VOLUME 13, ISSUE 4, APRIL 2025 | IJIREEICE

5G AND AI IN WEARABLE MEDICAL DEVICES: A NEW ERA OF PREVENTIVE HEALTHCARE

admin — Wed, 26 Mar 2025 07:50:03 +0000

Abstract: The integration of 5G and artificial intelligence (AI) in wearable medical devices is revolutionizing healthcare by enabling continuous, cost-effective monitoring for patients. 5G connectivity enhances the healthcare industry by offering reduced latency, increased energy efficiency, and speeds up to 100 times faster than its predecessor, 4G LTE. The wearable medical device market, currently valued at over $40 billion, continues to experience significant growth and shows promise for the future. However, as more companies adopt 5G technology, several challenges must be addressed to realize its full potential. Key issues include improving battery life, investing in data security and privacy, and reducing infrastructure acquisition costs, particularly in developing and underdeveloped countries.

Keywords: Artificial intelligence, Wearable medical devices, machine learning (ML), predictive analytics and deep learning

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SMART VOICE-CONTROLLED APPLIANCE MANAGEMENT SYSTEM USING LABVIEW

admin — Wed, 26 Mar 2025 09:32:12 +0000

Abstract: Home automation has transformed the way electrical appliances are managed, improving convenience, energy efficiency, and accessibility. This research presents a smart voice-controlled appliance management system that allows users to operate household appliances using voice commands transmitted via Bluetooth to myRIO. The system is particularly designed to assist individuals by providing a hands-free and intuitive method for controlling electrical devices. Using LabVIEW, the system processes and interprets voice commands, subsequently triggering the activation or deactivation of appliances through relay control circuits. The methodology includes voice recognition, wireless data transmission via Bluetooth, real-time processing in myRIO, and execution of commands through relay switching. Experimental validation demonstrates high command accuracy, minimal latency, and reliable Bluetooth communication. This paper discusses the system’s design, working principles, testing outcomes, and future improvements. The proposed system is cost-effective, scalable, and practical for integration into smart homes and assistive technology applications.

Keywords: Home Automation, IoT, MyRIO, Bluetooth communication, LabVIEW.

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ADVANCED TRAFFIC CONTROL THROUGH IOT INTEGRATION

admin — Wed, 26 Mar 2025 09:47:51 +0000

Abstract: Advanced traffic control through IoT (Internet of Things) integration represents a transformative approach to urban mobility management, enabling smarter, more efficient, and responsive transportation systems. By leveraging IoT-enabled sensors, devices, and communication networks, real-time data collection and analysis are made possible, providing a holistic view of traffic conditions and allowing for dynamic decision-making. IoT devices, such as connected traffic signals, smart cameras, and vehicle-to-infrastructure (V2I) communication systems, can continuously monitor traffic flow, vehicle speeds, pedestrian movements, and environmental factors like weather or air quality. This data is then processed using advanced analytics, machine learning algorithms, and cloud computing, which can predict traffic congestion, optimize signal timings, reduce delays, and enhance safety measures. Furthermore, IoT integration enables the coordination of various transportation modes, such as public transit, ride-sharing services, and autonomous vehicles, leading to seamless intermodal connectivity. This connected infrastructure also allows for real-time updates and alerts to drivers, helping them navigate efficiently and avoid potential hazards. Additionally, IoT-enabled traffic systems can contribute to sustainable urban planning by reducing carbon emissions, improving energy efficiency, and minimizing traffic- related noise pollution. Ultimately, the integration of IoT in traffic control systems represents a crucial step towards the development of smart cities, where transportation infrastructure is not only automated but also intelligently adaptive to changing conditions, enhancing the overall quality of life for residents and visitors alike.

Keywords: Advanced traffic control through IoT integration uses smart sensors and real- time data to manage traffic better and reduce congestion. It connects vehicles and traffic lights to improve traffic flow and make roads safer.

