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XGBoost-Liver:An Intelligent Integrated Features Approach for Classifying Liver Diseases Using Ensemble XGBoost Training Model

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computers, Materials & Continua 2025年第4期83卷 1459-1474页

作者： Sumaiya Noor Salman A.AlQahtani Salman Khan Business and Management Sciences Department Purdue UniversityWest LafayetteIN 47907USA New Emerging Technologies and 5G Network and Beyond Research Chair Department of Computer EngineeringCollege of Computer and Information SciencesKing Saud UniversityRiyadh11564Saudi Arabia Department of Computer Science Abdul Wali Khan University MardanMardan23200KPKPakistan

The liver is a crucial gland and the second-largest organ in the human body and also essential in digestion,metabolism,detoxification,and *** diseases result from factors such as viral infections,obesity,alcohol consumption,injuries,or genetic *** significant health risks and demand timely diagnosis and treatment to enhance survival ***,diagnosing liver diseases relied heavily on clinical expertise,often leading to subjective,challenging,and time-intensive ***,early detection is essential for effective intervention,and advancements in machine learning(ML)have demonstrated remarkable success in predicting various conditions,including Chronic Obstructive Pulmonary Disease(COPD),hypertension,and *** study proposed a novel XGBoost-liver predictor by integrating distinct feature methodologies,including Ranking and Statistical Projection-based strategies to detect early signs of liver *** Fisher score method is applied to perform global interpretation analysis,helping to select optimal features by assessing their contributions to the overall *** performance of the proposed model has been extensively evaluated through k-fold cross-validation ***,the performance of the proposed model is evaluated using individual and hybrid ***,the XGBoost-Liver model performance is compared to that of commonly used classifier ***,its performance is compared with the existing state-of-the-art computational *** experimental results show that the proposed model performed better than the existing predictors,reaching an average accuracy rate of 92.07%.This paper demonstrates the potential of machine learning to improve liver disease prediction,enhance diagnostic accuracy,and enable timely medical interventions for better patient outcomes.

关键词： Machine learning deep neural network SHAP(SHapley Additive exPlanation) liver disease classifica-tion SMOTE(synthetic minority over-sampling technique)

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Self-Learning Based Dependable Offloading Optimization in Semi-Trusted Vehicular Edge Computing and networks

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8141-8157页

作者： Li, Xuehan Jing, Tao Li, Ruinian Wang, Xiaoxuan Yan, Yu Fan, Xin Huo, Yan Yu, Fei Richard Beijing Jiaotong University School of Electronic and Information Engineering National Engineering Research Center of Advanced Network Technologies Beijing100044 China Bowling Green State University Department of CS Bowling GreenOH43403 United States Beijing Forestry University School of Information Science and Technology Beijing100083 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

As vehicular edge computing and networks (VECONs) emerge, Internet of vehicles (IoVs) is gaining significant advantages. To provide users with dependable services, VECONs need to offer efficient and secure task offloading services. Unfortunately, little effort has been put into addressing security issues such as the risk of privacy exposure when offloading data. Modeling and optimizing offloading dependability are affected by these issues. To address these concerns, a dependable three-layer offloading framework involving blockchain technology is presented in this paper, with fully homomorphic encryption (FHE) algorithm incorporated in order to mitigate privacy exposure risks. The framework divides tasks reasonably and introduces a novel measure of offloading dependability called cost from energy consumption, delay, and privacy exposure risk (cEDP) for constructing dependable offloading optimization. Additionally, a self-learning based dependable privacy preservation offloading (SDPPO) algorithm is presented that is capable of solving the optimal offloading problem in a decentralized manner. Simulation results show that the SDPPO algorithm outperforms its counterparts in terms of faster convergence speed, better cEDP convergence results, and improved scalability and tunability characteristics. © 1967-2012 IEEE.

