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Quality of service aware routing protocols in wireless multimedia sensor networks: survey

International Journal of Information Technology (Singapore) 2022年第2期14卷 789-800页

作者： Chiwariro, Ronald .N, Thangadurai Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Karnataka Bangalore India Department of Electronics and Communication Engineering Faculty of Engineering and Technology Centre for Emerging Technologies JAIN (Deemed-to-be University) Karnataka Bangalore India

Improvements in nanotechnology have introduced contemporary sensory devices that are capable of gathering multimedia data in form of images, audio and video. Wireless multimedia sensor networks handles such type of heterogeneous traffic. Since these networks are an emergent of wireless sensor networks, they inherit constraints that exist in these traditional networks. This paper is a survey of characteristics and requirements of wireless multimedia sensor networks and approaches to mitigating existing challenges in these networks. Further presents a review of recent research on multipath routing protocols and multi-channel media access protocols that offer quality of service assurances in handling multimedia data. The survey shows that there still exist bottlenecks in current routing solutions when applied to wireless multimedia sensor networks due to the nature of traffic involved as well as inherent characteristics, requirements and constraints. The findings are useful in the discovery of solutions to the challenges and issues. The authors further suggest approaches and plausible solutions to the problems in order to assist in the development of protocols that achieve quality of service in wireless multimedia sensor networks. © 2020, Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Energy efficiency Multi-channel media access control Multipath routing Quality of service

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Optimal Deep Convolutional Neural Network for Vehicle Detection in Remote Sensing Images

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computers, Materials & Continua 2023年第2期74卷 3117-3131页

作者： Saeed Masoud Alshahrani Saud S.Alotaibi Shaha Al-Otaibi Mohamed Mousa Anwer Mustafa Hilal Amgad Atta Abdelmageed Abdelwahed Motwakel Mohamed I.Eldesouki Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

Object detection(OD)in remote sensing images(RSI)acts as a vital part in numerous civilian and military application areas,like urban planning,geographic information system(GIS),and search and rescue *** recognition from RSIs remained a challenging process because of the difficulty of background data and the redundancy of recognition *** latest advancements in deep learning(DL)approaches permit the design of effectual OD *** study develops an Artificial Ecosystem Optimizer with Deep Convolutional Neural Network for Vehicle Detection(AEODCNN-VD)model on Remote Sensing *** proposed AEODCNN-VD model focuses on the identification of vehicles accurately and *** detect vehicles,the presented AEODCNN-VD model employs single shot detector(SSD)with Inception network as a baseline *** addition,Multiway Feature Pyramid Network(MFPN)is used for handling objects of varying sizes in *** features from the Inception model are passed into theMFPNformultiway andmultiscale feature ***,the fused features are passed into bounding box and class prediction *** enhancing the detection efficiency of the AEODCNN-VD approach,AEO based hyperparameter optimizer is used,which is stimulated by the energy transfer strategies such as production,consumption,and decomposition in an *** performance validation of the presentedmethod on benchmark datasets showed promising performance over recent DL models.

关键词： Object detection remote sensing vehicle detection artificial ecosystem optimizer convolutional neural network

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Recognition of Urdu Handwritten Alphabet Using Convolutional Neural Network (CNN)

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computers, Materials & Continua 2022年第11期73卷 2967-2984页

作者： Gulzar Ahmed Tahir Alyas Muhammad Waseem Iqbal Muhammad Usman Ashraf Ahmed Mohammed Alghamdi Adel A.Bahaddad Khalid Ali Almarhabi Department of Computer Science University of South AsiaLahore54000Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Computer Science GC Women UniversitySialkot53310Pakistan Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia Department of Information System Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computing in Al-QunfudahUmm Al-Qura UniversityMakkah24381Saudi Arabia

Handwritten character recognition systems are used in every field of life nowadays,including shopping malls,banks,educational institutes,*** is the national language of Pakistan,and it is the fourth spoken language in the ***,it is still challenging to recognize Urdu handwritten characters owing to their cursive *** paper presents a Convolutional Neural Networks(CNN)model to recognize Urdu handwritten alphabet recognition(UHAR)offline and online *** research contributes an Urdu handwritten dataset(aka UHDS)to empower future works in this *** offline systems,optical readers are used for extracting the alphabets,while diagonal-based extraction methods are implemented in online ***,our research tackled the issue concerning the lack of comprehensive and standard Urdu alphabet datasets to empower research activities in the area of Urdu text *** this end,we collected 1000 handwritten samples for each alphabet and a total of 38000 samples from 12 to 25 age groups to train our CNN model using online and offline ***,we carried out detailed experiments for character recognition,as detailed in the *** proposed CNN model outperformed as compared to previously published approaches.