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Animal Detection system using OpenCV

admin — Wed, 26 Mar 2025 09:50:43 +0000

Abstract: The project is aimed at developing an animal detection system using OpenCV, a very commonly used computer vision tool, without involving any sophisticated machine learning models. The system detects and classifies animals instantaneously in real-time using simple image processing techniques based on images and video feeds. With some of the techniques such as edge detection, background subtraction, contour finding, and motion tracking, this system can detect animals in diverse settings such as towns, farms, and forests with a high degree of accuracy. This method enables accurate detection of animals without the need for pre-trained AI models, thus simplifying the device and rendering it weightless and facilitating quicker deployment. It suits applications such as wildlife monitoring, security, and agricultural management, where effective and real-time tracking of animals is absolutely important. The strong image processing capability of OpenCV allows the system to run effectively.

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AI-Based Fake News Detection System

admin — Wed, 26 Mar 2025 10:16:00 +0000

Abstract: The rapid spread of false information in the digital age poses a serious threat to society, influencing how people think and make decisions. As a result, identifying fake news has become essential to ensuring the reliability of information found online. Traditional fact-checking methods often rely on slow, labor-intensive manual processes that are increasingly ineffective given the volume and speed of misinformation. This has led to growing interest in machine learning-based solutions for automating fake news detection. In this study, we propose a fake news classification model that uses Logistic Regression for classification and TF-IDF (Term Frequency-Inverse Document Frequency) vectorization for feature extraction, helping to distinguish between real and fake news articles more efficiently.However, many existing fake news detection systems face significant challenges. Traditional models often struggle to adapt to evolving misinformation patterns, leading to outdated or inaccurate results. Additionally, class imbalances in datasets — where one type of news (real or fake) heavily outweighs the other — can create biased predictions. Feature extraction techniques commonly used in older models also fail to capture the deeper, semantic meaning of text, resulting in subpar classification performance. Moreover, many models lack the ability to generalize across diverse datasets, which limits their effectiveness in real-world applications. These challenges highlight the need for a more reliable and adaptable system for fake news detection.

To address these issues, we propose an enhanced machine learning-based detection system. Our approach incorporates Logistic Regression alongside TF-IDF vectorization for effective feature extraction. We also introduce stratified train-test splitting to maintain class distribution during training and use RandomOverSampler to combat class imbalances by generating synthetic samples for underrepresented classes. To thoroughly evaluate performance, we measure accuracy, precision, recall, and visualize results using a confusion matrix, providing a clearer picture of how well the model performs.In addition to these core techniques, our system introduces several novel improvements. We implement automatic dataset validation to identify and handle missing or imbalanced labels, ensuring data is ready for training without manual intervention. If one class is significantly underrepresented or missing altogether, our model performs class augmentation, generating synthetic data to restore balance. We also introduce an interactive user prediction feature, allowing users to input custom news articles for real-time classification. This interactive component enhances the model’s practicality, making it a valuable tool for everyday use. These improvements collectively enhance model reliability, resulting in a more robust, accurate, and adaptable fake news detection system capable of keeping up with the ever-changing landscape of misinformation.

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SOLAR POWER MONITORING AND MANAGEMENT USING IOT

admin — Wed, 26 Mar 2025 10:36:18 +0000

Abstract: The goal of this project is to improve efficiency and dependability by managing and monitoring solar energy characteristics in real time. The system monitors important solar metrics including light intensity, voltage, and current using the ESP32 microcontroller, which has integrated WiFi and energy efficiency. To ensure accurate data gathering, a voltage sensor keeps an eye on the solar panels’ output voltage while the ACS712 current sensor measures the current produced by the panels. Through the processing and wireless transmission of this data by the ESP32, customers can utilize a mobile application to track solar performance in real time. One noteworthy aspect is the automated light control system, which maintains constant illumination by turning on an artificial light source when the solar voltage drops below a predetermined threshold. Even in a variety of environmental circumstances, its feature guarantees dependable operation. Applications such as real-time solar power monitoring, renewable energy management, and smart lighting systems are ideal for the suggested system. This project optimizes energy consumption and supports consistent illumination by providing a cost-effective, efficient, and sustainable method of controlling solar power. This helps to improve the use of renewable resources and make smarter energy consumption decisions.

