International Journal of Intelligent Enterprise (24 papers in press)
Solving Quadratic Assignment Problem by Symbiotic Organisms Search Algorithm
by Heba Rashad, Abdel Nasser H. Zaied, Yongquan Zhou
Abstract: Symbiotic Organisms Search algorithm (SOS) is a new meta-heuristic algorithm based on the symbiotic relationship between the biological, which was proposed in recent years. In this paper, we propose new robust and powerful metaheuristic algorithm called an improved version of symbiotic organisms search algorithm integrated with construction algorithm and mutation operators (ISOS) to solve quadratic assignment problems. The Quadratic Assignment Problem (QAP) considers NP-hard combinatorial optimization problem and has various practical applications. A set of benchmarks from QAPLIB Library are employed to evaluate the performance of proposed algorithm. ISOS is compared to several algorithms in the literature. The comparisons show that the proposed algorithm shows reliable, efficient and promising results.
Keywords: Symbiotic organisms search algorithm; Optimization; Quadratic assignment problem; Combinatorial Optimization.
Gandhian management perspective for enhancing productivity and innovation in Public Sector Organization.
by Shashi Kant, Pramod Chandra, Vinay Sharma, Rajat Agrawal
Abstract: This paper underpins the importance of establishing Public Sector Undertakings (PSU) in India with special reference to one of the key industrial organizations, Bharat Heavy Electricals Ltd (BHEL), which focuses on elements of innovation that drives productivity inculcating the Gandhian management perspective. In the present competitive world, BHEL and other industrial PSUs are passing through a transitional phase of enhancing their productive competitiveness through both process and product innovation for sustainable growth. This requires introduction and adaption of non-conventional or modern modes of management based on the principles of Mahatma Gandhi. The need, importance and implication of Gandhian perspective in industrial organization got reemphasize. Mahatma Gandhis fundamental philosophical approach might not be radically different from that of others but it exists in the form of an approach that structurally recommends a pathway of wealth generation, productivity, wealth distribution, and economic prosperity for all. The paper intends to find traces of Gandhian management perspective in Indian PSUs and also tries to elicit relevant Gandhian elements of industrial organization management.
Keywords: Public sector undertaking; ethical management; sustainable leadership; Gandhian management perspective; Gandhian innovation.
A Study on the Barriers to Lean Manufacturing Implementation for Small-Scale Industries in Himachal Region (India)
by Anbesh Jamwal, Ankur Aggarwal, Sumit Gupta, Parul Sharma
Abstract: In todays competitive market, lean philosophy has a vital role to play which is mainly governed by a reduction in cost, ameliorated quality, and higher expectations of customers as well as superior productivity. It has been globally witnessed that the companies which implement lean manufacturing in their system result into improved productivity and better customer satisfaction, along with the cost reduction. Lean manufacturing is the most important productivity improvement tool. The aim of this research is to identification and confirmation of the barriers to lean implementation in small-scale industries in the Himachal Region (India) and to build up a relationship between the barriers. A Total Interpretive Structural Modeling approach is applied to identify the relationship of various barriers. It is evident from the research that the cost of facilities is a major barrier to implementing lean in the small-scale industries. However, the government gives the financial support for the small-scale industries in Himachal Pradesh.
Keywords: Lean Manufacturing (LM); Barriers; Total Interpretive Structural Modeling (TISM); Inter-relationship.
Special Issue on: Smart City Applications
Performance study of Routing Protocols based on node mobility in MANETs
by Younes BEN CHIGRA, Abderahim GHADI, Mohamed BOUHORMA
Abstract: Mobile Ad Hoc network (MANET) is established by a set of smart mobile nodes without any central intelligence. Hence, nodes auto-discover their surrounding and establish needed links between source and destination node. Moreover, mobile nodes change arbitrary their position, which lead to frequent link failure. Therefore, the dynamic behaviour of nodes has a drastic impact on networks performances such as end-to-end delay, packet loss ratio and traffic control that is generated during route update process. Consequently, the main issue in MANETs operations is the design of an efficient routing protocol able to deliver required quality of service in case of nodes mobility and manage the intrinsic limitation of wireless networks, like scares bandwidth, limited energy and processing capabilities. The purpose of this paper is to study the impact of nodes mobility on routing performances and look forward an appropriate optimisation that might enhance efficiency of routing protocol in high dynamic environment.
