International Journal of Intelligent Enterprise (73 papers in press)
Incorporating carbon emissions in queuing models to determine lot sizes and inventory buffers in a supply chain
by Petrus Setya Murdapa, Nyoman Pujawan, Putu Dana Karningsih, Arman Hakim Nasution
Abstract: In this paper, we present a supply chain model that considers both inventory-related costs and emissions. We used the queueing-based performance model wherein emissions in three stages of supply chain activities are captured. The model was solved by the decomposition approach. For model validation, we have used a discrete event simulation. The computation results show that the two results, i.e., the decomposition approach and the simulation, are very close, indicating the accuracy the approach that we used. Experiments were conducted to test the applicability of the model. The numerical examples show that the change in parameter values is not always responded the same way by the total inventory-related costs and the emission costs, indicating the importance of including these two response variables in the model.
Keywords: Carbon emission; inventory buffering; lot sizing; performance model; supply chain.
Entrepreneurial passion facing its ecosystems obstacles: The case of Tunisia
by SAMIRA BOUSSEMA
Abstract: Entrepreneurship is one of the most appropriate remedies to the various economic crises. It is presented as a complex process that faces several barriers, thereby inhibiting a projects implementation phase. In fact, after a careful review of the literature, we noticed that empirical research on reasons behind unimplemented entrepreneurial projects are very rare, suggesting a failure in modeling the process in general and the pre-start phase in particular. In this paper, we try then to identify the main constraints to entrepreneurial passion in Tunisia by studying a representative sample of project promoters who have been unable to carry out their business projects. Using structural equation methods, we found that these promoters face barriers like a lack in entrepreneurial training and services provided by supporting organizations.
Keywords: Entrepreneurial passion; unimplementation of projects; Structural modeling.
Implementing just-in-time (JIT) based supply chain for the bulk items in an integrated steel plant
by Ram Naresh Roy
Abstract: Due to increasing global competition, organizations are continuously improving their operational practices and cost efficiency to get a competitive edge. This paper involves a case study in ABC Steel plant (anonymised) dealing with a huge amount of bulk-materials handling and transportation, and proposes a JIT-based model of handling and transporting which may lead to potential cost savings. The paper discusses the important requirements of JIT procurement and transportation through a literature review. The existing system of ABC Steel plant has been studied and modelled as a pull-system, and an MRP model has been used to calculate the amount of various raw materials needed for making the hot-iron or steel. The total costs of transporting bulk materials from various sources for steelmaking in the existing system (model-1) and the proposed JIT system (model-2) have been calculated. The differences between the two indicated the potential savings for different levels of safety-stock and different levels of JIT implementation.
Keywords: JIT supply chain; logistics; bulk materials; integrated steel plant; MRP model; cost-savings and productivity; lean procurement.
Framework to Identify a Set of Univariate Time Series Forecasting Techniques to aid in Business Decision Making
by IRAM NAIM, Tripti Mahara
Abstract: Forecasting is generally involved in business activities to anticipate or predict the future. With availability of numerous techniques and models, forecasters regularly face a genuine issue to select the most appropriate technique for different time series available in an organization. Most of the time, it is not possible to find one technique that can be used for all-time series as the selection is dependent upon the characteristics of a time series. Hence, the research proposes a selection tree to aid in decision making based upon availability of type of dataset and time series characteristics. The framework is validated using four real case studies. This study also presents advancement to existing forecasting method selection tree by exploring a new dimension of complex seasonal pattern for long time series.
Keywords: Univariate Time Series; Time series Pattern; model selection; trend analysis; seasonal data; complex seasonality; long time series.
Innovativeness, Environment and Performance of Small and Medium-sized Enterprises (SMEs) in the Manufacturing Sector in Malaysia
by Mandy Mok Kim Man, May Chiun Lo
Abstract: This study examines innovativeness and environmental factors that can influence the performance of SMEs (small and medium-sized enterprises) in the Malaysian manufacturing sector. A theoretical framework is developed, based on innovativeness, environment and SMEs performance. The present study examines the fundamental nature of innovativeness and relates these various elements of innovativeness to SMEs performance. Firstly, in the Malaysian context, the government interferes in the market through new business policies, increasing or decreasing the interest rate, controlling money supply and implementing competition policy law. Secondly, the role of government has also been neglected in most previous research in measuring the environment influences on business in spite of their importance in determining the environment indicators, such as, environmental uncertainty and intensity of competition. The present study shows that the innovativeness has significant impact on SMEs performance and the environment variables have moderating effect on the relationship between the innovativeness and SMEs performance.
Keywords: innovativeness; SMEs; environment; performance; technology; entrepreneurship.
The Impact of Industry 4.0 on Sustainability and the Circular Economy Reporting Requirements
by Sukhraj Takhar, Kapila Liyanage
Abstract: The traditional linear economic system focuses on the mass production of products, using available resources, at the lowest possible cost. Sustainability recognizes the impact of dwindling natural resources, as a result of mass production and directs us towards the use of more sustainable resources. The Circular Economy (CE) proposes the adoption of an open loop manufacturing system where products are designed using resources which enable products to be repaired, reused, repurposed and recycled. To analyse the impacts of sustainability and CE initiatives, accurate data needs to be collected. Industry 4.0 promotes interconnectivity, enabling real-time data collection, communication and data analytics. This paper contributes to existing literature by identifying a research gap on how sustainability and CE model data reporting needs may be met using Industry 4.0 technologies. Using a literature review and survey on real-world adoption, a set of reporting requirements for sustainability and CE models are identified- The conclusions provide an assessment of how Industry 4.0 may aid reporting needs.
Keywords: Circular economy; Industry 4.0; Intelligent manufacturing; Sustainability; Supply chain management.
Modelling for system fluctuations advancing buffer management delivering on the theory of constraints
by Arnesh Telukdarie
Abstract: The aircraft component manufacturing industry can be considered a high value manufacturing value chain, due to the nature of the delivery space. South Africa currently supplies various components into the international Aerospace industry. A key strategic operational tool-set is the Theory of Constraints (ToC) manufacturing methodology. This research propositions a review and optimization of the current Mode of Operation (MOO) specific to work in progress management. A simulation based approach is adopted to test the scenarios for potential work schedule optimization, including incorporation of the ToC principals. The results are insightful, specific to throughput and cost management. The research and simulation serves as a significant opportunity for the company to integrate and optimize leading towards industry 4.0 delivery.
Keywords: Manufacturing Systems; Industry 4.0; Theory of Constraints (ToC); Multimethod Simulation Modelling; Production optimization.
Supply network configuration archetypes for the circular exploitation of solid waste
by Naoum Tsolakis, Dimitris Zissis, Jagjit Singh Srai
Abstract: This research aims to use network configuration theory to propose circular supply chain archetypes for the valorisation of solid waste. The proposed network configuration archetypes are differentiated by their levels of geographic dispersion, each representing coherent clusters of waste material and supply network characteristics for the valorisation of waste streams, namely: centralised, semi-centralised and decentralised. The different types of solid waste require local (e.g. wood, organic waste), regional (e.g. glass, plastics and rubber, paper and cardboard) or pan-regional (e.g. metals and alloys) network configuration options primarily dictated by the intrinsic physico-chemical properties of the wasted material and constraints related to the processing technologies. Furthermore, the proposed network configuration archetypes dictate operational considerations, such as procurement and pre-processing options for the wasted feedstocks, along with upscale production opportunities and distribution of the value-added intermediates or end-products.
Keywords: circular supply networks; supply chain configuration archetypes; solid waste; renewable feedstocks; commercial value and viability.
Impact of Social Media Advertising on Millennials Buying Behaviour
by Taanika Arora, Arvind Kumar, Bhawna Agarwal
Abstract: The phenomenal growth of Social Media sites, has enticed the companies to target their consumers by advertising through most used mediums, hence it becomes crucial for the advertisers to carefully design the ads thereafter also check its effectiveness The purpose of this paper is to propose a conceptual model which determines the impact of various advertising content factors such as Informativeness, Entertainment, Credibility, Interactivity and Privacy Concerns on Attitude of Indian Millennials towards Social Media Advertising. Using non-probability sampling, the data was collected using the online questionnaire through Google Forums from a total of 470 social media users. The adapted scales have been validated through Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA), after which path analysis has been applied using SPSS AMOS 22.0 for testing the various formulated hypothesis. The results indicated significant relationships which can be useful in understanding the Attitude and Behavioural Responses of Indian Millennials towards Social Media Advertising. The study can be useful to the marketers, advertisers and brand managers in designing advertisements on social media sites by embedding certain essential features which can positively shape up the attitudes and further develop behavioral responses.
Keywords: Social Media; Millennials; Informativeness; Entertainment; Credibility; Interactivity; Privacy Concerns; Social Media Advertising; Attitude; Behavioural Responses; Indians.
Special Issue on: ICACB'18 Advanced Intelligent and Communication Systems
OBJECT CATEGORIZATION AND FLAME APPREHENSION
by Santhosh Kumar B, Velliangiri S, Ajayan J
Abstract: Object categorization is a customary errand of PC observation which includes deciding if a picture contains some particular class of question. The thought is firmly related with acknowledgment, arrangement, and misgiving. There are numerous techniques to speak to a division of articles, from shape investigation, or neighbourhood inscriptions, for example, SIFT, and so forth. The possible point is to extricate semantics from video to be utilized as a part of larger amount action examination undertakings. Arranging the kind of an uncovered video question is a significant advance in accomplishing this objective. Nonetheless, late research has demonstrated that question groups and their areas in pictures can be found in a freely way too. Our contemporary question arrangement calculation influences utilization of the closer view pixel to delineate to every individual associated area to make a blueprint for the protest. Customarily fire sensors which sense the nearness of specific particles created by smoke and fire by photometry were utilized to recognize fire. Normal sensors mean to detect particles, accordingly, an essential shortcoming of point locators is that they are separate limited and decay in open spaces. In this paper we depict the computational models utilized in our way to deal with achieve the objectives indicated previously
Keywords: Fire identification; FlameColor; Contour Pattern; Object categorization.
