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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Renu Kachhoria Love Mittal Prakash Divakaran Vinay Singh N. Thangarasu S. Rajarajeswari Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune INDIA Department of Computer Science Mangalayatan University Aligarh India Department of Bussiness & Management Himalayan University Itanagar India Department of Coputing & Information Technology Usha Martin University Ranchi India Department of Computer Science Karpagam Academy of Higher Education Coimbatore Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai

Advanced Neural community Architectures, consisting of Convolution Neural Networks (CNNs), have emerged as powerful solutions to demanding situations in extracting complicated and elusive information from large, high-dimensional datasets. CNNs have effective feature extraction abilities that may be used to identify subtle patterns, traits, and relationships in those datasets. but, effective architectures must be tailored to the particular nature of the hassle that allows you to maximize the potential of these fashions. CNNs were used effectively in some of domain names to discover styles in facts that could otherwise remain hidden. As an example, CNNs have been used inside the medical domain to quickly and accurately become aware of cardiologic patterns in electrocardiograms. Similarly, they have been used within the security domain to perceive capability threats earlier than they occur. In each domain names, the capability to extract cognitive facts from numerous datasets speedy and appropriately has saved lives and avoided failures. The exercise of the use of superior neural architectures to permit statistics extraction is complex and requires thoughtful layout. The structure must recall the traits of the project, which include the records kind, the range of dimensions concerned, and the amount of noise or outliers, to determine which strategies are maximum suitable for every specific utility.

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Advancing Patient Care for Autonomous Saline Level Alert System with Cloud-Based Isolation Forest Algorithm

Advancing Patient Care for Autonomous Saline Level Alert Sys...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： Nandhini R G. Sumathi S. Ramya Saravanan Thulasi Ishwarya M. V S. Murugan Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai Tamil Nadu India Department of Information Technology Kongunadu College of Engineering and Technology (Autonomous) Trichy Tamil Nadu India Department of Cyber Security Paavai Engineering College Namakkal Tamil Nadu India Customer and Product Support Services John Deere India Pvt Ltd Pune Maharashtra India Artificial intelligence and Data science Department Agni College of Technology Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

This work presents a novel method for improving patient care by creating a self-sufficient system to detect saline levels using an Isolation Forest Algorithm (IFA) hosted on the cloud. Standard practices for checking saline levels in IV infusion systems often include human intervention, which might cause important discrepancies to go undetected for longer periods of time. It presents a system that uses machine learning to identify aberrant saline levels in real-time, improving patient safety and relieving healthcare providers of some of their workload. The IFA, a powerful anomaly detection tool that works particularly well with high-dimensional data like that produced by intravenous infusion devices, is at the heart of our strategy. It gains scalability, flexibility, and real-time processing capabilities crucial for early alerting in clinical settings by implementing our algorithm on the cloud. Extensive testing and validation with both synthetic and real-world datasets prove that our suggested approach reliably and effectively detects abnormal saline levels with few false alarms. By providing healthcare practitioners with prompt alerts of possible infusion anomalies and allowing proactive action to avert adverse events, the autonomous saline level alarm system has major implications for patient care. The technology provides smooth interaction with current healthcare infrastructure by moving computing duties to the cloud, which eliminates the need for extra hardware and maintenance burdens.

关键词： Scalability Medical services Forestry Real-time systems Safety Streams Standards Research and development Optimization Testing

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A novel deep learning-based spider wasp optimization approach for enhancing brain tumor detection and physical therapy prediction

Computer Methods and Programs in Biomedicine Update

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Computer Methods and Programs in Biomedicine Update 2025年 7卷

