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An Enhanced computer Vision and Clustering Approach for Agricultural Produce Classification 1

An Enhanced Computer Vision and Clustering Approach for Agri...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Srivats, R. Kalyanasundaram, V. Prethija, G. Sharma, Abhiram School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Chennai India

ISBN: (纸本)9798331518943

Agricultural supply chains face specific challenges in ensuring that produce is efficiently categorized, processed, and transported from farmers to end consumers. One of the key difficulties lies in accurately classifying agricultural products based on their quality and characteristics, which directly impacts decision-making processes regarding their handling, storage, and transportation. To address the research gap, this research paper proposes an advanced system that employs a K-means classifier integrated with computer Vision techniques. The K-means algorithm, known for its ability to group data points into distinct clusters, is used here to classify agricultural produce by analysing visual features derived through edge detection and image augmentation. This proposed method enables the system to effectively identify and categorize produce into quality-based clusters, which allows for more targeted and efficient handling procedures. By automating the classification process, this proposed solution significantly reduces human error and its subjectivity in sorting agricultural goods. The system demonstrated a high performance with an overall accuracy of 99.03%, and for certain produce categories, it achieved optimal levels of precision 99.12%, recall 99.03%, and F1-score 99.035% These metrics highlight the system's robustness in differentiating between classes of agricultural produce, ensuring that each product is categorized correctly according to pre-defined standards. The Kmeans classifier, combined with the use of computer Vision techniques, facilitates faster and more reliable decision-making processes, which in turn reduces delays in sorting and transportation. This enhanced classification method not only optimizes resource allocation but also minimizes waste by ensuring that produce is handled in the most efficient manner. Ultimately, the proposed system benefits all stakeholders in the agricultural supply chain, improving operational efficiency, reducing cost

关键词： K-means clustering

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Fine-Tuning PID Constants Using Optimizers for an Autonomous Underwater Vehicle (AUV) 6

Fine-Tuning PID Constants Using Optimizers for an Autonomous...

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6th International Conference on Control and Robotics, ICCR 2024

作者： Gunturu, Shasank Shrote, Sumukh Sriraman, Harini Murugadass, Arthi School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Chennai India

ISBN: (纸本)9798331518158

This paper introduces a novel method to fine-tune the Proportional Integral Derivative (PID) controller for the MIRA Autonomous Underwater Vehicle (AUV), which secured the runners-up position at the TAC Challenge 2024. Traditional PID tuning, often relying on manual adjustments, falls short for the complex dynamics of AUVs. Our method combines gradient descent with advanced optimizers, Adam and Adagrad, to fine-tune PID parameters (Kp, Ki, Kd) across six degrees of freedom. A special cost function using the square root of squared errors weighted by time intervals enhances control accuracy and response efficiency. Through testing, the Adam optimizer demon-strated superior real-time performance compared to Adagrad, with its adaptive learning rate enabling quicker convergence and more reliable results in the dynamic underwater environment. Adam's handling of the AUV's changing needs contributed to stable and precise real-time docking during the competition, highlighting its effectiveness in improving control accuracy and robustness in challenging underwater conditions. © 2024 IEEE.

关键词： Cost functions

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Developing an Advanced Skin Disease Detection System by Integrating Hybrid Neural Network with Yolo V8 Model 3

Developing an Advanced Skin Disease Detection System by Inte...

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3rd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Pavithra, S. Tamilarasi, K. Shashidhar, Soham Shahzil, Mohammad Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350391565

Skin conditions that are caused by viruses, allergies, and fungi are common all over the world. Although sophisticated technologies such as lasers provide accurate diagnosis, their high price points restrict accessibility. Image processing makes automated dermatology screening possible in order to address this. After features are classified using multiclass SVM, users are given comprehensive illness information. This technique remarkably achieves 100 percent accuracy in identifying three different skin conditions. When used with morphology-based segmentation, image segmentation methods like adaptive thresholding and K-means clustering are very helpful in identifying diseases. This model attempts to accurately detect and analyze skin illnesses in spite of obstacles such as low lesion-to-skin contrast and *** similarities between diseased and non-diseased areas. Through the application of filters to reduce noise and the conversion of images to grayscale for improved processing, it offers significant diagnostic evidence, supporting doctors in their decision-making and reducing the possibility of adverse effects. © 2024 IEEE.