Efficient Wideband Filter Using Closed Loop Resonator with coupling lines
by Oudaya Coumar, S. Tamilselvan
Abstract: This paper is about a wideband filter using closed loop resonator with inter-digital coupling lines. The square resonator is used as closed loop structure integrated with inter-digital coupling on both sides which plays a major key role in this filter design. The proposed wideband filter can be employed in UWB receivers since the operating band of UWB is matching with operating bandwidth of this filter. Design and EM Simulation of the UWB filters characteristics are discussed in this work. The proposed UWB filter produces tremendous bandwidth ranges from 2.3 GHz to 8 GHz. The filter evaluation parameters like return loss, insertion loss, phase and group delay are obtained and their responses are analysed. The complete size measurement of the filter is achieved to be 39mm
Keywords: Insertion Loss (IL); Return Loss (RL); square resonator; and ultra-wideband filter (UWB).
AN ANALYSIS OF COMMITMENT AMONG COLLEGE TEACHERS
by Lovelin Auguskani P, Sreedevi V, Jerlin Priya
Abstract: The study An Analysis of Commitment among College Teachers. was carried out Nagercoil at Kanyakumari District. The study helps to understand the commitment level of college teachers. The study was conducted with a sample size of 158 teaching staff, data has been collected through questionnaire. This research paper is through analyzed with primary data which was collected conveyance sampling techniques.This study investigated the commitment among college teachers. The findings from the analysis indicated that the level of organizational commitment is high among the college teachers in Nagercoil at Kanyakumari District. One Way analysis is used to find Variance between the category of appointment and the three commitments level (affective, continuance, and normative) and find variance between the designation and commitment. Majority of government aided respondents are having high level of affective, and normative commitment and self finance respondents having high level of continuance commitment. Assistant professors are having high level of affective, and continuance commitments, Associate Professors are having high level of normative commitment. In order to maintain this level the management should take various initiatives which will motivate the faculty to remain committed towards their job and responsibilities.
Keywords: Affective; Continuance; Normative; Education; Teachers.
LIFI Based Smart Systems for Industrial Monitoring
by Prabakaran N., Naresh K., Kannadasan Rajendran
Abstract: Light Fidelity (Li-Fi) is an unfolding technology which can be used to transfer data through light. It is a complete transformation to the world of wireless data transfer. Harald Haas from the University of Edinburgh, United Kingdom termed and introduced Light Fidelity to world through the global talk show in which he demonstrated of a Li-Fi prototype at the TED Global conference in Edinburgh on 12th July 2011. Challenging the pre-existing data transfer model namely the Wireless Fidelity (Wi-Fi) on various parameters such as speed, safety, reliability eco-friendliness and efficiency. The light emitting diode in a Li-Fi system is the source of data transfer utilizing visible light as medium of communication. This can provide greater download capacity in comparison to the existing wired or wireless networks due to higher bandwidth of light. With such high potential every electronic day to day use commodity that has role of light over it can be thought of to be used as an internet accesses point. Since it can't penetrate through walls they have short range but are highly secure in the confinement of the surrounding in comparison to Wi-Fi and overcome the radio frequency bandwidth availability issues in the near future.
Keywords: Bandwidth Light emitting diode:rn Light Fidelity; Visible light Communication; Wireless Fidelity;.
Comparison of Automated Leaf Recognition Techniques
by Mahmudul Hassan, Arnab Kumar Maji
Abstract: Plant plays an important role in different ways in human life and atmosphere. There are large numbers of plant species in the world. Plant species plays a vital role in many domains such as preventing some the diseases, farming, environment, discovery of new drug and other related areas. Recognition of plant species without expert understanding is a huge task. There has been great demand for applying automatic computer vision technologies to increase botanical knowledge. Using leaf features and traits, the classification and identification of plant is carried out. Leaf features like shape, texture and venation are the features most frequently used to differentiate the plant species. Different methodologies are there to extract the feature and to classify the leaf images using classifier. In this paper we are going to discuss on different leaf recognition approaches along with feature extraction methods and their performances.
Keywords: ANN(Articial Neural Network); CNN(Convolution Neural Network); Deep learning; PNN(Probabilistic Neural Network); SVM(Support Vector Machine).
A Detail Study on Context-Aware Architectures in Internet of Things
by Deeba Kanmani, Saravanaguru RA.K
Abstract: Internet of things is used to get the data around the world at your fingertip. As we move towards IoT the role of sensors has become vital. These sensors generate a vast amount of data. Sensors are used to sense and collects parallel information from the given set. We propose Context-aware for an Internet of things architectures which computerize the task. In this paper, we will discuss about the architecture of context-aware, IoT and context-aware reasoning. The principle objective of this paper is to audit existing architecture identified with context-aware and IoT and to make the comparative analysis table. The principle point is to enable the clients to present the issues and our proposed design enhances and delivers important information.
Keywords: Context aware; Internet of Things; Middleware; Context Reasoning; Nature Inspired Algorithm; Middleware architecture.
ENHANCING SECURITY BY TWO WAY DECRYPTION OF MESSAGE PASSING OF EMR IN PUBLIC CLOUD
by PRATHAP R, MOHANASUNDARAM R
Abstract: Encryption is one of the most critical and mandatory technique to provide security in outsourced data. Message passing is the most unsecure place of transfer of dangerous information's, and this message passing is made the end to end encryption to avoid a centralized security agent to access the data. Existing methods of encryption only provide end to end encryption which is not feasible for certain situations like implementing authentication. When the end to end encryption is made, the messages are always not known to the central authentication agent like CBI, in this paper, we provide a two side decryption algorithm that can be decrypted by two entities (one receiver and the other is the central authentication agent). Thus improving the message passing security by allowing the centralized authentication agent to read the transferring words. We implemented this two-way decryption in trip database dataset, and the experiment results prove that our proposed algorithm improves the security of message passing of Electronic Medical Records in public cloud while comparing with existing encryption algorithms.
Keywords: Cloud; EMR; Encryption; Decryption; Authentication; Authorization.
A Hybrid Medical Image Coding Based on Block Truncation Coding and Residual Vector Quantization
by P. Chitra, M. Mary Shanthi Rani
Abstract: The advancement of medical field has witnessed tremendous growth with state of the art imaging technologies for accurate diagnosis, which in turn demands efficient storage of medical images. Further enhance the image quality RVQ is implemented in the proposed method. The proposed work aims at developing an effective algorithm for compressing medical images exploiting the advantages of Block Truncation coding(BTC) and Residual Vector Quantization(RVQ). The advantage of the Block Truncation Coding (BTC) is twofold: 1. It is simple to implement and 2. Involves less computational complexity. RVQ is used to enhance the quality further. In the proposed method, the input image is compressed using BTC in the first phase. The residual error out of first phase is subjected to RVQ in the second phase. The novel feature of the proposed method is the adaptive procedure of RVQ based on the variance of residual vectors. High variant residual vectors are subject to vector quantization and low variant vectors to scalar quantization respectively. Furthermore, the residual values are normalized to positive values, so as to preserve their sign, before quantization. Experimental results show the superior performance of the proposed method in terms of compression metrics.
Keywords: Block Truncation Coding; Residual Vector Quantization; Image Compression.
An Incremental Approach for Hierarchical Community Mining in Evolving Social Graphs
by Keshab Nath, Swarup Roy, Sukumar Nandi
Abstract: Community members which are highly connected with each other inside a community tends to create sub-communities, commonly termed as intrinsic or hierarchical communities. Finding intrinsic communities help us to reach out specific user needs, understanding the network dynamics and unveiling the functional and hidden aspects in the network, which is difficult without unveiling intra and inter-community all kinds of relationship. With the passage of time, members of a community may acquire different interests, leads to movement of members within different communities. Frequent changes in the relationship of members towards a community make the task of community
detection even more challenging. In this work, we propose a new community detection method, embedded communities from evolving networks (ECEnet), for handling intrinsic communities in evolving networks. We adopt a density variation concept to detect the intrinsic communities in growing networks. We use a new membership function to measure the contiguity of a member towards a community. We use both synthetic and real-world social networks for our experimentation. Experimental results reveal that ECEnet is successful in detecting intrinsic or hierarchical communities in a dynamic scenario.
Keywords: intrinsic communities; hierarchical communities; evolving networks; dynamic communities; incremental clustering; embedded cluster; density variation.
Product recommendation system using optimal switching hybrid algorithm
by P. Bhuvaneshwari, A. Nagaraja Rao
Abstract: With the advent of e-commerce on the internet era, people prefer to do online shopping by just clicking the corresponding website. E-commerce website holds a million of products with a lot of information about it. As it holds the wide variety of products in the same category, the customer feels difficult in choosing the desired one they exposed to. To overcome this information overload issue and to attract the customers, the term recommendation system was introduced in an e-commerce system. The recommendation system works as a heart in the business strategy of e-commerce companies like Amazon, Flip-Kart, ebay, etc. initially, collaborative filtering technique was utilised by most of the recommendation engine for reaching out the customers by providing the right product and the services at the right time. Even though it is popular and highly valuable, it faces the challenge of recommending the products for the new user (cold start problem). In this study, we propose an optimal switching hybrid approach (OSHA) to overcome the above problem where demographic filtering technique is employed to find the similarity of users and the combination of CF prediction mechanism provide the basis for processing the recommendations. The OSHA is the combination of collaborative filtering and demographic filtering techniques where it switches between the context depending on the scenario. Similarity prediction measure and K nearest neighbour algorithm are used to predict the similar kind of users with size K. The experimental results show that the proposed algorithm performs better and improves the performance of the recommender system than the standalone technique.
Keywords: e-commerce; collaborative filtering technique; cold start problem; optimal switching hybrid approach; OSHA; demographic filtering technique; k-nearest neighbour algorithm.
Changed Detection of Landsat 8 Imagery using Object Based Image Analysis with Particle Swarm Optimization
by Amitabha Nath, Amos Bortiew, Goutam Saha
Abstract: This paper addresses the problem of classification of hyperspectral remote sensing image and detection of any changes in the land use pattern using it. Traditional pixel based classification approaches often fail to achieve acceptable accuracy in classifying Landsat images because of its complexity. Therefore, present work aims to apply object based-image analysis (OBIA), which is a concept that combines segmentation and classification together into one unit. We propose a hybrid OBIA architecture, augmented with particle swarm optimisation (PSO) technique to fine tune different hyperparameters involved with it. We present its success on a classification problem where two sets of landsat-8 images captured in the year 2016 and 2017 are considered as input and OBIA is applied for classifying the images into four major land use classes and detect any changes in these classes over the period of time. The results are then compared with best pixel based classification approach known as random forest (RF) classifier to determine its effectiveness in classification of hyperspectral images. Statistical measures like precision, overall accuracy and kappa coefficient are used as a parameter for comparison.
