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International Communication Systems and Networks and Workshops, COMSNETS

作者： Suman Nandi Giridhar Maji Animesh Dutta Dept. of Computer Science and Engineering National Institute of Technology Durgapur West Bengal India Dept. of Electrical Engineering Asansol Polytechnic West Bengal India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

Exploring influential spreaders and predicting missing links in complex networks is essential for understanding and effectively controlling network dynamics. This paper presents a Graph Convolutional Network (GCN)-based link prediction method to estimate the probability of future link formation. We incorporate node features that capture local and global topological connectivity structures and feed these into the GCN model, where convolutional layers aggregate neighboring information and transform node features. This approach enables the model to capture structural patterns by integrating local and global information from neighboring nodes. In the final layer, the GCN model computes a prediction score representing the likelihood of an edge’s existence, using insights gained during training. Finally, considering the predicted links, we update the network structure and introduce a novel centrality method called Emerging Spreader Centrality (ESC) to identify emerging spreaders within this augmented network. We conduct two separate experiments to evaluate the performance of the GCN-based link prediction and the ESC method, comparing their effectiveness with various state-of-the-art methods. Results demonstrate that our approach not only effectively predicts future links but also identifies emerging spreaders in the augmented networks.

关键词： Training Graph convolutional networks Communication systems Computational modeling Aggregates Transforms Complex networks Predictive models Feeds

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2021 Monthly Rice Production in Chinese Coastal Provinces 3

2021 Monthly Rice Production in Chinese Coastal Provinces

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3rd International Conference on Big Data Analytics and Practices, IBDAP 2022

作者： Parmar, Ajeet Munasinghe, Thilanka Tubbs, Heidi Anyamba, Assaf Rensselaer Polytechnic Institute Department of Electrical Computer and Systems Engineering Troy United States Rensselaer Polytechnic Institute Department of Information Technology and Web Science Troy United States Oak Ridge National Laboratory Geospatial Science and Human Security Division Oak Ridge United States

ISBN: (数字)9781665469388

ISBN: (纸本)9781665469388

This paper explores the dynamics of rice production in the Chinese provinces of Liaoning, Jilin, Heilongjiang, Shanghai, Jiangsu, and Zhejiang and seeks to predict monthly rice production in the months of April through October using precipitation and Normalized Difference Vegetation Index as the predictor variables available. We utilize ridge and lasso regression models to predict the rice yield. Results indicate that a lasso regression model with an R2 value of 0.9991501 with an adjusted R2 value of 0.9991502 and a ridge regression model with an R2 value of 0.9865443 with an adjusted R2 value of 0.9870637 are possible. The lasso regression model does not account for all predictor variables while the ridge regression model does. Both models could be expanded upon to include more observations. © 2022 IEEE.

关键词： Regression analysis

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Federated Learning Augmented with Convolutional Neural Networks for Brain Cancer Classification

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Procedia computer science 2025年 258卷 2617-2626页

作者： Joseph Bamidele Awotunde Christiana Oluwakemi Abikoye Biswajit Brahma Esau Taiwo Oladipupo Anjan Bandyopadhyay McKesson Corporation USA 32559 Lake Bridgeport St Fremont CA 94555 USA Computer Science Department The Federal Polytechnic Bida Niger State Nigeria School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India

Leveraging MRI images for the categorization of brain tumors is a critical yet complex endeavor within the realm of medical imaging. Precise identification is crucial for prompt and effective treatment; however, the intricate nature of tumor morphology and variability in imaging often pose obstacles. Traditional practices largely rely on the manual examination of MRI images, supplemented by classic machine learning (ML) strategies. However, these techniques typically lack the robustness and scalability necessary for precise and automated tumor categorization. Key challenges include extensive manual processing, vulnerability to human error, limited ability to handle voluminous datasets, and insufficient adaptability to diverse tumor forms and imaging scenarios. To address these challenges, this paper introduces the use of federated learning, a ML-based model that integrates data from various entities, coupled with Convolutional Neural Networks (CNNs) for the classification of brain cancer. Federated learning facilitates the decentralized training of models across numerous clients while preserving the confidentiality of data, thus meeting the critical demand for privacy in the management of medical data. The model’s design incorporates transfer learning and a pre-trained CNN to refine its performance on the brain tumor dataset. The empirical evidence suggests that this integrated approach surpasses conventional techniques in the precise classification of brain cancer types. These insights underscore the promise of federated learning in conjunction with CNNs for medical image analysis, particularly in diagnosing brain cancer. This methodology could enhance the precision and efficiency of cancer classification, leading to improved treatment strategies and, ultimately, better patient care outcomes.

