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Crop weed separation through image-level segmentation: an ensemble of modified U-Net and encoder–decoder

Neural Computing and Applications 2025年 1-24页

作者： Ganapathy, Sannasi Srinivasan, Srinitish Bhopal462 002 India School of Computer Science and Engineering Vellore Institute of Technology Chennai600 127 India

Crop weed segmentation is one of the most challenging tasks in the field of computer vision. This is because, unlike other object detection or segmentation tasks, crop and weed are similar in terms of spectral features, shape, dimensions, etc. For precision agriculture to flourish in terms of smart spraying of crops, efficient systems to distinguish between crop and weed are the need of the hour, which if precise, will take a huge step toward solving the issue of food scarcity. To tackle this issue, we propose new ensemble architecture of two models—a U-Net with a modified backbone and an encoder–decoder. These networks learn to distinguish between soil and crop and soil and weed, respectively, whose ensemble gives state-of-the-art results on pixel-wise annotations of combined crop and weed images. Moreover, it also learns that the model captures un-annotated features since each component of the architecture learnt either crop or weed features to high precision. Finally, the proposed architecture is compared with the U-Net and SegNet, which are popular segmentation networks, and consistently achieved better results. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Semantic Segmentation

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RGB Pixel Combination Method for Underwater Camouflaged Image Categorization using Graph Neural Networks

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IEEE Access 2025年 13卷 84341-84356页

作者： Dhamodharan, K. Pandi, Thanapal Vellore Institute of Technology School of Advanced Sciences Department of Mathematics Vellore India Vellore Institute of Technology School of Computer Science Engineering and Information Systems (SCORE) Vellore India

Underwater imaging applications require sophisticated image processing techniques aided by computer Vision (CV). The intensity and light variations under the water demand camouflaged object, plant, and living organism detection in deep seas. This article introduces the Edge-Recognition Method using Graph Neural Networks (ERM-GNN) to address visual differentiations of such particles with precise categorization. This method is reliable in detecting camouflaged object edges and classifying their foregrounds and backgrounds. In the edge detection and object classification process, color features are utilized, relying on RGB variants observed near the edges of the foreground and background images. The edge depth intensity feature is trained repeatedly using the GNN, forming layers between the initial and final pixels of the regions. This approach imposes an xC_y pixel combination to analyze depth intensity until the foreground and backgrounds are categorized. The neural network trains the hidden layers by differentiating categorized and non-categorized RGB variants for in-depth detection. Consequently, this combination increases the chances of camouflaged underwater object categorization with better precision. This method outperforms state-of-the-art approaches such as G+L-CET, FEGM, and MFEM by a wide margin, improving recognition accuracy by 14.48%, precision by 12.81%, and object classification by 9.82%. These noteworthy improvements highlight the method's efficacy and potential use in marine biology and environmental surveillance, necessitating accurate underwater object detection. © 2013 IEEE.

关键词： Camouflaged Image CV Depth Intensity GNN Underwater Objects

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Recommendation System using Emotion Detection 3

Recommendation System using Emotion Detection

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

作者： Susindra, S. Sweta, B. Keerthikha Shri, R. Brindha, K. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331527549

The need for a personalized user experience brought recommendation systems to the forefront of digital innovation. However, traditional approaches tend to often forget human emotions, which represent a critical driver in decision making. The paper proposes a new framework in which the detection of emotions becomes part of the recommendation system through advanced facial expression analysis and sentiment evaluation. The paper discusses a hybrid architecture combining emotion recognition via CNN and a collaborative filtering technique for dynamic recommendations. The study outcome demonstrated an enormous improvement in user engagement and satisfaction. It further underlines the transformative potential of emotion-aware systems. © 2025 IEEE.

关键词： Emotion Recognition

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Analysing theoretical and neural distinguishers in Salsa 128 bits

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International Journal of Ad Hoc and Ubiquitous Computing 2025年第1期49卷 43-59页

作者： Karthika, S.K. Singh, Kunwar Department of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Chennai India Department of Computer Science and Engineering National Institute of Technology Tamilnadu Tiruchirappalli India

One of the finalists for the eSTREAM projects in 2005 was Salsa, created by Daniel J. Bernstein. Salsa is a widely recognised stream cipher that gained prominence after multiple cryptanalytic techniques were applied to the popular stream cipher RC4. Salsa offers two variations, with key sizes of 128 bits and 256 bits, depending on the seed key’s length. Salsa has undergone multiple key recovery attacks, particularly targeting the 256-bit variant, reaching up to its eighth round. Additionally, numerous experimental attacks have been conducted on Salsa, leaving room for further theoretical analysis. Theoretical analysis plays a crucial role in identifying vulnerable aspects of the cipher, enabling the design of stronger ciphers that are resistant to attacks. In a study by Dey and Sarkar (2021), they conducted a theoretical analysis to examine the origins of distinguishers found in experimental attacks on Salsa 256 bits and Chacha 256 bits. Inspired by their work, our paper focuses on the theoretical analysis of a differential attack on Salsa 128 bits, specifically up to four rounds. We mathematically established the probabilities of various observations and our theoretical analysis aligns with the experimental findings for Salsa 128 bits. Further, we have also found neural distinguisher for Salsa 128 bits based on the model proposed by Gohr (2019) in CRYPTO 2019. The designed model has found neural distinguishers at round 4 with 72.1% accuracy. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Uncertainty analysis

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Deep Learning-Based Enhanced Object Detection for Humanoid Robots 1

Deep Learning-Based Enhanced Object Detection for Humanoid R...

