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IoT-based System for Multi-Head Dual Attention Generative Adversarial Network-based Soil Nutrition and Plant Disease Detection in Smart Agriculture 8

IoT-based System for Multi-Head Dual Attention Generative Ad...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Shukla, Arvind Kumar Krishnaraj, Kowsalyadevi Venkatesh, R.J. Santhanalakshmi, M. Iftm University Department of Computer Applications Uttar Pradesh Moradabad India Sona College of Technology Department of Computer Science and Engineering Tamil Nadu Salem India Sri Venkateswara College of Engineering Department of Eee Tamil Nadu Chennai India Department of Computer Science and Engineering Tamil Nadu Dindigul India

ISBN: (纸本)9798350376425

The Internet of Things (IoT) has been increasingly popular in recent years due to its many uses. Regarding the agricultural industry, automated systems for identifying plant diseases offer mixed benefits and some drawbacks. Hence Multi-Head Dual Attention Generative Adversarial Network (MHDAGAN) based plant disease detection in IoT is proposed. In order to address the absence of infrastructure in this area, the proposed approach seeks to identify stress to plants caused by crop diseases, environmental imbalance, and soil fertility. The proposed scheme uses nodes to capture plant leaf images, with a sink node collecting information from an automated module for IoT-based monitoring. The Combined Bilateral Parametric Filtering (CBPF) is used to pre-process the nodes' images so they are appropriate for the plant disease detection. After pre-processing, DenseNet201is proposed for extracting the Statistical features of the images. Then Multi-Head Dual Attention Generative Adversarial Network (MHDAGAN) is proposed for segmenting and classifying the plant leaf as healthy and disease. In order to improve the classification accuracy, Humboldt Squid Optimization Algorithm is proposed. Utilizing criteria such as sensitivity, accuracy, energy of nodes and specificity on various IoT environments, the detection performance is verified overall. According to the simulation findings, the suggested strategy performs better in terms of detection, with an accuracy of 99.46%. © 2024 IEEE.

关键词： Plant diseases

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Supervised and unsupervised learning for characterising the industrial material defects

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International Journal of Business Intelligence and Data Mining 2022年第2期21卷 233-246页

作者： Radha, P. Selvakumar, N. Raja Sekar, J. Johnsonselva, J.V. Department of Computer Applications Mepco Schlenk Engineering College Sivakasi India Department of Mechanical Engineering Mepco Schlenk Engineering College Sivakasi India Department of Computer Science and Engineering Mepco Schlenk Engineering College Sivakasi India

The ultrasonic-based NDT is used in industries to examine the internal defects without damaging the components since the materials used in the industrial standard components must be 100% perfect. The ultrasonic signals are difficult to interpret, and the domain expert has to concentrate at every sampling point to identify the defect. Hence, the existing ultrasonic-based NDT method is improved by applying IoT, machine learning, and deep learning techniques to process the ultrasonic data. This work integrates NDT and IoT to analyse the properties of materials using deep learning-based supervised model and filter outliers using unsupervised model like density-based clustering method. After analysing the different categories of defects, the notifications are sent to various stakeholders to either repair or replace the defective components through their mobile using advanced communication techniques to avoid expensive experimentation or maintenance. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Nondestructive examination

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A quantitative investigation of immigrants integration and detention in Europe

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Quality and Quantity 2025年第1期59卷 253-274页

作者： Ballerini, Veronica Seri, Emiliano Department of Statistics Computer Science Applications “G. Parenti” University of Florence Florence Italy Department of Enterprise Engineering “Mario Lucertini” University of Rome Tor Vergata Rome Italy

