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CNN Based Multi-Object Segmentation and Feature Fusion for Scene Recognition

Computers, Materials & Continua 2022年第12期73卷 4657-4675页

作者： Adnan Ahmed Rafique yazeed yasin ghadi.suliman aalsuhibany Samia Allaoua Chelloug Ahmad Jalal Jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Korea

Latest advancements in vision technology offer an evident impact on multi-object recognition and scene *** sceneunderstanding task is a demanding part of several technologies,like augmented reality-based scene integration,robotic navigation,autonomous driving,and tourist *** visual information in contextually unified segments,convolution neural networks-based approaches will significantly mitigate the clutter,which is usual in classical frameworks during scene *** this paper,we propose a convolutional neural network(CNN)based segmentation method for the recognition of multiple objects in an ***,after acquisition and preprocessing,the image is segmented by using ***,CNN features are extracted from these segmented objects,and discrete cosine transform(DCT)and discrete wavelet transform(DWT)features are *** the extraction of CNN features and computation of classical machine learning features,fusion is performed using a fusion ***,to select theminimal set of features,genetic algorithm-based feature selection is *** order to recognize and understand the multi-objects in the scene,a neuro-fuzzy approach is *** objects in the scene are recognized,the relationship between these objects is examined by employing the object-to-object relation ***,a decision tree is incorporated to assign the relevant labels to the scenes based on recognized objects in the *** experimental results over complex scene datasets including SUN Red Green Blue-Depth(RGB-D)and Cityscapes’demonstrated a remarkable performance.

关键词： Convolutional neural network decision tree feature fusion neurofuzzy system

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Improving the Ambient Intelligence Living Using Deep Learning Classifier

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Computers, Materials & Continua 2022年第10期73卷 1037-1053页

作者： yazeed yasin ghadi Mouazma Batool Munkhjargal Gochoo suliman aalsuhibany Tamara al Shloul Ahmad Jalal Jeongmin Park Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science and Software Engieering United Arab Emirates UniversityAl Ain15551UAE Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Humanities and Social Science Al Ain UniversityAl Ain15551UAE Department of Computer Engineering Tech University of KoreaGyeonggi-do15073Korea

Over the last decade,there is a surge of attention in establishing ambient assisted living(AAL)solutions to assist individuals live *** a social and economic perspective,the demographic shift toward an elderly population has brought new challenges to today’s *** can offer a variety of solutions for increasing people’s quality of life,allowing them to live healthier and more independently for *** this paper,we have proposed a novel AAL solution using a hybrid bidirectional long-term and short-term memory networks(BiLSTM)and convolutional neural network(CNN)*** first pre-processed the signal data,then used timefrequency features such as signal energy,signal variance,signal frequency,empirical mode,and empirical mode *** convolutional neural network-bidirectional long-term and short-term memory(CNN-biLSTM)classifier with dimensional reduction isomap algorithm was then used to select ideal *** assessed the performance of our proposed system on the publicly accessible human gait database(HuGaDB)benchmark dataset and achieved an accuracy rates of 93.95 percent,*** reveal that hybrid method gives more accuracy than single classifier in AAL *** suggested system can assists persons with impairments,assisting carers and medical personnel.

关键词： Ambient assisted living convolutional neural network dimensionality reduction frequency-time features wearable technology

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Student’s Health Exercise Recognition Tool for E-Learning Education

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Intelligent Automation & Soft Computing 2023年第1期35卷 149-161页

作者： Tamara al Shloul Madiha Javeed Munkhjargal Gochoo suliman aalsuhibany yazeed yasin ghadi Ahmad Jalal Jeongmin Park Department of Humanities and Social Science Al Ain UniversityAl Ain15551UAE Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science and Software Engineering United Arab Emirates UniversityAl Ain15551UAE Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Korea

