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An Efficient Automated Technique for Classification of Breast Cancer Using Deep Ensemble Model

Computer Systems science & Engineering 2023年第7期46卷 897-911页

作者： Muhammad Zia Ur Rehman Jawad Ahmad Emad Sami Jaha Abdullah Marish Ali Mohammed A.Alzain Faisal Saeed Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan School of Computing Edinburgh Napier UniversityEdinburghEH105DTUK Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia DAAI Research Group Department of Computing and Data ScienceSchool of Computing and Digital TechnologyBirmingham City University15 Bartholomew RowBirminghamB55JUUK

Breast cancer is one of the leading cancers among *** has the second-highest mortality rate in women after lung *** detection,especially in the early stages,can help increase survival ***,manual diagnosis of breast cancer is a tedious and time-consuming process,and the accuracy of detection is reliant on the quality of the images and the radiologist’s ***,computer-aided medical diagnosis has recently shown promising results,leading to the need to develop an efficient system that can aid radiologists in diagnosing breast cancer in its early *** research presented in this paper is focused on the multi-class classification of breast *** deep transfer learning approach has been utilized to train the deep learning models,and a pre-processing technique has been used to improve the quality of the ultrasound *** proposed technique utilizes two deep learning models,Mobile-NetV2 and DenseNet201,for the composition of the deep ensemble *** learning models are fine-tuned along with hyperparameter tuning to achieve better ***,entropy-based feature selection is *** cancer identification using the proposed classification approach was found to attain an accuracy of 97.04%,while the sensitivity and F1 score were 96.87%and 96.76%,*** performance of the proposed model is very effective and outperforms other state-of-the-art techniques presented in the literature.

关键词： Breast cancer image enhancement ensemble model transfer learning feature selection

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Comparative Analysis of Classifier Performance on Reduced Feature Sets

Comparative Analysis of Classifier Performance on Reduced Fe...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Raed Alazaidah Wael Hadi Suhaila Abu Owaida Alaa Bani-bakr Mo'ath Alluwaici Muhyeeddin Alqaraleh Department of Data Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Information Security Faculty of Information Technology University of Petra Amman Jordan Department of Computer Science Faculty of Information Technology Al-al-Bayt University Mafraq Jordan Department of Cyber Security Faculty of Information Technology Zarqa University Zarqa Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Software Engineering Department Faculty of Information Technology Zarqa University Zarqa Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

In this study, we examine the impact of feature reduction on the performance of various classifiers within the Weka environment. Utilizing the Pima Indians Diabetes database, we compare the Accuracy, Precision, Recall, F1-measure, and other relevant metrics across classifiers like BayesNet, NaiveBayes, and NaiveBayesMul when applied to full and reduced feature sets. The reduction process involves halving the attribute count, aiming to assess the efficacy of machine learning models in scenarios with constrained feature availability. Our results indicate significant differences in the performance of the models, offering insights into the trade-offs between model complexity and predictive power in medical dataset applications.

关键词： Measurement Productivity data analysis Accuracy Systematics databases Machine learning Predictive models Diabetes Reliability

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Unleashing the Potential of Decentralized Federated Learning in Healthcare: A Comprehensive Survey on Insights and Challenges 9

Unleashing the Potential of Decentralized Federated Learning...

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9th IEEE International Conference on data science in Cyberspace, DSC 2024

作者： Cheng, Haibo Liu, Wenjian Qu, Youyang Fu, Kexue Wang, Qinyuan City University of Macau Faculty of Data Science China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China

ISBN: (纸本)9798350391367

Federated learning (FL) is a distributed learning method that reduces data transmission and privacy risks. However, the existence of a central server may become a target of attackers, resulting in an increased risk of user privacy leakage. The emergence of Decentralized Federated Learning (DFL) is posited to enhance data privacy and system reliability by reducing reliance on central servers and enhancing local data control, especially in the medical field, where personal health data is protected. This research investigates the application of (DFL) in the medical domain, focusing on its limitations. This study employs a systematic literature review, adhering to PRISMA guidelines, to comprehensively understand DFL's current landscape and challenges in healthcare. The key findings highlight four principal obstacles faced by DFL: security and privacy, communication efficiency, data and model heterogeneity, and incentive mechanisms. It also explores the potential for DFL to facilitate personalized healthcare treatments by large datasets distributed across various medical institutions. This research fills a significant void by systematically reviewing DFL technologies in healthcare. It provides insights into applications, challenges, and future directions for participants seeking secure and efficient healthcare data handling. © 2024 IEEE.

