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1st International Conference on Electronics, Communication and Signal Processing, ICECSP 2024

作者： Saini, Rashmi Singh, Suraj Govind Ballabh Pant Institute of Engineering and Technology Dept. of Computer Science and Engineering Pauri Garhwal India

ISBN: (纸本)9798350364590

Land use and land cover (LULC) classification is crucial for planning and sustainable development, especially in mountainous regions. This study focuses on the Tehsil Karnaprayag, situated in the vicinity of the Greater Himalaya. With a total area of 1858.903 km2 and elevations ranging from 647m to 7641m. Using multispectral Sentinel-2 imagery and Machine Learning techniques i.e. Extreme gradient boosting (XGBoost) and K-nearest neighbor (KNN). This study aims to classify seven distinct land cover classes: snow, forest, water bodies, grassland, agricultural land, fallow land, and built-up areas. The classification results demonstrate the effectiveness of XGBoost classifier achieving an accuracy of 90.22% and a kappa value of 0.882, while KNN achieved an accuracy of 88.59% with a kappa value of 0.862. The analysis reveals that forest and fallow land are the predominant land cover types in the study area, constituting nearly 60% and 15% of the total area respectively. Notably, XGBoost exhibits the highest class-specific accuracy of 97.60% for the snow class, demonstrating its utility in discriminating between challenging land cover types. This research highlights the importance of LULC classification in informing land management strategies and facilitating sustainable development initiatives in mountainous regions. The findings provide valuable insights into the spatial distribution and composition of land cover in Tehsil Karnaprayag, essential for effective planning and conservation efforts in the Himalayan ecosystem. © 2024 IEEE.

关键词： Sustainable development

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Reinforcement Learning-based Hybrid Routing Algorithms for Vehicular Ad Hoc Networks 16

Reinforcement Learning-based Hybrid Routing Algorithms for V...

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16th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2025 / 8th International Conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Boroujeni, Narges Haghighati Datta, Suprakash Firouzmakan, Pouya Telus Corp Toronto Canada Electrical Engineering and Computer Science Dept York University Toronto Canada

This paper proposes a new hybrid routing algorithm, ReLeVR, for vehicular ad hoc networks (VANETs). Our algorithm aims to efficiently solve the problem of routing messages from vehicles to specific geographical locations over VANETs. We assume that a VANET consists of vehicles and roadside units (RSUs). First, we propose a simple strategy to find the optimal locations for RSUs with the objective of minimizing their number. Our strategy computes RSU locations using available prior traffic information. Then we use Q-learning, a reinforcement learning algorithm, to learn from traffic flow patterns and compute a routing policy for the vehicles. This policy determines which grid the message should be sent to in the next step along the way for each location in the city. The routing policy is broadcast to all of the vehicles in the VANET. We demonstrate through simulations on real traffic data that our algorithm outperforms an older algorithm GPSR and a recent Q-learning-based algorithm, QGrid G in terms of metrics like the delivery ratio and delay. We observe that the number of RSUs used by our algorithm is significantly lower than that of a recent algorithm, QTAR. © 2025 Elsevier B.V.. All rights reserved.

关键词： Reinforcement learning

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Adaptive Weather Routing for Advanced Ship Navigation: A Comprehensive Review 3

Adaptive Weather Routing for Advanced Ship Navigation: A Com...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Venkatesan, Iyyappan Dharanish, M.B. Muthumari Lakshmi, S. Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu Chennai600069 India

ISBN: (纸本)9798331527549

Maritime navigation offers a comprehensive approach to optimising ship navigation, in such a way that guarantees the safety and effectiveness of the journey through multiple techniques, like the adaptive weather routing technique, which uses the predictive model and the current weather conditions for efficient navigation. The review mainly emphasizes the basic principles and computational simulation for sustainable navigation in advancing the route defining algorithm. Discusses about the conventional route defining algorithms and dynamic route defining algorithm by adjusting for wind, waves, and currents. A comparison analysis shows how well different algorithms work in various weather conditions, and case studies show how they are used in the actual world, while new trends are being discussed, major problems also concern the need for accurate climate predictions, high-speed data processing, and an efficient response plan. The Advancements in artificial intelligence and composite path forecasting also address issues related to major needs, such as accurate weather forecasts, high-speed data processing, and effective reaction tactics. Through this insights of this review provides on future directions for adaptive weather routing research and development that the sustainable growth of intelligent naval navigation is supported. © 2025 IEEE.

