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Recognition of American sign language using modified deep residual CNN with modified canny edge segmentation

Multimedia Tools and Applications 2025年 1-28页

作者： Hariharan, U. Devarajan, Devikanniga Kumar, P. Santhosh Rajkumar, K. Meena, M. Akilan, T. Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab Mohali140413 India Department of Computer Science and Engineering GITAM University Karnataka Bengaluru561203 India Department of Information Technology Faculty of Engineering and technology SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600089 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Tambaram602109 India Research Coordinator Dept. of IQAC Amity University Kolkata West Bengal 700135 India

American Sign Language (ASL) recognition aims to recognize hand gestures, and it is a crucial solution to communicating between the deaf community and hearing people. However, existing sign language recognition algorithms still have some drawbacks, such as difficulty recognizing hand movements and low recognition accuracy for most sign language recognition. A Modified Convolutional Neural Network (MCNN) deep residual 101 classifier-based American Sign Language identification system has been developed to address this problem. There are three main parts presents in the method. The first part is pre-processing the images to remove the noise, enhance the contrast of the picture, adjust the contrast level and smoothen the picture using various filters. The second part is the segmentation, and it’s used to partition the image using a modified canny edge detection method by removing all weak edges present in the image. Finally, classification will be done using the Modified CNN deep residual 101 classifiers. By classifying the image, the American Sign Language is accurately identified. This process is conducted through images. The outcome shows that the suggested approach has a 0.97% accuracy and a 0.05% False Positive Rate. Other CNN architectures such as resNet 50 and resNet 18 reached 0.90% and 0.80% accuracy, respectively, which is lower than our proposed method. In addition, the resNet 101 classifier effectively recognizes the difficult hand gestures through the image data. It obtains high recognition accuracy for 36 signs is 0 to 9 numbers and a to z alphabets from American Sign Language. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Cocktail Party Effect Using Parallel Intra and Inter Self-attention 3rd

Cocktail Party Effect Using Parallel Intra and Inter Self-at...

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3rd International Conference on Advances in information and Communication technology, ICTA 2024

作者： Tan, Ha Minh Quoc, Nguyen Kim Vu, Duc-Quang Dang, An Thi, Duyen Nguyen Faculty of Information Technology Nguyen Tat Thanh University Thai Nguyen Viet Nam Department of Computer Science and Information System Thai Nguyen University of Education Thai Nguyen Viet Nam Faculty of Computer Science Phenikaa University Ha Noi Viet Nam Thai Nguyen University of Information and Communication Technology Thai Nguyen Viet Nam

ISBN: (纸本)9783031809422

The self-attention architecture is also considered to be a significant contribution in sequence processing tasks. It has the ability to highlight the distinctive features of a sequence, has been very successful in natural language processing and speech separation. In addition, the intra- and inter-architecture of the dual-path bi-directional long-short term memory framework has been demonstrated to be very effective for processing extremely long sequences. Based on these properties, we design a core architecture called intra- and inter-self-attention framework. Parallel models is more efficient and stable than a single model. In this research, we propose a parallel intra- and inter-self-attention framework (PI2SA) for the cocktail party effect task. The experimental results demonstrate that our PI2SA is remarkably effective. It outperforms both traditional self-attention models and a basic intra- and inter-architecture. Our PI2SA achieves competitive results with state-of-the-art deep learning models on the cocktail party effect problem. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep neural networks

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A Study of Predicting Social Networking Sites(SNS) Media Disorder Survey Data Using a Machine Learning Approach

A Study of Predicting Social Networking Sites(SNS) Media Dis...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Munia, Jerin Akther Tahmiduzzaman, K.B.M. Saad, Izazul Haque Islam, Md Mynul Akash, Abdul Hady Zumma, Md. Thoufiq Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh University of Technology Sydney Faculty of Engineering and Information Technology Sydney Australia University of Asia Pacific Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331509828

This study uses survey data and machine learning algorithms to forecast social media disorder in people. A total of 600 individuals answered questions on their social media usage patterns, internet habits, demographics, and signs of social media disorder. This dataset was used to train a number of machine learning models, such as Gaussian Naive Bayes (GaussianNB), Random Forest, K-Nearest Neighbors (KNN), Decision Tree, and Support Vector Machine (SVM). According to the findings, the GaussianNB model had the greatest accuracy of 97%, followed by SVM (94%), Random Forest (91%), Decision Tree (92%), and KNN (90%). These results demonstrate how well machine learning can identify those who may be at risk for social media disorder. In order to improve prediction performance, future research will concentrate on increasing feature size, diversifying datasets, and refining models. Furthermore, incorporating these models into user-friendly apps and intervention programs may offer useful resources for the early diagnosis and treatment of social media illness. © 2025 IEEE.

