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4th International Conference on Communication, Devices and Computing (ICCDC 2023)

作者： Singh, Ritwiz Kashyap, Keshav Mukherjee, Rajesh Bera, Asish Chakraborty, Mamata Dalui Department of Computer Science and Engineering Haldia Institute of Technology WB Haldia India Department of Computer Science and Information Systems Birla Institute of Technology and Science Pilani Rajasthan Pilani Campus 333031 India Department of Computer Science and Engineering National Institute of Technology West Bengal Durgapur India

ISBN: (纸本)9789819927098

Human gender classification based on biometric features is a major concern for computer vision due to its vast variety of applications. The human ear is popular among researchers as a soft biometric trait, because it is less affected by age or changing circumstances and is non-intrusive. In this study, we have developed a deep convolutional neural network (CNN) model for automatic gender classification using the samples of ear images. The performance is evaluated using four cutting-edge pre-trained CNN models. In terms of trainable parameters, the proposed technique requires significantly less computational complexity. The proposed model has achieved 93% accuracy on the EarVN1.0 ear dataset. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural network models

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Neural Gas Counter Propagation Neural Network (NG CPNN): A Novel Model for Face Recognition

Neural Gas Counter Propagation Neural Network (NG CPNN): A N...

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2024 International Conference on science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Olagunju, Kazeem Michael Samuel Falohun, Adeleye Oke, Alice Oluwafunke Adebiyi, Marion O. Landmark University Directorate of Centre for Systems and Information Services Omu-Aran Nigeria LAUTECH Department of Computer Engineering Ogbomoso Nigeria Landmark University Department of Computer Science Omu-Aran Nigeria

ISBN: (纸本)9798350358155

The advent of biometric technology has enhanced security in various ways, especially by lowering the propensity for circumvention which was obtainable in traditional recognition measures such as the use of passwords, tokens, smart cards, PIN etc. Particularly facial recognition is currently a hot topic, which has evolved over time. A face recognition system is supposed to automatically recognize faces in pictures and videos by receiving a two-dimensional face image as input and comparing it to a predefined database of faces to outputs a discernible face image in the end. Despite the various algorithms used in face recognition, the problems of occlusion, light conditions and facial variation still persist. However, this study has implemented and presented Neural Gas Counter Propagation Neural Network (NG CPNN) as a novel model for face recognition *** implementation of model was carried out using 600 face images, pre-processed by histogram equalization, while face features were extracted by Principal Component Analysis. The novelty of NG CPNN was established by utilizing computational time, sensitivity, and accuracy as performance assessment measures at four distinct threshold values. The result shows computation time of 72.36s, accuracy of 96.25%, sensitivity of 98.33% at 0.8, 0.8, and 0.2 thresholds respectively. Finally, it was concluded based on empirical result and statistical evidence that NG-CPN is a novel model for face recognition. NG CPNN can be employed in access control systems, international borders, smartphone technology, and many more. © 2024 IEEE.

关键词： artificial intelligence biometrics face recognition image processing NG- CPNN pattern recognition

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Successful Integration of IoT and Blockchain Technologies Using Several Machine Learning Algorithms 5

Successful Integration of IoT and Blockchain Technologies Us...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Calarany, C. Indumathy, M. Senthilraja, P. Suganya, D. Gopi Vardhan, V. Rahul, Yelisetty Shanmukh Tagore Government Arts and Science College Department of Computer Science Puducherry India Rajiv Gandhi College of Engineering & Technology Department of Information Technology Kirumampakkam India K.S.Rangasamy College of Technology Department of Computer Science Tamil Nadu India Rajiv Gandhi College of Engineering & Technology Department of Computer Science Kirumampakkam India Data Science and Business Systems Srm Institute of Science and Technology Kattankulathur India