关键词： Weather forecasting

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Design of Digitized Visual Search Task for Assessment of Aging Effects 2

Design of Digitized Visual Search Task for Assessment of Agi...

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2nd IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2024

作者： Navalyal, Geeta Navalyal, Apoorva Naik, Sayali Dept. of Computer Science & Engineering Uttarakhand Dehradun India University of Florida Dept. of Electrical & Computer Engineering GainesvilleFL United States Grant Thornton Karnataka Bengaluru India

ISBN: (纸本)9798350372847

Mild cognitive impairments (MCI) develop in adults due to the normal aging process and may sometimes progress to a more serious cognitive decline stage resulting in Dementia. If the cognitive decline is identified at the MCI stage, it can be delayed or prevented from progressing by different interventions. A typical indicator of cognitive impairments that might be caused due to an injury, aging, or other factors is found to be a decreased search efficiency in visual search tasks. Here we propose a model to assess this efficiency by designing a digitized, easy-to-use visual search task capable of incorporating multiple variables in the visual stimulus, affecting the search capabilities. The variables under consideration include the number of distracting elements, target size, target distance from the center of the stimulus area, and habituation effects. This designed stimulus is tested on participants from different age groups. © 2024 IEEE.

关键词： aging cognitive impairments Visual search task

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Forecasting Cryptocurrency Prices in the DeFi Ecosystem Using Predictive Models

Forecasting Cryptocurrency Prices in the DeFi Ecosystem Usin...

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2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Sruthi, Srigiri Saravanan, Deivanai Jyotsna, C. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798331538538

In the rapidly evolving cryptocurrency markets, accurate price prediction is crucial for investors and traders. This task investigates the efficacy of advanced machine learning and deep learning for forecasting cryptocurrency prices. The proposed idea utilizes Autoregressive Integrated Moving Average (ARIMA) models, Bidirectional Long Short-Term Memory (Bi- LSTM) networks, Gated Recurrent Units (GRU), and Long Short-Term Memory networks improved with Self-Attention (LSTM-Self-Attention). Numerous studies were conducted using the historical data available. Root Mean Squared Error (RMSE) were used to evaluate the models. The study tries to show the impact of outside factors such as economic data and market belief in purchase to improve the precision of the forecast. This research offers investors and policy makers insight that demonstrates the ability of sophisticated machine learning models to navigate the intricacies of cryptocurrency marketplaces. Incorporating these approaches into a single predictive framework enables accurate forecasting and the identification of anomalies in DeFi markets. The findings contribute to the decision making that is strategic DeFi investments, providing tools for better prediction and analysis, and laying the groundwork for future advancements in DeFi analytics. © 2025 IEEE.

关键词： Prediction models

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A Few-Shot Learning Approach for Sound Source Distance Estimation Using Relation Networks 23

A Few-Shot Learning Approach for Sound Source Distance Estim...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Sobhdel, Amirreza Razavi-Far, Roozbeh Palade, Vasile Urmia University Dept. of Electrical and Computer Engineering Iran University of New Brunswick Faculty of Computer Science Canada Centre for Computational Science Coventry University United Kingdom

ISBN: (纸本)9798350374889

In this paper, we study the performance of few-shot learning, specifically meta learning empowered few-shot relation networks, over supervised deep learning and conventional machine learning approaches in the problem of Sound Source Distance Estimation (SSDE). In previous research on deep supervised SSDE, low accuracies have often resulted from the mismatch between the training data (from known environments) and the test data (from unknown environments). By performing comparative experiments on a sufficient amount of data, we show that the few-shot relation network outperforms other competitors including eXtreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Convolutional Neural Network (CNN), and MultiLayer Perceptron (MLP). Hence it is possible to calibrate a microphone-equipped system, with a few labeled samples of audio recorded in a particular unknown environment to adjust and generalize our classifier to the possible input data and gain higher accuracies. © 2024 IEEE.

