Accurate wind power forecasting is critical for system integration and stability as renewable energy reliance grows.Traditional approaches frequently struggle with complex data and non-linear connections. This article...Accurate wind power forecasting is critical for system integration and stability as renewable energy reliance grows.Traditional approaches frequently struggle with complex data and non-linear connections. This article presentsa novel approach for hybrid ensemble learning that is based on rigorous requirements engineering concepts.The approach finds significant parameters influencing forecasting accuracy by evaluating real-time Modern-EraRetrospective Analysis for Research and Applications (MERRA2) data from several European Wind farms usingin-depth stakeholder research and requirements elicitation. Ensemble learning is used to develop a robust model,while a temporal convolutional network handles time-series complexities and data gaps. The ensemble-temporalneural network is enhanced by providing different input parameters including training layers, hidden and dropoutlayers along with activation and loss functions. The proposed framework is further analyzed by comparing stateof-the-art forecasting models in terms of Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE),respectively. The energy efficiency performance indicators showed that the proposed model demonstrates errorreduction percentages of approximately 16.67%, 28.57%, and 81.92% for MAE, and 38.46%, 17.65%, and 90.78%for RMSE for MERRAWind farms 1, 2, and 3, respectively, compared to other existingmethods. These quantitativeresults show the effectiveness of our proposed model with MAE values ranging from 0.0010 to 0.0156 and RMSEvalues ranging from 0.0014 to 0.0174. This work highlights the effectiveness of requirements engineering in windpower forecasting, leading to enhanced forecast accuracy and grid stability, ultimately paving the way for moresustainable energy solutions.展开更多
Face recognition technology automatically identifies an individual from image or video sources.The detection process can be done by attaining facial characteristics from the image of a subject face.Recent developments...Face recognition technology automatically identifies an individual from image or video sources.The detection process can be done by attaining facial characteristics from the image of a subject face.Recent developments in deep learning(DL)and computer vision(CV)techniques enable the design of automated face recognition and tracking methods.This study presents a novel Harris Hawks Optimization with deep learning-empowered automated face detection and tracking(HHODL-AFDT)method.The proposed HHODL-AFDT model involves a Faster region based convolution neural network(RCNN)-based face detection model and HHO-based hyperparameter opti-mization process.The presented optimal Faster RCNN model precisely rec-ognizes the face and is passed into the face-tracking model using a regression network(REGN).The face tracking using the REGN model uses the fea-tures from neighboring frames and foresees the location of the target face in succeeding frames.The application of the HHO algorithm for optimal hyperparameter selection shows the novelty of the work.The experimental validation of the presented HHODL-AFDT algorithm is conducted using two datasets and the experiment outcomes highlighted the superior performance of the HHODL-AFDT model over current methodologies with maximum accuracy of 90.60%and 88.08%under PICS and VTB datasets,respectively.展开更多
Recently,Internet of Things(IoT)devices produces massive quantity of data from distinct sources that get transmitted over public networks.Cybersecurity becomes a challenging issue in the IoT environment where the exis...Recently,Internet of Things(IoT)devices produces massive quantity of data from distinct sources that get transmitted over public networks.Cybersecurity becomes a challenging issue in the IoT environment where the existence of cyber threats needs to be resolved.The development of automated tools for cyber threat detection and classification using machine learning(ML)and artificial intelligence(AI)tools become essential to accomplish security in the IoT environment.It is needed to minimize security issues related to IoT gadgets effectively.Therefore,this article introduces a new Mayfly optimization(MFO)with regularized extreme learning machine(RELM)model,named MFO-RELM for Cybersecurity Threat Detection and classification in IoT environment.The presented MFORELM technique accomplishes the effectual identification of cybersecurity threats that exist in the IoT environment.For accomplishing this,the MFO-RELM model pre-processes the actual IoT data into a meaningful format.In addition,the RELM model receives the pre-processed data and carries out the classification process.In order to boost the performance of the RELM model,the MFO algorithm has been employed to it.The performance validation of the MFO-RELM model is tested using standard datasets and the results highlighted the better outcomes of the MFO-RELM model under distinct aspects.展开更多
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 he...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.展开更多
Recently,Internet of Things(IoT)devices have developed at a faster rate and utilization of devices gets considerably increased in day to day lives.Despite the benefits of IoT devices,security issues remain challenging...Recently,Internet of Things(IoT)devices have developed at a faster rate and utilization of devices gets considerably increased in day to day lives.Despite the benefits of IoT devices,security issues remain challenging owing to the fact that most devices do not include memory and computing resources essential for satisfactory security operation.Consequently,IoT devices are vulnerable to different kinds of attacks.A single attack on networking system/device could result in considerable data to data security and privacy.But the emergence of artificial intelligence(AI)techniques can be exploited for attack detection and classification in the IoT environment.In this view,this paper presents novel metaheuristics feature selection with fuzzy logic enabled intrusion detection system(MFSFL-IDS)in the IoT environment.The presented MFSFL-IDS approach purposes for recognizing the existence of intrusions and accomplish security in the IoT environment.To achieve this,the MFSFL-IDS model employs data pre-processing to transform the data into useful format.Besides,henry gas solubility optimization(HGSO)algorithm is applied as a feature selection approach to derive useful feature vectors.Moreover,adaptive neuro fuzzy inference system(ANFIS)technique was utilized for the recognition and classification of intrusions in the network.Finally,binary bat algorithm(BBA)is exploited for adjusting parameters involved in the ANFIS model.A comprehensive experimental validation of the MFSFL-IDS model is carried out using benchmark dataset and the outcomes are assessed under distinct aspects.The experimentation outcomes highlighted the superior performance of the MFSFL-IDS model over recentapproaches with maximum accuracy of 99.80%.展开更多