Keywords: Landsat 8 image; RF; object based-image analysis; OBIA; particle swarm optimisation; PSO; changed detection; accuracy assessment.
An Analytical Hierarchical Process based Weighted Assessment of factors contributing Precipitation
by Vaishnavi Balaji, J. Karthikeyan, Kiran Yarrakula
Abstract: The analytical hierarchical process is a prioritising algorithm used in the field of multi-criteria decision making system. AHP has a special capability to handle intangible criteria, this exclusive nature strengthened its popularity. In this work, AHP is exercised to prioritise parameters that contribute to precipitation. Precipitation is any form of water that reaches the earth surface from the atmosphere such as rain, dew, snow, etc. Recent researches consider precipitation as one of the crucial phenomena of climate change and lots of modelling techniques are proposed to predict precipitation using varied parameters. The significance of the study is to address minor fallout of AHP in rank reversal and uncertainty in ranking the given parameters, by attempting to prioritise six basic climatic parameters such as maximum temperature, wind velocity, relative humidity, solar radiation and elevation, which are considered to contribute precipitation.
Keywords: analytical hierarchical processing; AHP; precipitation; climatic factors affecting precipitation; multi-criteria decision making algorithm.
Special Issue on: Computational Intelligence in Sustainable Informatics Systems
High Performance Inventive System for Gait Automation and Detection of Physically Disabled Persons
by Vinothkanna Rajendran, Vijayakumar Thangavel, Prabakaran Narayanasamy
Abstract: Physically challenged persons may face many difficulties in the present modern environment as most of the commercial facilities and utilities for a day to day life is designed for normal people to lead a sophisticated life. Particularly people physically disabled face struggles in escalators in malls and public transportation places. It is very difficult for the disabled individual to be identified as one among in a large crowd and they normally feel unconformable to step inside in a running escalator. This research work proposes a novel method to identify the physically challenged persons from a large crowd by their nature of legs, walking pattern and hand sticks and provide necessary preference for them to get inside the escalators. Gait automation and detection mechanism is used for person identification for all gait events and deep learning based neural network (DLNN) is used for learning the patterns and making the system to automatically identify the physically challenged. Experimental results shows that the proposed system automatically measures all the angle of gait events with an accuracy level of 95.4% and thus offers a cost effective solution for gait kinematic analysis for disabled peoples.
Keywords: Physically disabled; Gait; Deep learning neural network (DLNN),.
Optimizing QoS with Load Balancing in Cloud Computing applying Dual Fuzzy Technique
by Chintureena Thingom, Ganesh Kumar R
Abstract: Objective: Cloud computing has become a necessity when the internet usage has increased drastically. This research paper objective is to optimize Quality of Service in Cloud Computing using Dual Fuzzy Technique.
Method: With the competition to provide the best quality service at Cloud data center has increased multiple fold, we are analyzing the parameters of average response time, average completion time, average CPU utilization and Job success. Cloud-Sim Simulator has been used to predict and extensively bring out the best technique. The mathematical model is also used to provide reliable and valid result.
Findings: To achieve the best result, the load in datacenter needs to be efficiently distributed, so that it is managed to process maximum service requests with the best service response time and very few failures. In this paper, we applied Dual Fuzzy Technique for the load balancing in the cloud data center in such way that the response time and execution time is optimized compared to the available systems. The findings were extensive and unique where the existing systems were not very reliable and valid. The graphical representation have pointed out the difference. The parameters used have pointed that Dual Fuzzy Technique can provide the best optimized Quality of Service.
Applications/Improvement: With this technique, cloud computing service provider can provide better quality service. More research work in the future can look up for any other better load balancing algorithm.
Keywords: Cloud Computing; Dual Fuzzy; Quality of Service; Cloud-Sim; Load Balancing.
Study paper on Internet of Things and its utilized protocols with application
by Roopa Jayasingh, Remus Dominic D’mello, Abhishek Soren, Ankit Alex Hansdak, Bidyut Mondal
Abstract: The people of the 21st century have now entered a new era of computing technology which is known by a very common name: the Internet of Things (IOT). Although it is also known by certain other names like machine to machine, Internet of Everything and many more, one thing common in this technology is that it is happening in real and has got quite a potential to shape the future of computer technology in the coming days.IoT is basically a paradigm which generally consists of interactive smart machines which communicate with other smart machines, resulting into generation of informative data which are used to produce necessary actions that controls and command things hence making our day-to-day life a lot simpler. The following thesis is an extensive reference to the utilities, applications and possibilities of the internet of Things.
Keywords: Internet of Things; Message Queuing Telemetry Transport; Constrained Application Protocol; XMPP; Asynchronous messaging; Open System Intercommunication;.
Design of Master Controller Test Kit for the Railway Diesel Locomotives
by KISHORE KUMAR KAMARAJUGADDA, Movva Pavani
Abstract: In the present paper, a master controller test kit for the diesel locomotives is designed, developed and the prototype is tested. Master controller test kit is used by the locomotive driver to drive the engine which is a human-machine interface which primarily performs three essential functions such as controlling the regression, progression, and locomotive braking. Stick type master controller is a modular based compact design which is a crew friendly for smooth operation and comfort. In the proposed model, the operation of solenoids is indicated by LEDs, and dynamic braking is shown by Digital Voltmeter for measuring the drop across braking control pot (BKCP)
coil. By using this test stand a layman can also check the master controller in the section itself which saves staff-hours and most significant loco downtime. The tested prototype is economical, and its performance is comparable with the existing microcontroller based master controllers.
Keywords: diesel locomotive; throttle handle; reverser handle; master controller testing kit; directional handle; dynamic brake handle.
Special Issue on: Innovative Business and Organisational Transformation Practices
Workplace Spirituality and Employees Readiness for Change as Precursors to Innovative Work Behaviour: an Empirical Examination
by Nimitha Aboobaker, Zakkariya K.A., Manoj Edward
Abstract: Organizational performance and sustainability in the contemporary technological world is largely driven by the relentless change and continuous innovations brought about by employees. In this context, this paper investigates the relationship between workplace spirituality and innovative work behavior, based on an empirical research conducted among 208 employees working in information technology sector. The study also examined the mediating role of individual readiness for change in the aforementioned relationship. The three dimensions of workplace spirituality had varying influences on outcome variables. Employees with higher experiences of meaningful work and alignment with organizational values had higher readiness for change and innovative work behaviour. Findings of this study revealed no significant direct effect of sense of community on innovative work behaviour of the employees. Nevertheless, sense of community had an indirect effect on innovative work behaviour, through the mediating role of readiness to change. Implications for employees experience of workplace spirituality and outcomes, so as to meet the challenges of a VUCA (volatile, uncertain, complex and ambiguous) business environment are elaborated.
Keywords: Alignment with organizational values; Innovative Work Behaviour; Job Design; Meaningful Work; Organizational Culture; Readiness for Change; Sense of Community; Teamwork; Workplace Spirituality.
Impact of Organizational Justice on Perception of Ability-Job-Fit in Changing Environment
by MRINALI Tikare
The study aims to investigate the association/relationship among Procedural Justice, Distributive Justice and Perceived-Ability-Job-Fit.
This quantitative study has adopted the objectivism approach and is deductive in nature. Data were collected by using Multi-stage Probability Sampling Method from 274 Class-I employees working in Multinational General Insurance Company a subsidiary of Central Government of India. The scales developed by Sweeney & MaFarlin (1997) for Procedural Justice (PJ) & Distributive Justice (DJ) and Abdel-Halim (1981) for Ability-Job-Fit was selected. The techniques used for data analysis were Descriptive Statistics, Pearson Correlation and Multiple Regression.
The respondents have positive perception about their ability to perform the job and they are optimistic about the fairness of prevailing procedures and distribution of rewards followed by their organization which is indicated by high mean scores. The positive DJ & PJ perceptions proportionately increase the perception of Ability-Job-Fit. The result of Multiple Regression analysis indicated that two independent variables (DJ & PJ) explained the variance in Perceived-Ability-Job-Fit (Dependent Variable). The perception of PJ is making a statistically significant unique contribution to the prediction of the Perceived-Ability-Job-Fit.
The findings will be useful for employees to reduce stress by understanding the Fit Concepts. The HR practitioners can predict the behavior of their employees by executing the fairness practices in the system. The top-management can win trust and thereby expect support from their employees, even during dynamic/volatile business environments by implementing appropriate PJ in the organization.
For the first time, the study has attempted to establish a direct relationship between fairness perception and ability fit/congruence perception of employees. It may lead the future researchers to work in-depth on this model. The analysis of the study did not consider the effect of demographic variables. It should be noted that there are several concerns while generalizing the findings of this study.
Keywords: Distributive/Procedural Justice; Perceived-Ability-Job-Fit; experienced government employees.
Economic, Political and Institutional Determinants of Foreign Direct Investment Inflow in Emerging and Developing Asia
by Anil Kumar Goyal
Abstract: This study investigates the role of Economic, Political and Institutional factors in attracting foreign direct investment (FDI) inflow in top 5 host economies of Emerging and Developing Asia consisting of China, Hong Kong, Singapore, India, and Viet Nam for a period of eleven years for the period 2006-2016. The study is based on determinants, identified from literature review on the basis of their relevance and significance, of FDI inflow in top 5 host economies of Developing Asia. Annual data of dependent and independent variables for the period ranging from 2006-2016 has been collected from World Development Indicators, World Governance Indicators, World Bank. In the present study independent variables are categorized as Economic, Political and Institutional factors on the basis of their nature, effect and significance. The study uses Fixed Effects Panel Regression in order to measures the significance of determinants of FDI inflows in top 5 host economies of Developing Asia. Findings of Panel Regression Model reveal that most of the Economic Variables seem to be statistically significant and determinants FDI inflow as compared to Institutional and Political variables of FDI. After imputing variables into Economic, Political and Institutional, multiple regression estimates indicate that the coefficients of Economic and Institutional factors are significant as determinants of FDI inflow in Developing Asia.
