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Optimizing Heart-Kidney Interaction for Cancer Detection through Physiological Process Simulation as a Decision Support System

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IEEE Access 2025年 13卷 79169-79179页

作者： Jaddi, Najmeh Sadat Abdullah, Salwanj Othnman, Zalinda Yahya, Yazrina Nazri, Mohd Zakree Ahmad Goh, Say Leng Alvankarian, Fatemeh Alvankarian, Jafar Iranian eUniversity Faculty of Computer Engineering Tehran Iran Faculty of Information Science and Technology Center for Artificial Intelligence Technology Selangor Bangi43600 Malaysia Center for Software Technology and Management Faculty of Information Science and Technology UniversitiKebangsaan Malaysia Selangor Bangi43600 Malaysia Universiti Malaysia Sabah KampusAntarabangsa Labuan Optimisation and Analytics Research Group Faculty of Computing and Informatics Labuan87000 Malaysia University of Maryland Department of Electrical & Computer Engineering College ParkMD20742 United States Tehran University Science and Technology Park RayanMiniFab Tehran Iran

Decision support system (DSS) is a computer-based tool used to improve decision-making capabilities for any organization, using analysis of the available *** heart-kidney (HK) model proposed in this paper as a DSS simulates the physiological processes of the heart and kidneys in the human body to support cancer detection. The first part of the HK algorithm is inspired by the functions of the heart, where blood molecules (solutions) are sent to the heart, simulating the processes of oxygenation and the pumping of oxygenated blood to the kidneys as a local search. The solutions generated by the heart are then fed into the kidney-inspired algorithm (KA), which performs filtration, reabsorption, secretion, and excretion. The heart component enhances the KA's local search capability, guiding the algorithm toward optimal solutions. The HK model is evaluated on eight test functions, with results compared to those of the heart algorithm and KA alone. Additionally, the HK algorithm's effectiveness as a decision support system is tested on benchmark classification and time series prediction problems, with statistical analysis against existing methods in the literature. Finally, the HK algorithm is applied to cancer detection from microRNA data, demonstrating its potential in optimizing decision-making processes in healthcare. © 2013 IEEE.

关键词： Diseases

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Breast Cancer Stage Identification Using Machine Learning 11th

Breast Cancer Stage Identification Using Machine Learning

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11th EAI International Conference on Advancement of Science and Technology, ICAST 2023

作者： Arega, Birtukan Shegaw Mengsitu, Tesfahunegn Minwuyelet Faculty of Computing Bahir Dar Institute of Technology Bahir Dar University Bahir Dar Ethiopia Department of Information Convergence Engineering Pusan National University Busan Korea Republic of

ISBN: (纸本)9783031641503

This paper presents a machine learning model for determining the stages of breast cancer. The model normalizes images, eliminates noise, and removes artifacts through preprocessing, segmentation, and classification. Patching is used to extract the region of interest (ROI), while multi-thresholding is used for segmentation. The suggested models, including CNN-KNN, VGGNet-16, CNN-naïve Bayes, and CNN-SVM, were tested on a dataset of images from the CBIS-DDSM database. Experiments were conducted using full mammogram images and segmented ROI images, resulting in accuracies of 39%, 85%, 86%, and 91%, respectively. The CNN-SVM model achieved a 6% increase in classification accuracy compared to the state-of-the-art VGGNet-16 model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

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Face Recognition CCTV for Automated Attendance Tracking

Face Recognition CCTV for Automated Attendance Tracking

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IEEE Students' Conference on Electrical, Electronics and Computer Science (SCEECS)

作者： Sai Sindhu Kola Harsha Kenchi P. Rama V. Angayarkanni J. Kalaivani S. Sivakumar Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur TN India Department of Computing Technologies College of Engineering and TechnologyFaculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur TN India Computing Technologies College of Engineering and TechnologyFaculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur TN India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai Department of Computing Technologies Faculty of Engineering and Technology SRMIST Kattankulathur

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

This study investigates the design and execution of an automated attendance tracking system using facial recognition CCTV based . Facial recognition technology and CCTV cameras are integrated in this system to provide a seamless, frictionless way to track and record attendance in real time. In addition to boosting attendance record accuracy, the approach reduces the administrative burden associated with using traditional methods. The suggested system is designed to recognise people when they enter or leave a monitored area by leveraging developments in computer vision. It accomplishes this automatically and requires no interaction from humans. Furthermore, the system's use of the Internet of Things makes it flexible and scalable to a variety of business situations, including offices, schools, and large-scale events. This is made feasible via real-time data synchronising and reporting.

