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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Manjula, R. Jagadeesan, S. Ramalingam, Venkadeshan Vetriselvi, T. Johnpeter, T. Srm Institute of Science and Technology College of Engineering and Technology Department of Computing Technologies Chennai India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Tamilnadu Vellore India Information Technology Department University of Technology and Applied Sciences-Shinas Branch Sultanate of Oman Oman Vellore Institute of Technology School of Computer Science and Engineering Department of Iot Tamilnadu Vellore India K.Ramakrishnan College of Engineering Department of Computer Science and Engineering Trichy India

ISBN: (纸本)9798350313987

Hard hearing and deaf people are communicating with other people using the sign language. This is the only way of communication of such people to convey Since this is the only means by which these individuals can communicate, it is imperative that others be able to understand their language. Sign language recognition by computer begins with the acquisition of sign gestures and ends with recognition and conversion to text or voice. Static and dynamic are the two ways of sign gestures used. This study aims at focusing on the dynamic gesture recognition and the steps for recognizing sign language are discussed. The collected data are preprocessed and feature are extracted for classification. Finally the results are all investigated and developed for single user. © 2023 IEEE.

关键词： Gesture analysis Hand gesture recognition Hand tracking Sign language

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Enhanced Metaheuristics-Based Clustering Scheme for Wireless Multimedia Sensor Networks

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computers, Materials & Continua 2022年第11期73卷 4179-4192页

作者： R.Uma Mageswari Sara A.Althubiti Fayadh Alenezi E.Laxmi Lydia Gyanendra Prasad Joshi Woong Cho Department of Computer Science and Engineering Vardhaman College of Engineering(Autonomous)HyderabadTelangana501218India Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Software Convergence Daegu Catholic UniversityGyeongsan38430Korea

Traditional Wireless Sensor Networks(WSNs)comprise of costeffective sensors that can send physical parameters of the target environment to an intended *** the evolution of technology,multimedia sensor nodes have become the hot research topic since it can continue gathering multimedia content and scalar from the target *** existence of multimedia sensors,integrated with effective signal processing and multimedia source coding approaches,has led to the increased application of Wireless Multimedia Sensor Network(WMSN).This sort of network has the potential to capture,transmit,and receive multimedia *** energy is a major source in WMSN,novel clustering approaches are essential to deal with adaptive topologies of WMSN and prolonged network *** this motivation,the current study develops an Enhanced Spider Monkey Optimization-based Energy-Aware Clustering Scheme(ESMO-EACS)for *** proposed ESMO-EACS model derives ESMO algorithm by incorporating the concepts of SMO algorithm and quantum *** proposed ESMO-EACS model involves the design of fitness functions using distinct input parameters for effective construction of clusters.A comprehensive experimental analysis was conducted to validate the effectiveness of the proposed ESMO-EACS technique in terms of different performance *** simulation outcome established the superiority of the proposed ESMO-EACS technique to other methods under various measures.

关键词： Wireless multimedia sensor networks clustering spider monkey optimization algorithm energy efficiency metaheuristics quantum computing

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Unsupervised Rail Surface Defect Detection Method 9

Unsupervised Rail Surface Defect Detection Method

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9th International Conference on Intelligent Informatics and BioMedical sciences, ICIIBMS 2024

作者： Zhao, ChenYu Han, Yi Ouyang, Ningkang Zhao, YiFan School of Artificial Intelligence Zhengzhou Software Vocational and Technical College Zhengzhou China Anyang Institute of Technology Engineering Department of Computer Science and Information Anyang China North China University of Water Resources and Electric Power School of Information Engineering Zhengzhou China

ISBN: (纸本)9798350363043

Railroad travel has become an essential part of modern life, therefore, rail surface defect detection has become a problem that cannot be ignored. However, most of the currently existing methods often require a large number of defect samples for learning, which are labor-intensive and very difficult to obtain. In order to alleviate the data dependence of the detection model, this paper proposes an unsupervised method. Specifically, we use Transformer's attention mechanism to construct the encoder-decoder structure and train it only on normal samples with the purpose of reconstruction, so that those regions that cannot be reconstructed will be regarded as defects at the time of detection. To prevent the model from generalizing its generalization ability to those unknown defective samples, i.e., to avoid overgeneralization, we introduce a noise perturbation in the training so that the model focuses on the reconstruction of normal features. Experiments show that the proposed unsupervised approach outperforms a host of supervised models with low data requirements and can effectively address the current challenges of track defect detection. © 2024 IEEE.

