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Improving Thyroid Disorder Diagnosis via Ensemble Stacking and Bidirectional Feature Selection

Computers, Materials & Continua 2024年第3期78卷 4225-4241页

作者： muhammad Armghan Latif Zohaib Mushtaq Saad Arif Sara Rehman muhammad Farrukh qureshi Nagwan Abdel Samee Maali Alabdulhafith Yeong Hyeon Gu Mohammed A.Al-masni Department of Computer and Information System Cleveland State UniversityOhio44115USA Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Biomedical Engineering Riphah International UniversityIslamabad44000Pakistan Department of Electrical Engineering Riphah International UniversityIslamabad44000Pakistan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Artificial Intelligence College of Software and Convergence TechnologySejong UniversitySeoul05006Korea

Thyroid disorders represent a significant global health challenge with hypothyroidism and hyperthyroidism as two common conditions arising from dysfunction in the thyroid *** and timely diagnosis of these disorders is crucial for effective treatment and patient *** research introduces a comprehensive approach to improve the accuracy of thyroid disorder diagnosis through the integration of ensemble stacking and advanced feature selection *** forward feature selection,sequential backward feature elimination,and bidirectional feature elimination are investigated in this *** ensemble learning,random forest,adaptive boosting,and bagging classifiers are *** effectiveness of these techniques is evaluated using two different datasets obtained from the University of California Irvine-Machine Learning Repository,both of which undergo preprocessing steps,including outlier removal,addressing missing data,data cleansing,and feature *** experimentation demonstrates the remarkable success of proposed ensemble stacking and bidirectional feature elimination achieving 100%and 99.86%accuracy in identifying hyperthyroidism and hypothyroidism,*** enhancing detection accuracy,the ensemble stacking model also demonstrated a streamlined computational complexity which is pivotal for practical medical *** significantly outperformed existing studies with similar objectives underscoring the viability and effectiveness of the proposed *** research offers an innovative perspective and sets the platform for improved thyroid disorder diagnosis with broader implications for healthcare and patient well-being.

关键词： Ensemble learning random forests boosting dimensionality reduction machine learning smart healthcare computer aided diagnosis

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Fuzzy-Based Automatic Epileptic Seizure Detection Framework

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Computers, Materials & Continua 2022年第3期70卷 5601-5630页

作者： Aayesha muhammad Bilal qureshi muhammad Afzaal muhammad Shuaib qureshi Jeonghwan Gwak Shaheed Zulfikar Ali Bhutto Institute of Science and Technology IslamabadPakistan Department of Computer Science&IT University of Lakki MarwatKPKPakistan Stockholm University StockholmSweden Department of Computer Science School of Arts and SciencesUniversity of Central AsiaKyrgyz Republic Department of Software Korea National University of TransportationChungju27469Korea Department of Biomedical Engineering Korea National University of TransportationChungju27469Korea Department of AI Robotics Engineering Korea National University of TransportationChungju27469Korea Department of IT&Energy Convergence(BK21 FOUR) Korea National University of TransportationChungju27469Korea

Detection of epileptic seizures on the basis of Electroencephalogram(EEG)recordings is a challenging task due to the complex,non-stationary and non-linear nature of these biomedical *** the existing literature,a number of automatic epileptic seizure detection methods have been proposed that extract useful features from EEG segments and classify them using machine learning *** characterizing features of epileptic and non-epileptic EEG signals overlap;therefore,it requires that analysis of signals must be performed from diverse *** studies analyzed these signals in diverse domains to identify distinguishing characteristics of epileptic EEG *** pose the challenge mentioned above,in this paper,a fuzzy-based epileptic seizure detection model is proposed that incorporates a novel feature extraction and selection method along with fuzzy *** proposed work extracts pattern features along with time-domain,frequencydomain,and non-linear analysis of *** applies a feature selection strategy on extracted features to get more discriminating features that build fuzzy machine learning classifiers for the detection of epileptic *** empirical evaluation of the proposed model was conducted on the benchmark Bonn EEG *** shows significant accuracy of 98%to 100%for normal *** classification cases while for three class classification of normal ***-ictal *** accuracy reaches to above 97.5%.The obtained results for ten classification cases(including normal,seizure or ictal,and seizure-free or inter-ictal classes)prove the superior performance of proposed work as compared to other state-of-the-art counterparts.

