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Integrated Intelligence and communication systems (ICIICS), International Conference on

作者： K. Sangeetha Supriya Jeno Lovesum S P Renuka Arora Rishu Bhatia Sunita Yadwad Natrayan L Department of Computer Science & Engineering Christ university Bengaluru India Department of Computer Science & Engineering Jagannath University NCR Bahadurgarh India Department of Electronics and Communication Engineering Ganga Institute of Technology and Management Jhajjar India Department of Computer Science and Engineering Faculty of Engineering and Technology Parul institute of Engineering and Technology Parul University Vadodara India Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

The proliferation of Internet of Things (loT) devices has introduced significant security challenges due to the increased attack surface and the inherent vulnerabilities of interconnected systems. This paper proposes a novel security framework that integrates Artificial Intelligence (AI)-infused software and Virtual Replica Models, or Digital Twins, to enhance the robustness of loT systems. The framework leverages AI-driven anomaly detection and predictive analysis to identify potential threats in real-time, while Digital Twins provide a virtual replica of the loT ecosystem for continuous monitoring and simulation-based threat assessment. By fusing these technologies, the proposed solution enables proactive security measures, ensuring rapid response to cyberattacks and continuous system optimization. Extensive simulations and experimental results demonstrate the system's ability to reduce attack detection times by 30%, while improving overall system resilience and minimizing false positive rates. This approach provides a scalable and adaptable solution for securing loT infrastructures in various industries.

关键词： Industries Transport protocols Adaptation models Biological system modeling Software Real-time systems Threat assessment Digital twins Artificial intelligence Anomaly detection

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An Explainable Artificial Intelligence Framework for the Predictive Analysis of Hypo and Hyper Thyroidism Using Machine Learning Algorithms

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Human-Centric Intelligent systems 2023年第3期3卷 211-231页

作者： Hossain, Md. Bipul Shama, Anika Adhikary, Apurba Raha, Avi Deb Uddin, K. M. Aslam Hossain, Mohammad Amzad Islam, Imtia Murad, Saydul Akbar Munir, Md. Shirajum Bairagi, Anupam Kumar Department of Information and Communication Engineering Noakhali Science and Technology University Noakhali Bangladesh Department of System Engineering University of South Alabama Mobile USA Computer Science and Engineering Discipline Khulna University Khulna Bangladesh Mandy Dental College and Hospital Dhaka Bangladesh School of Computer Science and Computer Engineering The University of Southern Mississippi Hattiesburg USA Department of Electrical and Computer Engineering Old Dominion University Norfolk USA

The thyroid gland is the crucial organ in the human body, secreting two hormones that help to regulate the human body’s metabolism. Thyroid disease is a severe medical complaint that could be developed by high Thyroid Stimulating Hormone (TSH) levels or an infection in the thyroid tissues. Hypothyroidism and hyperthyroidism are two critical conditions caused by insufficient thyroid hormone production and excessive thyroid hormone production, respectively. Machine learning models can be used to precisely process the data generated from different medical sectors and to build a model to predict several diseases. In this paper, we use different machine-learning algorithms to predict hypothyroidism and hyperthyroidism. Moreover, we identified the most significant features, which can be used to detect thyroid diseases more precisely. After completing the pre-processing and feature selection steps, we applied our modified and original data to several classification models to predict thyroidism. We found Random Forest (RF) is giving the maximum evaluation score in all sectors in our dataset, and Naive Bayes is performing very poorly. Moreover selecting the feature by using the feature importance method RF provides the best accuracy of 91.42%, precision of 92%, recall of 92% and F1-score of 92%. Further, by analyzing the characteristics and behavior of the dataset, we identified the most important features (TSH, T3, TT4, and FTI) of the dataset. In terms of accuracy and other performance evaluation criteria, this study could advocate the use of effective classifiers and features backed by machine learning algorithms to detect and diagnose thyroid disease. Finally, we did some explainability analysis of our best classifier to understand the internal black-box of our machine learning model and datasets. This study could further pave the way for the researcher as well as healthcare professionals to analyze thyroid disease in real time applications.

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Enhancing Retrieval Quality Through Big Data and AI Technologies 3

Enhancing Retrieval Quality Through Big Data and AI Technolo...