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1st International Conference on Sustainable Energy Technologies and Computational Intelligence, SETCOM 2025

作者： Negi, Akshit Patel, Harshita Vellore Institute of Technology School of Computer Science Engineering & Information Systems Vellore India

ISBN: (纸本)9798331520540

In the contemporary world, humanoid robots are likely to play a key role in various fields, including health care, domestic service, hospitality, business, and military and security activities. The robots are employed to assist people in accomplishing specific missions or sometimes substitute human beings in hazardous situations. To accomplish these missions effectively, humanoid robots should be very good at recognition and subsequent processing of objects. The main goal of this research is to enhance the object recognition capabilities Leverage the advancements in deep learning to provide the control of Robotis-Op3 humanoid robots. In this work, the researchers analyzed popular deep neural models, among which are VGG16 and ResNet, highly promoted for their robust object recognition ability, as well as the more recent CNN architectures such as SSD MobileNetV3. This research improves object recognition capabilities on hu-manoid robots as such to enhance performance. in different domains, allowing them to better recognize and interact with the objects around them. This development will then allow more efficient and effective utilization of humanoid robots, extending their application and contribution in society. © 2025 IEEE.

关键词： Anthropomorphic robots

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MediHealth: An AI-Driven Mobile Solution for Enhanced Health Care Decision-Making and Accessibility 3

MediHealth: An AI-Driven Mobile Solution for Enhanced Health...

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

作者： Raj, Sukrutha Shekhar, Shashank Baskaran, P. School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore632014 India

ISBN: (纸本)9798331527549

Mobile health (mHealth) applications have revolutionized healthcare by enhancing patient engagement, providing tools for self-management, and supporting better health outcomes. This paper explores the development of an innovative mHealth application designed to empower patients in making informed healthcare decisions. The application leverages advanced features such as AI powered chatbots, personalized healthcare recommendations, and a report and prescription summarizer to simplify complex medical information. By allowing users to easily find appropriate healthcare professionals, access detailed prescription data, and receive symptom-based specialist recommendations, the app facilitates effective self-management of chronic conditions. Furthermore, it offers proactive care prompts, personalized advice, and health behavior reminders to promote better overall health outcomes. Despite challenges in data security, privacy, and equitable access, the study emphasizes the importance of a user-centered design approach in ensuring the widespread adoption and success of mHealth applications. Ultimately, the goal is to enhance healthcare delivery by improving patient engagement, facilitating seamless access to healthcare resources, and enabling well-informed decision-making. This application presents a holistic solution for advancing patient care across diverse healthcare settings. © 2025 IEEE.

关键词： Electronic health record

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Optimizing Image Retrieval in Cloud Servers with TN-AGW: A Secure and Efficient Approach

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Journal of The Institution of Engineers (India): Series B 2025年第2期106卷 459-473页

作者： Ponnuviji, N.P. Nirmala, G. Kokila, M. L. Sworna Priyadharshini, S. Indra Department of Computer Science and Engineering RMK College of Engineering and Technology Puduvoyal Thiruvallur India Department of Computer Science and Engineering RMD Engineering College Chennai India Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nādu Kattankulathur Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

The increasing use of cloud-based image storage and retrieval systems has made ensuring security and efficiency crucial. The security enhancement of image retrieval and image archival in cloud computing has received considerable attention in transmitting data and ensuring data confidentiality among cloud servers and users. Various traditional image retrieval techniques regarding security have developed in recent years but they do not apply to large-scale environments. This paper introduces a new approach called Triple network-based adaptive grey wolf (TN-AGW) to address these challenges. The TN-AGW framework combines the adaptability of the Grey Wolf Optimization (GWO) algorithm with the resilience of Triple Network (TN) to enhance image retrieval in cloud servers while maintaining robust security measures. By using adaptive mechanisms, TN-AGW dynamically adjusts its parameters to improve the efficiency of image retrieval processes, reducing latency and utilization of resources. However, the image retrieval process is efficiently performed by a triple network and the parameters employed in the network are optimized by Adaptive Grey Wolf (AGW) optimization. Imputation of missing values, Min–Max normalization, and Z-score standardization processes are used to preprocess the images. The image extraction process is undertaken by a modified convolutional neural network (MCNN) approach. Moreover, input images are taken from datasets such as the Landsat 8 dataset and the Moderate Resolution Imaging Spectroradiometer (MODIS) dataset is employed for image retrieval. Further, the performance such as accuracy, precision, recall, specificity, F1-score, and false alarm rate (FAR) is evaluated, the value of accuracy reaches 98.1%, the precision of 97.2%, recall of 96.1%, and specificity of 917.2% respectively. Also, the convergence speed is enhanced in this TN-AGW approach. Therefore, the proposed TN-AGW approach achieves greater efficiency in image retrieving than other existing

