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Advances in Multi-Spectral Image Categorization Through Convolutional Neural Networks with an Optimized Activation Function 2

Advances in Multi-Spectral Image Categorization Through Conv...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Raman, Ramakrishnan Kumar, Vikram Pillai, Biju G. Verma, Apurv Rastogi, Shailesh Meenakshi, R. India Srv Media Sri Balaji University Pune India Parul Institute of Technology Parul University Department of Computer Science & Engineering Engineering and Technology India Pune India Chennai Institute of Technology Computer Science and Engineering India

ISBN: (纸本)9798350373110

In recent years, deep learning technologies have significantly advanced the field of multi-spectral image analysis. This paper presents a novel advancement through the development of a specialized Convolutional Neural Network (CNN) architecture that incorporates an optimized activation function designed specifically for categorizing multi-spectral images. Our research introduces a CNN architecture with depth-wise separable convolutions, batch normalization, dropout layers, and fully connected layers, engineered to efficiently analyze both spatial and spectral dimensions of multi-spectral data. The cornerstone of this innovation is the optimized activation function, empirically refined to enhance learning dynamics and computational efficiency, effectively addressing common issues like vanishing and exploding gradients. Through extensive testing on a diverse dataset of environmental and man-made objects, our model demonstrated superior performance metrics such as accuracy (94.2%), precision (93.8%), recall (92.9%), and F1 score (93.3%), significantly outperforming conventional models. These results highlight the effectiveness of the tailored activation function in learning complex patterns in multi-spectral images, thus improving categorization capabilities. This study underscores the potential of tailored deep learning structures in enhancing the accuracy and robustness of multi-spectral image categorization, with profound implications for environmental monitoring, agriculture, and remote sensing. © 2024 IEEE.

关键词： Convolutional neural networks

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SolaceNet - Anonymity Ensured Online Therapy Platform for Overcoming Barriers in Mental Health Assistance

SolaceNet - Anonymity Ensured Online Therapy Platform for Ov...

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

作者： Sri, B. Usha Dasoju, Vigneshwar Avinash, G.J. Kokutam, Hemanth Kumar Jilla, Manohar B V Raju Institute Of Technology Computer Science Engineering Medak India

ISBN: (纸本)9798350371567

This research paper presents SolaceNet, a cutting-edge online treatment platform that prioritizes user confidentiality while addressing mental health concerns. The website provides private therapy sessions via a secure interface and links users with qualified mental health specialists. SolaceNet also includes a chatbot function that gives consumers immediate answers to questions about health. The objective of SolaceNet's development is to improve mental health support's accessibility by creating a private, encouraging environment for those in need of help. © 2024 IEEE.

关键词： User interfaces

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Lung Disease classification from chest X-ray images using Ensemble Learning

Lung Disease classification from chest X-ray images using En...

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2024 IEEE Calcutta Conference, CALCON 2024

作者： Ghosh, Risheek Talukdar, Arpita Heritage Institute of Technology Computer Science and Engineering Kolkata India Kolkata India

ISBN: (纸本)9798331516666

Lung diseases include conditions like asthma, COPD, interstitial lung disease, sarcoidosis, and pneumonia. This overview focuses on pneumonia and the role of X-ray imaging in its diagnosis. Pneumonia, characterized by lung inflammation and infection, causes fever, cough, difficulty breathing, and chest pain, often requiring hospitalization. Diagnosis involves clinical evaluation and medical imaging, with chest X-rays confirming pneumonia by showing lung areas filled with pus, mucus, or other substances. This study presents an ensemble model to identify pneumonia from chest X-rays, enhancing performance and reliability. The model uses transfer learning with three deep learning architectures: VGG-16, VGG-19, and ResNet-50. Each model, pre-trained on substantial datasets, captures complex features and patterns. Combining their predictive capabilities, the ensemble model outperforms individual models, providing a robust diagnostic tool for pneumonia, improving healthcare efficiency. © 2024 IEEE.

