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Detection of Cataracts from Retinal Fundus Image Using Ensemble Machine Learning Approach

Detection of Cataracts from Retinal Fundus Image Using Ensem...

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2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Hasan, Md. Nahid Hossain, Md. Ali Rajshahi University of Engineering and Technology Dept. of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350316001

Cataract is a common eye condition that causes clouding of the eye's natural lens, resulting in blurry vision and it is very common in older ages people. However, it can also occur in younger aged people due to various genetical effects, diabetes, smoking, and prolonged exposure to sunlight. Cataracts can be treated through surgery to remove the clouded lens and replaced by an artificial one, which can significantly improve the vision. This problem can be reduced through early detection, regular eye examinations etc. In this research, a Contrast Limited Histogram Equalization (CLAHE) of retinal fundus images is applied for a better presentation of the above cataract effects. These improved images are fed to a machine learning-based model for effective detection. Finally, the ensembling of the proposed machine learning-based classifiers is performed for the accurate detection of cataracts. The proposed model was tested experimentally on a real dataset and achieves excellent performance with accuracy, precision, and recall scores of 99.67%, 100%, and 99.25%, respectively which outperform the baseline methods. The robustness of the proposed ensemble model is evaluated through 5-fold cross-validation and achieved an average accuracy of 99.6%. The implementation code can be accessed from the GitHub repository: https://***/nahid-tech/cataract-detection. © 2023 IEEE.

关键词： Image enhancement

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Detection of Bullying Text: A Multi-faceted Approach using Machine Learning and Natural Language Processing 5

Detection of Bullying Text: A Multi-faceted Approach using M...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Kumar, Kotyada Mohan Kiran Ullas, Karthik Reddy, Vivek Srikar Snehitha, Munaga Sai Malkhed, Mahadevi E. Kumar, K. Dinesh Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India Amrita School of Engineering Amrita Vishwa Vidyapeetham Dept. of Electronics and Computer Engineering Bengaluru India

ISBN: (纸本)9798331504403

With the dawn of the new age of communication, i.e., social media, the world has come into the palms of every individual at every corner of the world. However, easier access to messaging implies easier forms of abuse. As the usage of social media increases, the other side of communication, bullying, also increases. Cyberbullying refers to the concept of harassing individuals or groups through means of the internet with the help of messages or multimedia. Cyberbullying has a huge impact on mental health and research is ongoing in this area to accurately detect bullying text. Existing models are embedded into social media platforms to either ban or apply sanctions on the users involved in the passage of such materials. The proposed study aims to develop a model with a similar approach to detect cyberbullying in comment text. Also, sentiment analysis is performed on the comment text to get an idea of the message's context, thus improving the chances of bullying detection. The work is divided into two stages. In the first stage, the text and the sentiment score are directly passed to the machine learning models. Later, during the second stage word embedding using Word2Vec, a natural language processing technique, is performed on the comment text to obtain vectorized features which are then given to the machine learning models along with sentiment features. It was observed that the latter model shows high levels of success and that it can be deployed in real-world applications. © 2024 IEEE.

关键词： Adversarial machine learning

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Prediction and detection of nutrition deficiency using machine learning

Prediction and detection of nutrition deficiency using machi...

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2023 IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2023

作者： Kumar Mishra, Amit Tripathi, Neha Gupta, Ashish Upadhyay, Deepak Kumar Pandey, Neeraj Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Dept. of Computer Science and Engineering Dehradun India Tula's Institute Dept. of Computer Science and Engineering Dehradun India Dit University School of Computing Dehradun India

ISBN: (纸本)9781665474924

The plant producers have a hard time identifying nutritional inadequacies in their crops. The capacity to recognize these comprehensive nutritional deficiencies could help regulate crops properly. Using image processing, Convolutional Neural Network (CNN), the researchers were able to categorize and identify complete nutritional deficiencies in various cultivars. The prototypes would provide prescribed plant fertilizers once nutrient insufficiency was recognized. Iron (Fe), magnesium (Mg), potassium (K), nitrogen (N), calcium (Ca) and complete nutrition were examined. For classifying the image processing techniques were used to turn the images into grayscale & binary data. Using identification and prediction, CNN predicts complete nutritional deficiencies in the plant. CNN high accuracy of detection and diagnosis of nutrient deficits in different cultivars, according to the results compared with Artificial Neural Network (ANN) and DenseNet-121. The design has been tested and the results demonstrate a better way to classify and diagnose complete nutritional deficits in different cultivars. © 2023 IEEE.

