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检索条件"机构=Institute for Machine Learning Department of Computer Science"

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Virtual Scribe for Visually Impaired Learners Through Interactive Speech-Based Application 1

引用

2nd International Conference on Human-Centric Smart Computing, ICHCSC 2023

作者： Sanjeetha, R. Peter, T. John Kallimani, Jagadish S. Sambhram Institute of Technology Bangalore India Department of Computer Science and Engineering Sambhram Institute of Technology Bangalore India Department of Artificial Intelligence and Machine Learning M S Ramaiah Institute of Technology Bangalore India

ISBN: (数字)9789819977116

ISBN: (纸本)9789819977109

Blind people face various challenges every day, something that people take for granted. One such problem that blind students face is the need for a scribe to write their exam. The onus falls on the blind student to find themselves a scribe which in itself is a difficult task. But even if a blind student has found a scribe, the scribes come with their own set of problems, such as linguistic capability and illegible handwriting. To solve this problem, V-Scribe or virtual scribe is being proposed which is a web application that would completely replace a human scribe. The application uses speech recognition, text-to-speech and a natural language understanding module to understand the requests given by the blind student and give back an appropriate response. The DIET classifier developed by Rasa is trained, obtaining an accuracy of 96.7% for intent recognition and 94.7% for entity recognition. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Students

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machine learning and Deep learning Approaches for Automated Diabetic Retinopathy Diagnosis

Machine Learning and Deep Learning Approaches for Automated ...

引用

2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Fulkar, Bhushan Burle, Rushikesh Patil, Pawan Gaurkhade, Sanskruti Gundewar, Swapnil Pacharaney, Utkarsha Datta Meghe Institute of Higher Education and Research Department of Artificial Intelligence and Data Science Wardha India Department of Artificial Intelligence and Machine Learning Maharashtra Wardha India Datta Meghe Institute of Higher Education and Research Department of Computer Science and Design Wardha India Datta Meghe Institute of Higher Education and Research Department of Artificial Intelligence and Machine Learning Wardha India

ISBN: (纸本)9798350375237

Since diabetic retinopathy (DR) is the primary cause of blindness in working-age adults worldwide, vision loss must be avoided by receiving early detection and treatment. In recent years, deep learning techniques have become more and more useful tools for automated drug discovery, possibly offering solutions to overcome the limitations of traditional screening methods. In-depth descriptions of the most recent deep learning methods for DR detection are provided in this review article. We begin by discussing the importance of DR screening, its prevalence, and the challenges faced by healthcare systems in managing this condition. Next, we discuss different deep learning architectures used for DR detection, such as CNNs and RNNs, and their combinations. We investigate how to improve the precision, resilience, and interpretability of DR detection models through the use of explainable AI, ensemble methods, transfer learning, and attention mechanisms. We also examine publicly available datasets, evaluation metrics, and preprocessing techniques that are commonly used to benchmark DR detection algorithms. We discuss the limitations and possible uses of deep learning-based deep readout detection, emphasizing the significance of large-scale, diverse datasets, the interpretability of the model, and real-world deployment concerns. The overall objectives of this review are to advance the field of automated DR detection by highlighting recent developments, compiling existing knowledge, and identifying areas that require further research. © 2024 IEEE.

关键词： Convolutional neural networks

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Harnessing NLP and Deep learning to Decode Disease-Gene Interactions in Lung Cancer 1

Harnessing NLP and Deep Learning to Decode Disease-Gene Inte...

引用

1st International Conference for Women in Computing, InCoWoCo 2024

作者： Rani, K. Mary Sudha Prasad, V. Kamakshi Chaitanya Bharathi Institute of Technology Department of Artificial Intelligence and Machine Learning Hyderabad India Jawaharlal Nehru Technological University Hyderabad Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331518943

Lung cancer remains a critical global health challenge, necessitating advanced predictive models to enhance diagnostic and prognostic accuracy. This study employs natural language processing (NLP) and deep learning to advance personalized lung cancer prognosis and treatment response prediction. Using a synthetic dataset, our novel neural network model integrates clinical data with patient narratives to extract meaningful insights, correlating patient transcripts with existing datasets. The model interprets lung cancer histology types, gene sets, and genetic mutations, achieving 85% accuracy in predicting treatment responses, with a sensitivity of 0.88 and specificity of 0.83. By merging clinical and transcript-based data, this research provides clinicians with a reliable tool to support personalized medicine and informed decision-making. The results highlight the potential of AI-driven approaches to improve disease-gene association analysis, paving the way for innovative computational methodologies in lung cancer research. © 2024 IEEE.

关键词： Lung cancer

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Feature Extraction and Similarity-Based Movie Recommendation Using Bag-of-Words Model

Feature Extraction and Similarity-Based Movie Recommendation...

