arXiv subjects

Marcellin Atemkeng

Publications and source records attributed to Marcellin Atemkeng.

26 records · Page 2Linked to original sources

arXiv2023

Visual Interpretable and Explainable Deep Learning Models for Brain Tumor MRI and COVID-19 Chest X-ray Images

Deep learning shows promise for medical image analysis but lacks interpretability, hindering adoption in healthcare. Attribution techniques that explain model reasoning may increase trust in deep learning among clinical stakeholders. This paper aimed to evaluate attribution methods for illuminating how deep neural networks analyze medical images. Using adaptive path-based gradient integration, we attributed predictions from brain tumor MRI and COVID-19 chest X-ray datasets made by recent deep convolutional neural network models. The technique highlighted possible biomarkers, exposed model biases, and offered insights into the links between input and prediction. Our analysis demonstrates the method's ability to elucidate model reasoning on these datasets. The resulting attributions show promise for improving deep learning transparency for domain experts by revealing the rationale behind predictions. This study advances model interpretability to increase trust in deep learning among healthcare stakeholders.

cs.LG↗

arXiv2023

Label Assisted Autoencoder for Anomaly Detection in Power Generation Plants

One of the critical factors that drive the economic development of a country and guarantee the sustainability of its industries is the constant availability of electricity. This is usually provided by the national electric grid. However, in developing countries where companies are emerging on a constant basis including telecommunication industries, those are still experiencing a non-stable electricity supply. Therefore, they have to rely on generators to guarantee their full functionality. Those generators depend on fuel to function and the rate of consumption gets usually high, if not monitored properly. Monitoring operation is usually carried out by a (non-expert) human. In some cases, this could be a tedious process, as some companies have reported an exaggerated high consumption rate. This work proposes a label assisted autoencoder for anomaly detection in the fuel consumed by power generating plants. In addition to the autoencoder model, we added a labelling assistance module that checks if an observation is labelled, the label is used to check the veracity of the corresponding anomaly classification given a threshold. A consensus is then reached on whether training should stop or whether the threshold should be updated or the training should continue with the search for hyper-parameters. Results show that the proposed model is highly efficient for reading anomalies with a detection accuracy of $97.20\%$ which outperforms the existing model of $96.1\%$ accuracy trained on the same dataset. In addition, the proposed model is able to classify the anomalies according to their degree of severity.

cs.LG↗

arXiv2022

Creating awareness about security and safety on highways to mitigate wildlife-vehicle collisions by detecting and recognizing wildlife fences using deep learning and drone technology

In South Africa, it is a common practice for people to leave their vehicles beside the road when traveling long distances for a short comfort break. This practice might increase human encounters with wildlife, threatening their security and safety. Here we intend to create awareness about wildlife fencing, using drone technology and computer vision algorithms to recognize and detect wildlife fences and associated features. We collected data at Amakhala and Lalibela private game reserves in the Eastern Cape, South Africa. We used wildlife electric fence data containing single and double fences for the classification task. Additionally, we used aerial and still annotated images extracted from the drone and still cameras for the segmentation and detection tasks. The model training results from the drone camera outperformed those from the still camera. Generally, poor model performance is attributed to (1) over-decompression of images and (2) the ability of drone cameras to capture more details on images for the machine learning model to learn as compared to still cameras that capture only the front view of the wildlife fence. We argue that our model can be deployed on client-edge devices to inform people about the presence and significance of wildlife fencing, which minimizes human encounters with wildlife, thereby mitigating wildlife-vehicle collisions.