关键词： Federated learning

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Mapping the Scientific Landscape of Integrated Scheduling Towards Industry 5.0 with A Bibliometric Analysis 6

Mapping the Scientific Landscape of Integrated Scheduling To...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Putri Ardyanti, Anak Agung Ayu Xie, Zhiqiang Karnyoto, Andrea Stevens Pardamean, Bens Estiyanti, Ni Made School of Computer Science and Technology Harbin University of Science and Technology Harbin150000 China Faculty of Information Technology and Design Primakara University Bali 80226 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia School of Management and Business University of Debrecen Debrecen Hungary

The industry is rapidly transitioning from the 4.0 era to the 5.0 era, prompting renewed interest among scholars in scheduling problems. They allow operations to process and assemble various components simultaneously. This bibliometric analysis provides a comprehensive understanding of the diverse research perspectives on the potential challenges of integrated scheduling within the framework. We identified 357 Scopus articles and analysed co-occurrence keyword (CNK) by VOSViewer tools and Global Citation Score (GCS) by Scopus tools. The research data obtained from 1993 to 2023 mainly in the subject areas of Engineering, Computer science, Maths, and others. CNK identified six cluster keywords, and nine most cited articles based on normalised GCS. These results contribute to our understanding of the development of scheduling algorithms, shifting from an emphasis on manufacturing transfers to contemporary demand-centric research directions. Furthermore, this research provides a valuable perspective for policymakers, plant personnel, and manufacturing managers to make informed decisions. © 2025 The Authors. Published by Elsevier B.V.

关键词： Job shop scheduling

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Decompose, Enrich, and Extract! Schema-aware Event Extraction using LLMs.

Decompose, Enrich, and Extract! Schema-aware Event Extractio...

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International Conference on information Fusion

作者： Fatemeh Shiri Farhad Moghimifar Reza Haffari Yuan-Fang Li Van Nguyen John Yoo Department of Data Science and AI Faculty of Information Technology Monash University Melbourne Australia Information Sciences Division Defence Science and Technology Group Adelaide Australia

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

Large Language Models (LLMs) demonstrate significant capabilities in processing natural language data, promising efficient knowledge extraction from diverse textual sources to enhance situational awareness and support decision-making. However, concerns arise due to their susceptibility to hallucination, resulting in contextually inaccurate content. This work focuses on harnessing LLMs for automated Event Extraction, introducing a new method to address hallucination by decomposing the task into Event Detection and Event Argument Extraction. Moreover, the proposed method integrates dynamic schema-aware augmented retrieval examples into prompts tailored for each specific inquiry, thereby extending and adapting advanced prompting techniques such as Retrieval-Augmented Generation. Evaluation findings on prominent event extraction benchmarks and results from a synthesized benchmark illustrate the method’s superior performance compared to baseline approaches.

关键词： Accuracy Event detection Large language models Natural languages Decision making Knowledge graphs Benchmark testing Cognition data mining Reliability

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Evaluation Method of Motor Coordination Ability in Children Based on Machine Vision

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Tsinghua science and technology 2025年第2期30卷 633-649页

作者： Yi Lei Dawei Shu Miao Yu Donglin Shi Jianqiang Li Yanjie Chen Faculty of Information Technology Beijing University of TechnologyBeijing 100124China Department of Automation Tsinghua UniversityBeijing 100084China Pharmacovigilance Research Center for information technology and Data Science Cross-strait Tsinghua Research InstituteXiamen 361000China Institute of Systems Engineering Dalian University of TechnologyDalian 116024China Physical Education Department Hebei Sports UniversityShijiazhuang 050049China Department of Children’s Health Care Center Beijing Children’s HospitalCapital Medical UniversityNational Center for Children’s HealthBeijing 100045China

