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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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Inventory and demand management for FMCG companies by investigating SCM uncertainty causes and operational solutions

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Quality and Quantity 2025年 1-25页

作者： Nasiri, G. Reza Saboori, Ghazaleh Monfared, Fatemeh Department of Industrial Engineering Faculty of Engineering Alzahra University Tehran Iran Faculty of Information Technology University of Jyvaskyla P.O. Box 35 (Agora) Jyvaskyla Finland

The impact of uncertainty on the efficiency of supply chain management is a significant factor that must be considered. This requires an understanding of the underlying causes of uncertainty and the implementation of appropriate strategies to address them. Furthermore, this can lead to an improvement in material and information flow as well as an enhancement in liquidity. The present study is being conducted in two phases: a qualitative and a quantitative phase. The objective of the qualitative phase is to identify the underlying causes of uncertainty through library research. In the quantitative phase, our research contributes to the existing literature by combining the Analytic Hierarchy Process and Cognitive Maps approaches to identify the relationships between the sources of uncertainty at each level of the Fast-moving Consumer Goods supply chain. The results revealed nine effective sources of uncertainty in the supply chain. Although the Analytic Hierarchy Process questionnaire is not without its shortcomings, including the potential for biased expert opinion, we have implemented measures to mitigate these issues and improve the reliability of the questionnaire. Moreover, experts have identified potential solutions to mitigate uncertainty in a supply chain. These include conducting a comprehensive assessment, developing contingency plans and protocols, implementing technology solutions, investing in training, and focusing on customer preferences. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.

关键词： Cognitive maps Demand management FMCG industry Inventory management Supply chain management Uncertainty causes

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Neutrosophic Sets and Systems: A Decade of Scientific Contribution and Growth

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Neutrosophic Sets and Systems 2025年 81卷 438-465页

作者： Saqlain, Muhammad Merigó, José M. Kumam, Poom Department of Mathematics King Mongkut’s University of Technology Thonburi Bangkok10140 Thailand School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney 81 Broadway UltimoNSW2007 Australia

Neutrosophic Sets and Systems (NSS) has become an important Journal for neutrosophic theory and its applications in uncertainty modeling and decision sciences. In 2023, NSS celebrated its 10th anniversary, marking a decade of contributions to scientific literature. Motivated by this event, this paper provides a critical bibliometric analysis of NSS’s evolution as a leading academic journal in neutrosophy. This study explores the publication trends, citation impact, most productive authors, institutions, and global collaboration networks of NSS. The study maps bibliographic coupling, keyword co-occurrence and co-citation analysis using data from the Scopus database, secondary search using NSS webpage, and visualization tools like the VOSviewer. The findings identify Neutrosophic Sets, Decision-Making, and Fuzzy Sets as the most frequently studied keywords that highlight the increasing integration of neutrosophic approaches into multi-criteria decision-making (MCDM) and soft computing. The results indicate the most significant areas of research that are topology, soft set theory, hypersoft sets, and uncertainty modeling. The findings highlight the NSS increasing international collaboration, with contributions from America, Middle East, and Asia. Additionally, the analysis explores NSS’s alignment with sustainable development goals (SDGs), revealing notable contributions to good health and well-being (SDG 3), quality education (SDG 4), and peace, justice, and strong institutions (SDG 16). These contributions highlight how neutrosophic sets and systems is valuable across many researchers, from public health to governance to education. Overall, this paper serves as a fundamental study for researchers looking to explore emerging trends and novel applications of neutrosophic theories across diverse scientific and practical domains. © 2025, University of New Mexico. All rights reserved.

关键词： Bibliographies

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Experiments on Taking Loss Methods in Imitation Learning With Bilateral Control

Experiments on Taking Loss Methods in Imitation Learning Wit...

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2025 IEEE International Conference on Mechatronics, ICM 2025

作者： Tagashira, Yusuke Okamura, Kazuya Akagawa, Tetsuro Sakaino, Sho National Institute of Technology Hachinohe College Electrical and Information Engineering Course Aomori Japan University of Tsukuba Faculty of Engineering Information and Systems Tsukuba Japan

ISBN: (纸本)9798331533892

Recently, motion generation technology using deep learning has been attracting attention as a method for robots to acquire motions adapted to their environment. The combination of imitation learning and bilateral control enables imitation of movements at speeds comparable to those of humans. Previous studies have investigated learning that inputs external information, such as one-hot vectors or image features, but learning to choose actions based on dynamic external information is still being studied. If a camera is used to collect external information, high-level image processing is required, resulting in heavy processing. We thought that the robot's motion generation could cover the problem without high-level image processing. Therefore, this paper examines the task of grasping an object that moves autonomously, independent of the robot, using low-resolution images. We first tried a model that takes loss based on the transformation of angle information into arm hand coordinates, whereas the previous learning model takes loss based on angle information. The results showed that, in imitation learning based on bilateral control, using hand coordinate loss caused a bias in learning the joint angle responses. It also became clear that it is important to learn the joint angle responses with equal weights. © 2025 IEEE.

