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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Prasad, Arpana Asha, V. Nirmala, A.P. Madhushree, S. Kumar, Mrinal Sreeja, S.P. New Horizon College of Engineering Master of Computer Applications Bengaluru India

ISBN: (纸本)9781665462167

This study explores the use of machine learning approaches for addiction prediction. Addiction is a major public health problem, and there is a need for reliable methods of predicting which individuals are at risk for developing substance use disorders. Machine learning has emerged as a powerful tool for predictive modelling, and has been applied successfully to a variety of tasks in the field of medicine. A proposed Machine Learning model for addiction prediction from an ongoing study is presented in this paper. © 2023 IEEE.

关键词： Forecasting

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Forecasting COVID-19 Pandemic using Prophet, LSTM, hybrid GRU-LSTM, CNN-LSTM, Bi-LSTM and Stacked-LSTM for India 6

Forecasting COVID-19 Pandemic using Prophet, LSTM, hybrid GR...

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6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Prakash, Satya Jalal, Anand Singh Pathak, Pooja GLA University Department of Computer Engineering and Applications UP Mathura India

ISBN: (纸本)9798350346961

The COVID-19 Pandemic has been around for four years and remains a health concern for everyone. Although things are somewhat returning to normal, increased incidence of COVID-19 cases in some regions of the world (such as China, Japan, France, South Korea, etc.) has bred worry and anxiety in world, including India. The scientific community, which includes governmental organizations and healthcare facilities, was eager to learn how the COVID-19 Pandemic would develop. The current work makes an attempt to address this question by employing cutting-edge machine learning and Deep Learning algorithms to anticipate the daily incidence of COVID-19 for India over the course of the next six months. For the purpose famous timeseries algorithms were implemented including LSTM, Bi-Directional LSTM and Stacked LSTM and Prophet. Owing to success of hybrid algorithms in specific problem domains- the present study also focuses on such algorithms like GRU-LSTM, CNN-LSTM and LSTM with Attention. All these models have been trained on timeseries dataset of COVID-19 for India and performance metrics are recorded. Of all the models, the simplistic algorithms have performed better than complex and hybrid ones. Owing to this best result was obtained with Prophet, Bidirectional LSTM and Vanilla LSTM. The forecast reveals flat nature of COVID-19 case load for India in future six months.. © 2023 IEEE.

关键词： COVID-19

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Breast Cancer classification using Neural networks

Breast Cancer classification using Neural networks

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2023 IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2023

作者： Asha, V. Saju, Binju Mathew, Serene Athira, M.V. Swapna, Y. Sreeja, S.P. New Horizon College of Engineering Master of Computer Applications Bengaluru India

ISBN: (纸本)9781665462631

Nowadays, due to lack of awareness of breast cancer and its signs that show, as well as methods for prevention, causes them to be one of the most deadly types of cancer and the death rate has significantly increased. Hence, in order to stop the spread of cancer, early identification at a nearly stage is critical as well as important. Breast cancer is further classified in to two types, malignant and benign. This study used machine learning techniques and neural network methods to classify the breast cancer types. A system is automated to carry out its opinion that is also automated, for breast cancer. This approach uses DNN (deep neural network), CNN (Convolutional Neural Network) and ANN Artificial Neural Network) and RFE (recursive feature elimination) for feature selection. DNN is applied with a multitude of layers of functions processing is applied to categorize the breast cancer data set. The result shows DNN is comparatively more outperforming with an accuracy of 97%. © 2023 IEEE.

关键词： Convolutional neural networks

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A Survey on Aspect-based Sentiment Analysis for Online Purchased Product Feedback 10

A Survey on Aspect-based Sentiment Analysis for Online Purch...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Prakash, Yuvraj Sharma, Dilip Kumar Gla University Dept. of Computer Engineering and Applications Mathura India

ISBN: (纸本)9789380544472

Customer review can have a significant impact on the development of a product and the innovations that need to be done in it from time to time. Nowadays, it is really easy to get customer reactions. The organization may examine user opinions that are favorable or unfavorable about the product in the feedback or comments sections of social media and online retail platforms. As the success or failure of a product is directly tied to how well consumers like it, understanding their opinions may help businesses prepare for the future and improve their products. In this research, we explored studies that used aspect-based sentiment analysis to assess user opinions of online sold items. This method employs aspect terms by determining a connection between the terms to analyze the sentiment of the feedback. It means analyzing customer comments and statements about a product and drawing meaning from them. We have discussed articles from the last five years that analyzed the sentiment using Aspect based Sentiment Analysis on social media and e-commerce media. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Sentiment analysis

