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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Md Ismail Hossain Siddiqui Zishad Hossain Limon Shakil Khan Mahbub Alam Khan Hamdadur Rahman Shimanto Krishna Chakroborty Shamim Forhad Abdullah Al-Sakib S M Arafat Rahman Shafiur Rahman Dept. of Engineering/Industrial Management Westcliff University Irvine CA USA Dept. of Computer Science Westcliff University Irvine CA USA Dept. of Business Analytics International American University CA USA Dept. of Management Information System Pacific State University USA Dept. of Management of Information System International American University USA Dept. of Computer Science and Engineering East West University Dhaka Bangladesh Dept. of Electrical and Electronics Engineering American International University Bangladesh Dept. of Information Technology Westcliff University USA Dept. of Software Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Eggplant diseases present major challenges to agricultural productivity, leading to yield losses and threatening farmers livelihoods. Traditional diagnostic methods tend to be resource-intensive, subjective, and often inaccurate, making them unsuitable for large-scale use. This study addresses these shortcomings by proposing an Ensemble Transfer Learning (ETL) approach for precise classification of eggplant diseases. The model combines the strengths of ConvNeXt, EfficientNetV2, NFNet, and RegNet architectures, enhancing generalization and minimizing overfitting. Two datasets containing over 19,800 images were utilized for training and validation, with advanced preprocessing and data augmentation techniques applied to ensure high input data quality and model robustness. To assess performance, evaluation metrics such as accuracy, F1-score, MCC, and PR-AUC were used. The model achieved an impressive classification accuracy of 99.76%, surpassing state-of-the-art methods and demonstrating strong generalization across various conditions. A web-based application was also developed, enabling real-time disease diagnostics through a user-friendly interface. This application provides farmers with actionable insights, promoting sustainable farming practices by reducing crop losses and minimizing the need for chemical interventions. However, its reliance on high-quality labeled images limits its applicability in uncontrolled environments. Future research will aim to integrate more diverse datasets and develop mobile-based solutions to enhance scalability and usability. This study emphasizes the potential of ensemble transfer learning to transform agricultural disease diagnosis and improve crop productivity.

关键词： Productivity Accuracy Transfer learning Crops computer architecture Real-time systems Medical diagnosis Chemicals Diseases Farming

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Data-Driven Decision Management: Multi-Modal Analytics in a Health Case Study 19

Data-Driven Decision Management: Multi-Modal Analytics in a ...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Agomuo, Okechukwu Clement Khan, Zohaib Otuomasirichi, Agomuo Ijeoma Uzoma, Agomuo Kingsley Muzamal, Junaid Hussain Department of Operations and Information Systems University of Utah Salt Lake City United States Dept. of Software Engineering FAST NUCES Lahore Pakistan Department of Agricultural Economics University of Nigeria Enugu Nigeria Center for Environmental Management and Green Energy University of Nigeria Enugu Nigeria Fast School of Computing Fast NUCES Lahore Pakistan

ISBN: (纸本)9798331507817

In an era where data is abundant yet underutilized, organizations across various sectors face challenges in extracting actionable insights from complex, multi-modal datasets. To address these challenges, this study proposes a versatile data analytics framework designed to enhance predictive accuracy and support informed decision-making. The framework integrates structured and unstructured data, leveraging advanced Transformer architectures to capture temporal dynamics and complex interactions within the data. In a healthcare use case, the framework demonstrated its effectiveness by achieving an Accuracy of 0.854, an F1-Score of 0.895, and an AUC-ROC of 0.930, significantly outperforming traditional models. By incorporating SHAP for interpretability, the framework ensures that its predictions are both accurate and transparent, making it a valuable tool for decision-makers. While healthcare is one application, the framework’s flexibility allows it to be adapted to a wide range of industries. Future work will focus on validating the framework across different sectors and integrating it into operational workflows to optimize decision-making and outcomes. © 2025 IEEE.

关键词： Electronic health record

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Joint Energy and Computation Workload Management for Geo-distributed Data Centers

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2025年

作者： Wang, Ran Wu, Rixin Liu, Linfeng Yi, Changyan Zhu, Kun Wang, Ping Niyato, Dusit Nanjing University of Aeronautics and Astronautics Collaborative Innovation Center of Novel Software Technology and Industrialization College of Computer Science and Technology Nanjing China Nanjing University of Posts and Telecommunications School of Computer Science and Technology Nanjing China York University Dept. of Electrical Engineering and Computer Science Lassonde School of Engineering Canada Nanyang Technological University School of Computer Science and Engineering Singapore

