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| 1 | Modified Harris Hawks Optimization Based Test Case Prioritization for Software Testing显示文摘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. | Manar Ahmed Hamza Abdelzahir Abdelmaboud Souad Larabi-Marie-Sainte Haya Mesfer Alshahrani Mesfer Al Duhayyim Hamza Awad Ibrahim Mohammed Rizwanullah Ishfaq Yaseen | 2022 | Computers, Materials & Continua2022，，7： | 1 |
| 2 | Energy Aware Data Collection with Route Planning for 6G Enabled UAV Communication显示文摘With technological advancements in 6G and Internet of Things(IoT), the incorporation of Unmanned Aerial Vehicles (UAVs) and cellularnetworks has become a hot research topic. At present, the proficient evolution of 6G networks allows the UAVs to offer cost-effective and timelysolutions for real-time applications such as medicine, tracking, surveillance,etc. Energy efficiency, data collection, and route planning are crucial processesto improve the network communication. These processes are highly difficultowing to high mobility, presence of non-stationary links, dynamic topology,and energy-restricted UAVs. With this motivation, the current research paperpresents a novel Energy Aware Data Collection with Routing Planning for6G-enabled UAV communication (EADCRP-6G) technique. The goal of theproposed EADCRP-6G technique is to conduct energy-efficient cluster-baseddata collection and optimal route planning for 6G-enabled UAV networks.EADCRP-6G technique deploys Improved Red Deer Algorithm-based Clustering (IRDAC) technique to elect an optimal set of Cluster Heads (CH) andorganize these clusters. Besides, Artificial Fish Swarm-based Route Planning(AFSRP) technique is applied to choose an optimum set of routes for UAVcommunication in 6G networks. In order to validated whether the proposedEADCRP-6G technique enhances the performance, a series of simulationswas performed and the outcomes were investigated under different dimensions.The experimental results showcase that the proposed model outperformed allother existing models under different evaluation parameters. | Mesfer Al Duhayyim Marwa Obayya Fahd N.Al-Wesabi Anwer Mustafa Hilal Mohammed Rizwanullah Majdy M.Eltahir | 2022 | Computers, Materials & Continua2022，，4： | 1 |
| 3 | Deep Learning Enabled Computer Aided Diagnosis Model for Lung Cancer using Biomedical CT Images显示文摘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. | Mohammad Alamgeer Hanan Abdullah Mengash Radwa Marzouk Mohamed K Nour Anwer Mustafa Hilal Abdelwahed Motwakel Abu Sarwar Zamani Mohammed Rizwanullah | 2022 | Computers, Materials & Continua2022，，10： | 1 |
| 4 | Deep Learning Empowered Cybersecurity Spam Bot Detection for Online Social Networks显示文摘Cybersecurity encompasses various elements such as strategies,policies,processes,and techniques to accomplish availability,confidentiality,and integrity of resource processing,network,software,and data from attacks.In this scenario,the rising popularity of Online Social Networks(OSN)is under threat from spammers for which effective spam bot detection approaches should be developed.Earlier studies have developed different approaches for the detection of spam bots in OSN.But those techniques primarily concentrated on hand-crafted features to capture the features of malicious users while the application of Deep Learning(DL)models needs to be explored.With this motivation,the current research article proposes a Spam Bot Detection technique using Hybrid DL model abbreviated as SBDHDL.The proposed SBD-HDL technique focuses on the detection of spam bots that exist in OSNs.The technique has different stages of operations such as pre-processing,classification,and parameter optimization.Besides,SBD-HDL technique hybridizes Graph Convolutional Network(GCN)with Recurrent Neural Network(RNN)model for spam bot classification process.In order to enhance the detection performance of GCN-RNN model,hyperparameters are tuned using Lion Optimization Algorithm(LOA).Both hybridization of GCN-RNN and LOA-based hyperparameter tuning process make the current work,a first-of-its-kind in this domain.The experimental validation of the proposed SBD-HDL technique,conducted upon benchmark dataset,established the supremacy of the technique since it was validated under different measures. | Mesfer Al Duhayyim Haya Mesfer Alshahrani Fahd NAl-Wesabi Mohammed Alamgeer Anwer Mustafa Hilal Mohammed Rizwanullah | 2022 | Computers, Materials & Continua2022，，3： | 1 |
