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15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Polydoropoulos, Andreas Nestoras, Fotios Groumpos, Peter P. University of Patras Department of Electrical and Computer Engineering Patras26500 Greece

ISBN: (纸本)9798350368833

The public service sector plays a crucial role in delivering efficient and high-quality services to citizens. In order to meet the increasing demands and expectations, it is imperative to explore innovative approaches for improving service efficiency and quality. This paper presents a novel framework that combines State Fuzzy Cognitive Maps (FCMs) and Artificial Intelligence (AI) techniques to optimize service delivery in the public service sector. Drawing on inputs such as the number of applications per employee, the regulatory environment, employee competence and the budget allocation, the proposed framework captures the key factors influencing service efficiency and quality. By employing State FCMs, the complex relationships and interdependencies among these factors are modeled and analyzed, providing a comprehensive understanding of the system's behavior. The integration of AI techniques further empowers the framework by enabling data-driven decision-making and optimization processes. Through simulation and optimization algorithms, the system's performance is evaluated and strategies are devised to enhance service efficiency and quality. The outputs of the framework are focused on improved and higher levels of service quality, addressing factors such as budget allocation, process optimization, service responsiveness and accuracy. The paper discusses real-world case studies and practical implementations of the proposed framework in the public service sector. It highlights the benefits of this approach, including enhanced employee competence, streamlined operations, personalized service delivery, and increased citizen satisfaction. Moreover, it emphasizes the importance of continuous evaluation and refinement to ensure the sustained improvement of service efficiency and quality. © 2024 IEEE.

关键词： Budget control

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Curvature-Based Piecewise Linear Approximation Method of GELU Activation Function in Neural Networks 2

Curvature-Based Piecewise Linear Approximation Method of GEL...

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2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2024

作者： Mohaidat, Tamador Khan, Md Rahat Kader Khalil, Kasem University of Mississippi Department of Electrical and Computer Engineering MS United States

ISBN: (纸本)9798331529529

Artificial neural networks (ANNs) rely significantly on activation functions for optimal performance. Traditional activation functions such as ReLU and Sigmoid are commonly used. However, they have computational and hardware efficiency constraints. This work presents a unique piecewise linear (PWL) approximation approach for the Gaussian Error Linear Unit (GELU) activation function that uses curvature values. We split the GELU function into pieces based on curvature values and then used the minimal squares approach to figure out each segment's PWL approximation function equations. To determine the most appropriate function expression with a specified number of segments, we employ an optimization technique that minimizes both maximum and average absolute errors. This demonstrates that the PWL approximation of the GELU function accurately converges to the original function as the piecewise points' number increases. The proposed method is intended to improve hardware efficiency while maintaining high accuracy. EExperimental findings indicate that the PWL approximation of GELU obtains {9 7. 9 6%} accuracy on the MNIST dataset. This method offers a promising alternative for improving the performance and efficiency of ANN activation algorithms in hardware applications. © 2024 IEEE.

关键词： Artificial Neural Networks curvature value GELU piecewise linear approximation method

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Application of the Deep Convolutional Neural Network for the Classification of Auto Immune Diseases

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computers, Materials & Continua 2023年第10期77卷 647-664页

作者： Fayaz Muhammad Jahangir Khan Asad Ullah Fasee Ullah Razaullah Khan Inayat Khan Mohammed ElAffendi Gauhar Ali Department of Computer Science and Information Technology Sarhad University of Science and Information TechnologyPeshawar25000Pakistan Department of Computer Science University of Engineering and TechnologyMardan23200Pakistan EIAS Data Science and Blockchain Lab College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia

