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Improved Attentive Recurrent Network for Applied Linguistics-Based Offensive Speech Detection

computer Systems science & Engineering 2023年第11期47卷 1691-1707页

作者： Manar Ahmed Hamza Hala J.Alshahrani Khaled Tarmissi Ayman Yafoz Amira Sayed A.Aziz Mohammad Mahzari Abu Sarwar Zamani Ishfaq Yaseen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Applied Linguistics College of LanguagesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkahSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of English College of Science&HumanitiesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Applied linguistics is one of the fields in the linguistics domain and deals with the practical applications of the language studies such as speech processing,language teaching,translation and speech *** ever-growing Online Social Networks(OSNs)experience a vital issue to confront,i.e.,hate *** the OSN-oriented security problems,the usage of offensive language is the most important threat that is prevalently found across the *** on the group targeted,the offensive language varies in terms of adult content,hate speech,racism,cyberbullying,abuse,trolling and *** these,hate speech is the most intimidating form of using offensive language in which the targeted groups or individuals are intimidated with the intent of creating harm,social chaos or *** Learning(ML)techniques have recently been applied to recognize hate speech-related *** current research article introduces a Grasshopper Optimization with an Attentive Recurrent Network for Offensive Speech Detection(GOARN-OSD)model for social *** GOARNOSD technique integrates the concepts of DL and metaheuristic algorithms for detecting hate *** the presented GOARN-OSD technique,the primary stage involves the data pre-processing and word embedding ***,this study utilizes the Attentive Recurrent Network(ARN)model for hate speech recognition and *** last,the Grasshopper Optimization Algorithm(GOA)is exploited as a hyperparameter optimizer to boost the performance of the hate speech recognition *** depict the promising performance of the proposed GOARN-OSD method,a widespread experimental analysis was *** comparison study outcomes demonstrate the superior performance of the proposed GOARN-OSD model over other state-of-the-art approaches.

关键词： Applied linguistics hate speech offensive language natural language processing deep learning grasshopper optimization algorithm

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Deer Hunting Optimization with Deep Learning Enabled Emotion Classification on English Twitter Data

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computer Systems science & Engineering 2023年第12期47卷 2741-2757页

作者： Abdelwahed Motwakel Hala J.Alshahrani Jaber S.Alzahrani Ayman Yafoz Heba Mohsen Ishfaq Yaseen Amgad Atta Abdelmageed Mohamed I.Eldesouki Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMakkahSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Currently,individuals use online social media,namely Facebook or Twitter,for sharing their thoughts and *** of emotions on social networking sites’finds useful in several applications in social welfare,commerce,public health,and so *** is expressed in several means,like facial and speech expressions,gestures,and written *** recognition in a text document is a content-based classification problem that includes notions from deep learning(DL)and natural language processing(NLP)*** article proposes a Deer HuntingOptimizationwithDeep Belief Network Enabled Emotion Classification(DHODBN-EC)on English Twitter Data in this *** presented DHODBN-EC model aims to examine the existence of distinct emotion classes in *** the introductory level,the DHODBN-EC technique pre-processes the tweets at different ***,the word2vec feature extraction process is applied to generate the word embedding *** emotion classification,the DHODBN-EC model utilizes the DBN model,which helps to determine distinct emotion class ***,the DHO algorithm is leveraged for optimal hyperparameter adjustment of the DBN *** extensive range of experimental analyses can be executed to demonstrate the enhanced performance of the DHODBN-EC approach.A comprehensive comparison study exhibited the improvements of the DHODBN-EC model over other approaches with increased accuracy of 96.67%.

关键词： Deer hunting optimization deep belief network emotion classification Twitter data sentiment analysis english corpus

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Dart Games Optimizer with Deep Learning-Based Computational Linguistics Named Entity Recognition

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Intelligent Automation & Soft computing 2023年第9期37卷 2549-2566页

