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Enhancing reliability in coronary artery disease prediction using ensemble learning for key attribute identification

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Life Cycle Reliability and Safety engineering 2025年 1-14页

作者： Upadhyay, Sachin Sagar, Anil Kumar Roy, Nihar Ranjan Department of Computer Science and Engineering School of Engineering and Technology Sharda University Knowledge Park III Greater Noida India Department of Computer Science and Engineering School of Engineering and Technology VIPS-TC Delhi Pitampura India

Cardiovascular disease (CAD) is a significant public health concern, affecting a large population worldwide. Early diagnosis and management of CAD can minimize the risk of acute myocardial infarction and improve patient outcomes. Assessment tools like SBP, cholesterol, pulse rate, and ST segment depression can help identify causes early and manage them effectively. Management includes medication therapy, healthy dietary habits, and exercise. Several machine learning (ML) methodologies have been researched to enhance CAD predictions, including AdaBoost, ANNs, J48, Decision Tree, K Nearest Neighbor (KNN), Naïve Bayes, and Random Forest. However, single models still lack sufficient capacity to address the complexity and flexibility of CAD. Ensemble learning, which uses multiple classifiers to boost predictability, has been employed to address these issues. The system was developed after benchmarking it with multiple classifiers on a Cleveland cardiac disease dataset. The ensemble method showed a 92.11% accuracy rate, far better than the highest performing classifier operating individually. This suggests the possibility of practical solutions for CAD prediction using ensemble methods, enabling precise early diagnosis and efficient targeted treatment. Comparing ensemble learning for CAD predictors reveals how these approaches can revolutionize medicine by enabling early diagnosis and personalized treatment plans. There is a need to further develop these methods for clinical application, such as creating practical tools for easier application by healthcare workers and integrating sophisticated techniques. In conclusion, ensemble learning methods represent significant advancements in CAD prediction, with superior performance in identifying critical attributes and enhancing predictive accuracy. As healthcare evolves with the integration of intelligent technologies, the adoption of ensemble learning methods holds great promise for enhancing patient outcomes and reducing the

关键词： ANN CAD Decision Trees Ensemble KNN Machine learning Random Forest

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Unilateral Control for Social Welfare of Iterated Game in MobileCrowdsensing

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Journal of computer science & technology 2025年第2期40卷 531-551页

作者： Ji-Qing Gu Chao Song Jie Wu Li Lu Ming Liu School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731 China 2 School of Computer Science Chengdu University of Information TechnologyChengdu 610225China Center for Networked Computing Temple UniversityPhiladelphiaPA 19122U.S.A

Mobile crowdsensing is a popular platform that takes advantage of the onboard sensors and resources on mobile nodes. The crowdsensing platform chooses to assign several sensing tasks each day, whose utility is based on the quality of harvested sensing data, the payment of transmitting data, and the recruitment of mobile nodes. An Internet serviceprovider (ISP) selects a portion of access points (APs) to power on for uploading data, whose utility depends on threeparts: the traffic income of transmitting sensing data, the energy cost of operating APs, and the energy cost of data transmissions by APs. The interaction between the crowdsensing platform and ISP is formulated as an iterated game, with social welfare defined as the sum of their expected utilities. In this paper, our objective is to unilaterally control social welfare without considering the opponent’s strategy, with the aim of achieving stable and maximized social welfare. Toachieve this goal, we leverage the concept of a zero-determinant strategy in the game theory. We introduce a zero-determinant strategy for the vehicular crowdsensing platform (ZD-VCS) and analyze it in discrete and continuous models in thevehicular crowdsensing scenario. Furthermore, we analyze an extortion strategy between the platform and ISP. Experimental results demonstrate that the ZD-VCS strategy enables unilateral control of social welfare, leading to a high andstable value.

关键词： iterated game social welfare control vehicular crowdsensing zero-determinant strategy

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High-speed encrypted traffic classification by using payload features

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Digital Communications and Networks 2025年第2期11卷 412-423页

作者： Xinge Yan Liukun He Yifan Xu Jiuxin Cao Liangmin Wang Guyang Xie School of Cyber Science and Engineering Southeast UniversityNanjing 211189China Nanjing Huafei Data Technology Co. Ltd.Nanjing 210019China School of Computer Science and Communication Engineering Jiangsu UniversityZhenjiang 212013China

Traffic encryption techniques facilitate cyberattackers to hide their presence and *** classification is an important method to prevent network ***,due to the tremendous traffic volume and limitations of computing,most existing traffic classification techniques are inapplicable to the high-speed network *** this paper,we propose a High-speed Encrypted Traffic Classification(HETC)method containing two ***,to efficiently detect whether traffic is encrypted,HETC focuses on randomly sampled short flows and extracts aggregation entropies with chi-square test features to measure the different patterns of the byte composition and distribution between encrypted and unencrypted ***,HETC introduces binary features upon the previous features and performs fine-grained traffic classification by combining these payload features with a Random Forest *** experimental results show that HETC can achieve a 94%F-measure in detecting encrypted flows and a 85%–93%F-measure in classifying fine-grained flows for a 1-KB flow-length dataset,outperforming the state-of-the-art comparison ***,HETC does not need to wait for the end of the flow and can extract mass computing *** average time for HETC to process each flow is only 2 or 16 ms,which is lower than the flow duration in most cases,making it a good candidate for high-speed traffic classification.

