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Multi-lingual news stack based ensemble classification for deep news feed analysis

Neural computing and Applications 2025年 1-12页

作者： Sakthivel, Rakesh Kumar Nagasubramanian, Gayathri Sankayya, Muthuramalingam Kadry, Seifedine Rocha, Alvaro Karnataka Bengaluru India Department of Information Technology Thiagarajar College of Engineering Madurai India Faculty of Applied Computing and Technology Noroff University College Kristiansand Norway Alvaro Rocha ISEG University of Lisbon Lisbon Portugal

Analyzing and extracting effective words of news feeds are critical tasks. News making sources may feed their news articles in different manners based on their own perspective. In this situation, finding likelihood samples from the vast news feeds is an essential task. Moreover, acquiring optimal word deviations, news varieties, sentiment metrics, and multi-lingual analysis are major tasks of this proposed system. To achieve this massive news feed analysis task, the proposed system uses specially designed multi-classifier units and news stack manipulation procedures. The proposed system integrates different classifiers and dynamic news stacks to handle datasets of multi-lingual attributes. The results show that the proposed method performs with better accuracy rate than existing techniques. This gives 10 to 15% higher accuracy rate than existing works. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Data accuracy

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Machine Learning-Driven QoT Prediction for Enhanced Optical Networks in DWDM system

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IEEE Access 2025年 13卷 80445-80460页

作者： Sakthivel, Sudha Belgaum, Mohammad Riyaz Sajak, Aznida Abu Bakar Alam, Muhammad Mansoor Su'ud, Mazliham Mohd Universiti Kuala Lumpur Advanced Telecommunication Technology Research Cluster Malaysia University of Technology and Applied Sciences Muscat133 Oman Multimedia University Faculty of Computing and Informatics Cyberjaya63100 Malaysia Arab Open University Faculty of Computer Studies 18211 Bahrain Riphah International University Faculty of Computing Islamabad46000 Pakistan

This research demonstrated the machine learning (ML) classifiers with regression learning to improve an optical system’s quality of transmission (QoT). In Optical Communication, the data can be communicated from source to destination through the established lightpaths. However, as the signal traverses the optical links and devices, its QoT may deteriorate due to various impairments. The QoT is an essential component that determines the connectivity of an optical network. Therefore, ensuring a QoT guarantee is necessary to establish a successful lightpath. Predicting the QoT before establishing lightpaths can guide the routing and allocation of resources required for the lightpaths. In this research, using ML models an appropriate QoT analytical prediction model is developed computationally. Simulations were conducted at a 10 Gbps data rate per channel for 64-channel DWDM systems. The proposed model significantly improves in detecting fiber nonlinearity, and performance was studied using Q-factor, BER, and noise power. The results indicate that the SVM-based classifier with regression learning performs better than any other classifiers discussed in this research. This study assesses the efficiency of the proposed ML models in predicting the QoT for established lightpaths. Results indicate that all the ML classifiers with Regression models can accurately predict the transmission quality for over 90% of lightpaths. However, the proposed SVM-based classifier with a regression model demonstrates superior generalization, with a nearly perfect QoT prediction rate of around 99% for the established lightpaths. In the network planning stage, residual margins are added to compensate for inaccuracies, which ensures accurate signal reception. The proposed ML model achieved a lightpath residual margin with a 0.7 dB error. © 2013 IEEE.

关键词： Fiber to the x

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Computational approaches for circRNA-disease association prediction:a review

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Frontiers of Computer Science 2025年第4期19卷 99-113页

作者： Mengting NIU Yaojia CHEN Chunyu WANG Quan ZOU Lei XU Center for Informational Biology University of Electronic Science and Technology of ChinaChengdu 610054China School of Applied Chemistry and Biological Technology Shenzhen Polytechnic UniversityShenzhen 518055China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu 610054China Yangtze Delta Region Institute(Quzhou) University of Electronic Science and Technology of ChinaQuzhou 324000China Faculty of Computing Harbin Institute of TechnologyHarbin 150006China

