|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Simulation, Modeling, and Optimization of Intelligent Kidney Disease Predication Empowered with Computational Intelligence Approaches显示文摘Artificial intelligence(AI)is expanding its roots in medical diagnostics.Various acute and chronic diseases can be identified accurately at the initial level by using AI methods to prevent the progression of health complications.Kidney diseases are producing a high impact on global health and medical practitioners are suggested that the diagnosis at earlier stages is one of the foremost approaches to avert chronic kidney disease and renal failure.High blood pressure,diabetes mellitus,and glomerulonephritis are the root causes of kidney disease.Therefore,the present study is proposed a set of multiple techniques such as simulation,modeling,and optimization of intelligent kidney disease prediction(SMOIKD)which is based on computational intelligence approaches.Initially,seven parameters were used for the fuzzy logic system(FLS),and then twenty-five different attributes of the kidney dataset were used for the artificial neural network(ANN)and deep extreme machine learning(DEML).The expert system was proposed with the assistance of medical experts.For the quick and accurate evaluation of the proposed system,Matlab version 2019 was used.The proposed SMOIKD-FLSANN-DEML expert system has shown 94.16%accuracy.Hence this study concluded that SMOIKD-FLS-ANN-DEML system is effective to accurately diagnose kidney disease at initial levels. | Abdul Hannan Khan Muhammad Adnan Khan Sagheer Abbas Shahan Yamin Siddiqui Muhammad Aanwar Saeed Majed Alfayad Nouh Sabri Elmitwally | 2021 | Computers, Materials & Continua2021，，5： | 2 |
| 2 | Intelligent Forecasting Model of COVID-19 Novel Coronavirus Outbreak Empowered with Deep Extreme Learning Machine显示文摘An epidemic is a quick and widespread disease that threatens many lives and damages the economy.The epidemic lifetime should be accurate so that timely and remedial steps are determined.These include the closing of borders schools,suspension of community and commuting services.The forecast of an outbreak effectively is a very necessary but difficult task.A predictive model that provides the best possible forecast is a great challenge for machine learning with only a few samples of training available.This work proposes and examines a prediction model based on a deep extreme learning machine(DELM).This methodology is used to carry out an experiment based on the recent Wuhan coronavirus outbreak.An optimized prediction model that has been developed,namely DELM,is demonstrated to be able to make a prediction that is fairly best.The results show that the new methodology is useful in developing an appropriate forecast when the samples are far from abundant during the critical period of the disease.During the investigation,it is shown that the proposed approach has the highest accuracy rate of 97.59%with 70%of training,30%of test and validation.Simulation results validate the prediction effectiveness of the proposed scheme. | Muhammad Adnan Khan Sagheer Abbas Khalid Masood Khan Mohammed AAl Ghamdi Abdur Rehman | 2020 | Computers, Materials & Continua2020，，9： | 1 |
| 3 | Alzheimer Disease Detection Empowered with Transfer Learning显示文摘Alzheimer’s disease is a severe neuron disease that damages brain cells which leads to permanent loss of memory also called dementia.Many people die due to this disease every year because this is not curable but early detection of this disease can help restrain the spread.Alzheimer’s ismost common in elderly people in the age bracket of 65 and above.An automated system is required for early detection of disease that can detect and classify the disease into multiple Alzheimer classes.Deep learning and machine learning techniques are used to solvemanymedical problems like this.The proposed system Alzheimer Disease detection utilizes transfer learning on Multi-class classification using brain Medical resonance imagining(MRI)working to classify the images in four stages,Mild demented(MD),Moderate demented(MOD),Non-demented(ND),Very mild demented(VMD).Simulation results have shown that the proposed systemmodel gives 91.70%accuracy.It also observed that the proposed system gives more accurate results as compared to previous approaches. | Taher M.Ghazal Sagheer Abbas Sundus Munir M.A.Khan Munir Ahmad Ghassan F.Issa Syeda Binish Zahra Muhammad Adnan Khan Mohammad Kamrul Hasan | 2022 | Computers, Materials & Continua2022，，3： | 1 |
