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12th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Shambharkar, Shubhangi Kumar, Anil Dari, Sukhvinder Singh Joshi, Ira Bhoite, Sonali Prashant Shirkande, Aparna S. Indurkar, Ira Computer Technology Department Yeshwantrao Chavan College of Engineering Maharashtra Nagpur India Department of Artificial Intelligence and Data Science Poornima Institute of Engineering and Technology Rajasthan Jaipur India Pune India Electronics and Communication Engineering Arya College of Engineering Rajasthan Jaipur India Department of Information Technology Vishwakarma Institute of Information Technology Pune Maharashtra Pune India S. B. Patil College of Engineering Indapur Maharashtra Pune India Department of Neuroscience DMCOP Datta Meghe College of Physiotherapy Maharashtra Nagpur India

ISBN: (纸本)9789819601462

The overall increase in cyber-attacks which takes place has had a major impact on the working of an organization and its related forensics department. With increased amount of breaches, the technical aspects of a developer are at stake. This had therefore led to the evolution of innovation and automation in the security domain. However, the risks which are associated with the same remains stagnant;since the digital transformation have been on a constant improvization factor with respect to the strategies thus involved. In such a scenario, the usage and implementation and artificial intelligence (AI) becomes a mandatory task. Cyber-attacks and breaches which are based on AI concepts have had a major impact and have revolutionized the overall working of the networking sector. An increase in the cyber-attack surface has been observed vividly. With this increase in the resilience of the networking sector, the author of the proposed research paper tends to highlight the need for AI- and ML-based decisive strategies which must be amalgamated in cyber security. The study also aims to focus on the role of various cyber forensic departments that have been successfully adopted by multiple companies. An understanding and working of a cyber-criminal is also needed in such a scenario. Hence, the knowledge of cyber security along with cyber forensic appears to be fruitful. The research paper thereby presents a systematic review of AI amalgamated with cyber forensics and provides various problem statements and methodologies associated with the same. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： AI Cyber forensics Methodologies ML Resilience

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CAMP-GNN: A Constraint-Aware Message-Passing model for Optimal Resource Allocation in Software Projects

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IEEE Access 2025年 13卷 87965-87977页

作者： Asif, Muhammad Aldosari, Fahd M. Badawood, Donia Y. Salam, Abdu Bacha Khan University Department of Computer Science Charsadda24420 Pakistan Umm Alqura University Department of Computer and Networks Engineering Makkah Saudi Arabia Umm Alqura University Department of Data Science Makkah Saudi Arabia Abdul Wali Khan University Department of Computer Science Mardan23200 Pakistan

Resource allocation in software projects is a critical challenge, with inefficiencies in handling dynamic constraints such as skill mismatches, evolving task dependencies, and budget overruns leading to widespread cost and schedule overruns. To address these issues, this paper introduces CAMP-GNN, a novel constraint-aware graph neural network (GNN) framework designed to optimize resource allocation in software projects, addressing challenges like skill mismatches, evolving task dependencies, and budget overruns. These issues often lead to significant cost and schedule overruns, and CAMP-GNN aims to resolve them by dynamically adapting to project constraints. The framework integrates urgency, skill alignment, and budget slack directly into its message-passing layers, allowing for more precise and context-aware task-resource assignments. A key contribution is SoftStat, the first structured dataset that combines resource pools, allocations, and project details, enabling the modeling of complex task-resource dependencies and financial/temporal constraints. CAMP-GNN also introduces domain-specific message aggregation rules to unify urgency, skill alignment, and budget slack, advancing GNNs beyond basic relational modeling. A novel preprocessing pipeline is proposed to convert raw project timelines, skill dependencies, and budget thresholds into machine-learnable features, ensuring effective integration with computational models. Validated on both synthetic and real-world datasets, CAMP-GNN demonstrates superior performance with a 0.12 MAE, RMSE of 0.18 in allocation, F1 of 0.89 in budget violations, and 085 in deadline violations, 18% cost savings, and a 27% reduction in deadline breaches, outperforming existing methods. Future work will focus on scaling the framework for large-scale projects and improving interpretability for broader adoption. © 2013 IEEE.