关键词： Medical image processing electroencephalography machine learning fuzzy system models seizure detection epileptic seizure virtualization

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A Survey on the Role of Complex Networks in IoT and Brain Communication

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Computers, Materials & Continua 2023年第9期76卷 2573-2595页

作者： Vijey Thayananthan Aiiad Albeshri Hassan A.Alamri muhammad Bilal qureshi muhammad Shuaib qureshi Cybersecurity Department Faculty of ComputingEngineering and ScienceUniversity of SouthWalesWalesUK Department of Computer Science Faculty of Computing and Information Technology(FCIT)King Abdulaziz UniversityJeddah21589Saudi Arabia Computer Science Department Umm Al-Qura UniversityMakkah21955Saudi Arabia Department of Computer Science&IT University of Lakki MarwatLakki Marwat28420KPKPakistan Department of Computer Engineering Chungnam National UniversityDaejeon34134Republic of Korea

Complex networks on the Internet of Things(IoT)and brain communication are the main focus of this *** benefits of complex networks may be applicable in the future research directions of 6G,photonic,IoT,brain,etc.,communication *** data traffic,huge capacity,minimal level of dynamic latency,*** some of the future requirements in 5G+and 6G communication *** emerging communication,technologies such as 5G+/6G-based photonic sensor communication and complex networks play an important role in improving future requirements of IoT and brain *** this paper,the state of the complex system considered as a complex network(the connection between the brain cells,neurons,etc.)needs measurement for analyzing the functions of the neurons during brain ***,we measure the state of the complex system through *** 5G+/6G-based photonic sensor nodes,finding observability influenced by the concept of contraction provides the stability of *** IoT or any sensors fail to measure the state of the connectivity in the 5G+or 6G communication due to external noise and attacks,some information about the sensor nodes during the communication will be ***,neurons considered sing the complex networks concept neuron sensors in the brain lose communication and ***,affected sensor nodes in a contraction are equivalent to compensate for maintaining stability *** this compensation,loss of observability depends on the contraction size which is a key factor for employing a complex *** analyze the observability recovery,we can use a contraction detection algorithm with complex network *** survey paper shows that contraction size will allow us to improve the performance of brain communication,stability of neurons,etc.,through the clustering coefficient considered in the contraction detection *** addition,we discuss the scalability of IoT communication using 5G+/6G

关键词： Complex networks emerging communication IoT based on 6G systems neuroscience photonic technology

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NewBee: Context-Free Grammar (CFG) of a New Programming Language for Novice Programmers

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Intelligent Automation & Soft Computing 2023年第7期37卷 439-453页

作者： muhammad Aasim qureshi muhammad Asif Saira Anwar Department of Computer Science Bahria University Lahore54000Pakistan Department of Law Science and TechnologyUniversity of Bologna40126Italy Department of Multidisciplinary Engineering Texas A&M UniversityCollege Station77843USA

Learning programming and using programming languages are the essential aspects of computer science *** use programming languages to write their *** computer programs(students or practitioners written)make computers artificially intelligent and perform the tasks needed by the *** these programs,the computer may be visioned as a pointless *** the premise of writing programs is situated with specific programming languages,enormous efforts have been made to develop and create programming ***,each program-ming language is domain-specific and has its nuances,syntax and seman-tics,with specific pros and *** language-specific details,including syntax and semantics,are significant hurdles for novice ***,the instructors of introductory programming courses find these language specificities as the biggest hurdle in students learning,where more focus is on syntax than logic development and actual implementation of the *** the conceptual difficulty of programming languages and novice students’struggles with the language syntax,this paper describes the design and development of a Context-Free Grammar(CFG)of a programming language for the novice,newcomers and students who do not have computer science as their *** to its syntax proximity to daily conversations,this paper hypothesizes that this language will be easy to use and understand by novice *** paper systematically designed the language by identifying themes from various existing programming languages(e.g.,C,Python).Additionally,this paper surveyed computer science experts from industry and academia,where experts self-reported their satisfaction with the newly designed *** results indicate that 93%of the experts reported satisfaction with the NewBee for novice,newcomer and non-Computer Sci-ence(CS)major students.