| 4 | Autonomous Parking-Lots Detection with Multi-Sensor Data Fusion Using Machine Deep Learning Techniques显示文摘The rapid development and progress in deep machine-learning techniques have become a key factor in solving the future challenges of humanity.Vision-based target detection and object classification have been improved due to the development of deep learning algorithms.Data fusion in autonomous driving is a fact and a prerequisite task of data preprocessing from multi-sensors that provide a precise,well-engineered,and complete detection of objects,scene or events.The target of the current study is to develop an in-vehicle information system to prevent or at least mitigate traffic issues related to parking detection and traffic congestion detection.In this study we examined to solve these problems described by(1)extracting region-of-interest in the images(2)vehicle detection based on instance segmentation,and(3)building deep learning model based on the key features obtained from input parking images.We build a deep machine learning algorithm that enables collecting real video-camera feeds from vision sensors and predicting free parking spaces.Image augmentation techniques were performed using edge detection,cropping,refined by rotating,thresholding,resizing,or color augment to predict the region of bounding boxes.A deep convolutional neural network F-MTCNN model is proposed that simultaneously capable for compiling,training,validating and testing on parking video frames through video-camera.The results of proposed model employing on publicly available PK-Lot parking dataset and the optimized model achieved a relatively higher accuracy 97.6%than previous reported methodologies.Moreover,this article presents mathematical and simulation results using state-of-the-art deep learning technologies for smart parking space detection.The results are verified using Python,TensorFlow,OpenCV computer simulation frameworks. | Kashif Iqbal Sagheer Abbas Muhammad Adnan Khan Atifa Ather Muhammad Saleem Khan Areej Fatima Gulzar Ahmad | 2021 | Computers, Materials & Continua2021，，2： | 1 |
| 5 | Convolutional Neural Network Based Intelligent Handwritten Document Recognition显示文摘This paper presents a handwritten document recognition system based on the convolutional neural network technique.In today’s world,handwritten document recognition is rapidly attaining the attention of researchers due to its promising behavior as assisting technology for visually impaired users.This technology is also helpful for the automatic data entry system.In the proposed systemprepared a dataset of English language handwritten character images.The proposed system has been trained for the large set of sample data and tested on the sample images of user-defined handwritten documents.In this research,multiple experiments get very worthy recognition results.The proposed systemwill first performimage pre-processing stages to prepare data for training using a convolutional neural network.After this processing,the input document is segmented using line,word and character segmentation.The proposed system get the accuracy during the character segmentation up to 86%.Then these segmented characters are sent to a convolutional neural network for their recognition.The recognition and segmentation technique proposed in this paper is providing the most acceptable accurate results on a given dataset.The proposed work approaches to the accuracy of the result during convolutional neural network training up to 93%,and for validation that accuracy slightly decreases with 90.42%. | Sagheer Abbas Yousef Alhwaiti Areej Fatima Muhammad A.Khan Muhammad Adnan Khan Taher M.Ghazal Asma Kanwal Munir Ahmad Nouh Sabri Elmitwally | 2022 | Computers, Materials & Continua2022，，3： | 1 |
| 6 | A Fused Machine Learning Approach for Intrusion Detection System显示文摘The rapid growth in data generation and increased use of computer network devices has amplified the infrastructures of internet.The interconnectivity of networks has brought various complexities in maintaining network availability,consistency,and discretion.Machine learning based intrusion detection systems have become essential to monitor network traffic for malicious and illicit activities.An intrusion detection system controls the flow of network traffic with the help of computer systems.Various deep learning algorithms in intrusion detection systems have played a prominent role in identifying and analyzing intrusions in network traffic.For 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.The 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 attacks.The proposed IDS-FMLT system model obtained 95.18%validation accuracy and a 4.82%miss rate in intrusion detection. | Muhammad Sajid Farooq Sagheer Abbas Atta-ur-Rahman Kiran Sultan Muhammad Adnan Khan Amir Mosavi | 2023 | Computers, Materials & Continua2023，，2： | 0 |