The increasing demands of data computation and storage for cloud-based services motivate the development and deployment of large-scale data centers (DCs). The energy demand of these devices is rising rapidly and becoming a noticeable challenge for current power networks. The smart grid (SG) is deemed as the future power system paradigm enabling more affordable and sustainable energy supply, which can effectively relieve the load pressure from DCs. Moreover, with growing concerns regarding harmful emissions due to combustion of fossil fuels, the exploitation of renewable energy sources (RES) has attracted extensive attention, which can benefit SGs and DCs, as well as society at large. However, the geo-distributed property of DCs and SGs and the uncertain nature of RES production pose severe challenges to the optimal management of computation and energy resources in such a tripartite coupling system. Focusing on these issues, a joint energy and computation workload management framework is proposed for enabling a sustainable DC paradigm with distributed RES. Specifically, a three-layer game is formulated to model the iterations among entities including the energy market, data center operators (DCOs), and SGs. The market includes a certain amount of RES that must be dispatched. The SG offers the DCO an electricity selling price while simultaneously importing RES from the market at a buying price in order to maximize the benefit. The DCO allocates the workload to different DCs, aiming to minimize the costs of energy consumption and carbon emissions. The interactive processes between different entities are further decomposed into two coupling Stackelberg games. We obtain the equilibrium state of the game and prove its uniqueness and optimality. Simulation experiments are conducted to evaluate the performance of the joint energy and computation workload management scheme and show its superiority over counterparts in utilizing renewable energy and reducing emissions. Furthe

关键词： Smart power grids

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Deep Learning and IoT-Enabled Accident Detection and Reporting for Smart Cities Domain

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KSII Transactions on Internet and Information systems 2025年第4期19卷 1140-1166页

作者： Javed, Rawal Mazhar, Tehseen Aoun, Muhammad Shahzad, Tariq Al-AlShaikh, Halah A. Ghadi, Yazeed Yasin Saudagar, Abdul Khader Jilani Khan, Muhammad Amir School of Automation Central South University Changsha China School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Science and Information Technology School Education Department Government of Punjab Layyah31200 Pakistan Department of CS IT Ghazi University Punjab Dera Ghazi Khan Pakistan School of Electrical Engineering Dept. of Electrical and Electronic Eng. Science University of Johannesburg Johannesburg2006 South Africa Riyadh11432 Saudi Arabia Department of Computer Science and Software Engineering Al Ain University Abu Dhabi12555 United Arab Emirates School of Computing Sciences College of Computing Informatics and Mathematics University Technology MARA Selangor Shah Alam40450 Malaysia

Even though numerous ICT-based solutions have been put forth for the detection of accidents and rescue missions, they often suffer from compatibility issues with different vehicles and are accompanied by high costs. This paper introduces a cutting-edge intelligent system designed for accident detection and rescue, mirroring human cognitive functions through integrating the Internet of Things (IoT) and artificial intelligence. A specialized kit for IoT has been created to identify accidents and gather comprehensive incident-related data, such as pressure, position, gravitational force, and speed. This information is then transmitted to the cloud for processing. A deep learning model is implemented in the cloud environment to verify the IoT module's output and activate the rescue mechanism when an accident is detected. The DL module, employing ensemble transfer learning with dynamic weights, aims to minimize false detection rates. Because there isn't an appropriate dataset available, a personalized dataset is generated from diverse online videos. To validate the proposed method, a comparative analysis is conducted on Yolo V4. Experimental results indicate that Yolo V4 outperforms, achieving training, validation, and test accuracies of 98.2% each. To assess the system's real-world applicability, it undergoes validation using a robot car model. Copyright © 2025 KSII.

关键词： Deep learning

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Machine Learning in Agriculture and Crop Management: A Comprehensive Review

Machine Learning in Agriculture and Crop Management: A Compr...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Rachna Rathore Archana Ganesh Said Preeti Jnagra Haritha Murari Saloni Bansal Harshita Singh Dept. of Mathematics Jiwaji University Gwalior India Dept. of Computer Engineering AISSMS’s Institute Of Information Technology Pune India Dept. of Biotechnology Lingayas Vidyapeeth Haryana India Dept. of Software Engineer Spark Infotech Inc Phoenix Arizona USA Dept. of Computer Eng. & Applications GLA University Mathura India Dept. of CSE (AIML) CSE-APEX Chandigarh University Chandigarh India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Machine learning (ML) has transformed agriculture and crop management, revolutionizing modern farming techniques. By leveraging ML, farmers can accurately monitor and analyze agricultural data, gaining valuable insights into crop health, soil conditions, weather patterns, and pest infestations. Through the analysis of satellite imagery, sensor data, and historical records, ML models can predict optimal planting times, forecast yields, and identify potential disease outbreaks, enabling more efficient resource management. These innovations promote precision agriculture, reduce waste, and support sustainability. This paper explores the latest developments in ML applications for agriculture, emphasizing their potential to enhance food security, minimize environmental impact, and boost farm profitability through data-driven decision-making.

