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检索条件"机构=Artificial Intelligence and Machine Learning Faculty of Engineering and Technology"

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Advancing Alzheimer's Nodule Detection Through a Comprehensive Multi-Scene Deep learning Framework 10

Advancing Alzheimer's Nodule Detection Through a Comprehensi...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Premkumar, C. Rajeshwari, R. Remya, R. Cholaathiraj, M.S. Reethika, A. Bharathidasan, S. Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Sathyamangalam India Cmr Institute of Technology Department of Computer Science and Engineering Bengaluru India Vel Tech Rangarajan Dr Sagunthala R&d institute of science and Technology Department of Electronics and Communication Engineering Chennai India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Sathyamangalam India Sri Ramakrishna Engineering College Department of Electronics and Communication Engineering Coimbatore India Erode Sengunthar Engineering College Department of Electronics and Communication Engineering Erode India

ISBN: (纸本)9798350384369

Determining the precise location of Alzheimer's nodules is essential for estimating the risk of brain cancer. Conventional CAD modules, including MRI, PET, and CT, struggle with feature extraction and segmentation due to time limits and complexity. This study proposes an efficient brain nodule detection technique based on the Multi-Scene Deep learning Framework (MSDLF) that makes use of the vesselness filter. A four-channel CNN model is developed using information from two image sequences to enhance radiologists' ability to recognise four-stage nodules. This adaptable model may be used in two different classes. The outcomes demonstrate how well the MSDLF performs while processing substantial volumes of image data for brain nodule recognition, improving with accuracy 98.86% and 98.45% of sensitivity by using decreasing false positives. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Bangladeshi Traffic Sign Recognition and Classification using CNN with Different Kinds of Transfer learning through a new (BTSRB) Dataset 2

Bangladeshi Traffic Sign Recognition and Classification usin...

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2nd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Sayeed, Md. Abu Saiful Islam, Md. Islam, Md Babul Kumar Pareek, Piyush Rohan, Tanbin Islam Chittagong University of Engineering & Technology Electrnoics and Telecommunication Engineering Chattogram Bangladesh University of Calabria School of Ict Dimes Lab Rende Italy Nitte Meenakshi Institute of Technology Artificial Intelligence and Machine Learning Bengaluru India Bahari Data Research House Dhaka Bangladesh

ISBN: (纸本)9798350347456

An accident is the cry of a lifetime. The role of traffic signs is most important in preventing or mitigating accidents. In many cases, while driving, traffic signs are not visible in the conventional approach;if traffic sign detection and recognition can alert the driver of important sign guidance ahead, it will help in accident mitigation. Traffic sign recognition plays a monumental role in expert systems, such as traffic assistance driving systems and automatic driving systems. The prime purpose of this paper is to design and identify a computer-based system that can spontaneously detect the direction of a road sign. For this research work, we have created our own dataset, which is called the Bangladeshi Traffic Sign Recognition Benchmark (BTSRB) dataset. The dataset, BTSRB, was created by capturing images from different angles and under different parameters and conditions. A total of 7320 images were collected to create this comprehensive database. This dataset called BTSRB all the images collected from Bangladesh. In this paper, we used five different types of models (CNN, Inception V3, MobileNetV2, ResNet50, and VGG16), which are pre-trained on the ImageNet dataset. Later, we finetuned the pre-trained model and used transfer learning. The main challenge of this research is collecting datasets from a country like Bangladesh, where no recognized dataset is available. When compared to another model, the accuracy of this model is greater than 91%. This paper emphasizes the significance of traffic sign recognition in expert systems and the necessity for a well-established dataset in nations where such resources are not readily available. © 2023 IEEE.

关键词： Accidents

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Early Predicting Alzheimer's Disease Using Multiple machine learning Techniques

Early Predicting Alzheimer's Disease Using Multiple Machine ...

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artificial intelligence for Innovations in Healthcare Industries (ICAIIHI), International Conference on

作者： Rushikesh Burle Leelkanth Dewangan Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Mahartashtra India

To neurodegenerative disorders in machine learning methods are used to predict Alzheimer's disease. Even while the symptoms don't seem severe at first, they do gradually increase symptoms and behaviour to change and day to day life working strategies are not work properly up to the mark. One predominant form Alzheimer's disease is a kind of dementia. A few psychological indicators that can be used to predict AD include age, as per features how many visits there have been, the MMSE, and education.

