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A hybrid TwinSVM-HHO model for multilingual spam review detection using sentiment features and pre-trained embeddings

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Expert Systems with Applications 2025年 287卷

作者： Al-Zoubi, Ala’ M. Mora, Antonio M. Faris, Hossam Qaddoura, Raneem Department of Artificial Intelligence and Data Science Faculty of Science and Information Technology Al-Zaytoonah University of Jordan Amman Jordan Department of Signal Theory Telematics and Communications ETSIIT and CITIC University of Granada Granada Spain King Abdullah II School for Information Technology The University of Jordan Amman Jordan Research Centre for Information and Communications Technologies of the University of Granada (CITIC-UGR) University of Granada Granada 18010 Spain School of Computing and Informatics Al Hussein Technical University Amman Jordan

The detection of spam reviews in multilingual environments remains a challenging task due to linguistic diversity, data imbalance, and semantic complexity. This paper proposes a novel hybrid model that integrates Twin Support Vector Machine (TwinSVM) with Harris Hawks Optimization (HHO) for simultaneous parameter optimization and feature selection. To enhance semantic understanding, sentiment-based features are incorporated alongside pre-trained word embedding models—BERT, FastText, and MUSE—across English, Arabic, and Spanish datasets. Our approach generates 24 high-quality datasets using embeddings with 100 and 400 dimensions, including a combined multilingual set. Experimental results demonstrate that our proposed HHO-TwinSVM model consistently outperforms conventional classifiers and metaheuristic-enhanced SVMs, achieving accuracy improvements of up to 9.44 % and enhanced robustness in low-resource languages. This integrated framework represents a scalable and adaptable solution for multilingual spam detection. Four detailed experiments were conducted in this study, each designed to address and demonstrate a specific aspect of the proposed approach. Across all experiments, the method outperformed existing algorithms, achieving impressive accuracy rates of 92.9741 %, 89.0314 %, 80.3580 %, and 85.0859 % on Arabic, English, Spanish, and multilingual datasets, respectively. Subsequently, sentiment analysis features were incorporated to further enhance detection performance, resulting in improvements of 1.0994 %, 2.6674 %, 9.4430 %, and 8.7448 %, respectively. A comprehensive analysis of the experimental results, including the influence of reviews and sentiment features, is also presented. © 2025 Elsevier Ltd

关键词： Detection HHO Multilingual Pre-trained Security Sentiment analysis Spam reviews Twin SVM Word embeddings

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Channel-shuffled transformers for cross-modality person re-identification in video

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Scientific Reports 2025年第1期15卷 1-13页

作者： Kasantikul, Rangwan Kusakunniran, Worapan Wu, Qiang Wang, Zhiyong Faculty of Information and Communication Technology Mahidol University 999 Phuttamonthon 4 Road Nakhon Pathom Salaya 73170 Thailand School of Electrical and Data Engineering University of Technology Sydney 15 Broadway Ultimo 2007 NSW Australia School of Computer Science The University of Sydney Camperdown 2006 NSW Australia

Effective implementation of person re-identification (Re-ID) across different modalities (such as daylight vs night-vision) is crucial for Surveillance applications. information from multiple frames is essential for effective re-identification, where visual components from individual frames become less reliable. While transformers can enhance the temporal information extraction, the large number of channels required for effective feature encoding introduces scaling challenges. This could lead to overfitting and instability during training. Therefore, we proposed a novel Channel-Shuffled Temporal Transformer (CSTT) for processing multi-frame sequences in conjunction with a ResNet backbone to form Hybrid Channel-Shuffled Transformer Net (HCSTNET). Replacing fully connected layers in standard multi-head attention with ShuffleNet-like structures is important for integration of transformer attention with a ResNet backbone. Applying ShuffleNet-like structures reduces overfitting through parameter reduction with channel-grouping, and further improves learned attention using channel-shuffling. According to our tests with the SYSU-MM01 dataset in comparison against simple averaging of multiple frames, only the temporal transformer with channel-shuffling achieved a measurable improvement over the baseline. We have also investigated the optimal partitioning of feature maps therein. © The Author(s) 2025.