Circular RNA(circRNA)is a covalently closed RNA molecule formed by back *** role of circRNAs in posttranscriptional gene regulation provides new insights into several types of cancer and neurological *** are associated with multiple diseases and are emerging biomarkers in cancer diagnosis and *** associations prediction is one of the current research hotspots in the field of *** research on circRNAs has made great progress,the traditional biological method of verifying circRNA-disease associations is still a great challenge because it is a difficult task and requires much ***,advances in computational methods have made considerable progress in circRNA *** review comprehensively discussed the functions and databases related to circRNA,and then focused on summarizing the calculation model of related predictions,detailed the mainstream algorithm into 4 categories,and analyzed the advantages and limitations of the 4 *** not only helps researchers to have overall understanding of circRNA,but also helps researchers have a detailed understanding of the past algorithms,guide new research directions and research purposes to solve the shortcomings of previous research.

关键词： circular RNA disease association prediction machine learning data mining deep learning

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Enhancing Classification Algorithm Recommendation in Automated Machine Learning: A Meta-Learning Approach Using Multivariate Sparse Group Lasso

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Computer Modeling in Engineering & Sciences 2025年第2期142卷 1611-1636页

作者： Irfan Khan Xianchao Zhang Ramesh Kumar Ayyasamy Saadat M.Alhashmi Azizur Rahim School of Software Dalian University of TechnologyDalian116620China Faculty of Information and Communication Technology Universiti Tunku Abdul RahmanKampar31900Malaysia College of Computing and Informatics University of SharjahSharjah27272United Arab Emirates Department of Computer Systems Engineering University of Engineering and Applied SciencesSwat19200Pakistan

The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods have become impractical due to their resource *** Machine Learning(AutoML)systems automate this process,but often neglect the group structures and sparsity in meta-features,leading to inefficiencies in algorithm recommendations for classification *** paper proposes a meta-learning approach using Multivariate Sparse Group Lasso(MSGL)to address these *** method models both within-group and across-group sparsity among meta-features to manage high-dimensional data and reduce multicollinearity across eight meta-feature *** Fast Iterative Shrinkage-Thresholding Algorithm(FISTA)with adaptive restart efficiently solves the non-smooth optimization *** validation on 145 classification datasets with 17 classification algorithms shows that our meta-learning method outperforms four state-of-the-art approaches,achieving 77.18%classification accuracy,86.07%recommendation accuracy and 88.83%normalized discounted cumulative gain.

关键词： Meta-learning machine learning automated machine learning classification meta-features

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YOLOCSP-PEST for Crops Pest Localization and Classification

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Computers, Materials & Continua 2025年第2期82卷 2373-2388页

作者： Farooq Ali Huma Qayyum Kashif Saleem Iftikhar Ahmad Muhammad Javed Iqbal Department of Software Engineering University of Engineering and TechnologyTaxila47050Pakistan Department of Computer Science&Engineering College of Applied Studies&Community ServiceKing Saud UniversityRiyadh11362Saudi Arabia Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science University of Engineering and TechnologyTaxila47050Pakistan

Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome the issue of pest detection on crops. We have developed the YOLOCSP-PEST model for Pest localization and classification. With the Cross Stage Partial Network (CSPNET) backbone, the proposed model is a modified version of You Only Look Once Version 7 (YOLOv7) that is intended primarily for pest localization and classification. Our proposed model gives exceptionally good results under conditions that are very challenging for any other comparable models especially conditions where we have issues with the luminance and the orientation of the images. It helps farmers working out on their crops in distant areas to determine any infestation quickly and accurately on their crops which helps in the quality and quantity of the production yield. The model has been trained and tested on 2 datasets namely the IP102 data set and a local crop data set on both of which it has shown exceptional results. It gave us a mean average precision (mAP) of 88.40% along with a precision of 85.55% and a recall of 84.25% on the IP102 dataset meanwhile giving a mAP of 97.18% on the local data set along with a recall of 94.88% and a precision of 97.50%. These findings demonstrate that the proposed model is very effective in detecting real-life scenarios and can help in the production of crops improving the yield quality and quantity at the same time.

