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Optimizing Healthcare Delivery through CloudBased Clinical Decision Support Systems

Optimizing Healthcare Delivery through CloudBased Clinical D...

2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Ebenezar, U. Samson Jenila Vennila, G. Balakrishnan, T. Suresh Krishnan, Prabhakar Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India Mohan Babu University School of Computing Department of Ai & Ml Andhra Pradesh Tirupati India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Amrita Vishwa Vidyapeetham Center for Cyber Security Systems and Networks Amritapuri-campus India

ISBN: (纸本)9798350373783

The experiment aims at evaluating how four machine learning algorithms work: Decision Tree, Random Forest, SVM, and Neural Network, in a task, of binary classification. Models were evaluated using comprehensive metrics such as accuracy, precision, recall, F-score and AUC that revealed a good diagnostic performance. Models selected for the experiment and parameters tested on real datasets show steady changes in performance. The Neural Network model remained on top, achieving accuracy, precision, recall, F-score and AUC-ROC of 91.8%, 92.1%, 91.3%, 91.7% and 0.93 respectively. Random Forest displayed a competitive accordance along with the accuracy of 88.6%, precision of 89.2%, recall of 88.7%, and the F-score of 89.0%, and the AUC of 0.91. From the results, Support Vector Machine and Decision Tree models posted lower accuracy, recall, and fscores compared to the Neural Network and Random Forest models in most cases. These reveal the need for the improvement of the suitable machine learning model that comes from specific task needs as well as the consideration of each one of factors including accuracy, interpretability, and computational efficiency. Extensive and development of the model may result in more comprehensive understanding of how the model leads classifying of real-world applications such as medical diagnoses, fraud detection and target marketing. © 2024 IEEE.

关键词： Random forests

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Triple voting: hybrid cardiovascular diseases prediction model

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International Journal of Applied Decision sciences 2025年第3期18卷 257-281页

作者： Solomon, Dahlak Daniel Kumar, Karan Sonia Kanwar, Kushal Polat, Kemal Yogananda School of AI Computers and Data Science Shoolini University Himachal Pradesh Solan India Mullana Ambala133207 India Department of Computer Science and Engineering and Information Technology Jaypee University of Information and Technology Himachal Pradish Solan India Department of Electrical and Electronics Engineering Bolu Abant Izzet Baysal University Bolu Turkey

Currently, cardiovascular diseases are a high-risk cause of death in both developed and developing countries. Thus, heart disease prognosis has received substantial interest in the medical field worldwide. The incidence of heart disorders is escalating at an alarming rate, and it is crucial and worrisome to anticipate their occurrence. Predicting and detecting cardiovascular disease using machine learning and data mining might be clinically useful, but difficult. There are numerous machine learning algorithms accessible, several studies have developed machine learning algorithms for early cardiac disease prediction to help physicians suggest medical treatments. The accuracy of the model will be evaluated to determine whether the performance of the model is accurate or not. Seven machine learning methods are compared in this study, with the data obtained from the UCI Laboratory’s cardiovascular patient database. In essence, this research presents a majority voting-based hybrid model which is called triple voting. The hybrid model uses voting of Naïve Bayes, logistic regression (LR) and support vector machines (SVM) experimental outcomes show the proposed triple voting model’s accuracy is 89%, which is higher than the individual models and other proposed hybrid models. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Pulmonary diseases

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BCViMer 1.0: A Deep Network Pipeline for the Identification of Breast Cancer Using MRI Data 3

BCViMer 1.0: A Deep Network Pipeline for the Identification ...

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3rd IEEE International Conference on Industrial Electronics: Developments and Applications, ICIDeA 2025

作者： Das, Tejaswini Ratha, Ashis Kumar Dash, Sushree Sasmita Pandey, Rakesh Mohanty, Sabyasachi Nayak, Debasish Swapnesh Kumar Utkal University Department of Statistics Bhubaneswar India School of Computer science and Engineering IILM University Uttar Pradesh Greater Noida India IIMT College of Engineering Department of AI & DS Greater Noida India Centurion University of Technology and Management Department of Computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9798331533205

