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Exploring the Impact of IoT data Diversity on Machine Learning Model 5

Exploring the Impact of IoT Data Diversity on Machine Learni...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Bisen, Dhananjay Venu, Nookala Dubey, Aditya Garg, Priyanka Salendra, Suresh Devi, T. Lakshmi Madhav Institute of Technology & Science Center for Internet of Things M.P Gwalior474 005 India ABV Indian Institute of Information Technology and Management Data Science and Quantum Computing M.P Gwalior474 015 India Balaji Institute of Technology & Science Dept. of Compter Science and Engineering Telangana Warangal506 331 India Siddhartha Institute of Engineering and Technology Dept.of CSE-Data Science Hyderabad501506 India

ISBN: (纸本)9798331530013

The past few years have witnessed a remarkable change by the rapid expansion of the Internet of Things (IoT). The increasing spread of networked devices in various areas of the Internet of Things has resulted in a huge amount of machine data. This data contains rich information that can be harnessed across domains such as healthcare, agriculture, smart cities, and manufacturing. To effectively analyze and capitalize this information, the integration of machine learning (ML) methods is essential. Thus, well-known machine learning techniques including Logistic Regression, Decision Tree, KNN, SVM, Random Forest, and Naive Bayes are used in paper. The performance of the algorithms is analyzed using three diverse IoT datasets and compared to one another using pre-defined measures such as accuracy, precision, recall, F1-score and execution time. Furthermore, test findings shown that the random forest algorithm consistently outperformed other algorithms for a variety of datasets with up to 98% accuracy. © 2024 IEEE.

关键词： Adversarial machine learning

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Necessity of Information Security in Safeguarding Smart Grid Infrastructure 5

Necessity of Information Security in Safeguarding Smart Grid...

引用

5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Joshi, Namra Khandelwal, Achala Joshi, Shubham SVKM's Institute of Technology Department of Electrical Engineering Maharashtra Dhule India Thakur College of Engineering & Technology Kandivali Mumbai India Vishwakarma Institute of Information Technology Dept. of Artificial Intelligence & Data Science Pune India

ISBN: (纸本)9798331504403

The Indian economy is continuously growing, so the power demand is increasing abruptly. The power extraction from non-conventional energy sources has also increased in the past 10 years. Somehow generation and demand are matched. However, a competent infrastructure is needed to handle power generated from multiple sources. Smart Grid Systems has excellent properties to handle such situations in power systems. The highlights of the fundamentals of smart grid, recent developments in Smart Grid as well as Importance of Information Security in Smart Grid is discussed. © 2024 IEEE.

关键词： Power grids

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A Dual-Port, and Single-Element Planar Inverted-F Antenna for Current 4G/5G Portable Applications 21

A Dual-Port, and Single-Element Planar Inverted-F Antenna fo...

引用

21st International Multi-Conference on Systems, Signals and Devices, SSD 2024

作者： Alja'Afreh, Saqer S. Altakhaineh, Amjaad T. Al-Shamaileh, Mallak H. Matarneh, Aser M Daoud, Omar R. Al-Khawaldah I, Mohammad School of Engineering and Computing American International University Electrical and Computing Engineering Al-Jahra Kuwait Pioneers International Academy Data Science and Machine Learning Dept. Amman Jordan Al-Ahliyya Amman University Communications and Computer Engineering Amman Jordan

ISBN: (纸本)9798350374131

A first-hand, low-profile, two-port planar inverted-F antenna is proposed for current 4G/5G sub-3 GHz hand-portable multiple-input and multiple-output (MIMO) applications. The design features a profile of about 5 mm and dual-coplanar feeds, which are isolated using only a modified top plate, while the shared ground plane is not perturbed as in previous designs. An open-edge slot on the top of the PIFA provides a 3λ/4 current path. The proposed antenna covers approximately a 15% impedance bandwidth fraction in the range of 2.45-2.85 GHz. The simulated results, including scattering parameters, far-field radiation patterns, and the envelope correlation coefficient, all exhibit excellent features. © 2024 IEEE.

关键词： Antenna feeders

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Noninvasive assessment of spatio-temporal recurrence in atrial fibrillation 28

Noninvasive assessment of spatio-temporal recurrence in atri...

引用

28th European Signal Processing Conference, EUSIPCO 2020

作者： Bonizzi, Pietro Zeemering, Stef van Rosmalen, Frank Schotten, Ulrich Karel, Joël Dept. of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands Dept. of Physiology Maastricht University Maastricht Netherlands Dept. of Biomedical Engineering Maastricht University Maastricht Netherlands

ISBN: (纸本)9789082797053

Propagation of Atrial Activity during atrial fibrillation (AF) is a complex phenomenon characterized by a certain degree of recurrence (periodic repetition). In this study, we investigated the possibility to detect recurrence noninvasively from body surface potential map recordings in patients affected by persistent AF, and localize this recurrence both in time and space. Results showed that clusters of recurrence can be identified from body surface recordings in these patients. Moreover, the number of clusters detected and their location on the top-right of the back of the torso were significantly associated with AF recurrence 4 to 6 weeks after electrical cardioversion. This suggests that noninvasive quantification of recurrence in persistent AF patients is possible, and may contribute to improve patient stratification. © 2021 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Noninvasive medical procedures

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Hybrid Deep Learning Approach for Nonfunctional Software Requirements Classifications 3

Hybrid Deep Learning Approach for Nonfunctional Software Req...

