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)...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.展开更多
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 ...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.展开更多
Proper waste management models using recent technologies like computer vision,machine learning(ML),and deep learning(DL)are needed to effectively handle the massive quantity of increasing waste.Therefore,waste classif...Proper waste management models using recent technologies like computer vision,machine learning(ML),and deep learning(DL)are needed to effectively handle the massive quantity of increasing waste.Therefore,waste classification becomes a crucial topic which helps to categorize waste into hazardous or non-hazardous ones and thereby assist in the decision making of the waste management process.This study concentrates on the design of hazardous waste detection and classification using ensemble learning(HWDC-EL)technique to reduce toxicity and improve human health.The goal of the HWDC-EL technique is to detect the multiple classes of wastes,particularly hazardous and non-hazardous wastes.The HWDC-EL technique involves the ensemble of three feature extractors using Model Averaging technique namely discrete local binary patterns(DLBP),EfficientNet,and DenseNet121.In addition,the flower pollination algorithm(FPA)based hyperparameter optimizers are used to optimally adjust the parameters involved in the EfficientNet and DenseNet121 models.Moreover,a weighted voting-based ensemble classifier is derived using three machine learning algorithms namely support vector machine(SVM),extreme learning machine(ELM),and gradient boosting tree(GBT).The performance of the HWDC-EL technique is tested using a benchmark Garbage dataset and it obtains a maximum accuracy of 98.85%.展开更多
Cybersecurity-related solutions have become familiar since it ensures security and privacy against cyberattacks in this digital era.Malicious Uniform Resource Locators(URLs)can be embedded in email or Twitter and used...Cybersecurity-related solutions have become familiar since it ensures security and privacy against cyberattacks in this digital era.Malicious Uniform Resource Locators(URLs)can be embedded in email or Twitter and used to lure vulnerable internet users to implement malicious data in their systems.This may result in compromised security of the systems,scams,and other such cyberattacks.These attacks hijack huge quantities of the available data,incurring heavy financial loss.At the same time,Machine Learning(ML)and Deep Learning(DL)models paved the way for designing models that can detect malicious URLs accurately and classify them.With this motivation,the current article develops an Artificial Fish Swarm Algorithm(AFSA)with Deep Learning Enabled Malicious URL Detection and Classification(AFSADL-MURLC)model.The presented AFSADL-MURLC model intends to differentiate the malicious URLs from genuine URLs.To attain this,AFSADL-MURLC model initially carries out data preprocessing and makes use of glove-based word embedding technique.In addition,the created vector model is then passed onto Gated Recurrent Unit(GRU)classification to recognize the malicious URLs.Finally,AFSA is applied to the proposed model to enhance the efficiency of GRU model.The proposed AFSADL-MURLC technique was experimentally validated using benchmark dataset sourced from Kaggle repository.The simulation results confirmed the supremacy of the proposed AFSADL-MURLC model over recent approaches under distinct measures.展开更多
Hyperspectral remote sensing/imaging spectroscopy is a novel approach to reaching a spectrum from all the places of a huge array of spatial places so that several spectral wavelengths are utilized for making coherent ...Hyperspectral remote sensing/imaging spectroscopy is a novel approach to reaching a spectrum from all the places of a huge array of spatial places so that several spectral wavelengths are utilized for making coherent images.Hyperspectral remote sensing contains acquisition of digital images from several narrow,contiguous spectral bands throughout the visible,Thermal Infrared(TIR),Near Infrared(NIR),and Mid-Infrared(MIR)regions of the electromagnetic spectrum.In order to the application of agricultural regions,remote sensing approaches are studied and executed to their benefit of continuous and quantitativemonitoring.Particularly,hyperspectral images(HSI)are considered the precise for agriculture as they can offer chemical and physical data on vegetation.With this motivation,this article presents a novel Hurricane Optimization Algorithm with Deep Transfer Learning Driven Crop Classification(HOADTL-CC)model onHyperspectralRemote Sensing Images.The presentedHOADTL-CC model focuses on the identification and categorization of crops on hyperspectral remote sensing images.To accomplish this,the presentedHOADTL-CC model involves the design ofHOAwith capsule network(CapsNet)model for generating a set of useful feature vectors.Besides,Elman neural network(ENN)model is applied to allot proper class labels into the input HSI.Finally,glowworm swarm optimization(GSO)algorithm is exploited to fine tune the ENNparameters involved in this article.The experimental result scrutiny of the HOADTL-CC method can be tested with the help of benchmark dataset and the results are assessed under distinct aspects.Extensive comparative studies stated the enhanced performance of the HOADTL-CC model over recent approaches with maximum accuracy of 99.51%.展开更多
Data mining process involves a number of steps fromdata collection to visualization to identify useful data from massive data set.the same time,the recent advances of machine learning(ML)and deep learning(DL)models ca...Data mining process involves a number of steps fromdata collection to visualization to identify useful data from massive data set.the same time,the recent advances of machine learning(ML)and deep learning(DL)models can be utilized for effectual rainfall prediction.With this motivation,this article develops a novel comprehensive oppositionalmoth flame optimization with deep learning for rainfall prediction(COMFO-DLRP)Technique.The proposed CMFO-DLRP model mainly intends to predict the rainfall and thereby determine the environmental changes.Primarily,data pre-processing and correlation matrix(CM)based feature selection processes are carried out.In addition,deep belief network(DBN)model is applied for the effective prediction of rainfall data.Moreover,COMFO algorithm was derived by integrating the concepts of comprehensive oppositional based learning(COBL)with traditional MFO algorithm.Finally,the COMFO algorithm is employed for the optimal hyperparameter selection of the DBN model.For demonstrating the improved outcomes of the COMFO-DLRP approach,a sequence of simulations were carried out and the outcomes are assessed under distinct measures.The simulation outcome highlighted the enhanced outcomes of the COMFO-DLRP method on the other techniques.展开更多