Due to the recently increased requirements of e-learning systems,multiple educational institutes such as kindergarten have transformed their learning towards virtual *** student health exercise is a difficult task but an important one due to the physical education needs especially in young *** proposed system focuses on the necessary implementation of student health exercise recognition(SHER)using a modified Quaternion-basedfilter for inertial data refining and data fusion as the pre-processing ***,cleansed data has been segmented using an overlapping windowing approach followed by patterns identification in the form of static and kinematic signal ***,these patterns have been utilized to extract cues for both patterned signals,which are further optimized using Fisher’s linear discriminant analysis(FLDA)***,the physical exercise activities have been categorized using extended Kalmanfilter(EKF)-based neural *** system can be implemented in multiple educational establishments including intelligent training systems,virtual mentors,smart simulations,and interactive learning management methods.

关键词： E-learning exercise recognition online physical education student’s healthcare

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An Intelligent HealthCare Monitoring Framework for Daily Assistant Living

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Computers, Materials & Continua 2022年第8期72卷 2597-2615页

作者： yazeed yasin ghadi Nida Khalid suliman A.Alsuhibany Tamara al Shloul Ahmad Jalal Jeongmin Park Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Humanities and Social Science Al Ain UniversityAl Ain15551UAE Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Korea

Human Activity Recognition(HAR)plays an important role in life care and health monitoring since it involves examining various activities of patients at homes,hospitals,or ***,the proposed system integrates Human-Human Interaction(HHI)and Human-Object Interaction(HOI)recognition to provide in-depth monitoring of the daily routine of *** propose a robust system comprising both RGB(red,green,blue)and depth *** particular,humans in HHI datasets are segmented via connected components analysis and skin detection while the human and object in HOI datasets are segmented via saliency *** track the movement of humans,we proposed orientation and thermal features.A codebook is generated using Linde-Buzo-Gray(LBG)algorithm for vector ***,the quantized vectors generated from image sequences of HOI are given to Artificial Neural Network(ANN)while the quantized vectors generated from image sequences of HHI are given to K-ary tree hashing for *** are two publicly available datasets used for experimentation on HHI recognition:Stony Brook University(SBU)Kinect interaction and the University of Lincoln’s(UoL)3D social activity ***,two publicly available datasets are used for experimentation on HOI recognition:Nanyang Technological University(NTU)RGB-D and Sun Yat-Sen University(SYSU)3D HOI *** results proved the validity of the proposed system.

关键词： Artificial neural network human-human interaction humanobject interaction k-ary tree hashing machine learning

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Self-Care Assessment for Daily Living Using Machine Learning Mechanism

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Computers, Materials & Continua 2022年第7期72卷 1747-1764页

作者： Mouazma Batool yazeed yasin ghadi.suliman A.Alsuhibany Tamara al Shloul Ahmad Jalal Jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Humanities and Social Science Al Ain UniversityAl Ain15551UAE Department of Computer Engineering Korea Polytechnic University237 Sangidaehak-ro Siheung-siGyeonggi-do15073Korea

Nowadays,activities of daily living(ADL)recognition system has been considered an important field of computer *** and optical sensors are widely used to assess the daily living activities in healthy people and people with certain *** conventional ADL utilizes RGB optical sensors but an RGB-D camera with features of identifying depth(distance information)and visual cues has greatly enhanced the performance of activity *** this paper,an RGB-D-based ADL recognition system has been ***,human silhouette has been extracted from the noisy background of RGB and depth images to track human movement in a *** on these silhouettes,full body features and point based features have been extracted which are further optimized with probability based incremental learning(PBIL)***,random forest classifier has been used to classify activities into different *** n-fold crossvalidation scheme has been used to measure the viability of the proposed model on the RGBD-AC benchmark dataset and has achieved an accuracy of 92.71%over other state-of-the-art methodologies.