| 5 | Indoor Electromagnetic Radiation Intensity Relationship to Total Energy of Household Appliances显示文摘The rapid technological developments in the modern era have led to increased electrical equipment in our daily lives,work,and homes.From this standpoint,the main objective of this study is to evaluate the potential relationship between the intensity of electromagnetic radiation and the total energy of household appliances in the living environment within the building by measuring and analyzing the strength of the electric field and the entire electromagnetic radiation flux density of electrical devices operating at frequencies(5 Hz to 1 kHz).The living room was chosen as a center for measurement at 15 homes in three different environmental regions(urban,suburbs,and open areas).The three measurement methods are(Mode 1:people in a sitting position with electrical appliances on.Mode 2:People in a standing position with electrical appliances on.Mode 3:People are in the upright positionwhile turning off the electrical devices)in the living room.These measurement methods and their results reinforce the importance of this research.The results showed that the average electric field strengthmeasured inMode 2 ismuch greater than the two methods,and we also found less electromagnetic radiation in Mode 3 than in the two modes.All results remain within the recommended overall exposure developed by the International Committee for the Prevention of Non-Ionizing Radiation and the International Electrotechnical Commission. | Murad A.A.Almekhlafi Lamia Osman Widaa Fahd N.Al-Wesabi Mohammad Alamgeer Anwer Mustafa Hilal Manar Ahmed Hamza Abu Sarwar Zamani Mohammed Rizwanullah | 2022 | Computers, Materials & Continua2022，，3： | 0 |
| 6 | An Intelligent Hazardous Waste Detection and Classification Model Using Ensemble Learning Techniques显示文摘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%. | Mesfer Al Duhayyim Saud S.Alotaibi Shaha Al-Otaibi Fahd N.Al-Wesabi Mahmoud Othman Ishfaq Yaseen Mohammed Rizwanullah Abdelwahed Motwakel | 2023 | Computers, Materials & Continua2023，，2： | 0 |
| 7 | CryptoNight Mining Algorithm with YAC Consensus for Social Media Marketing Using Blockchain显示文摘Social media is a platform in which user can create,share and exchange the knowledge/information.Social media marketing is to identify the different consumer’s demands and engages them to create marketing resources.The popular social media platforms are Microsoft,Snapchat,Amazon,Flipkart,Google,eBay,Instagram,Facebook,Pin interest,and Twitter.The main aim of social media marketing deals with various business partners and build good relationship with millions of customers by satisfying their needs.Disruptive technology is replacing old approaches in the social media marketing to new technology-based marketing.However,this disruptive technology creates some issues like fake news,insecure,inconsistency,inaccuracy and so on.These issues contribute economic instability in the society,diminishing the level of trustworthy.To overcome these issues,this paper we present blockchain as disruptive technology for social media marketing.Blockchain plays a vital role on social media marketing by providing secure to the company page in the website.The properties of disruptive potential of blockchain on social media marketing is transparency,security,reliability and immutability.This paper presents a new framework for disruptive technology in blockchain social media marketing using fusion of CryptoNight mining algorithm with YAC consensus algorithm[BCDSMM-CNYAC].This mining algorithm provides high CPU efficiency,high dimensionality of secure and detecting falsifying data attack in the social media marketing.For the data analysis we proposed ANOVA analysis method regarding to the factors of age,time,frequency visiting times of social media platform.For reliability analysis of data Cronbach’s alpha tests are implemented. | Anwer Mustafa Hil Fahd N.Al-Wesabi Hadeel Alsolai Ola Abdelgney Omer Ali Nadhem Nemri Manar Ahmed Hamza Abu Sarwar Zamani Mohammed Rizwanullah | 2022 | Computers, Materials & Continua2022，，5： | 0 |
| 8 | Modeling of Artificial Intelligence Based Traffic Flow Prediction with Weather Conditions显示文摘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. | Mesfer Al Duhayyim Amani Abdulrahman Albraikan Fahd N.Al-Wesabi Hiba M.Burbur Mohammad Alamgeer Anwer Mustafa Hilal Manar Ahmed Hamza Mohammed Rizwanullah | 2022 | Computers, Materials & Continua2022，，5： | 0 |
| 9 | Integration of Fog Computing for Health Record Management Using Blockchain Technology显示文摘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. | Mesfer AI Duhayyim Fahd N.Al-Wesabi Radwa Marzouk Abdalla Ibrahim Abdalla Musa Noha Negm Anwer Mustafa Hilal Manar Ahmed Hamza Mohammed Rizwanullah | 2022 | Computers, Materials & Continua2022，，5： | 0 |