The recent developments in Multimedia Internet of Things(MIoT)devices,empowered with Natural Language Processing(NLP)model,seem to be a promising future of smart devices.It plays an important role in industrial models...The recent developments in Multimedia Internet of Things(MIoT)devices,empowered with Natural Language Processing(NLP)model,seem to be a promising future of smart devices.It plays an important role in industrial models such as speech understanding,emotion detection,home automation,and so on.If an image needs to be captioned,then the objects in that image,its actions and connections,and any silent feature that remains under-projected or missing from the images should be identified.The aim of the image captioning process is to generate a caption for image.In next step,the image should be provided with one of the most significant and detailed descriptions that is syntactically as well as semantically correct.In this scenario,computer vision model is used to identify the objects and NLP approaches are followed to describe the image.The current study develops aNatural Language Processing with Optimal Deep Learning Enabled Intelligent Image Captioning System(NLPODL-IICS).The aim of the presented NLPODL-IICS model is to produce a proper description for input image.To attain this,the proposed NLPODL-IICS follows two stages such as encoding and decoding processes.Initially,at the encoding side,the proposed NLPODL-IICS model makes use of Hunger Games Search(HGS)with Neural Search Architecture Network(NASNet)model.This model represents the input data appropriately by inserting it into a predefined length vector.Besides,during decoding phase,Chimp Optimization Algorithm(COA)with deeper Long Short Term Memory(LSTM)approach is followed to concatenate the description sentences 4436 CMC,2023,vol.74,no.2 produced by the method.The application of HGS and COA algorithms helps in accomplishing proper parameter tuning for NASNet and LSTM models respectively.The proposed NLPODL-IICS model was experimentally validated with the help of two benchmark datasets.Awidespread comparative analysis confirmed the superior performance of NLPODL-IICS model over other models.展开更多
Melanoma remains a serious illness which is a common formof skin cancer.Since the earlier detection of melanoma reduces the mortality rate,it is essential to design reliable and automated disease diagnosis model using...Melanoma remains a serious illness which is a common formof skin cancer.Since the earlier detection of melanoma reduces the mortality rate,it is essential to design reliable and automated disease diagnosis model using dermoscopic images.The recent advances in deep learning(DL)models find useful to examine the medical image and make proper decisions.In this study,an automated deep learning based melanoma detection and classification(ADL-MDC)model is presented.The goal of the ADL-MDC technique is to examine the dermoscopic images to determine the existence of melanoma.The ADL-MDC technique performs contrast enhancement and data augmentation at the initial stage.Besides,the k-means clustering technique is applied for the image segmentation process.In addition,Adagrad optimizer based Capsule Network(CapsNet)model is derived for effective feature extraction process.Lastly,crow search optimization(CSO)algorithm with sparse autoencoder(SAE)model is utilized for the melanoma classification process.The exploitation of the Adagrad and CSO algorithm helps to properly accomplish improved performance.A wide range of simulation analyses is carried out on benchmark datasets and the results are inspected under several aspects.The simulation results reported the enhanced performance of the ADL-MDC technique over the recent approaches.展开更多
Sign language includes the motion of the arms and hands to communicate with people with hearing disabilities.Several models have been available in the literature for sign language detection and classification for enha...Sign language includes the motion of the arms and hands to communicate with people with hearing disabilities.Several models have been available in the literature for sign language detection and classification for enhanced outcomes.But the latest advancements in computer vision enable us to perform signs/gesture recognition using deep neural networks.This paper introduces an Arabic Sign Language Gesture Classification using Deer Hunting Optimization with Machine Learning(ASLGC-DHOML)model.The presented ASLGC-DHOML technique mainly concentrates on recognising and classifying sign language gestures.The presented ASLGC-DHOML model primarily pre-processes the input gesture images and generates feature vectors using the densely connected network(DenseNet169)model.For gesture recognition and classification,a multilayer perceptron(MLP)classifier is exploited to recognize and classify the existence of sign language gestures.Lastly,the DHO algorithm is utilized for parameter optimization of the MLP model.The experimental results of the ASLGC-DHOML model are tested and the outcomes are inspected under distinct aspects.The comparison analysis highlighted that the ASLGC-DHOML method has resulted in enhanced gesture classification results than other techniques with maximum accuracy of 92.88%.展开更多
Recently,urbanization becomes a major concern for developing as well as developed countries.Owing to the increased urbanization,one of the important challenging issues in smart cities is waste management.So,automated ...Recently,urbanization becomes a major concern for developing as well as developed countries.Owing to the increased urbanization,one of the important challenging issues in smart cities is waste management.So,automated waste detection and classification model becomes necessary for the smart city and to accomplish better recyclable waste management.Effective recycling of waste offers the chance of reducing the quantity of waste disposed to the land fill by minimizing the requirement of collecting raw materials.This study develops a novel Deep Consensus Network with Whale Optimization Algorithm for Recycling Waste Object Detection(DCNWORWOD)in Smart Cities.The goal of the DCNWO-RWOD technique intends to properly identify and classify the objects into recyclable and non-recyclable ones.The proposed DCNWO-RWOD technique involves the design of deep consensus network(DCN)to detect waste objects in the input image.For improving the overall object detection performance of the DCN model,the whale optimization algorithm(WOA)is exploited.Finally,Na飗e Bayes(NB)classifier is used for the classification of detected waste objects into recyclable and non-recyclable ones.The performance validation of theDCNWO-RWOD technique takes place using the open access dataset.The extensive comparative study reported the enhanced performance of the DCNWO-RWOD technique interms of several measures.展开更多
