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QMCS-IAS: Quantum mechanism and compression sensing-based image authentication and security technique

Multimedia Tools and Applications 2024年 1-27页

作者： Singh, Ranjeet Kumar Tripathi, Kuldeep Narayan Kaur, Gagandeep Agrawal, Rohit Department of Computer Science and Engineering Madhav Institute of Technology & ampScience Race Course Road MP Gwalior474005 India

Ensuring the security and protection of digital content transmitted over a network is a critical challenge, as data can take various forms, such as text, images, audio, and videos, all of which can be easily manipulated by hackers. Watermarking is one of the practical approaches to address data authentication, copyright protection, and document ownership issues. The primary objective of this research is to develop hybrid watermark encryption and as well as compression of watermark embedding mechanism that ensures secure and efficient watermarking of images using quantum chaos maps and discrete wavelet transformation. This study also introduces a novel dual watermarking approach combining quantum chaos-based encryption and discrete wavelet transformation to achieve robust watermark embedding, also its maintain the capacity of watermarked images. The method involves encrypting watermark components using measurement matrices derived from quantum chaos maps, followed by pixel exchange procedures and embedding into specific frequency sub-bands of the host image, thereby enhancing both the security and quality of watermarking. The objective of this work is to maintain the capacity of watermarked images during the dual embedding process using a compression mechanism. Experimental results show the proposed approach consistently outperforms the existing approach in terms of watermark image quality and robustness. The correlation coefficient (CC) coefficients for the proposed algorithm generally range between 0.95 and 0.99, indicating superior watermark preservation and retrieval capabilities. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Pixels

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A Deep Learning Model to Detect Fake News about COVID-19

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Recent Advances in computer science and Communications 2023年第9期16卷 58-66页

作者： Shanmugavel, Selva Birunda Rangaswamy, Kanniga Devi Muthukannan, Muthiah Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Tamil Nadu India School of Computer Science and Engineering Vellore Institute of Technology Chennai Tamil Nadu Chennai India Department of Civil Engineering KCG College of Technology Chennai India

Aims/Background: Twitter has rapidly become a go-to source for current events coverage. The more people rely on it, the more important it is to provide accurate data. Twitter makes it easy to spread misinformation, which can have a significant impact on how people feel, especially if false information spreads around COVID-19. Methodology: Unfortunately, twitter was also used to spread myths and misinformation about the illness and its preventative immunization. So, it is crucial to identify false information before its spread gets out of hand. In this research, we look into the efficacy of several different types of deep neural networks in automatically classifying and identifying fake news content posted on social media platforms in relation to the COVID-19 pandemic. These networks include long short-term memory (LSTM), bi-directional LSTM, convolutional-neural-networks (CNN), and a hybrid of CNN-LSTM networks. Results: The "COVID-19 Fake News" dataset includes 42,280, actual and fake news cases for the COVID-19 pandemic and associated vaccines and has been used to train and test these deep neural networks. Conclusion: The proposed models are executed and compared to other deep neural networks, the CNN model was found to have the highest accuracy at 95.6%. © 2023 Bentham science Publishers.

关键词： COVID-19

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DICNet: A Novel CNN Model based on DenseNet with Interleaved Convolutional Block Attention Module for the Classification of Breast Cancer Histopathology Images

DICNet: A Novel CNN Model based on DenseNet with Interleaved...

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2024 International Conference on Smart Systems for Applications in Electrical sciences, ICSSES 2024

作者： Kumar, Arvind Singh, Chandan Sachan, Manoj Kumar Sant longowal Institute of Engineering and Technology Department of Computer Science and Engineering Sangrur India Punjabi University Department of Computer Science Patiala India

ISBN: (纸本)9798350364040

Histopathology image analysis is crucial for the accurate diagnosis of breast cancer (BC), which is the most prevalent cancer among women. The prompt secondary opinions based on automated analysis can assist pathologists, reduce workload, and minimize interobserver variability. This paper introduces the DICNet, a novel convolutional neural network (CNN) architecture based on DenseNet with interleaved convolutional block attention module (CBAM) to classify the BC histopathology images. Specifically, the interleaved attention mechanism utilizes inter-channel and inter-spatial relationships among convolutional features to accentuate more informative and discriminative features. Furthermore, the pooled responses from multiple convolutional layers with varying spatial resolutions are concatenated to obtain a multi-scale representation. The classification performance of the proposed model is evaluated on the publicly available benchmark BreaKHis dataset with four magnification levels. The proposed DICNet model achieves an overall best classification accuracy of 94.02 % at the 40× magnification level. Additionally, DICNet demonstrates enhanced classification performance at every magnification level, and significantly outperforms existing state-of-the-art approaches. © 2024 IEEE.

