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Results in engineering 2025年 26卷

作者： P, Udayakumar Subhashini, R. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

This study proposes a model for Alzheimer's Disease (AD) diagnosis called the Connectome Graph Attention Network (CGAT), designed to improve the classification of AD stages. By integrating multimodal neuroimaging data and clinical diagnosis information, CGAT constructs a connectome graph for each individual, with brain regions as nodes and the connections between them as edges. CGAT enhances classification by employing an attention mechanism that highlights critical brain region connections. A unique feature of the model is its capacity to merge features from diverse imaging modalities effectively, using modality-specific loss weighting to ensure balanced data contribution in the final classification. Developed in PyTorch Geometric (PyG), CGAT was optimized through the NNI AutoML toolkit. The model demonstrated a notable AUROC score of 94.00, outperforming other graph-based models like GCN, GraphSAGE, GGNN, and ChebNet, especially in differentiating EMCI from LMCI. Visualizations using t-SNE revealed CGAT's ability to distinctly separate cognitive states, emphasizing important connections among brain regions. Overall, CGAT efficiently combines multimodal neuroimaging data for improved, multi-class AD stage classification. © 2025 The Author(s)

关键词： Neurodegenerative diseases

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Trauma-Informed Insights from Co-Design of Self-Disclosure Robots with Domestic Abuse Survivors 20

Trauma-Informed Insights from Co-Design of Self-Disclosure R...

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20th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2025

作者： Morris, Nyomi Carrine Williams, Tom Jelen, Ben Colorado School of Mines MIRRORLab GoldenCO United States Rose-Hulman Institute of Technology Department of Computer Science and Software Engineering Terre HauteIN United States

ISBN: (纸本)9798350378931

Survivors of domestic abuse face significant challenges securing recovery resources, such as housing, mental health care, and social connections. Accessing these resources requires survivors to disclose their status as survivors of domestic abuse;a process that can be traumatic and emotionally burdensome. In this work, we consider how social robots might help domestic abuse survivors to address challenges surrounding self-disclosure. To do so, we conducted a three-session co-design workshop involving 8 participants from a domestic abuse shelter. Our results provide three key contributions: (1) new insights into the benefits and barriers to self-disclosure;(2) new insights into the ways that social robots can help survivors to better achieve those benefits and overcome those barriers before, during, and after disclosure, including key design recommendations associated with each of these phases;and (3) observations into how a trauma-informed computing perspective may enable more effective design work in Human-Robot Interaction. © 2025 IEEE.

关键词： Social robots

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Smart Grid Security Framework for Data Transmissions with Adaptive Practices Using Machine Learning Algorithm

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computers, Materials & Continua 2025年第3期82卷 4339-4369页

作者： Shitharth Selvarajan Hariprasath Manoharan Taher Al-Shehari Hussain Alsalman Taha Alfakih School of Built Environment Engineering and ComputingLeeds Beckett UniversityLeedsLS13HEUK Department of Computer Science and Engineering Chennai Institute of TechnologyChennai602109India Centre for Research Impact & Outcome Chitkara UniversityChandigarh140401India Department of Electronics and Communication Engineering Panimalar Engineering CollegeChennai600123India Department of Self-Development Skill Common First Year DeanshipKing Saud UniversityRiyadh11362Saudi Arabia Department of Computer Science College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

This research presents an analysis of smart grid units to enhance connected units’security during data *** major advantage of the proposed method is that the system model encompasses multiple aspects such as network flow monitoring,data expansion,control association,throughput,and *** addition,all the above-mentioned aspects are carried out with neural networks and adaptive optimizations to enhance the operation of smart grid ***,the quantitative analysis of the optimization algorithm is discussed concerning two case studies,thereby achieving early convergence at reduced *** suggested method ensures that each communication unit has its own distinct channels,maximizing the possibility of accurate *** results in the provision of only the original data values,hence enhancing *** power and line values are individually observed to establish control in smart grid-connected channels,even in the presence of adaptive settings.A comparison analysis is conducted to showcase the results,using simulation studies involving four scenarios and two case *** proposed method exhibits reduced complexity,resulting in a throughput gain of over 90%.

