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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Suganthi, N. Mohana Subhashni, R. Chitra, A. Rajakumari, S. Priya, T. Anitha, P. Caroline Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India St.Peter's Institute of Higher Education and Research Department of Computer Applications Avadi Chennai54 India EastPoint College of Higher Education Department of Computer Application Jnana Prabha East Point Campus Karnataka Bengaluru560049 India

ISBN: (纸本)9798331509828

Monitoring fetal development and guaranteeing mother health depend on correct estimate of fetal gestational age. Conventional approaches of gestational age estimate mostly depend on manual estimations from ultrasonic scans, which may be laborintensive and prone to human mistake. Variability in operators, picture quality, and fetal position might potentially affect current methods and cause possible errors and inconsistent outcomes. We offer the Ultrasound Images Using Deep Learning Techniques (UI-DLT) framework, a strong, automated method for fetal gestational age estimate, to handle these problems. UI-DL T analyzes ultrasonic pictures and enhances the accuracy and consistency of gestational age estimations by using deep learning-more especially, convolutional neural networks (CNNs). UI-DLT reduces human error and standards outcomes across several clinical environments by automating the analytic process. CNNs are used in the proposed system to categorize and examine ultrasonic pictures, therefore spotting important information on fetal development and growth. By extracting deep picture elements that match developmental phases, our method guarantees accurate and consistent gestational age estimations, thereby beyond the capacity of conventional manual techniques. Experimental data on ultrasonic image datasets shows significant increases in estimating accuracy of the UI-DLT framework. This approach not only improves the accuracy of gestational age prediction but also helps to progress clinical decision-making and mother-fetal health care. © 2025 IEEE.

关键词： Ultrasonic imaging

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Semantic Communication in Dynamic Channel Scenarios: Collaborative Optimization of Dual-Pipeline Joint Source-Channel Coding and Personalized Federated Learning

arXiv

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arXiv 2025年

作者： Yan, Xingrun Zuo, Shiyuan Lyu, Yifeng Fan, Rongfei Hu, Han School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China Institute of Computer Application Technology Norinco Groups Beijing China School of Information and Electronics Beijing Institute of Technology Beijing China

Semantic communication is designed to tackle issues like bandwidth constraints and high latency in communication systems. However, in complex network topologies with multiple users, the enormous combinations of client data and channel state information (CSI) pose significant challenges for existing semantic communication architectures. To improve the generalization ability of semantic communication models in complex scenarios while meeting the personalized needs of each user in their local environments, we propose a novel personalized federated learning framework with dual-pipeline joint source-channel coding based on channel awareness model (PFL-DPJSCCA). Within this framework, we present a method that achieves zero optimization gap for non-convex loss functions. Experiments conducted under varying SNR distributions validate the outstanding performance of our framework across diverse datasets. Copyright © 2025, The Authors. All rights reserved.

关键词： Latent semantic analysis

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Optimized Dense Neural Network Architecture for Bone Marrow Cancer Classification Using Custom Callbacks

Optimized Dense Neural Network Architecture for Bone Marrow ...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： G. Deepa Y. Kalpana Department of Computer Science VELS Institute of Science Technology and Advanced Studies (VISTAS) Chennai India Department of Computer Application and IT VELS Institute of Science Technology and Advanced Studies (VISTAS) Chennai India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Classification of bone marrow cancer is a crucial step in the analysis of several hematological cancers, such as multiple myeloma and leukemia. This study shows better dense neural network (DNN) architecture for significant the variation among the five main types of bone marrow cells includes lymphocytes, myelocytes, band neutrophils, and plasma cells. To find and diagnose bone marrow cancers like multiple myeloma, acute myeloid leukemia (AML), and acute lymphoblastic leukemia (ALL) early on, it is important to be able to accurately identify these cell types. The suggested DNN model uses unique callbacks to optimize training, avoid overfitting, and guarantee faster convergence. These callbacks include learning rate scheduling, early pausing, and dynamic weight modifications. The system preprocesses the high-resolution bone marrow image database to enhance contrast, adapt to fluctuations in illumination, and utilize dense layers for cell classification and convolutional layers for feature extraction. After extensive hyperparameter tuning, a model that classified these five categories of bone marrow cells obtained an overall accuracy rate of 95%. In terms of accuracy, recall, and F1 scores, the model performs better than conventional machine learning techniques while retaining computing economy. The model is able to increase training times without sacrificing classification performance by incorporating custom callbacks. With its enormous clinical application potential, our improved DNN architecture offers pathologists a dependable and effective tool for accurately classifying bone marrow cells. By improving bone marrow cancer diagnosis accuracy, this approach can improve patient care and treatment results.

