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A Review on Implementation of Wearable Devices in Healthcare 7

A Review on Implementation of Wearable Devices in Healthcare

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Suman, Saurabh Choudhary, Vishal Kumar Pacharaney, Utkarsha Kumar, Abhishek Kumar, Dhiraj Raushan, Rahul Faculty of Engineering And Technology Department of Computer Science And Design Maharashtra Wardha India Faculty of Engineering And Technology Department of Computer Science And Medical Engineering Maharashtra Wardha India Faculty of Engineering And Technology Department of Artificial Intelligence And Machine Learning Maharashtra Wardha India

ISBN: (纸本)9798350359299

The capacity to get additional patient data, including clinical, behavioral, and self-monitored data, has enhanced by the expanding usage of wearable technology. Large volumes of previously unobtainable data are now available, providing a chance to create new instruments with sophisticated analysis and decision support systems for application in clinical settings. Additionally, it gives patients additional options by arming them with personalized decision-support resources and information, enabling them to take charge of their own health care while maintaining their sense of autonomy. Especially well-suited for miniaturization and integration into microfluidic devices are biosensors with electrochemical sensors. Applications cover activities in the domains of diagnostics, food and environmental control, and the identification of complete cells, the parts of cells, proteins, and tiny compounds. Microfluidic technologies can be as basic as sample transport tubes or as sophisticated as platforms with extra components for sample preparation and sensing electrode integration. The kind of detection, application, and sample material all influence the microfluidics design. The next issue for the healthcare industry will be how to use newly acquired information in a way that benefits patients, healthcare organizations, and professionals all while reducing costs and workloads. Given the current limitations, the challenge for wearable technology designers is to provide solutions that healthcare practitioners can readily integrate and use. © 2024 IEEE.

关键词： Biosensors

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Advanced Energy Consumption Forecasting in Smart Grids Using Quantum Boltzmann machines 2

Advanced Energy Consumption Forecasting in Smart Grids Using...

引用

2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Velayutham, P. Balaji, V.S. Krissh Shankaran, N. Aarupadai Veedu Institute of Technology Department of Computer Science and Engineering India Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning India

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388916

Smart grid systems are seen as the next big thing to energy distribution and management with more efficiency, stability, and sustainability in power distribution and energy management systems. It is challenging to predict the amount of energy consumption in smart grid system because of its randomness resulting from various factors such as consumer's behavior, weather conditions. This is mainly because traditional forecasting techniques are usually inadequate in capturing these complexities and as a result resources are poorly allocated and organizational processes become inefficient. The work provides the energy consumption forecasting in smart grids through the application of Quantum Boltzmann machine(QBM) in the smart grid utilizing historical data. Some of the variables incorporated in the data are reaction time, power balance, and price elasticity coefficients customized through smart meters. Once raw data is preprocessed, QBMs are developed to establish an empirical probability model that will predict actual future energy utilization characteristics. The QBM is tested against the actual consumption data and focuses on the visualization of energy profile and identification of points like local extremes. © 2024 IEEE.

关键词： Smart power grids

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Building a Data Lake for Power BI in the Cloud: A Review on Utilizing Cloud Storage Services for Large Datasets 2

Building a Data Lake for Power BI in the Cloud: A Review on ...

引用

2nd DMIHER International Conference on artificial intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Gurbade, Viraj Vijay Verma, Prateek Gundewar, Swapnil Bramhe, Vijendra Singh Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Vit Bhopal University School of Computing Science and Engineering Kothrikalan Madhya Pradesh Sehore466114 India