关键词： Programming language formal language computer language language grammar simple syntax programming language novice programmer

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De-convolution and De-noising of SAR Based GPS Images Using Hybrid Particle Swarm Optimization

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Chinese Journal of Electronics 2023年第1期32卷 166-176页

作者： Rizwan Sadiq muhammad Bilal qureshi muhammad Mohsin Khan Dept.of Electrical and Electronic Engineering Koc University Dept.of Electrical and Computer Engineering COMSATS University Dept.of Radiology and Biomedical Imaging Yale University Sino-Pak Center for Artificial Intelligence (SPCAI) Pak-Austria Fachhochschule:Institute of Applied Sciences and Technology (PAF-IAST)

Synthetic aperture radar(SAR) imaging is an efficient strategy which exploits the properties of microwaves to capture images. A major concern in SAR imaging is the reconstruction of image from back scattered signals in the presence of noise. The reflected signal consist of more noise than the target signal and it is a challenging problem to reduce the noise in the collected signal for better reconstruction of an image. Current studies mostly focus on filtering techniques for noise removal. This can result in an undesirable point spread function causing extreme smearing effect in the desired image. In order to handle this problem, a computational technique, particle swarm optimization(PSO) is used for de-noising purpose and later the target performance is further improved by an amalgamation of Wiener ***, to improve the de-noising performance we have exploited the singular value decomposition based morphological filtering. To justify the proposed improvements we have simulated the proposed techniques and results are compared with the conventional existing models. The proposed method revealed considerable decrease in mean square error compared to Wiener filter and PSO techniques. Quantitative analysis of image restoration quality are also presented in comparison with Wiener filter and PSO based on the improvement in signal to noise ratio and peak signal to noise ratio.

关键词： Radar remote sensing Wiener filters Filtering Statistical analysis Radar polarimetry Particle swarm optimization Synthetic aperture radar

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Pseudo NLP Joint Spam Classification Technique for Big Data Cluster

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Computers, Materials & Continua 2022年第4期71卷 517-535页

作者： WooHyun Park Nawab muhammad Faseeh qureshi Dong Ryeol Shin Department of Electrical and Computer Engineering Sungkyunkwan UniversitySuwon16419Korea Department of Computer Education Sungkyunkwan UniversitySeoul03063Korea

Spam mail classification considered complex and error-prone task in the distributed computing *** are various available spam mail classification approaches such as the naive Bayesian classifier,logistic regression and support vector machine and decision tree,recursive neural network,and long short-term memory ***,they do not consider the document when analyzing spam mail *** approaches use the bagof-words method,which analyzes a large amount of text data and classifies features with the help of term frequency-inverse document *** there are many words in a document,these approaches consume a massive amount of resources and become infeasible when performing classification on multiple associated mail documents ***,spam mail is not classified fully,and these approaches remain with ***,we propose a term frequency topic inverse document frequency model that considers the meaning of text data in a larger semantic unit by applying weights based on the document’s ***,the proposed approach reduces the scarcity problem through a frequency topic-inverse document frequency in singular value decomposition *** proposed approach also reduces the dimensionality,which ultimately increases the strength of document *** evaluations show that the proposed approach classifies spam mail documents with higher accuracy using individual document-independent processing *** evaluations show that the proposed approach performs better than the logistic regression model in the distributed computing environment,with higher document word frequencies of 97.05%,99.17%and 96.59%.

关键词： NLP big data machine learning TFT-IDF spam mail

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A Novel Auto-Annotation Technique for Aspect Level Sentiment Analysis

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Computers, Materials & Continua 2022年第3期70卷 4987-5004页

作者： muhammad Aasim qureshi muhammad Asif Mohd Fadzil Hassan Ghulam Mustafa muhammad Khurram Ehsan Aasim Ali Unaza Sajid Department of Computer Sciences Bahria UniversityLahore Campus54000Pakistan Computer and Information Science Department University TeknologiPetronas32610Malaysia

In machine learning,sentiment analysis is a technique to find and analyze the sentiments hidden in the *** sentiment analysis,annotated data is a basic ***,this data is manually *** annotation is time consuming,costly and laborious *** overcome these resource constraints this research has proposed a fully automated annotation technique for aspect level sentiment *** is created from the reviews of ten most popular songs on *** of five aspects—voice,video,music,lyrics and song,are *** N-Gram based technique is *** dataset consists of 369436 reviews that took 173.53 s to annotate using the proposed technique while this dataset might have taken approximately 2.07 million seconds(575 h)if it was annotated *** the validation of the proposed technique,a sub-dataset—Voice,is annotated manually as well as with the proposed ***’s Kappa statistics is used to evaluate the degree of agreement between the two *** high Kappa value(i.e.,0.9571%)shows the high level of agreement between the *** validates that the quality of annotation of the proposed technique is as good as manual annotation even with far less computational *** research also contributes in consolidating the guidelines for the manual annotation process.

