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Care Compass: Monitoring made easy, Care made effective 3

Care Compass: Monitoring made easy, Care made effective

3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Manhas, Prabal Chandra, Joydeep Kaur, Ramanjot Sahay, Rashi Chandigarh University Punjab India Chandigarh University Dept.of Computer Science and Engineering Punjab Mohali India Manav Rachna International Institute of Research and Studies Faridabad India

ISBN: (纸本)9798331515911

Traditional healthcare focused on hospitals and clinics proved out to be inadequate, especially during the COVID-19 pandemic and various emergency crisis, Care Compass, is an innovative and affordable smart wearable device aimed at addressing critical health challenges faced by today's youth. The 21st century witnessed a large-scale transformation with the integration of Internet of Things and smart devices ranging from healthcare to manufacturing sector. The device integrates smart IoT devices and other technologies such as cloud computing platforms, health management mobile application with machine learning algorithms, offering real-time personalized health recommendations and continuous monitoring of vital health parameters. Emphasis is placed on accessibility and affordability, making Care Compass a viable solution for a broader demographic. The integration of Care Compass with clinical workflows is facilitated through collaboration with healthcare providers, enhancing the utility and effectiveness of the device in real-world applications. The results from initial testing demonstrate the potential of Care Compass to significantly improve health outcomes and quality of life for young individuals. © 2025 IEEE.

关键词： Electronic health record

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Text-based Emotion Recognition using Sentiment Analysis 1

Text-based Emotion Recognition using Sentiment Analysis

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1st International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2022

作者： Chris Jonathan, D. Juliet, Sujitha Karunya Institute of Technology and Sciences Dept.of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665497107

The reason for sentiment analysis is to distinguish the fundamental mentalities of individuals locally. The Internet is where clients of the world put their perspectives to various things. They talk about regular issues, whine about what they use, and offer positive or negative viewpoints about the item. This makes the Internet a significant source of data for opinion mining and sentiment analysis. In Software engineering, sentiment analysis has many purposes such as, recognizing thoughts that will further develop customers message conveyance and distinguishing their thoughts on a composed email. Furthermore, phenomenal composed data must be utilized for few articulations, accordingly lessening the possibilities of accurate analysis. The authors have gathered data on the utilization of instant messages that can be utilized to determine such issues. The fundamental reason for the task is to offer a concise composed expression, which is just enthusiastic. The proposed model aims to utilize two strategies and to realize what works best. The hardest part in sentiment analysis is figuring out the text(s) as per the thoughts. So, this model analysis data from corpora text and recognizes emotion out of it. In addition to customer satisfaction assessment and brand belief analysis, sentiment analysis through text is widely used currently. © 2022 IEEE.

关键词： Machine learning

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Monolithic 3D Printed Micro-Optofluidic Device for Two-phase Flow Monitoring and Bioapplications 13

Monolithic 3D Printed Micro-Optofluidic Device for Two-phase...

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13th IEEE International Conference and Expositions on Electric and Power engineering, EPEi 2024

作者： Saitta, Lorena Cutuli, Emanuela Celano, Giovanni Tosto, G Claudio Guarino, Francesca Cicala, Gianluca Bucolo, Maide University of Catania Dept.of Civil Engineering and Architecture Catania95125 Italy University of Catania Dept. of Electrical Electronic and Computer Science Engineering Catania95125 Italy University of Catania Dept. of Biomedical and Biotechnological Science Catania95125 Italy

ISBN: (纸本)9798350356199

In this work, a biocompatible 3D printed micro-optofluidic (MoF) device designed for monitoring two-phase flows is presented. It was manufactured using the Projection Micro-Stereolithography (PμSL) 3D printing technique combined with a biocompatible resin. This device successfully monitored both an air-water slug-flow and a two-phase mixture of micrometric eukaryotic yeast cells (Saccharomyces cerevisiae) suspended in a phosphate-buffered saline (PBS) solution. Its working principle relies on the absorption phenomenon, since different light transmissions are correlated to either the fluid or the cells interfering with a laser beam in a microchannel section. To accomplish this, the MoF device was designed with two micrometric slots for the insertion of optical fibers to capture the light signal. Both the micro-optical and microfluidic components, each 200 μm in size, were integrated into the MoF device as a monolithic structure. A wide experimental campaign was performed on the MoF device investigating different flow rates and cells concentrations. The optimal operative condition for achieving the most robust working ability were determined using a Design of Experiments (DoE) approach. © 2024 IEEE.

关键词： Two phase flow

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Achieving Linear and Systematic Perspectives to detect stroke rehabilitation exercise posture using Neural Network 3

Achieving Linear and Systematic Perspectives to detect strok...