Sentiment Analysis(SA),a Machine Learning(ML)technique,is often applied in the literature.The SA technique is specifically applied to the data collected from social media sites.The research studies conducted earlier u...Sentiment Analysis(SA),a Machine Learning(ML)technique,is often applied in the literature.The SA technique is specifically applied to the data collected from social media sites.The research studies conducted earlier upon the SA of the tweets were mostly aimed at automating the feature extraction process.In this background,the current study introduces a novel method called Quantum Particle Swarm Optimization with Deep Learning-Based Sentiment Analysis on Arabic Tweets(QPSODL-SAAT).The presented QPSODL-SAAT model determines and classifies the sentiments of the tweets written in Arabic.Initially,the data pre-processing is performed to convert the raw tweets into a useful format.Then,the word2vec model is applied to generate the feature vectors.The Bidirectional Gated Recurrent Unit(BiGRU)classifier is utilized to identify and classify the sentiments.Finally,the QPSO algorithm is exploited for the optimal finetuning of the hyperparameters involved in the BiGRU model.The proposed QPSODL-SAAT model was experimentally validated using the standard datasets.An extensive comparative analysis was conducted,and the proposed model achieved a maximum accuracy of 98.35%.The outcomes confirmed the supremacy of the proposed QPSODL-SAAT model over the rest of the approaches,such as the Surface Features(SF),Generic Embeddings(GE),Arabic Sentiment Embeddings constructed using the Hybrid(ASEH)model and the Bidirectional Encoder Representations from Transformers(BERT)model.展开更多
Recently,Internet of Things(IoT)has been developed into a field of research and it purposes at linking many sensors enabling devices mostly to data collection and track applications.Wireless sensor network(WSN)is a vi...Recently,Internet of Things(IoT)has been developed into a field of research and it purposes at linking many sensors enabling devices mostly to data collection and track applications.Wireless sensor network(WSN)is a vital element of IoT paradigm since its inception and has developed into one of the chosen platforms for deploying many smart city application regions such as disaster management,intelligent transportation,home automation,smart buildings,and other such IoT-based application.The routing approaches were extremely-utilized energy efficient approaches with an initial drive that is,for balancing the energy amongst sensor nodes.The clustering and routing procedures assumed that Non-Polynomial(NP)hard problems but bio-simulated approaches are utilized to a recognized time for resolving such problems.With this motivation,this paper presents a new blockchain with Enhanced Hunger Games Search based Route Planning(BCEHGS-RP)scheme for IoT assisted WSN.The presented BCEHGS-RP model majorly employs BC technology for secure communication in the IoT supportedWSN environment.In addition,an effective multihop route planning approach was designed by the use of EHGS technique.The proposed EHGS technique is derived from the concept of Hill Climbing strategy(HCS)and HGS algorithm.Moreover,a fitness function with two parameters namely residual energy(RE)and intercluster distance to elect optimal routes.The performance validation of the BCEHGS-RP model is experimented with under diverse number of nodes.Extensive experimental outcomes highlighted the better performance of the BCEHGS-RP technique on recent approaches.展开更多
Combined Economic and Emission Dispatch(CEED)task forms multi-objective optimization problems to be resolved to minimize emission and fuel costs.The disadvantage of the conventional method is its incapability to avoid...Combined Economic and Emission Dispatch(CEED)task forms multi-objective optimization problems to be resolved to minimize emission and fuel costs.The disadvantage of the conventional method is its incapability to avoid falling in local optimal,particularly when handling nonlinear and complex systems.Metaheuristics have recently received considerable attention due to their enhanced capacity to prevent local optimal solutions in addressing all the optimization problems as a black box.Therefore,this paper focuses on the design of an improved sand cat optimization algorithm based CEED(ISCOA-CEED)technique.The ISCOA-CEED technique majorly concen-trates on reducing fuel costs and the emission of generation units.Moreover,the presented ISCOA-CEED technique transforms the equality constraints of the CEED issue into inequality constraints.Besides,the improved sand cat optimization algorithm(ISCOA)is derived from the integration of tra-ditional SCOA with the Levy Flight(LF)concept.At last,the ISCOA-CEED technique is applied to solve a series of 6 and 11 generators in the CEED issue.The experimental validation of the ISCOA-CEED technique ensured the enhanced performance of the presented ISCOA-CEED technique over other recent approaches.展开更多
Nowadays,security plays an important role in Internet of Things(IoT)environment especially in medical services’domains like disease prediction and medical data storage.In healthcare sector,huge volumes of data are ge...Nowadays,security plays an important role in Internet of Things(IoT)environment especially in medical services’domains like disease prediction and medical data storage.In healthcare sector,huge volumes of data are generated on a daily basis,owing to the involvement of advanced health care devices.In general terms,health care images are highly sensitive to alterations due to which any modifications in its content can result in faulty diagnosis.At the same time,it is also significant to maintain the delicate contents of health care images during reconstruction stage.Therefore,an encryption system is required in order to raise the privacy and security of healthcare data by not leaking any sensitive data.The current study introduces Improved Multileader Optimization with Shadow Image Encryption for Medical Image Security(IMLOSIE-MIS)technique for IoT environment.The aim of the proposed IMLOSIE-MIS model is to accomplish security by generating shadows and encrypting them effectively.To do so,the presented IMLOSIE-MIS model initially generates a set of shadows for every input medical image.Besides,shadow image encryption process takes place with the help of Multileader Optimization(MLO)withHomomorphic Encryption(IMLO-HE)technique,where the optimal keys are generated with the help of MLO algorithm.On the receiver side,decryption process is initially carried out and shadow image reconstruction process is conducted.The experimentation analysis was carried out on medical images and the results inferred that the proposed IMLOSIE-MIS model is an excellent performer compared to other models.The comparison study outcomes demonstrate that IMLOSIE-MIS model is robust and offers high security in IoT-enabled healthcare environment.展开更多
