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Herb compounds screening as meningitis inhibitor candidates using neural network and random forest methods

AIP Conference Proceedings 2025年第1期3250卷

作者： Riska Aprilia Mohammad Hamim Zajuli Al Faroby Muhammad Adib Kamali Muhammad Dzulfikar Fauzi Information Technology Study Program Faculty of Information Technology and Business Institut Teknologi Telkom Surabaya Surabaya Indonesia Data Science Study Program Faculty of Information Technology and Business Institut Teknologi Telkom Surabaya Surabaya Indonesia Informatics Study Program Faculty of Information Technology and Business Institut Teknologi Telkom Surabaya Surabaya Indonesia

Meningitis is an inflammation of the meninges that occurs in the protective lining of the brain and spinal cord caused by bacterial, viral, or fungal infections. This disease is difficult to recognize because it has initial symptoms like the flu where the patient has a fever and headache. Current efforts to prevent the disease by strengthening antibodies. Meanwhile, drug candidates for the treatment of this disease still have not found optimal results in reducing mortality due to meningitis. This study aims to find and analyses herbal compound candidates that might be inhibitors of meningitis. Compound data was acquired from a validated open database. The data acquired are smiles of the chemical bond structure of the compounds. In the data processing process, compound feature extraction is required by applying the concept of molecular fingerprint. The results of feature extraction are used as datasets to build classification models by applying the Multilayer Perceptron (MLP) and Random Forest algorithms. The two models are compared, and a more robust model is selected to be used as a prediction model for herbal compound search. The MLP model has a better accuracy of 0.97 compared to the Random Forest model. The results of screening using the MLP learning model obtained Symphytine, cis-Linalool oxide, and 3-O-Methylcalopocarpin compounds have the highest probability compared to thousands of other herbal compounds. This candidate compound can be used as a recommendation for drug discovery to treat patients who contract Meningitis.

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Hybrid Optimization Framework for Energy Efficiency Maximization in NOMA-Aided Internet of Vehicles with beyond Diagonal RIS

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IEEE Internet of Things Journal 2025年

作者： Yi, Xiaoxi Wang, Han Zhang, Chi Song, Huiling Dutta, Ashit Kumar Yichun University College of Physical Science and Technology Jiangxi Yichun 336000 China City University of Macau Faculty of Data Science Macao 999078 Macao Yichun University College of Physical Science and Engineering Yichun 336000 China Harbin University of Commerce College of Basic Science Heilongjiang Harbin 150006 China AlMaarefa University Ad Diriyah College of Applied Sciences Department of Computer Science and Information Systems Riyadh 13713 Saudi Arabia

The integration of beyond diagonal reconfigurable intelligent surfaces (BD-RIS) with non-orthogonal multiple access (NOMA) in the Internet of Vehicles (IoV) networks presents the transformative potential for 6G vehicular communications, promising unprecedented gains in energy and spectral efficiency. However, existing research fails to address the joint optimization of BD-RIS configuration, NOMA power allocation, and dynamic IoV association-a critical oversight given their inherent interdependence in practical deployment scenarios. Current literature predominantly examines these components in isolation, with conventional RIS architectures and orthogonal multiple access schemes, leading to suboptimal performance in high-mobility vehicular environments. This work bridges this gap by proposing a novel hybrid optimization framework for NOMA-aided BD-RIS assisted IoV networks. The proposed framework simultaneously optimizes the IoV association with the base station (BS), NOMA power allocation, and BD-RIS phase shift design to maximize the energy efficiency of the system while ensuring the minimum signal-to-interference plus noise ratios (SINRs) of IoVs. The proposed framework is formulated as non-linear optimization due to joint decision variables and rate expressions of IoVs, resulting in an NP-hard problem where achieving a joint optimal solution is computationally complex. To handle this complexity, the original joint formulation is decomposed into three subproblems: IoV association with BS, BS power allocation, and BD-RIS phase shift design. An efficient iterative solution is then developed through the synergistic combination of deep reinforcement learning, first-order Taylor expansion, and manifold optimization methods. For comprehensive evaluation, we introduce a NOMA-aided conventional RIS assisted IoV framework as a benchmark. Extensive numerical results based on Monte Carlo simulations demonstrate that the proposed NOMA-aided BD-RIS assisted IoV network achieves