ISBN: (纸本)9798350383522

Internet of Things (IoT) produces massive amounts of data that need to be processed and saved securely. The strong features of Blockchain makes it as a best candidate for storing the data received from IoT sensors. However, there is a need of concern to take care of the challenges associated with both IoT and blockchain paradigms. Firstly, the enormous amount of data should be effortlessly handled by Blockchain network, without adding much complexity. Secondly, the heterogeneous nature of data that are received from various IoT Sensors should be stored within the blockchain. In this paper, a conceptual framework based on Multimodal Machine Learning (MML) algorithm is proposed as an interface between the IoT sensors and Blockchain networks. The MML algorithm is used to understand the different modalities of the data and provides a way to manage the heterogeneity in that data. The MML algorithms provides an intelligent data processing that allows for real-time data storage in the blockchain networks. The conceptual frame assumes that audio, video, and text are the three types of data as received from IoT. These inputs are processed in simplistic Recurrent neural network RNN that acts as MML and then passed to Blockchain networks for secure storage. Simulation of the above said conceptual framework is performed assuming all other relevant requirements of IoT, MML and Blockchain are taken into account with default parameters and values. The results obtained provide a promising note for future scope in this direction. © 2024 IEEE.

关键词： IoT MML RNN

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ICON: Improving Inter-Report Consistency in Radiology Report Generation via Lesion-aware Mixup Augmentation

ICON: Improving Inter-Report Consistency in Radiology Report...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Hou, Wenjun Cheng, Yi Xu, Kaishuai Hu, Yan Li, Wenjie Liu, Jiang Department of Computing The Hong Kong Polytechnic University Hong Kong Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

ISBN: (纸本)9798891761681

Previous research on radiology report generation has made significant progress in terms of increasing the clinical accuracy of generated *** this paper, we emphasize another crucial quality that it should possess, i.e., inter-report consistency, which refers to the capability of generating consistent reports for semantically equivalent *** quality is even of greater significance than the overall report accuracy in terms of ensuring the system's credibility, as a system prone to providing conflicting results would severely erode users' ***, existing approaches struggle to maintain inter-report consistency, exhibiting biases towards common patterns and susceptibility to lesion *** address this issue, we propose ICON, which Improves the inter-report CONsistency of radiology report *** to enhance the system's ability to capture similarities in semantically equivalent lesions, our approach first involves extracting lesions from input images and examining their ***, we introduce a lesion-aware mixup technique to ensure that the representations of the semantically equivalent lesions align with the same attributes, achieved through a linear combination during the training *** experiments on three publicly available chest X-ray datasets verify the effectiveness of our approach, both in terms of improving the consistency and accuracy of the generated reports. © 2024 Association for Computational Linguistics.

关键词： Radiology

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Investigating User Preferences for Conversation Design of Voice Assistant systems using Linguistic Features 67th

Investigating User Preferences for Conversation Design of Vo...

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67th International Annual Meeting of the Human Factors and Ergonomics Society, HFES 2023

作者： Sargsyan, Lilit Choi, Seungju Kim, Sang-Hwan Department of Industrial and Manufacturing Systems Engineering College of Engineering and Computer Science University of Michigan – Dearborn DearbornMI United States

As the use of voice assistant (VA) systems is increasing, conversation design in the system is important for effective human-system interaction. The objective of the study was to investigate the level of user preference for VA outputs in terms of linguistics. Answers of three VA systems for each of the nine questions were collected and categorized for distinctive linguistic factors such as type of theme, thematic progression, number of predications, and ellipsis. The VA answers were evaluated through an online survey. Results show that linguistic factors and features significantly affect user preference for VA outputs. The results imply that the linguistic features need to be considered for designing voice interaction communications as a natural interaction method. © 2023 Human Factors and Ergonomics Society.

关键词： Linguistic Features User perception Voice Assistant systems

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Image Hiding with High Robustness Based on Dynamic Region Attention in the Wavelet Domain

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computer Modeling in engineering & sciences 2024年第10期141卷 847-869页

作者： Zengxiang Li Yongchong Wu Alanoud Al Mazroa Donghua Jiang Jianhua Wu Xishun Zhu Department of Information Engineering Gongqing CollegeNanchang UniversityJiujiang332020China School of Information Engineering Gongqing Institute of Science and TechnologyJiujiang332020China Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhou511400China School of Information Engineering Nanchang UniversityNanchang330031China School of Mathematics and Statistics Hainan Normal UniversityHaikou571158China