In this paper, we question whether different attitudes of European countries’ policies toward migrant integration correspond to different propensities to hold foreigners in prison. As a first attempt to test the existence of such an association, we cluster 34 European countries for the year 2019, modelling the dimensions of the Migrant Integration Policy Index (MIPEX). Leveraging finite mixtures of multivariate Gaussian, we identify three groups of countries with a similar level of integration. Then, we estimate the relative proportion of foreigners held in prison among clusters, relying on UNODC and UNDESA data and exploiting Fisher’s noncentral hypergeometric (FNCH) model. We aim to introduce the FNCH model on clusters as a new quantitative tool to investigate demographic and social research issues. Results show that, in the less virtuous cluster in terms of migrants’ integration, foreigners are almost twice more exposed to detention than in the other clusters. Moreover, looking at the differences within clusters, we find that foreigners have a different propensity to be held in prison with respect to citizens. The proposed approach adds new valuable insights to the MIPEX and provides a novel perspective on an important and highly debated phenomenon, such as foreigners in prison, through the lenses of migrants’ integration. From a policymaking perspective, there emerges a need for more attention and further investigation on the paths underlying such an association between migrants’ integration and detention. To this aim, it is essential that countries enhance data collection and access, especially on migrant incarceration. © The Author(s) 2024.

关键词： Foreigners in prison MIPEX Model-based clustering Noncentral hypergeometric distribution Odds ratios

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An Approach for Enhancing Data Storage Capacity in Quick Response Code using Zip Compression Technique

An Approach for Enhancing Data Storage Capacity in Quick Res...

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2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Bhardwaj, Cheshtaa Garg, Hitendra G.L Bajaj Group of Institutions Department of Computer Science and Engineering Mathura India Gla University Department of Computer Engineering and Applications Mathura India

ISBN: (纸本)9798350322309

A 2-D matrix barcode known as Quick Response Code stores data in four distinct encoding modes. Nowadays, it can be found on almost every product, billboard, digital transaction, and so on because of its growing popularity. It is now so user- and mobile-friendly that users can seamlessly transit from offline to online environments. It is used to store the information, allowing people to interact in real world using Smartphone and also as an information sharing medium. The primary objective of this work is to use ZIP compression to improve QR code data storage. This paper will provide a brief explanation of how QR code data storage capacity is enhanced by ZIP compression. As ZIP compression is a lossless compression technique, the result demonstrates how more data can be stored in QR codes of different sizes without losing any information in the form of compressed data. Additionally, after scanning the QR code, the compressed data is decompressed to retrieve the original data. © 2023 IEEE.

关键词： Data compression

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Deep Learning Techniques for Object Detection in Underwater Environments

Deep Learning Techniques for Object Detection in Underwater ...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Praveen, R.V.S. Maindola, Meenakshi Anandhi, R.J. Thandra, Nagarjuna Bansal, Saloni Sharma, Vishal LTIMindtree Limited Telangana Hyderabad India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India New Horizon College of Engineering Department of Information Science Engineering Bangalore India MLR Institute of Technology Dept. Computer Science and Engineering Telangana Hyderabad India GLA University Department of Computer Engineering and Applications Mathura India Lovely Professional University Phagwara India

ISBN: (纸本)9798331512965

The degradation of image quality caused by things like light absorption, scattering, and distortion makes object detection in underwater environments a unique challenge. applications like marine research, environmental monitoring, and autonomous underwater vehicles (AUVs) are restricted in their effectiveness by traditional image processing techniques, which often struggle with these complexities. Recent years have seen the rise of deep learning techniques as a potent weapon in the fight against these obstacles. In order to accurately detect objects underwater, this paper investigates the use of deep learning models, specifically convolutional neural networks (CNNs). We explore cutting-edge models and methods like attention mechanisms, transfer learning, and hybrid deep learning architectures to improve detection performance in challenging underwater environments. Furthermore, we utilize data augmentation techniques and synthetic dataset generation to tackle the critical issue of limited labeled underwater datasets. From a detection accuracy and robustness perspective, the experimental results show that our suggested deep learning method is light years ahead of the competition. Marine exploration, autonomous underwater vehicle navigation, and underwater surveillance systems can all benefit from this study's new insights into how to better detect objects in real time while submerged. © 2025 IEEE.