关键词： Urdu handwritten text recognition handwritten dataset convolutional neural network artificial intelligence machine learning deep learning

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Identification of Candidate Drugs and Molecular Pathways for Pulmonary Arterial Hypertension and Chronic Kidney Disease 5

Identification of Candidate Drugs and Molecular Pathways for...

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5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Hosen, Md. Faruk Yasmin, Mst. Farjana Basar, Md. Abul Trisha, Arifa Ferdoushi Shadhin, Khairul Alam Mollah, Md. Sarwar Uddin, Muhammad Shahin Department of Computing and Information System Ashulia Dhaka Bangladesh Department of Computer Science and Engineering Purbachal American CityKanchon 1460 Bangladesh Department of Software Engineering Ashulia Dhaka Bangladesh Mawlana Bhashani Science and Technology University Department of Information and Communication Technology Tangail1902 Bangladesh

ISBN: (纸本)9798350394290

Pulmonary arterial hypertension (PAH) is biggest preventable cause of early deaths, and hence one of the global preferences of World Health Organization for determent. PAH also induces the vast majority of patients with chronic kidney disease (CKD). Both PAH and CKD are inherently connected, but the common molecular pathways and genes of these two disorders have not yet been demonstrated. In this study, we aimed to determine the similar molecular pathways and therapeutic hub proteins in PAH and CKD that may be used to anticipate the progression of the disease. We used Limma package to conduct differential analysis of gene transcripts of PAH and CKD obtained from the Gene Expression repository. The functional annotations of the genes were determined with the use of pathway analysis. Then proteinâprotein interaction (PPI) networks were constructed to determine the hub proteins, and a clustering technique was applied to determine the most significant PPI elements. We separately evaluated the PAH and CKD gene expression-based datasets and discovered 30 differentially expressed genes (DEGs) shared by both PAH and CKD. The pathways of KEGG revealed that the most prevalent DEGs are associated with the Malaria and African trypanosomiasis. Gene ontology (GO), TF and miRNA analysis and module investigation is treated the forthcoming work of this study. Finally, various drug compounds have been suggested based on the concordant DEGs. © 2023 IEEE.

关键词： Proteins

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Enhancing Healthcare Delivery Through IoT Applications in Remote Patient Monitoring and Telemedicine 3

Enhancing Healthcare Delivery Through IoT Applications in Re...

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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Yogi, Kottala Sri Gowda V, Dankan Manoj Kumar, S.B. Nithya, R. Begum, A.Yasmine Suganthi.n Symbiosis Institute of Business Management Hyderabad Symbiosis International University Department of Operations Pune India Bms Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India Bgsit Adichunchanagiri University Faculty of Engineering Management & Technology Dept. of Ece Karnataka 571448 India Vivekanandha College of Engineering for Women Department of Computer Science and Engineering Tamilnadu Namakkal India Mohan Babu University School of Engineering Department of Electronics and Communication Engineering Tirupati India Dayananda Sagar College of Engineering Department of Electrical and Electronics Engineering Karnataka Bangalore India

ISBN: (纸本)9798350364569

This paper presents how IoT plays a vital role in improving delivery of healthcare services especially through remote patient monitoring and telemedicine. The proposed system also employs IoT devices aimed at passing various health indicators like;pulse rate, blood glucose levels, blood pressure, and physical activity to continually and objectively provide real-time data in the body. This particular study, the participants with chronic diseases, normal population and elderly people also participated in the test of the system. Some of the findings include;it is possible to measure the daily steps corresponding to the a calorie expenditure, the sleep score ranging between 60-80 and oxygen saturation percentages ranging from 96%-99% in various postures. The system also proved the correlation between the percentage of variation in ambient temperature and the percentage of variation in the pulse rate which was quite sensitive to the changes in temperatures. Based on these observations, it is possible to conclude that IoT-based monitoring can improve patient's conditions as long as underlying issues will be detected at an early stage and necessary actions will be taken in time. The study also also demonstrates the application of telemedicine in making healthcare more accessible especially for the people from rural settings or in developing countries. The results show a good direction regarding the future of the system;however, security issues, privacy relating to data as well as integration are key hurdles in this connection. © 2024 IEEE.