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3rd International Conference on Communication, Computing and Digital Systems, C-CODE 2023

作者： Rahman, Kiramat Ghani, Anwar Ahmad, Rashid Sajjad, Syed Haseeb International Islamic University Dept. of Software Engineering Islamabad Pakistan Big Data Research Center Jeju National University Jeju Korea Republic of Inernational Islamic University Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350332391

This paper explores the concept of algorithmic hybridization, which involves combining various machine learning (ML) algorithms to enhance performance by utilizing the benefits of both simultaneously. This study presents a framework that utilizes a combination of long short-term memory (LSTM) and bidirectional LSTM (BiLSTM) with artificial neural networks (ANN) to classify non-functional requirements (NFR). The task of NFR classification is challenging due to the scarcity of supervised learning data. The effectiveness of the proposed approach was assessed by comparing the performance of our integrated model with that of single LSTM and BiLSTM models. To conduct this evaluation, we combined two datasets consisting of 1000 non-functional requirements (NFR). The experimental findings revealed that the proposed approach is effective as the hybrid models exhibited better precision, recall, and F-1 score compared to its counterparts. © 2023 IEEE.

关键词： Requirements engineering

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Cyber Complaint Automation System

Cyber Complaint Automation System

引用

Advanced Computing and Communication Technologies for High Performance Applications

作者： Anand V Prabhu MJ Jefiya Joel Denny Joseph Tisa Sunny Cerene Mariam Abraham Computer Science and Engineering dept. of Artificial Intelligence and Data Science

Due to the Internet's quick spread and the digitali-sation of commercial activity, cybercrime has risen dramatically. Computers or data are the intended victim of cybercrime. This paper is about “Cyber Complaint Automation System”. The current cyber cell of Kerala police has a time comsuming approach. Complaint can be given in two ways. A written complaint can be given to cyber police station. Second option is to use cyber crime reporting portal. In this portal user has to undergo four different steps to sucessfully register the complaint. These complaints are stored in a database and manually classified into respective departments. This methodology of a cyber complaint takes a great deal of effort and time. The time wasted on current system helps the criminal in covering his track. As a result, the victim's time and money are squandered, and police receive criticism. We are working to create a software that will drastically shorten the time it takes to handle a complaint in an effort to remedy this scenario. Our system automates the classification process and prioritize emergency complaints. Thus helps the police to apprehend the criminal faster.

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A Novel Design of Secured BlockChain Assisted Land Registration Mechanism by using Enhanced Verification Principles

A Novel Design of Secured BlockChain Assisted Land Registrat...

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2023 IEEE International Conference on Research Methodologies in knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Prabu, R.T. Hemalatha, R. Madhini, M. Diwakaran, S. Senthilkumar, V. Thiyaneswaran, B. Dept. Electronics and Communication Saveetha School of Engineering Chennai India Dept. Computer Science Engineering St.Joseph College of Engineering Chennai India Dept. Electronics and Communication S.A.Engineering College Chennai India Dept. Master of Comp. Applns Kalasalingam Academy of Res. & Edn. Srivilliputhur India Department of Data Science VelTech Multitech Engg. College Chennai India Dept. Electronics & Communication Sona College of Technology Salem India

ISBN: (纸本)9798350305708

In the contemporary world, safeguarding data has assumed paramount importance, prompting numerous industrial sectors to fortify their defenses against potential hackers. In this landscape, blockchain has emerged as a cutting-edge technology that empowers peers to securely exchange various assets, including currency, financial documents, and land properties. Operating as an open-source public network, blockchain eliminates the need for a central authority. Instead, it facilitates peer-to-peer interactions, ensuring that transactions, value transfers, and data sharing are authenticated by all interconnected nodes within the network. The conventional process of land registration, characterized by its sluggishness and complexity, involves multiple intermediaries and is susceptible to instances of fraudulent and counterfeit land transfers. To address these challenges, the paper introduces a conceptual framework for blockchain-based land registry systems. By analyzing existing frameworks and scrutinizing the robust strategies and concepts they employ, the paper establishes connections that culminate in the creation of this conceptual framework. The proposed solution in the paper revolves around the secure transfer of land ownership through blockchain technology. This solution circumvents the need for intermediaries, allowing buyers and sellers to conduct land ownership transactions seamlessly using the Ethereum network. By leveraging the power of blockchain, this solution aims to mitigate the vulnerabilities and inefficiencies inherent in traditional land registration processes, ushering in a more secure, efficient, and transparent approach to managing land ownership transactions. © 2023 IEEE.