关键词： Unsupervised learning

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Achieving Efficient and Accurate Segmentation for Autonomous Driving 9

Achieving Efficient and Accurate Segmentation for Autonomous...

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9th International Conference on Intelligent Informatics and BioMedical sciences, ICIIBMS 2024

作者： Ouyang, Ningkang Dong, Jie Han, Yi Zhao, ChenYu North China University of Water Resources and Electric Power School of Information Engineering Zhengzhou China Anyang Institute of Technology Engineering Department of Computer Science and Information Anyang China Zhengzhou Software Vocational and Technical College School of Artificial Intelligence Zhengzhou China

ISBN: (纸本)9798350363043

Autonomous driving technology, as a mainstream trend in today's technological development, holds significant commercial value. Semantic segmentation, a core technology in this field, faces challenges with current mainstream models due to their complex structures, large numbers of parameters, and the need for high-performance hardware. These issues hinder their application in mobile devices. Therefore, this paper proposes a road scene semantic segmentation algorithm based on dynamic asymmetric convolution and capsule networks, named Asymmetric Capsule Convolutional Networks (ACCN). Specifically, the algorithm uses dynamic asymmetric convolution to construct a baseline network that combines dynamic convolution and capsule dynamic routing algorithms to enhance the model's representational capabilities. Additionally, a dispersed attention module is built to strengthen the dependency between features to achieve more adaptable network weights. Experiments demonstrate that ACCN achieves a good balance between segmentation accuracy and detection speed. On the CamVid and Cityscapes test datasets, the proposed method in this paper achieved an average Intersection over Union (mIoU) of 80.8% at 55.2 fps, showcasing better trade-offs in segmentation accuracy and efficiency. The experimental results indicate the effectiveness of our method. © 2024 IEEE.

关键词： Semantic Segmentation

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Earthworm Optimization with Improved SqueezeNet Enabled Facial Expression Recognition Model

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computer Systems science & engineering 2023年第8期46卷 2247-2262页

作者： N.Sharmili Saud Yonbawi Sultan Alahmari E.Laxmi Lydia Mohamad Khairi Ishak Hend Khalid Alkahtani Ayman Aljarbouh Samih M.Mostafa Computer Science and Engineering Department Gayatri Vidya Parishad College of Engineering for WomenVisakhapatnamAndhra PradeshIndia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia King Abdul Aziz City for Science and Technology RiyadhKingdom of Saudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong TebalPenang14300Malaysia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11564Saudi Arabia Department of Computer Science University of Central AsiaNaryn722600Kyrgyzstan Faculty of Computers and Information South Valley UniversityQena83523Egypt

Facial expression recognition(FER)remains a hot research area among computer vision researchers and still becomes a challenge because of high intraclass *** techniques for this problem depend on hand-crafted features,namely,LBP,SIFT,and HOG,along with that a classifier trained on a database of videos or *** execute perform well on image datasets captured in a controlled condition;however not perform well in the more challenging dataset,which has partial faces and image ***,many studies presented an endwise structure for facial expression recognition by utilizing DL ***,this study develops an earthworm optimization with an improved SqueezeNet-based FER(EWOISN-FER)*** presented EWOISN-FER model primarily applies the contrast-limited adaptive histogram equalization(CLAHE)technique as a pre-processing *** addition,the improved SqueezeNet model is exploited to derive an optimal set of feature vectors,and the hyperparameter tuning process is performed by the stochastic gradient boosting(SGB)***,EWO with sparse autoencoder(SAE)is employed for the FER process,and the EWO algorithm appropriately chooses the SAE ***-ranging experimental analysis is carried out to examine the performance of the proposed *** experimental outcomes indicate the supremacy of the presented EWOISN-FER technique.

关键词： Facial expression recognition deep learning computer vision earthworm optimization hyperparameter optimization

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Machine Learning-based Multi-path Reliable and Energy-efficient Routing Protocol for Underwater Wireless Sensor Networks 20

Machine Learning-based Multi-path Reliable and Energy-effici...