Motor coordination is crucial for preschoolers’development and is a key factor in assessing childhood *** diagnostic methods often rely on subjective manual *** paper presents a machine vision-based approach aimed at improving the objectivity and adaptability of *** method proposed involves the extraction of key points from the human skeleton through the utilization of a lightweight pose estimation network,thereby transforming video assessments into evaluations of keypoint *** study uses different methods to handle static and dynamic actions,including regularization and Dynamic Time Warping(DTW)for spatial alignment and temporal discrepancies.A penalty-adjusted single-frame pose similarity method is used to evaluate *** lightweight pose estimation model reduces parameters by 85%,uses only 6.6%of the original computational load,and has an average detection missing rate of less than 1%.The average error for static actions is 0.071 with a correlation coefficient of 0.766,and for dynamic actions it is 0.145 with a correlation coefficient of *** results confirm the proposed method’s effectiveness,which includes customized visual components like motion waveform graphs to improve accuracy in pediatric healthcare diagnoses.

关键词： developmental coordination disorder action quality assessment pose estimation dynamic time warping decision support system

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A Study on the Pricing of Used Sailboats: Utilizing Random Forest Models to Analyze the Impact of Multiple Factors 3

A Study on the Pricing of Used Sailboats: Utilizing Random F...

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3rd IEEE International Conference on Electrical Engineering, Big data and Algorithms, EEBDA 2024

作者： Zhou, Huimin Qian, Jiajun Sun, Feifan School of Mathematics and Big Data Chongqing University of Arts and Sciences Chongqing China Suzhou University of Science and Technology Faculty of Electronic and Information Engineering Suzhou China College of Economics and Management Northeast Forestry University Harbin China

ISBN: (纸本)9798350380989

This study explores the impact of various factors on the valuation of sailboats. The research delves into the mathematical correlation between the prices of secondhand sailboats and 11 specific factors. Initially, any missing values were removed, outliers were substituted, and box plots were utilized to validate the precision of the sample data. Furthermore, five supplementary parameters were sourced from public websites, encompassing displacement, draft, beam, material, and GDP. Subsequently, after organizing the data, categorical variables were encoded using sequence and label encoding. Following this, multiple linear regression models, ridge regression models, and random forest models were constructed using the entire dataset. Through a comparative analysis of the three models' accuracy, the random forest model was chosen as the ultimate model for elucidating the pricing of each sailboat. The predictive precision of the random forest model was scrutinized, yielding a prediction accuracy of R2=0.786. © 2024 IEEE.

关键词： Mathematical relationship Multiple linear regression models Random forest model Sailboats

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Legal-Onto Model for Efficient Land Law Updates in Vietnam 11th

Legal-Onto Model for Efficient Land Law Updates in Vietnam

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11th International Conference on Future data and Security Engineering, FDSE 2024

作者： D. T. Do, Huy D. Nguyen, Hien T. Pham, Vuong Nguyen, Tri-Hai Faculty of Computer Science University of Information Technology Ho Chi Minh City Viet Nam Faculty of Mathematics and Computer Science University of Science Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam Computer Science Department New Mexico State University Las Cruces United States Institute of Data Science and Artificial Intelligence Sai Gon University Ho Chi Minh City Viet Nam Faculty of Information Technology School of Technology Van Lang University Ho Chi Minh City Viet Nam

