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

1st Symposium on Machine Learning and Metaheuristics Algorit...

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

ISBN: (纸本)9789811543005

The proceedings contain 23 papers. The special focus in this conference is on machine learning and metaheuristics algorithms, and applications. The topics include: CNN and stacked LstM Model for Indian Sign Language Recognition;air Quality Monitoring and Classification Using machine learning;Facial Emotion Recognition Using Shallow CNN;lead Artist Identification from Music Videos Using Auto-encoders;Enhanced Differential Evolution-Based EEG Channel Selection;An Efficient Word Embedded Click-Bait Classification of YouTube Titles Using SVM;NSGA III for CNC End Milling Process Optimization;steering Angle Estimation for Self-driving Car Using Deep learning;a Textural Feature Based Cytological Image Classification Using Artificial Neural Network;proposing Contextually Relevant Advertisements for Online Videos;analyzing Architectures of Reservoir Computing Models Using Particle Swarm Intelligence;comparison of metaheuristics for the Allocation of Resources for an After-School Program in Remote Areas of India;A C-LstM with Attention Mechanism for Question Categorization;quicklyCook: A User-Friendly Recipe Recommender;early Detection of Suicidal Predilection on Instagram Social Networking Service;a Cost-Effective Data Node Management Scheme for Hadoop Clusters in Cloud Environment;deep Temporal Analysis of Twitter Bots;comparison of metaheuristics for a Vehicle Routing Problem in a Farming Community;A Brain Computer Interface Based Visual Keyboard System Using SSVEP and Electrooculogram;stock Market Prediction Using a Hybrid Model;an Internet of Things Framework to Forecast Indoor Air Quality Using machine learning.

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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...

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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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Air Quality Monitoring and Classification Using machine learning 1

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

作者： Tlais, Sami HajjHussein, Hassan Sakr, Fouad Hallani, Mohamad Ahmad, Abdel-Mehsen El-Bazzal, Zouhair Department of Biological and Chemical Sciences School of Arts and Sciences Lebanese International University Beirut Lebanon Department of Computer Communications Engineering School of Engineering Lebanese International University Bekaa Campus Beirut Lebanon

ISBN: (数字)9789811543012

ISBN: (纸本)9789811543005

Air pollution is considered one of the biggest health threats after it has become the fourth leading cause of death in the world. According to the Health Effect Institute (HEI), 95% of the world’s population is currently breathing polluted air. This paper highlights the importance of using machine learning algorithms to classify and predict air pollution based on collected real-time environmental data. These algorithms would help decision makers and responsible authorities to take action to alleviate this critical situation. machine learning algorithms will be evaluated with offline data and real-time data which will be collected through pollution sensors as a model study. The obtained results revealed that Artificial Neural Network had the best performance and the highest accuracy among KNN, SVM, and Naïve Bayes Classifier. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Support vector machines

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Comparison of metaheuristics for a Vehicle Routing Problem in a Farming Community 1

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

作者： Mohan, Aravind Dileep, Anandhu Ajayan, Sreesankar Gutjahr, Georg Nedungadi, Prema Amrita Vishwa Vidyapeetham Amritapuri Kollam India Department of Mathematics Amrita Vishwa Vidyapeetham Amritapuri Kollam India

ISBN: (数字)9789811543012

ISBN: (纸本)9789811543005

In a farming community, different types of commodities may need to be transported to different destinations, like the market, storage unit or a processing unit, during the harvest season. To organize efficient transportation in such a setting, the problem is formulated as a Vehicle Routing Problem with Pickups and Deliveries, by considering a virtual field and a virtual destination for delivery of each commodity. To solve this particular problem instance, four common metaheuristics - iterative hill-climbing, guided local search, tabu search, and simulated annealing - were tried and their performances based on total tour lengths for different run times were compared. Basic implementations of these metaheuristics were done using Google OR tools. Guided local search was found to produce good solutions quicker than others. In the long run, tabu search was able to find a slightly better solution. Simulated annealing was prone to get trapped in a local optimum for hours. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Vehicle routing

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An Internet of Things Framework to Forecast Indoor Air Quality Using machine learning 1

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

作者： Rastogi, Krati Lohani, Divya Department of Computer Science and Engineering Shiv Nadar University Gautam Buddha Nagar Greater NoidaUttar Pradesh India

ISBN: (数字)9789811543012

ISBN: (纸本)9789811543005

Good air quality is critical to healthy and productive life in the indoor environment. Quality of air inside buildings depends on several factors. Till now, it was not possible to measure and monitor indoor air quality (IAQ) through mobile phones and wireless sensors in real time, as standardized miniature sensors were not commercially available. With the introduction of wireless air quality sensors that can pair with most contemporary smartphones, it is now possible to monitor indoor environment pervasively in real time, take precautionary measures, and maintain a healthy life. In this work, we have created an IoT sensing system to monitor and analyse the time variation of carbon dioxide in a university classroom. Using the sensed data, forecast models have been developed to predict the build-up of carbon dioxide, an indicator of IAQ. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Carbon dioxide

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Hybrid Duty Cycle Algorithm for Industrial WSNs Using machine learning 1

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

作者： Nicolas, Charbel Ahmad, Abdel-Mehsen Hernan, Jose Rubio Habib, Gilbert LaRRIS Faculty of Sciences Lebanese University Fanar Lebanon School of Engineering Lebanese International University Beirut Lebanon Electronics and Physics Télécom SudParis Institut Polytechnique de Paris Evry France