关键词： Convolutional neural networks

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Analysis of Various Hyperparameters for Predicting Hypertension using Machine Learning Algorithms 8

Analysis of Various Hyperparameters for Predicting Hypertens...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Duggal, Shallu Sood, Shivani Singh, Amanpreet Singh, Harjeet Malarvel, Muthukumaran Lovely Professional University School of Computer Applications Phagwara144411 India Chitkara Institute of Engineering and Technology Chitkara University Punjab 140401 India Department of Computer Science and Engineering India

ISBN: (纸本)9798350367904

Predicting hypertension, or high blood pressure, is crucial because it's a risky condition that frequently doesn't exhibit any obvious symptoms or indicators until major problems arise. By using predictive models for early identification, the risk of uncontrolled hypertension-related cardiovascular events, strokes, and organ damage can be decreased to take quick action. The study aims to predict the diagnosis of hypertension using three machine learning techniques: Logistic Regression, Gaussian Naive Bayes, and Support Vector Machine(SVM). With the focus on improving accuracy and reliability, the various hyperparameters of each individual algorithm are tuned using the cross validation and grid search approaches. After extensive testing and parameter modification, the SVM algorithm was found to be the most accurate predictor, which achieved a significant accuracy of 9 7. 0 0 %. The best hyper-parameters achieved for SVM are c value 4 and kernel as 'rbf'. The models' performance is evaluated using accuracy, specificity, sensitivity, F1-score, or AUC (Area Under the Curve). This study facilitates proactive healthcare measures by offering insightful information to determine the best algorithms for the early prediction of hypertension. © 2024 IEEE.

关键词： Prediction models

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An Improved KNN Algorithm to Identify Parkinson's Disease on Voice Ailment

An Improved KNN Algorithm to Identify Parkinson's Disease on...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Sukanya, N.S. Shinty, P.K. New Horizon College Of Engineering Department Of Master Of Computer Applications Karnataka Bengaluru India School Of Science And Computer Studies Cmr University Karnataka Bengaluru India

ISBN: (纸本)9798350371567

In recent times, researchers have extended their research on Parkinson's Disease (PD) using Artificial Neural Network techniques. PD is an age related progressive neurological ailment that affects millions of older people worldwide. There is a need to identify PD accurately as early as possible. Identifying PD at an early stage is a challenging procedure. This paper aims to use machine learning models such as Artificial Neural Networks (ANN) and an enhanced k-Nearest Neighbour (kNN) techniques to identify PD at an early stage. To detect the PD, an audio data of older people is used in this study. An automatic process classifies an audio data into two categories such as PD patient and healthful individual. The proposed model uses the weights and the nearest neighbourhood concepts to predict the audio samples. The results of the improved kNN classifier produces optimal results by classifying the audio datasets into healthy and unhealthy categories. © 2024 IEEE.

关键词： Diagnosis

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Object Detection in Real-Time Surveillance Using Deep Learning-Based YOLO Framework

Object Detection in Real-Time Surveillance Using Deep Learni...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Kukreti, Sanjeev Praveen, R.V.S. Bansal, Saloni Raju, Hemanth Singh, Navdeep Begum, Rowsonara Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India LTIMindtree Limited Telangana Hyderabad India GLA University Department of Computer Engineering and Applications Mathura India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Lovely Professional University Phagwara India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798331512965

The continual growth of the computer vision and artificial intelligence fields overtime have provided the basis for more efficient and accurate surveillance. This paper examines the use of YOLO, a deep learning model that is used in the detection of objects, for use in real-time surveillance systems. The added benefits of identifying and labeling multiple objects in a single pass forward makes YOLO ideal for real-time surveillance. Unlike other approaches to object detection such as region-based methods or methods with multiple stages of detection, YOLO cast object detection as a regression problem where boxes and class probabilities are predicted jointly. This leads to a far improved performance when compared with their slower counterparts without the sacrifice of precision. In this work, the YOLO model is finetuned on a large database of surveillance videos to improve object detection at low lighting and in the presence of occlusion and crowding. The developed system has a high detection speed and accuracy;the application areas will include uses such as security inspection, traffic regulation, and anomaly detection. This is done to affirm that using YOLO is more efficient and faster than the other object detection algorithms in real-time surveillance tasks. The results indicate directions for further improvements to guarantee public safety and improve efficient surveillance systems. © 2025 IEEE.