关键词： 6G beyond diagonal RIS diagonal RIS energy efficiency optimization Internet of vehicles non-orthogonal multiple access

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ICGNN: Graph Neural Network Enabled Scalable Beamforming for MISO Interference Channels

arXiv

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arXiv 2025年

作者： He, Changpeng Lu, Yang Ai, Bo Dobre, Octavia A. Ding, Zhiguo Niyato, Dusit School of Computer Science and Technology Beijing Jiaotong University Beijing100044 China School of Electronics and Information Engineering Beijing Jiaotong University Beijing100044 China Faculty of Engineering and Applied Science Memorial University St. John’sNLA1C 5S7 Canada Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi127788 United Arab Emirates College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

This paper investigates the graph neural network (GNN)-enabled beamforming design for interference channels. We propose a model termed interference channel GNN (ICGNN) to solve a quality-of-service constrained energy efficiency maximization problem. The ICGNN is two-stage, where the direction and power parts of beamforming vectors are learned separately but trained jointly via unsupervised learning. By formulating the dimensionality of features independent of the transceiver pairs, the ICGNN is scalable with the number of transceiver pairs. Besides, to improve the performance of the ICGNN, the hybrid maximum ratio transmission and zero-forcing scheme reduces the output ports, the feature enhancement module unifies the two types of links into one type, the subgraph representation enhances the message passing efficiency, and the multi-head attention and residual connection facilitate the feature extracting. Furthermore, we present the over-the-air distributed implementation of the ICGNN. Ablation studies validate the effectiveness of key components in the ICGNN. Numerical results also demonstrate the capability of ICGNN in achieving near-optimal performance with an average inference time less than 0.1 ms. The scalability of ICGNN for unseen problem sizes is evaluated and enhanced by transfer learning with limited fine-tuning cost. The results of the centralized and distributed implementations of ICGNN are illustrated. © 2025, CC BY.

关键词： Graph neural networks

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Joint Covert and Secure Communication for SWIPT Assisted CNOMA Systems

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IEEE Internet of Things Journal 2025年

作者： Huang, Gaojian Lei, Yuxin Li, Xingwang Khan, Wali Ullah Wang, Gongpu Nallanathan, Arumugam Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454003 China City University of Macau Faculty of Data Science 999078 China Henan Polytechnic University School of Surveying and Land Information Engineering Jiaozuo454003 China Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454099 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Luxembourg City1855 Luxembourg Beijing Jiaotong University Beijing Key Laboratory of Transportation Data Analysis and Mining School of Computer and Information Technology Beijing100044 China Institute of Internet of Things Wuxi214111 China Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of

With the rapid advancement of physical-layer security technology, the covert and secure communication has become crucial in safeguarding wireless communication systems. In this paper, we propose a joint covert and secure transmission scheme for simultaneous wireless information and power transfer (SWIPT) assisted cooperative non-orthogonal multiple access (CNOMA) systems. In the CNOMA system, a greedy relay transmits the confidential information to the far user (Carol), with the assistance of the near user (Bob). Meanwhile, as a SWIPT node, Bob is self-sustained by harvesting energy from relay. What's more, a warden (Alice) and non-colluding eavesdroppers (Eves) always attempt to detect and capture the confidential information, respectively. To counteract the attacks from Alice and Eves, a jamming-assisted scheme is employed. For the proposed system model, we derive closed-form expressions for the detection error probability (DEP) and the average minimum detection error probability (AMDEP) of Alice. Additionally, closed-form expressions for the outage probability (OP) of users and the intercept probability (IP) of Eves are obtained. Furthermore, to maximize the effective covert rate (ECR) of Carol, an optimization problem is formulated, subject to covertness and security constraints. Numerical results are provided to demonstrate the impact of the system parameters on covert and secure performance, with the results showing perfect agreement with the theoretical analysis. © 2014 IEEE.

