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Secure multimedia communication: advanced asymmetric key authentication with grayscale visual cryptography

Mathematical Biosciences and engineering 2024年第3期21卷 4762-4778页

作者： Liu, Tao Vairagar, Shubhangi Adagale, Sushadevi Karthick, T. Karunya, Catherine Esther Blesswin, A. John Mary, G. Selva Tianjin Sino-German University of Applied Sciences Tianjin300350 China Department of Artificial Intelligence and Data Science Dr. D. Y. Patil Institute of Technology Pimpri Pune411018 India Department of Computer Engineering KJEI's Trinity Academy of Engineering Pune411048 India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur603203 India Computer Science and Engineering School of Computing SRM Institute of Science and Technology Kattankulathur603203 India Directorate of Learning and Development SRM Institute of Science and Technology Kattankulathur603203 India

The secure authentication of user data is crucial in various sectors, including digital banking, medical applications and e-governance, especially for images. Secure communication protects against data tampering and forgery, thereby bolstering the foundation for informed decision-making, whether managing traffic, enhancing public safety, or monitoring environmental conditions. Conventional visual cryptographic protocols offer solutions, particularly for color images, though they grapple with challenges such as high computational demands and reliance on multiple cover images. Additionally, they often require third-party authorization to verify the image integrity. On the other hand, visual cryptography offers a streamlined approach. It divides images into shares, where each pixel represented uniquely, thus allowing visual decryption without complex computations. The optimized multi-tiered authentication protocol (OMTAP), which is integrated with the visual sharing scheme (VSS), takes secure image sharing to the next level. It reduces share count, prioritizes image fidelity and transmission security, and introduces the self-verification of decrypted image integrity through asymmetric key matrix generators, thus eliminating external validation. Rigorous testing has confirmed OMTAP's robustness and broad applicability, thereby ensuring that decrypted images maintain their quality with a peak signal-to-noise ratio (PSNR) of 40 dB and full integrity at the receiver's end. © 2024 American Institute of Mathematical sciences. All rights reserved.

关键词： Medical applications

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Enhancing Gastrointestinal Image Classification: A Fusion of CNN and Vision Trasformers 2

Enhancing Gastrointestinal Image Classification: A Fusion of...

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2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Ganesh, Bachu Annamalai, R. Jayapal, Senthil Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India Collage of Computing and Information Science University of Technology and Applied Sciences Ibra Oman

ISBN: (纸本)9798350383652

In order to meet the pressing demand for early diagnosis in healthcare, we proposed a unique hybrid architecture in this study that is intended for the categorization of gastrointestinal (GI) illnesses. With fewer trainable parameters, faster training times, and less storage needed for model weights, our suggested architecture prioritizes efficiency achieves an impressive 94% accuracy. To effectively diagnose GI disorders, this design combines convolutional neural networks (CNNs) with Transformers. The creation of this hybrid architecture, which boasts excellent accuracy in illness categorization, decreased storage requirements, and efficiency in training, is one of the research's accomplishments. Our proposed model demonstrates its flexibility for many uses, encouraging simple implementation and expandability. Extensive tests performed on a large dataset of endoscopic images highlight our hybrid architecture's better performance over current techniques. With this study, diagnostics for gastrointestinal diseases have advanced significantly, opening the door to future diagnostic systems that will be more precise and effective. The proposed architecture has outperformed the traditional pre-trained models both in classification accuracy and time to train the model with no weights. © 2024 IEEE.

关键词： Endoscopy

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Accurate Real-World Co-ordinate Finding and the Object Classification for the Robot Manipulator 3

Accurate Real-World Co-ordinate Finding and the Object Class...