IIF(Indirect Immune Florescence)has gained much attention recently due to its importance in medical *** primary purpose of this work is to highlight a step-by-step methodology for detecting autoimmune *** use of IIF for detecting autoimmune diseases is widespread in different medical *** 80 different types of autoimmune diseases have existed in various body *** IIF has been used for image classification in both ways,manually and by using the computer-Aided Detection(CAD)*** data scientists conducted various research works using an automatic CAD system with low *** diseases in the human body can be detected with the help of Transfer Learning(TL),an advanced Convolutional Neural Network(CNN)*** baseline paper applied the manual classification to the MIVIA dataset of Human Epithelial cells(HEP)type II cells and the Sub Class Discriminant(SDA)analysis technique used to detect autoimmune *** technique yielded an accuracy of up to 90.03%,which was not reliable for detecting autoimmune disease in the mitotic cells of the *** the current research,the work has been performed on the MIVIA data set of HEP type II cells by using four well-known models of *** augmentation and normalization have been applied to the dataset to overcome the problem of overfitting and are also used to improve the performance of TL *** models are named Inception V3,Dens Net 121,VGG-16,and Mobile Net,and their performance can be calculated through parameters of the confusion matrix(accuracy,precision,recall,and F1 measures).The results show that the accuracy value of VGG-16 is 78.00%,Inception V3 is 92.00%,Dense Net 121 is 95.00%,and Mobile Net shows 88.00%accuracy,***,DenseNet-121 shows the highest performance with suitable analysis of autoimmune *** overall performance highlighted that TL is a suitable and enhanced technique compared to its ***,the proposed technique is used

关键词： Indirect immune fluorescence computer-aided diagnosis transfer learning confusion matrix

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An Evolutionary Compact Deep Transfer Learning with CNN for Hyper-Parameter Tuning in Temporal Sorting of Plant Growth 13

An Evolutionary Compact Deep Transfer Learning with CNN for ...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Mousavirad, Seyed Jalaleddin Shallari, Irida O'Nils, Mattias Mid Sweden University Department of Computer and Electrical Engineering Sundsvall Sweden

ISBN: (纸本)9798350308365

The efficient management of agricultural resources requires a deep understanding of plant growth dynamics. This research focuses on Sweden's forestry sector and explicitly addresses the crucial early stages of pine tree development. The main difficulty in categorising plant growth over time is that instances within a given category are not identical, while instances from different categories may have similarities. In this context, we present a novel measurement system that integrates the capa-bilities of evolutionary computation and deep transfer learning using image data. The image acquisition system includes a tray of plates that moves through a nursery, generating a dataset captured over 44 days of plant growth. Our newly proposed algorithm, EvoSqueezeNet, employs various search strategies for SqueezeNet deep transfer learning to find proper hyper-parameters. We opted for SqueezeNet based on our preliminary studies, revealing its superior performance compared to other pre-trained models in our case study. Given that our approach is independent of any specific evolutionary algorithm, we utilised five distinct search strategies. These include Differential Evolution (DE), Particle Swarm Optimisation (PSO), Covariance Ma-trix Adaptation Evolution Strategy (CMA-ES), Comprehensive Learning PSO (CLPSO), and Linear Population Size Reduction Success-History Adaptation DE (LSHADE). Consequently, we proposed five EvoSqueezeNet schemes for temporal plant growth categorisation. One characteristic of our proposed model is that it uses a limited computation budget for search strategies, en-hancing its applicability in real-world applications. The proposed EvoSqueezeNet methodology demonstrates an error reduction of more than 40%, showcasing its superior performance compared to competing methods. © 2024 IEEE.

关键词： Budget control

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Probabilistic Feamework for Modeling and Analyzing Complex Networks 15

Probabilistic Feamework for Modeling and Analyzing Complex N...

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

作者： Nair, Lekshmi S Cheriyan, Jo Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India SAINTGITS College of Engineering Department of Computer Science and Engineering Kottayam India

ISBN: (纸本)9798350370249

Complex networks, characterised by intricate structures arising from the relationships and interactions among their constituent elements, play a pivotal role in various domains such as social networks, biological systems, and technological infrastructures. The evolution and dynamics of complex networks are analysed for various applications such as link prediction, information diffusion, and recommendation systems. Recent research has focused on the study of structural motifs and the probabilistic analysis of motifs to capture the dynamics of the network. The motifs, considered building blocks of the larger network structure, are recurrent patterns in the network. This study focuses on the different probabilistic models built on triads, which are sets of three interconnected nodes. We investigate six different probability distributions, including Poisson, Gamma, Exponential, Power Law, Gaussian, Lorentzian, and Gaussian Mixture Models (GMMs) that can be applied in the complex network to capture the insight of the connectivity pattern. The applications of this research extend to refining network models, improving simulations, and enhancing our understanding of the likelihood of observing distinct patterns within complex social networks. © 2024 IEEE.