关键词： Technological innovation Systematics Reviews Weather forecasting Machine learning Predictive models Soil Satellite images Sustainable development Crop yield

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Transparent AI for Adaptive Fraud Detection 19

Transparent AI for Adaptive Fraud Detection

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Agomuo, Okechukwu Clement Uzoma, Agomuo Kingsley Khan, Zohaib Otuomasirichi, Agomuo Ijeoma Muzamal, Junaid Hussain Department of Operations and Information Systems University of Utah Salt Lake City United States Center for Environmental Management and Green Energy University of Nigeria Enugu Nigeria Dept. of Software Engineering FAST NUCES Lahore Pakistan Department of Agricultural Economics University of Nigeria Enugu Nigeria Fast School of Computing Fast NUCES Lahore Pakistan

ISBN: (纸本)9798331507817

The rapid evolution of digital financial services has significantly increased the vulnerability of financial systems to fraudulent activities. Traditional machine learning models have offered substantial progress in detecting fraudulent transactions;however, they often lack adaptability and transparency, critical for dynamic financial environments and regulatory compliance. This paper introduces a novel approach that integrates Deep Reinforcement Learning (DRL) with Explainable Artificial Intelligence (XAI) to enhance fraud detection capabilities in cloud-based financial platforms. Our model, tested on the synthetic PaySim mobile money transaction dataset, leverages the adaptive nature of DRL to continuously learn and update its fraud detection strategies based on evolving transaction patterns. Concurrently, the integration of XAI provides clear, interpretable insights into the decision-making process, thus ensuring transparency and building trust. The proposed model demonstrates superior performance with an achieved accuracy of 99.5% and an F1-score of 99.0%, significantly outperforming traditional fraud detection systems. These results not only underscore the efficacy of our model in improving the accuracy of fraud detection but also highlight its capability in offering significant improvements in understanding the underlying reasons for each decision, thereby aiding compliance with financial regulations. The practical implications of this research highlight its potential to provide a robust, scalable, and transparent solution for real-time fraud detection, which is essential for safeguarding the integrity of modern financial systems. © 2025 IEEE.

关键词： Decentralized finance

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Predicting Optimal Placement of Electric Vehicle Charge Stations Using Machine Learning: A Case Study in Glasgow, UK

Predicting Optimal Placement of Electric Vehicle Charge Stat...

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Iranian Conference on Renewable Energy and Distributed Generation (ICREDG)

作者： Arash Mousaei Yahya Naderi dept. Computer and Electrical Engineering University of Tabriz Tabriz Iran Sustainable Energy Systems Department Ricardo AEA Glasgow U.K

ISBN: (数字)9798331525132

ISBN: (纸本)9798331525149

Given the reduced costs of establishing Electric Vehicle (EV) charging stations and the need to improve their accessibility, it is essential for EV developers and governments to identify optimized locations for new stations. This study presents a predictive modeling approach using Machine Learning to determine the optimal sites for EV charging stations in Glasgow. While various machine learning methods are available for this purpose, we focus on Linear Regression due to its simplicity and high accuracy. By analyzing a comprehensive dataset of 207 AC charging stations-including factors latitude, longitude, population density, number of kinds of plugs, total number of plugs, price per kWh, traffic conditions, distance to the nearest station, Air Quality Index (AQI), and land value - we aim to provide urban planners and policymakers with actionable insights. The study evaluates the performance of the Linear Regression model in predicting the most suitable locations for charging stations. The results indicate that the proposed model achieved impressive accuracy, with R 2 , Mean Absolute Error (MAE), and Mean Squared Error (MSE) values of 0.89, 0.05, and 0.08, respectively. This research contributes to the ongoing efforts in urban planning for sustainable mobility by offering a data-driven methodology for EV infrastructure development.

关键词： Accuracy Atmospheric modeling Linear regression Machine learning Predictive models Charging stations Electric vehicle charging Indexes Population density Plugs

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Automated Weed Species Classification in Rice Cultivation Using Deep Learning