关键词： Deep learning

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Plant Disease Diagnosis and Image Classification Using Deep Learning

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computers, Materials & Continua 2022年第5期71卷 2125-2140页

作者： Rahul Sharma Amar Singh Kavita N.Z.Jhanjhi Mehedi Masud Emad Sami Jaha Sahil Verma School of Computer Applications Lovely Professional University Jalandhar144411India Department of Computer Science and Engineering Chandigarh UniversityMohaliIndia Department of Computer Science and Engineering Tailor’s UniversityMalaysia Department of Computer Science College of Computer and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science Faculty of Computer and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

Indian agriculture is striving to achieve sustainable intensification,the system aiming to increase agricultural yield per unit area without harming natural resources and the *** farming employs technology to improve *** and accurate analysis and diagnosis of plant disease is very helpful in reducing plant diseases and improving plant health and food crop *** disease experts are not available in remote areas thus there is a requirement of automatic low-cost,approachable and reliable solutions to identify the plant diseases without the laboratory inspection and expert’s *** learning-based computer vision techniques like Convolutional Neural Network(CNN)and traditional machine learning-based image classification approaches are being applied to identify plant *** this paper,the CNN model is proposed for the classification of rice and potato plant leaf *** leaves are diagnosed with bacterial blight,blast,brown spot and tungro *** leaf images are classified into three classes:healthy leaves,early blight and late blight *** leaf dataset with 5932 images and 1500 potato leaf images are used in the *** proposed CNN model was able to learn hidden patterns from the raw images and classify rice images with 99.58%accuracy and potato leaves with 97.66%*** results demonstrate that the proposed CNN model performed better when compared with other machine learning image classifiers such as Support Vector Machine(SVM),K-Nearest Neighbors(KNN),Decision Tree and Random Forest.

关键词： Plant diseases detection CNN image classification deep learning in agriculture

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A Music Recommender System to Identify the Emotional State of a Person Using Machine Learning Algorithms 4

A Music Recommender System to Identify the Emotional State o...

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

作者： Muruganantham, A. Malarvizhi, C. Divya Vahini, S. Saranya, D. Uthiramoorthy, A. Karnataka Bengaluru India Psg Institute of Advanced Studies Department of Computer Science Coimbatore India Dr.Mahalingam College of Engineering and Technology Department of Computer Applications Tamil Nadu Pollachi India Sri Eshwar College of Engineering Department of Information Technology Coimbatore India Rathinam College of Arts and Science Department of Computer Science India

ISBN: (纸本)9798331523923

In today's digital world, customized recommendation algorithms can improve user experiences in many different areas. Within the sphere of intelligent human-technology interaction, this paper specifically examines a music recommender system driven by machine learning algorithms. It figures out what mood users are in and can provide an appropriate response in real time. The system being propounded in this essay uses advanced technology in facial expression recognition. With the help of a webcam, it aims to accurately detect user emotions, including joy, sadness, and anger. Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Support Vector Machines (SVM) have been employed to effectively classify people's mood and recommend an appropriate song. Our extensive tests and examinations show that CNN, RNN, and SVM models are all very good at guessing users' moods - with high precision, recall, F1-score, and accuracy percentages. Furthermore, in-depth analyses like confusion matrixes illuminate how well each model serves as an emotion recognizer. This study's findings are representative of future prospects in affective computing around the globe with major implications for intelligent recommendation systems to which it's important that the models are sensitive. In conclusion, the suggested Music Recommender System is something new for taking a more personal view: We can be as deeply entertained as the web allows us to be - make tailored entertainment paths in the digital era. © 2025 IEEE.

关键词： Convolutional neural networks

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