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Ensemble Machine Learning Based Identification of Pediatric Epilepsy

Computers, Materials & Continua 2021年第7期68卷 149-165页

作者： Shamsah Majed Alotaibi atta-ur-rahman Mohammed Imran Basheer Muhammad Adnan Khan Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Faculty of Computing Riphah School of Computing&InnovationRiphah International UniversityLahorePakistan

Epilepsy is a type of brain disorder that causes recurrent *** is the second most common neurological disease after Alzheimer’*** effects of epilepsy in children are serious,since it causes a slower growth rate and a failure to develop certain *** the medical field,specialists record brain activity using an Electroencephalogram(EEG)to observe the epileptic *** detection of these seizures is performed by specialists,but the results might not be accurate due to human errors;therefore,automated detection of epileptic pediatric seizures might be the optimal *** paper investigates the detection of epileptic seizures by applying supervised machine learning *** techniques applied on the data of patients with ages seven years and below from children’s hospital boston massachusetts institute of technology(CHB-MIT)scalp EEG database of epileptic pediatric signals.A group of Naïve Bayes(NB),Support vector machine(SVM),Logistic regression(LR),k-nearest neighbor(KNN),Linear discernment(LD),Decision tree(DT),and ensemble learning methods were applied to the classification *** results demonstrated the outperformance of the present study by achieving 100%for all parameters using the Ensemble learning model in contrast to state-of-the-art studies in the ***,the SVM model achieved performance with 98.3%for sensitivity,97.7%for specificity,and 98%for *** results of the LD and LR models reveal the lower performance i.e.,the sensitivity at 66.9%–68.9%,specificity at 73.5%–77.1%,and accuracy at 70.2%–73%.

关键词： Pediatric epilepsy ensemble learning machine learning SVM EEG data

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Intelligent Cloud Based Heart Disease Prediction System Empowered with Supervised Machine Learning

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Computers, Materials & Continua 2020年第10期65卷 139-151页

作者： Muhammad Adnan Khan Sagheer Abbas Ayesha atta Allah Ditta Hani Alquhayz Muhammad Farhan Khan atta-ur-rahman Rizwan Ali Naqvi Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Science Government College UniversityLahore54000Pakistan Department of Information Sciences Division of Science&TechnologyUniversity of EducationLahore54000Pakistan Department of Computer Science College of ScienceMajmaah UniversityMajmmah11952Saudi Arabia Department of Forensic Sciences University of Health SciencesLahore54000Pakistan Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Unmanned Vehicle Engineering Sejong UniversitySeoul05006Korea

The innovation in technologies related to health facilities today is increasingly helping to manage patients with different *** most fatal of these is the issue of heart disease that cannot be detected from a naked eye,and attacks as soon as the human exceeds the allowed range of vital signs like pulse rate,body temperature,and blood *** real challenge is to diagnose patients with more diagnostic accuracy and in a timely manner,followed by prescribing appropriate treatments and keeping prescription errors to a *** developing countries,the domain of healthcare is progressing day by day using different Smart healthcare:emerging technologies like cloud computing,fog computing,and mobile *** health records(EHRs)are used to manage the huge volume of data using cloud *** reduces the storage,processing,and retrieval cost as well as ensuring the availability of *** learning procedures are used to extract hidden patterns and data *** this research,a combination of cloud computing and machine learning algorithm Support vector machine(SVM)is used to predict heart *** results have shown that the proposed intelligent cloud-based heart disease prediction system empowered with a Support vector machine(SVM)-based system model gives 93.33%accuracy,which is better than previously published approaches.

关键词： Cloud computing machine learning healthcare

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Modeling and Verification of Aircraft Takeoff Through Novel Quantum Nets

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Computers, Materials & Continua 2022年第8期72卷 3331-3348页

作者： Maryam Jamal Nazir Ahmad Zafar atta-ur-rahman Dhiaa Musleh Mohammed A.Gollapalli Sghaier Chabani School of Systems and Technology University of Management&Technology(UMT)C-II Block C 2 Phase 1 Johar TownLahore54770PunjabPakistan Department of Computer Science COMSATS University Islamabad–Sahiwal CampusSahiwal57000PunjabPakistan Department of Computer Science(CS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)Dammam31441Saudi Arabia Department of Computer Information System(CIS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)Dammam31441Saudi Arabia