Cardiovascular problems have become the predominant cause of death worldwide and a rise in the number of patients has been observed lately.Currently,electrocardiogram(ECG)data is analyzed by medical experts to determi...Cardiovascular problems have become the predominant cause of death worldwide and a rise in the number of patients has been observed lately.Currently,electrocardiogram(ECG)data is analyzed by medical experts to determine the cardiac abnormality,which is time-consuming.In addition,the diagnosis requires experienced medical experts and is error-prone.However,automated identification of cardiovascular disease using ECGs is a challenging problem and state-of-the-art performance has been attained by complex deep learning architectures.This study proposes a simple multilayer perceptron(MLP)model for heart disease prediction to reduce computational complexity.ECG dataset containing averaged signals with window size 10 is used as an input.Several competing deep learning and machine learning models are used for comparison.K-fold cross-validation is used to validate the results.Experimental outcomes reveal that the MLP-based architecture can produce better outcomes than existing approaches with a 94.40%accuracy score.The findings of this study show that the proposed system achieves high performance indicating that it has the potential for deployment in a real-world,practical medical environment.展开更多
Recently,computer aided diagnosis(CAD)model becomes an effective tool for decision making in healthcare sector.The advances in computer vision and artificial intelligence(AI)techniques have resulted in the effective d...Recently,computer aided diagnosis(CAD)model becomes an effective tool for decision making in healthcare sector.The advances in computer vision and artificial intelligence(AI)techniques have resulted in the effective design of CAD models,which enables to detection of the existence of diseases using various imaging modalities.Oral cancer(OC)has commonly occurred in head and neck globally.Earlier identification of OC enables to improve survival rate and reduce mortality rate.Therefore,the design of CAD model for OC detection and classification becomes essential.Therefore,this study introduces a novel Computer Aided Diagnosis for OC using Sailfish Optimization with Fusion based Classification(CADOC-SFOFC)model.The proposed CADOC-SFOFC model determines the existence of OC on the medical images.To accomplish this,a fusion based feature extraction process is carried out by the use of VGGNet-16 and Residual Network(ResNet)model.Besides,feature vectors are fused and passed into the extreme learning machine(ELM)model for classification process.Moreover,SFO algorithm is utilized for effective parameter selection of the ELM model,consequently resulting in enhanced performance.The experimental analysis of the CADOC-SFOFC model was tested on Kaggle dataset and the results reported the betterment of the CADOC-SFOFC model over the compared methods with maximum accuracy of 98.11%.Therefore,the CADOC-SFOFC model has maximum potential as an inexpensive and non-invasive tool which supports screening process and enhances the detection efficiency.展开更多
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...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.展开更多
Sentiment analysis(SA)of the Arabic language becomes important despite scarce annotated corpora and confined sources.Arabic affect Analysis has become an active research zone nowadays.But still,the Arabic language lag...Sentiment analysis(SA)of the Arabic language becomes important despite scarce annotated corpora and confined sources.Arabic affect Analysis has become an active research zone nowadays.But still,the Arabic language lags behind adequate language sources for enabling the SA tasks.Thus,Arabic still faces challenges in natural language processing(NLP)tasks because of its structure complexities,history,and distinct cultures.It has gained lesser effort than the other languages.This paper developed a Multi-versus Optimization with Deep Reinforcement Learning Enabled Affect Analysis(MVODRL-AA)on Arabic Corpus.The presented MVODRL-AAmodelmajorly concentrates on identifying and classifying effects or emotions that occurred in the Arabic corpus.Firstly,the MVODRL-AA model follows data pre-processing and word embedding.Next,an n-gram model is utilized to generate word embeddings.A deep Q-learning network(DQLN)model is then exploited to identify and classify the effect on the Arabic corpus.At last,the MVO algorithm is used as a hyperparameter tuning approach to adjust the hyperparameters related to the DQLN model,showing the novelty of the work.A series of simulations were carried out to exhibit the promising performance of the MVODRL-AA model.The simulation outcomes illustrate the betterment of the MVODRL-AA method over the other approaches with an accuracy of 99.27%.展开更多
Recent developments on Internet and social networking have led to the growth of aggressive language and hate speech.Online provocation,abuses,and attacks are widely termed cyberbullying(CB).The massive quantity of use...Recent developments on Internet and social networking have led to the growth of aggressive language and hate speech.Online provocation,abuses,and attacks are widely termed cyberbullying(CB).The massive quantity of user generated content makes it difficult to recognize CB.Current advancements in machine learning(ML),deep learning(DL),and natural language processing(NLP)tools enable to detect and classify CB in social networks.In this view,this study introduces a spotted hyena optimizer with deep learning driven cybersecurity(SHODLCS)model for OSN.The presented SHODLCS model intends to accomplish cybersecurity from the identification of CB in the OSN.For achieving this,the SHODLCS model involves data pre-processing and TF-IDF based feature extraction.In addition,the cascaded recurrent neural network(CRNN)model is applied for the identification and classification of CB.Finally,the SHO algorithm is exploited to optimally tune the hyperparameters involved in the CRNN model and thereby results in enhanced classifier performance.The experimental validation of the SHODLCS model on the benchmark dataset portrayed the better outcomes of the SHODLCS model over the recent approaches.展开更多
