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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Dwivedi, Mani Dwivedi, Anuj Kr. KIET Group of Institutions Delhi-NCR Department of Computer Science and Engineering Ghaziabad India Galgotias College of Engineering and Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798331523893

Artificial intelligence (AI) systems are evolving from static, task-specific models to dynamic, agent-based systems that perform well in a variety of scenarios. We present an Interactive Agent Foundation Model using a novel multi-task agent training methodology that includes many pretraining techniques such as language modelling, visual masked autoencoders, and next-action prediction. This framework's adaptability and versatility have been shown in the robotics, gaming AI, and healthcare sectors, yielding results that are pertinent to the situation. Effective multimodal and multitask learning is made possible by utilizing a variety of data sources, such as textual information, gaming data, robotics sequences, and large-scale video datasets. Our method offers a viable way forward for creating adaptable, proactive, multimodal systems. © 2025 IEEE.

关键词： Robot learning

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Cardiovascular Disease Analysis Using Correlational Analysis and Association Rules Mining for In-depth Analysis to Identify Predominant Variables

Cardiovascular Disease Analysis Using Correlational Analysis...

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2023 International Conference on computer science, Information Technology and engineering, ICCoSITE 2023

作者： Siswanto, Boby Soeparno, Haryono Sianipar, Nesti F. Budiharto, Widodo Bina Nusantara University BINUS Graduate Program- Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University Engineering Faculty Food Technology Department Jakarta Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350320954

Cardiovascular disease is one of the dangerous non-communicable disorders or diseases that has become one of the causes of death worldwide. Various studies have been conducted to prevent cardiovascular disease in the world. This study analyzed cardiovascular disease medical record data from the Kaggle public dataset by implementing correlational analysis combined with association rule mining to identify variables that are the predominant cause of cardiovascular disease. Correlational analysis can analyze the interrelationships between variables in a dataset, but not in depth. Association rule mining can identify the interrelationships of variables in the form of frequent item sets, which can be calculated for their support and confidence values. The result of this study is a combination of correlation analysis with association rule mining that can identify predominant variables to cause cardiovascular disease. Found that the variable gender=woman, height=short ( © 2023 IEEE.

关键词： Association rules

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Enhanced Visuals through Software-Optimized Memory Pipeline 2

Enhanced Visuals through Software-Optimized Memory Pipeline

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

作者： Ramesh, P.S. Senthilvadivu, S. Sudha, I. Kumar, T. Rajesh Naveen, V. Nagarajan, B. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Saveetha University Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Simats Department of Computer Science and Engineering Tamil Nadu Chennai India Madanapalle Institute of Technology and Science Department of Computer Science and Technology Madanapalle India Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Coimbatore India

ISBN: (纸本)9798350387933

Image processing algorithms, which supply the image quality, are used by modern mobile devices to capture images. These methods need more RAM to process an image and fix these problems. Software pipelines are utilized to reduce memory usage as much as feasible while maintaining the highest level of flexibility and affordability. Our method uses a line-based buffer software pipeline, which improves image quality and runs at a minimal cost. By transferring only the output to the next phase, the algorithm operates on an individual basis. Each step is completed in less time. Using picture enhancement and noise reduction techniques results in increased productivity, reduced memory consumption, and greater image quality. The applied framework demonstrates noise reduction with the PSNR algorithm and yields good results. © 2024 IEEE.

关键词： Image quality

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Evaluating the Impact of Risk Management Frameworks on IT Audits: A Comparative Analysis of COSO, COBIT, ISO/IEC 27001, and NIST CSF 5

Evaluating the Impact of Risk Management Frameworks on IT Au...

