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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Khan, Hafsah Arora, Deepak Rizvi, Sheenu Amity School of Engineering and Technology Amity University Lucknow Department of Computer Science & Engineering India

ISBN: (纸本)9798350321425

The study proposes an experimental model for robot-mediated systems in healthcare. The framework outline and required modules are discussed successively alongside the methodology needed to achieve the system's base functioning. The concept of response systems and their importance in building an efficient agent for healthcare support are also discussed. The study looks at the concept of human-machine interfaces, medical robotic systems, methods of implementation and design to achieve the conception, the underlying effects, and the public's opinion on technology in healthcare. The proposed system derives from the issues faced in conveying the full capabilities of a healthcare network. The idea behind the proposal is to provide a means to leverage different components of healthcare systems to attain a holistic model capable of serving various patient and staff needs. © 2023 IEEE.

关键词： Health care

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A deep-feature based estimation algorithm (DFEA) for catastrophic forgetting

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16771-16784页

作者： Thanikkal, Jibi G. Dubey, Ashwani Kumar Thomas, M.T. Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh UP Noida201313 India Department of Electronics and Communication Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh UP Noida201313 India Department of Botany St. Thomas College Kerala Thrissur India

A deep learning network is super-efficient for image processing. The artificial brain of the deep learning system is highly capable of solving the categorization of problems. However, deep learning has several restrictions on the intelligence of the human brain. The human brain can update and improve knowledge by analyzing the features of images. In the artificial brain, the arrangements of the pixels are used for image understanding. This pixel-based continuous learning tendency of the artificial brain of deep learning leading to reducing the knowledge of previously achieved relevant information of the system is called the catastrophic forgetting problem of deep learning. Recently, many researchers reported that the catastrophic forgetting problem affects the result of deep learning. So, in this study, we are providing an excellent solution for the catastrophic forgetting problem of deep learning by restricting the direct analysis of input images with image morphological features. This image morphological feature-based deep learning system helps the artificial brain to concentrate on image features. This image morphological features system-based study achieved a stable learning graph with 80% average learning accuracy. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Learning systems

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Multilingual Text Recognition System 1

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7th International Conference on Microelectronics, Electromagnetics and Telecommunication, ICMEET 2022

作者： Maithani, Mansi Meher, Dibyasha Gupta, Sumita Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Noida India

ISBN: (数字)9789811988653

ISBN: (纸本)9789811988646

Most of us take our reading and writing abilities for granted until the onset of visual impairment. After all, literacy is a key to personal independence and access to information. Tasks that require little thinking on our part, such as reading lables and business cards, can prove to be very challenging for visually impaired people. The proposed approach suggests the use of OCR techniques to extract information from printed and electronic documents and convert this information into a voice output for the benefit of the visually impaired. This paper considers the functioning of two of the common OCR engines, Tesseract OCR and EasyOCR. We have used these tools for the text recognition of Indian languages, Bengali and Tamil along with English text. We conclude this paper with a comparative study of these two tools by considering business cards as an input. From business cards we have tried to extract all legible data by using Tesseract and EasyOCR and compared these tools based on their accuracy parameter. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Optical character recognition

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Emotion based Music Player Through Speech Recognition 13

Emotion based Music Player Through Speech Recognition

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13th International Conference on Cloud Computing, Data science and engineering, Confluence 2023

作者： Malik, Mihika Gupta, Sumita Amity University Amity School of Engineering and Technology Department of Computer Science and Engineering Noida India

ISBN: (纸本)9781665462631

Music is a crucial part of our everyday lives;it provides a sense of stability and calmness to the mind and body giving birth to the concept of Music therapy. Conceptualizing the aspect of an emotion-based music player for the use case of Music Therapy pertains to be an innovative solution for Mental Health Issues. This paper aims to recognize variations in data for human emotion detection through their vocal parameters along with highlighting aspects of SER i.e., speech emotion detection. © 2023 IEEE.

关键词： Emotion Recognition

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Recommendation System Algorithms For Music Therapy 13

Recommendation System Algorithms For Music Therapy

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13th International Conference on Cloud Computing, Data science and engineering, Confluence 2023

作者： Singh, Anannmaya Gupta, Sumita Amity University Amity School of Engineering and Technology Department of Computer Science and Engineering Noida India

ISBN: (纸本)9781665462631

A recommendation system is an artificial intelligence or machine learning system that uses big data to suggest or recommend products to consumers. In this paper analysis of different types of recommendation system such as collaborative filtering & content filtering has been done through the help of implementations of different types of algorithms. Through those implementations an optimal algorithm which best suits our purpose of making a music recommendation system will be selected. Initially we will be looking at the different approaches implemented in the research papers and study them. In the paper implementations have been done for three algorithms i.e., Popularity based recommendation system, Item based recommendation system and Content filtering-based recommendation system. After the implementation of these approaches, it was found that item-based recommendation is the best model for the project and meets all our requirements. This paper also discusses most of the known approaches and gives a deep insight to their working & methodology. © 2023 IEEE.

关键词： Recommender systems

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A Sentiment Analysis Based Approach for Customer Segmentation

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Recent Patents on engineering 2022年第2期16卷 32-42页

作者： Bhatnagar, Anisha Bhatia, Madhulika Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Noida India

Background: Customer Segmentation is the process of dividing customers into groups based on some demographic factors in order to get an idea of the targeted audience for a product and to best market said product. Objective: Sentiment Analysis on customer reviews is one way that this process can be enhanced to get not just demographic information but subjective information and preferences as well. Methods: In this study, Long Short-Term Memory model, a deep learning technique, has been applied for Sentiment Analysis and its results have been used to perform Customer Segmentation on demographic data containing information such as age and gender. Segmentation was performed using Spectral Clustering. Cluster Labels were extracted to perform supervised classification using different supervised algorithms, such as Support Vector Machines, Random Forests, Decision Trees and Logistic Regression. Results: An accuracy of 90.9% was achieved by the LSTM model. An accuracy of 100% was achieved by the Random Forest and Decision Tree Classifiers. © 2022 Bentham science Publishers.