引用

International Conference on Smart Systems and Wireless Communication, SSWC 2024

作者： Raj, Swati Dutta, Jhalak Bandopadhyay, Saikat Prasad, Rajesh Department of Computer Science and Engineering Christ University Bengaluru India Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India Department of Artificial Intelligence and Machine Learning Netaji Subhash Engineering College Kolkata India Department of Computer Science and Engineering MIT Art Design and Technology University Pune India

ISBN: (纸本)9789819613472

Recommender systems have become pervasive in guiding users through a lot of choices available in today’s digital landscape. This paper presents a content-based recommender system focused specifically for movie recommendations, offering a viable alternative to collaborative filtering methods. Unlike collaborative filtering, which relies on user ratings and demographic data, the proposed system operates without such prerequisites, making it particularly advantageous in scenarios with limited user engagement. By leveraging content attributes such as title, genre, cast, and crew, the system identifies similarities between movies and recommends those akin to a user’s preferences. Through innovative preprocessing techniques and feature extraction methodologies, the system transforms textual descriptions into numerical vectors, enabling efficient recommendation generation. The proposed model contributes to addressing critical challenges inherent in collaborative filtering, including the cold start problem, data sparsity, and popularity bias. Furthermore, it underscores the broad applicability of recommender systems across various domains and emphasizes the continuous evolution of interface designs and algorithms within the research community. Overall, the proposed content-based recommender system offers a promising solution for delivering personalized movie recommendations while advancing the state of the art in recommendation technology. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bag-of-words Cosine similarity Filtering system Recommender system TF-IDF

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DETECTION OF PULMONARY EMBOLISM WITH IMPROVED XCEPTION ARCHITECTURE 10

DETECTION OF PULMONARY EMBOLISM WITH IMPROVED XCEPTION ARCHI...

引用

10th International Conference on AI and the Digital Economy, CADE 2024

作者： Paulraj, Getzi Jeba Leelipushpam Princess, P. Joyce Beryl Jebadurai, Immanuel Johnraja Celshia, S. Division of Computer Science and Engineering Karunya Institute of Technology and Sciences India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences India

ISBN: (纸本)9781837241880

The blocking of arteries in the lungs through blood clots is known as pulmonary embolism. This blood vessel block usually originates in the legs and travels to the lungs causing fatal damage. An imaging test called Computed Tomography (CT) pulmonary angiography scans the lungs for blood clots. The preferred method for identifying pulmonary embolism is CT pulmonary angiography. Manual examination by radiologists increases the workload, consumes more time and also requires experience for correct examination. The objective of this paper is to propose an improved Xception architecture based on Convolutional Neural Networks. The existing architecture is improvised by replacing the Relu activation function with the Swish activation function. This improves the feature extraction of the algorithm thereby improving efficiency in terms of loss and accuracy. The algorithm has been trained and tested using the standard RSNA-STR Pulmonary Embolism CT Dataset. It is evident from the experimental results that our model achieves an improvement of 3.4% and 1.4% in terms of accuracy and loss respectively. © The Institution of Engineering & Technology 2024.

关键词： Blood vessels

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Revamping Primary Programming Education with Augmented Reality Teaching via EnvisionAR 5

Revamping Primary Programming Education with Augmented Reali...

引用

5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Annie Silviya, S.H. Anitha, R. Gayathri, P. Hiranmaie, N. Harini, R. Hareene, D. Rajalakshmi Institute of Technology Computer Science and Engineering Chennai India Rajalakshmi Engineering College Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798350385649

This study introduces a transformative approach to primary programming education, leveraging Augmented Reality (AR) teaching methodologies through the innovative EnvisionAR platform. In response to the dynamic technological landscape, this research addresses the need for engaging, immersive learning experiences for young learners. The EnvisionAR platform, a pioneering fusion of AR technology and tailored curriculum, caters to the cognitive development stages of primary school students. By seamlessly integrating virtual elements into the physical environment, students engage in interactive, hands-on learning that cultivates creativity, problem solving skills, and a profound understanding of programming concepts. This research signifies a broader potential for AR integration in diverse educational disciplines. © 2024 IEEE.

关键词： Augmented reality

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Detection of Mango Leaves Disease using CSUBW based Residual Attention with EfficientNet 2

Detection of Mango Leaves Disease using CSUBW based Residual...

