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6th International Conference on Communication, Devices and Networking, ICCDN 2022

作者： Awotunde, Joseph Bamidele Sur, Nath Samarendra Imoize, Agbotiname Lucky Misra, Sanjay Gaber, Tarek Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Kwara State Ilorin240003 Nigeria Department of Electronics and Communication Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University East Sikkim Rangpo Majitar737136 India Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Lagos100213 Nigeria Department of Electrical Engineering and Information Technology Institute of Digital Communication Ruhr University Bochum44801 Germany Department of Applied Data Science Institute of Energy Technology Halden Norway Faculty of Computers and Informatics Suez Canal University Ismailia41522 Egypt School of Science Engineering and Environment University of Salford ManchesterM5 4WT United Kingdom

ISBN: (纸本)9789819919826

Alzheimer's disease (AD) is a long-lasting, degenerative brain illness for which there is now no successful treatment. However, there are medications that can delay its growth and stop its tracks. Therefore, stopping and regulating the progression of AD depends greatly on its early diagnosis. computer-aided diagnosis (CAD) in health care has become more and more dependent on deep learning technologies. This has help in early diagnosis and detection of various disease in health care. New techniques for the early diagnosis of neurocognitive abnormalities are now possible thanks to advancements in medical imaging and computational capacity in an effort to stop or slow down cognitive aging. Therefore, this study proposes a residual network (ResNet) type 50 for early diagnosis of AD. Convolutional neural networks (CNNs) are first used in the study for features extraction and then make the ultimate diagnosis using the ResNet-50. To create a better architecture, the problem of AD diagnosis will be addressed by utilizing and incorporating ResNet-50, which improves the current categorization problem's accuracy to its maximum. With a 98.8% accuracy rate, the proposed model using ResNet-50 produces encouraging findings for AD prediction. The study reveals that the model can effectively circumvent CNNs for the diagnostic issue with AD. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computer aided diagnosis

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An Ensemble Models for the Prediction of Sickle Cell Disease from Erythrocytes Smears

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EAI Endorsed Transactions on Pervasive Health and Technology 2023年 9卷

作者： Ayoade, Oluwafisayo Babatope Oladele, Tinuke Omolewa Imoize, Agbotiname Lucky Adeloye, Jerome Adetoye Awotunde, Joseph Bamidele Olorunyomi, Segun Omotayo Faboya, Olusola Theophilus Idowu, Ayorinde Oladele Department of Computing and Information Science School of Pure and Applied Sciences College of Science Bamidele Olumilua University of Education Science & Technology Ekiti State Ikere-Ekiti Nigeria Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Kwara State Nigeria Department of Electrical and Electronics Faculty of Engineering University of Lagos Lagos State Akoka 100213 Nigeria Ekiti State Data Center Office Old Governor’s Office Ekiti State Ado-Ekiti Nigeria

INTRODUCTION: The human blood as a collection of tissues containing Red Blood Cells (RBCs), circular in shape and acting as an oxygen carrier, are frequently deformed by multiple blood diseases inherited from parents. These hereditary diseases of blood involve abnormal haemoglobin (Hb) or anemia which are major public health issues. Sickle Cell Disease (SCD) is one of the common non-communicable disease and genetic disorder due to changes in hematological conditions of the RBCs which often causes the inheritance of mutant Hb genes by the patient.. OBJECTIVES: The process of manual valuation, predictions and diagnosis of SCD necessitate for a passionate time spending and if not done properly can lead to wrong predictions and diagnosis. Machine Learning (ML), a branch of AI which emphases on building systems that improve performance based on the data they consume is appropriate. Despite previous research efforts in predicting with single ML algorithm, the existing systems still suffer from high false and wrong predictions. METHODS: Thus, this paper aimed at performing comparative analysis of individual ML algorithms and their ensemble models for effective predictions of SCD (elongated shapes) in erythrocytes blood cells. Three ML algorithms were selected, and ensemble models were developed to perform the predictions and metrics were used to evaluate the performance of the model using accuracy, sensitivity, Receiver Operating Characteristics-Area under Curve (ROC-AUC) and F1 score metrics. The results were compared with existing literature for model(s) with the best prediction metrics performance.. RESULTS: The analysis was carried out using Python programming language. Individual ML algorithms reveals that their accuracies show MLR=87%, XGBoost=90%, and RF=93%, while hybridized RF-MLR=92% and RF-XGBoost=99%. The accuracy of RF-XGBoost of 99% outperformed other individual ML algorithms and Hybrid models. CONCLUSION: Thus, the study concluded that involving hybridized M

关键词： Forecasting

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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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Temporary Symmetric Key Based Message Verification Protocol for Smart Energy Networks 7

Temporary Symmetric Key Based Message Verification Protocol ...