关键词： Resource allocation

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FedCosA: Optimised Federated Learning Model Using Cosine Annealing

FedCosA: Optimised Federated Learning Model Using Cosine Ann...

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2025 International Conference on Emerging Technologies for Intelligent Systems, ETIS 2025

作者： John, Binu Menon, Varun G. Adhikari, Mainak Scms School of Engineering and Technology Department of Computer Science & Engineering Kerala Ernakulam683576 India Apj Abdul Kalam Technological University Kerala Thiruvananthapuram695016 India School of Data Science Indian Institute of Science Education and Research Kerala Thiruvananthapuram695551 India

ISBN: (纸本)9798331507541

The Federated Learning paradigm allows multiple devices or nodes to contribute to model training without sharing raw data, ensuring that sensitive information remains on the client side while still benefiting from collective learning. However, FL suffers several problems, including slow convergence, overfitting, and communication cost, particularly in Non-Independent and Identically Distributed (Non-IID) data settings. To solve these concerns, we offer an improved Federated Averaging (FedAvg) method that includes the Adam optimizer, cosine annealing learning rate scheduler, and weight decay (L2 regularisation). The Adam optimizer generates adaptive learning rates, but the cosine annealing schedule refines the learning rate over time for greater stability. Weight decay reduces overfitting by penalizing big model parameters. Furthermore, picking a subset of customers in each cycle lowers communication costs. Experimental findings on the MNIST dataset show that our strategy outperforms existing FL algorithms like FedAvg, FedSGD, FedNova, and FedSAM in test accuracy and loss minimization while significantly lowering communication load and training time. These enhancements make the suggested approach more effective and scalable for FL across several settings. © 2025 IEEE.

关键词： Federated learning

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T³Planner: Multi-Phase Planning Across Structure-Constrained Optical, IP, and Routing Topologies

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IEEE Journal on Selected Areas in Communications 2025年第5期43卷 1823-1839页

作者： Yijun Hao Shusen Yang Fang Li Yifan Zhang Cong Zhao Xuebin Ren Peng Zhao Chenren Xu Shibo Wang National Engineering Laboratory for Big Data Analytics Xi’an Jiaotong University Xi’an China National Engineering Laboratory for Big Data Analytics and the Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an China School of Computer Science Peking University Beijing China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China National Engineering Laboratory for Big Data Analytics XiÃan Jiaotong University XiÃan China

Network topology planning is an essential multi-phase process to build and jointly optimize the multi-layer network topologies in wide-area networks (WANs). Most existing practices target single-phase/layer planning, and are incapable of satisfying all rigorous topological structure constraints (e.g., dual-homing rings) defined by network standards and operators, especially in large-scale networks. These significantly limit their usability and performance in production networks. We consider a general topology planning problem with typical structure constraints over three essential phases (greenfield, reconfiguration, and site expansion) and topological layers (optical, IP, and routing topologies). We present, T3Planner, a novel practical solver to this problem in production. Specifically, we develop a structure-driven encoder based on graph neural network (GNN) for concise structure encoding, and design a new learning framework with optical-centric layer compression/reconstruction and rule-aided reinforcement learning (RL) for fast convergence and high performance. Extensive experiments on nine real topologies demonstrate that T3Planner scales to large optical networks with hundreds of sites, saves 46.6% cost, and supports $3.12\times $ more demand when compared to related existing approaches.

关键词： Topology Network topology Planning IP networks Optical fiber networks Routing Optical fibers Optical feedback Production Graph neural networks

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A Trace-Selection Preprocessing Approach of Discovering Structurally Complete ICN-Process Models from Process Logs 3rd

A Trace-Selection Preprocessing Approach of Discovering Str...