关键词： Differential privacy

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Accountable Distributed Access Control With Privacy Preservation for Blockchain-Enabled Internet of Things Systems: A Zero-Trust Security Scheme

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IEEE Internet of Things Journal 2025年第11期12卷 17936-17947页

作者： Fang, He Xu, Li Nan, Guoshun Zheng, Danyang Zhao, Haitao Wang, Xianbin Fujian Normal University College of Computer and Cyber Security Fuzhou350007 China Beijing University of Posts and Telecommunications National Engineering Research Center for Mobile Network Technologies Beijing100876 China Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu610031 China Nanjing University of Posts and Telecommunications Department of Wireless Communication Key Lab of Jiangsu Province Nanjing210003 China Western University Department of Electrical and Computer Engineering LondonONN6A 5B9 Canada

While being able to avoid single point failures, emerging decentralized security techniques are facing new challenges of reliability, robustness, and privacy preservation in blockchain-enabled Internet-of-Things (IoT) systems. To circumvent these issues, a zero-trust security scheme is proposed through distributed access control, enhanced authentication, dynamic authorization, and privacy preservation enabled by the consortium blockchain. The proposed scheme integrates three key components, i.e., a distributed recommendation mechanism, where multiple authorized nodes are utilized as referrers to efficiently confer their trust on a new public entity for enhanced authentication;an anonymous credential generation strategy, which is developed for the new entity to further protect its privacy from linking attacks;and an adaptive reputation update strategy, which is proposed for evaluating the nodes' behaviors in the system for accountability and dynamic multiple-level authorization. The proposed scheme is implemented in a Hyperledge Fabric and the results show that it significantly enhances security and protects private information. © 2014 IEEE.

关键词： Differential privacy

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DCGSD: Low-Light Image Enhancement with Dual-Conditional Guidance Sparse Diffusion Model

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Jin, Haiyan Wang, Jing Zuo, Fengyuan Su, Haonan Xiao, Zhaolin Wang, Bin Zhang, Yuanlin Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Department of Computer Science and Engineering NO. 5 South Jinhua Road Shaanxi Xi'an710048 China

When restoring low-light images, most methods largely overlook the ambiguity due to dark noise and lack discrimination for region and shape representations, resulting in invalid feature enhancement. In this work, we propose a physically explainable and prior guidance model for low-light image enhancement, termed Dual-Conditional Guidance Sparse Diffusion (DCGSD). Specifically, we introduce an elaborately designed Luminance Structure Guidance Head, which can be easily plugged into the existing diffusion model to emphasize the value of the luminance and structural representation. Furthermore, for reliable noise analysis, we provide a novel Sparse Attention Enhancement Module that is adaptively empowered to exploit the most useful region-to-region dependencies. This dynamic selection makes the diffusion process from dense to sparse, thus improving the efficiency of the reasoning noise distributions. To avoid noise amplification, we further present a Skip Calibration Module, which can be used to refine the local neighborhood that contains noisy and structural information. Extensive experiments have been performed to verify the superiority of the proposed method. DCGSD shows that leveraging dual-conditional guidance can support the diffusion model to produce sharper and more realistic results. © 1991-2012 IEEE.

关键词： Feature extraction

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Multi-Agent Deep Reinforcement Learning-Based Cooperative Perception and Computation in VEC

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IEEE Internet of Things Journal 2025年

作者： Zhao, Liang Li, Longjia Tan, Zhiyuan Hawbani, Ammar He, Qiang Liu, Zhi Shenyang Aerospace University School of Computer Science Shenyang110136 China Edinburgh Napier University School of Computing EdinburghEH10 5DT United Kingdom Northeastern University School of Medicine and Biological Information Engineering Shenyang110169 China The University of Electro-Communications Department of Computer and Network Engineering Tokyo Japan

Connected and autonomous vehicles (CAVs) are an important paradigm of intelligent transportation systems. Cooperative perception (CP) and vehicular edge computing (VEC) enhance CAVs' perception capacity of the region of interest (RoI) while alleviating the pressure of intensive computation on onboard resources. However, existing CP and computation schemes are based on inefficient broadcast communications and still face challenges such as highly dynamic communication link channel conditions caused by vehicle mobility, and limited computing resources in VEC environments. Considering the delay sensitivity of CAVs' perception tasks and the need for enhanced perception, we propose a unicast-based cooperative perception and computation scheme to achieve more efficient resource utilization and perception task execution in VEC scenarios. Our goal is to maximize CP gain and minimize task execution delay by optimizing the decision of each ego CAVs. To solve the sequential decision-making problem of multi-objective optimization, we propose a solution based on improved multi-agent proximal policy optimization deep reinforcement learning, where CAVs agents make adaptive decisions distributed based on partial observations. Simulation results show that compared with the baseline algorithm, our proposed scheme effectively reduces the execution delay of ego CAVs perception tasks and ensures a high perception gain. © 2025 IEEE.

