Precision agriculture involves manipulation of variations in field productivity, maximization of income, scale backing of wastes, and minimizing of the impact on surroundings using automated machine-controlled information assortment and documentation. This work focuses on the efficient control of farm irrigation by exploiting the capabilities of Internet of Things (IoT) and Big Data-based Decision Support System (DSS) to generate adequate valve control commands. Three varieties of prediction techniques such as Deep Neural Network (DNN), Random Forest (RF) and Resilient Back-Propagation Neural Network model are tested to predict soil Moisture Content (MC) in one hour advance by considering 6 numbers of different sensors. The real-time data collection is performed using the proposed IoT node deployment strategy tested in the field. An integrated IoT-based DSS framework is proposed to accumulate 17 numbers of soil and environmental parameters to predict future variation of soil MC in 1 h advance. Further, Structural Similarity (SSIM) Index is used to visualize and maintain uniform MC all over the agriculture area during the entire cropping period. Site and zone specific irrigation control scheme is tested in the test site using fuzzy logic-based weather dependent model. The complete system architecture, deployment strategy and performance of the proposed IoT-based DSS mechanism is discussed in this article.

The current investigation explains the chemical reaction and bioconvection process for an inclined magnetized Cross nanofluid over an inclined cylinder using a spectral relaxation approach. Additionally, the facts concerning swimming gyrotactic microorganisms, non-uniform thermal conductivity, and variable decrease or increase in heat sources are taken together. Each profile is checked for inclined and orthogonal magnetic impact. Appropriate transformations made for conversion of nonlinear PDEs into systems of ODEs. For obtaining numerical results, a spectral relaxation approach is utilized, and graphs are plotted with each physical parameter attached. It is well established that the temperature field intensifies owing to an amplification of thermal conduction and Brownian diffusivity phenomena. The heat transfer rate amplifies owing to a magnification in magnetic parameter and thermal conductivity, but the velocity field diminishes as a result of magnification in the Weissenberg number and power law index. Amplification in the reaction rate constant parameter diminishes the concentration field. Activation energy is the key factor responsible for magnification in the concentration field. Furthermore, smooth agreement is found during comparison with the existing literature. Statistical analysis is also conducted for physical quantities.

Generative Artificial Intelligence (AI) has rapidly become a mainstream technology capable of autonomously producing realistic and diverse media content, including text, images, video, music, and code. As models like GPT, DALL·E, and Stable Diffusion transform industries such as entertainment, marketing, education, and software development, they also introduce a complex landscape of security and privacy risks. This chapter, per the authors, explores critical vulnerabilities such as prompt injection, adversarial attacks, data poisoning, and model inversion, which can lead to unethical outputs and exposure of sensitive training data. It further examines threats arising from large-scale data scraping, copyright violations, and lack of user consent, while also addressing ethical issues tied to misinformation, deepfakes, and surveillance. The chapter reviews technical safeguards including differential privacy, federated learning, watermarking, adversarial training, and secure deployment methods like sandboxing and real-time monitoring.

Internet of Things (IoT) connects many physical devices to many individuals from earth to space. This new concept covers almost all applications such as smart cities, industries, autonomous systems, and underground setups. This article summarizes the application of IoT-driven wireless nodes and IoT decision support platform in underground mining areas where location estimation of the miners is a critical task for the mining administrators. The underground mining accident is one of the most critical environmental disasters where life risk of the miners is a very crucial factor to be addressed. There are very few research work reported to find real-time location of the miner without any Global Positioning System (GPS) and mobile signals. This work demonstrates an efficient local positioning mechanism where low-cost and high battery capacity WiFi nodes are used to find the location of the miner during crude environmental conditions. The whole local positioning system works on the basis of Receiver Signal Strength Indicator (RSSI) value of the miner nodes. This article also demonstrates the useful technique to increase the battery life more than one year. Apart from all the above technological uses, it has been proposed that the localization algorithm can be used as potential technological key for local positioning in underground mines.