关键词： Traffic classification Flow analysis Information entropy Machine learning Randomness test

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Implementing Newly Proposed Dynamic Tree Structure: Development, Integration, and Optimization

Implementing Newly Proposed Dynamic Tree Structure: Developm...

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Banerjee, Pallab Kumari, Sweety Kumar, Biresh Thakur, Kanika Gaurav, Kumar Kumar, Purushottam Amity University Jharkhand Dept. of Computer Science Engineering Jharkhand Ranchi India Galgotia University Dept. of Computer Science Engineering Greater Noida campus India Amity University Jharkhand Amity School of Information Technology Jharkhand Ranchi India

ISBN: (纸本)9798350385199

An innovative data structure called a 'dynamic tree' has important uses in algorithmic time complexity optimization. It creates well-organized trees by modifying the properties of several single-node trees, therefore preserving the forest structure. This structure is essential to improving the effectiveness of many algorithms. In this work, I particularly address the 'Maximum Flow method,' a crucial subtopic in the larger field of dynamic trees. One basic method for solving network flow issues is the Maximum Flow Algorithm, but it frequently runs into issues with time complexity. My research attempts to overcome these obstacles by using dynamic trees to enhance the efficiency of the algorithm. This work reduces the time complexity of network flow problems, leading to more effective solutions. The findings have potential implications in a variety of computing and real-world scenarios. © 2024 IEEE.

关键词： Ancestor Cut Evert Find Key Link Root Top tree

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AutoBots: A Botnet Intrusion Detection Scheme Using Deep Autoencoders 4th

AutoBots: A Botnet Intrusion Detection Scheme Using Deep Aut...

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4th International Conference on Computing, Communications, and Cyber-Security, IC4S 2022

作者： Verma, Ashwin Bhattacharya, Pronaya Prasad, Vivek Kumar Datt, Rajan Tanwar, Sudeep Department of Computer Science and Engineering Institute of Technology Nirma University Gujarat Ahmedabad382481 India Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University West Bengal Kolkata700135 India

ISBN: (纸本)9789819914784

Recently, with the massive exchange of data over Internet of Things (IoT) ecosystems, attacks surfaces have also intensified. In IoT, connected devices share data over open channels and thus are highly vulnerable to security and privacy attacks. Botnet-based attacks have been found to have a significant effect on the network-based system. Thus, in this paper, we present a scheme AutoBots, which differentiates the normal and anomaly behaviour of IoT devices among the connected network. To exploit this, we consider diverse parameters like network behaviour profiles and apply autoencoders to classify and detect anomalous traffic from normal traffic. We used the BASHLITE and MIRAI IoT botnet setup and trained our network with the N-BaIoT dataset that has both benign and malicious network traffic. We compared our scheme for metrics like attack detection time, attack detection with respect to hourly traffic, deep residual accuracy, and residual loss. The presented results signify the efficacy of the proposed scheme against conventional bot-detection schemes. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Intrusion detection

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Optimized Neural Network for Automated Cardiovascular Disease Classification: Meta-heuristic Enabled Model

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SN computer science 2025年第1期6卷 1-14页

作者： Kusuma, S. Jothi, K.R. Department of Computer Science & Engineering (Data Science) Madanapalle Institute of Technology & Science Madanapalle India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Cardiovascular disease remains a major issue for mortality and morbidity, making accurate classification crucial. This paper introduces a novel heart disease classification model utilizing Electrocardiogram (ECG) signals. The model consists of two main steps: feature extraction using a refined Lion Algorithm with New Mutated evaluation (LA-NM) and classification through an ensemble approach combined with a Neural Network (NN). The proposed model significantly outperforms traditional classifiers. At 80% training data, it achieves accuracy improvements of 8.4% to 3.11% over Neural Network (NN), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and ensemble models. At 90% training data, it shows precision improvements ranging from 1.03% to 0.31% better than the comparison models and specificity improvements ranging from 13.92% to 0.52%. These results underscore the effectiveness of the model and suggest its potential for further development in personalized treatment recommendations based on patient history. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Ensemble models Feature extraction Heart disease classification Neural network Optimization

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Event-Driven Attention Network:A Cross-Modal Framework for Efficient Image-Text Retrieval in Mass Gathering Events

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computers, Materials & Continua 2025年第5期83卷 3277-3301页

作者： Kamil Yasen Heyan Jin Sijie Yang Li Zhan Xuyang Zhang Ke Qin Ye Li School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China School of Information and Software Engineering University of Electronic Science and Technology ofChinaChengdu611731China Kashi Institute of Electronics and Information Industry Kashi844508China