关键词： Lung cancer

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Machine learning Approaches for Autism Spectrum Disorder Prediction 10

Machine learning Approaches for Autism Spectrum Disorder Pre...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Harish Sree Prasanna Kumar, D. Malardhas, Julia Punitha Devapriya, I. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350353068

The neurodevelopmental illness known as autism spectrum disorder is a lifelong condition marked by a persistent or limited development of social-communication skills, cognitive abilities, activities, and behavior. The symptoms of autism are easier to see and identify in children between the ages of two and three. A prediction system based on traditional machine learning techniques, such as support vector machines, random forests, multiple layer perceptrons, naive Bayes, convolution neural networks, and deep neural networks, accounts for most of the research on autism spectrum disorder. Accuracy, precision, and recall are among the performance indicators used to verify the proposed models. This study investigated and evaluated the prediction of autism spectrum disorders using standard criteria including application type, simulation method, comparison approach, and input data. The purpose of this work is to offer a consolidated framework for autism spectrum disorder prediction to researchers. With the best results, the random forest method performed better than traditional machine learning algorithms. 97.8% accuracy percentage was achieved. Readers can better comprehend the underlying mechanisms and structures of the investigated frameworks with the aid of these work flow representations. © 2024 IEEE.

关键词： Support vector machines

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Sign Language Recognition Systems: A Comprehensive Review 1

Sign Language Recognition Systems: A Comprehensive Review

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1st International Conference on Pioneering Developments in computer science and Digital Technologies, IC2SDT 2024

作者： Aggarwal, Rahul Meena, Anjali Kaur, Navleen Singh, Rajveer Patel, Suryamani National Institute of Technology Jalandhar Department of Computer Science and Engineering Jalandhar India

ISBN: (纸本)9798350365016

Recently, emphasis has been placed on the applications of ISL since they assist people with HIs in their day- to-day lives in diversely populated India. This review paper aims to present years of development in real-time ISL recognition systems, the progress they brought, the issues they face, and the other ways it is being utilized in this ever-evolving area. We critically review the approaches for real-time ISL recognition from two aspects - the method and the assessment procedure based on the literature survey and recent developments and tendencies in the ISL research field. The current review accrues previous literature research to identify critical deficits and future suggestions to enrich the literature on assistive technologies for the deaf and hard-of-hearing people in India. © 2024 IEEE.

关键词： Adversarial machine learning

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Deep Learning Approaches for Air Quality Forecasting: An LSTM and CNN Perspective 2

Deep Learning Approaches for Air Quality Forecasting: An LST...

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2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Lakshmi, S. Krishnamoorthy, A. Vellore Institute of Technology School of Computer Science Engineering Vellore632014 India

ISBN: (数字)9798350368109

ISBN: (纸本)9798350368109

Nowadays, air pollution has become a significant and urgent environmental concern, because of human activities, growing industries, and urbanization. Prolonged exposure to elevated levels of air pollution might result in health issues. Hence, the ability to forecast pollution levels is essential for prompt action and efficient control of air quality. PM2.5 is a notable air pollutant comprised of minuscule particles, each measuring less than 2.5 micrometers in diameter. Rising PM2.5 concentrations exacerbate the risk to human wellness. This research examines the precise forecasting of long-term results of PM2.5 pollutants. The levels of PM2.5 help decrease air pollution and prevent its adverse effects. This study proposes a hybrid CNN-LSTM approach that uses the CNN algorithm to acquire spatial characteristics and the LSTM framework to derive temporal details. Also, this model analyzes data from five highly polluted stations in Delhi, and the data was collected from 01/01/2019 to 10/12/2020 from the Central Pollution Control Board in India. Correlation coefficients between PM2.5 and various substances aid in identifying comparable pollutants and evaluating their collective health effects. To assess the efficacy of these models, it is important to measure such as Mean Square Error and Mean Absolute Error (MAE). The hybrid model exceeds the baseline models, leading to the lowest error rate. © 2024 IEEE.

关键词： Mean square error

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Cyberbullying Predictive Analysis on Twitter(X) Data with Multi-model Supervised Technique 7th

Cyberbullying Predictive Analysis on Twitter(X) Data with Mu...

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7th IFIP TC 12 International Conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Manepalli, Vijaya Vardhan Meka, Srikar Supriya, S. Sreekrishna, M. Gopika, G.S. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India

ISBN: (纸本)9783031699856

The study presents a predictive analysis approach for cyberbullying detection on Twitter, utilizing a multi-model supervised technique. The research aims to develop an effective strategy to identify and classify instances of cyberbullying before they escalate further on social media platforms. A dataset of tweets related to cyberbullying was collected, preprocessed, and transformed into numerical features. Three machine learning models, namely Support Vector Machines (SVM), Random Forest (RF), and Deep Neural Networks (DNN), were trained and evaluated to predict the presence of cyberbullying in tweets. The results demonstrated the effectiveness of the proposed technique, achieving high accuracy, precision, recall, and F1-score. Furthermore, additional experiments were conducted to investigate the impact of different feature engineering methods on model performances. The findings highlight the importance of considering various linguistic and context-based features in cyberbullying detection tasks. Overall, this study contributes to the ongoing efforts in developing proactive measures to combat cyberbullying, providing insights and potential approaches for monitoring and addressing this prevalent issue on Twitter. © IFIP International Federation for Information Processing 2024

关键词： Tweets

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Optimizing Heart Disease Prediction with Feature Sensitivity and Risk Reduction Strategies Using Machine Learning Classifiers 2024

Optimizing Heart Disease Prediction with Feature Sensitivity...