关键词： Image classification

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IWF-ECTIC: Improved Wiener filtering and ensemble of classification model for tourism image classification

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Multimedia Tools and Applications 2024年 1-38页

作者： Bhosale, Tejaswini A. Pushkar, S. Department of Computer Science and Engineering Birla Institute of Technology Jharkhand Mesra835215 India Birla Institute of Technology Ranchi Mesra835215 India

Social media is so widely used;travelers frequently post texts, photos, and videos about their experiences online. These user-generated content (UGCs) significantly affect how travelers perceive Tourist Destination Image (TDI) and directly influence their decision-making. UGC photos represent passengers' visual preferences for a certain place. Considering the significance of photographs, a lot of researchers have tried to analyze them using machine learning methods. Nevertheless, a limitation of research efforts employing these techniques for tourism image analysis is that they cannot precisely categorize the unique photos found in specific tourist destinations using predetermined images. In order to get rid of these challenges, this work proposes a Tourism image classification model termed as Improved Weiner Filtering and Ensemble of Classification model for Tourism Image Classification (IWF-ECTIC) model which includes steps like preprocessing, segmentation, feature extraction, and classification. The preprocessing is done via improved wiener filtering which provides a more robust assessment of image quality. The segmentation is done by the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) model which can effectively handle outliers and noisy points. Subsequent to the segmentation, MLGBPHS (Modified Local Gabor Binary Pattern Histogram Sequence), MBP (Median Binary Pattern), color, shape and Improved Entropy-based features are extracted in the feature extraction stage to reduce the dimensionality of the data. Finally, ensemble classification is done by combining the Multihead Convolutional Neural Network with Attention Mechanism (MHCNN-AM), Bidirectional Long Short-Term Memory (Bi-LSTM), and Recurrent Neural Network (RNN) classifiers to classify the images. To improve the classification accuracy, optimal training will be done in MHCNN-AM, Bi-LSTM, and RNN classifier using the proposed Cuckoo Updated Chimp Optimization (CUCO) algorit

关键词： Wiener filtering

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Marine Life Analysis Based on Ocean Water Level Rise and Climate Change Using Underwater Imaging Techniques

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 657-669页

作者： Mate, Anuja Dinesh Arulananth, T.S. Sathiya, T. Jyothi, B. Swarna Raja, A. Wasim Yalawar, Mrutyunjaya S. Computer Technology Sou.Venutai Chavan Polytechnic Pune India Department of Electronics and Communication Engineering MLR Institute of Technology Telangana Hyderabad 500043 India Department of Computer Science and Engineering Sona College of Technology Tamil Nadu Salem India Computer Science & amp Engineering Madanapalle Institute of Technology & amp Science Andhra Pradesh Madanapalle India Artificial Intelligence and Data Science Sri Krishna College of Engineering and Technology Coimbatore India Department of Computer Science and Engineering CMR Engineering College Hyderabad India

Forecasting sea levels is crucial for harbour operations and coastal structure design. The oceans make up two-thirds of Earth’s surface;therefore, historically, the marine economy has been extremely diversified as well as abundant. In order to explore as well as harness this difficult environment, prominent businesses and ecological groups are developing and deploying novel gadgets backed by state-of-the-art marine mechatronics solutions. This research proposes a novel method of marine life monitoring with ocean water level rise analysis based on climate changes by underwater imaging using machine learning techniques. Here, the input has been collected as underwater images as well as processed for noise removal and normalisation. Then, these images were segmented for monitoring marine life using U-Net graph cut fuzzy clustering with a spatial neural network. Then this segmented image has been classified using multilayer region-based Q-belief vector neural networks for the analysis of sea level rise analysis based on climate change training. Experimental analysis has been carried out for various marine life monitoring datasets and climate change datasets in terms of training accuracy, specificity, recall, AUC, and Kappa-coefficient. Proposed techniques achieved specificity 93%, training accuracy 91%, AUC 95%, recall 96%, and Kappa-coefficient 90%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Climate changes Machine learning techniques Marine life monitoring Q-belief vector Water level rise analysis