关键词： Deep learning classification of pests YOLOCSP-PEST pest detection

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INITIAL VALUE PROBLEMS FOR NONLINEAR NEUTRAL DELAY Ψ-CAPUTO FRACTIONAL HYBRID DIFFERENTIAL EQUATIONS WITH BOUNDED DELAYS

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Journal of Mathematical Sciences (United States) 2025年 1-11页

作者： Malghi, Hayat Taqbibt, Abdellah El Omari, M’hamed Qaffou, Abdelaziz Hilal, Khalid Department of Mathematics Applied Mathematics and Scientific Computing Laboratory Faculty of Sciences and Technology Beni Mellal Sultan Moulay Slimane University P.O. Box 523 Beni Mellal 23000 Morocco

In this work, we explore the existence of solutions to an initial value problem for nonlinear neutral delay Ψ-Caputo fractional hybrid differential equations with bounded delays. The existence results are established using fixed point theorems. To demonstrate the practical applicability of our findings, we provide an illustrative example. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Fixed point Hybrid differential equation Neutral delay differential equation Ψ-Caputo derivative

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A low Mach number asymptotic analysis of dissipation-reducing methods for curing shock instability

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applied Mathematics and Mechanics(English Edition) 2025年第4期46卷 723-744页

作者： Hongping GUO Xun WANG Zhijun SHEN National Key Laboratory of Computational Physics Institute of Applied Physics and Computational MathematicsBeijing100088China Faculty of Mathematics Baotou Teachers’CollegeBaotouInner Mongolia Autonomous Region014030China School of Mathematical Sciences Peking UniversityBeijing100871China PKU-Changsha Institute for Computing and Digital Economy Changsha410205China Center for Applied Physics and Technology Peking UniversityBeijing100871China

We are intrigued by the issues of shock instability,with a particular emphasis on numerical schemes that address the carbuncle phenomenon by reducing dissipation rather than increasing *** a specific class of planar flow fields where the transverse direction exhibits vanishing but non-zero velocity components,such as a disturbed onedimensional(1D)steady shock wave,we conduct a formal asymptotic analysis for the Euler system and associated numerical *** analysis aims to illustrate the discrepancies among various low-dissipative numerical ***,a numerical stability analysis of steady shock is undertaken to identify the key factors underlying shock-stable *** verify the stability mechanism,a consistent,low-dissipation,and shock-stable HLLC-type Riemann solver is presented.

关键词： Riemann solver numerical shock instability low Mach number HLLC

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Designing a hybrid neural network framework for real-time anomaly detection in cybersecurity applications

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International Journal of Information technology (Singapore) 2025年 1-7页

作者： Jain, Aaditya Ead, Waleed M. Alshahrani, Mohammed Altamimi, Jawaher Suliman Aklan, Fawaz Rihan, Shaza Dawood College of Computing Sciences and Information Technology Teerthanker Mahaveer University Uttar Pradesh Moradabad India Faculty of Computing and Information Al-Baha University Al-Baha Saudi Arabia Faculty of Computers and Artificial Intelligence Beni-Suef University Beni-Suef Egypt Computers Department Applied College Najran University Najran Saudi Arabia Applied College Najran University Najran Saudi Arabia Department of Computer Science Faculty of Applied Science Taiz University Taiz P.O. Box 6803 Taiz Yemen

As cybersecurity is becoming a prominent domain, it is an imperative to provide real-time anomaly detection for early threat detection and to keep networks safe. In this paper, an optimized hybrid neural network-framework is proposed to detect anomalies at several attack vectors with high efficacy. The framework comprises convolutional neural network equipped with long-short term memory (CNN-LSTM) for temporal and spatial representation of data. As the system accommodates new data and keeps on learning, it adjusts itself becoming a significant mean in counteracting against emerging threats with less latency in detection. In order to verify the efficiency and robustness of the proposed framework, a series of real-time simulation is designed using key performance metrics such as detection rate, false alarm rate and processing time. The results show higher improvement in accuracy and speed of anomaly detection, which makes the framework a useful instrument to reinforce cybersecurity defense during real-time scenarios. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.