关键词： Blockchain Land Registration land registry systems

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Personalized Stress Detection using a Lightweight Machine Learning Framework With Convenient Wrist-Worn Sensors

Personalized Stress Detection using a Lightweight Machine Le...

引用

2024 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2024

作者： Hasan, M. Mithun, S. Mahmud, M. Baral, S. Poudel, K. Middle Tennessee State University Computational And Data Science MurfreesboroTN United States University Of Memphis Dept. Of Computer Science TN United States University Of Memphis Dept. Of Electrical And Computer Engineering TN United States Middle Tennessee State University Dept. Of Computer Science MurfreesboroTN United States Univ. Of Tennessee Health Science Center Div. Of General Internal Medicine MemphisTN United States

ISBN: (纸本)9798350388572

Over 70% Americans experience daily stress, making real-time, accurate stress detection crucial for timely intervention, and promoting physical and mental well-being. In this study, we detect stress using machine learning from publicly available wrist-worn sensor data. We examined both population and individual levels, focusing on simple, computation friendly statistical features for reduced processing to facilitate adoptions in real-life mHealth applications. Our framework demonstrates that stress detection achieves over 96% accuracy at both the population and individual levels, with the Extreme Gradient Boosting classifier achieving the highest predictive accuracy. We found significant variability between individuals and achieved an average accuracy of 99.63% with area under the curve (AUC) 99.23%, F1-score 99.00%, precision, and recall 99.22%. This approach offers a promising solution for real-life stress detection due to its high accuracy and simplicity. © 2024 IEEE.

关键词： LightGBM Machine learning Personalization Stress detection Wrist-band sensors

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Self-Adaptive Smart Lighting System using Yolov8 and ESP32

Self-Adaptive Smart Lighting System using Yolov8 and ESP32

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International Conference on Electrical Energy Systems (ICEES)

作者： Virum Ranka Dwisha Shah Devam Shah Pradnya Saval Dept. of Computer Science and Engineering (Data Science) Dwarkadas J. Sanghvi College of Engineering Mumbai India

ISBN: (数字)9798350353778

ISBN: (纸本)9798350353785

In the pursuit of sustainable living and enhanced user comfort, the integration of smart technologies into everyday devices has become paramount. This paper highlights the importance of dynamic lighting control in improving energy efficiency and the overall quality of life. Traditional lighting systems often fail to account for the dynamic nature of human activities, resulting in unnecessary energy wastage and suboptimal lighting conditions. The proposed system seeks to bridge the gap between existing lighting systems and the evolving needs of modern indoor spaces, with its focus on adaptability, efficiency, and user-centricity, promoting a more comfortable and nurturing environment. Although earlier studies have explored various aspects of smart lighting technologies, there remains a paucity of comprehensive solutions that integrate machine learning with sensor technologies to create truly adaptive environments. Novel algorithms have been utilized to optimize both visual comfort and energy efficiency. This system achieves up to 45-70% energy savings compared to existing lighting systems, making it a promising solution for both environmental and economic concerns.

关键词： Visualization Machine learning algorithms Energy conservation Smart homes Activity recognition Energy efficiency Real-time systems Trajectory Sensors Smart lighting

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Empirical Analysis of Classical and Quantum Algorithms for Portfolio Optimization: Enhancing Financial Decision-Making Through Quantum Computing

Empirical Analysis of Classical and Quantum Algorithms for P...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Krish Valecha Anuradha Yeole Atharv Salian Shruti Mathur Dept. of Computer Science and Engineering (Data Science) Dwarkadas J. Sanghvi College of Engineering Mumbai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

This research investigates the efficacy of quantum and classical algorithms in the context of portfolio optimization, focusing on a dataset comprising 20 equities from India's National Stock Exchange over a duration of three years. The study employs quantum methodologies such as the Quantum Approxi-mate Optimization Algorithm (QAOA), the Variational Quantum Eigensolver (VQE), and the Exact Minimum Eigen Solver, alongside conventional strategies including Mean-Variance Op-timization, Genetic Algorithms, and Simulated Annealing. The methodology for this study includes data preprocessing, issue designing, and performance evaluation based on metrics such as MSE, RMSE, and Mean Absolute Error. The results obtained show quantum algorithms exhibit better performance over the classical methods regarding prediction accuracy and risk-return profiles, especially the Exact Minimum Eigen hSolver and QAOA. The Exact Minimum Eigen Solver and QAOA had the lowest MSE values (0.006 and 0.005, respectively), whereas the best-performing classical technique, the Genetic Algorithm, had an MSE of 0.0105. These findings imply that quantum algorithms provide significant advantages in portfolio optimization problems, with the potential to revolutionize financial optimization tactics. The research can be further unfolded into avenues for future inquiry, including expansion of the choice of assets, alternative data sources integration, as well as hybrid quantum-classical models to efficiently enhance portfolio management strategies.

关键词： Quantum algorithm Accuracy Soft sensors Simulated annealing Transforms Prediction algorithms Stock markets Optimization Portfolios Genetic algorithms

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