Presently,smart cities play a vital role to enhance the quality of living among human beings in several ways such as online shopping,e-learning,ehealthcare,etc.Despite the benefits of advanced technologies,issues are ...Presently,smart cities play a vital role to enhance the quality of living among human beings in several ways such as online shopping,e-learning,ehealthcare,etc.Despite the benefits of advanced technologies,issues are also existed from the transformation of the physical word into digital word,particularly in online social networks(OSN).Cyberbullying(CB)is a major problem in OSN which needs to be addressed by the use of automated natural language processing(NLP)and machine learning(ML)approaches.This article devises a novel search and rescue optimization with machine learning enabled cybersecurity model for online social networks,named SRO-MLCOSN model.The presented SRO-MLCOSN model focuses on the identification of CB that occurred in social networking sites.The SRO-MLCOSN model initially employs Glove technique for word embedding process.Besides,a multiclass-weighted kernel extreme learning machine(M-WKELM)model is utilized for effectual identification and categorization of CB.Finally,Search and Rescue Optimization(SRO)algorithm is exploited to fine tune the parameters involved in the M-WKELM model.The experimental validation of the SRO-MLCOSN model on the benchmark dataset reported significant outcomes over the other approaches with precision,recall,and F1-score of 96.24%,98.71%,and 97.46%respectively.展开更多
Internet of things(IoT)and cloud computing(CC)becomes widespread in different application domains such as business,e-commerce,healthcare,etc.The recent developments of IoT technology have led to an increase in large a...Internet of things(IoT)and cloud computing(CC)becomes widespread in different application domains such as business,e-commerce,healthcare,etc.The recent developments of IoT technology have led to an increase in large amounts of data from various sources.In IoT enabled cloud environment,load scheduling remains a challenging process which is applied for ensuring network stability with maximum resource utilization.The load scheduling problem was regarded as an optimization problem that is solved by metaheuristics.In this view,this study develops a new Circle Chaotic Chameleon Swarm Optimization based Load Scheduling(C3SOA-LS)technique for IoT enabled cloud environment.The proposed C3SOA-LS technique intends to effectually schedule the tasks and balance the load uniformly in such a way that maximum resource utilization can be accomplished.Besides,the presented C3SOA-LS model involves the design of circle chaotic mapping(CCM)with the traditional chameleon swarm optimization(CSO)algorithm for improving the exploration process,shows the novelty of the work.The proposed C3SOA-LS model computes an objective with the minimization of energy consumption and makespan.The experimental outcome implied that the C3SOA-LS model has showcased improved performance and uniformly balances the load over other approaches.展开更多
Mathematical modelling has been extensively used to measure intervention strategies for the control of contagious conditions.Alignment between different models is pivotal for furnishing strong substantiation for polic...Mathematical modelling has been extensively used to measure intervention strategies for the control of contagious conditions.Alignment between different models is pivotal for furnishing strong substantiation for policymakers because the differences in model features can impact their prognostications.Mathematical modelling has been widely used in order to better understand the transmission,treatment,and prevention of infectious diseases.Herein,we study the dynamics of a human immunodeficiency virus(HIV)infection model with four variables:S(t),I(t),C(t),and A(t)the susceptible individuals;HIV infected individuals(with no clinical symptoms of AIDS);HIV infected individuals(under ART with a viral load remaining low),and HIV infected individuals with two different incidence functions(bilinear and saturated incidence functions).A novel numerical scheme called the continuous Galerkin-Petrov method is implemented for the solution of themodel.The influence of different clinical parameters on the dynamical behavior of S(t),I(t),C(t)and A(t)is described and analyzed.All the results are depicted graphically.On the other hand,we explore the time-dependent movement of nanofluid in porous media on an extending sheet under the influence of thermal radiation,heat flux,hall impact,variable heat source,and nanomaterial.The flow is considered to be 2D,boundary layer,viscous,incompressible,laminar,and unsteady.Sufficient transformations turn governing connected PDEs intoODEs,which are solved using the proposed scheme.To justify the envisaged problem,a comparison of the current work with previous literature is presented.展开更多
Opinion Mining(OM)studies in Arabic are limited though it is one of the most extensively-spoken languages worldwide.Though the interest in OM studies in the Arabic language is growing among researchers,it needs a vast...Opinion Mining(OM)studies in Arabic are limited though it is one of the most extensively-spoken languages worldwide.Though the interest in OM studies in the Arabic language is growing among researchers,it needs a vast number of investigations due to the unique morphological principles of the language.Arabic OM studies experience multiple challenges owing to the poor existence of language sources and Arabic-specific linguistic features.The comparative OM studies in the English language are wide and novel.But,comparative OM studies in the Arabic language are yet to be established and are still in a nascent stage.The unique features of the Arabic language make it essential to expand the studies regarding the Arabic text.It contains unique featuressuchasdiacritics,elongation,inflectionandwordlength.Thecurrent study proposes a Political Optimizer with Probabilistic Neural Network-based Comparative Opinion Mining(POPNN-COM)model for the Arabic text.The proposed POPNN-COM model aims to recognize comparative and non-comparative texts in Arabic in the context of social media.Initially,the POPNN-COM model involves different levels of data pre-processing to transform the input data into a useful format.Then,the pre-processed data is fed into the PNN model for classification and recognition of the data under different class labels.At last,the PO algorithm is employed for fine-tuning the parameters involved in this model to achieve enhanced results.The proposed POPNN-COM model was experimentally validated using two standard datasets,and the outcomes established the promising performance of the proposed POPNN-COM method over other recent approaches.展开更多
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 us...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%.展开更多
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 Inte...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.展开更多