关键词： Channel Shuffling RGB-IR Re-ID Surveillance Transformer Video-Based Person Re-identification

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FIR-GNN: A Graph Neural Network using Flow Interaction Relationships for Intrusion Detection of Consumer Electronics in Smart Home Network

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IEEE Transactions on Consumer Electronics 2025年

作者： Fu, Mengyi Wang, Pan Liu, Shidong Chen, Xuejiao Zhou, Xiaokang Nanjing University of Post and Telecommunications School of Modern Posts Nanjing China China Electric Power Research Institute Nanjing China Nanjing Vocational College of Information Technology School of Communications Nanjing China Kansai University Faculty of Business Data Science Osaka565-0823 Japan Riken Riken Center for Advanced Intelligence Project Tokyo103-0027 Japan

In the smart home scenario, the Consumer Internet of Things (CIoT) deeply integrates into daily life with various Consumer Electronics (CEs) like home cameras, smart speakers, smoke/fire detectors, VR/AR game boxes/handles, and future home medical terminals. However, CEs face multiple risks due to attack concealment and protocol differences. Against this backdrop, embedding Network Intrusion Detection System (NIDS) in the smart home gateway is proposed. Despite Machine Learning (ML) and Deep Learning (DL) enhancing network intrusion detection, challenges remain in sample collection, traffic feature expression, and gateway resource constraints. To address these, we propose FIR-GNN. It constructs a FIRG graph for traffic pattern capture, uses edge-wise graph attention in FIR-GNN for semi-supervised learning, and selects features by SHAP to cut resource consumption. Experiments show FIR-GNN improves classification performance by 3-5% on BoT-IoT and CICIDS2017 data, safeguarding smart home CEs. © 2025 IEEE.

关键词： Graph neural networks

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Utilization of Machine Learning Algorithms to Monitor the Growth Path of Fishes in Marine Aquaculture 15th

Utilization of Machine Learning Algorithms to Monitor the Gr...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Ajibade, Samuel-Soma M. Essah, Richard Tapales, Catherine P. Antecristo, Charmaine P. Cababat, Feliciana G. Oyebode, Oluwadare Joshua Akintoye, Kayode A. Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University Selangor Malaysia Department of Computer and Information Sciences Sunway University Selangor Malaysia Department of Computer Science and Engineering Chandigarh University Punjab India College of Arts and Sciences Cebu Technological University Cebu Moalboal Philippines Cebu Technological University Cebu Moalboal Philippines College of Education Cebu Technological University Cebu Moalboal Philippines Department of Civil and Environmental Engineering ABUAD Ado Ekiti Nigeria Department of Computer Science The Federal Polytechnic Ekiti State Ado Ekiti Nigeria

ISBN: (纸本)9783031810855

Under a realistic cellular metabolic economic model of Nile tilapia, this work investigates Q-learning fish growth trajectory analysis. we offer two Q-learning data mining algorithms the best management policy using simulated data of optimum growth projections starting at the early growth stage and ending at appropriate market share. The early or primitive Q-learning technique learns optimum feed supplying regulatory regime for fish cultivation rate for the fished present the cages, whereas other one continuously adjusts appropriate nutrition controls the law for agricultural fish rate of increase in tanks together within ideal temperature pattern. The cumulative percentage weight of fishes through both vessels on land and afloat cages was tracked with relative dynamic model errors of 1.7% and 6.6%, respectively, using Q-learning control rules. Furthermore, contrasted to drifting cages where its water temp is controlled at 29.7 ℃, the eating and heat management policies minimise the proportional rate of feeding along with wastage of food in by 11%. data, algorithms, and performance of DL approaches used in adaptive aquaculture farms are also examined. In a nutshell, our goal is to give scholars and clinicians a greater grasp of the latest advancements of deep learning in aquaculture, which will help them develop smart aquacultural applications. With improved machine - learning technologies, a video trying to track biological early warning has made significant progress. Artificial intelligence has helped researchers better understand behavioural reactions to pharmacological and environmental stress. We discuss pioneer efforts in exact monitoring of a gathering of individuals in 2d/3d space in this paper, which introduces the fundamentals of activity recognition. Video tracking's professional and operational difficulties are discussed. The toxicological study based on fish behavioral data is then summarized. © The Author(s), under exclusive license to Springer Nature

关键词： artificial intelligence cellular metabolic data mining algorithms pharma logical trajectory

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Safe and Reliable Diffusion Models via Subspace Projection

arXiv

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arXiv 2025年

作者： Chen, Huiqiang Zhu, Tianqing Wang, Linlin Yu, Xin Gao, Longxiang Zhou, Wanlei Faculty of Data Science City University of Macau China School of Computer Science University of Queensland QLD Australia Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center 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

