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2nd International Conference on Data Science and Information System, ICDSIS 2024

作者： Chakrabarti, Chandrima Narula Institute Of Technology Department Of Computer Science And Engineering Kolkata India

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381665

Utilizing delay tolerant networks (DTN) via smartphones is a practical approach, especially when traditional networks are compromised. The use of short-range communication technologies like Bluetooth or WiFi ensures connectivity among ground nodes, albeit with limitations in determining efficient routes across the entire disaster area. Incorporating Unmanned Aerial Vehicles (UAVs) into the network introduces a dynamic element that can significantly enhance message delivery speed and route determination However, the introduction of UAVs also brings new challenges, particularly in ensuring the security and reliability of communication. Malicious attacks, such as message dropping or manipulation, can disrupt the network's overall functionality and effectiveness. This paper proposes the use of monitor nodes to track both UAVs and ground nodes, identifying and mitigating malicious nodes to ensure successful message delivery. Simulation is done using Opportunistic Network Environment (ONE). The simulator provides valuable insights into the proposed methods' effectiveness. © 2024 IEEE.

关键词： Delay tolerant networks

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Exploring Transformer-Based Approaches for Hyperspectral Image Classification: A Comparative Analysis 3rd

Exploring Transformer-Based Approaches for Hyperspectral Ima...

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3rd International Conference on Machine Vision and Augmented Intelligence, MAI 2023

作者： Arya, Rajat Kumar Srivastava, Rajeev Department of Computer Science and Engineering Indian Institute of Technology-BHU Varanasi India

ISBN: (纸本)9789819743582

Hyperspectral imaging (HSI) is a technique that captures and analyzes multiple spectral bands for each pixel in an image. Convolutional neural networks (CNNs) effectively extract local features for HSI classification but struggle with capturing sequential spectral signatures. To address this, we conducted a comparative study to assess the performance of transformers, which can handle sequential information, in HSI classification. We reviewed a few models in the literature, analyzing their architecture and performance. Using the Indian Pines dataset, we evaluated models like ViT, SSRN-ViT, SpectralFormer, and Double-ViT. Our results show that Double-ViT models outperform transformers for HSI classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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MediMind: A Comprehensive Health Prediction and Record-Keeping Platform 6th

MediMind: A Comprehensive Health Prediction and Record-Keep...

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6th International Conference on Information Systems and Management Science, ISMS 2023

作者： Naik, Darshana A. Sharma, Aditya Mahajan, Danish Khandelwal, Dhruv Gajya, Arpit Department of Computer Science and Engineering Ramaiah Institute of Technology Bangalore India

ISBN: (纸本)9783031707889

The "MediMind" project signifies a revolutionary fusion of predictive analytics, centered on diabetes and heart disease risk assessment, coupled with state-of-the-art skin disease prediction and robust Electronic Health Record (EHR) management. By employing advanced machine learning models like Logistic Regression and Support Vector Machines (SVM) for diabetes and heart disease predictions, alongside the cutting-edge Convolutional Neural Network (ResNet-50) for precise skin disease classification, "MediMind" achieves unparalleled accuracy and applicability. The predictive models exhibit exceptional performance metrics, boasting high accuracy, precision, recall, and F1-score. Seamless integration into Electronic Health Records (EHR) ensures immediate implementation in clinical decision-making, providing healthcare professionals with precise insights into diabetes and heart disease risks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Electronic health record

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Brinjal Leaf Diseases Detection Based on Deep Convolutional Neural Network 4

Brinjal Leaf Diseases Detection Based on Deep Convolutional ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Anu Priya, A. Rajaram, K. Kit - Kalaignarkarunanidhi Institute of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331523923

Brinjal leaf diseases, such as leaf spot, mosaic virus, and wilt, would critically reduce crop yield quality if the disease is spread during an early stage. Such reasons have made early identification imperative to ensure effective prevention and management. This article, therefore, aims at coming up with an effective technique of identifying brinjal leaf diseases using VGG16 CNN model. Traditional identification methods are based on manually examining plant diseases, an effort known to be highly labour-consuming, subjective, and even unreliable. In addition, most of the available machine learning methods fail in scalability, computational cost, and specific domain problems. However, our technique makes use of the high ability of VGG16 architecture to extract automatically relevant features from leaf images and then classify them with high accuracy. Used Eggplant Leaf Disease Dataset which contains six classes. The central idea of our approach is to employ deep convolutional layers of VGG16 to extract discriminative features from the images followed by classification using fully connected layers and softmax activation. The approach obtains 98.2% accuracy for determining of Brinjalleaf diseases. This work will impact the agricultural sector by means of providing a fast automated solution that gives accurate insights about early detection of leaf disease occurrence, hence reducing losses to crops and enhancing production. © 2025 IEEE.

