Journal of Scientific & Industrial Research

P. Parthiban ¹, S. Amalaldhasan ¹, R. Dhanalakshmi ²

Affiliations
1 National Institute of Technology, Tiruchirappalli-620015, IN
2 National Institute of Technology Nagaland, Dimapur-797103, IN

Abstract

Eco Design integrates environmental thinking into product design and packaging including in its production, consumption and disposal of the product life cycle in the supply chain. In today‘s scenario eco design is very important for saving our environment. This papers aims to investigate the technology, organization and environment factors of the eco design that influence the adoption of Green Supply Chain Management using Fuzzy Quality Function Deployment (FQFD). Quality function deployment (QFD) is a planning and problem-solving methodology used to translate customer requirements (CRs) into technical requirements (TRs) in the Course of new product development (NPD). In the proposed model, fifteen fundamental requirements of customers are identified and eight main factors of eco design are derived to satisfy the overall requirements as detailed. The importance of the customer requirements and relationship strength were identified as linguistic data. We have collected data for the criteria from the decision makers of the automotive industry. Under different situation the values of subjective data are often inaccurate so we have applied Fuzzy Quantitative Approach to overcome this deficiency of high subjectivity and low reliability. This study shows the fuzzy logic using Quality Function Deployment for easy decision making. So this proposed method shows the final ranking of the important eco design factors that influences the adoption of Green Supply Chain Management in the automotive industry. The final result of paper gives Stakeholder Cooperation is the most important factors of eco design.

Keywords

Eco Design, Green Supply Chain Management (GSCM), Fuzzy Quality Function Deployment (FQFD).

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R. Dhanalakshmi ¹, P. D. D. Dominic ², G. Muruganantham ³, P. Parthiban ⁴

Affiliations
1 Department of Computer Science and Engineering, National Institute of Technology Nagaland, Dimapur, Nagaland, IN
2 Department of Computer and Information Sciences, Universiti Teknologi Petronas, MY
3 Department of Management Studies, National Institute of Technology, Tiruchirappalli-620015, Tamil Nadu, IN
4 Department of Production Engineering, National Institute of Technology, Tiruchirappalli-620015, Tamil Nadu, IN

Abstract

Wireless networks are the topic of growing interest due to their ability to control the physical environment even from remote locations. Intelligent routing, bandwidth allocation and power control techniques are critical for these networks. Also, finding a best path between the communication end points is a challenge in these networks. In this paper we propose Ant Mobility Mode (AMM), an on-demand, multi-path routing algorithm that exercises power control and coordinates the nodes to communicate with one another, in wireless networks. The main goal of this protocol is to reduce the overhead, congestion, and stagnation, while increasing the throughput of the network. AAM proves to be a promising option for the mobility pattern wireless networks like MANETs.

Keywords

Ant Mobility Mode, Wireless Networks, Multi-Path Routing Algorithm.

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K. D. Prashanth ¹, P. Parthiban ¹, R. Dhanalakshmi ²

Affiliations
1 National Institute of Technology, Tiruchirappalli-620015, IN
2 National Institute of Technology Nagaland, Dimapur-797103, IN

Abstract

Heavy Industry is an industry that involves one or more characteristics such as Large and Heavy Products; Large or Heavy Equipment and facilities; or Complex and Numerous Processes. Outsourcing in heavy industry sector has gained a strategic importance in order to survive in the cost and quality conscious environment. In order to meet the customer demand promptly, any industry must have a healthy and competent supplier base. The purpose of this paper is to evaluate and rank the criteria affecting the performance of the suppliers of a Heavy Industry (BHEL, Tiruchirappalli) by using Multi Criteria Decision Making Tool (MCDM) – Fuzzy Analytical Hierarchy Process (AHP). The Criteria and Sub Criteria affecting the supplier performance have been decided by a team of experts from the industry and through literature study. AHP derives the relative weights of the criteria using pairwise comparison between criteria in a hierarchical system. It also provides a clear picture to the industry about the importance of various factors affecting the performance of the suppliers. This research helps the industry and its suppliers to identify the factors in which they need to strengthen in order to improve their performance.

Keywords

Heavy Industry, Supplier Performance Evaluation, Criteria and Sub Criteria, Fuzzy AHP, Multi Criteria Decision Making Tool.

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R. Dhanalakshmi ¹, Utkarsh M. Khaire ²

Affiliations
1 Department of Computer Science and Engineering, National Institute of Technology Puducherry, Karaikal-609609, IN
2 Department of Computer Science and Engineering, National Institute of Technology Nagaland, Dimapur-797103, IN

Abstract

Cancer is an invasive disease if it detects at a later stage. We strongly believe that the early detection of cancer can increase the efficiency of treatment and decreases the mortality rate. The microarray is a technique where we can study thousands of genes in a short amount of time when we compared to any other traditional methods. The main drawback of microarray data is its “curse of dimensionality” problem. Since it has a very large number of genes (features) as compared to a number of samples, it creates computational instability for a single system to give an effective result. Hadoop processes big data in divide and conquer manner in its master-slave architecture to project results in a short amount of time. Random forest is an ensemble technique for feature selection and classification. To select a relevant feature from all feature set of microarray data, we need to randomly permute the value of features and check for misclassification rate. If the misclassification rate changes then that particular feature is important. With the proposed method, the accuracy level for detecting cancer at the early stage is effectively improved.