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3rd International Conference on Smart Generation Computing, communication and Networking, SMART GENCON 2023

作者： Mishra, Abhinav Singh, Akhilendra Pratap Shankar, Amar Kaushik, Harshita Divya, K. Disha, S. Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Food Technology Faculty of Engineering and Technology Karnataka Ramnagar India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Wankhede Department of Computer Engineering Pune India

ISBN: (纸本)9798350319125

Huge facts and AI technologies have superior speedy to end up the primary drivers of innovation and progress in lots of one-of-a-kind areas, along with statistics retrieval. through the evaluation of big datasets of formerly retrieved documents and question logs, AI algorithms are capable of expect and perceive the maximum applicable files and also are used to higher recognize and respond to consumer queries. additionally, AI technology which include natural language processing are already beginning to shape the manner human beings engage with engines like Google, permitting extra conversational, person-friendly seek experience. eventually, massive information and AI have the capability to help form semantically based collections of documents with keywords and entities robotically extracted from content, making it easier for a search engine to analyze and connect files higher. This may in the end result in better retrieval satisfactory and greater dynamic records exploration studies for users of search engines. © 2023 IEEE.

关键词： analyze dynamic exploration records search

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Wearable Analytics and Early Diagnostic of COVID-19 Based on Two Cohorts 14

Wearable Analytics and Early Diagnostic of COVID-19 Based on...

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14th International Congress on Ultra Modern Telecommunications and Control systems and Workshops, ICUMT 2022

作者： Skibinska, Justyna Hosek, Jiri Channa, Asma Brno University of Technology Unit of Electrical Engineering Tampere University Faculty of Electrical Engineering and Communication Brno Czech Republic Brno University of Technology Faculty of Electrical Engineering and Communication Brno Czech Republic University Politehnica of Bucharest University Mediterranea of Reggio Calabria Computer Science Department Diies Department Bucharest Romania

ISBN: (数字)9798350398663

ISBN: (纸本)9798350398663

The outbreak of the COVID-19 pandemic forced a need to create screening tests to diagnose the disease. To answer this challenge, this paper introduces the support methodology for COVID-19 early detection based on wearable and machine learning likewise on two various cohorts. We compare the level of detection of the COVID-19 disease, Influenza, and Healthy Control (HC) thanks to the usage of machine learning classifiers likewise changes in heart rate and daily activity. The features obtained as the parameters of the ratio of heart rate to the variable of the number of steps proved to have the highest statistical importance. The COVID-19 cases versus HC were possible to be distinguished with 0.73 accuracy by the XGBoost algorithm, whereas COVID-19 cases, Influenza vs. HC were able to be differentiated on similar level of accuracy: in 0.72 by Support Vector Machine. The multiclass classification between the cases achieved a 0.57 F1-score for three classes by XGBoost. For early diagnosis, this solution could serve as an extra test for clinicians during the pandemic, and the result shows which metric could be useful for creating the machine learning model. © 2022 IEEE.

关键词： COVID-19

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Advanced IOT Traffic Light Control system 5

Advanced IOT Traffic Light Control System

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5th International Conference on Advances in Computational Tools for engineering Applications, ACTEA 2023

作者： Hamad, Martin Abou El Hajj, Antonio Daou, Roy Abi Zeid Hayek, Ali Haidar, Gaby Abou Lebanese German University Faculty of Public Health Biomedical Technologies Department Lebanon MART Learning Education and Research Center Lebanon University of Kassel Department of Computer Architecture and System Programming Kassel Germany American University of Science and Technology Engineering Faculty Zahle Lebanon

ISBN: (纸本)9798350309959

For several years, traffic congestion has been a major problem in big cities where the number of cars and different means of transportation has been increasing significantly. The problem of congestion is becoming more and more critical, and if not treated smartly this issue will negatively affect drivers by wasting time and fuel gas while waiting for hours in lanes. This paper presents a new and smart way to mitigate this issue in an affordable cost, minimum processing power, and low power consumption. This concept takes into consideration the majority of the cases that may cause congestion and presents a smart and accurate outputs to ease traffic flow leading to the prediction of the peak hours of traffic congestion for smarter control. A model is designed to study the case of a four lanes crossroad with two traffic lights and two LCD monitors. The strategy in reading data is divided into two parts: real data from sensors and pre collected data from google maps to create a kind of a predicted pattern over a certain time interval. The responsiveness of the system is analyzed thoroughly, and the accuracy of all possible cases is carefully considered and evaluated. Each part of the system was tested alone, and the overall system is still in an ongoing testing phase. The results have shown minimum faulty errors and accepted outputs that can lead to safe traffic control decisions. Finally, integrating more IoT devices and sensors between V2V, V2P, V2I with the help of artificial intelligence will definitely optimize this system with higher accuracy. © 2023 IEEE.

关键词： Traffic congestion

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Current Problems and Proposed Solutions for the Electronic Commerce Sector 1

Current Problems and Proposed Solutions for the Electronic C...

