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    Ant Colony and Whale Optimization Algorithms Aided by Neural Networks for Optimum Skin Lesion Diagnosis: A Thorough Review
    (Mdpi, 2024) Mukhlif, Yasir Adil; Ramaha, Nehad T. A.; Hameed, Alaa Ali; Salman, Mohammad; Yon, Dong Keon; Fitriyani, Norma Latif; Syafrudin, Muhammad
    The adoption of deep learning (DL) and machine learning (ML) has surged in recent years because of their imperative practicalities in different disciplines. Among these feasible workabilities are the noteworthy contributions of ML and DL, especially ant colony optimization (ACO) and whale optimization algorithm (WOA) ameliorated with neural networks (NNs) to identify specific categories of skin lesion disorders (SLD) precisely, supporting even high-experienced healthcare providers (HCPs) in performing flexible medical diagnoses, since historical patient databases would not necessarily help diagnose other patient situations. Unfortunately, there is a shortage of rich investigations respecting the contributory influences of ACO and WOA in the SLD classification, owing to the recent adoption of ML and DL in the medical field. Accordingly, a comprehensive review is conducted to shed light on relevant ACO and WOA functionalities for enhanced SLD identification. It is hoped, relying on the overview findings, that clinical practitioners and low-experienced or talented HCPs could benefit in categorizing the most proper therapeutical procedures for their patients by referring to a collection of abundant practicalities of those two models in the medical context, particularly (a) time, cost, and effort savings, and (b) upgraded accuracy, reliability, and performance compared with manual medical inspection mechanisms that repeatedly fail to correctly diagnose all patients.
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    Consequential Advancements of Self-Supervised Learning (SSL) in Deep Learning Contexts
    (Mdpi, 2024) Abdulrazzaq, Mohammed Majid; Ramaha, Nehad T. A.; Hameed, Alaa Ali; Salman, Mohammad; Yon, Dong Keon; Fitriyani, Norma Latif; Syafrudin, Muhammad
    Self-supervised learning (SSL) is a potential deep learning (DL) technique that uses massive volumes of unlabeled data to train neural networks. SSL techniques have evolved in response to the poor classification performance of conventional and even modern machine learning (ML) and DL models of enormous unlabeled data produced periodically in different disciplines. However, the literature does not fully address SSL's practicalities and workabilities necessary for industrial engineering and medicine. Accordingly, this thorough review is administered to identify these prominent possibilities for prediction, focusing on industrial and medical fields. This extensive survey, with its pivotal outcomes, could support industrial engineers and medical personnel in efficiently predicting machinery faults and patients' ailments without referring to traditional numerical models that require massive computational budgets, time, storage, and effort for data annotation. Additionally, the review's numerous addressed ideas could encourage industry and healthcare actors to take SSL principles into an agile application to achieve precise maintenance prognostics and illness diagnosis with remarkable levels of accuracy and feasibility, simulating functional human thinking and cognition without compromising prediction efficacy.
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    The connection between anemia and limitations in daily activities among older males: the critical role of dynapenia
    (Springer science and business media deutschland GmbH, 2024) Karışmaz, Abdulkadir; Soysal, Pınar; Eren, Rafet; Serin, İstemi; Aslan, Ceyda; Rahmati, Masoud; Yon, Dong Keon; Smith, Lee
    Aim The aim of the present study was to examine the relationship between anemia and basic and instrumental activities of daily living in older male patients. Methods A total of 223 older males attending one geriatric outpatient clinic were included in this cross-sectional study. Anemia was defined as a hemoglobin level below 13 g/dL. Patients' demographic characteristics, comorbidities, and comprehensive geriatric assessment parameters were also recorded. Handgrip strength of < 27 kg for males was accepted as dynapenia. Basic Activities of Daily Living (BADL) and Instrumental Activities of Daily Living (IADL) questionnaires were used to evaluate functional capacity. Results The mean age (standard deviation) of the participants was 80.17 (7.69) years. The prevalence of patients with anemia was 43.9%. There was differences between anemic and non-anemic groups in terms of presence of diabetes mellitus (DM), congestive heart failure (CHF), chronic kidney disease (CKD), malnutrition, dynapenia, geriatric depression, BADL and IADL scores (all p < 0.05). In multivariate analysis, after adjusting for all confounding variables except for dynapenia, patients with anemia were associated with reduced BADL and IADL (all p < 0.05). After adjusting for all confounding variables including dynapenia, deterioration in total BADL and IADL scores did not remain significant in the anemic group compared to the non-anemic group (p > 0.05). Conclusion Close to one in two older outpatient men had anemia. Anemic men had a higher incidence of DM, CHF, CKD, malnutrition, geriatric depression and dynapenia. Anemia was associated with dependence in both BADL and IADL in older men. However, comorbidities, nutritional status, depressive mood and, specifically muscle strength, were important contributors to this association.