Breast cancer ranks among the foremost causes of cancer-related deaths in women globally, with early diagnosis via advanced imaging techniques significantly enhancing patient outcomes. Convolutional neural networks (CNNs) demonstrate considerable potential in the automation of breast cancer detection. Nonetheless, their constrained capacity to capture global image features may hinder their performance. This study presents BCViMer 1.0, a deep learning pipeline utilizing the Vision Transformer (ViT) to overcome existing limitations and enhance breast cancer classification accuracy from MRI images. This research aimed to assess the efficacy of BCViMer 1.0 relative to established CNN architectures, specifically ResNet-50, ResNet-101, VGG-16, and VGG-19. Experimental results indicate that BCViMer 1.0 excels these CNN models, attaining an average classification accuracy that is 4.15% higher. The enhanced performance of BCViMer 1.0 is due to its capacity to capture long-range dependencies and global context in MRI images, facilitated by the self-attention mechanism of the ViT. In conclusion, BCViMer 1.0 exhibits superior diagnostic accuracy compared to conventional CNN-based models, representing a significant advancement in breast cancer detection. This study emphasizes the capabilities of Vision Transformers in medical imaging and establishes a basis for future investigation in clinical applications. © 2025 IEEE.

关键词： Convolutional neural networks

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Innovative Approaches in Network Architecture: Enhancing Connectivity and Performance in the Era of 5G and Beyond

Innovative Approaches in Network Architecture: Enhancing Con...

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

作者： Sunita Pachar Satti Sudha Mohan Reddy Lakshmi Narasimhan Srinivasagopalan Balla Mounica B Srinivasa Rao Sivakoteswararao Katta IBM Department GLA University Department of ECE SRKR Engineering College India Software Development Enterprise Architecture Plano TX USA Computer Science and Engineering (AI&ML) Vignan Institute of Technology and Science Hyderabad Ace Engineering College Hyderabad Department of Mechanical KIETW Kakinada

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

This research study examines the evolving ecosystem of network applications for enhancing connectivity and performance in 5G and beyond (B5G) networks. The objective is to streamline large-scale deployment of vertical applications through a middleware layer, facilitating interactions among network operators and third parties based on varying trust levels. The proposed model addresses the complexity and computational demands of enabling adaptable, secure, and scalable applications for diverse 5G platforms. In particular, this study highlights the 5G-EPICENTRE project, which enables traffic management and dynamic control, especially for missioncritical Public Protection and Disaster Relief (PPDR) applications. By optimizing resource allocation, the model reduces computational costs while meeting the unique demands of PPDR services, such as high-quality video and data for emergency operations. The model's effectiveness will be evaluated through experiments leveraging 5G Core (5GC) control-plane capabilities, with a focus on quality of service (QoS) and latency. Practical limitations, including the integration challenges of multi-network APIs, are discussed in the conclusion.

关键词： Adaptation models 5G mobile communication Computational modeling Scalability Ecosystems Quality of service Telecommunications Complexity theory Resource management Protection

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Implementing Lightweight Post-Quantum Signatures for Enhanced Security in Green IoE

Implementing Lightweight Post-Quantum Signatures for Enhance...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： H. Manoj T. Gadiyar Sivakumar KK Nainee Patel Shadaksharaiah C Amit Lathigara Vanguri Smitha Department of Information Science and Engineering Canara Engineering College Benjanapadavu India School of Liberal Arts and Sciences Mohan Babu University Tirupati India Department of Computer Science & Engineering IES Insititute of Technology and Management IES University Bhopal India Department of Computer Science and Design Bapuji Institute of Engineering & Technology (BIET) Davangere India Department of Computer Science and Engineering School of Engineering RK University Rajkot India Department of Computer Science and Engineering (AI&ML) CMR Engineering College Hyderabad India

ISBN: (数字)9798331533663

ISBN: (纸本)9798331533670

Joining people, processes, data, and gadgets in a sustainable way is called the environmentally friendly Internet of Everything (Green IoE). Traditional encryption techniques, however, face serious security threats from the advent of quantum computing. Discrete logarithmic and factorisation issues make current cryptography systems like RSA and ECC susceptible to quantum attacks. In order to improve Green IoE security without sacrificing energy efficiency, this article suggests using lightweight post-quantum signature methods. The promising members are the ones derived from the lattice, hash, or multivariate quadratic theory when it comes to the choice between security and performance. This research identifies how it is possible to integrate quantum-safe security for Green IoE applications without eradicating the low power requirements inherent in these systems. It is by combining these little post-quantum signatures into Green IoE that it becomes possible to come up with a safe system that meets the set environmental standards. Realizing the need to construct a dependable safety system with Green IoE in the post-quantum period, the research evaluates the strength and speed of the proposed cryptography algorithms.

