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Transformer-Based Self-Supervised Imputation for Medical Data 4

Transformer-Based Self-Supervised Imputation for Medical Dat...

引用

4th International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2024

作者： Singh, Aryan Kumar Saikia, Arpit Baro, Pranita Borah, Malaya Dutta National Institute of Technology Silchar Dept. of Computer Science and Engineering Cachar India

ISBN: (纸本)9798350391183

Dealing with missing data is necessary to develop a solid predictive medical research model. This work presents a hybrid dynamic imputation method devoted to addressing the challenge of missing data, specifically focusing on medical datasets. In this work, we investigate the incompleteness of the dataset using a sophisticated measure called the Incompleteness Factor. This metric is used to guide a hybrid dynamic approach. A novel Transformer-based Self-Supervised imputation method is employed to learn complex data dependencies to impute missing data. This paper introduces one potential solution to obtain model-level accuracy increase for tasks like disease prediction, patient prognosis, etc., over medical datasets. The proposed method aims to remove the measurement biases caused by incompleteness and preprocess the data to enhance the credibility and clinical relevance of the experimental results. © 2024 IEEE.

关键词： Self-supervised learning

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Leveraging Convolutional Neural Network for Real-Time Facial Emotion Detection

Leveraging Convolutional Neural Network for Real-Time Facial...

引用

2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Thakur, Sujata Negi Pandey, Muskan Tiwari, Vaibhavi Graphic Era Hill University Dept. of Computer Science and Engineering Haldwani India

ISBN: (纸本)9798350379716

Facial emotion detection is a very relevant research domain, having its applications in a wide diversified field of topics that include statistical analysis, the social sciences, and computer vision. Developing methods for machines that can recognize and interpret human emotions in real-time has been revolutionary for improving human-computer interactions, enhancing security systems, and helping with substantial insights from psychological studies. In this paper, it is proposed to develop a new model of emotion recognition where emotions through facial images will be classified based on seven different emotional features. Taking grayscale photos from a dataset called fer2013, we train a Convolutional Neural Network for the purpose of identifying these emotions. As far as the approach applied in this paper is concerned, the process of emotion identification is basically divided into three major phases: face detection, feature extraction, and emotion classification. We tried different dept.s and layers of CNNs, achieving as high an accuracy as 89.98%, which has huge potential for deep learning in advancing FER technologies. The contribution with this research is to developing efforts on more intuitive and responsive AI systems that can understand and respond to human emotions in an emotional sense. © 2024 IEEE.

关键词： Convolutional neural networks

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A Comprehensive Survey of Blockchain Technology-Trust as a Service 3

A Comprehensive Survey of Blockchain Technology-Trust as a S...

引用

3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Muthu, S. Esakki Kartheeban, K. Kalasalingam Academy of Research and Education Dept. of Computer Science and Engineering Srivilliputtur India

ISBN: (纸本)9798350396157

In today's rapidly changing digital world, security and trust remain as an essential element supporting a range of communications, transactions, and distributed systems. With the invention of blockchain technology, a trust may now be provided as 'Trust as a Service (TaaS),' a decentralized, verifiable, and essential asset rather than a centralized digital network. In a decentralized and distributed ledger network, the blocks are connected in chronological order, and information is saved using distributed ledger techniques. Since the start of the deployment, blockchain has kept a record of all transactions. This research study analyses the explores the blockchain technology and analyze how it functions as a trusted provider across a range of industries. Also, this study investigates the fundamental ideas behind blockchain, its development, and the cryptographic security systems. This research study illustrates how blockchain maintains confidence in data quality, security, transparency, and immutability across a range of applications, including banking, supply chain, healthcare, and governance. This study also emphasizes the difficulties and possibilities present in the rapidly developing blockchain ecosystem and further discusses about the issues and challenges and various blockchain attacks, solutions, and tools. Finally, the open issues and solutions are analysed. © 2024 IEEE.

关键词： Blockchain

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AI-Based Indian Currency Detection for Visually Challenged Users

AI-Based Indian Currency Detection for Visually Challenged U...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Shopika, S. Senthil Velan, A.S. Dharun Prakash, S.R. Pavithra, S. Lakshmi, R Poornima Ponnusamy, R. Chennai Institute of Technology Dept. of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798331510022

