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High-Efficiency Video Coder in Pruned Environment Using Adaptive Quantization Parameter Selection

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Computers, Materials & Continua 2022年第10期73卷 1977-1993页

作者： Krishan Kumar Mohamed Abouhawwash Amit Kant Pandit shubham mahajan Mofreh A.Hogo School of Electronics&Communication Engineering Shri Mata Vaishno Devi UniversityKatra182320India Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast Lansing48824MIUSA Electrical Engineering Department Faculty of EngineeringBenha UniversityBenha13518Egypt

The high-efficiency video coder(HEVC)is one of the most advanced techniques used in growing real-time multimedia applications ***,they require large bandwidth for transmission through bandwidth,and bandwidth varies with different video sequences/*** paper proposes an adaptive information-based variable quantization matrix(AIVQM)developed for different video formats having variable energy *** quantization method is adapted based on video sequence using statistical analysis,improving bit budget,quality and complexity ***,to have precise control over bit rate and quality,a multi-constraint prune algorithm is proposed in the second stage of the AI-VQM technique for pre-calculating K numbers of *** same should be handy to selfadapt and choose one of the K-path automatically in dynamically changing bandwidth availability as per requirement after extensive testing of the proposed algorithm in the multi-constraint environment for multiple paths and evaluating the performance based on peak signal to noise ratio(PSNR),bit-budget and time complexity for different videos a noticeable improvement in rate-distortion(RD)performance is *** the proposed AIVQM technique,more feasible and efficient video sequences are achieved with less loss in PSNR than the variable quantization method(VQM)algorithm with approximately a rise of 10%–20%based on different video sequences/formats.

关键词： Adaptive quantization high-efficient video coding(HEVC) quad-tree rate-distortion optimization(RDO) video compression variable quantization method(VQM) quantization parameter(QP)

Covid-19 Detection from Chest X-Ray Images Using Advanced Deep Learning Techniques

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Computers, Materials & Continua 2022年第1期70卷 1541-1556页

作者： shubham mahajan Akshay Raina Mohamed Abouhawwash Xiao-Zhi Gao Amit Kant Pandit School of Electronics&Communication Engineering Shri Mata Vaishno Devi UniversityKatra182320India School of Electrical Engineering Shri Mata Vaishno Devi UniversityKatra182320India Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast Lansing48824MIUSA School of Computing University of Eastern FinlandKuopio70210Finland

Like the Covid-19 pandemic,smallpox virus infection broke out in the last century,wherein 500 million deaths were reported along with enormous economic *** unlike smallpox,the Covid-19 recorded a low exponential infection rate and mortality rate due to advancement inmedical aid and *** analytics,machine learning,and automation techniques can help in early diagnostics and supporting treatments of many reported *** paper proposes a robust and efficient methodology for the early detection of COVID-19 from Chest X-Ray scans utilizing enhanced deep learning *** study suggests that using the Prediction and Deconvolutional Modules in combination with the SSD architecture can improve the performance of the model trained at this *** used a publicly open CXR image dataset and implemented the detectionmodelwith task-specific pre-processing and near 80:20 *** achieved a competitive specificity of 0.9474 and a sensibility/accuracy of 0.9597,which shall help better decision-making for various aspects of identification and treat the infection.

关键词： Machine learning deep learning object detection chest X-ray medical images Covid-19

Deep Learning Based Automated Diagnosis of Skin Diseases Using Dermoscopy

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Computers, Materials & Continua 2022年第5期71卷 3145-3160页

作者： Vatsala Anand Sheifali Gupta Deepika Koundal shubham mahajan Amit Kant Pandit Atef Zaguia Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjabIndia Department of Systemics School of Computer ScienceUniversity of Petroleum&Energy StudiesDehradunUttrakhandIndia School of Electronics&Communication Shri Mata Vaishno Devi UniversityKatra182320India Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Biomedical image analysis has been exploited considerably by recent technology involvements,carrying about a pattern shift towards‘automation’and‘error free diagnosis’classification methods with markedly improved accurate diagnosis productivity and cost *** paper proposes an automated deep learning model to diagnose skin disease at an early stage by using Dermoscopy *** proposed model has four convolutional layers,two maxpool layers,one fully connected layer and three dense *** the convolutional layers are using the kernel size of 3∗3 whereas the maxpool layer is using the kernel size of 2∗*** dermoscopy images are taken from the HAM10000 *** proposed model is compared with the three different models of ResNet that are ResNet18,ResNet50 and *** models are simulated with 32 batch size and Adadelta *** proposed model has obtained the best accuracy value of 0.96 whereas the ResNet101 model has obtained 0.90,the ResNet50 has obtained 0.89 and the ResNet18 model has obtained value as ***,features obtained from the proposed model are more capable for improving the classification performance of multiple skin disease *** model can be used for early diagnosis of skin disease and can also act as a second opinion tool for dermatologists.

