Forest is one of the main sources of living organisms. Its needs start from the human breath to usage of the wood. But due to many reasons, area occupied by the forest is reducing every year. The reasons behind these environmental impacts are natural disasters (forest fires), deforestation activities, and unlawful actions. Forest fire could be creating most serious threat to wild animals and resources of human welfares. The primary phenomenon of the wild fire occurrence is circumstance hotness of the forest. The dry and hot atmosphere caused the fire in the forest. The deforestation and smuggling activities are also tridents to the available forest. The main consideration of this article is to detect wildfires in advance and protect forest resources from social crimes through advanced sensor integration in the IoT (Internet of Things) environment. A smart forest alert monitoring system has been proposed in this article to avoid forest mishap over by automated self‐decision‐making protective actions such as parameter measures and alert and implementing the harm mitigation actions related to the hot temperature, humidity, smoke, and smuggling of trees. All the sensors work as per the algorithm designed by the specific application of IoT (Internet of Things). The accurate predictions of the forest fire events and ensuring the forest safety have been tested and verified by a conducted case study on the real forest zone environment.

The focus of the paper is on the renewable energy-based rural electrification system. Two concepts have been used in finding solutions to the issues related to this micro-grid system. The first is the best fitted model of hybrid configuration system, which utilizes village-owned resources, such as abundant biomass and solar irradiation. The second is that observed improved performance in terms of system sizing, techno-economic performance, and environmental stability of the hybrid model over different power storage backup media. The practical aspects and suggestion over the hybrid village electrification system have been presented through a case study of Korkadu village, Puducherry state, India. A comparative analysis of the hybrid model has been made with different power storage backup systems, namely the solid-state battery (lead acid-ASM battery, Li-ion-ASM battery) and flow battery (Redflow ZBM2 battery) arrangement. The result revealed hybrid rural electrification system with LI battery as the most favourable choice of the electrification system to the village, considering that its economic factor includes the total net present cost, cost of energy are found to be lowest. The technical parameters of power production capacity surface and battery state of charge were observed to be optimum with reduction in the environmental pollution parameter (GHS emission) value.

Machine learning has emerged as a promising method for predicting breast cancer using quantum computation techniques. Quantum machine learning algorithms, such as quantum support vector machines (QSVMs), are demonstrating superior efficiency and economy in tackling complex problems compared to traditional machine learning methods. When compared with classical support vector machine, the quantum machine produces remarkably accurate results. The suggested quantum SVM model in this study effectively resolved the binary classification problem for diagnosing malignant breast cancer. This work introduces an enhanced approach to breast cancer diagnosis by integrating QSVM with elitist non‐dominated sorting genetic optimization (ENSGA), leveraging the strengths of both techniques to achieve more accurate and efficient classification results. ENSGA plays a crucial role in optimising QSVM parameters, ensuring that the model attains the best possible classification accuracy while considering multiple objectives simultaneously. Moreover, the quantum kernel estimation method demonstrated exceptional performance by achieving high accuracy within an impressive execution time of 0.14 in the IBM QSVM simulator. The seamless integration of quantum computation techniques with optimisation strategies such as ENSGA highlights the potential of quantum machine learning in revolutionising the field of healthcare, particularly in the domain of breast cancer diagnosis.