Publication Search

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.

This study explores the application of machine learning to predict students' GPA based on behavioral and time-related factors, including study hours, extracurricular activities, sleep, social interactions, and physical activity. Seven regression algorithms were employed to evaluate predictive accuracy using metrics such as MAE, RMSE, and R2 Among these, Regularized Linear Regression demonstrated the highest accuracy and interpretability, highlighting its suitability for this dataset. The findings emphasize the potential of machine learning in identifying key predictors of academic performance and offer practical applications for personalized academic advising and time management. This research provides a data-driven framework to support students and educators in optimizing learning outcomes.

Natural disasters such as earthquakes, hurricanes, and floods pose significant risks to critical infrastructure. AI-driven disaster response systems provide real-time analytics, predictive modeling, and automated response strategies to mitigate damage and improve recovery efforts. This paper explores how AI-powered drones, satellite imagery, and sensor networks enhance disaster monitoring and decision-making. Additionally, the study discusses the role of AI in optimizing emergency resource allocation and predicting infrastructure vulnerabilities. Through an analysis of past disaster management strategies, this research aims to propose AI-integrated frameworks that enhance disaster preparedness and resilience.

Natural disasters such as earthquakes, hurricanes, and floods pose significant risks to critical infrastructure. AI-driven disaster response systems provide real-time analytics, predictive modeling, and automated response strategies to mitigate damage and improve recovery efforts. This paper explores how AI-powered drones, satellite imagery, and sensor networks enhance disaster monitoring and decision-making. Additionally, the study discusses the role of AI in optimizing emergency resource allocation and predicting infrastructure vulnerabilities. Through an analysis of past disaster management strategies, this research aims to propose AI-integrated frameworks that enhance disaster preparedness and resilience.

Energy efficiency has become a critical focus in manufacturing plants due to rising operational costs and increasing environmental concerns. The growing importance of energy management is driven by the need to reduce energy consumption, lower emissions, and enhance overall operational efficiency. Traditional maintenance practices, such as reactive and preventive maintenance, often lead to unnecessary downtime, high repair costs, and inefficient energy usage. In contrast, predictive maintenance (PdM), supported by Internet of Things (IoT)-enabled sensor networks, offers a proactive approach to minimizing energy waste by predicting equipment failures before they occur. This study develops a predictive maintenance framework using IoT-based sensor networks to optimize energy usage and reduce energy losses in manufacturing plants. The research begins with an overview of IoT sensor network architectures and their applications in industrial automation, including sensors such as temperature, vibration, and pressure sensors. It explores predictive analytics techniques, such as machine learning and artificial intelligence, used for failure prediction, which are key to enhancing energy efficiency. The study emphasizes how predictive maintenance contributes to industrial sustainability by reducing carbon footprints and optimizing energy consumption. The research methodology involves the installation of IoT sensors in critical machinery, real-time data analysis using machine learning algorithms for failure prediction, and energy consumption measurement before and after implementing IoT-based interventions. The results show significant improvements in energy consumption efficiency and operational productivity. Predictive maintenance led to reduced unplanned downtime, increased equipment reliability, and a more sustainable manufacturing process. However, challenges such as sensor integration, initial setup costs, and data security concerns were identified. The study concludes with recommendations for integrating IoT-based predictive maintenance systems into manufacturing plants to further optimize energy usage and promote sustainability.

This research focuses on the application of predictive analytics in digital classrooms to track and predict student performance. The study aims to address the limitations of traditional teacher judgment, which often relies on limited data points and subjective assessments. The research proposes a machine learning-driven approach that utilizes data from Learning Management Systems (LMS), including student engagement, academic performance, and attendance, to predict student success or failure with greater accuracy. Various machine learning techniques, such as Support Vector Machine (SVM) and Random Forest (RF), are applied to develop a predictive model that can identify at-risk students early. The findings show that the model achieves an accuracy rate of over 85%, with key predictors including past academic performance and student engagement. This model outperforms traditional assessment methods by providing real-time, data-driven insights that enable timely interventions. The study concludes that predictive analytics has significant potential to enhance educational outcomes by offering personalized support and improving curriculum design. However, challenges such as data integration, fairness, and privacy concerns must be addressed for broader implementation.

This study explores the integration of predictive analytics into supply chain management within national e-commerce enterprises. Predictive analytics, which utilizes historical data combined with machine learning algorithms, regression analysis, and time series forecasting, has shown significant improvements in operational efficiency. The study focuses on four key areas: demand forecasting, inventory management, transportation optimization, and customer satisfaction. By predicting demand more accurately, e-commerce platforms can reduce stockouts and overstock situations, streamline logistics routes, and lower logistics costs. The implementation of predictive analytics led to a 20% reduction in delivery times and a 15% decrease in logistics costs, thereby enhancing customer satisfaction. However, the study also highlights challenges in integrating real-time data from multiple sources and scaling predictive models across diverse product categories and geographic regions. The results emphasize the need for e-commerce platforms to invest in technology that enables seamless data integration and the development of region-specific predictive models. The findings are compared with industry benchmarks, showing that the improvements in logistics and supply chain performance align with global trends. Based on these results, the study recommends best practices for implementing predictive analytics, including effective data collection, machine learning model training, and scalability considerations. By following these practices, e-commerce companies can optimize their supply chains, reduce operational costs, and increase customer satisfaction, positioning them for greater competitive advantage in the marketplace.

This article has been retracted at the request of the Editor-in-Chief. The journal was alerted to issues within this article, including significant overlap in content, methodology, and visual materials with another previously published article: "Social Engineering Attack Classifications on Social Media Using Deep Learning" (DOI: 10.32604/cmc.2023.032373) published in Computers, Materials & Continua in 2023. Upon thorough investigation, it was found that the article substantially reproduces ideas, methodologies, and figures from the original work without proper attribution, violating the ethical standards of the journal and academic publishing. The authors were contacted and asked to provide an explanation for these concerns. The corresponding author acknowledged the oversight and accepted responsibility for the duplication. Consequently, the authors formally requested the withdrawal of the paper. As per journal policy, the Editor-in-Chief has decided to retract the article due to a breach of publication ethics. The journal sincerely regrets that these issues were not detected during the manuscript screening and review process and apologizes to the authors of the original article, as well as to the readers of the journal. For more information on the journal’s ethical policies, please visit: Retraction Policy.

This paper compares various machine learning models in their ability to predict financial trends, with a focus on time-series analysis. We evaluate models such as linear regression, decision trees, support vector machines, and deep learning, measuring their performance based on accuracy, computational cost, and interpretability. Our results reveal that deep learning models offer superior accuracy but are less interpretable, while simpler models, though less accurate, provide better insight into the underlying data. This research provides guidelines for selecting suitable models based on specific financial applications.

Urban traffic congestion is a growing problem in Indonesian cities, affecting economic productivity and quality of life. This research explores the development of a smart traffic management system utilizing Internet of Things (IoT) sensors and artificial intelligence (AI) algorithms to analyze traffic patterns and optimize flow. The proposed system collects real-time data and uses predictive analytics to adjust traffic signals dynamically. Field tests in Jakarta demonstrate a 15% improvement in traffic flow and reduced travel times during peak hours. The findings suggest significant potential for scalable smart city solutions in urban traffic management across Indonesia.