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| 1 | Hyperparameter Tuned Deep Learning Enabled Intrusion Detection on Internet of Everything Environment显示文摘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. | Manar Ahmed Hamza Aisha Hassan Abdalla Hashim Heba G.Mohamed Saud S.Alotaibi Hany Mahgoub Amal S.Mehanna Abdelwahed Motwakel | 2022 | Computers, Materials & Continua2022，，12： | 1 |
| 2 | Hyperparameter Tuned Deep Learning Enabled Cyberbullying Classification in Social Media显示文摘Cyberbullying(CB)is a challenging issue in social media and it becomes important to effectively identify the occurrence of CB.The recently developed deep learning(DL)models pave the way to design CB classifier models with maximum performance.At the same time,optimal hyperparameter tuning process plays a vital role to enhance overall results.This study introduces a Teacher Learning Genetic Optimization with Deep Learning Enabled Cyberbullying Classification(TLGODL-CBC)model in Social Media.The proposed TLGODL-CBC model intends to identify the existence and non-existence of CB in social media context.Initially,the input data is cleaned and pre-processed to make it compatible for further processing.Followed by,independent recurrent autoencoder(IRAE)model is utilized for the recognition and classification of CBs.Finally,the TLGO algorithm is used to optimally adjust the parameters related to the IRAE model and shows the novelty of the work.To assuring the improved outcomes of the TLGODLCBC approach,a wide range of simulations are executed and the outcomes are investigated under several aspects.The simulation outcomes make sure the improvements of the TLGODL-CBC model over recent approaches. | Mesfer Al Duhayyim Heba G.Mohamed Saud S.Alotaibi Hany Mahgoub Abdullah Mohamed Abdelwahed Motwakel Abu Sarwar Zamani Mohamed I.Eldesouki | 2022 | Computers, Materials & Continua2022，，12： | 1 |
| 3 | A text mining technique using asso- ciation rules extraction 显示文摘 | Hany Mahgoub Dietmar RSsner Nabil Ismail Fawzy Torkey | 2008 | International Journal of Computational Intelligence2008，，4： | 1 |
| 4 | Artificial Intelligence Based Clustering with Routing Protocol for Internet of Vehicles显示文摘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. | Manar Ahmed Hamza Haya Mesfer Alshahrani Fahd NAl-Wesabi Mesfer Al Duhayyim Anwer Mustafa Hilal Hany Mahgoub | 2022 | Computers, Materials & Continua2022，，3： | 0 |
| 5 | 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 |
| 6 | Analysis and Assessment of Wind Energy Potential of Socotra Archipelago in Yemen显示文摘The increasing use of fossil fuels has a significant impact on the environment and ecosystem,which increases the rate of pollution.Given the high potential of renewable energy sources inYemen and the absence of similar studies in the region,this study aims to examine the potential of wind energy in Socotra Island.This was done by analyzing and evaluating wind properties,determining available energy density,calculating wind energy extracted at different altitudes,and then computing the capacity factor for a number of wind turbines and determining the best.The average wind speed in Socotra Island was obtained from the Civil Aviation and Meteorology Authority data,only for the five-year data currently available.The results showed high wind speeds from June to September(9.85-14.88 m/s)while the wind speed decreased for the rest of the year.The average wind speed in the five years was 7.95 m/s.The average annual wind speed,wind energy density,and annual energy density were calculated at different altitudes(10,30,and 50 m).According to the International Wind Energy Rating criteria,the region of Socotra Island falls under Category 7 and is classified as‘Superb’for most of the year.This study provides useful information for developing wind energy and an efficient wind approach. | Murad A.Almekhlafi Fahd N.Al-Wesabi Imran Khan Nadhem Nemri Khalid Mahmood Hany Mahgoub Noha Negm Amin M.El-Kustaban Ammar Zahary | 2022 | Computers, Materials & Continua2022，，1： | 0 |
