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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Nair, Rahul Anil Suresh, Dev Karan Rajeswari, D. Srm Institute of Science and Technology School of Computing College of Engineering and Technology Department of Data Science and Business Systems Tamil Nadu Kattankulathur India

ISBN: (纸本)9781665462167

Battery life of EV (electric vehicles) depends on various factors. These factors include temperature of the battery, duration of use or the type and condition of the road traversed by the vehicle. This may cause the battery to discharge faster than it used to. Understanding what may cause the battery life to shorten can help us alleviate the given problem and can preserve the battery for longer time. This research focuses on the effects road conditions have on battery life to be able to recommend the favourable conditions to extend it. It proposes to find the routes taken by the vehicles and classifying them based on their battery health upon reaching the destination. In this paper, an attempt has been made to predict the category of the routes by employing Transfer learning via a pre-trained model VGG-16. Observations indicate that paths which have more turns and curves are generally worse for battery life as compared to straight and narrow paths. Another model which is an artificial neural network has been developed to predict the based on the average vehicle speed and the distance covered by the vehicle. The VGG-16 model predicts the data with which it is validated with an accuracy of 84.29% and the testing data with an accuracy of 83.56%. The artificial neural network model predicts the category of the data with an accuracy of 88.02%. © 2023 IEEE.

关键词： Deep learning

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Music Recommendation based on Facial Expressions using Data Augmentation 3

Music Recommendation based on Facial Expressions using Data ...

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Kapoor, Tanisha Ganguly, Arnaja Rajeswari, D. SRM Institute of Science and Technology Kattankulathur School of Computing College of Engineering and Technology Department of Data Science and Business Systems Chennai603203 India

ISBN: (纸本)9798350343632

Emotion recognition from facial expressions plays a pivotal role in human-computer interaction, psychology, marketing, and various other domains. This research paper presents a comprehensive study and implementation of an automated emotion recognition system based on deep learning techniques. The project addresses the challenge of accurately classifying a wide range of emotions from facial images in real-time. Leveraging Convolutional Neural Networks (CNNs) and modern deep learning approaches, the system demonstrates high accuracy in identifying emotions, including happiness, sadness, anger, and more. This project used the image dataset FER-2013 for the mood prediction portion of the project, and Spotify music data to predict emotions for music recommendation. Both the datasets utilized are freely available on Kaggle. The proposed system can achieve an accuracy of 91.23% in predicting the users' emotion based on their facial expressions. © 2023 IEEE.

关键词： CNN Emotion recognition Facial expression analysis FER-2013 Music Recommendation

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Air Pollutant Prediction Using Shallow Architecture Machine Learning Algorithms 8th

Air Pollutant Prediction Using Shallow Architecture Machine ...

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8th World Conference on Smart Trends in systems Security and Sustainability, WorldS4 2024

作者： Olaifa, Moses Malele, Vusumuzi Department of Computer Systems Engineering Faculty of ICT Tshwane University of Technology Pretoria South Africa Unit of Data Science and Computing Vaal Campus North-West University Vanderbijlpark South Africa

ISBN: (纸本)9789819786947

Over the past few decades, mortality rates associated with air quality pollution have risen in numerous countries around the globe. This pollution stems from different factors such as meteorological conditions, human activities from urban industrialization, vehicular emission, and so on. Air pollution constituents such as NO2, CO, SO, and coarse and fine particulate matter for different locations have been analyzed to learn both correlation and inter-dependency for long and short terms. However, many of these studies overlook datasets collected using low-cost tools and equipment. Most of the time, datasets of this nature are marred with inconsistencies and gaps at irregular intervals. This may render the air pollutant data unsuitable. In order to test the suitability of machine learning algorithms on such dataset, this study conducts a time-series forecast analysis using the prophet algorithm. Two additional models including linear regressor and random forest regressor are trained and tested on the same dataset and a comparative analysis of the three models is performed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Detection of Brain Tumor from MRI Images Using VGG-16 CNN Model with Transfer Learning 3

Detection of Brain Tumor from MRI Images Using VGG-16 CNN Mo...