关键词： Convolution

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Merging Realms: Classical and Quantum-Enhanced Deep Learning Techniques for CIFAR-10 Classification

Merging Realms: Classical and Quantum-Enhanced Deep Learning...

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2024 IEEE International Conference on Computing, Semiconductor, Mechatronics, Intelligent systems and Communications, COSMIC 2024

作者： Bobbili, Sai Jayanth Chandrappa, S. Reddy, V. Keshava Guru Prasad, M.S. Deepa, K. Sharma, Mansi Dept. of Computer Science and Engg. Dehradun India Dept. of Computer Science and Engineering Bengaluru India VTU Department of ECE India NMAMIT Department of ECE Nitte India

ISBN: (纸本)9798331517892

Classical and quantum-enhanced deep learning techniques are revolutionizing areas of artificial intelligence. This leads to substantial innovations in various domains, including the image processing. The CIFAR-10 dataset is a widely used and benchmarked dataset for assessing the performance of deep learning models. In this proposed method, the CIFAR-10 dataset is used for image classification in an effort to improve model performance. The modern and strong models are employed, such as CNN, InceptionV3, DenseNet, and a Hybrid Quantum-DenseNet. The precision, recall, accuracy, loss, and F1-score metrics are used in this analysis to assess these models. From the analysis, it is observed that CNN and Hybrid Quantum-DenseNet attained 77%-78% accuracy. The InceptionV3 reached the maximum accuracy of 90.15%. The performance of the developed hybrid model was exceptionally well across a range of parameters. DenseNet achieved an accuracy of up to 84%. © 2024 IEEE.

关键词： Adversarial machine learning

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Attention-Based AdaptSepCX Network for Effective Student Action Recognition in Online Learning 5

Attention-Based AdaptSepCX Network for Effective Student Act...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Dey, Arnab Anand, Anubhav Samanta, Subhajit Sah, Bijay Kumar Biswas, Samit Dept. of Computer Science and Technology Indian Institute of Engineering Science and Technology Shibpur Howrah711103 India

In the realm of online learning and distance education, the issue of inadequate supervision looms large, posing a significant obstacle. This paper delves into the challenges posed by the lack of supervision in online learning environments and proposes an innovative solution to understand and recognize students' behaviors. This study's primary objective is to detect and recognize students' actions in images captured through webcam. This task distinguishes itself from the well-established video-based student action recognition domain, which relies on temporal cues. Recognizing student actions from images intensifies the complexity of the problem. To meet this challenge, a novel deep learning model named AdaptSepCX Attention, specifically designed for student action recognition in online learning environments, is introduced. The proposed method exhibits exceptional performance with 92.73% validation accuracy on the Student Online Action Image dataset (SOAId), a carefully curated collection comprising 2029 student-centric images. The proposed model outperforms well-established models such as DenseNet121, NASNet Mobile, ConvXNet, DELVS1 and MobileNetV2 in student action recognition. Action recognition for students has broader implications beyond the online classroom. It has the potential to revolutionize educational technology, making online learning more interactive and engaging. Enabling machines to understand and respond to student actions enhances education, personalizes learning, and supports students' academic success and well-being. This research enhances the understanding of student involvement in online learning and offers an effective solution for recognizing actions from images. © 2024 The Authors. Published by Elsevier B.V.

关键词： Students

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IoT-Driven Precision Crop Recommendation System Using LSTM & TSC-NET for Sustainable Agriculture 1

IoT-Driven Precision Crop Recommendation System Using LSTM &...

