Energy is an essential element for any civilized country’s social and economic development,but the use of fossil fuels and nonrenewable energy forms has many negative impacts on the environment and the ecosystem.The ...Energy is an essential element for any civilized country’s social and economic development,but the use of fossil fuels and nonrenewable energy forms has many negative impacts on the environment and the ecosystem.The Republic of Yemen has very good potential to use renewable energy.Unfortunately,we find few studies on renewable wind energy in Yemen.Given the lack of a similar analysis for the coastal city,this research newly investigates wind energy’s potential near the Almukalla area by analyzing wind characteristics.Thus,evaluation,model identification,determination of available energy density,computing the capacity factors for several wind turbines and calculation of wind energy were extracted at three heights of 15,30,and 50meters.Average wind speeds were obtained only for the currently available data of five recent years,2005–2009.This study involves a preliminary assessment of Almukalla’s wind energy potential to provide a primary base and useful insights for wind engineers and experts.This research aims to provide useful assessment of the potential of wind energy in Almukalla for developing wind energy and an efficient wind approach.The Weibull distribution shows a perfect approximation for estimating the intensity of Yemen’s wind energy.Depending on both theWeibullmodel and the results of the annual wind speed data analysis for the study site in Mukalla,the capacity factor for many turbines was also calculated,and the best suitable turbine was selected.According to the International Wind Energy Rating criteria,Almukalla falls under Category 7,which is,rated“Superb”most of the year.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
Sign language recognition can be considered as an effective solution for disabled people to communicate with others.It helps them in conveying the intended information using sign languages without any challenges.Recen...Sign language recognition can be considered as an effective solution for disabled people to communicate with others.It helps them in conveying the intended information using sign languages without any challenges.Recent advancements in computer vision and image processing techniques can be leveraged to detect and classify the signs used by disabled people in an effective manner.Metaheuristic optimization algorithms can be designed in a manner such that it fine tunes the hyper parameters,used in Deep Learning(DL)models as the latter considerably impacts the classification results.With this motivation,the current study designs the Optimal Deep Transfer Learning Driven Sign Language Recognition and Classification(ODTL-SLRC)model for disabled people.The aim of the proposed ODTL-SLRC technique is to recognize and classify sign languages used by disabled people.The proposed ODTL-SLRC technique derives EfficientNet model to generate a collection of useful feature vectors.In addition,the hyper parameters involved in EfficientNet model are fine-tuned with the help of HGSO algorithm.Moreover,Bidirectional Long Short Term Memory(BiLSTM)technique is employed for sign language classification.The proposed ODTL-SLRC technique was experimentally validated using benchmark dataset and the results were inspected under several measures.The comparative analysis results established the superior performance of the proposed ODTL-SLRC technique over recent approaches in terms of efficiency.展开更多
With the advent of the Internet of Things(IoT),several devices like sensors nowadays can interact and easily share information.But the IoT model is prone to security concerns as several attackers try to hit the networ...With the advent of the Internet of Things(IoT),several devices like sensors nowadays can interact and easily share information.But the IoT model is prone to security concerns as several attackers try to hit the network and make it vulnerable.In such scenarios,security concern is the most prominent.Different models were intended to address these security problems;still,several emergent variants of botnet attacks like Bashlite,Mirai,and Persirai use security breaches.The malware classification and detection in the IoT model is still a problem,as the adversary reliably generates a new variant of IoT malware and actively searches for compromise on the victim devices.This article develops a Sine Cosine Algorithm with Deep Learning based Ransomware Detection and Classification(SCADL-RWDC)method in an IoT environment.In the presented SCADL-RWDCtechnique,the major intention exists in recognizing and classifying ransomware attacks in the IoT platform.The SCADL-RWDC technique uses the SCA feature selection(SCA-FS)model to improve the detection rate.Besides,the SCADL-RWDC technique exploits the hybrid grey wolf optimizer(HGWO)with a gated recurrent unit(GRU)model for ransomware classification.A widespread experimental analysis is performed to exhibit the enhanced ransomware detection outcomes of the SCADL-RWDC technique.The comparison study reported the enhancement of the SCADL-RWDC technique over other models.展开更多
Earlier notification and fire detection methods provide safety information and fire prevention to blind and visually impaired(BVI)individuals in a limited timeframe in the event of emergencies,particularly in enclosed...Earlier notification and fire detection methods provide safety information and fire prevention to blind and visually impaired(BVI)individuals in a limited timeframe in the event of emergencies,particularly in enclosed areas.Fire detection becomes crucial as it directly impacts human safety and the environment.While modern technology requires precise techniques for early detection to prevent damage and loss,few research has focused on artificial intelligence(AI)-based early fire alert systems for BVI individuals in indoor settings.To prevent such fire incidents,it is crucial to identify fires accurately and promptly,and alert BVI personnel using a combination of smart glasses,deep learning(DL),and computer vision(CV).The most recent technologies require effective methods to identify fires quickly,preventing damage and physical loss.In this manuscript,an Enhanced Fire Detection System for Blind and Visually Challenged People using Artificial Intelligence with Deep Convolutional Neural Networks(EFDBVC-AIDCNN)model is presented.The EFDBVC-AIDCNN model presents an advanced fire detection system that utilizes AI to detect and classify fire hazards for BVI people effectively.Initially,image pre-processing is performed using the Gabor filter(GF)model to improve texture details and patterns specific to flames and smoke.For the feature extractor,the Swin transformer(ST)model captures fine details across multiple scales to represent fire patterns accurately.Furthermore,the Elman neural network(ENN)technique is implemented to detect fire.The improved whale optimization algorithm(IWOA)is used to efficiently tune ENN parameters,improving accuracy and robustness across varying lighting and environmental conditions to optimize performance.An extensive experimental study of the EFDBVC-AIDCNN technique is accomplished under the fire detection dataset.A short comparative analysis of the EFDBVC-AIDCNN approach portrayed a superior accuracy value of 96.60%over existing models.展开更多
