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Fog Computing in Healthcare: Enhancing Recommendations and Real-Time Monitoring 5th

Fog Computing in Healthcare: Enhancing Recommendations and R...

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5th international conference on advances in Information Communication technology and Computing, AICTC 2024

作者： Salih, Ali Fadhil Al-Bakri, Nadia F. Computer Science Department College of Sciences AL Nahrain University Jadriya Baghdad Iraq

ISBN: (纸本)9789819761050

the embedded fog computing is motivating a major prototype transferal in healthcare systems. Branded by decentralized data processing and storage, moving these functions closer to the source of data generation, fog computing extends the power of the cloud to the edge of the network. Fog computing existence within the healthcare sector has pivotal role in enhancing healthcare recommendation services and real-time monitoring capabilities. this study aims to highlight the transformative impact of fog computing on healthcare, offering a detailed examination of how it addresses the shortcomings of traditional cloud-based models. Also, this study explores the technical mechanisms behind fog computing, illustrating how its unique architecture enables faster, more efficient, and more responsive services compared to centralized cloud models which can significantly impact patient care and outcomes. Furthermore, future directions in this field is discussed, paving the way for more advanced, efficient, and patient-centric healthcare solutions. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Fog computing

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A Comparative Selection of Best Activation Pair Layer in Convolution Neural Network for Sentence Classification using Deep Learning Model 7

A Comparative Selection of Best Activation Pair Layer in Con...

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7th international conference on Communication and Electronics Systems, ICCES 2022

作者： Nahar, Lutfun Zabu, Zinat Ara Raihan, Ahamed Islam, Md. Inzamamul Emon, Md. Ismail Siddiqi Noakhali Science and Technology University Department of Computer Science and Telecommunication Engineering Noakhali Bangladesh Feni University Department of Computer Science and Engineering Feni Bangladesh Morgan State University Department of Physics BaltimoreMD United States Brac University 66 Mohakhali Department of Electrical and Electronics Engineering Dhaka Bangladesh

ISBN: (数字)9781665496346

ISBN: (纸本)9781665496346

Many natural language processing jobs need sentiment classification of text content. there is an urgent need, particularly with the rise of social media, to extract meaningful information from vast volumes of data upon that Internet utilizing sentiment analysis. We're interested in adopting deep learning models to handle sentiment classification because of the advances that have been made. We present a framework named fastText along with Convolutional Neural Network in this study (CNN). To commence, we use fastText to create words vector representations that will be fed into the CNN. FastText's purpose is to generate a generative model of a word as well as reflect word distance. this one will enable the parameters to also be established at an advantageous CNN point, which will help neural nets to perform much better in this circumstance. Second, we create a CNN architecture that is appropriate for sentiment analysis. We use two sets of convolutional layers along with pooling layers in this design. this is the first time, to our knowledge, that a 9-layer architecture and design model is developed based on fastText as well as CNN was used to assess the sentim ent of phrases. We use the Rectified Linear Unit (ReLU), Normalization, and Dropout techniques to improve the accuracy and generalizability of our model. We put our methodology to the test on a publicly available dataset of movie review extracts with five labels: negative, slightly negative, neutral, moderately positive, and positive. In this dataset, ourReLU pairwise network obtains a test accuracy of 96.4 percent, outperforming existing neural network models. © 2022 IEEE.

关键词： Convolutional neural networks

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the Effect of Comorbidity on the Survival Rates of COVID-19 Using Quantum Machine Learning 7th

The Effect of Comorbidity on the Survival Rates of COVID-19 ...

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7th international conference on Data Management, Analytics and Innovation, ICDMAI 2023

作者： Shahapure, Arsheyee Banerjee, Anindita Deshmukh, Rehan Computer Science and Engineering Dr. Vishwanath Karad MIT-World Peace University Paud Road Pune411036 India Centre for Development of Advanced Computing Corporate Research and Development C-DAC Innovation Park Panchavati Pashan Pune411008 India School of Biosciences and Technology Dr. Vishwanath Karad MIT-World Peace University Paud Road Pune411036 India