关键词： Decision trees

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Extensive Analysis of Deep Learning Algorithms for Automatic Detection of Liver Anomalies Using MRI and CT Scanned Images 4

Extensive Analysis of Deep Learning Algorithms for Automatic...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Chakraverti, Sugandha Sharma, Smita Shukla, Vijay Amity School of Engineering and Technology Amity University Uttar Pradesh Department of Computer Science and Engineering Greater Noida Campus India Amity School of Engineering and Technology Amity University Uttar Pradesh Greater Noida Campus India Greater Noida Institute of Technology Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350387490

The liver's segmentation during medical imaging is crucial for the diagnosis and prognosis of liver diseases., including fibroid cysts, malignant growths, and other pathological problems. The World Health Organization says that alcohol addiction, non-alcoholic fatty liver disease, chronic hepatitis infections, and genetic predispositions are among the risk factors that make liver cancer a primary cause of cancer-related death. Radiologists' traditional manual assessment of CT and MRI images takes a lot of time and is prone to mistakes. Medical picture segmentation has undergone a revolution because of developments in deep learning, namely convolutional neural networks (CNNs)., which automate feature extraction and increase accuracy. This comprehensive analysis investigates many deep learning techniques, such as CNNs, U-Net, autoencoders, GANs, and transformers, that are used for liver segmentation. The above-mentioned models have revealed a significant improvement in efficiency and accuracy in segmentation. These models' evaluation and assessment have been eased with datasets such as LiTS, CHAOS, MSD, SLIVER07, and many others. Some of the performance indices that are used to measure the effectiveness of these mathematical models. Despite this, there are still problems that persist such as the processing requirements, morphological variation, and dependency on labeled data. To improve model clinical relevance in the future, more work should be done to make them less sensitive to noise and to increase model interpretability. This analysis raises questions for further development to overcome the current limitations and reveal the potential of deep learning to transform the identification of liver abnormalities. © 2024 IEEE.

关键词： computerized tomography

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Electroencephalogram-based deep learning framework for the proposed solution of e-learning challenges and limitations

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International Journal of Intelligent Information and Database Systems 2022年第3期15卷 295-310页

作者： Pathak, Dharmendra Kashyap, Ramgopal Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Chhattisgarh Raipur492001 India

There is a high surge in usage of online e-learning platforms due to the current ongoing COVID-19 scenario. There are specific problems that persist in the current e-learning online models, i.e., validations and tracking of students’ learning curves, validation of presented course material, content-based personalisation as per the requirements of the students, identification of learning disabilities among students, etc. Our paper proposes the deep learning model to solve the issues related to existing machine learning models of manual feature extraction and training on limited data. Also, real-time e-learning data will be collected from students wearing EEG-headband while taking online classes. It solves the issues associated with conventional machine learning models and historical data. The proposed CNN model will classify the students on different grades and help in the development of an automated framework for the tracking of a student learning curve, providing recommendations for the betterment of e-learning course materials. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Feature extraction

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Momentary sensor based cricket ball design for bowlers performance analysis

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Multimedia Tools and Applications 2024年 1-34页

作者： Balbudhe, Pravin Sharma, Rika Solanki, Sachin Department of Computer Science & amp Engineering Amity School of Engineering and Technology Amity University Raipur Chhattisgarh India Directorate of Technical Education Government Polytechnic Campus Maharashtra Sadar Nagpur India

With rapid development in gaming technology, we have seen different apparatuses helping players, coaches, umpires, refries, and students for better decision-making and performance enhancement. This research work focused on development of the momentary sensors-based cricket ball for capturing minimal details of ball activity which cannot be reviewed or tracked with normal human eyes of cameras. This is more challenging because changes in ball motions are at the highest speed and need a high-speed sensor to track the details. We are presenting the custom-modified cricket ball having a perfect weight proportioned microcontroller and momentary sensor-based logic board inside. This hardware inside is capable of recording every minor rotational, traveler, and geographical information of the ball activity so that the data can be utilized for performance analysis. The experimental result shows that not all the information could be gathered from the single or similar working principal type of sensor, rather it needs the fusion of different sensors and still needs the right time synchronization between all sensor modules to capture the right statistical information. We achieved the result and presented it to the best coaches in the town, to have their opinions. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Decision making

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Customer Segmentation of E-commerce data using K-means Clustering Algorithm 13

Customer Segmentation of E-commerce data using K-means Clust...

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13th International Conference on Cloud Computing, Data science and engineering, Confluence 2023

作者： Rajput, Lucky Singh, Shailendra Narayan Amity University Amity School of Engineering and Technology Department of Computer Science and Engineering Noida India

ISBN: (纸本)9781665462631

Several data mining techniques, such as classification, clustering, regression, etc., are used to determine the purchasing behaviour of customers to create value for money in businesses. In this paper, clustering is implemented on a real-time data set of an e-commerce firm that aims to decide whether to focus on its website or mobile application. The K-means clustering algorithm is used to segment and cluster the users for the same purpose because of the scattered nature of the data and to find hidden patterns in the data set. To define the number of clusters, the elbow method is used, and customers are grouped with respect to different attributes. Based on the analysis, a decision is made about which set of customers to target. © 2023 IEEE.

关键词： K-means clustering

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