Large-scale text-to-image (T2I) diffusion models have revolutionized image generation, enabling the synthesis of highly detailed visuals from textual descriptions. However, these models may inadvertently generate inappropriate content, such as copyrighted works or offensive images. While existing methods attempt to eliminate specific unwanted concepts, they often fail to ensure complete removal—allowing the concept to reappear in subtle forms. For instance, a model may successfully avoid generating images in Van Gogh’s style when explicitly prompted with "Van Gogh", yet still reproduce his signature artwork when given the prompt "Starry Night". In this paper, we propose SAFER, a novel and efficient approach for thoroughly removing target concepts from diffusion models. At a high level, SAFER is inspired by the observed low-dimensional structure of the text embedding space. The method first identifies a concept-specific subspace Sc associated with the target concept c. It then projects the prompt embeddings onto the complementary subspace of Sc, effectively erasing the concept from the generated images. Since concepts can be abstract and difficult to fully capture using natural language alone, we employ textual inversion to learn an optimized embedding of the target concept from a reference image. This enables more precise subspace estimation and enhances removal performance. Furthermore, we introduce a subspace expansion strategy to ensure comprehensive and robust concept erasure. Extensive experiments demonstrate that SAFER consistently and effectively erases unwanted concepts from diffusion models while preserving generation quality. © 2025, CC BY.

关键词： Embeddings

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Efficient COVID-19 detection using data mining algorithms: a comparison of basic and hybrid approaches

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Soft Computing 2025年第3期29卷 1437-1451页

作者： Saidi, Mohammad Gheibi, Mohammad Ghazikhani, Adel Lotfata, Aynaz Chahkandi, Benyamin Familsamavati, Sajad Behzadian, Kourosh Department of Computer Science Imam Reza International University Mashhad Iran MS of Software Engineering Information and Communication Technology Unit Mashhad Municipality Fire and Safety Services Organization Mashhad Iran Big Data Lab Imam Reza International University Mashhad Iran Institute for Nanomaterials Advanced Technologies and Innovation Technical University of Liberec Liberec46117 Czech Republic Tallinn10615 Estonia Department of Pathology Microbiology and Immunology School of Veterinary Medicine University of California Davis95616 United States Department of Urban and Regional Planning San Jose State University San Jose95192 United States Faculty of Civil and Environmental Engineering Gdansk University of Technology Narutowicza Street 11/12 80-233 Gdansk Poland Tecnologico de Monterrey Escuela de Ingeniería y Ciencias Monterrey Mexico School of Computing and Engineering University of West London London United Kingdom

Accurate and efficient diagnosis of COVID-19 remains a significant challenge due to the limitations of current detection methods, such as blood tests and chest scans, which can be time-consuming and error-prone. This study aims to compare the performance of basic and hybrid data mining algorithms in diagnosing COVID-19, using blood test results and clinical information to identify the most effective approach. A dataset of 200 records from suspected and infected COVID-19 patients, with 23 characteristics and one diagnostic class, was analysed. Nine data mining algorithms were tested: four basic algorithms (Naive Bayes, Support Vector Machine, Decision Tree, K-Nearest Neighbor) and five hybrid algorithms (Random Forest, AdaBoost, Majority Voting, XGBoost, Bagging). The study also integrated Response Surface Methodology (RSM) and Adaptive-Network-based Fuzzy Inference System (ANFIS) to enhance model performance. The Bagging algorithm demonstrated superior performance with an accuracy of 88%, sensitivity of 74%, and F-criterion of 78%. The integration of RSM and ANFIS further showed that a smart model could be developed for efficient pandemic crisis management, achieving up to 100% accuracy when considering key factors like AST, Albumin, and CRP. The findings suggest that Bagging and hybrid data mining algorithms can significantly improve COVID-19 detection, reducing time and errors in identifying exposed individuals. The study highlights the potential of combining machine learning techniques with RSM-ANFIS models for effective pandemic management and decision-making in medical settings. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Support vector machines

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Multimodal Skin Cancer Prediction: Integrating Dermoscopic Images and Clinical Metadata with Transfer Learning

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Open Bioinformatics Journal 2025年 18卷