ISBN: (纸本)9789819604333

In the rapidly evolving economic and social landscape of Vietnam, land remains a pivotal resource, crucial for economic growth and social stability. The recent enactment of the Land Law 2024, which supersedes the 2013 legislation, necessitates continuous updates for land users, investors, and regulatory authorities to ensure compliance and optimize land utilization. Traditional legal information retrieval systems are constrained by keyword-based searches and pre-set responses, which are inadequate for addressing specific user queries or rapidly integrating new legislative updates. This paper presents an advanced technological solution employing the Legal-Onto model to represent and update legal knowledge. The Legal-Onto model, inspired by relational ontology and keyphrase graphs, captures the semantic content of legal documents. By employing natural language processing techniques, the system automatically compares old and new legal texts, identifying differences at the article, point, and clause levels. This enables efficient updates and provides a valuable tool for legal professionals. It demonstrates the potential of artificial intelligence in legal contexts, offering a more effective and accessible approach to legal information. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： information Retrieval Intelligent System Knowledge-Based Systems Legal Document Ontology Vietnamese Land Law

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A Novel 3D Medical Image Segmentation Model Using Improved SAM

A Novel 3D Medical Image Segmentation Model Using Improved S...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Kuang, Yuansen Ma, Xitong Zhao, Jing Wang, Guangchen Zeng, Yijie Liu, Song Key Laboratory of Computing Power Network and Information Security Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9781665410205

3D medical image segmentation is an essential task in the medical image field, which aims to segment organs or tumours into different labels. A number of issues exist with the current 3D medical image segmentation task: existing models cannot simultaneously obtain the space correlation and depth correlation of 3D slices;previous models suffer from local detail loss of positional embedding in 3D images;previous approaches often have blurring of boundaries in segmenting 3D images. To solve these shortcomings, we propose a 3D medical image segmentation model named TPM-SAM. In our model, we design a twinchannel image encoder to simultaneously capture the space correlation and depth correlation of 3D slices through a multi-head attention mechanism and improved adapters. Furthermore, we design a prompt encoding generator, which divides the volumetric image into small blocks and better captures the local detail information. In addition, we introduce a multi-layer aggregation decoder by employing U-Net with multi-level skip connection to solve the blurring of boundaries in processing 3D images. Finally, we experimented and evaluated our model on KiTS21 and LiTS17 datasets to compare with other baseline models. © 2024 IEEE.

关键词： Image segmentation

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A Survey of Artificial Intellegence Techniques to Detect Intrusion Attack in Communication Network 1

A Survey of Artificial Intellegence Techniques to Detect Int...

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1st International Conference on Cognitive Computing and Engineering Education, ICCCEE 2023

作者： Shankar Siva, S. Thanh Hung, Bui Chakrabarti, Prasun Industrial University of Ho Chi Data Science Laboratory Faculty of IT Minh City Viet Nam KG Reddy College of Engineering and Technology Department of CSE Telangana Moinabad India Industrial University of Ho Chi Data Science Laboratory Faculty of Information Technology Minh City Viet Nam LTM SLS Baroda University Gujarat Vadodara391510 India

ISBN: (纸本)9798350332803

Networks are a huge part of modern life, and cyber security has become a crucial area of research. An intrusion detection system (IDS) is a device that examines network traffic to confirm security, availability, and integrity. Despite years of research, conventional IDSs continue to struggle with enhancing detection performance, lowering False Alarm Rates (FAR), and identifying novel assaults. Many academics have concentrated on constructing IDSs that leverage Artificial Intelligence (AI) technologies to overcome the difficulties mentioned above. With extreme accuracy, AI algorithms can reliably identify the main distinctions among normal and anomalous data. Furthermore, AI approaches are very generalizable, allowing them to identify previously undiscovered assaults. IDS methods based on ML, DL, and meta-heuristic optimization have recently been proposed as workable alternatives for spotting intrusions throughout the network. This survey presents an IDS taxonomy that classifies and summarizes AI-based IDS research using data items as the primary dimension. This article offers a thorough analysis of recent studies on the application of AI in IDSs, including surveys and suggestions for cutting-edge techniques. Following that, recent developments and advances in AI-based network IDS are explored, as well as the proposed strategy, assessment metrics, and dataset selection. This study highlighted many research obstacles and presented future direction for research in developing AI-based Network IDS using the inadequacies of the existing approaches. © 2023 IEEE.

关键词： Wireless networks

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