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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Sreelekshmi, V. Nair, Jyothisha J. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

Among women breast cancer is the second leading cause of cancer. Emergence of Artificial Intelligence(AI) in the medical care leads to good survival rate by diagnosing and effective prognosis of the breast cancer patients. Scientific findings show that supervised deep learning model is highly dependent on the size of the training set, which must be manually labeled by experienced radiologists. The freely available biomedical imaging dataset are small in size mostly. In addition, obtaining large medical image files is difficult due to privacy and legal reasons. Thus the deep learning models tend to overfit and fail to provide a general result. Also the recent studies indicate that early detection of breast cancer (BC) is crucial to achieve favorable treatment results and reduce associated mortality. Data augmentation is the most widely used approach to address the aforementioned problem. Our proposed architecture consist of the pectoral muscle removal of the dataset, then the variational auto encoders used for data augmentation and then the U-Net and its varients used for breast cancer classification. The dice similarity score obtained for the model after the pectoral muscle segmentation is 97.53% and the classification rate accuracy is 98.5% by use of the Attention Residual U-Net than the other two models. © 2024 The Authors. Published by Elsevier B.V.

关键词： Classification (of information)

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Exploring Eye Gaze Patterns in Three Dimensions: An Innovative Visualization Dashboard 2

Exploring Eye Gaze Patterns in Three Dimensions: An Innovati...

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

作者： Akshay, S. Yashwanth, M.A. Manohar, N. Jipeng, Tian Amudha, J. School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science Karnataka Mysore India Computer Science College Zhongyuan University of Technology Department of Computer Science China School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350383652

Eye tracking is a method of measuring and analyzing the movements of the eyes as they move and focus on different objects in the visual field. This technique has been used in a wide range of fields, including psychology, neuroscience and human-computer interaction. Eye tracking provides valuable insights into how individuals process visual information, including attentional biases, cognitive processing, and decision-making. Traditionally, eye tracking has relied on licensed software and specialized hardware, making it costly and less accessible for some researchers and applications. However, with recent technological advancements and by leveraging the capabilities of webcams, researchers, and practitioners can now perform eye-tracking experiments without the need for expensive equipment. By utilizing webcams, researchers can conduct eye-tracking experiments in a cost-effective manner, making the method more accessible to a broader range of researchers and practitioners. © 2024 IEEE.

关键词： Human computer interaction

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Pan-Sharpening Using Fusion of 2D-DFRFT, 2D-DWT, and CNN

Pan-Sharpening Using Fusion of 2D-DFRFT, 2D-DWT, and CNN

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Varma, Gadiraju Hemanth Sai Phaniveer, Gudivada Lohit Sai Srivallabh, Tippa Pramodh, Tripuramallu Nidhin Prabhakar, T.V. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331517953

In recent years, the demand for high-resolution remote sensing imaging has increased in a variety of fields, including environmental monitoring, urban planning, agriculture, and disaster management. Multispectral imaging, which gathers information at several wavelengths, can provide useful insights for such applications. However, the spatial resolution of multi- spectral images frequently falls short of the necessary criteria for thorough analysis. Pansharpening approaches overcome this issue by combining high-resolution panchromatic (PAN) images with lowerresolution multispectral data, thereby improving spa- tial details while keeping spectral features. In this article, we present a unique pan-sharpening technique that makes use of the synergies between the two-dimensional discrete fractional Fourier transform (2D-DFRFT), the twodimensional discrete wavelet transform (2D-DWT), and convolutional neural network (CNN) models. By combining these complex mathematical transforms with deep learning approaches, we provide a holistic solution for improving both spectral and spatial resolution. Our method's main innovation is its multi-stage procedure, which starts with converting the PAN and multispectral images using 2DDFRFT and 2D-DWT, then extracts features using a CNN model. Subse- quent high-pass filtering of the converted images refines spatial information, allowing for a more precise fusion process. The inclusion of angle parameters associated with both 2D-DFRFT and 2D-DWT provides an additional degree of freedom, allowing for finer control over the fusion process and producing better results. We test the performance of our proposed pan-sharpening system using real-world satellite pictures from IKONOS and GeoEye-1 via extensive experiments in MATLAB. © 2024 IEEE.