关键词： Computer science Electric potential Computer vision Accuracy Face recognition Cameras Real-time systems Internet of Things Monitoring Business

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Cervical Cancer Risk Prediction using SMOTE and Explainable AI

Cervical Cancer Risk Prediction using SMOTE and Explainable ...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Agarwal, Priyansh Chaudhary, Annanya Sarvanan, T.R. Srm Institute of Science and Technology School of Computing Department of Computational Intelligence India Srm Institute of Science and Technology Faculty of Engineering & Tech. Department of Computational Intelligence India

ISBN: (纸本)9798331508685

Cervical cancer, accounting for 7.9% of cancers in women globally, is a critical health challenge, particularly due to its asymptomatic nature in early stages. This study proposes a machine learning-driven framework to predict cervical cancer risk using clinical data, addressing class imbalance through SMOTE and optimizing model performance via GridSearchCV. By evaluating five classifiers - K-Nearest Neighbors, Random Forest, Support Vector Classifier, Logistic Regression, and Decision Trees - this research identifies the Decision Tree model as the most robust, achieving superior precision, recall, and accuracy metrics. Additionally, Explainable AI techniques such as SHAP and LIME were employed to enhance interpretability, offering clinicians actionable insights into key risk factors driving predictions. The results demonstrate the effectiveness of machine learning in facilitating early detection and personalized treatment strategies for cervical cancer, thereby advancing its integration into clinical applications. © 2025 IEEE.

关键词： Lung cancer

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High-Efficiency Asymmetrically Designed Three-Phase Virtual 48-pulse Power Supply for Electrolytic Hydrogen

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IEEE Transactions on Power Electronics 2025年第8期40卷 10335-10342页

作者： Tian, Hanlei Cui, Haoran Han, Wei Sathik M, Jagabar Liang, Guozhuang Chen, Maolin Mekhilef, Saad Sustainable Energy and Environment Thrust The Hong Kong University of Science and Technology (GZ) Guangzhou China Electrical Engineering School of Hebei University of Science and Technology Shijiazhuang China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India College of Electrical Engineering Sichuan University Chengdu China School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne VIC Australia

To address the low-voltage, high-current requirements in hydrogen production applications, a virtual 48-pulse three-phase rectifier is proposed, and achieves the equivalent performance of four parallel 12-pulse rectifiers (12-PR). The proposed approach builds upon the conventional 12-PR design and incorporates novel Asymmetric Current Injection Units (ACIU). The ACIU is installed on the DC side of the AC-DC converter, generating a specific current at the DC link to modulate and enhance the operating modes of the 12-PR. Consequently, the 12-pulse is extended to 48, resulting in an almost sinusoidal input current with a Total Harmonic Distortion (THD) of 3.2% and an output current with a ripple rate (RR) of 2.3%, thus significantly improving power quality. Furthermore, the asymmetric design of the current injection units eliminates the need for a phase-shifting transformer. The efficacy of the proposed rectifier for electrolytic hydrogen production is validated using a 2.2 kW experimental prototype. © 1986-2012 IEEE.

关键词： Voltage Hydrogen Rectifiers Production Windings Circuits Inductors Transformers Harmonic analysis Electrochemical processes

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Precision Forecasting of Stock Prices: Leveraging XGBoost and Technical Indicator for Advanced Predictive Modeling 1

Precision Forecasting of Stock Prices: Leveraging XGBoost an...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Yuvaraj, S. Chenni Kumaran, J. Vinston Raja, R. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology School of Computing Department of Computational Intelligence Kattankulathur Chennai India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529246

The investigation aims to evaluate the performance of machine learning techniques, particularly the XGBoost regression method, for stock price prediction with the help of technical indicators. The research targets the adjusted closing price to reduce the autocorrelation problem in time series. The Five Days and Ten Days Exponential Moving Averages (EMA-5 and EMA-10) are involved in refining feature selection. The performance of the model was evaluated using different error metrics, namely, Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R2). The results indicate that the XGBoost model, combined with EMA-5 and EMA-10, achieves high predictive performance, obtaining an r2 value of 0.98. It implies that incorporating short-term EMAs into advanced machine learning techniques drastically improves stock price forecasting. © 2025 IEEE.

关键词： Time series

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Automatic computation of bending sequences for double-head wire bending machines

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International Journal of Computer Integrated Manufacturing 2025年第4期38卷 451-462页

作者： Baraldo, Andrea Bascetta, Luca Caprotti, Fabrizio Chourasiya, Sumit Ferretti, Gianni Ponti, Angelo Sakcak, Basak BLM SPA - BLM GROUP via Selvaregina 30 CO Cantù Italy Politecnico di Milano Dipartimento di Elettronica Informazione e Bioingegneria Milano Italy Center of Ubiquitous Computing Faculty of Information Technology and Electrical Engineering University of Oulu Oulu Finland

Determining a feasible bending sequence for a double-head bending machine is a challenging and time-consuming task, even for an expert operator. An automatic bending sequence computation algorithm for a single-head bending machine was proposed in a previous work, exploiting A (Formula presented.) graph search algorithm and the representation of the workpiece as an equivalent manipulator. This paper extends that approach to the case of a double-head bending machine, whose complexity, in terms of number of possible solutions to the problem of computing the feasible bending sequences, and of determination of the better sequences, that should exploit parallelism thanks to the execution of simultaneous bends on the two heads, is definitely higher. In this case, the wire has been modelled using two serial-link manipulators with a common base, and the A (Formula presented.) cost function has been modified to promote simultaneous bends. An axes reachability-based path generator and the possibility of partial sequence search have been incorporated as well, to manage the run-time challenges due to an increased number of bends. In this way, an efficient bending sequence computation algorithm for a double-head wire bending machine has been devised, whose performance has been assessed on two industrial test cases, demonstrating its effectiveness and usefulness. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Wire

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Identification of Potentially Unpredictable Behavior in the Execution of Service Orchestrations

Identification of Potentially Unpredictable Behavior in the ...