| 7 | Resource Assessment of Wind Energy Potential of Mokha in Yemen with Weibull Speed显示文摘The increasing use of fossil fuels has a significant impact on the environment and ecosystem,which increases the rate of pollution.Given the high potential of renewable energy sources in Yemen and other Arabic countries,and the absence of similar studies in the region.This study aims to examine the potential of wind energy in Mokha region.This was done by analyzing and evaluating wind properties,determining available energy density,calculating wind energy extracted at different altitudes,and then computing the capacity factor for a few wind turbines and determining the best.Weibull speed was verified as the closest to the average actual wind speed using the cube root,as this was verified using 3 criteria for performance analysis methods(R^(2)=0.9984,RMSE=0.0632,COE=1.028).The wind rose scheme was used to determine the appropriate direction for directing the wind turbines,the southerly direction was appropriate,as the winds blow from this direction for 227 days per year,and the average southerly wind velocity is 5.27 m/s at an altitude of 3 m.The turbine selected in this study has a tower height of 100m and a rated power of 3.45 MW.The capacitance factor was calculated for the three classes of wind turbines classified by the International Electrotechnical Commission(IEC)and compared,and the turbine of the first class was approved,and it is suitable for the study site,as it resists storms more than others.The daily and annual capacity of a single,first-class turbine has been assessed to meet the needs of 1,447 housing units in Mokha region.The amount of energy that could be supplied to each dwelling was around 19 kWh per day,which was adequate to power the basic loads in the home. | Abdulbaset El-Bshah Fahd N.Al-Wesabi Ameen M.Al-Kustoban Mohammad Alamgeer Nadhem Nemri Majdy M.Eltahir Hany Mahgoub Noha Negm | 2021 | Computers, Materials & Continua2021，，10： | 0 |
| 8 | Peak-Average-Power Ratio Techniques for 5G Waveforms Using D-SLM and D-PTS显示文摘Multicarrier Waveform(MCW)has several advantages and plays a very important role in cellular systems.Fifth generation(5G)MCW such as Non-Orthogonal Multiple Access(NOMA)and Filter Bank Multicarrier(FBMC)are thought to be important in 5G implementation.High Peak to Average Power Ratio(PAPR)is seen as a serious concern in MCW since it reduces the efficiency of amplifier use in the user devices.The paper presents a novel Divergence Selective Mapping(DSLM)and Divergence Partial Transmission Sequence(D-PTS)for 5G waveforms.It is seen that the proposed D-SLM and PTS lower PAPR with low computational complexity.The work highlighted a combination of multi-data block partial transmit schemes along with tone reservation.In this,an overlapping factor is used to determine the number of data blocks for every group.Here,considering only those data blocks that have minimum signal power,the use of DSLM and DPTS are required to eliminate the segment’s peaks.Simulation results reveal that the suggested hybrid technique proves to be better than the conventional PTS scheme.Furthermore,the power saving performance of FBMC and NOMA is compared with the Orthogonal Frequency Division Multiplexing(OFDM)waveform. | Himanshu Sharma karthikeyan Rajagopal G.Gugapriya Rajneesh Pareek Arun Kumar HayaMesfer Alshahrani Mohamed K.Nour Hany Mahgoub Mohamed Mousa Anwer Mustafa Hilal | 2023 | Computer Systems Science & Engineering2023，45，5： | 0 |
| 9 | Intelligent Optimization-Based Clustering with Encryption Technique for Internet of Drones Environment显示文摘The recent technological developments have revolutionized the functioning of Wireless Sensor Network(WSN)-based industries with the development of Internet of Things(IoT).Internet of Drones(IoD)is a division under IoT and is utilized for communication amongst drones.While drones are naturally mobile,it undergoes frequent topological changes.Such alterations in the topology cause route election,stability,and scalability problems in IoD.Encryption is considered as an effective method to transmit the images in IoD environment.The current study introduces an Atom Search Optimization basedClusteringwith Encryption Technique for Secure Internet of Drones(ASOCE-SIoD)environment.The key objective of the presented ASOCE-SIoD technique is to group the drones into clusters and encrypt the images captured by drones.The presented ASOCE-SIoD technique follows ASO-based Cluster Head(CH)and cluster construction technique.In addition,signcryption technique is also applied to effectually encrypt the images captured by drones in IoD environment.This process enables the secure transmission of images to the ground station.In order to validate the efficiency of the proposed ASOCE-SIoD technique,several experimental analyses were conducted and the outcomes were inspected under different aspects.The comprehensive comparative analysis results established the superiority of the proposed ASOCE-SIoD model over recent approaches. | Dalia H.Elkamchouchi Jaber S.Alzahrani Hany Mahgoub Amal S.Mehanna Anwer Mustafa Hilal Abdelwahed Motwakel Abu Sarwar Zamani Ishfaq Yaseen | 2022 | Computers, Materials & Continua2022，，12： | 0 |
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