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1st International Conference on computer science and Emerging Technologies, CSET 2023

作者： Wahshat, Hassan Al Al-Rousan, Riyad Al-Haithami, Wafa Abdulkarem Ahmad Saany, Syarilla Iryani Mohamad, Fatma Susilawati Kumar, Rohit Ajloun National University Faculty of Business Department of Management Information System Ajloun Jordan Department of Virtual and Augmented Reality Jordan Mediu Faculty of Finance and Administrative Sciences Malaysia UniSZA Faculty of Informatics and Computing Malaysia School of Computer Science and Engineering Jain Deemed to Be University Bangalore India

ISBN: (纸本)9798350341737

The rapid growth of electronic commerce (e-commerce) has revolutionized the global business landscape, transforming the way consumers and businesses interact, purchase, and sell goods and services. However, this sector faces a myriad of challenges that impede its sustainable growth and development. This research paper critically examines the current problems encountered by the electronic commerce sector and proposes viable solutions to address these issues. The study identifies three major problem areas affecting the sector: (1) Security and Privacy Concerns, (2) Logistics and Supply Chain Bottlenecks, and (3) Customer Experience and Trust. Regarding security and privacy, the proliferation of cyber-attacks, data breaches, and identity theft has eroded consumer confidence, hindering e-commerce adoption. The paper aims to investigate the latest technologies and best practices, such as blockchain, advanced encryption techniques, and robust authentication methods, to fortify e-commerce platforms against cyber threats and safeguard user data. The second problem area, logistics and supply chain bottleneck, poses challenges related to order fulfilment, inventory management, and last-mile delivery. Through an in-depth analysis, the research proposes innovative approaches like utilizing machine learning algorithms for predictive inventory management, integrating Internet of Things (IoT) devices for real-time tracking, and exploring drone and autonomous vehicle deliveries to enhance overall logistics efficiency. The third issue addressed in this paper pertains to customer experience and trust. The inability to tangibly experience products before purchase, coupled with ambiguous return policies, often deters potential customers from engaging in e-commerce transactions. The study delves into augmented reality (AR) and virtual reality (VR) applications to simulate product experiences and advocates for transparent and customer centric return policies. This research paper sheds li

关键词： Supply chains

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Noisy ECG Signal Data Transformation to Augment Classification Accuracy

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computers, Materials & Continua 2022年第5期71卷 2191-2207页

作者： Iqra Afzal Fiaz Majeed Muhammad Usman Ali Shahzada Khurram Akber Abid Gardezi Shafiq Ahmad Saad Aladyan Almetwally M.Mostafa Muhammad Shafiq Department of Information Technology University of GujratGujrat50700Pakistan Department of Computer Science University of GujratGujrat50700Pakistan Faculty of Computing The Islamia University of BahawalpurBahawalpur63100Pakistan Department of Computer Science COMSATS University IslamabadIslamabadPakistan Industrial Engineering Department College of EngineeringKing Saud UniversityP.O.Box 800Riyadh 11421Saudi Arabia Department of Information Systems College of Computer and Information SciencesKing Saud UniversityP.O.Box 800Riyadh11421Saudi Arabia Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

In this era of electronic health,healthcare data is very important because it contains information about human *** addition,the Internet of Things(IoT)revolution has redefined modern healthcare systems and management by providing continuous *** this case,the data related to the heart is more important and requires proper *** the analysis of heart data,Electrocardiogram(ECG)is *** this work,machine learning techniques,such as adaptive boosting(AdaBoost)is used for detecting normal sinus rhythm,atrial fibrillation(AF),and noise in ECG signals to improve the classification *** proposed model uses ECG signals as input and provides results in the form of the presence or absence of disease AF,and classifies other signals as normal,other,or *** article derives different features from the signal using Maximal Information Coefficient(MIC)and minimum Redundancy Maximum Relevance(mRMR)technique,and then classifies them based on their *** the ECG contains some kind of noise and irregular data streams so the purpose of this study is to remove artifacts from the ECG signal by deploying the method of Second-Order-Section(SOS)(filter)and correctly classify *** features were extracted to improve the detection of ECG *** with existing methods,this work gives promising results and can help improve the classification accuracy of the ECG signals.

关键词： ECG atrial fibrillation adaptive boosting heart rate variability

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CFOL: A Superficial Machine Learning Methodology to Identify Malwares by Using Convolutional Fuzzy Optimization Logic

CFOL: A Superficial Machine Learning Methodology to Identify...