关键词： Image segmentation

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Deep Learning Techniques for Crop Disease Detection and Classification: A Data-Driven Analysis 3

Deep Learning Techniques for Crop Disease Detection and Clas...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Chenat, Ananya Singh, Gaurang Patel, Omkumar J. Gupta, Shalini Dhar, Anuj Revathi, M. Vellore Institute of Technology Chennai School of Computer Science and Engineering Chennai600127 India

ISBN: (纸本)9798350369083

The agricultural sector is the main source of livelihood to a prominent section of our country, thus making it a cornerstone of the Indian economy. As it significantly contributes to the country's GDP, maintaining agricultural stability and enhancing productivity becomes very crucial. Conditions like disease outbreaks, changing climate and bad quality pesticides among others have an adverse effect on crop quality. By adopting advanced technological solutions into traditional farming, we can address this challenge and make way for a more robust agricultural sector. The paper presents a comprehensive comparative analysis of 4 distinct deep learning models - ResNet, EfficientNet, Vision Transformers (ViT), and You Only Look Once (YOLO) for the detection and classification of crop diseases through image analysis. The performance of these 4 models are evaluated on various metrics that include training and validation loss, accuracy, F1 score, specificity, and sensitivity. Results from the experiments show ViT and YOLO emerging as top performers. YOLO with its unique approach of single-pass object detection and ViT leveraging the Transformer based architecture showcase better accuracy and efficiency in this domain as compared to EfficientNet and ResNet. The findings of this study highlight the potential of deep learning models in revolutionizing agricultural technology. © 2024 IEEE.

关键词： Contrastive Learning

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An Empirical Comparison of Machine and Deep Learning Algorithms for Predicting Maternal Health Risk 5th

An Empirical Comparison of Machine and Deep Learning Algorit...

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5th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： N, Srinivasa Gupta Valarmathi, B. Kavitha, A. Gopini, Sai Bhuvan Seela, Hari Kumar Department of Manufacturing Engineering School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Mathematics School of Advanced of Sciences Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9783031770777

Maternal health is among the greatest challenges in the world, especially in rural areas as there lack medical practitioners, they do not have easily accessible publics clinics and transport is difficult. Therefore, high rates of maternal as well as infant morbidity and mortalities are recorded. This research utilizes Artificial Intelligence (AI) with machine learning algorithms to forecast and address maternal health hazards right at their onset stage. The current research utilizes the concept of AI along with many Machine Learning (ML) methods like the Ensemble Learning Model (ELM), Random Forest (RF), K-Nearest Neighbour (KNN), Decision-Tree (DT), XG-Boost (XGB), Cat Boost (CB), and Gradient Boosting (GB), along with Synthetic Minority Over-sampling Technique (SMOTE) algorithm used for dealing with the problem class imbalance within the data set. SMOTE algorithm is utilized for the dataset balancing process. The handling system involves refining data preprocessing with the help of feature engineering and robust data cleaning which makes sure that anomalies do not erode the reliability of the predictive model. The existing methods [1] used RF (90%), DT (87%), XGB (85%), CB (86%), and GB (81%) algorithms and were compared with the accuracies of the proposed models like Logistic Regression (LR), Ensemble Learning Bagging (ELB), Ensemble Learning Stacking (ELS), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The existing methods used only imbalance dataset. The accuracies of the proposed models with using SMOTE algorithm (balanced dataset) are LR (61.33%), KNN (81%), ELB (92.33%), ELS (90.66%) CNN (40.67%), RNN (59.67%), LSTM (54%), GRU (56%) respectively. Among these methods, ELB achieved 92.33% of accuracy with using SMOTE algorithm using imbalanced dataset. Whereas the accuracies of the proposed models without using SMOTE algorithm (imbalanced dataset) are LR (66.09%), KNN (68.47%)

关键词： Convolutional neural networks

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4D-Rossler Hyperchaotic System for Image Encryption and Decryption with High Security 10

4D-Rossler Hyperchaotic System for Image Encryption and Decr...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Agarwal, Soumil Byramdharmika, M. Dhathri, Sree Thanikaiselvan, V. Subashanthini, S. Amirtharajan, Rengarajan Vellore Institute of Technology School of Electronics Engineering Vellore India Engineering and Information Systems Vellore Institute of Technology School of Computer Science Vellore India Sastra Deemed University School of Electrical & Electronics Engineering Thanjavur India

ISBN: (纸本)9798350353068

In today's digital world, safeguarding data at all times is important, especially with the widespread use of images across various processes. Image encryption is vital in concealing sensitive information through cryptographic techniques, rendering data indecipherable to unauthorized parties. However, existing encryption systems often fall short in terms of both efficiency and security. This research explores chaos theory for image encryption methodologies to address these limitations. A comprehensive study of image encryption fundamentals was conducted, including chaos theory, various chaotic maps, DNA encoding, and encryption system evaluation metrics. Implementation of different chaotic maps such as the logistic map, tent map, and combined logistic-tent map, along with 3D chaotic systems like the Lorenz and Rossler systems, was performed for encryption purposes. Additionally, the 4D extension of the Rossler hyperchaotic system was introduced for enhanced encryption capabilities. Encryption methods were evaluated using metrics such as UACI, NPCR, correlation coefficients, image entropy and histogram analysis, with results compared and tabulated. This research advances image encryption techniques by leveraging chaos theory to achieve heightened security and efficiency in safeguarding image data. © 2024 IEEE.