Hidden capacity,concealment,security,and robustness are essential indicators of hiding ***,hiding algorithms tend to focus on algorithmic capacity,concealment,and security but often overlook the robustness of the *** practical applications,the container can suffer from damage caused by noise,cropping,and other attacks during transmission,resulting in challenging or even impossible complete recovery of the secret *** image hiding algorithm based on dynamic region attention in the multi-scale wavelet domain is proposed to address this issue and enhance the robustness of hiding *** this proposed algorithm,a secret image of size 256×256 is first decomposed using an eight-level Haar wavelet *** wavelet transform generates one coefficient in the approximation component and twenty-four detail bands,which are then embedded into the carrier image via a hiding *** the recovery process,the container image is divided into four non-overlapping parts,each employed to reconstruct a low-resolution secret *** lowresolution secret images are combined using densemodules to obtain a high-quality secret *** experimental results showed that even under destructive attacks on the container image,the proposed algorithm is successful in recovering a high-quality secret image,indicating that the algorithm exhibits a high degree of robustness against various *** proposed algorithm effectively addresses the robustness issue by incorporating both spatial and channel attention mechanisms in the multi-scale wavelet domain,making it suitable for practical *** conclusion,the image hiding algorithm introduced in this study offers significant improvements in robustness compared to existing *** ability to recover high-quality secret images even in the presence of destructive attacksmakes it an attractive option for various *** research and experimentation can explore the algorith

关键词： Image hiding robustness wavelet transform dynamic region attention

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Intelligent identification of hate speeches to address the increased rate of individual mental degeneration

Intelligent identification of hate speeches to address the i...

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2022 International Conference on ENTERprise Information systems, CENTERIS 2022 - International Conference on Project MANagement, ProjMAN 2022 and International Conference on Health and Social Care Information systems and Technologies, HCist 2022

作者： Ava, Lamima Tabassum Karim, Asif Hassan, Md. Mehedi Faisal, Fahad Azam, Sami Al Haque, A.S.M. Farhan Zaman, Sadika Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh College of Engineering IT and Environment Charles Darwin University NT Australia Department of Computer Science and Engineering North Western University Khulna Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Electronic Systems Aalborg University Copenhagen Denmark Department of Computer Science and Engineering North Western University Khulna Bangladesh

Hate speech is a public statement that demonstrates resentment or provokes disturbance towards a person or group often based upon race, age, religion, sexual orientation, minority group, psychical disability, political belief, etc. Such an act is a leading cause of mental degeneration in individuals observed throughout the world. We have witnessed an upsurge in the spreading of hateful speech through videos in recent times due to increased social media usage. Researchers are working on this issue because it has become more frequent on several social media platforms, and it leads to low self-esteem and has significant negative impacts on human life. In this work, we focus on collecting data from such videos as nowadays people are sharing numerous videos of this negative nature on platforms like Facebook and YouTube. The audio data of these videos were then converted into text to build the dataset, and we applied some classifier models to our dataset. In this paper, we utilized a transfer learning Bidirectional Encoder Representations from Transformers (BERT) model that gives state-of-The-Art outcomes. More precisely, we fine-Tuned our model based on transfer learning to evaluate BERT's capacity to capture hostile contexts inside YouTube videos. We examined Fine-Tuning BERT;with different learning rates and listed the outcomes. We train the BERT by freezing all the hyperparameters but with various random seeds to evaluate our suggested Fine-Tuning approach. Compared to previous methodologies that used our dataset, our proposition fared better in terms of accuracy and execution time. © 2022 The Authors. Published by ELSEVIER B.V.

关键词： Classification (of information)

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Depth First:Optimal Path Discovery Between Designated Nodes in Random Ring-Based Graphs

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China Communications 2024年第9期21卷 225-241页

作者： Li Qi Xu Jiasheng Zhang Haonan Kang Huquan Fu Luoyi Long Fei Wang Xinbing Department of Electronic Engineering Shanghai Jiao Tong UniversityShanghai 200240China Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China State Key Laboratory of Media Convergence Production Technology and Systems Xinhua News AgencyBeijing 100077China