关键词： Blood pressure

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Identification of Molecular Biomarkers and Key Pathways among Idiopathic pulmonary fibrosis (IPF), Chronic Obstructive Pulmonary Disease (COPD) and Lung cancer 26

Identification of Molecular Biomarkers and Key Pathways amon...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Yasmin, Mst. Farjana Faruk Hosen, Md. Basar, Md. Abul Alam Shadhin, Khairul Trisha, Arifa Ferdoushi Shahin Uddin, Muhammad Mawlana Bhashani Science and Technology University Department of Information and Communication Technology Tangail1902 Bangladesh Department of Computing and Information System Dhaka Ashulia Bangladesh Department of Computer Science and Engineering Kanchon Purbachal American City1460 Bangladesh Department of Software Engineering Dhaka Ashulia Bangladesh

ISBN: (纸本)9798350359015

Lung cancer (LC), idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary disease (COPD) are the most fatal disorders in the globe, generating frequent human issues. Having IPF and COPD are the risk factors of the LC, but the molecular mechanisms that underlie among IPF, COPD, and LC are not yet elucidated. In this study, we looked for shared molecular indicators and pathways that might explain how individuals with IPF, COPD, and LC are related to one another. GSE24206, GSE76925 and GSE18842 microarray datasets are utilized for IPF, COPD and LC samples. The preprocessing of datasets has done using R language, and then concordant differentially expressed genes (DEGs) are discovered. After that the protein-protein interactions (PPIs) are built using the similar DEGs, and the hub genes are determined using topological analysis. ETS1, MSH2, SORD, RORA and NEDD9 are the PPI network's top 5 hub genes. The pathways of KEGG demonstrated that the concordant DEGs are related to the colorectal cancer and pathways in cancer. Future work for this project will focus on miRNA, TF, and gene ontology (GO) analyses, as well as module analysis networks. Finally, based on the concordant DEGs, a number of potential medications have been suggested. © 2023 IEEE.

关键词： COPD Differential expressed genes Drug compounds enrichment analysis Hub gene IPF Lung cancer Protein-protein network

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A Position-Aware Transformer for Image Captioning

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computers, Materials & Continua 2022年第1期70卷 2065-2081页

作者： Zelin Deng Bo Zhou Pei He Jianfeng Huang Osama Alfarraj Amr Tolba School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Computer Science and Cyber Engineering Guangzhou UniversityGuangzhou510006China Advanced Forming Research Centre University of StrathclydeRenfrewshirePA49LJGlasgowUnited Kingdom Department of Computer Science Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Mathematics and Computer Science Faculty of ScienceMenoufia UniversityEgypt

Image captioning aims to generate a corresponding description of an *** recent years,neural encoder-decodermodels have been the dominant approaches,in which the Convolutional Neural Network(CNN)and Long Short TermMemory(LSTM)are used to translate an image into a natural language *** these approaches,the visual attention mechanisms are widely used to enable deeper image understanding through fine-grained analysis and even multiple steps of ***,most conventional visual attention mechanisms are based on high-level image features,ignoring the effects of other image features,and giving insufficient consideration to the relative positions between image *** this work,we propose a Position-Aware Transformer model with image-feature attention and position-aware attention mechanisms for the above *** image-feature attention firstly extracts multi-level features by using Feature Pyramid Network(FPN),then utilizes the scaled-dot-product to fuse these features,which enables our model to detect objects of different scales in the image more effectivelywithout increasing *** the position-aware attentionmechanism,the relative positions between image features are obtained at first,afterwards the relative positions are incorporated into the original image features to generate captions more *** are carried out on the MSCOCO dataset and our approach achieves competitive BLEU-4,METEOR,ROUGE-L,CIDEr scores compared with some state-of-the-art approaches,demonstrating the effectiveness of our approach.

关键词： Deep learning image captioning transformer attention position-aware

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Cephalopods Classification Using Fine Tuned Lightweight Transfer Learning Models

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Intelligent Automation & Soft Computing 2023年第3期35卷 3065-3079页

作者： P.Anantha Prabha G.Suchitra R.Saravanan Department of Computer Science&Engineering Sri Krishna College of TechnologyCoimbatore641042Tamil NaduIndia Department of Electronics and Communication Engineering Government College of TechnologyCoimbatore641013Tamil NaduIndia Department of Marine Pharmacology Faculty of Allied Health SciencesChettinad Academy of Research and EducationKelambakkamChengalpattu603103Tamil NaduIndia

Cephalopods identiﬁcation is a formidable task that involves hand inspection and close observation by a *** observation and iden-tiﬁcation take time and are always contingent on the involvement of experts.A system is proposed to alleviate this challenge that uses transfer learning techni-ques to classify the cephalopods *** the proposed method,only the Lightweight pre-trained networks are chosen to enable IoT in the task of cephalopod ***,the efﬁciency of the chosen models is determined by evaluating their performance and comparing theﬁ***,the models areﬁne-tuned by adding dense layers and tweaking hyperparameters to improve the classiﬁcation of *** models also employ a well-tuned Rectiﬁed Adam optimizer to increase the accuracy ***,Adam with Gradient Cen-tralisation(RAdamGC)is proposed and used inﬁne-tuned models to reduce the training *** framework enables an Internet of Things(IoT)or embedded device to perform the classiﬁcation tasks by embedding a suitable lightweight pre-trained ***ﬁne-tuned models,MobileNetV2,InceptionV3,and NASNet Mobile have achieved a classiﬁcation accuracy of 89.74%,87.12%,and 89.74%,***ﬁndings have indicated that theﬁne-tuned models can classify different kinds of *** results have also demonstrated that there is a signiﬁcant reduction in the training time with RAdamGC.