| 10 | An Optimized Deep Learning Model for Emotion Classification in Tweets显示文摘The task of automatically analyzing sentiments from a tweet has more use now than ever due to the spectrum of emotions expressed from national leaders to the average man.Analyzing this data can be critical for any organization.Sentiments are often expressed with different intensity and topics which can provide great insight into how something affects society.Sentiment analysis in Twittermitigates the various issues of analyzing the tweets in terms of views expressed and several approaches have already been proposed for sentiment analysis in twitter.Resources used for analyzing tweet emotions are also briefly presented in literature survey section.In this paper,hybrid combination of different model’s LSTM-CNN have been proposed where LSTMis Long Short TermMemory andCNNrepresents ConvolutionalNeural Network.Furthermore,the main contribution of our work is to compare various deep learning and machine learning models and categorization based on the techniques used.The main drawback of LSTM is that it’s a timeconsuming process whereas CNN do not express content information in an accurate way,thus our proposed hybrid technique improves the precision rate and helps in achieving better results.Initial step of our mentioned technique is to preprocess the data in order to remove stop words and unnecessary data to improve the efficiency in terms of time and accuracy also it shows optimal results when it is compared with predefined approaches. | Chinu Singla Fahd NAl-Wesabi Yash Singh Pathania Badria Sulaiman Alfurhood Anwer Mustafa Hilal Mohammed Rizwanullah Manar Ahmed Hamza Mohammad Mahzari | 2022 | Computers, Materials & Continua2022，，3： | 0 |
| 11 | Political Optimizer with Probabilistic Neural Network-Based Arabic Comparative Opinion Mining显示文摘Opinion Mining(OM)studies in Arabic are limited though it is one of the most extensively-spoken languages worldwide.Though the interest in OM studies in the Arabic language is growing among researchers,it needs a vast number of investigations due to the unique morphological principles of the language.Arabic OM studies experience multiple challenges owing to the poor existence of language sources and Arabic-specific linguistic features.The comparative OM studies in the English language are wide and novel.But,comparative OM studies in the Arabic language are yet to be established and are still in a nascent stage.The unique features of the Arabic language make it essential to expand the studies regarding the Arabic text.It contains unique featuressuchasdiacritics,elongation,inflectionandwordlength.Thecurrent study proposes a Political Optimizer with Probabilistic Neural Network-based Comparative Opinion Mining(POPNN-COM)model for the Arabic text.The proposed POPNN-COM model aims to recognize comparative and non-comparative texts in Arabic in the context of social media.Initially,the POPNN-COM model involves different levels of data pre-processing to transform the input data into a useful format.Then,the pre-processed data is fed into the PNN model for classification and recognition of the data under different class labels.At last,the PO algorithm is employed for fine-tuning the parameters involved in this model to achieve enhanced results.The proposed POPNN-COM model was experimentally validated using two standard datasets,and the outcomes established the promising performance of the proposed POPNN-COM method over other recent approaches. | Najm Alotaibi Badriyya B.Al-onazi Mohamed K.Nour Abdullah Mohamed Abdelwahed Motwakel Gouse Pasha Mohammed Ishfaq Yaseen Mohammed Rizwanullah | 2023 | Intelligent Automation & Soft Computing2023，，6： | 0 |
| 12 | An Optimized Algorithm for CR-MIMO Wireless Networks显示文摘With the rapid development of wireless communication technology,the spectrum resources are increasingly strained which needs optimal solutions.Cognitive radio(CR)is one of the key technologies to solve this problem.Spectrum sensing not only includes the precise detection of the communication signal of the primary user(PU),but also the precise identification of its modulation type,which can then determine the a priori information such as the PU’service category,so as to use this information to make the cognitive user(CU)aware to discover and use the idle spectrum more effectively,and improve the spectrum utilization.Spectrum sensing is the primary feature and core part of