Anshit Mukherjee

CHATGPT is a chatbot system that applies the large scale of this pre-trained language model to produce natural and application-like responses to the users' messages. The new proposed application called CHATGPT seeks to offer the user an improved way of practicing generative models and at the same time engage the user in a fun and resourceful interaction with an artificially intelligent agent. With this tool that researchers are still exploring, it is our intention in this paper to solve some rather difficult models. We are much interested to know how these models will advance this smart tool to enable it play a crucial role in the future. In this paper various versions of the interactive soft set models are explained along with the scope of effectiveness. It has been determined that the ChatGPT has achieved greater results in terms of performance in comparison with other instruments that is based on the full concept of AI.

The microbiome has emerged as the critical player in human health and disease, with profound implication for hematological malignancies. Understanding the complex interaction between the microbiome hematopoietic system hold great promise for improving diagnosis prognosis and treatment outcomes. In recent years Artificial Intelligence has revolutionized our ability to analyze vast dast sets and extract meaningful insights. This manuscript provides an overview of the intersection between AI, microbiome research and hematological malignancies highlighting the transformative potential of AI driven approaches in this field.

Cancer is one of the most serious threats to human health and life. Despite the advances in conventional therapies, such as surgery, chemotherapy, radiotherapy, and immunotherapy, there are still many challenges and limitations in achieving effective and precise cancer treatment. Nanorobots, inspired by natural biological nanomachines, offer a promising alternative for cancer diagnosis and therapy. Nanorobots are nanoscale devices that can perform various tasks under the guidance of external stimuli, such as magnetic fields, light, ultrasound, or chemical gradients. Nanorobots can be designed to target specific cancer cells or tissues, deliver drugs or genes, sense tumor biomarkers, perform minimally invasive surgery, or combine multiple functions for comprehensive treatment. In this chapter, the authors review the recent progress and applications of bio-inspired nanorobots for cancer diagnosis and therapy, with a focus on magnetic field-driven nanorobots. They also discuss the challenges and future perspectives of nanorobots in clinical translation.

In the pursuit of optimizing renewable energy sources, the selection of solar plant installation sites presents a complex decision-making challenge that involves multiple criteria. This research introduces a groundbreaking algorithm, leveraging quantum computing techniques to enhance Multi-Criteria Decision Making (MCDM) for solar plant site selection. The proposed algorithm harnesses the superposition and entanglement properties of quantum bits to evaluate extensive datasets and criteria with unprecedented speed and accuracy. By integrating quantum versions of established MCDM methods such as the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), the algorithm provides a sophisticated tool for decision-makers. The research demonstrates the algorithm's superiority over classical methods through rigorous simulation and validation processes. The findings suggest that quantum-enhanced MCDM can significantly streamline the solar plant site selection, paving the way for a more efficient deployment of solar energy infrastructure and contributing to a sustainable energy future.

The smart energy domain poses substantial challenges in future research, necessitating advanced investigation into optimizing smart customizable networks using artificial intelligence (AI) and machine learning (ML). With renewable energy (RE) being pivotal for global development amid climate change, AI introduces new paradigms to reshape activities, demanding revamped energy infrastructure and RE deployment strategies. The chapter explores the adoption of AI in future smart cities research with considerable economic benefits. Moreover, in the field of environmental science and engineering (ESE), the ML's potential in revolutionizing ESE by addressing complex problems and outlining crucial components for successful ML implementation in ESE, correct model development, proper interpretation, and applicability analysis has been done. The renewable source of energy like solar and wind energy can be generated in place where they are found in plenty. The ML application of energy source tracing and the prediction of energy type with multiple models of efficiencies has been highlighted.

Aims: The objective of Veritas AI is to revolutionize the domain of lie detection through the deployment of a cutting-edge algorithm within the realms of computational linguistics and artificial intelligence. Study Design: Veritas AI is conceptualized as a groundbreaking framework that integrates advanced syntactic and semantic analysis, leveraging generative pre-trained transformers to identify linguistic cues indicative of deception. Place and Duration of Study: The research underpinning Veritas AI's algorithm was meticulously executed at the Abacus CSE Lab over a period from December 2022 to March 2024, ensuring a robust empirical foundation for the system's validation and optimization. Methodology: Employing a deep learning neural network at its core, Veritas AI is trained on a diverse dataset comprising both truthful and deceptive dialogues. This training is complemented by multimodal biometric interrogation techniques and sophisticated natural language processing algorithms. Results: The empirical results underscore Veritas AI's unparalleled accuracy in discerning truth, marked by its ability to provide real-time adaptive feedback and maintain robust performance across various communication scenarios. Conclusion: In conclusion, Veritas AI stands as a testament to the symbiotic potential of human ingenuity and machine learning. Its precision-engineered algorithm, underpinned by empirical validation, heralds a transformative leap in the field of automated veracity assessment, setting a new benchmark for truth analysis in the digital age.

ChatGPT is a conversational language model that can interact with users naturally and engagingly. It is based on GPT, a large-scale neural network that can generate coherent and diverse text from a given prompt. ChatGPT is trained using reinforcement learning from human feedback, which allows it to adapt to different contexts and preferences. However, ChatGPT also faces some limitations and challenges, such as producing inaccurate or nonsensical answers, being sensitive to input phrasing, and being over-verbose or repetitive. In this paper, we propose to view ChatGPT as a fuzzy system, which can capture the uncertainty and ambiguity inherent in natural language. We argue that fuzzy logic and fuzzy sets can provide a useful framework for analyzing, evaluating, and improving ChatGPT's performance and behavior. We illustrate how fuzzy concepts such as membership functions, linguistic variables, and fuzzy rules can be applied to ChatGPT's input, output, and internal representations. We also discuss some potential benefits and drawbacks of using fuzzy methods for ChatGPT, as well as some open questions and future directions for research.

In this research, the analytical study is aimed to observe the multi-level administration in the primary school educational system prevailing in India. It also investigates the functioning body of decentralized education administration with numerical analysis.

Robotic systems often require engineers to write code to specify the desired behaviour of the robots. This process is slow, costly, and inefficient, as it involves multiple iterations and manual tuning. ChatGPT is a tool that leverages a large language model (LLM) to enable natural language interaction, code generation, and learning from feedback for robotic applications. ChatGPT allows users, who may not have technical expertise, to provide high-level instructions and feedback to the LLM, while observing the robot's performance. ChatGPT can produce code for various scenarios of robots, using the LLM's knowledge to control different robotic factors. ChatGPT can also be integrated with other platforms, such as Snapchat and Duolingo, to enhance the user experience and management. ChatGPT is a novel tool that facilitates a new paradigm in robotics, where users can communicate with and teach robots using natural language.

Fossil fuels are a leading source of global warming pollution. Even the waste products are hazardous to public health and the environment. Considering the business cost and environmental concerns, the technology of 'Bio-waste-to-Energy' is recognized as an increasingly important renewable source of energy. Here we have produced potassium hydroxide (KOH) activated microporous carbon from tea wastage. We have fabricated flexible Polyvinyl alcohol (PVA: easily processable, cheap and eco-friendly polymer) based films using Activated Carbon (AC) as nanofiller with different weight percentage. Conductivity observation of the sample was done to study their prospect as the dielectric materials in energy storage, microwave absorption applications. Depending upon the results, we can apply the Activated Carbon (AC) based polyvinyl alcohol (PVA) dielectric film to make flexible, environmental friendly energy/storage devices, microwave absorbents.