Keywords: Determinants of FDI inflow; Economic; Political and Institutional factors; top 5 host economies of Emerging and Developing Asia consisting of China; Hong Kong; Singapore; India; and Viet Nam; Panel Data; Fixed Effect; Multiple Regression.
Price Discovery and Volatility Transmission in the Spot and Futures Market of Pepper: An Empirical Analysis
by Asha Nadig, Viswanathan T
Abstract: Pepper, the king of spices, is one of the oldest and widely traded spices across the world over many centuries. As a commodity traded in the spot, futures and export market, global demand and supply play a crucial role in shaping pepper price and volatility. As price risks are integral to farmers and traders, forecasting successive prices will be of great help to them. The price risk can be minimised through effective hedging. The futures market provides a platform for both hedging and speculation. Hence understanding the relationship between spot and future market is essential for the traders of commodities. Understanding the relationship between the two markets include price forecasting, price discovery and volatility spill over between the spot and futures market.
This paper examines the price discovery mechanism and volatility transmission between the spot and futures prices of pepper. Applying the statistical, seasonal variation and econometric models for forecasting, forecasting accuracy is tested. By applying the simple linear regression model, the study concluded that the subsequent price of pepper in the spot market cannot be predicted appropriately. The Holt Winters model gives biased estimate of future prices. The goodness of fit analysed through the Akaike information criterion (AIC) gives better values of forecasting. The ARIMA model is the appropriate model to forecast the price of pepper.
Keywords: Forecasting; Price discovery; Cointegration; Volatility spill over.
Analyzing the Entrepreneurial Intentions through Intellectual Capital: Evidences from India
by Ahmed Musa Khan, Mohd Yasir Arafat, Mohd Anas Raushan
Abstract: Intellectual capital is defined as the knowledge that can be converted into value. Intellectual capital has received a considerable attention from in the field of innovation performance. Still, there is a paucity of research which identifies the role of intellectual capital in creating ventures. This research is an attempt to examine the influence of intellectual capital on start-ups. A large data set of responses from 3360 respondents from India has been provided by the largest entrepreneurship research project Global Entrepreneurship Monitor has been used. A logistic regression technique is employed to measure the influence of intellectual capital on entrepreneurial intentions. The results show that all the components of intellectual capital, human capital, structural capital and relational capital have a positive and significant impact on entrepreneurial intentions. The study suggests that policies should be proposed to develop human capital, structural capital and facilitate interaction between existing and potential entrepreneurs so that new venture creation can be fostered. This research falls among the initial studies investigating the relationship between intellectual capital and entrepreneurial intentions. The review of literature reveals that very few studies based on large data set are conducted in developing countries like India.
Keywords: Intellectual Capital; Entrepreneurial Intentions; Human Capital; Structural Capital; Relational Capital.
Special Issue on: Advances in Approaches and Methods for Decision Making Using Optimisation and Artificial Intelligence Techniques
Sustainable Supply Chain Risk Mitigation: A mixed method approach
by Madhukar Chhimwal, Saurabh Agrawal, Girish Kumar
Abstract: The objective of this research is to find a way to minimize risk in the supply chain by identifying the critical success factors and analyzing the relationship between sundry critical success factors. Proposed study construct a model of the critical success factors using the Interpretive Structural Modeling approach and test the model using regression analysis technique. Analysis of the results indicates that there are some critical success factors which have high driving power and low dependence that require utmost attention and are of great paramount while other cluster consists of those critical success factors which are highly dependent and need futuristic actions. In this work, only regression analysis technique is used to validate the model that is developed using Interpretive Structural Modeling approach. This type of relegation will help the supply chain managers to distinguish between independent and dependent critical success factors and how the relationships among the critical success factors will efficaciously minimize the risk in a supply chain. This study can be considered as a base study for the practitioners and academicians who are working in the area of risk management for achieving sustainability in the supply chain.
Keywords: Sustainable supply chain management; Interpretive Structural Modeling; Regression analysis; Risk Mitigation.
Gender differences in the perception of the Quality of College Life in Spanish University
by Juan Jose Blazquez-Resino, Edyta Gołąb-Andrzejak, Santiago Gutierrez-Broncano
Abstract: Through the current research, we intend to analyze how students differ in their levels of Quality of College Life (QCL) according to gender, and how this relates to overall Quality of Life (QoL) and loyalty (measured by Identification and Word of Mouth [WoM]) to their specific university. The survey included 243 students attending public university in Spain. The results obtained through the analysis of data allows affirming that there are differences between women and men both in the configuration of the Quality of their College Life and in its effect on the loyalty shown towards the college. This paper contributes towards an improved comprehension regarding the differences between the students according to their gender, so that managers can develop strategies better adapted to students.
Keywords: Quality of College Life (QCL); Student Satisfaction; Installations Satisfaction; Education Satisfaction; Social Satisfaction; Quality of Life (QoL); Positive Word of Mouth (WoM); Identification; Gender Differences; The Effect of Gender; University.
Performance Measurement of various AI techniques for Energy Estimation and its Optimization using Sensitivity Analysis
by Yashish Swami, Navjot Singh, Umang Soni
Abstract: The objective of this project is to predict Energy Performance of a Building (EPB) in terms of heating and cooling load by using various Artificial Intelligence (AI) techniques then measuring the corresponding strength of each input and its effect on the output in order to identify the most significant input from the lot by using Sensitivity Analysis. EPB can help in efficient construction of buildings as well as put a leash on dwindling Natural Resources and Global Warming. The various intelligent techniques used in this project are Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), ANFIS-GA (Genetic Algorithm) and ANFIS-PSO (Particle Swarm Optimization. In order to identify the most significant input, we are using a technique based on Sensitivity Analysis, which is called the Connection Weight Algorithm. In the end, performance of the AI techniques is compared to select the best performing model.rn
Keywords: Energy Performance of Building; Artificial Neural Network; Adaptive Neuro-Fuzzy Inference System; ANFIS-GA; ANFIS-PSO; Sensitivity Analysis; Heating Load; Cooling Load.
Optimization of Software Release Time using Adaptive Neuro Fuzzy Approach
by Shubhra Gautam, Deepak Kumar, L.M. Patnaik
Abstract: In this research study, the optimal release time of the software to be released is determined by using adapative neuro fuzzy optimization method. Adaptive neuro fuzzy optimization method has the advantage of fuzzy logic and neural network both. Different input criteria are taken in the paper based on which the optimal release time of the software is determined. The two important factors that govern the release time of any software are Cost and Reliability. Cost and reliability are estimated with respect to mean number of failures. Software reliability growth models are used to find the mean number of failures in any software. First, a software release time growth model is discussed which gives us the mean number of failures, these will be used to determine the cost and reliability. Second, an adaptive neuro fuzzy method is discussed for optimization by which the optimal time of release of spftware is obtained. Numerical examples are taken and results obtained are compared with different other optimization methods. From the results of numerical example, we can conclude that neuro fuzzy optimization method is best for software release time optimization.
Keywords: Software testing; Optimal release time,reliability,cost ; fuzzy logic; neuro fuzzy.
The Effect of Travel Safety Experience on Passenger Satisfaction
by Pankaj Deshwal
Abstract: Purpose The aim of the study is to ascertain the dimensions of safety experience of the female passengers as regards to metro rail services provided by the Delhi metro rail in India. The impact of these dimensions on satisfaction is to be assessed.rnDesign/methodology/approach The female safety experience dimensions are obtained by performing an exploratory factor analysis. The research was conducted using survey method. A structured instrument was used to collect responses from the female passengers. Further, the reliability of the dimensions is ascertained followed by multiple linear regression analysis using SPSS s/w.rnFindings As a major output of the present research, nine dimensions viz. experience with other passengers, teasing experience, feeling towards metro rail, staff experience, outside metro station experience, compartment experience, behaviour experience, entry experience and recovery experience emerged as dimensions that lead to female passenger satisfaction as regards Delhi metro rail services. Further, the outputs of multiple linear regression revealed out of these nine dimensions, five dimensions, namely, feeling towards metro rail, experience with other passengers, recovery experience and staff experience are found to be statistically significant at 1 or 5 percent significance level.rnOriginality/value Authors believe this is the first effort that ascertains the dimensions that lead to female safety experience as regards to Delhi metro rail services and explore the impact of these dimensions on female passenger overall satisfaction. rn
Keywords: Female; safety experience; satisfaction; metro rail; regression; factor analysis.
Special Issue on: Big Intelligent Enterprise For Sustainable Computing
FINANCIAL ACCESS INDICATORS OF FINANCIAL INCLUSION: A COMPARATIVE ANALYSIS OF SAARC COUNTRIES
by Ravikumar Thangaraj
Abstract: Financial inclusion provides access to formal financial services at reasonable cost to the financially excluded people. Financial inclusion has been one of the most sought after topics in recent times for policy makers, researchers and academicians. Definition of financial inclusion varies from region to region. Financial inclusion is measured using different indicator. The important indicators of financial inclusion measurement include access indicators, usage indicators, quality indicators and financial education indicators. Most of the researchers use access indicators and usage indicators to measure financial inclusion. Access indicators comprise of demographic and geographic branch penetration, demographic and geographic ATM penetration and population per branch. This study focuses on comparative analysis of access indicators of financial inclusion in SAARC countries. The study is based on secondary data available in the Central Banks of SAARC nations, International Monetary Fund, World Bank and Asian Development Bank. The study has found and analyzed about the countries which has performed well in each indicator of financial access.
Keywords: Financial access; financial inclusion; Indicators; SAARC.
A Novel Method for Predicting Kidney Diseases Using Optimal Artificial Neural Network in Ultrasound Images
by Balamurugan S.P., G. Arumugam
Abstract: The main aim of this research is to design and develop an efficient approach for predicting ultrasound kidney diseases using multiple stages. Nowadays, kidney disease prediction is one of the crucial procedures in surgical and treatment planning for ultrasound images. Therefore, in this paper, we propose a novel ultrasound kidney diseases prediction using the artificial neural network (ANN). To achieve the concept, we comprise the proposed system into four modules such as preprocessing, feature extraction, feature selection using OGOA and disease prediction using ANN. Initially, we eliminate the noise present in the input image using the optimal wavelet and bilateral filter. Then, a set of GLCM features are extracted from each input image and then we select the important features using oppositional grasshopper optimization algorithm (OGOA). To classify the image as normal or abnormal, the proposed method utilize an artificial neural network (ANN). The performance of the proposed method is evaluated using accuracy, sensitivity, and specificity. The experimentation results show that the proposed system attains the maximum accuracy of 95.83% which is high compared to existing methods.