关键词： Convolutional neural networks

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Tolerant Testing and Distance Estimation for Distributions Under Memory Constraints 50th

Tolerant Testing and Distance Estimation for Distributions...

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50th International Conference on Current Trends in Theory and Practice of computer Science, SOFSEM 2025

作者： Roy, Sampriti Vasudev, Yadu Department of Computer Science and Engineering Indian Institute of Technology Madras Chennai India

ISBN: (纸本)9783031826962

We investigate tolerant testing and distance estimation problems in distribution testing when the amount of memory is limited. In particular, our aim is to provide a good estimate of the distance measure (in total variation distance) using constant memory words. Though the problems are well understood in the standard access model with an Ω(nlogn) lower bound, it has not been explored under memory constraints. We propose algorithms that require O(nϵ7) queries for estimating distance to a known distribution and O(nϵ6) queries (where ϵ∈(0,1) is an error parameter) for estimating distance to uniformity that use only O(1) memory words. Our algorithms match the Ω(nϵ4) lower bound established by [19] in terms of the dependence on n. Additionally, we present an (ϵ,cϵ) tolerant closeness tester in terms of 2 distance for all c≥3, which requires O(n3mϵ2) samples and O(m) bits of memory for logn/ϵ4≤m≤n/ϵ2. Furthermore, we outline an O(n2ϵ12mϵ24) trade-off for (ϵ1,ϵ2) tolerant closeness testing problem (0121 distance, where logn≤m≤nlogn is the available memory in bits. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Distribution testing Property testing Streaming

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Vision Aid For Blind People Using YOLOV8 2

Vision Aid For Blind People Using YOLOV8

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2nd International Conference on Networking, Embedded and Wireless Systems, ICNEWS 2024

作者： More, Priyanka Sangamkar, Satvik Vishwakarma Institute Of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350367461

This assistive system for the visually impaired integrates real-time YOLOv8 object detection with distance estimation and text-to-speech conversion, empowering users with enriched environmental awareness. Leveraging robust datasets and architectures, it identifies over 90 everyday objects amidst clutter while concurrently approximating object proximity through trigonometric translations of size metrics. Audio cues enunciate recognized items and distances in real time for accessibility. This combination of precise visual recognition, spatial metrics, and assistive feedback holds promise for enhanced mobility, independence, and safety during navigation for visually impaired individuals. © 2024 IEEE.

关键词： Vision aids

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Weighted Ensemble Learning for Accident Severity Classification Using Social Media Data

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SN computer Science 2024年第5期5卷 1-20页

作者： Raul, Sanjib Kumar Rout, Rashmi Ranjan Somayajulu, D.V.L.N. Department of Computer Science and Engineering National Institute of Technology Telangana Warangal 506004 India Department of Computer Science and Engineering Indian Institute of Information Technology Design and Manufacturing (IIITDM) Andhra Pradesh Kurnool 518002 India

Due to the heavy increase in social media usage, like Twitter, there is a growing interest in the research community in developing automation tools like accident severity-based tweet classification models. These tools aid in automatically extracting severity information from the accident tweet content. Moreover, prediction models are essential for predicting the severity of an accident to increase the safety efficacy of the road traffic system. However, the difficulty lies in the collection of sufficient labeled data. We propose a model called weighted ensemble-based self-training with decision tree (WESTDT), a semi-supervised methodology with a dynamic data labeling strategy. The base classifier in this model is a homogeneous weighted ensemble of decision tree classifiers for better prediction of pseudo labels. We also propose a novel performance measure called risk factor to estimate the amount of risk present in the application using the prediction model. The proposed model outperformed the state-of-the-art model, namely reliable semi-supervised ensemble learning (RESSEL), and the baseline models, namely decision tree (DT) and self-training with decision tree (STDT), in terms of both the traditional and the proposed measures. The results indicate that the proposed framework outperforms all other models on all datasets in terms of precision, recall, and accuracy by a range of 5–18.3%, 3.9–9.3%, and 6.6%, respectively. These findings can be helpful for the development of efficient and sustainable systems for traffic management and safety. It is also crucial for assisting government authorities in devising prompt, proactive strategies to prevent traffic accidents and enhance road safety. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Accident severity classification Decision tree Self-training Semi-supervised learning Weighted ensemble learning

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Comparative Analysis of Machine Learning Algorithms in Fish Survival Prediction

Comparative Analysis of Machine Learning Algorithms in Fish ...

