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Advancing Search Engine Functionality Through AI-Powered Audio Transcription

Advancing Search Engine Functionality Through AI-Powered Aud...

引用

2024 International Conference on Advances in Computing Research on Science engineering and Technology, ACROSET 2024

作者： Poply, Saksham Sharma, Arpit Kumar Rathore, Pramod Singh SystemsManipal University Department of IoT and Intelligent Jaipur India Manipal University Department of Computer and Communcation Engineering Jaipur India Manipal University Department of Computer and Communication Engineering Jaipur India

ISBN: (纸本)9798350388800

Efficiently navigating through extensive datasets is fundamental for enhancing productivity, facilitating informed decision-making, and fostering innovation. To achieve this, the integration of state-of-the-art technologies such as artificial intelligence (AI) and advanced search engines has become imperative in maximizing the utilization of available information. This paper presents a novel approach to address the challenges associated with traditional search methodologies. Contrary to conventional methods focusing solely on optimizing speed and indexes, our proposed solution leverages transcript generation for video/audio content distributed across the internet. By integrating transcripts into the search process, our approach aims to significantly enhance search capabilities, allowing for more precise and efficient retrieval of relevant multimedia content. Through empirical evaluation and comparative analysis, we demonstrate the efficacy and superiority of our method in improving search efficiency and accuracy. This research contributes to advancing the field of information retrieval by introducing a smart solution tailored to meet the evolving demands of contemporary digital ecosystems. © 2024 IEEE.

关键词： Decision making

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Enhancing Fashion Design with Conditional Generative Adversarial Networks: A CGAN Approach Using the Fashion-MNIST Dataset 12

Enhancing Fashion Design with Conditional Generative Adversa...

引用

12th International Conference on Internet of Everything, Microwave, Embedded, Communication and Networks, IEMECON 2024

作者： Kumar, Mukul Sharma, Arpit Kumar Rathore, Pramod Singh Department of IoT and Intelligent Systems Manipal University Jaipur Rajasthan Jaipur India Department of Computer and Communcation Engineering Manipal University Jaipur India

ISBN: (纸本)9798350387315

This research delves into the promising realm of Conditional Generative Adversarial Networks (CGANs) to explore their potential for crafting unique fashion item images. Leveraging the robust capabilities of the TensorFlow framework, along with essential tools such as NumPy and Matplotlib, we embark on a journey of model development and visualization. Our canvas of choice is the Fashion-MNIST dataset, meticulously curated to provide a rich tapestry for our CGAN to glean insights into the intricate nuances of various clothing articles. Throughout this paper, we peel back the layers of the CGAN architecture, unraveling its inner mechanisms, elaborating on the challenging yet gratifying training process, and meticulously evaluating the quality of the images it generates. © 2024 IEEE.

关键词： Generative adversarial networks

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Deep Learning for Low-Light Image Enhancement: A U-Net Approach

Deep Learning for Low-Light Image Enhancement: A U-Net Appro...

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2024 IEEE International Conference on Intelligent Computing and Emerging Communication Technologies, ICEC 2024

作者： Maniyar, Varun Raj, Aditya Sharma, Arpit Kumar Singh Rathore, Pramod Manipal University Jaipur Department of IoT and Intelligent Systems Rajasthan Jaipur India Manipal University Jaipur Department of Computer and Communcation Engineering India Manipal University Jaipur Department of Computer and Communication Engineering India

ISBN: (纸本)9798331508432

The existing solutions in the field of low light image enhancement are either too complex to implement and use or introduce unwanted noise during the enhancement process. To overcome these hurdles, we have developed a straightforward solution that prioritize simplicity yet provides high accuracy, minimizing noise and maximining enhancement quality. The main motive to develop this project was to utilize Convolutional Neural Networks (CNN) to improve the clarity of a picture clicked in low-light environments by capturing fine details of input image and enhancing those details. We trained the model using 'Mean Squared Error' loss and the 'Adam' optimizer. The output images are much more understandable, making it easier to identify object's and colour whereas also enhancing the overall quality of image. Our project showcases the capability of CNNs for image enhancement tasks, specifically within the low-light domain. The resultant model of this project can be used for day-to-day tasks which do not require complex implementations such as surveillance in residential areas. Future works may explore further improvements in this field by either introducing custom loss functions or a deeper neural network. © 2024 IEEE.

关键词： Convolutional neural networks

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Quality of Red Wine: Analysis and Comparative Study of Machine Learning Models 5

Quality of Red Wine: Analysis and Comparative Study of Machi...

