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2nd IEEE International Conference on Data Science and Network Security, ICDSNS 2024

作者： Kumar, Sachin Deepa, B. Kavitha, T. Tamilselvi, M. Sathiyapriya, V. Natarajan, B. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India Srmist Ramapuram Campus Department of Electroncis and Communication Engineering Ramapuram India MA.M College of Engineering and Technology Department of Computer Science and Engineering Trichy India Roever Engineering College Department of Computer Science and Engineering Perambalur India Knowledge Institute of Technology Department of Computer Science and Engineering Salem India School of Computer Science and Engineering Vellore Institue of Technology Chennai Campus Chennai India

ISBN: (纸本)9798350373110

Sign language enhances the communication capabilities of the deaf-mute community, allowing for a deeper understanding of their needs and emotions. These languages are highly structured and visual, using gestures and various upper body movements such as those of the hands, face, eyes, and gaze. Researchers face numerous challenges in recognizing and translating the diverse variations in sign movements, which requires specialized expertise in computer vision and artificial intelligence. Sign language recognition and translation research has garnered global attention. This research work introduces a novel methodology for generating sign gesture videos from text inputs by integrating various intelligent techniques. The proposed model employs an enhanced generative adversarial network (GAN) to create sign videos from input sentences. Experiments with the proposed VideoGAN model using diverse sign language datasets from multiple countries have demonstrated its effectiveness. The research outcomes highlight its contribution to high-quality video production, with improved evaluation metrics underscoring the model's superior performance. © 2024 IEEE.

关键词： Generative adversarial networks

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A Real-Time Hand Sign Language Recognition System for Threatening Situations Using Deep Learning 12

A Real-Time Hand Sign Language Recognition System for Threat...

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12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Shwany, Zardasht Abdulaziz Abdulkarim Ismail, Shayda Khudhur Khoshnaw, Karwan Hoshyar Khalid Mustafa, Twana Saeed Karabatak, Murat Othman, Nashwan Adnan Erbil Polytechnic University Physiotherapy Department Erbil Iraq Ministry of Higher Education and Scientific Research Network Department Erbil Iraq Erbil Polytechnic University Department of Information and Communication Technology Engineering Erbil Iraq Firat University Department of Software Elazig Turkey College of Science Knowledge University Department of Computer Science Erbil Iraq Knowledge University Department of Computer Engineering Erbil Iraq Al-Kitab University Department of Computer Engineering Altun Kupri Iraq

ISBN: (纸本)9798350330366

Hand sign language has been done as physical movements in natural languages that humans have used since ancient times, along with letters, words, and spoken language. This paper presents a real-time method to identify threatening signs by criminals during interrogation, which increases their criminal rank and helps to reach conclusions. The proposed method is to install a camera of appropriate quality in front of the offender, record hand gestures in a specific area of the hand, apply some image processing techniques, such as contrast enhancement techniques, to the image to facilitate recognition as input, and then classify the images using a convolutional neural network (CNN) for a specific problem with high specifications for that topic by and using AlexNet. The accuracy of the presented method is more than 94.2% in real-time testing. © 2024 IEEE.

关键词： Convolutional neural networks

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Optimizing mechanical strength and thermal stability of sisal fiber-plastic composites by high-temperature treatment

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Interactions 2025年第1期246卷 1-15页

作者： Mungle, Nischal P. Jadhav, Varsha D. Kulkarni, Sumant Khanderao D.S.Vijayan Saminathan, Rajasekaran Satishkumar, P. Department of Mechanical Engineering Yeshawantrao Chavan College of Engineering Maharashtra Wanadongari Nagpur India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Technology Maharashtra Pune India School of Civil Engineering REVA University Rukmini Knowledge Park Karnataka Kattigenahalli Bengaluru India Tamilnadu Chennai India Department of Mechanical Engineering College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department of Mechanical Engineering Rathinam Technical Campus Tamilnadu Coimbatore India

