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2024 IEEE International Conference on Unmanned systems, ICUS 2024

作者： Luo, Fuyu Yu, Yunbo Guo, Xiaoye Systems Engineering Research Institute China State Shipbuilding Corporation Beijing China Department of Computer Science Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350384185

This Multi-agent collaborative perception is a hot topic in the current field of machine learning. Through communication and sharing of perceived scene information between agents, this approach can tackle issues such as occlusion and sparse observations present in single-agent observations. Existing studies have shown that localization errors are a significant factor affecting the accuracy of perception results. Furthermore, most prior work has focused solely on the performance of algorithms in individual tasks without considering multi-task scenarios. In this paper, a multitask collaborative perception method based on consensus perception is proposed. It integrates local perception information from each intelligent agent to form a global perception consensus, which is then used to correct the spatial feature maps of the agents, alleviating the impact of localization errors on perception results. Additionally, the use of a feature selection module based on spatial confidence maps and a multi-scale attention feature fusion module optimizes the performance of the collaborative perception process. By setting up multiple decoders in parallel, a single model is able to handle diverse tasks. Experimental results demonstrate that the proposed consensus-based collaborative perception method effectively mitigates the negative impact of localization errors on perception performance, and the algorithm can achieve good perception results across multiple tasks. © 2024 IEEE.

关键词： Feature Selection

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Steel Defect Detection Using a ResNet-Inspired Model and Fourier Transforms 4

Steel Defect Detection Using a ResNet-Inspired Model and Fou...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Anand, Philip Sudharsan, Rohit Madhavan Ramachandran, Vishwath Prusty, Manas Ranjan Vellore Institute of Technology School of Computer Science and Engineering Chennai India Vellore Institute of Technology Centre for Cyber Physical Systems Chennai India

ISBN: (纸本)9798350343670

Defect detection in steel materials is a critical task to ensure product quality and reliability in various industrial applications. Traditional methods for detecting defects in steel surfaces are often labor-intensive, time-consuming, and prone to subjectivity and errors. However, the advent of deep learning-based models and techniques has provided a promising avenue for automating this process, significantly reducing error rates. In this research, the authors propose a Steel Defect Detection system with the help of a custom designed deep neural network inspired by the ResNet architecture. Notably, the model incorporates innovative attention layers not previously integrated into similar architectures, enhancing its predictive capabilities. The authors make use of data augmentation techniques to improve the model performance to generalize, and detect defects in imperceptible data. The novel multiclass semantic segmentation model has achieved over 91% accuracy and can be used to automate the Steel defect detection process, which can remarkably decrease the costs and time associated with manually inspecting the Steel sheets. © 2024 IEEE.

关键词： Attention layers augmentation Deep learning Residual Network Steel Defect

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Wind Speed Prediction Using Chicken Swarm Optimization with Deep Learning Model

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computer systems science & engineering 2023年第9期46卷 3371-3386页

作者： R.Surendran Youseef Alotaibi Ahmad F.Subahi Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennaiIndia Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Computer Science University College of Al JamoumUmm Al-Qura UniversityMakkah21421Saudi Arabia

High precision and reliable wind speed forecasting have become a challenge for *** events,namely,strong winds,thunderstorms,and tornadoes,along with large hail,are natural calamities that disturb daily *** accurate prediction of wind speed and overcoming its uncertainty of change,several prediction approaches have been presented over the last few *** wind speed series have higher volatility and nonlinearity,it is urgent to present cutting-edge artificial intelligence(AI)*** this aspect,this paper presents an intelligent wind speed prediction using chicken swarm optimization with the hybrid deep learning(IWSP-CSODL)*** presented IWSP-CSODL model estimates the wind speed using a hybrid deep learning and hyperparameter *** the presented IWSP-CSODL model,the prediction process is performed via a convolutional neural network(CNN)based long short-term memory with autoencoder(CBLSTMAE)*** optimally modify the hyperparameters related to the CBLSTMAE model,the chicken swarm optimization(CSO)algorithm is utilized and thereby reduces the mean square error(MSE).The experimental validation of the IWSP-CSODL model is tested using wind series data under three distinct *** comparative study pointed out the better outcomes of the IWSP-CSODL model over other recent wind speed prediction models.

关键词： Weather wind speed predictive model chicken swarm optimization hybrid deep learning

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Sinhala Sign Language Learning System for Hearing Impaired Community 4

Sinhala Sign Language Learning System for Hearing Impaired C...

