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Valluvan: Processing Name Board Images to Enhance Communication for Native Tamil Speakers 1

引用

5th International Conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

作者： Ramesh, Sriramachandran Prasanna Kumar, N. Murali Krishnan, D.A. Parveen Sultana, H. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (数字)9789819718412

ISBN: (纸本)9789819718405

The Valluvan app is a language solution for native Tamil speakers. The system emphasizes the recognition of name boards, translation, and speech output to enhance communication and access to information. The app utilizes advanced technology to analyze images and text on name boards, transforming it into readable text for machines using sophisticated algorithms, the text is then translated to Tamil. The Valluvan app utilizes text-to-speech conversion methods to deliver speech output in Tamil, enhancing users’ understanding of translated content. The paper presents an overview of the app, discussing its methodology, implementation details, experimental results, and analysis. An app with potential to empower native Tamil speakers by facilitating access to and comprehension of information from diverse language sources. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image enhancement

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Face Identification of Suspects Using Sequential -Deep Convolutional Neural Network 2

Face Identification of Suspects Using Sequential -Deep Convo...

引用

2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Munusamy, Vaishnavi Senthilkumar, Sudha School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350328202

In terms of increasing terrorism, criminal behaviour, and anti-social events, there has been a need for safety systems to identify criminals. Face Recognition is one of the vibrant technologies that are very useful for person identification, especially in criminal investigation. Convolutional layers are used in our proposed system for feature extraction, and dense layers are used for classification. ReLU activation functions use four convolutional layers with 3 × 3 kernels and 128/64 filters to add non-linearity. Carefully placing dropout layers prevents overfitting, and Max Pooling layers down sample spatial dimensions. The model produces encouraging outcomes on a customized dataset comprising 4288 photos that capture different angles and concealed/hidden images of four subjects. © 2024 IEEE.

关键词： Face recognition

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An Enhanced Feature Fusion Network Model for Cervical Cancer Prediction

An Enhanced Feature Fusion Network Model for Cervical Cancer...

引用

2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Pragatheeswari, B. Mary, Jameela Priya, S. Roshni Dhivyaa, C.R. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331540821

Healthcare providers and researchers that work with patients who have cervical cancer face a significant challenge because it is one of the world's most common causes of death. Worldwide, cervical cancer is one of the major causes of death among women according to the World Health Organization (WHO). Pap smear, also called the Pap Test, is used to examine the cells present in the cervix for abnormal cells. However, manual examination of these pap smears is prone to human error. This work aims to explore the use of deep learning models to detect cancer from pap smear images and develop an ensemble model to improve the accuracy. Our proposed model uses pretrained CNN models through transfer learning to extract features, perform feature concatenation followed by dimensionality reduction and hyperparameter tuning, which are then fed into a neural network for multiclass classification. Hyperparameters are fine-tuned with the help of Bayesian optimization to improve the model's performance. Our proposed model has achieved an accuracy of 96.4%, sensitivity of 96.41% and specificity of 96.66%. © 2024 IEEE.

关键词： Dimensionality reduction

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Flourishing Fields: Modernizing Farming with Real Time AI-powered Tomato Disease Detection and Monitoring

Flourishing Fields: Modernizing Farming with Real Time AI-po...

引用

2024 International Conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Sriram, G. Saran, D. Vignesh, M. Nallusamy, Priyanka Arumuga Arun, R. Adaline Suji, R. Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798331541170

The agricultural sector faces significant challenges due to plant diseases, which can drastically reduce crop yields and quality. This paper presents a cutting-edge solution for real-time detection and instant remediation of tomato plant diseases using a web-based interface. Leveraging deep learning models and advanced image processing techniques, our system accurately identifies ten distinct tomato diseases from leaf images. The core of the system is built using TensorFlow and OpenCV, and it operates through a user-friendly interface developed with Streamlit. This interface not only detects diseases in real-time but also provides actionable solutions, recommending specific fertilizers and treatment protocols. This holistic approach will pinpoint the location of infected crops, apply targeted treatments, and monitor the effectiveness of interventions. By harnessing technology, we aspire to empower farmers with timely information and tools to enhance crop health, reduce losses, and increase agricultural productivity. Our initial focus on tomatoes provides a robust framework that can be adapted to other crops, paving the way for smarter, more resilient agricultural practices. © 2024 IEEE.

