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Fusion-Based Machine Learning Architecture for Heart Disease Prediction

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Computers, Materials & Continua 2021年第5期67卷 2481-2496页

作者： Muhammad Waqas Nadeem Hock Guan Goh Muhammad Adnan Khan Muzammil Hussain Muhammad faheem mushtaq Vasaki a/p Ponnusamy Faculty of Information and Communication Technology(FICT) Universiti Tunku Abdul Rahman(UTAR)KamparPerak31900Malaysia Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Computer Science Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Computer Science School of Systems and TechnologyUniversity of Management and TechnologyLahore54000Pakistan Department of Information Technology Khwaja Fareed University of Engineering and Information TechnologyRahim Yar Khan64200Pakistan

The contemporary evolution in healthcare technologies plays a considerable and signicant role to improve medical services and save human *** disease or cardiovascular disease is the most fatal and complex disease which it is hardly to be detected through our naked eyes,as numerous people have been suffering from this disease *** attacks occur when the ranges of vital signs such as blood pressure,pulse rate,and body temperature exceed their normal *** efcient diagnosis of heart diseases could play a substantial role in the eld of cardiology,while diagnostic time could be *** has been a key challenge for researchers and medical experts to diagnose heart diseases accurately and ***,machine learning-based techniques are used for the diagnosis with higher accuracy,using datasets compiled from former medical patients’*** recent years,numerous studies have been presented in the literature propose machine learning techniques for diagnosing heart ***,the existing techniques have some limitations in terms of their *** this paper,a novel Support Vector Machine(SVM)based architecture for heart disease prediction,empowered with a fuzzy based decision level fusion,is *** SVMbased architecture has improved the accuracy signicantly as compared to existing solutions,where 96.23%accuracy has been achieved.

关键词： Heart disease machine learning support vector machine fuzzy logic fusion cardiovascular

Enabling Smart Cities with Cognition Based Intelligent Route Decision in Vehicles Empowered with Deep Extreme Learning Machine

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Computers, Materials & Continua 2021年第1期66卷 141-156页

作者： Dildar Hussain Muhammad Adnan Khan Sagheer Abbas Rizwan Ali Naqvi Muhammad faheem mushtaq Abdur Rehman Afrozah Nadeem School of Computational Sciences Korea Institute for Advanced StudySeoul02455Korea Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Unmanned Vehicle Engineering Sejong UniversitySeoul05006Korea Department of Information Technology Khwaja Fareed University of Engineering and Information TechnologyRahim Yar Khan64200Pakistan

The fast-paced growth of artificial intelligence provides unparalleled opportunities to improve the efficiency of various industries,including the transportation *** worldwide transport departments face many obstacles following the implementation and integration of different vehicle *** of these tasks is to ensure that vehicles are autonomous,intelligent and able to grow their repository of *** learning has recently been implemented in wireless networks,as a major artificial intelligence branch,to solve historically challenging problems through a data-driven *** this article,we discuss recent progress of applying machine learning into vehicle networks for intelligent route decision and try to focus on this emerging *** Extreme Learning Machine(DELM)framework is introduced in this article to be incorporated in vehicles so they can take human-like *** present GPS compatibility issues make it difficult for vehicles to take real-time decisions under certain *** leads to the concept of vehicle controller making *** proposed DELM based system for self-intelligent vehicle decision makes use of the cognitive memory to store route *** overcomes inadequacy of the current in-vehicle route-finding technology and its *** the relevant route-related information for the ride will be provided to the user based on its *** the DELM method,a high degree of precision in smart decision taking with a minimal error rate is *** investigation,it has been observed that proposed framework has the highest accuracy rate with 70%of training(1435 samples)and 30%of validation(612 samples).Simulation results validate the intelligent prediction of the proposed method with 98.88%,98.2%accuracy during training and validation respectively.

关键词： DELM ANN IoT feedforward route decision prediction smart city

Intelligent Breast Cancer Prediction Empowered with Fusion and Deep Learning

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Computers, Materials & Continua 2021年第4期67卷 1033-1049页

作者： Shahan Yamin Siddiqui Iftikhar Naseer Muhammad Adnan Khan Muhammad faheem mushtaq Rizwan Ali Naqvi Dildar Hussain Amir Haider School of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan School of Computer Science Minhaj University LahoreLahore54000Pakistan Department of Computer Science&Information Technology Superior UniversityLahore54000Pakistan Department of Computer Science Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Information Technology Khwaja Fareed University of Engineering and Information TechnologyRahim Yar Khan64200Pakistan Department of Unmanned Vehicle Engineering Sejong UniversitySeoul05006Korea School of Computational Sciences Korea Institute for Advanced Study(KIAS)85 HoegiroDongdaemunguSeoul02455Korea Department of Intelligent Mechatronics Engineering Sejong UniversitySeoul05006Korea

Breast cancer is the most frequently detected tumor that eventually could result in a significant increase in female mortality *** to clinical statistics,one woman out of eight is under the threat of breast *** and inheritance patterns may be a reason behind its spread among ***,some preventive measures,such as tests and periodic clinical checks can mitigate its risk thereby,improving its survival chances *** diagnosis and initial stage treatment can help increase the survival *** that purpose,pathologists can gather support from nondestructive and efficient computer-aided diagnosis(CAD)*** study explores the breast cancer CAD method relying on multimodal medical imaging and decision-based *** multimodal medical imaging fusion,a deep learning approach is applied,obtaining 97.5%accuracy with a 2.5%miss rate for breast cancer prediction.A deep extreme learning machine technique applied on feature-based data provided a 97.41%***,decisionbased fusion applied to both breast cancer prediction models to diagnose its stages,resulted in an overall accuracy of 97.97%.The proposed system model provides more accurate results compared with other state-of-the-art approaches,rapidly diagnosing breast cancer to decrease its mortality rate.