ISBN: (纸本)9798331528713

A data lake is a centralized repository where you may store both structured and unstructured data at any scale. Unlike typical data warehouses, which need data structure for storage, data lakes allow you to store raw data in its natural state. This adaptability makes data lakes suitable for storing large volumes of various data kinds, including photographs, videos, log files, sensor data, and others. When integrating a data lake with Power Business intelligence (BI), organizations can harness the power of both technologies to gain valuable insights from their data. In the realm of data-driven decision-making, organizations face the challenge of managing and extracting insights from vast and diverse datasets, a task conventional on-premises storage solutions often struggle with due to the exponential growth of data. Thus, the adoption of more scalable and flexible alternatives becomes imperative. Cloud-based data lakes emerge as transformative solutions, offering unparalleled scalability, accessibility, cost-effectiveness, and advanced security protocols. This paper discuss the concept of constructing a cloud-based data lake optimized for Power BI, leveraging leading cloud storage services like Azure Data Lake Storage (ADLS) and Amazon S3. By seamlessly integrating Power BI with the cloud data lake, organizations can overcome traditional storage limitations and reap numerous benefits. The practical applications in advanced analytics, customer insights, IoT data management, and data discovery underscore the real-world utility of cloud data lakes. The integration of Power BI with a cloud data lake augments efficient data analysis and self-service analytics potential, offering organizations a comprehensive solution for deriving actionable insights from their data assets. © 2024 IEEE.

关键词： Decision making

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Rice Leaf Disease Diagnosis Using Dense EfficientNet Model 1

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23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Roopa Devi, E.M. Shanthakumari, R. Rajadevi, R. Anusuyaa Harini Lokesh Department of Information Technology Kongu Engineering College Perundurai India Department of Artificial Intelligence and Machine Learning Kongu Engineering College Perundurai India

ISBN: (数字)9783031648472

ISBN: (纸本)9783031648465

Global food security and agricultural sustainability are seriously threatened by the paddy leaf diseases that is rapidly spread. Four of the most common widespread disease that affect rice leaf are Hispa, brown spot, Leaf blast and as well as Healthy leaf are identified. Quickly and correctly identifying a specific disease in order to implement effective strategies for its control and treatment. After the dataset performed the necessary pre-processing, a variety of deep learning algorithms, comprising VGG19, Resnet50, and InceptionV3, were applied on it. Our deep learning model is based on the EfficientNetB3 architecture, which has demonstrated excellent performance in image analysis applications. The EfficientNetB3 model is trained on the collected dataset, employing transfer learning to leverage pre-trained weights and accelerate convergence. To enhance the discriminative power of the model, employ feature extraction techniques. These techniques extract relevant features from the images and provide valuable information for disease detection. The integration of deep learning and feature extraction techniques, specifically using EfficientNetB3 demonstrates the potential of modern technologies in the field of agriculture. The ultimate objective of this integrated technique is to enable agricultural precision by providing farmers with a technology that is capable of identifying and monitoring plant diseases precisely and efficiently. It aids to sustainable agricultural production by providing focused interventions and minimising resource use. In summary, the originality in this work is found in the careful coupling of cutting-edge deep learning architecture, transfer learning, and feature extraction approaches, showcasing the advances being made in disease identification for the improvement of agricultural sustainability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Feature extraction

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Enhancing Fault Detection and Diagnosis through an Integrated Chaos-RNN Model

Enhancing Fault Detection and Diagnosis through an Integrate...

引用

2024 International Conference on Intelligent and Innovative Practices in engineering and Management, IIPEM 2024

作者： Vijay, K. Vijay, Priya Sudharsan, R. Swathy, B. Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India Rajalakshmi Engineering College Department of Information Technology Chennai India

ISBN: (纸本)9798350390049

One of the most challenging problems is to identify the anomaly or fault in machines and materials, though identifying the faults are a tedious task once the fault or anomaly is identified. Identifying the reason or the root cause for the fault or anomaly is the expected or actual solution for the problem. Smart manufacturing based on Digital Twin has provided solutions for several non-trivial problems such as detecting the time taken for product delivery, fault detection in materials, machines, delivery and many more real time problems. To detect the anomaly real-time and identify or reason out the root cause behind the fault a novel digital twin architecture comprising of several novel modules is proposed;improving the productivity of the outcome from manufacturing. A Digital Twin architecture for identifying the Root Cause of a Fault in a Smart Manufacturing environment based on Anomaly Detection and Time Series analysis is proposed. The architecture is expected to improve the overall production rate and reduce the down time in identifying the root cause of the fault occurred. An artificial Neural Network that works on the principle of Chaos Theory is proposed and experimented with single and multi-class anomaly. © 2024 IEEE.