The formal modeling and verification of aircraft takeoff is a challenge because it is a complex safety-critical *** task of aircraft takeoff is distributed amongst various computer-based controllers,however,with the growing malicious threats a secure communication between aircraft and controllers becomes highly *** research serves as a starting point for integration of BB84 quantum protocol with petri nets for secure modeling and verification of takeoff *** integrated model combines the BB84 quantum cryptographic protocol with powerful verification tool support offered by petri *** model certain important properties of BB84,a new variant of petri nets coined as Quantum Nets are proposed by defining their mathematical foundations and overall system dynamics,furthermore,some important system properties are also abstractly *** proposed QuantumNets are then applied for modeling of aircraft takeoff process by defining three quantum nets:namely aircraft,runway controller and gate *** authentication between quantum nets,the use of external places and transitions is demonstrated to describe the encryptiondecryption process of qubits ***,the developed takeoff quantum network is verified through simulation offered by colored petri-net(CPN)***,reachability tree(RT)analysis is also performed to have greater confidence in feasibility and correctness of the proposed aircraft takeoff model through the Quantum Nets.

关键词： Aircraft takeoff BB84 quantum cryptography petri-nets quantum nets formal modeling and verification

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A Fused Machine Learning Approach for Intrusion Detection System

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Computers, Materials & Continua 2023年第2期74卷 2607-2623页

作者： Muhammad Sajid Farooq Sagheer Abbas atta-ur-rahman Kiran Sultan Muhammad Adnan Khan Amir Mosavi School of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam 31441Saudi Arabia Department of CIT The Applied CollegeKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Software Gachon UniversitySeongnam13120Korea John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

The rapid growth in data generation and increased use of computer network devices has amplified the infrastructures of *** interconnectivity of networks has brought various complexities in maintaining network availability,consistency,and *** learning based intrusion detection systems have become essential to monitor network traffic for malicious and illicit *** intrusion detection system controls the flow of network traffic with the help of computer *** deep learning algorithms in intrusion detection systems have played a prominent role in identifying and analyzing intrusions in network *** this purpose,when the network traffic encounters known or unknown intrusions in the network,a machine-learning framework is needed to identify and/or verify network *** Intrusion detection scheme empowered with a fused machine learning technique(IDS-FMLT)is proposed to detect intrusion in a heterogeneous network that consists of different source networks and to protect the network from malicious *** proposed IDS-FMLT system model obtained 95.18%validation accuracy and a 4.82%miss rate in intrusion detection.

关键词： Fused machine learning heterogeneous network intrusion detection

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Authenblue: A New Authentication Protocol for the Industrial Internet of Things

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Computers, Materials & Continua 2021年第4期67卷 1103-1119页

作者： Rachid Zagrouba Asayel AlAbdullatif Kholood AlAjaji Norah Al-Serhani Fahd Alhaidari Abdullah Almuhaideb atta-ur-rahman Department of Computer Information System College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Computer Science College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia

The Internet of Things(IoT)is where almost anything can be controlled and managed remotely by means of *** the IoT evolution led to quality of life enhancement,many of its devices are *** lack of robust key management systems,efficient identity authentication,low fault tolerance,and many other issues lead to IoT devices being easily targeted by *** this paper we propose a new authentication protocol called Authenblue that improve the authentication process of IoT devices and Coordinators of Personal Area Network(CPANs)in an Industrial IoT(IIoT)*** study proposed Authenblue protocol as a new Blockchainbased authentication *** enhance the authentication process and make it more secure,Authenblue modified the way of generating IIoT identifiers and the shared secret keys used by the IIoT devices to raise the efficiency of the authentication *** enhance the authentication protocol that other models rely on by enhancing the approach used to generate the User Identifier(UI).The UI values changed from being static values,sensors MAC addresses,to be generated values in the inception *** approach makes the process of renewing the sensor keys more secure by renewing their UI values instead of changing the secret *** this study,Authenblue has been simulated in the Network Simulator 3(NS3).Simulation results show an improved performance compared to the related work.