作者： Daoud, Suleiman Nasayreh, Ahmad Nahar, Khalid M.O. Abedalaziz, Wlla k. Alayasreh, Salem M. Gharaibeh, Hasan Bashkami, Ayah Jaradat, Amer Jarrar, Sultan Al-Hawamdeh, Hammam Ezugwu, Absalom E. Zitar, Raed Abu Smerat, Aseel Snasel, Vaclav Abualigah, Laith Neurosurgery Department Faculty of Medicine Jordan University of Science and Technology Irbid 22110 Jordan Department of Information Technology and Computer Sciences Yarmouk University Irbid 211633 Jordan Artificial Intelligence and Data Innovation Office King Hussein Cancer Center Amman Jordan Department of Rehabilitation science Faculty of applied medical science Jordan University of Science and Technology King Abdullah University Hospital Irbid Jordan Department of Medical Laboratory Sciences Al Balqa Applied University Salt Jordan Unit for Data Science and Computing North-West University 11 Hofman Street Potchefstroom 2520 South Africa College of Engineering and Computing Liwa University Abu Dhabi United Arab Emirates Faculty of Educational Sciences Al-Ahliyya Amman University Amman 19328 Jordan Computer Technologies Engineering Mazaya University College Nasiriyah Iraq Faculty of Electrical Engineering and Computer Science VŠB-Technical University of Ostrava Poruba-Ostrava 70800 Czech Republic Computer Science Department Al al-Bayt University Mafraq 25113 Jordan

A brain tumor, one of the deadliest disorders, is characterized by the abnormal growth of synapses in the brain. Early detection can improve brain tumor diagnosis, and accurate diagnosis is essential for effective treatment. Researchers have developed several deep-learning classification methods to diagnose brain tumors. Moreover, these types of tumorscan significantly impair physical activity, presenting a broad spectrum of symptoms. As a result, each patient requires an individualized physical therapy treatment plan tailored to their specific needs. However, some challenges remain, including the need for a competent expert in classifying brain tumors using deep learning models, as well as the challenge of creating the most accurate deep learning model for brain tumor classification. To address these challenges, we present a highly accurate and efficient methodology based on advanced metaheuristic algorithms and deep learning. To identify different types of pediatric brain tumors, we specifically develop an optimal residual learning architecture. We also present the Spider Wasp Optimization (SWO) algorithm, which aims to improve performance by feature selection. The algorithm enhances the effectiveness of optimization by balancing the speed of convergence and diversity of solutions. We first convert the algorithm from continuous to binary, combine it with the K-Nearest Neighbor (KNN) algorithm for classification, and evaluate it on a dataset of brain MRI images collected from King Abdullah Hospital. Our analysis revealed that in terms of metrics such as accuracy, sensitivity, specificity, and f1-score, it outperformed other conventional algorithms. We demonstrate the overall effectiveness of the proposed model by using it to select the optimal features extracted from the Resnet50V2 model for pediatric brain tumor detection. We compared the proposed SWO+KNN model with other deep learning architectures such as MobileNetV2, Resnet50V2, and machine learning algorithms

关键词： Brain tumor Deep learning Feature selection Machine learning Metaheuristic optimization Pediatric Physical therapy Rehabilitation

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Assessment of Mixing Efficiency in a Planar Passive Micromixer With T-shaped Configuration

Assessment of Mixing Efficiency in a Planar Passive Micromix...

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Electrical, Energy, and artificial intelligence (EICEEAI), International engineering Conference on

作者： Jafar Ababneh Mohanad A. Deif Ahmad Solyman Mansour Y. Ibrahim Mohamed Hafez Yosef M. Ashmawy The Faculty of Information Technology Cyber security Zarqa University Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of Artificial intelligence College of Information Technology Misr University for Science & Technology (MUST) 6th of October City Egypt School of Computing Engineering and Built Environment Glasgow Caledonian University Glasgow UK Department of Bioelectronics Modern University of Technology and Information (MTI) University Cairo Egypt Faculty of Engineering FEQS INTI-IU-University Nilai Malaysia

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

Microfluidic devices have garnered considerable interest owing to their prospective utilization in diverse domains, encompassing chemical synthesis, biological analysis, and medicinal research. Micromixers are critical in adequate fluid mixing at a microscale within the array of devices under consideration. This study aims to offer a comprehensive analysis of the efficacy of the T-shaped micromixer configuration in scenarios that necessitate accurate and expeditious mixing. This study examines the performance of a T micromixer through simulation and analysis. The findings demonstrate that T micromixers exhibit some drawbacks that result in suboptimal mixing efficiency. The attainment of a desirable level of mixing efficiency can be accomplished by utilizing splitting-recombination and chaotic advection mechanisms. The study's outcomes indicate that the T micromixer demonstrates its maximum mixing effectiveness, roughly 60% when the Reynolds number (Re) is at or below 0.5. Nevertheless, it has been observed that the T micromixer encounters a decrease in mixing effectiveness as the Reynolds number escalates within the range of 0.5 to 15.