Short-term traffic flow prediction (TFP) is an important area inintelligent transportation system (ITS), which is used to reduce traffic congestion. But the avail of traffic flow data with temporal features and period...Short-term traffic flow prediction (TFP) is an important area inintelligent transportation system (ITS), which is used to reduce traffic congestion. But the avail of traffic flow data with temporal features and periodicfeatures are susceptible to weather conditions, making TFP a challengingissue. TFP process are significantly influenced by several factors like accidentand weather. Particularly, the inclement weather conditions may have anextreme impact on travel time and traffic flow. Since most of the existing TFPtechniques do not consider the impact of weather conditions on the TF, it isneeded to develop effective TFP with the consideration of extreme weatherconditions. In this view, this paper designs an artificial intelligence based TFPwith weather conditions (AITFP-WC) for smart cities. The goal of the AITFPWC model is to enhance the performance of the TFP model with the inclusionof weather related conditions. The proposed AITFP-WC technique includesElman neural network (ENN) model to predict the flow of traffic in smartcities. Besides, tunicate swarm algorithm with feed forward neural networks(TSA-FFNN) model is employed for the weather and periodicity analysis. Atlast, a fusion of TFP and WPA processes takes place using the FFNN modelto determine the final prediction output. In order to assess the enhancedpredictive outcome of the AITFP-WC model, an extensive simulation analysisis carried out. The experimental values highlighted the enhanced performanceof the AITFP-WC technique over the recent state of art methods.展开更多
The unstructured growth of abnormal cells in the lung tissue creates tumor.The early detection of lung tumor helps the patients avoiding the death rate and gives better treatment.Various medical image modalities can h...The unstructured growth of abnormal cells in the lung tissue creates tumor.The early detection of lung tumor helps the patients avoiding the death rate and gives better treatment.Various medical image modalities can help the physicians in the diagnosis of disease.Many research works have been proposed for the early detection of lung tumor.High computation time and misidentification of tumor are the prevailing issues.In order to overcome these issues,this paper has proposed a hybrid classifier of Atrous Spatial Pyramid Pooling(ASPP)-Unet architecture withWhale Optimization Algorithm(ASPP-Unet-WOA).To get a fine tuning detection of tumor in the Computed Tomography(CT)of lung image,this model needs pre-processing using Gabor filter.Secondly,feature segmentation is done using Guaranteed Convergence Particle Swarm Optimization.Thirdly,feature selection is done using Binary Grasshopper Optimization Algorithm.This proposed(ASPPUnet-WOA)is implemented in the dataset of National Cancer Institute(NCI)Lung Cancer Database Consortium.Various performance metric measures are evaluated and compared to the existing classifiers.The accuracy of Deep Convolutional Neural Network(DCNN)is 93.45%,Convolutional Neural Network(CNN)is 91.67%,UNet obtains 95.75%and ASPP-UNet-WOA obtains 98.68%.compared to the other techniques.展开更多
Agriculture 4.0,as the future of farming technology,comprises numerous key enabling technologies towards sustainable agriculture.The use of state-of-the-art technologies,such as the Internet of Things,transform tradit...Agriculture 4.0,as the future of farming technology,comprises numerous key enabling technologies towards sustainable agriculture.The use of state-of-the-art technologies,such as the Internet of Things,transform traditional cultivation practices,like irrigation,to modern solutions of precision agriculture.To achieve effectivewater resource usage and automated irrigation in precision agriculture,recent technologies like machine learning(ML)can be employed.With this motivation,this paper design an IoT andML enabled smart irrigation system(IoTML-SIS)for precision agriculture.The proposed IoTML-SIS technique allows to sense the parameters of the farmland and make appropriate decisions for irrigation.The proposed IoTML-SIS model involves different IoT based sensors for soil moisture,humidity,temperature sensor,and light.Besides,the sensed data are transmitted to the cloud server for processing and decision making.Moreover,artificial algae algorithm(AAA)with least squares-support vector machine(LS-SVM)model is employed for the classification process to determine the need for irrigation.Furthermore,the AAA is applied to optimally tune the parameters involved in the LS-SVM model,and thereby the classification efficiency is significantly increased.The performance validation of the proposed IoTML-SIS technique ensured better performance over the compared methods with the maximum accuracy of 0.975.展开更多
Oral cancer is the most commonly occurring‘head and neck cancers’across the globe.Most of the oral cancer cases are diagnosed at later stages due to absence of awareness among public.Since earlier identification of ...Oral cancer is the most commonly occurring‘head and neck cancers’across the globe.Most of the oral cancer cases are diagnosed at later stages due to absence of awareness among public.Since earlier identification of disease is essential for improved outcomes,Artificial Intelligence(AI)and Machine Learning(ML)models are used in this regard.In this background,the current study introduces Artificial Intelligence with Deep Transfer Learning driven Oral Cancer detection and Classification Model(AIDTLOCCM).The primary goal of the proposed AIDTL-OCCM model is to diagnose oral cancer using AI and image processing techniques.The proposed AIDTL-OCCM model involves fuzzy-based contrast enhancement approach to perform data pre-processing.Followed by,the densely-connected networks(DenseNet-169)model is employed to produce a useful set of deep features.Moreover,Chimp Optimization Algorithm(COA)with Autoencoder(AE)model is applied for oral cancer detection and classification.Furthermore,COA is employed to determine optimal parameters involved in AE model.A wide range of experimental analyses was conducted on benchmark datasets and the results were investigated under several aspects.The extensive experimental analysis outcomes established the enhanced performance of AIDTLOCCM model compared to other approaches with a maximum accuracy of 90.08%.展开更多
Early detection of lung cancer can help for improving the survival rate of the patients.Biomedical imaging tools such as computed tomography(CT)image was utilized to the proper identification and positioning of lung c...Early detection of lung cancer can help for improving the survival rate of the patients.Biomedical imaging tools such as computed tomography(CT)image was utilized to the proper identification and positioning of lung cancer.The recently developed deep learning(DL)models can be employed for the effectual identification and classification of diseases.This article introduces novel deep learning enabled CAD technique for lung cancer using biomedical CT image,named DLCADLC-BCT technique.The proposed DLCADLC-BCT technique intends for detecting and classifying lung cancer using CT images.The proposed DLCADLC-BCT technique initially uses gray level co-occurrence matrix(GLCM)model for feature extraction.Also,long short term memory(LSTM)model was applied for classifying the existence of lung cancer in the CT images.Moreover,moth swarm optimization(MSO)algorithm is employed to optimally choose the hyperparameters of the LSTM model such as learning rate,batch size,and epoch count.For demonstrating the improved classifier results of the DLCADLC-BCT approach,a set of simulations were executed on benchmark dataset and the outcomes exhibited the supremacy of the DLCADLC-BCT technique over the recent approaches.展开更多
