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1st IEEE International Conference on Emerging research in Computational science, ICERCS 2023

作者： Poornima, D. Premalatha, J. Sarojini Abirami, G. Triveni, K. Bobby, M.P. Sathyabama Institute of Science & Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350359756

Real-time Artificial Intelligence (AI) and Machine Learning (ML) programs have emerged as a number one motive force for considerably altering patient care and management inside the healthcare control area. Staff shortages and affected person document management are among the fundamental problems dealing with the healthcare industry. In an effort to fight those issues, actual-time AI and ML technology offer a demonstration of optimism with the aid of facilitating quick decisions and higher affected person care. Having those technologies work together ensures that time-sensitive healthcare statistics is processed quick, which improves both useful resource allocation and service shipping. However, there are challenges related to the implementation of real-time AI and ML packages in healthcare management. Interoperability, the mixing of disparate records sources, and privateness and security concerns all pose great difficulties. In addition, an intensive and powerful approach is needed to assure the processing of healthcare facts in real-time. To better manipulate patient facts inside electronic health information, an IoT-based totally Artificial Deep Neural Network (IoT-ADNN) is offered in this studies. There are a huge style of revolutionary uses for actual-time (AI-ML) in healthcare administration. These gear provide healthcare carriers with actual-time information, which in flip results in better resource allocation and a more gratifying affected person revel in. In addition, they enhance information security and accessibility, making sure privateness and ease of get entry to to touchy healthcare facts. Data transmission and shipping quotes, postpone estimation accuracy, energy performance, and throughput have all been dramatically improved. The importance and usefulness of actual-time AI and ML systems in healthcare management are emphasised by means of these measurements. © 2023 IEEE.

关键词： Deep neural networks

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Predicting health insurance claim frauds using supervised machine learning technique 8

Predicting health insurance claim frauds using supervised ma...

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8th IEEE International Conference on science, technology, engineering and Mathematics, ICONSTEM 2023

作者： Veena, K. Supriya, S. Deepa, D. Cruz Antony, J. Karpura Dheepan, G.M. Pavan, Ayila Sri Dharma Siva Pagadalahariprasad Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350347791

The healthcare sector is growing quickly, making it a complex system. At the same time, fraud in this sector is becoming a serious issue. Misuse of the medical insurance systems is one of the problems. The automated detection of healthcare scams uses data mining and machine learning approaches. In this work, an effort is made to provide a review of healthcare industry frauds and the methods for spotting them. A proposed solution to address issues in health insurance claims involves using a machine learning model that focuses on utilizing appropriate methods, identifying key data sources, and examining the properties of healthcare data. To gain insight into the patterns of the features, univariate and bivariate analysis are conducted on the data. Afterwards, proper data visualization is performed to determine which feature has the greatest impact. Based on the pre-processed data, a machine learning model is constructed and compared with four algorithms: Logistic Regression, Random Forest, Decision Tree Classifier, and Naive Bayes. The highest accuracy was achieved through the Decision Tree Classifier algorithm, with an accuracy of 97.03%. © 2023 IEEE.

关键词： Health insurance

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High-Frequency Performance with Microwave and Millimeter-Wave Methods 1

High-Frequency Performance with Microwave and Millimeter-Wav...

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1st IEEE International Conference on Emerging research in Computational science, ICERCS 2023

作者： Poornima, D. Jany Shabu, S.L. Refonaa, J. Karunya, K. Bobby, M.P. Sathyabama Institute of Science & Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350359756

Modern wireless communication, radar systems, and the realization of new technologies like 5G and beyond all rely heavily on high-frequency performance. The use of microwave and millimeter-wave techniques (MMWT) could provide revolutionary new uses for vast amounts of data at extremely low latency. High-frequency performance optimization is still a challenging and essential problem. Interference Mitigation, Antenna Design, Signal Processing, and Regulatory Compliance are all considered to be sizeable obstacles towards accomplishing High-Frequency Performance. The motive of the research is to advocate the use of Regulatory Compliance Frequency Band Analysis (RCFBA) for the purpose of figuring out target frequencies in accordance with utility necessities and acting spectrum scanning and evaluation with the intention to assess regulatory constraints and available channels. Maximizing signal energy even as keeping off interference via cautiously choosing and positioning antennas. Enabling higher data throughput, decrease latency, and a wider variety of applications for high-frequency systems by way of easing their adoption and deployment in fields like telecommunications, radar systems, satellite communique, and 5G networks. High-frequency performance can be modelled and evaluated with the help of present day modeling tools, that may then be used to optimize gadget parameters, propagation, and antenna designs. It equips users with the way for thorough simulation evaluation, optimization, and fulfillment in excessive-frequency packages, taking into account the deployment of excessive-frequency structures that healthy the desires of contemporary packages and regulatory standards. © 2023 IEEE.