| 7 | Personality Detection Using Context Based Emotions in Cognitive Agents显示文摘Detection of personality using emotions is a research domain in artificial intelligence.At present,some agents can keep the human’s profile for interaction and adapts themselves according to their preferences.However,the effective method for interaction is to detect the person’s personality by understanding the emotions and context of the subject.The idea behind adding personality in cognitive agents begins an attempt to maximize adaptability on the basis of behavior.In our daily life,humans socially interact with each other by analyzing the emotions and context of interaction from audio or visual input.This paper presents a conceptual personality model in cognitive agents that can determine personality and behavior based on some text input,using the context subjectivity of the given data and emotions obtained from a particular situation/context.The proposed work consists of Jumbo Chatbot,which can chat with humans.In this social interaction,the chatbot predicts human personality by understanding the emotions and context of interactive humans.Currently,the Jumbo chatbot is using the BFI technique to interact with a human.The accuracy of proposed work varies and improve through getting more experiences of interaction. | Nouh Sabri Elmitwally Asma Kanwal Sagheer Abbas Muhammad A.Khan Muhammad Adnan Khan Munir Ahmad Saad Alanazi | 2022 | Computers, Materials & Continua2022，，3： | 0 |
| 8 | Modelling Intelligent Driving Behaviour Using Machine Learning显示文摘In vehicular systems,driving is considered to be the most complex task,involving many aspects of external sensory skills as well as cognitive intelligence.External skills include the estimation of distance and speed,time perception,visual and auditory perception,attention,the capability to drive safely and action-reaction time.Cognitive intelligence works as an internal mechanism that manages and holds the overall driver’s intelligent system.These cognitive capacities constitute the frontiers for generating adaptive behaviour for dynamic environments.The parameters for understanding intelligent behaviour are knowledge,reasoning,decision making,habit and cognitive skill.Modelling intelligent behaviour reveals that many of these parameters operate simultaneously to enable drivers to react to current situations.Environmental changes prompt the parameter values to change,a process which continues unless and until all processes are completed.This paper model intelligent behaviour by using a‘driver behaviour model’to obtain accurate intelligent driving behaviour patterns.This model works on layering patterns in which hierarchy and coherence are maintained to transfer the data with accuracy from one module to another.These patterns constitute the outcome of different modules that collaborate to generate appropriate values.In this case,accurate patterns were acquired using ANN static and dynamic non-linear autoregressive approach was used and for further accuracy validation,time-series dynamic backpropagation artificial neural network,multilayer perceptron and random sub-space on real-world data were also applied. | Qura-Tul-Ain Khan Sagheer Abbas Muhammad Adnan Khan Areej Fatima Saad Alanazi Nouh Sabri Elmitwally | 2021 | Computers, Materials & Continua2021，，9： | 0 |
| 9 | Intelligent Ammunition Detection and Classification System Using Convolutional Neural Network显示文摘Security is a significant issue for everyone due to new and creative ways to commit cybercrime.The Closed-Circuit Television(CCTV)systems are being installed in offices,houses,shopping malls,and on streets to protect lives.Operators monitor CCTV;however,it is difficult for a single person to monitor the actions of multiple people at one time.Consequently,there is a dire need for an automated monitoring system that detects a person with ammunition or any other harmful material Based on our research and findings of this study,we have designed a new Intelligent Ammunition Detection and Classification(IADC)system using Convolutional Neural Network(CNN).The proposed system is designed to identify persons carrying weapons and ammunition using CCTV cameras.When weapons are identified,the cameras sound an alarm.In the proposed IADC system,CNN was used to detect firearms and ammunition.The CNN model which is a Deep Learning technique consists of neural networks,most commonly applied to analyzing visual imagery has gained popularity for unstructured(images,videos)data classification.Additionally,this system generates an early warning through detection of ammunition before conditions become critical.Hence the faster and earlier the prediction,the lower the response time,loses and potential victims.The proposed IADC system provides better results than earlier published models like VGGNet,OverFeat-1,OverFeat-2,and OverFeat-3. | Gulzar Ahmad Saad Alanazi Madallah Alruwaili Fahad Ahmad Muhammad Adnan Khan Sagheer Abbas Nadia Tabassum | 2021 | Computers, Materials & Continua2021，，5： | 0 |