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7th IEEE International Energy Conference, ENERGYCON 2022

作者： Nyangaresi, Vincent Omollo Abduljabbar, Zaid Ameen Ma, Junchao Sibahee, Mustafa A. Al Tom Mboya University College Faculty of Biological & Physical Sciences Homabay40300 Kenya University of Basrah Department of Computer Science College of Education for Pure Sciences Basrah61004 Iraq Shenzhen Technology University College of Big Data and Internet Shenzhen518118 China

ISBN: (纸本)9781665479820

Smart grid networks require sufficient protection so as to safeguard the sensitive data that is exchanged among the smart meter, gateway node, data aggregators and utility service providers. To this end, numerous schemes have been presented based on a myriad of techniques such as public key cryptography, bilinear pairing operations, device identities and elliptic curve cryptography among others. However, many security loopholes still lurk in these protocols. In addition, performance is another serious issue that needs to be solved especially for smart gas meters. In this paper, a symmetric key based message verification protocol is presented. Extensive security evaluation is executed to show that the proposed protocol offers mutual authentication, key agreement and untraceability. It is also resilient against man-in-the-middle, verifier leakage, message replay, impersonation and known session key attacks. In terms of performance, the proposed protocol has the least communication costs and lower computation overheads compared with its peers. © 2022 IEEE.

关键词： Authentication

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A Density Peak Clustering Method based on Improved Siphon Effect 12

A Density Peak Clustering Method based on Improved Siphon Ef...

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2022 12th International Workshop on computer science and Engineering, WCSE 2022

作者： Liu, Jiyuan Xia, Xiaona School of Co1mputer Science Qufu Normal University Shandong Rizhao276826 China Faculty of Education Qufu Normal University Shandong Qufu273165 China Chinese Academy of Education Big Data Qufu Normal University Shandong Qufu273165 China School of Computer Science Qufu Normal University Shandong Rizhao276826 China

ISBN: (纸本)9789811839597

To solve the problem that the density peak clustering algorithm needs to manually select the clustering center, a density peak clustering algorithm based on improved siphon effect (IDPC) is designed. IDPC completes the clustering of research objects with various distribution shapes by removing the maximum cluster center weight in the descending sequence, calculating and iterating the potential difference, and selecting the relative equilibrium point. A comprehensive comparative experiment is carried out on UCI data set. The experimental results show that IDPC can accurately and automatically select the cluster center and cluster number, improve the performance indexes such as Fowles mallows score, Rand index and adjusted mutual information, and can adaptively process data sets with low dimension and various distribution shapes. © 2022 WCSE. All Rights Reserved.

关键词： Siphons

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Node Localization Method in Wireless Sensor Networks Using Combined Crow Search and Weighted Centroid Method

SSRN

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SSRN 2023年

作者： Sankaranarayanan, Suresh Rajaram, V. Srividhya, S. Lorenz, Pascal Rodrigues, Joel J.P.C. Department of Computer Science College of Computer Science and Information Technology King Faisal University Al Hofuf Saudi Arabia Department of Networking and Communications School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur603203 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur603203 India University of Haute Alsace Colmar France Qingdao266555 China Instituto de Telecomunicações Covilhã6201-001 Portugal

Localization of nodes is critical for acquiring access to diverse nodes that would provide services in remote places. Single-anchor localization techniques suffer from a co-linearity problem, resulting in poor performance. The reliable multiple anchor selection method can be computationally intensive and requires a lot of processing power and time to identify suitable anchor nodes. There has been work where researchers have employed nature-inspired algorithms over conventional optimization method towards localization of unknown nodes in WSN. These methods have challenges like longer time and lot of processing power to solve. In addition, these methods are sensitive to parameter selection impacting the ***, we in this research work have employed the nature-inspired crow search algorithm for selecting the suitable anchors from the population thereby reducing errors in identifying the location of unknown nodes. In addition, the weighted centroid method is proposed for the identification of the exact location of an unknown node. This makes the weighted centroid method and the crow search algorithm a more trustworthy and efficient way for node localization in wireless sensor networks. The Crow Search-based Weighted Centroid Localization (CS-WCL) has outperformed WCL and DV-Hop with the use of the best anchor node in terms of Localization Error, Total data packets, and Energy Consumption. This resulted in CSWCL outperforming other nature-inspired algorithms for node localization. © 2023, The Authors. All rights reserved.

关键词： Energy utilization

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Asynchronous Fractional Multi-Agent Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing

arXiv

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arXiv 2024年

作者： Jin, Lyudong Tang, Ming Pan, Jiayu Zhang, Meng Wang, Hao Zhejiang University University of Illinois at Urbana–Champaign Institute Zhejiang University Haining314400 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518000 China School of Software Technology Zhejiang University Ningbo315048 China Department of Data Science and AI Faculty of Information Technology Monash University MelbourneVIC3800 Australia