CR.Classical sensing algorithms includes energy detection,cyclostationary feature detection,matched filter detection,and so on.The energy detection algorithm has a simple structure and does not require prior knowledge of the PU transmitter signal,but it is easily affected by noise and the threshold is not easy to determine.The combination of multiple-input multiple-output(MIMO)with CR improves the spectral efficiency and multipath fading utilization.To best utilize the PU spectrum while minimizing the overall transmit power,an iterative technique based on semidefinite programming(SDP)and minimum mean squared error(MMSE)is proposed.Also,this article proposed a new method for max-min fairness beamforming.When compared to existing algorithms,the simulation results show that the proposed algorithms perform better in terms of total transmitted power and signal-tointerference plus noise ratio(SINR).Furthermore,the proposed algorithm effectively improved the system performance in terms of number of iterations,interference temperature threshold and balance SINR level which makes it superior over the conventional schemes. | Imran Khan Fahd N.Al-Wesabi Marwa Obayya Anwer Mustafa Hilal Manar Ahmed Hamza Mohammed Rizwanullah Fahad Ahmed Al-Zahrani Hirofumi Amano Samih M.Mostafa | 2022 | Computers, Materials & Continua2022，，4： | 0 |
| 13 | Optimized Stacked Autoencoder for IoT Enabled Financial Crisis Prediction Model显示文摘Recently,Financial Technology(FinTech)has received more attention among financial sectors and researchers to derive effective solutions for any financial institution or firm.Financial crisis prediction(FCP)is an essential topic in business sector that finds it useful to identify the financial condition of a financial institution.At the same time,the development of the internet of things(IoT)has altered the mode of human interaction with the physical world.The IoT can be combined with the FCP model to examine the financial data from the users and perform decision making process.This paper presents a novel multi-objective squirrel search optimization algorithm with stacked autoencoder(MOSSA-SAE)model for FCP in IoT environment.The MOSSA-SAE model encompasses different subprocesses namely preprocessing,class imbalance handling,parameter tuning,and classification.Primarily,the MOSSA-SAE model allows the IoT devices such as smartphones,laptops,etc.,to collect the financial details of the users which are then transmitted to the cloud for further analysis.In addition,SMOTE technique is employed to handle class imbalance problems.The goal of MOSSA in SMOTE is to determine the oversampling rate and area of nearest neighbors of SMOTE.Besides,SAE model is utilized as a classification technique to determine the class label of the financial data.At the same time,the MOSSA is applied to appropriately select the‘weights’and‘bias’values of the SAE.An extensive experimental validation process is performed on the benchmark financial dataset and the results are examined under distinct aspects.The experimental values ensured the superior performance of the MOSSA-SAE model on the applied dataset. | Mesfer Al Duhayyim Hadeel Alsolai Fahd N.Al-Wesabi Nadhem Nemri Hany Mahgoub Anwer Mustafa Hilal Manar Ahmed Hamza Mohammed Rizwanullah | 2022 | Computers, Materials & Continua2022，，4： | 0 |
| 14 | Cuckoo Optimized Convolution Support Vector Machine for Big Health Data Processing显示文摘Big health data collection and storing for further analysis is a challenging task because this knowledge is big and has many features.Several cloud-based IoT health providers have been described in the literature previously.Furthermore,there are a number of issues related to time consumed and overall network performance when it comes to big data information.In the existing method,less performed optimization algorithms were used for optimizing the data.In the proposed method,the Chaotic Cuckoo Optimization algorithm was used for feature selection,and Convolutional Support Vector Machine(CSVM)was used.The research presents a method for analyzing healthcare information that uses in future prediction.The major goal is to take a variety of data while improving efficiency and minimizing process time.The suggested method employs a hybrid method that is divided into two stages.In the first stage,it reduces the features by using the Chaotic Cuckoo Optimization algorithm with Levy flight,opposition-based learning,and distributor operator.In the second stage,CSVM is used which combines the benefits of convolutional neural network(CNN)and SVM.The CSVM modifies CNN’s convolution product to learn hidden deep inside data sources.For improved economic flexibility,greater protection,greater analytics with confidentiality,and lower operating cost,the suggested approach is built on fog computing.Overall results of the experiments show that the suggested method can minimize the number of features in the datasets,enhances the accuracy by 82%,and decrease the time of the process. | Eatedal Alabdulkreem Jaber S.Alzahrani Majdy M.Eltahir Abdullah Mohamed Manar Ahmed Hamza Abdelwahed Motwakel Mohamed I.Eldesouki Mohammed Rizwanullah | 2022 | Computers, Materials & Continua2022，，11： | 0 |