文摘Accurate wind power forecasting is critical for system integration and stability as renewable energy reliance grows.Traditional approaches frequently struggle with complex data and non-linear connections. This article presentsa novel approach for hybrid ensemble learning that is based on rigorous requirements engineering concepts.The approach finds significant parameters influencing forecasting accuracy by evaluating real-time Modern-EraRetrospective Analysis for Research and Applications (MERRA2) data from several European Wind farms usingin-depth stakeholder research and requirements elicitation. Ensemble learning is used to develop a robust model,while a temporal convolutional network handles time-series complexities and data gaps. The ensemble-temporalneural network is enhanced by providing different input parameters including training layers, hidden and dropoutlayers along with activation and loss functions. The proposed framework is further analyzed by comparing stateof-the-art forecasting models in terms of Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE),respectively. The energy efficiency performance indicators showed that the proposed model demonstrates errorreduction percentages of approximately 16.67%, 28.57%, and 81.92% for MAE, and 38.46%, 17.65%, and 90.78%for RMSE for MERRAWind farms 1, 2, and 3, respectively, compared to other existingmethods. These quantitativeresults show the effectiveness of our proposed model with MAE values ranging from 0.0010 to 0.0156 and RMSEvalues ranging from 0.0014 to 0.0174. This work highlights the effectiveness of requirements engineering in windpower forecasting, leading to enhanced forecast accuracy and grid stability, ultimately paving the way for moresustainable energy solutions.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2023R349)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.This study is supported via funding from Prince Sattam bin Abdulaziz University Project Number(PSAU/2023/R/1444).
文摘Face recognition technology automatically identifies an individual from image or video sources.The detection process can be done by attaining facial characteristics from the image of a subject face.Recent developments in deep learning(DL)and computer vision(CV)techniques enable the design of automated face recognition and tracking methods.This study presents a novel Harris Hawks Optimization with deep learning-empowered automated face detection and tracking(HHODL-AFDT)method.The proposed HHODL-AFDT model involves a Faster region based convolution neural network(RCNN)-based face detection model and HHO-based hyperparameter opti-mization process.The presented optimal Faster RCNN model precisely rec-ognizes the face and is passed into the face-tracking model using a regression network(REGN).The face tracking using the REGN model uses the fea-tures from neighboring frames and foresees the location of the target face in succeeding frames.The application of the HHO algorithm for optimal hyperparameter selection shows the novelty of the work.The experimental validation of the presented HHODL-AFDT algorithm is conducted using two datasets and the experiment outcomes highlighted the superior performance of the HHODL-AFDT model over current methodologies with maximum accuracy of 90.60%and 88.08%under PICS and VTB datasets,respectively.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number(RGP 2/142/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R161)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4210118DSR06).
文摘Recently,Internet of Things(IoT)devices produces massive quantity of data from distinct sources that get transmitted over public networks.Cybersecurity becomes a challenging issue in the IoT environment where the existence of cyber threats needs to be resolved.The development of automated tools for cyber threat detection and classification using machine learning(ML)and artificial intelligence(AI)tools become essential to accomplish security in the IoT environment.It is needed to minimize security issues related to IoT gadgets effectively.Therefore,this article introduces a new Mayfly optimization(MFO)with regularized extreme learning machine(RELM)model,named MFO-RELM for Cybersecurity Threat Detection and classification in IoT environment.The presented MFORELM technique accomplishes the effectual identification of cybersecurity threats that exist in the IoT environment.For accomplishing this,the MFO-RELM model pre-processes the actual IoT data into a meaningful format.In addition,the RELM model receives the pre-processed data and carries out the classification process.In order to boost the performance of the RELM model,the MFO algorithm has been employed to it.The performance validation of the MFO-RELM model is tested using standard datasets and the results highlighted the better outcomes of the MFO-RELM model under distinct aspects.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R77)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4331004DSR15).
文摘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.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R319),Princess Nourah bint Abdulrahman University,Riyadh,Saudi ArabiaThe authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4310373DSR27).
文摘Recently,Internet of Things(IoT)devices have developed at a faster rate and utilization of devices gets considerably increased in day to day lives.Despite the benefits of IoT devices,security issues remain challenging owing to the fact that most devices do not include memory and computing resources essential for satisfactory security operation.Consequently,IoT devices are vulnerable to different kinds of attacks.A single attack on networking system/device could result in considerable data to data security and privacy.But the emergence of artificial intelligence(AI)techniques can be exploited for attack detection and classification in the IoT environment.In this view,this paper presents novel metaheuristics feature selection with fuzzy logic enabled intrusion detection system(MFSFL-IDS)in the IoT environment.The presented MFSFL-IDS approach purposes for recognizing the existence of intrusions and accomplish security in the IoT environment.To achieve this,the MFSFL-IDS model employs data pre-processing to transform the data into useful format.Besides,henry gas solubility optimization(HGSO)algorithm is applied as a feature selection approach to derive useful feature vectors.Moreover,adaptive neuro fuzzy inference system(ANFIS)technique was utilized for the recognition and classification of intrusions in the network.Finally,binary bat algorithm(BBA)is exploited for adjusting parameters involved in the ANFIS model.A comprehensive experimental validation of the MFSFL-IDS model is carried out using benchmark dataset and the outcomes are assessed under distinct aspects.The experimentation outcomes highlighted the superior performance of the MFSFL-IDS model over recentapproaches with maximum accuracy of 99.80%.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Small Groups Project under Grant Number(120/43)Princess Nourah Bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R263)+1 种基金Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura Universitysupporting this work by Grant Code:(22UQU4310373DSR36).