关键词： Federated learning Machine learning Brain tumor MRI image , Data privacy Cancer classification

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Developing a Robust Intrusion Detection System Using SMOTE and Hybrid SVNN Model 4

Developing a Robust Intrusion Detection System Using SMOTE a...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Kiranmayee, B. Devi, M. Sharmila Susheela, Kalva Dhumpati, Raghu Penubaka, Kiran Kumar Reddy Naidu, U Ganesh Kurnool India Santhiram Engineering College Department of Cse Nandyal India Sir C R Reddy College for Women Department of Computer Science Eluru India Bahrain Polytechnic College Department of CSE Bahrain Mlr Institute of Technology Department of CSE-AIML Hyderabad India B v Raju Institute of Technology Telangana Medak India

ISBN: (纸本)9798331523923

This paper proposes an integrated approach to build up IDS with proper effectiveness toward the rising need for strong network security. Network traffic anomaly detection and classification are one of the major aims and enhance the security layer against various types of cyber threats. This study is a methodical approach in which a diverse set of data is first extracted from Kaggle. The collected dataset is a comprehensive one that includes various kinds of network traffic data. The first step includes preprocessing the data, i.e., handling missing values, removing erroneous entries, and dealing with outliers using the Interquartile Range (IQR) method. To counter class imbalance, Synthetic Minority Oversampling Technique (SMOTE) is utilized in generating synthetic samples in underrepresented classes for generalization of models. The feature selection algorithm relies on the Chi-Square Lasso Feature Selection method. This method unites Chi-Square tests to select statistically significant features with Lasso regularization for feature dimension reduction with the goal of overfitting minimization. For building models, a hybrid of Support Vector Machine and K-Nearest Neighbors, which is termed as SVNN, is used. By using soft voting to aggregate the outputs of SVM and KNN, the proposed SVNN model improves the prediction accuracy, the degree of robustness, and adaptability. The presented methodology has achieved high classification accuracy 93.28%, precision 93.49%, recall 93.28%, F1 score 93.23%, and low RMSE of 0.2592. These metrics demonstrate that the model is reliable and of practical use for intrusion detection in dynamic, high-dimensional environments of networks, providing a proper solution to modern security network challenges. © 2025 IEEE.

关键词： Network intrusion

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Study on indoor pollutants emission in Akure, Ondo State, Nigeria

Arab Gulf Journal of Scientific Research

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Arab Gulf Journal of Scientific Research 2024年第4期42卷 1643-1663页

作者： Abulude, Francis Olawale Suriano, Domenico Oluwagbayide, Samuel Dare Akinnusotu, Akinyinka Abulude, Ifeoluwa Ayodeji Awogbindin, Emmanuel Environmental and Sustainable Research Group Science and Education Development Institute Akure Nigeria Department of Sustainable Development Research Center of Brindisi ENEA-Italian National Agency for New Technologies Energy and Environment Brindisi Italy Department of Agricultural and Bio-Environmental Engineering Federal Polytechnic Idah Nigeria Department of Science Laboratory Technology Rufus Giwa Polytechnic Owo Nigeria Department of Agricultural Economics Bioeconomy and Food Systems Justus – Liebig University (JLU) Giessen Germany Department of Computer Science Bamidele Olumilua University of Education Science and Technology Ekiti Ikere Nigeria

Purpose: This study aimed to characterize the concentrations of indoor pollutants (such as carbon dioxide (CO2), ozone (O3), nitrogen dioxide (NO2) and sulfur dioxide (SO2), as well as particulate matter (PM) (PM1, PM... 详细信息

Purpose: This study aimed to characterize the concentrations of indoor pollutants (such as carbon dioxide (CO₂), ozone (O₃), nitrogen dioxide (NO₂) and sulfur dioxide (SO₂), as well as particulate matter (PM) (PM₁, PM_2.5 and PM₁₀) in Akure, Nigeria, as well as the relationship between the parameters’ concentrations. Design/methodology/approach: The evaluation, which lasted four months, used a low-cost air sensor that was positioned two meters above the ground. All sensor procedures were correctly carried out. Findings: CO₂ (430.34 ppm), NO₂ (93.31 ppb), O₃ (19.94 ppb), SO₂ (40.87 ppb), PM₁ (29.31 µg/m3), PM_2.5 (43.56 µg/m³), PM₁₀ (50.70 µg/m³), temperature (32.4°C) and relative humidity (50.53%) were the average values obtained. The Pearson correlation depicted the relationships between the pollutants and weather factors. With the exception of April, which had significant SO₂ (18%) and low PM₁₀ (49%) contributions, NO₂ and PM₁₀ were the most common pollutants in all of the months. The mean air quality index (AQI) for NO₂indicated that the AQI was “moderate” (51–100). In contrast to SO₂, whose AQI ranged from “moderate” to “very unhealthy,” O₃'s AQI ranged from “good” (50) to “unhealthy” (151–200). Since PM₁, PM_2.5 and PM₁₀ made up the majority of PC1’s contribution, both PM_2.5 and PM₁₀ were deemed “hazardous.” Practical implications: The practical implication of indoor air pollution is long-term health effects, including heart disease, lung cancer and respiratory diseases such as emphysema. Indoor air pollution can also cause long-term damage to people’s nerves, brain, kidneys, liver and other organs. Originality/value: Lack of literature in terms of indoor air qu

