Journal of Applied Artificial Intelligence

Machine Learning Models to Identify and Classify Clickbait Headlines Accurately

2024-05-28T14:57:05+00:00

One potential research problem related to clickbait data could be to investigate the impact of clickbait headlines on news consumption and perception of news credibility. The objective of using Machine Learning (ML) models to analyze clickbait data in this work is to determine an accurate model for identifying and classifying clickbait headlines, understand the features that make them successful, evaluate the model's performance in real-world scenarios, and compare the performance of different ML models to select the best one for clickbait classification. By achieving these objectives, the research could provide valuable insights into the mechanisms behind clickbait and the effectiveness of ML models in detecting and mitigating its impact. This research could inform the development of more effective algorithms and tools for combating clickbait and improving news literacy. The suggested methodology for detecting clickbait using machine learning involves collecting a large amount of clickbait and non-clickbait headlines, pre-processing and cleaning the data, identifying and extracting relevant features, selecting an appropriate ML algorithm, training and evaluating the model, making necessary adjustments, and deploying the final model in a production environment to detect clickbait in real-world data. The specific steps and details may vary depending on the task complexity and data availability.

A Review of Frameworks for Evaluating the Security Performance of E-Government Systems

2024-12-24T07:16:43+00:00

The use of information and communication technology (ICT) is rapidly expanding throughout society. Different ICTs are used by governments to communicate with their country's citizens and other e-government initiative stakeholders. The e-government initiative faces various internal and external challenges, including limited funding, rapid technological advancements, internet accessibility for the public, and concerns about privacy and security. To address these challenges, several frameworks have been proposed that help improve E-government performance. In order to measure the effectiveness of e-government, this paper aims to identify various constructs and their relationships by providing a summary of the proposed frameworks and models for electronic government development

Efficacy of Two Hidden Layers Artificial Neural Network Synapticity for Deep Learning: A Case of Pattern Recognition

2024-11-15T17:03:18+00:00

Most research works in Artificial Neural Network (ANN) are accustomed with the use of single hidden layer (SHL) topology without giving considerations to the problem type, its complexity and desired depth of supervised or unsupervised learning. This could be partly due to the inherent complexities associated with the use of more than one hidden layer which in turn affects solution efficiency. However, the trade-off occasionally is between efficiency and effectiveness of result. When effectiveness is prioritized perhaps for sensitive or mission critical systems, then multiple hidden layers can become advantageous. This research has investigated the ability of an Artificial Neural Network (ANN) with two hidden layer topology to exhibit deep learning behaviour in comparison with a single hidden layer architecture ANN system. A two hidden layer (THL) Neural Network was developed and implemented using Microsoft Visual Studio programming suite and applied to a pattern recognition problem. The gradient descent optimization of the back propagation algorithm in a feed forward scheme was used in the development of the supervised ANN which consisted of thirty inputs at the input layer, two hidden layers with five nodes and a single output layer with one node for a Boolean response. Normalized images mapped into a pattern extraction template using principal component analysis (PCA) of the original images served as pre-processed inputs to the two hidden layer architecture with an initial learning rate of η = 0.1 and maximum tolerable rate of η = 0.4 for fast convergence. Iterations for validation of the feed forward back propagation algorithm using three image patterns showed that over 96% recognition of presented data was recorded. Graphical comparison of the results obtained from separate iterative sessions of the One Hidden Layer (OHL) and (THL) architectures under same input-output dataset revealed more visible traits of attained deep learning by the two hidden layer architecture due to enhanced synapticity of additional nodes.

YorubaAI: Bridging Language Barrier with Advanced Language Models

2025-03-12T09:32:26+00:00

YorubaAI addresses the digital divide caused by language barriers, particularly for Yoruba language speakers who struggle to interact with advanced large language models (LLMs) like GPT-4, which primarily support high-resource languages. This study develops a system, named YorubaAI, for seamless communication in Yoruba language with LLMs. The YorubaAI enables users to input and receive responses in Yoruba language, both in text and audio formats. To achieve this, a speech-to-text (STT) model is fine-tuned for automatic Yoruba language speech recognition while a text-to-speech (TTS) model is employed for conversion of Yoruba language text to speech equivalent. Direct communication with LLM in low-resource languages like Yoruba language typically yields poor results. To prevent this, a generation technique known as retrieval-augmented generation (RAG) is utilized to augment the LLM's existing knowledge with additional information. The RAG is formed through creation of a database of questions and answers in Yoruba language. This database serves as the primary knowledge base that the YorubaAI uses to retrieve relevant information with respect to the question asked. The content of the created questions and answers database is converted into vector embeddings using Google’s Language-Agnostic BERT Sentence Embedding (LaBSE) model to yield numerical representations that capture the semantic meaning of the texts. The embeddings generated from the Yoruba questions database are stored in a vector store database. These embeddings were essential for efficient search and retrieval.The the two models (STT and TTS models) were integrated with a LLM using a user-friendly interface that was built using the Gradio framework. The STT model achieved a word error rate of 13.06% while the TTS model generated natural-sounding Yoruba language speech. YorubaAI correctly responded to various queries in pure Yoruba language syntax and thus successfully bridges the AI accessibility gap for Yoruba language speakers.