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Prediction of banana leaf disease by intelligent algorithm through digital image using neural network technique

Multimedia Tools and Applications 2025年 1-17页

作者： Nagu, Bharathiraja Kaur, Gaganpreet Shanmugavelu, Murugesan M.R.M, Veeramanickam Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Information Technology Tagore Engineering College Tamil Nadu Chennai India Apex Institute of Technology-Computer Science and Engineering Chandigarh University Punjab India

A lot of research shows that there could be several reasons why the duality of agricultural products has been reduced. Plant diseases make up one of the most important components of this quality. Therefore, the reduction of plant diseases allows for much higher product quality. The automated diagnosis of plant diseases using CNN techniques is presented in this article. The simple approach alters the 25 color channels and 24H histograms. The Banana Leaf Disease Detection Technique (BLDDT), which uses two attention mechanisms to assess, identify and categorize traits, was also suggested in this research. In the analysis phase, the testing carried out using the reference information reveals that the disease, detection system may be reasonably more appropriate than the current techniques because it both identifies and diagnoses the diseased/infected areas. The researchers were able to identify Esca, black decay, and isariopsis ––––with a 99.93% accuracy level using the suggested disease detector. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

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Climate Geoscience–Based Disaster Management in Healthcare Analysis by Markov Q-Transfer Adversarial with Cloud Computing

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Remote Sensing in Earth Systems sciences 2025年第2期8卷 474-483页

作者： Srinivas, Jhade Rebecca, B. Bhargavi, Kovvuri N Swetha, Velma Reddy Spandana, S. Degala, Divya Priya Department of Computer Science and Engineering-Data Science KG Reddy College of Engineering and Technology Moinabad Telangana Hyderabad India Computer Science and Engineering Marri Laxman Reddy Institute of Technology and Management Telangana Dundigal 500043 India Department of CSE Aditya University AP Surampalem India Department of Computer Science and Engineering (AI-ML) Malla Reddy Engineering College Hyderabad India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Bowrampet Telangana Hyderabad India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Analysis and reaction to natural disasters have made extensive use of deep learning methods using semantic segmentation networks. These implementations’ foundation is based on convolutional neural networks (CNNs), which are capable of precisely identifying and locating the relevant areas of interest within satellite imagery or other types of remote sensing data. This helps with rescue planning, restoration efforts, and disaster evaluation. Difficulties in the provision of healthcare services, due to natural disasters, are made worse by the existing problems prevalent in most healthcare systems. The aim of this research is to propose a novel technique in climate change–based disaster management with their public healthcare analysis using cloud computing with deep learning techniques. In this research, the cloud network has been used in LiDAR image collection for climate change–based disaster-struck regions and their public healthcare analysis. This collected image has been classified and optimized using a deep Markov Q-transfer adversarial neural network with binary grey whale swarm optimization. Experimental analysis has been carried out for various disaster management–based public healthcare datasets in terms of classification accuracy, specificity, NSE, sensitivity, and F-1 score. The proposed technique attained a classification accuracy of 97%, F-1 score of 94%, specificity of 96%, sensitivity of 95%, and NSE of 57%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Adversarial neural network Cloud computing Disaster management LiDAR image Whale swarm optimization

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A hybrid convolutional neural network for sarcasm detection from multilingual social media posts

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Multimedia Tools and Applications 2025年第16期84卷 15867-15895页

作者： Pandey, Rajnish Kumar, Abhinav Singh, Jyoti Prakash Tripathi, Sudhakar Department of Information Technology IET Dr Rammanohar Lohia Awadh University Ayodhya Uttar Pradesh Faizabad India Department of Computer Science and Engineering Motilal Nehru National Institute of Technology Allahabad Prayagraj211004 India Department of Computer Science and Engineering National Institute of Technology Patna Patna India Department of Information Technology Rajkiya Engineering College Uttar Pradesh Ambedkarnagar India