关键词： Machine learning power systems security smart grid

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Integrating GIS-Remote Sensing: A Comprehensive Approach to Predict Oceanographic Health and Coastal Dynamics

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Remote Sensing in Earth systems sciences 2025年第1期8卷 200-212页

作者： Krishnamoorthy, R. Tanaka, Kazuaki Begum, M. Amina Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai India Department of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan Department of ECE Sri Venkateswaraa College of Technology Tamilnadu Chennai India

The monitoring of oceanographic and coastal dynamics is essential for understanding the effects of climate change, predicting natural disasters, and managing coastal resources. Remote sensing technology, particularly through satellite imagery, has revolutionized the ability to collect vast amounts of environmental data over large spatial and temporal scales. However, the complexity of these datasets, which often include intricate spatial patterns and long-term temporal trends, requires advanced machine learning models capable of handling both dimensions effectively. In this study, we present a Hybrid GIS-Transformer Network for analyzing oceanographic and coastal dynamics using remote sensing data. The proposed model integrates the spatial feature extraction capabilities of the GIS network with the temporal analysis strengths of the transformer model. This approach enables a comprehensive understanding of both spatial and temporal patterns in high-resolution satellite imagery, allowing for more accurate detection and prediction of environmental changes. Our methodology was tested across multiple tasks, including coastal erosion detection, sea surface temperature (SST) prediction, wave height prediction, and ocean currents detection. The Hybrid GIS-Transformer model consistently outperformed existing models such as CNN, ResNet50, MobileNet, EfficientNet, and LSTM. For coastal erosion detection, the hybrid model achieved an accuracy of 96.12%, while for SST prediction, the accuracy was 95.43%. In wave height prediction, the model achieved an accuracy of 93.02%, and for ocean currents detection, an accuracy of 91.88% was recorded. These results demonstrate the model’s superiority in capturing both fine spatial details and long-term temporal dynamics compared with individual models. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Coastal dynamics Deep learning Environmental monitoring Hybrid GIS-Transformer Machine learning Oceanographic analysis Remote sensing Sea surface temperature prediction Spatial feature extraction Temporal analysis Wave height prediction

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An with effective ensemble classifier for network intrusion detection system

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Peer-to-Peer Networking and Applications 2025年第2期18卷

作者： Nandhini, U. Santhosh Kumar, S.V.N. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

The increasing frequency of network attacks poses a significant challenge in maintaining network security. In response to security vulnerabilities, the employment of Intrusion Detection systems (IDS) has become crucial. An IDS is a software application, which monitors network traffic to monitor and identify various malicious actions in the network. Network Intrusion Detection systems (NIDS) specifically examine network traffic to identify and monitor the various malicious activities identified by the network nodes. However, many existing IDS solutions encounter difficulties in achieving optimal feature selection, aiming for high classification accuracy while minimizing false alarm rates. In the proposed system, the Linear Discriminant Analysis (LDA) technique is employed to reduce the dataset dimensionality. Further, an Enhanced Whale Optimization Algorithm (EWOA) is utilized for efficient feature selection for global optimization tasks. To perform classification, an ensemble classifier is proposed which employs Random Forest (RF) and XGBoost. The main objective of the proposed approach is to improve classification accuracy while minimizing the false alarm rate by combining the benefits of RF and XGBoost. The proposed work is implemented by using the python jupyter framework. The experimental results show that the proposed system has an overall classification accuracy of 96.08%, a false alarm rate of 0.01%, and an overall detection rate of 94.03%, by using the NSL-KDD dataset when it is compared with their counterparts. © The Author(s).

关键词： Application programs

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Endpoint Detection in Breast Images for Automatic Classification of Breast Cancer Aesthetic Results 1st

Endpoint Detection in Breast Images for Automatic Classifi...

