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Decentralized Trust: Visual Cryptography for Transparent E-Voting Solutions

Decentralized Trust: Visual Cryptography for Transparent E-V...

引用

2024 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Asmitha, M. Kavitha, C.R. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543617

E-voting system is a digital mechanism that enables people to vote electronically, ensuring confidentiality, security, and transparency. Visual cryptography (VC) is a cryptographic technique where a secret image is divided into two or more shares, and only by superimposing these shares can the secret be revealed. When combined, visual cryptography offers a unique and secure way to enhance e-voting systems, providing features such as transparency, privacy, and tamper-proof mechanisms. It has an exceptional way of splitting a secret image into n pieces such that each piece reveals no information about the whole. Whenever these shares are stacked or overlaid, the original image is recreated again. This means that every share taken alone does not provide any clues to what is in the hidden content, and so it is a very good option for safeguarding shared information. In terms of electronic voting systems, visual cryptography can be used as a possible solution to addressing issues such as vote tampering, coercion and confidentiality of voters. This paper aims to enhance the security and confidentiality of electronic voting systems through the implementation of visual cryptography. A high level of security and verifiability can be realized with e-voting system through visual cryptography. © 2024 IEEE.

关键词： Voting machines

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Image Dehazing Using Pix2Pix GAN and Inception 8

Image Dehazing Using Pix2Pix GAN and Inception

引用

8th IEEE International Conference on Information and Communication Technology, CICT 2024

作者： Mithul, C. Vardhan, Puvvadi Harsha Varun, Sai Ss, Harsha Vardhan Nidhin, Prabhakar T.V. Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amrita School of Computing Bengaluru India

ISBN: (纸本)9798331531539

Remote sensing imagery is commonly used in variety of visual data applications, including military surveillance, earthquake damage assessment, autonomous vehicles, and satellite monitoring. However, atmospheric haze is known to reduce visibility in these scenes, which negatively impacts the analysis and compromises the accuracy of advanced applications. The suggested method makes use of both generative adversarial networks (GANs) and Inception Blocks to enhance the expression power of the Generator by adding features from multiple convolutional paths, enabling it to generate more detailed images, than a generator that creates dehazed images from hazy input and a discriminator that separates produced images from real photos make up the components of this GAN based dehazing model. The dehazed pictures are further refined using a CNN-based enhancement network, which further corrects colour distortions, captures minute details, and improves overall output. This approaches success is demonstrated by experimenting with remote sensing datasets, which outperform conventional dehazing approaches and highlight the potential of GANs and CNNs to reduce haze and enhance image quality. The technique shows the potential to clear foggy satellite images. © 2024 IEEE.

关键词： Damage detection

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Smart Steps to Sporting Success: IoT-Driven Footwear Innovations 15

Smart Steps to Sporting Success: IoT-Driven Footwear Innovat...

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

作者： Thangam, S. Joshith, Gadi Santhosh, S. Harish, V. Jesy Janet Kumari, J. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

Using the cloud platform Blynk IoT, smart sneakers with built-in step and calorie monitoring were created with athletes in mind. The primary characteristic of the smart shoe is its ability to detect early signals of cramping in the muscles during athletic exercises. This allows for prompt response to protect the athlete's foot and performance. It also acknowledges the vulnerabilities that athletes may encounter during training;this feature guarantees that athletes can notify the user by sending them messages in the event of any potential hazard. Beyond features for health and safety, the smart shoe calculates steps and tracks activity, giving athletes useful information for their training and recuperation regimens. With a focus on safety first, this project tackles important issues like sensor accuracy, device longevity, user comfort, and data security to produce a solution that improves athletic performance. By providing athletes with a comprehensive tool for health monitoring and personal safety, the smart shoe seeks to redefine wearable technology in sports. © 2024 IEEE.

关键词： Wearable sensors

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Matrix Factorization for Augmented Deep Learning Model Generation 2

Matrix Factorization for Augmented Deep Learning Model Gener...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Nived, P.A. Harikumar, Sandhya Anil, Aditya Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

This paper aims to address two significant challenges of Deep Learning(DL) model generation, high computational cost involved during the training phase and data interpretability from high dimensional data. The computational cost is curbed using centralized as well as distributed matrix factorization technique known as CUR decomposition. CUR decomposition has the advantage of reducing the dimensions of the dataset without data transformation and minimal information loss. Though Singular Value De-composition(SVD) is the best matrix decomposition technique with least reconstruction error, it transforms the data and hence the original data cannot be interpreted from the decomposed matrices while applying DL technique. So CUR decomposition is leveraged to reduce the number of features in high-dimensional data while preserving the essential information. Extensive experimental analysis is conducted to evaluate and compare the performance of CUR and SVD techniques based on reconstruction error and time complexity in centralized as well as distributed setting. The results exhibit that CUR outperforms SVD in terms of computational efficiency, especially in a distributed setting where the decomposition time for CUR was a fraction compared to SVD. At the same time DL models generated from this reduced dataset exhibit comparable accuracy with the original high dimensional dataset as well as reduced dataset obtained through SVD. This work also enhances the accessibility of the CUR algorithm to users, for which an application is developed that enables users to execute the algorithm on any dataset. The user-friendly interface of the application facilitates the upload of datasets, configuration of desired decomposition rank, and execution of the algorithm with a single click. © 2024 Elsevier B.V.. All rights reserved.