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3rd International Conference on Embedded Systems and Artificial Intelligence, ESAI 2024

作者： Sharan, Sudeep Thanh, Hung Do Nauth, Peter Dominguez-Jimenez, Juan Jose Frankfurt University of Applied Sciences Faculty of Computer Science and Engineering Frankfurt am Main Germany University of Cádiz UCASE Software Engineering Group School of Engineering Cádiz Spain

ISBN: (纸本)9798331522056

This paper presents an object detection and classification of the objects using Deep Learning (DL). The integration of object detection algorithms and depth camera developed is capable of providing robots, such as self developed assistive intelligent robot called 'ROSWITHA' (RObot System WITH Autonomy) with the ability to detect an object and interpret their surrounding environment. It also provides the real-world coordinates/position of the object in that environment, which helps the robot to perform some applications, such as grabbing and serving, etc. You Only Look Once (YOLO) is one of the most popular object detection algorithms which is treated as a single regression problem and is processed by a single neural network. In this work, the YOLOv5 (YOLO version 5) network is implemented on a depth camera 'RealSense' for processing object detection and estimating the position of the objects. The model is trained on reduced/filtered application-required objects (e.g. bottle and cup) from the available COCO dataset, and the training outcome alongside the performance of the overall system was evaluated. The results exhibited a satisfactory performance of the model in accurately detecting and classifying the objects with a high confidence score ranging from the least to7 2% to the highest of9 0%. The confidence score could also be adjusted by the user on the developed GUI to attain the most certain predictions. © 2024 IEEE.

关键词： Bottles

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Metaheuristic Based Clustering with Deep Learning Model for Big Data Classification

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computer Systems science & engineering 2023年第1期44卷 391-406页

作者： R.Krishnaswamy Kamalraj Subramaniam V.Nandini K.Vijayalakshmi Seifedine Kadry Yunyoung Nam Department of Electronics and Communication Engineering University College of Engineering AriyalurAriyalur621704India Department of Biomedical Engineering Faculty of EngineeringKarpagam Academy of Higher EducationCoimbatore641021India Department of Computer Science and Engineering Sona College of TechnologySalem636005India Department of Electronics and Communication Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai600077India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Recently,a massive quantity of data is being produced from a distinct number of sources and the size of the daily created on the Internet has crossed two *** the same time,clustering is one of the efficient techniques for mining big data to extract the useful and hidden patterns that exist in ***-based clustering techniques have gained significant attention owing to the fact that it helps to effectively recognize complex patterns in spatial *** data clustering is a trivial process owing to the increasing quantity of data which can be solved by the use of Map Reduce *** this motivation,this paper presents an efficient Map Reduce based hybrid density based clustering and classification algorithm for big data analytics(MR-HDBCC).The proposed MR-HDBCC technique is executed on Map Reduce tool for handling the big *** addition,the MR-HDBCC technique involves three distinct processes namely pre-processing,clustering,and *** proposed model utilizes the Density-Based Spatial Clustering of Applications with Noise(DBSCAN)techni-que which is capable of detecting random shapes and diverse clusters with noisy *** improving the performance of the DBSCAN technique,a hybrid model using cockroach swarm optimization(CSO)algorithm is developed for the exploration of the search space and determine the optimal parameters for density based ***,bidirectional gated recurrent neural network(BGRNN)is employed for the classification of big *** experimental validation of the proposed MR-HDBCC technique takes place using the benchmark dataset and the simulation outcomes demonstrate the promising performance of the proposed model interms of different measures.

关键词： Big data data classification clustering mapreduce dbscan algorithm

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Semi-analytical finite element method applied for characterizing micropolar fibrous composites

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applied Mathematics and Mechanics(English Edition) 2024年第12期45卷 2147-2164页

作者： J.A.OTERO Y.ESPINOSA-ALMEYDA R.RODRIGUEZ-RAMOS J.MERODIO School of Engineering and Sciences Monterrey Institute of TechnologyEstado de Mexico 52926Mexico Institute of Engineering and Technology Autonomous University of Ciudad JuarezCiudad JuarezChihuahua 32310Mexico Postgraduate Program in Computational Modeling in Science and Technology Federal Fluminense UniversityRıo de Janeiro 27255-125Brazil Faculty of Mathematics and Computer Science University of HavanaLa Habana 10400Cuba Department of Mathematics Applied to ICT School of Informatics Systems EngineeringTechnical University of MadridMadrid 28031Spain

