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Generalized Multilingual AI-Powered System for Detecting Fake News in India: A Comparative Analysis of Machine Learning Algorithms

Generalized Multilingual AI-Powered System for Detecting Fak...

引用

2024 International Conference on Advances in Data engineering and Intelligent computing Systems, ADICS 2024

作者： Mahesh, M. Sai Thanusri, V. Deepak, K. Amrita School Of Computing Amrita Vishwa Vidyapeetham Department Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350364828

The impact of fake news in the present-day world is very high. It has the potential to alter anything irrespective of the profile. Not all fake news is dangerous but some may even ruin the lives of many. There are a lot of existing Fake News Detecting models but most of them mainly struggle to achieve and maintain the accuracy of the model as well as consistent updating of the datasets. Some models face difficulty in multi-language detection while other models struggle in automatic language detection. The proposed model serves as a solution for all the above-mentioned problems and it also includes web scrapping. The cross-dataset experimentation is performed using machine learning techniques for impactful results which primarily focus on Indian news. This model outperforms existing standards with an astonishing 99% accuracy using the logistic regression algorithm. © 2024 IEEE.

关键词： Fake detection

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Secure ML Evaluation on Encrypted Data with Fully Homomorphic Encryption and Concrete ML 5

Secure ML Evaluation on Encrypted Data with Fully Homomorphi...

引用

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

作者： Gaddam, Thanmai Chennupalle, Divya Kavitha, C.R. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350376685

In healthcare sector, preserving patient data privacy is vital. The increasing popularity of machine learning (ML) models for classification tasks on sensitive medical datasets calls for the widespread use of strong encryption approaches to protect data. This work looks at employing homomorphic encryption to safeguard healthcare datasets for machine learning (ML) classification tasks. The performance of fully homomorphic encryption (FHE) with Cheon-Kim-Kim-Song(CKKS) on medical datasets is evaluated in terms of the time taken to encrypt/decrypt the datasets used for ML tasks and the time taken to perform fundamental operations like addition and dot product. In this study, ML models such as Logistic Regression, Support Vector Machine, Decision Tree, and XGBoost are trained on two datasets: heart disease prediction and breast cancer prediction, and then tested on unencrypted, quantized, and fully homomorphic encrypted data using scikit-learn and Concrete ML. The models are evaluated using accuracy, precision, recall and f1-score. This paper evaluates the practicality of using homomorphic encryption to maintain the confidentiality of data without significantly affecting performance of ML models, emphasising its potential benefits in healthcare. © 2024 IEEE.

关键词： Adversarial machine learning

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Building a Secure and Scalable Finance Chatbot: Client-Server Architecture, Load Balancing, and TLS Security 4

Building a Secure and Scalable Finance Chatbot: Client-Serve...

引用

4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Reddy, T. Anisha Akhilesh, S. Manogna, Sodum Bhaskaran, Sreebha Kavitha, C.R. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331540364

This research introduces a new and improved model for a financial chatbot based on deep learning, machine learning, and natural language processing to provide suitable financial services and increase the financial literacy of users. It utilizes a detailed financial database, another complex machine learning structure for market forecasts., and natural language processing to parse user questions. Moreover, there is the customer resource management feature that applies machine learning in the analysis of financial data and natural language processing for understanding the user's intent. The paper also looks into the use of transport layer security (TLS) encryption to secure the flow of the sensitive information, the data that is to be transmitted from the chatbot server to the user clients. By integrating the dimensions of concepts such as financial analysis, personal finance advice, and data transmission, this research provides an extensive and secure approach to improving one's financial situation. © 2024 IEEE.

关键词： Financial data processing

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Evaluation of RPL and SDN-WISE in UAV assisted WSN data collection 2

Evaluation of RPL and SDN-WISE in UAV assisted WSN data coll...

引用

2nd International Conference on computer Vision and Internet of Things, ICCVIoT 2024

作者： Deepankumar, S. Rengaraj, R. Gupta, Boggarapu Mithil Raj Kumar Nalinadevi, K. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9781837242672

This paper evaluates the performance of the Routing Protocol for Low-Power and Lossy Networks (RPL) and the Software-Defined WIreless SEnsor network (SDN-WISE) approach in a IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN) network scenario. In this network scenario a mobile sink acting as an Unmanned Aerial Vehicle (UAV) is used for data collection in Wireless Sensor Networks (WSNs). A novel methodology is presented for generating efficient paths for the mobile UAV sink. It involves leveraging the K means clustering algorithm for finding the centroids which can cover the sensors within the transmission range. The Ant Colony Optimization technique is then used for traversing the centroids to solve the Travelling Salesmen problem (TSP). Key performance metrics such as average end-to-end delay, packet delivery ratio and energy consumption are analysed under varying conditions and sensor node densities. The study provides valuable insights into optimising UAV-assisted WSNs for enhanced performance and longevity in real-world applications. © The Institution of engineering & Technology 2024.

关键词： Ant colony optimization

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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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Gesture Recognition Technology in Smart Gloves Enhanced by Machine Learning 4

Gesture Recognition Technology in Smart Gloves Enhanced by M...

