A NOVEL BIOMETRIC APPROACH FOR AUTHENTICATION IN PERVASIVE COMPUTING ENVIRONMENTS

A NOVEL BIOMETRIC APPROACH FOR AUTHENTICATION IN PERVASIVE COMPUTING ENVIRONMENTS

Rachappa1 ,Divyajyothi M G 1 and Dr. D H Rao2 1Jain University, Bangalore 2S.G. Balekundri Institute of Technology, Belgaum India

ABSTRACT

The paradigm of embedding computing devices in our surrounding environment has gained more interest in recent days. Along with contemporary technology comes challenges, the most important being the security and privacy aspect. Keeping the aspect of compactness and memory constraints of pervasive devices in mind, the biometric techniques proposed for identification should be robust and dynamic. In this work, we propose an emerging scheme that is based on few exclusive human traits and characteristics termed as ocular biometrics, promising utmost security and reliability. Complex iris recognition and retinal scanning algorithms have been discussed which promises achievement of accurate results. The performance and vast applications of these algorithms on pervasive computing devices is also addressed.

KEYWORDS Pervasive computing, Biometrics, Privacy, Security, Iris recognition, Advanced computing, Authentication Original Source URL: http://aircconline.com/acii/V3N2/3216acii02.pdf http://airccse.org/journal/acii/vol3.html

An Intelligent Method for Accelerating the Convergence of Different Versions of SGMRES Algorithm

An Intelligent Method for Accelerating the Convergence of Different Versions of SGMRES Algorithm

Mohadeseh Entezari Zarch, Seyed AbolfazlShahzadeh Fazeli and Mohammad Bagher Dowlatshahi, Yazd University, Iran

ABSTRACT

In a wide range of applications, solving the linear system of equations Ax = b is appeared. One of the best methods to solve the large sparse asymmetric linear systems is the simplified generalized minimal residual (SGMRES(m)) method. Also, some improved versions of SGMRES(m) exist: SGMRES-E(m, k) and SGMRES-DR(m, k). In this paper, an intelligent heuristic method for accelerating the convergence of three methods SGMRES(m), SGMRES-E(m, k), and SGMRES-DR(m, k) is proposed. The numerical results obtained from implementation of the proposed approach on several University of Florida standard matrixes confirm the efficiency of the proposed method.

KEYWORDS Artificial Intelligence, Heuristic Algorithms, Linear systems of equations, SGMRES. Original Source URL: http://aircconline.com/acii/V3N2/3216acii01.pdf http://airccse.org/journal/acii/vol3.html

Text Mining: open Source Tokenization Tools – An Analysis

Text Mining: open Source Tokenization Tools – An Analysis

S.Vijayarani and R.Janani Bharathiar University, India Professor, IIITM-K, Trivandrum, India

ABSTRACT

Text mining is the process of extracting interesting and non-trivial knowledge or information from unstructured text data. Text mining is the multidisciplinary field which draws on data mining, machinelearning, information retrieval, computational linguistics and statistics. Important text mining processes are information extraction, information retrieval, natural language processing, text classification, content analysis and text clustering. All these processes are required to complete the preprocessing step before doing their intended task. Pre-processing significantly reduces the size of the input text documents and the actions involved in this step are sentence boundary determination, natural language specific stop- word elimination, tokenization and stemming. Among this, the most essential and important action is the tokenization. Tokenization helps to divide the textual information into individual words. For performing tokenization process, there are many open source tools are available. The main objective of this work is to analyze the performance of the seven open source tokenization tools. For this comparative analysis, wehave taken Nlpdotnet Tokenizer, Mila Tokenizer, NLT K Word Tokenize, TextBlob Word Tokenize, MBSP Word Tokenize, Pattern Word Tokenize and Word Tokenization with Python NLTK. Based on the results, we observed that the Nlpdotnet Tokenizer tool performance is better than other tools.

KEYWORDS Text Mining, Preprocessing, Tokenization, machine learning, NLP Original Source URL: http://aircconline.com/acii/V3N1/3116acii04.pdf http://airccse.org/journal/acii/vol3.html

A Literature Survey on Recommendation System Based on Sentimental Analysis

A Literature Survey on Recommendation System Based on Sentimental Analysis

Achin Jain, Vanita Jain and Nidhi Kapoor, BharatiVidyapeeth College of Engineering, India

ABSTRACT

Recommender systems have grown to be a critical research subject after the emergence of the first paper on collaborative filtering in the Nineties. Despitethe fact that educational studies on recommender systems, has extended extensively over the last 10 years, there are deficiencies in the complete literature evaluation and classification of that research. Because of this, we reviewed articles on recommender structures, and then classified those based on sentiment analysis. The articles are categorized into three techniques of recommender system, i.e.; collaborative filtering (CF), content based and context based. We have tried to find out the research papers related to sentimental analysis based recommender system. To classify research done by authors in this field, we have shown different approaches of recommender system basedon sentimental analysis with the help of tables. Our studies give statistics, approximately trends in recommender structures research, and gives practitioners and researchers with perception and destiny route on the recommender system using sentimental analysis. We hope that this paper enables all and sundry who is interested in recommender systems research with insight for destiny.

