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Japanese Sign Language Recognition by Combining Joint Skeleton-Based Handcrafted and Pixel-Based Deep Learning Features with Machine Learning Classification

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computer Modeling in engineering & sciences 2024年第6期139卷 2605-2625页

作者： Jungpil Shin Md.Al Mehedi Hasan Abu Saleh Musa Miah Kota Suzuki Koki Hirooka School of Computer Science and Engineering The University of AizuAizuwakamatsu965-8580Japan Department of Computer Science&Engineering Rajshahi University of Engineering&TechnologyRajshahi6204Bangladesh

Sign language recognition is vital for enhancing communication accessibility among the Deaf and hard-of-hearing *** Japan,approximately 360,000 individualswith hearing and speech disabilities rely on Japanese Sign Language(JSL)for ***,existing JSL recognition systems have faced significant performance limitations due to inherent *** response to these challenges,we present a novel JSL recognition system that employs a strategic fusion approach,combining joint skeleton-based handcrafted features and pixel-based deep learning *** system incorporates two distinct streams:the first stream extracts crucial handcrafted features,emphasizing the capture of hand and body movements within JSL ***,a deep learning-based transfer learning stream captures hierarchical representations of JSL gestures in the second ***,we concatenated the critical information of the first stream and the hierarchy of the second stream features to produce the multiple levels of the fusion features,aiming to create a comprehensive representation of the JSL *** reducing the dimensionality of the feature,a feature selection approach and a kernel-based support vector machine(SVM)were used for the *** assess the effectiveness of our approach,we conducted extensive experiments on our Lab JSL dataset and a publicly available Arabic sign language(ArSL)*** results unequivocally demonstrate that our fusion approach significantly enhances JSL recognition accuracy and robustness compared to individual feature sets or traditional recognition methods.

关键词： Japanese Sign Language(JSL) hand gesture recognition geometric feature distance feature angle feature GoogleNet

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Masked Face Recognition Using MobileNet V2 with Transfer Learning

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computer Systems science & engineering 2023年第4期45卷 293-309页

作者： Ratnesh Kumar Shukla Arvind Kumar Tiwari Department of Computer Science&Engineering Dr.APJ Abdul Kalam Technical UniversityLucknow226021India Department of Computer Science&Engineering KNITSultanpur228118Uttar PradeshIndia

Corona virus(COVID-19)is once in a life time calamity that has resulted in thousands of deaths and security *** are using face masks on a regular basis to protect themselves and to help reduce corona virus *** the on-going coronavirus outbreak,one of the major priorities for researchers is to discover effective *** important parts of the face are obscured,face identification and verification becomes exceedingly *** suggested method is a transfer learning using MobileNet V2 based technology that uses deep feature such as feature extraction and deep learning model,to identify the problem of face masked *** the first stage,we are applying face mask detector to identify the face ***,the proposed approach is applying to the datasets from Canadian Institute for Advanced Research10(CIFAR10),Modified National Institute of Standards and Technology Database(MNIST),Real World Masked Face Recognition Database(RMFRD),and Stimulated Masked Face Recognition Database(SMFRD).The proposed model is achieving recognition accuracy 99.82%with proposed *** article employs the four pre-programmed models VGG16,VGG19,ResNet50 and *** extract the deep features of faces with VGG16 is achieving 99.30%accuracy,VGG19 is achieving 99.54%accuracy,ResNet50 is achieving 78.70%accuracy and ResNet101 is achieving 98.64%accuracy with own *** comparative analysis shows,that our proposed model performs better result in all four previous existing *** fundamental contribution of this study is to monitor with face mask and without face mask to decreases the pace of corona virus and to detect persons using wearing face masks.

