Election Gifting and the Ordeal of Democracy in Nigeria

Over-time, researchers have failed to show how citizens share, if not all, a part of the blame in Nigeria's failing democracy. Through election gifting, the community of voters in Nigeria have been selling their commonwealth to politicians who should not be anywhere close to power. Using secondary sources of data and relying on Robert Dahl’s theory of democracy, the paper argues that since the people are the custodians of the political mandate in any democracy, they should be held accountable when there is a problem. The paper further identifies the history, effect, scope, and predisposing factors of election gifting in Nigeria. This paper focuses on the fact that the non-evaluative and clientelist approach to democracy are the reason for the sad realities in Nigeria and concludes that only the engagement in a civil society can revive Nigeria’s failing democracy

DEVELOPMENT OF A DOOR LOCK SECURITY SYSTEM BASED ON AUTOMATIC SPEECH RECOGNITION

The security of homes and properties are of utmost importance and various methods have been employed by researchers to achieve this aim. This work developed a door lock security system based on automatic speech recognition. The developed system was programmed to recognize certain users and give a particular user access to a door. In training the system, MFCC feature extraction technique was used to extract appropriate features from five different individual’s speech signal and Vector Quantization using LBG algorithm (VQLBG) was done to recognize a speaker. A prototype door was designed and implemented to test the developed system.Euclidean distance was used in calculating the parametric representation of individual speech signal to be recognized and an accuracy of 75% was obtained by calculating the Word Error Rate (WER). Results show that the developed system is more reliable in securing door lock in homes than traditional method.&nbsp

Enhancing Alzheimer's disease prediction using random forest: A novel framework combining backward feature elimination and ant colony optimization

Background Alzheimer's disease (AD) represents a significant global health challenge due to its increasing prevalence and the limitations of current diagnostic approaches. Early detection is crucial as pathological changes occur 10-15 years before clinical symptoms manifest, yet current diagnostic methods typically identify the disease at moderate to advanced stages. Machine learning techniques offer promising solutions for early prediction, but face challenges related to feature selection and hyperparameter optimization. Objective To develop an enhanced predictive model for Alzheimer's disease by integrating advanced feature selection techniques with nature-inspired hyperparameter optimization for Random Forest classifiers while ensuring robust validation and statistical significance testing. Methods This study employed three feature selection techniques (Whale Optimization Algorithm, Artificial Bee Colony, and Backward Elimination Feature Selection) and two hyperparameter optimization algorithms (Artificial Ant Colony Optimization and Bald Eagle Search) to improve Random Forest model performance. A dataset comprising 2,149 instances with 34 features was preprocessed using MinMax normalization and Synthetic Minority Oversampling Technique (SMOTE) applied only to training data to prevent data leakage. Statistical significance testing using McNemar's test was conducted to compare model performances. Model performance was evaluated using accuracy, precision, recall, F1-score, and AUC with confidence intervals calculated using bootstrap sampling. Results The combination of Backward Elimination Feature Selection with Artificial Ant Colony Optimization achieved the highest performance (95% accuracy ± 1.2%, 95% precision ± 1.1%, 94% recall ± 1.3%, 95% F1-score ± 1.0%, 98% AUC ± 0.8%), outperforming other methodological combinations and conventional machine learning algorithms with statistically significant improvements (p < 0.001). This approach identified 26 significant features associated with Alzheimer's disease. Additionally, nature-inspired optimization algorithms demonstrated substantial computational efficiency advantages over empirical approaches (18 minutes versus 133 minutes). Conclusion The integration of advanced feature selection with nature-inspired hyperparameter optimization enhances Alzheimer's disease prediction accuracy while improving computational efficiency. However, external validation on independent datasets and prospective clinical studies are needed to establish real-world utility. This methodological framework offers promising applications for early diagnosis and intervention planning, with potential extensions to other complex medical prediction tasks

Explainable AI for Parkinson's disease prediction: A machine learning approach with interpretable models.

Parkinson's Disease (PD) is a chronic, progressive neurological disorder with significant clinical and economic impacts globally. Early and accurate prediction remains challenging with traditional diagnostic methods due to subjectivity, delayed diagnosis, and variability. Machine Learning (ML) approaches offer potential solutions, yet their clinical adoption is hindered by limited interpretability. This study aimed to develop an interpretable ML model for early and accurate PD prediction using comprehensive multimodal datasets and Explainable Artificial Intelligence (XAI) techniques. The study applied five ML algorithms: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Logistic Regression (LR), Random Forest (RF), XGBoost, and a stacked ensemble method to a publicly available dataset (n = 2105) from Kaggle. Data encompassed demographic, medical history, lifestyle, clinical symptoms, cognitive, and functional assessments with specific inclusion/exclusion criteria applied. Preprocessing involved normalization, Synthetic Minority Oversampling Technique (SMOTE), and Sequential Backward Elimination (SBE) for feature selection. Model performance was evaluated via accuracy, precision, recall, F1-score, and Area Under Curve (AUC). The best-performing model (RF with feature selection) was interpreted using SHAP and LIME methods. Random Forest combined with Backward Elimination Feature Selection achieved the highest predictive accuracy (93 %), precision (93 %), recall (93 %), F1-score (93 %), and AUC (0.97). SHAP and LIME analyses indicated UPDRS scores, cognitive impairment, functional assessment, and motor symptoms as primary predictors, enhancing clinical interpretability. The study demonstrated the effectiveness of an interpretable RF model for accurate PD prediction. Integration of ML and XAI significantly improves clinical decision-making, diagnosis timing, and personalized patient care. [Abstract copyright: Copyright © 2025 The Author(s). Published by Elsevier Masson SAS.. All rights reserved.