关键词： Training Adaptation models Accuracy Computational modeling Neural networks Leukemia Cells (biology) computer architecture Bones Overfitting

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An influence study of task-irrelevant factors on the neural network accuracy

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Signal, Image and Video Processing 2025年第7期19卷 1-11页

作者： Liu, Yi Zhang, Changsheng Zhao, Haitong Zhang, Chen Wu, Wenjing Department of Software Engineering Northeastern University Shenyang110167 China Department of Computer Application Technology Northeastern University Shenyang110167 China College of Computer Science and Engineering Ningxia Institute of Science and Technology Shizuishan753000 China

In the field of data-driven deep learning, the dataset quality is one of the essential factors that determine the neural network (NN) accuracy. During the sampling process, the sample may contain many task-irrelevant factors. However, there is a lack of study on the influence of task-irrelevant factors in the dataset on the NN accuracy. This paper analyzes the influence of task-irrelevant factors through mathematical derivation, and finds that regularization cannot solve the problem of task-irrelevant factors affecting the NN inference performance. To reduce the influence of task-irrelevant factors, this paper proposes an idea, making the mathematical expectation of task-irrelevant features in the training set to be zero. Furthermore, this paper proposes a method that can reduce the influence of the background factor on the image classification model. To verify the effectiveness of this method, a series of controlled experiments are conducted on the image classification and object detection task. Experimental results show that this method not only effectively improves the accuracy of the classification model, but also improves the precision of the object detection model. The code related with this paper can be download from https://***/CaedmonLY/BackgroundFactor/tree/master. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Object detection

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application of adaptive guard channel reservation under hard handoff constraint in wireless cellular network

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International Journal of Wireless and Mobile Computing 2025年第2期28卷 170-185页

作者： Sahu, Promod Kumar Pati, Hemanta Kumar Pradha, Sateesh Kumar Department of Computer Science and Application Utkal University Vani Vihar Odisha Bhubaneswar India Department of Computer Science and Engineering International Institute of Information Technology Bhubaneswar Odisha Bhubaneswar India

Voice is the king of communication in wireless cellular network (WCN). Again, WCNs provide two types of calls, i.e., new call (NC) and handoff call (HC). Generally, HCs have higher priority than NCs because call dropping gives negative impact to users as compared to call blocking. So, to handle this problem some channels are reserved for HCs. If more channels are reserved for HCs, then handoff call dropping probability (HCDP) decreases at the same time as new call blocking probability (NCBP) increases;if fewer channels are reserved for HCs, HCDP increases and NCBP decreases. If channels are reserved for HCs by considering target HCDP then HCDP will be below the target and NCBP will be minimum for that target. So, keeping this in mind in this paper, we propose a mathematical model that estimates optimal number of guard channels (GCs) for HCs by considering target HCDP and keeping NCBP minimum. The mathematical model proposed assume static traffic parameters. However, in reality, the user traffic is dynamic and usually inherited through its testbed implementation. To obtain optimal performance we propose an adaptive GC reservation scheme by considering hard handoff constraint while implementing this model through testbed. Finally, we have verified testbed results with those of the model-based approach applying the parameters of different Global System for Mobile Communication (GSM) standards to evaluate its applicability to real systems. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Testbeds

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Utilization of hesitant and intuitionistic fuzzy sets (HFS-IFS) in computational intelligence for decision modeling

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International Journal of Information technology (Singapore) 2025年 1-8页

作者： Sharma, Suvarna Dangi, Dharmendra Dixit, Dheeraj Kumar Gupta, Rashmi Kumar, Jeetendra Bhagat, Amit Department of Computer Science and Applications Dr. Vishwanath Karad MITWorld Peace University Pune India Information Technology Indian Institute of Information Technology (IIIT) C/O- Maulana Azad National Institute of Technology (MANIT) Bhopal India Computer Science and Engineering Madhav Institute of Technology & Science (MITS) Madhya Pradesh Gwalior India Computer Science and Application Atal Bihari Vajpayee University Chhattisgarh Bilaspur India Computer Applications Maulana Azad National Institute of Technology Bhopal India

This paper focuses on the utilization of hesitant and intuitionistic fuzzy sets (HFS & IFS) in a computational intelligent approach, mainly for decision modelling under complex vague surroundings. Indeed, classical fuzzy sets can be powerful in some applications, but they may not describe the broad range of epistemic imprecision and non-stationary uncertainty involved in making decisions at any given time. HFS & IFS overcome it by providing the ability to quantify degrees of hesitation and membership uncertainty. HFS allows a decision maker to express the multiple reference degrees associated with an option and also show uncertainty about how to assign that precise level of degree or real value. On the other hand, IFS allows for more possibilities of representing ambiguity. In this research, we incorporate the proposed model into a computational intelligent approach for improving decision-making, particularly in the context of multi-criteria decision analysis (MCDA), resource allocation and task completion parameters. The results show that the proposed HFS-IFS method presents improved performance in terms of accuracy and uncertainty treatment across a series of case studies. © Bharati Vidyapeeth's institute of computer applications and Management 2025.

