International Journal of Enterprise Network Management (14 papers in press)
Performance and Emission Characteristics of a DI Diesel Engine using Diestrol Blends and diesel as fuel
by BIBIN CHIDAMBARANATHAN, Seeni Kannan Pauldurai, Devan Ponnusamy Kumarasami, Rajesh Rajamoni
Abstract: Biofuels, namely, biodiesel and ethanol produced from renewable energy sources are used as fuels in the blended form along with diesel to investigate the performance and emission characteristics of a DI diesel engine. Diestrol blend consists of diesel, biodiesel/methyl ester and ethanol. In diestrol blends, ethanol percentage is steadily elevated with an incremental factor of 5% culminating into three blends with a maximum percentage of 15% by volume and named as EB5, EB10 and EB15 respectively. A comprehensive analysis of engine performance characteristics such as brake thermal efficiency, brake specific fuel consumption, exhaust gas temperature and emission characteristics such as Carbon monoxide, Carbon dioxide, unburned hydrocarbon, oxides of nitrogen and Smoke opacity were carried out. From the above investigation, it was found that brake thermal efficiency increased by 3%, 5% and oxides of nitrogen emission decreased by 23%, 24.5% when compared to diesel and B20 respectively.
Keywords: diesel engines; diestrol; ethanol; emissions; methyl esters; punnai oil; performance; ternary blends.
Prediction of carotid atherosclerosis in patients with impaired glucose tolerance A performance analysis of machine learning techniques
by Maruthamuthu A, M. Punniyamoorthy, Swetha Manasa Paluru, Sindhura Tammuluri
Abstract: The focus of this paper is to examine factors associated with carotid atherosclerosis in patients with impaired glucose tolerance (IGT), and to predict the rapid progression of carotid intima-media thickness (IMT). The proposed machine learning methods performed well and accurately predicted the progression of carotid IMT. The linear support vector machine, non-linear support vector machine with a radial basis kernel function, multilayer perceptron (MLP), and Naive Bayes method were employed. A comparison of these methods was conducted using the Brier score, and the accuracy was tested using a confusion matrix.
Keywords: Multilayer Perceptron; Support Vector Machine; Radial Basis Kernel function; Impaired Glucose Tolerance; Carotid Atherosclerosis; Naive Bayesian model; Brier Score.
Do you gain by Green Supply Chain Management (GSCM)?
by Balakrishnan AS, Jayshree Suresh
Abstract: The importance of green has increased due to the environmental change. The burning of oil and other fossil fuels releases carbon dioxide, which rises, blankets the earth and traps heat. On environmental issues, there are intensive studies which have been dealt with extensively by practitioners and academicians. There is an increasing pressure on businesses to improve economic and environmental performance. Green supply chain management (GSCM) is an emerging approach for economic and ecological benefit to manufacturers. This paper presents the case study on how GSCM practiced in Ford India in the areas of logistics, packaging and manufacturing processes, how GSCM influence with firm performance, and its gain by extending across firms in developing markets such as India.
Keywords: Green supply chain management; GSCM; logistics; manufacturing; packaging; India.
An Analytical Study of Lean Implementation Measures in Pump Industries in India
by Mohan Prasad M, Ganesan K, Paranitharan K.P, Rajesh R
Abstract: The manufacturing industries in India are gearing up to face the challenges namely the quality, timely delivery and satisfying customer need in the international market. This prompted some large manufacturing industries to implement lean thinking in their manufacturing process. Most of the manufacturing companies are yet to take up this task. Particularly, the pump manufacturing industries which are mostly occupied by SMEs are still to follow the suit. In this context, this research study has made sincere attempt to survey the implementation of Lean in pump manufacturing industries in India through an instrument consisting of seven lean implementation measures namely, Reasons to Implement Lean Practice, Lean Tools/concepts Employed in the Company, Reasons for Low Priority Towards Lean Implementation, Major Barriers in Lean Practices, Evaluation of Level of Waste in the Company, Success Factors of Lean Practicing in the Company and Lean Performance Indicators. A survey type research was conducted and the results indicated that identified lean implementation measures were found to be significant in achieving lean implementation in pump manufacturing industries.