In this experiment, high-temperature polyethylene terephthalate (PT) was mixed with epoxy resin (ER) that had been thinned with acetone. Sisal fibers were coated with the resulting product. Composites of Coated treated sisal fibre-reinforced PT (CTSF-PT) were made using a constant fiber loading of 10 wt% and processing temperatures of 230, 250, and 270ºC. The fibers were treated with NaOH. These composites were then characterized for thermal, mechanical and morphological performances. When comparing findings obtained at 250 and 270ºC, the optimal processing temperature was found to be 230ºC. This temperature resulted in the best thermal and mechanical properties, and excellent interfacial adhesion among the coated fibers and the PT. In comparison to the uncoated SF, the coated composites showed a 9.5% reduction in degradation onset temperature and a 13% improvement in decomposition temperature following resin coating, according to thermogravimetric analysis. The CSF-PT composites surpassed the uncoated composites by 2.18% and 2.8% in terms of glass transition (Tg) and melting temperature (Tm) measured by differential scanning calorimetry (DSC), with peak values at 122.2 °C and 265.1 °C, respectively. Coated Sisal-reinforced PT (CSF-PT) and coated-treated PT (CTSF-PT) composites outperformed raw Sisal (SF-PT) composites in terms of overall qualities and morphological properties, regardless of processing temperature. Applying resin to natural fibers enhances their heat resistance and the bond between the coated fibers and the plastic matrix, making them better suited for engineering plastic composites. Applications involving high temperatures, such as construction and automotive industries are well-suited to the resulting composites. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Automotive industry

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Supply Chain Strategies Supported by Blockchain Technology: Open Problems and Challenges

Supply Chain Strategies Supported by Blockchain Technology: ...

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2023 International Conference on Advancement in Computation and computer Technologies, InCACCT 2023

作者： Rana, Sumit Kumar Rana, Arun Kumar Naz, Sardar M N Islam Singh, Ashutosh Kumar Bhushan, Bharat Panipat Institute of Engineering and Technology Department of Computer Science and Engineering Haryana Panipat India Galgotia College of Engineering Knowledge Park I Department of Computer Science Engineering Uttar Pradesh Greater Noida201310 India ISILC Victoria University Melbourne Australia Kurukshetra University NIT Department of Computer Applications Kurukshetra India Sharda Universi Knowledge Park III Department of Computer Science Engineering Greater Noida201310 India

ISBN: (纸本)9798350396485

The most prevalent issue in the supply chain ecosystem is introducing transparency to the system. Another issue is fostering trust among various stakeholders to support successful process execution. Depending on the domain and the stakeholders, the supply chain process may change. Thus, solving the issue is challenging. Blockchain technology, sometimes referred to as distributed ledger technology, is beneficial in many ways in a variety of industries, including finance and healthcare. Blockchain based model can resolve the issues of traditional supply chain ecosystem. It creates a decentralized and transparent system where every decision is taken with the help of consensus of the associated stakeholders. This supports an immutable ledger so unauthorized modification is not supported. In this article, we address the issues that the supply chain ecosystem faces as well as the benefits of blockchain technology in resolving these issues. As well as some of the remaining challenges and opportunities, other aspects of blockchain technology are explored that will enable it flourish in future supply chains. © 2023 IEEE.

关键词： Supply chains

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Performance Analysis of Health Care Expense Anticipation using Computational Intelligence 1

Performance Analysis of Health Care Expense Anticipation usi...

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1st International Conference on Innovative engineering Sciences and Technological Research, ICIESTR 2024

作者： Gopalakrishnan, A. Kumarganesh, S. Divya Bharathi, G. Anthoniraj, S. Natarajan, Suresh Kumar Gochhait, Saikat Knowledge Institute of Technology Department of Artificial Intelligence and Data Science Salem India Knowledge Institute of Technology Department of Electronics and Communication Engineering Salem India Mahendra Institute of Technology Department of Computer Science and Engineering Namakkal India School of Computer Science and Engineering Ramanagara India Symbiosis International Deemed University Symbiosis Institute of Digital and Telecom Management Maharashtra Pune India

ISBN: (纸本)9798350348637

The escalating costs of healthcare globally necessitate the development of accurate prediction models to address the financial strain on individuals, families, businesses, and governments. This research employs linear regression models to forecast healthcare expenditures, analyzing the intricate relationship between various factors and costs. A comprehensive literature survey explores topics such as the impact of environmental pollution on healthcare spending, prediction of surgical expenses, budget impact analysis for cancer treatment, and the integration of big data in healthcare infrastructure investments. The methodology involves gathering a diverse dataset encompassing patient demographics, medical history, treatment methods, and associated expenditures. Exploratory data analysis employs Pearson's correlation coefficient to identify trends and correlations between variables. Two linear regression models are proposed and compared, with the second model excluding non-significant variables for enhanced accuracy and interpretability. Results indicate that the second model, with refined feature selection, outperforms the first in terms of mean squared error and R-squared values. A comparative analysis of different studies underscores the diverse findings in healthcare economics, ranging from the impact of CO2 emissions on health to predicting surgery costs and budgetary savings in cancer treatment. The research contributes to the growing body of knowledge in healthcare economics, providing decision-makers with a valuable tool for precise expenditure estimation. The emphasis on linear regression models aids stakeholders in understanding factors influencing healthcare costs, guiding resource allocation, policy creation, and financial planning for more sustainable and effective healthcare systems. © 2024 IEEE.