With recent advances made in Internet of Vehicles(IoV)and Cloud Computing(CC),the Intelligent Transportation Systems(ITS)find it advantageous in terms of improvement in quality and interactivity of urban transportatio...With recent advances made in Internet of Vehicles(IoV)and Cloud Computing(CC),the Intelligent Transportation Systems(ITS)find it advantageous in terms of improvement in quality and interactivity of urban transportation service,mitigation of costs incurred,reduction in resource utilization,and improvement in traffic management capabilities.Many trafficrelated problems in future smart cities can be sorted out with the incorporation of IoV in transportation.IoV communication enables the collection and distribution of real-time essential data regarding road network condition.In this scenario,energy-efficient and reliable intercommunication routes are essential among vehicular nodes in sustainable urban computing.With this motivation,the current research article presents a new Artificial Intelligence-based Energy Efficient Clustering with Routing(AI-EECR)Protocol for IoV in urban computing.The proposed AI-EECR protocol operates under three stages namely,network initialization,Cluster Head(CH)selection,and routing protocol.The presented AI-EECR protocol determines the CHs from vehicles with the help of Quantum Chemical Reaction Optimization(QCRO)algorithm.QCROalgorithmderives a fitness function with the help of vehicle speed,trust level,and energy level of the vehicle.In order to make appropriate routing decisions,a set of relay nodeswas selected usingGroup Teaching Optimization Algorithm(GTOA).The performance of the presented AI-EECR model,in terms of energy efficiency,was validated against different aspects and a brief comparative analysis was conducted.The experimental outcomes established that AI-EECR model outperformed the existing methods under different measures.展开更多
Mobile communication and the Internet of Things(IoT)technologies have recently been established to collect data from human beings and the environment.The data collected can be leveraged to provide intelligent services...Mobile communication and the Internet of Things(IoT)technologies have recently been established to collect data from human beings and the environment.The data collected can be leveraged to provide intelligent services through different applications.It is an extreme challenge to monitor disabled people from remote locations.It is because day-to-day events like falls heavily result in accidents.For a person with disabilities,a fall event is an important cause of mortality and post-traumatic complications.Therefore,detecting the fall events of disabled persons in smart homes at early stages is essential to provide the necessary support and increase their survival rate.The current study introduces a Whale Optimization Algorithm Deep Transfer Learning-DrivenAutomated Fall Detection(WOADTL-AFD)technique to improve the Quality of Life for persons with disabilities.The primary aim of the presented WOADTL-AFD technique is to identify and classify the fall events to help disabled individuals.To attain this,the proposed WOADTL-AFDmodel initially uses amodified SqueezeNet feature extractor which proficiently extracts the feature vectors.In addition,the WOADTLAFD technique classifies the fall events using an extreme Gradient Boosting(XGBoost)classifier.In the presented WOADTL-AFD technique,the WOA approach is used to fine-tune the hyperparameters involved in the modified SqueezeNet model.The proposedWOADTL-AFD technique was experimentally validated using the benchmark datasets,and the results confirmed the superior performance of the proposedWOADTL-AFD method compared to other recent approaches.展开更多
Mobile edge computing(MEC)provides effective cloud services and functionality at the edge device,to improve the quality of service(QoS)of end users by offloading the high computation tasks.Currently,the introduction o...Mobile edge computing(MEC)provides effective cloud services and functionality at the edge device,to improve the quality of service(QoS)of end users by offloading the high computation tasks.Currently,the introduction of deep learning(DL)and hardware technologies paves amethod in detecting the current traffic status,data offloading,and cyberattacks in MEC.This study introduces an artificial intelligence with metaheuristic based data offloading technique for Secure MEC(AIMDO-SMEC)systems.The proposed AIMDO-SMEC technique incorporates an effective traffic prediction module using Siamese Neural Networks(SNN)to determine the traffic status in the MEC system.Also,an adaptive sampling cross entropy(ASCE)technique is utilized for data offloading in MEC systems.Moreover,the modified salp swarm algorithm(MSSA)with extreme gradient boosting(XGBoost)technique was implemented to identification and classification of cyberattack that exist in the MEC systems.For examining the enhanced outcomes of the AIMDO-SMEC technique,a comprehensive experimental analysis is carried out and the results demonstrated the enhanced outcomes of the AIMDOSMEC technique with the minimal completion time of tasks(CTT)of 0.680.展开更多
Internet of Medical Things (IoMT) is a breakthrough technologyin the transfer of medical data via a communication system. Wearable sensordevices collect patient data and transfer them through mobile internet, thatis, ...Internet of Medical Things (IoMT) is a breakthrough technologyin the transfer of medical data via a communication system. Wearable sensordevices collect patient data and transfer them through mobile internet, thatis, the IoMT. Recently, the shift in paradigm from manual data storage toelectronic health recording on fog, edge, and cloud computing has been noted.These advanced computing technologies have facilitated medical services withminimum cost and available conditions. However, the IoMT raises a highconcern on network security and patient data privacy in the health caresystem. The main issue is the transmission of health data with high security inthe fog computing model. In today’s market, the best solution is blockchaintechnology. This technology provides high-end security and authenticationin storing and transferring data. In this research, a blockchain-based fogcomputing model is proposed for the IoMT. The proposed technique embedsa block chain with the yet another consensus (YAC) protocol building securityinfrastructure into fog computing for storing and transferring IoMT data inthe network. YAC is a consensus protocol that authenticates the input datain the block chain. In this scenario, the patients and their family membersare allowed to access the data. The empirical outcome of the proposedtechnique indicates high reliability and security against dangerous threats.The major advantages of using the blockchain model are high transparency,good traceability, and high processing speed. The technique also exhibitshigh reliability and efficiency in accessing data with secure transmission. Theproposed technique achieves 95% reliability in transferring a large number offiles up to 10,000.展开更多