关键词： Air quality index Indoor air quality Nigeria Pollutants United States environmental protection agency (USEPA) World health organization (WHO)

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Enabling Cross-Language Data Integration and Scalable Analytics in Decentralized Finance

arXiv

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arXiv 2023年

作者： Flynn, Conor Bennett, Kristin P. Erickson, John S. Green, Aaron Seneviratne, Oshani Electrical and Computer Systems Engineering Rensselaer Polytechnic Institute Troy United States Mathematical and Computer Sciences Rensselaer Polytechnic Institute Troy United States Future of Computing Institute Rensselaer Polytechnic Institute Troy United States Mathematical Sciences Rensselaer Polytechnic Institute Troy United States Computer Science Rensselaer Polytechnic Institute Troy United States

With the agile development process of most academic and corporate entities, designing a robust computational back-end system that can support their ever-changing data needs is a constantly evolving challenge. We propose the implementation of a data and language-agnostic system design that handles different data schemes and sources while subsequently providing researchers and developers a way to connect to it that is supported by a vast majority of programming languages. To validate the efficacy of a system with this proposed architecture, we integrate various data sources throughout the decentralized finance (DeFi) space, specifically from DeFi lending protocols, retrieving tens of millions of data points to perform analytics through this system. We then access and process the retrieved data through several different programming languages (R-Lang, Python, and Java). Finally, we analyze the performance of the proposed architecture in relation to other high-performance systems and explore how this system performs under a high computational load. © 2023, CC BY-NC-SA.

关键词： Finance

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Investigation of a New Stator Permanent Magnet with the effects of Flux-Reversal and Flux-Switching

Investigation of a New Stator Permanent Magnet with the effe...

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Power Electronic and Drive Systems and Technologies Conference (PEDSTC)

作者： Mohammad Reza Sarshar Mohammad Amin Jalali Kondelaji Mojtaba Mirsalim Seyed Hamid Montazeri Department of Electrical Engineering Amirkabir University of Technology (Tehran Polytechnic) Tehran Iran Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology (MIT) Cambridge USA

ISBN: (数字)9798331533946

ISBN: (纸本)9798331533953

This article studies a new stator-permanent magnet (PM) motor with flux-switching (FS) and flux-reversal (FR) effects synergies. The proposed structure benefits from the splitting stator pole PMs and consequent-pole FRPM, which reduces the PM utilization compared to the conventional ones. Also, the proposed structure is optimized by a random search multiobjective algorithm to attain the structure with the highest output torque and minimum torque ripple. Additionally, the proposed motor is compared to a conventional FSPM to investigate the advantages of the main motor. Using finite element analysis (FEA) studies, the flux density distribution results show that the FR effect lowers the saturation risk compared to FSPM. Also, the back-EMF generation of the proposed motor has improved by 135% compared to its benchmark. Correspondingly, fullload results show that the proposed topology has enhanced the average torque by 75% and deteriorated the ripple by about 55% compared to FSPM. Therefore, the proposed structure offers a profitable for high torque density applications.

关键词： Magnetic flux Torque Switches Stators Motors Permanent magnets Power electronics Topology Torque measurement Saturation magnetization

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Smart Production and Manufacturing System Using Digital Twin Technology and Machine Learning

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SN computer science 2023年第5期4卷 1-10页

作者： Yadav, Ranjeet Roopa, Y. Mohana Lavanya, M. Ramesh, J.V.N. Chitra, N. Thulasi Babu, Gadde Raghu Electronics and Communication Engineering Government Polytechnic Daman UT Administration of Dadra and Nagar Haveli and Daman and Diu Daman and Diu 396210 India Department of CSE Institute of Aeronautical Engineering Telangana Hyderabad India Department of CSE Sri Venkateswara College of Engineering & Technology (Autonomous) R.V.S. Nagar Andhra Pradesh Chittoor 517127 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Dist. AP Vaddeswaram India Computer Science and Information Technology MLR Institute of Technology Hyderabad India Department of Mechanical Engineering VNR Vignana Jyothi Institute of Engineering and Technology Nizampet (S.O) Telangana Hyderabad 500090 India