作者： Panneerselvam, Ramya Balasubramaniam, Sathiyabhama Sureshkumar, Vidhushavarshini Ravi, Vinayakumar Maidin, Siti Sarah CSE Sona College of Technology Tamilnadu Salem India CSE SRM Institute of science and Technology Tamilnadu India Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar Saudi Arabia Faculty of Data Sciences and Information Technology INTI International University Persiaran Perdana BBN Putra NilaiNegeri Sembilan Nilai 71800 Malaysia

Background: Skin cancers exist as the most pervasive cancers in the world;to increase the survival rates, early prediction has become more predominant. Many conventional techniques frequently depend on visual review of clinical information and dermoscopic illustrations. In recent technological developments, the enthralling algorithms of combining modalities are used for increasing diagnosis accuracy in deep learning. Methods: Our research proposes a multi-faceted approach for the prediction of skin cancer that incorporates clinical metadata with dermoscopic visuals. The pre-trained convolutional neural networks, like EfficientNetB3, were used for dermoscopic images along with transfer learning techniques to excavate some of the visual attributes in this study. Moreover, TabNet was used for processing the clinical metadata, including age, gender, and medical history. The features obtained from both fusion techniques were integrated to enhance the prediction accuracy. The benchmark datasets, like ISIC 2018, ISIC 2019, and HAM10000, were used to assess the model. Results: The proposed multi-faceted system achieved 98.69% accuracy in the classification of skin cancer, surpassing the model that used dermoscopic snapshots with clinical data. The convergence of images with clinical metadata has substantially enhanced prediction resilience, demonstrating the importance of multimodal deep learning in skin lesion diagnosis. Conclusion: This research focused mainly on the efficiency of integrating dermoscopic visuals and clinical information using transfer learning for skin cancer prediction. The proposed system offers a promising tool for improving diagnostic accuracy, and further research could explore its application in other medical fields requiring multimodal data integration. © 2025 The Author(s). Published by Bentham Open.

关键词： Attention fusion Deep learning Dermoscopy EfficientNetB3 Multimodal Skin cancer TabNet

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A deep reinforcement learning approach for wind speed forecasting

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Engineering Applications of Computational Fluid Mechanics 2025年第1期19卷

作者： Band, Shahab S. Lin, Ting Jia Qasem, Sultan Noman Ameri, Rasoul Shahmirzadi, Danyal Aslam, Muhammad Shamrooz Pai, Hao-Ting Salwana, Ely Mousavi, Amir International Graduate School of AI 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 Saudi Arabia Computer Science Department Faculty of Applied Science Taiz University Taiz Yemen Department of Information Management National Yunlin University of Science and Technology Douliu Taiwan Graduate School of Engineering Science and Technology National Yunlin University of Science and Technology Douliu Taiwan Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Big Data Business Analytics National Pingtung University Pingtung Taiwan Institute of Visual Informatics Universiti Kebangsaan Malaysia Selangor Bangi Malaysia Institute of the Information Society Ludovika University of Public Service Budapest Hungary John Von Neumann Faculty of Informatics Obuda University Budapest Hungary Department of Information and Computing Systems Abylkas Saginov Karaganda Technical University Karaganda Kazakhstan

The conventional wind forecasting methods often struggle to handle the non-stationary and inconsistent wind patterns. This paper presents a hybrid method of Empirical Wavelet Transf... 详细信息

The conventional wind forecasting methods often struggle to handle the non-stationary and inconsistent wind patterns. This paper presents a hybrid method of Empirical Wavelet Transform (EWT) and Deep Reinforcement Learning (DRL) for wind speed modeling to overcome the forecasting challenges. The EWT method transforms the original wind speed series into several independent modes and a residual series. In addition, the DRL method is utilised to optimise the weights associated with three distinct supervised deep learning models, i.e., Long Short-Term Memory (LSTM), Convolutional Neural Networks with LSTM (CNN-LSTM), and CNN with Gated Recurrent Units (CNN-GRU). The performance of the proposed EWT-DRL is evaluated against deep learning models, including LSTM, CNN-LSTM, CNN-GRU, and their coupling with EWT. The combination of EWT and the DRL (EWT-DRL) method achieves a Mean Absolute Error (MAE) of 0.151, a Mean Squared Error (MSE) of 0.060, a Root Mean Squared Error (RMSE) of 0.192, and a correlation coefficient (R) of 0.9913. These results indicate the effectiveness of EWT-DRL in improving accuracy for wind speed modeling. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： artificial intelligence deep reinforcement learning empirical wavelet transform long short-term memory machine learning Renewable energy wind speed forecasting