关键词： Angular geometric features decision tree classifier human activity recognition probability based incremental learning ridge detection

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Pedestrian Physical Education Training Over Visualization Tool

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Computers, Materials & Continua 2022年第11期73卷 2389-2405页

作者： Tamara al Shloul Israr Akhter suliman A.Alsuhibany yazeed yasin ghadi Ahmad Jalal Jeongmin Park Department of Humanities and Social Science Al Ain UniversityAl Ain15551UAE Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Korea

E-learning approaches are one of the most important learning platforms for the learner through electronic *** study techniques are useful for other groups of learners such as the crowd,pedestrian,sports,transports,communication,emergency services,management systems and education sectors.E-learning is still a challenging domain for researchers and developers to find new trends and advanced tools and *** of them are currently working on this domain to fulfill the requirements of industry and the *** this paper,we proposed a method for pedestrian behavior mining of aerial data,using deep flow feature,graph mining technique,and convocational neural *** input data,the state-of-the-art crowd activity University of Minnesota(UMN)dataset is adopted,which contains the aerial indoor and outdoor view of the pedestrian,for simplification of extra information and computational cost reduction the pre-processing is *** flow features are extracted to find more accurate ***,to deal with repetition in features data and features mining the graph mining algorithm is applied,while Convolution Neural Network(CNN)is applied for pedestrian behavior *** proposed method shows 84.50%of mean accuracy and a 15.50%of error ***,the achieved results show more accuracy as compared to state-ofthe-art classification algorithms such as decision tree,artificial neural network(ANN).

关键词： Artificial intelligence behavior mining convolution neural network(CNN) deep flow e-learning environment

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Body Worn Sensors for Health Gaming and e-Learning in Virtual Reality

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Computers, Materials & Continua 2022年第12期73卷 4763-4777页

作者： Mir Mushhood Afsar Shizza Saqib yazeed yasin ghadi.suliman A.Alsuhibany Ahmad Jalal Jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Computer Engineering Tech University of Korea237 Sangidaehak-roSiheung-siGyeonggi-do15073Korea

Virtual reality is an emerging field in the whole *** problem faced by people today is that they are more indulged in indoor technology rather than outdoor ***,the proposed system introduces a fitness solution connecting virtual reality with a gaming interface so that an individual can play first-person *** system proposed in this paper is an efficient and cost-effective solution that can entertain people along with playing outdoor games such as badminton and cricket while sitting in the *** track the human movement,sensors Micro Processor Unit(MPU6050)are used that are connected with Bluetoothmodules andArduino responsible for sending the sensor data to the ***,the sensor data is sent to a machine learning model,which detects the game played by the *** detected game will be operated on human gestures.A publicly available dataset named IM-Sporting Behaviors is initially used,which utilizes triaxial accelerometers attached to the subject’s wrist,knee,and below neck regions to capture important aspects of human *** main objective is that the person is enjoying while playing the game and simultaneously is engaged in some kind of sporting *** proposed system uses artificial neural networks classifier giving an accuracy of 88.9%.The proposed system should apply to many systems such as construction,education,offices and the educational *** experimentation proved the validity of the proposed system.

关键词： Artificial neural networks bluetooth connection inertial sensors machine learning virtual reality exergaming

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A Highly Secured Image Encryption Scheme using Quantum Walk and Chaos

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Computers, Materials & Continua 2022年第10期73卷 657-672页

作者： Muhammad Islam Kamran Muazzam A.Khan suliman A.Alsuhibany yazeed yasin ghadi Arshad Jameel Arif Jawad Ahmad Department of Computer Science Quaid-i-Azam UniversityIslamabad45320Pakistan Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Computer Science and Software Engineering Al Ain UniversityAbu Dhabi15551UAE Institute for Energy and Environment University of StrathclydeGlasgowG11XQUK School of Computing Edinburgh Napier UniversityEdinburghEH105DTUK