Keywords: Routing protocols; Network performances; Mobile Ad Hoc Network (MANETs); Mobility; Traffic overhead; Routing metrics.
A memetic algorithm for fleet size and mix vehicle routing problems with electric modular vehicles
by Dhekra Rezgui, Jouhaina Chaouachi Siala, Wassila Aggoune-Mtalaa, Hend Bouziri
Abstract: This work deals with an extension of the well-known Vehicle RoutingrnProblem with Time Windows (VRPTW), where the fleet consists of electric modular vehicles (EMVs). The main drawback of managing electric vehicles isrnthat they have a limited range. Here the vehicles are modular which means that payload modules are carried by a cabin module and can be detached at certain customer locations allowing the rest of the vehicle to continue the tour. This can also permit to recharge the battery of some modules to further capitalize on the gained energy. To tackle the resulting research problem, a comprehensive mathematical formulation is proposed to take into account the multiple constraints linked with the modularity, the electric charging, time windows to serve the customers and capacity issues. Due to the NP-hardness of the problem, a memetic algorithm is implemented and tested for designing good quality solutions in reasonable computational times. Extensive computational experiments carried out on some benchmark instances show the effectiveness of both the problem formulation and the memetic algorithm.
Keywords: Urban logistics; Vehicle Routing Problem; Metaheuristics; Electric Modular Vehicles.
Micro Navigation System for Smart Parking Area
by Hicham LAHDILI, Zine El Abidine Alaoui Ismaili
Abstract: Despite its limited accuracy, Navigation systems depending on GPS are currently attracting considerable interest due to their increasing convenience in the field of transportation area. So far, most outdoor navigation applications are using GPS and the absence of a low-cost alternative with acceptable accuracy satisfying the indoor applications requirements has become one of the motivational topics to follow up. Therefore, different studies have been recently conducted in order to develop an accurate indoor localization system. In this paper, we will propose a micro-navigation system for parking area with a visual positioning system (VPS) based on the surveillance camera network adaptation and the image processing theory. From the data provided by the VPS, a navigation tool will be presented besides. The proposed system has achieved an accuracy error of 0.08M, and proved a remarkable improvement in terms of finding parking places time-consuming. We believe that we have designed an innovative solution responding to traffic jams and pollution issues as well as stress and finding parking places time-consuming.
Keywords: smart parking; indoor navigation application; vehicle positioning system; real time; image processing.
Smart Cities Reconsidered: the Entrepreneurial Challenge
by Domingos Santos
Abstract: The concept of the smart city acquired increasing expression and has become a strongly discussed field in the recent past among, either the research community, either the political stakeholders. Studying and intervening on the urban dynamics requires a shift from focusing on forms of knowledge and innovation outputs towards focusing on learning and innovation dynamics, exploring the diverse dimensions of knowledge building and promoting social capital. This paper aims, first, to contribute to clarify the meaning of the word smart in the context of urban development through an approach based on an in-depth literature review of pertinent studies and then, to establish the links with the dimensions of entrepreneurship that can help guide more effective urban development and planning policies, illustrating with the Smart Coimbra initiative. Besides the paper aims to inform and improve policy-making on the subject of promoting entrepreneurial mindsets and contexts, speculating on some principles and guidelines that may help fertilize urban dynamics and build smarter and more entrepreneurial cities.
Keywords: Smart city; urban development; entrepreneurship; entrepreneurship policy; innovation policy.
Architecture of a Decision Support System Based on Big Data for Monitoring Type 2 Diabetics
by Boudhir Anouar Abdelhakim, Ben Ahmed Mohammed, Fellaji Soumaya
Abstract: Type 2 diabetes is one of chronic diseases that require continuous and real-time monitoring to prevent the occurrence of complications. On the one hand, the doctor must have information about the patient's daily life (vital signs, stress, sedentary lifestyle, physical activities, nutrition, etc.). On the other hand, the prescribed treatment must be evaluated each time to test the validity of the diagnosis. To achieve this goal, a decision support system based on the Big Data Mining technology must be designed in order to have a centralized knowledge of diabetics. This system will improve the quality of monitoring and treatment from the different data collected. Thus, this paper presents an architecture of a decision support system allowing doctors to monitor the health status of their patients, based on data collected from different resources, in order to enrich the knowledge database and prescribe new treatments based on similar cases and experiences of doctors and patients belonging to this system.
