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2nd symposium on machine learning and metaheuristics algorithms, and applications, somma 2020

2nd Symposium on Machine Learning and Metaheuristics Algorit...

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2nd symposium on machine learning and metaheuristics algorithms, and applications, somma 2020

ISBN: (纸本)9789811604188

The proceedings contain 19 papers. The special focus in this conference is on 2nd symposium on machine learning and metaheuristics algorithms, and applications, somma 2020. The topics include: Exam Seating Allocation to Prevent Malpractice Using Genetic Multi-optimization Algorithm;Big Data: Does BIG Matter for Your Business?;modelling Energy Consumption of Domestic Households via Supervised and Unsupervised learning: A Case Study;machine learning and Soft Computing Techniques for Combustion System Diagnostics and Monitoring: A Survey;Traffic Sign Classification Using ODENet;Analysis of UNSW-NB15 Dataset Using machine learning Classifiers;concept Drift Detection in Phishing Using Autoencoders;detection of Obfuscated Mobile Malware with machine learning and Deep learning Models;CybSecMLC: A Comparative Analysis on Cyber Security Intrusion Detection Using machine learning Classifiers;smart Security and Surveillance System in Laboratories Using machine learning;Deep Neural Networks with Multi-class SVM for Recognition of Cross-Spectral Iris Images;gaze Fusion-Deep Neural Network Model for Glaucoma Detection;deep learning Based Stable and Unstable Candle Flame Detection;emotion Recognition from Facial Expressions Using Siamese Network;activity Modeling of Individuals in Domestic Households Using Fuzzy Logic;Stock Price Prediction Using machine learning and LSTM-Based Deep learning Models;an Improved Salp Swarm Algorithm Based on Adaptive β -Hill Climbing for Stock Market Prediction.

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Deep learning Based Stable and Unstable Candle Flame Detection 1

引用

2nd symposium on machine learning and metaheuristics algorithms, and applications, somma 2020

作者： Khan, Amir Ansari, Mohammad Samar Aligarh Muslim University Aligarh India Software Research Institute Athlone Institute of Technology Athlone Ireland

ISBN: (数字)9789811604195

ISBN: (纸本)9789811604188

This paper presents a deep learning based solution for identification of normal and abnormal candle flames, controlled and uncontrolled flames. Candle flames affected by external factors like wind, improper combustion of fuel etc. Proposed CNN based deep neural network can successfully classify the stable and unstable candle flame with an accuracy of 67% for generated test set and an accuracy of 83% for random images taken from open source on internet. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Combustion

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machine learning and Soft Computing Techniques for Combustion System Diagnostics and Monitoring: A Survey 1

引用

2nd symposium on machine learning and metaheuristics algorithms, and applications, somma 2020

作者： Khan, Amir Khan, Mohd. Zihaib Ansari, Mohammad Samar University Womens’ Polytechnic Aligarh Muslim University Aligarh India University Polytechnic Aligarh Muslim University Aligarh India Software Research Institute Athnlone Institute of Technology Athlone Ireland Department of Electronics Engineering Aligarh Muslim University Aligarh India

ISBN: (数字)9789811604195

ISBN: (纸本)9789811604188

Combustion systems are ubiquitous in nature and are employed under varied conditions to comply with the specific demands of the applications they are used in. Combustion control and optimization techniques are essential for efficient and reliable monitoring of the combustion process. This paper presents a comprehensive review of combustion monitoring diagnostics and prognostics which have been researched thoroughly using various soft-computing techniques incorporating state-of-the-art machine learning (ML) and Deep learning (DL) techniques. Regarding the combustion systems, there are three primary areas which have been investigated viz. (i) combustion state monitoring, (ii) radical emissions and their concentration measurement, and (iii) 3-D flame image reconstruction. This paper reviews these areas along with recent advancements in the flame imaging techniques. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Soft computing

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Smart Security and Surveillance System in Laboratories Using machine learning 1

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2nd symposium on machine learning and metaheuristics algorithms, and applications, somma 2020

作者： Patil, Ashwini Shetty, Krupali Hinge, Shweta Tejaswini, G. Anni Shinay, V. Budihal, Suneeta V. Iyer, Nalini Sujata, C. KLE Technological University Hubballi Karnataka India