关键词： Fusion feature breast cancer prediction deep learning convolutional neural network multi-modal medical image fusion decision-based fusion

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

Depression Intensity Classification from Tweets Using Fast Text Based Weighted Soft Voting Ensemble

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Computers, Materials & Continua 2024年第2期78卷 2047-2066页

作者： Muhammad Rizwan Muhammad faheem mushtaq Maryam Rafiq Arif Mehmood Isabel de la Torre Diez Monica Gracia Villar Helena Garay Imran Ashraf Department of Artificial Intelligence The Islamia University of BahawalpurBahawalnagar63100Pakistan Department of Information Technology Khwaja Fareed University of Engineering and ITRYKhan64200Pakistan Department of Computer Science&Information Technology The Islamia University of BahawalpurBahawalnagar63100Pakistan Department of Signal Theory and Communications and Telematic Engineering Unviersity of ValladolidPaseo de Belén 15Valladolid47011Spain Faculty of Social Science and Humanities Universidad Europea del Atlántico Isabel Torres 21Santander39011Spain Department of Project Management Universidad Internacional IberoamericanaCampeche24560México Fundación Universitaria Internacional de Colombia Bogotá111611Colombia Universidad Internacional Iberoamericana AreciboPuerto Rico00613USA Universidade Internacional do Cuanza Cuito46703Angola Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541South Korea

Predicting depression intensity from microblogs and social media posts has numerous benefits and applications,including predicting early psychological disorders and stress in individuals or the general public.A major challenge in predicting depression using social media posts is that the existing studies do not focus on predicting the intensity of depression in social media texts but rather only perform the binary classification of depression and moreover noisy data makes it difficult to predict the true depression in the social media *** study intends to begin by collecting relevant Tweets and generating a corpus of 210000 public tweets using Twitter public application programming interfaces(APIs).A strategy is devised to filter out only depression-related tweets by creating a list of relevant hashtags to reduce noise in the ***,an algorithm is developed to annotate the data into three depression classes:‘Mild,’‘Moderate,’and‘Severe,’based on International Classification of Diseases-10(ICD-10)depression diagnostic *** baseline classifiers are applied to the annotated dataset to get a preliminary idea of classification performance on the *** FastText-based model is applied and fine-tuned with different preprocessing techniques and hyperparameter tuning to produce the tuned model,which significantly increases the depression classification performance to an 84%F1 score and 90%accuracy compared to ***,a FastText-based weighted soft voting ensemble(WSVE)is proposed to boost the model’s performance by combining several other classifiers and assigning weights to individual models according to their individual *** proposed WSVE outperformed all baselines as well as FastText alone,with an F1 of 89%,5%higher than FastText alone,and an accuracy of 93%,3%higher than FastText *** proposed model better captures the contextual features of the relatively small sample class and aids in the detection of

关键词： Depression classification deep learning FastText machine learning

Coronavirus: A “Mild” Virus Turned Deadly Infection

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Computers, Materials & Continua 2021年第5期67卷 2631-2646页

作者： Rizwan Ali Naqvi Muhammad faheem mushtaq Natash Ali Mian Muhammad Adnan Khan Atta-ur-Rahman Muhammad Ali Yousaf Muhammad Umair Rizwan Majeed Department of Unmanned Vehicle Engineering Sejong UniversitySeoul05006Korea Department of Information Technology Khwaja Fareed University of Engineering and Information TechnologyRahim Yar Khan64200Pakistan School of Computer and Information Technology Beaconhouse National UniversityTarogilLahore53700Pakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Faculty of Information Technology University of Central PunjabLahore54782Pakistan Directorate of IT The Islamia University of BahawalpurBahawalpur63100Pakistan

Coronaviruses are a family of viruses that can be transmitted from one person to *** strains have only been mild viruses,but the current form,known as coronavirus disease 2019(COVID-19),has become a deadly *** outbreak originated in Wuhan,China,and has since spread *** symptoms of COVID-19 include a dry cough,sore throat,fever,and nasal *** drugs,pathogen–host interaction,and 2 weeks of isolation have been recommended for the treatment of the *** operating procedures,such as the use of face masks,hand sanitizer,handwashing with soap,and social distancing,are also ***,travel bans for cities,states,and countries have been put in place,along with lockdowns to control the *** restrictions,mask use,sanitizer or soap use,and avoidance of touching the face and nose have produced encouraging results,whereas the effectiveness of antibiotics has not been *** results of isolation for the recovery of infected people have also been *** bans and lockdowns have caused a slump in economies,and unemployment has risen sharply,resulting in an increase in mental health cases *** date,vaccines have been developed and are in use in certain countries,but following standard operating procedures remain *** countries following the guidelines can eradicate this *** Zealand was the rst country to eliminate the virus from their territory.