ISBN: (数字)9789811543012

ISBN: (纸本)9789811543005

Wireless Sensor Networks (WSNs) are used to monitor physical or environmental conditions. Due to energy and bandwidth constraints, wireless sensors are prone to packet loss during communication. To overcome the physical constraints of WSNs, there is an extensive renewed interest in applying data-driven machine learning methods. In this paper, we present a mission-critical surveillance system model for industrial environments. In our proposed system, a decision tree algorithm is installed on a centralized server to predict the wireless channel quality of the wireless sensors. Based on the machine-learning algorithm directives, wireless sensor nodes can proactively adapt their duty cycle to mobility, interference and hidden terminal. Extensive simulation results validate our proposed system. The prediction algorithm shows a classification accuracy exceeding 73%, which allows the duty cycle adaptation algorithm to significantly minimize the delay and energy cost compared to using pure TDMA or CSMA/CA protocols. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Forecasting

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Lead Artist Identification from Music Videos Using Auto-encoders 1

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

作者： Chandrashekaraiah, Prajwal Kashyap, Pranav H. Hatwar, Sriharsha Murthy, Srinivasa Department of Computer Science PES University Bengaluru India

ISBN: (数字)9789811543012

ISBN: (纸本)9789811543005

Music videos are now widely popular due to internet applications like YouTube and Facebook. Identifying lead artist from a music video is important in several scenarios including video classification, recommendation, etc. The proposed architecture involves using several image processing and machine learning techniques to identify the lead artist in the video. Firstly, selective frames are extracted from the video. Next, using DLib (a Deep learning Library in C++) and YOLO (You Only Look Once) all the faces and corresponding human bodies are extracted from these frames. Auto Encoders are then used to encode and compare the facial features. Finally, the lead artist is selected by ranking the faces in order of frequency of occurrence in the video. Tests were performed on several music videos that were downloaded from the internet and the lead artist was identified successfully in most of them with an overall accuracy of the system being approximately 70%. © Springer Nature Singapore Pte Ltd. 2020.

关键词： learning systems

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Early Detection of Suicidal Predilection on Instagram Social Networking Service 1st

Early Detection of Suicidal Predilection on Instagram Social...

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

作者： Rohini, K.R. Rajendran, Gayathri Arjun, V. Poornachandran, Prabaharan Jyothi Engineering College APJ Abdul Kalam Technological University ThrissurKerala India Amrita School of Engineering Amrita Vishwa Vidyapeetham AmritapuriKerala India

ISBN: (纸本)9789811543005

Social Networks has become the biggest medium of expression of one’s thoughts and emotions. With more than one-third of the global population expressing their thoughts, opinions and events, these networks become rich with direct indicators about the subject. Multimedia based social networking services are gaining popularity than text-based ones. In this work, we analyze the data from Instagram, which is one of the largest image and video-based social networking service with more than one billion users. This paper analyzes the social media posts on Instagram with the goal of finding when the subject feels low that could potentially lead to a suicidal tendency in the initial stages. In this study, the Instagram posts were analysed using different features that are more inclined to exhibit suicidal behaviour. The best accuracy is achieved by J48 binary tree classifier with a classification accuracy of 87.68%. © Springer Nature Singapore Pte Ltd. 2020.

关键词： machine learning

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steering Angle Estimation for Self-driving Car Using Deep learning 1

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

作者： Kumar, Ajay Palaniswamy, Suja Department of Computer Science and Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru560035 India

ISBN: (数字)9789811543012

ISBN: (纸本)9789811543005

The contemporary age has seen a tremendous increase in the number of road accidents. Traffic accidents are commonly caused by driver error, mobile phone usage, in-car audio and video entertainment systems, and extensive traffic. The road accident in India causes one death every four minutes. Imagine if everyone can easily and safely get around while driving is not tired, drunk or distracted. Self-driving means of transport are those in which drivers are never required to drive the vehicle. In self-driving car, time spent on travel may well be time spent doing what one needs, because all driving is handled by the car. Also referred to as autonomous or "driverless" cars, they mix sensors and code to manage, navigate, and drive the vehicle. The self-driving cars have huge potential to alter the means of transportation. We have proposed an end-to-end method based on deep learning for estimating the steering angle and the accuracy obtained is 98.6%. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Automobile steering equipment

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Exploring the potential of metaheuristics-based clustering in load profiling applications 1

Exploring the potential of metaheuristics-based clustering i...

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1st Conference on Energy Transition in the Mediterranean Area (SyNERGY MED)

作者： Panapakidis, Ioannis P. Christoforidis, Georgios C. Technol Educ Inst Thessaly Dept Elect Engn Larisa 41110 Greece Western Macedonia Univ Appl Sci Dept Elect Engn Kozani 50100 Greece

ISBN: (纸本)9781728130873

The clustering tool is ideal for scientific and engineering problems where no information about the data structure is available. Usually, in load profiling applications no sufficient information is a priori given for the consumers' classes, their consumption patterns and other attributes that will aid on building a descriptive model for the consumption trends. Many clustering algorithms of different type have been proposed. However, no attention has been placed so far in the utilization of metaheuristics for building robust clustering tools. The scope of this study is to develop a hybrid algorithm that combines partitional clustering and Particle Swarm Optimization (PSO). Simulation results indicate the superiority of the proposed algorithm over conventional clustering approaches.

关键词： clustering load profiles machine learning optimization swarm intelligence

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