关键词： Machine learning natural language processing annotation semi-annotated technique reviews annotation text annotation corpus annotation

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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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AI-Enabled Grouping Bridgehead to Secure Penetration Topics of Metaverse

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Computers, Materials & Continua 2022年第12期73卷 5609-5624页

作者： Woo Hyun Park Isma Farah Siddiqui Nawab muhammad Faseeh qureshi Department of Electrical and Computer Engineering Sungkyunkwan UniversitySuwon16419Korea Department of Computer Education Sungkyunkwan UniversitySeoul03063Korea Department of Software Engineering Mehran University of Engineering&TechnologyJamshoroPakistan

With the advent of the big data era,security issues in the context of artificial intelligence(AI)and data analysis are attracting research *** the metaverse,which will become a virtual asset in the future,users’communication,movement with characters,text elements,etc.,are required to integrate the real and ***,they can be exposed to ***,various hacker threats *** example,users’assets are exposed through notices and mail alerts regularly sent to users by *** the future,hacker threats will increase mainly due to naturally anonymous ***,it is necessary to use the natural language processing technology of artificial intelligence,especially term frequency-inverse document frequency,word2vec,gated recurrent unit,recurrent neural network,and long-short term ***,several application versions are ***,research on tasks and performance for algorithm application is *** propose a grouping algorithm that focuses on securing various bridgehead strategies to secure topics for security and safety within the *** algorithm comprises three modules:extracting topics from attacks,managing dimensions,and performing ***,we create 24 topic-based *** normal and spam mail attacks to verify our algorithm,the accuracy of the previous application version was increased by∼0.4%-1.5%.

关键词： Metaverse security computational linguistics grouping bridgehead AI

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Ambient BackCom in beyond 5G NOMA networks: A multi-cell resource allocation framework

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Digital Communications and Networks 2022年第6期8卷 1005-1013页

作者： Wali Ullah Khan Fida Hussain Memon Kapal Dev muhammad Awais Javed Dinh-Thuan Do Nawab muhammad Faseeh qureshi Interdisciplinary Centre for Security Reliability and Trust(SnT)University of Luxembourg1855 Luxembourg CityLuxembourg Department of Electrical Engineering Sukkur IBA University Pakistan Department of Institute of Intelligent Systems University of JohannesburgSouth Africa Department of Electrical and Computer Engineering COMSATS University Islamabad44000IslamabadPakistan Electrical Engineering Department University of Colorado DenverDenverCO80204USA Department of Computer Education Sungkyunkwan UniversitySeoulSouth Korea

The research of Non-Orthogonal Multiple Access (NOMA) is extensively used to improve the capacity of networks beyond the fifth-generation. The recent merger of NOMA with ambient Backscatter Communication (BackCom), though opening new possibilities for massive connectivity, poses several challenges in dense wireless networks. One such challenge is the performance degradation of ambient BackCom in multi-cell NOMA networks under the effect of inter-cell interference. Driven by providing an efficient solution to the issue, this article proposes a new resource allocation framework that uses a duality theory approach. Specifically, the sum rate of the multi-cell network with backscatter tags and NOMA user equipment is maximized by formulating a joint optimization problem. To find the efficient base station transmit power and backscatter reflection coefficient in each cell, the original problem is first divided into two subproblems, and then the closed form solution is derived. A comparison with the Orthogonal Multiple Access (OMA) ambient BackCom and pure NOMA transmission has been provided. Simulation results of the proposed NOMA ambient BackCom indicate a significant improvement over the OMA ambient BackCom and pure NOMA in terms of sum-rate gains.

关键词： Beyond fifth-generation Non-orthogonal multiple access Ambient BackCom Spectrum efficiency optimization

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