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6th International conference on communication and computational technologies, ICCCT 2024

作者： Jain, Samyak Pal, Aarush Andrew, J. Maheshwari, Saatvik Department of Computer Science and Engineering and Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

ISBN: (纸本)9789819774258

This study addresses gastrointestinal cancer radiation therapy challenges by implementing advanced deep learning techniques. We focus on automating manual segmentation tasks during treatment planning to enhance efficiency and patient tolerance. Our methodology uses a diverse 3D MRI scan dataset to employ image analysis, Convolutional Neural Networks (CNNs), and Image Segmentation. The primary architectural choices involve the U-Net and InceptionResNetv2 models, contributing to developing a predictive segmentation model. This substantial result underscores the effectiveness of our approach in achieving accurate segmentations in the clinical setting. The overarching objective of our project is to automate the segmentation of the stomach and intestines in MRI scans, thereby streamlining radiation therapy planning for improved efficiency and patient care. In our evaluations, the implemented model demonstrates a commendable dice coefficient of 0.7, as indicated by metrics such as dice loss. Anticipated outcomes encompass reduced treatment times, enhanced patient comfort, and optimised tumour radiation doses while minimising healthy tissue exposure. The successful implementation of this advanced model holds the potential to revolutionise current radiation therapy practices, significantly elevating cancer treatment outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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A Smart Heart Disease Diagnostic System Using Deep Vanilla LSTM

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computers, Materials & Continua 2023年第10期77卷 1251-1279页

作者： Maryam Bukhari Sadaf Yasmin Sheneela Naz Mehr Yahya Durrani Mubashir Javaid Jihoon Moon Seungmin Rho Department of Computer Science COMSATS University IslamabadAttock CampusAttockPakistan Department of Computer Science COMSATS University IslamabadIslamabadPakistan Department of Computer Science UET TaxilaTaxilaPakistan Department of AI and Big Data Soonchunhyang UniversityAsanKorea Department of Industrial Security Chung-Ang UniversitySeoul06974Korea

Effective smart healthcare frameworks contain novel and emerging solutions for remote disease diagnostics,which aid in the prevention of several diseases including heart-related *** this context,regular monitoring of cardiac patients through smart healthcare systems based on Electrocardiogram(ECG)signals has the potential to save many *** existing studies,several heart disease diagnostic systems are proposed by employing different state-of-the-art methods,however,improving such methods is always an intriguing area of ***,in this research,a smart healthcare system is proposed for the diagnosis of heart disease using ECG *** proposed framework extracts both linear and time-series information on the ECG signals and fuses them into a single framework *** linear characteristics of ECG signals are extracted by convolution layers followed by Gaussian Error Linear Units(GeLu)and time series characteristics of ECG beats are extracted by Vanilla Long Short-Term Memory Networks(LSTM).Following on,the feature reduction of linear information is done with the help of ID Generalized Gated Pooling(GGP).In addition,data misbalancing issues are also addressed with the help of the Synthetic Minority Oversampling Technique(SMOTE).The performance assessment of the proposed model is done over the two publicly available datasets named MIT-BIH arrhythmia database(MITDB)and PTB Diagnostic ECG database(PTBDB).The proposed framework achieves an average accuracy performance of 99.14%along with a 95%recall value.

关键词： Smart systems deep learning ECG signals heart disease concurrent learning LSTM generalized gated pooling

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Launching a data Analytics Major: How Collaboration and Agility Built a Successful Program

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American Mathematical Monthly 2025年第1期132卷 77-88页

作者： Forrester, Richard Marshall, Emily Forrester, Jeffrey Richeson, David Schaefer, Jennifer Department of Data Analytics and Department of Mathematics and Computer Science Dickinson College Carlisle 17013 PA United States Department of Economics Denison University Granville 43023 OH United States Department of Data Analytics and Department of Mathematics and Computer Science Dickinson College Carlisle 17013 PA United States Department of Mathematics and Computer Science Dickinson College Carlisle 17013 PA United States

In this article, we share the details of Dickinson College’s journey to establish a data analytics major. Designed to provide students with the technical proficiency required to become a data scientist, Dickinson’s data analytics major also incorporates values of a liberal arts education, including interdisciplinary learning, critical thinking, thoughtful analysis, effective communication, and ethical considerations. We describe how sustained collaboration, a detailed plan to engage the campus community, partnerships with alumni, and support from senior leaders were essential to this achievement. We also discuss the challenges and successes encountered before and after the approval of the major. © 2024 Mathematical Association of America.

