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Recent advancements in natural language processing (NLP) have catalyzed the development of models capable of generating coherent and contextually relevant responses. Such models are applied across a diverse array of applications, including but not limited to chatbots, expert systems, question-and-answer robots, and language translation systems. Large Language Models (LLMs), exemplified by OpenAI's Generative Pretrained Transformer (GPT), have significantly transformed the NLP landscape. They have introduced unparalleled abilities in generating text that is not only contextually appropriate but also semantically rich. This evolution underscores a pivotal shift towards more sophisticated and intuitive language understanding and generation capabilities within the field. Models based on GPT are developed through extensive training on vast datasets, enabling them to grasp patterns akin to human writing styles and deliver insightful responses to intricate questions. These models excel in condensing text, extending incomplete passages, crafting imaginative narratives, and emulating conversational exchanges. However, GPT LLMs are not without their challenges, including ethical dilemmas and the propensity for disseminating misinformation. Additionally, the deployment of these models at a practical scale necessitates a substantial investment in training and computational resources, leading to concerns regarding their sustainability. ChatGPT, a variant rooted in transformer-based architectures, leverages a self-attention mechanism for data sequences and a reinforcement learning-based human feedback (RLHF) system. This enables the model to grasp long-range dependencies, facilitating the generation of contextually appropriate outputs. Despite ChatGPT marking a significant leap forward in NLP technology, there remains a lack of comprehensive discourse on its architecture, efficacy, and inherent constraints. Therefore, this survey aims to elucidate the ChatGPT model, offering an in-depth exploration of its foundational structure and operational efficacy. We meticulously examine Chat-GPT's architecture and training methodology, alongside a critical analysis of its capabilities in language generation. Our investigation reveals ChatGPT's remarkable aptitude for producing text indistinguishable from human writing, whilst also acknowledging its limitations and susceptibilities to bias. This analysis is intended to provide a clearer understanding of ChatGPT, fostering a nuanced appreciation of its contributions and challenges within the broader NLP field. We also explore the ethical and societal implications of this technology, and discuss the future of NLP and AI. Our study provides valuable insights into the inner workings of ChatGPT, and helps to shed light on the potential of LLMs for shaping the future of technology and society. The approach used as Eco-GPT, with a three-level cascade (GPT-J, J1-G, GPT-4), achieves 73% and 60% cost savings in CaseHold and CoQA datasets, outperforming GPT-4. [ABSTRACT FROM AUTHOR]

In the operation of power grid, worldwide, non-technical losses (NTLs) occur in a massive amount of proportion which is observed up to 40% of the total electric transmission and distribution losses. These dominant losses severely affect to adverse the performance of all the private and public distribution sectors. By rectifying these NTLs, the necessity of establishing new power plants will automatically be cut down. Hence, NTLs have become a critical challenge to do research in this emerging area for researchers of power systems due to the limitations of the current methodologies to detect and fix up these prominent type of losses. The existing survey so for basically contains the detail of identification of non-technical losses by machine and deep learning methods while this paper is a complete trouble shooting to resolve this issue by systematic approach. To address this, causes of NTLs along with its impact on economies and types of NTLs are elaborated in various countries. In addition, we have also prepared a comparative analysis based on several essential parameters. Further, implementation process of detection of NTLs or electricity theft based on Machine Learning or Deep Learning has also been demonstrated. Moreover, major challenges of detection of NTLs or electricity theft based on ML and Deep Learning, and its possible solutions are also described. Hence, definitely this comprehensive survey will help to the leading researchers to reach a new height in this thrust area. [ABSTRACT FROM AUTHOR]

Regression analysis is a statistical method employed to establish the relationship between "independent variables" and "dependent variables." This widely utilized analysis technique is capable of addressing numerous issues. Given the complexity and imprecision of real-world events, simplistic models often prove inadequate. Consequently, many researchers have turned to fuzzy set theory to enhance the accuracy of statistical regression analysis. These endeavors have bolstered the adaptability of various analysis doctrines. This paper offers a comprehensive and well-structured review of the literature, theory, and practice pertaining to fuzzy regression analysis. Furthermore, a straightforward and efficient method for retrieving data from wine databases is presented herein. The data is further elucidated through the use of charts and tables. The regression model leverages this data to provide a precise representation of wine quality. Additionally, we examine the assumptions underlying regression analysis. [ABSTRACT FROM AUTHOR]