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1st International Conference on Innovative Sustainable Technologies for Energy, Mechatronics and Smart systems, ISTEMS 2024

作者： Satya Sandeep, K. Lakshmi Sahithi, K. Shabana Azmi, Sd. Renuka Devi, J. Nirmala Jyothi, B. Vignan 'S Nirula Institute of Technology and Science for Women Dept. of Computer Science and Engineering Guntur India

ISBN: (数字)9798350384246

ISBN: (纸本)9798350384246

Andhra Pradesh is a well-known agricultural state with a major contribution to the production of rice, cotton, and chilies it is also known as the 'Rice Bowl of India'. Unpredictable weather events have been a concern to the state, it is proving to show a negative impact on the state's farming population, nevertheless the state still contributes materially to the country's cultivation production. on thorough research and historical analysis, it is stated that the food diffidence and conservationist problems arise from the established age-old agricultural methods, these methods are being followed without any systematic scientific pursuit. this study pursues to solve the mentioned problem by transitioning agricultural practices with the use of Internet of Things (IoT) technologies. A preferred multi-class classification model leveraging IoT data. the data primarily includes meteorological parameters such as soil NPK values, temperature, humidity, and more. The hypothesized model, a hybrid of Long Short-Term Memory (LSTM) and Time Series - Convolutional Neural Networks (TSC-NET), which recommends suitable crops for the cropland location, it is accomplished by integrating Time-space data. the food grain acreage in Andhra Pradesh is seen dwindling by 4.2 % in 2022-2023, underscoring the susceptibility of farmers to monsoon failures. In addition, macroeconomically speaking, the slowdown in agrarian development disrupts economy's advancement as a whole, advocating for agrarian development. The IoT -driven crop classification model provides the novel approach to enhance crop selection and optimize production potential. therefore, improving the precision and efficacy of crop forecasting while enabling farmers to make well-informed decisions accounting for soil conditions and climate data. It intends to tackle both macro-level economic concerns and micro-level farmer vulnerabilities, this research levels to improve the sustainability and efficiency of agriculture in Andhra Pr

关键词： Internet of things

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Ensembled Obstructive Sleep Apnea Detection Using Extra Tree Ensemble Technique 2

Ensembled Obstructive Sleep Apnea Detection Using Extra Tree...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Khan, Atiya Biswas, Saroj Kr. Chunka, Chukhu Barman, Subhas National Institute of Technolgy Silchar Dept. of Computer Science & Engg. Silchar India Jalpaiguri Government Engineering Collage Dept. of Computer Science & Engg West Bengal India

ISBN: (纸本)9798350371314

Obstructive Sleep Apnea (OSA) is a sleep-related disorder with repetitive episodes of complete or partial upper airway obstruction during sleep. These episodes result in disrupted breathing patterns and reduced oxygen levels in the blood. Electroencephalogram is a valuable tool in detecting and diagnosing sleep apnea as it provides insights into the disruptions of sleep caused by repeated apnea events. This paper proposes an efficient model to detect OSA named Ensembled Expert System for Obstructive Sleep Apnea Detection (EESOSAD) using EEG physiological sensors and an Extra Tree ensemble classifier. The proposed model incorporates Discrete Wavelet Transform with Daubechies order-8 for decomposing EEG signal into multiple sub-bands and extracting statistical features like - Maximum, Minimum, Mean, Standard Deviation, Kurtosis, Power, Energy, Root Mean Square, and Variance. A state-of-the-art data preprocessing pipeline is also designed and developed for treating corrupted, missing, duplicates, and outliers, as medical data are prone to noise, anomalies, and inconsistencies that can distort patterns and compromise analysis validity. Finally, for the classification task, the Extra Tree ensemble classifier is used for OSA detection to comprehensively evaluate the performance of the developed data pre-processing and feature fusion pipeline. Multiple evaluation metrics, including Precision, Recall, Accuracy, F1-score, and AUC-ROC curve, have been employed to compare and assess the model's effectiveness. Furthermore, diverse state-of-the-art ensemble learning models have been incorporated for benchmark comparison and discussion. The proposed EESOSAD model achieved an average accuracy of 59% and outperformed multiple benchmark models with an optimal performance margin. © 2024 IEEE.

关键词： Discrete wavelet transforms

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Driver Assistance System, based on the Convolutional Neural Network 16

Driver Assistance System, based on the Convolutional Neural ...