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3rd International Conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2022

作者： Umamageswari, A. Deepa, S. Banu, J.Faritha SRM Institute of Science and Technology Dept.of Computer Science and Engineering Ramapuram Campus Chennai India

ISBN: (纸本)9781665456647

A lot of individuals suffer from motor impairment these days, led by various factors. The cause can be a stroke, accident, or even old age. Rehabilitation process for these people can be costly and ineffective, if not done properly. Integration of physiotherapy with technology can have positive outcomes. However, existing approaches are not much effective and are complex in their own ways. We present a unique system that consists of two parts: A model and a model for evaluating overall performance. With easy-To-understand instructions, the model provides feedback on exercise correctness. Individuals with chronic stroke did a variety of rehabilitation exercises while wearing inertial measurement units to capture upper body motion, and the model extracts clinically accepted features from the joint dataset. Human labelers detected primitives and labelled and segmented the associated dataset. We created a convolutional neural network model that outperformed existing models after performing automatic classification of these primitives. A data pre-processing module is included in the model to compute discrete entities of varying physical movements in sensor data. In addition, instance normalization replaces batch normalization. When the approach is applied to new patients, this leads to an increase in efficacy and probable distributional alterations. The simulations show that the virtual rehabilitation strategy suggested in this paper is more effective in existing systems than standard methods and introduces new thresholds. © 2022 IEEE.

关键词： Neural network models

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Comparative Analysis of Deep Learning Algorithms for Handwritten Digit Recognition 3

Comparative Analysis of Deep Learning Algorithms for Handwri...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Savaram, Padmaja Bandu, Sasidhar Lakshitha, B. Prince Sattam bin Abdulaziz University Dept.of Computer Science Al-Kharj Saudi Arabia Prince Sattam bin Abdulaziz University Dept. of English Al-Kharj Saudi Arabia Vnrvjiet Jntu Dept. of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331518592

Handwritten digit recognition presents a considerable challenge in pattern recognition due to the inherent variability in digit size, stroke width, orientation, and margin alignment. The difficulty is further exacerbated when different individuals write the same digit with varied stylistic nuances. Discriminating between visually similar digits, such as 1 and 7, 5 and 6, 3 and 8, 2 and 5, and 2 and 7, adds an additional layer of complexity. The offline handwritten digit recognition approach discussed in this paper leverages a range of machine learning methodologies. The objective is to establish robust and accurate digit recognition techniques, utilizing several algorithms including Artificial Neural Networks, Convolutional Neural Networks, K-Nearest Neighbors, and Recurrent Neural Networks. These algorithms are evaluated using the Modified National Institute of Standards and Technology (MNIST) dataset and the US Postal Service (USPS) dataset to identify the most effective model. The selection of the optimal machine learning approach is determined by factors such as dataset size, data complexity, and required accuracy. The Convolutional Neural Networks model demonstrated superior performance, achieving an accuracy of 98.58% with 10,000 MNIST images and 94.7% accuracy with 2007 USPS images when compared with other models. © 2024 IEEE.

关键词： Contrastive Learning

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IoT: Communication protocols and security threats

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Internet of Things and Cyber-Physical Systems 2023年第1期3卷 1-13页

作者： Gerodimos, Apostolos Maglaras, Leandros Ferrag, Mohamed Amine Ayres, Nick Kantzavelou, Ioanna School of Computer Science and Informatics University of Thessaly Lamia Greece School of Computing at Edinburgh Napier University Edinburgh United Kingdom Technology Innovation Institute Abu Dhabi United Arab Emirates School of Computer Science and Informatics De Montfort University Leicester United Kingdom School of Engineering Dept.of Informatics and Computer Engineering University of West Attica Athens Greece

In this study, we review the fundamentals of IoT architecture and we thoroughly present the communication protocols that have been invented especially for IoT technology. Moreover, we analyze security threats, and general implementation problems, presenting several sectors that can benefit the most from IoT development. Discussion over the findings of this review reveals open issues and challenges and specifies the next steps required to expand and support IoT systems in a secure framework. © 2023 The Authors

关键词： Network architecture

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An Efficient Machine Learning Model for Identification of Seagrasses through Morphometrics 2

An Efficient Machine Learning Model for Identification of Se...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Dubey, Ashwani Kumar Thanikkal, Jibi G Dilipan, E. Sharma, Puneet Shukla, Manoj Kumar Amity University Uttar Pradesh Aset Dept.of Electronics and Communication Engineering UP Noida India Amity University Uttar Pradesh Aset Dept.of Computer Science and Engineering UP Noida India Saveetha University Center for Marine Omics Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai77 India Sr University School of Computer Science & Ai Telangana Warangal India

ISBN: (纸本)9798350360523

Seagrasses are marine flowering plants capable of completing their life cycle underwater. Globally, 72 species of seagrasses have been identified, including 15 species from India. Artificial intelligence/ Machine learning techniques are gaining wide use in many fields, and their applications in marine sciences are still in the initial stage. Considering this, a study is attempted to identify seagrass species through Artificial intelligence/ Machine learning techniques. Since the characteristics of seagrass vary depending on location and other environmental factors, identifying seagrass species is regarded as a difficult task. In the present study, a known seagrass species is taken as a reference and its leaf morphological features are considered for shape description and deep learning-based identification. Leaf morphometrics of seagrass species are drawn to develop the databases. Morphological feature extraction algorithm, descriptor generation algorithm, and deep learning-based identification are applied for species identification. Existing artificial intelligence/ machine learning models use image pixels as input to the deep learning model. However, this is the first attempt to recognize seagrasses through their morphological properties. Here, the leaf shape property is utilized for seagrass identification. The proposed machine learning model received 60% accuracy during testing. Additional investigation was possible with the same algorithm for future seagrass species identification. Utilizing Artificial intelligence/ Machine learning algorithms may lead to identifying new seagrass species. © 2024 IEEE.