For denomination recognition, this Indian Currency Detection System will use a CNN trained on an extremely large and diverse set of Indian banknotes from 10 to 2000. It possesses an amicable interface through which images can be captured using the camera of the user's smartphone, providing real-time audio feedback to denominate the banknote without requiring any visual assistance. Made with the features of capturing an image, real-time recognition, and user-friendly design, these are made to add a great experience for smooth navigation by a blind person. The novel and innovative features make this Indian Currency Detection System unique. Using CNN, which has been trained on the comprehensive set of Indian banknotes from 10 to 2000, the software lets a user capture banknote image directly by the smartphone camera. It allows knowing the denomination through real-time audio feedback without any visual assistance. Such characteristics include image capture, real-time identification, and designing user-friendly user interfaces that make easy navigation of the surroundings possible for the visually impaired. It does more than detect currency. It significantly contributes to the discussion regarding the development and availability of more assistive technologies. It is usable and aims to bring about greater accessibility and inclusivity in promoting financial independence among visually impaired people. Contributing to the ability of cash transactions among the visually impaired to gain confidence in freedom and security, this project aligns with a broader discussion related to assistive technologies. © 2024 IEEE.

关键词： Assistive technology

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A Deep Learning Approach to Enhance Air Quality Prediction: Comparative Analysis of LSTM, LSTM with Attention Mechanism and BiLSTM

A Deep Learning Approach to Enhance Air Quality Prediction: ...

引用

2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Bhardwaj, Deepanshu Ragiri, Prakash Rao Netaji Subhas University of Technology Dept. of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350364866

In this study, we address the challenge of predicting air quality indices (AQIs) by leveraging Long Short-Term Memory (LSTM) networks. Recognizing the dynamic nature of air quality data, we explore the efficacy of three LSTM variants - standard LSTM, LSTM with an attention mechanism, and Bidirectional LSTM (BiLSTM) - in capturing temporal dependencies and improving forecast accuracy. Our methodology involved preprocessing a comprehensive dataset of air quality measures, followed by an experimental design that tested each model variant under different sequence lengths (30 and 365 days). The models were evaluated based on their mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) across three response variables: Nitrogen Oxides (NO), Particulate Matter up to 10 micrometers in size (PM10), and Particulate Matter up to 2.5 micrometers in size (PM2.5). Notably, when comparing the RMSE values across the three models, the LSTM with the attention mechanism consistently outperformed the standard LSTM and BiLSTM models, particularly with a sequence length of 30 days, indicating a superior ability to capture the intricacies of temporal dependencies in environmental data. Moreover, the shorter sequence length generally yielded better predictive performance, suggesting that longer sequences might introduce unnecessary complexity or overfitting. These indications of our study suggest a promising direction for the deployment of attention-based models in real-time AQI prediction systems, which could enhance the accuracy of environmental forecasts and inform public health decisions more effectively. Future work will explore the integration of additional environmental factors, the refinement of model frameworks, and the expansion of the dataset to include multi-city comparisons. © 2024 IEEE.

关键词： Mean square error

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Architectural Design for AdhaarAssist - A Forward-Looking Lock-Free STM Solution for Adhaar

Architectural Design for AdhaarAssist - A Forward-Looking Lo...

引用

2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Jafar, Sana Pranveer Singh Institute of Technology Dept. of Computer Science and Engineering Kanpur India

ISBN: (纸本)9798350385199

Any parallel program is judged based on its efficiency in doing multiple tasks at the same time without getting stuck due to an underlying problem. The main purpose of a parallel program is to effectively and quickly solve a large and complicated problem at hand. This is why;parallel programs form the basis of many powerful supercomputers where parallel computing is supported through special hardware which is complemented with parallel software achieving hundreds of petaflop/s of performance. The biggest challenge for a parallel programmer is to build a program that has a minimum tradeoff for increased parallelism and good concurrency control among the parallel tasks. One of the biggest applications of parallelism is the huge, ever-increasing database of Adhaar biometric. Its crucial to have a robust, fast and efficient way of accessing and manipulating this data so that along with faster results, consistency of the database is also maintained. This paper proposes a schema AdhaarAssist which is a lock-free STM based algorithm that will work on a shared in-memory data structure. Some of its features are increased concurrency by lazy conflict detection, partial aborts that optimize the algorithm for maximum throughput when complex and lengthy transactions are employed, memory barriers and timestamping. The algorithm proposed in this paper will open the way for leveraging it for distributed-shared in-memory databases like the one used by Adhaar. © 2024 IEEE.