关键词： Adversarial machine learning

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CMV2U-Net: A U-shaped network with edge-weighted features for detecting and localizing image splicing

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Journal of Forensic sciences 2025年第0期70卷 2025 Apr 3页

作者： Akram, Arslan Jaffar, Muhammad Arfan Rashid, Javed Boulaaras, Salah Mahmoud Faheem, Muhammad Faculty of Computer Science and Information Technology The Superior University Lahore Pakistan Information Technology Services University of Okara Okara Pakistan Department of Mathematics College of Science Qassim University Buraydah Saudi Arabia VTT Technical Research Centre of Finland Espoo Finland

The practice of cutting and pasting portions of one image into another, known as “image splicing,” is commonplace in the field of image manipulation. Image splicing detection using deep learning has been a hot research topic for the past few years. However, there are two problems with the way deep learning is currently implemented: first, it is not good enough for feature fusion, and second, it uses only simple models for feature extraction and encoding, which makes the models vulnerable to overfitting. To tackle these problems, this research proposes CMV2U-Net, an edge-weighted U-shaped network-based image splicing forgery localization approach. An initial step is the development of a feature extraction module that can process two streams of input images simultaneously, allowing for the simultaneous extraction of semantically connected and semantically agnostic features. One characteristic is that a hierarchical fusion approach has been devised to prevent data loss in shallow features that are either semantically related or semantically irrelevant. This approach implements a channel attention mechanism to monitor manipulation trajectories involving multiple levels. Extensive trials on numerous public datasets prove that CMV2U-Net provides high AUC and F₁ in localizing tampered regions, outperforming state-of-the-art techniques. Noise, Gaussian blur, and JPEG compression are post-processing threats that CMV2U-Net has successfully resisted. © 2025 American Academy of Forensic sciences.

关键词： digital forensics feature fusion image authentication image forgery localization image splicing detection U-shaped network

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A Novel Pressure Sensor with Composite Sensitive Film for High Precision Measurement at Broad Range 38

A Novel Pressure Sensor with Composite Sensitive Film for Hi...

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38th IEEE International Conference on Micro Electro Mechanical Systems, MEMS 2025

作者： Chen, Xiaopeng Ling, Sijia Tong, Hanyang Xiao, Yujing Zhang, Jin Yu, Zhengyin Jin, Qinghui Ningbo University The Faculty of Electrical Engineering and Computer Science China Chinese Academy of Sciences Shanghai Institute of Microsystem and Information Technology China

ISBN: (纸本)9798331508890

Microelectromechanical system (MEMS) based pressure sensors have been utilized for decades;however, new trends in pressure sensors have recently emerged, such as increased sensitivity, a broader range and reduced chip size. This paper presents a tiny 0.6mm×0.8mm piezoresistive pressure sensor that offers exceptional sensitivity (0.27mV/kPa/3.3V) at a wide range of pressures (0-1MPa). The innovative composite pressure-sensitive film of the sensor is composed of a 10μm "*"-shaped single-crystalline silicon structure and a remarkably thin thickness of only 2.5μm poly-silicon film. It is developed utilizing the thin-film under bulk-silicon technique. And simultaneous improvement of detection sensitivity and linearity is achieved by etching out the nonlinear deformation region of the film. © 2025 IEEE.

