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Agricultural consumer Internet of Things devices: Methods for optimizing data aggregation

Alexandria Engineering Journal 2025年 125卷 692-699页

作者： Mengash, Hanan Abdullah Mahgoub, Hany Alshuhail, Asma Darem, Abdulbasit A. Majdoubi, Jihen Yafoz, Ayman Alsini, Raed Alghushairy, Omar Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science Applied College at Mahayil King Khalid University Saudi Arabia Department of Information Systems College of Computer Sciences & Information Technology King Faisal University Saudi Arabia Center for Scientific Research and Entrepreneurship Northern Border University Arar73213 Saudi Arabia Engineering and Technology Unit Applied College Majmaah University Al Majmaah11952 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Information Systems and Technology College of Computer Science and Engineering University of Jeddah Jeddah21589 Saudi Arabia

With the advent of state-of-the-art computer and digital technology, modern civilisation has been immensely facilitated and optimised. The Internet of Things (IoT) has grown in importance in recent years, allowing us to monitor our physical environments and broadening our horizons. The "practice, science, or art" of farming is defined as tending to land, growing crops with the use of different tools and techniques, and then selling the harvested food. If farmers optimise their operations with the help of a Wireless Sensor Network (WSN), they will be able to work much more efficiently and effectively. Data aggregation involves collecting information from multiple sensors. The data aggregation process is optimised by applying metaheuristic techniques. A Genetic Algorithm (GA) is a method for modelling evolution that uses mutation, crossover, and natural selection as its building blocks. The key benefit of the Artificial Bee Colony (ABC) approach is that it simultaneously considers local and global search, and it doesn't get trapped calculating its local minima. Naturalistic algorithms like ALO model their hunting behaviour after that of ant-lions and doodlebugs. It manages to find a happy medium between exploration and exploitation with just one operator. Experimental evidences show that the proposed metaheuristic technique, ABC-ALO, which combines the best elements of Artificial Bee Colony and Ant Lion Optimisation, is superior to existing metaheuristic approaches in terms of lifetime computation, or the number of alive nodes at different round counts. © 2025

关键词： Genetic algorithms

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Time-aware Medication Recommendation via Intervention of Dynamic Treatment Regimes 34

Time-aware Medication Recommendation via Intervention of Dyn...

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34th ACM Web Conference, WWW 2025

作者： Li, Yishuo Zhang, Qi Lu, Wenpeng Peng, Xueping Zhang, Weiyu Si, Jiasheng Gong, Yongshun Hu, Liang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Department of Computer Science and Technology Tongji University Shanghai China Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia School of Software Shandong University Jinan China

ISBN: (纸本)9798400712746

Medication recommendation aims to suggest personalized drug combinations to patients based on their longitudinal medical histories stored in electronic health record (EHR) datasets. Patients’ Dynamic Treatment Regimes (DTRs) determine how patients’ drug combinations change along with the evolution of disease treatment. DTRs are effective for comprehending disease-treatment dynamics and for recommending a timely and personalized combination of medications for patients. However, existing medication recommender systems (MRSs) overlook the multiple treatment pathways generated by the intervention of DTRs and can only recommend a single treatment paradigm, ignoring the fact that patients may be at different treatment stages and thus require different treatment regime. Such disregard leads to a significant limitation in recommending personalized medication combinations tailored to different treatment stages, yielding greatly compromised accuracy and applicability of MRSs. Moreover, existing methods often overlook the time interval information over patients’ successive visits, which is critical to indicate patients’ treatment evolution. To address these significant gaps, we propose a Time-aware Medication Recommendation Framework via Intervention of Dynamic Treatment Regimes, called MR-DTR. To explicitly illustrate the intervention processes of DTRs on similar patients, we employ a co-guided graph to connect various patient sequences. In addition, to fully utilize the time interval information, we design a time-aware guidance mechanism dedicated to the co-guided graph to efficiently learn medication representation using the patient’s guidance information. We also introduce relative time intervals in the encoder to act as positional information. Extensive experiments on two real-world datasets demonstrate that MR-DTR surpasses state-of-the-art models in terms of recommendation performance. © 2025 Copyright held by the owner/author(s).