ISBN: (数字)9789811604195

ISBN: (纸本)9789811604188

The paper proposes to design and develop a smart authentication system in laboratory as a part of security and surveillance. To address the unauthorized entry in the laboratory, a smart alert system is designed and developed. The authentic entry to any laboratory will reduce the student response to hazards and accidents, risks to acceptable levels. The proposed methodology uses face detection and recognition techniques for the student authentication. Based on the results, the attendance is updated in the attendance data base if the authorized users enter the laboratory else the details will be sent to the course instructors through the registered mails. The authentic student is also verified for wearing the personal protective equipment during the entry to the laboratory. By this, we can reduce the vandalism occurring in laboratories and maintain the integrity. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Face recognition

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Concept Drift Detection in Phishing Using Autoencoders 1

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2nd symposium on machine learning and metaheuristics algorithms, and applications, somma 2020

作者： Menon, Aditya Gopal Gressel, Gilad Amrita Vishwa Vidyapeetham Kollam Kerala India Georgia Institute of Technology AtlantaGA United States

ISBN: (数字)9789811604195

ISBN: (纸本)9789811604188

When machine learning models are built with non-stationary data their performance will naturally decrease over time due to concept drift, shifts in the underlying distribution of the data. A common solution is to retrain the machine learning model which can be expensive, both in obtaining new labeled data and in compute time. Traditionally many approaches to concept drift detection operate upon streaming data. However drift is also prevalent in semi-stationary data such as web data, social media, and any data set which is generated from human behaviors. Changing web technology causes concept drift in the website data that is used by phishing detection models. In this work, we create "Autoencoder Drift Detection" (ADD) an unsupervised approach for a drift detection mechanism that is suitable for semi-stationary data. We use the reconstruction error of the autoencoder as a proxy to detect concept drift. We use ADD to detect drift in a phishing detection data set which contains drift as it was collected over one year. We also show that ADD is competitive within ±24% with popular streaming drift detection algorithms on benchmark drift datasets. The average accuracy on the phishing data set is.473 without drift detection and using ADD is increased to.648. © 2021, Springer Nature Singapore Pte Ltd.

关键词： machine learning

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Detection of Obfuscated Mobile Malware with machine learning and Deep learning Models 1

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2nd symposium on machine learning and metaheuristics algorithms, and applications, somma 2020

作者： Dhanya, K.A. Dheesha, O.K. Gireesh Kumar, T. Vinod, P. TIFAC CORE in Cyber Security Amrita School of Engineering Amrita University Coimbatore India SCMS School of Engineering and Technology Cochin India

ISBN: (数字)9789811604195

ISBN: (纸本)9789811604188

Obfuscation techniques are used by malware authors to conceal malicious code and surpass the antivirus scanning. machine learning techniques especially deep learning techniques are strong enough to identify obfuscated malware samples. Performance of deep learning model on obfuscated malware detection is compared with conventional machine learning models like Random Forest (RF), Classification and Regression Trees (CART) and K Nearest Neighbour (KNN). Both Static (hardware and permission) and dynamic features (system calls) are considered for evaluating the performance. The models are evaluated using metrics which are precision, recall, F1-score and accuracy. Obfuscation transformation attribution is also addressed in this work using association rule mining. Random forest produced best outcome with F1-Score of 0.99 with benign samples, 0.95 with malware and 0.94 with obfuscated malware with system calls as features. Deep learning network with feed forward architecture is capable of identifying benign, malware, obfuscated malware samples with F1-Score of 0.99, 0.96 and 0.97 respectively. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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Modelling Energy Consumption of Domestic Households via Supervised and Unsupervised learning: A Case Study 1

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2nd symposium on machine learning and metaheuristics algorithms, and applications, somma 2020

作者： Shah, Shahid Mehraj Communication Control and Learning Lab Department of Electronics and Communication Engineering National Institute of Technology Srinagar Jammu and Kashmir India