| 10 | A Neuro-Fuzzy Approach to Road Traffic Congestion Prediction显示文摘The fast-paced growth of artificial intelligence applications provides unparalleled opportunities to improve the efficiency of various systems.Such as the transportation sector faces many obstacles following the implementation and integration of different vehicular and environmental aspects worldwide.Traffic congestion is among the major issues in this regard which demands serious attention due to the rapid growth in the number of vehicles on the road.To address this overwhelming problem,in this article,a cloudbased intelligent road traffic congestion prediction model is proposed that is empowered with a hybrid Neuro-Fuzzy approach.The aim of the study is to reduce the delay in the queues,the vehicles experience at different road junctions across the city.The proposed model also intended to help the automated traffic control systems by minimizing the congestion particularly in a smart city environment where observational data is obtained from various implanted Internet of Things(IoT)sensors across the road.After due preprocessing over the cloud server,the proposed approach makes use of this data by incorporating the neuro-fuzzy engine.Consequently,it possesses a high level of accuracy by means of intelligent decision making with minimum error rate.Simulation results reveal the accuracy of the proposed model as 98.72%during the validation phase in contrast to the highest accuracies achieved by state-of-the-art techniques in the literature such as 90.6%,95.84%,97.56%and 98.03%,respectively.As far as the training phase analysis is concerned,the proposed scheme exhibits 99.214% accuracy. The proposed prediction modelis a potential contribution towards smart cities environment. | Mohammed Gollapalli Atta-ur-Rahman Dhiaa Musleh Nehad Ibrahim Muhammad Adnan Khan Sagheer Abbas Ayesha Atta Muhammad Aftab Khan Mehwash Farooqui Tahir Iqbal Mohammed Salih Ahmed Mohammed Imran BAhmed Dakheel Almoqbil Majd Nabeel Abdullah Omer | 2022 | Computers, Materials & Continua2022，，10： | 0 |
| 11 | Automated File Labeling for Heterogeneous Files Organization Using Machine Learning显示文摘File labeling techniques have a long history in analyzing the anthological trends in computational linguistics.The situation becomes worse in the case of files downloaded into systems from the Internet.Currently,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 files.Currently,no significant work is done on automated file labeling during the organization of heterogeneous user files.A few attempts have been made in topic modeling.However,one major drawback of current topic modeling approaches is better results.They rely on specific language types and domain similarity of the data.In 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 files.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. | Sagheer Abbas Syed Ali Raza MAKhan Muhammad Adnan Khan Atta-ur-Rahman Kiran Sultan Amir Mosavi | 2023 | Computers, Materials & Continua2023，，2： | 0 |
| 12 | Intelligent Model for Predicting the Quality of Services Violation显示文摘Cloud computing is providing IT services to its customer based on Service level agreements(SLAs).It is important for cloud service providers to provide reliable Quality of service(QoS)and to maintain SLAs accountability.Cloud service providers need to predict possible service violations before the emergence of an issue to perform remedial actions for it.Cloud users’major concerns;the factors for service reliability are based on response time,accessibility,availability,and speed.In this paper,we,therefore,experiment with the parallel mutant-Particle swarm optimization(PSO)for the detection and predictions of QoS violations in terms of response time,speed,accessibility,and availability.This paper also compares Simple-PSO and Parallel MutantPSO.In simulation results,it is observed that the proposed Parallel MutantPSO solution for cloud QoS violation prediction achieves 94%accuracy which is many accurate results and is computationally the fastest technique in comparison of conventional PSO technique. | Muhammad Adnan Khan Asma Kanwal Sagheer Abbas Faheem Khan T.Whangbo | 2022 | Computers, Materials & Continua2022，，5： | 0 |
