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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Wilfred Blessing N. R Hemalatha Gunasekaran Hariharan B. Jaber Saleh Salim Al Jabri Kavitha S. J Sutherlin Subitha G. College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Sultanate of Oman Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Department of Computer Science and Engineering Stella Mary's College of Engineering Aruthenganvilai Tamil Nadu India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Health care plays a vital responsibility in a human life span. World countries targets to provide better care for their citizens. Digital transformations and innovations in Artificial Intelligence (AI) technologies helps in deep for the novel solutions to speed-up the decision making process in health management and patient consulting. AI branches including Machine Learning (ML), Deep Learning (DL), Neural Networks (NN), Natural Language Processing (NLP) and so forth can support innovations in heal care domain. This paper presents the comprehensive and noble review on recent existing tools and techniques and also recommends advanced techniques for health care industry that are to be used in Sultanate of Oman. Furthermore, overcoming AI challenges are resulted and presented through the study. This work mainly aimed to produce the multiple dimensions and challenges that helps to develop AI powered real-life applications in health care support system.

关键词： Deep learning Technological innovation Ethics Reviews Decision making Surgery Medical services Natural language processing Medical diagnostic imaging Diseases

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AI-Enhanced Healthcare: Densenet Based Alzheimer's Disease Prediction and Online Consultation for Accessible Medical Support

AI-Enhanced Healthcare: Densenet Based Alzheimer's Disease P...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Improvements in patient care and early disease detection possibilities are two of the ways artificial intelligence is transforming the healthcare industry. Alzheimer's disease, a neurodegenerative disease that worsens with time, is among the most difficult diseases to exactly identify in early stages. Patients thus delay in treatment and experience a decline in their quality of life. Research is still under progress, thus knowledge about how AI-driven prediction models could be combined with readily available healthcare services remains lacking. This work presents a special artificial intelligence-powered system consisting of DenseNet-based deep learning for Alzheimer's disease prediction with an online consultation platform. The proposed approach analyzes brain MRI data and classifies Alzheimer's stages with higher accuracy using a DenseNet architecture, well-known for its exceptional performance in the field of feature extraction. Comprising three thousand magnetic resonance imaging (MRI) images, the dataset has been preprocessed for noise reduction and tuned for models performance. At 98.5%, the DenseNet model exceeded other methods including VGGNet and ResNet in accuracy.

关键词： Accuracy Magnetic resonance imaging Redundancy Medical services Predictive models Brain modeling Real-time systems Alzheimer's disease Medical diagnostic imaging Overfitting

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Reinforcement Learning Driven Smart Charging Algorithms to Enhance Battery Lifespan and Grid Sustainability

Reinforcement Learning Driven Smart Charging Algorithms to E...

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2024 International Conference on Advances in Modern Age Technologies for Health and Engineering science, AMATHE 2024

作者： Sivakumar, S. Yadav, Mali Ram Bharathi, A. Akila, D. Dineshkumar, P. Banupriya, V. Vel Tech Rangarajan Dr.Sagunthala R And D Institute Of Science And Technology Department Of Electrical And Electronics Engineering Chennai India Lovely Professional University Department Of Physical Education Punjab India Vels Institute Of Science Technology And Advanced Studies Department Of Information Technology Pallavaram India Saveetha College Of Liberal Arts And Sciences Simats Department Of Computer Applications Chennai India Sona College Of Technology Department Of Information Technology Tamil Nadu Salem India M. Kumarasamy College Of Engineering Department Of Computer Science And Business Systems Karur India

ISBN: (纸本)9798350371567

This study investigates the use of RL-driven smart charging algorithms, particularly the Deep Deterministic Policy Gradients (DDPG) model, to maximise grid sustainability and battery lifetime. The current state of this field's study is lacking in real-time adaptation to different settings and in handling the ever-changing nature of charging profiles. By comparison, the suggested approach demonstrates a significant efficiency boost through the use of RL principles, which allow the model to learn and adjust its charging tactics. In specifically, the strategy contributes to a 10% improvement in total grid efficiency while cutting peak demand by 25%, minimising charge-discharge cycles by 20%, and reducing battery degradation rates by 15%. Not only do these measurements show that the suggested method works, but they also show how unique it is in offering a smart charging solution that is both sensitive and adaptive. This research fills in the gaps left by other studies and proposes an optimisation technique that is more dynamic and works in real-time. Its influence on the health of individual batteries and the sustainability of the grid as a whole is noteworthy. © 2024 IEEE.