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A machine learning Approach for Early Prediction of Autism Spectrum Disorder using eXtreme Gradient Boosting and Logistic Regression Framework 1

A Machine Learning Approach for Early Prediction of Autism S...

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1st International Conference on Software, Systems and Information technology, SSITCON 2024

作者： Hassan, Muntather Muhsin Laxmi, H. Bhagya Sasikala, M. Kumar, S. Senthil Alamelu, M. The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Maisammaguda Department of Electronics and Communication Engineering Hyderabad India Sona College of Technology Department of Information Technology Salem India New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Chennai India Bannari Amman Institute of Technology Sathyamangalam Department of Artificial Intelligence and Machine Learning Erode India

ISBN: (纸本)9798350352931

Nowadays, Autism Spectrum Disorder (ASD) is a compound neurodevelopmental disorder that affects millions of persons universally, impacting their communication, communal interaction, and behavior. ASD is a diverse condition that exhibits differently in each individual, from trivial complications with social cues to severe challenges with parallel communication. ASD has become a demanding concern for healthcare professionals, and educationalists, highlighting the need for early detection, effective intervention, and wide-ranging support systems. As a result, an approach has proposed for predicting ASD at early stages, as the existing models have overfitting, class imbalance and more complexity problems. To overcome these issues a hybrid model of eXtreme Gradient Boosting and Logistic Regression (XGBoost-LR) has proposed, which involves with preprocessing of Synthetic Minority Oversampling Technique (SMOTE) and Standard Scalar Standardization to balance class and to improve interpretability. Next, feature extraction is performed with Recursive Feature Elimination (RFE) and cross validation using 5foldCV to reduce overfitting and improve generalizability. The XGBoost-LR model gives a precise rate in predicting and classifying the stages of ASD. The proposed XGBoost-LR model gives the better results than the other existing methods Support Vector machine (SVM) in terms of metrices accuracy, precision, recall, specificity and f1 score respectively. © 2024 IEEE.

关键词： Diseases

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Application of machine learning Algorithms for Analyzing Sentiments Using Twitter Dataset

Application of Machine Learning Algorithms for Analyzing Sen...

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Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： Bhairavi Rewatkar Aditya Barhate Prateek Verma Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

Sentiment analysis is a Natural Language Processing (NLP) approach used to determine whether a piece of text is negative, positive, or neutral based on the emotions it expresses. Despite advancements, several challenges persist in this field, including handling sarcasm, context ambiguity, and the presence of noisy data. Recent techniques, such as deep learning models and transformer-based approaches, have improved accuracy but still face limitations in computational efficiency and real-time analysis. This article employs machine learning (ML) techniques to analyze text and determine the sentiments of reviews shared on Twitter, aiming to address these challenges. The proposed system extracts attributes from the text, such as the sentiment polarity (whether the sentiment is negative or positive) and the topic or theme of the discussion. A comparison with prior work on sentiment analysis in tweets is presented, along with a comprehensive study of feature extraction and representation methods. The study evaluates the performance of six classifiers: Naïve Bayes, Decision Tree, Logistic Regression, Support Vector machine (SVM), Gradient Boosting, and Random Forest, based on different feature extraction techniques. Using the Bag of Words technique, SVM achieved the highest accuracy of 94.58%, while using term frequency, Random Forest showed the highest accuracy of 94.99%. The developed algorithms for sentiment analysis in tweets are useful for analyzing social feedback, product surveys, and social media comments. Combining neural networks and deep learning improves the accuracy of sentiment analysis and provides opportunities to analyze various types of emotions, enabling proactive responses to public sentiment.

关键词： Support vector machines Deep learning Sentiment analysis Accuracy machine learning algorithms Social networking (online) Reviews

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Hybrid Fuzzy Assisted RNN Algorithm for Diabetic Retinopathy Identification

Hybrid Fuzzy Assisted RNN Algorithm for Diabetic Retinopathy...