关键词： Budget control

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Enhanced Deep Learning Approach for Software Defect Forecasting

Enhanced Deep Learning Approach for Software Defect Forecast...

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2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems, AICERA/ICIS 2023

作者： Anju, A.J. Judith, J.E. Eranad Knowledge City Technical Campus Department of Computer Science Engineering Kerala India Noorul Islam Centre for Higher Education Department of Computer Science Engineering Tamil Nadu India

ISBN: (纸本)9798350303452

In the realm of defect prediction in complex systems, the integration of advanced machine learning techniques holds promise for enhancing accuracy and efficiency. This research introduces a novel approach by combining a Deep Adaptive Neural Network (ARNN) with the optimization capabilities of Cuckoo Search (CSO) for defect and non-defect data classification. Th e proposed model leverages the temporal dependencies within the data using a deep ARNN architecture, allowing for the exploration of intricate patterns and correlations over time. To optimize the model's performance, the Cuckoo Search Optimization algorithm is employed. CSO, inspired by the reproductive behavior of cuckoos, offers an effective mechanism for fine-tuning the parameters of the deep ARNN, optimizing its ability to discern subtle variations between defect and non-defect instances. The efficacy of the proposed approach is evaluated on benchmark datasets, showcasing its capability to achieve superior predictive accuracy compared to conventional methods. © 2023 IEEE.

关键词： Defects

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ML Based Intrusion Detection Scheme for various types of attacks in a WSN using C4.5 and CART classifiers

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Materials Today: Proceedings 2023年 80卷 3769-3776页

作者： Gite, Pratik Chouhan, Kuldeep Murali Krishna, K. Kumar Nayak, Chinmaya Soni, Mukesh Shrivastava, Amit Department of Computer Science and Engineering IPSA-IES Indore AB Road MP Indore India Department of Computer Science and Engineering I.T.S Engineering College Greater Noida India Department of Electronics and Communication Engineering Vignan's Institute of Information Technology Duvvada Andhra Pradesh Visakhapatnam India Faculty of Emerging Technology Sri Sri University Odisha751001 India Department of Computer Science & Engineering Jagran Lakecity University Bhopal India Department of Computer Science Engineering Swami Vivekanand College of Engineering Khandwa Road Near Tolnaka Vivekanand Knowledge City Indore India

A key component for preserving a WSN's integrity is designing an intrusion detection system (IDS). This article covers several sorts of security threats that may occur in a WSN. It proposes a detection of malicious nodes in a WSN. This system identifies four common assaults (i.e., black hole, wormhole, gray hole, and DDoS attacks) and is based on a Base Station machine learning (ML) algorithm (BS). The suggested ML algorithm continually analyzes data patterns from each node. Based on this technique, BS recognizes the network's harmful behavior and provides notifications to neighbor nodes to prevent attacker. First, the various assaults are examined and their characteristics are derived in terms of network parameters. Furthermore, the gathered pattern trains an ML algorithm. Then, the attacker node is effectively and properly classified within the BS. The NS2 simulator simulated the needed secure WSN. The experimental findings revealed excellent accuracy of the suggested method for attack detection. This precision can enhance network performance in terms of energy usage and packet delivery (PDR). © 2021

关键词： Base stations

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A Simple but Effective Approach for Unsupervised Few-Shot Graph Classification 24

A Simple but Effective Approach for Unsupervised Few-Shot Gr...