Keywords: Ultrasound image; neural network; multi-kernel k-means clustering; GLCM features; segmentation; classification; bilateral filter; OGOA.
Recurrent Neural Network based Speech Recognition using MATLAB
by Praveen James, Mun Hou Kit, Chockalingam Aravind Vaithilingam, Alan Tan Wee Chiat
Abstract: The purpose of this paper is to design an efficient Recurrent Neural Network (RNN) based speech recognition system using software with Long Short-Term Memory (LSTM). The design process involves the implementation of speech acquisition, pre-processing, feature extraction, training and pattern recognition tasks for a small vocabulary sentence recognition system using RNN. A vocabulary of 80 words which constitute 20 sentences is used to train and test a vanilla LSTM network. The depth of the layer is chosen as 20, 42 and 60 and the accuracy of each system is determined. The results reveal that the maximum accuracy of 89% is achieved when the depth of the hidden layer is 42. Design complexity and processing time are considered when the signal acquisition, pre-processing, feature extraction, and training algorithm are implemented. In this paper, there are 5 layers namely, the input layer, the fully connected layer, the SoftMax layer, the output layer and one hidden LSTM layer that can be increased for more complex design requirements. The LSTM network stores previous values and is the core component of the speech recognition system. Since the depth of the hidden layer is fixed for a task, increased performance can be achieved by increasing the number of hidden layers. However, the processing time increases as the number of layers increase which necessitates a dedicated hardware device.
Keywords: Speech Recognition; Feature extraction; Pre-processing; RNN; hidden layer; MATLAB.
Real Time Noisy Dataset Implementation of Optical Character Identification Using CNN
by Anand R
Abstract: Optical Character Recognition (OCR) is one of the major research problem in real time applications and its used to recognize all the characters in an image. As English is a universal language, character recognition in English is a challenging task. Deep learning approach is one of the solution for the recognition of optical characters. Aim of this research work is to perform character recognition using Convolutional Neural Network with LeNET Architecture. Dataset used in this work is scanned passport dataset for generating all the characters and digits using tesseract. The Dataset has training set of 60795 and testing set of 7767. Total samples used are 68562 which is separated by 62 labels. Till now there is no research on predicting all 52 characters and 10 digits. The algorithm used in this work is based on deep learning with appropriate some layer which shows significant improvement in accuracy and reduced the error rate. The developed model was experimented with test dataset for prediction and can produce 93.4% accuracy on training, and 86.5% accuracy on the test dataset.
Keywords: Convolutional Neural Networks; Scanned Passport; Deep Learning; Classification; Optical Character Recognition; Discrete Wavelet Transform.
S-Transform Based Efficient Copy Move Forgery Detection Technique in Digital Images
by Rajeev Rajkumar, Sudipta Roy, Manglem Singh
Abstract: Copy-move Forgery (CMF), which copies a part of a picture and pastes it into another location, is one of the common strategies for digital image tampering. Due to the arrival of high-performance hardware and the compact use of image processing software, empowers creating image forgeries easy that are undetectable by the naked eye. For CMF detection, we suggest an efficient and vigorous method that could take care of numerous geometric ameliorations including rotation, scaling, and blurring. In the projected CMF detection system, we use Stock Well Transform (S-Transform) which hybrids the advantages of both Scale Invariant Feature Transform (SIFT) and Wavelet Transform (WT) to extract the key points and their descriptors from the overlapped image blocks. Furthermore, Euclidean distance (ED) between the overlapped blocks are measured to detect the similarities and to identify the tampered or forged region in the image. Besides, a novel Fuzzy min max Neural Network based Decision Tree (FMMNN-DT) classifier is used to recognize the duplicated regions in the forgery image. The proposed system is tested and validated using MICC-F220 dataset and we present comparison among the proposed outcomes with some existing ones which ensure the significance of the proposed.
Keywords: CMF; S-Transform; Feature Extraction; Fuzzy min-max classifier; Decision Tree Classifier.
A Computational Perception of Locating Multiple Longest Common Subsequence in DNA Sequences
by TAMILPAVAI GURUSAMY, SRIPATHY PADHMA R, VISHNUPPRIYA C
Abstract: Bioinformatics is an active research area which combines biological matter as well as computer science research. The longest common subsequence (LCS) is one of the indispensable issue to be unraveled viably in computational science. Discovering LCS is fundamental undertaking in Deoxyribonucleic Acid (DNA) arrangement investigation and other molecular biology. In this paper, new calculation for discovering LCS of two DNA successions and its area is proposed. The objective of this created framework is to discover the area and length of all subsequences which introduces in the two arrangements. To achieve this, DNA sequences are stored in an array and the comparison of DNA sequences are performed using matching algorithm. At the end of matching process, group of subsequence are obtained. Then the length and location of the matched subsequence are computed. After completing the matching process, longest common subsequence(s) is located. In this proposed work, maximally obtained length of LCS is 8. Finally, the computation time is calculated for locating LCS in DNA sequences. In addition to this, computation time is analyzed by gradually increasing the length (in characters count) of DNA sequences from 100, 200, 300, 400 and 500. It concludes that computation time for locating LCS in various lengths of DNA sequences took few seconds difference only.
Keywords: Computational biology; DNA; longest common subsequence; matching algorithm.
Analysis of Double chambered - Single and cascaded Microbial Fuel Cell: Characterization study based on the enrichment of fuel
by G. Thenmozhi, J. Sreelatha, S. Gobinaath
Abstract: Need for green energy, depletion of fossil fuels becomes the immediate requirement for building a clean and sustainable society. Among the various methods of sustainable energy sources, Microbial Fuel cell is an emerging field with vast history as it converts the naturally available materials or bio-products into electricity with the help of microbes. Hence microbial fuel cell is an energy transducer. The experimental set-up is a double chambered microbial fuel cell with four single units among which two are separate and other two single units are cascaded into one. Cow dung and sheep worm kept in the anodic chamber are used both individually and also in combination. vermicompost, curd etc are added to promote the growth of bacteria into it. With this setup, the variation of voltage in the microbial fuel cell with respect to time is observed. Also the performance of microbial fuel cell with fuel enrichment is analyzed. rn
Keywords: Microbial Fuel Cell; energy transducer; cascaded MFC; double chambered microbial fuel cell; cow dung; sheep worm; vermicompost; fuel enrichment; clean energy; Characterization study.
Enhanced Media Independent Handover for vertical handover decision in MANET
by Jagan Nath, Rajesh Kumar Aggarwal, Yudhvir Singh
Abstract: In heterogeneous Mobile Ad Hoc Network (MANET), seamless connectivity of a mobile node is the important challenge. Due to the mobility of the node, it may loss seamless connection. So, vertical handover techniques were presented to solve this issue. However, communication might get cancelled when handover takes place. This results in call drop and some other issues. So as to overcome these issues, an Enhanced Media Independent Handover/IEEE802.21 (EMIH) for vertical handover decision is presented in this paper. In this standard, Adaptive Neuro-Fuzzy Inference System (ANFIS) is included to select optimal network for vertical handover. Simulation results show that performance of the proposed approach outperforms that of the existing approach in terms of handover probability, drop, etc.
Keywords: Mobile Ad hoc Network; vertical handover; optimal network; Adaptive Neuro-Fuzzy Inference System (ANFIS); Received Signal Strength (RSS); Average Bit Rate (ABR); handover probability.
Regulations on sustainability reporting as a global force in shaping business enterprises: Evidence from India
by Mathivanan Periasamy, Kasilingam Ramaiah
Abstract: McKinsey in 2010 identified the larger role of the state as a business regulator as one of the five global forces that shape business enterprises. In the recent past, this was very evident in India when both the government and the stock market regulator introduced changes in business responsibility reporting of Indian enterprises. Intelligent enterprises adapt swiftly to changing regulatory mechanisms be it voluntary or mandatory. In this paper, we discuss how Indian enterprises respond to sustainability reporting requirements both in the voluntary and mandatory regimes. Among the variables identified for our study companys age, industry type, market capitalization and listing status of the company including index type influences global sustainability reporting practices in India.
Keywords: GRI; Sustainability reporting in India; Mandatory BRR; Sustainability index; Sustainable companies; SRTs.
Design of cost effective transistor by software simulation for profitable production
by Debasis Mukherjee
Abstract: Reduction of process cost is the key factor for profitability in any industry. Semiconductor industry is also not an exception of this rule. In this paper, a novel transistor structure has been proposed with reduced process cost and almost same functionality compared to conventional MOSFET transistor. Details fabrication steps of the novel transistor have been proposed. Working of the proposed structure resembles conventional MOSFET, but structure wise there are many differences. Necessity of source extension and drain extension has been uninvolved, resulting less fabrication cost and higher concentration of transistors in same chip area. Another improvement is removal of gate spacer, resulting cutting down of process cost. Both the conventional MOSFET and the proposed one have been simulated by Sentaurus TCAD toolkit for 7 nm technology generation. The performance of the proposed transistor has been found satisfactory compared to the conventional MOSFET as per the guidance given in International Technology Roadmap for Semiconductors or ITRS 2013 version.
Keywords: 7 nm; cost; CMOS; device level; fabrication; ITRS; MOSFET; process cost; production; profitability; TCAD; VLSI.
A Supervised Multimodal Search Re ranking Technique using Visual Semantics
by Nikhila T Bhuvan, M. Sudheep Elayidom
Abstract: The multimedia content in a webpage is usually given least importance in webpage ranking. A better user satisfaction could be achieved if the web pages are ranked based on multiple modalities rather than just depending on the textual content. A better ranking of the web pages is proposed using natural language descriptions of images along with the textual content in a webpage is being proposed. The inter-modal correspondences between text and visual data is learned using the Convolutional Neural Network assisted by the datasets of images and their sentence descriptors. The model is based on Convolutional Neural Networks over images to generate the image descriptor and Dandelion API for their similarity measure with the Query. The image description is algorithmically generated rather depending on the image annotations present. Finally, it has been proven that the re-ranked web pages using the generated descriptions significantly outperform the state of art retrieval models.