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Incipient motion of sediment by turbulent kinetic energy in the presence of emergent vegetation in the outer sidewall of a bend

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ISH Journal of Hydraulic engineering 2025年第2期31卷 139-147页

作者： Rismani, Nasim Afzalimehr, Hossein Asghari-Pari, Seyed-Amin Nazari-Sharabian, Mohammad Karakouzian, Moses Department of Civil Engineering Iran University of Science and Technology Tehran Iran Department of Civil Engineering Behbahan Khatam Alanbia University of Technology Behbahan Iran Department of Mathematics Engineering and Computer Science West Virginia State University Institute WV United States Department of Civil and Environmental Engineering and Construction University of Nevada Las Vegas NV United States

This study examines the influence of vegetation on sediment movement in a 180° river bend with rigid emergent vegetation on the outer side. Using an Acoustic Doppler Velocimeter, researchers collected 504 velocity profiles under conditions with and without vegetation, across four different vegetation densities. The findings indicate that vegetation reduces the critical velocity needed for sediment movement by 59%. Additionally, the presence of vegetation increases turbulent kinetic energy, with the highest levels observed at the lowest vegetation density, and decreasing as vegetation density increases. This pattern suggests that vegetation not only modifies flow dynamics but also affects the threshold conditions for sediment transport, especially on the outer side of the bend. For example, turbulent kinetic energy is amplified 18 times at a 150-degree angle. In contrast, the bare bed exhibited minimal turbulent kinetic energy, underscoring the significant role of vegetation in altering flow structures. This study highlights the critical importance of emergent vegetation in river management and sediment control, demonstrating a more pronounced effect on the outer bend than the inner side, thus contributing to the sustainability and management of natural streams. © 2024 Indian Society for Hydraulics.

关键词： Kinetic energy

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Deep learning based classification of motor imagery EEG signals using an improved path finder optimization algorithm

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2024年第3期8卷 115-124页

作者： Jayashekar, Vishwesh Pandian, Raviraj Basavarajegowda, Rajashekar Mallajamma Department of Computer Science & Engineering GSSS Institute of Engineering and Technology for Women Mysuru Affiliated to VTU Karnataka Belagavi India

Motor Imagery Brain-computer Interfaces (MI-BCIs) are systems based on AI that collect patterns of brain activities in mental movement and translate these movements through external devices. The identification of motor intention by evaluating Electroencephalogram (EEG) signals is an important issue in applications related to Brain-computer Interfaces (BCI). In this paper, an Improved Path Finder Optimization Algorithm (IPFOA) is proposed to improve the process of selecting features. The data is collected from the BCI competition IV 2a dataset and the stage of pre-processing takes place through sliding windows and 6th order Butterworth filter. The feature will be extracted from the pre-processed data using the hybrid Convolutional Neural Network (CNN) and Common Spatial Pattern (CSP) method. After this stage, the required features are selected from the extracted features using the proposed IPFOA algorithm. Finally, the classification of EEG signals takes place using a Stacked autoencoder, a classifier based on a Deep Neural Network. The experimental results show that the proposed approach achieved a better accuracy of 98.40% which is comparatively higher than the existing approaches. © (2024), (Society of Sytematic Innovation). All rights reserved.

关键词： Brain computer Interface Convolutional Neural Networks Deep Learning Electroencephalogram Motor Imagery Path Finder Optimization

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FAKE CURRENCY DETECTION USING CONVOLUTIONAL NEURAL NETWORK AND IMAGE PROCESSING 15

FAKE CURRENCY DETECTION USING CONVOLUTIONAL NEURAL NETWORK A...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Srilatha, Boddu Ithvika, C.H. Meenakshi, Patil Pradhan, Sasmita Kumari Rani, K. Pushpa Jayanth, M. Department of Computer Science and Engineering MLR Institute of Technology Hyderabad500043 India Department of Computer Science and Engineering AVN Institute of Engineering and Technology Hyderabad500043 India Department of of CSE-CyS DS and AI&DS VNR VJIET Bachupally Hyderabad India