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Document Analysis using Deep Learning Techniques 4

Document Analysis using Deep Learning Techniques

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Shireesha, K.B. Thejas, P. Anupama Kumar, S. Rv College of Engineering Department of Master of Computer Applications Bangalore India

ISBN: (纸本)9798350300093

Document layout analysis is an automated process that recognizes and extracts the content and structure of various documents, including books, newspapers, and forms, using computer vision and machine learning techniques. Its main purpose is to enable accurate and efficient processing of vast amounts of digital documents in fields like digital preservation, document management, and information retrieval. Document layout analysis is widely applicable and can help with tasks such as data extraction, document classification, and information retrieval, which makes it an essential component of many modern information processing systems. This implements the layout parser and CNN architecture. The layout parser is a Python library wherein the library contains all the algorithms like R-CNN, CNN, and Faster-CNN. CNN is used to classify different components of the document based on their position in the layout, Spacy is used to extract the document and perform the natural processing tasks on the extracted text. The result is a structured representation of the layout and contents of a document that can be used for better document understanding, and information retrieval. © 2023 IEEE.

关键词： Information retrieval

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BatStyler: Advancing Multi-category Style Generation for Source-free Domain Generalization

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第6期35卷 5419-5430页

作者： Xu, Xiusheng Qi, Lei Zhou, Jingyang Geng, Xin Southeast University School of Computer Science and Engineering China Interdisciplinary Applications Southeast University Key Laboratory of New Generation Artificial Intelligence Technology Ministry of Education 211189 China

Source-Free Domain Generalization (SFDG) aims to develop a model that performs on unseen domains without relying on any source domains. However, the implementation remains constrained due to the unavailability of training data. Research on SFDG focus on knowledge transfer of multi-modal models and style synthesis based on joint space of multiple modalities, thus eliminating the dependency on source domain images. However, existing works primarily work for multi-domain and less-category configuration, but performance on multi-domain and multi-category configuration is relatively poor. In addition, the efficiency of style synthesis also deteriorates in multi-category scenarios. How to efficiently synthesize sufficiently diverse data and apply it to multi-category configuration is a direction with greater practical value. In this paper, we propose a method called BatStyler, which is utilized to improve the capability of style synthesis in multi-category scenarios. BatStyler consists of two modules: Coarse Semantic Generation and Uniform Style Generation modules. The Coarse Semantic Generation module extracts coarse-grained semantics to prevent the compression of space for style diversity learning in multi-category configuration, while the Uniform Style Generation module provides a template of styles that are uniformly distributed in space and implements parallel training. Extensive experiments demonstrate that our method exhibits comparable performance on less-category datasets, while surpassing state-of-the-art methods on multi-category datasets. Code is available here. © 1991-2012 IEEE.

关键词： Knowledge transfer

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Recent Trends in Sign Language Detection System Using Machine Learning Algorithms 2

Recent Trends in Sign Language Detection System Using Machin...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Sharma, Keshav Jain, Naman Upadhyay, Adarsh Mishra, Annu Chaturvedi, Ravi Prakash Pruthi, Jyoti Sharda School of Engineering & Technology Department of Computer Science & Engineering Greater Noida India Sharda School of Engineering & Technology Department of Computer Science & Applications Greater Noida India

ISBN: (纸本)9798350368109

This study summarises current advances in sign language recognition systems, emphasising trends, problems, and prospects. Twenty key research publications are analysed, spanning a wide range of sign language recognition topics, from low-power bio-inspired armband solutions to sophisticated neural network structures. These technologies' incorporation into human-robot interactions, as well as their potential to improve communication accessibility, are highlighted. The communication gap limits social integration, access to services, information, and education for individuals who rely on sign language. To address this issue, our research aims to develop an innovative method for real-time translation of sign language into spoken and written languages. We propose a comprehensive system called "SignText"that utilizes Large Language Models (LLMs) and Python programming to interpret and translate sign language into subtitles. Through this approach, we aim to bridge the communication gap and enhance the lives of individuals who use sign language. Initial results demonstrate promising accuracy, with an average recognition rate of over 90% for basic signs and gestures, and up to 85% and 70% for more complex and similar signs, respectively. These results show significant progress toward fostering greater inclusion and accessibility in social interactions and daily life. © 2024 IEEE.