关键词： Accuracy Quantum computing Green products Stability criteria Robustness Timing Cryptography Sustainable development Streams Standards

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Optimizing IoT Threat Mitigation with Artificial Intelligence in Banking: A Multi-Objective Approach 2

Optimizing IoT Threat Mitigation with Artificial Intelligenc...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Pratap Singh Chauhan, Rudra Sonker, Sanjav Kumar Kaur, Manpreet Sharma, Chhaya Singh, Robin Singh, Ramendra Shri Shankracharya Institute of Professionals Management and Technology Department of CSE-AI and Ai Ml Raipur India Ajay Kumar Garg Engineering College Department of Information Technology Ghaziabad India Institute of Computing Chandigarh University Gharuan Mohali India Raj Kumar Goel Institute of Technology Department of Computer Science & Engineering Ghaziabad India Graphic Era Hill University Dehradun India Ghaziabad India

ISBN: (纸本)9798350371055

This research introduces a novel ai-based mechanism for optimizing threat mitigation in IoT banking systems, addressing the growing vulnerabilities in this critical sector. The proposed mechanism, characterized by a precision of 0.88 and a balanced recall of 0.79, offers a robust defense against cyber threats. Leveraging a Deep Neural Architecture known as Pointer Networks, the mechanism adapts dynamically, ensuring high accuracy in threat identification (precision) while comprehensively covering potential threats (recall), resulting in a harmonious F1 score of 0.83. Through extensive threat-specific evaluations, the mechanism proves versatile, exhibiting high performance in scenarios involving malware (precision: 0.89, recall: 0.82, F1 score: 0.85), denial of service (DoS) attacks (precision: 0.87, recall: 0.78, F1 score: 0.82), and unauthorized access attempts (precision: 0.90, recall: 0.81, F1 score: 0.85). Scalability testing further validates its practical applicability, maintaining precision and F1 score values across varying sizes of IoT ecosystems. This research establishes the proposed mechanism as a potent and adaptable cybersecurity tool, poised to fortify the resilience of IoT banking systems against the dynamic landscape of cyber threats. © 2024 IEEE.

关键词： Artificial Intelligence Banking Cybersecurity IoT Threat Mitigation

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The Power of Second Chance: Personalized Submodular Maximization with Two Candidates 17th

The Power of Second Chance: Personalized Submodular Maximiz...

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17th International Conference on Combinatorial Optimization and Applications, COCOA 2024

作者： Yuan, Jing Tang, Shaojie Department of Computer Science and Engineering University of North Texas Denton United States Center for AI Business Innovation Department of Management Science and Systems University at Buffalo Buffalo United States

ISBN: (纸本)9789819644476

Most of existing studies on submodular maximization focus on selecting a subset of items that maximizes a single submodular function. However, in many real-world scenarios, we might have multiple user-specific functions, each of which models the utility of a particular type of user. In these settings, our goal would be to choose a set of items that performs well across all the user-specific functions. One way to tackle this problem is to select a single subset that maximizes the sum of all of the user-specific functions. Although this aggregate approach is efficient in the sense that it avoids computation of sets for individual functions, it really misses the power of personalization - for it does not allow to choose different sets for different functions. In this paper, we introduce the problem of personalized submodular maximization with two candidate solutions. For any two candidate solutions, the utility of each user-specific function is defined as the better of these two candidates. Our objective is, therefore, to select the best set of two candidates that maximize the sum of utilities of all the user-specific functions. We have designed effective algorithms for this problem. We also discuss how our approach generalizes to multiple candidate solutions, increasing flexibility and personalization in our solution. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

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IoT-Based Livestock Monitoring and Management System Using Machine Learning Algorithms

IoT-Based Livestock Monitoring and Management System Using M...