关键词： Performance evaluation Fluids Ethanol Software Biology Computational efficiency Microfluidics

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(AIAM2019) artificial intelligence in Software engineering and inverse: Review

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International Journal of Computer Integrated Manufacturing 2020年第10-11期33卷 1129-1144页

作者： Shehab, Mohammad Abualigah, Laith Jarrah, Muath Ibrahim Alomari, Osama Ahmad Daoud, Mohammad Sh. Computer Science Artificial Intelligence Department Aqaba University of Technology Aqaba Jordan Faculty of Computer Sciences and Informatics Amman Arab University Amman Jordan Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Parit Raja Malaysia Department of Computer Engineering Istanbul Gelisim University Istanbul Turkey Faculty of Engineering Al Ain University of Science and Technology Abu Dhabi United Arab Emirates

artificial intelligence (AI) and Software engineering are considered as significant fields to solve various problems. However, there are weaknesses in certain problem-solving in each field. Thus, this paper is a broad-based review of using artificial intelligence (AI) to improve software engineering (SE), and vice versa. As well as it intends to review the techniques developed in artificial intelligence from the standpoint of their application in software engineering. The aim of this review is highlighted in how the previous study benefited from incorporating the advantages of both fields. The researchers and practitioners on AI and SE belong to a wide range of audiences from the domains of optimization, engineering, data mining, clustering, etc., who will benefit from this study and areas for potential future research. © 2020 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Application programs

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Multi-objective Aerial IRS-assisted ISAC Optimization via Generative AI-enhanced Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Xie, Wenwen Sun, Geng Wang, Jiacheng Du, Hongyang Kang, Jiawen Huang, Kaibin Leung, Victor C.M. College of Computer Science and Technology Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Department of Electrical and Electronic Engineering The University of Hong Kong 999077 Hong Kong School of Automation Guangdong University of Technology Guangzhou510006 China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518115 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

Integrated sensing and communication (ISAC) has garnered substantial research interest owing to its pivotal role in advancing the development of next-generation (6G) wireless networks. However, achieving a performance balance between communication and sensing in the dual-function radar communication (DFRC)-based ISAC system remains a significant challenge. In this paper, an aerial intelligent reflecting surface (IRS)-assisted ISAC system is explored, where a base station (BS) supports dual-functional operations, enabling both data transmission for multiple users and sensing for a blocked target, with the channel quality enhanced by an IRS mounted on the unmanned aerial vehicle (UAV). Moreover, we formulate an integrated communication, sensing, and energy efficiency multi-objective optimization problem (CSEMOP), which aims to maximize the communication rate of the users and the echo rate of the target, while minimizing UAV propulsion energy consumption by jointly optimizing the BS beamforming matrix, IRS phase shifts, the flight velocity and angle of the UAV. Considering the non-convexity, trade-off, and dynamic nature of the formulated CSEMOP, we propose a generative diffusion model-based deep deterministic policy gradient (GDMDDPG) method to solve the problem. Specifically, the diffusion model is incorporated into the actor network of DDPG to improve the action quality, with noise perturbation mechanism for better exploration and recent prioritized experience replay (RPER) sampling mechanism for enhanced training efficiency. Simulation results indicate that the GDMDDPG method delivers superior performance compared to the existing methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Natural Language Communication with a Conversational Teachable Agent

arXiv

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arXiv 2022年

作者： Love, Rachel Law, Edith Cohen, Philip R. Kulić, Dana The Department of Electrical and Computer Systems Engineering Monash University Australia The David R. Cheriton School of Computer Science University of Waterloo Waterloo Canada Openstream Inc. The Department of Data Science and Artificial Intelligence Faculty of Information Technology Monash University Australia The Department of Electrical and Computer Systems Engineering Department of Mechanical and Aerospace Engineering Monash University Australia