文摘Sentiment Analysis(SA),a Machine Learning(ML)technique,is often applied in the literature.The SA technique is specifically applied to the data collected from social media sites.The research studies conducted earlier upon the SA of the tweets were mostly aimed at automating the feature extraction process.In this background,the current study introduces a novel method called Quantum Particle Swarm Optimization with Deep Learning-Based Sentiment Analysis on Arabic Tweets(QPSODL-SAAT).The presented QPSODL-SAAT model determines and classifies the sentiments of the tweets written in Arabic.Initially,the data pre-processing is performed to convert the raw tweets into a useful format.Then,the word2vec model is applied to generate the feature vectors.The Bidirectional Gated Recurrent Unit(BiGRU)classifier is utilized to identify and classify the sentiments.Finally,the QPSO algorithm is exploited for the optimal finetuning of the hyperparameters involved in the BiGRU model.The proposed QPSODL-SAAT model was experimentally validated using the standard datasets.An extensive comparative analysis was conducted,and the proposed model achieved a maximum accuracy of 98.35%.The outcomes confirmed the supremacy of the proposed QPSODL-SAAT model over the rest of the approaches,such as the Surface Features(SF),Generic Embeddings(GE),Arabic Sentiment Embeddings constructed using the Hybrid(ASEH)model and the Bidirectional Encoder Representations from Transformers(BERT)model.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R237)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4310373DSR30).
文摘Recently,Internet of Things(IoT)has been developed into a field of research and it purposes at linking many sensors enabling devices mostly to data collection and track applications.Wireless sensor network(WSN)is a vital element of IoT paradigm since its inception and has developed into one of the chosen platforms for deploying many smart city application regions such as disaster management,intelligent transportation,home automation,smart buildings,and other such IoT-based application.The routing approaches were extremely-utilized energy efficient approaches with an initial drive that is,for balancing the energy amongst sensor nodes.The clustering and routing procedures assumed that Non-Polynomial(NP)hard problems but bio-simulated approaches are utilized to a recognized time for resolving such problems.With this motivation,this paper presents a new blockchain with Enhanced Hunger Games Search based Route Planning(BCEHGS-RP)scheme for IoT assisted WSN.The presented BCEHGS-RP model majorly employs BC technology for secure communication in the IoT supportedWSN environment.In addition,an effective multihop route planning approach was designed by the use of EHGS technique.The proposed EHGS technique is derived from the concept of Hill Climbing strategy(HCS)and HGS algorithm.Moreover,a fitness function with two parameters namely residual energy(RE)and intercluster distance to elect optimal routes.The performance validation of the BCEHGS-RP model is experimented with under diverse number of nodes.Extensive experimental outcomes highlighted the better performance of the BCEHGS-RP technique on recent approaches.
基金supported via funding from Prince Sattam bin Abdulaziz University project number(PSAU/2023/R/1444)The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:22UQU4340237DSR65.
文摘Combined Economic and Emission Dispatch(CEED)task forms multi-objective optimization problems to be resolved to minimize emission and fuel costs.The disadvantage of the conventional method is its incapability to avoid falling in local optimal,particularly when handling nonlinear and complex systems.Metaheuristics have recently received considerable attention due to their enhanced capacity to prevent local optimal solutions in addressing all the optimization problems as a black box.Therefore,this paper focuses on the design of an improved sand cat optimization algorithm based CEED(ISCOA-CEED)technique.The ISCOA-CEED technique majorly concen-trates on reducing fuel costs and the emission of generation units.Moreover,the presented ISCOA-CEED technique transforms the equality constraints of the CEED issue into inequality constraints.Besides,the improved sand cat optimization algorithm(ISCOA)is derived from the integration of tra-ditional SCOA with the Levy Flight(LF)concept.At last,the ISCOA-CEED technique is applied to solve a series of 6 and 11 generators in the CEED issue.The experimental validation of the ISCOA-CEED technique ensured the enhanced performance of the presented ISCOA-CEED technique over other recent approaches.
基金the Deanship of Scientific Research at King Khalid University for funding this work through Small Groups Project under Grant Number(241/43)Princess Nourah Bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R319)Princess Nourah Bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4340237DSR30).
