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5th International Symposium on Signal and Image Processing, ISSIP 2024

作者： Singh, Jagendra Saraswat, Mala Upreti, Kamal Agrawal, Gaurav Hasan, Dler Salih Shaik, Nazeer School of Computer Science Engineering and Technology Bennett University Greater Noida India School of Computer Science Engineering & Technology Bennett University Greater Noida India Delhi-NCR Ghaziabad India KIET Group of Institution Ghaziabad India Department of Computer Science & Information Tech. University of Salahaddin-Erbil Erbil Iraq Department of Computer Science & Engineering Srinivasa Ramanujan Institute of Technology Autonomous Anantapur India

ISBN: (纸本)9789819795147

The main objective of this research was the development and evaluation of an IoT- and machine learning-based health monitoring system capable of protecting patient’s vital organs through cloud diagnosis. This could be achieved by connecting a set of sensors, including temperature, pressure, heart rate, and oxygen sensors, to the patient and allowing them to communicate with the cloud to transmit real-time data via IoT technologies. The data could be further analyzed and predicted using cloud-based machine learning algorithms. This study investigated the performance of different machine learning models, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Naive Bayes (NB), and Negative Decision Trees (DT), for the purpose of patient’s health prediction based on the sensor data. After experimentation and evaluation, we found that the ANN model demonstrated the best predictive ability, with an accuracy level of 99.45%. The SVM, NB, and DT models also demonstrated good performance, with the accuracies of 96.5%, 94.34%, and 91.2%, respectively. Therefore, this research demonstrated that IoT and machine learning technologies could be successfully employed in healthcare for remote patient monitoring and timely prediction. The created system allows for real-time monitoring, which enables early prediction, potentially leading to improved patient outcomes, cost savings, and higher efficiency of provided care. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Remote patient monitoring

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The Electricity Management for Domestic Applications Through Hybrid Grid

The Electricity Management for Domestic Applications Through...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Kumar, Sanjay Kalra, Bharti Kannagi, A. Noida Institute of Engineering and Technology Department of Mechanical Engineering Uttar Pradesh Greater Noida India Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Bangalore India

ISBN: (纸本)9798350357769

In order to effectively manage power demand, optimize energy usage, and integrate distributed renewable energy production, home energy management systems, or HEMSs, are essential. The primary aim is to optimize energy efficiency while maintaining customer comfort. Numerous factors, including as energy prices, meteorological conditions, load the profiles, and customer comfort levels, influence how HEMSs function. They greatly reduce the amount of power used in both personal and business smart networks as they become more and more common. This study performs a comprehensive analysis of the body of research on HEMS, including key ideas, configurations, and the supporting technologies that support their operation. Giving a thorough review of the status of HEMS innovation and its uses today is the aim. The study also discusses popular communication methods used in demand management applications and analyzes current advancements in HEMS computing. The study that follows provides readers with a comprehensive overview of current and upcoming developments in HEMS technology and solutions. Presenting a polished and original synthesis of the current material while preserving its integrity and excluding any needless elaboration is the aim. © 2023 IEEE.

关键词： communication technologies demand response distributed renewable energy energy efficiency HEMS Home Energy Management Systems load scheduling and sustainable energy management microgrids smart grid

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ML-Orchestrated Malware Detection Framework for Windows APIs in Critical Infrastructure Systems 2

ML-Orchestrated Malware Detection Framework for Windows APIs...

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Vaghasiya, Shayalkumar Gupta, Rajesh Mohapatra, Santoshinee Tanwar, Sudeep Garg, Deepak Institute of Technology Nirma University Department of Computer Science and Engineering Gujarat 382481 India Sr University School of Computer Science and Ai Telangana Warangal India

ISBN: (纸本)9798350387933

The security of critical infrastructure systems is crucial as they provide basic amenities and services necessary for functioning of a community, also they represent important part of country's economy. An attack on this critical infrastructure systems can have significant consequences. In this paper we discuss the This paper addresses the increasing security challenges in critical infrastructure systems due to cyber threats, particularly malware and propose a framework based on machine learning for robust malware classification. Our proposed framework includes a dataset of Windows API functions used by the software, preprocessing techniques, and the XGBoost model which achieves an accuracy of 86.98%. Using this model, we can detect and categorize five different types of malwares to provide security against malware attacks. The results show model's ability to successfully classify different kinds of malware. Realtime monitoring is another feature of this framework which is important for actively protecting critical infrastructure systems. © 2024 IEEE.

关键词： Machine learning

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Machine Learning Classifiers for Social Media Bots Detection on Twitter using Explainable AI 2

Machine Learning Classifiers for Social Media Bots Detection...

