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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 signals.In 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 algorithms.Some characterizing features of epileptic and non-epileptic EEG signals overlap;therefore,it requires that analysis of signals must be performed from diverse perspectives.Few studies analyzed these signals in diverse domains to identify distinguishing characteristics of epileptic EEG signals.To 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 classifiers.The proposed work extracts pattern features along with time-domain,frequencydomain,and non-linear analysis of signals.It applies a feature selection strategy on extracted features to get more discriminating features that build fuzzy machine learning classifiers for the detection of epileptic seizures.The empirical evaluation of the proposed model was conducted on the benchmark Bonn EEG dataset.It shows significant accuracy of 98%to 100%for normal vs.ictal classification cases while for three class classification of normal vs.inter-ictal vs.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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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 filter.Moreover, 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： Remote sensing Synthetic aperture radar imaging Wiener filter Particle swarm optimization

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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 paper.The benefits of complex networks may be applicable in the future research directions of 6G,photonic,IoT,brain,etc.,communication technologies.Heavy data traffic,huge capacity,minimal level of dynamic latency,etc.are some of the future requirements in 5G+and 6G communication systems.In 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 communication.In 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 communication.Here,we measure the state of the complex system through observability.Using 5G+/6G-based photonic sensor nodes,finding observability influenced by the concept of contraction provides the stability of neurons.When 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 lost.Similarly,neurons considered sing the complex networks concept neuron sensors in the brain lose communication and connections.Therefore,affected sensor nodes in a contraction are equivalent to compensate for maintaining stability conditions.In this compensation,loss of observability depends on the contraction size which is a key factor for employing a complex network.To analyze the observability recovery,we can use a contraction detection algorithm with complex network properties.Our 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 algorithm.In addition,we discuss the scalability of IoT communication using 5G+/6G-based photonic technology.

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

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Aldosterone synthase inhibitor“Baxdrostat”for resistant hypertension:A clinical approach or futuristic idea?

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Chronic Diseases and Translational Medicine 2024年第2期10卷 146-148页

作者： muhammad Osama Siddiqui Ayaan Ahmed qureshi Arooba Siddiqui Noor ul ain Liaquat National Hospital and Medical College KarachiSindhPakistan Karachi Medical and Dental College KarachiSindhPakistan

The American College of Cardiology/American Heart Association defines resistant hypertension(RH)as a clinical blood pressure(BP)reading of>130/80 mmHg in patients taking three antihypertensive drugs,including a renin–angiotensin system inhibitor,a calcium channel blocker(CCB),and a diuretic at well-tolerated doses.1,2 It is reported from multiple population-based surveys that in the United States,there is an approximately 12%–15%prevalence of RH among adults diagnosed with hypertension.2 A prospective study demonstrates that 20%of people diagnosed with RH had primary hyperaldosteronism.3,4 In addition to the classic three antihypertensive drugs,spironolactone and mineralocorticoid are also administered for RH.However,with respect to the safety profile of spironolactone,it has been reported to have several side effects such as low testosterone production,menstrual irregularities,and excessively raised serum potassium levels,leaving the drug unfit for the longitudinal therapeutic purpose of treating RH.

关键词： hypertension drugs hypertensive

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Improving Thyroid Disorder Diagnosis via Ensemble Stacking and Bidirectional Feature Selection

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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 gland.Accurate and timely diagnosis of these disorders is crucial for effective treatment and patient care.This research introduces a comprehensive approach to improve the accuracy of thyroid disorder diagnosis through the integration of ensemble stacking and advanced feature selection techniques.Sequential forward feature selection,sequential backward feature elimination,and bidirectional feature elimination are investigated in this study.In ensemble learning,random forest,adaptive boosting,and bagging classifiers are employed.The 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 reduction.Extensive experimentation demonstrates the remarkable success of proposed ensemble stacking and bidirectional feature elimination achieving 100%and 99.86%accuracy in identifying hyperthyroidism and hypothyroidism,respectively.Beyond enhancing detection accuracy,the ensemble stacking model also demonstrated a streamlined computational complexity which is pivotal for practical medical applications.It significantly outperformed existing studies with similar objectives underscoring the viability and effectiveness of the proposed scheme.This 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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Limits of Depth: Over-Smoothing and Over-Squashing in GNNs

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Big Data Mining and Analytics 2024年第1期7卷 205-216页

作者： Aafaq Mohi ud din Shaima qureshi Department of Computer Science and Engineering National Institute of Technology SrinagarSrinagar 190006India

Graph Neural Networks(GNNs)have become a widely used tool for learning and analyzing data on graph structures,largely due to their ability to preserve graph structure and properties via graph representation learning.However,the effect of depth on the performance of GNNs,particularly isotropic and anisotropic models,remains an active area of research.This study presents a comprehensive exploration of the impact of depth on GNNs,with a focus on the phenomena of over-smoothing and the bottleneck effect in deep graph neural networks.Our research investigates the tradeoff between depth and performance,revealing that increasing depth can lead to over-smoothing and a decrease in performance due to the bottleneck effect.We also examine the impact of node degrees on classification accuracy,finding that nodes with low degrees can pose challenges for accurate classification.Our experiments use several benchmark datasets and a range of evaluation metrics to compare isotropic and anisotropic GNNs of varying depths,also explore the scalability of these models.Our findings provide valuable insights into the design of deep GNNs and offer potential avenues for future research to improve their performance.