关键词： Extraction

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Deep Learning Innovations for Underwater Waste Detection: An In-Depth Analysis

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IEEE Access 2025年 13卷 88917-88929页

作者： Walia, Jaskaran Singh Haridass, Kavietha Pavithra, L.K. Vellore Institute of Technology School of Computer Science and Engineering Chennai India Vellore Institute of Technology Centre for Human Movement Analytics Chennai India

Underwater waste detection is a critical challenge for preserving aquatic ecosystems, particularly due to inherent underwater distortions such as light refraction, occlusion, and scattering. In this study, we present a novel deep learning framework for real-time underwater waste detection by evaluating state-of-the-art object detection algorithms on a manually annotated custom dataset comprising 10,000 curated augmented images across various water bodies to simulate real-world turbidity, illumination, and occlusion, consisting of three classes: Underwater Trash, Rover, and Biological Life. Our approach incorporates robust feature extraction and specialized data augmentation techniques that effectively mitigate the adverse effects of underwater distortions. We investigate multiple architectures, including YOLOv8n, YOLOv7, YOLOv6s, YOLOv5s, Faster R-CNN, and Mask R-CNN, analyzing key parameters such as mean average precision (mAP), inference speed, and computational efficiency. Our results demonstrate that YOLO-based models achieve faster & accurate performance, with YOLOv8n and YOLOv7 reaching a detection accuracy of 96%, compared to 81% and 83% for Faster R-CNN and Mask R-CNN, respectively. Computationally, YOLOv8n achieves 76 ms per frame, while YOLOv5s runs at 14 ms per frame, confirming real-time viability for AUV deployment. The proposed approach offers significant advantages over existing methods by enabling rapid, accurate detection with low computational overhead, thereby paving the way for integration with autonomous underwater vehicles (AUVs) for environmental monitoring and waste management. © 2013 IEEE.

关键词： CNN computer Vision Deep Learning Image processing Image Segmentation Object Detection Robotics Underwater Trash YOLO

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Sentiment Analysis of Financial News: Implications for Corporate Strategy in Risk Management, Mergers and Marketing

Sentiment Analysis of Financial News: Implications for Corpo...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Sendhil, R. Lijo, Joshua Abinaya, E. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331509828

This research study examines the ways to use sentiment analysis on financial news for corporate strategy making. We examine the impact of sentiment in financial news on corporate decisions (beyond technology) regarding risk management, mergers and marketing strategy using recent developments from research into sentiment analysis as input and contrasts it with our findings. This study presents unsupervised and supervised sentiment analysis methods, statistical methods and AI tools for sentiment analysis, and their contributions to identifying business needs. In addition, the paper underscores existing difficulties and received a number of limitations on current techniques in sentiment analysis that have to be tended for better supporting corporate strategy. © 2025 IEEE.

关键词： Decentralized finance

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An Energy Efficient Hybrid Communication Protocol for Large Area Wireless Sensor Networks 4

An Energy Efficient Hybrid Communication Protocol for Large ...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Karthigeyan, Shreyas Bhol, Sampark Jain, Akshat Yadav, Karthik Sinha, Akash Padma Priya, R. School of Computer Science and Information Systems Vellore Institute of Technology Vellore Campus Tamil Nadu Vellore632014 India School of Computer Science and Engineering Vellore Institute of Technology Vellore Campus Tamil Nadu Vellore632014 India Department of Computer Science and Engineering Maulana Azad National Institute of Technology Madhya Pradesh Bhopal462003 India

Energy conservation is an indispensable aspect of the protocols designed for Wireless Sensor Networks (WSNs). The communication protocols for WSN fall mainly under two categories: centralized and distributed. Centralized protocols are not feasible for real-world WSNs as the network can span across large areas where most of the nodes cannot directly communicate with the base station (BS). The routing issues in such networks can be addressed using multi-hop protocols. However, the process of BS guided clustering in such methods cannot be performed. Distributed clustering methods that minimize the intervention of BS in clustering can provide feasible solutions in such cases, but the energy expenditure is considerably high. This paper, hence, proposes a hybrid clustering based routing protocol for large WSN that employs certain aspects of centralized as well as that of distributed protocols. Experimental evaluation of the proposed protocol in terms of network lifetime gives promising results that clearly indicate the energy efficiency and consistency of the proposed work in different scenarios. The proposed work can prove beneficial in real-life scenarios where the deployed nodes are far away from the BS. © 2025 The Authors. Published by Elsevier B.V.

关键词： Hybrid power

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