关键词： Smart manufacturing

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Enhancing Passenger Convenience: An Efficient Speech Driven Enquiry System for Railway Stations 2

Enhancing Passenger Convenience: An Efficient Speech Driven ...

引用

2nd IEEE International Conference for Women in Innovation, technology and Entrepreneurship, ICWITE 2024

作者： Sakri, Leena Biradar, S.R. Kulkarni, Mitali Pramod Patilkukarni, Swarna Sahana, K. SDM College of Engineering and Technology Department of Artificial Intelligence and Machine Learning Dharwad India SDM College of Engineering and Technology Department of Information Science and Engineering Dharwad India

ISBN: (纸本)9798350383287

This paper presents a speech-based query system for railway stations. The system allows passengers to ask their queries in the form of speech and receive answers in the form of speech. It provides answers to a range of frequently asked queries, such as train information, seat availability, and PNR status, and is designed to be comprehensive and intuitive. Through the use of a combination of natural language processing, speech recognition, and database querying, the system can generate prompt and accurate responses. The speech recognition component translates the passenger's speech input into text. After parsing the text input, the natural language processing component determines the precise question the traveller is posing. The database querying component retrieves the relevant information from the railway database and generates a speech output tailored to the passenger's query. Most queries receive a response in less than 10 seconds, making the system accurate and efficient. This implementation of a speech- based query system at train stations holds promise for enhancing the overall passenger experience. By allowing passengers to ask their queries as a speech, the system can make it easier for passengers to get the information they need quickly and easily. © 2024 IEEE.

关键词： Python

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Early Predicting Alzheimer's Disease Using Multiple machine learning Techniques 1

Early Predicting Alzheimer's Disease Using Multiple Machine ...

引用

1st International Conference on artificial intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Burle, Rushikesh Dewangan, Leelkanth Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Mahartashtra Wardha442001 India

ISBN: (纸本)9798350330915

To neurodegenerative disorders in machine learning methods are used to predict Alzheimer's disease. Even while the symptoms don't seem severe at first, they do gradually increase symptoms and behaviour to change and day to day life working strategies are not work properly up to the mark. One predominant form Alzheimer's disease is a kind of dementia. A few psychological indicators that can be used to predict AD include age, as per features how many visits there have been, the MMSE, and education. © 2023 IEEE.

关键词： Alzheimer Disease(AD) Convolution Neural Network (CNN) machine learning parameters (MLP) Mini-Mental State Examination (MMSE) Support Vector machine (SVM)

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LSTM based Soft-Sensor for Estimating Nitrate Concentration in Aquaponics pond 3

LSTM based Soft-Sensor for Estimating Nitrate Concentration ...

引用

3rd International Conference for Innovation in technology, INOCON 2024

作者： Dharshan, A. Kumar, Purushottam Ravimaran, S. Srinivasulu Reddy, U. Saranathan College of Engineering Department of Artificial Intelligence and Data Science Trichy India National Institute of Technology Artificial Intelligence Machine Learning & Data Analytics Lab Trichy India National Institute of Technology Department of Computer Applications Machine Learning & Data Analytics Lab Trichy India