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20th International Conference on Frontiers of information Technology, FIT 2023

作者： Khan, Zahid Ullah Aman, Muhammad Rahman, Wazir Ur Khan, Faran Jamil, Tooba Hashim, Rifhat Harbin Engineering University College of Underwater Acoustic Engineering Harbin150001 China University of Science and Technology Bannu Department of Software Engineering Pakistan Barani Institute of Information and Technology Department of Computer Science Rawalpindi Pakistan

ISBN: (纸本)9798350395785

The research on applications for Underwater Wireless Sensor Networks (UWSNs) including the investigation of submerged resources, gathering oceanographic data, conducting operational surveillance, and prevention from natural disasters is rapidly increasing. Traditional wireless sensor networks (TWSNs) are distinct from UWSNs. Routing protocols of TWSNs are also distinct from UWSNs routing protocols in terms of energy efficiency. Most of routing protocols have been developed to extract data from the ocean floor to the surface of waters. There are many challenges for indigenous designing of an efficient routing protocol for UWSNs, such as, design of reliable path for forwarding communicating data packets, managing the movement of nodes, configuration of sensor nodes, removing void communicating nodes and increasing the power efficiency of the system. This research focuses on designing a novel Machine learning-based multi-path reliable and energy-efficient routing protocol (M2RE2RP) for UWSNs. It will select an efficient path among the existing communication paths used by sensor nodes, which will ultimately increase the lifespan of the network. The simulation experiment results dictate that the proposed routing model M2RE2RP achieves better performance in terms of network throughput, end-to-end latency, total energy consumption and network lifetime. © 2023 IEEE.

关键词： Routing protocols

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Blockchain Assisted Intrusion Detection System Using Differential Flower Pollination Model

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computers, Materials & Continua 2022年第12期73卷 4695-4711页

作者： Mohammed Altaf Ahmed Sara A Althubiti Dronamraju Nageswara Rao ELaxmi Lydia Woong Cho Gyanendra Prasad Joshi Sung Won Kim Department of Computer Engineering College of Computer Engineering&SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Computer Science and Engineering Chitkara University Institute of Engineering and TechnologyChitkara UniversityPunjab140401India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Software Convergence Daegu Catholic UniversityGyeongsan38430Korea Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-siGyeongbuk-do38541Korea

Cyberattacks are developing gradually sophisticated,requiring effective intrusion detection systems(IDSs)for monitoring computer resources and creating reports on anomalous or suspicious *** the popularity of Internet of Things(IoT)technology,the security of IoT networks is developing a vital *** of the huge number and varied kinds of IoT devices,it can be challenging task for protecting the IoT framework utilizing a typical *** typical IDSs have their restrictions once executed to IoT networks because of resource constraints and ***,this paper presents a new Blockchain Assisted Intrusion Detection System using Differential Flower Pollination with Deep Learning(BAIDS-DFPDL)model in IoT *** presented BAIDS-DFPDLmodelmainly focuses on the identification and classification of intrusions in the IoT *** accomplish this,the presented BAIDS-DFPDL model follows blockchain(BC)technology for effective and secure data transmission among the ***,the presented BAIDSDFPDLmodel designs Differential Flower Pollination based feature selection(DFPFS)technique to elect ***,sailfish optimization(SFO)with Restricted Boltzmann Machine(RBM)model is applied for effectual recognition of *** simulation results on benchmark dataset exhibit the enhanced performance of the BAIDS-DFPDL model over other models on the recognition of intrusions.

关键词： Internet of things feature selection intrusion detection blockchain security deep learning

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Smart Farmer Education System for Disease Identification, Severity Assessment and Infestation Management (Brinjal) 17

Smart Farmer Education System for Disease Identification, Se...

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17th IEEE International Conference on Industrial and information Systems, ICIIS 2023

作者： Bandara, Keshara Vidhanaarachchi, Samitha Nanayakkara, A.A.E. Kulasinghe, Kavindu Rajapakshe, Sanka Wickramasinghe, Asela Yapa, Kanishka Sri Lanka Institute of Information Technology Department of Information Technology New Kandy Road Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka University of Ruhuna Faculty of Agriculture Department of Soil Science Mapalana Kamburupitiya Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350323634

The agriculture industry is one of the most significant sources of foreign exchange and employment in the Sri Lankan market. Therefore, small crops play a crucial role in ensuring the food security of the population as they are integral components of Sri Lankan cuisine. However, industry experts have identified inefficient disease and pest management as a hindrance to production. This issue requires immediate attention and proper action, which can be effectively addressed through the utilization of ICT technologies. Consequently, this research paper proposes a smart system that aims to assist farmers and agriculture professionals by providing them with farmer education, disease and infestation management tools, as well as progression level calculations. The implementation of this system aims to uplift the agriculture industry in Sri Lanka. The developed 'AI-based mobile application system for Brinjal Diseases,' which is based on analyzing the eating patterns of leafhopper damage, presents an innovative approach to identify the most significant threats to Brinjal, specifically focusing on leafhopper damage. This proposed system effectively informs various stakeholders, including the Area Agricultural Research Officer, Seed Certification and Plant Protection Center (SCPPC), Agricultural Service Center, Office of the Register of Pesticides (ROP), Plant Protection Service (PPS), as well as farmers, regarding the dispersion and prevention of brinjal diseases and pest infections. © 2023 IEEE.