关键词： Convolutional Neural Network(CNN) deep learning face recognition system COVID-19 dataset and machine learning based models

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Redundant Transmission Control Algorithm for Information-Centric Vehicular IoT Networks

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computers, Materials & Continua 2023年第8期76卷 2217-2234页

作者： Abdur Rashid Sangi Satish Anamalamudi Mohammed SAlkatheiri Murali Krishna Enduri Anil Carie Mohammed AAlqarni Department of Computer Science College of Science and TechnologyWenzhou-Kean UniversityWenzhou325060China Department of Computer Science Engineering SRM University APAmaravati522502India Department of Cybersecurity College of Computer Science&EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Software Engineering College of Computer Science&EngineeringUniversity of JeddahJeddahSaudi Arabia

Vehicular Adhoc Networks(VANETs)enable vehicles to act as mobile nodes that can fetch,share,and disseminate information about vehicle safety,emergency events,warning messages,and passenger ***,the continuous dissemination of information fromvehicles and their one-hop neighbor nodes,Road Side Units(RSUs),and VANET infrastructures can lead to performance degradation of VANETs in the existing hostcentric IP-based ***,Information Centric Networks(ICN)are being explored as an alternative architecture for vehicular communication to achieve robust content distribution in highly mobile,dynamic,and errorprone *** ICN-based Vehicular-IoT networks,consumer mobility is implicitly supported,but producer mobility may result in redundant data transmission and caching inefficiency at intermediate vehicular *** paper proposes an efficient redundant transmission control algorithm based on network coding to reduce data redundancy and accelerate the efficiency of information *** proposed protocol,called Network Cording Multiple Solutions Scheduling(NCMSS),is receiver-driven collaborative scheduling between requesters and information sources that uses a global parameter expectation deadline to effectively manage the transmission of encoded data packets and control the selection of information *** results for the proposed NCMSS protocol is demonstrated to analyze the performance of ICN-vehicular-IoT networks in terms of caching,data retrieval delay,and end-to-end application *** end-to-end throughput in proposed NCMSS is 22%higher(for 1024 byte data)than existing solutions whereas delay in NCMSS is reduced by 5%in comparison with existing solutions.

关键词： Caching data dissemination redundancy control ICN-vehicular IoT networks

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Human and Machine Vision Based Indian Race Classification Using Modified-Convolutional Neural Network

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computer Systems science & engineering 2023年第3期44卷 2603-2618页

作者： Vani A.Hiremani Kishore Kumar Senapati Department of Computer Science&Engineering Birla Institute of TechnologyMesraRanchi835215JharkhandIndia

The inter-class face classification problem is more reasonable than the intra-class classification *** address this issue,we have carried out empirical research on classifying Indian people to their geographical *** work aimed to construct a computational classification model for classifying Indian regional face images acquired from south and east regions of India,referring to human *** have created an Automated Human Intelligence System(AHIS)to evaluate human visual *** of AHIS response showed that face shape is a discriminative feature among the other facial *** have developed a modified convolutional neural network to characterize the human vision response to improve face classification *** proposed model achieved mean F1 and Matthew Correlation Coefficient(MCC)of 0.92 and 0.84,respectively,on the validation set,outperforming the traditional Convolutional Neural Network(CNN).The CNN-Contoured Face(CNN-FC)model is developed to train contoured face images to investigate the influence of face ***,to cross-validate the accuracy of these models,the traditional CNN model is trained on the same *** an accuracy of 92.98%,the Modified-CNN(M-CNN)model has demonstrated that the proposed method could facilitate the tangible impact in intra-classification problems.A novel Indian regional face dataset is created for supporting this supervised classification work,and it will be available to the research community.

关键词： Data collection and preparation human vision analysis machine vision canny edge approximation method color local binary patterns convolutional neural network

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Realtime Object Detection Through M-ResNet in Video Surveillance System

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Intelligent Automation & Soft Computing 2023年第2期35卷 2257-2271页

作者： S.Prabu J.M.Gnanasekar Department of Computer Science&Engineering Karpaga Vinayaga College of Engineering and TechnologyChengalpattuTamilnadu603308India Department of Computer Science&Engineering Sri Venkateshwara College of Engineering&TechnologyChennai602001TamilnaduIndia

