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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

引用

Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA), International Conference on

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

ISBN: (数字)9798331517953

ISBN: (纸本)9798331517960

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.

关键词： Technological innovation Satellites Quantum computing Urban planning Process control Feature extraction Mathematical models Convolutional neural networks Spatial resolution Remote sensing

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Secure Fuzzy Keyword Search in Cloud computing using Levenshtein Distance and Cryptographic Indexing

Secure Fuzzy Keyword Search in Cloud Computing using Levensh...

引用

Data Intelligence and Cognitive Informatics (ICDICI), International Conference on

作者： Jahnavi M Haritha K Gowtham R S V Shinu M Rajgopal Dept of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

Growing usage of the cloud for computing, the necessity for safe and security storage and retrieval of information procedures has become important. This study provides a novel way to tackle the difficulty of searching across encrypted information in the cloud with supporting an inherent fuzzy in queries made by users. The purpose is to allow users to execute searches for keywords on their secure data saved in the cloud while maintaining the secrecy of the data. The approach we propose uses cryptographic methods, notably to secure the information before transferring it to the service. To support fuzzy searches for keywords, we apply approaches such as secure indexing building and similarity rating. This enables users to receive useful data regardless of whether their search request includes errors in spelling, typos, or small changes in the terms. The system structure makes sure a cloud server stays clueless to the details of the encrypted information and the queries used for searching. In addition, we incorporate safety measures to defend against illegal access and to preserve integrity of data throughout the searching process. Thorough tests and experiments, we show the speed as well as effectiveness of our strategy in the areas of search performance, processing overhead, and speed of response, not just enhancing the security of data provided by users but additionally provides an efficient method for safe and flexible retrieval of data in cloud-based systems of storage.

关键词： Cloud computing Fault tolerance Data privacy Web services Keyword search Fault tolerant systems User experience Safety Cryptography Indexing

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Encryption scheme using Mealy Machine

Encryption scheme using Mealy Machine

引用

Global Conference for Advancement in Technology (GCAT)

作者： Hemanth Ramasubbu Sricharan Silaparasetty Mehul Singh Bhakuni Niharika Panda Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

This novel encryption scheme leverages a Mealy machine for dynamic encryption. To significantly enhance data security, it enforces strong key requirements (with mixed uppercase letters, digits, and special characters). The encryption process involves pre-processing data, creating a character mapping matrix, integrating a Mealy machine based on the matrix sum for additional complexity, generating and validating a strong key, and manipulating the data with the key matrix and Mealy machine outputs. Decryption follows a similar approach to recover the original data, making the system efficient for both encryption and decryption. This work paves the way for a secure and versatile encryption solution.

关键词： Data protection Encryption Delays Complexity theory Data mining Data communication Optimization

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A Comprehensive Analysis of Machine Learning Algorithms for Suitable Crop Prediction in Agriculture

A Comprehensive Analysis of Machine Learning Algorithms for ...

引用

Cognitive Robotics and Intelligent Systems (ICC - ROBINS), International Conference on

作者： Kriti Priya Shah Subodh Narayan Sah Kadam Prajwal Dharmaraj K Dinesh Kumar Dept of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372755

Agricultural productivity is major concern for the global food community. According to the 2011 census, about 49% of the people in India are engaged in farming or related activities. Agriculture provides almost 17% of the country's GDP. So, it's necessary to grow suitable crops at suitable locations to maximize the production of crops and revenue obtained from them to help the country’s economy. Many statistical methods have been used from past years like doing field survey and analyze the obtained data, obtaining mathematical equations that give relationship between crop-related parameters like temperature and rainfall, techniques like correlation analysis, regression analysis, ANOVA, etc. In this comparative analysis, the prediction of suitable crop is done using supervised machine learning prediction approaches such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Least Absolute Shrinkage & Selection Operator (LASSO) regression, Linear Descriptive analysis (LDA), Nearest Centroid Classifier and Voting regression. KNN & SVM, both models achieved the highest accuracy among others, which is 97.78%. The main goal of this comparative study is that comparing different machine learning classification techniques for suitable crop prediction helps to enhance the production of crops and thus contributes to the growth of agriculture in the country. This comparative study provides valuable insights and guidance for suitable crop plantation.