Keyword: Green energy, IOT, Remote storage, Solar panels, Real-time monitoring, solar panel

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PC BASED MONITORING OF HUMAN BIOLOGICAL SIGNALS USING LABVIEW

admin — Wed, 26 Mar 2025 17:16:51 +0000

Abstract: PC-based monitoring of human biological signals using LabVIEW is an indirect method for measuring human biological parameters, which helps determine whether a person is in a normal or abnormal condition. Human body parameters such as body temperature, pulse rate, blood pressure, and respiration rate are commonly used to assess health. These parameters are measured from the external surface of the body, which characterizes the indirect method of measurement. Sensors like LM 35, XD 58C, and 6820 are used to measure temperature, pulse, and blood pressure signals. These signals are then interfaced with the LabVIEW simulator through myRIO. The obtained signals are compared with normal parameter values, and if any abnormalities are detected, the system provides an output indicating the issue.

Keywords: LabVIEW, myRIO, Temperature sensors, Pulse sensor, Pressure sensor, Pulse rate, Blood Pressure.

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CR0P YIELD PREDICTION USING DEEP XG BOOST ALGORITHM

admin — Thu, 27 Mar 2025 04:34:00 +0000

Abstract: Because crop yield is dependent on a number of variables, it is a difficult task to predict. Even though a lot of models have been created so far in the literature, they still need to be improved because their performance is inadequate. In order to assess the performance of the underlying algorithms in relation to various performance criteria, we created deep learning-based models for this study. The XGBoost machine learning (ML) algorithm, convolutional neural networks (CNN), XGBoost, and recurrent neural networks (RNN) are the algorithms that were assessed in this study. According to the environmental, soil, silt, nitrogen, clay, ocd, ocs, pHH2O, sand, soc, ceo, water, and crop parameters, we estimated crop yield for the case study.

Keywords: XGBOOST,Crop yield learning algorithms.

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DIABETES EARLY DIAGNOSIS AND HEALTH MONITORING SYSTEM

admin — Thu, 27 Mar 2025 05:03:06 +0000

Abstract: The “AI-Powered Diabetes Early Diagnosis and Health Monitoring Platform” project introduces an innovative method for the early detection and management of diabetes, a growing global health concern. Utilizing advanced machine learning algorithms, this initiative focuses on analyzing and interpreting complex medical data to accurately forecast the likelihood of diabetes onset in individuals. The predictive model integrates a thorough examination of multiple factors, including genetic predispositions, lifestyle habits, environmental influences, and pre-existing medical conditions, all of which play a crucial role in shaping an individual’s risk profile for developing diabetes.
A standout feature of this system is its emphasis on personalized medicine. By considering each individual’s unique health profile and risk factors, the model delivers customized risk evaluations, enabling more precise and effective prevention strategies. This tailored approach not only enhances patient outcomes but also supports the efficient use of healthcare resources

Keywords: Artificial intelligence, Machine learning, Diabetes, Disease prediction.

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DETECTION AND IFENTIFICATION OF PILLS USING MACHINE LEARNING

admin — Thu, 27 Mar 2025 07:01:04 +0000

Abstract: In the medical field, precise pill identification and detection are essential for avoiding prescription mistakes and guaranteeing patient safety. Machine learning must be used to automate the process because traditional manual approaches are tedious and susceptible to human mistake. Due to changes in illumination, background noise, and picture quality, conventional rule-based image processing methods—which depend on texture, colour, and shape—frequently have accuracy and robustness issues. Using Convolutional neural networks, more commonly trained on a dataset of pill pictures with data augmentation for improved generalisation, this study suggests a deep learning-based method to overcome these drawbacks. Multiple convolutional, maximal pooling, and layer dropouts are included into the model to improve feature extraction and lessen overfitting. Accuracy under various circumstances is ensured by validating performance on a distinct dataset.

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