关键词： Dynamic Treatment Regimes Electronic Health Record Medication Recommendation Recommender Systems

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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, Salwani Othman, Zalinda Yahya, Yazrina Zakree Ahmad Nazri, Mohd Leng Goh, Say Alvankarian, Fatemeh Alvankarian, Jafar Iranian eUniversity Faculty of Computer Engineering Tehran96351-14578 Iran Universiti Kebangsaan Malaysia Center for Artificial Intelligence Technology Faculty of Information Science and Technology Bangi43600 Malaysia Universiti Kebangsaan Malaysia Center for Software Technology and Management Faculty of Information Science and Technology Bangi43600 Malaysia Universiti Malaysia Sabah Kampus Antarabangsa Labuan Optimisation and Analytics Research Group Faculty of Computing and Informatics Labuan87000 Malaysia University of Maryland Department of Electrical and Computer Engineering College ParkMD20742 United States RayanMiniFab Tehran University Science and Technology Park Tehran*** Iran

A decision support system (DSS) is a computer-based tool used to improve decision-making capabilities for any organization by analyzing the available data. The 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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Network intrusion detection based on feature fusion of attack dimension

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Journal of Supercomputing 2025年第6期81卷 1-33页

作者： Sun, Xiaolong Gu, Zhengyao Zhang, Hao Gu, Jason Liu, Yanhua Dong, Chen Ye, Junwei College of Computer and Data Science Fuzhou University Fuzhou350116 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Chinese Ministry of Education Fuzhou350116 China Department of Electrical and Computer Engineering Dalhousie University HalifaxNSB3J 1Z1 Canada

Network traffic anomaly detection involves the rapid identification of intrusions within a network through the detection, analysis, and classification of network traffic data. The variety of cyberattacks encompasses diverse attack principles. Employing an indiscriminate feature selection strategy may lead to the neglect of key features highly correlated with specific attack types. This oversight could diminish the recognition rate for that category, thereby impacting the overall performance of the detection model. To address this issue, this paper proposes a network traffic anomaly detection model based on the fusion of attack-dimensional features. Firstly, construct binary classification datasets independently for each attack class and perform individual feature selection to extract positively correlated features for each class. The features are then fused by employing a combination methods. Subsequently, based on the fused sub-datasets, base classifiers are trained. Finally, an ensemble learning approach is introduced to integrate the predictions of individual classifiers, enhancing the robustness of the model. The proposed approach, validated on NSL-KDD and UNSW-NB15 benchmark datasets, outperforms the latest methods in the field by achieving a 2% and 7% increase in precision on weighted averages. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Feature Selection

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Pose Estimation of Spacecraft Using Dual Transformers and Efficient Bayesian Hyperparameter Optimization

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International Journal of Advanced computer science and Applications 2025年第4期16卷 629-644页

作者： Raja, N. Kannaiya Ramesh, Janjhyam Venkata Naga El-Ebiary, Yousef A. Baker Muniyandy, Elangovan Reddy, N. Konda Kumar, Vanipenta Ravi Devarasetty, Prasad School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh Bhopal466114 India Department of CSE Graphic Era Hill University Dehradun248002 India Department of CSE Graphic Era Deemed To Be University Uttarakhand Dehradun248002 India Faculty of Informatics and Computing UniSZA University Malaysia Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602 105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of Engineering Mathematics K L University Greenfields Guntur Dist Andhra Pradesh Vaddeswaram522302 India Department of Mathematics Annamacharya University New Boyanapalli Rajampet India Department of Computer Science and Engineering DVR & Dr HS MIC College of Technology Andhra Pradesh Kanchikacherla India

Spacecraft pose estimation is an essential contribution to facilitating central space mission activities like autonomous navigation, rendezvous, docking, and on-orbit servicing. Nonetheless, methods like Convolutional Neural Networks (CNNs), Simultaneous Localization and Mapping (SLAM), and Particle Filtering suffer significant drawbacks when implemented in space. Such techniques tend to have high computational complexity, low domain generalization capacity for varied or unknown conditions (domain generalization problem), and accuracy loss with noise from the space environment causes such as fluctuating lighting, sensor limitations, and background interference. In order to overcome these challenges, this study suggests a new solution through the combination of a Dual-Channel Transformer Network with Bayesian Optimization methods. The innovation is at the center with the utilization of EfficientNet, augmented with squeeze-and-excitation attention modules, to extract feature-rich representations without sacrificing computational efficiency. The dual-channel architecture dissects satellite pose estimation into two dedicated streams—translational data prediction and orientation estimation via quaternion-based activation functions for rotational precision. Activation maps are transformed into transformer-compatible sequences via 1×1 convolutions, allowing successful learning in the transformer's encoder-decoder system. To maximize model performance, Bayesian Optimization with Gaussian Process Regression and the Upper Confidence Bound (UCB) acquisition function makes the optimal hyperparameter selection with fewer queries, conserving time and resources. This entire framework, used here in Python and verified with the SLAB Satellite Pose Estimation Challenge dataset, had an outstanding Mean IOU of 0.9610, reflecting higher accuracy compared to standard models. In total, this research sets a new standard for spacecraft pose estimation, by marrying the versatility of deep le