引用

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

作者： Kumari, Sonam Misra, Anuranjan Wahi, Amitabh Rathore, Pramod Singh Greater Noida Institute of Technology Department of Computer Science & Engineering Greater Noida India Bhagwant University Department of Computer Science & Engineering Rajasthan Ajmer India Manipal University Department of Computer and Communcation Engineering Jaipur India

ISBN: (纸本)9798350321425

Wine is an alcoholic beverage made from different varieties of grapes after fermenting of grapes. There are different styles of wine varieties depends upon types of grapes used (with or without peeling of skins of grapes) and strains of yeast like red wine, white wine, rose wine, orange or amber wine. Wine can be made from others fruits, grains, cashew coconut, honey etc. Quality and taste of wine depends upon making process and aging of wine. Top 3 Wine producer countries in world are Italy, Spain, and France as per 2021 report. Red wine contains 5.5 to 20.5% of alcohol in it. Consumption of Red wine in right amount is good for health but consumption in large amount is bad for health. The quality of red wine may be predicted by using artificial intelligence techniques with the help of different chemicals parameters as attributes. In this paper, red wine datasets were used for training and testing purpose for the classification of red wine into 6 categories. 6 classes of data were converted into two class based on the quality index. Four different approaches of machine learning algorithms were applied to predict the classification of red wine on two class datasets. It was found that out of four algorithms, the decision tree classifier predicted better performance result of red wine quality compared to other machine learning classifiers. © 2023 IEEE.

关键词： Wine

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AI Enabled- Information Retrival Engine (AI-IRE) in Legal Services: An Expert-Annotated NLP for Legal Judgements 2

AI Enabled- Information Retrival Engine (AI-IRE) in Legal Se...

引用

2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Kanhaiya, Kishan Naveen Sharma, Arpit Kumar Gautam, Kamlesh Rathore, Pramod Singh Netaji Subhas University of Technology Delhi India National Institute of Technology Kurukshetra India Manipal University Department of Computer and Communcation Engineering Jaipur India Poornima College of Engineering Department of Advance Computing Rajasthan Jaipur India

ISBN: (纸本)9798350325799

The relationships between the law and fact, and the use of the facts to support a legal conclusion requires practice. This study introduces a technique called the "legal analysis of data extraction"which helps students/law practitioners to quickly assess a law article on the basis of the extracted attributes, and then apply their understanding in order to support a legal conclusion. To ease the processing of the law text data and retrieval of important information (entities) like names of Judges, appellants, respondents, judgment dates, etc. by utilizing NLP processes and ML techniques. This study demonstrates a novel approach of advanced information retrieval in the law articles leveraging natural language processing and machine learning techniques which are productized and used on live data as well. In this custom NER, aggregate accuracy of 95% is achieved using text classification followed by a regex fallback and flagging mechanism. This study consists of the following sections- a) Introduction b) Related Work c) About Dataset d) Methodology and approach e) Fallback mechanisms f) Results and Evaluations g) Conclusions & Future Scope with h) References. © 2023 IEEE.

关键词： Engines

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Advancing Search Engine Functionality Through AI-Powered Audio Transcription

Advancing Search Engine Functionality Through AI-Powered Aud...

引用

Advances in Computing Research on Science engineering and Technology (ACROSET), International Conference on

作者： Saksham Poply Arpit Kumar Sharma Pramod Singh Rathore Department of IoT and Intelligent SystemsManipal University Jaipur India Department of Computer and Communcation Engineering Manipal University Jaipur India Department of Computer and Communication Engineering Manipal University Jaipur India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

关键词： Productivity Technological innovation Navigation Ecosystems Focusing Speech recognition Search engines Streaming media Indexes Artificial intelligence

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Deep Learning for Low-Light Image Enhancement: A U-Net Approach

Deep Learning for Low-Light Image Enhancement: A U-Net Appro...

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Intelligent Computing and Emerging Communication Technologies (ICEC), International Conference on

作者： Varun Maniyar Aditya Raj Arpit Kumar Sharma Pramod Singh Rathore Department of IoT and Intelligent Systems Manipal University Jaipur Jaipur Rajasthan India Department of Computer and Communcation Engineering Manipal University Jaipur India Department of Computer and Communication Engineering Manipal University Jaipur India

ISBN: (数字)9798331508432

ISBN: (纸本)9798331508449

The existing solutions in the field of low light image enhancement are either too complex to implement and use or introduce unwanted noise during the enhancement process. To overcome these hurdles, we have developed a straightforward solution that prioritize simplicity yet provides high accuracy, minimizing noise and maximining enhancement quality. The main motive to develop this project was to utilize Convolutional Neural Networks (CNN) to improve the clarity of a picture clicked in low-light environments by capturing fine details of input image and enhancing those details. We trained the model using ‘Mean Squared Error’ loss and the ‘Adam’ optimizer. The output images are much more understandable, making it easier to identify object’s and colour whereas also enhancing the overall quality of image. Our project showcases the capability of CNNs for image enhancement tasks, specifically within the low-light domain. The resultant model of this project can be used for day-to-day tasks which do not require complex implementations such as surveillance in residential areas. Future works may explore further improvements in this field by either introducing custom loss functions or a deeper neural network.