Line-of-sight clarity and assurance are essential because they are considered the golden rule in wireless network planning,allowing the direct propagation path to connect the transmitter and receiver and retain the st...Line-of-sight clarity and assurance are essential because they are considered the golden rule in wireless network planning,allowing the direct propagation path to connect the transmitter and receiver and retain the strength of the signal to be received.Despite the increasing literature on the line of sight with different scenarios,no comprehensive study focuses on the multiplicity of parameters and basic concepts that must be taken into account when studying such a topic as it affects the results and their accuracy.Therefore,this research aims to find limited values that ensure that the signal reaches the future efficiently and enhances the accuracy of these values’results.We have designed MATLAB simulation and programming programs by Visual Basic.NET for a semi-realistic communication system.It includes all the basic parameters of this system,taking into account the environment’s diversity and the characteristics of the obstacle between the transmitting station and the receiving station.Then we verified the correctness of the system’s work.Moreover,we begin by analyzing and studying multiple and branching cases to achieve the goal.We get several values from the results,which are finite values,which are a useful reference for engineers and designers of wireless networks.展开更多
Generally,software testing is considered as a proficient technique to achieve improvement in quality and reliability of the software.But,the quality of test cases has a considerable influence on fault revealing capabi...Generally,software testing is considered as a proficient technique to achieve improvement in quality and reliability of the software.But,the quality of test cases has a considerable influence on fault revealing capability of software testing activity.Test Case Prioritization(TCP)remains a challenging issue since prioritizing test cases is unsatisfactory in terms of Average Percentage of Faults Detected(APFD)and time spent upon execution results.TCP ismainly intended to design a collection of test cases that can accomplish early optimization using preferred characteristics.The studies conducted earlier focused on prioritizing the available test cases in accelerating fault detection rate during software testing.In this aspect,the current study designs aModified Harris Hawks Optimization based TCP(MHHO-TCP)technique for software testing.The aim of the proposed MHHO-TCP technique is to maximize APFD and minimize the overall execution time.In addition,MHHO algorithm is designed to boost the exploration and exploitation abilities of conventional HHO algorithm.In order to validate the enhanced efficiency of MHHO-TCP technique,a wide range of simulations was conducted on different benchmark programs and the results were examined under several aspects.The experimental outcomes highlight the improved efficiency of MHHO-TCP technique over recent approaches under different measures.展开更多
Muscular Dystrophy (MD) is a group of inherited muscular diseases that are commonly diagnosed with the help of techniques such asmuscle biopsy, clinical presentation, and Muscle Magnetic Resonance Imaging(MRI). Among ...Muscular Dystrophy (MD) is a group of inherited muscular diseases that are commonly diagnosed with the help of techniques such asmuscle biopsy, clinical presentation, and Muscle Magnetic Resonance Imaging(MRI). Among these techniques, Muscle MRI recommends the diagnosis ofmuscular dystrophy through identification of the patterns that exist in musclefatty replacement. But the patterns overlap among various diseases whereasthere is a lack of knowledge prevalent with regards to disease-specific patterns.Therefore, artificial intelligence techniques can be used in the diagnosis ofmuscular dystrophies, which enables us to analyze, learn, and predict forthe future. In this scenario, the current research article presents an automated muscular dystrophy detection and classification model using SynergicDeep Learning (SDL) method with extreme Gradient Boosting (XGBoost),called SDL-XGBoost. SDL-XGBoost model has been proposed to act as anautomated deep learning (DL) model that examines the muscle MRI dataand diagnose muscular dystrophies. SDL-XGBoost model employs Kapur’sentropy based Region of Interest (RoI) for detection purposes. Besides, SDLbased feature extraction process is applied to derive a useful set of featurevectors. Finally, XGBoost model is employed as a classification approach todetermine proper class labels for muscle MRI data. The researcher conductedextensive set of simulations to showcase the superior performance of SDLXGBoost model. The obtained experimental values highlighted the supremacyof SDL-XGBoost model over other methods in terms of high accuracy being96.18% and 94.25% classification performance upon DMD and BMD respectively. Therefore, SDL-XGBoost model can help physicians in the diagnosis of muscular dystrophies by identifying the patterns of muscle fatty replacementin muscle MRI.展开更多
Internet of Everything(IoE),the recent technological advancement,represents an interconnected network of people,processes,data,and things.In recent times,IoE gained significant attention among entrepreneurs,individual...Internet of Everything(IoE),the recent technological advancement,represents an interconnected network of people,processes,data,and things.In recent times,IoE gained significant attention among entrepreneurs,individuals,and communities owing to its realization of intense values from the connected entities.On the other hand,the massive increase in data generation from IoE applications enables the transmission of big data,from contextawaremachines,into useful data.Security and privacy pose serious challenges in designing IoE environment which can be addressed by developing effective Intrusion Detection Systems(IDS).In this background,the current study develops Intelligent Multiverse Optimization with Deep Learning Enabled Intrusion Detection System(IMVO-DLIDS)for IoT environment.The presented IMVO-DLIDS model focuses on identification and classification of intrusions in IoT environment.The proposed IMVO-DLIDS model follows a three-stage process.At first,data pre-processing is performed to convert the actual data into useful format.In addition,Chaotic Local Search Whale Optimization Algorithm-based Feature Selection(CLSWOA-FS)technique is employed to choose the optimal feature subsets.Finally,MVO algorithm is exploited with Bidirectional Gated Recurrent Unit(BiGRU)model for classification.Here,the novelty of the work is the application of MVO algorithm in fine-turning the hyperparameters involved in BiGRU model.The experimental validation was conducted for the proposed IMVO-DLIDS model on benchmark datasets and the results were assessed under distinct measures.An extensive comparative study was conducted and the results confirmed the promising outcomes of IMVO-DLIDS approach compared to other approaches.展开更多
The human motion data collected using wearables like smartwatches can be used for activity recognition and emergency event detection.This is especially applicable in the case of elderly or disabled people who live sel...The human motion data collected using wearables like smartwatches can be used for activity recognition and emergency event detection.This is especially applicable in the case of elderly or disabled people who live self-reliantly in their homes.These sensors produce a huge volume of physical activity data that necessitates real-time recognition,especially during emergencies.Falling is one of the most important problems confronted by older people and people with movement disabilities.Numerous previous techniques were introduced and a few used webcam to monitor the activity of elderly or disabled people.But,the costs incurred upon installation and operation are high,whereas the technology is relevant only for indoor environments.Currently,commercial wearables use a wireless emergency transmitter that produces a number of false alarms and restricts a user’s movements.Against this background,the current study develops an Improved WhaleOptimizationwithDeep Learning-Enabled Fall Detection for Disabled People(IWODL-FDDP)model.The presented IWODL-FDDP model aims to identify the fall events to assist disabled people.The presented IWODLFDDP model applies an image filtering approach to pre-process the image.Besides,the EfficientNet-B0 model is utilized to generate valuable feature vector sets.Next,the Bidirectional Long Short Term Memory(BiLSTM)model is used for the recognition and classification of fall events.Finally,the IWO method is leveraged to fine-tune the hyperparameters related to the BiLSTM method,which shows the novelty of the work.The experimental analysis outcomes established the superior performance of the proposed IWODL-FDDP method with a maximum accuracy of 97.02%.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