| 15 | Big Data Analytics with Artificial Intelligence Enabled Environmental Air Pollution Monitoring Framework显示文摘Environmental sustainability is the rate of renewable resourceharvesting, pollution control, and non-renewable resource exhaustion. Airpollution is a significant issue confronted by the environment particularlyby highly populated countries like India. Due to increased population, thenumber of vehicles also continues to increase. Each vehicle has its individualemission rate;however, the issue arises when the emission rate crosses thestandard value and the quality of the air gets degraded. Owing to the technological advances in machine learning (ML), it is possible to develop predictionapproaches to monitor and control pollution using real time data. With thedevelopment of the Internet of Things (IoT) and Big Data Analytics (BDA),there is a huge paradigm shift in how environmental data are employed forsustainable cities and societies, especially by applying intelligent algorithms.In this view, this study develops an optimal AI based air quality prediction andclassification (OAI-AQPC) model in big data environment. For handling bigdata from environmental monitoring, Hadoop MapReduce tool is employed.In addition, a predictive model is built using the hybridization of ARIMAand neural network (NN) called ARIMA-NN to predict the pollution level.For improving the performance of the ARIMA-NN algorithm, the parametertuning process takes place using oppositional swallow swarm optimization(OSSO) algorithm. Finally, Adaptive neuro-fuzzy inference system (ANFIS)classifier is used to classify the air quality into pollutant and non-pollutant.A detailed experimental analysis is performed for highlighting the betterprediction performance of the proposed ARIMA-NN method. The obtainedoutcomes pointed out the enhanced outcomes of the proposed OAI-AQPCtechnique over the recent state of art techniques. | Manar Ahmed Hamza Hadil Shaiba Radwa Marzouk Ahmad Alhindi Mashael M.Asiri Ishfaq Yaseen Abdelwahed Motwakel Mohammed Rizwanullah | 2022 | Computers, Materials & Continua2022，，11： | 0 |
| 16 | Weather Forecasting Prediction Using Ensemble Machine Learning for Big Data Applications显示文摘The agricultural sector’s day-to-day operations,such as irrigation and sowing,are impacted by the weather.Therefore,weather constitutes a key role in all regular human activities.Weather forecasting must be accurate and precise to plan our activities and safeguard ourselves as well as our property from disasters.Rainfall,wind speed,humidity,wind direction,cloud,temperature,and other weather forecasting variables are used in this work for weather prediction.Many research works have been conducted on weather forecasting.The drawbacks of existing approaches are that they are less effective,inaccurate,and time-consuming.To overcome these issues,this paper proposes an enhanced and reliable weather forecasting technique.As well as developing weather forecasting in remote areas.Weather data analysis and machine learning techniques,such as Gradient Boosting Decision Tree,Random Forest,Naive Bayes Bernoulli,and KNN Algorithm are deployed to anticipate weather conditions.A comparative analysis of result outcome said in determining the number of ensemble methods that may be utilized to improve the accuracy of prediction in weather forecasting.The aim of this study is to demonstrate its ability to predict weather forecasts as soon as possible.Experimental evaluation shows our ensemble technique achieves 95%prediction accuracy.Also,for 1000 nodes it is less than 10 s for prediction,and for 5000 nodes it takes less than 40 s for prediction. | Hadil Shaiba Radwa Marzouk Mohamed K Nour Noha Negm Anwer Mustafa Hilal Abdullah Mohamed Abdelwahed Motwakel Ishfaq Yaseen Abu Sarwar Zamani Mohammed Rizwanullah | 2022 | Computers, Materials & Continua2022，，11： | 0 |