关键词： Cephalopods transfer learning lightweight models classiﬁcation deep learning ﬁsh IoT

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Microwave assisted pyrolysis of biomass feedstock fundamentals and the effect of process parameters - A Review 4

Microwave assisted pyrolysis of biomass feedstock fundamenta...

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4th International Conference on Emerging Electrical Energy, Electronics and Computing, ICE4CT 2022

作者： Hamzah, Husam Talib Veluru, Sridevi Hasan, Hikmat Mohammed Abed, Hayder Yousif Mahdi, Husam Salah Hamdullah, Noor Abdulqader Tukarambai, M. Poiba, Venkata Rao Srikanth, R. Department of Chemical Engineering College of Engineering Andhra University Visakhapatnam India Department of Electrical Engineering Faculty of Engineering Ferdowsi University of Mashhad Mashhad Iran Department of Electrical Engineering University of Technology Baghdad Iraq Department of Computer Science and System Engineering College of Engineering Andhra University Visakhapatnam India Electrical and Computer Engineering School of Engineering and Architecture Altinbas University Istanbul Turkey Department of Chemical Engineering Anil Neerukonda Institute of Technology and Sciences Sangivalasa India

Globally, biomass usage as a supply of non-depletable resources materials used in the production of energy at their rawest state is an issue. Pyrolysis is a method of thermally treating biomass that as a consequence in the formation of liquid, products, both solid and gaseous. Elevated heating is required to transform the complicated composition of the biomass's underlying structure matrix into usable products. Heating with a microwave oven has the potential to become a competitive option. to more traditional methods of heating. Owing to its ease of operation and rapid heating rate, it has recently been widely employed in pyrolysis. The goals of this research are to found the principles of MW-assisted pyrolysis action and to examine some critical issues characteristics of operation that have an influence on the yield of a product. This procedure appears to be governed by a number of operational factors for instance, microwave power and temperature degree, the addition of microwave absorbers and their concentration, initial relative humidity, primary sweep gas flow rate/time spent in residence. © Published under licence by IOP Publishing Ltd.

关键词： Biomass

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Semantic Segmentation and YOLO Detector over Aerial Vehicle Images

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computers, Materials & Continua 2024年第8期80卷 3315-3332页

作者： Asifa Mehmood Qureshi Abdul Haleem Butt Abdulwahab Alazeb Naif Al Mudawi Mohammad Alonazi Nouf Abdullah Almujally Ahmad Jalal Hui Liu Faculty of Computing and AI Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Cognitive Systems Lab University of BremenBremen28359Germany

Intelligent vehicle tracking and detection are crucial tasks in the realm of highway ***,vehicles come in a range of sizes,which is challenging to detect,affecting the traffic monitoring system’s overall *** learning is considered to be an efficient method for object detection in vision-based *** this paper,we proposed a vision-based vehicle detection and tracking system based on a You Look Only Once version 5(YOLOv5)detector combined with a segmentation *** model consists of six *** the first step,all the extracted traffic sequence images are subjected to pre-processing to remove noise and enhance the contrast level of the *** pre-processed images are segmented by labelling each pixel to extract the uniform regions to aid the detection phase.A single-stage detector YOLOv5 is used to detect and locate vehicles in *** detection was exposed to Speeded Up Robust Feature(SURF)feature extraction to track multiple *** on this,a unique number is assigned to each vehicle to easily locate them in the succeeding image frames by extracting them using the feature-matching ***,we implemented a Kalman filter to track multiple *** the end,the vehicle path is estimated by using the centroid points of the rectangular bounding box predicted by the tracking *** experimental results and comparison reveal that our proposed vehicle detection and tracking system outperformed other state-of-the-art *** proposed implemented system provided 94.1%detection precision for Roundabout and 96.1%detection precision for Vehicle Aerial Imaging from Drone(VAID)datasets,respectively.

关键词： Semantic segmentation YOLOv5 vehicle detection and tracking Kalman filter SURF

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