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A charge system search based watermarking technique for gray-scale images using DWT–SVD

Multimedia Tools and Applications 2024年第18期83卷 53743-53757页

作者： Gupta, Ritu Singh, Abhilasha Department of Information Technology Bhagwan Parshuram Institute of Technology GGSIPU Delhi India Department of Computer Science and Engineering SRM Institute of Science and Technology Delhi-NCR Campus Modinagar Ghaziabad India

This paper put forward an embedded scheme to execute image watermarking in light of the discrete wavelet transform (DWT), singular value decomposition (SVD) and Charge System Search (CSS) method. In the proposed scheme, Charge System Search technique is applied to advance the multiple scaling factors (MSFs). To approve the proposed scheme, six different images are used to inspect its performance and its outcomes are likewise contrasted with the past work announced in writing. From the results, it very well may be seen that proposed watermarking technique is a vigorous and proficient methodology in the field of grayscale image watermarking technology. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.

关键词： Discrete wavelet transforms

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Transformer-Based Embeddings for Greek Language Categorization 24

Transformer-Based Embeddings for Greek Language Categorizati...

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24th IEEE/ACIS International Conference on computer and Information Science, ICIS 2024

作者： Liapis, Charalampos M. Kyritsis, Konstantinos Perikos, Isidoros Paraskevas, Michael University of Patras Computer Technology Institute & Press 'Diophantus' Mathematics Dept. Patras26504 Greece University of Peloponnese Electrical and Computer Engineering Dept. Patras Greece University of Patras Computer Engineering and Informatics Department Patras Greece University of Peloponnese Computer Technology Institute and Press 'Diophantus' Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350376647

The Greek School Network (GSN) provides support to students, teachers, and school units in secondary education across Greece. Handling numerous user queries manually can be challenging, necessitating the development of an automated system for accurate categorization of these queries. This paper presents a comparative study of various transformer-based models for multi-class text categorization of Greek language queries submitted to the GSN helpdesk. We introduce a new experimental balanced dataset and extract vector representations from eleven transformer-based models. These representations are evaluated using ten classic machine learning classifiers. Our findings highlight the superior performance of the Multilingual E5 Text Embeddings model, particularly when paired with the extreme gradient-boosting classifier. This combination demonstrates a clear advantage in accurately categorizing user queries, paving the way for more efficient automated helpdesk systems. © 2024 IEEE.

关键词： Contrastive Learning

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A Meticulous Range of Forensics Tech Advancement in the field of IoT, IIoT and MIoT 5

A Meticulous Range of Forensics Tech Advancement in the fiel...

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

作者： Matta, Priya Kamalraj, R. Saxena, Kumud Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Science and Information Technology Bangalore India Noida Institute of Engineering and Technology Department of Computer Science Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350383522

Wide application of millions of connected, intelligent, and flexible gadgets in critical infrastructures like medical facilities, public transit, sustainability, and home automation has been driven by latest expansions in intelligence technology and hardware. Clients profit from consistency and accessibility when data is transmitted across the internet deprived of the need for any kind of person to person or person-to-computer communication however this additionally creates new prospects for hackers and increases a number of complicated and challenging issues for the area of online forensics. Regardless of the fact that IoT records may be a valuable evidence source, forensics professionals must deal with a wide range of issues, starting the massive diversity of IoT devices and unrecognized setups to the multi-tenant platforms of cloud and the ensuing multijurisdictional lawsuits. Encryption at all levels, which compromises users' rights regarding confidentiality for the benefit of forensics study, is another difficulty. The aim of this work is to recognize and address the issues connected through the complex procedure of IoT based inquiries, particularly the privacy and security problems. The study also examines the application of IoT forensics across different industries. © 2024 IEEE.

关键词： IoT (Internet of things) IoT forensics privacy issues and digital forensics Security issues

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Know How Much Sensitive Precision and Recall Validity Measures Are? 27th

Know How Much Sensitive Precision and Recall Validity Measu...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Mishra, Sumit Swain, Srinibas Prakash, Ved Department of Computer Science and Engineering Rajiv Gandhi Institute of Petroleum Technology Amethi Jais India Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati Guwahati India

ISBN: (纸本)9783031781650

For the performance evaluation of the clustering algorithm, evaluation metrics are used. For this purpose, the obtained set of clusters are compared with the actual set of clusters (or gold standard). Various evaluation metrics have been proposed in the past. One important question regarding these evaluation metrics is – how good are these metrics for evaluating the performance of the clustering algorithm? Wagner et al. have proposed some of the properties of these evaluation metrics. The evaluation metric should also have a high sensitivity value to capture the change in the clustering result/gold standard along with these properties. In this paper, we compute the sensitivity of two commonly used evaluation metrics – Precision and Recall. We also show that the sensitivity of Precision and Recall is polynomial with respect to the number of data-points. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Clustering algorithms

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Early Detection of Autism Spectrum Disorder (ASD)-A Deep Learning Approach 1st

Early Detection of Autism Spectrum Disorder (ASD)-A Deep Lea...

