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Fuzzy-Based Automatic Epileptic Seizure Detection Framework

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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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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Synthetic data as an investigative tool in hypertension and renal diseases research

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World Journal of Methodology 2025年第1期15卷 9-13页

作者： Aleena Jamal Som Singh Fawad qureshi Sidney Kimmel Medical College Thomas Jefferson UniversityPhiladelphiaPA 19107United States School of Medicine University of Missouri Kansas CityKansasMO 64106United States Division of Nephrology and Hypertension Mayo ClinicRochesterMN 55905United States

There is a growing body of clinical research on the utility of synthetic data derivatives,an emerging research tool in *** nephrology,clinicians can use machine learning and artificial intelligence as powerful aids in their clinical decision-making while also preserving patient *** is especially important given the epidemiology of chronic kidney disease,renal oncology,and hypertension ***,there remains a need to create a framework for guidance regarding how to better utilize synthetic data as a practical application in this research.

关键词： Synthetic data Artificial intelligence Nephrology Blood pressure Research Editorial

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A modified fractional least mean square algorithm for chaotic and nonstationary time series prediction

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Chinese Physics B 2014年第3期23卷 159-164页

作者： bilal Shoaib Ijaz Mansoor qureshi Ihsanulhaq Shafqatullah Department of Electronic Engineering Faculty of Engineering and Technology International Islamic University Department of Electrical Engineering AIR University School of Engineering and Applied Sciences ISRA University

A method of modifying the architecture of fractional least mean square （FLMS） algorithm is presented to work with nonlinear time series prediction. Here we incorporate an adjustable gain parameter in the weight adaptation equation of the original FLMS algorithm and absorb the gamma function in the fractional step size parameter. This approach provides an interesting achievement in the performance of the filter in terms of handling the nonlinear problems with less computational burden by avoiding the evaluation of complex gamma function. We call this new algorithm as the modified fractional least mean square （MFLMS） algorithm. The predictive performance for the nonlinear Mackey glass chaotic time series is observed and evaluated using the classical LMS, FLMS, kernel LMS, and proposed MFLMS adaptive filters. The simulation results for the time series with and without noise confirm the superiority and improvement in the prediction capability of the proposed MFLMS predictor over its counterparts.

关键词： fractional least mean square kernel methods Reimann-Lioville derivative Mackey glass timeseries

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Effect of Firing Temperature on the Electromagnetic Properties of Electronic Transformer Cores Developed by Using Nanosized Mn–Zn Ferrite Powders

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Acta Metallurgica Sinica(English Letters) 2015年第2期28卷 159-163页

作者： Hassan Waqas Ammad Hussain qureshi muhammad Shahzad Materials Division Directorate of Technology PINSTECH

Current investigation deals with the effect of grain growth process as a function of sintering temperature on the electromagnetic properties （initial permeability, impedance, and gain） of Mn-Zn ferrite （MZF） cores （toroids）. By employing auto combustion process, nanosized [（20 ± 5） nm] MZF powders were synthesized and toroid shape cores were obtained after subsequent calcination and firing （sintering） process. It was observed that the submicron structure （0.5 μm） achieved in the ferrite core sintered at 1,000 ℃ was gradually transformed into micron size grains by increasing the firing temperature i.e., 1,100, 1,200, and 1,350 ℃. The results reveal that MZF core sintered at low temperature （1,000 ℃） showed high impedance, low initial permeability, and narrow working frequency range i.e., 3-15 MHz. However, the improvement in initial permeability, sintered density, and operational frequency range （100 kHz-17 MHz） was observed at high temperature （1,350 ℃） firing in the inert environment. This synthesized MZF core is more suitable for miniaturized switch mode power supply applications.

关键词： Mn-Zn ferrites Nanosized powders Impedance Sintering

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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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Artificial Intelligence Based Meteorological Parameter Forecasting for Optimizing Response of Nuclear Emergency Decision Support System

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原子能科学技术 2024年第10期58卷 2068-2076页

作者： bilal Ahmed Khan HASEEB ur Rehman QAISAR Nadeem muhammad Ahmad Naveed qureshi JAWARIA Ahad muhammad Naveed Akhtar AMJAD Farooq MASROOR Ahmad Pakistan Institute of Engineering and Applied Sciences Islamabad 44000Pakistan

This paper presents a novel artificial intelligence (AI) based approach to predict crucial meteorological parameters such as temperature,pressure,and wind speed,typically calculated from computationally intensive weather research and forecasting (WRF) *** meteorological data is indispensable for simulating the release of radioactive effluents,especially in dispersion modeling for nuclear emergency decision support *** of meteorological conditions during nuclear emergencies using the conventional WRF model is very complex and ***,a new artificial neural network (ANN) based technique was proposed as a viable alternative for meteorological prediction.A multi-input multi-output neural network was trained using historical site-specific meteorological data to forecast the meteorological *** evaluation of this technique was conducted to test its performance in forecasting various parameters including atmospheric pressure,temperature,and wind speed components in both East-West and North-South *** performance of developed network was evaluated on an unknown dataset,and acquired results are within the acceptable range for all meteorological *** show that ANNs possess the capability to forecast meteorological parameters,such as temperature and pressure,at multiple spatial locations within a grid with high accuracy,utilizing input data from a single ***,accuracy is slightly compromised when predicting wind speed *** mean square error (RMSE) was utilized to report the accuracy of predicted results,with values of 1.453℃for temperature,77 Pa for predicted pressure,1.058 m/s for the wind speed of U-component and 0.959 m/s for the wind speed of *** conclusion,this approach offers a precise,efficient,and wellinformed method for administrative decision-making during nuclear emergencies.

关键词： prediction of meteorological parameters weather research and forecasting model artificial neural networks nuclear emergency support system

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Acute fractures of the pediatric foot and ankle

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World Journal of Pediatrics 2015年第1期11卷 14-20页

作者： Mansur Halai bilal Jamal Paul Rea Mobeen qureshi Anand Pillai Department of Trauma and Orthopedics University Hospitials of South ManchesterManchester M239LTUK

Background:Injuries around the foot and ankle are *** is a paucity of literature,outside that of specialist orthopedic journals,that focuses on this subject in the pediatric *** sources:In this review,we outline pediatric foot and ankle fractures in an anatomically oriented manner from the current *** aim is to aid the emergency department doctor to manage these challenging injuries more efectively in the acute ***:These injuries require a detailed history and examination to aid the ***,plain radiographs are suficient,but more complex injuries require the use of magnetic resonance *** is dependent on the proximity to skeletal maturity and the degree of displacement of *** have a marked ability to remodel after fractures and therefore mainstay treatment is immobilization by a cast or *** fxation,although uncommon in this population,may be necessary with adolescents,certain unstable injuries or in cases with displaced articular *** the setting of severe foot trauma,skin compromise and compartment syndrome of the foot must be ***:The integrity of the physis,articular surface and soft tissues are all equally important in treating these injuries.

关键词： ankle foot fractures

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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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