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Dynamic Distributed Model Compression for Efficient Decentralized Federated Learning and Incentive Provisioning in Edge Computing Networks

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6293-6314页

作者： Asheralieva, Alia Niyato, Dusit Wei, Xuetao Southern University of Science and Technology Department of Computer Science and Engineering 1088 Xueyuan Avenue Shenzhen China Nanyang Technological University College of Computing and Data Science 50 Nanyang Avenue Singapore639798 Singapore

We study decentralized federated learning (DFL) in edge computing networks where edge nodes (ENs) collaboratively train their artificial intelligence (AI) models in a serverless manner without sharing local data. We consider the following critical DFL challenges: i) scarce bandwidth resources of ENs;ii) dynamic, heterogeneous edge environment;iii) incentive provisioning and complex tradeoffs between the DFL performance and training costs. To resolve these challenges, we develop a new model compression method where ENs utilize dynamic, non-identical compression rates to improve the communication efficiency of DFL under time-varying, heterogeneous resource constraints. We show that our method can be formulated as a graphical Markov potential game where ENs act as players deciding on their compression factors and the number of data samples used for model updates. Each EN is incentivized to participate in DFL through rewards based on the EN's contribution to training. We prove that our game has a dominant pure-strategy Nash equilibrium (NE) maximizing its potential function and propose a dynamic distributed compression algorithm in which each EN can find its dominant strategy independently. We show that this algorithm converges to the Pareto-optimal NE, representing the most efficient solution of our game enhancing the DFL performance with minimal costs. © 2002-2012 IEEE.

关键词： Federated learning

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The Implicit Bias of Adam on Separable data 38

The Implicit Bias of Adam on Separable Data

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhang, Chenyang Zou, Difan Cao, Yuan Department of Statistics and Actuarial Science School of Computing and Data Science The University of Hong Kong Hong Kong Department of Computer Science School of Computing and Data Science Institute of Data Science The University of Hong Kong Hong Kong Department of Statistics and Actuarial Science School of Computing and Data Science Department of Mathematics The University of Hong Kong Hong Kong

Adam has become one of the most favored optimizers in deep learning problems. Despite its success in practice, numerous mysteries persist regarding its theoretical understanding. In this paper, we study the implicit bias of Adam in linear logistic regression. Specifically, we show that when the training data are linearly separable, the iterates of Adam converge towards a linear classifier that achieves the maximum ∞-margin in direction. Notably, for a general class of diminishing learning rates, this convergence occurs within polynomial time. Our result shed light on the difference between Adam and (stochastic) gradient descent from a theoretical perspective. © 2024 Neural information processing systems foundation. All rights reserved.

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Cumulative Step Size Adaptation for Adaptive SEMO in Integer Space 13th

Cumulative Step Size Adaptation for Adaptive SEMO in Integ...

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13th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2025

作者： Rudolph, Günter Wagner, Markus Department of Computer Science TU Dortmund University Dortmund Germany Department of Data Science and AI Monash University Clayton Australia

ISBN: (纸本)9789819635054

Parameter control involves dynamically adjusting the parameter values of the evolutionary algorithm throughout the optimization process, including parameters like mutation rate and operator selection. Self-adaptation can improve the performance and robustness of the algorithm, however, parameter control mechanisms themselves need to be designed and configured carefully. In this article, we review the cumulative step size adaptation method originally proposed for single-objective optimization over continuous variables and recast it for deployment in multiobjective optimization over unbounded integer space. We contribute a systematic investigation of its hyperparameters which shows that while (1) the very best configurations remain problem-specific, (2) the performance of the algorithm is largely independent of the self-adaptation scheme’s parameterization and initial configuration. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Integer linear programming

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Modelling a Multi-Perceptron Q Network Layer for Analyzing Patient Condition in Healthcare Sector

Modelling a Multi-Perceptron Q Network Layer for Analyzing P...

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

作者： Karthi, M. Manirajan, S.P. Prince, A. Santhosh, R. Department of Artificial Intelligence and Data Science India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331509828

Chronic diseases like chronic respiratory diseases, diabetes, heart disease (HD) and cancer are the important causes of mortality globally. The diagnosis of Heart-related diseases with a variety of symptoms or characteristics is incredibly complex. Internet of Things (loT) embedded solution has improved with the increase in smart wearable devices. However, people who undergo heart attacks unexpectedly have poor survival rates. As a result, an Internet of Things based Multi-Perceptron Q-Network (MQN)-based monitoring program is suggested for cardiac patients to assist in identifying heart disease and administering medication appropriately. The technology based on MQN is used to securely encrypt this sensor data before sending it to the cloud. The encrypted and decrypted data is eventually done and MQN classifier is used to complete the classification process. The two categories of data that make up the classified outcomes are i) regular and ii) irregular. It indicates the individual's cardiac status, and if the results are peculiar, the doctor is notified via text so that the patient can receive treatment. When compared to current algorithms, the MQN for HD diagnosis exhibits better enhancement in the estimated investigational results. Furthermore, when contrasted against the current MQN, the introduced Multi-layered Perceptron (MLP) system facilitates safe data transmission yields the maximum security level of 96%, and is accomplished in the shortest amount of time for both decryption and encryption. © 2025 IEEE.