Conversational teachable agents offer a promising platform to support learning, both in the classroom and in remote settings. In this context, the agent takes the role of the novice, while the student takes on the role of teacher. This framing is significant for its ability to elicit the Protégé effect in the student-teacher, a pedagogical phenomenon known to increase engagement in the teaching task, and also improve cognitive outcomes. In prior work, teachable agents often take a passive role in the learning interaction, and there are few studies in which the agent and student engage in natural language dialogue during the teaching task. This work investigates the effect of teaching modality when interacting with a virtual agent, via the web-based teaching platform, the Curiosity Notebook. A method of teaching the agent by selecting sentences from source material is compared to a method paraphrasing the source material and typing text input to teach. A user study has been conducted to measure the effect teaching modality on the learning outcomes and engagement of the participants. The results indicate that teaching via paraphrasing and text input has a positive effect on learning outcomes for the material covered, and also on aspects of affective engagement. Furthermore, increased paraphrasing effort, as measured by the similarity between the source material and the material the teacher conveyed to the robot, improves learning outcomes for participants. © 2022, CC BY.

关键词： Students

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IoT-Enabled Table Turnover for Dining Experiences with AdaBoost Algorithm

IoT-Enabled Table Turnover for Dining Experiences with AdaBo...

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Innovation in technology (APCIT), Asia Pacific Conference on

作者： Chitra Sabapathy Ranganathan N. Mohankumar G. Sumathi A. Nithya P. Sudha Juliet M. Muthulekshmi Mphasis Corporation Chennai Tamil Nadu India Symbiosis Institute of Technology (SIT) Symbiosis International (Deemed University) Nagpur Maharashtra India Department of Artificial Intelligence & Data Science Kongunadu College of Engineering and Technology (Autonomous) Trichy Tamil Nadu India Department of Information Technology St. Joseph’s Institute of Technology Chennai Tamil Nadu India Department of Electronics and Instrumentation Engineering Meenakshi College of Engineering Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350361537

ISBN: (纸本)9798350361544

Traditional dining experiences are undergoing a period of transition because of the growing use of Internet of Things (IoT) technologies and machine learning in the restaurant business. This article presents revolutionary IoT-driven technology, intended to maximize the table turnover rate and improve client happiness. The system can gather real-time data on the occupancy status of each table since it places occupancy sensors at each table. This allows for the effective distribution of tables as they become available. model is software that automates table cleaning alerts, staff coordination, and predictive analytics for resource planning. It is designed to connect seamlessly with queue management and point-of-sale (POS) systems. This novel strategy improves the restaurant's efficiency, reduces customers' wait times, and maximizes earning potential during peak hours. The suggested solution offers a chance to change dining venues by combining the capabilities of the IoT with dynamic table management. This will result in a better customer dining experience and improved operational results for companies.

关键词： Technological innovation Machine learning Solids Real-time systems Software Sensors Internet of Things

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Blockchain and Machine Learning Integration for Safe data Sharing in Finance and HR

Blockchain and Machine Learning Integration for Safe Data Sh...

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Intelligent Algorithms for Computational intelligence Systems (IACIS), International Conference on

作者： T. B Sivakumar E. Prathiba Shweta Sharma Rashmi Saket Prasad M. S. Ramaratnam V. Krishnamoorthy Department of CSE School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Artificial Intelligence and Data Science Mailam Engineering College Mailam India Department of Business Administration Shambhunath Institute of Engineering & Technology Prayagraj India D Y Patil PGDM Institute Pune Akurdi India Department of Management Studies Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya Kanchipuram India Department of Management Studies Kongu Engineering College Erode Perundurai India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

Modern finance and HR businesses have challenges in exchanging data due to centralized systems that are subject to hacking and lack transparency. The study proposes employing blockchain technology and machine intelligence (ML) to address these issues holistically. The proposed system enhances integrity and resilience to assaults by utilizing a decentralized ledger for secure data exchange. ML algorithms enable predictive analytics, risk management, and personalized insights. The proposed system outperforms the existing system in terms of accuracy, security, and efficiency. The accuracy of fraud detection increases from 0.75 to 0.90, as does the estimate of staff turnover from 0.80 to 0.95. The proposed system achieves an overall accuracy of 95%, surpassing the existing system's 85%. Blockchain and ML integration can alter data management methods in finance and HR industries, with improved security measures resulting in a risk score of 2 compared to 7 in the existing system.