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3rd International Conference on Advances in Information and Communication Technology, ICTA 2024

作者： Pham, Dinh-Lam Batzaya, Tsagaantsooj Kim, Kyoung-Sook Chang, Jeong-Hyun Kim, Kwanghoon Pio CCSRI Kyonggi University Gyeonggi Suwon16227 Korea Republic of Department of Public Safety Bigdata Graduate School of Kyonggi University Gyeonggi Suwon16227 Korea Republic of Data Process Engineering Research Laboratory Faculty of AI Computer Science and Engineering Kyonggi University Gyeonggi Suwon16227 Korea Republic of

ISBN: (纸本)9783031809422

In business process life-cycle management and reengineering through process mining, it is crucial for the process mining system to discover structurally safe and complete business process models from process logs. However, most process mining systems typically suffer from discovering inaccurate and unstructured business process models due to various types of anomalous traces hidden in process logs. In this paper, we therefore propose a process log preprocessing approach that purifies a corresponding process log dataset by selecting a group of reasonable process log traces and applying to discover an essential business process model satisfying the structural completeness requirement of matched-pairing and proper-nesting properties. The theoretical basis of the proposed approach is the mathematical graph model of information control net model that is the typical representation of business process models. Finally, we practically demonstrate the conceptual excellence and functional correctness of the proposed preprocessing approach through experimental verifications using real business process enactment log datasets available in the 4TU Center for Research data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Reengineering

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Cloud-Enhanced Transfer Learning Framework for Medical Image Analysis: Advancing Diagnostic Speed and Accuracy in Healthcare 3

Cloud-Enhanced Transfer Learning Framework for Medical Image...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Prabu, S. Chandra, N. Subhash Bhavani, M. Varma, V. K. Chaitanya Soni, Mukesh Mahendra Institute of Technology Department of Electronics and Communication Engineering Namakkal India Cvr College of Engineering Department of Computer Science and Engineering Mangalpalle India Rajalakshmi Institute of Technology Department of Artificial Intelligence and Data Science Chennai India Sagi Rama Krishnam Raju Engineering College Department of Mechanical Engineering Bhimavaram India Dr. D. Y. Patil Vidyapeeth Dr. D. Y. Patil School of Science & Technology Pune India

ISBN: (纸本)9798331508456

When it comes to medical image analysis, problems arise due to the scarce amount of data and computational resources in medical environments. This is because, as earlier stated, cloud settings demand efficient models capable of delivering more reliable diagnostic results without straining the cloud's computing resources. In this work, we presented a transfer learning based on DenseNet-201 as well ResNet-152 architectures with a new large dataset containing 15,000 medical images of different modality. By utilizing the distributed cloud computing and hypermeters precision strategy, the proposed model was found to have 95.8 % of accuracy, 94.2% sensitivity, and 96.3%specificity. It also reduced the processing time by sixty-eight percent over standard methods across the facilities. Five different datasets validated the proposed approach, achieving an external accuracy of 92.4%, indicating improved generalization performance. The enhancement of accuracy to approximately 90% and the reduction in required computation demonstrate its feasibility for extensive clinical applications on the cloud, spanning across hospitals. © 2025 IEEE.

关键词： Distributed cloud

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An Overview of IoT-based ECG Wavelength Analysis for Remote Patient Monitoring

An Overview of IoT-based ECG Wavelength Analysis for Remote ...

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Advancement in Computation & computer Technologies (InCACCT), International Conference on

作者： Kamrul Islam Najnin Sultana Nishu Md. Samiul Islam Md Tanvir Chowdhury Habibur Rahman Mahabubur Rahman Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Statistics and Data Science Jahangirnagar University Dhaka Bangladesh

