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Graph Convolutional Neural Network Based Malware Detection in IoT-Cloud Environment

Intelligent Automation & Soft Computing 2023年第6期36卷 2897-2914页

作者： Faisal SAlsubaei Haya Mesfer Alshahrani Khaled Tarmissi Abdelwahed Motwakel Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24211Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16242Saudi Arabia

Cybersecurity has become the most significant research area in the domain of the Internet of Things(IoT)owing to the ever-increasing number of cyberattacks.The rapid penetration of Android platforms in mobile devices has made the detection of malware attacks a challenging process.Furthermore,Android malware is increasing on a daily basis.So,precise malware detection analytical techniques need a large number of hardware resources that are signifi-cantly resource-limited for mobile devices.In this research article,an optimal Graph Convolutional Neural Network-based Malware Detection and classification(OGCNN-MDC)model is introduced for an IoT-cloud environment.The pro-posed OGCNN-MDC model aims to recognize and categorize malware occur-rences in IoT-enabled cloud platforms.The presented OGCNN-MDC model has three stages in total,such as data pre-processing,malware detection and para-meter tuning.To detect and classify the malware,the GCNN model is exploited in this work.In order to enhance the overall efficiency of the GCNN model,the Group Mean-based Optimizer(GMBO)algorithm is utilized to appropriately adjust the GCNN parameters,and this phenomenon shows the novelty of the cur-rent study.A widespread experimental analysis was conducted to establish the superiority of the proposed OGCNN-MDC model.A comprehensive comparison study was conducted,and the outcomes highlighted the supreme performance of the proposed OGCNN-MDC model over other recent approaches.

关键词： Cybersecurity IoT cloud malware detection graph convolution network

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Predictive Multimodal Deep Learning-Based Sustainable Renewable and Non-Renewable Energy Utilization

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Computer Systems Science & Engineering 2023年第10期47卷 1267-1281页

作者： Abdelwahed Motwakel MarwaObayya Nadhem Nemri Khaled Tarmissi Heba Mohsen Mohammed Rizwanulla Ishfaq Yaseen Abu Sarwar Zamani Department of Information Systems College of Business Administration in Hawtat bani TamimPrince Sattam bin Abdulaziz UniversityHowtat Bani Tamim16622Saudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Recently,renewable energy(RE)has become popular due to its benefits,such as being inexpensive,low-carbon,ecologically friendly,steady,and reliable.The RE sources are gradually combined with non-renewable energy(NRE)sources into electric grids to satisfy energy demands.Since energy utilization is highly related to national energy policy,energy prediction using artificial intelligence(AI)and deep learning(DL)based models can be employed for energy prediction on RE and NRE power resources.Predicting energy consumption of RE and NRE sources using effective models becomes necessary.With this motivation,this study presents a new multimodal fusionbased predictive tool for energy consumption prediction(MDLFM-ECP)of RE and NRE power sources.Actual data may influence the prediction performance of the results in prediction approaches.The proposed MDLFMECP technique involves pre-processing,fusion-based prediction,and hyperparameter optimization.In addition,the MDLFM-ECP technique involves the fusion of four deep learning(DL)models,namely long short-termmemory(LSTM),bidirectional LSTM(Bi-LSTM),deep belief network(DBN),and gated recurrent unit(GRU).Moreover,the chaotic cat swarm optimization(CCSO)algorithm is applied to tune the hyperparameters of the DL models.The design of the CCSO algorithm for optimal hyperparameter tuning of the DL models,showing the novelty of the work.A series of simulations took place to validate the superior performance of the proposed method,and the simulation outcome emphasized the improved results of the MDLFM-ECP technique over the recent approaches with minimum overall mean absolute percentage error of 3.58%.

关键词： Sustainability renewable energy power source energy prediction deep learning fusion model metaheuristics

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Sine Cosine Optimization with Deep Learning-Based Applied Linguistics for Sentiment Analysis on COVID-19 Tweets

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Computers, Materials & Continua 2023年第6期75卷 4767-4783页

