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Segmentation of Retinal Pigment Epithelium Layer in Retinal OCT Images 5

Segmentation of Retinal Pigment Epithelium Layer in Retinal ...

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5th IEEE International Conference on Advances in Electronics, computers and Communications, ICAECC 2023

作者： Sheeba, T.M. Antony Raj, S. Albert Anand, M. SRM Institute of Science and Technology Kattankulathur Faculty of Science and Humanities Department of Computer Applications Chennai India SRM Institute of Science and Technology Kattankulathur Faculty of Engineering and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798350305449

Optical Coherence Tomography (OCT) is a relatively recent procedure for studying eyes that has proven to be quite beneficial. We propose automatic detection of the RPE (Retinal Pigment Epithelium) layer in Retinal OCT images in this work. The bottom layer of the retina in retinal OCT images is known as the RPE layer, which is extracted in this study and may be studied to diagnose retinal disorders. After effectively increasing the quality of the OCT image using the Wiener filter, we extract the Retinal Pigment Epithelium layer using the Threshold approach. The proposed approach is tested using 40 OCT images of the retina. The results demonstrate 95.50 percent accuracy in RPE layer segmentation. © 2023 IEEE.

关键词： Optical tomography

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Enhanced Audio-Visual Speech Enhancement via Attention-based Sparse Graph Convolutional Neural Networks with Fire Hawk Optimization 3

Enhanced Audio-Visual Speech Enhancement via Attention-based...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Hebri, Dheeraj Joon, Rakesh Kumar Mohan, M. Ramapraba, P.S. Dhulipudi, Venkata Satish Narasimharao, Jonnadula Department of Master of Computer Applications Srinivas Institute of Technology Karnataka Mangalore574143 India Department of Electronics and Communication Engineering Ganga Institute of Technology and Management Haryana 124104 India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Tamil Nadu Chennai641407 India Department of Electrical and Electronics Engineering Panimalar Engineering College Tamil Nadu Chennai600123 India Department of Electronics and Communication Engineering BVC Institute of Technology and Science Andhra Pradesh 533221 India Department of Computer Science and Engineering CMR Technical Campus Telangana Hyderabad501401 India

ISBN: (纸本)9798331532420

Audio-Visual Speech Enhancement (AVSE) has the vision to enhance speech quality and requite intelligibility by using both audio and video inputs, which is quite useful in noisy conditions. This approach acknowledges the fact that modalities such as lip movement may be equally informative of the auditory signals as the actual sound in cases where audio signals are distorted. In this research, an innovation approach to the AVSE is presented, which involves several state-of-the-art techniques. The data preprocessing step uses Z-score Min-Max Normalization to normalize the input data to help enhance the feature extraction process. Differentiable adaptive short-time Fourier transform (DA-STFT) is employed as the method of audio feature extraction that provides key temporal information. In the feature extraction process, a local directional structural pattern (LDSP) is utilized for the extraction of visual features since it examines the moving patterns on the face effectively. The fusion of data is done using a quadruplet depth-wise separable fusion convolutional neural network (QDSFCNN), which enhances the amalgamation of the audio and visual cues. Classification is done using an attention-based sparse graph convolutional neural network, known as ASGCNN, since the focus is on the relevant stipulations. Finally, to achieve the highest performance level, the fire hawk optimizer (FHO) is used for tuning parameters in the presented model. This combined approach presents advanced developments in AVSE, which provide improved speech quality in difficult conditions. © 2024 IEEE.

关键词： Convolutional neural networks

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IoT-Genomics Analysis for Personal Health with Cybergenomics and Cloud Technology 10

IoT-Genomics Analysis for Personal Health with Cybergenomics...