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33rd ACM Web Conference, WWW 2024

作者： Liu, Yonghao Huang, Lan Cao, Bowen Li, Ximing Giunchiglia, Fausto Feng, Xiaoyue Guan, Renchu College of Computer Science and Technology Jilin University Changchun China Department of Information Engineering and Computer Science University of Trento Trento Italy Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education China

ISBN: (纸本)9798400701719

Graphs, as a fundamental data structure, have proven efficacy in modeling complex relationships between objects and are therefore found in wide web applications. Graph classification is an essential task in graph data analysis, which can effectively assist in extracting information and mining content from the web. Recently, few-shot graph classification, a more realistic and challenging task, has garnered great research interest. Existing few-shot graph classification models are all supervised, assuming abundant labeled data in base classes for meta-training. However, sufficient annotation is often challenging to obtain in practice due to high costs or demand for expertise. Moreover, they commonly adopt complicated meta-learning algorithms via episodic training to transfer prior knowledge from base classes. To break free from these constraints, in this paper, we propose a simple yet effective approach named SMART for unsupervised few-shot graph classification without using any labeled data. SMART employs transfer learning philosophy instead of the previously prevailing meta-learning paradigm, avoiding the need for sophisticated meta-learning algorithms. Additionally, we adopt a novel mixup strategy to augment the original graph data and leverage unsupervised pretraining on these data to obtain the expressive graph encoder. We also utilize the prompt tuning technique to alleviate the overfitting and low fine-tuning efficiency caused by the limited support samples of novel classes. Extensive experimental results demonstrate the superiority of our proposed approach, significantly surpassing even leading supervised few-shot graph classification models. Our code is available here. © 2024 ACM.

关键词： Graph neural networks

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Chaos-Based Novel Watermarked Satellite Image Encryption Scheme

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computer Modeling in engineering & Sciences 2025年第4期143卷 1049-1070页

作者： Mohamed Medani Yahia Said Nashwan Adnan Othman Farrukh Yuldashev Mohamed Kchaou Faisal Khaled Aldawood Bacha Rehman Applied College of Muhayil Aseer King Khalid UniversityAbha62529Saudi Arabia Center for Scientific Research and Entrepreneurship Northern Border UniversityArar73213Saudi Arabia Department of Computer Engineering College of EngineeringKnowledge UniversityErbil44001Iraq Department of Computer Engineering Al-Kitab UniversityAltun Kupri36001Iraq Department of Informatics and Its Teaching Methods Tashkent State Pedagogical UniversityTashkent100070Uzbekistan Department of Industrial Engineering College of EngineeringUniversity of BishaBishaP.O.Box 001Saudi Arabia Department of Computer Science Solent UniversitySouthamptonSO140YNUK

Satellite images are widely used for remote sensing and defence applications,however,they are subject to a variety of *** ensure the security and privacy of these images,theymust be watermarked and encrypted before ***,this paper proposes a novel watermarked satellite image encryption scheme based on chaos,Deoxyribonucleic Acid(DNA)sequence,and hash *** watermark image,DNA sequence,and plaintext image are passed through the Secure Hash Algorithm(SHA-512)to compute the initial condition(keys)for the Tangent-Delay Ellipse Reflecting Cavity Map(TD-ERCS),Henon,and Duffing chaotic maps,*** bitwise XOR and substitution,the TD-ERCS map encrypts the watermark *** ciphered watermark image is embedded in the plaintext *** embedded plaintext image is permuted row-wise and column-wise using the Henon chaotic *** permuted image is then bitwise XORed with the values obtained from the Duffing *** additional security,the XORed image is substituted through a dynamic *** evaluate the efficiency and performance of the proposed algorithm,several tests are performed which prove its resistance to various types of attacks such as brute-force and statistical attacks.

关键词： DNA sequence TD-ERCS chaoticmap henon chaoticmap duffing chaotic system SHA-512 encryption technique watermark embedding

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STGTP: Enhancing Stance Detection with Graph and Text Embedding through the Post-Graph Model

STGTP: Enhancing Stance Detection with Graph and Text Embedd...

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2024 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems, SPICES 2024

作者： Bhavani, Samineni Kommanti, Hima Bindu National Institute of Technology Department of Computer Science and Engineering Andhra Pradesh India Rajiv Gandhi University of Knowledge Technologies Andhra Pradesh India

ISBN: (纸本)9798350376135

This paper presents STGTP, which combines graph and text-based techniques for stance detection of social media posts. This model learns from the text in the post and the relationships between the posts due to users conversations and interactions. The proposed model uses the embeddings constructed using the textual content and an interaction graph of the posts. The concatenated embeddings are fed to a CNN module for predicting the stance label. Results over the ConvinceMe and CreateDebate datasets show that our proposed STGTP surpasses existing stance detection models by up to 3% in terms of Macro F1-score. © 2024 IEEE.

关键词： Graph embeddings

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