作者： Abdelwahed Motwakel Hala J.Alshahrani Abdulkhaleq Q.A.Hassan Khaled Tarmissi Amal S.Mehanna Ishfaq Yaseen Amgad Atta Abdelmageed Mohammad Mahzari 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 English College of Science and Arts at MahayilKing Khalid UniversityMuhayil63763Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24211Saudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of English College of Science&HumanitiesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Applied linguistics is an interdisciplinary domain which identifies,investigates,and offers solutions to language-related real-life problems.The new coronavirus disease,otherwise known as Coronavirus disease(COVID-19),has severely affected the everyday life of people all over the world.Specifically,since there is insufficient access to vaccines and no straight or reliable treatment for coronavirus infection,the country has initiated the appropriate preventive measures(like lockdown,physical separation,and masking)for combating this extremely transmittable disease.So,individuals spent more time on online social media platforms(i.e.,Twitter,Facebook,Instagram,LinkedIn,and Reddit)and expressed their thoughts and feelings about coronavirus infection.Twitter has become one of the popular social media platforms and allows anyone to post tweets.This study proposes a sine cosine optimization with bidirectional gated recurrent unit-based senti-ment analysis(SCOBGRU-SA)on COVID-19 tweets.The SCOBGRU-SA technique aimed to detect and classify the various sentiments in Twitter data during the COVID-19 pandemic.The SCOBGRU-SA technique follows data pre-processing and the Fast-Text word embedding process to accomplish this.Moreover,the BGRU model is utilized to recognise and classify sen-timents present in the tweets.Furthermore,the SCO algorithm is exploited for tuning the BGRU method’s hyperparameter,which helps attain improved classification performance.The experimental validation of the SCOBGRU-SA technique takes place using a benchmark dataset,and the results signify its promising performance compared to other DL models.

关键词： Applied linguistics deep learning sentiment analysis COVID-19 pandemic sine cosine optimization twitter

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

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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 therapy.The ever-growing Online Social Networks(OSNs)experience a vital issue to confront,i.e.,hate speech.Amongst the OSN-oriented security problems,the usage of offensive language is the most important threat that is prevalently found across the Internet.Based on the group targeted,the offensive language varies in terms of adult content,hate speech,racism,cyberbullying,abuse,trolling and profanity.Amongst 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 violence.Machine Learning(ML)techniques have recently been applied to recognize hate speech-related content.The current research article introduces a Grasshopper Optimization with an Attentive Recurrent Network for Offensive Speech Detection(GOARN-OSD)model for social media.The GOARNOSD technique integrates the concepts of DL and metaheuristic algorithms for detecting hate speech.In the presented GOARN-OSD technique,the primary stage involves the data pre-processing and word embedding processes.Then,this study utilizes the Attentive Recurrent Network(ARN)model for hate speech recognition and classification.At last,the Grasshopper Optimization Algorithm(GOA)is exploited as a hyperparameter optimizer to boost the performance of the hate speech recognition process.To depict the promising performance of the proposed GOARN-OSD method,a widespread experimental analysis was conducted.The 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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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 discipline.It deals with understanding written and spoken language from a computational viewpoint.Further,the domain also helps construct the artefacts that are useful in processing and producing a language either in bulk or in a dialogue setting.Named Entity Recognition(NER)is a fundamental task in the data extraction process.It concentrates on identifying and labelling the atomic components from several texts grouped under different entities,such as organizations,people,places,and times.Further,the NER mechanism identifies and removes more types of entities as per the requirements.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 methods.The conventional ways of managing the tasks range from rule-related and hand-crafted feature-related Machine Learning(ML)techniques to Deep Learning(DL)techniques.In this aspect,the current study introduces a novel Dart Games Optimizer with Hybrid Deep Learning-Driven Computational Linguistics(DGOHDL-CL)model for NER.The presented DGOHDL-CL technique aims to determine and label the atomic components from several texts as a collection of the named entities.In the presented DGOHDL-CL technique,the word embed-ding process is executed at the initial stage with the help of the word2vec model.For the NER mechanism,the Convolutional Gated Recurrent Unit(CGRU)model is employed in this work.At last,the DGO technique is used as a hyperparameter tuning strategy for the CGRU algorithm to boost the NER’s outcomes.No earlier studies integrated the DGO mechanism with the CGRU model for NER.To exhibit the superiority of the proposed DGOHDL-CL technique,a widespread simulation analysis was executed on two datasets,CoNLL-2003 and OntoNotes 5.0.The 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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Enhanced Crow Search with Deep Learning-Based Cyberattack Detection in SDN-IoT Environment

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Intelligent Automation & Soft Computing 2023年第6期36卷 3157-3173页

作者： Abdelwahed Motwakel Fadwa Alrowais Khaled Tarmissi Radwa Marzouk Abdullah Mohamed Abu Sarwar Zamani Ishfaq Yaseen Mohamed I.Eldesouki Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMeccaSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharj16436Saudi Arabia