关键词： Deep learning

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Echoes of Truth: Unraveling Homophily in Attributed Networks for Rumor Detection 5

Echoes of Truth: Unraveling Homophily in Attributed Networks...

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

作者： Rithish, S.V. Prabu, C.R. Anuush, M.B. Deepthi, L.R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

The surge in information and misinformation during the COVID-19 pandemic, particularly regarding 5G-related rumors, was prominent on various social media platforms, notably Twitter. This study addresses the pervasive issue of rumor propagation on Twitter by employing diverse machine learning models, clustering techniques, and node detection algorithms to gain insights into the dynamics of rumor dissemination. The primary focus is identifying influential individuals who significantly impact the spread of these rumors and had an accuracy of 0.86 while classifying various labels. The study reveals network structures and key influencers within each cluster by categorizing users into clusters. While it is commonly observed that users with a more extensive follower base have a broader impact on rumor dissemination, our innovative approach integrates advanced community detection algorithms and methods, the Overlapnodedetect algorithm and Rumorsourcetrace algorithm for identifying influential characters to uncover latent homophily structures within these clusters. The objective is identifying crucial users responsible for initiating and perpetuating these rumors. © 2024 The Authors. Published by Elsevier B.V.

关键词： Complex networks

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Neural Machine Translation Using a Pivot Approach: Dogri to English 15

Neural Machine Translation Using a Pivot Approach: Dogri to ...

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

作者： Nair, Aarathi Rajagopalan Sheshadri, Shailashree K. Dhanush, A. Pradyumna, N.V.S. Gupta, Deepa Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

The progress of Neural Machine Translation for Indic languages is gradually achieving a noteworthy result in terms of translation accuracy. However, its advancement is impeded by the lack of rich parallel corpora, a limitation not shared by non-Indic languages. As a result, most languages of India are still categorized as low-resource or zero-resource languages. Various state-of-the-art architectures have been developed to address these challenges, with the predominant approach being the pivot-based technique. This technique, rooted in transfer learning, utilizes an auxiliary language as a pivot to assist in translation. In this study, a pivotbased Neural Machine Translation (NMT) architecture is designed for translating Dogri to English, with Hindi as the pivot. Dogri, being a low-resource language, shares linguistic similarities with Hindi. Furthermore, Hindi-to-English state-of-the-art models are accessible, ensuring high translation accuracy. Through the implementation of this pivot-based translation model, a BLEU score of 20.97, SacreBLEU of 21.54 and chrF of 50.01 is attained. © 2024 IEEE.

关键词： Neural machine translation

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Crop Irrigation Advisory System Using Federated Logistic Regression 7th

Crop Irrigation Advisory System Using Federated Logistic Reg...

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7th IFIP TC 12 International Conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Gardas, Deepthi Karthi, R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9783031699856

Federated learning (FL) is a collaborative learning algorithm that builds ML models using knowledge from distributed clients at various locations. Federated learning is valuable when data is collected from heterogeneous environments, and a unifiedML model needs to be built with minimal sharing of data. In this study, a federated irrigation advisory system is developed for an agricultural application that helps the farmer to water their field based on agricultural field parameters. The application of machine learning techniques in the agricultural domain is still in its infancy. Agricultural farms employ different sensors and have natural variations in soil type and environmental conditions. A federated model framework is suitable for the development of an irrigation advisory system as heterogeneous data is gathered using a variety of sensors spread across several farms. FL-based Logistic Regression is designed to predict whether to water the field or not based on the field parameters: temperature, humidity, soil moisture content, number of days since planting the crop, and crop type. Federated learning algorithms are designed with a client-server architecture. Flower, an open-source federated framework, which used to build and test the irrigation advisory application. Two clients and a server are used to build the FL-based Logistic Regression model by exchange of model parameters. Weights and bias of the logistic regression model are aggregated by the server from multiple clients. These aggregated model parameters are sent to clients to build the regression model with its own data. Experiments were conducted to understand the performance of FL in learning the model parameters for prediction. The model’s performance is evaluated using metrics to understand the converge patterns and parameters that affect FL. © IFIP International Federation for Information Processing 2024.