关键词： Inductive power transmission

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ICT: Iterative Clustering with Training: Preliminary Results

ICT: Iterative Clustering with Training: Preliminary Results

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International Conference on New Trends in Computing sciences (ICTCS)

作者： Ahmad B. Hassanat Ahmad S. Tarawneh Ahmad S. Alhasanat Mansoor Alghamdi Khalid Almohammadi Zakaria Tarawneh Dept. Computer Science Faculty of Information Technology Mutah University Karak Jordan Dept. Data Science and Artificial Intelligence Mutah University Karak Jordan School of Foundation Studies and Mathematics Oryx Universal College Doha Qatar Dept of Mathematics Al-Hussein Bin Talal University Maan Jordan Dept. of Computer Science Applied College University of Tabuk Tabuk Saudi Arabia CiTIUS Universidade de Santiago de Compostela (USC) Spain

ISBN: (数字)9798331523411

ISBN: (纸本)9798331523428

We introduce Iterative Clustering with Training (ICT), a hybrid approach that combines clustering with iterative classifier-based refinement. Unlike traditional clustering methods, ICT leverages supervised learning to reassign ambiguous boundary points, improving cluster quality. The proposed approach starts with an initial clustering phase, followed by the computation of silhouette scores to separate high-confidence core points from uncertain boundary points. A classifier (e.g., Random Forest) is then trained on core points to predict labels for boundary points, refining clusters in an iterative process until convergence is achieved. To avoid trivial clusters, minimum cluster ratio constraints are implemented. Experimental results on multiple benchmark datasets show that ICT outperforms traditional clustering methods such as k-means, HDBSCAN, and spectral clustering across several evaluation metrics, such as Adjusted Rand Index, Normalized Mutual information, and Accuracy. ICT particularly excels at handling clusters that overlap, have noise, and complex geometries, where standard methods often fail. Key advantages include adaptive core selection, iterative refinement, and classifier flexibility, making ICT suitable for applications in customer segmentation, anomaly detection, and biological data analysis.

关键词： Training Measurement Clustering methods Roads Refining Supervised learning Semisupervised learning Iterative methods Standards Unsupervised learning

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Methodology for Real Estate Recommendation Based on Customer Behavior Knowledge as Context-Approach 15th

Methodology for Real Estate Recommendation Based on Customer...

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15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2023

作者： Vu, Sang Nguyen, Tinh Nguyen, Truc Pham, Vuong T. Huynh, Tai Nguyen, Hien D. University of Information Technology Ho Chi Minh City Viet Nam Faculty of Mathematics and Computer Science University of Science Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam Institute of Data Science and Artificial Intelligence Sai Gon University Ho Chi Minh City Viet Nam Kyanon Digital Ho Chi Minh City Viet Nam Computer Science Department New Mexico State University Las Cruces United States

ISBN: (纸本)9783031875687

Real estate recommendation systems present a significant challenge due to the multifaceted nature of buyer decision-making. This growing field necessitates the development of a holistic recommendation approach. This study proposes the Est-Model, which captures customer behavior related to real estate purchase decisions. The Est-Model leverages a relational knowledge model to organize property features. This model is then integrated with a database containing user profiles and real estate information. To address the cold-start problem for both items and users, the study introduces a novel real estate recommendation methodology. Utilizing a session-based recommendation framework, the system effectively manages sequential and contextual data to predict user preferences for future interactions. Empirical evaluations demonstrate the superiority of this approach over existing methodologies within the top-n recommendation paradigm. These findings are validated through experiments conducted on a real estate dataset. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Consultation Customer behaviors Intelligent system Knowledge-based systems Many-cold-start-users Recommendation system

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A two-stage deep learning-based hybrid model for daily wind speed forecasting

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Heliyon 2025年第1期11卷 e41026页

作者： Band, Shahab S. Ameri, Rasoul Qasem, Sultan Noman Mehdizadeh, Saeid Gupta, Brij B. Pai, Hao-Ting Shahmirzadi, Danyal Salwana, Ely Mosavi, Amir Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Computer Science Department College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh 11432 Saudi Arabia Computer Science Department Faculty of Applied Science Taiz University Taiz 6803 Yemen Water Engineering Department Urmia University Urmia Iran Department of Computer Science and Information Engineering Asia University Taichung 413 Taiwan Symbiosis Centre for Information Technology (SCIT) Symbiosis International University Pune India Center for Interdisciplinary Research University of Petroleum and Energy Studies (UPES) Dehradun India Department of Big Data Business Analytics National Pingtung University Pingtung Taiwan of Engineering Science and Technology National Yunlin University of Science and Technology 123 University Road Yunlin Douliou 64002 Taiwan Institute of Visual Informatics Universiti Kebangsaan Malaysia Selangor Bangi 43600 Malaysia John von Neumann Faculty of Informatics Obuda University Budapest Hungary Ludovika University of Public Service Budapest Hungary