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16th International Conference on Contemporary computing, IC3 2024

作者： Saranya, G. Tyagi, Amit Kumar Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Fashion Technology National Institute of Fashion Technology New Delhi India

ISBN: (纸本)9798400709722

Coronary heart disease (CHD) is associated with a loss of coronary artery blood supply to the heart. The risk of coronary cardiac disease is decreased in physical activity in epidemiologic studies. Researches have shown that regular exercise and the risk of heart disease have an inverse correlation. Sedentary people have the chance of CHD about twice as high-intensity workouts. Among persons with and without CHD, low physical activity levels have been reported to be significant predictors of all-cause and cardiovascular mortality. Evaluating a heart disease risk prediction model using machine learning classifier by considering a feature, number of hours of exercise per week and average calories burnt per day reduces the risk of developing heart disease. The optimum exercise level to stop CHD, however, is uncertain. Increased levels of exercise increase the risk of heart disease, if the patient increases in age. Exercise, both decrease and increase the risk of heart disease. Assessment of the risk of heart disease is very crucial based on effect of just exercise. Data Stratification is one solution where for a given age group, exercise decreases the risk of developing the disease. Various classifiers are used to assess the model. SVM with RBF kernel, KNN and Random Forest gives better accuracy compared to other classifiers. © 2024 Copyright held by the owner/author(s).

关键词： Risk assessment

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Deep Learning-Based Detection of Fungal Diseases in Apple Plants Using YOLOv8 Algorithm

Deep Learning-Based Detection of Fungal Diseases in Apple Pl...

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2024 International Conference on Advances in Data Engineering and Intelligent computing Systems, ADICS 2024

作者： BalaChandralekha, S. Thangakumar, J. Hindustan Institute Of Technology And Science Department Of Computer Science And Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350364828

Over the last decade, the identification of plant illnesses by automatic means has become a challenging field. In recent times, computer vision has made it feasible to identify diseases quickly and accurately, which has enhanced yield by allowing for the necessary treatment. Graph Neural Networks (GNNs), Convolutional Neural Networks (CNNs), Faster-CNN, Mask R-CNN, Siamese Networks, are the models built on deep learning have been applied to diagnose and cure crop disease. These models accomplish well when it comes to identifying plant diseases, but they have trouble with complex field images and frames with several leaves. The goal of this study is to enhance their application in real-world agriculture, where problems with intricate images and multiple leaves are frequent. This paper uses a YOLOv8 model to identify fungal diseases that affect apple leaves, including cedar rust, scab leaves, and black rot. The dataset used for the implementation was gathered from a Kaggle of 540 images, and the model obtained 86% mAP, outperforming the existing YOLOX model which has achieved 73%mAP. © 2024 IEEE.

关键词： Convolutional neural networks

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OCR-Based Extraction of Expiry Dates and Batch Numbers in Medicine Packaging for Error-Free Data Entry 7

OCR-Based Extraction of Expiry Dates and Batch Numbers in Me...

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7th International Conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Kavin, S. Shirley, C.P. Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372816

To guarantee patient safety and regulatory compliance, healthcare providers must accurately enter batch numbers and expiration dates from drug packaging. But manual entry is prone to mistakes, which could have unfavourable effects. This project suggests an automated method for extracting batch numbers and expiration dates from the back of pharmaceutical packaging by using optical character recognition (OCR) technology. In order to improve OCR accuracy, the research makes use of preprocessing techniques and a broad dataset of photos from medical packaging. The retrieved text is analysed to determine the batch number and expiration date, and data correctness is ensured by validation procedures. The end product solution reduces errors in healthcare data management by offering an intuitive user interface for effective data extraction. By means of thorough inspection and evaluation, The experiment shows how the OCR-based system can reliably and effectively extract important information from pharmaceutical packaging. Through the reduction of data entry errors and the facilitation of error-free information retrieval from drug packaging, this effort seeks to improve patient safety and streamline healthcare procedures. © 2024 IEEE.

关键词： Data accuracy

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