Keywords

Microarray, Random Forest, Hadoop.

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R. Dhanalakshmi ¹, B. B. Sinha ²

Affiliations
1 Department of Computer Science and Engineering, National Institute of Technology Puducherry, Karaikal-609609, IN
2 Department of Computer Science and Engineering, National Institute of Technology Nagaland, Dimapur-797103, IN

Abstract

The long tail of diverse consumption of resources online by the customers raises a challenge for the e-commerce websites and service providers. Recommender system offers a vigorous way to cope up with the aforementioned challenge. In this paper, we have proposed a hybrid cohort rating prediction technique which relies on high cohort users and high cohort items to make predictions. Our model significantly improves the retention of recommender system showing encouraging results when compared with existing traditional recommender systems.

Keywords

Recommender System, Pearson Correlation, Adjusted Cosine similarity, Collaborative filtering, MAE, RMSE.

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K. Dharun Prashanth ¹, P. Parthiban ¹, R. Dhanalakshmi ²

Affiliations
1 National Institute of Technology, Tiruchirappalli-620015, IN
2 National Institute of Technology Nagaland, Dimapur-797103, IN

Abstract

The purpose of this paper is to evaluate the performance of the suppliers of a heavy industry and to rank them based on their performance by using Multi Criteria Decision Making Tool (MCDM) – TOPSIS Method. The Criteria and Sub Criteria for the supplier performance evaluation has been decided by a team of experts from the manufacturing industry. DEMATEL is used to calculate the weightage of the criteria and TOPSIS is used to evaluate and rank the suppliers based on these criteria. This paper ranks the suppliers of the industry based on their performance. It also provides a clear picture about various factors affecting the performance of the suppliers. This research provides an insight to all the suppliers as to where they stand with respect to their performance. It helps them identify the factors in which they need to strengthen in order to improve their performance. It also provides a competitive environment for improving their performance which ultimately aids the manufacturing industry with better results from the suppliers.

Keywords

Supplier Performance Evaluation, Vendor Performance, Supplier Evaluation, DEMATEL, TOPSIS, Multi Criteria Decision Making (MCDM).

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S. N. Sathiya Narayana ¹, P. Parthiban ², Anbuchezhian ³, R. Dhanalakshmi ⁴

Affiliations
1 Department of Mechanical Engineering, Jayaram College of Engineering and Technology, Tiruchirappalli 620 015, IN
2 Department of Production Engineering, National Institute of Technology, Tiruchirappalli 620 015, IN
3 Department of Mechanical Engineering, RMD Engineering college, Kavaraipettai 600 077, IN
4 Department of Computer Science and Engineering, Indian Institute of Information Technology Tiruchirappalli, Trichy 620 015, IN

Abstract

For several years manufacturing industry has been flourishing in India and among the manufacturing industries automobile is one of the fundamental. This paper focuses on the execution of the lean and green supply chain in the medium scale automobile industrial sectors. The critical factors are discovered dependent on expert’s opinion and through questionnaires sent to the industries. The significance and the degrees of the factors are taken from interpretive structural modeling. Utilizing these levels, the critical weights of each factor are obtained through analytic hierarchy process (AHP). The critical weights are used in TOPSIS and Fuzzy TOPSIS, to find out the efficiency of the industries.

Keywords

AHP, Fuzzy TOPSIS, Manufacturing industry, Structural Modeling.

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P. Valarmathi ¹, R. Dhanalakshmi ², Narendran Rajagopalan ¹

Affiliations
1 National Institute of Technology Puducherry, IN
2 Indian Institute of Information Technology Tiruchirappalli, IN

Abstract

The exponential increase in the volume of online data has generated a confront of overburden of data for online users, which slow down the suitable access to products of pursuit on the Web. This contributed to the need for recommendation systems. Recommender system is a special form of intelligent technique that takes advantage of past user transactions on products to give recommendations of products. Collaborative filtering has turn out to be the commonly adopted method of providing users with customized services, except that it endures the problem of sparsely rated inputs. For collaborative filtering, we introduce a deep learning-based architecture which evaluates a discrete factorisation of vectors from sparse inputs. The characteristics of the products are retrieved using a deep learning model, denoising auto encoders. The traditional collaborative filtering algorithm that predicts and uses the past history of consumer interest and product characteristics are updated with the characteristics obtained by deep learning model for sparse rated inputs. The results of sparse data problem tested on MovieLens data set will greatly enhance the user and product transaction.

Keywords

Collaborative Filtering, Neural Network, Sparse Inputs.

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