| 17 | Deep Learning with Image Classification Based Secure CPS for Healthcare Sector显示文摘Cyber-Physical System(CPS)involves the combination of physical processes with computation and communication systems.The recent advancementsmade in cloud computing,Wireless Sensor Network(WSN),healthcare sensors,etc.tend to develop CPS as a proficient model for healthcare applications especially,home patient care.Though several techniques have been proposed earlier related to CPS structures,only a handful of studies has focused on the design of CPS models for health care sector.So,the proposal for a dedicated CPS model for healthcare sector necessitates a significant interest to ensure data privacy.To overcome the challenges,the current research paper designs a Deep Learning-based Intrusion Detection and Image Classification for Secure CPS(DLIDIC-SCPS)model for healthcare sector.The aim of the proposed DLIDIC-SCPS model is to achieve secure image transmission and image classification process for CPS in healthcare sector.Primarily,data acquisition takes place with the help of sensors and detection of intrusions is performed using Fuzzy Deep Neural Network(FDNN)technique.Besides,Multiple Share Creation(MSC)approach is used to create several shares of medical image so as to accomplish security.Also,blockchain is employed as a distributed data storage entity to create a ledger that provides access to the client.For image classification,Inception v3 with Fuzzy Wavelet Neural Network(FWNN)is utilized that diagnose the disease from the applied medical image.Finally,Salp Swarm Algorithm(SSA)is utilized to fine tune the parameters involved in WNN model,thereby boosting its classification performance.A wide range of simulations was carried out to highlight the superiority of the proposed DLIDIC-SCPS technique.The simulation outcomes confirm that DLIDIC-SCPS approach demonstrates promising results in terms of security,privacy,and image classification outcomes over recent state-of-the-art techniques. | Ahmed S.Almasoud Abdelzahir Abdelmaboud Faisal S.Alsubaei Manar Ahmed Hamza Ishfaq Yaseen Mohammed Abaker Abdelwahed Motwakel Mohammed Rizwanullah | 2022 | Computers, Materials & Continua2022，，8： | 0 |
| 18 | Artificial Intelligence Based Data Offloading Technique for Secure MEC Systems显示文摘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. | Fadwa Alrowais Ahmed S.Almasoud Radwa Marzouk Fahd N.Al-Wesabi Anwer Mustafa Hilal Mohammed Rizwanullah Abdelwahed Motwakel Ishfaq Yaseen | 2022 | Computers, Materials & Continua2022，，8： | 0 |
| 19 | Deep Transfer Learning Driven Oral Cancer Detection and Classification Model显示文摘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%. | Radwa Marzouk Eatedal Alabdulkreem Sami Dhahbi Mohamed K.Nour Mesfer Al Duhayyim Mahmoud Othman Manar Ahmed Hamza Abdelwahed Motwakel Ishfaq Yaseen Mohammed Rizwanullah | 2022 | Computers, Materials & Continua2022，，11： | 0 |
| 20 | Asymptomatic dengue infection in adults of major cities of Pakistan显示文摘Objective: To determine the asymptomatic dengue infection in adults of Pakistani population.Methods: This study was conducted in five major cities(Islamabad, Karachi, Lahore,Multan, and Peshawar) of Pakistan.A total of 5 230 adults aged 18 years and above without a history of dengue fever at any point in their life were enrolled from participating laboratories.Those who were confirmed for dengue previously were excluded.Of the total, 62.6%(n = 3 276) were male with an average age of 34.6 years.Participants were briefed about the objectives of the study, and written consent was obtained to perform dengue Ig G test using enzyme linked immunosorbent assay.The brief information related to age, gender and area was also taken on proforma.Results: Overall 32.3%(n = 1 691) was having asymptomatic dengue infection which was 67.5%(n = 756) in Karachi followed by 39.1%(n = 391) in Islamabad, 29.9%(n =316) in Lahore and 21%(n = 228) in Peshawar and none from Multan.More males were affected with asymptomatic dengue infection than females.The asymptomatic dengue infection was significantly higher in different cities; however, there was no significant difference with respect to age groups.Conclusions: The asymptomatic dengue infection is higher in cities i.e.Karachi,Islamabad and Lahore which are at risk of developing secondary dengue infections.There is a need of awareness among the public about secondary dengue infection. | Ibrar Rafique Muhammad Arif Nadeem Saqib Muhammad Arif Munir Huma Qureshi Ijaz-ul-Haq Taseer Rizwan Iqbal Waqaruddin Ahmed Tasleem Akhtar Rizwanullah | 2017 | Asian Pacific Journal of Tropical Medicine2017，10，10： | 0 |
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