In the realm of emerging real-time networked applications such as industrial cyber-physical systems (CPS), the Age of Information (AoI) has emerged as a pivotal metric for evaluating the timeliness. To meet the high computational demands, such as those in smart manufacturing within CPS, mobile edge computing (MEC) presents a promising solution for optimizing computing and reducing AoI. In this work, we study the timeliness of computational-intensive updates and explore jointly optimizing the task updating (when to generate a task) and offloading (where to process a task) policies to minimize AoI. Specifically, we consider edge load dynamics and formulate a task scheduling problem to minimize the expected time-average AoI. Solving this problem is challenging due to the fractional objective introduced by AoI and asynchronous decision-making of the semi-Markov game (SMG). To this end, we propose a fractional reinforcement learning (RL) framework. We first introduce a fractional single-agent RL framework and further propose a fractional multi-agent RL framework, including convergence analysis for both scenarios. To tackle the challenge of asynchronous decision-making in SMG, we further design an asynchronous model-free fractional multi-agent RL algorithm, where each mobile device can determine the task updating and offloading decisions without knowing the real-time system dynamics and decisions of other devices. Experimental results show that when compared with the best baseline algorithm, our proposed algorithms reduce the average AoI by up to 52.6%. Copyright © 2024, The Authors. All rights reserved.

关键词： Markov processes

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Attributed Multi-order Graph Convolutional Network for Heterogeneous Graphs

arXiv

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arXiv 2023年

作者： Chen, Zhaoliang Wu, Zhihao Zhong, Luying Plant, Claudia Wang, Shiping Guo, Wenzhong College of Computer and Data Science Fuzhou University Fuzhou350116 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China Faculty of Computer Science University of Vienna Vienna1090 Austria ds:UniVie Vienna1090 Austria

Heterogeneous graph neural networks aim to discover discriminative node embeddings and relations from multi-relational networks. One challenge of heterogeneous graph learning is the design of learnable meta-paths, which significantly influences the quality of learned embeddings. Thus, in this paper, we propose an Attributed Multi-Order Graph Convolutional Network (AMOGCN), which automatically studies meta-paths containing multi-hop neighbors from an adaptive aggregation of multi-order adjacency matrices. The proposed model first builds different orders of adjacency matrices from manually designed node connections. After that, an intact multi-order adjacency matrix is attached from the automatic fusion of various orders of adjacency matrices. This process is supervised by the node semantic information, which is extracted from the node homophily evaluated by attributes. Eventually, we utilize a one-layer simplifying graph convolutional network with the learned multi-order adjacency matrix, which is equivalent to the cross-hop node information propagation with multi-layer graph neural networks. Substantial experiments reveal that AMOGCN gains superior semi-supervised classification performance compared with state-of-the-art competitors. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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Characterizing Zagreb Index Bounds in Trees with Specified Metric Dimension

arXiv

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arXiv 2024年

作者： Ali, Waqar Husin, Mohamad Nazri Bin Nadeem, Muhammad Faisal Special Interest Group on Modeling and Data Analytics Faculty of Computer Science and Mathematics Universiti Malaysia Terengganu Terengganu Kuala Nerus21030 Malaysia Department of Mathematics COMSATS University Islamabad Lahore Campus Lahore54000 Pakistan

Consider a simple graph G = (V, E), where V are the vertices and E are the edges. The first Zagreb index, M1(G) = Pv∈V ψG(v)2. The second Zagreb index, M2(G) = Puv∈E ψG(u)ψG(v). The metric dimension of a graph refers to the smallest subset of vertices in a resolving set such that the distances from these vertices to all others in the graph uniquely identify each vertex. In this paper, we characterize bounds for the Zagreb indices of trees, based on the order of the tree and its metric dimension. Furthermore, we identify the trees that achieve these extremal bounds, offering valuable insights into how the metric dimension influences the behavior of the Zagreb indices in tree structures. © 2024, CC BY.

关键词： Trees (mathematics)

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What should a neuron aim for? Designing local objective functions based on information theory

arXiv

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arXiv 2024年

作者： Schneider, Andreas C. Neuhaus, Valentin Ehrlich, David A. Makkeh, Abdullah Ecker, Alexander S. Priesemann, Viola Wibral, Michael Faculty of Physics Institute for the Dynamics of Complex Systems University of Göttingen Germany Max Planck Institute for Dynamics and Self-Organization Germany Campus Institute for Dynamics of Biological Networks University of Göttingen Germany Institute of Computer Science Campus Institute Data Science University of Göttingen Germany

In modern deep neural networks, the learning dynamics of the individual neurons is often obscure, as the networks are trained via global optimization. Conversely, biological systems build on self-organized, local learning, achieving robustness and efficiency with limited global information. We here show how self-organization between individual artificial neurons can be achieved by designing abstract bio-inspired local learning goals. These goals are parameterized using a recent extension of information theory, Partial Information Decomposition (PID), which decomposes the information that a set of information sources holds about an outcome into unique, redundant and synergistic contributions. Our framework enables neurons to locally shape the integration of information from various input classes, i.e. feedforward, feedback, and lateral, by selecting which of the three inputs should contribute uniquely, redundantly or synergistically to the output. This selection is expressed as a weighted sum of PID terms, which, for a given problem, can be directly derived from intuitive reasoning or via numerical optimization, offering a window into understanding task-relevant local information processing. Achieving neuron-level interpretability while enabling strong performance using local learning, our work advances a principled information-theoretic foundation for local learning strategies. Copyright © 2024, The Authors. All rights reserved.

关键词： Information theory

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