关键词： Graph Neural Networks(GNNs) learning on graphs over-smoothing over-squashing isotropic-GNNs anisotropic-GNNs

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Aggravation of Cancer,Heart Diseases and Diabetes Subsequent to COVID-19 Lockdown via Mathematical Modeling

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Computer Modeling in Engineering & Sciences 2024年第7期140卷 485-512页

作者： Fatma Nese Efil Sania qureshi Nezihal Gokbulut Kamyar Hosseini Evren Hincal Amanullah Soomro Near East University Department of MathematicsNicosia99010North Cyprus Mehran University of Engineering and Technology Department of Basic Sciences and Related StudiesJamshoro76090Pakistan Department of Computer Science and Mathematics Lebanese American UniversityP.O.Box 13-5053BeirutLebanon Near East University Mathematics Research CenterNicosia99010North Cyprus

The global populationhas beenandwill continue to be severely impacted by theCOVID-19 epidemic.The primary objective of this research is to demonstrate the future impact of COVID-19 on those who suffer from other fatal conditions such as cancer,heart disease,and diabetes.Here,using ordinary differential equations(ODEs),two mathematical models are developed to explain the association between COVID-19 and cancer and between COVID-19 and diabetes and heart disease.After that,we highlight the stability assessments that can be applied to these models.Sensitivity analysis is used to examine how changes in certain factors impact different aspects of disease.The sensitivity analysis showed that many people are still nervous about seeing a doctor due to COVID-19,which could result in a dramatic increase in the diagnosis of various ailments in the years to come.The correlation between diabetes and cardiovascular illness is also illustrated graphically.The effects of smoking and obesity are also found to be significant in disease compartments.Model fitting is also provided for interpreting the relationship between real data and the results of thiswork.Diabetic people,in particular,need tomonitor their health conditions closely and practice heart health maintenance.People with heart diseases should undergo regular checks so that they can protect themselves from diabetes and take some precautions including suitable diets.The main purpose of this study is to emphasize the importance of regular checks,to warn people about the effects of COVID-19(including avoiding healthcare centers and doctors because of the spread of infectious diseases)and to indicate the importance of family history of cancer,heart diseases and diabetes.The provision of the recommendations requires an increase in public consciousness.

关键词： COVID-19 mathematical modeling cancer diabetes heart diseases sensitivity analysis

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Road Traffic Monitoring from Aerial Images Using Template Matching and Invariant Features

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Computers, Materials & Continua 2024年第3期78卷 3683-3701页

作者： Asifa Mehmood qureshi Naif Al Mudawi Mohammed Alonazi Samia Allaoua Chelloug Jeongmin Park Department of Creative Technologies Air UniversityIslamabad46000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Engineering Tech University of KoreaGyeonggi-do15073South Korea

Road traffic monitoring is an imperative topic widely discussed among researchers.Systems used to monitor traffic frequently rely on cameras mounted on bridges or roadsides.However,aerial images provide the flexibility to use mobile platforms to detect the location and motion of the vehicle over a larger area.To this end,different models have shown the ability to recognize and track vehicles.However,these methods are not mature enough to produce accurate results in complex road scenes.Therefore,this paper presents an algorithm that combines state-of-the-art techniques for identifying and tracking vehicles in conjunction with image bursts.The extracted frames were converted to grayscale,followed by the application of a georeferencing algorithm to embed coordinate information into the images.The masking technique eliminated irrelevant data and reduced the computational cost of the overall monitoring system.Next,Sobel edge detection combined with Canny edge detection and Hough line transform has been applied for noise reduction.After preprocessing,the blob detection algorithm helped detect the vehicles.Vehicles of varying sizes have been detected by implementing a dynamic thresholding scheme.Detection was done on the first image of every burst.Then,to track vehicles,the model of each vehicle was made to find its matches in the succeeding images using the template matching algorithm.To further improve the tracking accuracy by incorporating motion information,Scale Invariant Feature Transform(SIFT)features have been used to find the best possible match among multiple matches.An accuracy rate of 87%for detection and 80%accuracy for tracking in the A1 Motorway Netherland dataset has been achieved.For the Vehicle Aerial Imaging from Drone(VAID)dataset,an accuracy rate of 86%for detection and 78%accuracy for tracking has been achieved.

关键词： Unmanned Aerial Vehicles(UAV) aerial images dataset object detection object tracking data elimination template matching blob detection SIFT VAID

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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 education.Students use programming languages to write their programs.These computer programs(students or practitioners written)make computers artificially intelligent and perform the tasks needed by the users.Without these programs,the computer may be visioned as a pointless machine.As the premise of writing programs is situated with specific programming languages,enormous efforts have been made to develop and create programming languages.However,each program-ming language is domain-specific and has its nuances,syntax and seman-tics,with specific pros and cons.These language-specific details,including syntax and semantics,are significant hurdles for novice programmers.Also,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 program.Considering 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 major.Due to its syntax proximity to daily conversations,this paper hypothesizes that this language will be easy to use and understand by novice programmers.This 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 language.The 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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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 environment.There 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 algorithms.However,they do not consider the document when analyzing spam mail content.These 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 frequency.Because 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 together.Thus,spam mail is not classified fully,and these approaches remain with loopholes.Thus,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 topic.Moreover,the proposed approach reduces the scarcity problem through a frequency topic-inverse document frequency in singular value decomposition model.Our proposed approach also reduces the dimensionality,which ultimately increases the strength of document classification.Experimental evaluations show that the proposed approach classifies spam mail documents with higher accuracy using individual document-independent processing computation.Comparative 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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