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Artificial Intelligence is a popular topic these days. It is believed that artificial intelligence will be the solution to all existing societal problems, reducing human effort while also improving human interpretation of the world around them. Artificial intelligence, within the realm of computer science, is focused on developing machines capable of emulating human thought processes, operations, and adaptability. At present, artificial intelligence finds utility across diverse domains, including but not limited to medicine, engineering, agriculture, self-driving vehicles, and aviation. Another emerging arena for AI deployment is in energy platforms. This paper delves into the applications of artificial intelligence and their significance in fostering sustainability across various trends. Consequently, it provides insights and analysis regarding the longterm viability of AI, encompassing environmental and economic development. Furthermore, it highlights the positive impact of AI applications on sustainable development, encompassing aspects such as fulfilling energy requirements, generating employment opportunities, and enhancing environmental preservation.

The problem of power regulation has been well studied and there exist number of approaches in regulating the power supply to multi storage buildings. Some of the method uses number of supply units present in each floor, and number of control units present in the floors of the building. However, they suffer to achieve higher performance in power regulation. To handle this issue, a Genetic Algorithm Based power regulation model (GAPRM) is presented in this paper. The model considers various factors like number of electric stoves, no of heater, no of iron, no of vacuum cleaner, no of television, no of hair dryer, no of refrigerator, no of electric kettle, no of microwave oven, no of washing machines and Voltage requirement of each towards power regulation. The model fetches the details of each storage level and identifies the set of all electric devices present in the floor. Using these details, the method converts them into feature vector. With the incoming voltage, the method applies genetic algorithm to identify maximum units can be triggered with the supply at each floor. The genetic algorithm generates number of mutations and for each mutation; the method computes the fitness value in the name of Smart Voltage Fitness (SVF) measure. Based on the value of SVF, the method populates a subset of population. For the selected population, the method computes Power Regulation Value (PRV). According to the value of PRV, specific voltage has been regulated to the specific storage. This will be iterated for each storage level of the building. The proposed method improves the performance of power regulation in smart multi storage building.

The globalized and fiercely competitive nature of the international market has expanded the range of demands across all sectors of the agri-food business. The dairy business needs to adjust to the prevailing market conditions by enhancing resource efficiency, adopting environmentally sustainable practices, promoting transparency, and ensuring security. The Internet of Things (IoT), Edge Computing (EC), and deep learning play pivotal roles in facilitating these advancements as they enable the digitization of various components within the value chain. Solutions that depend on human observation via visual inspections are susceptible to delayed detection and potential human mistakes and need more scalability. The growing herd numbers raise a significant worry due to the potential negative impact on cow health and welfare, particularly about extended or undiscovered lameness. This condition has severe consequences for cows, eventually leading to a decline in milk output on the farm. To address this issue, an Integrated Approach to Dairy Farming (IA-DF) has been developed, which utilizes sophisticated Artificial Intelligence (AI) and data analytics methodologies using mobile applications to continuously monitor livestock and promptly detect instances of lameness in cattle. Initially, the VGG16 model, pre-trained on the ImageNet dataset, was used as the underlying architecture to extract the sequence of feature vectors associated with each video. This approach was adopted to circumvent the limitations of conventional feature engineering methods, which tend to be both time-consuming and labor-intensive with deep learning-based classification algorithms. IA-DF can extract semantic details from historical data in both forward and backward directions, hence enabling precise identification of fundamental behaviors shown by dairy cows.

The agricultural sector serves as a fundamental cornerstone of the economies of numerous countries, necessitating technological advancements despite limited financial resources. The Internet of Things (IoT) presents a novel aspect within the field of soil health monitoring, which has significant implications for advancing smart agriculture and farming practices. Integrating conventional agricultural practices with cutting-edge technologies, such as the IoT and Wireless Sensor Networks (WSN), can foster Smart Agriculture (SA). This paper presents IoT Integration for Enhanced Turmeric Cultivation (IoT-ETmC) in the context of SA. The TurmFox IoT and Edge-to-Cloud (ETC) technology can analyze gathered data and send it to the user through internet connectivity. The work involves the implementation of TurmFox in experiments focused on turmeric cultivation. The results demonstrate a notable improvement in the quality of turmeric as a direct outcome of this intervention. The curcumin levels in the given product are notably higher, ranging from 4450 to 5450 mg per 120g. This paper also aims to demonstrate the intuitive configuration of sensor-to-actuator connections for implementing desired SA. The real-time data obtained from Turmfox provides information on the pH values, moisture levels, and temperature, allowing for observing dynamic variations in environmental conditions within the specified period. The pH level was 6.5 at 09:00, with a moisture content of 51 g/m3 and a temperature of 293 K.