关键词： Deep reinforcement learning

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In-Ear System for Monitoring Lower Jaw Motion: A Novel Approach to Speech Intention Detection in Laryngectomy Patients

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IEEE Transactions on Biomedical engineering 2025年 PP卷 PP页

作者： Musikic, Nevena Jokic, Diana Josipovic, Ksenija Chepeha, Douglas B. Popovic, Milos R. the Institute of Biomedical Engineering University of Toronto TorontoONM5S 3G9 Canada the KITE—Toronto Rehabilitation Institute University Health Network TorontoONM5S 3G9 Canada Department of Electrical & Computer Engineering University of Toronto TorontoONM5S 3G9 Canada the Toronto General Research Institute University Health Network TorontoONM5S 3G9 Canada the University of Michigan Michigan Medicine Department of Otolaryngology-Head & Neck Surgery MI48109 United States

Restoring speech following laryngectomy remains a significant clinical challenge. Current artificial voice systems often require manual operation, reducing their practicality and social acceptability, while underscoring the critical need for hands-free solutions. In this study, we explore an untapped biomarker—deformation of the outer ear canal caused by lower jaw motion—as a discreet and reliable indicator of speech intention. By monitoring these deformations, we propose a novel in-ear device designed to enable intuitive, hands-free control of artificial voice systems. The device prototype incorporates four infrared proximity sensors housed within a 3D-printed enclosure to detect volumetric changes in the outer ear canal associated with lower jaw movements. Preliminary testing demonstrated the device’s capability to capture fundamental jaw motions (protrusion, retraction, depression/elevation, and lateral shifting), integrated movements (chewing, coughing), and speech patterns (isolated vowels, single words, and sentences). Validation against surface electromyography signals and audio recordings confirmed the device’s accuracy in detecting activity onset and termination across all tested movements and speech patterns. Furthermore, reproducibility of the recorded signals across independent trials highlighted the reliability of the proposed system. This proof-of-concept study established the feasibility of using outer ear canal deformation as a novel biomarker for detecting speech intention. By providing a non-invasive, discreet, and socially acceptable solution, this approach has the potential to transform the quality of life for laryngectomy patients, facilitating their reintegration into social and professional environments with greater ease and autonomy. © 2025 IEEE computer Society. All rights reserved.

关键词： Proximity sensors

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MULGONET: An interpretable neural network framework to integrate multi-omics data for cancer recurrence prediction and biomarker discovery

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Fundamental Research 2025年

作者： Lan, Wei Tang, Zhentao Liao, Haibo Chen, Qingfeng Chen, Yi-Ping Phoebe Zhang, Zhaolei Wang, Jianxin Guangxi Key Laboratory of Multimedia Communications and Network Technology School of Computer Electronic and Information Guangxi University Guangxi 530004 China Department of Computer Science and Information Technology La Trobe University 3086 VIC Australia Donnelly Centre Department of Computer Science and Department of Molecular Genetics University of Toronto M5S 1A1 ON Canada School of Computer Science and Engineering Central South University Hunan 410083 China

Multi-omics cancer data provides complementary views of tumorigenesis and progression. Technical challenges exist in integrating these heterogeneous data into deep learning models to better understand tumorigenesis and predict cancer recurrence. We herein propose a novel end-to-end deep learning method (MULGONET) for cancer recurrence prediction and biomarker discovery. First, MULGONET can effectively solve the curse of dimensionality and the lack of model interpretability in multi-omics data integration. Second, it explores interactions and regulatory relationships between genes and GO terms, thus providing biological insights. Benchmark results show that MULGONET outperforms other contemporary classification methods. It achieves AUPRs of 0.774 ± 0.015, 0.873 ± 0.003 and 0.702 ± 0.011 on the bladder, pancreatic and stomach cancer datasets, respectively. We also show that MULGONET can effectively identify prognostic genes and GO terms associated with cancer recurrence. © 2025