ISBN: (纸本)9798350370249

The increased use of modern printing and scanning technologies has led to a significant rise in counterfeit currency production, posing a serious threat to global economies. To tackle this growing issue, our project, titled "Fake currency detection using Convolutional Neural Networks and Image Processing," introduces an innovative solution that utilizes artificial intelligence (AI) and machine learning for efficient counterfeit detection. Financial institutions, banks, and businesses are facing heightened vulnerability to counterfeit currency, resulting in considerable financial losses and a decrease in the value of genuine money. Current currency detection systems often rely on time-consuming traditional methods and manual inspection, which are prone to human error. Even the counterfeit detection machines in use have limitations when it comes to identifying sophisticated counterfeit notes. Our project addresses these challenges by proposing an advanced system that integrates convolutional neural networks (CNNs) and image processing techniques. Given the advancements in printing and scanning technologies, counterfeiting has evolved into a more sophisticated and widespread problem. Traditional currency detection methods, rooted in hardware and image processing, have proven to be inefficient and time-consuming. Hence, there is a critical need for a more robust and rapid solution to detect counterfeit currency. Our proposed approach employs a transfer-learned CNN, a deep learning model trained on a dataset comprising real and fake currency images. The CNN learns the intricate features of both genuine and counterfeit banknotes, allowing it to accurately identify fake currency in real-time. The transfer learning process enables the CNN to leverage knowledge gained from a diverse dataset, improving its ability to recognize subtle patterns associated with counterfeit notes. The primary components of our project include a diverse dataset with images of real and fake currenc

关键词： Convolutional neural networks

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A deep Q-learning model for sequential task offloading in edge AI systems

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Intelligent and Converged Networks 2024年第3期5卷 207-221页

作者： Dong Liu Shiheng Gu Xinyu Fan Xu Zheng Department of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 610000China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of ChinaShenzhen 515100China Department of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu 610000China

Currently,edge Artificial Intelligence(AI)systems have significantly facilitated the functionalities of intelligent devices such as smartphones and smart cars,and supported diverse applications and *** fundamental supports come from continuous data analysis and computation over these *** the resource constraints of terminal devices,multi-layer edge artificial intelligence systems improve the overall computing power of the system by scheduling computing tasks to edge and cloud servers for *** efforts tend to ignore the nature of strong pipelined characteristics of processing tasks in edge AI systems,such as the encryption,decryption and consensus algorithm supporting the implementation of Blockchain ***,this paper proposes a new pipelined task scheduling algorithm(referred to as PTS-RDQN),which utilizes the system representation ability of deep reinforcement learning and integrates multiple dimensional information to achieve global task ***,a co-optimization strategy based on Rainbow Deep Q-Learning(RainbowDQN)is proposed to allocate computation tasks for mobile devices,edge and cloud servers,which is able to comprehensively consider the balance of task turnaround time,link quality,and other factors,thus effectively improving system performance and user *** addition,a task scheduling strategy based on PTS-RDQN is proposed,which is capable of realizing dynamic task allocation according to device *** results based on many simulation experiments show that the proposed method can effectively improve the resource utilization,and provide an effective task scheduling strategy for the edge computing system with cloud-edge-end architecture.

关键词： edge computing task scheduling reinforcement learning Rainbow Deep Q-Learning(RainbowDQN)

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Strategies of Passive Design Buildings in Cold and Arid Climates: A Review

Strategies of Passive Design Buildings in Cold and Arid Clim...

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International Conference on Interdisciplinary Approaches in Civil engineering for Sustainable Development, IACESD 2023

作者： Bhargavi, C. Ganesh, C.R. Manoj Kumar, M. Kishore, G.R. Department of Civil Engineering Jyothy Institute of Technology Bengaluru560082 India Department of Computer Science and Engineering Jyothy Institute of Technology Bengaluru India Department of Information Science and Engineering Jyothy Institute of Technology Bengaluru India

ISBN: (纸本)9789819709090

Passive design strategies have gained increasing attention in recent years as a means of improving the energy efficiency and comfort of buildings. These strategies aim to reduce the energy consumption of buildings by utilizing natural resources such as sunlight, wind, and vegetation. This article provides a review of the literature on passive design strategies and their performance in buildings in cold and arid climate zones. Snow regions present unique challenges and opportunities for sustainable building design due to extreme cold temperatures, heavy snowfall, and the need for effective insulation. The review emphasizes about implementation of passive design strategies, including site analysis, building orientation, insulation, snow management, and efficient heating systems. Passive design of buildings in arid climates focuses on utilizing natural elements and strategies to maximize energy efficiency and thermal comfort without relying heavily on mechanical cooling systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Energy efficiency

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