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3rd International Conference on Pervasive computing and Social Networking, ICPCSN 2023

作者： Singh Rathore, Bhanu Pratap Dave, Rutul Dhanasekaran, K. Priyadarsini, K. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai Kattankulathur603203 India

ISBN: (纸本)9798350322842

Brain tumors are a serious health concern caused by the abnormal growth of cells in or around the brain. These tumors can either be benign or malignant. Few of the causes for Brain tumors are genetic mutations, exposure to radiation, or environmental factors. Early detection of brain tumors is essential for effective treatment and improved patient outcomes. To address this, a novel approach has been proposed for brain tumor detection using transfer learning with the VGG-16 convolutional neural network (CNN) architecture. By leveraging the pre-trained VGG-16 model on the ImageNet dataset, which provides a broad range of data for learning and representing complex features of various objects, meaningful features can be extracted from MRI scans and differentiate between tumor and normal tissue accurately. This refined VGG-16 model can provide a more precise and reliable diagnosis, which is crucial for successful treatment. This research study demonstrates that the utilization of VGG-16 model for brain tumor diagnosis can provide an efficient and accurate method for identifying brain tumors. Moreover, this approach can be applied to develop automated systems to assist medical professionals in diagnosing and treating brain tumors more effectively. © 2023 IEEE.

关键词： Convolutional neural networks

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Shortest Job First with Gateway-based Resource Management Strategy for Fog Enabled Cloud computing 4

Shortest Job First with Gateway-based Resource Management St...

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4th IEEE International Conference on Data engineering and Communication systems, ICDECS 2024

作者： Anbazhagan, K. Kumar, Rotash Thilagavathy, R. Anuradha, D. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Government Engineering College Department of Electronics and Communication Engineering Rajasthan Jhalawar India College of Engineering and Technology Srm Institute of Science and Technology Kattankulathur Department of Computing Technologies Chennai India Panimalar Engineering College Department of Computer Science and Business Systems Chennai India

ISBN: (纸本)9798350393354

In recent years, fog computing has extended cloud services to the network edge, lowering network latency and congestion and overcoming cloud computing restrictions. Many hurdles, however, must be overcome before the benefits of the fog-IoT paradigm for low-latency applications can be completely realized. The most challenging difficulty in IoT applications is developing efficient resource management strategies that maximize throughput while minimizing delay. IoT and fog computing, rather than simply utilizing cloud computing technology, have brought intriguing new data management solutions. Fog computing makes real-time analytics applications conceivable. After combining fog computing technologies with Internet of Things (IoT) applications, the system can reply in milliseconds. Existing fog resource managers focus on a subset of fog resource management parameters such as system response time, network bandwidth, energy consumption, and latency. No Fog resource management takes multiple factors into account at the same time for decision-making, which will become increasingly crucial in intelligent homes. Gateway-based Fog computing pioneered the introduction of the shortest job (SJFGFC) to address these difficulties. The technique is validated in an IoT-based imaginative house automation scenario before being evaluated in a small-scale bright home experiment using the iFogSim toolbox and empirical models. © 2024 IEEE.

关键词： Fog computing

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Interaction between the Distribution System Operator and the Battery Storage Operator for Flexibility Procurement Services

Interaction between the Distribution System Operator and the...

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2022 Power System and Green Energy Conference, PSGEC 2022

作者： Badanjak, Domagoj Pandzic, Hrvoje University of Zagreb Faculty of Electrical Engineering and Computing Department of Energy and Power Systems Zagreb Croatia

ISBN: (数字)9781665499934

ISBN: (纸本)9781665499934

Fast penetration of the renewable energy sources and transition toward sustainable energy supply paradigm is currently an ongoing process. It comes with many environmental benefits, but it also creates many challenges. Distribution System Operators are forced to move on from the fit-and-forget approach and take more active role in operating distribution system to face problems such as bidirectional power flows, congestion and voltage limits violation. This article, taking into consideration that regulated entities cannot interfere with market activities, proposes interaction between distribution system operator and battery storage operator for flexibility procurement and investing in battery storage units at the most critical points in the network. Firstly, using AC OPF, most critical nodes are detected and then optimal energy and power capacity of the battery storage units are determined. Value of the flexibility service is determined by observing the profit difference from an optimal market participation strategy with and without network constraints. © 2022 IEEE.

关键词： Investments

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Lessons learned from requirements gathering for virtual reality simulators

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Virtual Reality & Intelligent Hardware 2021年第5期3卷 407-422页

作者： Vivian GÓMEZ Kelly PEÑARANDA Pablo FIGUEROA Department of Systems and Computing Engineering Universidad de Los AndesBogotá7620001Colombia

Background This paper shows how current collaborative virtual environments(VEs)such as Mozilla Hubs and AltspaceVR can aid in the task of requirements gathering in VR for simulation and *** We performed a qualitative study on our use of these technologies in the requirements gathering of two *** Our results show that requirements gathering in virtual reality has an impact on the process of requirements *** report advantages and shortcomings that will be of interest to future *** example,we found that VR sessions for requirements gathering in current VEs could benefit from better pointers and better sound *** Current VEs are useful for the requirements gathering task in the development of VR simulators and VR training environments.