关键词： Biomarker discovery Cancer recurrence Deep learning Interpretability Multi-omics data

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Dynamic load balancing with learning model for Sudoku solving system

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Digital Communications and networks 2020年第1期6卷 108-114页

作者： Nattapong Kitsuwan Praphan Pavarangkoon Hendro Mulyo Widiyanto Eiji Oki Department of Computer and Network Engineering The University of Electro-Communications1-5-1ChofugaokaChofu-shiTokyo182-8585Japan

This paper proposes a dynamic load balancing with learning model for a Sudoku problem solving system that has multiple workers and multiple *** objective is to minimise the total processing time of problem *** load balancing with learning model distributes each Sudoku problem to an appropriate pair of worker and solver when it is received by the *** information of the estimated solution time for a specific number of given input values,the estimated finishing time of each worker,and the idle status of each worker is used to determine the worker-solver *** addition,the proposed system can estimate the waiting period for each *** results show that the system has shorter processing time than conventional alternatives.

关键词： Dynamic load balancing Learning model Sudoku

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Intrusion Detection System with Customized Machine Learning Techniques for NSL-KDD Dataset

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computers, Materials & Continua 2023年第12期77卷 4025-4054页

作者： Mohammed Zakariah Salman A.AlQahtani Abdulaziz M.Alawwad Abdullilah A.Alotaibi Department of Computer Science College of Computer and Information ScienceKing Saud UniversityRiyadh11495Saudi Arabia New Emerging Technologies and 5G Network and Beyond Research Chair Department of Computer EngineeringCollege of Computer and Information SciencesKing Saud UniversityRiyadh11495Saudi Arabia Department of Computer Engineering College of Computer and Information ScienceKing Saud UniversityRiyadh11495Saudi Arabia

Modern networks are at risk from a variety of threats as a result of the enormous growth in internet-based *** consuming time and resources,intrusive traffic hampers the efficient operation of network *** effective strategy for preventing,detecting,and mitigating intrusion incidents will increase productivity.A crucial element of secure network traffic is Intrusion Detection System(IDS).An IDS system may be host-based or network-based to monitor intrusive network *** unusual internet traffic has become a severe security risk for intelligent *** systems are negatively impacted by several attacks,which are slowing *** addition,networked communication anomalies and breaches must be detected using Machine Learning(ML).This paper uses the NSL-KDD data set to propose a novel IDS based on Artificial Neural networks(ANNs).As a result,the ML model generalizes sufficiently to perform well on untried *** NSL-KDD dataset shall be utilized for both training and *** this paper,we present a custom ANN model architecture using the Keras open-source software *** specific arrangement of nodes and layers,along with the activation functions,enhances the model’s ability to capture intricate patterns in network *** performance of the ANN is carefully tested and evaluated,resulting in the identification of a maximum detection accuracy of 97.5%.We thoroughly compared our suggested model to industry-recognized benchmark methods,such as decision classifier combinations and ML classifiers like k-Nearest Neighbors(KNN),Deep Learning(DL),Support Vector Machine(SVM),Long Short-Term Memory(LSTM),Deep Neural network(DNN),and *** is encouraging to see that our model consistently outperformed each of these tried-and-true techniques in all *** result underlines the effectiveness of the suggested methodology by demonstrating the ANN’s capacity to accurately assess the effectiveness of the developed strategy

关键词： Artificial neural networks intrusion detection system classification NSL-KDD dataset machine and deep-learning neural network

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PRMT5 promotes full-length HTT expression by repressing multiple proximal intronic polyadenylation sites

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Nucleic Acids Research 2025年第8期53卷 gkaf347页