关键词： Self-supervised learning

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A review on 3D image reconstruction on specific and generic objects

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Materials Today: Proceedings 2023年 80卷 2400-2405页

作者： Shalma, H. Selvaraj, P. Dept. of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Chennai India Dept. of Information Technology SRM Institute of Science and Technology Kattankulathur Chennai India

With the emergence of various techniques involved in deep learning the researchers of computer vision tends to focus on the strategies such as object recognition and segmentation of image. This has inclined to accomplish the deep learning techniques in 3D reconstruction of both specific and generic objects. As the space for reconstruction of 3D images either in single or multi view has envisioned the researchers to concentrate on the available technologies used for reconstruction. With the available built in methods and technologies in deep learning, the performance of the proposed methods were reviewed and analyzed using several parameters. As the remaking of 2D images is still in the beginning stage, it is important to study the 3D shape representations, various network architecture, methodologies and approaches behind 3D reconstruction. In this work a review of deep learning methods for single or multiple RGB images of specific and generic object 3D reconstruction was done. Several methods and their importance were also discussed along with the challenges encountered and with further research directions. This paper critically analyzes the various 3D Shaped Representations, 3D Data Network Architectures, depth Estimation methods, Multi View Representations and the Data Representation Techniques. © 2021

关键词： Object recognition

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Network Intrusion Detection by Integrating BI-LSTM for Enhanced Security 3

Network Intrusion Detection by Integrating BI-LSTM for Enhan...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Lakshmi Priya, P. Lohith, Kandibanda Johnson, Joel M. Jyotsna, C. Aiswariya Milan, K. Bengaluru Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering India

ISBN: (纸本)9798331523893

The rising interconnectivity of digital systems has brought about the challenges of network security as high risks of data theft and unauthorized access through intrusions in networks. This paper details the development of a Network Intrusion Detection System that uses deep learning techniques and is designed to detect normal as well as anomalous activities within the network, such as zero-Day vulnerabilities, as discussed has proven that this model was capable of obtaining excellent accuracy, up to 99.25%, with precision and recall metric being well achieved, owed to advanced techniques, in this case, hyperparameter tuning. This will mean the model's capability to clearly differentiate legitimate and malicious traffic. In addition, explainable AI techniques such as SHAP and LIME deliver insights into the contributions or importance of various features about a model's decision-making. © 2025 IEEE.

关键词： Network intrusion

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Quantifying the Impact of Non-Code Text Block on Java Code Quality: Multi-Model Framework 3

Quantifying the Impact of Non-Code Text Block on Java Code Q...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Vvs, Sriharsha Nair, Pranav B. Akarsh, S.V. Narmada, Nakka Pati, Peeta Basa Amrita School of Computing Dept. of Computer Science & Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331508456

Non-code elements in programming, such as comments, documentation, and whitespace, play a crucial role in improving code readability, maintainability, and collaboration. However, their inclusion introduces overhead affecting code processing tools such as compilers and interpreters and static analyzers. In essence, this study investigates the impact of non-code blocks on the performance metrics of time complexity, space complexity, and execution time. A regression model was used to analyze the proportion of non-code elements in Java code and their effect on the performance metrics. The impact is quantified with the aim of providing empirical insights into how the density of non-code affects performance. Such findings are intended to help Java developers make decisions regarding code structuring and documentation while balancing aspects of maintainability and efficiency. The model's performance is validated by achieving an R2 score of 0.67, which demonstrates an improvement over previous methods and highlights its potential for real-world educational applications. © 2025 IEEE.

关键词： Java programming language

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Efficient Parking Spot Allocation in a Grid-Based Parking Lot

Efficient Parking Spot Allocation in a Grid-Based Parking Lo...

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2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Gandham, Bhavika Muthukumar, Sai Smrithi Vijayekkumaran, Trisha Krishna, Nagasarapu Sarayu Bhaskaran, Sreebha Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331538538

Parking management in urban areas faces significant challenges, including congestion, inefficient allocation, and unauthorized use of parking spaces. Building on a previous study that proposes a dynamic parking management system using a grid-based structure that categorizes parking spots as long-term or short-term, allowing for flexible and efficient allocation, this study focuses on optimizing and extending the system for greater efficiency. The system incorporates and compares four algorithms - Dijkstra's, Bellman-Ford, Content-Based Search (CBS), and Jump Point Search (JPS) - along with a novel Combined Search Algorithm, designed to optimize parking allocation and exit determination. The proposed Combined Search Algorithm demonstrated superior efficiency through comprehensive evaluation, reducing allocation and exit determination times by approximately 18% compared to Dijkstra's, 25% to Bellman- Ford, 20% to CBS, and 15% to JPS. These results highlight the approach's potential to significantly improve parking management by optimizing resource utilization, minimizing conflicts, and enhancing user experience. © 2025 IEEE.

关键词： Resource allocation

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