A semi-analytical finite element method(SAFEM),based on the two-scale asymptotic homogenization method(AHM)and the finite element method(FEM),is implemented to obtain the effective properties of two-phase fiber-reinforced composites(FRCs).The fibers are periodically distributed and unidirectionally aligned in a homogeneous *** framework addresses the static linear elastic micropolar problem through partial differential equations,subject to boundary conditions and perfect interface contact *** mathematical formulation of the local problems and the effective coefficients are presented by the *** local problems obtained from the AHM are solved by the FEM,which is denoted as the *** numerical results are provided,and the accuracy of the solutions is analyzed,indicating that the formulas and results obtained with the SAFEM may serve as the reference points for validating the outcomes of experimental and numerical computations.

关键词： semi-analytical approach fiber-reinforced composite(FRC) effective property finite element method(FEM) asymptotic homogenization method(AHM) micropolar elasticity

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Automated Radiology Report Generation from Chest X-ray Scans Using Deep Learning 6th

Automated Radiology Report Generation from Chest X-ray Scans...

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6th International Conference on Recent Innovations in Computing, ICRIC 2023

作者： Veer, V. Druva Priya, S. Baghavathi Tamilselvi, M. Department of Computer Science and Engineering Amrita School of Computing AmritaVishwaVidyapeetham Chennai India Information Technology Department University of Technology and Applied Sciences Nizwa Oman

ISBN: (纸本)9789819778614

A chest X-ray is a common diagnostic tool for many thoracic illnesses. Interpreting these images and coming up with accurate diagnostic results is a difficult and time-consuming task for radiologists. Recent results using deep learning approaches to automate the analysis of chest X-rays are promising. This paper provides a comprehensive description of deep learning techniques used to chest X-ray processing for the diagnosis of several diseases, including TB, pneumonia, and COVID-19. This paper provide a summary of the primary deep learning architectures, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and attention models, that are used for disease classification, localization, segmentation, and report generation from chest X-rays. This paper also discuss explainability techniques that are used to offer transparency and increase trust in model predictions. Along with existing issues, future directions are taken into consideration, such as multimodal learning, few-shot learning, and model evaluation. This paper explains the model which takes less time and give the optimal results. In order to facilitate future research into developing AI systems that are more accurate, understandable, and advantageous from a medical standpoint, this article provides an overview of the most current developments in deep learning techniques for automated chest X-ray diagnosis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Analysis of Artificial Intelligence Techniques on Sustainable Employee Performance Management

Analysis of Artificial Intelligence Techniques on Sustainabl...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Siluvainathan, Panneerselvam Padmakala, S. Selvakumar, A. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Applied Machine Learning Chennai India

ISBN: (纸本)9798331510022

Sustainable employee performance management (EPM) models are key to achieving the SDGs such as Promoting decent work and economic growth, reducing inequality, ensuring quality education, promoting gender equality, fostering innovation, sustainable practices like Green Human Resource Management, and Building Partnerships in work environments. Such performance management models would require data-driven insights to arrive at the right decisions in recruiting, assessing, rewarding, ensuring good employee health, and retaining employees for strategic advantages to institutions and countries alike. AI technologies could be employed for effective employee performance management with checks and balances as they have immense learning and predictive potential to avoid human errors and biases. This work presents EPM elements, an analysis of the EPM framework, EPM measurement techniques using AI techniques, Metrics for EPM, and finally, the future direction of research. © 2024 IEEE.

关键词： AI Employee Performance Management System Performance Metrics

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Clustering based Probabilistic I/O Scheduling for Burst-Buffers Equipped HPC 14

Clustering based Probabilistic I/O Scheduling for Burst-Buff...

