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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Aghalya, S. Sarasu, R. Mishra, Nilamadhab Arunachalam, G. Gobinath, D. Naganathan, Suresh Babu Thandullu St. Joseph's College of Engineering Omr Department of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Spatial Informatics Tamil Nadu Chennai India Vit Bhopal University School of Computing Science and Engineering Madhya Pradesh India Department of Electronics and Communication Engineering Tamil Nadu Namakkal India Rajalakshmi Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

The study presents an advanced neural network model for real-time remote evaluation and treatment of rheumatic diseases using sensor fusion. The model was developed for use in this investigation. The suggested system uses a wide variety of sensing modalities, such as image, motion, and biometric sensors. This is accomplished via artificial intelligence (AI) and the data gleaned from sensors. The neural network, which was trained on a dataset that was both varied and labeled, performs autonomous analyses of input data in order to accurately predict and categorize rheumatic diseases. The integration of the model into remote medical treatment is made easier by the fact that it is run on a Raspberry Pi inside a tele-rheumatology framework. This unique strategy not only improves diagnostic accuracy but also expands rheumatology services to geographically inaccessible or disadvantaged groups. As a result, the landscape of rheumatic healthcare delivery is completely transformed as a result of this new approach. For the purpose of ensuring the ethical use of this revolutionary technology, the procedures for data security and ethical concerns have been given priority. © 2024 IEEE.

关键词： Neural network models

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Application of Neural Network in the Prediction Models of Machine Learning Based Design

Application of Neural Network in the Prediction Models of Ma...

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2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2022

作者： Gehlot, Anita Ansari, Bushra Khatoon Arora, Deepika Anandaram, Harishchander Singh, Bhagwant Arias-Gonzáles, José Luis Uttaranchal University Uttaranchal Institute of Technology India Institute of Engineering and Technology AKTU Department of Civil Engineering Lucknow India Lloyd Institute of Engineering & Technology Computer Science & Engineering Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Tamil Nadu Coimbatore India SS Informatics Cluster School of Computer Science Uttarakhand Dehradun248007 India Pontificia Universidad Católica Del Peru Peru

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

This research examines way to pass, among the most dynamic and quickly-evolving technology fields in the telecommunication sector. Data may be sent between two locations wirelessly without the usage of digital downloads like wires and cables. Business telecom companies have advanced as a consequence and joined the big data age. Among the most potential machine learning methods for interpreting this deluge of information is computer science, which is employed in a range of business and scholarly research situations. This paper gives a broad overview of technology advancements and big data analytics, as well as possible uses for them in succeeding cellular networks. Apply sophisticated analytics to determine what mobile users' demands are, and then utilise that information to raise the effectiveness of "social wireless channels of communication."The huge, integrated, information machine educational approach it offers, which consists of extracting the features, simulation models, and forecasting enhancements, is particularly noteworthy. The key benefit of the proposed methodology is the logic of robust mathematical methods, issue conceptualization within the context of cellular networks, and reliance on a vast amount of data that represents both the bandwidth as well as other stringent user needs. and the ability to create a procedure. In a communication network, data is normally sent from the transmitter to the receiver across a distance. Using communication systems, the transmitter may be positioned somewhere within a few metres. We will examine existing strategies, their advantages and disadvantages, and potential developments in underwater sensors right now. We had discovered a fresh method of investigation. © 2022 IEEE.

关键词： Information management

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Estimating Uncertainty in Deep Learning Methods and Applications

Estimating Uncertainty in Deep Learning Methods and Applicat...