Keywords: Automatic image annotation; Convolutional Neural Networks; image descriptor; multimodality search; search re ranking; semantic similarity.
Intelligent Systems for Volumetric Feature Recognition from CAD Mesh Models
by Vaibhav Hase, Yogesh Bhalerao, Saurabh Verma, G. Vikhe Patil
Abstract: This paper presents an intelligent technique to recognize the volumetric features from CAD mesh models based on hybrid mesh segmentation. The hybrid approach is an intelligent blending of facet based, vertex based, rule-based, and artificial neural network (ANN) based techniques. Comparing with existing state-of-the-art approaches, the proposed approach does not depend on attributes like curvature, minimum feature dimension, number of clusters, number of cutting planes, the orientation of model and thickness of the slice to extract volumetric features. ANN based intelligent threshold prediction makes hybrid mesh segmentation automatic. The proposed technique automatically extracts volumetric features like blends and intersecting holes along with their geometric parameters. The proposed approach has been extensively tested on various benchmark test cases. The proposed approach outperforms the existing techniques favorably and found to be robust and consistent with coverage of more than 95% in addressing volumetric features.
Keywords: CAD mesh model; hybrid mesh segmentation; volumetric feature recognition.
Advanced Graphical based Security Approach to Handle Hard AI Problems based on Visual Security
by VENKATA SATYA VIVEK TAMMINEEDI, RAJAVARMAN V.N
Abstract: Security is the main aspect to explore human data from different web oriented applications present in Artificial Intelligence (AI). It is very difficult to use different web applications without security to access data in various places. So that various types of security related approaches were introduced to use services in securely in outside environment, but they have some limitations to protect data from outside attackers (Hackers). So that in this paper, we propose and introduce a Novel and Advanced Security Model to provide security from outside attackers in AI related web oriented applications. In this approach, we follow the basic features related to Captcha as a Graphical password to enable security services in our proposed approach. Using Captcha graphical passwords in our approach, we describe pushing attacks, pass-on attacks and guessing attacks in web applications with random selection of Captcha passwords to use web services. Our experimental results show efficient security relations when compare to existing security approaches in terms of Captcha generation, time and other parameters present in web security applications.
Keywords: Captcha as a graphical password; Directory based push attacks; Security attacks; Visual cryptography and Captcha based dictionary attacks.
Influence of Human Resource Management (HRM) Practices on the Organizational Commitment with Specific Reference to Selected Hotels in Chennai
by Dheera V.R, Jayasree Krishnan
Abstract: This study investigates the influence of Human Resource Management (HRM) practices on the Organizational Commitment in Hospitality Industry. The study hypothesizes that HRM practices (Employee Motivation, Rewards and Awards, Grievance Handling, Employee Engagement, Performance Appraisal and Training and Development) will be positively related to commitment to organization and career. The study was conducted with randomly selected employees (300 numbers) of leading hotels in Chennai, Tamilnadu. The statistical results of the data collected from the employees of hotels reveal that majority of the six HRM practices have direct positive and significant relationships with commitment to organization and career. The employees of the hotels felt that Grievance Handling function of HRM practices has to be given more importance and Performance Appraisal System has to be more effective in a manner to motivate employees to perform better.
Keywords: HRM Practices; Motivation; Rewards and Awards; Grievance Handling; Employee Engagement; Performance Appraisal; Training and Development; Commitment to Organization and Career; Hotel Industry.
Rendezvous Agents-based Routing Protocol (RARP) for delay sensitive data transmission over wireless sensor networks with mobile sink
by V.T. VENKATESWARLU, P.V. NAGANJANEYULU, D.N. RAO
Abstract: The data collected by the sensor nodes will be transferred to sink in traditional wireless sensor networks. The data transmission routes either direct or through the route established using intermediate nodes in between source sensor and sink. Due to the constrained energy reserves of the sensors, a transmission route should operate with minimal energy that tends to longevities the survival of the network. The other critical requirement of these sensor networks is the transmitting delay sensitive data with the ability of fault tolerance and ordering the data to be transmitted that is done by the factors involved to define the priority of that data. The numerous contributions observed in contemporary literature are dealing with the objectives stated. However, these objectives are still grabbing the attention of the present research domain, which is due to the phenomenal changes in network topologies, and the dense span of the network regions. This manuscript endeavored to portray a novel routing strategy to transmit delay-sensitive data from sensors to sink that aimed to achieve fault tolerance, priority-based transmission and longevities the network lifespan. The network context of the proposal is a sensor network with the mobile sink. A novel routing is portrayed that partitions the network area into regions and establishes rendezvous agents for the mobile sink at all of these regions and defines a method to order the areas which is followed by the mobile sink to visit the regions. The process of ordering the regions is furbished under many quality of service objectives portrayed in this manuscript. The performance of the proposed model assessed through simulation study and the same is compared with other contemporary model having similar objectives. The energy consumption efficiency under optimal packet delivery with minimal latency is the objective considered for the performance analysis.
Keywords: LEACH protocol; PASCCC; line-based data dissemination; rendezvous points; smoke/CO system.
Design of Data scoring model for Big Data
by Ranjan Kumar Dash
Abstract: The huge volume and variety of data stored in big data provide more accurate predictive platform for the users. However, the decision-making process becomes a tedious task due to requirement of much computational time and memory to access them. Thus, a solution to the said problem is data scoring that provides the selection of only those variables or features that impact the decision-making process to a greater extend. To cater the need of an efficient data scoring model, the work carried out in this paper proposes a new data scoring model for big data. The proposed model uses Adaptive LASSO as the statistical method. The steps involved in the design of the proposed model are outlined with proper explanation. The model is trained and tested by k-fold cross validation technique. The performance of the model is measured using ROC curve. The model is simulated using R and is applied on three distinct data sets. To make a comparison with LASSO, LASSO is also applied on these data sets. The simulated results reveal that the adaptive LASSO performs better than LASSO for large sized data sets.
Keywords: big data; regression analysis; data scoring; receiver operating characteristic curves; discriminant Analysis; decision tree; support vector machine; random forest.
Word Sense Disambiguation in Tamil using Indo Wordnet and Cross-Language Semantic Similarity
by Deepa Karuppaiah, P. M. Durai Raj Vincent
Abstract: Word sense disambiguation is the way to compute the correct sense of a word. It is considered as one of the important subtasks in natural language processing, machine translation and information retrieval. WSD found improving the overall performances of these systems. The job of WSD is to eliminate all senses of a word except the appropriate one as per the given context. The work in Tamil linguistics domain for information retrieval or natural language processing is very less. WSD can be performed in supervised and unsupervised manner. Here, we have proposed an unsupervised approach to disambiguate Tamil words in a given context using the context words and their dictionary gloss definitions. We have proposed two variants of our approach. The first approach uses the number of word overlapping between the glosses of context words whereas the second one uses the similarity between the glosses of context words with that of the ambiguous word. The second one found best among the two. For our approach, we have used Tamil Indo Wordnet, Oxford Tamil dictionary and English WordNet dictionary glosses. Our method achieves better result in recognizing correct senses in Tamil text.
Keywords: Word Sense Disambiguation; Natural Language Processing; Tamil WSD; Cross-Language Similarity; Gloss Vector Measure; IndoWordnet; Information Retrieval; Tamil shallow parser; Tamil Dictionary Glosses; Word Overlapping Measure.rnrn.
Factors influencing the change in consumer buying behavior in the competitive Era: An empirical study of consumer durable in Vietnam
by H.-C. Pham
Abstract: Aim and Purpose: The main purpose of this study is to determine the relationship between the variables influencing the consumer buying behavior. The consumer buying behavior has been identified in two forms consumer response and repeat purchase & recommendation. Scope: The study is theoretically limited to the consumer buying behavior and its antecedents. The study considers the responses from consumers of durable products of Vietnam. Methodology: This study is descriptive in which survey method has been used to collect the data. The data analysis has been done with the help of exploratory factor analysis and multiple regression. The sample size of 588 customers has been taken in the study to derive conclusions. Findings: The study finds that the constructs representing antecedents of consumer buying behavior such as advertising and promotion, celebrity endorsement, Effective CRM and Country of Origin have a significant impact on consumer buying behavior.
Keywords: Consumer buying behavior; multiple regression; competitiveera; exploratory factor analysis.
PREC - Hybrid Lightweight Cryptographic Approach to Enhance IoT Data Security
by M. Sruthi
Abstract: Data security in IoT is a significant area where IoT comprises of resource constrained devices. Hybrid cryptographic technique is a combination of symmetric and asymmetric algorithm. The advantage of hybrid cryptography is to speed up the process of data encryption with secure key transmission. Since IoT comprises of resource constrained devices we deploy the lightweight cryptographic algorithms which is specific for resource constrained devices with tailored S box and P box functions and less gates requirement. The proposed hybrid system uses PRESENT an ultra lightweight symmetric block cipher to encrypt the data and then ECC to secure key transmission. The results shows that proposed lightweight hybrid algorithm is better in terms of time taken to encrypt/decrypt which is must in a resource constrained environment.
Keywords: IoT; data security; hybrid cryptography; lightweight cryptography; light weight block ciphers.
Rapid Retrieval of Secured Data from the Sensor Cloud using a Relative Record Index and Energy Management of Sensors
by Geetha S, Deepalakshmi P
Abstract: A massive amount of data is produced by sensors. The data eventually finds a place in the cloud through a base station. Occasionally, the data collection process is disrupted as a result of the energy level of the sensor network. The energy of sensor batteries can be drained by voids. Void sensors do not propagate messages intended for the destination. We have addressed the issue of voids in sensors with the Dynamic Void Removal Algorithm. Data stored in the cloud is being used and retrieved by multiple customers through specifying the relative record index of the sensor data collected. A security mechanism is built with the help of the relative record index associated with sensor data collection. Authenticated customers are given a secret key to rapidly retrieve data from the cloud. Meanwhile sensor networks require a secure mutual authentication scheme in an anxious network environment; we use the Relative Record Index method to design a new user authentication procedure. Our etiquette can handle all problems thrown up by the former schemes. Furthermore, it enhances Wireless Sensor Network authentication with a higher degree of security than other protocols. Therefore, our protocol is more suited to an open and higher-security Sensor Network environment despite greater computation cost and energy.