关键词： Crowdsourcing

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CPDZ: A Credibility-Aware and Privacy-Preserving Data Collection Scheme with Zero-Trust in Next-Generation Crowdsensing Networks

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 2183-2199页

作者： Tang, Jianheng Fan, Kejia Yang, Shihao Liu, Anfeng Xiong, Neal N. Song, Houbing Herbert Leung, Victor C. M. Central South University School of Computer Science and Engineering Changsha410083 China Peking University School of Computer Science Beijing100871 China Sul Ross State University Department of Computer Mathematical and Physical Sciences AlpineTX79830 United States Department of Information Systems BaltimoreMD21250 United States Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Next-Generation Crowdsensing Networks (NGCNs) have become increasingly critical for smart cities, where data privacy and quality are pivotal concerns. Traditional trust mechanisms in crowdsensing mainly rely on static trust models, which are insufficient for dynamic security requirements. Zero-Trust security represents a promising opportunity, yet coming with notable challenges in NGCNs, including Unknown Workers Online Recruitment (UWOR), information Elicitation Without Verification (IEWV), Privacy Preserving Data Evaluation (PPDE), and Dynamic Trust Abrupt Shift (DTAS). To address these challenges, we propose a Credibility-aware and Privacy-preserving Data collection scheme with Zero-trust (CPDZ) for secure and quality data collection in NGCNs. First, our CPDZ scheme encompasses a quality worker recruitment strategy with combinatorial multi-armed bandit models, utilizing Thompson Sampling for the secure and efficient resolution of the UWOR. Second, an active dispatching scheme for unmanned aerial vehicles is crafted to collect data as a gold standard to assist in overcoming the IEWV challenge. Third, as for the PPDE challenge, we propose a lightweight privacy-preserving scheme for dependable truth discovery and secure trust verification. Fourth, the DTAS challenge is managed by a dual verification scheme that integrates short-term and long-term trust assessments, ensuring stability and adaptability of the zero-trust security in our CPDZ scheme. Experiments confirm the superiority of our CPDZ scheme, showing a 12.5% increase in recruitment revenue and a 57.8% reduction in relative error compared to existing approaches. © 1983-2012 IEEE.

关键词： Differential privacy

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A Gamified Virtual Learning Environment to Enhance Online Teaching and Learning Experience 7

A Gamified Virtual Learning Environment to Enhance Online Te...

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7th IEEE International conference for Convergence in Technology, I2CT 2022

作者： Tharaka, W.C.M.K. Dilanka, R.M.T. Perera, H.D.D.S. Rathnayake, R.H.C.S. Kodagoda, Nuwan Suriyawansa, Kushnara Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Sri Lanka

ISBN: (纸本)9781665421683

In-class teaching not only concentrates on lecture content delivery but also on maintaining strong mutuality between lecturer-students and student-student. Online lectures are gaining popularity due to the Covid-19 pandemic. However, the learning-teaching process has become ineffective because existing video conferencing solutions are not intended for academic purposes. This research was conducted to identify the pain points of online education and develop an enhanced software solution. A user survey confirmed that an isolated environment tends to diminish attentiveness during online lectures. Also, it is difficult for teachers to observe the attentiveness of all the students. As a solution, student attentiveness was measured using their facial expressions and collected data shown to lecturers through a virtual student behavioural environment. The physical separation causes students to feel isolated during lectures, which can negatively affect their academic development and social and psychological development. The developed application also provides a virtual group study environment as a feature. According to the results gathered in the user acceptance testing phase, it was found that the attention detection feature helps students keep their attention at a significant level. Further, 9 out of 10 teachers who participated in the testing acknowledged that the simulated student behavioural view could provide a more immersive experience. 71% of students preferred the new collaborative virtual environment, and students further elaborated that the virtual environment was more likely the physical group studies. 72% of students mentioned that the collaborative group study tool helped eliminate isolation during group studies. © 2022 IEEE.

关键词： E-learning

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