Keywords: Lean Practice; Lean Implementation Measures; Pump Manufacturing.
COMBINED STATIC ECONOMIC & EMISSION DISPATCH BY IMPROVED MOTH OPTIMISATION WITH VALVE POINT LOADING
by Vennila H, Rajesh R
Abstract: The need for electricity is the defining feature of the modern age. As the demand increases, so must the production. Thus, the size of power systems grows day by day. The problem of deciding the contribution of each generator in a power system is a complex one. Where some generators may be more fuel efficient, others may have a cleaner operation. The special methods like Valve Point Loading which help further improve efficiency, it is necessary to find new and better ways to determine the power to be supplied by each generator in a power system. This paper aims to find an optimum solution for the problem by the use of an algorithm inspired by the flight pattern of moths. Like a moth drawn to a flame, this algorithm zones in on the optimal solution, to minimise fuel cost as well as shrink emission of harmful gases. Moth Flame Optimization is a simple and robust method to discover the optimal solution in a vast search space. Thus, by implementing a heuristic algorithm like Moth Flame Optimization, the complex problem of finding the power to be generated by each generator in a power system can be vastly simplified and the optimal result can be easily and efficiently obtained.rnrn
Keywords: Economic Dispatch; Emission Dispatch; Improved Moth Flame ; heuristic algorithm.
A hybrid algorithm to solve the stochastic flow shop scheduling problems with machine break down
by MARICHELVAM MARIAPPAN KADARKARAINADAR
Abstract: A flow shop scheduling problem with uncertain processing times and machine break down is considered in this paper. The objective is to minimize the maximum completion time (makespan). As the problem is NP-hard (Non-deterministic Polynomial-time hard), a hybrid algorithm (HA) is proposed to solve the problem. The firefly algorithm (FA) is hybridized with the variable neighbourhood search (VNS) algorithm in the proposed HA. Extensive computational experiments are carried out with random problem instances to validate the performance of the proposed algorithm.
Keywords: scheduling; NP-hard; flow shop; makespan; firefly algorithm; variable neighbourhood search.
Critical review of literature and development of a framework for application of Artificial Intelligence in business
by Sanjay Mohapatra
Abstract: Artificial Intelligence has the ability to predict outcomes accurately and with reliability. The techniques have been used in several industries and domains. However, documenting results from different research that were conducted have not been documented. Also, most of the research have been carried out in developed countries and not much work have been published from other economies. As a result, there is a need to develop proper research background so that application of AIs can be sustainable and effective. The purpose of this study is to critically review different studies that have adopted AI in several domains, so that a theoretical framework guide for researchers and practitioners can be developed. This framework will also establish future trends in the said research area. From online databases, relevant articles and extracts were retrieved and were systematically analyzed. Using these inputs, a framework was developed. The findings of this study show that there is a gap between research work done and documentation available. The present applications of AI techniques require model based approach that brings in consistency in research as well as for industry. A paradigm shift in the framework based approach could lead to achieving a sustainable practice.
Keywords: Artificial Intelligence; Framework; Theoretical Study; AI applications.
Labor productivity improvement using hybrid Maynard Operation Sequence Technique and Ergonomic assessment
by Medha LNU, Sharath Kumar Reddy, Vimal KEK, Aravind Raj Sakthivel, Jayakrishna Kandasamy
Abstract: Productivity measures how efficiently productions inputs, such as labor and capital, are being used in an economy to produce a given level of output. With growing competition across the globe, contemporary organizations are under pressure to exploit the untapped potential of the labour. Maynard Operation Sequence Technique (MOST) is a work measurement system that can be easily implemented and practically maintained. The basic ergonomic analysis was also conducted for understanding the interactions among the labour and other elements of a system to optimize human well-being and overall system performance. In this article, MOST is used for time measurement study and minimization of fatigue among the operators by using ergonomics in a stamping unit. The primary objective of this review was to reduce the motion of a task in order to reduce the effort and time to perform the task to achieve higher production and better service level by the ergonomic approach. Ergonomics accounts the user's capabilities and limitations to ensure that tasks, functions, information, and environment suit each operator in any organization. Scoring sheets approach was used in conducting ergonomics study to decide the fitness of any unit on the basis safety and posture analysis of the operator. The hybrid approach (MOST-Ergo) can be used to improve the productivity of any organization by reducing the time and fatigue consumed by the operator during the operation.