Global adoption of wind energy continues to increase, while improving the efficiency of turbine settings requires reliable wind speed (WS) models. The latest models rely on artificial intelligence (AI) optimizations which constructs tests on a range of novel hybrid models to examine the reliability. Gradient Boosting (GB), Random Forest (RF), and Long Short-Term Memory (LSTM) are used in new combinations for data pre-processing. A Time Varying Filter-based Empirical Mode Decomposition (TVFEMD) model is coupled with the GB and LSTM standalone models, to create TVFEMD-GB and TVFEMD-LSTM hybrids, which are run in competition with each other. Eventually, a preferred hybrid form is established, simultaneous hybridization of TVFEMD with GB and LSTM. This study is the first to hybridize these fundamental systems, and create a TVFEMD-GB-LSTM model that can forecast WS. This study finds that the novel hybrid models exhibit superior performance to standalone GB and LSTM models, opening the pathway to alternative WS prediction techniques. © 2024 The Authors

关键词： Artificial intelligence Big data data science Forecasting Gradient boosting Long short-term memory Machine learning Wind speed

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Role of renewable energy and foreign direct investment toward economic growth in Egypt

Innovation and Green Development

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Innovation and Green Development 2025年第1期4卷

作者： Raihan, Asif Ibrahim, Said Ridwan, Mohammad Rahman, Md. Shoaibur Bari, A.B.M. Mainul Guneysu Atasoy, Filiz Institute of Climate Change National University of Malaysia Selangor Bangi 43600 Malaysia Faculty of Education National University of Malaysia Selangor Bangi 43600 Malaysia Department of Economics Noakhali Science and Technology University Noakhali 3814 Bangladesh Department of Agroforestry and Environment Hajee Mohammad Danesh Science and Technology University Dinajpur 5200 Bangladesh Department of Industrial and Production Engineering Bangladesh University of Engineering and Technology Dhaka 1000 Bangladesh Wilson Science Center Information and Data Science University of the Ozarks Clarksville 72830 AR United States

This investigation seeks to explore the influences of renewable energy and foreign direct investment (FDI) on Egypt's economy. Due to the function of FDI in promoting GDP expansion and renewable energy technology advancement, it is crucial to analyze the nexus between the economy, FDI, and green power. Egypt's annual data from 1990 to 2021 were employed in this research. By applying unit root tests, the data's stationarity was evaluated. The autoregressive distributed lag (ARDL) method was used to look into the connection between the variables in the long- and the short-run. Moreover, the reliability of the findings was reviewed using dynamic ordinary least squares (DOLS), fully modified ordinary least squares (FMOLS), and canonical cointegration regression (CCR) methodologies. As per the ARDL conclusions, an intensification of 1% in both renewable energy usage and FDI will be responsible for a boost of 1.02% and 1.87% in GDP over the long term, moreover to an upsurge of 0.54% and 1.11% over the short run. These outcomes suggest appropriate suggestions for the Egyptian government toward equitable development through promoting sustainable innovation in energy, improving FDI, and advancing sustainable economic expansion. © 2024

关键词： Economic growth Egypt FDI Renewable energy Sustainable development

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Decentralized Federated Graph Learning with Lightweight Zero Trust Architecture for Next-Generation Networking Security

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 1908-1922页

作者： Zhou, Xiaokang Liang, Wei Wang, Kevin I-Kai Yada, Katsutoshi Yang, Laurence T. Ma, Jianhua Jin, Qun Kansai University Faculty of Business Data Science Osaka565-0823 Japan RIKEN Center for Advanced Intelligence Project RIKEN Tokyo103-0027 Japan Hunan University of Technology and Business School of Computer Science Changsha410205 China Xiangjiang Laboratory Changsha410205 China The University of Auckland Department of Electrical Computer and Software Engineering Auckland1010 New Zealand St. Francis Xavier University Department of Computer Science AntigonishNSB2G 2W5 Canada Hosei University Faculty of Computer and Information Sciences Chiyoda-ku102-8160 Japan Waseda University Faculty of Human Sciences Tokorozawa359-1192 Japan

