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In today’s date Artificial intelligence (AI) has substantially transformed marketing strategies and specifically Viral Marketing by enhancing the content personalization, targeting the audience and real time campaign optimization. The study explored the Artificial Intelligence impact on Viral marketing with a comprehensive review of 20 literatures that highlights the diverse applications of AI such as predictive analytics, natural language processing (NLP) and AI-driven visual content creation. This study employed meta analysis approach to evaluate how effectively AI could boost marketing reach, engagement and return on investment (ROI). The finding of the study indicates a positive correlation between the efficiency of Viral Marketing campaigns and the integration of AI, despite the fact highlighting ethical and transparency. The study concludes with practical suggestions for using AI in Viral marketing in a responsible and efficient manner to enhance its potential while mitigating related dangers. This study also highlights AI’s revolutionary role in changing market dynamics.

The integration of Artificial Intelligence (AI) in personalized and adaptive learning environments has revolutionized the education sector by offering customized learning experiences tailored to individual student needs. This study explores the role of AI in enhancing adaptive learning through data-driven insights, intelligent tutoring systems, and real-time feedback mechanisms. By employing machine learning algorithms and natural language processing, AI-driven platforms can analyze student performance, predict learning patterns, and deliver personalized content. The study highlights the effectiveness of AI in addressing diverse learning styles, improving engagement, and optimizing educational outcomes. Furthermore, it discusses the implications of AI in fostering inclusive education and lifelong learning. The findings suggest that AI-powered learning environments significantly enhance student-centered education, promoting efficiency and accessibility.

The rapid advancement of technology has made artificial intelligence (AI), particularly generative chatbots, an integral part of everyday life. These chatbots, powered by Natural Language Processing (NLP) and deep learning technologies, are widely used in various fields such as customer service, education, and entertainment. However, the increasing prevalence of such technologies brings forth important philosophical concerns, particularly in the realm of axiology—the branch of philosophy that deals with the nature of values, including the practical and ethical implications of knowledge and technology. This study investigates the practical benefits and ethical responsibilities associated with generative chatbots, using ChatGPT as a case study. The research examines whether ChatGPT adheres to the axiological principles of science, specifically its usefulness in enhancing human life and its ethical responsibilities. Through a qualitative content analysis, this research evaluates the responses of ChatGPT to a series of questions based on the axiological framework outlined by Sumantri. The study focuses on two main aspects of science's axiological evaluation: the practical benefits of science and technology, and the ethical responsibilities tied to their application. The findings indicate that ChatGPT is capable of providing useful insights that contribute to human understanding, improve quality of life, simplify complex tasks, and offer solutions to various problems. However, the ethical considerations of AI technology, such as fairness, transparency, and accountability, remain a crucial area of concern. This research highlights the importance of balancing technological progress with ethical responsibility, emphasizing that AI systems like ChatGPT must be developed and applied in ways that align with human values to ensure their positive impact on society.

Artificial Intelligence (AI)plays a vital role in criminal investigations, offering innovative solutions to challenges faced by law enforcement. With its fast and accurate data analysis capabilities, AI can identify behavioral patterns, detect anomalies, and predict potential crimes. Technologies such as facial recognition, social network analysis, and natural language processing help speed up the investigation process and improve prosecution effectiveness. However, the application of AI also raises ethical challenges, including privacy issues and potential bias in algorithms. Therefore, it is important to develop a framework that ensures the responsible use of AI in a legal context.    

n today's digital era, customer reviews play a crucial role in purchasing decisions, but the large volume of reviews makes manual analysis difficult. Thus, a fast and accurate sentiment analysis method using Natural Language Processing (NLP) is needed. This research aims to analyze product reviews for the ZALIKA STORE 88 on Shopee using NLP. It involves preprocessing reviews, applying NLP techniques like tokenization, stemming, and lexical analysis, and automatically classifying sentiments. The analysis of ZALIKA STORE 88's reviews reveals mostly positive sentiments, with some negative and neutral reviews. The sentiment analysis achieved an 87% accuracy rate. This research is intended to help ZALIKA STORE 88 make informed decisions based on customer reviews.

Research This investigate consumer sentiment analysis to halal products using social media data with utilise intelligence artificial intelligence (AI). With background behind increasing estimated market value of halal products reach USD 2.02 Trillion in 2024, understanding deep about opinion consumer become very important. Research This adopt approach quantitative, using secondary data from social media platforms such as Twitter, Instagram, and Facebook. Through Natural Language Processing techniques and algorithms learning machine, sentiment analysis is performed For identify pattern positive, negative and neutral in perception consumers. Research results show that 60% of the total 10,000 reviews had positive sentiment, with halal food products receiving the highest positive sentiment. Factors influencing consumer sentiment include product quality, price, and transparency of information. In addition, the study found that the use of AI in sentiment analysis provides advantages in efficiency and accuracy, and is able to capture nuances in consumer opinions that are not Possible done by manual analysis. From the analysis this, can concluded that the marketing strategy of halal products must focus on improving quality and providing clear information about halal certification. This study not only provides insight for halal industry players, but also enriches the literature related to AI, sentiment analysis, and sharia economics.