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2023 International Conference on Energy, Materials and Communication engineering, ICEMCE 2023

作者： Sivaprakash, P. Gifta Jerith, G. Packiaraj, H. Manikandan, R.A. Thanish Kumar, T. Ashok, S. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu India Malla Reddy University School of Engineering Department of Artificial Intelligence & Machine Learning Hyderabad India

ISBN: (纸本)9798350393378

The most common cause of blindness in diabetics is diabetes retinopathy. The importance of early detection in preventing the DR cannot be overstated. However, because eye checkups are so expensive, many people let DR worsen and eventually lead to blindness. To identify persons with DR, the current study employs a color fundus image and a Fuzzy RNN algorithm. Although the purpose of this study is to save money, it could be a game changer for people with DR who cannot afford a medical diagnosis. Prior processing methods include green band extraction, histogram equalization, filtration, optic disc removal with organizational components on architecturally procedures, and illumination changes. The data acquired by GLCM contains contrast, correlation, energy, and homogeneity, and it is utilised to extract characteristics from the preprocessing results. This paper describes a threshold segmentation-based DR detection system. As a result of this study, a fuzzy-RNN methodology for identifying diabetic retinopathy has been developed. Threshold-based categorization is used to determine the diabetic zone. GLCM (Grey Level Co-occurrence Matrix) is the feature extraction technique applied in this case. These characteristics are utilised for categorization. For classification, an RNN classifier is utilised. To simulate the image processing using Phython in diabetic retinopathy is used to validate the final outcome. © 2023 IEEE.

关键词： Recurrent neural networks

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Detection of Leukemia from Histopathological Image using Deep learning Techniques 2

Detection of Leukemia from Histopathological Image using Dee...

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2nd IEEE International Conference on Advanced Technologies in Intelligent Control, Environment, Computing and Communication engineering, ICATIECE 2022

作者： Kumar, V Sunil Renukadevi, S. Pareek, Piyush Kumar De Albuquerque, Victor Hugo Costa Gupta, Deepak Khanna, Ashish Nitte Meenakshi Institute of Technology Department Artificial Intelligence and Machine Learning Bengaluru India Dayananda Sagar College of Engineering Department of Artificial Intelligence and Machine Learning Bengaluru India Graduate Program in Telecommunication Engineering Federal Institute of Education Science and Technology of Ceará Fortaleza Brazil Federal University of Ceará Department of Teleinformatics Engineering Brazil Maharaja Agrasen Institute of Technology GGSIPU CSE Dept India

ISBN: (纸本)9781665493963

Leukemia is a kind of cancer that is brought on by abnormalities in the body's white blood cells. An inspection of a blood smear that has been collected from the peripheral blood and then seen under a microscope is the method that is considered to be the gold standard for diagnosing leukemia. Techniques such as counting the cells in the blood and doing morphological analysis are two examples of methods that may be used to identify and diagnose leukemia. It is necessary to carefully examine a stained blood smear or an aspiration of bone marrow in order to correctly detect and diagnose leukemia. Both of these procedures involve the removal of bone marrow. This requires prior training and specialized expertise on the subject matter. A tedious and time-consuming operation, the manual evaluation of microscopic pictures of blood samples is one of the steps in the diagnostic procedure. The results are highly dependent on the degree of knowledge and experience that the laboratory worker has. Pathologists are often burdened with an excessive quantity of such data that has to be properly evaluated in order to arrive at a decision. This may be a time-consuming and difficult process. Red blood cells, white blood cells, and blood platelets are the three primary cell types that make up human blood. Platelets are also present in blood. Erythrocyte disease might refer to a collection of diseases that have an effect on hemoglobin. Hemoglobin is a protein found in red blood cells that is responsible for transporting oxygen to cells located throughout the body. This condition is referred to as sickle cell anemia. In sickle cell anemia, the blood contains abnormal hemoglobin molecules known as hemoglobin S. These molecules distort the structure of red blood cells, causing them to take on the form of a crescent or a reaping hook. There is a genetic form of anemia known as sickle cell anemia. This kind of anemia is caused by defective hemoglobin, which causes red blood cells to take on t

关键词： Hemoglobin

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A Comprehensive Review of Supervised learning Algorithms in Healthcare Applications

A Comprehensive Review of Supervised Learning Algorithms in ...