关键词： Dermoscopy images CNN deep learning classification optimizer ResNet diagnosis skin disease

MRMR Based Feature Vector Design for Efficient Citrus Disease Detection

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Computers, Materials & Continua 2022年第9期72卷 4771-4787页

作者： Bobbinpreet Sultan Aljahdali Tripti Sharma Bhawna Goyal Ayush Dogra shubham mahajan Amit Kant Pandit Department of Electronics&Communication Engineering Chandigarh UniversityMohali140413India Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Ronin Institute Mont ClairNJ07043USA School of Electronics&Communication Engineering Shri Mata Vaishno Devi UniversityKatra182320India

In recent times,the images and videos have emerged as one of the most important information source depicting the real time *** images nowadays serve as input for many applications and replacing the manual methods due to their capabilities of 3D scene representation in 2D *** capabilities of digital images along with utilization of machine learning methodologies are showing promising accuracies in many applications of prediction and pattern *** of the application fields pertains to detection of diseases occurring in the plants,which are destroying the widespread *** the disease detection process was done by a domain expert using manual examination and laboratory *** is a tedious and time consuming process and does not suffice the accuracy *** creates a room for the research in developing automation based methods where the images captured through sensors and cameras will be used for detection of disease and control its *** digital images captured from the field’s forms the dataset which trains the machine learning models to predict the nature of the *** accuracy of these models is greatly affected by the amount of noise and ailments present in the input images,appropriate segmentation methodology,feature vector development and the choice of machine learning *** ensure the high rated performance of the designed system the research is moving in a direction to fine tune each and every stage separately considering their dependencies on subsequent *** the most optimum solution can be obtained by considering the image processing methodologies for improving the quality of image and then applying statistical methods for feature extraction and *** training vector thus developed is capable of presenting the relationship between the feature values and the target *** this article,a highly accurate system model for detecting the diseases occurring in citrus fruits

关键词： Citrus diseases classification feature vector design plant disease detection redundancy reduction

Deep Learning Based Automated Detection of Diseases from Apple Leaf Images

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Computers, Materials & Continua 2022年第4期71卷 1849-1866页

作者： Swati Singh Isha Gupta Sheifali Gupta Deepika Koundal Sultan Aljahdali shubham mahajan Amit Kant Pandit Department of Electronics and Communication Engineering University Institute of TechnologyHimachal Pradesh UniversityShimla171005India Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjabIndia Department of Systemics School of Computer ScienceUniversity of Petroleum and Energy StudiesDehradunIndia Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia School of Electronics&Communication Shri Mata Vaishno Devi UniversityKatra185320India

In Agriculture Sciences, detection of diseases is one of the mostchallenging tasks. The mis-interpretations of plant diseases often lead towrong pesticide selection, resulting in damage of crops. Hence, the automaticrecognition of the diseases at earlier stages is important as well as economicalfor better quality and quantity of fruits. Computer aided detection (CAD)has proven as a supportive tool for disease detection and classification, thusallowing the identification of diseases and reducing the rate of degradationof fruit quality. In this research work, a model based on convolutional neuralnetwork with 19 convolutional layers has been proposed for effective andaccurate classification of Marsonina Coronaria and Apple Scab diseases fromapple leaves. For this, a database of 50,000 images has been acquired bycollecting images of leaves from apple farms of Himachal Pradesh (H.P)and Uttarakhand (India). An augmentation technique has been performedon the dataset to increase the number of images for increasing the *** performance analysis of the proposed model has been compared with thenew two Convolutional Neural Network (CNN) models having 8 and 9 layersrespectively. The proposed model has also been compared with the standardmachine learning classifiers like support vector machine, k-Nearest Neighbour, Random Forest and Logistic Regression models. From experimentalresults, it has been observed that the proposed model has outperformed theother CNN based models and machine learning models with an accuracy of99.2%.

关键词： Deep learning convolutional neural network apple leaves apple scab support vector machine

Optimization of Cognitive Radio System Using Self-Learning Salp Swarm Algorithm

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Computers, Materials & Continua 2022年第2期70卷 3821-3835页

作者： Nitin Mittal Harbinder Singh Vikas Mittal shubham mahajan Amit Kant Pandit Mehedi Masud Mohammed Baz Mohamed Abouhawwash Department of Electronics&Communication Engineering Chandigarh UniversityMohali140413India School of VLSI Design and Embedded Systems National Institute of TechnologyKurukshetra136119India School of Electronics&Communication Engineering Shri Mata Vaishno Devi UniversityKatra182320India Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Engineering College of Computer and Information TechnologyTaif UniversityTaif21994Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

CognitiveRadio(CR)has been developed as an enabling technology that allows the unused or underused spectrum to be used dynamically to increase spectral *** improve the overall performance of the CR systemit is extremely important to adapt or reconfigure the *** Decision Engine is a major module in the CR-based system that not only includes radio monitoring and cognition functions but also responsible for parameter *** meta-heuristic algorithms offer numerous advantages compared to traditional mathematical approaches,the performance of these algorithms is investigated in order to design an efficient CR system that is able to adapt the transmitting parameters to effectively reduce power consumption,bit error rate and adjacent interference of the channel,while maximized secondary user ***-Learning Salp Swarm Algorithm(SLSSA)is a recent meta-heuristic algorithm that is the enhanced version of SSA inspired by the swarming behavior of *** this work,the parametric adaption of CR system is performed by SLSSA and the simulation results show that SLSSA has high accuracy,stability and outperforms other competitive algorithms formaximizing the throughput of secondary *** results obtained with SLSSA are also shown to be extremely satisfactory and need fewer iterations to converge compared to the competitive methods.