关键词： Fruits

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Comparative and Preventive Analysis of Dictionary Attacks 1st

Comparative and Preventive Analysis of Dictionary Attacks

引用

1st International Conference on Sci/Tech and engineering, ICSTE 2023

作者： Shourya, Sanat Venkatachalam, Ilayaraja Patel, Harpal Mittal, Manit School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819947126

Hacking is one of the most widespread issues that the general public faces today. Hackers essentially use some social engineering techniques, combined with the publicly available information, to crack open the social media accounts, banking accounts, or cloud storage accounts. As a result, security and protection becomes the main motive for organizations running these platforms. The severity of threats linked to such incidents is tantamount to those linked with data leaks, security breaches, and security lapse events of their likes. Among others, dictionary attacks, a strategy employed for password cracking by trying every common-use word present in an extant wordlist, are the most common. Even though nowadays password strength classifiers are being used extensively almost everywhere which tends to keep these attacks at bay, they are still prone to hyper-personalized dictionary attacks as people are inclined to choose a password which revolves around their personal details itself. Consequently, the necessary awareness for the prevention of dictionary attacks is called for and is provided by performing an analysis of the time taken by different types of dictionary attacks and what type of password or otherwise is safe or not. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cybersecurity

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A Computationally efficient CNN-based Deep Learning Technique for Sickle Cell Detection

A Computationally efficient CNN-based Deep Learning Techniqu...

引用

2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Hemavarshini, S. Sathya Shree, S. Arumuga Arun, R. Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798331522988

An amino acid change in the hemoglobin protein causes sickle cell disease (SCD), a prevalent hereditary illness that causes red blood cells to take on a sickle shape. These malformed cells cause severe health complications, including painful episodes, organ damage, increased infection risk, and a significantly reduced lifespan. Accurate and timely diagnosis is essential for timely therapy and better results for patients. This study investigates the potential of various lightweight deep learning models, MobileNetV1, MobileNetV2, MobileNetV3 Small, and EfficientNetV0 as an efficient and accurate underlying model for SCD detection. All of these trained them on the publicly available Sickle Cell Disease Dataset, where various augmentation methods are used to enhance the resilience and generalization of the models, such as rotating, shifting, shearing, zooming, brightness, and contrast adjustments. The MobileNetV1 has achieved better performance with an accuracy of 93.2% and F1Score of 95.43% with a lesser complexity with computational parameters of 4.855 Million and a model size of 18.52 MB. These findings highlight the potential of these lightweight models to revolutionize SCD screening and diagnosis in resource-constrained environments, offering faster, more accessible, and cost-effective diagnostic tools, ultimately improving healthcare outcomes for individuals with SCD. © 2024 IEEE.

关键词： Patient treatment

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Pipeline Leak Detection System Using Machine Learning

Pipeline Leak Detection System Using Machine Learning

引用

2024 IEEE International Conference on Information technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Jaswanth, Chitrada Shiva, Golla Pavan Raj, Grandhi Naga Sai Rishi Thirunavukkarasan, M. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350382693

Leak detection is a critical concern across various sectors due to its potential environmental, economic, and safety implications. This paper presents a machine learning-based approach aimed at enhancing leak detection strategies, focusing on predicting the distance and diameter of leaks in pipelines. Leveraging a comprehensive dataset sourced from experimental setups featuring deliberate leaks, our model utilizes pressure and flow measurements to accurately identify and localize leaks. Through rigorous evaluation of multiple machine learning algorithms, including K-Nearest Neighbors, XGBoost, Decision Tree, Random Forest, and Support Vector Machine, we identify XGBoost for predicting leak distances and Random Forest for estimating leak diameters as optimal choices. The proposed approach demonstrates promising results in accurately detecting and quantifying pipeline leaks, offering valuable insights for ensuring pipeline integrity and safety. © 2024 IEEE.

