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Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Rushikesh Thote Aditya Barhate Abhay Tale Prateek Verma Department of Computer Science and Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

The integration of autonomous vehicles (AVs) into modern transportation systems presents both unprecedented opportunities and challenges, particularly in ensuring road safety and reducing accidents. This paper explores the application of Deep Reinforcement Learning (DRL) techniques to enhance decision-making processes in AVs, aiming to proactively prevent road accidents. By simulating complex driving environments, DRL algorithms enable AVs to learn optimal driving strategies through continuous interaction and feedback. We introduce a DRL-based framework that incorporates sensor data fusion, real-time environment analysis, and adaptive control mechanisms. Experimental results from simulated urban and highway scenarios demonstrate a significant reduction in collision rates and improved response to unforeseen obstacles compared to traditional rule-based systems. The findings underscore the potential of DRL in fortifying the reliability and safety of autonomous driving technologies.

关键词： Road accidents Reviews Decision making Transportation Transforms Deep reinforcement learning Road safety Safety Reliability Stress

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Smart Healthcare: Harnessing the Power of Machine Learning for Predictive Analysis

Smart Healthcare: Harnessing the Power of Machine Learning f...

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Engineering and Technology (PICET), Parul International Conference on

作者： Aditya Barhate Praveen Kumar Prateek Verma Nayan Jikar Abhay Tale Vaibhav Hikre Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Computer Science and Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798350369748

ISBN: (纸本)9798350369755

Smart healthcare employs cloud computing, the Internet of Things (IoT), and supercomputers to manage the healthcare system. Medical data storage volume is increasing, necessitating the adoption of Machine Learning (ML) models for predicting outcomes, particularly in the treatment of chronic illnesses. The trend of care personalization and technological integration is highlighted by the development of ML applications for clinical decision support systems, including real-time predictive analytics. Predictive analytics applications employ ML and Artificial Intelligence (AI) to forecast patient risks, outcomes, and treatment responses. Predictive analytics is more important in healthcare when it comes to detecting Alzheimer's disease, cardiovascular events, radiology, and other conditions. Therefore, the current study highlights the change in illness identification, medical imaging, and personalized medication brought about by ML and Predictive Analytics in Precision Medicine (PM). However, ethical concerns and the deployment of transparent AI models still need more research. This study anticipates a time when healthcare will be quicker, better informed, and more patient-centered by using the insights provided by intelligent healthcare data. By effectively saving patient lives, the use of ML in healthcare ushers in a new era for the industry.

关键词： Ethics Solid modeling Medical services Machine learning Radiology Supercomputers Real-time systems Internet of Things Predictive analytics Biomedical imaging

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Machine Learning for Wireless network Optimization: Algorithms and Applications for Adaptive Communication

Machine Learning for Wireless Network Optimization: Algorith...

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Advances in computer science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Madhira Srinivas S. Surya Prachi Deepak Thakar Amit Kumar Jain Department of Computer Science & Engineering Data Science Geethanjali College of Engineering & Technology (GCET) Hyderabad India Sri Ramachandra Faculty of Engineering & Technology (SRET) Sri Ramachandra Institute of Higher Education & Research Porur Chennai India Department of Computer Engineering KJ college of Engineering & Management Research Pune India Department of Electrical & Electronics Engineering Poornima University Jaipur Rajasthan India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

This project looks into the possibility of applying machine learning to optimize wireless networks for adaptive communication. Using 5G resource data, it applies preprocessing, exploratory analysis, and visualization to identify key trends: signal strength, latency, and bandwidth utilization. Machine learning algorithms predict resource allocation based on latency-sensitive and bandwidth-demanding applications. The approach emphasizes energy efficiency, scalability, and service quality, and dynamically adjusts communication strategies to meet diverse network demands. The findings indicate the machine learning importance in enhancing elasticity and robustness in a wireless network and its general function.

关键词： Machine learning algorithms Adaptive systems Wireless networks Scalability Machine learning Bandwidth Predictive models Prediction algorithms Resource management Optimization

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High-Level Synthesis for Microfluidic Biochips Considering Actual Volume Management and Channel Storage

High-Level Synthesis for Microfluidic Biochips Considering A...