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

作者： Prabakaran, T. Babu, A. Ramesh Hemalatha, S. Ramasamy, Jayabharathi Kovarasan, Rajesh Kambattan Elango, B. Joginpally B.R. Engineering College Department of Computer Science and Engineering Telangana Hyderabad India Vidya Vikas Institute of Engineering and Technology School of Computer Applications Karnataka Mysore India Tagore Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

Recent innovations at the convergence of genomics and the Internet of Things (IoT) have opened the way for an innovative strategy for administering individualized health care. This research study combines the potential of wearable health monitoring technologies with genetic insights to analyze the personal healthcare. A comprehensive knowledge of health dynamics may be achieved by seamlessly integrating real-time health data collected from IoT-enabled wearables with individual genetic profiles. Using algorithms for machine learning, connections between genetic differences and instantaneous health responses may be uncovered, paving the way for the development of individualized therapies and lifestyle recommendations. The suggested framework allows people to improve their health and gives medical professionals access to data-driven insights that can be used for the early diagnosis of diseases and the development of individualized treatment plans. The work that has been done sheds light on the revolutionary potential of merging genetics with the Internet of Things to establish a new paradigm of preventative and individualized medical treatment. © 2024 IEEE.

关键词： Genome

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Apple Fruit Flower Detection Using Auto-Encoder Convolutional Neural Network

Apple Fruit Flower Detection Using Auto-Encoder Convolutiona...

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2024 International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Srivastava, Yashi Rawat, Ruchira Shrivastava, Anurag Rashmi Bansal, Saloni Gupta, Priyanka Al-Fatlawy, Ramy Riad Lloyd Institute of Engineering & Technology Greater Noida India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Department of Computer Science and IT Noida International University U.P. India Department of Computer Engineering and Applications GLA University Mathura India Institute of Aeronautical Engineering Dundigal Hyderabad India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq

ISBN: (纸本)9798350355338

The yield estimation task altogether relies upon the way toward identifying and checking the quantity of fruits on trees. In production of fruit, basic yield the board choices are guided by bloom intensity, i.e., the quantity of flowers presents in a plantation. The intensity of Bloom technique is still commonly assessed by methods for human visual investigation. Mechanized PC vision frameworks for flower recognizable proof depend close by designed procedures that work just under explicit conditions and with restricted *** work comprises of four significant advances, (I) System preparing for Fully Convolutional Network (FCN), (ii) Preprocessing, (iii) Component Extraction, (iv) Division. Initially, a strategy for assessing high-resolution pictures with deep FCN on Graphics Processing Unit (GPU). Then, secondly a non-linear and linear algorithms are presented for lessening the image noise, so the exact flower identification can be ensured. Thirdly, highlight extraction is valuable to diminish the quantity of assets required for handling without losing significant or applicable data. By applying Local Binary Pattern (LBP), surface example likelihood can be summed up into a histogram. At last, isolate a image with high resolution into sub patches, assess all patches with the help of AECNN, at that point apply the refinement calculation on acquired scoremaps to figure final version of the mask segmentation. Trial results are led utilizing two datasets on flower pictures of AppleA and AppleB. Results are estimated regarding the measurements like Precision(P) and Recall(R). © 2024 IEEE.

关键词： Graphics processing unit

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Enhancing Facial Expression Recognition with a Directional Attentive Neural Network 3

Enhancing Facial Expression Recognition with a Directional A...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Singh, Jagendra Shelke, Nitin Arvind Masson, Palak Agrawal, Rishi Sharma, Amit Parashar, Amit Bennett University School of Computer Science Engineering & Technology Greater Noida India Raj Kumar Goel Institute of Technology Greater Noida India Gla University Department of Computer Engineering & Applications Mathura India Lovely Professional University School of Computer Application Phagwara India Pranveer Singh Institute of Technology Basic Science & Humanities-Chemistry Kanpur India

ISBN: (纸本)9798350384598

The proposed DANN was developed for better recognition of facial expression. The proposed DANN combines the attention and directional filters, which filter out features that focus on the informative facial regions while capturing the directional patterns in the facial queue. The average accuracy after benchmarking all the 10 experiment trials was in the very high level with the proposed DANN on datasets combining CK+, FER2013, and RAF-DB, with an average accuracy of 88.04%. The model showed a good trade-off between precision and recall: its average weighted F1 score was 0.881, while the average precision and recall were 0.883. This implies that the model will maximize the identification of true positive samples while, at the same time, minimizing that of false positives and false negatives. All these results are indicative of the robustness and reliability of the DANN architecture in classifying the facial expressions correctly across the different datasets and different experimental settings. The results suggest the high potential of DANN in furthering the state-of-the-art facial expression recognition with prosperous affective computing, human-computer interaction, and psychology. © 2024 IEEE.