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Jelena Matkovic Faculty of Mechanical Engineering Computing and Electrical Engineering University of Mostar Mostar Bosnia and Herzegovina

ISBN: (数字)9798331515799

ISBN: (纸本)9798331515805

The structure of business processes can vary significantly, encompassing complex behaviors that can become intricate and challenging to track or validate. This paper examines the constructs provided by the Web Services Business Process Execution Language standard that can be utilized to design possibly complex business process executions. It then identifies categories of potentially unpredictable behaviors, such as unintended parallelization or unwanted bottlenecks. The identified behaviors and proposed techniques for their early detection can serve as the basis for employing different further verification techniques. Furthermore, these behaviors can also be used as a reference for identifying similar behaviors in orchestrations of modern microservices, given the conceptual similarities in integrating such microservices into business processes.

关键词： Uncertainty Process modeling Semantics Microservice architectures Conductors Complexity theory Standards Business Testing Formal verification

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A Hybrid UNet and Vision Transformer Architecture with Multi-scale Fusion for Brain Tumor Segmentation 5th

A Hybrid UNet and Vision Transformer Architecture with Multi...

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5th International Conference on Medical Imaging and Computer Aided Diagnosis, MICAD 2024

作者： Ranjbarzadeh, Ramin Anari, Shokofeh Crane, Martin Bendechache, Malika School of Computing Faculty of Engineering and Computing Dublin City University Dublin Ireland Department of Accounting Economic and Financial Sciences Islamic Azad University South Tehran Branch Tehran Iran ADAPT Research Centre School of Computer Science University of Galway Galway Ireland

ISBN: (纸本)9789819638628

This research presents an advanced neural network architecture for the segmentation of brain tumors. Our suggested model for brain tumor segmentation incorporates the Vision Transformer (ViT) as a 3D encoder alongside a modified UNet architecture to effectively capture both global and local information in MRI data. The model contains a dual-encoder architecture: a 3D ViT encoder captures global contextual information throughout the MRI volume, while a concurrent convolutional encoder emphasizes localized features. The encoder routes are enhanced by skip connections, facilitating efficient feature transmission between the ViT and UNet layers. In the bottleneck, features from both encoders are integrated via a multi-scale fusion block, augmenting the model's capacity to discern intricate spatial hierarchies and tumor changes. The decoder pathway employs a hierarchical upsampling and multi-scale fusion framework, meticulously rebuilding the final segmentation map with a focus on spatial accuracy. This hybrid model integrates the ViT's capacity to capture long-range relationships with the UNet's proficiency in fine-grained spatial localization, yielding a strong segmentation output on the BraTS 2020 dataset that is adept at addressing the complex and diverse architecture of brain tumors in MRI. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Signal encoding

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Design and Development of a Small-Scale Green Hydrogen Vehicle:Hydrogen Consumption Analysis under Varying Loads for Zero-Emission Transport

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Energy engineering 2025年第5期122卷 1788-1803页

作者： Perry Yang Tchie Hunn Hadi Nabipour Afrouzi Research Centre for Sustainable Technologies Faculty of EngineeringComputing and ScienceSwinburne University of TechnologyJalan Simpang TigaKuching93350SarawakMalaysia College of Engineering Faculty of ComputingEngineering and the Built EnvironmentBirmingham City UniversityBirminghamB47XGUK

With growing interest in its potential applications across both stationary and transportation sectors,hydrogen has emerged as a promising alternative for environmentally responsible power *** replacing traditional fuels,hydrogen can significantly reduce greenhouse gas emissions in the transportation *** study focuses on the design and downsizing of a green hydrogen fuel cell car,aiming to scale the concept for larger *** components,including fuel cells,electrolysers,and solar panels,were evaluated through extensive laboratory *** reveal that variations in sunlight impact the solar panel’shydrogenproductionrate,withdifferences of approximately 4.9%attributed to changes in time and *** of consumption rates showed that a 17.4%increase in current consumption leads to a significant reduction in operational *** testing under varying loads demonstrated that higher current demands,such as those from a DC motor,accelerate hydrogen depletion,whereas lower currents extend operational *** results underscore the importance of maximizing solar energy efficiency,reducing reliance on conventional energy sources,and regulating consumption rates to optimize fuel cell *** hydrogen is produced using renewable energy,fuel cell technology is virtually ***,the study highlights the viability of powering vehicles with renewable energy,emphasizing the potential of green hydrogen fuel cell technology as a sustainable transportation solution.

关键词： Environmentally sustainable small scale solar hydrogen fuel cell car

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