The paradigm shift towards the Internet of Things(IoT)phe-nomenon and the rise of edge-computing models provide massive poten-tial for several upcoming IoT applications like smart grid,smart energy,smart home,smart health and smart transportation services.However,it also provides a sequence of novel cyber-security issues.Although IoT networks provide several advantages,the heterogeneous nature of the network and the wide connectivity of the devices make the network easy for cyber-attackers.Cyberattacks result in financial loss and data breaches for organizations and individuals.So,it becomes crucial to secure the IoT environment from such cyberattacks.With this motivation,the current study introduces an effectual Enhanced Crow Search Algorithm with Deep Learning-Driven Cyberattack Detection(ECSADL-CAD)model for the Software-Defined Networking(SDN)-enabled IoT environment.The presented ECSADL-CAD approach aims to identify and classify the cyberattacks in the SDN-enabled IoT envi-ronment.To attain this,the ECSADL-CAD model initially pre-processes the data.In the presented ECSADL-CAD model,the Reinforced Deep Belief Network(RDBN)model is employed for attack detection.At last,the ECSA-based hyperparameter tuning process gets executed to boost the overall classification outcomes.A series of simulations were conducted to validate the improved outcomes of the proposed ECSADL-CAD model.The experimental outcomes confirmed the superiority of the proposed ECSADL-CAD model over other existing methodologies.

关键词： Software defined networks artificial intelligence cybersecurity deep learning internet of things

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Improved Fruitfly Optimization with Stacked Residual Deep Learning Based Email Classification

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Intelligent Automation & Soft Computing 2023年第6期36卷 3139-3155页

作者： Hala J.Alshahrani Khaled Tarmissi Ayman Yafoz Abdullah Mohamed Abdelwahed Motwakel Ishfaq Yaseen Amgad Atta Abdelmageed Mohammad Mahzari 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 Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah22230Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16242Saudi Arabia Department of English College of Science&HumanitiesPrince Sattam bin Abdulaziz UniversityAlKharj16242Saudi Arabia

Applied linguistics means a wide range of actions which include addressing a few language-based problems or solving some language-based concerns.Emails stay in the leading positions for business as well as personal use.This popularity grabs the interest of individuals with malevolent inten-tions—phishing and spam email assaults.Email filtering mechanisms were developed incessantly to follow unwanted,malicious content advancement to protect the end-users.But prevailing solutions were focused on phishing email filtering and spam and whereas email labelling and analysis were not fully advanced.Thus,this study provides a solution related to email message body text automatic classification into phishing and email spam.This paper presents an Improved Fruitfly Optimization with Stacked Residual Recurrent Neural Network(IFFO-SRRNN)based on Applied Linguistics for Email Classification.The presented IFFO-SRRNN technique examines the intrinsic features of email for the identification of spam emails.At the preliminary level,the IFFO-SRRNN model follows the email pre-processing stage to make it compatible with further computation.Next,the SRRNN method can be useful in recognizing and classifying spam emails.As hyperparameters of the SRRNN model need to be effectually tuned,the IFFO algorithm can be utilized as a hyperparameter optimizer.To investigate the effectual email classification results of the IFFO-SRDL technique,a series of simulations were taken placed on public datasets,and the comparison outcomes highlight the enhancements of the IFFO-SRDL method over other recent approaches with an accuracy of 98.86%.

关键词： Email classification applied linguistics improved fruitfly optimization deep learning recurrent neural network

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Applied Linguistics with Mixed Leader Optimizer Based English Text Summarization Model

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Intelligent Automation & Soft Computing 2023年第6期36卷 3203-3219页

作者： Hala J.Alshahrani Khaled Tarmissi Ayman Yafoz Abdullah Mohamed Manar Ahmed Hamza Ishfaq Yaseen Abu Sarwar Zamani Mohammad Mahzari 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 UniversitySaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of English College of Science&HumanitiesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The term‘executed linguistics’corresponds to an interdisciplinary domain in which the solutions are identified and provided for real-time language-related problems.The exponential generation of text data on the Internet must be leveraged to gain knowledgeable insights.The extraction of meaningful insights from text data is crucial since it can provide value-added solutions for business organizations and end-users.The Automatic Text Summarization(ATS)process reduces the primary size of the text without losing any basic components of the data.The current study introduces an Applied Linguistics-based English Text Summarization using a Mixed Leader-Based Optimizer with Deep Learning(ALTS-MLODL)model.The presented ALTS-MLODL technique aims to summarize the text documents in the English language.To accomplish this objective,the proposed ALTS-MLODL technique pre-processes the input documents and primarily extracts a set of features.Next,the MLO algorithm is used for the effectual selection of the extracted features.For the text summarization process,the Cascaded Recurrent Neural Network(CRNN)model is exploited whereas the Whale Optimization Algorithm(WOA)is used as a hyperparameter optimizer.The exploitation of the MLO-based feature selection and the WOA-based hyper-parameter tuning enhanced the summarization results.To validate the perfor-mance of the ALTS-MLODL technique,numerous simulation analyses were conducted.The experimental results signify the superiority of the proposed ALTS-MLODL technique over other approaches.