文摘Nowadays,security plays an important role in Internet of Things(IoT)environment especially in medical services’domains like disease prediction and medical data storage.In healthcare sector,huge volumes of data are generated on a daily basis,owing to the involvement of advanced health care devices.In general terms,health care images are highly sensitive to alterations due to which any modifications in its content can result in faulty diagnosis.At the same time,it is also significant to maintain the delicate contents of health care images during reconstruction stage.Therefore,an encryption system is required in order to raise the privacy and security of healthcare data by not leaking any sensitive data.The current study introduces Improved Multileader Optimization with Shadow Image Encryption for Medical Image Security(IMLOSIE-MIS)technique for IoT environment.The aim of the proposed IMLOSIE-MIS model is to accomplish security by generating shadows and encrypting them effectively.To do so,the presented IMLOSIE-MIS model initially generates a set of shadows for every input medical image.Besides,shadow image encryption process takes place with the help of Multileader Optimization(MLO)withHomomorphic Encryption(IMLO-HE)technique,where the optimal keys are generated with the help of MLO algorithm.On the receiver side,decryption process is initially carried out and shadow image reconstruction process is conducted.The experimentation analysis was carried out on medical images and the results inferred that the proposed IMLOSIE-MIS model is an excellent performer compared to other models.The comparison study outcomes demonstrate that IMLOSIE-MIS model is robust and offers high security in IoT-enabled healthcare environment.
文摘Cardiovascular problems have become the predominant cause of death worldwide and a rise in the number of patients has been observed lately.Currently,electrocardiogram(ECG)data is analyzed by medical experts to determine the cardiac abnormality,which is time-consuming.In addition,the diagnosis requires experienced medical experts and is error-prone.However,automated identification of cardiovascular disease using ECGs is a challenging problem and state-of-the-art performance has been attained by complex deep learning architectures.This study proposes a simple multilayer perceptron(MLP)model for heart disease prediction to reduce computational complexity.ECG dataset containing averaged signals with window size 10 is used as an input.Several competing deep learning and machine learning models are used for comparison.K-fold cross-validation is used to validate the results.Experimental outcomes reveal that the MLP-based architecture can produce better outcomes than existing approaches with a 94.40%accuracy score.The findings of this study show that the proposed system achieves high performance indicating that it has the potential for deployment in a real-world,practical medical environment.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number(RGP 2/142/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R151)+1 种基金Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:22UQU4310373DSR13This research project was supported by a grant from the Research Center of the Female Scientific and Medical Colleges,Deanship of Scientific Research,King Saud University.
文摘Recently,computer aided diagnosis(CAD)model becomes an effective tool for decision making in healthcare sector.The advances in computer vision and artificial intelligence(AI)techniques have resulted in the effective design of CAD models,which enables to detection of the existence of diseases using various imaging modalities.Oral cancer(OC)has commonly occurred in head and neck globally.Earlier identification of OC enables to improve survival rate and reduce mortality rate.Therefore,the design of CAD model for OC detection and classification becomes essential.Therefore,this study introduces a novel Computer Aided Diagnosis for OC using Sailfish Optimization with Fusion based Classification(CADOC-SFOFC)model.The proposed CADOC-SFOFC model determines the existence of OC on the medical images.To accomplish this,a fusion based feature extraction process is carried out by the use of VGGNet-16 and Residual Network(ResNet)model.Besides,feature vectors are fused and passed into the extreme learning machine(ELM)model for classification process.Moreover,SFO algorithm is utilized for effective parameter selection of the ELM model,consequently resulting in enhanced performance.The experimental analysis of the CADOC-SFOFC model was tested on Kaggle dataset and the results reported the betterment of the CADOC-SFOFC model over the compared methods with maximum accuracy of 98.11%.Therefore,the CADOC-SFOFC model has maximum potential as an inexpensive and non-invasive tool which supports screening process and enhances the detection efficiency.
基金Princess Nourah Bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R281)Princess Nourah Bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4331004DSR10).
文摘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.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project Number (PNURSP2022R263)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Ara-bia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:22UQU4340237DSR38.
文摘Sentiment analysis(SA)of the Arabic language becomes important despite scarce annotated corpora and confined sources.Arabic affect Analysis has become an active research zone nowadays.But still,the Arabic language lags behind adequate language sources for enabling the SA tasks.Thus,Arabic still faces challenges in natural language processing(NLP)tasks because of its structure complexities,history,and distinct cultures.It has gained lesser effort than the other languages.This paper developed a Multi-versus Optimization with Deep Reinforcement Learning Enabled Affect Analysis(MVODRL-AA)on Arabic Corpus.The presented MVODRL-AAmodelmajorly concentrates on identifying and classifying effects or emotions that occurred in the Arabic corpus.Firstly,the MVODRL-AA model follows data pre-processing and word embedding.Next,an n-gram model is utilized to generate word embeddings.A deep Q-learning network(DQLN)model is then exploited to identify and classify the effect on the Arabic corpus.At last,the MVO algorithm is used as a hyperparameter tuning approach to adjust the hyperparameters related to the DQLN model,showing the novelty of the work.A series of simulations were carried out to exhibit the promising performance of the MVODRL-AA model.The simulation outcomes illustrate the betterment of the MVODRL-AA method over the other approaches with an accuracy of 99.27%.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R140)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:22UQU4310373DSR15.