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2nd International Conference on informatics, ICI 2023

作者： Gupta, Sarishty Sinha, Adwitiya Jaypee Institute of Information Technology Department of Computer Science and Engineering and Information Technology Noida India

ISBN: (纸本)9798350343830

Social networks have shaped modern society by revolutionizing how people connect and communicate with others around the world. Social media bots have become more prevalent as a result of social networks' phenomenal development and influence, nevertheless. These automated systems that are guided by algorithms or malicious actors can mimic human behavior and provide the impression that actual people are there. Growing concerns about the integrity of online debate, the dissemination of false information, and potential public opinion manipulation have been sparked by the use of social media bots. It is now necessary to create efficient procedures for identifying and thwarting social media threats in order to tackle this expanding danger. Such efforts use a variety of methods, including as examining profile metadata, observing behavioral patterns, and utilizing the strength of machine learning algorithms. In this paper, we propose an approach which uses multiple features of a user entity on Twitter to train a model for social media bot detection. We employed Twibot-22 dataset to conduct the experiment and compared the performance of our approach with established benchmark models in order to assess its efficacy. Remarkably, our Random Forest model outperformed the benchmarks, achieving an accuracy rate of 0.96. We utilized explainable AI technique to enhance the interpretability of random forest model. This study provides insights that are of immense value in refining the model and delving deeper into the distinguishing features of social bots and human users within the Twitter environment. © 2023 IEEE.

关键词： Lime

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Parametric Analysis of a Cricketer's Performance using Machine Learning Approach 7

Parametric Analysis of a Cricketer's Performance using Machi...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Vestly, Doddamani John Hariharan, Shanmugasundaram Kukreja, Vinay Prasad, Andraju Bhanu Swaraj, Kondawar Gopichand, Devarakonda Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350397253

This paper aimed to suggest the appropriate way to analyze the performance related to cricket players based on some predominant parameters such as batting, bowling, fielding, and overall contribution to the team success. In order to fetch the good result and success in cricket match, it is important to select the best players for the match. This is considered to be one of the crucial decision successful match outcomes. Eventually right actions and right decisions leads to success. These actions and decisions can be made through perfect selection of the machine learning algorithms with correct data sets and accurate attributes as well. This study makes recommendations for the best data source, approximate properties, and machine learning techniques. This paper aims to identify the factors that contribute to good performance and to provide insights that can be used by coaches and selectors in identifying and developing talented players. © 2023 IEEE.

关键词： Machine learning

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Intelligent System to Maximize Customer Experience in Restaurants 5

Intelligent System to Maximize Customer Experience in Restau...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Jayalal, R.U.A. Nimeshika, Y.E. Samarasekara, G.N.N. Wijerathna, G.R.C.I. Gamage, Narmada Gunathilake, Poojani Sri Lanka Institute of Information Technology Department of Computer Systems Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

With technological improvements in the restaurant industry, competition is fierce, and a restaurant's success depends on providing excellent and luxury customer satisfaction. The food industry has had to continuously advance with technology in order to adequately satisfy the wants of people leading increasingly luxurious lives. While some restaurants still rely on manpower, the majority are eager to advance with technology in order to improve the standard of their offerings and enhance their customers' experiences. In order to optimize customer satisfaction and experience, this research project intends to investigate the potential of intelligence in the restaurant business. This research has explored intelligent systems such as digital self-service menus for ordering food, augmented reality (AR) visualization systems for customers, the observation of chefs in the kitchen, and the usage of robots to serve meals. This research paper analyzes the advantages of automating restaurant operations and how to do so to enhance customer satisfaction. Overall, this study has the potential to transform the restaurant industry and increase customer satisfaction. © 2023 IEEE.

关键词： Augmented reality

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Drug Tracing In Healthcare Supply Chain Using Distributed Ledger technology

Drug Tracing In Healthcare Supply Chain Using Distributed Le...

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2023 IEEE International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2023

作者： Shaikh, Ayan Lokhande, Meghana Ahire, Prashant Sawalkar, Amey Shelke, Gaurav Shewale, Kartik Pimpri Chinchwad College of Engineering Department of Computer Engineering Pune India Symbiosis Institute of Technology Symbiosis International University Department of Computer Engineering Lavale Pune India

ISBN: (纸本)9798350397208

The distributed ledger technology, which is fundamentally a form of blockchain technology, is an example of an emergent technology in and of itself. In the context of cryptographic systems, it was conceived of simultaneously with the bitcoin application. The significance of the proposed system's role in the healthcare sector is implied by the fact that it aims to improve efficiency in the healthcare supply chain by incorporating distributed ledger technology. The fact that the chain of distribution of medical supplies involves multiple businesses makes it a complex system. In the context of the medical system, these kinds of issues can be familiar with the creation of fake drugs. Tracing pharmaceuticals is established utilizing technology known as distributed ledgers in order to combat the issues that arise during the manufacturing of drugs and to make the manufacturing process more reliable and transparent. Tracing will be done across the whole healthcare supply chain utilizing distributed ledger technology, also known as blockchain, and all of the players involved will have the ability to readily trace those transactions of pharmaceuticals in order to authenticate the medicine. © 2023 IEEE.