关键词： Anomaly detection Convolutional neural network (CNN) Cybersecurity Hybrid approach Long-short term memory (LSTM)

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Photoplethysmography Bio-Signal Extraction for Classifying Diabetes Mellitus Diseases Using Pretrained Deep Learning Networks

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SN Computer Science 2025年第2期6卷 1-14页

作者： Tarmizi, Syaidatus Syahira Ahmad Suriani, Nor Surayahani Mohd, Mohd Norzali Bin Hj Bagchi, Susama Debnath, Sanjoy Kumar Shah, Syed Mehr Ali Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Johor Parit Raja Malaysia Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India College of Computing & Information Sciences University of Technology & Applied Sciences Musandem Oman

This study investigates the association between the presence of blood glucose and blood pressure variations to determine an individual’s health. It classified diabetes mellitus by using a publicly available PPG-BP dataset of 72 subjects. Increasing diabetes trends due to undiagnosed disease were overcome through continuous finger cuff-based blood pressure measurements via photoplethysmography to extract diabetes related features including the baseline anthropometrical characteristics and signal morphology. The variations in blood pressure obtained from single channel PPG are processed to extract signal information and the results which are the STFT transformed version of the signal as the input data to investigate the meaningful patterns of the original raw, filtered and their derivatives between healthy and diabetic individuals. This can be followed by the state of the art of pretrained deep learning networks such as DenseNet121, DenseNet169, EfficientNetB0, EfficientNetB1, InceptionResNetV2, ResNet50, ResNet101 and Xception which were discussed in the comparative analysis that can be effectively used for classification on the respective sets of signals. The result indicates that InceptionResNetV2 and Xception networks outperform the rest of the pretrained deep learning used in the study with the highest recorded accuracy, precision and recall values which making them the most effective networks for classification problems. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blood glucose Blood pressure Diabetes mellitus Photoplethysmography

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GACL-Net:Hybrid Deep Learning Framework for Accurate Motor Imagery Classification in Stroke Rehabilitation

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Computers, Materials & Continua 2025年第4期83卷 517-536页

作者： Chayut Bunterngchit Laith H.Baniata Mohammad H.Baniata Ashraf ALDabbas Mohannad A.Khair Thanaphon Chearanai Sangwoo Kang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Computing Gachon UniversitySeongnam13120Republic of Korea Computer Science Department Faculty of Information TechnologyThe World Islamic Sciences and Education UniversityAmman11947Jordan Intelligent Systems Department Faculty of Artificial IntelligenceAl-Balqa Applied UniversityAl-Salt19117Jordan IT Infrastructure Department Qatrana Cement CompanyAmman11821Jordan Division of Industrial and Logistics Engineering Technology Faculty of Engineering and TechnologyKing Mongkut’s University of Technology North BangkokRayong CampusRayong21120Thailand

Stroke is a leading cause of death and disability worldwide,significantly impairing motor and cognitive *** rehabilitation is often hindered by the heterogeneity of stroke lesions,variability in recovery patterns,and the complexity of electroencephalography(EEG)signals,which are often contaminated by *** classification of motor imagery(MI)tasks,involving the mental simulation of movements,is crucial for assessing rehabilitation strategies but is challenged by overlapping neural signatures and patient-specific *** address these challenges,this study introduces a graph-attentive convolutional long short-term memory(LSTM)network(GACL-Net),a novel hybrid deep learning model designed to improve MI classification accuracy and ***-Net incorporates multi-scale convolutional blocks for spatial feature extraction,attention fusion layers for adaptive feature prioritization,graph convolutional layers to model inter-channel dependencies,and bidi-rectional LSTM layers with attention to capture temporal *** on an open-source EEG dataset of 50 acute stroke patients performing left and right MI tasks,GACL-Net achieved 99.52%classification accuracy and 97.43%generalization accuracy under leave-one-subject-out cross-validation,outperforming existing state-of-the-art ***,its real-time processing capability,with prediction times of 33–56 ms on a T4 GPU,underscores its clinical potential for real-time neurofeedback and adaptive *** findings highlight the model’s potential for clinical applications in assessing rehabilitation effectiveness and optimizing therapy plans through precise MI classification.

关键词： Motor imagery EEG stroke rehabilitation deep learning brain-computer interface

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