文摘Recent developments on Internet and social networking have led to the growth of aggressive language and hate speech.Online provocation,abuses,and attacks are widely termed cyberbullying(CB).The massive quantity of user generated content makes it difficult to recognize CB.Current advancements in machine learning(ML),deep learning(DL),and natural language processing(NLP)tools enable to detect and classify CB in social networks.In this view,this study introduces a spotted hyena optimizer with deep learning driven cybersecurity(SHODLCS)model for OSN.The presented SHODLCS model intends to accomplish cybersecurity from the identification of CB in the OSN.For achieving this,the SHODLCS model involves data pre-processing and TF-IDF based feature extraction.In addition,the cascaded recurrent neural network(CRNN)model is applied for the identification and classification of CB.Finally,the SHO algorithm is exploited to optimally tune the hyperparameters involved in the CRNN model and thereby results in enhanced classifier performance.The experimental validation of the SHODLCS model on the benchmark dataset portrayed the better outcomes of the SHODLCS model over the recent approaches.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under Grant Number(RGP 2/158/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R114),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Presently,smart cities play a vital role to enhance the quality of living among human beings in several ways such as online shopping,e-learning,ehealthcare,etc.Despite the benefits of advanced technologies,issues are also existed from the transformation of the physical word into digital word,particularly in online social networks(OSN).Cyberbullying(CB)is a major problem in OSN which needs to be addressed by the use of automated natural language processing(NLP)and machine learning(ML)approaches.This article devises a novel search and rescue optimization with machine learning enabled cybersecurity model for online social networks,named SRO-MLCOSN model.The presented SRO-MLCOSN model focuses on the identification of CB that occurred in social networking sites.The SRO-MLCOSN model initially employs Glove technique for word embedding process.Besides,a multiclass-weighted kernel extreme learning machine(M-WKELM)model is utilized for effectual identification and categorization of CB.Finally,Search and Rescue Optimization(SRO)algorithm is exploited to fine tune the parameters involved in the M-WKELM model.The experimental validation of the SRO-MLCOSN model on the benchmark dataset reported significant outcomes over the other approaches with precision,recall,and F1-score of 96.24%,98.71%,and 97.46%respectively.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under Grant Number(RGP 1/322/42)Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R136)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4340237DSR09).
文摘Internet of things(IoT)and cloud computing(CC)becomes widespread in different application domains such as business,e-commerce,healthcare,etc.The recent developments of IoT technology have led to an increase in large amounts of data from various sources.In IoT enabled cloud environment,load scheduling remains a challenging process which is applied for ensuring network stability with maximum resource utilization.The load scheduling problem was regarded as an optimization problem that is solved by metaheuristics.In this view,this study develops a new Circle Chaotic Chameleon Swarm Optimization based Load Scheduling(C3SOA-LS)technique for IoT enabled cloud environment.The proposed C3SOA-LS technique intends to effectually schedule the tasks and balance the load uniformly in such a way that maximum resource utilization can be accomplished.Besides,the presented C3SOA-LS model involves the design of circle chaotic mapping(CCM)with the traditional chameleon swarm optimization(CSO)algorithm for improving the exploration process,shows the novelty of the work.The proposed C3SOA-LS model computes an objective with the minimization of energy consumption and makespan.The experimental outcome implied that the C3SOA-LS model has showcased improved performance and uniformly balances the load over other approaches.
文摘Mathematical modelling has been extensively used to measure intervention strategies for the control of contagious conditions.Alignment between different models is pivotal for furnishing strong substantiation for policymakers because the differences in model features can impact their prognostications.Mathematical modelling has been widely used in order to better understand the transmission,treatment,and prevention of infectious diseases.Herein,we study the dynamics of a human immunodeficiency virus(HIV)infection model with four variables:S(t),I(t),C(t),and A(t)the susceptible individuals;HIV infected individuals(with no clinical symptoms of AIDS);HIV infected individuals(under ART with a viral load remaining low),and HIV infected individuals with two different incidence functions(bilinear and saturated incidence functions).A novel numerical scheme called the continuous Galerkin-Petrov method is implemented for the solution of themodel.The influence of different clinical parameters on the dynamical behavior of S(t),I(t),C(t)and A(t)is described and analyzed.All the results are depicted graphically.On the other hand,we explore the time-dependent movement of nanofluid in porous media on an extending sheet under the influence of thermal radiation,heat flux,hall impact,variable heat source,and nanomaterial.The flow is considered to be 2D,boundary layer,viscous,incompressible,laminar,and unsteady.Sufficient transformations turn governing connected PDEs intoODEs,which are solved using the proposed scheme.To justify the envisaged problem,a comparison of the current work with previous literature is presented.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R263)Princess Nourah bint Abdulrahman University,Riyadh,Saudi ArabiaThe authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:22UQU4310373DSR56.
文摘Opinion Mining(OM)studies in Arabic are limited though it is one of the most extensively-spoken languages worldwide.Though the interest in OM studies in the Arabic language is growing among researchers,it needs a vast number of investigations due to the unique morphological principles of the language.Arabic OM studies experience multiple challenges owing to the poor existence of language sources and Arabic-specific linguistic features.The comparative OM studies in the English language are wide and novel.But,comparative OM studies in the Arabic language are yet to be established and are still in a nascent stage.The unique features of the Arabic language make it essential to expand the studies regarding the Arabic text.It contains unique featuressuchasdiacritics,elongation,inflectionandwordlength.Thecurrent study proposes a Political Optimizer with Probabilistic Neural Network-based Comparative Opinion Mining(POPNN-COM)model for the Arabic text.The proposed POPNN-COM model aims to recognize comparative and non-comparative texts in Arabic in the context of social media.Initially,the POPNN-COM model involves different levels of data pre-processing to transform the input data into a useful format.Then,the pre-processed data is fed into the PNN model for classification and recognition of the data under different class labels.At last,the PO algorithm is employed for fine-tuning the parameters involved in this model to achieve enhanced results.The proposed POPNN-COM model was experimentally validated using two standard datasets,and the outcomes established the promising performance of the proposed POPNN-COM method over other recent approaches.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R281)Princess Nourah bint Abdulrahman University,Riyadh,SaudiArabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4331004DSR31).
文摘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%.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R281)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Ara-biaThe authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4331004DSR09).
文摘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.
