People who work in dangerous environments include farmers, sailors, travelers, and mining workers. Due to the fact that they must evaluate the changes taking place in their immediate surroundings, they must gather information and data from the real world. It becomes crucial to regularly monitor meteorological parameters such air quality, rainfall, water level, pH value, wind direction and speed, temperature, atmospheric pressure, humidity, soil moisture, light intensity, and turbidity in order to avoid risks or calamities. Enhancing environmental standards is largely influenced by IoT. It greatly advances sustainable living with its innovative and cutting-edge techniques for monitoring air quality and treating water. With the aid of various sensors, microcontroller (Arduino Uno), GSM, Wi-Fi, and HTTP protocols, the suggested system is a real-time smart monitoring system based on the Internet of Things. Also, the proposed system has HTTP-based webpage enabled by Wi-Fi to transfer the data to remote locations. This technology makes it feasible to track changes in the weather from any location at any distance. The proposed system is a sophisticated, efficient, accurate, cost-effective, and dependable weather station that will be valuable to anyone who wants to monitor environmental changes on a regular basis.

Tumor is defined as uncontrollable and indefinite growth of cells in the brain. Diagnosis should be earlier and proper treatment of tumors is essential to prevent permanent damage to the brain or even patient death. Manual detection of brain tumor from MRI images is most critical and takes more time. In this research letter a novel optimization technique has been proposed which overcome the problems of previous existing techniques. This paper presents automatic brain tumor detection, where image is segmented and classification is carried out on brain MRI images using genetic algorithm that is meta-heuristic optimization technique and support vector machine.

In this study, shell and heat exchangers are optimized using an integrated optimization framework. In this research, A structured Design of Experiments (DOE) comprising 16 trials was first conducted to systematically determine the essential parameters, including mass flow rates (mh, mc), temperatures (T1, t1, T2, t2), and heat transfer coefficients (€, TR, U). By identifying the first four principal components, PCA was able to determine 87.7% of the variance, thereby reducing the dimensionality of the problem. Performance-related aspects of the system are the focus of this approach. Key outcomes (€, TR, U) were predicted by 99% R-squared using the RSM models. Multiple factors, such as the mass flow rate and inlet temperature, were considered during the design process. The maximizing efficiency, thermal resistance, and utility were achieved by considering these factors. By using genetic algorithms, Pareto front solutions that meet the requirements of decision-makers can be found. The combination of the shell and tube heat exchangers produced better results than expected. Engineering and designers can gain practical insight into the mass flow rate, temperature, and key responses (€, TR, U) if they quantify improvements in these factors. Despite the importance of this study, it has several potential limitations, including specific experimental conditions and the need to validate it in other situations as well. Future research could investigate other factors that influence system performance. A holistic optimization framework can improve the design and engineering of heat exchangers in the future. As a result of the study, a foundation for innovative advancements in the field has been laid with tangible improvements. The study exceeded expectations by optimizing shell and heat exchanger systems using an integrated approach, thereby contributing significantly to the advancement of the field.

Brain tumor causes early unconsciousness of the human and lead to death by false findings and wrong treatment. The prompt identification and precise segmenting the brain medical imaging are crucial for diagnosis, planning of treatment, and patient results. Manual tumor detection in 3D brain Magnetic Resonance (MR) scans is time-taking and relatively unpredictable. Accurate detection and classification is main aim in prediction of the tumor size and type. This research proposes a customized CNN-based tumor segmented model which is an automated procedure for identifying and segmenting brain tumors in glioblastoma 3D Flair MRI as input based on a modified U-Net architecture. U-Net, a prominent deep learning model noted for its efficacy in picture segmentation tasks, has been modified to handle the complexities of three-dimensional medical data. These tumors, by nature, can take on almost any shape or size and appear in any region of the brain. These reasons force us to look into an artificial intelligence system that is very efficient and makes use of a flexible, high-capacity neural network. The recommended technique is tested on a publicly accessible datasets, proving its capacity to correctly segment diverse tumor types. The experimental results reveal a significant improvement in segmentation accuracy over traditional approaches, with high DSC, accurate tumor stage finding and other key performance measures. The proposed method is a promising tool for medical experts to help them identify brain tumors quickly and reliably, resulting in better patient care and more efficient diagnostic procedures.

Leaf diseases identification and sorting are essential responsibilities in crops, and they pose a significant challenge in today's agriculture and food processing environments since they affect crop inputs and yields. A more accurate and timelier prognosis of agricultural leaf diseases could aid in the creation of an immediate corrective method, greatly reducing economic and production losses. Deep learning technical developments have enabled investigators to significantly improve the performance and accuracy of object identification and recognition systems. The primary objective is to establish how to come up with a more applicable neural network framework for the stated duties. In the present article, we put forward the use of a convolutional neural network (CNN) approach for detecting crop leaf diseases in an assortment of plants employing images taken from plant leaves. As a result, it is able to appropriately assess the detection and classification processes. The proposed crop leaf disease forecasts include four stages: preprocessing to enhance and shrink images using the contrast stretching approach; segmentation using k-means clustering technique; feature extraction and classification using convolutional neural networks. Following that, we detected and displayed in the conversation box several segmentation features, classifier performance metrics, and statistical and structural features of the anticipated disease type. Finally, the acquired findings were compared to existing methodologies, and it was determined that the proposed plant disease prediction method is both accurate and efficient.

He study of tumor detection in brain is aimed for improvement of the required treatment for people that are suffering from brain tumor (BT). Brain tumors are aberrant cell evolutions in the brain, while cancer is an acronym employing to indicate tumors that are cancerous called malignant. CT or MRI investigations are used often to identify malignant regions in the brain. PET, cerebral arteriogram (CA), lumbar puncture (LP), and molecular testing (MT) can also be used to detect BTs. MRI scans are largely used in this investigation to examine the illness condition. The objectives of this study are to detect aberrant images and segment the tumor territory. The segmented mask can measure tumor density for therapeutic purposes.