关键词： High pass filters

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SACNN-IDS: A self-attention convolutional neural network for intrusion detection in industrial internet of things

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1398-1411页

作者： Mimonah Al Qathrady Safi Ullah Mohammed S.Alshehri Jawad Ahmad Sultan Almakdi Samar M.Alqhtani Muazzam A.Khan Baraq Ghaleb Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia Department of Computer Science Quaid-i-Azam UniversityIslamabadPakistan Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia School of Computing Engineering and the Built EnvironmentEdinburgh Napier UniversityEdinburghUK ICESCO Chair Big Data Analytics and Edge Computing Quaid-i-Azam UniversityIslamabadPakistan

Industrial Internet of Things(IIoT)is a pervasive network of interlinked smart devices that provide a variety of intelligent computing services in industrial *** IIoT nodes operate confidential data(such as medical,transportation,military,etc.)which are reachable targets for hostile intruders due to their openness and varied *** Detection Systems(IDS)based on Machine Learning(ML)and Deep Learning(DL)techniques have got significant ***,existing ML and DL-based IDS still face a number of obstacles that must be *** instance,the existing DL approaches necessitate a substantial quantity of data for effective performance,which is not feasible to run on low-power and low-memory *** and fewer data potentially lead to low performance on existing *** paper proposes a self-attention convolutional neural network(SACNN)architecture for the detection of malicious activity in IIoT networks and an appropriate feature extraction method to extract the most significant *** proposed architecture has a self-attention layer to calculate the input attention and convolutional neural network(CNN)layers to process the assigned attention features for *** performance evaluation of the proposed SACNN architecture has been done with the Edge-IIoTset and X-IIoTID *** datasets encompassed the behaviours of contemporary IIoT communication protocols,the operations of state-of-the-art devices,various attack types,and diverse attack scenarios.

关键词： convolutional neural network deep learning industrial internet of things intrusion detection self-attention

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A Three-Factor Authenticated Mobile Key Exchange Protocol Using Matrices Over Group Ring Structure 1

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2nd International Conference on Sustainable Technology and Advanced computing in Electrical engineering, ICSTACE 2023

作者： Chaudhary, Dharminder Senthamizh, G. Sreekutty, K.P. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9789819992355

ISBN: (纸本)9789819992348

Firstly, we know that the RSA/Discrete Logarithm assumptions don’t support devices with low computing. The factorization with discrete logarithm assumption uses only simple algebraic operations such as matrix multiplications, vector-matrix multiplications, and scalar matrix multiplications. Secondly, even if we’re still years away from having real quantum computers their ability is a significant threat to the cryptographic community. Therefore, we must be ready for future threats and computation complexity for low computing devices. In this article, we have introduced an advanced password-based anonymous authenticated exchange of keys protocol over a group ring structure over a non-commutative semigroup. As far as we are aware, this is the first authenticated exchange of keys protocol over a group ring structure over a non-commutative semigroup. The safety depends upon the toughness of factoring utilizing discrete logarithm assumption. Additionally, we analyzed both the complexity and security of the proposed protocol. This scheme is very efficient and includes only simple algebraic operations, matrix additions, and multiplications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Authentication

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MazeSolver: Exploring Algorithmic Solutions for Maze Navigation 15

MazeSolver: Exploring Algorithmic Solutions for Maze Navigat...

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

作者： Narendran, Advik Hothri, Anantha Saga, Hemanth Sahay, Apurvanand Bangalore Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering India

ISBN: (纸本)9798350370249

The initiative examines the efficiency of various maze-solving algorithms, including breadth-first search (BFS), depth-first search (DFS), A∗ search, Markov decision processes with policy iteration (MDP PI), value iteration (MDP VI), and an enhanced wall-follower algorithm. The study seeks to discover the best algorithm for the autonomy of navigation in harsh and non-obvious environments, which is a crucial step for robotics applications in logistics, exploration, and rescue operations. In a comparative analysis within a controlled maze environment, each algorithm's performance is evaluated based on path length, execution time, and memory usage. The study highlights the benefits of sophisticated decision-making and optimized pathfinding, particularly in MDP implementations and heuristic-based A∗ searches, addressing traditional limitations such as high computational costs and susceptibility to specific maze configurations. Results from this study are anticipated to enhance autonomous navigation systems, provide valuable educational insights, and improve operational efficiencies in critical applications. © 2024 IEEE.