Sarcasm in social media postings significantly impacts automated sentiment extraction due to its potential to invert the overall polarity of phrases. It poses a formidable challenge in extracting genuine sentiments from informal multilingual comments about events or individuals, especially when the message is in a multilingual form. The prevalence of noisy datasets further complicates this task. This work proposes a hybrid convolutional neural network (CNN-H) model to classify sarcastic and non-sarcastic statements. Unlike traditional CNN models, CNN-H integrates character and word embeddings, enhancing its capacity to handle the complexities of multilingual content. Evaluation on five benchmark datasets, including English (news headlines, Ghosh Tweet, Riloff Tweet), Hindi, and multilingual Hindi-English (Hinglish), demonstrates the superior performance of CNN-H. The model surpasses various state-of-the-art conventional machine-learning and deep-learning models, including CNN, achieving an impressive F1-score of 93.0% on the multilingual dataset. The incorporation of character and word embeddings proves pivotal in mitigating the impact of noisy datasets, prevalent in social media posts. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Sentiment analysis

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Multimodal Deep Learning Framework for Non-Intrusive Autism Spectrum Disorder Detection 3

Multimodal Deep Learning Framework for Non-Intrusive Autism ...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kavitha, G. Rajganesh, N. Rajalakshmi Institute of Technology Department of Computer Science and Engineering Chennai India Sri Venkateswara College of Engineering Department of Computer Science and Engineering Sriperumbudur Chennai India

ISBN: (纸本)9798331509675

The non-intrusive detection of Autism Spectrum Disorder (ASD) marks a significant advancement in early diagnosis and intervention. This approach allows users to upload videos to a web interface, where visual and auditory data are processed separately. Visual data is analyzed using a customized VGG19 Convolutional Neural Network (CNN) to identify facial expressions and movements indicative of autism, while auditory data is evaluated by a Recurrent Neural Network (RNN) to detect auditory cues like tone and rhythm. A voting mechanism across 10 frames determines the visual classification, labelling the subject autistic if most frames indicate autistic traits. Audio analysis provides an independent prediction. The subject is classified as autistic if either analysis indicates autistic features. By leveraging deep neural networks to process multimodal data, this method offers an accessible, early ASD detection tool for caregivers and professionals, promoting timely interventions and improved outcomes. © 2025 IEEE.

关键词： Convolutional neural networks

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SADCCNet: self-attention-based dense cascaded capsule network for bone cancer detection using deep learning approach

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Iran Journal of computer science 2025年 1-21页

作者： Kotwal, Jameer Gulab Sharma, Nand Kishore Somkunwar, Rachna Bavkar, Dnyaneshwar Deshmukh, Swapnil Khan, Vajid Korade, Nilesh Department of Computer Engineering Dr. D. Y. Patil Institute of Technology Maharashtra Pimpri Pune 411018 India Department of Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur 209305 India Department of Computer Engineering MGM College of Engineering & Technology Navi-Mumbai 410209 India Department of Computer Engineering G.S. Moze College of Engineering Balewadi Pune 411045 India Department of Computer Science and Engineering (Artificial Intelligence) Vishwakarma Institute of Technology Pune India

Bone cancer is a cancerous tumour that originates in bone tissue and causes inflammation, pain, and fractures. It could spread from other body areas or start in the bones. After a diagnosis, it is essential to begin appropriate treatment procedures at once, especially for malignant bone tumours. Several techniques were developed for bone cancer detection at an earlier stage. However, those techniques failed to provide efficient results due to their overfitting, higher false positive rate, etc. To overcome these kinds of issues, the proposed methodology is introduced to provide efficient performance. Initially, the data were gathered from Chonnam National University Hospital (CNUH) dataset. The pre-processing was done using the uprated non-local mean filtering (Up-NonMF) and contrast-limited ADAPTIVE histogram equalisation (CLAHE) for noise reduction and contrast enhancement. Then, the multi-stage UNet (Multi-SUnet) is used for segmentation. Finally, the proposed self-attention-based dense cascaded capsule network (SADCCNet) model is used to classify bone cancer. Here, the SADCCNet model is optimised using the binary zebra optimisation algorithm (BZOA) to improve its efficiency. This model is executed using the Python tool. The performance of the proposed technique is determined by comparing it with various existing models such as Res_UNet, MobiNet_V2, and VGG-16 models. The proposed model achieves 98.93% accuracy, 97.05% precision, 97.09% recall, 97% F1 score, and 97.35% Specificity. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Adaptive histogram equalisation Dense capsule networks Self-attention UNet

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Deep Convolutional Multi-Relational Graph Attention Network for Autonomous Drone Navigation in Urban Wind Zones

Deep Convolutional Multi-Relational Graph Attention Network ...

