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Chaotic Elephant Herd Optimization with Machine Learning for Arabic Hate Speech Detection

Intelligent Automation & Soft Computing 2024年第3期39卷 567-583页

作者： Badriyya B.Al-onazi Jaber S.Alzahrani Najm Alotaibi Hussain Alshahrani Mohamed Ahmed Elfaki Radwa Marzouk Heba Mohsen Abdelwahed Motwakel Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMecca24382Saudi Arabia Prince Saud AlFaisal Institute for Diplomatic Studies Riyadh12735Saudi Arabia Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqra11911Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in Egypt New CairoNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipAl-Kharj16278Saudi Arabia

In recent years,the usage of social networking sites has considerably increased in the Arab *** has empowered individuals to express their opinions,especially in ***,various organizations that operate in the Arab countries have embraced social media in their day-to-day business activities at different *** is attributed to business owners’understanding of social media’s importance for business ***,the Arabic morphology is too complicated to understand due to the availability of nearly 10,000 roots and more than 900 patterns that act as the basis for verbs and *** speech over online social networking sites turns out to be a worldwide issue that reduces the cohesion of civil *** this background,the current study develops a Chaotic Elephant Herd Optimization with Machine Learning for Hate Speech Detection(CEHOML-HSD)model in the context of the Arabic *** presented CEHOML-HSD model majorly concentrates on identifying and categorising the Arabic text into hate speech and *** attain this,the CEHOML-HSD model follows different sub-processes as discussed *** the initial stage,the CEHOML-HSD model undergoes data pre-processing with the help of the TF-IDF ***,the Support Vector Machine(SVM)model is utilized to detect and classify the hate speech texts made in the Arabic ***,the CEHO approach is employed for fine-tuning the parameters involved in *** CEHO approach is developed by combining the chaotic functions with the classical EHO *** design of the CEHO algorithm for parameter tuning shows the novelty of the work.A widespread experimental analysis was executed to validate the enhanced performance of the proposed CEHOML-HSD *** comparative study outcomes established the supremacy of the proposed CEHOML-HSD model over other approaches.

关键词： Arabic language machine learning elephant herd optimization TF-IDF vectorizer hate speech detection

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Trends and Challenges of Sustainable Agriculture in the Iraqi Environment

Trends and Challenges of Sustainable Agriculture in the Iraq...

引用

2022 IEEE International Conference on Blockchain, Smart Healthcare and Emerging Technologies, SmartBlock4Health 2022

作者： Al-Asadi, Hanan Hussein Goga, Maria Ali, Haider Abdullah Popa, Ramona Cristina Oludele, Awodele University POLITEHNICA of Bucharest Faculty of Automatic Control and Computers Bucharest Romania University of Technology-Iraq Baghdad Iraq Technical University of Civil Engineering of Bucharest UTCB Department for Training of Teaching Personnel Bucharest Romania Madenat Alelem University College Baghdad Iraq Babcock University Computer Science Department Ogun State Nigeria

ISBN: (纸本)9781665460149

The use of Agriculture Intelligent Systems (AISs) in Iraq has become popular among farmers. The greenhouse is a solution for plant growth, which uses an effective microclimate to simulate seasons and to use alternative energy sources like solar, thermal, and photovoltaic systems for microclimate control. Greenhouses are considered a successful solution with developments and achievements in the technology of this field. However, problems like high costs, difficulties in installation and maintenance, or the need for some procedures should be considered. Because Iraq is close to the equator (3669 km), it receives sufficient solar power. It also has large areas available for the building of solar greenhouse systems. The farmers need an affordable and functional smart greenhouse to enable and encourage them to use the environment efficiently. We intend to develop low-cost, accurate, and easy-to-use greenhouse systems controlled by artificial intelligence algorithms. Farmers will be able to control the greenhouse by using simple commands. Machine learning algorithms provide real-time and effective analysis that could support farmers in decision-making and provide relevant data for researchers and policymakers. AISs algorithms can lead to a sustainable and productive agriculture system. The system can be used in Iraq with sufficient government support and subsidy. © 2022 IEEE.

关键词： Intelligent systems

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6D Pose Estimation Based on Edge-Enhanced Point Pair Features for Surgical Navigation 8

6D Pose Estimation Based on Edge-Enhanced Point Pair Feature...

