Computer Science and Engineering

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Text mining has emerged as a very popular tool in the past years and helped in concluding valuable facts from widespread heterogeneous data in domain of climate science. When we navigate through the abysses of the text data from climate science domain there are many challenges that needs attention to use the maximum potential of this approach. The paper first highlights those voids that needs to be filled with detailed literature review followed by an innovative algorithm with detailed explanation how the algorithm overcomes the mentioned voids previously stated.
Empirical validation and graphical interpretation are also provided to support the efficiency of our algorithm in comparison with other existing advanced algorithms in this domain presently in use. Also, we mentioned challenges evolved due to our new algorithm followed by future scopes and conclusion.

Influencer marketing has evolved as a critical tool in this digital world for brands to develop powerful relationship with customers and turn them to loyal advocates. The traditional advertising strategies are gradually becoming less effective and this creates an inherent force on brands to find some authentic solution to connect to their audience. This chapter actually presents a novel algorithm that can smartly optimize the influencer marketing campaigns to achieve their respective aims. The chapter actually initially includes an analysis that contains in-depth review of the existing research on influencer marketing followed by the development of mathematical paradigm. This paradigm can foretell the success of influencer campaigns with high preciseness. The beauty of this algorithm is that it not only looks at the standard metrics like reach and engagement but also utilizes advanced semantic analysis to detect the effect of content generated by the influencer on the target audience. The results clearly indicate that our analytical model is the optimal choice in precisely forecasting campaign outcomes and improving brand-influencer collaborations. Brands can recognise new opportunities for growth and building a community of passionate supporters by smartly combining data analytics with deep understanding of influencer marketing.

The drug discovery process is characterized by its complexity, lengthy timelines, and high costs, often exceeding a decade and billions of dollars to develop new therapeutics. Traditional methods can be inefficient, leading to low success rates. This study explores the integration of artificial intelligence (AI), specifically ChatGPT, a natural language processing model by OpenAI, to enhance drug discovery. We propose a systematic framework for leveraging ChatGPT in three key areas: generating novel drug candidates, predicting pharmacokinetic (PK) and pharmacodynamic (PD) properties, and optimizing drug design. Our experiments involved literature analysis and feedback mechanisms, revealing that ChatGPT can effectively analyze vast scientific literature to extract insights on drug mechanisms and interactions. Furthermore, it demonstrated proficiency in generating novel chemical structures tailored to specific biological activities and optimizing existing compounds through iterative feedback loops. Preliminary results indicate that ChatGPT significantly accelerates candidate identification and enhances predictive capabilities regarding ADME and toxicity profiles. Despite its potential, challenges such as data privacy, regulatory compliance, and the need for domain-specific adaptations remain. This research underscores ChatGPT's transformative role in addressing longstanding challenges in drug discovery, paving the way for more efficient therapeutic development processes.

Flooding represents one of the most significant global threats, endangering lives, damaging property, and disrupting ecosystems. Timely and accurate flood predictions are critical for effective disaster response and resource allocation. However, traditional flood prediction models face challenges related to data availability, computational complexity, and inherent uncertainties. This paper introduces a novel approach utilizing quantum computing to enhance flood prediction and disaster response. We developed a quantum variational hydrological model that leverages quantum neural networks (QNNs) to simulate complex flood dynamics with high resolution and speed. Additionally, we implemented a quantum game-theoretic decision support system that employs Nash equilibrium and quantum reinforcement learning to optimize flood mitigation strategies and resource allocation. Our model was calibrated and validated using diverse data sources, including satellite imagery, rainfall data, and social media posts. The performance of our quantum-based approach was evaluated using metrics such as root mean square error (RMSE) and flood extent. The results demonstrate a significant improvement in prediction accuracy compared to existing models, alongside reduced response times and minimized economic losses. By quickly identifying irregularities in rainfall patterns and predicting flood occurrences, our model facilitates more effective emergency management. This research highlights the transformative potential of quantum computing in addressing the challenges of flood management. While our approach shows promise, it also faces limitations related to the scalability of quantum technologies and uncertainties in flood scenarios. Future research will focus on extending this methodology to various regions and disaster types while exploring the ethical implications of deploying quantum computing in disaster management contexts.

The intersection of Artificial Intelligence (AI) and image processing has given rise to a transformative era, redefining how visual data is handled across various sectors. This collaboration harnesses the potential of advanced Al algorithms, particularly deep learning models, to revolutionize image recognition, classification, and enhancement. Automated image recognition and classification, powered by deep learning models like convolutional neural networks (CNNs), have found applications in facial recognition, object detection, and scene understanding. This not only enhances security systems and autonomous vehicles but also streamlines content management through efficient categorization. Image enhancement and restoration benefit from Al algorithms trained on extensive datasets. In medical imaging. satellite analysis, and historical photo restoration, these algorithms discern intricate details, improving visual aesthetics, clarity, and accuracy. This application holds promise in advancing fields where precise visual data is crucial. Medical image analysis and diagnostics experience a significant leap forward with Al in image processing. Deep learning models excel in tasks like tumor detection and organ segmentation, expediting diagnostics and improving overall accuracy. The impact of Al in healthcare extends beyond efficiency to the enhancement of patient outcomes. Real-time video analysis and surveillance leverage Al- powered image processing for object tracking, behaviour analysis, and anomaly detection. This integration enhances security systems, public safety initiatives, and traffic management, showcasing the role of Al in fostering situational awareness. Augmented and virtual reality (AR and VR) applications benefit from personalized user experiences driven by Al. Understanding user interactions and preferences, Al algorithms adapt virtual environments, overlay relevant information, and optimize visual content delivery. This personalization enhances the immersive nature of AR and VR applications in gaming, education, and training. Keywords: Artificial Intelligence, Image processing, Al algorithms.

The pursuit of engaging more women in academic innovation is a critical endeavour that addresses the underrepresentation of women in the field of technology and innovation. Despite the growing recognition of diversity as a catalyst for innovation, women remain significantly underrepresented in academic innovation. This critical review examines the barriers that hinder female academics and participation in innovation and proposes strategies to foster their involvement. The review highlights the importance of inclusive policies and practices that can create a supportive environment for women to contribute to technological advancements and innovation. It underscores the need for systemic changes that encourage women's engagement throughout the innovation lifecycle, from ideation to commercialization. By implementing targeted interventions and cultivating an ecosystem that values diversity, academic institutions can unlock the full potential of women innovators and drive progress in research and development. The review calls for a concerted effort to dismantle the structural and cultural barriers that impede women's full participation in academic innovation, advocating for a paradigm shift towards greater equity and inclusion in the academic sphere. Engaging more women in academic innovation is not only a matter of social justice but also a strategic imperative to enhance the quality and impact of research and innovation in the global knowledge economy. This chapter serves as a valuable resource for policymakers, educators, and innovation leaders seeking to promote gender diversity and drive transformative change in academia and beyond. The review concludes with a call to action for stakeholders across the academic and innovation ecosystems to embrace diversity as a strength and to commit to creating a more inclusive and innovative future. Engaging more women in academic innovation is essential for fostering a culture of creativity, collaboration, and excellence that can address the complex challenges of the 21st century.