cs.CV↗

arXiv2022

The extended HI halo of NGC 4945 as seen by MeerKAT

Observations of the neutral atomic hydrogen (HI) in the nuclear starburst galaxy NGC 4945 with MeerKAT are presented. We find a large amount of halo gas, previously missed by HI observations, accounting for 6.8% of the total HI mass. This is most likely gas blown into the halo by star formation. Our maps go down to a $3σ$ column density level of $5\times10^{18} cm^{-2}$ . We model the HI distribution using tilted-ring fitting techniques and find a warp on the galaxy's approaching and receding sides. The HI in the northern side of the galaxy appears to be suppressed. This may be the result of ionisation by the starburst activity in the galaxy, as suggested by a previous study. The origin of the warp is unclear but could be due to past interactions or ram pressure stripping. Broad, asymmetric HI absorption lines extending beyond the HI emission velocity channels are present towards the nuclear region of NGC 4945. Such broad lines suggest the existence of a nuclear ring moving at a high circular velocity. This is supported by the clear rotation patterns in the HI absorption velocity field. The asymmetry of the absorption spectra can be caused by outflows or inflows of gas in the nuclear region of NGC 4945. The continuum map shows small extensions on both sides of the galaxy's major axis that might be signs of outflows resulting from the starburst activity.

astro-ph.CO↗

arXiv2022

Automatic Speech Recognition And Limited Vocabulary: A Survey

Automatic Speech Recognition (ASR) is an active field of research due to its large number of applications and the proliferation of interfaces or computing devices that can support speech processing. However, the bulk of applications are based on well-resourced languages that overshadow under-resourced ones. Yet, ASR represents an undeniable means to promote such languages, especially when designing human-to-human or human-to-machine systems involving illiterate people. An approach to design an ASR system targeting under-resourced languages is to start with a limited vocabulary. ASR using a limited vocabulary is a subset of the speech recognition problem that focuses on the recognition of a small number of words or sentences. This paper aims to provide a comprehensive view of mechanisms behind ASR systems as well as techniques, tools, projects, recent contributions, and possible future directions in ASR using a limited vocabulary. This work consequently provides a way forward when designing an ASR system using limited vocabulary. Although an emphasis is put on limited vocabulary, most of the tools and techniques reported in this survey can be applied to ASR systems in general.

cs.AI↗

arXiv2022

Predicting Fuel Consumption in Power Generation Plants using Machine Learning and Neural Networks

The instability of power generation from national grids has led industries (e.g., telecommunication) to rely on plant generators to run their businesses. However, these secondary generators create additional challenges such as fuel leakages in and out of the system and perturbations in the fuel level gauges. Consequently, telecommunication operators have been involved in a constant need for fuel to supply diesel generators. With the increase in fuel prices due to socio-economic factors, excessive fuel consumption and fuel pilferage become a problem, and this affects the smooth run of the network companies. In this work, we compared four machine learning algorithms (i.e. Gradient Boosting, Random Forest, Neural Network, and Lasso) to predict the amount of fuel consumed by a power generation plant. After evaluating the predictive accuracy of these models, the Gradient Boosting model out-perform the other three regressor models with the highest Nash efficiency value of 99.1%.

cs.LG↗

arXiv2021

Xova: Baseline-Dependent Time and Channel Averaging for Radio Interferometry

Xova is a software package that implements baseline-dependent time and channel averaging on Measurement Set data. The uv-samples along a baseline track are aggregated into a bin until a specified decorrelation tolerance is exceeded. The degree of decorrelation in the bin correspondingly determines the amount of channel and timeslot averaging that is suitable for samples in the bin. This necessarily implies that the number of channels and timeslots varies per bin and the output data loses the rectilinear input shape of the input data.

astro-ph.IM↗

arXiv2020

Filling the uv-gaps of the current VLBI network in Africa

In the African continent, South Africa has world-class astronomical facilities for advanced radio astronomy research. With the advent of the Square Kilometre Array project in South Africa (SA SKA), six countries in Africa (SA SKA partner countries) have joined South Africa to contribute towards the African Very Long Baseline Interferometry (VLBI) Networks (AVN). Each of the AVN countries will soon have a single-dish radio telescope that will be part of the AVN, the European VLBI Network, and the global VLBI network. The SKA and the AVN will enable very high sensitivity VLBI in the southern hemisphere. In the current AVN network, there is a gap in coverage in the central African region. This work analyses the scientific impact if new antennas were to be built or old telecommunication facilities were to be converted to radio telescopes in each of the six countries in central Africa i.e. Cameroon, Gabon, Congo, Equatorial Guinea, Chad, Central African Republic. The work also discusses some economical and skills transfer impacts of having a radio interferometer in this area of Africa.

astro-ph.IM↗