关键词： Coronavirus severe acute respiratory syndrome middle east respiratory syndrome world health organization

Intelligent Cloud Based Load Balancing System Empowered with Fuzzy Logic

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Computers, Materials & Continua 2021年第4期67卷 519-528页

作者： Atif Ishaq Khan Syed Asad Raza Kazmi Ayesha Atta Muhammad faheem mushtaq Muhammad Idrees Ilyas Fakir Muhammad Safyan Muhammad Adnan Khan Awais Qasim Department of Computer Science Government College UniversityLahore54000Pakistan Department of Information Technology Khwaja Fareed University of Engineering and Information TechnologyRahim Yar Khan64200Pakistan Department of Computer Science and Engineering University of Engineering and Technology LahoreNarowal Campus51600Pakistan Department of Computer Science Riphah International University Lahore CampusLahore54000Pakistan

Cloud computing is seeking attention as a new computing paradigm to handle operations more efficiently and *** computing uses dynamic resource provisioning and de-provisioning in a virtualized *** load on the cloud data centers is growing day by day due to the rapid growth in cloud computing *** in cloud computing is one of the fundamental properties,and elastic load balancing automatically distributes incoming load to multiple virtual *** work is aimed to introduce efficient resource provisioning and de-provisioning for better load *** this article,a model is proposed in which the fuzzy logic approach is used for load balancing to avoid underload and overload of resources.A Simulator in Matlab is used to test the effectiveness and correctness of the proposed *** simulation results have shown that our proposed intelligent cloud-based load balancing system empowered with fuzzy logic is better than previously published approaches.

关键词： Cloud computing fuzzy logic load balancing

Improving Queuing System Throughput Using Distributed Mean Value Analysis to Control Network Congestion

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Communications and Network 2015年第1期7卷 21-29页

作者： Faisal Shahzad Muhammad faheem mushtaq Saleem Ullah M. Abubakar Siddique Shahzada Khurram Najia Saher Department of Computer Science & IT The Islamia University of Bahawalpur Bahawalpur Pakistan College of Computer Science Chongqing University Chongqing China

In this paper, we have used the distributed mean value analysis (DMVA) technique with the help of random observe property (ROP) and palm probabilities to improve the network queuing system throughput. In such networks, where finding the complete communication path from source to destination, especially when these nodes are not in the same region while sending data between two nodes. So, an algorithm is developed for single and multi-server centers which give more interesting and successful results. The network is designed by a closed queuing network model and we will use mean value analysis to determine the network throughput (b) for its different values. For certain chosen values of parameters involved in this model, we found that the maximum network throughput for β≥0.7?remains consistent in a single server case, while in multi-server case for β≥ 0.5?throughput surpass the Marko chain queuing system.

关键词： Network Congestion Throughput Queuing System Distributed Mean Value Analysis

Insights into the 3D thermal structure of interplate earthquakes in the Colombia–Ecuador subduction zone

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Earth and Planetary Physics 2025年第02期 279-288页

作者： Rui Qu YingFeng Ji WeiLing Zhu Ye Zhu Haris faheem State Key Laboratory of Tibetan Plateau Earth System Environment and Resources (TPESER) Institute of Tibetan Plateau Research Chinese Academy of Sciences University of Chinese Academy of Sciences

Throughout the 20th century, several large megathrust earthquakes were observed in the Colombia–Ecuador subduction zone which widely ruptured plate interfaces, causing considerable damage and loss of life. The occurrence of earthquakes in subduction zones is thought to be closely related to the thermal structure of the incoming plate. However, in the case of the subducting Nazca Plate beneath the Colombia–Ecuador zone, the thermal structure remains unclear, especially its hydraulic distribution. On the basis of 3D thermal models, we present new insights into the plate interface conditions of Colombia–Ecuador interplate and megathrust earthquakes. We show that the plate geometry strongly affects the along-strike thermal structure of the slab beneath Colombia and Ecuador, with the subduction of the Carnegie Ridge playing an important role. Our results further reveal that the unique geometry of the Nazca Plate is the primary reason for the relatively high temperatures of the slab beneath Colombia. We suggest that the positions of the100–200 °C and 350–450 °C isotherms on the plate interface determine the updip and downdip limits of the seismogenic zone. For Colombia–Ecuador interplate earthquakes, the released fluids control the distribution of shallow-depth earthquakes, whereas the age and geometry of the slab control the distribution of intermediate-depth earthquakes. The average temperature of the plate interface at the upper limit of large megathrust earthquakes is hotter than previously thought, which is more consistent with our understanding of the Colombia–Ecuador subduction zone. We predict that the potential location of future large seismic events could be in the rupture zone of past seismic events or offshore of northern Colombia.

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