• Home
  • Ernest Domanaanmwi Ganaa
Ernest Domanaanmwi Ganaa

Ernest Domanaanmwi Ganaa
Hilla Limann Technical University · Information & Communication Technology

PhD. MSc. & BSc. Comp. Sci

About

22
Publications
10,898
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
87
Citations
Introduction
Dr. Ernest D. Ganaa is a lecturer in the ICT department of Hilla Limann Technical University, Ghana and holds a PhD in Computer Science from Jiangsu University, China. Ernest received his BSc. in Computer Science in 2008 and MSc. Information Technology in 2015, both from the Kwame Nkrumah University of Science and Technology (KNUST), Ghana. His research interest includes machine learning, pattern recognition and dimensionality reduction.
Additional affiliations
March 2022 - May 2023
University of Ghana
Position
  • Lecturer
Description
  • Part-Time Lecturer
November 2021 - present
SDD-UBIDS
Position
  • Lecturer
Description
  • Part-Time Lecturer
Education
February 2018 - September 2021
Jiangsu University
Field of study
  • Computer Science
September 2012 - November 2015
August 2004 - June 2008

Publications

Publications (22)
Article
Full-text available
In real‐world scenarios, pedestrian images often suffer from occlusion, where certain body features become invisible, making it challenging for existing methods to accurately identify pedestrians with the same ID. Traditional approaches typically focus on matching only the visible body parts, which can lead to misalignment when the occlusion patter...
Article
Full-text available
Founded on the premise that high-dimensional data can be characterized as data drawn from a union of several low-dimensional subspaces, subspace clustering has become famous due to the limitations of traditional clustering techniques such as k-means. Among the subspace clustering methods, spectral-based techniques have become increasingly popular i...
Article
Full-text available
This paper proposes a multi-view representation kernel ensemble Support Vector Machine. Unlike the conventional multiple kernel learning techniques which utilizes a common similarity measure over the entire input space, with the aim of solely learning their models via linear combination of basis kernels in single Reproducing Kernel Hilbert Space (R...
Article
Full-text available
Correlation Analysis is a popular technique for describing relationships between two datasets. In this paper, we proposed a correlation analysis framework via Joint Sample and Feature Selection (CAF-JSFS). Different from traditional correlation analysis where only feature selection is considered and each data point is treated equally, the significa...
Article
Full-text available
Patients, hospitals, sensors, researchers, providers, phones, and healthcare organisations are producing enormous amounts of data in both the healthcare and drug detection sectors. The real challenge in these sectors is to find, investigate, manage, and collect information from patients in order to make their lives easier and healthier, not only in...
Article
Full-text available
In this paper, we propose a novel Deep Auto-Encoders Ensemble model (DAEE) through assembling multiple deep network models with different activation functions. The hidden features obtained by our proposed model have better robustness in representation than traditional variants of auto-encoders because it aggregates the diversified feature represent...
Article
Full-text available
The last decade has witnessed a continuous boom in the application of machine learning techniques in pattern recognition, with much more focus on single-task learning models. However, the increasing amount of multimedia data in the real world also suggests that these single-task learning models have become unsuitable for complex problems. Hence, mu...
Article
Multi-label classification (MLC) is one of the challenging tasks in computer vision, where it confronts high dimensional problem both in output label and input feature spaces. This paper proposed solving MLC through multi-output residual embedding (MoRE), which learns appropriate distance metric by analyzing the residuals between input and output s...
Article
Full-text available
Canonical Correlation Analysis (CCA) and its kernel versions (KCCA) are well-known techniques adopted in feature representation and classification for images. However, their performances are significantly affected when the images are noisy and in multiple views. In this paper, the method of robust deflated canonical correlation analysis via feature...
Article
Full-text available
The matrix completion technique based on matrix factorization for recovering missing items is widely used in collaborative filtering, image restoration, and other applications. We proposed a new matrix completion model called hierarchical deep matrix completion (HDMC), where we assume that the variables lie in hierarchically organized groups. HDMC...
Article
Full-text available
Principal component analysis is a widely used technique. However, it is sensitive to noise and considers data samples to be linearly distributed globally. To tackle these challenges, a novel technique robust to noise termed deflated manifold embedding PCA is proposed. In this framework, we unify PCA with manifold embedding to preserve both global a...
Article
Remote sensing data are often adversely affected by noises due to atmospheric water absorption, transmission errors, sensor sensitivity, and saturation. Therefore, the performance of the classical principal component analysis (PCA) is negatively affected when dealing with such noisy data. To overcome this problem, a robust deflated PCA via multiple...
Chapter
The Canonical Correlation analysis (CCA), such as linear CCA and Kernel Canonical Correlation Analysis (KCCA) are efficient methods for dimensionality reduction (DR). In this paper, a method of sample factoring induced KCCA is proposed. Different from traditional KCCA method, sample factors are introduced to impose penalties on the sample spaces to...
Chapter
In this paper, we propose an instance factoring PCA (IFPCA) framework for dimension reduction in incomplete datasets. The advantage of IFPCA over the traditional PCA is that, a penalty is imposed on the instance space via a scaling-factor to suppress the effect of outliers in pursuing projections. We geometrically use two scaling-factor strategies,...
Thesis
Full-text available
The study uses remote technologies to provide tremendous support for network administrators by implementing a secure remote system administration application that runs on android smartphones to aid them administer their servers remotely when they (network administrators) are out stationed using their smartphones. The android app developed in eclips...
Article
Full-text available
Manifold alignment is very prevalent in machine learning for extracting common latent space from multiple datasets. These algorithms generally aim to achieve higher alignment accuracies by preserving the original structure while ensuring closeness between manifolds. This paper proposes a novel semi-supervised manifold alignment method that combines...
Article
Full-text available
The study investigates empirically the impact of innovation on firm productivity in Ghana. In examining the relationship between innovation and firm productivity, two robust Instrumental Variable estimation techniques (Two Stage Least Squares and Optimal Generalized Methods of Moment) have been employed so as to cure any endogeneity problems that m...
Article
Full-text available
The study investigates empirically the impact of innovation on firm productivity in Ghana. In examining the relationship between innovation and firm productivity, two robust Instrumental Variable estimation techniques (Two Stage Least Squares and Optimal Generalized Methods of Moment) have been employed so as to cure any endogeneity problems that m...
Article
Full-text available
This study sought to find out how Network Administrators make use of remote access tools in their daily activities as Network Administrators. It also investigated how effective and reliable these tools have been to Network Administrators in terms of the tools' ability to return desired results to the users (Network Administrators). From the finding...
Article
Full-text available
This research sought to find out how the use of mobile phones could be harnessed to enhance access to emergency services particularly in developing Countries. The lack of knowledge of the street names/house numbering/addressing system poses a challenge to the whole emergency response process in that; mobile phone users are unable to direct the emer...
Article
Full-text available
Vehicle and driver license registration is one of the many commodities that contribute significantly to the revenue generation capabilities of Ghana. However according to the investigation carried out for this research, 86.3 percent of respondents attest to the existence of fake vehicle and driver licenses. Thus, the government of Ghana loses a lot...
Article
Full-text available
In this paper we proposed advanced technologies to provide tremendous support for network administrators by implementing a secure remote system administration app that runs on android smartphones to aid them administer their servers remotely when they (network administrators) are out stationed using their smartphones. The android app developed in e...

Network

Cited By