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

作者： Pabitha, C. Joshua, K. Paul Pradeep, S. Sophia, S. Sharma, R. Rajesh Prabhu, D. Karthik Srm Valliammai Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Scad College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Tirunelveli India MAlla Reddy Engineering College for Women Department of Computer Science and Engineering Telangana Secunderabad India Karunya Institute of Technology and Sciences Department of computer Science and Engineering Tamil Nadu Coimbatore India Alliance University Department of Computer Technology Alliance College of Engineering and Design Bangalore India Ramco Institute of Technology Department of Electrical and Electronics Engineering Tamil Nadu Rajapalaiyam India

ISBN: (纸本)9798331509828

Drones are essential for civil engineering operations like logistics and data collecting. Current autonomous drone studies mainly concerns itself with safe path planning in static scenarios;however one of the major challenges of over urban environment is often dynamic factors such as wind, building layouts, and signal coverage that can significantly impact direction stability. For autonomous navigation across dynamic wind zones, this study proposed a new Shape-aware Mesh Dual Lightweight Deep Convolutional Multi-Relational Graph Attention Network with Growth Optimizer (SAMDLDCMRGANet-GO) that leverages local data from RGB cameras and GPS. The method incorporates a Dual Aggregation Transformer for feature extraction, Shape-aware Mesh Normal Filtering for preprocessing, and the LDCMRGAN et for efficient path planning. The proposed approach demonstrates its efficacy in navigating complicated surroundings while retaining high precision in obstacle avoidance and target acquisition, achieving an impressive 99.14% recall, 99.15% accuracy, an outstanding EVA of 0.99, and a low RMSE of 0.41. © 2025 IEEE.

关键词： Mesh generation

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An Energy Efficient IoT Based Smart Street Lighting Using Low Cost SOC 1

An Energy Efficient IoT Based Smart Street Lighting Using Lo...

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1st IEEE International Conference on Electronics, Computing, Communication and Control technology, ICECCC 2024

作者： Velu, Anitha Ramamoorthy, Raghu Manasa, S.M. Navulkumar, Devakirubai Sri Sairam College of Engineering Department of Electronics and Communication Engineering Bengaluru India The Oxford College of Engineering Department of Computer Science and Engineering Bengaluru India Jayam College of Engineering and Technology Department of Computer Science and Engineering Dharmapuri India

ISBN: (纸本)9798350371802

Internet of Things (IoT) has radically improved and modernized in every aspect of human existence. The term IoT is a recent trend that states the development a self-configurable network by connecting a variety of hardware objects, to internet without requiring human input to improve service delivery and automate processes by means of data transmission to the cloud. Streetlights are an essential component of any city, although they provide safer roadways, improved night vision, and increased visibility of public spaces it uses a significant amount of electricity and operates at maximum intensity from dusk till dawn. The wastage of electricity can be secured by having lights turn OFF on their own if it is not in use. This work presents an energy efficient IoT-based smart street lighting by using low-cost System On Chip (SOC) circuit. This work deploys an LDR sensor which acts as a decision device, to choose whether to switch ON or OFF the street light based on the level of ambient light. This simple switch has a relay at its output and is activated by either light or dark. This switching may be done using a cost-effective Wi-Fi module namely, ESP8266 for which the LDR sensed value is given as input. Any user can check the status of a street light whether it is ON/OFF from anywhere using internet in real time. It promises great dependability and remarkable stability for long-term. The proposed work is implemented using a NodeMCU board that is designed to supply the required light intensity at different times. © 2024 IEEE.