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International Conference on computer and Applications (ICCA)

作者： Abdulrahman Alqarafi Hina Batool Tahir Abbas Jamshaid Iqbal Janjua Sadaqat Ali Ramay Munir Ahmed College of Computer Science and Engineering Taibah University Medina Saudi Arabia Department of Computer Science TIMES Institute Multan Pakistan Al-Khawarizmi Institute of Computer Science University of Engineering & Technology (UET) Lahore Pakistan College of Informatics Korea University Seoul Republic of Korea

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

Uncertainty estimation in deep learning has emerged as a crucial area of research due to its significance in enhancing model reliability and decision-making in critical applications. This article explores various methods and applications of uncertainty estimation in deep learning, aiming to provide insights into its importance, methods, and potential impact. Through a comprehensive literature review and analysis, the study identifies key findings regarding the effectiveness, limitations, and ethical considerations associated with uncertainty estimation techniques. The results reveal the diverse range of methodologies employed, including Bayesian approaches, ensemble methods, and Monte Carlo sampling, each with its strengths and drawbacks. Furthermore, the article discusses the implications of uncertainty estimation in deep learning for fields such as healthcare, autonomous systems, and safety-critical domains. Overall, this study underscores the significance of uncertainty estimation in deep learning and provides valuable insights for researchers and practitioners in the field.

关键词： Deep learning Ethics Uncertainty Monte Carlo methods Decision making Estimation Medical services Reliability Artificial intelligence Systematic literature review

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Chromosome Feature Extraction and Ideogram-Powered Chromosome Categorization

Chromosome Feature Extraction and Ideogram-Powered Chromosom...

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作者： Pysarchuk, Oleksii Mironov, Yurii Faculty of Informatics and Computer Science National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" Kiev03056 Ukraine Faculty of Cybersecurity Computer and Software Engineering National Aviation University Kiev03058 Ukraine

Chromosomal diseases diagnostics is based on identifying chromosomes and detecting abnormalities in them. It is a sophisticated procedure that is performed manually or partially automated, therefore is prone to human error. Automation of such diagnostics is a complex multistage task that presents many challenges. One of such challenges is chromosome feature extraction and classification. Categorizing chromosomes is necessary for determining a diagnosis. This problem has been previously covered by research papers, but there is no complete solution by now. Moreover, the vast majority of proposals rely on Machine Learning (ML) and Neural Networks (NN). Usage of ML-powered solutions may pose a problem due to difficulty of dataset collection - medical data may be diverse, heterogeneous and hard to collect because of its sensitive nature. Given paper presents a proposal of chromosome feature extraction and categorization without NN. It uses computer Vision (CV) techniques for chromosome feature extraction: medial axis is extracted from a chromosome to identify its direction, and than chromosome bands (colored segments that make up distinct chromosome pattern) are identified along the axis. Having transformed chromosome image into a discrete piece of data, it is passed to a proposed multiple-criteria decision-making algorithm. This algorithm is designed to categorize chromosomes without learning dataset, instead making use of ideogram (schematic reference chromosome) data. The algorithm is focused on processing a chromosome as a set of segments, where each segment can be recognized by a special predicate. A predefined combination of such predicates should allow recognizing chromosome type. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Feature extraction

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TSO-DSO Interaction: Privacy-Preserving Optimal Power Flow with Distributed Generators Using a Machine Learning-Based Approach

TSO-DSO Interaction: Privacy-Preserving Optimal Power Flow w...

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IEEE PES Asia-Pacific Power and Energy engineering Conference (APPEEC)

作者： Burak Dindar Can Berk Saner Hüseyin Kemal Çakmak Veit Hagenmeyer Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Germany Department of Electrical and Computer Engineering National University of Singapore Singapore

ISBN: (数字)9798350318098

ISBN: (纸本)9798350318104

The integration of distributed generators (DGs) into the electrical grid has introduced new dimensions in power system management: Enhancing efficiency and operational flexibility of the grid requires taking into account the DGs located at the distribution level in dispatch decisions, while also considering the operational constraints of both distribution and transmission levels. However, the centralization of cooperation between transmission system operators (TSOs) and distribution system operators (DSOs) is troublesome due to privacy concerns of both parties. In present paper, we introduce a Machine Learning-Based Privacy-Preserving Optimal Power Flow (MLB-PP-OPF) framework. This method enables the seamless inclusion of DGs at the distribution level in optimal power flow calculations, all while adhering to the operational limits of both systems and maintaining data privacy. The proposed approach leverages three machine learning models to map the distribution system variables to active and reactive power flows at the point of common coupling, and to determine the feasible operating region of the distribution system in a privacy-preserving manner. Comparative case studies, performed on the coupled IEEE 30-bus transmission and 33-bus distribution test systems, demonstrate that the results achieved using MLB-PP-OPF align almost perfectly with those obtained through classical OPF, while maintaining data privacy.