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1st International Conference on Intelligent Computing, Smart Communication, and Network Technologies, ICICSCNT 2023

作者： Nirmalapriya, G. Ravikumar, K. Jayasri, M. PrasannaKumar, V. Department of ECE Rajalakshmi Institute of Technology Chennai India Department of Computer and Communication Engineering Rajalakshmi Institute of Technology Chennai India Computer Science Engineering Rajalakshmi Institute of Technology Chennai India

ISBN: (纸本)9783031759567

In this research, we put together a deep learning strategy that brings together the medical biomarker data from EEG tests and information from both clinical observations and inquiries to recognize the children with a Autism Spectrum Disorder (ASD). We expect that it can improve the effectiveness of identification significantly in addition to minimizing the expenditure. To begin with, our approach employed a pioneering tactic for selecting a neuronal signal landmarks derived from an eye movement, facial expression, and EEG results. This research showed that using a combination of Local Binary Pattern algorithm and deep learning classification led to a 87.50% accurate classification rate for multi-modal data merging. Finding a point to this method of detection as highly useful and effective for diagnosing ASD in young children. Graphs and confusion matrices unveiled different discrimination methods of EEG, personal behavior, and questionaries’ for discrepancy discrimination between ASD individuals and typically developing kids, with EEG being the most useful in drawing distinctions. Studies portray the physiological and behavioral important complementary characteristics. The combination of complementary information in this study proposed the deep learning approach has led to a significant improvement in classification accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Diseases

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AI-Driven Health: A Web App for Enhanced Healthcare Queries and Nutrition Analysis 5

AI-Driven Health: A Web App for Enhanced Healthcare Queries ...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Ushashree, P. Naik, Archana Sri, P. Amrutha Sai Geethanjali College of Engineering and Technology Department of Computer Science and Engineering Hyderabad India Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331504403

The AI-Driven Health chat assistant is an innovative healthcare solution that seamlessly integrates technology and care, enabling users to have natural language conversations about symptoms, treatments, and general health inquiries. The chatbot functions as a virtual assistant, enabling users to inquire about medical concerns and obtain clear explanations. The nutrition bot utilizes the advanced capabilities of Google Gemini to analyze food images, offering users comprehensive nutritional information. The proposed system undergoes constant refinement through regular testing and user feedback, guaranteeing a smooth and dependable platform for accessible healthcare interactions. The AI-Driven Health chat assistant strives to enhance the accessibility and comprehensibility of healthcare information, ultimately enhancing a healthier and more informed society. © 2024 IEEE.

关键词： Bot (Internet)

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Improving Maritime Data: A Machine Learning-based Model for Missing Vessel Trajectories Reconstruction

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IEEE Transactions on Vehicular technology 2025年第6期74卷 9565-9576页

作者： Chen, Jin Liang, Maohan Peng, Chang Zhang, Jixin Huo, Shengxu Sanya Institute Hunan University of Science and Technology China Department of Civil and Environmental Engineering National University of Singapore Singapore Singapore College of Computer Science and Electronic Engineering Hunan University China School of Computer Science Hubei University of Technology China

Advancements in maritime satellite technology have significantly impacted the maritime industry, enhancing both communication and safety at sea. These technological improvements have enabled Automatic Identification Systems (AIS) to transmit data through robust maritime communication networks. However, despite these advancements, AIS data often contain significant missing data due to limitations in both devices and network coverage. To overcome these limitations, this research presents an innovative approach for reconstructing missing points. In this paper, we first extracted the geometric shape and motion characteristics of vessels and constructed a decision tree using an adaptive sparse constraint mechanism to classify four types of vessel trajectories. Then, the vessel classification findings serve as input and the vessel acceleration features are constructed using the heading and velocity features of the trajectories at both ends. Finally, We employ a bidirectional Gate Recurrent Unit (GRU) network to simultaneously train on historical trajectory data. The method progressively shortens the reconstructed trajectory, thereby enhancing the accuracy of the reconstruction. In this paper, comparative experiments are conducted with several algorithms across various dimensions, including algorithm performance, trajectory types, and navigation scenarios. It is indicated by the experimental results that the algorithm discussed in this study outperforms baselines. Significant methodological advancements in vessel trajectory reconstruction are represented by this study, which also shows promising potential for application in intelligent maritime transportation systems. © 1967-2012 IEEE All rights reserved.

关键词： Waterway transportation

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A Novel Approach for Lightweight Sign Language Recognition Leveraging Google Mediapipe and Deep Neural Net 1

A Novel Approach for Lightweight Sign Language Recognition L...

