A brain tumor is a lethal neurological disease that affects the average performance of the brain and can be fatal. In India, around 15 million cases are diagnosed yearly. To mitigate the seriousness of the tumor it is essential to diagnose at the beginning. Notwithstanding, the manual evaluation process utilizing Magnetic Resonance Imaging (MRI) causes a few worries, remarkably inefficient and inaccurate brain tumor diagnoses. Similarly, the examination process of brain tumors is intricate as they display high unbalance in nature like shape, size, appearance, and location. Therefore, a precise and expeditious prognosis of brain tumors is essential for implementing the of an implicit treatment. Several computer models adapted to diagnose the tumor, but the accuracy of the model needs to be tested. Considering all the above mentioned things, this work aims to identify the best classification system by considering the prediction accuracy out of AlexNet, ResNet 50, and Inception V3. Data augmentation is performed on the database and fed into the three convolutions neural network (CNN) models. A comparison line is drawn between the three models based on accuracy and performance. An accuracy of 96.2% is obtained for AlexNet with augmentation and performed better than ResNet 50 and Inception V3 for the 120th epoch. With the suggested model with higher accuracy, it is highly reliable if brain tumors are diagnosed with available datasets.

This work aims to perform a holistic review regarding renewable energy mix, power production approaches, demand scenarios, power policies, and investments with respect to clean energy production in the southern states of India. Further, a thermoelectric-generator model is proposed to meet rural demands using a proposed solar dish collector technology. The proposed model is based on the idea of employing a parabolic concentrator and a thermoelectric (TE) module to generate electricity directly from the sun’s energy. A parabolic dish collector with an aperture of 1.11 m is used to collect sunlight and concentrate it onto a receiver plate with an area of 1.56 m in the proposed TE solar concentrator. The concentrated solar thermal energy is converted directly into electrical energy by using a bismuth telluride (BiTe)-based TE module mounted on the receiver plate. A rectangular fin heatsink, coupled with a fan, is employed to remove heat from the TE module’s cool side, and a tracking device is used to track the sun continuously. The experimental results show considerable agreement with the mathematical model as well as its potential applications. Solar thermal power generation plays a crucial part in bridging the demand–supply gap for electricity, and it can be achieved through rural electrification using the proposed solar dish collector technology, which typically has a 10 to 25 kW capacity per dish and uses a Stirling engine to generate power. Here the experimentation work generates a voltage of 11.6 V, a current of 0.7 A, and a power of 10.5 W that can be used for rural electrification, especially for domestic loads.

Degrading air quality is a matter of concern nowadays, and monitoring air quality helps us keep an eye on it. Air pollution is a pressing global issue with far-reaching impacts on public health and the environment. The need for effective and real-time monitoring systems has become increasingly apparent to combat this growing concern. Here, an innovative air pollution surveillance system (APSS) that leverages Internet of Things (IoT) technology to enable comprehensive and dynamic air quality assessment is introduced. The proposed APMS employs a network of Io enabled sensors strategically deployed across urban and industrial areas. These sensors are equipped to measure various pollutants, including particulate matter (PM2.5 and PM10), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ozone (O₃), carbon monoxide (CO), and volatile organic compounds (VOCs). Here, a regression model is created to forecast air quality using sensor data while taking into account variables including weather information, traffic patterns, and pollutants. Additionally, air quality categories (such as good, moderate, and harmful) are classified using classification algorithms based on preset thresholds. The IoT architecture facilitates seamless data transmission from these sensors to a centralized cloud-based platform. The developed APSS monitors the air quality using an MQ-135 gas sensor, and the data are shared over a web server using the Internet. An alarm will trigger when the air quality goes below a certain level. Also, the air quality, which is measured in parts per million (PPM), is displayed on the unit connected to it. Furthermore, when the PPM goes beyond a certain level, an alert message is sent to the air pollution control board, which takes preventive measures to control the pollution and also alerts the people, which helps each person in that society save their environment from pollution and have a good air quality environment. Additionally, the APSS offers user-friendly interfaces, accessible through web and mobile applications, to empower citizens with real-time air quality information. The effectiveness of the IoT-based air pollution monitoring system has been validated through successful field trials in urban and industrial environments, and it has the ability to provide real-time data insights and empower stakeholders in promoting environmental sustainability and fostering citizen engagement.

The technology exhibits the development of novel systems that enable control and monitoring regardless of distance or time. The internet of things helps people live and work smarter, as well as gain complete control over their lives. In addition to offering smart devices to automate homes, IoT is essential to business. This project demonstrates the IoT solution for monitoring temperature and humidity conditions, as well as the availability of electrical connectivity, independent of time or location. In a poultry house, both temperature and humidity levels should be monitored regularly in ensuring the system runs smoothly. The temperature sensor used to sense the temperature of the chicken. The fire sensor will alert the user if any fire accident occurs. Water level is also measured and the water pump motor will be turned on automatically to feed the chicken. Sensors are used in this system to detect weather/environmental parameters such as temperature and humidity. The Arduino microcontroller processes the information read from the sensors. The hardware is implemented successfully at different sites within the poultry shed. The experimental configuration was discovered to be quite effective. This initiative will benefit both the environment and the poultry business.