KEYWORDS Recommender systems; Literature review, Sentimentalanalysis Original Source URL: http://aircconline.com/acii/V3N1/3116acii03.pdf http://airccse.org/journal/acii/vol3.html

ERCA: Energy-Efficient Routing and Reclustering Algorithm for Cceftoextend Network Lifetime in WSNS

ERCA: Energy-Efficient Routing and Reclustering Algorithm for Cceftoextend Network Lifetime in WSNS

Muhammad K.Shahzad, Jae Kwan Lee and Tae Ho Cho Sungkyunkwan University, Republic of Korea

ABSTRACT

The pervasive application of wireless sensor networks (WNSs) is challenged by the scarce energy constraints of sensor nodes. En-route filtering schemes, especiallycommutative cipher based en-route filtering (CCEF) can saves energy with better filtering capacity. However,this approach suffer from fixed paths and inefficient underlying routing designed for ad-hoc networks. Moreover, with decrease in remaining sensor nodes, the probability of network partition increases. In this paper, we propose energy-efficient routing and re-clustering algorithm (ERCA) to address these limitations. In proposed scheme with reduction in the number of sensor nodes to certain thresh-hold the cluster size and transmission range dynamically maintain cluster node-density. Performance results show that our approach demonstrate filtering- power, better energy-efficiency, and an average gain over 285%in network lifetime.

KEYWORDS Wireless sensor networks, energy-efficiency, network lifetime, re-clustering, filtering-power. Original Source URL: http://aircconline.com/acii/V3N1/3116acii02.pdf http://airccse.org/journal/acii/vol3.html

Prediction of Lung Cancer Using Image Processing Techniques: A Review

Prediction of Lung Cancer Using Image Processing Techniques: A Review

Arvind Kumar Tiwari GGS College of Modern Technology, India

ABSTRACT

Prediction of lung cancer is most challenging problem due to structure of cancer cell, where most of t he cells are overlapped each other. The image processing techniques are mostly used for prediction of lung cancer and also for early detection and treatment to prevent the lung cancer. To predict the lung cancer various features are extracted from the images therefore, pattern recognition based approaches are useful to predict the lung cancer. Here, a comprehensive review for the prediction of lung cancer by previous researcher using image processing techniques is presented. The summary for the prediction of lung cancer by previous researcher using image processing techniques is also presented.

KEYWORDS Classification, lung cancer, accuracy, image processing techniques Original Source URL: http://aircconline.com/acii/V3N1/3116acii01.pdf http://airccse.org/journal/acii/vol3.html

An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Embedded Processor in Autonomous Vehicles

An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Embedded Processor in Autonomous Vehicles

Manoj C R Tata Consultancy Services Limited, India

ABSTRACT

Forward Collision Avoidance (FCA) systems in automobiles is an essential part of Advanced Driver Assistance System (ADAS) and autonomous vehicles. These devices currently use, radars as the main sensor. The increasing resolution of camera sensors, processing capability of hardware chipsets and advances in image processing algorithms, have been pushing the camera based features recently. Monocular cameras face the challenge of accurate scale estimation which limits it use as a stand-alone sensor for this application. This paper proposes an efficient system which can perform multi scale object detection which is being patent granted and efficient 3D reconstruction using structure from motion (SFM) framework. While the algorithms need to be accurate it also needs to operate real time in low cost embedded hardware. The focus of the paper is to discuss how the proposed algorithms are designed in such a way that it can be provide real time performance on low cost embedded CPU’s which makes use of only Digital Signal processors (DSP) and vector processing cores.

KEYWORDS Advanced driver assistance (ADAS), HOG, SFM, FCA, collision avoidance, 3D reconstruction, object detection, classification Original Source URL: http://aircconline.com/acii/V4N2/4217acii01.pdf http://airccse.org/journal/acii/vol4.html

Machine learning systems based on xgBoost and MLP neural network applied in satellite lithium-ion battery sets impedance estimation

Machine learning systems based on xgBoost and MLP neural network applied in satellite lithium-ion battery sets impedance estimation

Thiago H. R. Donato and Marcos G. Quiles National Space Research Institute,Sao Jose dos Campos, Brazil

ABSTRACT

In this work, the internal impedance of the lithium-ion battery pack (important measure of the degradation level of the batteries) is estimated by means of machine learning systems based on supervised learning techniques MLP - Multi Layer Perceptron - neural network and xgBoost - Gradient Tree Boosting. Therefore, characteristics of the electric power system, in which the battery pack is inserted, are extracted and used in the construction of supervised models through the application of two different techniques based on Gradient Tree Boosting and MultiLayer Perceptron neural network. Finally, with the application of statistical validation techniques,the accuracy of both models are calculated and used for the comparison between them and the feasibility analysis regarding the use of such models in real systems.

KEYWORDS Lithium-ion battery, Internal impedance, State of charge, Multi Layer Perceptron,Gradient Tree Boosting, xgBoost Original Source URL: http://aircconline.com/acii/V5N1/5118acii01.pdf http://airccse.org/journal/acii/current.html

Advanced Computational Intelligence: An International Journal (ACII)

 

http://airccse.org/journal/acii/index.html

ISSN : 2454 - 3934

Scope & Topics

Advanced Computational Intelligence: An International Journal (ACII) is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of computational intelligence. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced computational intelligence concepts and establishing new collaborations in these areas.

Authors are solicited to contribute to this journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the computational intelligence.

Topics considered include but are not limited to:

• Artificial intelligence
• Cellular automata
• Connectionist systems
• Distributed, Multimedia, Human Interface Systems
• Evolutionary computation
• Fuzzy logic
• Genetic algorithms
• Hybrid intelligent systems, Adaptation and Learning Systems
• Knowledge mining
• Neural networks
• Pattern recognition
• Self-organizing systems
• Soft computing
• Statistical models
• Symbolic machine learning

Paper Submission

Authors are invited to submit papers for this journal through E-mail: aciijournal@airccse.org. Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal.

Important Dates

• Submission Deadline  : April 08, 2018
• Notification                     : May 08,  2018
• Final Manuscript Due   : May 16,  2018
• Publication Date             : Determined by the Editor-in-Chief