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 an...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.展开更多
Object detection(OD)in remote sensing images(RSI)acts as a vital part in numerous civilian and military application areas,like urban planning,geographic information system(GIS),and search and rescue functions.Vehicle ...Object detection(OD)in remote sensing images(RSI)acts as a vital part in numerous civilian and military application areas,like urban planning,geographic information system(GIS),and search and rescue functions.Vehicle recognition from RSIs remained a challenging process because of the difficulty of background data and the redundancy of recognition regions.The latest advancements in deep learning(DL)approaches permit the design of effectual OD approaches.This study develops an Artificial Ecosystem Optimizer with Deep Convolutional Neural Network for Vehicle Detection(AEODCNN-VD)model on Remote Sensing Images.The proposed AEODCNN-VD model focuses on the identification of vehicles accurately and rapidly.To detect vehicles,the presented AEODCNN-VD model employs single shot detector(SSD)with Inception network as a baseline model.In addition,Multiway Feature Pyramid Network(MFPN)is used for handling objects of varying sizes in RSIs.The features from the Inception model are passed into theMFPNformultiway andmultiscale feature fusion.Finally,the fused features are passed into bounding box and class prediction networks.For enhancing the detection efficiency of the AEODCNN-VD approach,AEO based hyperparameter optimizer is used,which is stimulated by the energy transfer strategies such as production,consumption,and decomposition in an ecosystem.The performance validation of the presentedmethod on benchmark datasets showed promising performance over recent DL models.展开更多
Text classification or categorization is the procedure of automatically tagging a textual document with most related labels or classes.When the number of labels is limited to one,the task becomes single-label text cat...Text classification or categorization is the procedure of automatically tagging a textual document with most related labels or classes.When the number of labels is limited to one,the task becomes single-label text categorization.The Arabic texts include unstructured information also like English texts,and that is understandable for machine learning(ML)techniques,the text is changed and demonstrated by numerical value.In recent times,the dominant method for natural language processing(NLP)tasks is recurrent neural network(RNN),in general,long short termmemory(LSTM)and convolutional neural network(CNN).Deep learning(DL)models are currently presented for deriving a massive amount of text deep features to an optimum performance from distinct domains such as text detection,medical image analysis,and so on.This paper introduces aModified Dragonfly Optimization with Extreme Learning Machine for Text Representation and Recognition(MDFO-EMTRR)model onArabicCorpus.The presentedMDFO-EMTRR technique mainly concentrates on the recognition and classification of the Arabic text.To achieve this,theMDFO-EMTRRtechnique encompasses data pre-processing to transform the input data into compatible format.Next,the ELM model is utilized for the representation and recognition of the Arabic text.At last,the MDFO algorithm was exploited for optimal tuning of the parameters related to the ELM method and thereby accomplish enhanced classifier results.The experimental result analysis of the MDFO-EMTRR system was performed on benchmark datasets and attained maximum accuracy of 99.74%.展开更多
As the Internet of Things(IoT)endures to develop,a huge count of data has been created.An IoT platform is rather sensitive to security challenges as individual data can be leaked,or sensor data could be used to cause ...As the Internet of Things(IoT)endures to develop,a huge count of data has been created.An IoT platform is rather sensitive to security challenges as individual data can be leaked,or sensor data could be used to cause accidents.As typical intrusion detection system(IDS)studies can be frequently designed for working well on databases,it can be unknown if they intend to work well in altering network environments.Machine learning(ML)techniques are depicted to have a higher capacity at assisting mitigate an attack on IoT device and another edge system with reasonable accuracy.This article introduces a new Bird Swarm Algorithm with Wavelet Neural Network for Intrusion Detection(BSAWNN-ID)in the IoT platform.The main intention of the BSAWNN-ID algorithm lies in detecting and classifying intrusions in the IoT platform.The BSAWNN-ID technique primarily designs a feature subset selection using the coyote optimization algorithm(FSS-COA)to attain this.Next,to detect intrusions,the WNN model is utilized.At last,theWNNparameters are optimally modified by the use of BSA.Awidespread experiment is performed to depict the better performance of the BSAWNNID technique.The resultant values indicated the better performance of the BSAWNN-ID technique over other models,with an accuracy of 99.64%on the UNSW-NB15 dataset.展开更多
Wireless Sensor Network(WSN)consists of a group of limited energy source sensors that are installed in a particular region to collect data from the environment.Designing the energy-efficient data collection methods in...Wireless Sensor Network(WSN)consists of a group of limited energy source sensors that are installed in a particular region to collect data from the environment.Designing the energy-efficient data collection methods in largescale wireless sensor networks is considered to be a difficult area in the research.Sensor node clustering is a popular approach for WSN.Moreover,the sensor nodes are grouped to form clusters in a cluster-based WSN environment.The battery performance of the sensor nodes is likewise constrained.As a result,the energy efficiency of WSNs is critical.In specific,the energy usage is influenced by the loads on the sensor node as well as it ranges from the Base Station(BS).Therefore,energy efficiency and load balancing are very essential in WSN.In the proposed method,a novel Grey Wolf Improved Particle Swarm Optimization with Tabu Search Techniques(GW-IPSO-TS)was used.The selection of Cluster Heads(CHs)and routing path of every CH from the base station is enhanced by the proposed method.It provides the best routing path and increases the lifetime and energy efficiency of the network.End-to-end delay and packet loss rate have also been improved.The proposed GW-IPSO-TS method enhances the evaluation of alive nodes,dead nodes,network survival index,convergence rate,and standard deviation of sensor nodes.Compared to the existing algorithms,the proposed method outperforms better and improves the lifetime of the network.展开更多