关键词： Pressure sensors

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DDT-Net:Deep Detail Tracking Network for Image Tampering Detection

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computers, Materials & Continua 2025年第5期83卷 3451-3469页

作者： Jim Wong Zhaoxiang Zang Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang443002China College of Computer and Information Technology ChinaThree Gorges UniversityYichang443002China Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650504China

In the field of image forensics,image tampering detection is a critical and challenging *** methods based on manually designed feature extraction typically focus on a specific type of tampering operation,which limits their effectiveness in complex scenarios involving multiple forms of *** deep learningbasedmethods offer the advantage of automatic feature learning,current approaches still require further improvements in terms of detection accuracy and computational *** address these challenges,this study applies the UNet 3+model to image tampering detection and proposes a hybrid framework,referred to as DDT-Net(Deep Detail Tracking Network),which integrates deep learning with traditional detection *** contrast to traditional additive methods,this approach innovatively applies amultiplicative fusion technique during downsampling,effectively combining the deep learning feature maps at each layer with those generated by the Bayar noise *** design enables noise residual features to guide the learning of semantic features more precisely and efficiently,thus facilitating comprehensive feature-level ***,by leveraging the complementary strengths of deep networks in capturing large-scale semantic manipulations and traditional algorithms’proficiency in detecting fine-grained local traces,the method significantly enhances the accuracy and robustness of tampered region *** with other approaches,the proposed method achieves an F1 score improvement exceeding 30% on the DEFACTO and DIS25k *** addition,it has been extensively validated on other datasets,including CASIA and *** results demonstrate that this method achieves outstanding performance across various types of image tampering detection tasks.

关键词： Image forensics image tampering detection image manipulation detection noise flow Bayar

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Contemporary Impact of Cyberbullying on Youngsters: An Appraisal of Psychological, Emotional, Physical Health, and Academic Implications 19

Contemporary Impact of Cyberbullying on Youngsters: An Appra...

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19th International Conference on Ubiquitous information Management and Communication, IMCOM 2025

作者： Faith, Fatokun B. Long, Zalizah Awang Hamid, Suraya Malaysian Institute of Information Technology Universiti Kuala Lumpur Kuala Lumpur Malaysia Department of Information Systems Faculty of Computer Science IT Universiti Malaya Kuala Lumpur Malaysia

ISBN: (纸本)9798331507817

Cyberbullying is now a serious issue in contemporary times. The impact of cyberbullying on youngsters is of enormous concern in the public health sector as well as in the cybersecurity field. Several researchers have discussed the impact of cyberbullying from diverse perspectives, however, there is still a large gap in finding the paramount impact of cyberbullying in contemporary times, as well as providing solutions to eradicate this menace, especially as it deals with the negative effects of technology which is emerging daily. This study reviews via a scoping evidence approach suggesting the impact of cyberbullying on youngsters, focusing on the psychological, emotional, physical health, and academic implications. Findings from studies revealed that most youngsters face more of the psychological and emotional consequences of cyberbullying as compared to others discussed in the literature. Interestingly, there were also revelations of how cyberbullying had negative influences on the physical health and academic achievements of youngsters. These findings thus instigate the need for novel approaches that can help curb the rampancy of cyberbullying as well as ensure that decision-makers and stakeholders in the respective institutions put these impacts of cyberbullying into consideration when designing cybersecurity policies to ensure that victims of cyberbullying are assisted to recover, put in place technological solutions that can track and punish offenders and also reduce the rate of cyberbullying victimization. © 2025 IEEE.

关键词： Electronic health record

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Decentralizing Social Data: A Blockchain-Based Approach to Transparent and Trustworthy Social Networks 5

Decentralizing Social Data: A Blockchain-Based Approach to T...

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5th IEEE Middle East and North Africa COMMunications Conference, MENACOMM 2025

作者： El Ghor, Hussein Nakhal, Bilal Nasr, Sara Daher, Mohamad MUBS University ARCAS Research Center Faculty of Arts and Sciences Beirut Lebanon Beirut Arab University Mathematics and Computer Science Department Beirut Lebanon Lebanese University Faculty of Science Department of Computer Science Beirut Lebanon AUT University Department of Computer Science and Information Technology Beirut Lebanon

ISBN: (纸本)9798331519957

The rise of online social network (OSN) platforms has revealed pivotal issues with regards to data transparency and the concerns on the user's consent. Users are barely aware of how their data is used. To this end, this paper investigates the notion of applying blockchain technology for decentralizing social data, thus bringing more privacy and trust to the abstract concept of OSN platforms. We propose a Blockchain-Based Distributed Social Network System (B2DSN) that uses Hyperledger Fabric and InterPlanetary File System (IPFS) to secure sovereignty rights of data and offer greater control to users' data subjects. Hyperledger fabric is used to collect friends and data that are analyzed from social networks. We integrate consensus and verification methods directly within topic-based communities to provide trustworthiness of OSN platforms and eliminate the dissemination of rumors, and fake news. The feasibility of B2DSN is proved based on the integration of decentralized overlay network data flow, data sovereignty and verification and topic-based communities. The experimental simulation for data integrity demonstrates that the B2DSN model based on blockchain is feasible in securing and verifying the authenticity of social data even for large-scale OSNs. © 2025 IEEE.