This paper focuses on optimally determining the existence of connected paths between some given nodes in random ring-based *** as a fundamental underlying structure in network modeling,ring topology appears as commonplace in many realistic *** this,we consider graphs composed of rings,with some possible connected paths between *** prior knowledge of the exact node permutations on rings,the existence of each edge can be unraveled through edge testing at a unit cost in one *** problem examined is that of determining whether the given nodes are connected by a path or separated by a cut,with the minimum expected costs *** the problem into different cases based on different topologies of the ring-based networks,we propose the corresponding policies that aim to quickly seek the paths between nodes.A common feature shared by all those policies is that we stick to going in the same direction during edge searching,with edge testing in each step only involving the test between the source and the node that has been tested *** simple searching rule,interestingly,can be interpreted as a delightful property stemming from the neat structure of ring-based networks,which makes the searching process not rely on any sophisticated *** prove the optimality of the proposed policies by calculating the expected cost incurred and making a comparison with the other class of *** effectiveness of the proposed policies is also verified through extensive simulations,from which we even disclose three extra intriguing findings:i)in a onering network,the cost will grow drastically with the number of designated nodes when the number is small and will grow slightly when that number is large;ii)in ring-based network,Depth First is optimal in detecting the connectivity between designated nodes;iii)the problem of multi-ring networks shares large similarity with that of two-ring networks,and a larger number of ties between ri

关键词： connectivity analysis cost minimization path discover ring-based graph

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Suitable is the Best: Task-Oriented Knowledge Fusion in Vulnerability Detection 38

Suitable is the Best: Task-Oriented Knowledge Fusion in Vuln...

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Wang, Jingjing Huang, Minhuan Nie, Yuanpin Li, Xiang Du, Qianjin Kong, Wei Deng, Huan Kuang, Xiaohui Institute of Systems Engineering Academy of Military Sciences PLA China Department of Computer Science and Technology Tsinghua University China School of Information Science and Engineering Zhejiang Sci-Tech University China

Deep learning technologies have demonstrated remarkable performance in vulnerability detection. Existing works primarily adopt a uniform and consistent feature learning pattern across the entire target set. While designed for general-purpose detection tasks, they lack sensitivity towards target code comprising multiple functional modules or diverse vulnerability subtypes. In this paper, we present a knowledge fusion-based vulnerability detection method (KF-GVD) that integrates specific vulnerability knowledge into the Graph Neural Network feature learning process. KF-GVD achieves accurate vulnerability detection across different functional modules of the Linux kernel and vulnerability subtypes without compromising general task performance. Extensive experiments demonstrate that KF-GVD outperforms SOTAs on function-level and statement-level vulnerability detection across various target tasks, with an average increase of 40.9% in precision and 26.1% in recall. Notably, KF-GVD discovered 9 undisclosed vulnerabilities when employing on C/C++ open-source projects without ground truth. © 2024 Neural information processing systems foundation. All rights reserved.

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State of Health Estimation of Lithium-Ion Batteries Based on Relaxation Voltage Reconstruction and AM-LSTM Method

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7261-7273页

作者： Wei, Liangliang Xu, Hongzhang Sun, Yiwen Diao, Qi Tan, Xiaojun Fan, Yuqian Liu, Han School of Intelligent Systems Engineering Shenzhen518107 China Henan Institute of Science and Technology School of Computer Science and Technology Henan Xinxiang453003 China Dongguan Institute Sun Yat-sen University Guangdong Dongguan523808 China Hong Kong Polytechnic University Department of Building and Energy Engineering Hong Kong

It is essential to accurately estimate the state of health (SOH) for lithium-ion batteries from the perspectives of safety and reliability. Most existing data-driven methods are, however, based on charging or discharging data, which is relatively difficult to apply. This article proposes a novel SOH estimation approach based on the relaxation voltage reconstruction and a long short-term memory network with an attention mechanism (AM-LSTM) method. First, based on the relaxation voltage data, a complete relaxation curve is reconstructed with a gated recurrent unit (GRU) neural network. Next, health feature (HF) extraction is carried out on the reconstructed data, and the correlation is analyzed based on Pearson correlation analysis. Then, the SOH estimation is performed based on the AM-LSTM model. Various comparative studies have been conducted to verify the effectiveness of the proposed method by comparing it with relaxation voltage reconstruction and different SOH estimation methods. The experimental results demonstrate that the proposed method can effectively reconstruct the relaxation voltage and have good accuracy in estimating the SOH with a partial relaxation voltage curve. © 2015 IEEE.

关键词： Voltage Batteries Estimation Feature extraction Long short term memory Logic gates Data models Integrated circuit modeling Data mining Transportation

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