| 13 | Content Based Automated File Organization Using Machine Learning Approaches显示文摘In the world of big data,it’s quite a task to organize different files based on their similarities.Dealing with heterogeneous data and keeping a record of every single file stored in any folder is one of the biggest problems encountered by almost every computer user.Much of file management related tasks will be solved if the files on any operating system are somehow categorized according to their similarities.Then,the browsing process can be performed quickly and easily.This research aims to design a system to automatically organize files based on their similarities in terms of content.The proposed methodology is based on a novel strategy that employs the charactaristics of both supervised and unsupervised machine learning approaches for learning categories of digital files stored on any computer system.The results demonstrate that the proposed architecture can effectively and efficiently address the file organization challenges using real-world user files.The results suggest that the proposed system has great potential to automatically categorize almost all of the user files based on their content.The proposed system is completely automated and does not require any human effort in managing the files and the task of file organization become more efficient as the number of files grows. | Syed Ali Raza Sagheer Abbas Taher M.Ghazal Muhammad Adnan Khan Munir Ahmad Hussam Al Hamadi | 2022 | Computers, Materials & Continua2022，，10： | 0 |
| 14 | Vertical Pod Autoscaling in Kubernetes for Elastic Container Collaborative Framework显示文摘Kubernetes is an open-source container management tool which automates container deployment,container load balancing and container(de)scaling,including Horizontal Pod Autoscaler(HPA),Vertical Pod Autoscaler(VPA).HPA enables flawless operation,interactively scaling the number of resource units,or pods,without downtime.Default Resource Metrics,such as CPU and memory use of host machines and pods,are monitored by Kubernetes.Cloud Computing has emerged as a platform for individuals beside the corporate sector.It provides cost-effective infrastructure,platform and software services in a shared environment.On the other hand,the emergence of industry 4.0 brought new challenges for the adaptability and infusion of cloud computing.As the global work environment is adapting constituents of industry 4.0 in terms of robotics,artificial intelligence and IoT devices,it is becoming eminent that one emerging challenge is collaborative schematics.Provision of such autonomous mechanism that can develop,manage and operationalize digital resources like CoBots to perform tasks in a distributed and collaborative cloud environment for optimized utilization of resources,ensuring schedule completion.Collaborative schematics are also linked with Bigdata management produced by large scale industry 4.0 setups.Different use cases and simulation results showed a significant improvement in Pod CPU utilization,latency,and throughput over Kubernetes environment. | Mushtaq Niazi Sagheer Abbas Abdel-Hamid Soliman Tahir Alyas Shazia Asif Tauqeer Faiz | 2023 | Computers, Materials & Continua2023，，1： | 0 |
| 15 | Enabling Smart Cities with Cognition Based Intelligent Route Decision in Vehicles Empowered with Deep Extreme Learning Machine显示文摘The fast-paced growth of artificial intelligence provides unparalleled opportunities to improve the efficiency of various industries,including the transportation sector.The worldwide transport departments face many obstacles following the implementation and integration of different vehicle features.One of these tasks is to ensure that vehicles are autonomous,intelligent and able to grow their repository of information.Machine learning has recently been implemented in wireless networks,as a major artificial intelligence branch,to solve historically challenging problems through a data-driven approach.In this article,we discuss recent progress of applying machine learning into vehicle networks for intelligent route decision and try to focus on this emerging field.Deep Extreme Learning Machine(DELM)framework is introduced in this article to be incorporated in vehicles so they can take human-like assessments.The present GPS compatibility issues make it difficult for vehicles to take real-time decisions under certain conditions.It leads to the concept of vehicle controller making self-decisions.The proposed DELM based system for self-intelligent vehicle decision makes use of the cognitive memory to store route observations.This overcomes inadequacy of the current in-vehicle route-finding technology and its support.All the relevant route-related information for the ride will be provided to the user based on its availability.Using the DELM method,a high degree of precision in smart decision taking with a minimal error rate is obtained.During 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. | Dildar Hussain Muhammad Adnan Khan Sagheer Abbas Rizwan Ali Naqvi Muhammad Faheem Mushtaq Abdur Rehman Afrozah Nadeem | 2021 | Computers, Materials & Continua2021，，1： | 0 |