关键词： Blockchain hyperledger fabric IPFS privacy Social network transparency

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Performance *** Comparative Analysis of Multimodal Bilinear Pooling Fusion Approaches for Deep Learning-Based Visual Arabic-Question Answering Systems

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computer Modeling in Engineering & sciences 2025年第4期143卷 373-411页

作者： Sarah M.Kamel Mai A.Fadel Lamiaa Elrefaei Shimaa I.Hassan Electrical Engineering Department Faculty of Engineering at ShoubraBenha UniversityCairo11629Egypt Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer and Systems Engineering Faculty of Engineering and TechnologyBadr University in Cairo(BUC)Cairo11829Egypt Communication Systems Engineering Department Faculty of EngineeringBenha National UniversityObour11846QalyubiaEgypt

Visual question answering(VQA)is a multimodal task,involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate *** this paper,we propose a VQA system intended to answer yes/no questions about real-world images,in *** support a robust VQA system,we work in two directions:(1)Using deep neural networks to semantically represent the given image and question in a fine-grainedmanner,namely ResNet-152 and Gated Recurrent Units(GRU).(2)Studying the role of the utilizedmultimodal bilinear pooling fusion technique in the *** the model complexity and the overall model *** fusion techniques could significantly increase the model complexity,which seriously limits their applicability for VQA *** far,there is no evidence of how efficient these multimodal bilinear pooling fusion techniques are for VQA systems dedicated to yes/no ***,a comparative analysis is conducted between eight bilinear pooling fusion techniques,in terms of their ability to reduce themodel complexity and improve themodel performance in this case of VQA *** indicate that these multimodal bilinear pooling fusion techniques have improved the VQA model’s performance,until reaching the best performance of 89.25%.Further,experiments have proven that the number of answers in the developed VQA system is a critical factor that *** the effectiveness of these multimodal bilinear pooling techniques in achieving their main objective of reducing the model *** Multimodal Local Perception Bilinear Pooling(MLPB)technique has shown the best balance between the model complexity and its performance,for VQA systems designed to answer yes/no questions.

关键词： Arabic-VQA deep learning-based VQA deep multimodal information fusion multimodal representation learning VQA of yes/no questions VQA model complexity VQA model performance performance-complexity trade-off

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Evaluating BERT-based language models for detecting misinformation

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Neural Computing and Applications 2025年 1-32页

作者： Anggrainingsih, Rini Hassan, Ghulam Mubashar Datta, Amitava Information Technology and Data Science Faculty Sebelas Maret University Ir Sutami 36A Central Java Surakarta57126 Indonesia Computer Science and Software Engineering The University of Western Australia 35 Stirling Highway PerthWA6009 Australia

Online misinformation poses a significant challenge due to its rapid spread and limited supervision. To address this issue, automated rumour detection techniques are essential for countering the negative impact of false information. Previous research primarily focussed on extracting text features, which proved time-consuming and less effective. In this study, we contribute substantially to two domains: rumour detection on Twitter and the evaluation of text embeddings. We thoroughly analyse rumour detection models and compare the quality of text embeddings generated by various fine-tuned BERT-based models. Our findings indicate that our proposed models outperform existing techniques. Notably, when we test these models on combined datasets, we observe significant performance improvements with larger training and testing data sizes. We conclude that carefully considering the dataset, data splitting, and classification techniques is crucial for evaluating solution performance. Additionally, we find that differences in the quality of text embeddings between RoBERTa, BERT, and DistilBERT are insignificant. This challenges existing assumptions and highlights the need for future research to explore these nuances further. © The Author(s) 2025.

关键词： BERT-based models Comparative analysis Rumour detection Text embeddings

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