Abstract Changing customer perception towards organic food is inspiring people to shift to organic counterparts of conventionally available packaged food products. They are willing to purchase the organically manufactured and packaged products in spite of a premium price tag. They thus possess some inherent expectations post purchase of organic food products. The purpose of this study is to explore customer levels of satisfaction and dissatisfaction on various attributes or features of packaged organic food products available in Indian market. For this purpose, 26,905 Online Customer Reviews of 100 unique packaged organic food products were collected and analysed using Word Clouds and Bar Graphs. Text mining techniques of Natural Language Processing (NLP) were applied to transform the unstructured textual review data into a structured form for identification of meaningful patterns and useful insights. Results confirmed that the customers were more dissatisfied than they were satisfied on product features of Price and Smell. On the other hand, Flavor, Delivery and Taste were the factors of highest customer satisfaction as compared to dissatisfaction shown towards each of these product features. Overall, Taste, Quality and Packaging saw maximum number of customer reviews as well as overall customer satisfaction post purchase of organic food products online. The study has implications for common shoppers of organic food products, manufacturers, marketers as well as retailers. The research provides a better understanding of consumer opinion after purchase and consumption of organic food products online.

Abstract This paper presents a novel approach to enhance the performance of Automatic Voltage Regulator (AVR) systems in power systems using Deep Reinforcement Learning (DRL). The AVR plays a critical role in maintaining voltage stability and ensuring reliable power delivery. However, conventional control strategies, such as PID controllers, have limitations in handling complex and nonlinear power system dynamics. In this study, the application of DRL techniques is explored, particularly the Twin-Delayed Deep Deterministic Policy Gradient (TD3) algorithm, to AVR control. This algorithm offers the advantage of handling continuous action spaces and enable the controller to learn optimal control policies directly from the system's state information. The results show that the DRL approach outperforms the traditional PID and Neural Network-based control approaches, with the shortest time response and the best voltage regulation performance. The use of DRL in AVR system control shows promising potential for improving the efficiency and accuracy of power system control. This research provides insights into the advantages of DRL for process control and highlights its potential for future applications in power system control.

Abstract The proliferation of deep models characterized by an abundance of parameters has catalyzed research enthusiasm in the domain of AI systems. The emergence of novel computational modalities has brought forth numerous fresh challenges within the realm of cloud computing, encompassing aspects such as cost, performance, elasticity, and the intricate tradeoffs entailed therein.

Abstract Current photovoltaic systems are equipped with a monitoring system. The data is recorded with a predefined time base. The operator has a large amount of data. This article is a contribution to the analysis and understanding of this data. It proposes to estimate Generation from this data using machine learning. The study was conducted at the 1Mw generation site in Miroslava, Iasi, Romania. The visualization and interpretation of the generation data is presented in this article. Then, prediction techniques are presented to obtain an estimate of the plant’s generation. These techniques are: Simple Exponential Smoothing (SES), Autoregression (AR), Seasonal Auto-Regressive Integrated Moving Average with eXogenous factors (SARIMA), Holt Winter’s Exponential Smoothing (HWES), Long Short Term Memory neural (LSTM) and Convolutional Neural Network (CNN).

Abstract From traditional civil engineering and aerospace engineering to large portions of electrical, electronics, and computer engineering, and most importantly, the medical and clinical sciences, artificial intelligence (AI) is one of the most critical and useful tools for research. The human brain’s ability to interpret sensory data, solve issues, learn from past experiences and tests, store and retrieve data, etc., is where the current discipline starts. In particular, the power sector is on the cusp of inevitable change as a result of numerous restructuring efforts, and the power science community requires artificial intelligence resources for effective planning, operation, and control of the power system. Almost every AI method is logically conceived of as an optimization or decision-making problem. Power utilities can benefit from these AI methods because they provide novel approaches to efficient evaluation, effective management, and astute decision-making. We have learned more about the design process and created higher-quality goods and artifacts thanks in large part to the widespread use of AI techniques and approaches over the past few decades. When these disciplines work together, they produce cutting-edge architectures that can solve a wider range of design problems at once.

Abstract Purpose- The study aims to develop an efficient model to handle challenges of supply chain management in readymade garments industry. This paper illustrates the systematic literature reviews and experimental evidence-based study of readymade garments companies of a developing Asian country. The present study also finds the pattern for the healthy and sustained development of clothing enterprises. Design/methodology/approach- This paper illustrates the systematic literature reviews and experimental evidence-based study of readymade garments companies of a developing Asian country. The existing qualitative and quantitative studies were reviewed to find out the challenges in supply chain and the recommendable solutions are provided. The studies undertaken in Asian developing countries were only considered in this study. Findings- The innovation on supply chain management for readymade garments company in developing country will help enterprise to setup legal guidelines for smooth production process. The new innovative supply chain management has two characteristics, a balance of high efficiency and cost saving. Dual-source supply chain management, manufacturing outsourcing, and multi-supplier management are three elements of it. The ICT information management platform is a critical component in ensuring the supply chain flexibility of the clothing industry. Implications- Through the research of application and analysis of the innovative supply chain systems of readymade garment industry of Asian developing countries. It helps to setup guideline, and direction for the practical operation way of modern enterprise with innovative supply chain system knowledge and practice. Originality/value- All this relies on its unique and efficient supply chain management, this paper explains and analyzed innovative supply chain management. This paper also contributes by providing solution to overcome from the challenges of supply chain management in readymade garments industry. Paper Type- Research paper