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4th International Informatics and software engineering Conference, IISEC 2023

作者： Idushan, K.A.S. Dilshan, P.G.T. Jayasundera, P.S. Madhushan, K.L.B. Krishara, Jenny Wijendra, Dinuka Sri Lanka Institute of Information Technology Dept. of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350318036

This research aims to develop a comprehensive system for Sinhala Sign Language (SSL) that includes a learning system, dynamic sign detection, audio/video to sign conversion, and vocal training. SSL plays a crucial role in facilitating communication for individuals who are deaf or hard of hearing in Sri Lanka. The learning system provides a platform for learning SSL and includes a text-To-sign language interpreter. The dynamic sign detection system uses computer vision techniques to identify and interpret dynamic signs accurately. The audio/video to sign conversion system bridges the gap between spoken language and SSL by converting auditory information into visual representations. The vocal training system focuses on enhancing the vocal skills of cochlear implanted children. This research contributes to the development of effective communication and language skills for SSL users. © 2023 IEEE.

关键词： Learning systems

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Semantic2Graph: graph-based multi-modal feature fusion for action segmentation in videos

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Applied Intelligence 2024年第2期54卷 2084-2099页

作者： Zhang, Junbin Tsai, Pei-Hsuan Tsai, Meng-Hsun Department of Computer Science and Information Engineering National Cheng Kung University Tainan701 Taiwan Institute of Manufacturing Information and Systems National Cheng Kung University Tainan701 Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu300 Taiwan

Video action segmentation have been widely applied in many fields. Most previous studies employed video-based vision models for this purpose. However, they often rely on a large receptive field, LSTM or Transformer methods to capture long-term dependencies within videos, leading to significant computational resource requirements. To address this challenge, graph-based model was proposed. However, previous graph-based models are less accurate. Hence, this study introduces a graph-structured approach named Semantic2Graph, to model long-term dependencies in videos, thereby reducing computational costs and raise the accuracy. We construct a graph structure of video at the frame-level. Temporal edges are utilized to model the temporal relations and action order within videos. Additionally, we have designed positive and negative semantic edges, accompanied by corresponding edge weights, to capture both long-term and short-term semantic relationships in video actions. Node attributes encompass a rich set of multi-modal features extracted from video content, graph structures, and label text, encompassing visual, structural, and semantic cues. To synthesize this multi-modal information effectively, we employ a graph neural network (GNN) model to fuse multi-modal features for node action label classification. Experimental results demonstrate that Semantic2Graph outperforms state-of-the-art methods in terms of performance, particularly on benchmark datasets such as GTEA and 50Salads. Multiple ablation experiments further validate the effectiveness of semantic features in enhancing model performance. Notably, the inclusion of semantic edges in Semantic2Graph allows for the cost-effective capture of long-term dependencies, affirming its utility in addressing the challenges posed by computational resource constraints in video-based vision models. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Semantics

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Deep Learning-Based Histopathological Analysis for Colon Cancer Diagnosis: A Comparative Study of CNN and Transformer Models with Image Preprocessing Techniques 23th

Deep Learning-Based Histopathological Analysis for Colon Can...

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23rd International Conference on Intelligent systems Design and Applications, ISDA 2023

作者： Karthik, R. Thalanki, Vaibhav Yadav, Preyash Centre for Cyber Physical Systems Vellore Institute of Technology Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9783031648120

Colon cancer is a serious life-threatening type of cancer that affects the colon and rectum. Manual diagnosis of this disease is time-consuming and la-bor-intensive, requiring pathologists to carefully examine multiple tissue sections and slides per patient. Deep learning techniques offer promising solutions to these challenges. This research investigates the performance of various deep learning architectures, including ResNet50, InceptionResnetV2, NasNet, EfficientNetB0, DenseNet169, ViT, and MobileViT, in histopathology. Four stain normalization methods (Vahadane, Macenko, Reinhard, and ITK) and four image-denoising techniques (Wavelet-based denoising, Gaussian Filtering, Bilateral Filtering, and Median Filtering) were employed to ad-dress staining and noise issues. An ensemble is developed integrating a CNN and a transformer. CNNs excel at extracting local features from images through the use of convolutional layers and pooling operations, while the transformer can capture long-range dependencies. This allows the ensemble to capture intricate local patterns while also considering the relationships be-tween objects, regions, or features that may span the entire image. A quantitative comparative study on the ‘EBHI’ dataset was conducted, resulting in the development of an ensemble model achieving an accuracy of up to 92.02%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Image denoising

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Lens-Oppositional Wild Geese Optimization Based Clustering Scheme for Wireless Sensor Networks Assists Real Time Disaster Management

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computer systems science & engineering 2023年第7期46卷 835-851页

Recently,wireless sensor networks(WSNs)find their applicability in several real-time applications such as disaster management,military,surveillance,healthcare,*** utilization of WSNs in the disaster monitoring process has gained significant attention among research communities and ***-time monitoring of disaster areas using WSN is a challenging process due to the energy-limited sensor ***,the clustering process can be utilized to improve the energy utilization of the nodes and thereby improve the overall functioning of the *** this aspect,this study proposes a novel Lens-Oppositional Wild Goose Optimization based Energy Aware Clustering(LOWGO-EAC)scheme for WSN-assisted real-time disaster *** major intention of the LOWGO-EAC scheme is to perform effective data collection and transmission processes in disaster *** achieve this,the LOWGOEAC technique derives a novel LOWGO algorithm by the integration of the lens oppositional-based learning(LOBL)concept with the traditional WGO algorithm to improve the convergence *** addition,the LOWGO-EAC technique derives a fitness function involving three input parameters like residual energy(RE),distance to the base station(BS)(DBS),and node degree(ND).The proposed LOWGO-EAC technique can accomplish improved energy efficiency and lifetime of WSNs in real-time disaster management *** experimental validation of the LOWGO-EAC model is carried out and the comparative study reported the enhanced performance of the LOWGO-EAC model over the recent approaches.