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science, Technology, engineering and Management (ICSTEM), International Conference on

作者： Nagendar Yamsani K. Muthukumaran B Santhosh Kumar Asha V Navdeep Singh JoshuvaArockia Dhanraj School of Computer Science and Artificial Intelligence SR UNIVERSITY Warangal Department of Biomedical Engineering DSCET-Anna University Chennai Institute of Aeronautical Engineering Hyderabad Department of Computer Applications New Horizon College of Engineering Bangalore Lovely Professional University Phagwara Department of Computer Science and Engineering (AI&ML) School of Engineering Dayananda Sagar University Bengaluru Karnataka India

ISBN: (数字)9798350376913

ISBN: (纸本)9798350388473

This article provides a description of an Internet of Things (IoT) system that employs machine learning algorithms to provide real-time and precise insights into the health and behaviour of animals. A network of Internet of Things (IoT) sensors strategically placed throughout the cattle area of the proposed system is capable of monitoring a variety of attributes, including temperature, humidity, grazing patterns, and movements. The information gathered by these sensors is wirelessly transmitted to a central centre, where it is analysed by algorithms that acquire expertise through practice. To extract meaningful results from acquired data, supervised and unsupervised machine learning algorithms are implemented. The system maintains real-time surveillance of farmers or administrators and promptly alerts them to any anomalous events that may occur in regards to the animals' welfare or behaviour. This empowers them to expeditiously implement preventative measures against infections or optimise feeding regimens. Investigating historical data can yield significant insights that facilitate well-informed decision-making concerning enduring concerns like resource allocation or breeding initiatives. The incorporation of machine learning technology and the internet of things (IoT) into livestock management practices increases overall agricultural efficiency, productivity, and animal welfare.

关键词： Temperature sensors Temperature measurement Machine learning algorithms Animals Surveillance Sensor phenomena and characterization Real-time systems

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YOLOv8- Based Helmet and Vest Detection System for Safety Assessment

YOLOv8- Based Helmet and Vest Detection System for Safety As...

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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： Divyanka Thakur Priya Pal Amogh Jadhav Numaira Kable Bhagyalakshmi V Sonali Deshpande Department of Computer Science Engineering (AI&ML) Smt. Indira Gandhi College of Engineering Navi Mumbai India Department of Electronics and Telecommunication KC College of Engineering Management and Research Thane(E) India

Helmet and vest detection systems ensure worker safety in high-risk professions like construction, mining, and law enforcement. The latest version of the You Only Look Once (YOLO) object detection model, YOLOv8, offers significant advancements over its predecessors. YOLOv8 utilizes a fully convolutional neural network, comprising two major components: the backbone and the head, incorporating a self-attention mechanism to identify objects at various scales in the image. This model eliminates anchors, reducing box predictions and speeding up post-processing phases. Among the YOLOv8 family, YOLOv8-s stands out as the smallest and fastest model, with a size of 21 MB and a single epoch runtime of only 11 seconds. In this study, the YOLOv8-s model achieved an impressive 99.5% mean average precision (mAP) accuracy rate in real time. By implementing this system, the construction industry can significantly enhance safety practices through efficient and reliable monitoring of personal protective equipment usage at construction sites.

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Real-Time IoT Cybersecurity using Machine Learning-based ai Threat Detection System to Train Generative Robots

Real-Time IoT Cybersecurity using Machine Learning-based AI ...

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Trends in Material science and Inventive Materials (ICTMIM), International Conference on

作者： P Srilakshmi Koushik Reddy Chaganti Talachendri Suryam Sweety Julia I D. Chaithanya Jami Kavitha Department of CSE G Pulla Reddy Engineering College Dept. of Computer and Information Systems University of the Cumberlands Department of CSE (DATA SCIENCE) Vignan Institute of Technology and Science Department of AI&ML Malla Reddy University Department of CSE CVR College of Engineering Department of CSE Sanketika Vidya Parishad Engineering College

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Existing security protocols encounter major cybersecurity difficulties because the online devices' growing popularity continues to spread across networks. These security protocols are ineffective because they do not properly handle current cyber threats. The main goal of this study involves developing enhanced IoT cybersecurity through the development of a threat detection system which brings together adversarial training and deep learning models (CNN-LSTM) and Federated Learning (FL). The system enables distributed Internet of Things devices to work on security model development through Federated Learning while maintaining total privacy of their information. Security procedures controlled by generative artificial intelligence robots alongside real-time attack protection functions decrease security response durations. Through its Federated CNN-LSTM model the system upholds a 1.2% false positive rate alongside a 98.3% accuracy evaluation and 160 milliseconds of exact threat tracking time. The designed system sustains a minimal occurrence of incorrect alarm activations. The developed system provides real-time security for the Internet of Things framework because it enables adaptive protection systems while preserving user privacy in current IoT settings.

关键词： Training Privacy Protocols Federated learning Real-time systems Threat assessment Internet of Things computer security Protection Robots

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