关键词： Regulatory compliance

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Predicting Chances of Cardiovascular Diseases Through Integration of Feature Selection and Ensemble Learning 1

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11th International Conference on Recent Trends in Computing, ICRTC 2022

作者： Bhardwaj, Raghav Mishra, Shashvat Gupta, Isha Paliwal, Shweta Department of Computer Science and Engineering Meerut Institute of Engineering and Technology Meerut India

ISBN: (数字)9789811988257

ISBN: (纸本)9789811988240

Heart problems are among the top most notable problems in the field of medical science. The prediction of diseases related to heart is a peculiar challenge in the field of clinical analysis, and hence, it can be observed that heart problems have been the major death reason globally. The integration of different techniques of machine learning (ML) has proven to be an effective aid in decision making and helps in prediction from the diverse quantity of the data which is produced as a result of the observations in the field of health care. In this study, analysis of a real-time dataset has been performed using the integrated concept of feature selection. Different classifiers of ML are subject to parameter optimization, and values of classical performance measures are being observed. An interface has been designed to give user a pictorial view where the system will recommend what are the chances that an individual is suffering from heart disease. The identification of heart disease is based upon the proposed algorithm and identified features from the real-time dataset. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Electrocardiography

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A Framework for Collaborative Computing on Top of Mobile Cloud Computing to Exploit Idle Resources

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Annals of Data science 2023年第6期10卷 1635-1651页

作者： Babu, A. Ramesh Upadhayaya, Niraj Research Scholar Department of Computer Science & Engineering Jawaharlal Nehru Technological University Kukatpally Telangana Hyderabad500085 India J.B.Institute of Engineering & Technology Moinabad Telangana Hyderabad India

In the contemporary era, collaborative computing is the widely used model to exploit geographically distributed heterogeneous computing resources. Mobile Cloud Computing (MCC) offers an infrastructure that helps in offloading storage and computing resources to a public cloud. It has several advantages. However, in the context of modern Internet of Things based applications, it is essential to exploit idle resources of mobile devices as well. However, it is a challenging problem as mobile devices are resource-constrained and have mobility. Many existing MCC solutions concentrated on offloading tasks to outside mobile devices. In this paper, we investigate the possibility of using idle resources in mobile devices besides offloading tasks to the cloud. We proposed a novel algorithm known as Delay-aware Energy-Efficient Task Scheduling. The algorithm analyses locally available idle resources and schedules tasks over heterogeneous cores in mobile devices and also the cloud. In the process, it achieves strict deadlines associated with tasks and promotes energy conservation. A prototype application is built to simulate and evaluate the proposed algorithm. The experimental results revealed that the algorithm outperforms the existing baseline algorithms. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Mobile cloud computing

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Content recommendation based on recognised Emotion

Content recommendation based on recognised Emotion

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2023 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence, RAEEUCCI 2023

作者： Challa, Sireesha Gowri, Manohari V. Roobini, M.S. Nandhini, P. Daphine Desona Clemency, A.C. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350337426

E-commerce, video websites, and social networking sites are just a few examples of where recommendation algorithms are currently being used. As the amount and variety of data used in recommendation systems grows, more and more of it will be unstructured multimodal data, including text, images, and videos. Yet, it is difficult to employ unstructured multimodal data to enhance the effectiveness of recommendation schemes because of the illustration gap between different modalities. In this paper, we present a new multimodal framework for user-driven ensemble modelling based on the recommendation of mood descriptions. In contrast to standard approaches, our video reference formulation makes use of not just the audio stream but also the face (emotion) and textual modalities that together make up a video document's elementary data. Audio data is used as input by algorithms like Gaussian Naïve Bayes (GNB), Logistic Regression (LR), and Convolutional Neural Network (CNN), whereas text data is used by methods like, LR, Long-short Term Memory (LSTM), and Naive Bayes. The user's current disposition is used to inform recommendations for appropriate music, quotes, etc. generated by these ensemble models. Comprehensive experiments on real-world datasets demonstrate that, across a wide range of assessment measures, the proposed ensemble model greatly outperforms the state-of-the-art baselines. © 2023 IEEE.

关键词： Recommender systems

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Diabetic Foot Ulcer Detection and Classification using ConvMixer Model 5

Diabetic Foot Ulcer Detection and Classification using ConvM...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Anusha, N. Vidhya, R. Jani Anbarasi, L. Dhanya, D. Pushparani, S. Mohan, Geetha Vellore Institute of Technology School of Computer Science and Engineering Department of IoT Vellore Campus Vellore India Agni College of Technology Department of Information Technology Chennai India Vellore Institute of Technology School of Computer Science and Engineering Chennai India Mar Ephraem College of Engineering and Technology Department of Artificial Intelligence and Data Science Elavuvilai India Meenakshi College of Engineering Department of Cse Chennai India Institute of Applied Technology Computer Science Abu Dhabi United Arab Emirates