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)so...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%.展开更多
Arabic is one of the most spoken languages across the globe.However,there are fewer studies concerning Sentiment Analysis(SA)in Arabic.In recent years,the detected sentiments and emotions expressed in tweets have rece...Arabic is one of the most spoken languages across the globe.However,there are fewer studies concerning Sentiment Analysis(SA)in Arabic.In recent years,the detected sentiments and emotions expressed in tweets have received significant interest.The substantial role played by the Arab region in international politics and the global economy has urged the need to examine the sentiments and emotions in the Arabic language.Two common models are available:Machine Learning and lexicon-based approaches to address emotion classification problems.With this motivation,the current research article develops a Teaching and Learning Optimization with Machine Learning Based Emotion Recognition and Classification(TLBOML-ERC)model for Sentiment Analysis on tweets made in the Arabic language.The presented TLBOML-ERC model focuses on recognising emotions and sentiments expressed in Arabic tweets.To attain this,the proposed TLBOMLERC model initially carries out data pre-processing and a Continuous Bag Of Words(CBOW)-based word embedding process.In addition,Denoising Autoencoder(DAE)model is also exploited to categorise different emotions expressed in Arabic tweets.To improve the efficacy of the DAE model,the Teaching and Learning-based Optimization(TLBO)algorithm is utilized to optimize the parameters.The proposed TLBOML-ERC method was experimentally validated with the help of an Arabic tweets dataset.The obtained results show the promising performance of the proposed TLBOML-ERC model on Arabic emotion classification.展开更多
Sleep plays a vital role in optimum working of the brain and the body.Numerous people suffer from sleep-oriented illnesses like apnea,insomnia,etc.Sleep stage classification is a primary process in the quantitative ex...Sleep plays a vital role in optimum working of the brain and the body.Numerous people suffer from sleep-oriented illnesses like apnea,insomnia,etc.Sleep stage classification is a primary process in the quantitative examination of polysomnographic recording.Sleep stage scoring is mainly based on experts’knowledge which is laborious and time consuming.Hence,it can be essential to design automated sleep stage classification model using machine learning(ML)and deep learning(DL)approaches.In this view,this study focuses on the design of Competitive Multi-verse Optimization with Deep Learning Based Sleep Stage Classification(CMVODL-SSC)model using Electroencephalogram(EEG)signals.The proposed CMVODL-SSC model intends to effectively categorize different sleep stages on EEG signals.Primarily,data pre-processing is performed to convert the actual data into useful format.Besides,a cascaded long short term memory(CLSTM)model is employed to perform classification process.At last,the CMVO algorithm is utilized for optimally tuning the hyperparameters involved in the CLSTM model.In order to report the enhancements of the CMVODL-SSC model,a wide range of simulations was carried out and the results ensured the better performance of the CMVODL-SSC model with average accuracy of 96.90%.展开更多
Currently,individuals use online social media,namely Facebook or Twitter,for sharing their thoughts and emotions.Detection of emotions on social networking sites’finds useful in several applications in social welfare...Currently,individuals use online social media,namely Facebook or Twitter,for sharing their thoughts and emotions.Detection of emotions on social networking sites’finds useful in several applications in social welfare,commerce,public health,and so on.Emotion is expressed in several means,like facial and speech expressions,gestures,and written text.Emotion recognition in a text document is a content-based classification problem that includes notions from deep learning(DL)and natural language processing(NLP)domains.This article proposes a Deer HuntingOptimizationwithDeep Belief Network Enabled Emotion Classification(DHODBN-EC)on English Twitter Data in this study.The presented DHODBN-EC model aims to examine the existence of distinct emotion classes in tweets.At the introductory level,the DHODBN-EC technique pre-processes the tweets at different levels.Besides,the word2vec feature extraction process is applied to generate the word embedding process.For emotion classification,the DHODBN-EC model utilizes the DBN model,which helps to determine distinct emotion class labels.Lastly,the DHO algorithm is leveraged for optimal hyperparameter adjustment of the DBN technique.An extensive range of experimental analyses can be executed to demonstrate the enhanced performance of the DHODBN-EC approach.A comprehensive comparison study exhibited the improvements of the DHODBN-EC model over other approaches with increased accuracy of 96.67%.展开更多
Due to exponential increase in smart resource limited devices and high speed communication technologies,Internet of Things(IoT)have received significant attention in different application areas.However,IoT environment...Due to exponential increase in smart resource limited devices and high speed communication technologies,Internet of Things(IoT)have received significant attention in different application areas.However,IoT environment is highly susceptible to cyber-attacks because of memory,processing,and communication restrictions.Since traditional models are not adequate for accomplishing security in the IoT environment,the recent developments of deep learning(DL)models find beneficial.This study introduces novel hybrid metaheuristics feature selection with stacked deep learning enabled cyber-attack detection(HMFS-SDLCAD)model.The major intention of the HMFS-SDLCAD model is to recognize the occurrence of cyberattacks in the IoT environment.At the preliminary stage,data pre-processing is carried out to transform the input data into useful format.In addition,salp swarm optimization based on particle swarm optimization(SSOPSO)algorithm is used for feature selection process.Besides,stacked bidirectional gated recurrent unit(SBiGRU)model is utilized for the identification and classification of cyberattacks.Finally,whale optimization algorithm(WOA)is employed for optimal hyperparameter optimization process.The experimental analysis of the HMFS-SDLCAD model is validated using benchmark dataset and the results are assessed under several aspects.The simulation outcomes pointed out the improvements of the HMFS-SDLCAD model over recent approaches.展开更多
基金The authors thank 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 UniversityResearchers Supporting Project number(PNURSP2022R281)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research atUmmAl-Qura University for supporting this work by Grant Code:(22UQU4331004DSR06).