关键词： Deep learning Image color analysis Surveillance Noise Neural networks computer architecture Communications technology Convolutional neural networks Object recognition Image enhancement

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Quality of Red Wine: Analysis and Comparative Study of Machine Learning Models

Quality of Red Wine: Analysis and Comparative Study of Machi...

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International Conference on Inventive Research in Computing Applications (ICIRCA)

作者： Sonam Kumari Anuranjan Misra Amitabh Wahi Pramod Singh Rathore Department of Computer Science & Engineering Greater Noida Institute of Technology Greater Noida India Department of Computer Science & Engineering Bhagwant University Ajmer Rajasthan India India Department of Computer and Communcation Engineering Manipal University Jaipur India

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Deep Learning-Based Early Detection of Breast Cancer: Improving Accuracy and Efficiency in Diagnosis for Enhanced Patient Outcomes

Deep Learning-Based Early Detection of Breast Cancer: Improv...

引用

2024 International Conference on Communication, computer Sciences and engineering, IC3SE 2024

作者： Mapari, Shrikant Mahmoud, Amena Sathyamoorthy, Malathy Sasikumar, P. Saini, Shilpa Awad, Nancy Awadallah Pune India Kafrelsheikh University Faculty Of Computers And Information Department Of Computer Science Kafr El-sheikh Egypt Sophia University Department Of Information And Communcation Sciences Tokyo Japan Kpr Institute Of Engineering And Technology Department Of Information Technology Tamil Nadu Coimbatore India Malla Reddy Institute Of Engineering And Technology Hyderabad India Chandigarh University Mohali India Sadat Academy For Management Sciences Cairo Egypt

ISBN: (纸本)9798350366846

In Todays era breast cancer is a main reason of deaths among women across the world. If detected at early stage it increases the survival rates of the patients. In the last few years, deep learning techniques have been used in medical imaging in the early detection of disease. This research focuses on the development of a deep learning-based mechanism for the early detection of breast cancer by ultrasound scan images. The proposed technique in this paper presents the usage of CNNs to extract relevant features from ultrasound images. A large dataset of annotated ultrasound scans is used for training the deep learning model. To measure the effect of the developed system, extensive experiments and validations are conducted using independent test datasets. In our work, CNN models have been trained to identify the three cases of breast tissues such as normal, benign, and malignant, and were optimized using Recursive Feature Elimination Optimizer and divide it into 80% for the training set and 20% for the testing set. VGG-19, ResNet-101 and ResNeXt architectures optimized by Recursive Feature Elimination optimizer are used for training and validation of the breast cancer dataset and get the accuracy of 98.5% for the optimized VGG-19 model. © 2024 IEEE.

关键词： Large datasets

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Healthcare Information Systems and Study of Machine Learning Technology for Cancer Detection

Healthcare Information Systems and Study of Machine Learning...

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： T. Naresh B. Gouri S.Riyaz Banu Y. Sravanthi R. Lakshmi Narayana Y. Suneel Kumar Department of Electrical and Electronics Engineering KSRM College of Engineering (Autonomous) Kadapa Kadapa India Department of Computer Science Engineering KSRM College of Engineering (Autonomous) Kadapa Kadapa India Department of Computer Science Engineering Annamacharya University Rajampeta Kadapa India Department of Electronics and Communcation Engineering KSRM College of Engineering (Autonomous) Kadapa Kadapa India Department of Instrumentation SK University Anathapuram Ananthapuram India Department of Electrical and Electronics Engineering SRIT College of Engineering Kadapa India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Cancer remains a significant global health challenge, with early and accurate diagnosis crucial for effective treatment. While traditional methods have relied on statistical approaches, the complexity of cancer necessitates more sophisticated techniques. This study explores the application of machine learning in cancer diagnosis, focusing on the classification of tumors as benign or malignant. By analyzing multidimensional medical data, machine learning algorithms can effectively identify patterns and predict cancer risk with greater accuracy than traditional methods. The study investigates the potential and limitations of machine learning in cancer diagnosis, addressing challenges such as data quality, model interpretability, and ethical considerations. The findings of this research contribute to the advancement of cancer diagnosis and pave the way for improved patient outcomes.

关键词： Renewable energy sources Accuracy Machine learning algorithms Focusing Machine learning Medical services Medical diagnostic imaging Cancer Tumors Information systems

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