关键词： Markov processes

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Data-Driven Clustering and Insights for Rural Development in India 5

Data-Driven Clustering and Insights for Rural Development in...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Sha, Akhbar Madhan, S. Karthikeya, Moturi Megha, R. Swain, Dhruvjyoti Gopakumar, G. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

This study aims to reveal the complex socioeconomic structure of Indian villages by utilizing a dataset obtained from several government sources, which includes a wide range of 150 distinct variables. This study intends to uncover the underlying structure of these groups by using powerful data analytics and clustering approaches and further group Indian villages into meaningful clusters by finding socioeconomic differences and geographical details in India's rural landscape. The final research findings suggest a four-cluster strategy as the most insightful solution for providing a more detailed understanding of India's rural regions' diversity and its specific requirements. By describing the diversity of these clusters, this research study improves the framework for data-driven policy formation and development interventions that address the specific requirements of each group. Finally, this study highlights not only the potential of data-driven techniques in rural development, but also the importance of targeted customized solutions in solving India's rural development challenges. © 2024 The Authors. Published by Elsevier B.V.

关键词： Data Analytics

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Enhancing Multimedia Security through Advanced Image Authentication and Encryption 9

Enhancing Multimedia Security through Advanced Image Authent...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Chakradhar, Kotha Tharun, K. Thulasi Rajagopal, Shinu M. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350394474

With this flood of multimedia data, protecting it is an absolute necessity. This paper describes a complete method for securing images that combines encryption and watermarking. A technique of the sort suggested further prolongs picture encryption, by interweaving a Rubik's idea with advanced encryptions like those based on Triple DES and AES. As further, a brand new strong watermarking algorithm is displayed. In this way, a high degree of protection for the copyright and genuineness is hoped to be achieved. The proposed method has encouraging results ensuring harvested photographs 'accessibility, confidentiality and integrity. © 2024 IEEE.

关键词： Cryptography

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A Next-Gen Approach to Streamlined Canteen Management Systems using Mesa Framework 8

A Next-Gen Approach to Streamlined Canteen Management System...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Narendran, Advik Inuguri, Anantha Hothri Yashaswini, Manyam Sundaram, Srinidhi Khanna, Manju Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331505462

A next-generation canteen management system is proposed that employs intelligent software agents to streamline various tasks, such as order processing and customer service, within canteens. The system automates verification through ID card scanning and OTP authentication, ensuring secure and efficient operations. By integrating agents to manage orders and provide real-time updates, the system enhances the dining experience by reducing manual effort and improving service speed. This smart solution, designed to cater to institutional needs, provides seamless control over canteen processes while enhancing user satisfaction through a modern, technology-driven approach. © 2024 IEEE.

关键词： Intelligent agents

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Adapting Optimal Solutions to Travelling Salesman Problem 15

Adapting Optimal Solutions to Travelling Salesman Problem

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

作者： Madhusoodanan, Devika Vismaya, R. Hridyalakshmi Sahay, Apurvanand Amrita Vishwa Vidyapeetham Amrita School Of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

A comparative analysis of algorithms for the Traveling Salesman Problem (TSP) is proposed that evaluates four distinct approaches: Nearest neighbor (NN), 2-opt algorithm, Genetic Algorithm (GA), Simulated Annealing (SA) and a hybrid method of NN and 2-opt to determine its minimum distance achieved with minimum execution time. These algorithms are evaluated by their capability to find the shortest route and the total distance travelled in the given dataset. The evaluation process consists of execution time and route rendering. The results claim that the hybrid approach outsmarts all the other methods in terms of their execution time as it takes as low as 0.308 seconds as compared to other methods on the given dataset. This hybrid approach has the potential to improve the shortest distance in an iterative manner which balances the time versus distance objective criteria in a scalable manner. © 2024 IEEE.

关键词： 2-opt Compare Approaches Genetic Algorithm Hybrid Method Nearest neighbor Optimization Algorithms Simulated Cooling TSP

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