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16th Electrical engineering Faculty Conference, BulEF 2024

作者： Filipova-Petrakieva, Simona Yanev, Petar Dept. Fundamentals of Electrical Engineering Technical University of Sofia Sofia Bulgaria Dept. Computer Systems Technical University of Sofia Sofia Bulgaria

ISBN: (纸本)9798350391671

The continuously rising number of deaths due to car accidents on the roads is a critical problem nowadays. To reduce the number of risky situations on the road, it is necessary to increase the attention of drivers when driving motor vehicles. In this context, an advisory system to assist driving is proposed in this paper. The mathematical model uses a convolutional neural network to recognize traffic signs and detect real road situations. It is implemented using Python in the Jupyter Notebook environment. The following libraries were used: Tensorflow (Tensors and Keras libraries, for machine learning and deep learning, respectively), Tkinter (GUI library), Pillow (image processing library), matplotlib (data visualization library), NumPy (library for performing complex computations), and Pandas (data analysis and processing library). The developed application is installed on the onboard vehicle computer. The detected sign is displayed in an appropriate form on the automobile dashboard. Simulations of real traffic situations are implemented in the system. All this will facilitate driving and help drivers with less experience acquire the necessary driving reactions. © 2024 IEEE.

关键词： Convolutional neural networks

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Optimized 1D CNNs for Enhanced Early Detection and Accurate Prediction of COPD and Other Pulmonary Diseases

Optimized 1D CNNs for Enhanced Early Detection and Accurate ...

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2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Sahu, Prakash Jha, Sneha Kumar, Santosh IIIT-Naya Raipur Computer Science & Engineering India Csvtu Dept. of Data Science Chhattisgarh India

ISBN: (纸本)9798350364866

Artificial intelligence (AI) advancements have opened new avenues in medical diagnostics, particularly for the early detection of respiratory conditions such as Chronic Obstructive Pulmonary Disease (COPD). COPD, the most common respiratory disease, is marked by breathing difficulties, mucus (sputum) production, coughing, and wheezing. Early diagnosis is crucial for effective management and treatment. A one-dimensional convolutional neural network (1D CNN) model optimized with Adam and RMSprop algorithms is proposed for COPD detection. Using the ICBHI 2017 dataset, the model's performance with each optimizer was evaluated based on training and validation accuracy. The Adam optimizer achieved a training accuracy of 94% and a validation accuracy of 90%. In comparison, the RMSprop optimizer attained a training accuracy of 92% and a validation accuracy of 88%. These findings indicate that the Adam optimizer outperformed RMSprop in terms of accuracy. It showcased the potential of the proposed 1D CNN model as a reliable diagnostic tool, contributing to the advancement of respiratory health analytics. © 2024 IEEE.

关键词： Lung cancer

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A Proposed Model for Machine Learning-Based Early Chronic Kidney Disease Detection 3

A Proposed Model for Machine Learning-Based Early Chronic Ki...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Bhandurge, Sonam Sambrekar, Kuldeep Allagi, Shridhar Rajpurohit, Vijay S. Sannakki, Sanjeev Angadi Institute of Technology and Management Visvesvaraya Technological University Belagavi Dept. Computer Science and Engineering Karnataka Belagavi India Gogte Institute of Technology Visvesvaraya Technological University Belagavi Dept. Computer Science and Engineering Karnataka Belagavi India Kle Institute of Technology Hubli Visvesvaraya Technological University Belagavi Dept. Computer Science and Engineering India

ISBN: (纸本)9798350354171

Chronic kidney disease (CKD) is a sluggish and cumulative decline of kidney characteristics over the years. Early detection of CKD is essential when you consider that delayed detection and hospital treatment of the situation might eventually bring about the final stage of CKD and the patient's death. The prediction of diseases has grown to be appreciably impacted by means of the utilization of machine learning (ML) in healthcare. Thus, ML methods are the simplest in CKD prediction. The major goal is to use distinct supervised ML techniques, such as Support Vector Machine, Random Forest, Artificial Neural Network, and Logistic Regression, to examine and verify how correct they may be at detecting CKD. The proposed model provides various strategies for detecting CKD using medical facts which incorporates data preprocessing, strategies for handling missing values, and data reduction. It is noted that the clinical data has been classified with an overall accuracy of 100% by the Random Forest algorithm. The suggested ML model will help doctors identify chronic kidney ailments early on, which will be an effective manner of preventing the illness. © 2024 IEEE.

关键词： Support vector regression

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