关键词： Couplings Data privacy Reactive power Privacy Machine learning Generators Data models

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Cardiac Arrhythmia Disease Classifier Model Based on a Fuzzy Fusion Approach

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computers, Materials & Continua 2023年第5期75卷 4485-4499页

作者： Fatma Taher Hamoud Alshammari Lobna Osman Mohamed Elhoseny Abdulaziz Shehab Eman Elayat College of Technological Innovation Zayed UniversityDubaiUAE Department of Information Systems College of Computer and Information SciencesJouf UniversitySakakaSaudi Arabia Department of Electronics and Communications Engineering Delta Higher Institute for Engineering&TechnologyMansouraEgypt College of Computing and Informatics University of SharjahSharjahUnited Arab Emirates Department of Information Systems Mansoura UniversityMansoura35516Egypt Department of Teacher Preparation Faculty of Specific EducationMansoura UniversityMansouraEgypt

Cardiac diseases are one of the greatest global health *** to the high annual mortality rates,cardiac diseases have attracted the attention of numerous researchers in recent *** article proposes a hybrid fuzzy fusion classification model for cardiac arrhythmia *** fusion model is utilized to optimally select the highest-ranked features generated by a variety of well-known feature-selection *** ensemble of classifiers is then applied to the fusion’s *** proposed model classifies the arrhythmia dataset from the University of California,Irvine into normal/abnormal classes as well as 16 classes of ***,at the preprocessing steps,for the miss-valued attributes,we used the average value in the linear attributes group by the same class and the most frequent value for nominal ***,in order to ensure the model optimality,we eliminated all attributes which have zero or constant values that might bias the results of utilized *** preprocessing step led to 161 out of 279 attributes(features).Thereafter,a fuzzy-based feature-selection fusion method is applied to fuse high-ranked features obtained from different heuristic feature-selection *** short,our study comprises three main blocks:(1)sensing data and preprocessing;(2)feature queuing,selection,and extraction;and(3)the predictive *** proposed method improves classification performance in terms of accuracy,F1measure,recall,and precision when compared to state-of-the-art *** achieves 98.5%accuracy for binary class mode and 98.9%accuracy for categorized class mode.

关键词： Cardiac arrhythmia preprocessing missing values classification model fusion

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Bottleneck-Minimal Indexing for Generative Document Retrieval

arXiv

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arXiv 2024年

作者： Du, Xin Xiu, Lixin Tanaka-Ishii, Kumiko Waseda Research Institute for Science and Engineering Waseda University Japan Department of Mathematical Informatics The University of Tokyo Japan Department of Computer Science and Engineering Waseda University Japan

We apply an information-theoretic perspective to reconsider generative document retrieval (GDR), in which a document x ∈ X is indexed by t ∈ T , and a neural autoregressive model is trained to map queries Q to T . GDR can be considered to involve information transmission from documents X to queries Q, with the requirement to transmit more bits via the indexes T . By applying Shannon’s rate-distortion theory, the optimality of indexing can be analyzed in terms of the mutual information, and the design of the indexes T can then be regarded as a bottleneck in GDR. After reformulating GDR from this perspective, we empirically quantify the bottleneck underlying GDR. Finally, using the NQ320K and MARCO datasets, we evaluate our proposed bottleneck-minimal indexing method in comparison with various previous indexing methods, and we show that it outperforms those methods. © 2024, CC BY.

关键词： Signal distortion

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Visualization of Heart Rate Abnormality and Its Application for Detection of Possible False-positive Neonatal Sepsis

Visualization of Heart Rate Abnormality and Its Application ...