关键词： Support vector machines Surveys Temperature distribution Machine learning algorithms Rain Biological system modeling Crops

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A Novel Zero-Knowledge Authentication Protocol for Secure Communication in VANET

A Novel Zero-Knowledge Authentication Protocol for Secure Co...

引用

International Conference on computing and Networking Technology (ICCNT)

作者： Ardra Vinod Malavika Vinodkumar S V Mayur Pranav P Remyakrishnan Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Vehicular Ad Hoc Network (VANET) is a particular subclass of the mobile ad-hoc network that raises several security challenges, notably how users authenticate the network. The work explores using zero-knowledge proofs for secure authentication while preserving user privacy and aims at encrypted information exchange between onboard units. Zero-knowledge proofs enhance security by protecting against impersonation attacks. We strive to reduce the dependence on roadside units for computational tasks by integrating the central authentication server and sub-authentication servers. We reduce modular exponentiation operations during authentication, enhancing efficiency without compromising security. We use the protocol verifier tool called Proverif to verify the security of our protocol. Simulation using the NS-2 simulator validates the protocol by varying the vehicle density. This paper advances VANET security by combining zero-knowledge authentication with encrypted information exchange, strengthening security, efficiency, and data confidentiality while reducing reliance on RSUs. Our protocol ensures secure communication in VANET with minimum computational and communication overhead.

关键词： Privacy Protocols Prevention and mitigation Vehicular ad hoc networks Authentication Road safety Real-time systems Cryptography Servers Information exchange

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FretMate: ChatGPT-Powered Adaptive Guitar Learning Assistant 25

FretMate: ChatGPT-Powered Adaptive Guitar Learning Assistant

引用

30th International Conference on Intelligent User Interfaces, IUI 2025

作者： Shu, Xin Shi, Lei Cheng, Jiacheng Ouyang, Lingling Chu, Mengdi Shu, Xinhuan Open Lab Newcastle University Newcastle Upon Tyne United Kingdom Open Lab School of Computing Newcastle University Newcastle upon Tyne United Kingdom Computer Science and Engineering The Ohio State University ColumbusOH United States School of Computing Newcastle University Newcastle Upon Tyne United Kingdom

ISBN: (纸本)9798400713064

Learning to play the guitar poses significant challenges for beginners, who often choose to practice alone to avoid the embarrassment of making mistakes in front of others. This isolation leads to a lack of timely feedback and encouragement, resulting in frustration and decreased motivation. Traditional learning methods fail to provide personalized and immediate support. To address these issues, we propose a GPT-powered guitar learning assistant, FretMate, that provides immediate error correction, personalized learning paths, and emotional support. The design was informed by formative interviews with six guitar instructors and six learners. We evaluated our assistant against the traditional self-guided practice in a controlled two-week study with 16 participants. Results showed that participants using FretMate improved in skill acquisition, engagement, and motivation compared to the control group. We discuss the potential of integrating conversational AI into instrument learning to provide personalized instruction and emotional engagement. © 2025 Copyright held by the owner/author(s).

关键词： Federated learning

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Sentiment Analysis of Telugu News Articles Decoding Textual Nuances

Sentiment Analysis of Telugu News Articles Decoding Textual ...

引用

Emerging Trends in Information Technology and engineering (ic-ETITE), Conference on

作者： V Balaji Viswanadh Eswar Prasad K Bharathi Mohan G Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

In the field of Natural Language Processing, analyzing sentiment in Telugu presents distinctive challenges owing to the limited availability of annotated datasets. This paper presents a sophisticated method utilizing BERT (Bidirectional Encoder Representations from Transformers) for Telugu sentiment analysis, complemented by Part-of-Speech (POS) and Named Entity Recognition (NER) algorithms. The proposed system follows a multi-step process, leveraging BERT's bidirectional context understanding during pretraining, and subsequently fine-tuning the model with a focus on sentiment analysis. Additionally, the integration of POS and NER algorithms enhances the language model's capabilities by capturing grammatical components and identifying named entities in Telugu text. The model is evaluated on a diverse Telugu dataset, specifically the Telugu Sentiment Analysis Dataset (TSAD), demonstrating its proficiency in predicting sentiment labels. The study addresses the problem of limited annotated data in Telugu sentiment analysis and show-cases significant improvements in accuracy, precision, and recall compared to previous approaches. The proposed system achieves a notable accuracy of 92.07 %, illustrating its effectiveness in capturing the nuances of sentiment expression in Telugu.