ISBN: (纸本)9798331504403

Diabetic foot ulcer (DFUs) are a common and significant consequence of diabetes, frequently resulting in severe illness and amputations if not promptly identified and treated. The growing occurrence of DFUs is a substantial obstacle to healthcare globally. Precise and prompt categorization of DFUs is essential for efficient treatment and avoidance of consequences. This research analyzed ConvMixer model for comprehensive classification designed to accurately evaluate DFU. The DFU images are preprocessed using the watershed algorithm that helps in separating different regions of an image, such as the ulcer and surrounding tissues, to improve the neural network's ability to extract high-quality information. The framework utilizes depthwise and pointwise convolutions to mix spatial and channel-wise information in an efficient manner to effectively extract pertinent information from various clinical DFU dataset. The proposed work features a multi-layered depthwise and pointwise convolutional architecture, created especially to manage the complex patterns and variances found in various diabetic foot ulcer stages and variants (DFUs). By integrating these convolutions and skip associations, the network facilitates hierarchical extraction of features and robust representation of features. Experimental results on a substantial and varied DFU dataset demonstrate that the ConvMixer model achieves an impressive accuracy rate of 97.16%, with a precision, F1-score and a sensitivity of 97%, 97% and 99.08% respectively. © 2024 IEEE.

关键词： Diseases

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Real-Time Face Mask Detection System for Public Safety Based on ResNet50 & YOLOV2 15

Real-Time Face Mask Detection System for Public Safety Based...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Tun, Nay Kyi Godwin Premi, M.S. Myanmar Institute of Information Technology Mandalay Myanmar Department of Electronic Computer and Engineering Satayabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798350370249

The covid epidemic started in 2019 and has become the most terrifying disease that has destroyed many people's family lives, and it is frightening as an event that cannot be forgotten for the whole country to this day. To prevent the disease, the WHO has issued warnings to keep hands clean and to wear a mask whenever people go out to prevent the spread of the disease. For those involved in policy implementation;it is very difficult to identify people without a mask or wearing it in an improper manner. Crowded shopping centers, markets, hospitals, we all know that it has become an important matter to monitor whether the mask is mandatory in the departmental areas. However, monitoring in real time has become a waste of manpower and money. Therefore, even today, we still see the development of deep learning-based technologies that can monitor whether a facemask Detection based on CNN is implemented as one of the Deep Learning methods. In this system, transfer learning and ResNet-50 is used for Face Mask Detection. The dataset is collected from CCTV in the university and also standard dataset (Kaggel) for the training and testing. The main contribution of the proposed work is DL based real-time system development using the proposed CNN for determining whether the people on CCTV is wearing mask, not wearing mask and improperly mask. This system will provide better result in many position. © 2024 IEEE.

关键词： Classification CNN Detection ResNet50 YOLO

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Cyber-Ego: The Digital Self 9

Cyber-Ego: The Digital Self

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Saravanan, R. Aishwarya, R. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350397376

Cyber Ego expands to Cyber and Alter-ego. In this proposed paper, I have integrated four deep learning models for performing the tasks - natural language generation, text-to-speech, speech-driven facial animation and image animation as a combined framework to generates a video of the character speaking the generated content with the character's voice. Text data and audio voice lines have been collected and pre-processed to train the text-to-speech model. While pre-trained models are fine-tuned and used for the speech-driven facial animation model and image animation model. To put everything together in an interactive way, a Flask-based Python web application is created to provide as an interface that handles user inputs and generates the result. © 2023 IEEE.

关键词： Animation

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Data-Driven Insights: Leveraging Analytics for Predictive Modeling in Finance 4

Data-Driven Insights: Leveraging Analytics for Predictive Mo...

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

作者： Purwar, Mitali Deka, Upasana Raj, Himadri Ritu Upes Department of Computer Science & Engineering Dehradun India Noida Institute of Engineering & Technology Department of Computer Science & Engineering Greater Noida India Chandigarh College of Engineering Chandigarh Group of Colleges Department of Computer Science & Engineering Jhanjeri140307 India

ISBN: (纸本)9798350387490

A distinct gap in the most recent business intelligence literature is addressed by this research, which discusses the possibility of radical changes in the market based on the application of predictive analytics. The key goal is to evaluate the impact of the prediction models on ethics, expansiveness, precision and reliability. The evaluation is of a qualitative nature, and topic analysis along with case histories is used to acquire specific, contextualised data. Basic findings indicate that employment of market forecasting skills is enhanced through the use of predictive models since decisions are made much more deliberately. The analysis reveals that these models remain prone to change in a variety of contexts and demonstrates their capacity to deal with large data efficiently. Ethical issues is stressed out, which leads to the conclusion that there should be stricter rules for personal data protection and more openness for procedures to gain the compliance and trust. The conclusions underline the major competitive advantages that, if applied line by line, predictive analytics can offer, calling for more adaptability and sensitivity to the market trends. This report which proposes a 50-50 mix of value creation and integrity does, nonetheless;provide notice of potential ethical issues and data privacy. The guidelines for enhancing the accuracy of the prediction, which complies with the ethical standard are strengthening the information management system and developing novel predictive model technologies. Future research must continue to analyze shifts in predictive analytics understanding new technologies and possible changes in the market. © 2024 IEEE.

关键词： Prediction models

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