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1st Deep Breast Workshop on AI and Imaging for Diagnostic and Treatment Challenges in Breast Care, Deep-Breath 2024

作者： Freitas, Nuno Veloso, Carlos Mavioso, Carlos Cardoso, Maria J. Oliveira, Hélder P. Cardoso, Jaime S. Faculdade de Engenharia Universidade do Porto Porto Portugal Institute for Systems and Computer Engineering Technology and Science Porto Portugal Breast Unit Champalimaud Foundation Lisbon Portugal

ISBN: (纸本)9783031777882

Breast cancer is the most common type of cancer in women worldwide. Because of high survival rates, there has been an increased interest in patient Quality of Life after treatment. Aesthetic results play an important role in this aspect, as these treatments can leave a mark on a patient’s self-image. Despite that, there are no standard ways of assessing aesthetic outcomes. Commonly used software such as *** or BAT require the manual annotation of keypoints, which makes them time-consuming for clinical use and can lead to result variability depending on the user. Recently, there have been attempts to leverage both traditional and Deep Learning algorithms to detect keypoints automatically. In this paper, we compare several methods for the detection of Breast Endpoints across two datasets. Furthermore, we present an extended evaluation of using these models as input for full contour prediction and aesthetic evaluation using the *** software. Overall, the YOLOv9 model, fine-tuned for this task, presents the best results considering both accuracy and usability, making this architecture the best choice for this application. The main contribution of this paper is the development of a pipeline for full breast contour prediction, which reduces clinician workload and user variability for automatic aesthetic assessment. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Lung cancer

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Enhanced Federated RL with NEAT, Markov Chains, and Gaussian Processes

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Procedia computer science 2025年 260卷 742-751页

作者： Robert McMenemy Kallinatha H D Hamsaveni M Sachin D N Software Engineer Independent Researcher Glasgow Area Scotland UK Dept of Computer Science and Engineering Siddaganga Institute of Technology Tumakuru India Dept of Computer Science and Engineering Vidyavardhaka College of Engineering Mysuru India

Reinforcement Learning (RL) is a machine learning approach in which an agent learns to make decisions in an environment to maximize a cumulative reward. When combined with NeuroEvolution of Augmenting Topologies (NEAT), RL offers several advantages. NEAT is a genetic algorithm that optimizes the development of artificial neural networks by modifying both their structure and weights. When integrated with RL, NEAT can improve the learning process by merging evolutionary optimization with RL techniques. NEAT has demonstrated significant potential in evolving neural networks for RL tasks. However, traditional centralized training methods encounter scalability and data privacy issues. This paper investigates the integration of NEAT with Federated Learning (FL) and its enhancement with Markov Chains and Gaussian Processes to address certain issues. We propose a new framework that combines NEAT for neural network evolution with TensorFlow Federated (TFF) for decentralized training across multiple clients. Our approach is assessed using the BipedalWalker-v3 environment from OpenAI Gym. The experimental results show that our federated NEAT framework, augmented with Markov Chains and Gaussian Processes, achieves competitive performance while maintaining data privacy and reducing computational overhead on central servers. Additionally, we implement parallelization techniques using concurrent futures to enhance the efficiency of NEAT generations.

关键词： Federated Learning NEAT Reinforcement Learning TensorFlow Federated NeuroEvolution Privacy Decentralized Training Parallelization Markov Chains Gaussian Processes

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Accurate, Secure, and Efficient Semi-Constrained Navigation Over Encrypted City Maps

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 2642-2658页

作者： Li, Meng Chen, Yifei Gao, Jianbo Wu, Jingyu Zhang, Zijian He, Jialing Zhu, Liehuang Conti, Mauro Lin, Xiaodong Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei230002 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230002 China Hefei University of Technology Anhui Province Key Laboratory of Industry Safety and Emergency Technology Intelligent Interconnected Systems Laboratory of Anhui Province Hefei230002 China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100811 China University of Science and Technology of China School of Computer Science and Technology Hefei230026 China Beijing Institute of Technology Southeast Institute of Information Technology Fujian Putian351100 China Chongqing University College of Computer Science Chongqing400044 China University of Padua Department of Mathematics HIT Center Padua35131 Italy Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft2628 CD Netherlands University of Guelph School of Computer Science GuelphONN1G 2W1 Canada

Navigation services enable users to find the shortest path from a starting point S to a destination D, reducing time, gas, and traffic congestion. Still, navigation users risk the exposure of their sensitive location data. Our motivation arises from how users can accurately, securely, and efficiently navigate from S to Dwhile passing through k unordered stops, i.e., midway locations with a non-fixed visiting order. In this work, we formally define Semi-Constrained Navigation (SCN) and present a novel scheme Hermestoachieveaccurate,secure, andefficient SCN. Specifically, weproposeadivide-and-conquerapproachtostrikeagoodbalance betweenaccuracyandefficiency. It recursively depth-first-searches the whole area (a navigation tree) and invokes five carefully-crafted strategies stop-by-stop to compute three subpaths in three se quential subareas. We construct a path-distance oracle to encrypt the road graph and securely implement the strategies by using homomorphic encryption and garble circuits. We formally prove the security in the random oracle model and analyze the search complexity to be less than O(k2). We experiment over a real-world city map and compare with six baselines. Results show that path search with k = 4 among N = 1000 intersections requires 5.58 seconds with a 3.2% distance deviation rate and an 82.5% path similarity. © 2004-2012 IEEE.