In the current era of technological advancement, deep learning has become a widely discussed and utilized topic, particularly in image classification, object detection, and natural language processing. A significant development in deep learning is the Convolutional Neural Network (CNN), which is enhanced with various optimizations such as Adam, RMSProp, and SGD. This thesis implements the Inception v3 architecture for the deep learning model, utilizing these three optimization methods to classify malaria disease. The study aims to evaluate performance and determine the best optimization based on classification accuracy. The results indicate that the SGD optimization with a learning rate of 0.001 achieved an accuracy of 94%, RMSProp with learning rates of 0.001 and 0.0001 achieved an accuracy of 96%, and Adam with learning rates of 0.001 and 0.0001 achieved an accuracy of 95%.

This research delves into the crucial aspect of diversity within generative models, exploring both its understanding and potential enhancement. Diversity in generative models refers to the ability of the model to produce a wide range of outputs that cover the variability present in the underlying data distribution. Understanding diversity is fundamental for assessing the quality and applicability of generative models across various domains, including natural language processing, computer vision, and creative arts. We discusses existing methods and metrics for evaluating diversity in generative models and highlights the importance of diversity in promoting fairness, robustness, and creativity. It explores strategies for enhancing diversity in generative models, such as regularization techniques, diversity-promoting objectives, and novel architectures. By advancing our understanding of diversity and implementing techniques to enhance it, generative models can better capture the complexity and richness of real-world data, leading to improved performance and broader applicability.

Background: Public opinion analysis has become increasingly important in the digital era, where social media platforms generate large-scale textual data reflecting public perceptions toward environmental policies. Advances in Natural language processing (NLP) and machine learning enable systematic sentiment classification to support data-driven decision-making. Objective: This study aims to evaluate the effectiveness of several sentiment classification models in analyzing Indonesian-language social media data related to environmental policies. Method: The research employed a text mining pipeline including data crawling, preprocessing (case folding, tokenization, stopword removal, and stemming), and vectorization using TF-IDF. Three classification models Logistic Regression, Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) were trained and evaluated using accuracy and F1-score metrics. Results: Experimental findings indicate that LSTM achieved the highest performance with 91.7% accuracy and 91.2% F1-score, outperforming SVM (88.5%) and Logistic Regression (84.2%). Sentiment distribution analysis shows that public opinion is dominated by positive sentiment (47.5%), followed by neutral (32.0%) and negative (20.5%). Overall: The results demonstrate that deep learning-based models provide more robust contextual understanding and more reliable sentiment mapping for environmental policy analysis.

Generative artificial intelligence tools have recently attracted a great deal of attention. This is because of their huge advantages, which include ease of usage, quick generation of answers to requests, and the human-like intelligence they possess. This paper presents a vivid comparative analysis of the top 9 generative artificial intelligence (AI) tools, namely ChatGPT, Perplexity AI, YouChat, ChatSonic, Google's Bard, Microsoft Bing Assistant, HuggingChat, Jasper AI, and Quora's Poe, paying attention to the Pros and Cons each of the AI tools presents. This comparative analysis shows that the generative AI tools have several Pros that outweigh the Cons. Further, we explore the transformative impact of generative AI in Natural Language Processing (NLP), focusing on its integration with search engines, privacy concerns, and ethical implications. A comparative analysis categorizes generative AI tools based on popularity and evaluates challenges in development, including data limitations and computational costs. The study highlights ethical considerations such as technology misuse and regulatory challenges. Additionally, we delved into AI Planning techniques in NLP, covering classical planning, probabilistic planning, hierarchical planning, temporal planning, knowledge-driven planning, and neural planning models. These planning approaches are vital in achieving specific goals in NLP tasks. In conclusion, we provide a concise overview of the current state of generative AI, including its challenges, ethical considerations, and potential applications, contributing to the academic discourse on human-computer interaction.

This research aims to explore the impact of the level of analytical compliance on sentence structure in a text. Analytical compliance refers to the extent to which a system or method can understand and follow grammatical and syntactic rules in a particular language. Using a computational linguistics approach, this research analyzes how the level of analytical compliance can influence understanding and sentence structure in a text. This discovery has important implications in the development of natural language processing technology, especially in improving the system's ability to understand and produce sentences that conform to language norms. Increasing analytical compliance is expected to improve the quality of communication and overall understanding of text content. This research contributes to our understanding of the role of analytical compliance in shaping sentence structure in the context of computational linguistics.