关键词： Smart contract

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FedBully: A Cross-Device Federated Approach for Privacy Enabled Cyber Bullying Detection using Sentence Encoders

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Journal of Cyber Security and Mobility 2023年第4期12卷 465-496页

作者： Shetty, Nisha P. Muniyal, Balachandra Priyanshu, Aman Das, Vedant Rishi Department of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal576104 India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal576104 India

Cyberbullying has become one of the most pressing concerns for online platforms, putting individuals at risk and raising severe public concerns. Recent studies have shown a significant correlation between declining mental health and cyberbullying. Automated detection offers a great solution to this problem;however, the sensitivity of client-data becomes a concern during data collection, and as such, access may be restricted. This paper demonstrates FedBully, a federated approach for cyberbullying detection using sentence encoders for feature extraction. This paper introduces concepts of secure aggregation to ensure client privacy in a cross-device learning system. Optimal hyper-parameters were studied through comprehensive experiments, and a computationally and communicationally inexpensive network is proposed. Experiments reveal promising results with up to 93% classification AUC (Area Under the Curve) using only dense networks to fine-tune sentence embeddings on IID datasets and 91% AUC on non-IID datasets, where IID refers to Independent and Identically Distributed data. The analysis also shows that data independence profoundly impacts network performance, with AUC decreasing by a mean of 5.1% between Non-IID and IID. A rich and extensive study has also been performed on client network size and secure aggregation protocols, which prove the robustness and practicality of the proposed model. The novel approach presented offers an efficient and practical solution to training a cross-device cyberbullying detector while ensuring client-privacy. © 2023 River Publishers.

关键词： Learning systems

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Hybrid Fuzzy Decision-Making Model 4

Hybrid Fuzzy Decision-Making Model

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Kumar, Vikram Saini, Dimple Rastogi, Shailesh Raman, Ramakrishnan Verma, Apurv Meenakshi, R. Srv Media Pvt. Ltd India India Pune India Department of Cse India Computer Science and Engineering Faculty of Engineering and Technology Parul University India Computer Science and Engineering Chennai Institute of Technology India

ISBN: (纸本)9798350360165

Establishing a sustainable the healthcare system necessitates the development of social, economic, and environmental norms. Although many studies concentrate on these elements separately, an integrated method of handling evaluating environmentally friendly development in healthcare institutions is conspicuously lacking. To address this gap, this paper presents a technique for studying indicators of sustainable development in healthcare organizations by modifying dimensions, criteria, and characteristics. In all, the three sustainability aspects are covered by the study with 62 traits and 15 criteria identified. Furthermore, a unique implementation technique is proposed to enable this methodology to be applied to real-world issues. This paper makes three contributions: it provides a new methodology for gathering basic data to measure long-term viability levels in healthcare systems;it presents the ability to identify implementation weaknesses in sustainability aspects;and it determines the sustainability level of the healthcare system under imprecise conditions. The study findings provide practitioners and specialists with significant perspectives to assess the sustainability levels of healthcare systems and improve their overall efficacy. © 2024 IEEE.

关键词： Sustainable development goals

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ASocTweetPred: Mining and Prediction of Anti-social and Abusive Tweets for Anti-social Behavior Detection Using Selective Preferential Learning 13th

ASocTweetPred: Mining and Prediction of Anti-social and Abus...

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13th International Conference on Innovations in Bio-Inspired Computing and Applications, IBICA 2022, and 12th World Congress on Information and Communication Technologies, WICT 2022

作者： Bhaveeasheshwar, E. Deepak, Gerard Mala, C. Department of Computer Science and Engineering National Institute of Technology Tiruchirappalli India Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru India Manipal Academy of Higher Education Manipal Udupi India

ISBN: (纸本)9783031274985

In the present times, owing to a large number of social interactions via Online Social Networking platforms. Because the content is not moderated on the World Wide Web, especially on the Social Web, due to lack of moderation, there are many derogatory, Anti-Social, and racist comments. Specifically, Twitter, YouTube, Instagram, Facebook comments, and similar social network platforms. In this paper, ASocTweetPred for Mining and Prediction of Anti-Social and Abusive Tweets for Anti-Social Behavior Detection has been put forth by encompassing Ontologies of different phases, Knowledge Graph, and the classification of the dataset using the LSTM to generate and yield matching ontology snippets by computing the Semantic Similarity using the Bose Einstein’s Index and the APMI measure. Subsequently, the matched ontology snippets and the formulated Knowledge Graph are used in order to extract features by preferential selection using Shannon’s Entropy and Horn’s Index with differential step deviation measures. A triadic hybrid model of the LSTM, Bagging with Random Forest and SVC, and XGBoost classifiers are made use for Anti-Social Tweets prediction. An overall Precision of 97.93%, Recall of 98.09%, Accuracy of 98.01%, F-Measure of 98.00%, and with the lowest FNR of 0.02 has been achieved by the proposed ASocTweetPred framework. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Semantics

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