Keywords: maynard operation sequence technique; MOST; time study; standard time; productivity; ergonomic work posture analysis.
Special Issue on: BDSCC-2018 Big Data Innovation for Sustainable Intelligent Computing
Convergence of Partial Differential Equation Using Fuzzy Linear Parabolic Derivatives
by Shanthi Devi Palanisamy, Viswanathan Ramasamy
Abstract: Discovering solution for Partial Differential Equations (PDEs) is a very difficult. The exact solution can identify only in some particular cases. The numerical method for PDEs have been attains a greater significance growing in recent years. In this paper, we consider the convergence of Partial Differential Equation using Fuzzy Linear Parabolic (PDE-FLP) method on a finite domain. The method is based on the construction of PDE where the coefficients and initial conditions are obtained as fuzzy numbers and solved by linear parabolic derivatives. The linear parabolic derivative serves as the basis where Fuzzy form is considered to be solved as a numerical solution for PDEs. Firstly, the PDE form of two independent variables and the fuzzy representation of the two independent variables are derived. Secondly, Fuzzy Linear Parabolic derivative is provided for the convergence to a numerical solution for PDE. Fuzzy linear parabolic derivatives are employed to describe the wide variety of time dependent development. Parabolic derivatives are used in PDE-FLP because the coefficient is the same as the condition for the analytic solution. Finally, numerical results are given, which demonstrates the effectiveness and convergence of the PDE-FLP method. A detailed comparison between the approximate solutions obtained by these methods is discussed. Also, figurative representation to compare between the approximate solutions is also presented.
Keywords: Partial Differential Equation; Fuzzy; Linear Parabolic; Domain; Numerical solution.
Document Similarity Approach using Grammatical Linkages with Graph Databases
by Priya V, Umamaheswari K
Abstract: Document similarity had become essential in many applications such as document retrieval, recommendation systems, plagiarism checker etc. Many similarity evaluation approaches rely on word based document representation, because it is very fast. But these approaches are not accurate when documents with different language and vocabulary are used. When graph representation is used for documents they use some relational knowledge which is not feasible in many applications because of expensive graph operations. In this work a novel approach for document similarity computation which utilizes verbal intent has been developed. This improves the similarity by increasing the number of linkages using verbs between two documents. Graph databases were used for faster performance. The performance of the system is evaluated using various metrics like cosine similarity, jaccard similarity and Dice with different review datasets. The verbal intent based approach has registered promising results based on the links between two documents.
Keywords: Document similarity;Graph Database; Grammatical Linkages;knowledge graph; Text Similarity.
A CUSTOMER BASED SUPPLY CHAIN NETWORK DESIGN
by Anand.T Anand.T, Sudhakara Pandian R
Abstract: This study eventually synthesizes and proposes a new algorithm for a customer to a customer supply chain management system. Parallely we consider cost reductions in quantity rebate for inbound and outbound transportation of logistics. It utilizes an approximation procedure to simplify distance calculation details and builds up an algorithm to solve supply chain management issues using non- linear optimization technique. Numerical studies illustrate the solution procedure and influence of model parameters on Supply Chain Management and total costs. This study will result as a reference for top-level managements and organizations.
Keywords: Customer to Customer Network design; facility location-allocation; inventory policy; continuous approximation approach.
