An Interactive Visualization and Data Analysis of Supermarket Store
Breast Cancer Diagnosis Model Based on Convolutional Neural Networks’ Multiple Architectures.
A Generative AI Model for Forest Fire Prediction and Detection
Impact of Artificial Intelligence on Cyber Shopping in Kanniyakumari District
Role of Artificial Intelligence in Investment Management
A Study on Spending Patterns in the Digital Era with Special Reference to Tamilnadu
Artificial Intelligence in Investment Management, Asset Management and Warehouse Management
Influence of Digital Transformation and Artificial Intelligence in Business
A Study on Employee Perception towards Digital Marketing Services
Video Analytics for Optimizing Bank Services
A Comparative Analysis for Identifying the Polarity of People Based on Emotional Pulse in a Smart City
Video Analytics for Optimizing Bank Services
An Ensemble Technique to Predict Mental Illness using Data Mining Techniques
Role of Artificial Intelligence in Investment Management
Enhancing Donor Acquisition and Retention in Blood Banks via AI-Powered Decision Support Framework
Although the National Population Commission's forecasting efforts have become more accurate over the years, this work aims to use fuzzy logic to predict population growth in a quicker, simpler, more accurate, and more effective way. To accomplish this goal, various data collection technologies were employed to compile data from secondary sources, including the National Population Commission of Nigeria. A thorough literature evaluation on population forecasts and censuses has already been published. Implementing a proactive population forecast was built with a stated goal in mind. Python 3 was chosen as a reliable programming language for ODEINT (Ordinary Differential Equation Integration) for Natural Growth Model and Fuzzy Time Series library functions. Due to a performance accuracy of 99.6%, the model created for population census forecasting projects the future population at a dependable time.
Automated Short Answer Grading (ASAG) systems contribute immensely to providing prompt feedback to students, which eases the workload of instructors. This research focuses on the development of an optimized ASAG model using LSTM model and particle swarm optimization techniques to prevent model overfitting. The popular ASAG dataset by Mohler was utilized for the experiment. The dataset contains training samples from Computer Science department of the Federal University of Technology, Minna, Nigeria, with grades between 0 and 5. In order to effectively optimize the LSTM model parameters, which are learning rate and number of neurons in the LSTM layers, four experiments were performed, each with different particle population sizes (5, 10, 15 and 20). The results show that PS5 model produced the lowest RMSE and MAPE of 0.77697 and 44.5356%, respectively. The PS15 model, however, produced the highest RMSE and MAE of 0.80985 and 56.6192%, respectively. In order to validate the developed PSO-LSTM ASAG model, normal LSTM model for ASAG was implemented and tested. The PSO-LSTM has an RMSE value of 0.77687 and MAPE of 44.5356%, as compared with LSTM, which has an RMSE value of 0.9423 and MAPE of 85.73%. The results clearly show the superiority of the developed hybrid model in predicting the scores of short answer grading. The model's performance can be further improved by increasing the sample size and using other optimization algorithms, such as genetic algorithms or ant colony optimization. Further research can also investigate the effect of other variables, such as question complexity and student writing style, on the model's performance.
Cybercrime has emerged as a specialized domain, leveraging online communication networks with advanced specifications to identify cybercriminals through the application of cyber laws. Extensive research is underway to establish pertinent legal methodologies aimed at preventing and controlling cybercriminal activities. Over the past decade, considerable attention has been devoted to the compelling topics of malware and phishing detection, given the substantial damage inflicted upon internet users. Phishing website recognition represents recognizing these sites as potent tools for exploiting personal information and facilitating malicious activities. This paper introduces an innovative automatic categorization system designed to classify both malware identities and phishing websites. The system achieves this through the integration of clustering solutions, incorporating various clustering algorithms within a cluster ensemble scheme.
Blood banks play a critical role in ensuring a steady supply of safe blood for medical procedures. However, donor recruitment and retention pose significant challenges to the sustainability of blood banks. This study proposes an AIenabled decision-support system to optimize donor recruitment and retention strategies in blood banks. The system leverages machine learning algorithms to analyze historical donor data, demographic information, and external factors to predict donor behavior and identify potential strategies for improving recruitment and retention. By incorporating AI into decision-making processes, blood banks can make data-driven decisions, enhance the efficiency of donor management, and allocate resources effectively. This paper presents the methodology used to develop the AIenabled system and discusses its potential benefits and implications for blood bank operations. Experimental results demonstrate the effectiveness of the system in identifying successful recruitment and retention strategies. Overall, the research offers valuable insights into the application of AI in blood bank management, ultimately leading to more sustainable and efficient donor recruitment and retention practices.
With the explosive growth of information on the web, users face difficulties finding their desired information. There is a need to manage and cluster data efficiently. Although there are various multimedia database systems available for retrieval, most of the methods are not efficient enough. Artificial Intelligence (AI) is increasingly shaping media production and consumption, particularly in the field of video blogs (vlogs). This paper explores the intersection of AI and media, focusing on its implications for freedom of speech and media, content creation, video recommendations, content moderation, and content personalization. The objective is to extract relevant videos from a multimedia database, evaluate the performance of AI algorithms in processing video data, and demonstrate the effectiveness of these algorithms in enhancing the quality and accessibility of video blogs.