文摘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.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R237)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:(22UQU4331004DSR13).
文摘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.
基金the Deanship of Scientific Research at King Khalid University for funding this work underGrant Number(RGP 2/209/42)PrincessNourah 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:(22UQU4210118DSR27).
文摘Proper waste management models using recent technologies like computer vision,machine learning(ML),and deep learning(DL)are needed to effectively handle the massive quantity of increasing waste.Therefore,waste classification becomes a crucial topic which helps to categorize waste into hazardous or non-hazardous ones and thereby assist in the decision making of the waste management process.This study concentrates on the design of hazardous waste detection and classification using ensemble learning(HWDC-EL)technique to reduce toxicity and improve human health.The goal of the HWDC-EL technique is to detect the multiple classes of wastes,particularly hazardous and non-hazardous wastes.The HWDC-EL technique involves the ensemble of three feature extractors using Model Averaging technique namely discrete local binary patterns(DLBP),EfficientNet,and DenseNet121.In addition,the flower pollination algorithm(FPA)based hyperparameter optimizers are used to optimally adjust the parameters involved in the EfficientNet and DenseNet121 models.Moreover,a weighted voting-based ensemble classifier is derived using three machine learning algorithms namely support vector machine(SVM),extreme learning machine(ELM),and gradient boosting tree(GBT).The performance of the HWDC-EL technique is tested using a benchmark Garbage dataset and it obtains a maximum accuracy of 98.85%.
基金the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups Project under grant number(45/43)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:22UQU4310373DSR21.
文摘Cybersecurity-related solutions have become familiar since it ensures security and privacy against cyberattacks in this digital era.Malicious Uniform Resource Locators(URLs)can be embedded in email or Twitter and used to lure vulnerable internet users to implement malicious data in their systems.This may result in compromised security of the systems,scams,and other such cyberattacks.These attacks hijack huge quantities of the available data,incurring heavy financial loss.At the same time,Machine Learning(ML)and Deep Learning(DL)models paved the way for designing models that can detect malicious URLs accurately and classify them.With this motivation,the current article develops an Artificial Fish Swarm Algorithm(AFSA)with Deep Learning Enabled Malicious URL Detection and Classification(AFSADL-MURLC)model.The presented AFSADL-MURLC model intends to differentiate the malicious URLs from genuine URLs.To attain this,AFSADL-MURLC model initially carries out data preprocessing and makes use of glove-based word embedding technique.In addition,the created vector model is then passed onto Gated Recurrent Unit(GRU)classification to recognize the malicious URLs.Finally,AFSA is applied to the proposed model to enhance the efficiency of GRU model.The proposed AFSADL-MURLC technique was experimentally validated using benchmark dataset sourced from Kaggle repository.The simulation results confirmed the supremacy of the proposed AFSADL-MURLC model over recent approaches under distinct measures.
基金the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups Project under Grant Number(25/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R303)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:22UQU4340237DSR28.
文摘Hyperspectral remote sensing/imaging spectroscopy is a novel approach to reaching a spectrum from all the places of a huge array of spatial places so that several spectral wavelengths are utilized for making coherent images.Hyperspectral remote sensing contains acquisition of digital images from several narrow,contiguous spectral bands throughout the visible,Thermal Infrared(TIR),Near Infrared(NIR),and Mid-Infrared(MIR)regions of the electromagnetic spectrum.In order to the application of agricultural regions,remote sensing approaches are studied and executed to their benefit of continuous and quantitativemonitoring.Particularly,hyperspectral images(HSI)are considered the precise for agriculture as they can offer chemical and physical data on vegetation.With this motivation,this article presents a novel Hurricane Optimization Algorithm with Deep Transfer Learning Driven Crop Classification(HOADTL-CC)model onHyperspectralRemote Sensing Images.The presentedHOADTL-CC model focuses on the identification and categorization of crops on hyperspectral remote sensing images.To accomplish this,the presentedHOADTL-CC model involves the design ofHOAwith capsule network(CapsNet)model for generating a set of useful feature vectors.Besides,Elman neural network(ENN)model is applied to allot proper class labels into the input HSI.Finally,glowworm swarm optimization(GSO)algorithm is exploited to fine tune the ENNparameters involved in this article.The experimental result scrutiny of the HOADTL-CC method can be tested with the help of benchmark dataset and the results are assessed under distinct aspects.Extensive comparative studies stated the enhanced performance of the HOADTL-CC model over recent approaches with maximum accuracy of 99.51%.
基金the Deanship of Scientific Research at King Khalid University for funding this work under grant number(RGP 2/180/43)Princess Nourah bint Abdulrahman UniversityResearchers Supporting Project number(PNURSP2022R235)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research atUmmAl-Qura University for supporting this work by Grant Code:(22UQU4270206DSR01).