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2024 International Conference on Innovative Computing, Intelligent communication and Smart Electrical systems, ICSES 2024

作者： Suriya, N. Sowmya, M. Priyanka, P. Rajesh, K. Edlabadkar, A.P. Abbas, N. Chennai Institute of Technology Department of Electronics and Communication Engineering Chennai India Qis College of Engineering and Technology Department of Mba Pondur Road Vengamukkapalem Ongole Andhra Pradesh Prakasam District523272 India Qis College of Engineering and Technology Department of S&h Pondur Road Vengamukkapalem Ongole AndhraPradesh Prakasam District523272 India Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa University Faculty of Economics and Administrative Sciences Department of economic Zarqa Jordan College of Business Administration University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Malware poses a significant threat to the security and integrity of modern computer systems. Traditional malware detection methods, such as signature-based approaches, struggle to keep pace with the rapid evolution of malware. This paper presents Convolutional Fuzzy Optimization Logic (CFOL), a novel machine learning-based methodology for malware detection that leverages Convolutional Neural Networks (CNNs) enhanced with an Attention Mechanism. Data was collected from Kaggle's malware dataset, which was preprocessed to extract key features like file headers, API calls, and metadata. The CNN with Attention Mechanism demonstrated an accuracy of 97.80%, outperforming traditional models such as Random Forest and Support Vector Machines (SVM), which achieved accuracies of 91.3% and 89.6%, respectively. The Attention Mechanism allowed the model to prioritize the most critical malware features, significantly reducing the false positive rate (FPR) to 2.2%, one of the lowest among the tested models. Additionally, the system achieved high precision (98.2%), recall (97.1%), and F1 score (97.35%). The integration of fuzzy logic optimization further enhanced the decision-making process in cases of ambiguous classifications. The results validate the effectiveness of CFOL in detecting complex and evolving malware with high precision and minimal false positives. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhancing 6G-Satellite Network Integration with Artificial Intelligence: A Future communication Paradigm 22

Enhancing 6G-Satellite Network Integration with Artificial I...

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22nd International Learning and Technology Conference, L and T 2025

作者： Salah, Ibrahim Hussein, Aziza I. Wageh Lotfy, Mostafa Mabrook, Mohamed Mourad Almaaqal University College of Engineering Control and Computer Department Basrah61001 Iraq Effat University College of Engineering Energy and Technology Research Center Saudi Arabia Faculty of Technology and Education Beni-Suef University Dept. of Process Control Technology Beni-Suef Egypt Beni-Suef University Faculty of Navigation Science & Space Technology Space Communication Dept. Beni-Suef Egypt

ISBN: (纸本)9798331532215

The integration of 6G networks and satellite communications is set to revolutionize global connectivity, offering seamless coverage across terrestrial and non-terrestrial environments. Artificial Intelligence (AI) is essential for improving this integration, addressing challenges such as dynamic resource management, latency reduction, and network optimization. AI techniques like machine learning, deep learning, and reinforcement learning offer innovative solutions to handle the complexities of 6G-satellite networks. These advancements promise to significantly improve network efficiency, enhance data transmission reliability, and ensure seamless connectivity across different areas. Potential use cases include smart cities, autonomous vehicles, and Internet of Things (IoT) applications, where AI-driven 6G-satellite integration will be crucial. The proposed AI-enhanced 6G-satellite framework not only addresses current challenges but also lays the groundwork for a resilient, scalable, and globally interconnected communication infrastructure, offering a promising future. © 2025 IEEE.

关键词： Satellite communication systems

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Exams Item Analysis, Online Examination, and Education Evaluation system Via Internet 20

Exams Item Analysis, Online Examination, and Education Evalu...

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20th International Conference on Electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON 2023

作者： Chaidee, Kamonlak Boontasri, Kanchana Chaidee, Ekkachai Punthura, Kittichai Kumtip, Narong Pankham, Yotsanan Rajamangala University of Technology Lanna Chiangrai Faculty of Business Administration and Liberal Arts Department of Computer Information System Chiang Rai Thailand Rajamangala University of Technology Lanna Chiangrai Faculty of Science and Agricultural Technology Department of Information Technology Chiang Rai Thailand Rajamangala University of Technology Lanna Chiangrai Faculty of Electrical Engineering Department of Electrical Engineering Chiang Rai Thailand

ISBN: (纸本)9798350310467

The objective of this study is to develop an exams item bank management system on the internet. The exam item analysis system is used to determine the reliability of the exams using K-R 20 and K-R 21 formulas. The difficulty and power of discrimination values are determined using a choice analysis and percentages of high and lower groups technique. The create exams set system can be performed randomly or identified through the difficulty level. The examination can be performed online and the exam results can be immediately announced. The grading system is used to support the decision-making to evaluate learning achievement. The system's accuracy was tested and found to be accurate compared with the hand calculation results. The system was tested with a sample group and satisfaction was assessed through questionnaires. The satisfaction of teachers and students was highest, while that of administrators was high. The developed exams item bank management system operates as a web application with a security system for supporting in learning activity. © 2023 IEEE.

关键词： Decision making

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