Object detection plays a vital role in the video surveillance *** enhance security,surveillance cameras are now installed in public areas such as trafﬁc signals,roadways,retail malls,train stations,and ***,monitor-ing the video continually at a quicker pace is a challenging *** a consequence,security cameras are useless and need human *** primary difﬁculty with video surveillance is identifying abnormalities such as thefts,accidents,crimes,or other unlawful *** anomalous action does not occur at a high-er rate than usual *** detect the object in a video,ﬁrst we analyze the images pixel by *** digital image processing,segmentation is the process of segregating the individual image parts into *** performance of segmenta-tion is affected by irregular illumination and/or low *** factors highly affect the real-time object detection process in the video surveillance *** this paper,a modiﬁed ResNet model(M-Resnet)is proposed to enhance the image which is affected by insufﬁcient *** results provide the comparison of existing method output and modiﬁcation architecture of the ResNet model shows the considerable amount improvement in detection objects in the video *** proposed model shows better results in the metrics like preci-sion,recall,pixel accuracy,etc.,andﬁnds a reasonable improvement in the object detection.

关键词： Object detection ResNet video survilence image processing object quality

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Predicting Hate Words and Offensive Language: A Machine Learning Approach 8

Predicting Hate Words and Offensive Language: A Machine Lear...

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8th International Conference on Inventive Systems and Control, ICISC 202

作者： Sugandhi, K. Reddy, R. Uday Kumar Reddy, K. Ravi Kiran Reddy, B. Balla Department of Computer Science&Engineering Andhra Pradesh Guntur India

ISBN: (纸本)9798350386578

The internet has led to a significant increase in the amount of information available, which has resulted in the rise of online communication among users. Unfortunately, this has also resulted in the proliferation of toxic online texts, which can lead to bullying, harassment, and personal attacks. While there have been attempts to develop efficient models for predicting toxic comments online, the field is still in its nascent stage, hence new frameworks and strategies are needed to address this issue. Convolutional Neural Networks (CNNs) are a promising solution for text classification due to recent advancements in hardware and big data management. Additionally, the increasing amount of data available necessitates the development of novel machine learning tools for managing this information. This study compares CNNs with the traditional bag-of-words approach as well as more efficient methods for text classification. The results demonstrate that CNNs outperform the conventional method in classifying harmful comments, indicating the approach's potential for further research in this field. This work highlights the importance of developing innovative strategies to combat online toxicity and provides insight into how deep learning techniques can be used for text classification. © 2024 IEEE.

关键词： Adversarial machine learning

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A Proposed Architecture for Local-Host and AWS with Multiagent System

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Intelligent Automation & Soft Computing 2023年第6期36卷 2787-2802页

作者： Jaspreet Chawla Anil Kr Ahlawat Department of Computer Science&Engineering JSS Academy of Technical EducationNoidaAffiliated to AKTULucknow201301India Department of Computer Science&Engineering KIET Group of InstitutionsGhaziabadAffiliated to AKTULucknow201001India

Cloud computing has become one of the leading technologies in the world *** benefits of cloud computing affect end users *** are several cloud computing frameworks,and each has ways of monitoring and providing *** computing eliminates customer requirements such as expensive system configuration and massive infrastructure while improving dependability and *** the user’s perspective,cloud computing makes it easy to upload multiagents and operate on different web *** this paper,the authors used a restful web service and an agent system to discuss,deployments,and analysis of load performance parameters like memory use,cen-tral processing unit(CPU)utilization,network latency,etc.,both on localhost and an Amazon Web Service Elastic Cloud Computing(AWS-EC2)*** Java Agent Development Environment(JADE)tool has been used to propose an archi-tecture and conduct a comparative study on both local and remote *** is an open-source tool for maintaining applications on AWS *** focus of the study should be to reduce the complexity and time of load perfor-mance parameters by using an agent system on a cloud server instead of establish-ing a massive infrastructure on a local system,even for a small application.