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IEEE International Symposium on Quality Electronic Design

作者： Zhengyang Chen Yuhan Zhu Zhen Chen Zhisheng Chen Genggeng Liu College of Computer and Data Science Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing (Fuzhou University) Fuzhou China School of Informatics Xiamen University Xiamen China

ISBN: (数字)9798350309270

ISBN: (纸本)9798350309287

In recent years, microfluidic biochips have been widely applied in various fields of human society. The emergence of distributed channel-storage architecture enables fluid to be directly cached within the flow channels without being transported to the dedicated storage. However, the volume of the flow channels is limited, it is important to carefully consider the fluid volume to be cached to ensure it can be accommodated within the appropriate flow channels. In this paper, we propose a high-level synthesis method considering actual volume management and channel storage through integer linear programming, providing a rational analysis of fluid caching and the temporal relationships between different fluid-handling tasks. Experimental results conducted on multiple benchmark tests demonstrate the effectiveness of the proposed method in reducing the completion time of bioassay, the number of caches, the maximum fluid caching volume, and the number of fluid-handling tasks.

关键词： Fluids Biochips Estimation Cells (biology) Integer linear programming Benchmark testing Task analysis

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LightPRA: A Lightweight Temporal Convolutional network for Automatic Physical Rehabilitation Exercise Assessment

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computers in Biology and Medicine 2024年 173卷 108382-108382页

作者： Sardari, Sara Sharifzadeh, Sara Daneshkhah, Alireza Loke, Seng W. Palade, Vasile Duncan, Michael J. Nakisa, Bahareh Research Centre for Computational Science and Mathematical Modelling Coventry University Coventry United Kingdom School of Information Technology Faculty of Science Engineering and Built Environment Deakin University Geelong Vic Australia Department of Computer Science Swansea University Swansea United Kingdom School of Mathematics and Data Science Emirates Aviation University Dubai United Arab Emirates Centre for Sport Exercise and Life Sciences Coventry University Coventry United Kingdom

research evidence shows that physical rehabilitation exercises prescribed by medical experts can assist in restoring physical function, improving life quality, and promoting independence for physically disabled individuals. In response to the absence of immediate expert feedback on performed actions, developing a Human Action Evaluation (HAE) system emerges as a valuable automated solution, addressing the need for accurate assessment of exercises and guidance during physical rehabilitation. Previous HAE systems developed for the rehabilitation exercises have focused on developing models that utilize skeleton data as input to compute a quality score for each action performed by the patient. However, existing studies have focused on improving scoring performance while often overlooking computational efficiency. In this research, we propose LightPRA (Light Physical Rehabilitation Assessment) system, an innovative architectural solution based on a Temporal Convolutional network (TCN), which harnesses the capabilities of dilated causal Convolutional Neural networks (CNNs). This approach efficiently captures complex temporal features and characteristics of the skeleton data with lower computational complexity, making it suitable for real-time feedback provided on resource-constrained devices such as Internet of Things (IoT) devices and Edge computing frameworks. Through empirical analysis performed on the University of Idaho-Physical Rehabilitation Movement data (UI-PRMD) and KInematic assessment of MOvement for remote monitoring of physical REhabilitation (KIMORE) datasets, our proposed LightPRA model demonstrates superior performance over several state-of-the-art approaches such as Spatial–Temporal Graph Convolutional network (STGCN) and Long Short-Term Memory (LSTM)-based models in scoring human activity performance, while exhibiting lower computational cost and complexity. © 2024 The Authors

关键词： Convolution

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Efficient and High-quality Recommendations via Momentum-incorporated Parallel Stochastic Gradient Descent-Based Learning

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IEEE/CAA Journal of Automatica Sinica 2021年第2期8卷 402-411页

作者： Xin Luo Wen Qin Ani Dong Khaled Sedraoui MengChu Zhou the School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808 Hengrui(Chongqing)Artificial Intelligence Research Center Department of Big Data Analyses TechniquesCloudwalkChongqing 401331China the School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing 400065 Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China the Department of Computer and Information Science City College of Dongguan University of TechnologyDongguan 523419China the Department of Electrical and Computer Engineering Faculty of Engineeringand Center of Research Excellence in Renewable Energy and Power SystemsKing Abdulaziz UniversityJeddah 21481Saudi Arabia the Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA the Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz UniversityJeddah 21481Saudi Arabia

A recommender system(RS)relying on latent factor analysis usually adopts stochastic gradient descent(SGD)as its learning ***,owing to its serial mechanism,an SGD algorithm suffers from low efficiency and scalability when handling large-scale industrial *** at addressing this issue,this study proposes a momentum-incorporated parallel stochastic gradient descent(MPSGD)algorithm,whose main idea is two-fold:a)implementing parallelization via a novel datasplitting strategy,and b)accelerating convergence rate by integrating momentum effects into its training *** it,an MPSGD-based latent factor(MLF)model is achieved,which is capable of performing efficient and high-quality *** results on four high-dimensional and sparse matrices generated by industrial RS indicate that owing to an MPSGD algorithm,an MLF model outperforms the existing state-of-the-art ones in both computational efficiency and scalability.