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8th International Conference on Virtual Reality, ICVR 2022

作者： Liu, Chenyi Chen, Fei Deng, Lu Yi, Renjiao Zheng, Lintao Zhu, Chenyang School of Computer Science National University of Defense Technology Changsha China Second XiangYa Hospital Central South University Department of Spine Surgery Changsha China Clinical Nursing Teaching and Research Section Second Xiang Ya Hospital Changsha China College of Meteorology and Oceanography National University of Defense Technology Changsha China

ISBN: (数字)9781665479110

ISBN: (纸本)9781665479110

This paper proposes an efficient 6D pose estimation method for the automatic spinal surgical navigation system. Visual-based pose estimation is challenging in realistic surgical scenes due to the human spine's geometric complexity and symmetric ambiguity. Instead of considering the whole scene for pose estimation, we introduce a targeted down-sampling strategy that focuses more on edge area for efficient feature extraction of complex geometry. A pose hypothesis validation approach is then presented to resolve the symmetric ambiguity. Note that our method can also work on incomplete scenes since our sampling and validation do not require a whole model. We perform evaluations on two challenging datasets, which demonstrate the superiority of our method on pose estimation of geometrically complex, occluded, symmetrical objects. We also implemented a prototype of a surgical navigation system based on the proposed method, which may provide a viable option for improving the spinal clinical surgery system. © 2022 IEEE.

关键词： Geometry

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SSC- l0 : Sparse Subspace Clustering with the l0 Inequality Constraint 7th

SSC- l0 : Sparse Subspace Clustering with the l0 Inequality...

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7th Asian Conference on Pattern Recognition, ACPR 2023

作者： Wang, Yangbo Zhou, Jie Lin, Qingshui Lu, Jianglin Gao, Can National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China Basic Teaching Department Liaoning Technical University Huludao125105 China College of Engineering Northeastern University BostonMA02115 United States

ISBN: (纸本)9783031476648

Self-expression learning methods often obtain a coefficient matrix to measure the similarity between pairs of samples. However, directly using all points to represent a fixed sample in a class under the self-expression framework may not be ideal, as points from other classes participate in the representing process. To alleviate this issue, this study attempts to achieve representation learning between points only coming from the same class. In practice, it is easier for data points from the same class to represent each other than that from different classes. So, when reconstructing a point, if the number of non-zero elements in the coefficient vector is limited, a model is more likely to select data points from the class where the reconstructed point lies to complete the reconstruction work. Based on this idea, we propose Sparse Subspace Clustering with the l0 inequality constraint (SSC- l0 ). In SSC- l0, the l0 inequality constraint determines the maximum number of non-zero elements in the coefficient vector, which helps SSC- l0 to conduct representation learning among the points in the same class. After introducing the simplex constraint to ensure the translation invariance of the model, an optimization method concerning l0 inequality constraint is formed to solve the proposed SSC- l0, and its convergence is theoretically analyzed. Extensive experiments on well-known datasets demonstrate the superiority of SSC- l0 compared to several state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Learning systems

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Large Language Models are Few-shot Multivariate Time Series Classifiers

arXiv

引用

arXiv 2025年

作者： Chen, Yakun Li, Zihao Yang, Chao Wang, Xianzhi Xu, Guandong School of Computer Science Faculty of Engineering and Information Techology University of Technology Sydney 15 Broadway SydneyNSW2007 Australia Faculty of Information Technology and Engineering Ocean University of China 10587 China Centre for Learning Teaching and Technology The Education University of Hong Kong 610101 Hong Kong

Large Language Models (LLMs) have been extensively applied in time series analysis. Yet, their utility in the few-shot classification (i.e., a crucial training scenario due to the limited training data available in industrial applications) concerning multivariate time series data remains underexplored. We aim to leverage the extensive pre-trained knowledge in LLMs to overcome the data scarcity problem within multivariate time series. Specifically, we propose LLMFew, an LLM-enhanced framework to investigate the feasibility and capacity of LLMs for few-shot multivariate time series classification. This model introduces a Patch-wise Temporal Convolution Encoder (PTCEnc) to align time series data with the textual embedding input of LLMs. We further fine-tune the pre-trained LLM decoder with Low-rank Adaptations (LoRA) to enhance its feature representation learning ability in time series data. Experimental results show that our model outperformed state-of-the-art baselines by a large margin, achieving 125.2% and 50.2% improvement in classification accuracy on Handwriting and EthanolConcentration datasets, respectively. Moreover, our experimental results demonstrate that LLM-based methods perform well across a variety of datasets in few-shot MTSC, delivering reliable results compared to traditional models. This success paves the way for their deployment in industrial environments where data are limited. Our code is available at: https://***/r/llm-fewshot-mtsc-C608. Copyright © 2025, The Authors. All rights reserved.