| 16 | IoMT-Based Smart Monitoring Hierarchical Fuzzy Inference System for Diagnosis of COVID-19显示文摘The prediction of human diseases,particularly COVID-19,is an extremely challenging task not only for medical experts but also for the technologists supporting them in diagnosis and treatment.To deal with the prediction and diagnosis of COVID-19,we propose an Internet of Medical Things-based Smart Monitoring Hierarchical Mamdani Fuzzy Inference System(IoMTSM-HMFIS).The proposed system determines the various factors like fever,cough,complete blood count,respiratory rate,Ct-chest,Erythrocyte sedimentation rate and C-reactive protein,family history,and antibody detection(lgG)that are directly involved in COVID-19.The expert system has two input variables in layer 1,and seven input variables in layer 2.In layer 1,the initial identification for COVID-19 is considered,whereas in layer 2,the different factors involved are studied.Finally,advanced lab tests are conducted to identify the actual current status of the disease.The major focus of this study is to build an IoMT-based smart monitoring system that can be used by anyone exposed to COVID-19;the system would evaluate the user’s health condition and inform them if they need consultation with a specialist for quarantining.MATLAB-2019a tool is used to conduct the simulation.The COVID-19 IoMTSM-HMFIS system has an overall accuracy of approximately 83%.Finally,to achieve improved performance,the analysis results of the system were shared with experts of the Lahore General Hospital,Lahore,Pakistan. | Tahir Abbas Khan Sagheer Abbas Allah Ditta Muhammad Adnan Khan Hani Alquhayz Areej Fatima Muhammad Farhan Khan | 2020 | Computers, Materials & Continua2020，，12： | 0 |
| 17 | Understanding the Language of ISIS:An Empirical Approach to Detect Radical Content on Twitter Using Machine Learning显示文摘The internet,particularly online social networking platforms have revolutionized the way extremist groups are influencing and radicalizing individuals.Recent research reveals that the process initiates by exposing vast audiences to extremist content and then migrating potential victims to confined platforms for intensive radicalization.Consequently,social networks have evolved as a persuasive tool for extremism aiding as recruitment platform and psychological warfare.Thus,recognizing potential radical text or material is vital to restrict the circulation of the extremist chronicle.The aim of this research work is to identify radical text in social media.Our contributions are as follows:(i)A new dataset to be employed in radicalization detection;(ii)In depth analysis of new and previous datasets so that the variation in extremist group narrative could be identified;(iii)An approach to train classifier employing religious features along with radical features to detect radicalization;(iv)Observing the use of violent and bad words in radical,neutral and random groups by employing violent,terrorism and bad words dictionaries.Our research results clearly indicate that incorporating religious text in model training improves the accuracy,precision,recall,and F1-score of the classifiers.Secondly a variation in extremist narrative has been observed implying that usage of new dataset can have substantial effect on classifier performance.In addition to this,violence and bad words are creating a differentiating factor between radical and random users but for neutral(anti-ISIS)group it needs further investigation. | Zia Ul Rehman Sagheer Abbas Muhammad Adnan Khan Ghulam Mustafa Hira Fayyaz Muhammad Hanif Muhammad Anwar Saeed | 2021 | Computers, Materials & Continua2021，，2： | 0 |
| 18 | Intelligent Energy Consumption For Smart Homes Using Fused Machine-Learning Technique显示文摘Energy is essential to practically all exercises and is imperative for the development of personal satisfaction.So,valuable energy has been in great demand for many years,especially for using smart homes and structures,as individuals quickly improve their way of life depending on current innovations.However,there is a shortage of energy,as the energy required is higher than that produced.Many new plans are being designed to meet the consumer’s energy requirements.In many regions,energy utilization in the housing area is 30%–40%.The growth of smart homes has raised the requirement for intelligence in applications such as asset management,energy-efficient automation,security,and healthcare monitoring to learn about residents’actions and forecast their future demands.To overcome the challenges of energy consumption optimization,in this study,we apply an energy management technique.Data fusion has recently attracted much energy efficiency in buildings,where numerous types of information are processed.The proposed research developed a data fusion model to predict energy consumption for accuracy and miss rate.The results of the proposed approach are compared with those of the previously published techniques and found that the prediction accuracy of the proposed method is 92%,which is higher than the previously published approaches. | Hanadi AlZaabi Khaled Shaalan Taher M.Ghazal Muhammad A.Khan Sagheer Abbas Beenu Mago Mohsen A.A.Tomh Munir Ahmad | 2023 | Computers, Materials & Continua2023，，1： | 0 |