文摘Data mining process involves a number of steps fromdata collection to visualization to identify useful data from massive data set.the same time,the recent advances of machine learning(ML)and deep learning(DL)models can be utilized for effectual rainfall prediction.With this motivation,this article develops a novel comprehensive oppositionalmoth flame optimization with deep learning for rainfall prediction(COMFO-DLRP)Technique.The proposed CMFO-DLRP model mainly intends to predict the rainfall and thereby determine the environmental changes.Primarily,data pre-processing and correlation matrix(CM)based feature selection processes are carried out.In addition,deep belief network(DBN)model is applied for the effective prediction of rainfall data.Moreover,COMFO algorithm was derived by integrating the concepts of comprehensive oppositional based learning(COBL)with traditional MFO algorithm.Finally,the COMFO algorithm is employed for the optimal hyperparameter selection of the DBN model.For demonstrating the improved outcomes of the COMFO-DLRP approach,a sequence of simulations were carried out and the outcomes are assessed under distinct measures.The simulation outcome highlighted the enhanced outcomes of the COMFO-DLRP method on the other techniques.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R161)PrincessNourah 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:(22UQU4310373DSR33).
文摘The recent developments in Multimedia Internet of Things(MIoT)devices,empowered with Natural Language Processing(NLP)model,seem to be a promising future of smart devices.It plays an important role in industrial models such as speech understanding,emotion detection,home automation,and so on.If an image needs to be captioned,then the objects in that image,its actions and connections,and any silent feature that remains under-projected or missing from the images should be identified.The aim of the image captioning process is to generate a caption for image.In next step,the image should be provided with one of the most significant and detailed descriptions that is syntactically as well as semantically correct.In this scenario,computer vision model is used to identify the objects and NLP approaches are followed to describe the image.The current study develops aNatural Language Processing with Optimal Deep Learning Enabled Intelligent Image Captioning System(NLPODL-IICS).The aim of the presented NLPODL-IICS model is to produce a proper description for input image.To attain this,the proposed NLPODL-IICS follows two stages such as encoding and decoding processes.Initially,at the encoding side,the proposed NLPODL-IICS model makes use of Hunger Games Search(HGS)with Neural Search Architecture Network(NASNet)model.This model represents the input data appropriately by inserting it into a predefined length vector.Besides,during decoding phase,Chimp Optimization Algorithm(COA)with deeper Long Short Term Memory(LSTM)approach is followed to concatenate the description sentences 4436 CMC,2023,vol.74,no.2 produced by the method.The application of HGS and COA algorithms helps in accomplishing proper parameter tuning for NASNet and LSTM models respectively.The proposed NLPODL-IICS model was experimentally validated with the help of two benchmark datasets.Awidespread comparative analysis confirmed the superior performance of NLPODL-IICS model over other models.
基金the Deanship of Scientific Research at King Khalid University for funding this work under Grant Number(RGP 1/80/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R191)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Melanoma remains a serious illness which is a common formof skin cancer.Since the earlier detection of melanoma reduces the mortality rate,it is essential to design reliable and automated disease diagnosis model using dermoscopic images.The recent advances in deep learning(DL)models find useful to examine the medical image and make proper decisions.In this study,an automated deep learning based melanoma detection and classification(ADL-MDC)model is presented.The goal of the ADL-MDC technique is to examine the dermoscopic images to determine the existence of melanoma.The ADL-MDC technique performs contrast enhancement and data augmentation at the initial stage.Besides,the k-means clustering technique is applied for the image segmentation process.In addition,Adagrad optimizer based Capsule Network(CapsNet)model is derived for effective feature extraction process.Lastly,crow search optimization(CSO)algorithm with sparse autoencoder(SAE)model is utilized for the melanoma classification process.The exploitation of the Adagrad and CSO algorithm helps to properly accomplish improved performance.A wide range of simulation analyses is carried out on benchmark datasets and the results are inspected under several aspects.The simulation results reported the enhanced performance of the ADL-MDC technique over the recent approaches.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2023R263)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia+1 种基金The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura Universitysupporting this work by Grant Code:22UQU4310373DSR54.
文摘Sign language includes the motion of the arms and hands to communicate with people with hearing disabilities.Several models have been available in the literature for sign language detection and classification for enhanced outcomes.But the latest advancements in computer vision enable us to perform signs/gesture recognition using deep neural networks.This paper introduces an Arabic Sign Language Gesture Classification using Deer Hunting Optimization with Machine Learning(ASLGC-DHOML)model.The presented ASLGC-DHOML technique mainly concentrates on recognising and classifying sign language gestures.The presented ASLGC-DHOML model primarily pre-processes the input gesture images and generates feature vectors using the densely connected network(DenseNet169)model.For gesture recognition and classification,a multilayer perceptron(MLP)classifier is exploited to recognize and classify the existence of sign language gestures.Lastly,the DHO algorithm is utilized for parameter optimization of the MLP model.The experimental results of the ASLGC-DHOML model are tested and the outcomes are inspected under distinct aspects.The comparison analysis highlighted that the ASLGC-DHOML method has resulted in enhanced gesture classification results than other techniques with maximum accuracy of 92.88%.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number(RGP2/42/43)Princess Nourah bint Abdulrahman UniversityResearchers Supporting Project number(PNURSP2022R114)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Recently,urbanization becomes a major concern for developing as well as developed countries.Owing to the increased urbanization,one of the important challenging issues in smart cities is waste management.So,automated waste detection and classification model becomes necessary for the smart city and to accomplish better recyclable waste management.Effective recycling of waste offers the chance of reducing the quantity of waste disposed to the land fill by minimizing the requirement of collecting raw materials.This study develops a novel Deep Consensus Network with Whale Optimization Algorithm for Recycling Waste Object Detection(DCNWORWOD)in Smart Cities.The goal of the DCNWO-RWOD technique intends to properly identify and classify the objects into recyclable and non-recyclable ones.The proposed DCNWO-RWOD technique involves the design of deep consensus network(DCN)to detect waste objects in the input image.For improving the overall object detection performance of the DCN model,the whale optimization algorithm(WOA)is exploited.Finally,Na飗e Bayes(NB)classifier is used for the classification of detected waste objects into recyclable and non-recyclable ones.The performance validation of theDCNWO-RWOD technique takes place using the open access dataset.The extensive comparative study reported the enhanced performance of the DCNWO-RWOD technique interms of several measures.