The use of multimedia data sharing has drastically increased in the past few decades due to the revolutionary improvements in communication technologies such as the 4th generation(4G)and 5th generation(5G)*** have proposed many image encryption algorithms based on the classical random walk and chaos theory for sharing an image in a secure *** of the classical random walk,this paper proposes the quantum walk to achieve high image *** random walk exhibits randomness due to the stochastic transitions between states,on the other hand,the quantum walk is more random and achieve randomness due to the superposition,and the interference of the wave *** proposed image encryption scheme is evaluated using extensive security metrics such as correlation coefficient,entropy,histogram,time complexity,number of pixels change rate and unified average intensity *** experimental results validate the proposed scheme,and it is concluded that the proposed scheme is highly secured,lightweight and computationally *** the proposed scheme,the values of the correlation coefficient,entropy,mean square error(MSE),number of pixels change rate(NPCR),unified average change intensity(UACI)and contrast are 0.0069,7.9970,40.39,99.60%,33.47 and 10.4542 respectively.

关键词： Cryptography chaotic maps logistic map quantum walk security

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An Intelligent Framework for Recognizing Social Human-Object Interactions

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Computers, Materials & Continua 2022年第10期73卷 1207-1223页

作者： Mohammed Alarfaj Manahil Waheed yazeed yasin ghadi Tamara al Shloul suliman A.Alsuhibany Ahmad Jalal Jeongmin Park Department of Electrical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Humanities and Social Science Al Ain UniversityAl Ain15551UAE Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Computer Engineering Tech University of KoreaSiheung-si15073Gyeonggi-doKorea

Human object interaction(HOI)recognition plays an important role in the designing of surveillance and monitoring systems for healthcare,sports,education,and public *** involves localizing the human and object targets and then identifying the interactions between ***,it is a challenging task that highly depends on the extraction of robust and distinctive features from the targets and the use of fast and efficient ***,the proposed system offers an automated body-parts-based solution for HOI *** system uses RGB(red,green,blue)images as input and segments the desired parts of the images through a segmentation technique based on the watershed ***,a convex hullbased approach for extracting key body parts has also been *** identifying the key body parts,two types of features are ***,the entire feature vector is reduced using a dimensionality reduction technique called t-SNE(t-distributed stochastic neighbor embedding).Finally,a multinomial logistic regression classifier is utilized for identifying class labels.A large publicly available dataset,MPII(Max Planck Institute Informatics)Human Pose,has been used for system *** results prove the validity of the proposed system as it achieved 87.5%class recognition accuracy.

关键词： Dimensionality reduction human-object interaction key point detection machine learning watershed segmentation

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Home Automation-Based Health Assessment Along Gesture Recognition via Inertial Sensors

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Computers, Materials & Continua 2023年第4期75卷 2331-2346页

作者： Hammad Rustam Muhammad Muneeb suliman A.Alsuhibany yazeed yasin ghadi Tamara Al Shloul Ahmad Jalal Jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Humanities and Social Science Al Ain UniversityAl Ain15551UAE Department of Computer Engineering Korea Polytechnic University237 Sangidaehak-ro Siheung-siGyeonggi-do15073Korea

Hand gesture recognition (HGR) is used in a numerous applications,including medical health-care, industrial purpose and sports *** have developed a real-time hand gesture recognition system using inertialsensors for the smart home application. Developing such a model facilitatesthe medical health field (elders or disabled ones). Home automation has alsobeen proven to be a tremendous benefit for the elderly and disabled. Residentsare admitted to smart homes for comfort, luxury, improved quality of life,and protection against intrusion and burglars. This paper proposes a novelsystem that uses principal component analysis, linear discrimination analysisfeature extraction, and random forest as a classifier to *** have achieved an accuracy of 94% over the publicly benchmarked HGRdataset. The proposed system can be used to detect hand gestures in thehealthcare industry as well as in the industrial and educational sectors.

关键词： Genetic algorithm human locomotion activity recognition human–computer interaction human gestures recognition principal hand gestures recognition inertial sensors principal component analysis linear discriminant analysis stochastic neighbor embedding

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