关键词： Concurrency control

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Lip Reading Into Text Using Deep Learning 14

Lip Reading Into Text Using Deep Learning

引用

14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Manglani, Krishna Bahuguna, Utkarsh Gupta, Subhash Chand Amity School of Engineering and Technology Dept. of Computer Science Uttar Pradesh India

ISBN: (纸本)9798350344837

An essential part of computer vision, lip reading, has grown significantly and is now used in autonomous driving, public safety, and hearing-impaired communication. This work provides an in-dept. analysis of both conventional and deep learning-based lip reading systems, examining their development over time, difficulties encountered, and critical role in human communication. The suggested LipNet model, which leverages the complementary abilities of Long Short-Term Memory networks (LSTMs) for temporal modelling and Convolutional Neural Networks (CNNs) for spatial feature extraction, is a central focus of this research. When LipNet is tested using the GRID Corpus dataset, it outperforms baseline models in terms of performance. Saliency maps and viseme analysis highlight LipNet's ability to capture phonologically relevant areas in lip motions, providing insight into the network's attention processes. Apart from examining the importance of datasets such as OuluVs2and GRID Corpus, this study presents GhostNet, a lightweight model that tackles the requirement for effective network designs. The testing results highlight LipNet's competitive Character Error Rate (CER) and Word Error Rate (WER) in comparison to baseline models, proving that it is the best method for real-world use. The research ends with a summary of potential future developments in lip reading technology, such as modal integration, realtime applications, personalisation, ethical issues, and the goal of creating a communication environment that is more inclusive. This study highlights the effectiveness of LipNet and paves the way for future developments in the ever-evolving area of lip reading by examining a variety of methodologies. © 2024 IEEE.

关键词： Convolutional neural networks

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Intelligent IoT Platform for Real-Time Monitoring of Indoor Air Quality in Healthcare Facilities 12

Intelligent IoT Platform for Real-Time Monitoring of Indoor ...

引用

12th International Conference on Orange Technology, ICOT 2024

作者： Cheng, Yin-Ting Dept. Computer Science and Information Engineering SHU-TE University Kaohsiung Taiwan

ISBN: (纸本)9798331523602

In recent years, people have been confronted with unprecedented emerging infectious diseases. Medical institutions typically manage high-risk infectious diseases, which are transmitted via droplets or airborne routes, by using negative pressure isolation rooms to house patients and provide medical care. These isolation areas and rooms are specially designed to effectively prevent the spread of pathogens, with the goal of protecting healthcare personnel and preventing large-scale hospital-acquired infections. As most medical institutions have already completed the installation of hardware, wired network construction is not ideal for hospital environments, as it would require modifications to existing infrastructure, indirectly affecting the quality of the hospital environment, such as noise from construction and the layout of piping systems. Therefore, this study proposes an "Intelligent IoT Platform for Real-Time Monitoring of Indoor Air Quality in Healthcare Institutions." The system leverages smart sensors integrated with the Internet of Things (IoT), utilizing the small size and ease of deployment of the sensors as part of the overall technical framework. The platform also incorporates health and safety indicators relevant to hospital hygiene, enabling the monitoring of various environmental factors to ensure the safety of healthcare personnel. © 2024 IEEE.

关键词： Indoor air pollution

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Relocation Service Provider Application 3

Relocation Service Provider Application

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Shobha, Shobha Choudhary, Ankita Kour, Naindeep Prakash, Nitin Tejas, P.R. Nitte Meenakshi Institute of Technology Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350381931

This research aims to solve many problems people face when moving to a new place, such as housing, food, transportation, language differences and optimization. By creating recommendations, it is aimed to provide self-support to facilitate the transition process. The system will consider people's interests and consider different languages and cultures and budgets of people. This is useful in war or disaster environment. © 2024 IEEE.

关键词： Budget control

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Enhanced Machine Learning Approach for Detecting Spammers in Industrial Mobile Cloud Computing Environment 5

Enhanced Machine Learning Approach for Detecting Spammers in...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Bhavana, K. Sai Abhinandu, B. Sriyesh, P. Sai Rama, K. Satheesh Kumar, S. Institute of Aeronautical Engineering Dept. Computer Science & Information Technology Hyderabad India

ISBN: (纸本)9798331518097

This research study proposes an innovative machine learning algorithm, Spammer Identification Novel K-Means Extension (SINKEX), to fortify IoT mobile networks against commands and potential disruptions in industrial production. Utilizing an industrial mobile cloud dataset, the algorithm employs basic, content, and network features, including follower count, post frequency, message content, and hyperlink usage, for spam detection. After employing Pearson Correlation and Euclidean Distance to eliminate dissimilar records, Principal Component Analysis (PCA) is applied to remove irrelevant features. The SINKEX algorithm then labels unclassified records, designating an account as a SPAMMER or NON-SPAMMER based on features like follower count and post frequency. Comparisons with a hybrid fuzzy c-means clustering algorithm demonstrate SINKEX's superior performance, achieving higher accuracy, precision, and recall. The results are presented through an intuitive graphical user interface, showcasing the algorithm's effectiveness in securing industrial IoT networks against malicious activities. © 2024 IEEE.

关键词： Adversarial machine learning

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