关键词： Reinforcement learning

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Comparison of Edge computing Scheduling Algorithms 18

Comparison of Edge Computing Scheduling Algorithms

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18th Iberian Conference on Information Systems and Technologies, CISTI 2023

作者： Catali, Pedro S. Manacero, Aleardo Lobato, Renata S. Spolon, Roberta Cavenaghi, Marcos A. São Paulo State University - UNESP Computer Science and Statistics Department São José do Rio Preto Brazil São Paulo State University - UNESP Computing Department Bauru Brazil Humber Institute of Technology and Advanced Learning Toronto Canada

ISBN: (纸本)9789893347928

Through the advancement of the Internet of Things (IoT), the development of devices for task automation, data extraction, and communication between devices has become increasingly easy. But as a result, tens of zettabytes of data are being generated every year, causing excessive bandwidth consumption as well as slow response times for devices. One of the ways to solve the problem is with the use of Edge computing networks, such paradigm allows the transfer of the data processing to the edges of the network. Since the Edge is mostly composed of devices of varied and limited computational capacity, a good way to distribute the tasks that must be processed is needed. Therefore, efficient, and well tested, scheduling algorithms are a way to distribute tasks in such a way that the time required to perform them is minimized. This work explores the comparison o three distinct scheduling algorithms in Edge computing: the Modified Monte Carlo Tree Search;the Improved Binary Grey Wolf Optmizer and the Application-aware Scheduling Algorithm, analyzing their speed and efficiency as an evaluation metric, using the iSPD grid simulator. © 2023 ITMA.

关键词： Scheduling algorithms

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Generalizations across filler-gap dependencies in neural language models

arXiv

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arXiv 2024年

作者： Howitt, Katherine Nair, Sathvik Dods, Allison Hopkins, Robert Melvin Department of Linguistics University of Maryland United States Department of Computer Science University of Maryland United States University of Maryland Institute for Advanced Computer Studies United States

Humans develop their grammars by making structural generalizations from finite input. We ask how filler-gap dependencies, which share a structural generalization despite diverse surface forms, might arise from the input. We explicitly control the input to a neural language model (NLM) to uncover whether the model posits a shared representation for filler-gap dependencies. We show that while NLMs do have success differentiating grammatical from ungrammatical filler-gap dependencies, they rely on superficial properties of the input, rather than on a shared generalization. Our work highlights the need for specific linguistic inductive biases to model language acquisition. © 2024, CC BY.

关键词： Modeling languages

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Artificial Intelligence in Health Care computing and Assessments in Oman: an Effectual Study 2

Artificial Intelligence in Health Care Computing and Assessm...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Wilfred Blessing, N.R. Gunasekaran, Hemalatha Hariharan, B. Jabri, Jaber Saleh Salim Al Kavitha, S.J. Sutherlin Subitha, G. College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman SRM Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Kattankulathur India Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India Stella Mary's College of Engineering Department of Computer Science and Engineering Tamil Nadu Aruthenganvilai India

ISBN: (纸本)9798331505745

关键词： Nutrition

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Marine Predators Algorithm with Deep Learning-Based Leukemia Cancer Classification on Medical Images

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computer Modeling in Engineering & sciences 2024年第10期141卷 893-916页

作者： Sonali Das Saroja Kumar Rout Sujit Kumar Panda Pradyumna Kumar Mohapatra Abdulaziz S.Almazyad Muhammed Basheer Jasser Guojiang Xiong Ali Wagdy Mohamed Department of Computer Science&Engineering Gandhi Institute for TechnologyBPUTBhubaneswar752054India Department of Information Technology Vardhaman College of Engineering(Autonomous)Hyderabad501218TelanganaIndia Department of Electronic&Communication Engineering Vedang Institute of TechnologyBhubaneswar752010India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computing and Information Systems School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Research Centre for Human-Machine Collaboration(HUMAC) School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11931Jordan

In blood or bone marrow,leukemia is a form of cancer.A person with leukemia has an expansion of white blood cells(WBCs).It primarily affects children and rarely affects *** depends on the type of leukemia and the extent to which cancer has established throughout the *** leukemia in the initial stage is vital to providing timely patient *** image-analysis-related approaches grant safer,quicker,and less costly solutions while ignoring the difficulties of these invasive *** can be simple to generalize computer vision(CV)-based and image-processing techniques and eradicate human *** researchers have implemented computer-aided diagnosticmethods andmachine learning(ML)for laboratory image analysis,hopefully overcoming the limitations of late leukemia detection and determining its *** study establishes a Marine Predators Algorithm with Deep Learning Leukemia Cancer Classification(MPADL-LCC)algorithm onMedical *** projectedMPADL-LCC system uses a bilateral filtering(BF)technique to pre-process medical *** MPADL-LCC system uses Faster SqueezeNet withMarine Predators Algorithm(MPA)as a hyperparameter optimizer for feature ***,the denoising autoencoder(DAE)methodology can be executed to accurately detect and classify leukemia *** hyperparameter tuning process using MPA helps enhance leukemia cancer classification *** results are compared with other recent approaches concerning various measurements and the MPADL-LCC algorithm exhibits the best results over other recent approaches.