关键词： Computational Intelligence Decision modeling Hesitant fuzzy set (HFS) Intuitionistic fuzzy set (IFS) MCDA

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Recognition and evaluation of cutaneous condition through assorted artificial intelligence reliant algorithms

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International Journal of Information technology (Singapore) 2025年 1-13页

作者： Mishra, Manmohan Yadav, Ajay Kumar Mazumdar, Bireshwar Dass Gupta, Prashant K. Panwar, Arvind Bharadwaj, Shivam Department of Computer Application United Institute of Management Prayagraj India School of Computer Science & Engineering Technology Bennett University Plot Nos 8-11 TechZone II Uttar Pradesh Greater Noida 201310 India Galgotias University Plot No. 2 Yamuna Expy opposite Buddha International Circuit Sector 17A Uttar Pradesh Prayagraj 203201 India

Our skin is the hefty organ that envelops and shields body. It prevents us from numerous fatal and non fatal diseases. It is observed that due to bacteria or other causes of infection, skin faces certain minor or life threatening diseases. The most prioritized step toward restoring health is early illness signs identification. Identifying Cutaneous Condition from clinical images is one of the foremost challenges in medical image investigation. In the presented study we will enlighten the various Artificial Intelligence techniques falling under the categories of supervised machine learning including Probabilistic classifier (Naïve Bayes), Statistical algorithm (Logistic Regression), Ensemble learning (Random Decision Trees), Data analysis technique (Convolutional Neural Network) and Kernel approach (Support Vector Machine) to identify and classify the cutaneous condition appropriately so that corrective measures of skin treatment can be endow with. The proposed approach entails collecting images as input, preprocessing, segmenting, feature extraction and lastly applying the classification algorithms to derive the Cutaneous Condition categories. Additional trials are conducted using the different approaches as indicated and it was discovered from the suggested tests that the Convolutional Neural Network strategy yields the best results overall. The proposed model is trained, tested, and evaluated using the International Skin Imaging Collaboration (ISIC) 2019 challenge dataset and Human Against Machine with 10,000 training images (HAM10000) for the detection of manifold Cutaneous Condition. © Bharati Vidyapeeth's institute of computer applications and Management 2025.

关键词： Artificial intelligence Confusion matrix Convolutional neural networks Cutaneous condition Image processing Machine learning

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Communication Countermeasure Strategy Decision-Making Based on Incomplete Information Game

Communication Countermeasure Strategy Decision-Making Based ...

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International Conference on Advanced Communication technology (ICACT)

作者： Jinyue Liu Xiang Gao Haowei Yang Jihao Zhang Yuhan Liu Peng Gong National Key Laboratory of Mechatronic Engineering and Control School of Mechatronical Engineering Beijing Institute of Technology Beijing China Institute of Computer Application Technology Norinco Group Beijing China

ISBN: (数字)9791188428137

ISBN: (纸本)9798331507602

In modern battlefields, the stability of wireless communications is crucial for intelligence transmission, command coordination, and maintaining strategic advantage. However, with the rapid advancement of communication technologies, the electromagnetic environment has become increasingly complex, making intelligent decision-making in communication countermeasures a prominent research focus. However, most existing studies adopt deep learning and reinforcement learning methods, which require extensive data samples and computational resources. To address these limitations, this paper proposes a game-theoretic model for intelligent radio communication countermeasures under incomplete information. It quantitatively evaluates common attack and defense tactcis used in communication countermeasures and explores the mixed-strategy equilibrium of the game when both the attackers and defenders consist of multiple types. Furthermore, simulation-based comparative analyses are conducted to assess the factors influencing the utility of both the attacker and defender. The findings offer theoretical insights and decision-making support for the deployment of communication countermeasure strategies in practical military operations, contributing to significant strategic value.