| 19 | Marker-Based and Marker-Less Motion Capturing Video Data: Person and Activity Identification Comparison Based on Machine Learning Approaches显示文摘Biomechanics is the study of physiological properties of data and the measurement of human behavior.In normal conditions,behavioural properties in stable form are created using various inputs of subconscious/conscious human activities such as speech style,body movements in walking patterns,writing style and voice tunes.One cannot perform any change in these inputs that make results reliable and increase the accuracy.The aim of our study is to perform a comparative analysis between the marker-based motion capturing system(MBMCS)and the marker-less motion capturing system(MLMCS)using the lower body joint angles of human gait patterns.In both the MLMCS and MBMCS,we collected trajectories of all the participants and performed joint angle computation to identify a person and recognize an activity(walk and running).Using five state of the art machine learning algorithms,we obtained 44.6%and 64.3%accuracy in person identification using MBMCS and MLMCS respectively with an ensemble algorithm(two angles as features).In the second set of experiments,we used six machine learning algorithms to obtain 65.9%accuracy with the k-nearest neighbor(KNN)algorithm(two angles as features)and 74.6%accuracy with an ensemble algorithm.Also,by increasing features(6 angles),we obtained higher accuracy of 99.3%in MBMCS for person recognition and 98.1%accuracy in MBMCS for activity recognition using the KNN algorithm.MBMCS is computationally expensive and if we redesign the model of OpenPose with more body joint points and employ more features,MLMCS(low-cost system)can be an effective approach for video data analysis in a person identification and activity recognition process. | Syeda Binish Zahra Muhammad Adnan Khan Sagheer Abbas Khalid Masood Khan Mohammed A.Al-Ghamdi Sultan H.Almotiri | 2021 | Computers, Materials & Continua2021，，2： | 0 |
| 20 | Intelligent Decision Support System for COVID-19 Empowered with Deep Learning显示文摘The prompt spread of Coronavirus(COVID-19)subsequently adorns a big threat to the people around the globe.The evolving and the perpetually diagnosis of coronavirus has become a critical challenge for the healthcare sector.Drastically increase of COVID-19 has rendered the necessity to detect the people who are more likely to get infected.Lately,the testing kits for COVID-19 are not available to deal it with required proficiency,along with-it countries have been widely hit by the COVID-19 disruption.To keep in view the need of hour asks for an automatic diagnosis system for early detection of COVID-19.It would be a feather in the cap if the early diagnosis of COVID-19 could reveal that how it has been affecting the masses immensely.According to the apparent clinical research,it has unleashed that most of the COVID-19 cases are more likely to fall for a lung infection.The abrupt changes do require a solution so the technology is out there to pace up,Chest X-ray and Computer tomography(CT)scan images could significantly identify the preliminaries of COVID-19 like lungs infection.CT scan and X-ray images could flourish the cause of detecting at an early stage and it has proved to be helpful to radiologists and the medical practitioners.The unbearable circumstances compel us to flatten the curve of the sufferers so a need to develop is obvious,a quick and highly responsive automatic system based on Artificial Intelligence(AI)is always there to aid against the masses to be prone to COVID-19.The proposed Intelligent decision support system for COVID-19 empowered with deep learning(ID2S-COVID19-DL)study suggests Deep learning(DL)based Convolutional neural network(CNN)approaches for effective and accurate detection to the maximum extent it could be,detection of coronavirus is assisted by using X-ray and CT-scan images.The primary experimental results here have depicted the maximum accuracy for training and is around 98.11 percent and for validation it comes out to be approximately 95.5 percent while statistical parameters like sensitivity and specificity for training is 98.03 percent and 98.20 percent respectively,and for validation 94.38 percent and 97.06 percent respectively.The suggested Deep Learning-based CNN model unleashed here opts for a comparable performance with medical experts and it ishelpful to enhance the working productivity of radiologists. It could take the curvedown with the downright contribution of radiologists, rapid detection ofCOVID-19, and to overcome this current pandemic with the proven efficacy. | Shahan Yamin Siddiqui Sagheer Abbas Muhammad Adnan Khan Iftikhar Naseer Tehreem Masood Khalid Masood Khan Mohammed A.Al Ghamdi Sultan H.Almotiri | 2021 | Computers, Materials & Continua2021，，2： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员