Because there is more demand for freshwater around the world and the world’s population is growing at the same time, there is a severe lack of freshwater resources in the central part of the planet. The world’s current population of 7.2 billion people is expected to grow to over 9 billion by the year 2050. The vast majority of freshwater is used for things like cooking, cleaning, and farming. Most industrialised countries are in desperate need of smart irrigation systems, which are now a must-have because of how quickly technology is improving. In article presents IoT based Sensor integrated intelligent irrigation system for agriculture industry. IoT based humidity and soil sensors are used to collect soil related data. This data is stored in a centralized cloud. Features are selected by CFS algorithm. This will help in discarding irrelevant data. Clustering of data is performed by K means algorithm. This will help in keeping similar data together. Then classification model is build using the SVM, Random Forest and Naïve Bayes algorithm. Model is trained, validated and tested using the acquired data. Historical soil and humidity related data is also used in training the model. K-means SVM hybrid classifier is achieving better results for classification, prediction of water demand and saving fresh water by intelligent irrigation. K-means SVM hybrid classifier has achieved accuracy rate of 98.5 percent. Specificity, recall and precision of K-means SVM hybrid classifier is also higher than random forest and naïve bayes classifier.

According to the World Health Organization, heart disease is the biggest cause of death worldwide. It may be possible to bring down the overall death rate of individuals if cardiovascular disease can be detected in its earlier stages. If the cardiac disease is detected at an earlier stage, there is a greater possibility that it may be successfully treated and managed under the guidance of a physician. Recent advances in areas such as the Internet of Things, cloud storage, and machine learning have given rise to renewed optimism over the capacity of technology to bring about a paradigm change on a global scale. At the bedside, the use of sensors to capture vital signs has grown increasingly commonplace in recent years. Patients are manually monitored using a monitor located at the patient’s bedside; there is no automatic data processing taking place. These results, which came from an investigation of cardiovascular disease carried out across a large number of hospitals, have been used in the development of a protocol for the early, automated, and intelligent identification of heart disorders. The PASCAL data set is prepared by collecting data from different hospitals using the digital stethoscope. This data set is publicly available, and it is used by many researchers around the world in experimental work. The proposed strategy for doing research includes three steps. The first stage is known as the data collection phase, the data is collected using biosensors and IoT devices through wireless sensor networks. In the second step, all of the information pertaining to healthcare is uploaded to the cloud so that it may be analyzed. The last step in the process is training the model using data taken from already‐existing medical records. Deep learning strategies are used in order to classify the sound that is produced by the heart. The deep CNN algorithm is used for sound feature extraction and classification. The PASCAL data set is essential to the functioning of the experimental environment. The deep CNN model is performing most accurately.

This study addresses a critical gap in immersive engineering education by integrating browser-based WebVR with wearable inertial sensors to capture and enhance students’ physical interactions. In a randomized experiment (N = 93), participants experienced either (1) a WebVR environment augmented with real-time motion tracking or (2) traditional video-based instruction. Pre- and post-tests measured conceptual understanding, while questionnaires assessed immersion, perceived interaction control (the learner’s ability to manipulate and explore virtual components—for example, zooming in on a valve assembly), and motivation. Independent-samples t-tests revealed that the WebVR-sensor group achieved significantly greater learning gains (mean improvement 12%, p < .01) and higher motivation scores (4.1 vs. 3.2 on a 5-point scale, p < .001) than the control. Hierarchical regression showed that adding perceived interaction control increased explained variance in motivation by 9% (ΔR2 = .09, p < .05) beyond immersion alone. These results demonstrate that wearable-enhanced WebVR not only boosts engagement but also deepens conceptual comprehension by granting learners a stronger sense of agency directly supporting Quality Education(SDG 4) by improving engineering education through enhanced conceptual understanding and motivation. WebVR and wearable sensors advance educational technology. (SDG 9) WebVR’s accessibility reduces learning gaps across locations and socioeconomic status, supporting equality. (SDG 10) Wearable create healthier, more engaging environments. (SDG 3) Furthermore, WebVR prepares engineering students for employment, bridging skill gaps and fostering job-ready education. (SDG 8) Our findings offer practical guidance for deploying low-cost immersive platforms in engineering curricula and point toward future work on long-term retention, ethical handling of biometric data, and cross-cultural validation of immersive learning interventions.

By natural process, the world population will in-crease day by day. In rhythm, the tendency of the food will also be rising. So, it is a great challenge to supply food and increase its availability for the huge amount of people of the world, which helps to fulfill their fundamental civil rights. Nowadays, many agriculturedependent countries are fully dependent on the orthodox agriculture process. To develop such types of conditions, people should attract to modern cultivation practices. In the modern era, the Internet of things (IoT) is an optimal solution for preventing such types of challenges. IoT can connect with neoteric agriculture technology. Byusing this technique, farmers can directly connect themselves and control their felds and monitor their feld environmental conditions from anywhere around the world. By the use of unique forms of sensors, models, strategies, and technologies farmers can collect much real-time information and it helps a bearer to increase his productivity of farming. The goal of this paper is to reconsider some very recent works on smart and prompt agriculture processes. In this review paper, we have spoken about the techniques, and methodologies and summarized the state-of-the-art literature. We also made a comprehensive discussion and profound analysis of those recent works. Finally, we have given some future suggestions about smart farming techniques based on our discussion and analysis. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 8, Dec 2021 P 7-16