关键词： Disaster management real-time applications wireless sensor networks clustering bioinspired algorithms

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Automatic Detection of Weapons in Surveillance Cameras Using Efficient-Net

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computers, Materials & Continua 2022年第9期72卷 4615-4630页

作者： Erssa Arif Syed Khuram Shahzad Muhammad Waseem Iqbal Muhammad Arfan Jaffar Abdullah S.Alshahrani Ahmed Alghamdi Department of Computer Science Superior UniversityLahore54000Pakistan Department of Informatics&Systems University of Management&TechnologyLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Faculty of Computer Science&Information Technology Superior UniversityLahore54000Pakistan Department of Computer Science&Artificial Intelligence College of Computer Science&EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia

The conventional Close circuit television(CCTV)cameras-based surveillance and control systems require human resource *** all the criminal activities take place using weapons mostly a handheld gun,revolver,pistol,swords ***,automatic weapons detection is a vital requirement now a *** current research is concerned about the real-time detection of weapons for the surveillance cameras with an implementation of weapon detection using Efficient–*** time datasets,from local surveillance department’s test sessions are used for model training and *** consist of local environment images and videos from different type and resolution cameras that minimize the *** research also contributes in the making of Efficient-Net that is experimented and results in a positive *** results are also been represented in graphs and in calculations for the representation of results during training and results after training are also shown to represent our research ***-Net algorithm gives better results than existing *** using Efficient-Net algorithms the accuracy achieved 98.12%when epochs increase as compared to other algorithms.

关键词： Detection algorithms machine learning machine vision video surveillance

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Inverter Based on Arduino Mega Programming for Uninterruptible Power Supply Applications 2

Inverter Based on Arduino Mega Programming for Uninterruptib...

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2nd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2023

作者： Rao, Bhukya Nageswar Oswal, Riya A. Biraje, Swarup R. Hadimani, Lingaraj Kolhapur Institute of Technology College of Engineering Department of Computer Science and Business Systems Kolhapur India

ISBN: (纸本)9798350314045

The utilization of power electronic converters, specifically DC/AC inverters are progressively expanding across industries and household appliances due to their myriad benefits. These inverters commonly adopt a staircase voltage waveform derived from multiple DC sources, resulting in reduced harmonic distortion. This paper strives to enhance our understanding of the performance of a five-level inverter topology using Arduino Mega programming. The Arduino Mega programming will be employed to generate the required pulses for the suggested inverter. The efficacy of this inverter will be assessed through MATLAB/Simulink simulations substantiated through practical implementation using optocouplers and an Arduino Mega controller. The outcomes from the simulation and hardware experimentation confirm the effectiveness of the suggested inverter design. © 2023 IEEE.

关键词： Uninterruptible power systems

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ScDCMAC:adaptive feedback clustering of scRNA-seq data with deep gene correlation mining

ScDCMAC:adaptive feedback clustering of scRNA-seq data with ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Pang, Shanchen Wang, Ruiqian Wu, Wenhao Li, Hengxiao Wu, Jiarui Zhang, Yuanyuan Wang, Shudong College of Computer Science and Technology Qingdao Institute of Software Qingdao China Qingdao University of Technology School of Information and Control Engineering Qingdao China

ISBN: (纸本)9798350386226

Cluster assignment is crucial for analyzing single-cell RNA sequencing (scRNA-seq) data, essential for studying cell diversity and biological functions at the single-cell level. Deep learning-based clustering methods have been extensively applied in scRNA-seq data analysis. However, current deep models frequently overlook the deep exploration of potential gene correlation, resulting in networks lacking guidance from gene correlation information when learning hidden layer features. This paper proposes an adaptive feedback fuzzy clustering method based on deep gene correlation mining(scDCMAC). The deep gene correlation mining network employs contrast learning to compute the contrast loss on two cell similarity matrices, constructed from denoised autoencoder hidden layer features and original input features, to uncover potential gene correlation. The adaptive feedback clustering network further denoises the hidden layer features using a self-attention mechanism, computes the affiliation matrix, and supervises the denoising autoencoder's dimensionality reduction using the affiliation matrix. Experiments on 13 real scRNA-seq datasets demonstrate that scDCMAC excels in clustering accuracy and stability, showing great potential in scRNA-seq sample analysis. © 2024 IEEE.

关键词： Dimensionality reduction

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