作者： Mesfer Al Duhayyim Hala J.Alshahrani Khaled Tarmissi Heyam H.Al-Baity Abdullah Mohamed Ishfaq Yaseen Amgad Atta Abdelmageed Mohamed IEldesouki Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAl Aflaj16828Saudi Arabia Department of Applied Linguistics College of LanguagesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24211Saudi Arabia Department of Information Technology College of Computer and Information SciencesKing Saud UniversityP.O.Box 145111Riyadh4545Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Computational linguistics is an engineering-based scientific *** deals with understanding written and spoken language from a computational ***,the domain also helps construct the artefacts that are useful in processing and producing a language either in bulk or in a dialogue *** Entity Recognition(NER)is a fundamental task in the data extraction *** concentrates on identifying and labelling the atomic components from several texts grouped under different entities,such as organizations,people,places,and ***,the NER mechanism identifies and removes more types of entities as per the *** significance of the NER mechanism has been well-established in Natural Language Processing(NLP)tasks,and various research investigations have been conducted to develop novel NER *** conventional ways of managing the tasks range from rule-related and hand-crafted feature-related Machine Learning(ML)techniques to Deep Learning(DL)*** this aspect,the current study introduces a novel Dart Games Optimizer with Hybrid Deep Learning-Driven Computational Linguistics(DGOHDL-CL)model for *** presented DGOHDL-CL technique aims to determine and label the atomic components from several texts as a collection of the named *** the presented DGOHDL-CL technique,the word embed-ding process is executed at the initial stage with the help of the word2vec *** the NER mechanism,the Convolutional Gated Recurrent Unit(CGRU)model is employed in this *** last,the DGO technique is used as a hyperparameter tuning strategy for the CGRU algorithm to boost the NER’s *** earlier studies integrated the DGO mechanism with the CGRU model for *** exhibit the superiority of the proposed DGOHDL-CL technique,a widespread simulation analysis was executed on two datasets,CoNLL-2003 and OntoNotes *** experimental outcomes establish the promising performance of the DGOHDL-CL technique over other models.

关键词： Named entity recognition deep learning natural language processing computational linguistics dart games optimizer

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A Survey on Mobile Applications for Smart Agriculture: Making Use of Mobile Software in Modern Farming

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SN computer science 2021年第4期2卷 293页

作者： Oteyo, Isaac Nyabisa Marra, Matteo Kimani, Stephen Meuter, Wolfgang De Boix, Elisa Gonzalez Software Languages Laboratory Department of Computer Science Vrije Universiteit Brussel Pleinlaan 2 Elsene Brussels 1050 Belgium School of Computing and Information Technology Jomo Kenyatta University of Agriculture and Technology 62000 00200 Nairobi Kenya

The increasing global demand for food and nutrition security has raised the need to automate processes in modern farming. As such, a promising way to automate those processes is by using smart agriculture applications (SAAs). Different studies in the literature classify these applications based on agricultural themes, agricultural domains, and farming scenarios. However, this classification is not sufficient for researchers and industry to gain deeper insights on software engineering issues pertaining to SAAs. In this survey, we explore SAAs and further classify them based on architectural models, supported software engineering issues, and target mobile platforms. The survey results show that SAAs in general (1) follow different architectural models, (2) are targeted for different mobile platforms, and (3) satisfy different software engineering issues. Most importantly, the key findings from this study reveal that SAAs can fail to meet their intended purpose if developers ignore key software engineering issues. These findings can be used as a starting point for researchers and industry to implement smart agriculture related mobile applications. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Cloud computing Internet of things Mobile applications Smart agriculture applications Smart farming

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A Comprehensive Survey on RIS-Enhanced Physical Layer Security in UAV-Assisted Networks

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IEEE Internet of Things Journal 2025年

作者： Ahmed, Manzoor Soofi, Aized Amin Raza, Salman Li, Yongxiao Khan, Feroz Khan, Wali Ullah Asif, Muhammad Han, Zhu School of Computer and Information Science China Hubei Engineering University Institute for AI Industrial Technology Research Xiaogan City432000 China National University of Modern Languages Department of Computer Science Faisalabad38000 Pakistan National Textile University Department of Computer Science Faisalabad38000 Pakistan Beijing University of Posts and Telecommunications School of Electronic Engineering Beijing China Gachon University School of Computing Seongnam-si13120 Korea Republic of School of Computer Science Department of Electrical and Computer Engineering United States University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

This survey provides an in-depth examination of the role of reconfigurable intelligent surfaces (RIS) in enhancing physical layer security (PLS) within unmanned aerial vehicle (UAV)-assisted networks, which are essential for the secure and efficient operation of sixth-generation (6G) wireless communications. The study covers various types of RIS—passive, active, and hybrid—and their applications in both terrestrial and aerial environments to strengthen PLS. Key focus areas include advanced PLS techniques such as optimizing UAV trajectory, beamforming, and RIS phase-shift configurations, all aimed at improving secrecy rates (SRs) while mitigating the risks of eavesdropping and jamming. Moreover, the survey also addresses strategies for enhancing energy-efficient SRs and implementing anti-jamming mechanisms within UAV-assisted networks. Additionally, it explores the integration of RIS-UAV systems with emerging technologies such as non-orthogonal multiple access (NOMA), mobile edge computing (MEC), cognitive radio, and THz networks, demonstrating how security can be enhanced in such networks. Through detailed performance analysis, the paper highlights the transformative potential of RIS-equipped UAVs in overcoming the potential security challenges for future 6G networks. Finally, the survey presents lessons learned and identifies critical future research directions and open challenges, offering insights that will guide the development of robust and secure RIS-assisted UAV systems in next-generation wireless networks. © 2014 IEEE.