关键词： Internet of things

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A Deep Learning Approach to Industrial Corrosion Detection

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computers, Materials & Continua 2024年第11期81卷 2587-2605页

作者： Mehwash Farooqui Atta Rahman Latifa Alsuliman Zainab Alsaif Fatimah Albaik Cadi Alshammari Razan Sharaf Sunday Olatunji Sara Waslallah Althubaiti Hina Gull Department of Computer Engineering(CE) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Computer Science(CS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Computer Information Systems(CIS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia

The proposed study focuses on the critical issue of corrosion,which leads to significant economic losses and safety risks worldwide.A key area of emphasis is the accuracy of corrosion detection *** recent studies have made progress,a common challenge is the low accuracy of existing detection *** models often struggle to reliably identify corrosion tendencies,which are crucial for minimizing industrial risks and optimizing resource *** proposed study introduces an innovative approach that significantly improves the accuracy of corrosion detection using a convolutional neural network(CNN),as well as two pretrained models,namely YOLOv8 and *** leveraging advanced technologies and methodologies,we have achieved high accuracies in identifying and managing the hazards associated with corrosion across various industrial *** advancement not only supports the overarching goals of enhancing safety and efficiency,but also sets a new benchmark for future research in the *** results demonstrate a significant improvement in the ability to detect and mitigate corrosion-related concerns,providing a more accurate and comprehensive solution for industries facing these *** CNN and EfficientNetB0 exhibited 100%accuracy,precision,recall,and F1-score,followed by YOLOv8 with respective metrics of 95%,100%,90%,and 94.74%.Our approach outperformed state-of-the-art with similar datasets and methodologies.

关键词： Deep learning YOLOv8 EfficientNetB0 CNN corrosion detection Industry 4.0 sustainability

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SADAK: Simple, and Automatic Detection of Accidents on roads using Kolmogorov-Arnold Networks

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International Journal of Information technology (Singapore) 2025年 1-8页

作者： Dutta, Anurag Kumar, Pijush Kanti Lakshmanan, K. Ghosh, Soubhik Department of Computer Science Government College of Engineering & Textile Technology Calcutta Serampore India Department of Information Technology Government College of Engineering & Textile Technology Calcutta Serampore India Department of Mathematics Kuwait American School of Education Hawalli Salmiya Kuwait Department of Computer Science Bengal College of Engineering and Technology Calcutta Durgapur India

Road accidents are one of the major contributors to mortality in India with nearly 1.68 lakh deaths in 2023 accounting for nearly 19 deaths per hour. While road accidents aren’t always “fatal”, delayed detection leads to the loss of many lives. Deep Learning deliberately showed terrific results for a lot of tasks, but for a long time, the Deep Learning models utilized the standard Multi-Layered Perceptron (MLP) architecture as its’ skeleton. Recently, a state-of-the-art architecture—Kolmogorov Arnold Network (KAN) showed up, with variable activation functions in each edge of the graphical representation of the network. Several works have inherited the KAN architecture and have shown better results in many instances. This research proposes a class of accident detection models, SADAK (Simple, and Automatic Detection of Accidents on roads using Kolmogorov–Arnold Networks) for detection of road accidents inheriting KAN architecture. To benchmark its efficiency, several well-known MLP based architectures have been considered, alongside some state-of-the-art models, which result in accuracies as high as 89% using the MLP (Vision Transformer), and 97% using the KAN architecture (KAN-Convolutional-MLP). © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Deep learning Kolmogorov Arnold network Multi-layered perceptron Road accidents

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Enhancing COVID-19 Detection with YOLOv5 Object Detection Algorithm in Chest X-ray Images 3

Enhancing COVID-19 Detection with YOLOv5 Object Detection Al...

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3rd International Conference for Innovation in technology, INOCON 2024

作者： Duraisamy, Premkumar Arunkumar, D. Niranjani, V. Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350381931

In the ongoing fight against COVID-19, accurate diagnostic tools are crucial. This study focuses on the detection of COVID-19, primarily by analyzing Chest X-ray images to identify and understand the disease. We introduce a new approach that utilizes the YOLOv5 (You Only Look Once) object detection algorithm. YOLOv5 is known for its ability to quickly identify objects, and we have fine-tuned it to perform effectively with X-ray images. The result is a model that excels at identifying intricate COVID-19-related patterns in X-ray images, achieving a high accuracy score of 98%. By carefully adjusting and training the model with specific data, our method achieves even higher accuracy. This modification makes YOLOv5 a powerful tool for automating the detection of COVID-19, offering hope for early detection and timely action in the ongoing global fight against the pandemic. © 2024 IEEE.

关键词： COVID-19

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