关键词： Big data industrial application industrial data latent factor analysis machine learning parallel algorithm recommender system(RS) stochastic gradient descent(SGD)

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Impact of CLAHE-based image enhancement for diabetic retinopathy classification through deep learning 7

Impact of CLAHE-based image enhancement for diabetic retinop...

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7th International Conference on computer science and Computational Intelligence, CSCI 2022

作者： Hayati, Mira Muchtar, Kahlil Roslidar Maulina, Novi Syamsuddin, Irfan Elwirehardja, Gregorius Natanael Pardamean, Bens Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Universitas Syiah Kuala Banda Aceh23111 Indonesia Faculty of Medicine Universitas Syiah Kuala Banda Aceh23111 Indonesia Department of Computer and Networking Engineering Politeknik Negeri Ujung Pandang Makassar90245 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

Diabetic retinopathy (DR) is a type of diabetes mellitus that attacks the retina of the eye. DR will cause patients to experience blindness slowly. Generally, DR can be detected by using a special instrument called an ophthalmoscope to view the inside of the eyeball. However, in conditions where there is a very small difference between the normal image and the DR image, computer-based assistance is needed for maximizing image reading value. In this research, a method of image quality improvement will be carried out which will then be integrated with a classification algorithm based on deep learning. The results of image improvement using Contrast Limited Adaptive Histogram Equalization (CLAHE) shows that the average accuracy of the method on several models is very competitive, 91% for the VGG16 model, 95% for InceptionV3, and 97% for EfficientNet compared to the results original image which only has an accuracy of 87% for VGG16 model, 90% for InceptionV3 model, and 95% for EfficientNet. However, in ResNet34 better accuracy is obtained in the original image with an accuracy of 95% while in the CLAHE image the accuracy value is only 84%. The results of this comprehensive evaluation and recommendation of famous backbone networks can be useful in the computer-Aided diagnosis of diabetic retinopathy. © 2022 Elsevier B.V.. All rights reserved.

关键词： Image enhancement

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The Occurrence and Genetic Characteristics of Carbapenemase-Producing Enterobacteriaceae Isolated from Shared Water Bodies for Livestock and Human Utilization in Central Thailand

SSRN

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SSRN 2024年

作者： Songsaeng, Wipawee Kurilung, Alongkorn Prapasarakul, Nuvee Wongsurawat, Thidathip Am-In, Nutthee Lugsomya, Kittitat Sirichokchatchawan, Wandee College of Public Health Science Chulalongkorn University Bangkok Thailand Department of Biomedical Informatics University of Arkansas for Medical Sciences Little RockAR United States Department of Veterinary Microbiology Faculty of Veterinary Science Chulalongkorn University Bangkok Thailand Bangkok Thailand Division of Bioinformatics and Data Management for Research Research Group and Research Network Division Faculty of Medicine Siriraj Hospital Mahidol University Bangkok Thailand Department of Obstetrics Gynecology and Reproduction Faculty of Veterinary Science Chulalongkorn University Bangkok Thailand Veterinary and Life Science Murdoch University MurdochWA6150 Australia

The environmental dissemination of antimicrobial resistant Enterobacteriaceae poses a remarkable threat to public health. This study investigates the environmental presence and dissemination of carbapenemase-producing Enterobacteriaceae (CPE) in 30 important water bodies selected according to the interconnection and utilization by livestock and community people in central Thailand. From all selected samples, 6 isolates (20%) were identified as multi-drug resistant CPE including one E. coli, one K. pneumoniae and four E. roggemkampii carrying blaNDM-5, blaNDM-1 and blaIMI-1 genes respectively. The blaNDM-5 and blaNDM-1 genes were located on plasmids, p0111 and IncC, while blaIMI-1 was located on chromosomes. The plasmids also consisted of components that closely resembled those found in resistance plasmids obtained from clinical and environmental isolates worldwide. Additionally, through plasmid conjugation experiment, carbapenemase genes were transferable with high rate among bacterial species. These findings indicated that water bodies are polluted and there is an urgent need for integrated strategies to monitor and mitigate the spread of antibiotic resistance across human, animal, and environmental health domains in aquatic environments. © 2024, The Authors. All rights reserved.