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Visual Question Answering in Malayalam Text

Visual Question Answering in Malayalam Text

引用

Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Jose Joseph V Anand Ram YadhuKrishna P Anjali T Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amrita School of Computing Amritapuri India

ISBN: (数字)9798350396157

ISBN: (纸本)9798350396164

One of the most important tasks in combining computer vision and natural language understanding is Visual Question Answering (VQA). This paper focuses on the VQA problem in Malayalam, a language known for its unique Dravidian linguistic characteristics. The robust LXMERT model, a bidirectional encoder-decoder architecture capable of handling multimodal input, is used to achieve this goal. Extensive testing on an independent set of Malayalam images and questions highlights the feasibility of building a trustworthy VQA model on a Malayalam dataset. The method shows adaptability when answering questions about Malayalam images, which creates opportunities for image retrieval, annotation, and possibly image-based machine translation. This demonstrates how the model can be applied to real-world situations in a variety of fields.

关键词： Adaptation models Visualization Sentiment analysis Accuracy Image retrieval Linguistics Question answering (information retrieval)

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Enhancing Digital Image Security using Visual Cryptography Integrated with AES and RSA Encryption Techniques

Enhancing Digital Image Security using Visual Cryptography I...

引用

Delhi Section Conference (DELCON), IEEE

作者： Prajna Aasritha Adabala Rajani Sai Venkata Gowtham Thambi Jaishitha Vadluri Sai Rishisri Kavitha C. R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

Visual cryptography (VC) designed to secure visual information such as images and text by dividing it into multiple shares. A key advantage of visual cryptography is its ability to decrypt information without the need for computational devices, using simple human visual perception. The paper is centered on visual cryptography with examples such as Visual Secret Sharing, Advanced Encryption Standard (AES), and Rivest-Shamir-Adleman (RSA) during the encryption and decryption stages. The specific algorithms' efficiency in increasing security and improving image quality is discussed. The quality and overall accuracy of the reconstructed images are measured by the Peak Signal-to-Noise Ratio (PSNR). The benchmark images are used to compare the results in terms of encryption and decryption time and PSNR value. The findings signify the integration of AES and RSA in improving security without compromising image quality and computational complexity.

关键词： Image quality Visualization Protocols PSNR Image databases Digital images Encryption Standards Image reconstruction Visual perception

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CAMUS: Attribute-Aware Counterfactual Augmentation for Minority Users in Recommendation 23

CAMUS: Attribute-Aware Counterfactual Augmentation for Minor...

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2023 World Wide Web Conference, WWW 2023

作者： Ying, Yuxin Zhuang, Fuzhen Zhu, Yongchun Wang, Deqing Zheng, Hongwei Institute of Artificial Intelligence Beihang University Beijing China Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Engineering Beihang University Beijing China Beijing Academy of Blockchain and Edge Computing Beijing China Zhongguancun Lab Beijing China Sklsde School of Computer Science Beihang University Beijing China

ISBN: (纸本)9781450394161

Embedding-based methods currently achieved impressive success in recommender systems. However, such methods are more likely to suffer from bias in data distribution, especially the attribute bias problem. For example, when a certain type of user, like the elderly, occupies the mainstream, the recommendation results of minority users would be seriously affected by the mainstream users' attributes. To address this problem, most existing methods are proposed from the perspective of fairness, which focuses on eliminating unfairness but deteriorates the recommendation performance. Unlike these methods, in this paper, we focus on improving the recommendation performance for minority users of biased attributes. Along this line, we propose a novel attribute-aware Counterfactual Augmentation framework for Minority Users(CAMUS). Specifically, the CAMUS consists of a counterfactual augmenter, a confidence estimator, and a recommender. The counterfactual augmenter conducts data augmentation for the minority group by utilizing the interactions of mainstream users based on a universal counterfactual assumption. Besides, a tri-training-based confidence estimator is applied to ensure the effectiveness of augmentation. Extensive experiments on three real-world datasets have demonstrated the superior performance of the proposed methods. Further case studies verify the universality of the proposed CAMUS framework on different data sparsity, attributes, and models. © 2023 ACM.

关键词： Recommender systems

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