Keywords: Wireless Sensor Network (WSN); Sensor Cloud (SC); Void Sensors (VS); Dynamic Void Removal Algorithm (DVRA); Relative Record Index (RRI).
Comparative Study on IDS using Machine learning approaches for Software Defined Networks
by Muthamil Sudar K, Deepalakshmi P
Abstract: Software defined networking (SDN) is an emerging network approach that separates the data plane from control plane and enables programmable features to efficiently handle the network configuration in order to improve network performance and monitoring. Since SDN contains the logically centralised controller which controls the entire network, the attacker mainly focuses on causing vulnerability towards the controller. Hence there is a need of powerful tool called intrusion detection system (IDS) to detect and prevent the network from various intrusions. Therefore, incorporation of IDS into SDN architecture is essential one. Nowadays, machine learning (ML) approaches can provide promising solution for the prediction of attacks with more accuracy and with low error rate. In this paper, we surveyed about some machine learning techniques such as naive Bayes, decision tree, random forest, multilayer perceptron algorithms for IDS and compare their performance in terms of attack prediction accuracy and error rate. Additionally, we also discussed about the background of SDN, security issues in SDN, overview of IDS types and various machine learning approaches with the knowledge of datasets.
Keywords: intrusion detection system; IDS; machine learning; software defined networking; SDN; naive Bayes; decision trees; random forest; multilayer perceptron; datasets.
The effect of Lean on job satisfaction
by VARADARAJ ARAVAMUDHAN, ANANTH SENGODAN
Abstract: Lean principles and lean management are increasingly implemented in various sectors of organisations. Lean has shown visible effects in enhancing productivity, reducing wastage of time and materials while still maintaining customer satisfaction as well as employee satisfaction. Lean philosophy is about people understanding their motives and aspirations. Most of the literature works on lean say that the key driver for lean implementation is employee involvement and satisfaction with the process. Hence lean always focuses on employee motivation and their work performance. This thesis is proposed to study on the impact of lean on job satisfaction in organisations. The research data will be collected using the survey tool by distributing questionnaires to organisations that implement lean principles. The respondents who belong to the group that handles everyday work process and services will be selected to participate in the survey. The research survey data will be analysed using Microsoft Excel along with statistical tools like Correlation in order to have an in depth understanding on the findings of the research proposed through several hypothesis.
Keywords: enhancing productivity; reducing wastage of time; limitation of materials; employee motivation.
Reinforcement based Heterogeneous Ensemble for Anomaly Detection in Streaming Environment
by Sanjith S L, E. George Dharma Prakash Raj
Abstract: Intrusion detection in networks is a challenging process, mainly due to huge amount of data and the imbalanced nature of the data. Further, the ever-changing transmission patterns introduce concept drift, which also exhibits a huge challenge. This work presents a heterogeneous ensemble based prediction model to detect anomalies in the network environment. The major goal of the proposed model is to provide faster, more efficient real-time predictions and to enhance the reliability of the model by providing an iterative mechanism to handle concept drifts. The ensemble is created using three varied base learners and the results are aggregated using a voting combiner to provide results. Decision tree, random forest, and gradient boosting trees are used as the base learners. The varied nature of the learners enables effective performances in models. Further reinforcement and an iterative training component is introduced into the model to handle concept drift. Experiments were performed on benchmark intrusion detection data and the results indicate the high performing nature of the model. Comparisons were performed with recent state-of-the-art models in literature and they indicate improved performances of the proposed model, indicating the high performing nature of the proposed ensemble model.
Keywords: ensemble model; decision tree; random forest; gradient boosting trees; voting; anomaly detection.
Using Technological Modality to Learn Incidental and Intentional Vocabulary for Effective Communication
by Aravind B R, V. Rajasekaran
Abstract: Vocabulary is the flesh of a language, which is an indispensable constituent for a language. This research highlights the role of language learners acquisition in incidental and intentional vocabulary by using technological modality. Effective usage of vocabulary in communication and comprehension is crucial and demanding as well. English being the diplomatic language, and which is witnessed as a parameter for graduates, particularly in job acquisition. There are numerous teaching methods were followed for effective learning. In order to benefit, English as a second language (ESL) learners and English as a foreign language (EFL) learners, task-based learning (TBL) approach is observed to be an effective learning method. This paper
devices to use technology, entertainment, and design (TED) talk video with subtitles in the syllabus of TBL learning for effective learning of incidental and intentional vocabulary in language and succeeded by analysing the response from the students. The study reveals the significant development and interest in learning a new word by using the authentic instructional TED talk videos for vocabulary learning and vocabulary acquisition.
Keywords: English as a second language; ESL; English as a foreign language; EFL; task-based learning; TBL; vocabulary; English and communication.
CALL DETAIL RECORD BASED TRAFFIC DENSITY ANALYSIS USING GLOBAL k-MEANS CLUSTERING
by Suja C. Nair, Sudheep Elayidom, Sasi Gopalan
Abstract: With the expanding number of vehicles on the road is creating substantial traffic that is hard to control and maintain safety, particularly in extensive urban areas. To estimate the traffic density several works were carried out in the past. However, they are inappropriate and expensive due to the dynamics of traffic flow. Here we intend to use CDR to distinguish the traffic density location and to track the location of the mobile user. In our proposed method to discover the density scope of the traffic, we are using two algorithms called k-means clustering and the k nearest neighbour classification algorithms. The proposed technique will be tested among five different locations during the weekdays and the weekends, which show the noteworthiness of the proposed algorithm and show that our technique has high accuracy.
Keywords: traffic density; call detail records; CDR; data pre-processing; global K-means clustering algorithm; K-nearest neighbour classification; cell-tower ID; behavioral patterns; disposition; monitoring; predictable.
Indexing Documents With Reliable Indexing Techniques Using Apache Lucene In Hadoop
by E.Laxmi Lydia, Sivakoti Satyanarayan, K. Vijaya Kumar, Dasari Ramya
Abstract: Mostly 85% of the data is presented in the form of text, which is the human-readable format. Present educational, business, medical organisations, etc. making use of big data analytics for storage of data and processing that stored data by using information retrieval. Often times text documents have been transferred from one system to another system without any restrictions like, structured, unstructured and semi-structured data. Systems are well performed with high speed and less complexity only when it has all the data arranged in an orderly way. This paper describes how documents of text data are being Indexed using Apache Lucene with approaches in Hadoop. Most of the applications that deal with huge data over the internet are completely lacking. Use of effective analysis and techniques allow users in resulting
high-performance and a challenging option in leading big data analytics.
Keywords: Apache Lucene; indexing; big data; indexing techniques.
SMSS : Does Social,Mobile,Spatial and Sensor data have high impact on big data analytics
by CHEMMALAR SELVI, LAKSHMI PRIYA
Abstract: Big data refers to the huge torrent of large-scale datasets that are being generated at an exponential growth. Since we live in this digital world, the era of big data has emerged in part and parcel of our lives. The emergence of big data has reached in almost several domains like healthcare industry, telecom industry, molecular biology, biochemistry, physics, astronomy, computer science, business and others. In this paper, we have termed the types of big data by the form SMSS data which is simply meaning social, mobile, spatial and sensor data. This paper aims to provide the importance of big data analytics brought over the different types of big data extracted from heterogeneous data sources. To achieve this objective, we have made an
intensive study of several literatures and considered a variety of big data applications which are being discussed to showcase its value. Also, a generic framework is proposed that can be applicable to any kind of big data types extracted from such a diverse heterogeneous data sources. Finally, a few open source tools that can be used for processing the big data are presented.
Keywords: big data types; social data; spatial data; sensor data; mobile data.
OFFLINE STUDY FOR IMPLEMENTING HUMAN COMPUTER INTERFACE FOR ELDERLY PARALYZED PATIENTS USING ELECTROOCULOGRAPHY AND NEURAL NETWORKS
by S. Ramkumar, K.Sathesh Kumar, K. Maheswari, P.Packia Amutha Priya, G. Emayavaramban, J.Macklin Abraham Navamani
Abstract: Earlier days people with disability face lot of difficulty in communication due to neuromuscular attack. They are unable to share ideas and thoughts with others so they need some assist to overcome this condition. To overcome the condition, in this paper, we discussed the capabilities of designing electrooculogram (EOG)-based human computer interface (HCI) by ten subjects using power spectral density techniques and neural network. In this study, we compare the right hander performance with left hander performance. Outcomes of the study concluded that lefthander performance was marginally appreciated compared to right hander performance in terms of classification
accuracy with an average accuracy of 93.38% for all left hand subjects and 91.38% for all the right subjects using probabilistic neural network (PNN) and also we analysed that during the training left handers were interestingly participated and also they can able to perform the following eleven tasks easily compared with right handers.
Keywords: electrooculography; EOG; periodogram; human computer interface; HCI; probabilistic neural network; PNN.
Studies on European Call Option of Binomial Option Pricing Model Using Taguchis L27 Orthogonal Array
by Amir Ahmad Dar, N. Anuradha
Abstract: There are several parameters affecting the European call option value such as strike price K, the price of an underlying asset S0, volatility , time period t and interest rate r. In this paper, the binomial option pricing model is utilised to assess the estimation of a European call option. To explore the effects of input factors, Taguchi method of orthogonal L27 design experiment is carried out using an orthogonal array, analysis of variance (ANOVA), and analysis of mean (ANOM) were used. The purpose of this paper to find the best optimal combination by varying the parameters at constant interest rate r and the effects of parameters are discussed. The ANOM distinguishes which parameter influences higher on European call option value
and furthermore, it demonstrates the best combination where the European call option will get the greatest value. The ANOVA estimates the percentage contribution of every parameter on European call option and the analysis is carried out using MINITAB software.
Keywords: binomial model; Taguchi’s method; analysis of mean; ANOM; analysis of variance; ANOVA; option.
A new dominant point detection technique for polygonal approximation of digital planar closed curves.
by Kalaivani S, Bimal Kumar Ray
Abstract: On the visual perception, the real world objects are the collection of irregular polygons. Representation and understanding the irregular polygon is an interesting and major task in various research areas. However, approximation of the polygon is quite complex and challenge in different views. In this paper, a new approximation algorithm is proposed to represent the irregular polygon. The proposed method detects the dominant points which has high impact on the shape and supress the weak points. The obtained approximated polygon are with less vertices/points and retain the original shape with less approximation error value. The experiments of proposed algorithm are conducted using MPEG shape datasets to show its performance both in quantitative and qualitative aspect.