关键词： Genes

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HANE-SHC: Heterogeneous attributed network embedding with structural homophily contents

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Applied Intelligence 2025年第10期55卷 1-40页

作者： Fu, Yue Zhao, Shuliang Wu, Yongliang College of Information Engineering Hebei GEO University Hebei China College of Computer and Cyber Security Hebei Normal University Hebei China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security Hebei China Hebei Provincial Key Laboratory of Network and Information Security Hebei China School of Information Science and Technology Shijiazhuang Tiedao University Hebei China

Heterogeneous attributed networks (HANs) are ubiquitous in real-world, spanning domains from the academic network to the movie network. Heterogeneous attributed network embedding (HANE), a promising analytical technique in recent years, aims to generate low-dimensional dense vectors containing network structure and attribute content. Compared to the embedding approaches that only consider topology structure, researchers embed the network by leveraging both network topology and content to improve the quality of embedding vectors. However, when two nodes with a connected edge may have a small content similarity value, while two nodes without a connected edge may have a large content similarity value, so, in this situation, it is not appropriate to utilize the network topology and nodes’ content to generate embedding vectors. To overcome this challenge, we propose a novel approach of Heterogeneous Attributed networks Embedding based on Structural Homophily Contents, abbreviated as HANE-SHC, which is based on convolutional neural networks to leverage both the network structure and the content of nodes connected with the embedded node to generate the representation vectors of the embedded node. To our knowledge, this is the first study that proposes the Structural Homophily Contents to enhance the heterogeneous attributed network embedding. Through comprehensive evaluations on downstream tasks including clustering, classification, link prediction, and visualization, the experimental results demonstrate that the HANE-SHC outperforms the baselines. On the artificial synthetic networks with various network structure-content dissimilarity scores, the HANE-SHC model consistently produces high-quality embedding vectors regardless of the network dissimilarity scores between structure and content, significantly outperforming existing approaches.

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Large language models are fixated by red herrings: exploring creative problem solving and einstellung effect using the only connect wall dataset 23

Large language models are fixated by red herrings: exploring...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Saeid Alavi Naeini Raeid Saqur Mozhgan Saeidi John Giorgi Babak Taati Kite Research Institute Toronto Rehabilitation Institute University Health Network and Institute of Biomedical Engineering University of Toronto and Vector Institute for AI Department of Computer Science University of Toronto and Vector Institute for AI Department of Computer Science University of Toronto and Vector Institute for AI and Department of Biomedical Data Science Stanford University Department of Computer Science University of Toronto and Vector Institute for AI and Donnelly Centre for Cellular & Biomolecular Research University of Toronto Kite Research Institute Toronto Rehabilitation Institute University Health Network and Department of Computer Science University of Toronto and Institute of Biomedical Engineering University of Toronto and Vector Institute for AI

The quest for human imitative AI has been an enduring topic in AI research since its inception. The technical evolution and emerging capabilities of the latest cohort of large language models (LLMs) have reinvigorated the subject beyond academia to the cultural zeitgeist. While recent NLP evaluation benchmark tasks test some aspects of human-imitative behavior (e.g., BIG-bench's 'human-like behavior' tasks), few, if not none, examine creative problem. solving abilities. Creative problem solving in humans is a well-studied topic in cognitive neuroscience with standardized tests that predominantly use the ability to associate (heterogeneous) connections among clue words as a metric for creativity. Exposure to misleading stimuli — distractors dubbed red herrings — impede human performance in such tasks via the fixation effect and Einstellung paradigm. In cognitive neuroscience studies, such fixations are experimentally induced by pre-exposing participants to orthographically similar incorrect words to subsequent word-fragments or clues. The popular British quiz show Only Connect's Connecting Wall segment essentially mimics Mednick's Remote Associates Test (RAT) formulation with built-in, deliberate red herrings, which makes it an ideal proxy task to explore and study the fixation effect and Einstellung paradigm from cognitive neuroscience in LLMs. In this paper, we present the novel Only Connect Wall (OCW) dataset and report results from our evaluation of selected pre-trained language models and LLMs on creative problem solving tasks like grouping clue words by heterogeneous connections and identifying correct open knowledge domain connections in respective groups. We synthetically generate two additional datasets: OCW-Randomized, OCW-WordNet to further analyze our red-herrings hypothesis in language models. The code and link to the dataset are available at https://***/TaatiTeam/OCW.

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