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2D-DOA Estimation for Polarized Massive Antenna Array: Geometric and Algorithmic Guidelines

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IEEE Transactions on Vehicular technology 2025年

作者： Wen, Fangqing Wang, Han Gui, Guan Yuen, Chau Sari, Hikmet Adachi, Fumiyuki China Three Gorges University Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering Yichang443002 China Hubei University of Automotive Technology Institute of Vehicle Information Control and Network Technology Shiyan442002 China Yichun University College of Physical Sciences and Engineering Technology Yichun336000 China City University of Macau Faculty of Data Science China Nanjing University of Posts and Telecommunications College of Telecommunications and Information Engineering Nanjing210003 China Nanyang Technological University School of Electrical and Electronics Engineering 639798 Singapore Sendai980-8572 Japan

The utilization of an Electromagnetic Vector Sensor (EMVS) array presents valuable insights in wireless communications and radars by incorporating the advantages of spatial, temporal, and polarization diversities. This study focuses on addressing the problem of estimating the Two-Dimensional (2D) Direction-of-Arrival (DOA) for an arbitrarily spaced EMVS array. Firstly, the Cramer-Rao Bounds (CRB) for estimating the 2D-DOA and polarized parameters are derived, which provides design guideline for array geometry. As suggested by the CRB, a sparse planar EMVS array is a more appealing choice compared to the popular coprime EMVS array. Thereafter, a Parallel Factor (PARAFAC)-based weighted phase fitting algorithm is presented, designed to fully explore the aperture of the conformal array as well as capitalize on the tensorial structure inherent in array data. The proposed PARAFAC algorithm not only provides a closed-form solution for conformal arrays but also exhibits superior accuracy in 2D-DOA estimation compared to existing approaches. Numerical results corroborate the theoretical advantages of the proposed array geometry and PARAFAC algorithm. © 1967-2012 IEEE.

关键词： Cramer-Rao bounds

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6G communications through sub-Terahertz CMOS power amplifiers: Design challenges and trends

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Heliyon 2025年第10期11卷

作者： Lee, Jun Yan Wu, Duo Guo, Xuanrui Tan, Jian Ding Yew, Teh Jia Ng, Zi Neng Bhuiyan, Mohammad Arif Sobhan Miraz, Mahdi H. School of Artificial Intelligence and Robotics Xiamen University Malaysia Selangor Malaysia School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China School of Computing and Data Science Xiamen University Malaysia Selangor Malaysia School of Computing Faculty of Arts Science and Technology Wrexham University Wrexham United Kingdom Faculty of Computing Engineering and Science University of South Wales Pontypridd United Kingdom

The fifth-generation (5G) network faces limitations in supporting emerging applications, such as artificial intelligence (AI), virtual reality (VR) and digital twins. To overcome these confines, sub-Terahertz (sub-THz) and Terahertz (THz) technologies are considered to be key enablers of effective 6G wireless communications, offering higher transmission speeds, longer range and wider bandwidth. Achieving these capabilities requires careful engineering of 6G transceivers, with a focus on efficient power amplifiers (PAs) in the front-end, which play a critical role in effectively amplifying and transmitting signals over long distances. Complimentary metal-oxide-semiconductor (CMOS) technology-based PA in sub-THz suffers severe parasitic and limited maximum frequency, however, this has eventually been solved by different design architectures and scaling down of CMOS technology to break through the frequency limitations. In this article, we reviewed the potentials and capabilities of CMOS technology for designing 6G hardware, identified the state-of-art PA designs in the sub-THz band and then examined as well as compared the designs to identify the suitable design strategies for better performance. The circuit optimisation techniques, such as coupled-line, passive gain boosting method, zero-degree power splitting, load-pull matching, diode and capacitor linearisation for better gain, saturated output power and power added efficiency, are considered for the PA design architectures at different sub-THz bands. Furthermore, these methods are summarised and discussed with their advantages and disadvantages in lieu with their performances. The PA design trends, challenges and future perspectives are also presented and discussed. Therefore, this comprehensive review article will serve as a comparative study and reference for future PA designs for radio frequency integrated circuits (RFIC). © 2025 The Authors

关键词： 6G CMOS Front-end Power amplifier Sub-THz Transceiver Transmitter

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