ISBN: (纸本)9789819914135

We report the effect of comorbidity on the survival rates of COVID-19 using quantum machine learning. Recent understanding of the novel coronavirus aims to verify the target organ of the virus, which could lead shortly to significant advances in the diagnosis and treatment of infected patients. An overview of the impact of the SARS-CoV-2 virus based on many different parameters such as age, type of comorbidity, and gender has been studied. the data calculations are done manually by referring to parent articles and using machine learning and quantum machine learning algorithm. It is helpful to verify the target age group, gender at risk of infection, and survival rates of the person. the data used is classical data, and quantum algorithms were run on it. We found out that the accuracy has increased to the classical machine learning state vector machine results. We found that pulmonary diseases are the most harmful type of comorbidity when an individual gets infected with COVID-19. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Coronavirus

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Instantaneous Power based Effective Power Balance Operation in All-Electric Ship Microgrid

Instantaneous Power based Effective Power Balance Operation ...

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international conference on computer Applications In Electrical Engineering-Recent advances (CERA)

作者： K. Abinaya U. Sowmmiya Dept of Electrical and Electronics Engineering SRM Institute of Science and Technology Kattankulathur Chengalpattu Tamil Nadu India

Shipboard microgrids suffer from frequently varying, bulkier propulsion loads and the existing operation of diesel engines leads to increased emissions and thereby operational cost. Also, the propulsion loads and other fluctuating loads produce unbalance and nonlinearity in the system thereby demanding load compensation to maintain power quality. therefore, renewable sources and batteries are added to a hybrid shipboard microgrid (AC/DC network) to exert an uninterrupted power supply and to improve the power balance in the shipboard microgrid. the prime aim of this work is the usage of an interlinking converter capable of maintaining power balance in the system, bidirectional power transfer, and uninterrupted power supply through simple control methodology involving reference current generation based on instantaneous power theory. the instantaneous power theory involves the AC side source voltages and currents for the control and operates the interlinking converter at unity power during both PWM rectification and PWM inversion. the performance of the proposed control for an all-electric shipboard microgrid is validated in real-time through Software-In-Loop (SIL) approach by Opal 4510 Controller. the dynamic responses of the system through SIL are found to exhibit rectification at unity and preset power factors and also inversion at unity power factor with lesser total harmonic distortion.

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Text Data Augmentation Using Generative Adversarial Networks, Back Translation and EDA 7th

Text Data Augmentation Using Generative Adversarial Networks...

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Proceedings of the 7th international conference on advances in Computing and Data sciences, ICACDS 2023

作者： Ghadekar, Premanand Jamble, Manomay Jaybhay, Aditya Jagtap, Bhavesh Joshi, Aniruddha More, Harshwardhan Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9783031379390

Data or information augmentation techniques have been explored in NLP to create greater textual information for training. But, the overall performance advantage of present strategies is regularly marginal. Text augmentation can generate additional variations of the original text and improve the generalization ability of a machine learning model that processes natural language text data. the paper represents the performance of Generative Adversarial Networks for the overall performance in text classification. the results show that the Generative models give the best overall performance advantage over the EDA or back translation accuracy. the proposed models process text augmentation using GANs compared to methods like Easy Data Augmentation and back translation. the goal of EDA is to generate new, semantically similar sentences from an existing sentence, to increase the size and diversity of a dataset for training natural language processing models. Back Translation is an improved method with increased accuracy. Generative Adversarial Networks (GANs) generate new, synthetic data that is similar to a given training dataset using neural network architecture. In the context of text augmentation, GANs can be used to generate new, realistic text similar to a given text dataset. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Natural language processing systems

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Improved Forecasting of Electric Vehicle Charging Load using Neural Architecture Search

Improved Forecasting of Electric Vehicle Charging Load using...