关键词： Human computer interaction Virtual environment Requirements gathering Globally dispersed teams VR simulators and training

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Requirements, Challenges and Experiences of Practical Demand Response in Households 5

Requirements, Challenges and Experiences of Practical Demand...

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5th IEEE International Conference on Smart systems and Technologies, SST 2022

作者： Malbašić, Bruno Pandžić, Hrvoje Department of Energy and Power Systems University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia

ISBN: (数字)9781665482158

ISBN: (纸本)9781665482158

Due to the increasing integration of unmanageable renewable energy sources into the electric power system, active consumers are beginning to participate in its balancing. In order to study the possibility of demand response, i.e. the participation of smart household appliances for balancing the power system, the Demand Response Laboratory at the University of Zagreb Faculty of Electrical engineering and computing was designed. Today, it hosts a large number of controllable household appliances whose electricity consumption is continuously monitored. This paper brings preliminary results of the issues encountered when connecting and controlling smart devices within the laboratory and creating a single platform to enable their remote control. This is a prerequisite to assess the available flexibility and connectivity of the devices in the laboratory. © 2022 IEEE.

关键词： Laboratories

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Model of Visualization and Analytics for Open Data (Case: Election Voters & Kids Disability Category) 1

Model of Visualization and Analytics for Open Data (Case: El...

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Wibowo, Ripto Mukti Fakieh, Bahjat Ramzan, Muhammad Sher Alzahrani, Ahmed Saeed Siddiqui, Muazzam Alzahrani, Bander King Abdulaziz University Faculty of Computing and Information Technology Department of Information Systems Saudi Arabia King Abdulaziz University Faculty of Computing and Information Technology Department of Computer Science Saudi Arabia Indiana University Luddy School of Informatics Computing and Engineering Bloomington United States

ISBN: (纸本)9781665472753

Several regional head elections had to be postponed due to the pandemic, including in Indonesia because of the COVID-19 pandemic. Several big cities in Indonesia are of concern because of their large population and GDP. This study conducts analysis and testing of datasets taken from Open Data in a city in Indonesia. In addition to conducting research on regional head elections, we also present information on voters from the category of kids with disabilities. The steps used in this research are using regional mapping data of the city of Surabaya in the Election of the Regional Head. Download the data or dataset for the Regional Head Election ampersand Categories of kids with disabilities. Based on the dataset voters from the category of children with disabilities are more than 5 *** this research, we use Python to process our datasets & Big Data technology. Data cleaning or cleansing, Exploratory Data Analysis, and Empirical Cumulative Distribution Functions (ECDF) in python are also needed. Result from ECDF chart with steady increase (increment of 0.1). The highest variance value is in Electoral District 5 = 6.090 and the lowest value is in Electoral District 4 = 0.90. The result of Open Data is graphical data visualization and candidate scores to help as an alternative for the 2024 Regional Head Election and the Category of kids with disabilities. © 2023 IEEE.

关键词： Open Data

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Integrating random regret minimization-based discrete choice models with mixed integer linear programming for revenue optimization

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Iran Journal of Computer Science 2024年第1期8卷 21-35页

作者： Talebi, Amirreza Haeri Boroujeni, Sayed Pedram Razi, Abolfazl Department of Integrated Systems Engineering The Ohio State University Columbus OH United States School of Computing Clemson University Clemson 29632 SC United States ENSIMAG Grenoble Alpes University Grenoble France

This paper explores the critical domain of revenue management (RM) within operations research (OR), focusing on intricate pricing dynamics. Utilizing mixed integer linear programming (MILP) models, the study enhances revenue optimization by considering product prices as decision variables and emphasizing the interplay between demand and supply. Recent advancements in discrete choice models (DCMs), particularly those rooted in random regret minimization (RRM) theory, are investigated as potent alternatives to established random utility maximization (RUM)-based DCMs. Despite the widespread use of DCMs in RM, a significant gap exists between cutting-edge RRM-based models and their practical integration into RM strategies. The study addresses this gap by incorporating an advanced RRM-based DCM into MILP models, addressing pricing challenges in both capacitated and uncapacitated supply scenarios. The developed models demonstrate the feasibility and offer diverse interpretations of consumer choice behavior, drawing inspiration from established RUM-based frameworks. This research contributes to bridging the existing gap in the application of advanced DCMs within practical RM implementations. © This is a U.S. Government work and not under copyright protection in the US;foreign copyright protection may apply 2024.

关键词： Choice-based optimization Decision-making behavior Demand prediction Mixed integer linear programming Operations research Pricing

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