Adoption of digital twin (DT) in smart factories, which simulates an actual system that is manufacturing conditions and updates them in real-time, increased the output and decreased the costs and energy use which were some ways that this manifested. Fast-changing consumer demands have caused a sharp increase in factory transition in addition to producing fewer life cycles of a product. Such scenarios cannot be handled by conventional simulation and modeling techniques;we suggest a general framework for automating the creation of simulation models that are data-driven as the foundation for smart factory DTs. Our proposed framework stands out thanks to its data-driven methodology, which takes advantage of recent advances in machine learning and techniques for process mining, constant model validation, and updating. The framework's objective is to completely define and reduce the requirement for specialist knowledge to get the appropriate simulation models. A case study is used to demonstrate our framework. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Data-driven Digital twin Energy consumption Smart factories

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Adaptive Knowledge Graphs Enhance Medical Question Answering: Bridging the Gap Between LLMs and Evolving Medical Knowledge

arXiv

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arXiv 2025年

作者： Rezaei, Mohammad Reza Fard, Reza Saadati Parker, Jayson L. Krishnan, Rahul G. Lankarany, Milad Department of Biomedical Engineering University of Toronto Canada Department of Computer Science Worcester Polytechnic Institute United States Department of Biology University of Toronto Mississauga Canada Department of Computer Science University of Toronto Canada Vector Institute Canada

Large Language Models (LLMs) have greatly advanced medical Question Answering (QA) by leveraging vast clinical data and medical literature. However, the rapid evolution of medical knowledge and the labor-intensive process of manually updating domain-specific resources can undermine the reliability of these systems. We address this challenge with Adaptive Medical Graph-RAG (AMG-RAG), a comprehensive framework that automates the construction and continuous updating of Medical Knowledge Graphs (MKGs), integrates Chain-of-Thought (CoT) reasoning, and retrieves current external evidence (e.g., PubMed, WikiSearch). By dynamically linking new findings and complex medical concepts, AMG-RAG not only boosts accuracy but also enhances interpretability for medical queries. Evaluations on the MEDQA and MEDMCQA benchmarks demonstrate the effectiveness of AMG-RAG, achieving an F1 score of 74.1% on MEDQA and an accuracy of 66.34% on MEDMCQA—surpassing both comparable models and those 10 to 100 times larger. Importantly, these improvements are achieved without increasing computational overhead, underscoring the critical impact of automated knowledge graph generation and external evidence retrieval in delivering up-to-date, trustworthy medical insights. © 2025, CC BY.

关键词： Knowledge graph

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Many-Objective Multi-Verse Optimizer (MaOMVO): A Novel Algorithm for Solving Complex Many-Objective engineering Problems

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Journal of The Institution of Engineers (India): Series C 2024年第6期105卷 1467-1502页

作者： Kalita, Kanak Jangir, Pradeep Pandya, Sundaram B. Shanmugasundar, G. Chohan, Jasgurpreet Singh Abualigah, Laith Department of Mechanical Engineering Vel Tech Rangarajan Dr Sagunthala R &D Institute of Science and Technology Avadi600062 India Department of Machining Assembly and Engineering Metrology Faculty of Mechanical Engineering VSB-Technical University of Ostrava Ostrava Ostrava70800 Czech Republic Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India Jadara University Research Center Jadara University Irbid Jordan Department of Electrical Engineering Shri K.J. Polytechnic Bharuch392001 India Department of Mechanical Engineering Sri Sairam Institute of Technology Chennai600044 India Department of Mechanical Engineering and University Centre for Research and Development Chandigarh University Mohali140413 India Computer Science Department Al-Bayt University Mafraq25113 Jordan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura140401 India Research Center University of Tabuk Tabuk71491 Saudi Arabia MEU Research Unit Middle East University Amman11831 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia

Research has demonstrated the effectiveness of metaheuristic algorithms in addressing optimization challenges involving multi-objectives. These algorithms excel in generating a range of solutions that not only approach the Pareto front but are also evenly distributed. Multi-objective algorithms are effective for problems with two or three objectives. However, their efficiency diminishes in scenarios with many objectives, as the diversity selection and convergence pressure become less effective. Balancing convergence and diversity in multi-objective optimization pose a significant challenge. In response, this study introduces a novel many-objective multi-verse optimizer algorithm named MaOMVO for addressing many-objective problems. It integrates reference point and niche preserve to improve convergence and diversity and employs an innovative information feedback mechanism technique for population renewal. The superiority of the MaOMVO algorithm is evident in tests with MaF problems having 5, 8 and 15 objectives, as well as five real-world problems (RWMaOP1—RWMaOP5). It outperforms four leading algorithms Many-objective moth flame optimization, many-objective particle swarm optimization, non-dominated sorting genetic algorithm-III and reference vector guided evolutionary algorithm in terms of generational distance by 70%, inverted generational distance by 52%, spacing by 46.66%, spread by 55.55%, hypervolume by 52% and running time by 52% with concave, convex and mixed pareto fronts, confirming its robustness in diverse optimization scenarios. © The Institution of Engineers (India) 2024.

关键词： Genetic algorithms

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