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(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:(22UQU4331004DSR32).
文摘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.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R136)PrincessNourah 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:(22UQU4210118DSR28).
文摘Object detection(OD)in remote sensing images(RSI)acts as a vital part in numerous civilian and military application areas,like urban planning,geographic information system(GIS),and search and rescue functions.Vehicle recognition from RSIs remained a challenging process because of the difficulty of background data and the redundancy of recognition regions.The latest advancements in deep learning(DL)approaches permit the design of effectual OD approaches.This study develops an Artificial Ecosystem Optimizer with Deep Convolutional Neural Network for Vehicle Detection(AEODCNN-VD)model on Remote Sensing Images.The proposed AEODCNN-VD model focuses on the identification of vehicles accurately and rapidly.To detect vehicles,the presented AEODCNN-VD model employs single shot detector(SSD)with Inception network as a baseline model.In addition,Multiway Feature Pyramid Network(MFPN)is used for handling objects of varying sizes in RSIs.The features from the Inception model are passed into theMFPNformultiway andmultiscale feature fusion.Finally,the fused features are passed into bounding box and class prediction networks.For enhancing the detection efficiency of the AEODCNN-VD approach,AEO based hyperparameter optimizer is used,which is stimulated by the energy transfer strategies such as production,consumption,and decomposition in an ecosystem.The performance validation of the presentedmethod on benchmark datasets showed promising performance over recent DL models.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R263),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabiathe Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:22UQU4340237DSR35.
文摘Text classification or categorization is the procedure of automatically tagging a textual document with most related labels or classes.When the number of labels is limited to one,the task becomes single-label text categorization.The Arabic texts include unstructured information also like English texts,and that is understandable for machine learning(ML)techniques,the text is changed and demonstrated by numerical value.In recent times,the dominant method for natural language processing(NLP)tasks is recurrent neural network(RNN),in general,long short termmemory(LSTM)and convolutional neural network(CNN).Deep learning(DL)models are currently presented for deriving a massive amount of text deep features to an optimum performance from distinct domains such as text detection,medical image analysis,and so on.This paper introduces aModified Dragonfly Optimization with Extreme Learning Machine for Text Representation and Recognition(MDFO-EMTRR)model onArabicCorpus.The presentedMDFO-EMTRR technique mainly concentrates on the recognition and classification of the Arabic text.To achieve this,theMDFO-EMTRRtechnique encompasses data pre-processing to transform the input data into compatible format.Next,the ELM model is utilized for the representation and recognition of the Arabic text.At last,the MDFO algorithm was exploited for optimal tuning of the parameters related to the ELM method and thereby accomplish enhanced classifier results.The experimental result analysis of the MDFO-EMTRR system was performed on benchmark datasets and attained maximum accuracy of 99.74%.
基金This work was funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University,through the Research Groups Program Grant No.(RGP-1443-0048).
文摘As the Internet of Things(IoT)endures to develop,a huge count of data has been created.An IoT platform is rather sensitive to security challenges as individual data can be leaked,or sensor data could be used to cause accidents.As typical intrusion detection system(IDS)studies can be frequently designed for working well on databases,it can be unknown if they intend to work well in altering network environments.Machine learning(ML)techniques are depicted to have a higher capacity at assisting mitigate an attack on IoT device and another edge system with reasonable accuracy.This article introduces a new Bird Swarm Algorithm with Wavelet Neural Network for Intrusion Detection(BSAWNN-ID)in the IoT platform.The main intention of the BSAWNN-ID algorithm lies in detecting and classifying intrusions in the IoT platform.The BSAWNN-ID technique primarily designs a feature subset selection using the coyote optimization algorithm(FSS-COA)to attain this.Next,to detect intrusions,the WNN model is utilized.At last,theWNNparameters are optimally modified by the use of BSA.Awidespread experiment is performed to depict the better performance of the BSAWNNID technique.The resultant values indicated the better performance of the BSAWNN-ID technique over other models,with an accuracy of 99.64%on the UNSW-NB15 dataset.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Larg Groups project Under Grant Number(71/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R238)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:22UQU4340237DSR20.
文摘Wireless Sensor Network(WSN)consists of a group of limited energy source sensors that are installed in a particular region to collect data from the environment.Designing the energy-efficient data collection methods in largescale wireless sensor networks is considered to be a difficult area in the research.Sensor node clustering is a popular approach for WSN.Moreover,the sensor nodes are grouped to form clusters in a cluster-based WSN environment.The battery performance of the sensor nodes is likewise constrained.As a result,the energy efficiency of WSNs is critical.In specific,the energy usage is influenced by the loads on the sensor node as well as it ranges from the Base Station(BS).Therefore,energy efficiency and load balancing are very essential in WSN.In the proposed method,a novel Grey Wolf Improved Particle Swarm Optimization with Tabu Search Techniques(GW-IPSO-TS)was used.The selection of Cluster Heads(CHs)and routing path of every CH from the base station is enhanced by the proposed method.It provides the best routing path and increases the lifetime and energy efficiency of the network.End-to-end delay and packet loss rate have also been improved.The proposed GW-IPSO-TS method enhances the evaluation of alive nodes,dead nodes,network survival index,convergence rate,and standard deviation of sensor nodes.Compared to the existing algorithms,the proposed method outperforms better and improves the lifetime of the network.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through the Large Groups Project under grant number(71/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2023R203)+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:22UQU4340237DSR61This study is supported via funding from Prince Sattam bin Abdulaziz University project number(PSAU/2023/R/1444).