关键词： Authentication industrial internet of things security Authenblue blockchain NS3

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Automated File Labeling for Heterogeneous Files Organization Using Machine Learning

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Computers, Materials & Continua 2023年第2期74卷 3263-3278页

作者： Sagheer Abbas Syed Ali Raza MAKhan Muhammad Adnan Khan atta-ur-rahman Kiran Sultan Amir Mosavi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Science GC University LahorePakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Software Pattern Recognition and Machine Learning LabGachon UniversitySeongnam13120Korea Department of Computer Science College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam31441Saudi Arabia Department of CIT The Applied CollegeKing Abdulaziz UniversityJeddah31261Saudi Arabia John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

File labeling techniques have a long history in analyzing the anthological trends in computational *** situation becomes worse in the case of files downloaded into systems from the ***,most users either have to change file names manually or leave a meaningless name of the files,which increases the time to search required files and results in redundancy and duplications of user ***,no significant work is done on automated file labeling during the organization of heterogeneous user files.A few attempts have been made in topic ***,one major drawback of current topic modeling approaches is better *** rely on specific language types and domain similarity of the *** this research,machine learning approaches have been employed to analyze and extract the information from heterogeneous corpus.A different file labeling technique has also been used to get the meaningful and`cohesive topic of the *** results show that the proposed methodology can generate relevant and context-sensitive names for heterogeneous data files and provide additional insight into automated file labeling in operating systems.

关键词： Automated file labeling file organization machine learning topic modeling

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Aspect Level Songs Rating Based Upon Reviews in English

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Computers, Materials & Continua 2023年第2期74卷 2589-2605页

作者： Muhammad Aasim Qureshi Muhammad Asif Saira Anwar Umar Shaukat atta-ur-rahman Muhammad Adnan Khan Amir Mosavi Department of Computer Science Bahria University Lahore54000Pakistan Lahore Institute of Science and Technology Lahore 54792Pakistan Department of Multidisciplinary Engineering Texas A&M UniversityCollege Station77843USA Department of Computer Science College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam 31441Saudi Arabia Department of Software Gachon UniversitySeongnam13120Korea John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

With the advancements in internet facilities,people are more inclined towards the use of online *** service providers shelve their items for *** users post their feedbacks,reviews,ratings,*** the use of the *** enormous increase in these reviews has raised the need for an automated system to analyze these reviews to rate these *** Analysis(SA)is a technique that performs such decision *** research targets the ranking and rating through sentiment analysis of these reviews,on different *** a case study,Songs are opted to design and test the decision *** aspects of songs namely music,lyrics,song,voice and video are *** the reason,reviews of 20 songs are scraped from YouTube,pre-processed and formed a *** machine learning algorithms—Naïve Bayes(NB),Gradient Boost Tree,Logistic Regression LR,K-Nearest Neighbors(KNN)and Artificial Neural Network(ANN)are *** performed the best with 74.99%*** are validated using K-Fold.

关键词： Machine learning natural language processing songs reviews:sentiment analysis songs rating aspect level sentiment analysis reviews analysis text classification music

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Size-manipulation,compaction and electrical properties of barium manganite nanorods synthesized via the CHM method

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Progress in Natural Science:Materials International 2013年第4期23卷 388-394页

作者： Khizar Hayat M.A.Rafiq atta ur rahman Abid Ali Khan M.M.Hasan Materials Research Laboratory Institute of Physics and ElectronicsUniversity of PeshawarKhyber Pakhtunkhwa 25120Pakistan Micro and Nano Devices Group Department of Metallurgy and Materials EngineeringPakistan Institute of Engineering and Applied SciencesP.O.NiloreIslamabad 45650Pakistan Centre of Biotechnology & Microbiology University of PeshawarKhyber Pakhtunkhwa 25120Pakistan

A composite-hydroxide mediated method was employed to synthesize barium manganite ***,surface smoothness and uniformity of these nanorods were optimized by varying reaction temperature and reaction *** rods with an average diameter of 200 nm and length of1–1.5 μm were obtained at optimum conditions of 200 1C/48 *** dielectric study of these rods reveals that they have higher value of dielectric constant at lower frequencies which was attributed to the interfacial and rotational type ***,the increase in dielectric constant with temperature was attributed to the thermal activation of such ***,the analysis of ln(J) vs.E1/2characteristics in the temperature range of 300–400 K shows that possible operative conduction mechanism was of Poole–Frenkel *** value of βexp was found to be 4.85 times greater than the expected theoretical value of feld lowering coeffcient with an internal feld enhancement factor ofα2 *** high value of βexp may be due to some localized electric felds existing inside the sample.