关键词： Cognitive radio meta-heuristic algorithm cognitive decision engine salp swarm algorithm

Three Dimensional Optimum Node Localization in Dynamic Wireless Sensor Networks

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Computers, Materials & Continua 2022年第1期70卷 305-321页

作者： Gagandeep Singh Walia Parulpreet Singh Manwinder Singh Mohamed Abouhawwash Hyung Ju Park Byeong-Gwon Kang shubham mahajan Amit Kant Pandit Department of Electronics and Communication Engineering Lovely Professional UniversityJalandhar144411PunjabIndia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA Department of ICT Convergence Soonchunhyang UniversityAsan31538Korea School of Electronics&Communication Shri Mata Vaishno Devi UniversityKatra182320India

Location information plays an important role in most of the applications in Wireless Sensor Network(WSN).Recently,many localization techniques have been proposed,while most of these deals with two Dimensional ***,in Three Dimensional applications the task is complex and there are large variations in the altitude *** these 3D environments,the sensors are placed in mountains for tracking and deployed in air for monitoring pollution *** such applications,2D localization models are not *** to this,the design of 3D localization systems in WSNs faces new *** this paper,in order to find unknown nodes in Three-Dimensional environment,only single anchor node is *** the simulation-based environment,the nodes with unknown locations are moving at middle&lower layers whereas the top layer is equipped with single anchor node.A novel soft computing technique namely Adaptive Plant Propagation Algorithm(APPA)is introduced to obtain the optimized locations of these mobile *** target nodes are heterogeneous and deployed in an anisotropic environment having an Irregularity(Degree of Irregularity(DOI))value set to *** simulation results present that proposed APPAalgorithm outperforms as tested among other meta-heuristic optimization techniques in terms of localization error,computational time,and the located sensor nodes.

关键词： Wireless sensor networks localization particle swarm optimization h-best particle swarm optimization biogeography-based optimization grey wolf optimizer firefly algorithm adaptive plant propagation algorithm

Behavior of Delivery Robot in Human-Robot Collaborative Spaces During Navigation

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Intelligent Automation & Soft Computing 2023年第1期35卷 795-810页

作者： Kiran Jot Singh Divneet Singh Kapoor Mohamed Abouhawwash Jehad F.Al-Amri shubham mahajan Amit Kant Pandit Embedded Systems&Robotics Research Group Chandigarh UniversityMohali140413PunjabIndia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia School of Electronics and Communication Shri Mata Vaishno Devi UniversityKatra182320India

Navigation is an essential skill for *** becomes a cumbersome task for the robot in a human-populated environment,and Industry 5.0 is an emerging trend that focuses on the interaction between humans and *** behavior in a social setting is the key to human acceptance while ensuring human comfort and *** the advancement in robotics technology,the true use cases of robots in the tourism and hospitality industry are expanding in *** are very few experimental studies focusing on how people perceive the navigation behavior of a delivery robot.A robotic platform named“PI”has been designed,which incorporates proximity and vision *** robot utilizes a real-time object recognition algorithm based on the You Only Look Once(YOLO)algorithm to detect objects and humans during *** study is aimed towards evaluating human experience,for which we conducted a study among 36 participants to explore the perceived social presence,role,and perception of a delivery robot exhibiting different behavior conditions while navigating in a hotel *** participants’responses were collected and compared for different behavior conditions demonstrated by the robot and results show that humans prefer an assistant role of a robot enabled with audio and visual aids exhibiting social ***,this study can be useful for developers to gain insight into the expected behavior of a delivery robot.

关键词： Human-robot interaction robot navigation robot behavior collaborative spaces industrial IoT industry 5.0

Enhanced Heat Transfer Technology Based on Emission Reduction and Carbon Reduction in Cutting and Grinding

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Chinese Journal of Mechanical Engineering 2023年第4期36卷 1-3页

作者： Changhe Li Yanbin Zhang shubham Sharma School of Mechanical and Automotive Engineering Qingdao University of TechnologyQingdao 266520China Department of Mechanical Engineering and Advanced Materials Science Regional Center for Extension and DevelopmentCouncil of Scientific and Industrial Research(CSIR)-Central Leather Research Institute(CLRI)Jalandhar 144021India

Huge carbon emissions in machining process,which characterized by high energy consumption and usage of non-renewable resources,is becoming an obsession in the past *** the face of the international strategy of carbon peak,it is imperative to eliminate the usage of mineral cutting fluids and reduce energy consumption and carbon emissions by green cutting/grinding technologies,such as dry cutting,minimum quantity lubrication(MQL).

关键词： grinding lubrication eliminate