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All Journal PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Steyve, Nyatte
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Health Professions Materials Science & Nanotechnology

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A Hybrid Approach for Optimal Multi-Class Classification of Neglected Tropical Skin Diseases using Multi-Channel HOG Features Steyve, Nyatte; Steve, Perabi; Kedy, Mepouly; Ndjakomo, Salomé; Pierre, Ele
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 6 No. 2 (2024): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/hx3pcz75

Abstract

Neglected tropical skin diseases (NTDs) pose significant health challenges, especially in resource-limited settings. Early diagnosis is crucial for effective treatment and preventing complications. This study proposes a novel multi-class classification approach using multi-channel HOG features and a hybrid metaheuristic algorithm to improve the accuracy of NTD diagnosis. The method extracts optimal HOG features from images of Buruli Ulcer, Leprosy, and Cutaneous Leishmaniasis through different cell sizes, generating multiple training datasets. A hybrid Whale Optimization Algorithm and Shark Smell Optimization Algorithm (WOA-SSO) optimizes the Error Correcting Output Code (ECOC) framework for SVM, achieving superior multi-class classification performance. Notably, the multi-channel dataset, derived from averaging HOG features of different cell sizes, yields the highest accuracy of 89%. This study demonstrates the potential of the proposed method for developing mobile applications that facilitate early and accurate diagnosis of NTDs through image analysis, potentially improving patient outcomes and disease control. The hybrid metaheuristic algorithm plays a crucial role in optimizing the ECOC framework, enhancing the accuracy and efficiency of the multi-class classification process. This approach holds significant promise for revolutionizing NTD diagnosis and management, particularly in underserved communities.
Enhanced EV Battery Degradation Modeling in Tropical Environments via CVAE-GRU for Sustainable Transportation LOTCHOUANG FUSTE, Hervé; Marius, Kibong; Steyve, Nyatte; Emmanuel, Sapnken; Edwige, Mewoli; Gaston, Tamba
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2025 No. 1 (2025): Proceedings of 2025 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2025i1.610

Abstract

Electric Vehicle (EV) battery degradation in tropical environments remains poorly understood, with traditional linear models like OLS facing significant challenges such as multicollinearity, leading to unreliable insights into influential factors. This study aims to experimentally characterize lithium-ion battery degradation and comprehensively evaluate the influence of local climatic (temperature, humidity, dust) and driving conditions (road quality, mileage) in a Cameroonian tropical context, addressing the limitations of conventional statistical approaches. Our unique contribution involves providing empirical real-world data from a subSaharan environment and applying a novel hybrid CVAE-GRU methodology to capture complex non-linear and temporal dependencies. An embedded system continuously collected battery parameters (SoH, internal resistance) alongside environmental and driving data. The CVAE learns robust latent representations from these correlated inputs, while the GRU models their temporal dynamics for degradation prediction. Results confirm progressive SoH degradation, significantly accelerated by high temperatures, humidity, dust, and poor road quality. The CVAE-GRU approach effectively mitigates multicollinearity, offering superior accuracy and deeper insights into these influences. This work highlights the critical impact of tropical conditions on EV battery aging, providing crucial findings for developing adapted Battery Management Systems and fostering sustainable mobility in similar regions.
Co-Authors Edwige, Mewoli Emmanuel, Sapnken Gaston, Tamba Kedy, Mepouly LOTCHOUANG FUSTE, Hervé Marius, Kibong Ndjakomo, Salomé Pierre, Ele steve, Perabi