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Adopting new IoT technologies holds great potential for changing the patient healthcare landscape. While IoT-enabled healthcare applications have become increasingly popular, it’s important to note that rural areas often face poor healthcare infrastructure and limited access to quality medical care. However, IoT is a valuable asset, especially in emergencies like Covid-19. To make IoT even more helpful in this area, we will explore the existing IoT systems for Remote Patient Monitoring (RPM), reveal their shortcomings, and provide systematic fixes. In this study, we look at the advantages of integrating IoT devices with sensors, cloud technologies, and ECG wavelength analysis, particularly in RPM. IoT-enabled devices are adept at collecting and sending real-time information, enabling healthcare professionals to remotely monitor vital signs, essential indicators, and health parameters. While there are challenges associated with empowering remote patient monitoring with IoT, it can also improve healthcare outcomes and reduce costs. This technology fusion provides a glimmer of hope during a pandemic, promoting not only better health but a more fulfilling life. To sum up, this inquiry prepares the ground for a future where healthcare is personalized and patient-centric, with a range of approaches, resilience in the face of failure, and triumphs in overcoming challenges.

关键词： Patient monitoring Cloud computing Medical services Electrocardiography Real-time systems Sensors Mobile applications Internet of Things Servers Faces

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Enhancing acute leukemia classification through hybrid fuzzy C means and random forest methods

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Measurement: Sensors 2025年 39卷

作者： Lakshmi Narayanan, K. Santhana Krishnan, R. Harold Robinson, Y. Vimal, S. Rashid, Tarik A. Kausha, Chetna Mehedi Hassan, Md. Department of Electronics and Communication Engineering Francis Xavier Engineering College Tirunelveli India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology India Department of Computer Science and Engineering Francis Xavier Engineering College Tirunelveli India Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore India Department of Computer Science and Engineering University of Kurdistan Hewler Erbil Iraq Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Discipline Khulna University Khulna9208 Bangladesh

Leukemia is a category of cancer that is normally found in blood and bone marrow, and which causes rapid abnormal development in the making of white blood cells than the required amount. The produced white blood cells could be ineffective to fight against harmful infections and can even prejudice or restrict the capability of the bone marrow to generate red blood cells and blood platelets. If this is not diagnosed in the earlier stage, it may start to affect the function of the internal organs and cause death. Normally, entire blood counts image analysis and diagnosis are done manually which is an inaccurate and time-intensive process. In this proposed method the classification is tested with two Machine Learning algorithms which are Hybrid Fuzzy C Means (FCM) and Random Forest algorithm (RF) and Support Vector Machine for the detection and classification of Acute Leukemia disease and their performance was evaluated. The dataset comprised of 8637 images which included infected images, normal images and augmented images from different dataset providers and RGB to CMYK conversion with histogram equalization is applied for pre-processing, K means for Image Segmentation. Experimental results convey that Hybrid FCM and RF Algorithm attained an accuracy of 99.06 %, a sensitivity of 99.4 %, and a specificity of 97.8 % respectively, and the ROC (Receiver Operating Characteristic) curve shows that the result produced by the Hybrid FCM & RF based Classifier is best suitable in diagnosing the classification of the Acute Leukemia disease. The tool used for developing the proposed method was Matlab R2018 software. © 2025 The Authors

关键词： Lung cancer

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Piccolo: Large-Scale Graph Processing with Fine-Grained in-Memory Scatter-Gather

Piccolo: Large-Scale Graph Processing with Fine-Grained in-M...

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IEEE Symposium on High-Performance computer Architecture

作者： Changmin Shin Jaeyong Song Hongsun Jang Dogeun Kim Jun Sung Taehee Kwon Jae Hyung Ju Frank Liu Yeonkyu Choi Jinho Lee Department of Electrical and Computer Engineering Seoul National University School of Data Science Old Dominion Univeristy Samsung Electronics