引用

4th International Conference on Ubiquitous computing and Intelligent Information Systems, ICUIS 2024

作者： Thangam, S. Kandi, Nithin Charan, Dorai Sai Gosu, Vijay Kumari, J. Jesy Janet Virupaxappa, G. Amrita School of Computing Amrita Vishwa Vidhyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331529635

Gesture recognition systems are changing how people interact with devices, promising natural and intuitive hands-free control in various applications. Based on the ESP32 microcontroller and machine learning techniques, the proposed work is developing a compact, power-efficient system for recognizing hand gestures from data gathered by flex sensors and accelerometers. The entire system fits within memory constraints, as lightweight machine learning models are first trained on a computer using Python and then ideally deployed in optimized versions onto the ESP32. In this respect, the stream of pre-processed sensor data happens to be fed to the ML model for inference, sending back results to the ESP32, which hence triggers specific actions or commands. This fundamentally new approach shows the feasibility of machine learning on resource-constrained platforms, opening the road for wearable technologies that will be more clever and reactive. © 2024 IEEE.

关键词： Accelerometers

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Emotion and Cause Detection in Dialogues Using Capsule Networks 1

Emotion and Cause Detection in Dialogues Using Capsule Netwo...

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1st International Conference for Women in computing, InCoWoCo 2024

作者： Ganesh, Shyam K. Lavanya, G. Priya, S Baghavathi Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331518943

Identifying the sources of emotions expressed in text-to-text conversational dialogs significantly contributes to improving applications such as virtual assistants, customer support, and interventions aimed at improving mental well-being. Traditional approaches to emotion analysis are primarily focused on classification procedures rather than consideration of the underlying causes responsible for triggering specific emotional behaviors in various contexts. This paper has presented a new model that identifies emotions in conversational data but simultaneously enables the determination of the root causes of such emotions. The framework uses DistilBERT to generate subtle contextual embeddings, which incorporate the richness of meaning for each utterance. For that purpose, it uses a Capsule Network for the efficient modeling of sequential dependencies and hierarchical relationships that exist in dialog. This altogether makes this system decode more complex emotional triggers with greater accuracy. Furthermore, the model is designed to be scalable and efficient, meaning it can comfortably be deployed in any real-time environment with minimal computational resources. The Experimental Results, integrated A two-step model consisting of Distil BERT and Capsule Network greatly enhances the accuracy of emotion cause detection and can be a strong solution for applications requiring minute emotions and causal inferences from conversational data. © 2024 IEEE.

关键词： Emotion Recognition

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Addressing the Cold Start Challenge through the Implementation of Content-based Filtering and Hybrid Filtering for Movie Recommendation 15

Addressing the Cold Start Challenge through the Implementati...

引用

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

作者： Kolli, Mahi Mukil, L.D. Kartthikeyan, N. Pranavkrishnan, M. Sampath, Nalini Nair, Priyanka C. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350370249

Recommender systems are of great significance for the difficulty in increasing speed as well as the amount of online information of the users to sort out the relevant content as they offer personalized suggestions. Content-based filtering and collaborative filtering are the two very common types of recommender algorithms, but they are still facing some issues, e.g. sparsity and cold-start problems. To cope with these limitations and increase recommendation accuracy, this study proposes a new form of recommendation system that combines collaborative filtering and content-based filtering to form a hybrid recommender system. The system proposed integrates the advantages of both methods and, therefore, offers more precise and individualized recommendations. As an illustration, the hybrid method shows superiority compared to the traditional methods, which proves that deep learning might be the key to advancing recommendation systems. © 2024 IEEE.

关键词： Collaborative filtering

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An Ensemble Classification Model for Phishing Mail Detection 5

An Ensemble Classification Model for Phishing Mail Detection

引用

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

作者： Anirudh, S. Radha Nishant, P. Baitha, Sanjay Dinesh Kumar, K. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Benagaluru India

Phishing emails, which masquerade as legitimate correspondence, are a significant modern hazard that cybercriminals use to deceive people into disclosing personal information. This paper presents the complex tactics used by cyber adversaries in the always changing field of phishing detection by using a real-time dataset and sophisticated classification tools to thoroughly examine email properties. In addition to detection, this paper highlights the need for constant watchfulness, delves into the psychological and social engineering elements included in phishing emails, and promotes continuous user awareness. Furthermore, the research broadens its scope to encompass the wider implications for enterprises, clarifying possible financial and reputational fallout. It offers helpful advice on strengthening defences and acts as a crucial manual for protecting private and corporate data against the ever-changing and persistent danger landscape that phishing attempts provide. This research gives an exploration of new insights by cybercriminals used for insertion of malicious software using emails. By using attributes like email text, Email type, the research identifies the signs of fraud emails. © 2024 The Authors. Published by Elsevier B.V.

关键词： Electronic mail

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Solar Irradiance Forecasting using Improved Sample Convolution and Interactive learning 5

Solar Irradiance Forecasting using Improved Sample Convoluti...

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

作者： Subair, Ansil Gopakumar, G. Department of computer science and engineering Amrita School of Computing Amrita Viswa Vidyapeetham Amritapuri India

Renewable energy sources, driven by the global concern for climate change and the urgent need to reduce greenhouse gas emissions, are gaining increasing importance. Among them, solar energy stands out as a crucial renewable source and accurate solar irradiance forecasting is vital for its effective utilisation. Deep learning (DL) techniques have emerged as state-of-the-art solar irradiance forecasting methods in past years. However, traditional models that are widely used have inferior accuracy in forecasting. To bridge this gap, this paper presents an improved state-of-the-art model that leverages the convolutional property to forecast the time series data. We got a reduction of 8.32% in MAE compared to the base model. The paper's findings highlight the potential of DL-based approaches to significantly improve the accuracy of solar irradiance forecasts. © 2024 The Authors. Published by Elsevier B.V.

关键词： Forecasting

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