关键词： Text summarization deep learning hyperparameter tuning applied linguistics multi-leader optimizer

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Hunter Prey Optimization with Hybrid Deep Learning for Fake News Detection on Arabic Corpus

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Computers, Materials & Continua 2023年第5期75卷 4255-4272页

作者： Hala J.Alshahrani Abdulkhaleq Q.A.Hassan Khaled Tarmissi Amal S.Mehanna Abdelwahed Motwakel Ishfaq Yaseen Amgad Atta Abdelmageed Mohamed I.Eldesouki Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of English College of Science and Arts at MahayilKing Khalid UniversityMuhayil63763Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24211Saudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture 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

Nowadays,the usage of socialmedia platforms is rapidly increasing,and rumours or false information are also rising,especially among Arab nations.This false information is harmful to society and individuals.Blocking and detecting the spread of fake news in Arabic becomes critical.Several artificial intelligence(AI)methods,including contemporary transformer techniques,BERT,were used to detect fake news.Thus,fake news in Arabic is identified by utilizing AI approaches.This article develops a new hunterprey optimization with hybrid deep learning-based fake news detection(HPOHDL-FND)model on the Arabic corpus.The HPOHDL-FND technique undergoes extensive data pre-processing steps to transform the input data into a useful format.Besides,the HPOHDL-FND technique utilizes long-term memory with a recurrent neural network(LSTM-RNN)model for fake news detection and classification.Finally,hunter prey optimization(HPO)algorithm is exploited for optimal modification of the hyperparameters related to the LSTM-RNN model.The performance validation of the HPOHDL-FND technique is tested using two Arabic datasets.The outcomes exemplified better performance over the other existing techniques with maximum accuracy of 96.57%and 93.53%on Covid19Fakes and satirical datasets,respectively.

关键词： Arabic corpus fake news detection deep learning hunter prey optimizer classification model

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Optimal Quad Channel Long Short-Term Memory Based Fake News Classification on English Corpus

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Computer Systems Science & Engineering 2023年第9期46卷 3303-3319页

作者： Manar Ahmed Hamza Hala J.Alshahrani Khaled Tarmissi Ayman Yafoz Amal S.Mehanna 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 Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi 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 Cairo11845Egypt Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The term‘corpus’refers to a huge volume of structured datasets containing machine-readable texts.Such texts are generated in a natural communicative setting.The explosion of social media permitted individuals to spread data with minimal examination and filters freely.Due to this,the old problem of fake news has resurfaced.It has become an important concern due to its negative impact on the community.To manage the spread of fake news,automatic recognition approaches have been investigated earlier using Artificial Intelligence(AI)and Machine Learning(ML)techniques.To perform the medicinal text classification tasks,the ML approaches were applied,and they performed quite effectively.Still,a huge effort is required from the human side to generate the labelled training data.The recent progress of the Deep Learning(DL)methods seems to be a promising solution to tackle difficult types of Natural Language Processing(NLP)tasks,especially fake news detection.To unlock social media data,an automatic text classifier is highly helpful in the domain of NLP.The current research article focuses on the design of the Optimal Quad ChannelHybrid Long Short-Term Memory-based Fake News Classification(QCLSTM-FNC)approach.The presented QCLSTM-FNC approach aims to identify and differentiate fake news from actual news.To attain this,the proposed QCLSTM-FNC approach follows two methods such as the pre-processing data method and the Glovebased word embedding process.Besides,the QCLSTM model is utilized for classification.To boost the classification results of the QCLSTM model,a Quasi-Oppositional Sandpiper Optimization(QOSPO)algorithm is utilized to fine-tune the hyperparameters.The proposed QCLSTM-FNC approach was experimentally validated against a benchmark dataset.The QCLSTMFNC approach successfully outperformed all other existing DL models under different measures.

关键词： English corpus fake news detection social media natural language processing artificial intelligence deep learning

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