关键词： Traffic congestion

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Toward Drought Modeling in South Asia: Machine Learning Approaches, Challenges, and Opportunities

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IEEE Access 2025年 13卷 87654-87671页

作者： Mana Saleh Al Reshan Muhammad Owais Raza Naeem Ahmed Mahoto Adel Rajab Asadullah Shaikh Mohamed A. Elmagzoub Khairan D. Rajab Department of Information Systems College of Computer Science and Information Systems Najran University Najran Saudi Arabia Emerging Technologies Research Laboratory (ETRL) College of Computer Science and Information Systems Najran University Najran Saudi Arabia Department of Computer Engineering Istanbul Sabahattin Zaim University İstanbul Türkiye Department of Software Engineering Mehran University of Engineering and Technology Jamshoro Sindh Pakistan Department of Computer Science College of Computer Science and Information Systems Najran University Najran Saudi Arabia Department of Network and Communication Engineering College of Computer Science and Information Systems Najran University Najran Saudi Arabia

Drought is an environmental and economic problem. Sustainable ecosystems, water resources, food security, and ecosystem sustainability. Machine all are severely affected by drought. Due to the increasing frequency and severity of droughts caused by climate change. Effective drought modeling is crucial for early warning systems and risk mitigation. Recent advances in machine learning (ML) and deep learning (DL) techniques have been developed as potential drought modeling tools, which offer accurate and reliable drought detection. This review paper summarizes the drought modeling(Drought Prediction, Drought Detection and Drought Forecasting) approaches. This paper focuses on three main aspect. 1) The selection of the region for this study, for this study South Asia(SA) is selected as region of interest (ROI) that offer accurate drought modeling, providing policymakers and decision-makers with insightful information. The geographical scope of this study is the region of South Asia. This region is selected because of its heavy reliance on agriculture. 2) This paper focuses on the current and future trends, challenges, and advances of and vulnerability to droughts. The review offers a thorough grasp of how drought conditions are evaluated by gathering and analyzing the most important drought indicators and metrics specific to South Asia. The paper explores the current state-of-the-art in ML and DL for drought modeling. 3) This review encapsulates the indicator and metrics (Complex Machine learning and deep learning models) for drought modeling which are most relevant to the SA region. This study sum up as most common challenges in drought modeling are, highlighting current challenges such as incomplete and inconsistent datasets, lack of explainable and interpretable models, and unavailability of data for model uncertainty analysis. This study proposes that these problems can be solved with modern machine learning techniques such as explainable machine learning and federa

关键词： Droughts Machine learning Asia Deep learning Indexes Water resources Remote sensing Predictive models computer science Climate change Environmental monitoring Economics

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Sample Compression for Continual Learning

arXiv

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arXiv 2025年

作者： Comeau, Jacob Bazinet, Mathieu Germain, Pascal Subakan, Cem Computer Science and Software Engineering Department Laval University Canada Mila - Quebec Artificial Intelligence Institute Canada Computer Science and Software Engineering Department Concordia University Canada

Continual learning algorithms aim to learn from a sequence of tasks, making the training distribution non-stationary. The majority of existing continual learning approaches in the literature rely on heuristics and do not provide learning guarantees for the continual learning setup. In this paper, we present a new method called ‘Continual Pick-to-Learn’ (CoP2L), which is able to retain the most representative samples for each task in an efficient way. The algorithm is adapted from the Pick-to-Learn algorithm, rooted in the sample compression theory. This allows us to provide high-confidence upper bounds on the generalization loss of the learned predictors, numerically computable after every update of the learned model. We also empirically show on several standard continual learning benchmarks that our algorithm is able to outperform standard experience replay, significantly mitigating catastrophic forgetting. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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