关键词： Crop Irrigation crop Irrigation scheduling dataset Federated Learning Irrigation Advisory system Machine Learning

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Enhancement of Cassava Leaf Disease Detection Using InceptionNet and Squeeze and Excitation Attention Mechanism

Enhancement of Cassava Leaf Disease Detection Using Inceptio...

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2024 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2024

作者： Alagawadi, Anusha P Arunbalaji, C.G. Shivdutt, Sahen Singh, Rimjhim Padam Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331535476

Automated cassava leaf disease detection using images has gained immense importance as it is consumed by 800 million people as a staple crop across the globe. This paper proposes a novel approach to cassava leaf diseases classification based on InceptionV3 convolutional model augmented with Squeeze and Excitation attention mechanism for enhanced feature representation. The proposed work also attempts to improve robustness through extensive data augmentation, balanced sampling techniques and adaptive learning rate adjustment mechanism. For the evaluation of the performance of the proposed model, different techniques for explainability have been adopted, including Grad- CAM and LIME, which bring to the fore the mechanism of decision-making by our model. The proposed model is compared against six fine-tuned convolutional architectures and the results demonstrate the highest accuracy and F-score values for disease classification, thus showing the efficacy of the proposed approach. Over-all this research contributes to the general area of agricultural disease diagnosis but also sheds light on the interpretability of deep learning models for complex classification tasks. © 2024 IEEE.

关键词： Plant diseases

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Enhancing Hate Speech Detection in Tamil Code-Mix Content: A Deep Learning Approach with Multilingual Embeddings 5

Enhancing Hate Speech Detection in Tamil Code-Mix Content: A...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Vishak Anand, S. Prathap, Ishwar Gupta, Deepa Nair, Aarathi Rajagopalan Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350376685

The increasing amount of hate speech and language which are inappropriate on platforms of social media has emerged as a serious issue, prompting measures to counter it. This study examines hate speech and languages that are objectionable in a Tamil Code-Mix dataset, where Tamil content is provided in English. It assesses the performance of various deep learning classifiers that use different multilingual transformer embedding models, which includes many models such as DistilBERT, IndicBERT, XLM-RoBERTa, LASER, MuRIL, and BERT, in this domain. Among the models tested, an ensemble stacked model integrating Long Short-Term Memory and Gated Recurrent Units with XLM-RoBERTa embeddings had the greatest accuracy (0.76) and weighted F1-score (0.72). This exceptional performance underscores the potential of advanced ensemble models for distinguishing hate speech in code-mixed languages. The study's findings are notable for several reasons. They enhance understanding of hate speech in Tamil, particularly in its code-mixed variant, and offer insights into the application of advanced deep learning methods and multilingual embeddings for detecting harmful content. By improving accuracy and reliability in hate speech detection, this research contributes to fostering safer and more inclusive online environments for Tamil-speaking communities. In summary, the research advances the domain of hate speech identification and emphasizes the importance of technological solutions in creating more safer and very inclusive online spaces. © 2024 IEEE.

关键词： Contrastive Learning

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Safeguarding the Homeless Using ViTransformer in Violence Detection 3

Safeguarding the Homeless Using ViTransformer in Violence De...

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Bhuvaneswari, R. Sai Lasya, M. Natarajan, B. Tamilselvi, M. Geetha, K. Velammal, M. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India School of Computer Science and Engineering Vit University Tamilnadu Chennai India Roever Engineering College Department of Computer Science and Engineering Tamilnadu Perambalur India Karpagam College of Engineering Department of Computer Science and Engineering India

ISBN: (纸本)9798350361186

In recent years, the issue of homelessness has gained significant attention as societies grapple with finding solutions to alleviate the plight of those without stable shelter. To address this pressing concern, this research utilizes advanced video analysis techniques, specifically Vision Transformer, to detect the presence of individuals in homeless populations. By leveraging state-of-the-art technology, the research aims to not only identify individuals but also discern their actions and activities within the monitored environment. The methodology involves the initial detection of persons using Vi Transformer, an innovative deep learning model renowned for its accuracy in object recognition tasks. Once a person is successfully identified, this research further scrutinizes their activities, with a particular focus on detecting instances of conflict or fighting. This step holds immense potential or the safety and well-being of homeless individuals, as it enables the swift notification of nearby authorities or support services in cases of altercation. Crucially, this research delves into the performance metrics of both person detection and activity detection experiments, providing valuable insights into the efficacy of the Vi Transformer model in real-world applications. By assessing the system's accuracy, speed, and reliability, this study contributes to the development of proactive strategies for assisting homeless populations, emphasizing the importance of technology in addressing social challenges. © 2024 IEEE.

关键词： Object recognition

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