Study World College of Engineering (SWCE), in Coimbatore, Tamil Nadu, India is a private self-financing engineering institute. It is approved by AICTE and is affiliated to the Anna University Chennai.
The growing environmental issues regarding plastic waste have necessitated the need for sustainable and biodegradable alternatives to traditional plastic pots. Present study focuses on the production of eco-friendly biodegradable plant containers using various bio composites consisting of natural fibers such as pineapple leaves (PC), water hyacinth (WC), dried leaf litter (DC), banana fibers (BC), and coco peat with cornstarch acting as a natural adhesive. The main aim is to produce a nature-friendly alternative to conventional plastic pot maintaining structural integrity and durability. To assess the viability of the bio composite, several mechanical and environmental tests were carried out, such as compression, flexural strength, impact resistance, water absorption, and biodegradability tests. A direct planting test with the money tree (Epipremnum aureum) was conducted to qualitatively assess the practical applicability of the biopots under real-use conditions. All experimental data were analyzed statistically, and differences among the samples were considered significant at p < 0.05. Water absorption analysis showed that BC exhibited the highest absorption (96.40 ± 3.59
The project proposes an IoT based flood monitoring system utilizing Arduino Uno, soil moisture sensor, LCD, buzzer, Wi-Fi module (like ESP8266) and ThingSpeak cloud platform. It measures the moisture level of the soil and gives notifications when crossing a certain threshold, indicating imminent floods. The soil moisture sensor is used to detect the moisture content and it is shown on LCD screen for real time monitoring. A buzzer sounds to inform of high moisture levels. A Wi-Fi module enables wireless connectivity and sends data to the ThingSpeak cloud platform for centralized storage and analysis. Users may access real-time and historical data through an interface using Thing Speak. The Wi-Fi module is configured to connect to a specific network and send data to the server using a unique API key. With Thing Speak you can see, analyse and create alerts depending on the soil moisture, it is a complete solution to monitor floods. This low-cost solution, based on open-source hardware and cloud platforms, increases accessibility and ease of deployment and is appropriate for many environmental monitoring applications There is no question now about the need of understanding about environmental circumstances. Having environmental conditions can assist us to know more about our surroundings. In recent years, due to global warming, it's become more difficult to tell the sort of weather happening around us. So, the weather is hard to anticipate and must be checked often. So, we need good knowledge on the weather. So that the decision maker may take the proper weather decision. So, the decision maker has to create a system which can monitor and anticipate the weather in real time situations. The Internet of Things (IoT) is highly useful in forecasting and monitoring such sort of situation. thus, it can operate with real time data as well as the previously recorded data. The IoT provides data to the computational devices through Wireless Sensor Network (WSN) for result generation. Therefore, many have transitioned from predicting physical parameters of floods to computational real-time monitoring. The suggested system employs several atmospheric sensors including humidity, temperature, pressure and rain fall. The data captured is kept and sent to the device and therefore the result is received.
Lane and road signs are recognized by Advanced Driver Assistance Systems (ADAS). Road signs serve as warnings and guidelines for drivers, while lane detection helps them avoid collisions. In a single module, this work presents a revolutionary approach to lane and traffic sign detection. We can achieve this in two distinct phases. The first phase is detecting the presence of lane and road signage using a Gaussian mixture background model. It takes advantage of the quick radial transform, which has a high capacity for generalization, to extract the lane characteristics. To ascertain if a lane is present in the frame or not, we use a support vector machine (SVM) classifier. The SVM classifier has a higher accuracy and classification efficiency. The second stage evaluates road sign detection and extracts the set of features required for road sign identification using the histograms of the oriented gradient (HOG). The second phase involves determining whether the road sign will be visible in the frame and integrating lane and sign recognition into a single module.
When it comes to transporting people from emergency situations to hospitals and medical facilities and back, ambulances encounter several challenges. In India, 98.5
Rapid advances in deep learning and artificial intelligence (AI) technologies have revolutionized medical imaging and opened up previously unheard-of possibilities for early disease detection. This study explores two deep learning algorithms, convolutional neural networks (CNNs) and recurrent neural networks (RNNs), for use in the interpretation of medical pictures, including X-rays, MRIs, and CT scans. We review the effectiveness of these algorithms in identifying early-stage diseases, such as cancers, cardiovascular conditions, and neurodegenerative disorders, highlighting their potential to enhance diagnostic accuracy and improve patient outcomes. We also go over how these algorithms might be incorporated into clinical workflows, addressing issues with ethical considerations, model interpretability, and data variability. When it comes to illness detection, deep learning models outperform conventional imaging analysis techniques by utilizing massive datasets and cutting-edge training techniques. This project intends to add to the expanding corpus of research on artificial intelligence in healthcare by promoting the use of cutting-edge imaging technology to enable prompt diagnosis and treatment.