关键词： Convolutional neural networks

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Polyp image segmentation based on improved planet optimization algorithm using reptile search algorithm

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Neural computing and Applications 2025年第8期37卷 6327-6349页

作者： Abd Elaziz, Mohamed Al-qaness, Mohammed A. A. Al-Betar, Mohammed Azmi Ewees, Ahmed A. Department of Mathematics Faculty of Science Zagazig University Zagazig Egypt College of Physics and Electronic Information Engineering Zhejiang Normal University Jinhua321004 China Zhejiang Institute of Optoelectronics Jinhua321004 China Faculty of Computer Science and Engineering Galala University Suze435611 Egypt Ajman University Ajman346 United Arab Emirates College of Computing and Information Technology University of Bisha Bisha Saudi Arabia Department of Computer Damietta University Damietta Egypt Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan

To recognize the potential for colon polyps to develop into cancer over time, early diagnosis is crucial for preventative healthcare. Timely identification significantly improves the prognosis and treatment outcomes for colorectal cancer patients. Image segmentation is crucial in medical image analysis for accurate diagnosis and treatment planning. Therefore, in this study, we present an alternative multilevel thresholding polyp segmentation method (MPOA) to enhance the segmentation of polyp images. The proposed method is based on enhancing the planet optimization algorithm (POA) by integrating operators from the reptile search algorithm (RSA). The evaluation of the developed MPOA is tested with different polyp images and compared with other image segmentation approaches. The results highlight the superior capability of MPOA, as evidenced by various performance measures in effectively segmenting polyp images. Furthermore, metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and fitness values demonstrate that MPOA outperforms the basic version of POA and other methods. The evaluation outcomes underscore the significant impact of RSA in enhancing the performance of POA for the segmentation of polyp images. © The Author(s) 2025.

关键词： Diseases

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Exploring Multiscale Navigation of Homogeneous and Dense Objects with Progressive Refinement in Virtual Reality

Exploring Multiscale Navigation of Homogeneous and Dense Obj...

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IEEE Annual International Symposium Virtual Reality

作者： Leonardo Pavanatto Alexander Giovannelli Brian Giera Timo Bremer Haichao Miao Doug A. Bowman Department of Computer Science Center for Human-Computer Interaction Virginia Tech USA Center for Applied Scientific Computing Lawrence Livermore National Laboratory USA

ISBN: (数字)9798331536459

ISBN: (纸本)9798331536466

Locating small features in a large, dense object in virtual reality (VR) poses a significant interaction challenge. While existing multiscale techniques support transitions between various levels of scale, they are not focused on handling dense, homogeneous objects with hidden features. We propose a novel approach that applies the concept of progressive refinement to VR navigation, enabling focused inspections. We conducted a user study where we varied two independent variables in our design, navigation style (STRUCTURED vs. UNSTRUCTURED) and display mode (SELECTION vs. EVERYTHING), to better understand their effects on efficiency and awareness during multiscale navigation. Our results showed that unstructured navigation can be faster than structured and that displaying only the selection can be faster than displaying the entire object. However, using an everything display mode can support better location awareness and object understanding.

关键词： Human computer interaction Location awareness Three-dimensional displays Navigation Virtual reality Inspection

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Retrieval Augmented Recipe Generation

Retrieval Augmented Recipe Generation

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IEEE Workshop on Applications of computer Vision (WACV)

作者： Guoshan Liu Hailong Yin Bin Zhu Jingjing Chen Chong-Wah Ngo Yu-Gang Jiang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai Collaborative Innovation Center on Intelligent Visual Computing Singapore Management University

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

The growing interest in generating recipes from food images has drawn substantial research attention in recent years. Existing works for recipe generation primarily utilize a two-stage training method—first predicting ingredients from a food image and then generating instructions from both the image and ingredients. Large Multi-modal Models (LMMs), which have achieved notable success across a variety of vision and language tasks, shed light on generating both ingredients and instructions directly from images. Nevertheless, LMMs still face the common issue of hallu-cinations during recipe generation, leading to suboptimal performance. To tackle this issue, we propose a retrieval augmented large multimodal model for recipe generation. We first introduce Stochastic Diversified Retrieval Augmentation (SDRA) to retrieve recipes semantically related to the image from an existing datastore as a supplement, integrating them into the prompt to add diverse and rich context to the input image. Additionally, Self-Consistency Ensemble Voting mechanism is proposed to determine the most confident prediction recipes as the final output. It calculates the consistency among generated recipe candidates, which use different retrieval recipes as context for generation. Extensive experiments validate the effectiveness of our proposed method, which demonstrates state-of-the-art (SOTA) performance in recipe generation on the Recipe1M dataset.