关键词： Industries Training Adaptation models Accuracy Heuristic algorithms data security Computational modeling Finance Blockchains Predictive analytics

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Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?

arXiv

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arXiv 2024年

作者： Bassi, Pedro R.A.S. Li, Wenxuan Tang, Yucheng Isensee, Fabian Wang, Zifu Chen, Jieneng Chou, Yu-Cheng Roy, Saikat Kirchhoff, Yannick Rokuss, Maximilian Huang, Ziyan Ye, Jin He, Junjun Wald, Tassilo Ulrich, Constantin Baumgartner, Michael Maier-Hein, Klaus H. Jaeger, Paul Ye, Yiwen Xie, Yutong Zhang, Jianpeng Chen, Ziyang Xia, Yong Xing, Zhaohu Zhu, Lei Sadegheih, Yousef Bozorgpour, Afshin Kumari, Pratibha Azad, Reza Merhof, Dorit Shi, Pengcheng Ma, Ting Du, Yuxin Bai, Fan Huang, Tiejun Zhao, Bo Wang, Haonan Li, Xiaomeng Gu, Hanxue Dong, Haoyu Yang, Jichen Mazurowski, Maciej A. Gupta, Saumya Wu, Linshan Zhuang, Jiaxin Chen, Hao Roth, Holger Xu, Daguang Blaschko, Matthew B. Decherchi, Sergio Cavalli, Andrea Yuille, Alan L. Zhou, Zongwei Department of Computer Science Johns Hopkins University United States Department of Pharmacy and Biotechnology University of Bologna Italy Center for Biomolecular Nanotechnologies Istituto Italiano di Tecnologia Italy NVIDIA United States Germany Germany ESAT-PSI KU Leuven Belgium Faculty of Mathematics and Computer Science Heidelberg University Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Germany Shanghai Jiao Tong University China Shanghai Artificial Intelligence Laboratory China Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany DKFZ Germany School of Computer Science and Engineering Northwestern Polytechnical University China Australian Institute for Machine Learning The University of Adelaide Australia College of Computer Science and Technology Zhejiang University China Hong Kong University of Science and Technology Guangzhou China Hong Kong University of Science and Technology Hong Kong Faculty of Informatics and Data Science University of Regensburg Germany Faculty of Electrical Engineering and Information Technology RWTH Aachen University Germany Fraunhofer Institute for Digital Medicine MEVIS Germany Electronic & Information Engineering School Harbin Institute of Technology Shenzhen China China The Chinese University of Hong Kong Hong Kong Peking University China Department of Electrical and Computer Engineering Duke University United States Stony Brook University United States Department of Computer Science and Engineering Department of Chemical and Biological Engineering Division of Life Science Hong Kong University of Science and Technology Hong Kong Data Science and Computation Facility Fondazione Istituto Italiano di Tecnologia Italy Ecole Polytechnique Fédérale de Lausanne Switzerland

How can we test AI performance? This question seems trivial, but it isn’t. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified metrics, unfair comparisons, and short-term outcome pressure. As a consequence, good performance on standard benchmarks does not guarantee success in real-world scenarios. To address these problems, we present Touchstone, a large-scale collaborative segmentation benchmark of 9 types of abdominal organs. This benchmark is based on 5,195 training CT scans from 76 hospitals around the world and 5,903 testing CT scans from 11 additional hospitals. This diverse test set enhances the statistical significance of benchmark results and rigorously evaluates AI algorithms across out-of-distribution scenarios. We invited 14 inventors of 19 AI algorithms to train their algorithms, while our team, as a third party, independently evaluated these algorithms. In addition, we also evaluated pre-existing AI frameworks—which, differing from algorithms, are more flexible and can support different algorithms—including MONAI from NVIDIA, nnU-Net from DKFZ, and numerous other open-source frameworks. We are committed to expanding this benchmark to encourage more innovation of AI algorithms for the medical domain. Copyright © 2024, The Authors. All rights reserved.

关键词： Open systems

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