Keywords: Irregular polygon; curve; collinear points; dominant point; local deviation; global deviation; distortion error; split; merge; polygonal approximation.
Special Issue on: OSCM 2018 Sustainable Supply Chains and Circular Economy
Factors influencing Information and Communication Technology diffusion in Nigerias transport and logistics industry:an exploratory study .
by Anthony Ezenwa, Anthony Whiteing, Daniel Johnson, Akunna Oledinma
Abstract: Modern transport and logistics management is replete with the applications of advancements of Information and Communication Technology (ICT). However, the barriers to ICT innovation diffusion in the industry are widely acknowledged, particularly in the context of developing countries. Although ICT innovation diffusion reflects a continuum of conditional forces that coalesce around institutional forces; how different locally-evolved factors are influencing ICT diffusion in the developing logistics markets remains an under-researched issue. Moreover, ICT innovation acceptability and operative constraints of various stakeholders in the industry also remains a subject of concern. This paper combines primary qualitative studies (focus group discussion and in-depth expert interviews) to explore (i) how institutional forces in Nigeria are influencing ICT diffusion in the transport and logistics industry, using evidence from the local 3PL SMEs, (ii) the perceptions of the relevant stakeholders concerning ICT diffusion challenges in the industry and their roles in modulating them. The systematic analyses of the qualitative data, including content, thematic, magnitude coding, and interpretative procedures provide a useful way of understanding the mechanisms influencing ICT diffusion in Nigeria's transport and logistics industry and where discourses differ. In exploring the views of the stakeholders, one can observe that institutional forces are more oriented towards complexity than predictability, doing little to address either the structural challenges of the industry or the operational threats of the local logistics operators. The study concludes by illustrating that a turn towards institutional voids thinking will help advance sustainable ways to mitigate ICT diffusion challenges in the Nigerian transport and logistics industry.
Keywords: ICT; diffusion; 3PL SMEs; Nigeria; transport and logistics industry; innovation.
Drivers and barriers of consumer purchase intention of remanufactured mobile phones: a study on Indonesian consumers
by Didik Wahjudi, Shu San Gan, Yopi Yusuf Tanoto, Jerry Winata
Abstract: Remanufacturing is widely practiced because it reduces landfill, saves energy, and conserves natural resources. Little effort is given to exploring the acceptance of remanufactured products, especially the short life-cycle ones. Indonesia, the fourth largest mobile phone users, need to tackle the vast WEEE deriving from obsolete mobile phones. This study investigates drivers and barriers of consumer purchase intention for remanufactured mobile phones. Thirteen respondents were interviewed, representing different age groups, genders, and social classes. The key drivers are the affordable price, upgraded specification, and product warranty, while the regulatory concern, trend concern, and obsolescence concern are the main barriers. Perceived quality and quality assurance can be both drivers and barriers, depending on the level. This study recommends remanufacturers to focus on providing upgraded specification at an affordable price and providing information about the remanufacturing process. This study also argues the urgency for the government to enforce rigorous regulation against counterfeit products.
Keywords: remanufacturing; mobile phone; short life-cycle product; case study; Indonesia.
Circular Economy Business Model Design
by Arman Hakim Nasution, Mucharromatul Aula, Dewie Saktia Ardiantono
Abstract: The increasing influence of sustainability in the practice of supply chain management and operations can be attributed to the fact that the existing stakeholders within the organization are required to gain strong economic performance and be responsible for environmental and social performance. The implementation of circular economy in Indonesia has become one of the government's attention. This is evidenced by the master plan of circular economy implementation made until 2025. Although the masterplan that has been made is still focused on manufacturing companies, in this study, the author took a gap in the field of agribusiness, especially in the dairy industry. This research will conduct an optimal design of Circular Business Model Canvas (CBMC) for the dairy industry with expert opinion, calculated a prediction of new potential revenue streams of redesign result, and the relationship between circular economy with Sustainable Development Goals (SDGs). This research uses a qualitative method with explorative research design. The results show promising findings. A new business model for circular economy in the dairy industry is conducted. This new business model dairy industry will directly support five points of SDGs (point 6, 7, 8, 12, 15).
Keywords: CBMC; circular economy; dairy industry.
Consumers Perceptions of Circular Economy in the Hotel Industry: Evidence from Portugal
by Jorge Juliao, Marcelo Gaspar, Clarisse Alemão
Abstract: The purpose of this paper is to explore current consumers
Keywords: Circular Economy; Sustainability; Green Hotels; Consumer Behaviour; Green Products; Environment; Hospitality Industry; Tourism; Green Tourism; Ecotourism; Eco-traveling.
Sustainability as a driver of operational excellence - the relevance of variability in process operations
by Daniela Silva, Américo Azevedo
Abstract: Sustainable development is a widely spread concept nowadays, especially due to external pressure related with environmental and social issues, affecting all players of the supply chain. Sustainable policies must be adopted, such as improving process performance and reduce waste. With sustainability as a driver of operational excellence, this study is focused on the improvement of the production process of a company by reducing variability. A variability analysis was done to understand its root causes and act upon them, as well as a quantification of waste in the process. Finally, an improvement plan was delineated to mitigate the problems identified.
Keywords: Variability; lead-time; industrial production; waste quantification; operational excellence; sustainable standards; process operations.
Impacts of Industry 4.0 in Sustainable Food Manufacturing and Supply Chain
by Olumide Olajide Ojo, Satya Shah, Alec Coutroubis
Abstract: Integration of Sustainability and sustainable practices have been of paramount importance within most manufacturers' supply chain environment globally. Apart from the fact that every organisation now use this to improve on their Corporate Social Responsibility (CSR), this is also used as an opportunity to manage production and services within most firms efficiently. This sustainability is now a strategy adopted by most businesses to meet their customers' expectation considering the sustainable society awareness of which food manufacturing is not an exception. The use of several innovative strategies and incorporation of Industry 4.0 has been employed by some food manufacturers to meet up with this sustainability. This paper through a research study aims to bridge the interconnection between Industry 4.0 and sustainable practices within food manufacturing supply chain environments. This paper through a research study seeks to bridge the interconnection between Industry 4.0 and sustainable practices within food manufacturing supply chain environments
Keywords: Food Manufacturing; Industry 4.0; Logistics; Supply Chain; Sustainability; Sustainable Production; Corporate Social Responsibility.
Assessing Significant Factors for Sustainable Cold Chain Performance in Sri Lankan Context
by Keshala Wickrama Gunaratne, Pradeepa Jayaratne
Abstract: Purpose - Nearly one third of the food that is globally produced is wasted mostly during initial production and in transit or thrown away by wealthier economies due to unsuitability for consumption. The main ways of food spoilage occur during storage, transportation and production activities. While cold chains are becoming an imperative fragment in most of the developed countries at present, when it comes to developing countries like Sri Lanka there is huge loss incurred annually due to poor practices in this sector. Considering the importance of supply chain in the field of perishable commodities, the main objective of this study is to identify the contributing factors with a specific goal of reducing waste and facilitating sustainable performance in the cold supply chain.
Design/Methodology/Approach The evolution of the research field is analyzed with the use of extensive literature survey in five different industries that use cold chain practices in Sri Lanka. Study was carried out mainly in three stages. To identify the most significant factors, Exploratory Factor Analysis (EFA) was used followed by Confirmatory Factor Analysis (CFA). Finally, for the derivation of factor-based relationship model Structural Equation Modelling was used (SEM). Furthermore, the evolution of performance and sustainable measurement in cold chain were used to highlight the criticalities of the research field.
Findings The authors classify three main significant factors that impact sustainability in perishable supply chain. The paper highlights the inadequacies in the available literature to achieve food quality and nutrition with less wastage and what main areas that need focus to achieve sustainability goals. Moreover, these key areas are open for scholars to plan future research in this area.
Research limitations/implications The results presented from this paper is limited to large & medium scale entities that use cold chain practices anywhere along the supply chain. Also due to restrictions of information sharing from certain companies the sample size has a moderate number, with an above par response rate of 63%. The results of this study can be used by researchers to focus on specific industries at large scale to have more implications on sustainability. In addition, practitioners can develop a performance measurement framework including the main three factors that authors propose to achieve their sustainable goals within their food supply chain.
Originality/Value Research on perishable supply chain is receiving immense attention from both academia and practitioners due to significant relevance towards sustainability and food quality. The body of knowledge is yet immature and this paper provides a new perspective of thinking towards attaining sustainability performance. The work is original in the way authors integrate sustainability across the three key factors that identified across supply chain in every strategic, operational and tactical decisions. The context can be used by researchers and practitioners to develop practical sustainable cold chain performance framework.
Keywords: Sustainability; Green Logistics; Perishables; Cold Chain; Temperature Controlled Environment; Food Waste; Carbon Foot Print; Sustainable Performance.
Effect of Corporate Sustainable Development on Green Purchasing: Insights from ISO14001 Certified Manufacturing Companies in Malaysia
by Marini Nurbanum Mohamad, Charis Samuel Solomon Koilpillai
Abstract: The objective of this paper is to investigate the effect of corporate sustainable development on green purchasing. A conceptual model was recommended and empirically tested to identify the relationship of corporate sustainable development (environmental development, economic development and social development) towards green purchasing. Questionnaires were sent to all 621 ISO 14001 qualified manufacturing corporations in Malaysia and a total of 146 responses were obtained. A regression analysis was conducted to analyse the data and to test the hypotheses that corporate sustainable development has a positive effect towards green purchasing. Findings of this study identified that all three facets of corporate sustainable development (environmental development, economic development and social development) has a positive effect towards green purchasing. These results present empirical evidence concerning the significant relationship of corporate sustainable development towards green purchasing among ISO 14001 qualified manufacturing companies in Malaysia. Additionally, this research describes the significance of practicing green purchasing in the manufacturing industry which is particularly useful for managers to understand how the adoption of corporate sustainable development strategies by manufacturing firms would be justified, not just by merely moral grounds, but also the positive gains in companies.
Keywords: green purchasing; corporate sustainable development; environmental development; economic development; social development; Malaysia.