文摘Energy is an essential element for any civilized country’s social and economic development,but the use of fossil fuels and nonrenewable energy forms has many negative impacts on the environment and the ecosystem.The Republic of Yemen has very good potential to use renewable energy.Unfortunately,we find few studies on renewable wind energy in Yemen.Given the lack of a similar analysis for the coastal city,this research newly investigates wind energy’s potential near the Almukalla area by analyzing wind characteristics.Thus,evaluation,model identification,determination of available energy density,computing the capacity factors for several wind turbines and calculation of wind energy were extracted at three heights of 15,30,and 50meters.Average wind speeds were obtained only for the currently available data of five recent years,2005–2009.This study involves a preliminary assessment of Almukalla’s wind energy potential to provide a primary base and useful insights for wind engineers and experts.This research aims to provide useful assessment of the potential of wind energy in Almukalla for developing wind energy and an efficient wind approach.The Weibull distribution shows a perfect approximation for estimating the intensity of Yemen’s wind energy.Depending on both theWeibullmodel and the results of the annual wind speed data analysis for the study site in Mukalla,the capacity factor for many turbines was also calculated,and the best suitable turbine was selected.According to the International Wind Energy Rating criteria,Almukalla falls under Category 7,which is,rated“Superb”most of the year.
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 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(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:(22UQU4210118DSR02).
文摘Sign language recognition can be considered as an effective solution for disabled people to communicate with others.It helps them in conveying the intended information using sign languages without any challenges.Recent advancements in computer vision and image processing techniques can be leveraged to detect and classify the signs used by disabled people in an effective manner.Metaheuristic optimization algorithms can be designed in a manner such that it fine tunes the hyper parameters,used in Deep Learning(DL)models as the latter considerably impacts the classification results.With this motivation,the current study designs the Optimal Deep Transfer Learning Driven Sign Language Recognition and Classification(ODTL-SLRC)model for disabled people.The aim of the proposed ODTL-SLRC technique is to recognize and classify sign languages used by disabled people.The proposed ODTL-SLRC technique derives EfficientNet model to generate a collection of useful feature vectors.In addition,the hyper parameters involved in EfficientNet model are fine-tuned with the help of HGSO algorithm.Moreover,Bidirectional Long Short Term Memory(BiLSTM)technique is employed for sign language classification.The proposed ODTL-SLRC technique was experimentally validated using benchmark dataset and the results were inspected under several measures.The comparative analysis results established the superior performance of the proposed ODTL-SLRC technique over recent approaches in terms of efficiency.
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-1443-0051).
文摘With the advent of the Internet of Things(IoT),several devices like sensors nowadays can interact and easily share information.But the IoT model is prone to security concerns as several attackers try to hit the network and make it vulnerable.In such scenarios,security concern is the most prominent.Different models were intended to address these security problems;still,several emergent variants of botnet attacks like Bashlite,Mirai,and Persirai use security breaches.The malware classification and detection in the IoT model is still a problem,as the adversary reliably generates a new variant of IoT malware and actively searches for compromise on the victim devices.This article develops a Sine Cosine Algorithm with Deep Learning based Ransomware Detection and Classification(SCADL-RWDC)method in an IoT environment.In the presented SCADL-RWDCtechnique,the major intention exists in recognizing and classifying ransomware attacks in the IoT platform.The SCADL-RWDC technique uses the SCA feature selection(SCA-FS)model to improve the detection rate.Besides,the SCADL-RWDC technique exploits the hybrid grey wolf optimizer(HGWO)with a gated recurrent unit(GRU)model for ransomware classification.A widespread experimental analysis is performed to exhibit the enhanced ransomware detection outcomes of the SCADL-RWDC technique.The comparison study reported the enhancement of the SCADL-RWDC technique over other models.
基金the King Salman Centre for Disability Research for funding this work through Research Group No.KSRG-2024-068。
文摘Earlier notification and fire detection methods provide safety information and fire prevention to blind and visually impaired(BVI)individuals in a limited timeframe in the event of emergencies,particularly in enclosed areas.Fire detection becomes crucial as it directly impacts human safety and the environment.While modern technology requires precise techniques for early detection to prevent damage and loss,few research has focused on artificial intelligence(AI)-based early fire alert systems for BVI individuals in indoor settings.To prevent such fire incidents,it is crucial to identify fires accurately and promptly,and alert BVI personnel using a combination of smart glasses,deep learning(DL),and computer vision(CV).The most recent technologies require effective methods to identify fires quickly,preventing damage and physical loss.In this manuscript,an Enhanced Fire Detection System for Blind and Visually Challenged People using Artificial Intelligence with Deep Convolutional Neural Networks(EFDBVC-AIDCNN)model is presented.The EFDBVC-AIDCNN model presents an advanced fire detection system that utilizes AI to detect and classify fire hazards for BVI people effectively.Initially,image pre-processing is performed using the Gabor filter(GF)model to improve texture details and patterns specific to flames and smoke.For the feature extractor,the Swin transformer(ST)model captures fine details across multiple scales to represent fire patterns accurately.Furthermore,the Elman neural network(ENN)technique is implemented to detect fire.The improved whale optimization algorithm(IWOA)is used to efficiently tune ENN parameters,improving accuracy and robustness across varying lighting and environmental conditions to optimize performance.An extensive experimental study of the EFDBVC-AIDCNN technique is accomplished under the fire detection dataset.A short comparative analysis of the EFDBVC-AIDCNN approach portrayed a superior accuracy value of 96.60%over existing models.