关键词： Human robot interaction

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Prediction Analysis of Hypothyroidism by Association 3

Prediction Analysis of Hypothyroidism by Association

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3rd International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Saju, Binju Asha, V. Prasad, Arpana Varshitha, A. Abhishek, S. Sreeja, S.P. Master of Computer Applications New Horizon College of Engineering Bengaluru India

ISBN: (纸本)9781665494007

According to clinical data, '1 in 10' Indians suffer from thyroid disease. Hypothyroidism / Hypothyroidism is an endocrine disorder or condition in which the thyroid gland does not produce enough thyroid hormone. Predicting hypothyroidism in each individual based on profile and behavior is a challenging task in the current field. The current system is a scientific study in which experts suspect hypothyroidism based on symptoms. Hypothyroidism can only be confirmed after a battery of diagnostic tests. There are some populations that require more accurate testing to determine their thyroid status. Approximately 42 million people in India suffer from thyroid disease and hypothyroidism. There is no automation to predict hypothyroidism. System uses the concept of association-rule mining approach to find out the patterns of hypothyroidism. Frequent item set mining in dataset are used for application. The main focus includes minimizing the scanning of dataset and a better time complexity than the popular algorithms. The research tries to weigh all the hypothyroidism attributes indivually and calculate to predict the accuracy of the hypothyroisim. It evolves by analyzing the association that the attribute can contribute towards the occurrence of hypothyroidism. © 2023 IEEE.

关键词： Forecasting

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Privacy-preserving multi-keyword hybrid search over encrypted data in cloud

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 261-274页

作者： Singh, Niharika Kumar, Jitendra Singh, Ashutosh Kumar Mohan, Anand School of Computer Sciences Bennett University Uttar Pradesh Noida India CISIR Department of Electrical and Electronics Engineering University Teknologi Petronas Perak Seri Iskandar Malaysia Department of Computer Applications National Institute of Technology Tiruchirappalli Tamilnadu Tiruchirappalli India Department of Computer Applications National Institute of Technology Kurukshetra Haryana Kurukshetra India Department of Electronics Engineering Indian Institute of Technology-BHU Uttar Pradesh Varanasi India

Data outsourcing to the cloud is gaining popularity day by day in cloud computing. It requires a dynamic architecture that avoids loopholes and stops intruders from entering the cloud environment. To retrieve the enciphered documents from the cloud, the latest technique that is enrolled effectively is multi-keyword fuzzy search. The proposed research work uses this technique along with the n-gram corpus algorithm that helps to achieve relevancy score and sub-linear search time. This algorithmic combination is able to predict the privacy ranking measure of the retrieved data. To validate the opposed approach, simulation analysis has been performed to preserve user-oriented privacy in a cloud environment. The ranking precision of the proposed method is compared with (Fu et al. in IEEE Trans Inf Forensics Secur 12(8):1874–1884. https://***/10.1109/TIFS.2017.2692728, 2017) which uses a stemming algorithm and a matching algorithm based on the inner product. The results express the efficiency of the proposed approach by showing a relative improvement in security precision up to 55%. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

关键词： Privacy-preserving techniques

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Forecasting the Solar Power with Differentiation of Data Figures using Neural Networks

Forecasting the Solar Power with Differentiation of Data Fig...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Kavitha, T. Hemalatha, S. Kumar, R.V. Naveen Balan, S.Sakthi Sharan, V. Kongu Engineering College Perundurai Department of Computer Applications Erode India

ISBN: (纸本)9798350348217

The industrial sector relies heavily on solar power forecasting because it is dependable and readily available. High solar energy penetration is essential for the financial sustainability of power systems. The fact that solar energy is actually renewable is the most significant benefit of all the features of solar panels. Forecast models incorporate information from numerical weather prediction (NWP) factors such as cloud cover, sunshine, radiation, precipitation, etc. However, there is NTP Uncertainty in the assumption of the atmosphere's starting state. Advanced predictions are made in the meantime by machine learning algorithms. We need to estimate and compare the accuracy rates between Ann algorithm and Rnn algorithm for the outgoing solar power in this study using the dataset from the previous *** the upcoming month, the crucial machine learning algorithm will be created. The most extensively used 'multi-layer Neural Network' model of ANN and RNN should anticipate and compare electricity costs. Our research aims to accurately forecast solar power. The technique of forecasting the solar energy collected from solar panels for future use is attained by machine learning algorithms. The prediction and comparison will be carried out using the Artificial Neural Network and Recurrent Neural Network concepts in Machine Learning. © 2023 IEEE.

关键词： Solar energy

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