引用

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

作者： Kumar V, Sunil Jyothi, A.P. Nagamani, H.S. Sumanth, S. Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Ramaiah University of Applied Sciences Faculty of Engineering and Technology Department of Cse Bengaluru India Department of Computer Science Bengaluru India Department of Computer Science and Computer Applications Kolar India

ISBN: (纸本)9798350382952

Mangoes are one of the most popular fruits worldwide due to their taste and nutrition. Plant diseases may reduce plant quantity and quality. Disease management to improve crop quality and productivity requires early plant illness detection. Automation of plant disease detection reduces large farm surveillance. Since plants feed us, leaf problems must be diagnosed quickly and accurately. Our advanced learning method automatically detects leaf disease in several mango plant varieties. This study creates the RAM-EfficientNet, a novel illness classification network. The RAM block in EfficientNet extracts additional features and handles slight leaf lesion changes. Transfer learning was used to train EfficientNet on ImageNet to overcome the mango leaf disease sample size issue. The Cat Swarm Updated Black Widow Model (CSUBW), a hybrid optimization model, changes RAM-EfficientNet's weights to improve classification accuracy. Black Widow Optimization Algorithm (BWO) and Cat Swarm Optimization Algorithm (CSO) are combined to create the hybrid optimization model. The model's performance is then assessed to confirm its efficacy. © 2024 IEEE.

关键词： Fruits

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DeepDerm: Elevating Precision in Dermatological Diagnosis with Enhanced CNN 15

DeepDerm: Elevating Precision in Dermatological Diagnosis wi...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Mundada, Monica R. Seema, S. Sowmya J, B.J. Student, Sonia Fathima Ramaiah Institute of Technology Department of Masters of Compiuter Application Banglore India Ramaiah Institute of Technology Department of Computer Science and Engineering Banglore India Ramaiah Institute of Technology Department of Artificial Intelligence and Machine Learning Banglore India

ISBN: (纸本)9798350370249

Since human skin illnesses are among the most common dermatological conditions worldwide, visual observations are usually the first step in the diagnosis process. Accurate classification of skin biopsies and scrapings requires dermoscopic analysis and clinical screening due to medical criteria. It gets more difficult to classify skin diseases using medical photographs because of the complex forms, wide range of colors, and data security issues. In this study, we offer a dependable approach for the classification of 20 distinct types of skin illnesses, based on an improved Convolutional Neural Network (CNN) model. Our enhanced CNN design exceeds the standard CNN, which only achieves an accuracy of 31.38%, with an astounding accuracy of 98.21%. The accuracy of two benchmark algorithms, AlexNet and VGG19, is also not particularly high (32.14% and 52.30%, respectively). In contrast to traditional CNN models the enhanced CNN performs better, demonstrating its efficacy and dependability in accurately identifying a wide range of skin disorders. including well-known architectures like AlexNet and VGG19. Positive implications of this discovery include the development of more precise diagnostic tools for dermatology. © 2024 IEEE.

关键词： Benchmark Algorithms CNN Enhanced CNN Skin Disease Classification

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Automated Cleaning Solutions: Robotics Innovation in Homecare 4

Automated Cleaning Solutions: Robotics Innovation in Homecar...

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4th International Conference on Advancement in Electronics and Communication Engineering, AECE 2024

作者： Sujatha, E. Devi, S.Renuka Divya, M. Raju, D. Naveen Department of Computer Science and Engineering Chennai India Department of Artificial Intelligence and Machine Learning Chennai India Department of Computer Science and Engineering Chennai India R.M.K. Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350364729

A room cleaning robot, often known as a vacuum robot, is a clever appliance designed to simplify household cleaning tasks. It is equipped with sensors and brushes that allow it to navigate rooms, detect obstacles, and collect dirt and debris from floors. These robots can be programmed to operate autonomously, following predefined cleaning patterns or avoiding specific areas. They are also commonly controlled remotely through smartphone apps or voice commands, making them user- friendly and convenient. Room cleaning robots are known for their efficiency in reaching tight spaces and cleaning under furniture, which can be challenging for traditional vacuum cleaners. They often use rechargeable batteries, returning to their charging docks when the battery is low, ensuring uninterrupted cleaning They have become popular for their ability to maintain clean floors in homes and businesses without continuous human supervision. Additionally, their compact and sleek designs make them fit seamlessly into modern living spaces. As technology advances, these robots continue to evolve, offering improved cleaning performance and smarter features. © 2024 IEEE.

关键词： Vacuum cleaners

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Prediction of COVID-19 Severity Using Patient’s PHR 6th

Prediction of COVID-19 Severity Using Patient’s PHR

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6th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2021

作者： Bharathi, M.A. Meghana Kumar, K.J. Department of Artificial Intelligence and Machine Learning BMS Institute of Technology and Management Bangalore India Department of Computer Science BMS Institute of Technology and Management Bangalore India

ISBN: (纸本)9789811935749

A significant health crisis, including the current COVID-19 outbreak, presents us for an opportunity to think about it and focus on how we may improve the way we handle health care in the future to make us humans better prepared and capable of dealing with such an *** the COVID-19 trend has swayed irregularly, they have remained in the dark, unsure how much resources they will have even in the future *** these instances, difficult period to be capable of predicting exactly what sort of resources a person have it necessary now of a positive test, or perhaps even earlier, would take place extremely beneficial to organizations, as they will be able to get or make preparations with their resource required to save that patient’s *** aim of the work is to devise a system that would be both outlay and reliable. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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