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5th International Conference on Electrical, Communication and computer engineering, ICECCE 2024

作者： Salihu, Armend Dervishi, Ramadan UNI - Universum International College Department of Computer Science Kosovo Prishtina Serbia AAB College Faculty of Computer Science Kosovo Prishtina Serbia

ISBN: (纸本)9798331529437

In today's digital landscape, information technology (IT) audits are critical for ensuring an in digital landscape nowadays. Frameworks related to risk management provide a systematic approach to identify and reduce IT risks. Control frameworks provide a defined method for analyzing internal controls and ensuring the effectiveness of IT audits. This study focuses on the impact and effectiveness of four major frameworks on IT audits: COSO, COBIT, ISO/IEC 27001, and NIST CSF. We investigate how each framework organizes the audit process, aids in control gap discovery, improves audit efficiency, and allows for unambiguous reporting. In addition, we explain factors to consider while picking the best framework for an organization's specific risk profile. The study conducts a thorough assessment of their strengths, shortcomings, and applicability for various IT audit scenarios. © 2024 IEEE.

关键词： ISO Standards

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Revolutionizing Medical Diagnostics: Exploring Creativity in AI for Biomedical Image Analysis

Revolutionizing Medical Diagnostics: Exploring Creativity in...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Krishnamoorthy, P. Swetha, Devulapally Geetha, Pamidimukkala Sai Karunambiga, K. Ayyasamy, Ramesh Kumar Kiran, Ajmeera Thiruthangal Nadar College PG Department of Computer Science Tamil Nadu Chennai India Vaagdevi Engineering College Department of Computer Science and Engineering Telangana Bolli Kunta India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India Karpagam Institute of Technology Department of CSE Tamil Nadu Coimbatore India Faculty of Information and Communication Technology Kampar Malaysia MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331540685

Biomedical image analysis plays a crucial role in the early and accurate diagnosis of diseases, significantly impacting patient outcomes. This study presents an innovative approach to biomedical image analysis by integrating machine learning techniques with a user-friendly web interface. The system takes input images uploaded through the web interface and processes them using advanced machine learning algorithms implemented in Python. The goal is to automate the detection of diseases from these images, enhancing the efficiency and precision of diagnosis. Convolutional Neural Networks (CNNs), a specialized class of deep learning models designed for image analysis, are employed to learn and characterize disease-specific patterns. The proposed approach is evaluated using diverse biomedical images encompassing various diseases and conditions. Extensive experiments demonstrate the system's effectiveness, achieving high accuracy, sensitivity, and specificity in disease detection. Integrating machine learning with a web interface simplifies the diagnostic process and enables remote consultations, making healthcare services more accessible and timelier. In conclusion, this study presents a robust framework for automated biomedical image analysis, demonstrating the potential of machine learning and web technologies in revolutionizing healthcare diagnostics. The system's accuracy and user-friendly interface make it a valuable tool for healthcare professionals, contributing to early disease detection and improved patient care. © 2024 IEEE.

关键词： Convolutional neural networks

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Securing IoT Devices and Data: An Analysis of Blockchain Technology and Mechanisms 15

Securing IoT Devices and Data: An Analysis of Blockchain Tec...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Thakur, Sujata Negi Sidana, Sagar Sinha, Anurag Akthar, Haniya Kumar, Biresh Kumar, Nitish Raj, Petheru Maheswaran, S. Al-Khayyat, Ahmed Graphic Era Hill University Department of computer science and engineering India Maharshi Dayanand University Department of computer science and engineering Haryana Rohtak India School of Computing and information Science IGNOU New Delhi India Amity University Jharkhand Department of computer science and engineering Ranchi India Lovely Professional University Department of computer science and engineering Phagwara India Reliance Jio Platforms Ltd Bangalore India Kongu Engineering College Department of Electronics and Communication Engineering Tamilnadu 638060 India Islamic University Department of computer science and engineering Iraq