Effects of fluoxetine on functional outcomes after acute stroke (FOCUS): a pragmatic, double-blind, randomised, controlled trial

Background Results of small trials indicate that fluoxetine might improve functional outcomes after stroke. The FOCUS trial aimed to provide a precise estimate of these effects. Methods FOCUS was a pragmatic, multicentre, parallel group, double-blind, randomised, placebo-controlled trial done at 103 hospitals in the UK. Patients were eligible if they were aged 18 years or older, had a clinical stroke diagnosis, were enrolled and randomly assigned between 2 days and 15 days after onset, and had focal neurological deficits. Patients were randomly allocated fluoxetine 20 mg or matching placebo orally once daily for 6 months via a web-based system by use of a minimisation algorithm. The primary outcome was functional status, measured with the modified Rankin Scale (mRS), at 6 months. Patients, carers, health-care staff, and the trial team were masked to treatment allocation. Functional status was assessed at 6 months and 12 months after randomisation. Patients were analysed according to their treatment allocation. This trial is registered with the ISRCTN registry, number ISRCTN83290762. Findings Between Sept 10, 2012, and March 31, 2017, 3127 patients were recruited. 1564 patients were allocated fluoxetine and 1563 allocated placebo. mRS data at 6 months were available for 1553 (99·3%) patients in each treatment group. The distribution across mRS categories at 6 months was similar in the fluoxetine and placebo groups (common odds ratio adjusted for minimisation variables 0·951 [95% CI 0·839–1·079]; p=0·439). Patients allocated fluoxetine were less likely than those allocated placebo to develop new depression by 6 months (210 [13·43%] patients vs 269 [17·21%]; difference 3·78% [95% CI 1·26–6·30]; p=0·0033), but they had more bone fractures (45 [2·88%] vs 23 [1·47%]; difference 1·41% [95% CI 0·38–2·43]; p=0·0070). There were no significant differences in any other event at 6 or 12 months. Interpretation Fluoxetine 20 mg given daily for 6 months after acute stroke does not seem to improve functional outcomes. Although the treatment reduced the occurrence of depression, it increased the frequency of bone fractures. These results do not support the routine use of fluoxetine either for the prevention of post-stroke depression or to promote recovery of function. Funding UK Stroke Association and NIHR Health Technology Assessment Programme

Corruption practices and government effectiveness on human capital development in Nigeria

The study assessed the impact of corruption practices and government effectiveness (GE) on human capital development (HCD) in Nigeria between the years 2003 and 2020, Panel data from 2003 to 2020 were obtained from the database of United Nations Development Programme, World Development Indicators and CIP and were analysed using the ordinary least square method which is suitable for the dataset. The study found that corruption has a significant relationship with HCD in Nigeria while the relationship between GE and HCD is not significant. The research implication is that the persistent problem of slow and sometimes stagnant HCD and growth in Nigeria can be reversed by improving GE and by reducing corrupt practices in the country. The paper concluded that corruption practices have a very strong influence on HCD in Nigeria, while the relationship between GE and HCD is insignificant. It was recommended that Nigeria should institute stiffer punishments for offenses bothering on corruption practices.   Keywords: Corruption, human capital, development, government effectiveness, Nigeria.</jats:p

EFFECT OF GOVERNMENT EXPENDITURE ON AGRICULTURAL PRODUCTIVITY IN NIGERIA (1960-2008). A BOUNDS TESTING APPROACH

This study examines the effect of government expenditure on agricultural production in Nigeria between 1960 and 2008. The output of agricultural production proxied as agricultural GDP(LAGDP) was considered as a function of factors such as the amount of the lagged agricultural GDP, exchange rate (LER), government expenditure on agriculture(LEXP), structural adjustment programme (SAP), price of crude oil (LPo) and agricultural land (LD). Estimates, based on the autoregressive distributed lags modeling approach to integration and error correction specification, indicate that the exchange rate (LER), lagged value of agricultural GDP (LAGDP), crude oil price(LPo), structural adjustment programme (SAP), agricultural land (LD) and trend significantly determined agricultural output in Nigeria. The results further show that, the error correction mechanism (ECM) indicated a feedback of about 48.1% of the previous year’s disequilibrium from long-run agricultural output