关键词： Affective computing Attention mechanisms Deep learning Directional Attentive Neural Network Facial expression recognition

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Predicting Glaucoma Severity Levels in Human Eyes: Integrating CNN and Random Forest for accurate classification 1

Predicting Glaucoma Severity Levels in Human Eyes: Integrati...

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1st International Conference on Optimization Techniques for Learning, ICOTL 2023

作者： Suryavanshi, Ankita Kukreja, Vinay Dogra, Ayush Bhattacherjee, Abhishek Brar, Tejinder Pal Singh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Lovely Professional University Computer Science and Engineering Punjab Phagwara India Cgc Landran Department of Computer Applications Mohali India

ISBN: (纸本)9798350328042

This study prompts intervention and effective treatment, it is crucial to accurately categorize the various levels of glaucoma severity in human eyes. To accurately classify glaucoma sickness, this research introduces a novel methodology that combines Convolutional Neural Networks, also known as CNNs, and Random Forests synergistically. Our model displays amazing accuracy in differentiating between intensity classes by taking advantage of CNNs' skill at extracting complex information from medical pictures and Random Forests' talent for ensemble learning. The method includes careful data augmentation, preprocessing, and aggregation, which strengthens the model's durability. The effectiveness of the model over a range of glaucoma levels of severity may be understood by a thorough performance evaluation that takes variables like accuracy, recollection, and F1 score into account. The results highlight its potential as a tool for diagnosis for healthcare practitioners, enabling early detection. This study provides a transformational approach to improving glaucoma diagnosis and demonstrates the possibility of multidisciplinary collaboration between machine learning and medical field expertise. The results highlight the wider significance of technology-driven medical improvements in tackling complicated medical issues, with potential applications to other eye illnesses and beyond. © 2023 IEEE.

关键词： Health care

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A Novel Method for Pneumothorax Diagnosis and Segmentation Using Deep Convolution Neural Network

A Novel Method for Pneumothorax Diagnosis and Segmentation U...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Shanthi, S. Nirmaladevi, K. Mathiarasi, N. Tharani, As. Krithika Monisha, A. Kuppuchamy, R. Krishnamoorthy, N. Kongu Engineering College Department of Computer Science & Engineering Perundurai India PSNA College of Engineering and Technology Department of Computer Applications Tamilnadu Dindigul India Vellore Institute of Technology Department of Software Systems and Engineering Vellore Campus India

ISBN: (纸本)9798350348217

A pneumothorax is the accumulation of air in the pleural space between the chest and lungs. Air that is in the lungs during breathing can leak out of the lungs and become trapped between the chest and lungs in the thoracic cavity, causing chest pain, difficulty breathing, and ultimately the complete collapse of the taut lungs. pneumothorax. Therefore, early diagnosis and treatment should be started. Neural networks have proven effective in image classification tasks, and many researchers believe that training neural network models can detect radiologist-diagnosed chest abnormalities such as pneumonia and pneumothorax. is proving. Moreover, we utilize various techniques, including Stochastic Weight Averaging (SWA), aggressive data augmentation, and learning rate schedule. We use the chest x-ray dataset provided by the 2019 SIIM-ACR In this study, a ResNet-34 neural network is trained on a data set of chest x-rays containing over 10,675 images to create a model that predicts the presence of pneumothorax from chest *** suggested system receives a Dice Similarity Coefficient (DSC) of 0.8410 © 2023 IEEE.

