Identifying Chronic Kidney Failure through Machine Learning
Smart Water Management by Smart Sensors and IoT: Enhancing Efficiency and Sustainability with Automatic Pump Control Systems
IoT Based Voice Controlled Home Automation System using Google Assistant
IoT and its Evolution in Healthcare
Integration of Deep Learning and IoT for Diabetic Retinopathy Diagnosis using Retinal Fundus Images
Smart Innovations in IoT Technology
Integrating IoT, ML and Cloud Computing for Sustainable Agriculture: Opportunities and Challenges
Real-Time Monitoring and Assessment of the Indoor Air Quality Hazard Index using Deep Learning Approach
Security Challenges and Measures of IoT Devices and its Networks
IoT-Based Overvoltage, Overcurrent, and Over-Temperature Fault Detector in Industries
IoT Based Voice Controlled Home Automation System using Google Assistant
Security Challenges and Measures of IoT Devices and its Networks
IoT and its Evolution in Healthcare
Implementation of IoT Based Forest Fire Detection and Prevention System
A Blockchain Based Cyber Thread Detection System for IIoT Networks
Chronic kidney disease (CKD) is a major issue that has been growing at a constant rate. An individual can only survive for a few days without functioning kidneys, which leads to dialysis or kidney transplantation. In CKD, the kidneys are harmed and cannot cleanse blood as they normally do. Heart conditions, anemia, bone issues, excessive potassium and calcium levels, and anemia are among the frequent consequences of kidney failure. In the worst-case scenario, total renal failure necessitates a kidney transplant for survival. Early identification of CKD can significantly improve quality of life. Machine learning methods like Random Forest, Naïve Bayes, Decision Trees, SVM, and KNN are effective for early CKD identification. The Random Forest model, particularly, has shown excellent performance on the UCI CKD dataset, achieving 100% accuracy. Due to its superior accuracy compared to other models, the Random Forest classifier was employed for CKD prediction.
Automatic water pump control systems play a pivotal role in modern water management, offering efficient and reliable solutions for various applications. This literature highlights the key aspects of automatic water pump control, focusing on the integration of smart technology to enhance efficiency and reliability. The system comprises sensors, controllers, actuators, and connectivity features, allowing for real-time monitoring and intelligent control of water pumps. Sensors detect parameters such as water level, pressure, and flow rate, providing essential data for decision-making. A robust controller processes sensor inputs and executes control algorithms to optimize pump operation based on predefined logic or user-defined settings. Smart technology integration enables remote access and control via user-friendly interfaces such as web-based dashboards, mobile apps, or dedicated control panels. Users can monitor system status, adjust settings, and receive alerts or notifications remotely, enhancing convenience and accessibility. Connectivity features such as Wi-Fi, Bluetooth, or cellular communication enable seamless communication between the system and users' devices. Data logging and analysis capabilities record historical data and performance metrics, facilitating trend analysis and optimization of system parameters. Intelligent algorithms dynamically adjust pump operation based on factors such as demand fluctuations, energy prices, and weather conditions, maximizing efficiency and minimizing resource wastage. The automatic water pump control system offers numerous benefits, including improved efficiency, reliability, and cost savings. By automating pump operation and optimizing resource utilization, the system reduces energy consumption, minimizes water wastage, and enhances system longevity. These advantages make it an ideal solution for a wide range of applications, including agricultural irrigation, municipal water supply, industrial processes, and residential water systems.
Home automation has become increasingly popular in recent years due to advancements in technology and the desire for convenience and efficiency in daily life. This work explores the implementation of a home automation system using the Blynk app and IFTTT (If This Then That) platform. The system is designed to control the on/off status of two bulbs using a two-channel relay module, NodeMCU ESP32 microcontroller, and physical push buttons. The hardware components include a two-channel relay module, two bulbs, NodeMCU ESP32 microcontroller, and physical push buttons. The physical push buttons are connected between the ESP32 and the relay module, as well as between the power supply and the ESP32. The ESP32 serves as the central hub for the automation system, controlling the relays based on input from both physical push buttons and commands received via the Blynk app. Software development for this project is done using the Arduino IDE, which provides a user-friendly interface for programming the ESP32 microcontroller. The Blynk platform is utilized to create a virtual dashboard for monitoring and controlling the status of the relays remotely through the Blynk mobile app. Users can easily toggle the on/off status of the bulbs using the virtual buttons on the Blynk app, providing convenient control from anywhere with an internet connection. Furthermore, the system is integrated with the IFTTT platform to enable automation based on predefined triggers and actions. This allows users to create custom automation rules, such as turning on the lights at sunset or sending a notification when the lights are turned on/off. By leveraging the capabilities of both Blynk and IFTTT, the home automation system becomes more versatile and adaptable to the user's preferences and needs.
Many illnesses have seen a rise in prevalence in the past few years. Different technologies, such as the Internet of Things (IoT), artificial intelligence, and big data analysis, have been used in the healthcare industry. Efficiency in healthcare has been enhanced through the integration of IoT technology. Numerous medical devices in the healthcare industry utilize IoT technology, including the IoT-based patient monitoring system Dozee, which can monitor vital parameters such as heart rate, pulse rate, ECG, and BP. These parameters can be monitored on devices as well as on web servers. In the healthcare sector, IoT enhances quality of life. In the future, artificial intelligence will analyze data to provide precise patient information. Robotics, such as robotic surgery, have the potential to enhance the future of healthcare. IoT technology in healthcare focuses on remote patient monitoring systems and telemedicine services, providing patients with live data and keeping records up-to-date. The advancement of IoT devices includes wearable monitoring systems for patients and smartwatches, which can link with mobile phones to track patient information.
Diabetic Retinopathy (DR) is a leading cause of blindness worldwide, necessitating early detection through regular screening of retinal fundus images. Leveraging advancements in Deep Learning and the Internet of Things (IoT), this study proposes a novel automated diagnostic system. Deep Learning models, specifically Convolutional Neural Networks (CNNs), are trained on a large dataset of retinal images to classify them based on disease severity. IoT facilitates real-time image acquisition and transmission, enhancing accessibility and efficiency in remote or underserved areas. Key stages include preprocessing, feature extraction, and classification using state-of-the-art neural networks such as ResNet and EfficientNet. Evaluation metrics such as sensitivity, specificity, and accuracy validate the system's performance against clinical standards. Integration of IoT enables seamless data flow from image capture to diagnosis, optimizing healthcare delivery. This approach holds promise for scalable and cost-effective DR screening, potentially transforming diabetic eye care globally.