ISBN: (纸本)9798350370249

The Internet of Things, or IoT, has expanded substantially in recent years. Combining IoT and the Internet allows heterogeneous intelligent devices to process real-time data and conduct transactions. Thanks to the Internet of Things (IoT), almost everything and everything can be connected to the internet and covered. Smart housing, smart healthcare, smart irrigation, and many other uses for IoT are all common [2]. However, the IoT's dependability in terms of privacy and security presents significant development challenges. Consensus mechanisms, decentralization, smart contracts, and data encryption are all blockchain elements that are well suited for building IoT systems since they assist designers in preventing potential attacks and lowering transaction costs. Blockchain serves as a decentralized and open database platform, which can improve the performance of IoT security. We must consider the security challenges and concerns that come with blockchain technology in addition to its ability to provide us with more dependable and useful services. Blockchain technology creates a decentralised, tamper-proof ledger that can securely record and confirm transactions, offering a viable solution for improving the security of IoT devices. Using an explanation of the underlying blockchain technology, a discussion of the security issues that IoT devices must deal with, and a description of the possible advantages of using blockchain for IoT security, we investigate the utilization of blockchain innovation for safeguarding IoT gadgets in this paper. © 2024 IEEE.

关键词： Blockchain Consensus mechanism IoT

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Wireless IoT Gas and Smoke Detection System with ESP8266 and Blynk Integration 1

Wireless IoT Gas and Smoke Detection System with ESP8266 and...

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1st International Conference on Optimization Techniques for Learning, ICOTL 2023

作者： Arul Prakash, A. Subathra Rahin Batcha, R. Ramalingam, Vijay Arunkumar, G. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India Madanapalle Institute of Technology & Science Madanapalle Department of Computer Science & Engineering India

ISBN: (纸本)9798350328042

The Internet of Things (IoT) has revolutionized the way we interact with our surroundings, making it possible to monitor and control devices remotely. One critical application of IoT technology is in the domain of home and industrial safety, where the early detection of smoke and hazardous gases is of paramount importance. The IoT Smoke and gas Detector utilizes the ESP8266 microcontroller, a versatile and cost-effective IoT platform, as the central processing unit. This device is equipped with sensors capable of detecting smoke and a range of harmful gases, including carbon monoxide (CO), methane (CH4), and propane (C3H8). When dangerous levels of these substances are detected, the device triggers alarms, sends notifications to designated users, and records data for analysis. The Blynk platform, a user-friendly and customizable IoT dashboard, is used as the interface for this system. Users can access real-time data from the Smoke & Gas Detector on their smartphones or other devices, allowing them to monitor the safety of their environment remotely. © 2023 IEEE.

关键词： Microcontrollers

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Digital Twin-Based Automated Fault Diagnosis in Industrial IoT Applications

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computers, Materials & Continua 2023年第4期75卷 183-196页

作者： Samah Alshathri Ezz El-Din Hemdan Walid El-Shafai Amged Sayed Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Security Engineering LabPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Electrical Energy Engineering College of Engineering&TechnologyArab Academy for Science Technology&Maritime TransportSmart Village CampusEgypt

In recent years, Digital Twin (DT) has gained significant interestfrom academia and industry due to the advanced in information technology,communication systems, Artificial Intelligence (AI), Cloud Computing (CC),and Industrial Internet of Things (IIoT). The main concept of the DT isto provide a comprehensive tangible, and operational explanation of anyelement, asset, or system. However, it is an extremely dynamic taxonomydeveloping in complexity during the life cycle that produces a massive amountof engendered data and information. Likewise, with the development of AI,digital twins can be redefined and could be a crucial approach to aid theInternet of Things (IoT)-based DT applications for transferring the data andvalue onto the Internet with better decision-making. Therefore, this paperintroduces an efficient DT-based fault diagnosis model based on machinelearning (ML) tools. In this framework, the DT model of the machine isconstructed by creating the simulation model. In the proposed framework,the Genetic algorithm (GA) is used for the optimization task to improvethe classification accuracy. Furthermore, we evaluate the proposed faultdiagnosis framework using performance metrics such as precision, accuracy,F-measure, and recall. The proposed framework is comprehensively examinedusing the triplex pump fault diagnosis. The experimental results demonstratedthat the hybrid GA-ML method gives outstanding results compared to MLmethods like LogisticRegression (LR), Na飗e Bayes (NB), and SupportVectorMachine (SVM). The suggested framework achieves the highest accuracyof 95% for the employed hybrid GA-SVM. The proposed framework willeffectively help industrial operators make an appropriate decision concerningthe fault analysis for IIoT applications in the context of Industry 4.0.