关键词： BaMnO3 nanorods Electrical properties Dielectric properties DC conduction mechanism

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Modelling and Simulation of COVID-19 Outbreak Prediction Using Supervised Machine Learning

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Computers, Materials & Continua 2021年第3期66卷 2397-2407页

作者： Rachid Zagrouba Muhammad Adnan Khan atta-ur-rahman Muhammad Aamer Saleem Muhammad Faheem Mushtaq Abdur Rehman Muhammad Farhan Khan Department of Computer Information System College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Computer Science Riphah International UniversityLahore CampusLahore54000Pakistan Hamdard Institute of Engineering and Technology Hamdard UniversityIslamabad44000Pakistan Department of Information Technology Khwaja Fareed University of Engineering and Information TechnologyRahim Yar Khan64200Pakistan School of Computer Science NCBA&ELahore54000Pakistan Department of Forensic Sciences University of Health SciencesLahore54000Pakistan

Novel Coronavirus-19(COVID-19)is a newer type of coronavirus that has not been formally detected in *** is established that this disease often affects people of different age groups,particularly those with body disorders,blood pressure,diabetes,heart problems,or weakened immune *** epidemic of this infection has recently had a huge impact on people around the globe with rising mortality *** levels of mortality are attributed to their transmitting behavior through physical contact between *** is extremely necessary to monitor the transmission of the infection and also to anticipate the early stages of the disease in such a way that the appropriate timing of effective precautionary measures can be *** latest global coronavirus epidemic(COVID-19)has brought new challenges to the scientific *** Intelligence(AI)-motivated methodologies may be useful in predicting the conditions,consequences,and implications of such an *** forecasts may help to monitor and prevent the spread of these *** article proposes a predictive framework incorporating Support Vector Machines(SVM)in the forecasting of a potential outbreak of *** findings indicate that the suggested system outperforms cutting-edge *** method could be used to predict the long-term spread of such an outbreak so that we can implement proactive measures in *** findings of the analyses indicate that the SVM forecasting framework outperformed the Neural Network methods in terms of accuracy and computational *** proposed SVM system model exhibits 98.88%and 96.79%result in terms of accuracy during training and validation respectively.

关键词： Coronavirus outbreak machine learning artificial intelligence

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Data Fusion Architecture Empowered with Deep Learning for Breast Cancer Classification

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Computers, Materials & Continua 2023年第12期77卷 2813-2831页

作者： Sahar Arooj Muhammad Farhan Khan Tariq Shahzad Muhammad Adnan Khan Muhammad Umar Nasir Muhammad Zubair atta-ur-rahman Khmaies Ouahada Faculty of Computing Riphah International UniversityIslamabad45000Pakistan Department of Forensic Sciences University of Health SciencesLahore54000Pakistan Department of Electrical and Electronic Engineering Science University of JohannesburgP.O.Box 524Johannesburg2006South Africa School of Computing Skyline University CollegeUniversity City SharjahSharjah1797United Arab Emirates Riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Software Faculty of Artificial Intelligence&SoftwareGachon UniversitySeongnamGyeonggido13120Korea Department of Computer Science Bahria UniversityLahore CampusLahore54000Pakistan Department of Computer Science College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam31441Saudi Arabia

Breast cancer(BC)is the most widespread tumor in females worldwide and is a severe public health *** is the leading reason of death affecting females between the ages of 20 to 59 around the *** detection and therapy can help women receive effective treatment and,as a result,decrease the rate of breast cancer *** cancer tumor develops when cells grow improperly and attack the healthy tissue in the human *** are classified as benign or malignant,and the absence of cancer in the breast is considered *** learning,machine learning,and transfer learning models are applied to detect and identify cancerous tissue like *** research assists in the identification and classification of *** implemented the pre-trained model AlexNet and proposed model Breast cancer identification and classification(BCIC),which are machine learning-based models,by evaluating them in the form of comparative *** used 3 datasets,A,B,and *** fuzzed these datasets and got 2 datasets,A2C and *** A2C is the fusion of A,B,and C with 2 classes categorized as benign and *** B3C is the fusion of datasets A,B,and C with 3 classes classified as benign,malignant,and *** used customized AlexNet according to our datasets and BCIC in our proposed *** achieved an accuracy of 86.5%on Dataset B3C and 76.8%on Dataset A2C by using AlexNet,and we achieved the optimum accuracy of 94.5%on Dataset B3C and 94.9%on Dataset A2C by using proposed model BCIC at 40 epochs with 0.00008 learning *** proposed fuzzed dataset model using transfer *** fuzzed three datasets to get more accurate results and the proposed model achieved the highest prediction accuracy using fuzzed dataset transfer learning technique.

关键词： Breast cancer classification deep learning machine learning transfer learning learning rate

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