ISBN: (数字)9798331506476

ISBN: (纸本)9798331506483

Graph processing requires irregular, fine-grained random access patterns incompatible with contemporary off-chip memory architecture, leading to inefficient data access. This inefficiency makes graph processing an extremely memory-bound application. Because of this, existing graph processing accelerators typically employ a graph tiling-based or processing-in-memory (PIM) approach to relieve the memory bottleneck. In the tiling-based approach, a graph is split into chunks that fit within the on-chip cache to maximize data reuse. In the PIM approach, arithmetic units are placed within memory to perform operations such as reduction or atomic addition. However, both approaches have several limitations, especially when implemented on current memory standards (i.e., DDR). Because the access granularity provided by DDR is much larger than that of the graph vertex property data, much of the bandwidth and cache capacity are wasted. PIM is meant to alleviate such issues, but it is difficult to use in conjunction with the tiling-based approach, resulting in a significant disadvantage. Furthermore, placing arithmetic units inside a memory chip is expensive, thereby supporting multiple types of operation is thought to be impractical. To address the above limitations, we present Piccolo, an end-to-end efficient graph processing accelerator with fine-grained in-memory random scatter-gather. Instead of placing expensive arithmetic units in off-chip memory, Piccolo focuses on reducing the off-chip traffic with non-arithmetic function-in-memory of random scatter-gather. To fully benefit from in-memory scatter-gather, Piccolo redesigns the cache and miss-handling architecture (MHA) of the accelerator such that it can enjoy both the advantage of tiling and in-memory operations. Piccolo achieves a maximum speedup of 3.28 × and a geometric mean speedup of 1.62 ×, along with up to 59.7% reduction in energy consumption across various and extensive benchmarks.

关键词： Energy consumption Memory architecture Bandwidth Benchmark testing System-on-chip Standards Faces Arithmetic

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Decentralized Federated Graph Learning with Lightweight Zero Trust Architecture for Next-Generation Networking Security

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 1908-1922页

作者： Zhou, Xiaokang Liang, Wei Wang, Kevin I-Kai Yada, Katsutoshi Yang, Laurence T. Ma, Jianhua Jin, Qun Kansai University Faculty of Business Data Science Osaka565-0823 Japan RIKEN Center for Advanced Intelligence Project RIKEN Tokyo103-0027 Japan Hunan University of Technology and Business School of Computer Science Changsha410205 China Xiangjiang Laboratory Changsha410205 China The University of Auckland Department of Electrical Computer and Software Engineering Auckland1010 New Zealand St. Francis Xavier University Department of Computer Science AntigonishNSB2G 2W5 Canada Hosei University Faculty of Computer and Information Sciences Chiyoda-ku102-8160 Japan Waseda University Faculty of Human Sciences Tokorozawa359-1192 Japan

The rapid development and usage of digital technologies in modern intelligent systems and applications bring critical challenges on data security and privacy. It is essential to allow cross-organizational data sharing to achieve smart service provisioning, while preventing unauthorized access and data leak to ensure end users' efficient and secure collaborations. Federated Learning (FL) offers a promising pathway to enable innovative collaboration across multiple organizations. However, more stringent security policies are needed to ensure authenticity of participating entities, safeguard data during communication, and prevent malicious activities. In this paper, we propose a Decentralized Federated Graph Learning (FGL) with Lightweight Zero Trust Architecture (ZTA) model, named DFGL-LZTA, to provide context-aware security with dynamic defense policy update, while maintaining computational and communication efficiency in resource-constrained environments, for highly distributed and heterogeneous systems in next-generation networking. Specifically, with a re-designed lightweight ZTA, which leverages adaptive privacy preservation and reputation-based aggregation together to tackle multi-level security threats (e.g., data-level, model-level, and identity-level attacks), a Proximal Policy Optimization (PPO) based Deep Reinforcement Learning (DRL) agent is introduced to enable the real-time and adaptive security policy update and optimization based on contextual features. A hierarchical Graph Attention Network (GAT) mechanism is then improved and applied to facilitate the dynamic subgraph learning in local training with a layer-wise architecture, while a so-called sparse global aggregation scheme is developed to balance the communication efficiency and model robustness in a P2P manner. Experiments and evaluations conducted based on two open-source datasets and one synthetic dataset demonstrate the usefulness of our proposed model in terms of training performance, computa

关键词： Federated learning

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