作者： Yadav, Manisha Alqazzaz, Mona A. Ciamponi, Felipe E. Ho, Jolene C. Maron, Maxim I. Sababi, Aiden M. Macleod, Graham Ahmadi, Moloud Bullivant, Garrett Tano, Vincent Langley, Sarah R. Sánchez-Osuna, María Sachamitr, Patty Kushida, Michelle Bardile, Costanza Ferrari Pouladi, Mahmoud A. Kurtz, Rebecca Richards, Laura Pugh, Trevor Tyers, Mike Angers, Stephane Dirks, Peter B. Bader, Gary D. Truant, Ray Massirer, Katlin B. Barsyte-Lovejoy, Dalia Shechter, David Harding, Rachel J. Arrowsmith, Cheryl H. Prinos, Panagiotis Department of Medical Biophysics University of Toronto Toronto M5G1L7 ON Canada Princess Margaret Cancer Centre University Health Network Toronto M5G1L7 ON Canada Structural Genomics Consortium University of Toronto Toronto M5G1L7 ON Canada Center for Molecular Biology and Genetic Engineering University of Campinas (UNICAMP) Campinas 13083-872 Brazil Department of Biochemistry Albert Einstein College of Medicine Bronx 10461 NY United States Donnelly Centre for Cellular and Biomolecular Research University of Toronto Toronto M5S3E1 ON Canada Department of Molecular Genetics University of Toronto Toronto M5S3E1 ON Canada Leslie Dan Faculty of Pharmacy University of Toronto Toronto M5S3M2 ON Canada Developmental and Stem Cell Biology Program Arthur and Sonia Labatt Brain Tumor Research Centre The Hospital for Sick Children Toronto M5G0A4 ON Canada Lee Kong Chian School of Medicine Nanyang Technological University Singapore 636921 Singapore School of Biosciences Cardiff University Cardiff CF103AX United Kingdom Institute for Research in Immunology and Cancer Universitéde Montréal Montreal H3C3J7 QC Canada Department of Medical Genetics Centre for Molecular Medicine and Therapeutics Djavad Mowafaghian Centre for Brain Health British Columbia Children's Hospital Research Institute University of British Columbia Vancouver V5Z4H4 BC Canada Department of Biochemistry and Biomedical Sciences McMaster University Hamilton L8N3Z5 ON Canada Ontario Institute for Cancer Research University Health Network Toronto M5G0A3 ON Canada Division of Neurosurgery Department of Surgery University of Toronto Toronto M5S1A8 ON Canada Department of Laboratory Medicine and Pathobiology University of Toronto Toronto M5S1A8 ON Canada Lunenfeld-Tanenbaum Research Institute Sinai Health System Toronto M5G1X5 ON Canada Department of Computer Science University of Toronto M5S3E1 ON Canada Department of Pharmacology and Toxicology University of Toronto

Expansion of the CAG trinucleotide repeat tract in exon 1 of the Huntingtin (HTT) gene causes Huntington's disease (HD) through the expression of a polyglutamine-expanded form of the HTT protein. This mutation triggers cellular and biochemical pathologies, leading to cognitive, motor, and psychiatric symptoms in HD patients. Targeting HTT splicing with small molecule drugs is a compelling approach to lowering HTT protein levels to treat HD, and splice modulators are currently being tested in the clinic. Here, we identify PRMT5 as a novel regulator of HTT messenger RNA (mRNA) splicing and alternative polyadenylation. PRMT5 inhibition disrupts the splicing of HTT introns 9 and 10, leading to the activation of multiple proximal intronic polyadenylation sites within these introns and promoting premature termination, cleavage, and polyadenylation of the HTT mRNA. This suggests that HTT protein levels may be lowered due to this mechanism. We also detected increasing levels of these truncated HTT transcripts across a series of neuronal differentiation samples, which correlated with lower PRMT5 expression. Notably, PRMT5 inhibition in glioblastoma stem cells potently induced neuronal differentiation. We posit that PRMT5-mediated regulation of intronic polyadenylation, premature termination, and cleavage of the HTT mRNA modulates HTT expression and plays an important role during neuronal differentiation. © 2025 The Author(s).

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