A Hybridized SMOTE-ENN Approach on Imbalanced Dataset of Fraudulent Credit-Card Scenario
Using Machine Learning to Curb Fraudulent Transactions
AI-Driven Predictive Maintenance for Gold Processing Mills: Implementation and Experimental Evaluation Based on a Supervised Learning Framework
Social Media Content Impact Forecaster: AI-Based Pre-Post Prediction of Engagement
AI-Powered Chatbots for Customer Service: Effectiveness and User Satisfaction
A Study on Spending Patterns in the Digital Era with Special Reference to Tamilnadu
Enhancing Donor Acquisition and Retention in Blood Banks via AI-Powered Decision Support Framework
Computer-Based Fuzzy Logic for Forecasting the Population Census of Edo State, Nigeria
Artificial Intelligence in Investment Management, Asset Management and Warehouse Management
Influence of Digital Transformation and Artificial Intelligence in Business
A Comparative Analysis for Identifying the Polarity of People Based on Emotional Pulse in a Smart City
Video Analytics for Optimizing Bank Services
AI-Powered Chatbots for Customer Service: Effectiveness and User Satisfaction
Role of Artificial Intelligence in Investment Management
AI-Driven Predictive Maintenance for Gold Processing Mills: Implementation and Experimental Evaluation Based on a Supervised Learning Framework
Businesses and financial activities are now carried out effortlessly thanks to the advancement of information technology. Credit cards make it simple and comfortable to perform company activities remotely. However, this advancement is not without obstacles and compromises, since credit card fraud is expanding at an exponential rate. Thus, in order to address this difficulty using cutting-edge deep learning technology to detect fraud, the dataset in question must be easily available and balanced. However, most of the available datasets are not balanced, thereby potentially affecting the accuracy of the learning models to detect or classify. To this end, this study aims to hybridize the Synthetic Minority Oversampling Technique-Edited Nearest Neighbor (SMOTE-ENN) algorithm to balance the dataset and detect the possibility of fraud. SMOTE is taken into consideration in order to proffer a solution to the imbalanced nature of the dataset, which was acquired from the Kaggle repository based on the insight of the benchmark literature. The ENN, which is the deep neural network, would in turn receive the output from this process. Based on the results, the hybridized technique is promising because the model was able to record an accuracy and F1-score of 99%.
Fraud detection has become an increasingly critical challenge in sectors such as finance, e-commerce, and insurance, where the ability to process and analyze large datasets efficiently is essential. Machine learning (ML), a subset of artificial intelligence, offers a powerful approach to detecting fraudulent activities by learning from historical transaction data and identifying patterns indicative of fraud. This paper proposes a ML-based model for fraud detection, focusing on leveraging ensemble learning to combine two different models to address precision and efficiency of detection systems. The model aims to improve detection accuracy and reduce false positives in financial transactions. This study also highlights the potential benefits of leveraging singles Neural Networks and Random Forest which proven to slightly perform less than the proposed ensemble model. Through experimental validation, the proposed approach demonstrates its effectiveness in detecting fraudulent transactions and contributing to the broader goal of securing digital transactions.
This paper presents the development, implementation, and evaluation of an AI-based predictive maintenance (PdM) model designed specifically for gold processing mills. The work builds upon an original model developed using real- world operational data from Zimbabwean gold plants. Unlike previous generic frameworks, this research employs supervised learning algorithms, including logistic regression, decision trees, support vector machines (SVM), and random forests, rigorously evaluated for their effectiveness in detecting equipment failures. The Random Forest classifier demonstrated superior performance with a 98.25% accuracy and strong sensitivity to minority failure classes. A user interface was also developed using Streamlit to facilitate practical deployment and interaction with maintenance teams. The study confirms that AI-based PdM models, when appropriately engineered and evaluated, can significantly reduce unplanned downtime, enhance equipment reliability, and optimize maintenance schedules in gold processing environments.
Social media has transformed into a primary communication and marketing platform where content engagement plays a crucial role in user influence and business strategy. Traditional analytics tools provide insights only after content is posted, limiting users' ability to optimize engagement beforehand. This study presents a novel AI-based framework for predicting social media engagement before posting, using BERT and DistilBERT for textual analysis. By leveraging transformer-based embeddings, the approach analyzes past social media data, extracts meaningful textual patterns, and forecasts potential user engagement (likes, shares, comments). The proposed model is trained using historical social media datasets, ensuring robust prediction across various post types. The dataset includes captions, engagement metrics, and user interactions, preprocessed through text normalization, tokenization, and embeddings generation. Experimental results demonstrate that the model outperforms traditional NLP models, offering high accuracy in engagement prediction. The proposed approach enables content creators, marketers, and businesses to strategically optimize their posts before sharing, leading to higher engagement and better audience reach. The findings highlight the effectiveness of transformer models in predicting user behavior, paving the way for AI-driven content optimization in social media analytics.
AI-powered chatbots are one of the emerging technologies in the quickly changing world. AI chatbots are mostly utilized by websites, apps, and businesses. An artificial intelligence chatbot (AI) is computer software that mimics human speech using AI. AI-powered chatbots are frequently employed in lead generation, sales, and customer service. This study's primary goals are to determine how well chatbot technology meets customer needs and to examine user satisfaction with AI-powered chatbots. Convenience sampling was used to choose 90 respondents for this investigation. For this study, both primary and secondary data were gathered, and the data were analyzed using SPSS software. According to the results, the best way to use AI-powered chatbot technology is to provide 24/7 customer service. Age and user satisfaction are significantly correlated, as evidenced by the rejection of the null hypothesis for seamless transactions. Age and user satisfaction do not significantly correlate because criteria like brand awareness, integration with customer data, personalized answers, quick responses, customer interactions, and user-friendly design are all higher than 5%.