Keywords: Big data; Analytics; Hadoop;Lambda Architecture; Healthcare.
MultiPrime Cloud-RSA: A Fast Homomorphic Encryption Scheme for Data Confidentiality Protection in Clouds
by Khalid EL MAKKAOUI, Abderrahim Beni-Hssane, Abdellah Ezzati
Abstract: Concerns over data confidentiality are amongst the main barriersrnlimiting the widespread adoption of cloud solutions. Indeed, scientists haverninvented a new promising form of encryption, homomorphic encryption (HE),rnwhich can be considered as a powerful tool to get over these concerns. Sincerncloud environments are more vulnerable to attacks and since cloud consumersrnfrequently access to cloud computing services using light-weight devices, the HE schemes must be promoted, in terms of security level and running time, to operate efficiently. In El Makkaoui et al. (2017), we boosted the normal RSA cryptosystem at security level, Cloud-RSA. In this paper, we suggest a new fast variant of the Cloud-RSA scheme, MultiPime Cloud-RSA, to accelerate its decryption process. The variant uses a modulus of the form n = p1p2...pk for k>= 2 and employs the Chinese remaindering to decrypt. Theoretical and simulation results show that the new variant offers a large decryption speed-up over the Cloud-RSA scheme whilst maintaining a recommended security level.
Keywords: Cloud-RSA Scheme; Cloud Computing; Confidentiality; Multiplicative Homomorphism; Chinese Remainder Theorem (CRT); Fast Decryption.
IT GOVERNANCE IN COLLABORATION MODE: BUILDING IT COLLABORATION NETWORK USING A SOCIO TECHNICAL APPROACH BASED ON ACTOR NETWORK THEORY
by MOHAMMED SALIM BENQATLA, BOUCHAIB BOUNABAT
Abstract: Abstract: IT Governance of projects needs collaboration among several organisms. Collaboration is ensured by building network of collaboration between the collaborating entities; IT Collaboration between organizations can play an important role to achieve business objectives. In order to build such networks of collaboration at a real scale need modeling social interactions between different actors in order to share, analyze, and suggest improvements for a collaborative perspective. This paper describes a new tool for collaboratively modeling based on Actor Network Theory. *CollabANT system is based on Actor Network Theory and Game Theory algorithm that efficiently provides abstract models of collaboration between different actors aiming at uncovering cost allocations concerns. We demonstrate the effectiveness of our approach with a real case study. The analyze of *interessement phase reveals that we are able to increase the cost saving objectives within a collaborative mode. We also present a what-if simulation feature to assess the impact of scenarios related to future collaboration evolution. Furthermore, we provide a live deployment of the *CollabANT system that allows users to explore the dynamics of collaboration networks in place as well as their involvement over time.
Keywords: Actor Network Theory; IT governance; Cost-Sharing; Cooperative game theory; Shapley value; Collaboration Network; socio-technique.
Special Issue on: Digital Innovation and Intelligence Analysis of E-Business Collaborations and Societal Challenges
Clustering of Text Documents with Keyword weighting function
by CHRISTY A, MeeraGandhi.G G, Vaithyasubramanian S
Abstract: In this digital world, Data is available in abundance everywhere and it is growing at a phenomenal rate. Making data available readily for decision making is an important task of data analyst. In this article, we propose an unsupervised learning algorithm for text document clustering by adopting keyword weighting function. Documents are pre-processed and relevant keywords based on their weights are grouped together. Clustered Keyword Weighting (CKW) takes each class in the training collection as a known cluster, and searches for feature weights iteratively to optimize the clustering objective function, in order to retrieve the best clustering result. Performance of CKW is validated by clustering BBC news collection text collections. Experiments were conducted with simple K-Means, Hierarchical clustering algorithms and our keyword weighting and clustering approach has shown improved cluster quality compared to the other methods.rn
Keywords: Documents; Cluster; Unsupervised; Feature; K-Means; Normalized; etc. rn.
