GIT-Net: An Ensemble Deep Learning-Based GI Tract Classification of Endoscopic Images.

This paper presents an ensemble of pre-trained models for the accurate classification of endoscopic images associated with Gastrointestinal (GI) diseases and illnesses. In this paper, we propose a weighted average ensemble model called GIT-NET to classify GI-tract diseases. We evaluated the model on a KVASIR v2 dataset with eight classes.

From Twitter to Aso-Rock: A sentiment analysis framework for understanding Nigeria 2023 presidential election.

Introduction: Social media platforms such as Facebook, LinkedIn, Twitter, among others have been used as tools for staging protests, opinion polls, campaign strategy, medium of agitation and a place of interest expression especially during elections.

Aim: In this work, a Natural Language Processing framework is designed to understand Nigeria 2023 presidential election based on public opinion using Twitter dataset.

Methods: Two million tweets with 18 features were collected from Twitter containing public and personal tweets of the three top contestants - Atiku Abubakar, Peter Obi and Bola Tinubu - in the forthcoming 2023 Presidential election.

A machine learning prediction of academic performance of secondary school students using radial basis function neural network.

Background: Predictive models for academic performance forecasting have been a useful tool in the improvement of the administrative, counseling and instructional personnel of academic institutions.

Aim: The aim of this work is to develop a Radial Basis Function Neural Network for prediction of students' performance using their past academic records as well as their cognitive and psychomotor abilities.

Methods: We obtained data from a secondary school repository containing academic, cognitive and psychomotor scores of the students.

The Rise of Cloud Computing: Data Protection, Privacy, and Open Research Challenges-A Systematic Literature Review (SLR).

Cloud computing is a long-standing dream of computing as a utility, where users can store their data remotely in the cloud to enjoy on-demand services and high-quality applications from a shared pool of configurable computing resources. Thus, the privacy and security of data are of utmost importance to all of its users regardless of the nature of the data being stored. In cloud computing environments, it is especially critical because data is stored in various locations, even around the world, and users do not have any physical access to their sensitive data.

Attention Autoencoder for Generative Latent Representational Learning in Anomaly Detection.

Today, accurate and automated abnormality diagnosis and identification have become of paramount importance as they are involved in many critical and life-saving scenarios. To accomplish such frontiers, we propose three artificial intelligence models through the application of deep learning algorithms to analyze and detect anomalies in human heartbeat signals. The three proposed models include an attention autoencoder that maps input data to a lower-dimensional latent representation with maximum feature retention, and a reconstruction decoder with minimum remodeling loss.

Hyperspectral Imaging for Bloodstain Identification.

Blood is key evidence to reconstruct crime scenes in forensic sciences. Blood identification can help to confirm a suspect, and for that reason, several chemical methods are used to reconstruct the crime scene however, these methods can affect subsequent DNA analysis. Therefore, this study presents a non-destructive method for bloodstain identification using Hyperspectral Imaging (HSI, 397-1000 nm range).

A Hybrid Unsupervised Approach for Retinal Vessel Segmentation.

Retinal vessel segmentation (RVS) is a significant source of useful information for monitoring, identification, initial medication, and surgical development of ophthalmic disorders. Most common disorders, i.e.