Automated Weed Species Classification in Rice Cultivation Us...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Hamdadur Rahman Mahbub Alam Khan Shakil Khan Zishad Hossain Limon Md Ismail Hossain Siddiqui Shimanto Krishna Chakroborty Shamim Forhad Abdullah Al-Sakib Sheikh Shemanto Afridi Shafiur Rahman Dept. of Management of Information System International American University CA USA Dept. of Management Information System Pacific State University CA USA Dept. of Business Analytics International American University CA USA Dept. of Computer Science Westcliff University Irvine CA USA Dept. of Engineering/Industrial Management Westcliff University Irvine CA USA Dept. of Computer Science and Engineering East West University Dhaka Bangladesh Dept. of Electrical and Electronics Engineering American International University Bangladesh Dept. of Information Technology Westcliff University CA USA Dept. of Software Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Weeds in rice fields compete for essential resources such as water, nutrients, and light, leading to reduced crop yields, increased production costs, and environmental concerns. Traditional methods of weed detection are often labor-intensive, subjective, and inefficient, highlighting the need for automated approaches in precision agriculture. This study introduces an ensemble model designed for robust and accurate classification of rice weeds, addressing the shortcomings of conventional methods and enhancing detection scalability. The proposed ensemble model combines four deep learning architectures: EfficientNet-V2, SqueezeNet, InceptionResNet-v2, and DenseNet-201. By leveraging the complementary strengths of these models, we aim to improve performance. The study utilizes the Rice Field Weed Detection Dataset, which contains 3,632 high-resolution images that have been augmented to a total of 21,792 samples. Advanced preprocessing techniques such as resizing, normalization, and augmentation are applied to ensure effective feature extraction. Our model achieved a remarkable classification accuracy of 99.51%, significantly surpassing individual models and other state-of-the-art approaches. In addition, we developed a web-based application for real-time weed detection, providing a user-friendly platform for farmers and agricultural professionals to implement timely weed management strategies. This system supports sustainable farming by reducing the need for excessive herbicide use and promoting environmentally friendly practices. This research highlights the potential of ensemble deep learning for precise agricultural weed detection. Future work will focus on integrating diverse datasets and developing mobile applications to enhance accessibility and scalability.

关键词： Deep learning Accuracy Scalability Transfer learning Feature extraction Real-time systems Mobile applications Crop yield Water resources Farming

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Reinforcement Learning for Unrelated Parallel Machine Scheduling with Release Dates, Setup Times, and Machine Eligibility 24

Reinforcement Learning for Unrelated Parallel Machine Schedu...

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Proceedings of the Winter Simulation Conference

作者： Sang-Hyun Cho Hyun-Jung Kim Lars Mönch Dept. of Industrial and Systems Engineering Korea Advanced Institute of Science and Technology Daejeon Republic of Korea Dept. of Mathematics and Computer Science University of Hagen Universitätsstraße 1 Hagen GERMANY

ISBN: (纸本)9798331534202

This paper presents a novel approach for solving unrelated parallel machine scheduling problems through reinforcement learning. Notably, we consider three main constraints: release date, machine eligibility, and sequence- and machine-dependent setup time to minimize total weighted tardiness. Our work presents a new graph representation for solving the problem and utilizes graph neural networks combined with reinforcement learning. Experimental results show that our proposed method outperforms traditional dispatching rules and an apparent tardiness cost-based algorithm. Furthermore, since we represent and solve the problem using graphs, our method can be used regardless of the number of jobs or machines once trained.

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Disentangled Source-Free Personalization for Facial Expression Recognition with Neutral Target Data

arXiv

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arXiv 2025年

作者： Sharafi, Masoumeh Ollivier, Emma Zeeshan, Muhammad Osama Belharbi, Soufiane Pedersoli, Marco Koerich, Alessandro Lameiras Bacon, Simon Granger, Eric LIVIA ILLS Dept. of Systems Engineering ETS Montreal Canada LIVIA Dept. of Software and IT Engineering ETS Montreal Canada Dept. of Health Kinesiology & Applied Physiology Concordia University Montreal Canada Montreal Behavioural Medicine Centre CIUSSS Nord-de-l’Ile-de-Montréal Canada

Facial Expression Recognition (FER) from videos is a crucial task in various application areas, such as human-computer interaction and health monitoring (e.g., pain, depression, fatigue, and stress). Beyond the challenges of recognizing subtle emotional or health states, the effectiveness of deep FER models is often hindered by the considerable variability of expressions among subjects. Source-free domain adaptation (SFDA) methods are employed to adapt a pre-trained source model using only unlabeled target domain data, thereby avoiding data privacy and storage issues. Typically, SFDA methods adapt to a target domain dataset corresponding to an entire population and assume it includes data from all recognition classes. However, collecting such comprehensive target data can be difficult or even impossible for FER in healthcare applications. In many real-world scenarios, it may be feasible to collect a short neutral control video (displaying only neutral expressions) for target subjects before deployment. These videos can be used to adapt a model to better handle the variability of expressions among subjects. This paper introduces the Disentangled Source-Free Domain Adaptation (DSFDA) method to address the SFDA challenge posed by missing target expression data. DSFDA leverages data from a neutral target control video for end-to-end generation and adaptation of target data with missing non-neutral data. Our method learns to disentangle features related to expressions and identity while generating the missing non-neutral target data, thereby enhancing model accuracy. Additionally, our self-supervision strategy improves model adaptation by reconstructing target images that maintain the same identity and source expression. Experimental results on the challenging BioVid and UNBC-McMaster pain datasets indicate that our DSFDA approach can outperform state-of-the-art adaptation methods. Our code is available at: ***/MasoumehSharafi/DSFDA-for-Pain-Assessment. Copyright

关键词： Face recognition

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