关键词： Poisson distribution

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Cloud-Edge Framework for Multi-Vehicle Deployment Control using Spiking Neural Networks

Cloud-Edge Framework for Multi-Vehicle Deployment Control us...

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2024 IEEE Research and Applications of Photonics in Defense Conference, RAPID 2024

作者： Banad, Yaser Mike Sharif, Sarah Safura The University of Oklahoma Department of Electrical and Computer Engineering Oklahoma United States

ISBN: (纸本)9798350373097

Recent advances have spotlighted the use of unmanned aerial vehicles (UAVs) for defense and surveillance, focusing on cooperative monitoring and exploration. However, these UAV systems face significant operational challenges due to limited computational and energy resources, affecting their endurance and effectiveness in critical missions. This study proposes a novel approach using Spiking Neural Networks (SNNs) to overcome these limitations. SNNs, inspired by the human brain's processing efficiency, offer a shift towards more autonomous and adaptive UAV systems. Leveraging SNNs, we introduce a cloud-edge computational framework aimed at optimizing barrier coverage control and enhancing UAVs' monitoring capabilities within resource constraints. This SNN-based framework marks a considerable advancement in UAV operational capabilities, enabling longer missions, better area coverage, and adaptability to environmental changes. The effectiveness in achieving optimal barrier coverage sets a new benchmark for multivehicle systems in defense applications, underscoring the potential of brain-inspired computing in military operations and paving the way for future research in autonomous defense technologies. © 2024 IEEE.

关键词： Neural networks

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Intelligent Handover Management Enabled by O-RAN and Deep Reinforcement Learning 100

Intelligent Handover Management Enabled by O-RAN and Deep Re...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Dai, Jiongyu Mahboob, Shadab Wang, Haining Liu, Lingjia Virginia Tech Bradley Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798331517786

The increased number of connected devices and diverse quality of service demands in cellular networks present formidable challenges for efficient traffic management. In this paper, we present a handover control approach to tackle these challenges in 5G networks, integrating the Proximal Policy Optimization (PPO) framework with Reservoir Computing (RC). Our approach, aligned with the O-RAN architecture, offers a near-real-time solution for intelligent handover management, improving cell performance, and enhancing user experience. This renders our solution O-RAN compliant and deployable as an 'xApp' on the RAN Intelligent Controller (RIC). The introduced algorithm features a sequential state design including user-specific and cell-specific metrics. Harnessing the power of reservoir computing, our solution captures dynamic changes in state sequences while reducing training overhead and enhancing online training efficiency. The efficacy of our algorithm is demonstrated through evaluations on the ns3 platform, proving its superiority in system-level simulations. The experimental results affirm the effectiveness of the introduced algorithm in addressing the dynamic challenges of handover in cellular networks. © 2024 IEEE.

关键词： Deep reinforcement learning

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An Efficient Text Cleaning Pipeline for Clinical Text for Transformer Encoder Models 12

An Efficient Text Cleaning Pipeline for Clinical Text for Tr...

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12th IEEE International Conference on Intelligent Systems, IS 2024

作者： Ridoy, Shahriyar Zaman Sultana, Jannat Ria, Zinnat Fowzia Uddin, Mohammed Arif Rahman, Md Hasibur Rahman, Rashedur M. North South University Department of Electrical and Computer Engineering Dhaka Bangladesh