关键词： Chaotic systems

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A Deep Learning Approach for Classification of Vitiligo and Scar Images 10

A Deep Learning Approach for Classification of Vitiligo and ...

引用

10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Rajesh, E. Madanagopal, C. John, Reovwin Thanikaiselvan, V. Tamizharasi, T. Amirtharajan, Rengarajan Vellore Institute of Technology School of Electronics Engineering Vellore India Vellore Institute of Technology School of Computer Science and Engineering Vellore India Sastra Deemed University School of Electrical & Electronics Engineering Thanjavur613 401 India

ISBN: (纸本)9798350353068

Skin diseases such as vitiligo present significant challenges in accurate diagnosis due to their similarities with other conditions, often requiring time-consuming assessments by medical professionals. Leveraging advancements in deep learning and medical image processing, this research proposes a novel approach to efficiently and accurately classify these conditions. The proposed model comprises multiple convolutional layers for extracting intricate features from skin images, followed by max-pooling layers to downsample spatial dimensions while retaining essential information. Additionally, fully connected layers enable complex feature learning and classification. This architecture, with six convolutional layers progressively increasing in depth and spatial reduction through max-pooling, culminates in three fully connected layers for precise classification. This comprehensive design ensures effective classification between vitiligo and scar images. By utilising this architecture, our model achieves a high training accuracy of 93.53% and precision of 96.50%, demonstrating its effectiveness in discriminating between vitiligo and scar images. This approach holds promise for enhancing diagnostic accuracy and efficiency in clinical settings, contributing to improved patient outcomes and healthcare delivery. © 2024 IEEE.

关键词： Principal component analysis

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Study of Ocean Pattern's and Ocean Bed Using Applications of IOT 2

Study of Ocean Pattern's and Ocean Bed Using Applications of...

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

作者： Vishwanath, K. Reddy, Rachit Roy, Sritama Vellore Institute of Technology Department of Computer Science Chennai600127 India School of Electronics Engineering Vellore Institute of Technology Chennai600127 India

ISBN: (纸本)9798350328202

The ocean plays an integral role for influencing Earth's climate and sustaining diverse ecosystems as oceans covers almost 70 % of the earth's surface. It is important to monitor and control the ocean biodiversity through the advanced IoT technologies. The internet of Underwater things (IoUT) is an emerging technology which can connect the underwater objects as well as environment. IoUT is directly related with the navigation, disaster prediction, smart monitoring and intelligent system. Understanding the dynamics of the ocean is crucial for various applications, including weather forecasting, maritime operations, and environmental conservation. This research paper presents an in-depth analysis of an Ocean buoy Monitoring System that utilizes temperature, humidity, and geolocation data collected from buoys to predict ocean patterns, tides, and marine life. The system combines real-time sensor measurements with advanced data analysis techniques to provide valuable insights into oceanographic phenomena. © 2024 IEEE.

关键词： Buoys

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Enhancing Predictive Analytics Through Hybrid Machine Learning Models: A Comparative Analysis Of SVM-NB And Lasso-Ridge Techniques 2

Enhancing Predictive Analytics Through Hybrid Machine Learni...

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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Abdullah, A Sheik Mishra, Utkarsh Akash, K. Ravi, Athul Mohan Arvind, S.P. Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350372892

During the moment of data-driven decision-making, effective classification algorithms play a major part in different areas. This study showcases a comparative analysis of Support Vector Machines (SVM) and Naïve Bayes (NB), two standout machine learning systems, through various data sets. We suggest a unique hybrid method that cleverly blends SVM and NB, tackling their separate inadequacies while leveraging their strengths. Likewise, we present a hybrid model that combines lasso and ridge regression methods for improved performance. Our experimental outcomes reveal the superior predictive accuracy of the proposed SVM-NB hybrids over singular algorithms and the Lasso-Ridge Hybrid. This research highlights the potential of hybrid methods in advancing predictive analytics, providing a hopeful path for optimizing classification duties in practical situations. © 2024 IEEE.

关键词： Predictive analytics

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Text Feedback Classification using Machine Learning Techniques 2

Text Feedback Classification using Machine Learning Techniqu...

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Elakiya, E. Kanagaraj, R. Paturu, Tejus Nivethika, S. Deepa Kanmani, R.Sujithra School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India Vellore Institute of Technology Department of Computer Science and Engineering Tamil Nadu Vellore India National Institute of Technology Puducherry Department of Computer Science and Engineering Puducherry Karaikal India

ISBN: (纸本)9798350347579

The popularity of online shopping has grown worldwide, making it an integral part of many people's lives. As customers are free to express their emotions online, online sales have become a significant source of revenue. This enables obtaining honest feedback for various products, helping to understand not only what is popular but also the overall consensus. To make sense of the large amounts of product feedback and gauge the public's response, it is important to understand the widely held sentiments. Machine learning models provide a solution to extract feedback from text. Random Forest classifier produces the highest accuracy of 88 percentage. © 2023 IEEE.

关键词： Text processing

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