关键词： Automated fault diagnosis control system ML AI CC IIoT digital twins genetic algorithm GA-ML technique

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Drone-Based Intelligent System for Social Distancing Compliance Using YOLOv5 and YOLOv6 with Euclidean Distance Metric

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SN computer science 2024年第8期5卷 972页

作者： Parkavi, A. Alex, Sini Anna Pushpalatha, M.N. Shukla, Prashant Kumar Pandey, Ankur Sharma, Sachin Department of Computer Science and Engineering M S Ramaiah Institute of Technology Bangalore India Department of Computer Science and Engineering (AI and ML) M S Ramaiah Institute of Technology Bangalore India Department of Information Science and Engineering M S Ramaiah Institute of Technology Bangalore India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur 522302 India Department of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur India Manav Rachna International Institute of Research and Studies Faridabad India

The COVID-19 pandemic forced rigorous social distancing measures to be put in place to halt the virus’s spread;compliance was extremely difficult. In response, we have designed a highly advanced intelligent drone system that maintains and controls distance measures following social distance principles through advanced machine learning skills. This paper describes an intelligent monitoring and compliance system in public places using drone technology with a focus on social distance requirements. Boosting the efficiency of human detection in real-time video streams acquired by drones, our solution employs the You Only Look Once version 5 (YOLOv5) and YOLO version 6 (YOLOv6) object detection algorithms which are the most potent computer vision tools today. The distances between the recognized individuals are computed based on the Euclidean distance measurement to distinguish cases of nonadherence to social distance measures. Our technique integrates the recently developed deep learning models with aerial imagery and monitoring to make it easily scalable for population health surveillance. Thus, we prove impressively that the underlying inventive method works well in different environmental conditions and can potentially contribute to people’s safety in the event of a pandemic. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19 Drones Euclidean distance metric Social distancing YOLOv6

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Aspect Oriented Sentiment Analysis on Customer Reviews on Restaurant Using the LDA and BERT Method

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SN computer science 2023年第4期4卷 399页

作者： Lohith, C. Chandramouli, H. Balasingam, Udayabalan Arun Kumar, S. Department of Computer Science and Engineering Gopalan College of Engineering and Management Bengaluru India Department of Computer Science and Engineering East Point College of Engineering and Technology Bengaluru India Department of Information Science and Engineering East Point College of Engineering and Technology Bengaluru India Department of Computer Science and Engineering Nagarjuna College of Engineering and Technology Bengaluru India

This study presents a new approach for administering point-of-view-based assessment exams. The purpose of our ranking is to gain a sense of the best and most hilariously bad works of art. Given a collection of free-text clients, a specific object reviews. Our method begins with a handcrafted match to find viewpoints, use dependency courses in individual sentences. It then congregates by combining different notifications of a same location using measure of similarity based on the Word Net. Finally, it performs an each point of view obtains a score, which contributes to the overall score. A new assessment of a client’s reaction to a social occasion it is a part of it there is no need for any of that in our ideology express a seed word or space Data, as it merely makes use of an off-the-shelf word reference for evaluation we look at what makes people interested. Perceiving and rating points from online book reviews are the outcome of groundwork. We investigate the topic’s breadth. Diverse supply tendencies management approaches based on the multi sentence verification and interexamination work assessment of the rating are presented. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Aspect_modeling LDA Sentiment_analysis Topic_aspect_mapping Topic_modeling

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