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international conference on computer Applications In Electrical Engineering-Recent advances (CERA)

作者： Subhadeep Kar Sayanee Das Paramita Chattopadhyay Department of Electrical Engineering Indian Institute of Engineering Science and Technology Shibpur India

In the modern world, due to global climatic changes, the Automobile Sector is generally shifting towards Electric Vehicles due to its benefits over Internal Combustion Engines (ICE). this rapid increase in the Electric Vehicle Charging stations causes increase in overall Energy demand. thus, forecasting of power demand becomes a key ingredient for Electric Vehicle Charging Infrastructure Management. In this paper, two of the most popular Machine Learning forecasting models namely Bidirectional Long Short-Term Memory Network (Bi-LSTM) and Long Short-Term Memory Network (LSTM) are used for prediction of Electric Vehicle Charging Load demand whereby, both the network hyper parameters are tuned using traditional Grid Search Method and Neural Architecture Search (NAS) Method. the models are tested on the dataset provided by Electric Vehicle Supply Equipment (EVSE) using Adaptive Charging Networks (ACN) by California technology. the present work proposes a SARSA-based Neural Architecture Search (NAS) method to optimize the design of the forecasting models and results show satisfactory improvement in forecasting performance over models designed by conventional Grid Search-based approaches.

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A-Eye Tracker: Human Eye Defect Tracker and Analyzing Software 7th

A-Eye Tracker: Human Eye Defect Tracker and Analyzing Softwa...

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7th IFIP TC 12 international conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Swathishri, B. Swetha, R. Department of Information Technology St. Joseph’s Institute of Technology Chennai India

ISBN: (纸本)9783031699818

Eye tracking is a valuable method for detecting and analyzing eye movement in various research fields and healthcare environments. It can assist in patient diagnosis and treatment. However, the accuracy and reliability of eye-tracking tools have been a long-standing challenge in the field. this project presents a software application that utilizes DeepLabCut (DLC), a deep learning-based computer vision library, to track eye movements from a live stream video or a prerecorded video (.mp4) file. the application gives clinicians and researchers an affordable, reliable, and portable tool for eye tracking. this helps them identify and analyze eye defects more effectively. this software can generate useful outputs like eye movements as CSV data, and videos showing different labelled parts of the eye, and help doctors decide the most suitable treatment options over time. Additionally, this project showcases an example of a software application & its features built using the Dash Python framework. It is integrated with DLC API, which runs as a web service. Together they create an effective tool for eye tracking. this application offers an option to integrate a Convolution Neural Network (CNN) model into the web app resulting in customized features. It helps researchers to utilize it in their respective fields of study. the practical finding indicates that the developed application is highly effective in providing accurate and real-time eye-tracking capabilities. In summary, this project explores the difficulties and possible solutions in eye tracking using DLC. It also offers valuable insights into the future to research in this field. this project ‘A-Eye Tracker’ (Phonetically similar to AI). © IFIP international Federation for Information Processing 2024.

关键词： Diagnosis

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A Study on Qualia’s Experiential Model from the Concept of Service Innovation Design: A Case of 7-Eleven Service Experience in Taiwan 10th

A Study on Qualia’s Experiential Model from the Concept of ...

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10th international conference on Kansei Engineering and Emotion Research, KEER 2024

作者： Lin, Yi-Hang Lin, Po-Hsien Graduate School of Creative Industry Design National Taiwan University of Arts Taipei Taiwan Graduate School of Creative Industry Design National Taiwan University of Arts Professor Taipei Taiwan

ISBN: (纸本)9789819798896

the shift in the consumer landscape in Taiwan’s retail industry is due to technological advances and service differentiation. the added value of culture and creativity not only enriches lifestyles, but also changes consumers’ experiences and perceptions of products. this study uses the case study method to propose a research framework, combining the theories related to the three levels of qualia and service innovation, to explore in depth the correlation between the development of convenience superstores and qualia design, using 7-Eleven as a case study. the results are summarized as follows: (1) 7-Eleven has successfully shaped its differentiation and uniqueness after 2019 through "brand co-branded theme stores", and through the qualia analysis, it has found that the service contains moving stories and creative designs, and has finally realized the service innovation process of "shaping differences and establishing identity";(2) "Humanity" has been the core axis in the process of service enhancement, and the continuous development of science and technology has brought about more diversified experiences, rich products, and better service quality. the continuous development of technology has brought more diversified experiences, rich commodities, and a compounded field;(3) the innovative process of combining the concept of sustainable development through emotional space, emotional experiences, and qualia products can expand the influence of the specialty outlets, continue to build the image of "good neighbors in the community," and provide a new way of thinking about the development of "human nature" and "technology". © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Product development

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Deep Learning Ensemble for Diabetes Prediction: Integrating LSTM, DCNN, and SMOTE for Enhanced Risk Assessment 7th

Deep Learning Ensemble for Diabetes Prediction: Integrating ...