文摘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%.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R263)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:22UQU4340237DSR36The authors are thankful to the Deanship of Scientific Research at Najran University for funding thiswork under theResearch Groups Funding program grant code(NU/RG/SERC/11/7).
文摘Arabic is one of the most spoken languages across the globe.However,there are fewer studies concerning Sentiment Analysis(SA)in Arabic.In recent years,the detected sentiments and emotions expressed in tweets have received significant interest.The substantial role played by the Arab region in international politics and the global economy has urged the need to examine the sentiments and emotions in the Arabic language.Two common models are available:Machine Learning and lexicon-based approaches to address emotion classification problems.With this motivation,the current research article develops a Teaching and Learning Optimization with Machine Learning Based Emotion Recognition and Classification(TLBOML-ERC)model for Sentiment Analysis on tweets made in the Arabic language.The presented TLBOML-ERC model focuses on recognising emotions and sentiments expressed in Arabic tweets.To attain this,the proposed TLBOMLERC model initially carries out data pre-processing and a Continuous Bag Of Words(CBOW)-based word embedding process.In addition,Denoising Autoencoder(DAE)model is also exploited to categorise different emotions expressed in Arabic tweets.To improve the efficacy of the DAE model,the Teaching and Learning-based Optimization(TLBO)algorithm is utilized to optimize the parameters.The proposed TLBOML-ERC method was experimentally validated with the help of an Arabic tweets dataset.The obtained results show the promising performance of the proposed TLBOML-ERC model on Arabic emotion classification.
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(PNURSP2022R235)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:(22UQU4340237DSR10).
文摘Sleep plays a vital role in optimum working of the brain and the body.Numerous people suffer from sleep-oriented illnesses like apnea,insomnia,etc.Sleep stage classification is a primary process in the quantitative examination of polysomnographic recording.Sleep stage scoring is mainly based on experts’knowledge which is laborious and time consuming.Hence,it can be essential to design automated sleep stage classification model using machine learning(ML)and deep learning(DL)approaches.In this view,this study focuses on the design of Competitive Multi-verse Optimization with Deep Learning Based Sleep Stage Classification(CMVODL-SSC)model using Electroencephalogram(EEG)signals.The proposed CMVODL-SSC model intends to effectively categorize different sleep stages on EEG signals.Primarily,data pre-processing is performed to convert the actual data into useful format.Besides,a cascaded long short term memory(CLSTM)model is employed to perform classification process.At last,the CMVO algorithm is utilized for optimally tuning the hyperparameters involved in the CLSTM model.In order to report the enhancements of the CMVODL-SSC model,a wide range of simulations was carried out and the results ensured the better performance of the CMVODL-SSC model with average accuracy of 96.90%.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project Number (PNURSP2022R281)Princess Nourah bint Abdulrahman University,Riyadh,Saudi ArabiaDeanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code: (22UQU4340237DSR61).
文摘Currently,individuals use online social media,namely Facebook or Twitter,for sharing their thoughts and emotions.Detection of emotions on social networking sites’finds useful in several applications in social welfare,commerce,public health,and so on.Emotion is expressed in several means,like facial and speech expressions,gestures,and written text.Emotion recognition in a text document is a content-based classification problem that includes notions from deep learning(DL)and natural language processing(NLP)domains.This article proposes a Deer HuntingOptimizationwithDeep Belief Network Enabled Emotion Classification(DHODBN-EC)on English Twitter Data in this study.The presented DHODBN-EC model aims to examine the existence of distinct emotion classes in tweets.At the introductory level,the DHODBN-EC technique pre-processes the tweets at different levels.Besides,the word2vec feature extraction process is applied to generate the word embedding process.For emotion classification,the DHODBN-EC model utilizes the DBN model,which helps to determine distinct emotion class labels.Lastly,the DHO algorithm is leveraged for optimal hyperparameter adjustment of the DBN technique.An extensive range of experimental analyses can be executed to demonstrate the enhanced performance of the DHODBN-EC approach.A comprehensive comparison study exhibited the improvements of the DHODBN-EC model over other approaches with increased accuracy of 96.67%.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups Project under grant number(45/43)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:(22UQU4310373DSR16).
文摘Due to exponential increase in smart resource limited devices and high speed communication technologies,Internet of Things(IoT)have received significant attention in different application areas.However,IoT environment is highly susceptible to cyber-attacks because of memory,processing,and communication restrictions.Since traditional models are not adequate for accomplishing security in the IoT environment,the recent developments of deep learning(DL)models find beneficial.This study introduces novel hybrid metaheuristics feature selection with stacked deep learning enabled cyber-attack detection(HMFS-SDLCAD)model.The major intention of the HMFS-SDLCAD model is to recognize the occurrence of cyberattacks in the IoT environment.At the preliminary stage,data pre-processing is carried out to transform the input data into useful format.In addition,salp swarm optimization based on particle swarm optimization(SSOPSO)algorithm is used for feature selection process.Besides,stacked bidirectional gated recurrent unit(SBiGRU)model is utilized for the identification and classification of cyberattacks.Finally,whale optimization algorithm(WOA)is employed for optimal hyperparameter optimization process.The experimental analysis of the HMFS-SDLCAD model is validated using benchmark dataset and the results are assessed under several aspects.The simulation outcomes pointed out the improvements of the HMFS-SDLCAD model over recent approaches.