Special Issue on: Sustainable Computing for Enterprise Resource Planning Applications
MINING MASSIVE ONLINE LOCATION BASED SERVICES FROM USER ACTIVITY USING BEST FIRST GRADIENT BOOSTED DISTRIBUTED DECISION TREE
by Venkatesh M., Mohanraj V, Suresh Y
Abstract: User activity is predicted through the frequency in which the online substances in Location-Based Social Networks (LBSN) are produced and used by the consumer. Users are classified by researchers into a number of groups depending upon the level of their functioning. This takes place by creation of services based on their location, so that maximum number of user gets benefited. This work involves Gradient Boosted Distributed Decision Tree (GBDT) which is optimized on the basis of total iterations and shrinkage on using best algorithm. Implementation of the data is done through Hadoop network. A foursquare dataset is created using work, food, travel, park and shop. One of the most commonly used machine learning algorithm is Stochastic Gradient Boosted Decision Trees (GBDT) at present. Its advantages are that it can be interpreted easily with increased precision. However, most of the implementations are costly computationally and needs all training data in the main memory. The node with lowest lower bound is developed through Best First Search (BFS). Its own filing system is provided through Hadoop which is called Hadoop Distributed File System. The algorithm used is K-Nearest Neighbor (KNN) classifier algorithm.
Keywords: User activity; foursquare dataset; Stochastic Gradient Boosted Decision Trees (GBDT); Best-First Search (BFS) and K-Nearest Neighbor (KNN) classifier.
GRO AND WeGO - ALGORITHMIC APPROACHES TO INTEGRATE THE HETROGENOUS DATABASES AND ENHANCE THE EVALUATION OF ONTOLOGY MAPPING SYSTEMS IN THE SEMANTIC WEB
by Rajeswari Velliappan, Kavitha M, Dharmistan K.Varughese
Abstract: In the present day world, where information driven economy and information enhanced living standards rule everything, the sources of data from which the information is derived, are highly heterogeneous. The heterogeneity necessitates a mechanism for integrating data, existing in a variety of forms, before it is presented to the user in a fruitful manner. Different strategies have been developed with a number of implementations available to help the world population benefit from the ocean of data available across sources through massive network of computers. The Internet and World Wide Web, forming the backbone of the information highway will benefit from research solutions that enable people to retrieve data or information that fit their specific queries or requirements. Semantic web is an initiative in achieving that goal of machine processed information being available to us than requiring human intelligence for processing information. This work is carried out to address the heterogeneity problem that exists among data sources and provides a solution through the application of ontology. The ontology by itself is a structured data representation and intended for information processing through machine intelligence. Artificial intelligence is a thrust area for ontology applications. Ontology is a conceptual tool for handling semantic heterogeneity. The algorithmic approach adopted in the mapping solution system, considers the most common structure of ontology representation viz. the graph model. The graph nodes or the elements of ontology are compared carefully by a set of nine matching parameters to obtain various indices or scores as explained subsequently. Then a comprehensive similarity analysis is carried out to arrive at the degree of matching of individual nodes as well as the ontology in totality for an ontology alignment.
Keywords: Ontology; Semantic Web; Heterogeneous databases; GRO and WeGO.
Feature Selection and Instance Selection using Cuttlefish Optimization Algorithm through Tabu Search
by Karunakaran Velswamy, Suganthi Muthusamy, Rajasekar Velswamy
Abstract: Over the recent decades, the amount of data generated has been growing exponentially, the existing machine learning algorithms are not feasible for processing of such huge amount of data. To solve these issues the two commonly adopted schemes are, scaling up the data mining algorithms and another one is data reduction. Scaling up the data mining algorithms is not a best way, but data reduction is fairly possible. In this paper, cuttlefish optimization algorithm along with tabu search approach is used for data reduction. Dataset can be reduced, in two ways, one is the selecting optimal subset of features from the original dataset i.e., eliminating those features which are contributing lesser information another method is selecting optimal subset of instances from the original data set, i.e. is eliminating those instances which are contributing lesser information. Cuttlefish optimization algorithm with tabu search finds both optimal subset of features and instances. Optimal subset of feature and subset of instances obtained from the cuttlefish algorithm with tabu search provides a similar detection rate, high accuracy rate, lesser false positive rate and the lesser computational time for training the classifier that we obtained from the original data set.
Keywords: Data Reduction; Instance Selection; Feature Selection; Cuttlefish Optimization; Tabu Search; Machine learning; Artificial Intelligence.