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14th International Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2023

作者： Zha, Benbo Shen, Hong Zhuo, Hankz Hankui Luo, Zhijian Jiaying University School of Computer Meizhou China Sun Yat-sen University School of Computer Science and Engineering Guangzhou China Macao Polytechnic University School of Applied Sciences China

ISBN: (纸本)9798350371024

Modern High-Performance Computing (HPC) platforms usually consist of an intermediate high-throughput layer, Burst-Buffers (BBs), between computing nodes and underlying shared Parallel File System (PFS) to absorb the I/O bursts caused by concurrent I/O requests from different applications. As concurrent applications increase I/O demand, BBs may experience I/O contention due to its limited capacity. The existing probabilistic I/O scheduling method can schedule I/O under limited BBs' capacity, which can sense BBs' congestion via the Markov-Chain-based probability model. However, the probability model requires consistent I/O characteristics of applications, including similar I/O duration and longer application length, to obtain an accurate I/O load estimation. These consistency conditions do not often hold in realistic *** this paper, we proposed a probability I/O scheduling framework based on application clustering (PIOS) to eliminate the consistency requirement. The framework first clusters all applications by 1-D K-means according to their I/O phrase length. Next, the expected I/O workload of each cluster is calculated and then the BBs's capacity is partitioned according to the expected I/O workload. Finally, the probabilistic I/O scheduling is applied to each application cluster. The simulation results demonstrate our framework can adapt to inconsistency and show more efficiency. © 2023 IEEE.

关键词： File organization

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An Overview of Moving Object Detection Using YOLO Deep Learning Models 2

An Overview of Moving Object Detection Using YOLO Deep Learn...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Dwivedi, Upendra Joshi, Kireet Shukla, Surendra Kumar Rajawat, Anand Singh G L Bajaj Institute of Technology and Management Department of Applied Computational Science and Engineering Greater Noida India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India SVKM S Nmims Mpstme Department of Computer Science and Engineering Shirpur Campus Shirpur India School of Computer Sciences and Engineering Sandip University Nashik India

ISBN: (纸本)9798350371055

computer vision is a promising domain that focuses on emerging approaches, algorithms and technologies to provide computing capability to machine to analysis visual data, such as image files, videos files and real time video streaming. In computer Vision and image processing detecting object from images and videos has been topic of extensive research. This is accomplished by applying various computer vision techniques to analyze the visual data and determine class of objects from image and videos files. One widespread approach to object detection is using the deep learning models. YOLO (You Only Look Once) is a convolutional neural network that offers a fast and efficient solution to object detection using deep learning. It enable computer to handle real-time detection of objects in video frames and accurately locate and classify moving objects. YOLO is able to simultaneously detect and classify objects in an efficient manner using convolutional neural networks. © 2024 IEEE.

关键词： Deep Learning Image Processing Motion Detection Moving Object Detection YOLO

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Leveraging Ensemble Techniques for Enhanced Eye State Classification with EEG Data 2

Leveraging Ensemble Techniques for Enhanced Eye State Classi...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Priyadharshini Jayadurga, N. Chandralekha, M. Saleem, Kashif Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India King Saud University College of Applied Studies and Community Service Department of Computer Sciences and Engineering Riyadh Saudi Arabia

ISBN: (纸本)9798350371314

Brain-computer Interaction (BCI) is an indispensable component in the constantly evolving field of human-machine interaction, offering direct interaction between the human brain and external devices. The acquisition of electroencephalogram, or EEG, signals by BCI devices is vital for this connection. The objective of this study is to differentiate eye states (open and closed) with the goal of shedding light on the consequences of artifacts when performing various BCI tasks. The background study explores the key concepts of BCI, emphasizing the critical role that EEG signals play in the intricate relationship between humans and computers. Within the BCI framework, the problem statement accentuates the importance of eye state classification across tasks. In an effort to improve classification accuracy and eliminate artifacts, this research strategically integrates techniques through the implementation of an extensive approach and assessments of multiple ensemble strategies. The findings and analysis reveal the efficiency of ensemble approaches in understanding eye states, hence improving BCI applications and expanding human-machine interaction. © 2024 IEEE.

关键词： Electroencephalography

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