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2024 IEEE Bangalore Humanitarian technology Conference, B-HTC 2024

作者： Shirwaikar, Rudresh D. D'Souza, Lyzandra Bhangle, Anirudh Santosh Joshi, Sarang Deepak Jathar, Nupur Ajit Alvares, Jonathan Elroy Agnel Institute of Technology and Design Department of Computer Engineering Mapusa India

ISBN: (纸本)9798350344509

This research paper conducts an exploration of prevalent machine learning classification algorithms, emphasizing the refinement of Random Forest models to optimize performance. The initial phase involves a comparative analysis of Random Forest, k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Logistic Regression, and Naive Bayes Classification algorithms across the dataset, resulting in Random forest to be best performed among all with average accuracy of 61%. Subsequently, then we focus on enhancement of the Random Forest algorithm with random train-test split dataset, resulting in an enhanced average accuracy of 65% which is about 4% improvement to the previous accuracy and choosing the best traintest split dataset, along with fine tuning the model, improved the average accuracy to 73% which is about 12% improvement to previous accuracy which is significantly higher showcasing the significance of precise algorithmic tuning. The completion of our investigation lies in the creation of an integrated model which incorporates multiple Random Forest classifier models, each specifically trained for binary class classification, along with a comprehensive model designed for multiclass classification. This combined model excels with a peak accuracy of 82.75% for the best split, demonstrating its strong performance in classification tasks. This research not only contributes to the theoretical understanding of algorithm performance and optimization but also offers practical insights for the application of machine learning in challenging classification scenarios. © 2024 IEEE.

关键词： Classification (of information)

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Evaluating Public Opinion Through Twitter Sentiment Analysis 2

Evaluating Public Opinion Through Twitter Sentiment Analysis

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2nd International Conference on Networking, Embedded and Wireless Systems, ICNEWS 2024

作者： Jadhav, Narendra More, Priyanka Dixit, Akhilesh Sharma, Akshay Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350367461

In the digital age, social media platforms like Twitter have become a central forum for public discourse, reflecting diverse opinions on various topics. This paper explores the application of sentiment analysis to Twitter data to extract and quantify these opinions. This work processes and analyzes a large dataset of tweets using machine-learning techniques to identify underlying attitudes and divide them into positive, negative, and neutral categories. The methodology comprises preprocessing such as text normalization and noise removal, sentiment categorization using a model based on Natural Language Processing (NLP) techniques, and data gathering via the Twitter API. The results reveal patterns and trends in public sentiment on key issues, providing insights that can benefit businesses, policymakers, and researchers. This paper discusses the potential real-world applications of this technology in marketing, political science, and public relations. © 2024 IEEE.

关键词： Tweets

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Fastlane: A framework for building fast path network applications

Fastlane: A framework for building fast path network applica...

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2024 IEEE Conference on Network Function Virtualization and Software Defined Networks, NFV-SDN 2024

作者： Das, Debojeet Lakshya Vutukuru, Mythili Department of Computer Science and Engineering Indian Institute of Technology Bombay India

ISBN: (纸本)9798350380538

To leverage the full potential of high-speed network interface cards (NICs), it is essential for network applications to use high-performance packet I/O frameworks. However, porting applications to use these specialized frameworks is a tedious task. While prior work has proposed frameworks to ease the development of network functions over fast packet I/O mechanisms, there has not been much work done to ease the porting of applications that are built to run on the kernel network stack. This paper presents Fastlane, a framework to easily port the "fast path" of traditional network applications to use fast packet I/O mechanisms while retaining the "slow path" to run on the traditional network stack. Fastlane provides APIs by which application developers can configure, use, and switch between different fast packet I/O mechanisms like DPDK and CNDP in the application’s fast path. Additionally, Fastlane provides APIs for the fast path to communicate with the slow path running on the kernel network stack. Fastlane also allows applications to be deployed in an unprivileged Kubernetes pod, easing application development for cloud deployments. We evaluate the effectiveness of Fastlane by porting a production-grade 5G user plane function to use our framework. Our evaluation shows that Fastlane enables developers to build high-performance network applications easily and imposes minimal performance overheads. ©2024 IEEE.

关键词： HIgh speed networks

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