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artificial intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Aditya Barhate Abhay Tale Nayan Jikar Prateek Verma Praveen Kumar Prajyot Yesankar Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

Supervised learning has revolutionized the concept of personalization in treatment with the development of Precision Medicine. This review aims to provide a systematic analysis of the utilization of supervised learning in healthcare and its functions in diagnostics and prognoses, recommendations for treatment, and patient surveillance. Classification algorithms such as Logistic Regression, Support Vector machines, and Decision Trees. Regression techniques such as Linear Regression, Polynomial Regression, and Neural Networks. Ensemble methods such as Bagging, Boosting, and Stacking. Deep learning techniques such as Convolutional Neural Networks, Recurrent Neural Networks, and Long Short-Term Memory are discussed for their efficiency and applicability in various healthcare applications. There are strengths highlighted by the review including enhanced diagnostic capabilities and patient-targeted therapy alongside weaknesses such as quality of data, interpretability as well as compatibility with existing healthcare systems. This review gives a more generalized approach to how supervised learning algorithms help to enhance healthcare results and identifies the likely topic that requires more research in the future.

关键词： Industries Deep learning Ethics Systematics Reviews Precision medicine Supervised learning Medical services Classification algorithms Tuning

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Exploring Deep Neural Networks for Accurate Diabetic Retinopathy Prediction

Exploring Deep Neural Networks for Accurate Diabetic Retinop...

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2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Kujani, T. Arivubrakan, P. Raju, K. Sankar, P. Mani Reddy, B. Surekha Bhavana, E. Computer Science And Engineering Vel Tech Rangarajan Dr. Sagunthala R&d Institute Of Science And Technology Chennai India Artificial Intelligence And Machine Learning Vel Tech Rangarajan Dr. Sagunthala R&d Institute Of Science And Technology Chennai India Artificial Intelligence And Data Science Vel Tech Rangarajan Dr. Sagunthala R&d Institute Of Science And Technology Chennai India

ISBN: (纸本)9798350352689

Diabetic Retinopathy (DR) emerges as a serious complication associated with diabetes mellitus, causing damage to retinal blood vessels, and resulting in vision loss. The critical need for early detection and effective management to curb the progression towards vision impairment and potential blindness underscores the significance of this issue. To address these challenges, have propose a novel deep learning system utilizing a Densely connected Convolutional Network (DenseNet-201) for the efficient detection of DR. This model is specifically trained to classify fundus images into six distinct categories: no DR, mild, moderate, and severe, proliferative DR, and hard exudates. The datasets utilized in this study include the publicly available Diabetic Retinopathy Detection 2015 and Aptos 2019 blindness detection datasets from Kaggle. The proposed system comprises multiple stages, including Data Collection, Preprocessing, Augmentation, and Modeling. During the preprocessing phase, normalization techniques are to eliminate biases or distortions present in the fundus images. Furthermore, the augmentation of data is employed to enhance the diversity and quantity of the training dataset. In the modeling stage, have designed and implemented the deep learning model, with the robust and high-performing DenseNet-201 architecture selected as the model's backbone. The model exhibits a significant accuracy of 87.7% in its classification task. Additionally, have introduced a Deep Neural Network (DNN) model, achieving an impressive accuracy rate of 95.8%. These results underscore the effectiveness of our proposed system in accurately and efficiently detecting DR. The primary objective of this research is to develop a reliable and automated system for Diabetic Retinopathy detection, with the overarching goal of improving the quality of life for individuals affected by diabetes. © 2024 IEEE.

关键词： Deep neural networks

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Application Research of Ant Colony Algorithm in Cross Basin Reservoir Earthquake and Dam Strong Earthquake

Application Research of Ant Colony Algorithm in Cross Basin ...

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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Wang, Kouwu Chen, Zuwen Huang, Jie Luo, Xin Du, Weihua Aruna, T.M. Power China Guiyang Engineering Corporation Ltd. Guizhou Guiyang China Engineering Technology Research Center Guizhou Guiyang China GuiZhou Vocational Technology Institute Guizhou Guiyang China Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9798350398465

Ant colony algorithm is a modern intelligent optimization algorithm, which is essentially a pseudo-random search and parallel algorithm. It makes some complex problems that are difficult to be solved by conventional optimization algorithms better, and improves people's ability to solve practical engineering optimization problems. for the problems of reservoir earthquake and dam strong earthquake, this paper analyzes the optimal solution under different parameter settings through the study of ant colony algorithm and process, combined with sample *** results show that the improved ant colony algorithm can speed up the convergence speed of the algorithm and improve the overall quality of the solution. © 2023 IEEE.

关键词： Multiobjective optimization

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