关键词： Lung cancer

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A novel framework integrating ensemble transfer learning and Ant Colony Optimization for Knee Osteoarthritis severity classification

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Multimedia Tools and Applications 2024年第39期83卷 86923-86954页

作者： Malik, Isra Yasmin, Mussarat Iqbal, Ahmed Raza, Mudassar Chun, Chang-Jae Al-antari, Mugahed A. Department of Computer Science COMSATS University Islamabad Wah Campus Wah Cantt44000 Pakistan Department of Computer Science Sir Syed Case Institute of Technology Islamabad45230 Pakistan Department of Computer Science HITEC University Taxila Taxila47080 Pakistan Department of Artificial Intelligence and Data Science College of AI Convergence Daeyang AI Center Sejong University Seoul05006 Korea Republic of

Knee Osteoarthritis (KOA), the most prevalent joint disease, significantly impacts elderly mobility due to progressive cartilage degeneration. Early prediction is crucial for preventing disease progression and guiding effective treatment plans. This paper proposes an EnsembleTL-ACO, fully automated, computer-aided diagnosis (CAD) system for accurate and rapid KOA severity grading. The proposed CAD system leverages an ensemble transfer learning strategy to extract robust deep features by fusing multiple deep learning models. It combines features from two consecutive AI models: (1) AlexNet for implicit class-wise deep feature extraction from preprocessed data, and (2) a custom IsrNet for further feature depth. Unsupervised k-means clustering based on PCA dimensionality reduction decomposes each class into subgroups, further refining features. Finally, Ant Colony Optimization (ACO) selects the most informative features. Evaluated on the Osteoarthritis Initiative (OAI) dataset, the proposed system achieves high accuracy in classifying the five KOA severity grades. With 1000 optimized features, it reaches average overall accuracies of 89.89% and 85.44% using Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classifiers, respectively. This surpasses recent deep learning methods, demonstrating significant improvement. This novel CAD system presents a promising solution for practical applications, offering an accurate AI-powered tool for KOA diagnosis and management. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Ant colony optimization

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Model-based framework for exploiting sensors of IoT devices using a botnet: a case study with android

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Cyber-Physical Systems 2025年第1期11卷 1-46页

作者： Khaliq, Zubair Khan, Dawood Ashraf Baba, Asif Iqbal Ali, Shahbaz Farooq, Sheikh Umar Department of Computer Science University of Kashmir Jammu and Kashmir Srinagar India AI Thndr Dubai United Arab Emirates Department of Computer Science Tuskegee University TuskegeeAL United States Data Engineering hyke.ai Dubai United Arab Emirates

Botnets have become a severe security threat not only to the Internet but also to the devices connected to it. Factors like the exponential growth of IoT, the COVID-19 pandemic, and the ever-larger number of cybercriminals are incentivising the growth of botnets in this domain. The recent outbreak of botnets like Dark Nexus (derived from Qbot and Mirai), Mukashi, LeetHozer, Hoaxcalls, etc. shows the alarming rate at which this threat is converging. The botnets have attributes that make them an excellent platform for malicious activities in IoT devices used by organisations to safeguard the personal and confidential data of their customers, employees, and business partners. The IoT devices have built-in sensors or actuators that can be exploited to monitor or control the physical environment of the entities connected to them, thereby violating the fundamental concept of privacy-by-design of these devices. In this paper, we design and describe a modular botnet framework for IoT. The framework uses an enhanced centralised architecture associated with a novel ‘Domain Fluxing Technique’. The proposed framework will provide insights into how privacy in IoT devices can be incorporated at design time to check the sensors and actuators in these devices against malicious exploitation, consequently preserving privacy. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Internet of things

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Comparison of Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) Models on the Poverty Levels in Central Java in 2023

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IAENG International Journal of Applied Mathematics 2024年第12期54卷 2746-2757页

作者： Alya, Najma Attaqiya Almaulidiyah, Qothrotunnidha Farouk, Bailey Reshad Rantini, Dwi Ramadan, Arip Othman, Fazidah Engineering Department Data Science Technology Study Program Universitas Airlangga Indonesia Engineering Department Data Science Technology Study Program Faculty of Advanced Technology and Multi-discipline Universitas Airlangga Indonesia Information System Study Program Department of Industrial and System Engineering Telkom University Surabaya Campus Indonesia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaysia

Poverty is considered a serious global issue that must be immediately eradicated by Sustainable Development Goals (SDGs) 1, namely ending poverty anywhere and in any form. As a developing country, poverty is a complex issue faced by Indonesia. Tackling poverty in Indonesia has become a key focus of the government’s strategic priorities. This issue of poverty is experienced by all provinces in Indonesia, including Central Java. The poverty rate in Central Java stands at 10.77% or 3.79 million people. The causes of poverty are explained by several factors. Using these various factors, a mapping model can be conducted to determine the number of poor populations in Central Java. This research employs Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) approaches to compare the effectiveness of these models in analyzing the number of poor populations in Central Java in 2023. The kernel function weighting used in this study is Fixed Gaussian. The results showed that the MGWR model outperformed both the OLS regression and GWR models, achieving an Akaike Information Criterion (AIC) of 62.766, an R2 of 82.3%, and a Mean Squared Error (MSE) of 0.177. Consequently, it can be inferred that the MGWR model was more suitable for explaining the poverty levels in Central Java. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Logistic regression

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