关键词： Adversarial machine learning

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Characterization of ultraviolet communication in a mixed aerosol environment

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Optoelectronics Letters 2025年第2期21卷 77-84页

作者： ZHAO Taifei SUN Yuxin WANG Wuxin ZHANG Haijun PAN Feixiang WU Hao Artificial Intelligence Key Laboratory of Sichuan Province Sichuan University of Science&EngineeringZigong 643000China Faculty of Automation and Information Engineering Xi’an University of TechnologyXi’an 710048China

Wireless ultraviolet (UV) has strong scattering characteristics and can communicate through non-direct *** UV signals are transmitted in the atmosphere,they are affected by the absorption and scattering effects of atmospheric particles and atmospheric turbulence,resulting in attenuation of UV signal energy and reduced reliability of the communication *** paper focuses on the channel model of UV non-direct-view single scattering communication,and simulates and analyzes the communication characteristics of UV light in atmospheric turbulence and mixed aerosol environment under horizontal,vertical and oblique range communication *** results show that at equal relative humidity,the wireless UV non-directive scattering communication performance for vertical communication scenarios is more affected by the mixed aerosol environment and the communication performance is worse.

关键词： communication scattering ultraviolet

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A mathematical multi-objective optimization model and metaheuristic algorithm for effective advertising in the social internet of things

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Neural Computing and Applications 2025年第6期37卷 4797-4821页

作者： Molaei, Reza Rahsepar Fard, Kheirollah Bouyer, Asgarali Department of Computer Engineering and Information Technology University of Qom Qom Iran Department of Software Engineering Azarbaijan Shahid Madani University Tabriz Iran Department of Software Engineering Faculty of Engineering and Natural Science Istinye University Istanbul Turkey

The integration of social networks with the Internet of Things (IoT) has been explored in recent research, giving rise to the Social Internet of Things (SIoT). One promising application of SIoT is viral marketing, which plays a critical role in modern advertising. However, current solutions proposed for viral marketing are often impractical in real-world scenarios, and there has been limited investigation into viral marketing within the context of SIoT. To address this issue, we present a multi-objective integer linear programming model named ILP-ASoT that facilitates advertising through SIoT. Our approach introduces two realistic parameters: the node sociality rate and the node appropriateness rate as an advertising destination. By balancing the cost of selecting a seed set with the number of users who receive the advertisement, our model offers an appropriate trade-off. Since this is proven as an NP-hard problem, A PSO-based metaheuristic algorithm is proposed to provide near-optimal solutions for advertising in SIoT, called PSO-ASoT. Through simulations, we show that our PSO-ASoT algorithm outperforms state-of-the-art algorithms in several metrics, such as influence spread, advertising cost, and duplicated advertising rate. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Heuristic algorithms

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Optimized ensemble deep learning approach for accurate breast cancer diagnosis using transfer learning and grey wolf optimization

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Evolving Systems 2025年第2期16卷 1-17页

作者： Hassan, Esraa Saber, Abeer El-Sappagh, Shaker El-Rashidy, Nora Faculty of Artificial Intelligence Kafrelsheikh University Kafr Elsheikh Egypt Information Technology Department Faculty of Computers and Artificial Intelligence Damietta University Damietta34517 Egypt Faculty of Computer Science and Engineering Galala University Suez435611 Egypt

Breast cancer (BC) is one of the most significant threats to women’s health worldwide, affecting one in eight women and causing over 42,250 deaths in 2024. Early detection plays a crucial role in improving patient outcomes, with mammography being the primary screening method most health organizations recommend. This paper highlights the potential of deep learning (DL) in enhancing diagnostic Accuracy and speed. However, despite the promising results of DL models, the increasing complexity of breast cancer data presents a significant challenge in selecting the most relevant features. We propose a robust DL model incorporating transfer learning (TL) and grey wolf optimization (GWO) to enhance BC diagnosis. The architecture is developed by leveraging multiple deep neural networks, including ResNet and Inception, based on convolutional neural networks (CNNs). These networks are fine-tuned using a mammographic image dataset to optimize model weights and parameters for higher classification accuracy. Additionally, the Wisconsin Breast Cancer Dataset (WBCD) is utilized with GWO to refine feature selection further. Experimental results demonstrate that the proposed ensemble method improves robustness, generalization, and detection rates while minimizing false positives and negatives. Large-scale dataset evaluation yielded a precision of 0.942, sensitivity of 0.982, Accuracy of 0.965, and an area under the curve (AUC) value of 0.971. These findings suggest that the proposed framework could significantly enhance patient care and improve healthcare service organization and management. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Diseases

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Rainfall prediction based on PCA-BiLSTM algorithm 24

Rainfall prediction based on PCA-BiLSTM algorithm

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4th Asia-Pacific Artificial Intelligence and Big Data Forum, AIBDF 2024

作者： Deng, Pengying Parthasarathy, Rajamohan Huo, Sicong Ayyappan, Preethy College of Information Engineering Nanning University Guangxi Nanning China Faculty of Engineering Built Environment and Information Technology SEGi University Kuala Lumpur Malaysia