The rapid development and usage of digital technologies in modern intelligent systems and applications bring critical challenges on data security and privacy. It is essential to allow cross-organizational data sharing to achieve smart service provisioning, while preventing unauthorized access and data leak to ensure end users' efficient and secure collaborations. Federated Learning (FL) offers a promising pathway to enable innovative collaboration across multiple organizations. However, more stringent security policies are needed to ensure authenticity of participating entities, safeguard data during communication, and prevent malicious activities. In this paper, we propose a Decentralized Federated Graph Learning (FGL) with Lightweight Zero Trust Architecture (ZTA) model, named DFGL-LZTA, to provide context-aware security with dynamic defense policy update, while maintaining computational and communication efficiency in resource-constrained environments, for highly distributed and heterogeneous systems in next-generation networking. Specifically, with a re-designed lightweight ZTA, which leverages adaptive privacy preservation and reputation-based aggregation together to tackle multi-level security threats (e.g., data-level, model-level, and identity-level attacks), a Proximal Policy Optimization (PPO) based Deep Reinforcement Learning (DRL) agent is introduced to enable the real-time and adaptive security policy update and optimization based on contextual features. A hierarchical Graph Attention Network (GAT) mechanism is then improved and applied to facilitate the dynamic subgraph learning in local training with a layer-wise architecture, while a so-called sparse global aggregation scheme is developed to balance the communication efficiency and model robustness in a P2P manner. Experiments and evaluations conducted based on two open-source datasets and one synthetic dataset demonstrate the usefulness of our proposed model in terms of training performance, computa

关键词： Federated learning

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A Survey of Machine Learning Methods for Analyzing Synovitis Arthritis in Human Joints 19thth

A Survey of Machine Learning Methods for Analyzing Synovit...

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19th International Conference on Computer Aided Systems Theory, EUROCAST 2024

作者： Bąk, Artur Klempous, Ryszard Segen, Jakub Nikodem, Jan Rozenblit, Jerzy Chaczko, Zenon Kulbacki, Michal Gruszecka, Katarzyna Skoczyńska, Marta Atsushi, Ito Bożejko, Wojciech Jagielski, Dariusz Panejko, Anna Kowalczyk, Hubert Kluwak, Konrad Wojciechowski, Konrad Kulbacki, Marek Wroclaw University of Technology Wroclaw Poland Wroclaw University of Science and Technology Wroclaw Poland School of Electrical and Data Engineering University of Technology Sydney Ultimo Australia Department of Electrical and Computer Engineering University of Arizona Tucson United States Polish-Japanese Academy of Information Technology Warsaw Poland DIVE IN AI Wroclaw Poland Mathematical Institute University of Wroclaw Wroclaw Poland Faculty of Economics Chuo University Tokyo Japan Department of Rheumatology and Internal Diseases Lower Silesian Specialist Hospital Wroclaw Poland

ISBN: (纸本)9783031829598

Synovitis, characterized by inflammation of the synovial membrane in human joints, poses significant diagnostic and treatment challenges. This review presents methods and recent machine learning (ML) developments to analyze synovial arthritis in joints that help overcome these challenges. The methods described in the review include traditional ML algorithms, novel deep learning architectures and recent medical imaging techniques. Key challenges are discussed including the need for large and diverse datasets, model interpretability, generalization to different patient populations, dealing with data variability, and reducing computational complexity. The review also examines integrating multimodal data sources, advances in transfer learning, and developing robust, interpretable models as future directions. It includes enhancing early diagnostic capabilities, leveraging joint-on-a-chip simulations, and investigating signaling pathways in rheumatoid arthritis. This study aims to provide a consolidated resource for interdisciplinary researchers, clinicians and practitioners in the fields of rheumatology and medical imaging as it synthesizes current research to understand better ML methods in the detection of synovitis in human joints, paving the way for improved diagnostic and care capabilities over the patient. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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