ISBN: (纸本)9798350350982

It might be challenging to choose the best text preprocessing strategy in the field of natural language processing (NLP) due to the variety of techniques available. Given the popularity of transformer models, we wondered if preprocessing was necessary and, if so, what methods would improve the models' performance. Especially when working with clinical text data, accuracy is crucial. Our goal was to find an appropriate pre-processing pipeline for clinical texts that maintains or improves model performance. We experienced four common preprocessing techniques and their groupings on two datasets from MIMIC-3 and PubMed. We used four models: BERT base, BioBERT, BioClinicalBERT, and RoBERTa. The varied accuracy results from existing techniques inspired us to develop a new pipeline to improve accuracy. Our pipeline starts with removing repeated punctuation, normalizing the text with a CleanText function, and filtering less important words using TF-IDF scores to keep clinically applicable terms and moderate noise. Our results presented that our pipeline outperformed the base models. For the MIMIC-3 dataset, the BERT base model achieved 90.16% accuracy, and for the PubMed dataset, BioBERT achieved 64.20% accuracy. We also found that removing stop words decreased accuracy, while using TF-IDF either maintained or improved it up to 3%. Additionally, as we removed less important words from the documents our pipeline considerably reduced training time up to 17%. © 2024 IEEE.

关键词： Natural language processing systems

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Influence of Rise Time on the Aging of Electric Machine Windings

Influence of Rise Time on the Aging of Electric Machine Wind...

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2024 IEEE Conference on electrical Insulation and Dielectric Phenomena, CEIDP 2024

作者： Arafat, Easir Rahman, Md Asifur Islam, Farzana Ghassemi, Mona Laboratory Department of Electrical and Computer Engineering RichardsonTX United States

ISBN: (纸本)9798350366679

This study examines how fast rise times, which are common in modern power electronics and drive systems, affect the aging of electric machine windings. It focuses on how to ensure these windings can last longer and work reliably in electrical systems. A twisted pair magnet wire with insulation commonly used in wound machines was used to get experimental data to understand how different voltage waveforms can influence endurance testing of motor insulation systems powered by inverters. Unlike past studies that looked at comparatively slower rise times and fewer repetitions, this research specifically addresses the challenges posed by next-generation wide bandgap (WBG)-based conversion systems. These systems operate at very high speeds, up to 100 kV/μs, and switch frequencies up to 500 kHz, where both frequency and rise time are crucial factors affecting insulation aging over time. © 2024 IEEE.

关键词： Machine windings

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A GMM-CGABC Based Energy Replenishment Scheme for On-Demand Wireless Rechargeable Sensor Networks 1

A GMM-CGABC Based Energy Replenishment Scheme for On-Demand ...

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1st International Symposium on Parallel Computing and Distributed Systems, PCDS 2024

作者： Mouli, S.Sudhamsu Veeraiah, T. Mahesh, K. Singh, M.P. Ecole Centrale School of Engineering Mahindra University Department of Computer Science and Engineering Hyderabad India National Institute of Technology Department of Computer Science and Engineering Patna India

ISBN: (纸本)9798350349658

Wireless Sensor Networks (WSNs) are constrained by the limited energy capacity of Sensor Nodes (SNs), which hinders their perpetual operation. The advent of Wireless Energy Transfer (WET) technology has emerged as a promising solution to this challenge, enabling the creation of Wireless Rechargeable Sensor Networks (WRSNs). Recent research has shown growing interest in integrating Wireless Mobile Chargers (WMCs) into WRSNs to replenish the energy reserves of critical SNs through WET. A key aspect of WET involves using one or more WMCs to recharge SNs, with optimizing the charging schedule (i.e., the sequence in which SNs are charged) crucial in on-demand WRSNs. In addition, spatiotemporal constraints imposed by the requesting SNs must be considered when devising the WMC's charging schedule. To address these challenges, this work proposes the integration of a Gaussian Mixture Model with a Chaotic Gaussian Artificial Bee Colony (GMM-CGABC) to develop an efficient recharge schedule that revitalizes SNs. This model is designed to optimize SN deployment, maximize network coverage, minimize energy consumption, and extend network longevity. The proposed method determines the optimal charging path for WMCs within each cluster to minimize Average Charging Latency (ACL) while incorporating a novel strategy for fitness function evaluation. Extensive simulations demonstrate that the proposed system outperforms state-of-the-art algorithms, including First Come, First Served (FCFS), Nearest Job Next with Preemption (NJNP), and the Gravitational Search Algorithm (GSA), in terms of charging latency. © 2024 IEEE.

关键词： Inductive power transmission

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