文摘Short-term traffic flow prediction (TFP) is an important area inintelligent transportation system (ITS), which is used to reduce traffic congestion. But the avail of traffic flow data with temporal features and periodicfeatures are susceptible to weather conditions, making TFP a challengingissue. TFP process are significantly influenced by several factors like accidentand weather. Particularly, the inclement weather conditions may have anextreme impact on travel time and traffic flow. Since most of the existing TFPtechniques do not consider the impact of weather conditions on the TF, it isneeded to develop effective TFP with the consideration of extreme weatherconditions. In this view, this paper designs an artificial intelligence based TFPwith weather conditions (AITFP-WC) for smart cities. The goal of the AITFPWC model is to enhance the performance of the TFP model with the inclusionof weather related conditions. The proposed AITFP-WC technique includesElman neural network (ENN) model to predict the flow of traffic in smartcities. Besides, tunicate swarm algorithm with feed forward neural networks(TSA-FFNN) model is employed for the weather and periodicity analysis. Atlast, a fusion of TFP and WPA processes takes place using the FFNN modelto determine the final prediction output. In order to assess the enhancedpredictive outcome of the AITFP-WC model, an extensive simulation analysisis carried out. The experimental values highlighted the enhanced performanceof the AITFP-WC technique over the recent state of art methods.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under Grant Number(GRP/303/42)Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R203),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The unstructured growth of abnormal cells in the lung tissue creates tumor.The early detection of lung tumor helps the patients avoiding the death rate and gives better treatment.Various medical image modalities can help the physicians in the diagnosis of disease.Many research works have been proposed for the early detection of lung tumor.High computation time and misidentification of tumor are the prevailing issues.In order to overcome these issues,this paper has proposed a hybrid classifier of Atrous Spatial Pyramid Pooling(ASPP)-Unet architecture withWhale Optimization Algorithm(ASPP-Unet-WOA).To get a fine tuning detection of tumor in the Computed Tomography(CT)of lung image,this model needs pre-processing using Gabor filter.Secondly,feature segmentation is done using Guaranteed Convergence Particle Swarm Optimization.Thirdly,feature selection is done using Binary Grasshopper Optimization Algorithm.This proposed(ASPPUnet-WOA)is implemented in the dataset of National Cancer Institute(NCI)Lung Cancer Database Consortium.Various performance metric measures are evaluated and compared to the existing classifiers.The accuracy of Deep Convolutional Neural Network(DCNN)is 93.45%,Convolutional Neural Network(CNN)is 91.67%,UNet obtains 95.75%and ASPP-UNet-WOA obtains 98.68%.compared to the other techniques.
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 2/209/42).
文摘Agriculture 4.0,as the future of farming technology,comprises numerous key enabling technologies towards sustainable agriculture.The use of state-of-the-art technologies,such as the Internet of Things,transform traditional cultivation practices,like irrigation,to modern solutions of precision agriculture.To achieve effectivewater resource usage and automated irrigation in precision agriculture,recent technologies like machine learning(ML)can be employed.With this motivation,this paper design an IoT andML enabled smart irrigation system(IoTML-SIS)for precision agriculture.The proposed IoTML-SIS technique allows to sense the parameters of the farmland and make appropriate decisions for irrigation.The proposed IoTML-SIS model involves different IoT based sensors for soil moisture,humidity,temperature sensor,and light.Besides,the sensed data are transmitted to the cloud server for processing and decision making.Moreover,artificial algae algorithm(AAA)with least squares-support vector machine(LS-SVM)model is employed for the classification process to determine the need for irrigation.Furthermore,the AAA is applied to optimally tune the parameters involved in the LS-SVM model,and thereby the classification efficiency is significantly increased.The performance validation of the proposed IoTML-SIS technique ensured better performance over the compared methods with the maximum accuracy of 0.975.
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(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:(22UQU4310373DSR06).
文摘Oral cancer is the most commonly occurring‘head and neck cancers’across the globe.Most of the oral cancer cases are diagnosed at later stages due to absence of awareness among public.Since earlier identification of disease is essential for improved outcomes,Artificial Intelligence(AI)and Machine Learning(ML)models are used in this regard.In this background,the current study introduces Artificial Intelligence with Deep Transfer Learning driven Oral Cancer detection and Classification Model(AIDTLOCCM).The primary goal of the proposed AIDTL-OCCM model is to diagnose oral cancer using AI and image processing techniques.The proposed AIDTL-OCCM model involves fuzzy-based contrast enhancement approach to perform data pre-processing.Followed by,the densely-connected networks(DenseNet-169)model is employed to produce a useful set of deep features.Moreover,Chimp Optimization Algorithm(COA)with Autoencoder(AE)model is applied for oral cancer detection and classification.Furthermore,COA is employed to determine optimal parameters involved in AE model.A wide range of experimental analyses was conducted on benchmark datasets and the results were investigated under several aspects.The extensive experimental analysis outcomes established the enhanced performance of AIDTLOCCM model compared to other approaches with a maximum accuracy of 90.08%.
基金The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4310373DSR03).
文摘Early detection of lung cancer can help for improving the survival rate of the patients.Biomedical imaging tools such as computed tomography(CT)image was utilized to the proper identification and positioning of lung cancer.The recently developed deep learning(DL)models can be employed for the effectual identification and classification of diseases.This article introduces novel deep learning enabled CAD technique for lung cancer using biomedical CT image,named DLCADLC-BCT technique.The proposed DLCADLC-BCT technique intends for detecting and classifying lung cancer using CT images.The proposed DLCADLC-BCT technique initially uses gray level co-occurrence matrix(GLCM)model for feature extraction.Also,long short term memory(LSTM)model was applied for classifying the existence of lung cancer in the CT images.Moreover,moth swarm optimization(MSO)algorithm is employed to optimally choose the hyperparameters of the LSTM model such as learning rate,batch size,and epoch count.For demonstrating the improved classifier results of the DLCADLC-BCT approach,a set of simulations were executed on benchmark dataset and the outcomes exhibited the supremacy of the DLCADLC-BCT technique 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 2/25/42),Received by Fahd N.Al-Wesabi.www.kku.edu.sa.This research was funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University through the Fast-track Research Funding Program.