关键词： Leukemia cancer medical imaging image classification deep learning marine predators algorithm

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Preface to the 12th Workshop on Bibliometric-enhanced Information Retrieval at ECIR 2022 12

Preface to the 12th Workshop on Bibliometric-enhanced Inform...

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12th International Workshop on Bibliometric-Enhanced Information Retrieval, BIR 2022

作者： Frommholz, Ingo Mayr, Philipp Cabanac, Guillaume Verberne, Suzan School of Engineering Computing and Mathematical Sciences University of Wolverhampton United Kingdom GESIS Leibniz-Institute for the Social Sciences Cologne Germany University of Toulouse Computer Science Department IRIT UMR 5505 France Leiden Institute of Advanced Computer Science Leiden University Netherlands

This preface summarizes the 12th Workshop on Bibliometric-enhanced Information Retrieval (BIR). BIR 2022 was held as hybrid event at April 10th, 2022, co-located with the 44th European Conference on Information Retrie...

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Quantum computing Society of the Philippines: An NGO Perspective on Promoting Quantum Information, science, and Technology in the Philippines 5

Quantum Computing Society of the Philippines: An NGO Perspec...

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5th IEEE International Conference on Quantum computing and Engineering, QCE 2024

作者： Lopez, Dylan Josh Domingo Peramo, Elmer Ayson, Jabez Mendoza Masongsong, Angela Nicole S. Raquel, Lance Dominic C. Corpus, Bobby O. Department of Computer Technology De La Salle University Manila Philippines Advanced Science and Technology Institute Department of Science and Technology Quezon City Philippines Electronics Engineering Department Don Honorio Ventura State University Pampanga Philippines STEM Department De La Salle University Integrated School Manila Philippines Department of Physics University of San Carlos Cebu City Philippines Quantum Computing Society of the Philippines Manila Philippines

ISBN: (纸本)9798331541378

The development of Quantum Information science and Technology (QIST) as an emerging technology among developed and developing countries has caught the attention of governments, industries, and educational institutions. However, not all countries have equal starting points in promoting QIST amongst different sectors. Several players in various countries promote QIST as a discipline and education. Although significant developments in quantum technology are focused in Western countries, great innovation can also be seen in Eastern countries, specifically Asia. The Philippines is one of several developing countries in Asia that is in the process of adopting QIST in multiple sectors. In this paper, the Quantum computing Society of the Philippines (QCSP) is introduced as one of the arms of the country for promoting QIST to the public. Further in this paper, the initial impact of QIST promotion on various communities across the Philippines through multiple activities and educational efforts is explained. Several critical success stories for different audiences were described, wherein initial acceptance of QIST was evident through hackathons and lecture series. Events held show improvement in interest and proficiency in the foundations of QIST. © 2024 IEEE.

关键词： Quantum computers

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Anomaly Detection for Log using AutoM and Auto-Sklearn Classification Algorithm 15

Anomaly Detection for Log using AutoM and Auto-Sklearn Class...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Karthik, G.M. Gupta, Ankur Gupta, Sh Rajesh Jha, Amaresh Sivasangari, A. Mishra, B.P. School of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India Department of CSE RIMT University Godindgarh Mandi India Department of Management Sanskriti University Uttar Pradesh Mathura India School of Modern Media University of Petroleum and Energy Studies Dehradun India School of Computing Sathyabama Institute of Science and Technology Chennai India Department of Electronics JSS Academy of Technical Education NoidaSector-62 India

ISBN: (纸本)9798350370249

— In the realm of application development, logs represent a pivotal resource for the identification and comprehension of application performance, as well as the detection of anomalies, all of which are essential for ensuring the overall efficiency of an application. The proliferation of users has resulted in a substantial increase in the volume of logs available for analysis in the field of log analysis research. Typically, these logs exhibit an unstructured format, largely dictated by the developer's design choices. Consequently, there is a need for log parsing techniques to in still structure into these logs, enabling the application of machine learning algorithms for the prediction of anomalies. In the present research, the primary objective is the prediction of anomalies within Hadoop Distributed File System (HDFS) logs utilizing an AutoML (Automated Machine Learning) tool known as Auto-Sklearn. The research outcomes illustrate the effectiveness of the proposed model, which attains an impressive accuracy rate of 99% when applied to the dataset. This study underscores the critical role of logs in application development, highlights the importance of log structure transformation, and demonstrates the substantial predictive capabilities of the Auto-Sklearn model in the context of HDFS log analysis. ©2024 IEEE.

关键词： Anomaly detection

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