Fuzzy Association Rule Mining for Economic Development Indicators
by Deepesh Kumar Srivastava, Basav Roychoudhury, Harsh Vardhan Samalia
Abstract: This paper is focused on fuzzy mining approach to extract fuzzy association rules among the economic development indicators that are Net official development assistance received (ODA), foreign direct investment (FDI) and gross domestic product (GDP) for developing country India.This study is an attempt to explore the use of fuzzy association rule mining on time series data and to extract interesting association rules therefrom. The extracted rules exhibit the relative volatile nature of these three development indicators. A fuzzy membership function is used to transform the quantitative values of percentage change of each successive year datum into fuzzy sets in linguistic terms. The scalar cardinality of each linguistic term is calculated on the yearly data. Only those fuzzy association rules that qualified the criteria of minimum support and minimum confidence value were taken into consideration. The rules thus mined out exhibit quantitative regularities and can be used for the better suggestion to appropriate policy makers.
Keywords: Development indicators; Association rules; Fuzzy sets; Membership function.
Integrating the Power of Social Media Dataset Impact in Medical Diagnosis
by Suresh A, A. Jayanthiladevi
Abstract: Technology gives consumer the power to investigate products to label them and criticize them in equal measure, and more. The emergence of Internet-based social media has made it possible for one person to communicate with hundreds or even thousands of other people about products and the companies that provide them. Due the impact of consumer-to-consumer communications is a most impressiveness in the marketplace. Therefore many companies today have pages on social networks to complement the information held about products, the feedback of consumers about products and tend to relate more to a company after reading various reviews. This paper is discusses about the dataset used in this work for medical diagnosis, experimental scenario and also about obtained result and discussion of the proposed system and reason for achievements on decision making process. We have collected 10000 records from weblog dataset for experiments in this work for behavioural analysis. Initially, feature selection by using knowledge base and send it for classification using WEKA tool and JAVA. WEKA is a collection of machine learning algorithms for data mining tasks. The proposed algorithms applied to the dataset from Java code and it contains tools for data analysis and predictive modeling. The input dataset of the WEKA are used in the form of CSV file. The various results obtained by the proposed model and other models and classifiers. Performance of the proposed hybrid behaviour analysis model which is the combination of SMO classifier and Rule based Classification algorithm. The performance comparative analysis between the proposed rules based classifier, C4.5 and SMO. The performance of the proposed hybrid behaviour analysis model provides better performance than individual performance of other classifiers.
Keywords: social network; online buying; consumer behavior; and Rule based classifier: C4.5 and SMO and Hybrid model.
Heterogeneous Network Security Management
by Narmadha Ramakrishnan
Abstract: The main objective of this paper is to develop authentication delay model for inter domain or intra-domain authentications. Authentication is a security mechanism used to identify mobile nodes(MN) and prevent unauthorized usage when inter-domain or intra domain handoff happens. The latency due to authentication procedure is known as authentication delay. The authentication delay depends on Message propagation time, Message transmission time, Message encryption/decryption time, Authentication request service and waiting time at the Access Point, Authentication request service and waiting time at the server, Key encryption & decryption time, Key generation time and the registration request service and waiting time, etc., In existing systems, Authentication Delay modelling of homogeneous network domain has been carried out and actual values are not applied for modelling the authentication delay. In the proposed system Authentication Delay modelling of heterogeneous network domain will be carried out and actual values will be applied for modelling the authentication delay.rnrn
Keywords: Security; Interworking networks; Authentication; Key Management.
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),.
Special Issue on: Big Intelligent Enterprise For Sustainable Computing
Comparative Study on IDS using Machine learning approaches for Software Defined Networks
by Muthamil Sudar K, Deepalakshmi P
Abstract: Software Define 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 centralized 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. Now a day, 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: IDS; Machine learning (ML); Software defined networking (SDN); Naïve Bayes; Decision Trees; Random forest; Multilayer perceptron; Datasets.
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.
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).