文摘With recent advances made in Internet of Vehicles(IoV)and Cloud Computing(CC),the Intelligent Transportation Systems(ITS)find it advantageous in terms of improvement in quality and interactivity of urban transportation service,mitigation of costs incurred,reduction in resource utilization,and improvement in traffic management capabilities.Many trafficrelated problems in future smart cities can be sorted out with the incorporation of IoV in transportation.IoV communication enables the collection and distribution of real-time essential data regarding road network condition.In this scenario,energy-efficient and reliable intercommunication routes are essential among vehicular nodes in sustainable urban computing.With this motivation,the current research article presents a new Artificial Intelligence-based Energy Efficient Clustering with Routing(AI-EECR)Protocol for IoV in urban computing.The proposed AI-EECR protocol operates under three stages namely,network initialization,Cluster Head(CH)selection,and routing protocol.The presented AI-EECR protocol determines the CHs from vehicles with the help of Quantum Chemical Reaction Optimization(QCRO)algorithm.QCROalgorithmderives a fitness function with the help of vehicle speed,trust level,and energy level of the vehicle.In order to make appropriate routing decisions,a set of relay nodeswas selected usingGroup Teaching Optimization Algorithm(GTOA).The performance of the presented AI-EECR model,in terms of energy efficiency,was validated against different aspects and a brief comparative analysis was conducted.The experimental outcomes established that AI-EECR model outperformed the existing methods under different measures.
基金The authors extend their appreciation to the King Salman Center for Disability Research for funding this work through Research Group no KSRG-2022-030.
文摘Mobile communication and the Internet of Things(IoT)technologies have recently been established to collect data from human beings and the environment.The data collected can be leveraged to provide intelligent services through different applications.It is an extreme challenge to monitor disabled people from remote locations.It is because day-to-day events like falls heavily result in accidents.For a person with disabilities,a fall event is an important cause of mortality and post-traumatic complications.Therefore,detecting the fall events of disabled persons in smart homes at early stages is essential to provide the necessary support and increase their survival rate.The current study introduces a Whale Optimization Algorithm Deep Transfer Learning-DrivenAutomated Fall Detection(WOADTL-AFD)technique to improve the Quality of Life for persons with disabilities.The primary aim of the presented WOADTL-AFD technique is to identify and classify the fall events to help disabled individuals.To attain this,the proposed WOADTL-AFDmodel initially uses amodified SqueezeNet feature extractor which proficiently extracts the feature vectors.In addition,the WOADTLAFD technique classifies the fall events using an extreme Gradient Boosting(XGBoost)classifier.In the presented WOADTL-AFD technique,the WOA approach is used to fine-tune the hyperparameters involved in the modified SqueezeNet model.The proposedWOADTL-AFD technique was experimentally validated using the benchmark datasets,and the results confirmed the superior performance of the proposedWOADTL-AFD method compared to other 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 2/209/42)Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R77),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Mobile edge computing(MEC)provides effective cloud services and functionality at the edge device,to improve the quality of service(QoS)of end users by offloading the high computation tasks.Currently,the introduction of deep learning(DL)and hardware technologies paves amethod in detecting the current traffic status,data offloading,and cyberattacks in MEC.This study introduces an artificial intelligence with metaheuristic based data offloading technique for Secure MEC(AIMDO-SMEC)systems.The proposed AIMDO-SMEC technique incorporates an effective traffic prediction module using Siamese Neural Networks(SNN)to determine the traffic status in the MEC system.Also,an adaptive sampling cross entropy(ASCE)technique is utilized for data offloading in MEC systems.Moreover,the modified salp swarm algorithm(MSSA)with extreme gradient boosting(XGBoost)technique was implemented to identification and classification of cyberattack that exist in the MEC systems.For examining the enhanced outcomes of the AIMDO-SMEC technique,a comprehensive experimental analysis is carried out and the results demonstrated the enhanced outcomes of the AIMDOSMEC technique with the minimal completion time of tasks(CTT)of 0.680.
文摘Internet of Medical Things (IoMT) is a breakthrough technologyin the transfer of medical data via a communication system. Wearable sensordevices collect patient data and transfer them through mobile internet, thatis, the IoMT. Recently, the shift in paradigm from manual data storage toelectronic health recording on fog, edge, and cloud computing has been noted.These advanced computing technologies have facilitated medical services withminimum cost and available conditions. However, the IoMT raises a highconcern on network security and patient data privacy in the health caresystem. The main issue is the transmission of health data with high security inthe fog computing model. In today’s market, the best solution is blockchaintechnology. This technology provides high-end security and authenticationin storing and transferring data. In this research, a blockchain-based fogcomputing model is proposed for the IoMT. The proposed technique embedsa block chain with the yet another consensus (YAC) protocol building securityinfrastructure into fog computing for storing and transferring IoMT data inthe network. YAC is a consensus protocol that authenticates the input datain the block chain. In this scenario, the patients and their family membersare allowed to access the data. The empirical outcome of the proposedtechnique indicates high reliability and security against dangerous threats.The major advantages of using the blockchain model are high transparency,good traceability, and high processing speed. The technique also exhibitshigh reliability and efficiency in accessing data with secure transmission. Theproposed technique achieves 95% reliability in transferring a large number offiles up to 10,000.