ISBN: (纸本)9798350381931

In the field of aquaponics, where fish and plants coexist in a symbiotic environment, closely monitoring nitrate levels in the water is crucial due to their profound impact on aquatic and plant well-being. Traditional nitrate measurement methods are often time-consuming and costly. Various approaches, including first principles, IoT-based sensors, and machine learning-based soft sensors, have been attempted to address this challenge. However, these efforts face challenges such as expensive sensors, infrequent data collection, multistage data processing using limited sensor types, and the need for regular maintenance like cleaning and calibration. Additionally, varied environmental conditions affect sensor suitability for different water environments, and even some machine learning-based soft sensors have proven inaccurate. In response, soft sensors, especially deep learning-based ones, have gained prominence in industrial applications for their adaptability and accuracy. These sensors provide real-time insights into complex processes without requiring expensive hardware. In this study, an innovative solution was introduced using Long Short-Term Memory (LSTM) technology, a neural network architecture in deep learning known for capturing complex temporal patterns. LSTM is well-suited for modeling and predicting nitrate concentration changes in aquaponics, trained with extensive data collected from various aquaponic ponds. Through rigorous evaluation, a remarkable MSE value of 0.00074 and an impressive R-squared score of 0.98 were achieved, holding potential for scaling up to commercial applications, benefiting aquaponics operations, supporting researchers, and enhancing sustainability and productivity in aquaponic systems. © 2024 IEEE.

关键词： Nitrates

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Diagnosis of Advanced Diabetic Retinopathy from Fundus Image Using machine learning

Diagnosis of Advanced Diabetic Retinopathy from Fundus Image...

引用

2024 International Conference on artificial intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Mankar, Bhagyashri Bawankar, Bhushan Gabhane, Diksha Wanjari, Mohini Department of Information Technology Nagpur India J.D. College of Engineering Department of Artificial Intelligence and Machine Learning Nagpur India

ISBN: (纸本)9798331517953

Diabetic Retinopathy is an eye disease which mainly caused to the diabetic patients. The patients who have been suffering from diabetes since long time have major chances to suffer from Diabetic Retinopathy (DR) as well. If the DR is not detected early, the patient may experience vision loss, so early detection of this disease is very important. Micro-aneurysm and exudates are the features from which we can identify whether patients suffer from DR or not. Identifying retinal micro-aneurysms and exudates from the retinal fundus image is the primary goal of the proposed work to test for diabetic retinopathy. Firstly, the pre-processing is performed on Fundus image. Preprocessing techniques include adaptive median filtering, adaptive histogram equalization, and grey scale conversion. Feature extraction is utilized to extract the features from the fundus image after pre-processing. The extracted features are act as an input to the SVM classifier. The fundus image is classified as normal or impacted using a vector machine. © 2024 IEEE.

关键词： Median filters

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Classifying Parkinson's Disease Using Speech Features 2

Classifying Parkinson's Disease Using Speech Features

引用

2nd International Conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Soliman, Hala A. Elshourbagi, Mohammed S. Dahy, Malak A. Osman, Mena M. Kamal, Bassant A. Elaziz, Mohamed Abd El-Rashidy, Nora Galala University Faculty of Computer Science and Engineering Suez435611 Egypt Intelligence-Kferelshikh University-kaferelshikh Machine learning and information retrieval-faculty of artificial Egypt

ISBN: (纸本)9798350362633

This scientific paper focuses on the detection of Parkinson's Disease in individuals using vocal/speech features. Specifically, the study records an individual's pronunciation of the short 'a' sound (\\ \\\ \\\ae) and applies various Signal Processing Algorithms to process the recorded data. To address the data's class imbalance, both oversampling and undersampling techniques are implemented, and their results are compared. Additionally, a feature selection algorithm is employed to eliminate noise from the dataset while retaining the relevant features necessary for the model. Logistic regression, Support Vector machines (SVM), decision trees, and Ensemble learning methods are utilized to determine the likelihood of an individual having Parkinson's Disease. Furthermore, SHAP (SHapley Additive exPlanations) analysis is conducted to gain a deeper understanding of the impact of features on class determination for each instance. The Random Forest model achieves an accuracy of approximately 95% when trained on the oversampled dataset, surpassing the accuracy obtained from the undersampled dataset. Additionally, a graphical user interface (GUI) is successfully developed to predict the diagnosis of Parkinson's disease. © 2024 IEEE.

关键词： Neurodegenerative diseases

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