关键词： Drones

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Enhanced Artificial Bee Colony Based Optimization for Mitigating Replication in Large Data for Internet of Things (Iot)

SSRN

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SSRN 2022年

作者： Ramzan, Muhammad Sher Asghar, Anees Ullah, Ata Jhanji, Noor Zaman Alsolami, Fawaz Ahmad, Iftikhar Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Computer Science National University of Modern Languages Islamabad Pakistan School of Computer Science and Engineering SCE Taylor’s University Malaysia

Internet of Things (IoT) consists of complex and dynamically aggregated elements or smart entities that needs decentralized supervision for data exchanging throughout the different networks. A most commonly used swarm intelligence based approach named artificial bee colony (ABC) can be utilized in order to figure out the problems like clustering of gigantic data in IoT, complex optimization problems in IoT etc. In standard ABC algorithm the key issue is that every single food site is compared with every other food site to find out the finest solution in the neighboring regions. In this way an extensive number of redundant comparisons will be required which results in delayed convergence rate, more time consumption and increased delay will occur. This paper proposes an enhanced ABC (E-ABC) oriented optimized algorithm for data sharing and replication. E-ABC enhances the prior ABC-based schemes by decreasing the number of redundant comparisons. The proposed algorithm makes a comparison between the finest food sites with the neighboring sites that results in the exclusion of poor sources. The proposed algorithm was implemented in a replication scenario to verify its dominance over existing schemes with respect to convergence rate, data file accessibility and average response time. The results prove the superiority of the E-ABC over counterparts. © 2022, The Authors. All rights reserved.

关键词： Internet of things

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Artificial Bee Colony Based Gabor Parameters Optimizer (ABC-GPO) for Modulation Classification

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Computational Intelligence and Neuroscience 2022年第0期2022卷 9464633页

作者： Aljubayrin, Saad Sarfraz, Mubashar Ghauri, Sajjad A. Amirzada, Muhammad Rizwan Mezgebo Kebedew, Teweldebrhan Department of Computer Science College of Computing and Information Technology Shaqra University Shaqra Saudi Arabia Faculty of Engineering and Computer Science National University of Modern Languages Islamabad Pakistan School of Engineering & Applied Sciences ISRA University Islamabad Pakistan Ethio Telecom Addis Ababa Ethiopia

Modulation classification is one of the essential requirements in the various cognitive radio applications where prior information about the incoming signal is unknown. The modulation classification using a pattern recognition approach can be achieved in 2 modules: first, parameters are extracted from the noisy signal, and then feature selection is carried out using a Gabor filter network (GFN). In the second module, features are exploited for classification purposes. The modulation formats considered for the purpose of classification are BPSK, QPSK, 8PSK, 16PSK, 64PSK, 4FSK, 8FSK, 16FSK, QAM, 8QAM, 16QAM, 32QAM, and 64QAM. The Gabor filter parameters and weights of the adaptive filter are attuned using the Delta rule and recursive least square (RLS) algorithm until the cost function is minimized. In the end, the artificial bee colony (ABC) algorithm is used to optimize the Gabor parameters as well as the classifier's performance. The simulation results show the supremacy of the proposed classifier structure. © 2022 Saad AlJubayrin et al.

关键词： Adaptive filters

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Cloud-Based Intelligent Patient Triage Systems with SVM Classifiers for Emergency departments

Cloud-Based Intelligent Patient Triage Systems with SVM Clas...

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Innovations in Communications, Electrical and computer Engineering (ICICEC), International Conference on

作者： N V Shibu Sujatha M M. Xavier Suresh K. Sindhuja T Saju Raj C. Srinivasan Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Easwari Engineering College Chennai Tamil Nadu India Department of Physics School of Advanced Sciences and Languages VIT Bhopal University Bhopal Madhya Pradesh India Department of Information Technology J.J College of Engineering and Technology Tiruchirappalli Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376524