关键词： Information leakage

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Measurement of Body Vitals using Photoplethysmography (PPG) and Smartphone Sensors 2

Measurement of Body Vitals using Photoplethysmography (PPG) ...

引用

2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Furniturewala, Mahera Sivaraman, Arjun Anand, Anmol Kannuchamy, Tarun Sasikala, R. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350328202

Every day, millions of people use portable health gadgets to monitor body vitals such as heart rate, blood oxygen level, blood pressure, etc. However, this can also be facilitated just by using normal smartphones which are commonly available. Thus, this paper aims to provide first aid in the measurement of body vitals for early diagnosis of symptoms. The outcome of this paper is a framework to measure the key body vitals such as blood oxygen, heart rate, blood pressure and respiratory rate using the built-in sensors in a smartphone namely a camera, flashlight, accelerometer, and microphone. These vitals are captured by analyzing the photoplethysmography (PPG) signals obtained from the red intensities of the images of the fingertip and performing further arithmetic calculations on the obtained values. On the other hand, respiratory rate is calculated using the deviation in the values obtained in the accelerometer readings during the movement of the chest upwards and downwards. All the algorithms are computationally feasible on mobile devices and make use of the built-in smartphone sensors only, without the need for external devices. Also, smartphones have become cheaper than before and more people in the society have access to it, thus giving them a wider reach. The paper also compares the values for the vitals measured on pulse oximeters, blood pressure monitors, health bands, and smartphones with low-and high-quality cameras. Comparisons are made on a sample size of 100 measurements, and the mean percentage error for each type of body vital measurement is calculated with respect to each measuring device. © 2024 IEEE.

关键词： Smartphones

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From Code to Conundrum: Machine Learning's Role in Modern Malware Detection 2

From Code to Conundrum: Machine Learning's Role in Modern Ma...

引用

2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Jha, Vaibhavi Saxena, Akshat School of Computer Science & Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350370188

In this research paper, we'll be diving into the combination of malware analysis and machine learning to stepup security. Right now, our digital world is like an anthill: connected in every possible way. In this study, we used machine learning algorithms like Random Forests, K-Nearest Neighbours (KNN), and Logistic Regression for anomaly detection. Inside 60,000 benign and malware instances lie the answers we are looking for. We judge these three algorithms on how well they can detect accuracy, precision, recall, and Fl- score. We hope to give professionals practical insights to secure their systems from malware while constantly being aware of new threats. In a day where security is crucial, this work will connect past issues with machine learning today so that we can better prepare for problems tomorrow. © 2024 IEEE.

关键词： Logistic regression

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Automated Resume Parsing and Ranking using Natural Language Processing 3

Automated Resume Parsing and Ranking using Natural Language ...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Thangaramya, K. Logeswari, G. Gajendran, Sudhakaran Deepika Roselind, J. Ahirwar, Neha School of Computer Science and Engineering Vellore Institute of Technology Vellore India School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus India School of Electronics Engineering Vellore Institute of Technology Chennai Campus India

ISBN: (纸本)9798350372120

Advancements in networking and communication have revolutionized the recruitment process, leading to the development of modern resume parsing and ranking systems. With the proliferation of internet-based recruiting, numerous resumes are now stored in recruitment systems. However, traditional methods, such as manual processing and the utilization of unique resume templates, have limitations when dealing with unstructured documents like resumes. Resume parsing is a crucial technique that involves extracting essential details pertaining to applicants to be shortlisted for interview. This paper introduces the 'Resume Parser and Ranker,' an application designed to efficiently and automatically rank resumes, saving considerable time and manpower during the recruitment process. The application is powered by Natural Language Processing (NLP) approach, incorporates heuristic calculations to evaluate the final score of each candidate. The system utilizes Deep Learning (DL) for Named Entity Recognition (NER), achieving an impressive 93% accuracy in information extraction. Notably, this accuracy is close to the level achieved by humans, which typically does not exceed 96%. The high accuracy of the resume parser ensures reliable results, enabling companies to identify the most suitable candidates who can be called for further interview rounds. © 2024 IEEE.

关键词： Deep learning

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