Research on mass gathering events is critical for ensuring public security and maintaining social ***,most of the existing works focus on crowd behavior analysis areas such as anomaly detection and crowd counting,and there is a relative lack of research on mass gathering *** believe real-time detection and monitoring of mass gathering behaviors are essential formigrating potential security risks and ***,it is imperative to develop a method capable of accurately identifying and localizing mass gatherings before disasters occur,enabling prompt and effective *** address this problem,we propose an innovative Event-Driven Attention Network(EDAN),which achieves image-text matching in the scenario of mass gathering events with good results for the first *** image-text retrieval methods based on global alignment are difficult to capture the local details within complex scenes,limiting retrieval *** local alignment-based methods aremore effective at extracting detailed features,they frequently process raw textual features directly,which often contain ambiguities and redundant information that can diminish retrieval efficiency and degrade model *** overcome these challenges,EDAN introduces an Event-Driven AttentionModule that adaptively focuses attention on image regions or textual words relevant to the event *** calculating the semantic distance between event labels and textual content,this module effectively significantly reduces computational complexity and enhances retrieval *** validate the effectiveness of EDAN,we construct a dedicated multimodal dataset tailored for the analysis of mass gathering events,providing a reliable foundation for subsequent *** conduct comparative experiments with other methods on our dataset,the experimental results demonstrate the effectiveness of *** the image-to-text retrieval task,EDAN achieved the best performance on the R@5 metric,w

关键词： Mass gathering events image-text retrieval attention mechanism

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Securing Supply Chain - Blockchain: Leveraging ICC for Product Verification and Validation 2

Securing Supply Chain - Blockchain: Leveraging ICC for Produ...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Faiyaz, Mohd Sarfaraz Dhanda, Namrata Srivastava, Aryan Singh, Guru Dev Verma, Rajat Amity University Uttar Pradesh Department of Computer Science and Engineering Lucknow India Amity Institute of Information Technology Amity University Uttar Pradesh Lucknow India Pranveer Singh Institute of Technology Department of Computer Science and Engineering Kanpur India

ISBN: (纸本)9798350371055

This paper explores the importance of supply chain integrity in today's global business landscape, considering the substantial losses of over 500 billion annually attributed to counterfeit goods. It emphasizes the potential of emerging technologies like blockchain and QR codes in verifying and validating products. The International Chamber of Commerce (ICC), representing over 45-million-member companies, plays a crucial role in supply chain management, particularly in product verification using these technologies. The paper examines the ICC's efforts in standardization, policy advocacy, and industry collaborations related to supply chain management, focusing on publications, guidelines, initiatives, and activities for product verification. Real-world examples demonstrate the benefits of blockchain and QR codes, including enhanced transparency, traceability, and security against fraud. Industry reports highlight the financial implications of supply chain disruptions and counterfeit products, amounting to 4.2 trillion annually for businesses. Challenges and implications of implementing these technologies are also discussed, considering operational efficiency and sustainability in supply chains. In conclusion, the paper emphasizes the pivotal role of the ICC in supply chain management and suggests further research and practical applications for blockchain and QR codes in supply chains. © 2024 IEEE.

关键词： Blockchain International Chamber of Commerce (ICC) Product validation Product verification QR codes Supply chain management

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Deep Learning Assisted Intelligent Human computer Interaction for Next Generation Internet Applications

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第2期48卷 145-146页

作者： Hasan, Al Mehedi Hwang, Yong Seok Shin, Jungpil Dept. of Computer Science and Engineering Rajshahi University of Engineering and Technology Bangladesh Kwangwoon University Seoul Korea Republic of School of Computer Science and Engineering The University of Aizu Aizuwakamatsu Japan

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Estimating the cost of dining for two: An Integration of Sentiment Analysis and Feature Based Approach 5

Estimating the cost of dining for two: An Integration of Sen...

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Shashank, Salabh Behera, Rajat Kumar Amity University Auup Amity School of Engineering and Technology Uttar Pradesh Noida201301 India KIIT Deemed to be University School of Computer Engineering Odisha Bhubaneswar751024 India

ISBN: (纸本)9798331522100

In rapidly growing food industry, where more than 1200 orders are processed in a minute, it is essential for owners to optimize their services. This study focuses into the food industry and analyzes the approximate cost for two people along with the sentimental analysis using the data taken from Zomato. The approximate cost for two is based on various factors like restaurants location, availability of online ordering, table booking facilities, restaurant ratings and, cuisines offered. Since there is huge competition among restaurants, so some fake reviews can also be posted. Sentimental analysis is applied to check if the review posted is genuine or fake. Results found that there are around 10k fake reviews out of 51k total reviews. Various regression algorithms like Decision Tree, Random Forest, and others are used to calculate approximate cost for two. Also, various classification algorithms like Naïve Bayes, Support Vector, and others are used to verify sentiment analysis. The regression problem is analyzed using two different approaches: first by considering all the features and second by reducing the features by using Principal Component Analysis. Various computation metrics for regression as well as classification are computed, and the results are incorporated. © 2025 IEEE.

关键词： Food industry

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