关键词： Training computer vision Accuracy Computational modeling Stochastic processes Predictive models Reliability Faces

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Explainable Hybrid Model for Presaging Tumor Stage Classification with Survival Survey

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SN computer science 2025年第4期6卷 1-24页

作者： Manna, Sweta Mistry, Sujoy Zhao, Zhongming Dahal, Keshav Department of Computer Science and Engineering Maulana Abul Kalam Azad University of Technology Nadia W.B Kolkata 741249 India Center for Precision Health School of Biomedical Informatics The University of Texas Health Science Center at Houston Houston TX United States School of Engineering and Computing University of the West of Scotland Paisley PA1 2BE United Kingdom

Tumor stage classification with a survival survey during detection time is the most crucial part of cancer treatment. The survivability period is directly associated with the early detection of the stage. This study developed a hybrid model to accurately classify the tumor (T) stages with the survivability analysis. The study aims to provide an explainable Artificial Intelligence framework (such as SHAP, and SHAPASH), through which the trustability and interpretability of the proposed model can increase. Along with the XAI, this hybrid approach uses two statistical models, ANOVA and LASSO with the standard TNM (tumor, node, metastasis) method for the T-stage classification. Afterward, the survivability analysis for each T stage is shown by the Kaplan–Meier (KM) method, proportion hazard ratio (Cox). Where KM is used to represent the survivability of individual tumor stages in months and the Cox regression shows the risk factors of the events. Initially, the proposed model was examined on a full dataset that contained malignant and benign data for evaluating the performance of the model, therefore stage classification for T1, T2, and T3 performs for malignant data. The K-fold and repeated-stratified K-fold cross-validation are used to measure the accuracy of the whole dataset for lung and breast cancer. For lung cancer, maximum accuracy was attained by MLP–97.64 in K-fold cross-validation. In breast cancer maximum accuracy achieved by GPC-99.57 in repeated-stratified K-fold cross validation. The results of both datasets are compared with the state-of-the-art models. The study contributed by representing the significance of individual features for tumor staging using the advanced approach of XAI, the SHAPASH interface. The accuracy of the T1, T2, and T3 stages based on malignant data was demonstrated with various classifier models with cross-validation. Finally, it is expected that executing that approach will improve patient outcomes. By using, advanced artificial in

关键词： ANOVA Breast cancer Deep learning Explainable artificial intelligence F-score Gaussian process classification LASSO Lung cancer Machine learning Random-Forest Shapely additive explanation XGBoost

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A Vertical Federated Multiview Fuzzy Clustering Method for Incomplete Data

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IEEE Transactions on Fuzzy Systems 2025年第5期33卷 1510-1524页

作者： Li, Yan Hu, Xingchen Yu, Shengju Ding, Weiping Pedrycz, Witold Kiat, Yeo Chai Liu, Zhong National University of Defense Technology Laboratory for Big Data and Decision Changsha410073 China National University of Defense Technology School of Computer Changsha410073 China Nantong University School of Artificial Intelligence and Computer Science Nantong226019 China Gliwice44-100 Poland University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Nanyang Technological University College of Computing and Data Science 639798 Singapore

Multiview fuzzy clustering (MVFC) has gained widespread adoption owing to its inherent flexibility in handling ambiguous data. The proliferation of privatization devices has driven the emergence of new challenge in MVFC research works. Federated learning, a technique that can jointly train without directly using raw data, has gain significant attention in decentralized MVFC. However, their applicability depends on the assumptions of data integrity and independence between different views. In fact, while within distributed environments, data typically exhibits two challenging problems: 1) multiple views within a single client;2) incomplete data. Existing methods exhibit limitations in effectively addressing these challenges. Hence, in this study, we aim at achieving the effective clustering for incomplete data by a novel vertical federated MVFC framework. Specifically, a unified clustering framework is designed to capture both local client learning and global server training. For the local client learning, the data reconstruction strategy and prototype alignment strategy are introduced to ensure the preservation of data structure and refinement of clustering relationships, which mitigates the impact of incomplete data. Meanwhile, the global training process implements aggregation based on client-specific information. The whole process is realized based on the unified fuzzy clustering framework, promoting collaborative learning between client-specific and server information. Theoretical analyses and extensive experiments are carefully conducted to validate the effectiveness and efficiency of the proposed method from multiple perspectives. © 1993-2012 IEEE.

关键词： Data integrity

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