关键词： Time series

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Software Service for Studying Words of the Ukrainian Language 11

Software Service for Studying Words of the Ukrainian Languag...

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11th International Conference on Advanced computer Information Technologies, ACIT 2021

作者： Shevchuk, Ruslan Mykytiuk, Vitalii Miramontes, David Alfonso Yushko, Andriy Yurchyshyn, Tetiana Mokhun, Serhii West Ukrainian National University Department of Computer Science Ternopil Ukraine Us Pease Corps San DiegoCA United States West Ukrainian National University Department of Foreign Languages Information and Communication Technologies Ternopil Ukraine Ternopil Volodymyr Hnatiuk National Pedagogical University Department of Physics and Methods of Its Teaching Ternopil Ukraine

This paper addresses the issue of teaching Ukrainian as a foreign language with the help of modern software. The technical requirements and software service architecture for studying words of Ukrainian language were proposed. This service integrates word forms of Ukrainian words, their grammatical meanings, grammatical cases and means of expression. This allows to carry out effective management of arrays of lexicographic data and to receive examples of use of Ukrainian words in various contexts. The software service developed for learning Ukrainian words is based on the Single-Page Applications, REST API and uses the JavaScript-library React and the *** Core framework. © 2021 IEEE.

关键词： Application programs

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Improving Hospital Preparedness for Future Pandemics with Machine Learning and Queue Management Solutions

Improving Hospital Preparedness for Future Pandemics with Ma...

引用

International Conference on Intelligent Computing in Data sciences (ICDS)

作者： Khalil Bouramtane Said Kharraja Jamal Riffi Omar EL Beqqali Saïd Boujraf University of Jean Monnet Saint-Etienne IUT of Roanne LASPI RU ROANNE FRANCE Faculty of science Dhar El Mahraz Laboratory of Computer Science Signals Automation and Cognitivism (LISAC) University Sidi Mohamed Ben Abdellah Fez Morocco Dept. Biophysics & Clinical MRI Methods Faculty of Medicine Clinical Neuroscience Laboratory University of Sidi Mohamed Ben Abdellah Fez Morocco

ISBN: (数字)9798350351200

ISBN: (纸本)9798350351217

The COVID-19 pandemic has underscored the critical need for resilient and efficient hospital workflows capable of managing sudden increases in patient volume. This study presents a comprehensive approach that integrates advanced machine learning algorithms with queue management techniques to enhance hospital preparedness and response during pandemics. By Employing predictive analytics and classification systems, our methodology improves patient triage, optimizes resource allocation, and reduces wait times, thereby enhancing overall patient care. The study includes a detailed analysis of machine learning models used for patient prioritization and an exploration of queue management strategies to streamline patient flow. Our findings indicate substantial improvements in hospital efficiency and the ability to handle peak demands during health crises. This research provides actionable insights and a robust framework for healthcare administrators aiming to fortify their facilities against future pandemic scenarios, ultimately contributing to better healthcare outcomes.

关键词： Machine learning algorithms Pandemics Hospitals Heuristic algorithms Machine learning Data models Resource management Servers Predictive analytics Queueing analysis

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SAFE: A Sparse Autoencoder-Based Framework for Robust Query Enrichment and Hallucination Mitigation in LLMs

arXiv

引用

arXiv 2025年

作者： Abdaljalil, Samir Pallucchini, Filippo Seveso, Andrea Kurban, Hasan Mercorio, Fabio Serpedin, Erchin Electrical and Computer Engineering Texas A&M University College StationTX United States Dept of Statistics and Quantitative Methods University of Milano-Bicocca Italy CRISP Research Centre crispresearch.eu University of Milano-Bicocca Italy College of Science and Engineering Hamad Bin Khalifa University Doha Qatar