关键词： Learning algorithms

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An Effective Analysis of Machine Learning Models for Prediction of Heart Disease 2

An Effective Analysis of Machine Learning Models for Predict...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Patil, Rudragoud Kamkar, Shambahvi C. Goudar, Shwetha Indudhar K.L.S.Gogte Institute of Technology Dept of Computer Science and Engineering Karnataka Belagavi India K.L.S Gogte Institute of Technology Dept.of Master of Computer Applications Karnataka Belagavi India

ISBN: (纸本)9798350372892

Heart failure is a chronic illness that impacts millions of individuals worldwide. In order to forecast heart failure health status early and take the necessary activities to address this global issue an effective machine learning-based technique is required. Globally, heart disease is one of the leading causes of death. Medical professionals find it challenging to forecast since it demands experience and a deeper level of predictive knowledge. The healthcare industry operates in a knowledge-deficient but information-rich environment. Healthcare systems have access to a vast amount of data via the internet, but there aren't many useful tools for analyzing the data to find hidden patterns. Medical efficiency will increase with an automated system, which will also save money and time. This software uses information acquired from Kaggle to forecast the likelihood of a disease developing. To create a prediction model, the following algorithms are used: Random Forest (RF), support vector machine (SVM), K-nearest neighbor (KNN), Decision Tree (DT), and Logistic Regression (LR). Phases for feature selection and data preprocessing were finished before the models were created. When the models were evaluated based on accuracy and the F1-score, the RF model performed the best. © 2024 IEEE.

关键词： Adversarial machine learning

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Predictive Precision: A Comparative Study of ML in Alzheimer's Diagnosis

Predictive Precision: A Comparative Study of ML in Alzheimer...

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Trends in engineering Systems and Technologies (ICTEST), International Conference on

作者： E. Sujatha M. Subiksha Dept.of Computer Science and Engineering Saveetha Engineering College Chennai India

ISBN: (数字)9798350307795

ISBN: (纸本)9798350307801

Alzheimer's disease has become a significant contemporary health concern, impacting approximately 45 million individuals. This degenerative brain condition, prevalent among the elderly, lacks a well-defined etiology and pathogenesis. Dementia serves as the primary instigator, progressively inflicting damage upon brain cells and leading to a decline in cognitive functions such as thinking and reading. Those afflicted typically experience an average of 8 years of lifespan after the discernible onset of symptoms. However, this duration may vary, spanning from four to 20 years, contingent upon factors such as age and overall health. Addressing this critical issue, the integration of ML systems gives a great solution for disease prediction. The primary objective is to identify Dementia in diverse patient populations. This study encapsulates the outcomes and analyses derived from the detection of Dementia from many ML algorithms. The dataset collected from the Open Access Series of Imaging Studies (OASIS) serves as the foundational resource for system development. Comprising MRI data from 150 patients from the age gap of 60 to 96, this also undergoes comprehensive scrutiny and application across multiple machine learning *** ensuing comparative analysis seeks to ascertain the efficacy of different models in delivering optimal results. Through this investigation, the aim is to delineate the model that exhibits superior performance for the detection of Alzheimer's disease within the context of the OASIS dataset.

关键词： Analytical models Open Access Magnetic resonance imaging Sociology Prediction algorithms Data models Alzheimer's disease

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Visible Watermark Removal with Deep Learning Technology 6

Visible Watermark Removal with Deep Learning Technology

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6th International Symposium on computer, Consumer and Control, IS3C 2023

作者： Lin, Chia-Chen Wang, Pei-Yu Lin, Yan-Heng Huang, Hsuan-Chao Saberikamposhti, Morteza National Chin-Yi University of Technology Dept.of Computer Science and Information Engineering Taichung Taiwan Universiti Kebangsaan Malaysia Dept. of Information Science & Technology Selangor Malaysia

ISBN: (纸本)9798350301953

Watermarking is a technique used to assert ownership over an image, and can be categorized into visible and invisible forms based on the detectability of the watermark. Visible watermarking is more user-friendly and intuitive than invisible methods since it allows individuals to identify image ownership with their own eyes rather than relying on machine-based watermark decoders. To enhance the visual quality of watermarked images and ensure the original images can be fully recovered after visible watermark authentication, a visible watermark removal approach using deep learning-based inpainting is proposed in this paper. Experimental results demonstrate that the watermarked images carrying the visible watermark and auxiliary information achieve peak signal-to-noise ratios (PSNRs) ranging from 41.89 dB to 43.17 dB and structural similarity indices (SSIMs) up to 0.97 to 0.98. Furthermore, our hybrid recovery operations enable the complete restoration of the original images, making them easily readable. © 2023 IEEE.

关键词： Watermarking

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