关键词： Wireless communication Military communication Deep learning Decision making Games Reinforcement learning Communications technology Stability analysis Radio communication countermeasures Electromagnetics

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Identification of Key Genetic Drivers in Multidrug Resistance: A Co-Expression Network Analysis of Pseudomonas aeruginosa

Identification of Key Genetic Drivers in Multidrug Resistanc...

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Ambient Intelligence in Health Care (ICAIHC), International Conference on

作者： Sweta Padma Routray Saswati Mahapatra Debasish Swapnesh Kumar Nayak Enketeswara Subudhi Tripti Swarnkar Centre for Biotechnology Siksha ‘O’ Anusandhan (Deemed to be) University Bhubaneswar India Department of Computer Application Siksha ‘O’ Anusandhan (Deemed to be) University Bhubaneswar India Department of Computer Science and Engineering Centurion University of Technology and Management Bhubaneswar India Department of Computer Application National Institute of Technology Raipur India

ISBN: (数字)9798331530259

ISBN: (纸本)9798331530266

Antimicrobial resistance (AMR) in Pseudomonas aeruginosa, a critical pathogen in healthcare-associated infections, demands new insights into underlying genetic mechanisms. This study aimed to identify key regulatory genes contributing to multidrug resistance (MDR) through a comprehensive analysis of gene expression in clinical isolates. RNA-Seq data from 246 clinical isolates of P. aeruginosa was analyzed using Weighted Gene Co-Expression Network Analysis (WGCNA) to construct co-expression networks. Modules of co-expressed genes were identified, and hub genes with high connectivity were selected, focusing on those within and across modules, including the largest module enriched with resistance-related genes. Pathway enrichment was performed using STRING and ShinyGO, highlighting key resistance-associated pathways. Pathway enrichment analysis of hub genes identified polA as a central player in DNA repair and replication processes, crucial for bacterial survival under antibiotic stress. In addition to polA, other highly connected hub genes, such as dnaB, tatC, and ppnK, were enriched in pathways including protein transport and mismatch repair. Within the turquoise module, hub genes like dnaA, uvrD, and gyrA were primarily involved in ATP-binding, ligase activity, and biosynthetic processes, essential for antibiotic resistance. Network analysis further emphasized the centrality of polA and its role in bridging multiple resistance pathways, highlighting its potential as a therapeutic target. This research highlights polA and other critical hub genes as key regulatory elements within the MDR networks of P. aeruginosa, playing essential roles in pathways vital for bacterial survival. The identification of these genes provides promising biomarkers and potential therapeutic targets to counteract MDR in clinical settings.

关键词： Proteins Pathogens Microorganisms Antibiotics Network analyzers Medical services Maintenance engineering Gene expression Stress Immune system

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Dynamic Access Control in Multi-Layer Satellite Remote Sensing System Using Multi-Agent Deep Reinforcement Learning

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IEEE Transactions on Wireless Communications 2025年

作者： Hu, Han Lyu, Yifeng Fan, Rongfei Sui, Xiufeng Zhan, Cheng Niyato, Dusit Beijing Institute of Technology School of Information and Electronics Beijing100081 China Norinco Group Institute of Computer Application Technology Beijing100089 China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Beijing Institute of Technology School of Management Beijing100081 China Southwest University School of Computer and Information Science Chongqing400715 China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore

The Multi-Layer Satellite Remote Sensing (SRS) integrates data collection by Low Earth Orbit (LEO) satellites and data processing assistance from Medium Earth Orbit (MEO) satellites, thereby playing a crucial role in scientific exploration. However, effectively controlling access to LEO satellites for processing data, especially considering the frequent handovers caused by speed differences, presents a significant challenge to achieving high energy efficiency services. To address this challenge, we explore cooperative dynamic access control based on efficient communication mechanisms, with the aim of prioritizing processed data volume and meeting energy consumption requirements for satellites. Specifically, we formulate the access control issue as an optimization problem and integrate it into the framework of partially observable Markov decision process (POMDP), considering MEO satellites' limited observation ability. By employing Multi-agent Deep Reinforcement Learning (MADRL), we propose a novel dynamic access control algorithm named DAC to solve our featured problem. Specifically, for improving performance, communication-efficient cooperation among MEOs is enhanced through modeling decision-relevant information of fellow MEO satellites and maximizing mutual information with their actual data to extract precise awareness and enable the generation of concise message. Finally, we conduct comprehensive experiments and an ablation study spanning the Starlink, OneWeb, and Telesat mega-constellations. The results demonstrate that DAC increases the average system data processing volume by at least 13.5%, while meeting energy consumption constraints and outperforming baseline algorithms. © 2002-2012 IEEE.

关键词： Deep reinforcement learning

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