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7th IFIP TC 12 international conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Selvaraj, Ayyakkannu Satheesh Kumar, V. Bhudhwant, Dilip Tamane, Sharvari Khandelwal, Chhaya S. University Department of Information and Communication Technology MGM University Maharashtra Aurangabad India Department of Mechatronics Engineering Park College of Engineering and Technology Tamil Nadu Coimbatore India Department of Electronics and Computer Engineering Jawaharlal Nehru Engineering College MGM University Maharashtra Aurangabad India

ISBN: (纸本)9783031699856

Diabetes is a prevalent and chronic medical condition that poses a significant public health challenge worldwide. Accurate early prediction of diabetes risk can facilitate timely interventions and improve patient outcomes. In this study, we propose a comprehensive approach to diabetes risk assessment through the integration of deep learning techniques and data augmentation strategies. Our research leverages the power of deep learning ensembles by combining Long Short-Term Memory (LSTM) networks, Deep Convolutional Neural Networks (DCNN), and Synthetic Minority Over-sampling Technique (SMOTE) for addressing class imbalance. We begin by collecting and preprocessing a dataset comprising patient information, medical histories, and, where applicable, image data. the LSTM model is tailored to capture temporal dependencies in the data, while the DCNN extracts meaningful features. By integrating SMOTE, we tackle class imbalance challenges commonly encountered in medical diagnosis tasks. Our ensemble model seamlessly integrates the predictions from these diverse models, allowing for a holistic assessment of diabetes risk. Extensive evaluation and hyperparameter tuning are performed to optimize performance and ensure the model’s robustness. Cross-validation techniques are employed to gauge the model’s generalization capabilities. the proposed approach aims to not only provide accurate predictions but also to offer insights into the contributions of individual models within the ensemble. Ultimately, our research seeks to enhance diabetes risk assessment, offering a valuable tool for healthcare practitioners in making informed decisions and potentially improving the early detection and management of diabetes. this present study represents a significant step towards more reliable and comprehensive diabetes risk assessment, leveraging the potential of deep learning and ensemble techniques to advance predictive accuracy and broaden our understanding of this critical healthcare cha

关键词： Risk assessment

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Gun Detection Using Yolov7 5th

Gun Detection Using Yolov7

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5th international conference on advances in Information Communication technology and Computing, AICTC 2024

作者： Rizwana, Shaik Tomer, Vikas Singh, Prabhishek Diwakar, Manoj Yamsani, Nagendar National College of Ireland DublinD01 Ireland School of Computer Science Engineering and Technology Bennett University Greater Noida India CSE Graphic Era Deemed to Be University Dehradun India Graphic Era Hill University Dehradun India School of Computer Science and Artificial Intelligence SR University Warangal India

ISBN: (纸本)9789819761050

Guns have always been a problem and the main cause of disruptions to public safety across the globe. this is a serious problem that should not be disregarded. All public areas can benefit from monitoring and surveillance services provided by an autonomous visual gun detection model. Gun detection has never been able to obtain the right speed or accuracy in real time in previous works. A reliable model to detect guns will enable a prompt response and suggest safety precautions. After investigating various research papers for a Yolo algorithms-based gun detection model, I used the Yolo algorithms with prediction heads with two different datasets. the dataset contains curated gun images that were collected from multiple sources to train and validate the Yolo models. the Yolo gun detection model is a dependable and effective model for various images of firearms and their orientations, achieving 87% precision and 70% recall. the SOTA (state of the art) aimed at the deep neural architectures for security purposes is approached by this detection model. the most recent Yolo-based gun detection model allows automated surveillance and alert systems to identify firearm threats more quickly in real time. this model’s performance is adequate for embedded applications and video in closed-circuit televisions. the primary difficulties arise in situations where there is inadequate lighting and the firearm is partially visible, making it challenging to identify the object. A smaller number of True-Negative and False-Positive cases have resulted from the experiments. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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