文摘Line-of-sight clarity and assurance are essential because they are considered the golden rule in wireless network planning,allowing the direct propagation path to connect the transmitter and receiver and retain the strength of the signal to be received.Despite the increasing literature on the line of sight with different scenarios,no comprehensive study focuses on the multiplicity of parameters and basic concepts that must be taken into account when studying such a topic as it affects the results and their accuracy.Therefore,this research aims to find limited values that ensure that the signal reaches the future efficiently and enhances the accuracy of these values’results.We have designed MATLAB simulation and programming programs by Visual Basic.NET for a semi-realistic communication system.It includes all the basic parameters of this system,taking into account the environment’s diversity and the characteristics of the obstacle between the transmitting station and the receiving station.Then we verified the correctness of the system’s work.Moreover,we begin by analyzing and studying multiple and branching cases to achieve the goal.We get several values from the results,which are finite values,which are a useful reference for engineers and designers of wireless networks.
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.1/127/42)Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R237),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Generally,software testing is considered as a proficient technique to achieve improvement in quality and reliability of the software.But,the quality of test cases has a considerable influence on fault revealing capability of software testing activity.Test Case Prioritization(TCP)remains a challenging issue since prioritizing test cases is unsatisfactory in terms of Average Percentage of Faults Detected(APFD)and time spent upon execution results.TCP ismainly intended to design a collection of test cases that can accomplish early optimization using preferred characteristics.The studies conducted earlier focused on prioritizing the available test cases in accelerating fault detection rate during software testing.In this aspect,the current study designs aModified Harris Hawks Optimization based TCP(MHHO-TCP)technique for software testing.The aim of the proposed MHHO-TCP technique is to maximize APFD and minimize the overall execution time.In addition,MHHO algorithm is designed to boost the exploration and exploitation abilities of conventional HHO algorithm.In order to validate the enhanced efficiency of MHHO-TCP technique,a wide range of simulations was conducted on different benchmark programs and the results were examined under several aspects.The experimental outcomes highlight the improved efficiency of MHHO-TCP technique over recent approaches under different measures.
文摘Muscular Dystrophy (MD) is a group of inherited muscular diseases that are commonly diagnosed with the help of techniques such asmuscle biopsy, clinical presentation, and Muscle Magnetic Resonance Imaging(MRI). Among these techniques, Muscle MRI recommends the diagnosis ofmuscular dystrophy through identification of the patterns that exist in musclefatty replacement. But the patterns overlap among various diseases whereasthere is a lack of knowledge prevalent with regards to disease-specific patterns.Therefore, artificial intelligence techniques can be used in the diagnosis ofmuscular dystrophies, which enables us to analyze, learn, and predict forthe future. In this scenario, the current research article presents an automated muscular dystrophy detection and classification model using SynergicDeep Learning (SDL) method with extreme Gradient Boosting (XGBoost),called SDL-XGBoost. SDL-XGBoost model has been proposed to act as anautomated deep learning (DL) model that examines the muscle MRI dataand diagnose muscular dystrophies. SDL-XGBoost model employs Kapur’sentropy based Region of Interest (RoI) for detection purposes. Besides, SDLbased feature extraction process is applied to derive a useful set of featurevectors. Finally, XGBoost model is employed as a classification approach todetermine proper class labels for muscle MRI data. The researcher conductedextensive set of simulations to showcase the superior performance of SDLXGBoost model. The obtained experimental values highlighted the supremacyof SDL-XGBoost model over other methods in terms of high accuracy being96.18% and 94.25% classification performance upon DMD and BMD respectively. Therefore, SDL-XGBoost model can help physicians in the diagnosis of muscular dystrophies by identifying the patterns of muscle fatty replacementin muscle MRI.
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(46/43)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:(22UQU4210118DSR13).
文摘Internet of Everything(IoE),the recent technological advancement,represents an interconnected network of people,processes,data,and things.In recent times,IoE gained significant attention among entrepreneurs,individuals,and communities owing to its realization of intense values from the connected entities.On the other hand,the massive increase in data generation from IoE applications enables the transmission of big data,from contextawaremachines,into useful data.Security and privacy pose serious challenges in designing IoE environment which can be addressed by developing effective Intrusion Detection Systems(IDS).In this background,the current study develops Intelligent Multiverse Optimization with Deep Learning Enabled Intrusion Detection System(IMVO-DLIDS)for IoT environment.The presented IMVO-DLIDS model focuses on identification and classification of intrusions in IoT environment.The proposed IMVO-DLIDS model follows a three-stage process.At first,data pre-processing is performed to convert the actual data into useful format.In addition,Chaotic Local Search Whale Optimization Algorithm-based Feature Selection(CLSWOA-FS)technique is employed to choose the optimal feature subsets.Finally,MVO algorithm is exploited with Bidirectional Gated Recurrent Unit(BiGRU)model for classification.Here,the novelty of the work is the application of MVO algorithm in fine-turning the hyperparameters involved in BiGRU model.The experimental validation was conducted for the proposed IMVO-DLIDS model on benchmark datasets and the results were assessed under distinct measures.An extensive comparative study was conducted and the results confirmed the promising outcomes of IMVO-DLIDS approach compared to other approaches.
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(158/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R77)+1 种基金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:(22UQU4310373DSR52).
文摘The human motion data collected using wearables like smartwatches can be used for activity recognition and emergency event detection.This is especially applicable in the case of elderly or disabled people who live self-reliantly in their homes.These sensors produce a huge volume of physical activity data that necessitates real-time recognition,especially during emergencies.Falling is one of the most important problems confronted by older people and people with movement disabilities.Numerous previous techniques were introduced and a few used webcam to monitor the activity of elderly or disabled people.But,the costs incurred upon installation and operation are high,whereas the technology is relevant only for indoor environments.Currently,commercial wearables use a wireless emergency transmitter that produces a number of false alarms and restricts a user’s movements.Against this background,the current study develops an Improved WhaleOptimizationwithDeep Learning-Enabled Fall Detection for Disabled People(IWODL-FDDP)model.The presented IWODL-FDDP model aims to identify the fall events to assist disabled people.The presented IWODLFDDP model applies an image filtering approach to pre-process the image.Besides,the EfficientNet-B0 model is utilized to generate valuable feature vector sets.Next,the Bidirectional Long Short Term Memory(BiLSTM)model is used for the recognition and classification of fall events.Finally,the IWO method is leveraged to fine-tune the hyperparameters related to the BiLSTM method,which shows the novelty of the work.The experimental analysis outcomes established the superior performance of the proposed IWODL-FDDP method with a maximum accuracy of 97.02%.
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.