Efficient patient triage in emergency departments is crucial for the timely and successful allocation of resources and treatment in the constantly changing healthcare environment. To improve patient triage systems, this research suggests a new strategy that makes use of cloud computing and support vector machine (SVM) classifiers. The proposed solution endeavors to enhance patient outcomes in emergency departments by optimizing resource allocation via the use of cloud infrastructure's scalability and accessibility in conjunction with SVM algorithms' strong classification capabilities. Quickly classifying patients according to severity, the system integrates and analyzes data in real-time, allowing healthcare providers to prioritize high-risk situations and distribute resources appropriately. Healthcare practitioners can work together more efficiently and share patient data with cloud-based architecture, which promotes a more comprehensive approach to treatment. Results from the experiments prove that the system works as advertised and can be easily scaled up, highlighting its revolutionary potential to change the way emergency healthcare facilities handle patient triage. The growing body of evidence demonstrates the value of intelligent systems for healthcare delivery optimization and emergency patient care improvement via the use of technology.

关键词： Support vector machines Cloud computing Technological innovation Accuracy Scalability Medical services Real-time systems Safety Resource management Research and development

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Cloud-Enabled Predictive Vehicle Health Monitoring and Maintenance with LSTM Networks for Time-Series Analysis

Cloud-Enabled Predictive Vehicle Health Monitoring and Maint...

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Innovations in Communications, Electrical and computer Engineering (ICICEC), International Conference on

作者： Viyyapu Lokeshwari Vinya K. K. Manivannan S. K. Manivannan C. Sathiyamoorthy M. Jagadeeswaran B. Meenakshi Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Easwari Engineering College Chennai Tamil Nadu India Department of Physics School of Advanced Sciences and Languages VIT Bhopal University Bhopal Madhya Pradesh India Department of Information Technology J.J College of Engineering and Technology Tiruchirappalli Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376524

Maximizing operational efficiency and customer satisfaction in the automobile sector depends on maintaining vehicle health and avoiding maintenance downtime. Vehicle Internet of Things (IoT) devices capture massive volumes of sensor data, enabling predictive maintenance. This research offers cloud-based predictive vehicle health monitoring and maintenance systems using Long Short-Term Memory (LSTM) networks for time-series analysis. System architecture uses IoT sensors in automobiles to gather real-time data on engine temperature, oil pressure, and vibration. This data is stored, analyzed, and predicted on clouds. To assess time-series sensor data and forecast failures or maintenance requirements, LSTM networks, a form of recurrent neural network, are used. The proposed system uses data preprocessing to manage missing values and outliers, feature extraction to find significant sensor data patterns, and LSTM-based predictive modeling to predict vehicle health conditions. Technology may predict future faults by continually monitoring and evaluating the vehicle's state, allowing proactive maintenance to avoid expensive breakdowns and downtime. The cloud-based architecture is scalable and flexible, providing smooth connection with fleet management systems and remote vehicle health monitoring across scattered sites. The suggested method optimizes maintenance schedules and resource allocation using predictive insights from real-time sensor data to improve operational efficiency and save costs. It may improve predictive vehicle health monitoring and maintenance in the automobile sector, improving dependability, safety, and cost.

关键词： Cloud computing Costs Time series analysis Real-time systems Maintenance Safety Automobiles Internet of Things Monitoring Long short term memory

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Automatic Discovery of Heterogeneous Machine Learning Pipelines: An Application to Natural Language Processing 28

Automatic Discovery of Heterogeneous Machine Learning Pipeli...

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28th International Conference on Computational Linguistics, COLING 2020

作者： Estevez-Velarde, Suilan Gutiérrez, Yoan Montoyo, Andrés Almeida-Cruz, Yudivián School of Math and Computer Science University of Havana Cuba University of Alicante Spain Department of Languages and Computing Systems University of Alicante Spain

ISBN: (纸本)9781952148279

This paper presents AutoGOAL, a system for automatic machine learning (AutoML) that uses heterogeneous techniques. In contrast with existing AutoML approaches, our contribution can automatically build machine learning pipelines that combine techniques and algorithms from different frameworks, including shallow classifiers, natural language processing tools, and neural networks. We define the heterogeneous AutoML optimization problem as the search for the best sequence of algorithms that transforms specific input data into the desired output. This provides a novel theoretical and practical approach to AutoML. Our proposal is experimentally evaluated in diverse machine learning problems and compared with alternative approaches, showing that it is competitive with other AutoML alternatives in standard benchmarks. Furthermore, it can be applied to novel scenarios, such as several NLP tasks, where existing alternatives cannot be directly deployed. The system is freely available and includes in-built compatibility with a large number of popular machine learning frameworks, which makes our approach useful for solving practical problems with relative ease and effort. © 2020 COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference. All rights reserved.

关键词： Machine learning

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