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Business Intelligence Model (BIM) plays a vital role in forming a strategy and taking correct data-based steps in a modern generation to achieve a better demand forecasting result. An inevitable resolution support structure that helps the organization conduct data analyses throughout the business process has been considered a significant challenge. The prediction of potential demands for businesses is predicted with the help of artificial intelligence has been introduced in this research. Based on the intelligence technique, demand estimation is considered one of the company's major decision-making activities focused on Improving Holistic Business Intelligence Model (IHBIM). For predictions of demand, first raw data from the market is gathered, and then potential demand for sales/products is predicted according to requirements using IHBIM. This forecast is based on data obtained from multiple sources. Further, Artificial intelligence conducts data from various modules and calculates the goods/products' demands regularly, monthly, and quarterly has been integrated into IHBIM. The simulation results show that the accuracy of the demand forecast is non-compromising. Furthermore, the model's performance is validated by combining the projected results with accurate data and calculating the percentage error. [ABSTRACT FROM AUTHOR]

Substation equipment inspection is essential for the power industry. The expansion of the smart grid scale improves the transmission capacity and enhances the likelihood of power plant facilities failure. To ensure the safety of the electric power supply, it is essential to inspect substation equipment. Metal commercial equipment can be traversed by remote inspection robots equipped with magnetic wheels. It is possible to use robots like this to examine equipment and pipelines remotely. In many cases, these gadgets are able to scale vertical surfaces and even traverse obstacles with a variety of shapes. Finally, researchers in the field of robotics have indicated that challenges such as restricted onboard battery capacity, undependable line fault detection, electrical insulation, power mechanism, and advanced control techniques for outer wind disruption are highly promising research areas. To build an unmanned, intelligent, and succeeded substation, the substation progressively implements inspection robots instead of physical exertion. Hence, in this paper, the mobile-based Intelligent Tracking Framework (MITF) has been proposed using inspection robots. This inspection robot is autonomous and can be used for various tracking tools: visual, infrared, and partial charge–discharge camera. The robot is integrated with a camera and thermal infrared imager sensors that have been collectively designated as workload. These inspection sensors are used to detect environmental parameters such as reading meters, evaluation thermoelectric temperature. The accurate localization of working loads and the inspection robot electromagnetic interference within substations have been resolved. This mobile robot delivers innovative monitoring and precise detection for the unmanned substation and smart substation. The suggested approach's effectiveness is verified through experiment results based on the electrical equipment of the substation. The experimental outcome of the proposed method boosts the Meter Reading Analysis (94.19%), Transmission Capacity Analysis (98.5%), Workload Analysis (98.9%), Temperature Analysis (97.6%), and Safety Analysis (95.41%). [ABSTRACT FROM AUTHOR]

The literature review of the market has recently been increasingly analyzed based on the relative impact of the empirical determination of strategic success factors in e-commerce across various smart city platforms. To quantify the cumulative impacts of a given policy or ecommerce model, this research perspective has been focused on the effect of marketing practices and selected business models, differentiating direct and indirect factors on sales and profitability in a smart city platform. In this research, Hierarchical Advance Soft Computing Techniques (HASCT) has been proposed to analyze the performance factor of e-commerce in various sectors of smart cities platform. Furthermore, HASCT evaluates the relative value of the different strategic components using unrelated regression models in e-commerce. Here, the indirect impact of sales on profitability determines the overall flexibility of the marketing plan. Furthermore, the Fuzzy Decision-making trial and evaluation laboratory model (DEMATEL) has been introduced for effective decision making and measure the success factors of ecommerce. The numerical results have been executed, and the proposed HASCT method enhances customer buying behavior prediction of 95.6%, profitability ratio of 98.7%, the performance ratio of 96.8%, accuracy ratio of 97.7%, and less error rate 9.8% in smart cities platforms. [ABSTRACT FROM AUTHOR]