关键词： Image classification

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Product Recommendation System using Deep Learning based Recurrent Neural Network

Product Recommendation System using Deep Learning based Recu...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

ISBN: (纸本)9798350348217

Today's marketing strategies place a high priority on comprehending customer sentiments. It will not only give businesses a better understanding of how their clients view their goods and/or services, but it will also give them suggestions on how to enhance their offerings. We make an effort to comprehend the relationship between various factors in customer reviews on an online store for women's clothing. The proposed system also classifies each review, according to, whether it recommends the product in question or not, and whether it has a positive, negative, or neutral sentiment. With the exception of review titles and review texts, we used univariate and multivariate analyses on datasets features to accomplish these goals. Additionally, we have implemented a bidirectional recurrent neural network (RNN) with a long-short term memory unit (LSTM) for sentiment and recommendation. © 2023 IEEE.

关键词： Long short-term memory

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Diabetes Mellitus Prediction and Classification Using Firefly Optimization Based Support Vector Machine 2

Diabetes Mellitus Prediction and Classification Using Firefl...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Chitradevi, B. Supriya Chandra, N. Subhash Chitradevi, T.N. Alabdeli, Haider Department of Computer Applications Trichy India Nitte Meenakshi Institute of Technology Dept of ISE Bengaluru India Cvr College of Engineering Department of Cse Telangana Hyderabad India Bannari Amman Institute of Technology Department of Computer Science and Engineering Erode India The Islamic University Najaf Iraq

ISBN: (纸本)9798350382952

The Diabetes mellitus is the most dangerous disease all over the world and it is deadly disease determined by the rapid growth levels of the glucose in a blood. The Machine Learning (ML) approaches supports in the prediction and detection of the diabetes. This research aims to propose a Firefly Optimized-based Support Vector Machine (FSVM) for the prediction and classification of diabetes. The datasets utilized for diabetic prediction are PIMA Indian Diabetic dataset. The pre-processing is done by using the data normalization, cleaning as well as data splitting techniques. The Principal Component Analysis (PCA)-based feature selection approach is used for selecting the features in a acquired dataset. Finally, the ML-based algorithms are used for the classification of the diabetes disease. The effectiveness of proposed method is evaluated by PIMA dataset with the various performance metrices such as accuracy, precision, recall, and F1-score of values about 98.45%, 98.69%, 99.37%, and 98.25% respectively when compared to previous approaches such as Naïve Bayes, Integrated kernel as well as K-Nearest Neighbour. © 2024 IEEE.

关键词： Support vector machines

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Transformative Trends in Reinforcement Learning: From Deep Q-Learning to Real-World applications 2

Transformative Trends in Reinforcement Learning: From Deep Q...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Mani, Chandra Sharma, Tarun Kumar Yadav, Kanchan Gupta, Divya Raj Kumar Goel Institute of Technology Computer Science & Engineering UP Ghaziabad India Abes Engineering College Department of Computer Applications UP Ghaziabad India Gla University Department of Electrical Engineering UP Mathura India Miet Department of Bca UP Meerut India

ISBN: (纸本)9798350371055

Reinforcement studying (RL) is a type of machine studying that maximizes an agent's cumulative praise in an environment. As such, it has been more and more used to address a ramification of sequential selection-making issues successfully. Currently, there were most important breakthroughs within the RL space that have led to expanded research activity and the development of several novel techniques and packages. Those transformative traits include deep learning, model-free exploration, off-policy gaining knowledge of hierarchical techniques, meta-gaining knowledge of multi-agent training, deep reinforcement learning (DRL), and transfer learning. These new strategies to RL allow for the development of more efficient and powerful algorithms, which in flip allow more a hit education and alertness of reinforcement mastering marketers in lots of one of a kind domain names. Similarly, those additives of RL permit faster, more flexible, and stronger schooling for RL marketers. As a result, RL is rapidly turning into a part of many one of a kind areas of studies that require powerful choice-making systems, inclusive of robotics, herbal language processing, and healthcare. © 2024 IEEE.

关键词： Environment Healthcare Hierarchical Reinforcement Strategies

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