ISBN: (纸本)9798400710865

This paper examines key issues in rainfall prediction under the dual impacts of climate change and human activities. Accurate rainfall prediction is essential for disaster mitigation, infrastructure protection and sustainable development. This study evaluates the performance of PCA-BiLSTM, LSTM and BP algorithms on the Guangxi Nanning ***-BiLSTM combines the dimensionality reduction capability of PCA and the time series feature extraction capability of Bi-LSTM, and shows excellent performance in both low and high rainfall scenarios. The experimental results show that PCA-BiLSTM achieves the lowest MSE (20.24), RMSE (4.50), and MAPE (1.12%), which is better than LSTM (MSE: 48.91, RMSE: 6.99, MAPE: 1.46%) and BP (MSE: 49.73, RMSE: 7.05, MAPE: 2.12%). In terms of classification accuracy, PCA-BiLSTM reaches 92.54%, LSTM reaches 82.09% and BP reaches 73.13%.PCA-BiLSTM is good at capturing complex temporal patterns and categorizing rainfall, and is therefore well suited for precision forecasting tasks, but is computationally *** strikes a balance between accuracy and efficiency, whereas BP is applicable to the resource-limited situations. Overall, PCA-BiLSTM proved to be the most robust and accurate rainfall prediction method. © 2024 Copyright held by the owner/author(s).

关键词： Rain

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MCFF-Net: Multi-Scale Contextual Feature Fusion Network for Polyp Segmentation

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Journal of Shanghai Jiaotong University (Science) 2025年 1-13页

作者： Ma, Lei Yan, Jiangkai Shao, Dangguo Li, Jingtao Wang, Jiawei Yan, Yukun Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Yunnan Provincial Key Laboratory of Artificial Intelligence Kunming650500 China

In clinical practice, technologies for automatically segmenting polyps might greatly improve the speed and accuracy of medical diagnosis, significantly reducing the chances of patients developing colorectal cancer. Nevertheless, the segmentation of polyps presents a considerable challenge due to their low contrast, presence of occlusion, and intricate background disturbances. In this paper, we propose an innovative approach for segmenting polyps, called multi-scale contextual feature fusion network for polyp segmentation (MCFF-Net). Specifically, MCFF-Net includes three modules: feature enhancement module (FEM), global-local feature fusion module (GLFF), and cross-scale feature fusion module (CFFM). Among them, FEM is used to improve the features at each scale. Building on this, the CFFM enables comprehensive interaction among the information across various scales, enhancing the diversity of the feature scales extracted. Moreover, the GLFF fuses global-local features to generate a rough object localization map. This map further modulates the fusion features from the CFFM, facilitating more precise segmentation. On five publicly available datasets, our approach not only segments various polyp types but also surpasses existing methods in terms of accuracy and generalizability. © Shanghai Jiao Tong University 2024.

关键词： Diseases

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Parameters Estimation of Modified Triple Diode Model of PSCs Considering Charge Accumulations and Electric Field Effects Using Puma Optimizer

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Computer Modeling in engineering & Sciences 2025年第4期143卷 723-745页

作者： Amlak Abaza Ragab A.El-Sehiemy Mona Gafar Ahmed Bayoumi Electrical Engineering Department Faculty of EngineeringKafrelsheikh UniversityKafrelsheikh33516Egypt Department of Computer Engineering and Information College of Engineering inWadi AlddawasirPrince Sattam Bin Abdulaziz UniversityKharj16278Saudi Arabia Machine Learning and Information Retrieval Department Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt Electrical Department Faculty of Engineering and TechnologyBadr University in CairoBadr11829Egypt Physics and Mathematics Department Faculty of EngineeringKafrelsheikh UniversityKafrelsheikh33516Egypt College of Engineering Al-Ayen UniversityThi-Qar64001Iraq

Promoting the high penetration of renewable energies like photovoltaic(PV)systems has become an urgent issue for expanding modern power grids and has accomplished several challenges compared to existing distribution *** study measures the effectiveness of the Puma optimizer(PO)algorithm in parameter estimation of PSC(perovskite solar cells)dynamic models with hysteresis consideration considering the electric field effects on *** models used in this study will incorporate hysteresis effects to capture the time-dependent behavior of PSCs *** PO optimizes the proposed modified triple diode model(TDM)with a variable voltage capacitor and resistances(VVCARs)considering the hysteresis *** suggested PO algorithm contrasts with other wellknown optimizers from the literature to demonstrate its *** results emphasize that the PO realizes a lower RMSE(Root mean square errors),which proves its capability and efficacy in parameter extraction for the *** statistical results emphasize the efficiency and supremacy of the proposed PO compared to the other well-known competing *** convergence rates show good,fast,and stable convergence rates with lower RMSE via PO compared to the other five competitive ***,the lowermean realized via the PO optimizer is illustrated by the box plot for all optimizers.

关键词： Dynamic model of PSCs puma optimizer parameter estimation triple diode model

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