Despite the state-of-the-art performance of Large Language Models (LLMs), these models often suffer from hallucinations, which can undermine their performance in critical applications. In this work, we propose SAFE, a novel method for detecting and mitigating hallucinations by leveraging Sparse Autoencoders (SAEs). While hallucination detection techniques and SAEs have been explored independently, their synergistic application in a comprehensive system, particularly for hallucination-aware query enrichment, has not been fully investigated. To validate the effectiveness of SAFE, we evaluate it on two models with available SAEs across three diverse cross-domain datasets designed to assess hallucination problems. Empirical results demonstrate that SAFE consistently improves query generation accuracy and mitigates hallucinations across all datasets, achieving accuracy improvements of up to 29.45%. © 2025, CC BY.

关键词： Structured Query Language

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Spectralhomogeneitycross frequencies can be a quality metric for the large-scale resting EEGpreprocessing

arXiv

引用

arXiv 2023年

作者： Hu, Shiang Ruan, Jie Langer, Nicolas Bosch-Bayard, Jorge Lv, Zhao Yao, Dezhong Valdes-Sosa, Pedro Antonio Anhui Province Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University 111 Jiulong Road Hefei230601 China Methods of Plasticity Research Department of Psychology University of Zurich Zurich Switzerland The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation School of Life Science and Technology University of Electronic Science and Technology of China Chengdu China Cuban Center for Neurocience La Habana Cuba

The brain projectsrequire the collectionof massiveelectrophysiologicaldata,aiming to the longitudinal, sectional,or populational neuroscience *** metrics automatically label the data after centralizedpreprocessing. However, although thewaveforms-basedmetricsarepartially useful, theymay be unreliable by neglecting the spectral profiles. Here, we detected the phenomenon of parallel log spectra(PaLOS)that thescalp EEG power in the log scale were parallel to each otherfrom 10% of 2549 HBNEEG. This phenomenon was reproduced in 8% of 412 PMDT EEG from 4 databases. We designed the PaLOSindex (PaLOSi) to indicate this phenomenon by decomposing the cross-spectraatdifferent frequenciesinto the common principal component spaces. We found that the PaLOS biophysically implied a prominently dominant dipolein the source spacewhich wasimplausible for the resting EEG. And it may be practically resulted from *** with the 1966 normative EEG cross-spectra, theHBN and the PMDT EEG with PaLOS presented generally much higherelectrode pairwise coherences and highersimilarity of coherence-based network patterns, which went against the known frequency dependent characteristic of coherence networks. We suggestthe PaLOSi should lay in the range of0.4-0.7 for large resting EEG quality assurance. Copyright © 2023, The Authors. All rights reserved.

关键词： Quality assurance

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ASSESSING INTER-RATER RELIABILITY WITH HETEROGENEOUS VARIANCE COMPONENTS MODELS: FLEXIBLE APPROACH ACCOUNTING FOR CONTEXTUAL VARIABLES

arXiv

引用

arXiv 2022年

作者： Martinková, Patrícia Bartoš, František Brabec, Marek Institute of Computer Science The Czech Academy of Sciences Prague Czech Republic Faculty of Education Charles University Prague Czech Republic Department of Psychological Methods University of Amsterdam Amsterdam Netherlands

Inter-rater reliability (IRR), which is a prerequisite of high-quality ratings and assessments, may be affected by contextual variables such as the rater's or ratee's gender, major, or experience. Identification of such heterogeneity sources in IRR is important for implementation of policies with the potential to decrease measurement error and to increase IRR by focusing on the most relevant subgroups. In this study, we propose a flexible approach for assessing IRR in cases of heterogeneity due to covariates by directly modeling differences in variance components. We use Bayes factors to select the best performing model, and we suggest using Bayesian model-averaging as an alternative approach for obtaining IRR and variance component estimates, allowing us to account for model uncertainty. We use inclusion Bayes factors considering the whole model space to provide evidence for or against differences in variance components due to covariates. The proposed method is compared with other Bayesian and frequentist approaches in a simulation study, and we demonstrate its superiority in some situations. Finally, we provide real data examples from grant proposal peer-review, demonstrating the usefulness of this method and its flexibility in the generalization of more complex designs. © 2022, CC BY.

关键词： Inference engines

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