关键词： Amazon web service jade local host rest web service

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Real-time detection network for tiny traffic sign using multi-scale attention module

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science China(Technological sciences) 2022年第2期65卷 396-406页

作者： YANG TingTing TONG Chao School of Computer Science&Engineering Beihang UniversityBeijing 100191China

As one of the key technologies of intelligent vehicles, traffic sign detection is still a challenging task because of the tiny size of its target object. To address the challenge, we present a novel detection network improved from yolo-v3 for the tiny traffic sign with high precision in real-time. First, a visual multi-scale attention module(MSAM), a light-weight yet effective module, is devised to fuse the multi-scale feature maps with channel weights and spatial masks. It increases the representation power of the network by emphasizing useful features and suppressing unnecessary ones. Second, we exploit effectively fine-grained features about tiny objects from the shallower layers through modifying backbone Darknet-53 and adding one prediction head to yolo-v3. Finally, a receptive field block is added into the neck of the network to broaden the receptive field. Experiments prove the effectiveness of our network in both quantitative and qualitative aspects. The m AP@0.5 of our network reaches 0.965 and its detection speed is55.56 FPS for 512 × 512 images on the challenging Tsinghua-Tencent 100 k(TT100 k) dataset.

关键词： tiny object detection traffic sign detection multi-scale attention module real-time

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Hybrid Convolutional Neural Network and Long Short-Term Memory Approach for Facial Expression Recognition

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Intelligent Automation & Soft Computing 2023年第1期35卷 689-704页

作者： M.N.Kavitha A.RajivKannan Department of Computer Science&Engineering Kongu Engineering CollegePerunduraiErode638060Tamil NaduIndia Department of Computer Science&Engineering K.S.R.College of EngineeringTiruchengode637215Tamil NaduIndia

Facial Expression Recognition(FER)has been an importantfield of research for several *** of emotional characteristics is crucial to FERs,but is complex to process as they have significant intra-class *** characteristics have not been completely explored in static *** studies used Convolution Neural Networks(CNNs)based on transfer learning and hyperparameter optimizations for static facial emotional *** Swarm Optimizations(PSOs)have also been used for tuning ***,these methods achieve about 92 percent in terms of *** existing algorithms have issues with FER accuracy and ***,the overall FER performance is degraded *** address this issue,this work proposes a combination of CNNs and Long Short-Term Memories(LSTMs)called the HCNN-LSTMs(Hybrid CNNs and LSTMs)approach for *** work is evaluated on the benchmark dataset,Facial Expression Recog Image Ver(FERC).Viola-Jones(VJ)algorithms recognize faces from preprocessed images followed by HCNN-LSTMs feature extractions and FER ***,the success rate of Deep Learning Techniques(DLTs)has increased with hyperparameter tunings like epochs,batch sizes,initial learning rates,regularization parameters,shuffling types,and *** proposed work uses Improved Weight based Whale Optimization Algorithms(IWWOAs)to select near-optimal settings for these parameters using bestfitness *** experi-mentalfindings demonstrated that the proposed HCNN-LSTMs system outper-forms the existing methods.

关键词： Facial expression recognition Gaussianfilter hyperparameter optimization improved weight-based whale optimization algorithm deep learning(DL)

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Sentiment Drift Detection and Analysis in Real Time Twitter Data Streams

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computer Systems science & engineering 2023年第6期45卷 3231-3246页

作者： E.Susi A.P.Shanthi Department of Computer Science&Engineering Anna UniversityChennai600025Tamil NaduIndia

Handling sentiment drifts in real time twitter data streams are a challen-ging task while performing sentiment classifications,because of the changes that occur in the sentiments of twitter users,with respect to *** growing volume of tweets with sentiment drifts has led to the need for devising an adaptive approach to detect and handle this drift in real *** work proposes an adap-tive learning algorithm-based framework,Twitter Sentiment Drift Analysis-Bidir-ectional Encoder Representations from Transformers(TSDA-BERT),which introduces a sentiment drift measure to detect drifts and a domain impact score to adaptively retrain the classification model with domain relevant data in real *** framework also works on static data by converting them to data streams using the Kafka *** experiments conducted on real time and simulated tweets of sports,health care andfinancial topics show that the proposed system is able to detect sentiment drifts and maintain the performance of the classification model,with accuracies of 91%,87%and 90%,*** the results have been provided only for a few topics,as a proof of concept,this framework can be applied to detect sentiment drifts and perform sentiment classification on real time data streams of any topic.

关键词： Sentiment drift sentiment classification big data BERT real time data streams twitter

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