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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
Pages
Posts
Blog Post number 4
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 2
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 1
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
publications
A Hybrid Minkowski-Log-Cosh Loss Function for Robust LSTM-Based Time Series Forecasting
Published in IEEE Access, 2025
This research introduces a novel Hybrid Minkowski–Log–Cosh (MLC) loss function for robust LSTM-based time series forecasting in noisy environments. The Log-cosh loss function provides a tunable framework where parameter $p$ controls the trade-off between outlier robustness and error sensitivity. Validated on 11 years of malaria incidence data, MLC achieves 18.23% MAPE (vs. 20.96% for MSE) in clean data regimes and reduces mispredicted cases by approximately 117,000 in outlier-contaminated scenarios. The method maintains $\mathcal{O}(N)$ computational complexity while offering explicit gradient analysis and plug-and-play compatibility with deep learning models.
Recommended citation: Simsoba, K.-A., Oscar, N., & Mageto, T. (2025). "A Hybrid Minkowski-Log-Cosh Loss Function for Robust LSTM-Based Time Series Forecasting." IEEE Access, 13, 187307–187319.
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talks
Statistical Analysis of Online Learning During the COVID-19 Pandemic
Published:
This project examines the impact of online learning on university students’ academic performance during the COVID-19 pandemic using a statistical and data-driven approach.
Generative AI for Multimodal Breast Cancer Diagnosis and Staging
Published:
This project investigates generative and multimodal AI models for breast cancer diagnosis and staging, integrating histopathology images with structured clinical records.
Scalable Matrix Factorization and ALS-Based Recommendation Systems on MovieLens 32M
Published:
This project explores scalable recommendation systems based on matrix factorization, with a focus on Alternating Least Squares (ALS) for large-scale collaborative filtering.
Hybrid Minkowski–Log–Cosh Loss Function for Robust LSTM Time-Series Forecasting
Published:
This project presents a Hybrid Minkowski–Log–Cosh (MLC) loss function designed to improve the robustness of LSTM models on noisy, outlier-contaminated time-series data.
teaching
Mathematics and Physics Teacher
, Public High Schools, 2019
Delivered secondary-level instruction in mathematics and physics, with emphasis on conceptual understanding, analytical reasoning, and systematic problem-solving.
Tutor – Big Data Master’s Program
, École Polytechnique de Lomé, 2023
Graduate tutoring (volunteer) for first-year Master’s students in the Big Data program, covering inferential statistics and optimization.