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1st International Conference on Software, Systems and Information technology, SSITCON 2024

作者： Siju, Ijas Selvam, Prabu Vellore Institute of Technology School of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Tiruchirappalli India

ISBN: (纸本)9798350352931

The advent of technologies like Deep Learning has revolutionized human interaction, transcending language and disability barriers. Sign Language Recognition (SLR) systems have emerged as vital tools, facilitating seamless communication between individuals and between humans and machines through Interfaces. However, existing SLR systems pose significant challenges, including the demand for high accuracy, low latency, simplicity in development and deployment, lightweight design for edge compatibility, and efficient resource utilization during training. Addressing these challenges necessitates developing a system that balances training data accuracy, ease of training, and resource efficiency. Conventional Deep learning approaches often rely on high-dimensional image datasets for training HGR models, resulting in trade-offs between latency, accuracy, complexity, and resource requirements. To mitigate these challenges, this paper harnesses the capabilities of Google Mediapipe to extract a 21-point hand landmark vector from each training image. By leveraging this low-dimensional data, we train model that are accurate, lightweight, less complex, and exhibit low latency, making them compatible with edge devices. The hand gesture images were collected for several gestures, such as peace, okay, and stop. These images will be converted to landmark vectors using Mediapipe. These landmark points were used to train the Deep Neural Network architecture developed in Tensorflow. The model was trained with each hand gesture assigned with a common word in English and the proposed model was tested with a live webcam stream. The proposed model is developed in Python in the Google Colab IDE with the help of libraries and frameworks like OpenCV, Tensorflow and Google Mediapipe. The proposed model outperforms current state-of-the-art approaches by achieving an accuracy of 9 4. 8 8 %. © 2024 IEEE.

关键词： Convolutional neural networks

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FinSecure: Utilizing IoT Sensors for Formaldehyde Detection and Fish Freshness Detection for Enhancing Safety in Fish Consumption Using Machine Learning and Deep Learning

FinSecure: Utilizing IoT Sensors for Formaldehyde Detection ...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Harish, S. Somesh, K. Suntharavadivelan Venkatesan, M. Prabhavathy, P. National Institute of Technology Puducherry Department of Computer Science and Engineering Karaikal India Vellore Institute of Technology Department of Computer Science and Engineering and Information Systems Vellore India

ISBN: (数字)9798331540685

ISBN: (纸本)9798331540685

The global food supply heavily relies on fisheries, highlighting the crucial importance of ensuring the safety of fish products. However, the widespread application of antibiotics and the existence of compounds such as formaldehyde present significant hazards to both human consumers and the underwater ecosystem. These risks are compounded by processes like bio-magnification, further emphasizing the need for vigilant monitoring and regulation. This project presents a comprehensive approach to fisheries management and food safety monitoring through the integration of advanced machine learning, deep learning techniques and IoT sensor technology. The overall objective is to develop a model that can determine whether the given sample Fish Species is safe or unsafe for human consumption, by considering parameters: Fish Freshness Detection, using a HCHO Sensor to determine the Formaldehyde levels in the given Fish and finally using these parameters to determine whether the fish can be consumed or not. For the Fish Freshness Detection, utilizing Transfer Learning (pre-trained MobileNetV2 model), the system achieves an impressive accuracy of 99.02%. Thus, integrating all these models, determine whether the supplied fish is safe for consumption or not. By integrating these functionalities, the system contributes to proactive fisheries and food safety monitoring and protection. The holistic approach of this project, combining accurate positions the developed model and IoT application as valuable assets for sustainable aquaculture practices and the preservation of aquatic ecosystems. The project's outcomes contribute to the advancement of several interdisciplinary domains such as machine learning, and fisheries management, laying the groundwork for continued innovation in this interdisciplinary domain. © 2024 IEEE.

关键词： Fish

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Identifying Authentic Reviews on Amazon Dataset using Transformer-Based Models

Identifying Authentic Reviews on Amazon Dataset using Transf...

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2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Azam, Hadiqa Chaudhry, Hassan Nazeer Majid, Muhammad Computer Department University of Engineering and Technology Taxila Pakistan Computer Science Department Barani Institute of Information Technology Rawalpindi Pakistan

ISBN: (纸本)9798331504465

Reviews and ratings significantly impact businesses, either boosting their reputation or harming competitors. To manipulate these outcomes, some businesses falsify product reviews. This paper introduces a transformer-based model that utilizes GloVe (Global Vectors for Word Representation) semantic embeddings to detect fraudulent reviews. Our approach captures subtle linguistic patterns indicative of fake reviews by fine-tuning a pre-trained GloVe model on a labelled dataset. The GloVe embeddings improve the model’s grasp of word semantics, while an attention-based neural network classifies reviews as genuine or fraudulent. Our model has surpassed traditional machine learning methods, achieving 98% accuracy across various datasets. © 2024 IEEE.

关键词： Adversarial machine learning

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