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2023 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2023

作者： Lu, Yen-Tzu Chang, Wen-Yen Lan, Kun-Chan Lin, Yung-Chieh National Cheng Kung University Department of Institute of Medical Informatics Tainan Taiwan National Cheng Kung University Department of Computer Science and Information Engineering Tainan Taiwan National Cheng Kung University Hospital Department of Pediatrics Tainan Taiwan

ISBN: (纸本)9781665493840

Neonatal sepsis is a type of bloodstream infection that occurs within 28 days of birth, mostly in low birth weight or premature newborns. According to a 2020 report by the World Health organization, an estimated number of 1.3 million to 3.9 million newborns suffer from sepsis each year, and 4.0 million to 7.0 million die from sepsis-related complications. A newborn with an infection will show signs or changes in their appearance. Symptoms like temporary acceleration or slowing of the heart rate, and low HRV, are commonly associated with neonates who have sepsis Accurate detection of heart rate anomalies is essential for diagnosing sepsis. In this study, heart rate abnormality encompasses low HRV, transient acceleration or slowing of the heart rate, and other unusual events. The current methods of clinical observation of abnormalities are not necessarily intuitive or informative. For example, differentiation between proven sepsis and culture-negative sepsis is difficult by solely observing the average heart rate on a bedside monitor. To this end, we proposed a new visualization tool to help clinicians in the detection of suspected false-positive neonatal sepsis. Our visualization tool provides information such as the variability and distribution of the heart rates, which can be used to detect the abnormal events. These functions will help clinicians to detect possible false-positive sepsis and reduce unnecessary antibiotic treatment. © 2023 IEEE.

关键词： Visualization

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Editorial:Special Topic on Federated Learning for IoT and Edge Computing

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ZTE Communications 2022年第3期20卷 1-2页

作者： PAN Yi CUI Laizhong CAI Zhipeng LI Wei Faculty of Computer Science and Control Engineering at Shenzhen Institute of Advanced Technology Chinese Academy of SciencesChina the Academy of the United Nations Sciences and Technology Organization Ukrainian Academy of Engineering Sciences American Institute for Medical and Biological Engineering Institute of Engineering Technology the College of Computer Science and Software Engineering at Shenzhen University China Department of Computer Science and the College of Business at Georgia State University USA the Center for Distributed and High Performance Computing School of Computer ScienceThe University of Sydney the Australia-China Joint Research Center on Energy Informatics and Demand Response Technologies

Recent years have witnessed the proliferation of Internet of Things(IoT),in which billions of devices are connected to the Internet,generating an overwhelming amount of *** is challenging and infeasible to transfer an... 详细信息

关键词： cloud generating data.

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An efficient and autonomous dynamic resource allocation in cloud computing with optimized task scheduling

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Procedia computer Science 2024年 246卷 3654-3663页

作者： Safa Rabaaoui Héla Hachicha Ezzeddine Zagrouba University of Tunis El Manar Higher Institute of Computer Science Laboratory of Informatics Modeling and Information and Knowledge Processing (LIMTIC) 2 Rue Abou Rayhane Bayrouni Ariana 2080 Tunisia College of Computer Science and Engineering - University of Jeddah

Resource allocation, scheduling, and provisioning are indeed critical and complex issues in cloud computing environments, as they directly impact both the user experience and the profitability of cloud service providers. Also, the excessive use of resources and the fact that the required resources may exceed those available have raised additional challenges. The effective and optimal allocation of resources in this dynamic cloud environment poses a significant challenge due to the substantial increase in cloud utilization. Then, an energy-efficient task scheduling algorithm is required to improve the efficiency of the resource allocation process. This paper proposed an efficient and autonomous dynamic resource allocation in cloud computing with optimized task scheduling. We aim to minimize the cost of virtual machines and the makespan. Furthermore, its impact on the best response time and energy consumption has been studied. The simulation shows that our method gave better results than the former ones.

关键词： Cloud Computing Dynamic Resource allocation Multi-Agent System Energy consumption Task scheduling Cost Makespan

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