ISBN: (数字)9789811604195

ISBN: (纸本)9789811604188

Electricity energy billing system is prevalent in most of the places in the world. Also digitization of these electricity bills has also been successfully implemented in various underdeveloped countries as well. The vast amount of data is available regarding the energy consumption of consumers. In this paper we consider a case study of one city, about which we have electricity energy data for several years. We first classify consumers based on their average energy usage via clustering algorithms. We also have survey data of several houses. In that survey, we have building information, family information and also appliance information. We use various regression techniques to disaggregate the energy usage corresponding to various appliances. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Energy utilization

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Analysis of UNSW-NB15 Dataset Using machine learning Classifiers 1

引用

2nd symposium on machine learning and metaheuristics algorithms, and applications, somma 2020

作者： Dickson, Anne Thomas, Ciza College of Engineering Trivandrum Thiruvananthapuram Kerala India Directorate of Technical Education Thiruvananthapuram Kerala India

ISBN: (数字)9789811604195

ISBN: (纸本)9789811604188

Benchmark datasets are the inevitable tool required to scrutinize vulnerabilities and tools in network security. Current datasets lack correlation between normal and the real-time network traffic. Behind every evaluation and establishment of attack detection, such datasets are the cornerstone deployed by research community. Creating our own dataset is a herculean task. Hence analyzing the subsisting datasets aids to provide a thorough clarity on the effectiveness when deployed in real time environments. This paper work focus on analysis and comparison of UNSW-NB15 with NSL-KDD dataset based on performance analysis and accuracy using machine learning classifiers. Feasibility, reliability and dependability of the dataset is reviewed and discussed by considering various performance measures such as precision, recall, F-score, specificity using various machine learning classifiers Naïve Bayes, Logistic Regression, SMO, J48 and Random Forest. Experimental results give out its noticeable classification accuracy of 0.99 with the random forest classifier having 0.998 recall and specificity 0.999 respectively. Research studies reveal the fact that threat diagnosis using conventional dataset and sophisticated technologies cover only 25% of threat taxonomy and hence the poor performance of existing intrusion detection systems. Thorough analysis and exploration of the dataset will pave the way for the outstanding performance of the intelligent Intrusion Detection System. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Network security

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Comparison of metaheuristics for the Allocation of Resources for an After-School Program in Remote Areas of India 1st

Comparison of Metaheuristics for the Allocation of Resources...

引用

1st symposium on machine learning and metaheuristics algorithms, and applications, somma 2019

作者： Gutjahr, Georg Menon, Radhika Nedungadi, Prema Center for Research in Analytics and Technologies for Education Amrita Vishwa Vidyapeetham Amritapuri Kollam India

ISBN: (纸本)9789811543005

This paper describes an after-school education program in rural India. The program maintains 53 education centers in remote villages in 21 states of India. For the allocation of resources, we first describe the results of an multi-attribute utility assessment, where 130 teachers, coordinators, and people associated with the program specified their opinion on factors that are most important on running a successful after-school education center. We then formulate the problem of optimal resource allocating as a generalized assignment problem. To solve this problem, three different metaheuristics are compared: iterated hill climbing, tabu search, and simulated annealing. It is found that tabu search is giving the best performance. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Simulated annealing

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Activity Modeling of Individuals in Domestic Households Using Fuzzy Logic 1

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2nd symposium on machine learning and metaheuristics algorithms, and applications, somma 2020

作者： Dyuthi, Sristi Ram Shah, Shahid Mehraj Communication Control and Learning Lab Department of Electronics and Communication Engineering National Institute of Technology Srinagar Srinagar Jammu and Kashmir India Department of ECE University of California San Diego San Diego United States

ISBN: (数字)9789811604195

ISBN: (纸本)9789811604188

A model which predicts the activities of each individual in a household is developed. This model is used to simulate the activities of 25,000 households in a town in Kerala (a state in southern region of India). A fuzzy-logic based approach is used to estimate the probabilities of an individual to be in a particular state/activity. Then an optimization problem is formulated to compute the activity transitions. Further, the activity transitions of the individuals within a house are tied in an appropriate way. These activity transitions are then used to simulate a Markov chain of the activities for a sample set of households in a town in Kerala. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Fuzzy logic

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