Browsing by Author "Ocran, E."
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Item Estimating Exceedance Probability of Extreme Water Levels of the Akosombo dam(Science and Development, 2017) Ocran, E.; Doku-Amponsah, K.; Nortey, E.N.N.The Akosombo dam is a major source of electric energy in Ghana. Considering the current increase in the demand for electricity in the country, where such an increase in demand implies more pressure on the dam, it is of key interest to study the tail behaviour of the water levels of the dam. Such a study is important because the level of water in the dam determines the amount of electricity generated. The study employed the Univariate Extreme Value Theory to model the monthly maximum and minimum water levels of the dam. The Generalized Extreme Value Distribution was fitted to the data and the Maximum likelihood estimation method was employed to estimate the model parameters. The study indicated that, the water levels cannot fall below 226.00ft which is the critical water level of the Akosombo dam. It further showed that, the lowest ever level of water the dam can attain is 226.69ft and the highest 279.07ft. The study also found that, though the water cannot fall below the critical level, there was evidence of its falling below the minimum operation head.Item FaceNet recognition algorithm subject to multiple constraints: Assessment of the performance(Scientific African, 2024) Mensah, J.A.; Appati, J.K.; Boateng, E.K.A.; Ocran, E.; Asiedu, L.Literature has it that the performance of most face recognition algorithms still decline in multiple constrained environments (Occlusions and Expressions), despite the achieved successes of deep learning face recognition algorithms. Using expression variant test face images syn thetically occluded at 30% and 40% rates, the study evaluated the performance of FaceNet deep learning model for face recognition under the aforementioned constraints and when three (3) statistical multiple imputation methods (Multivariable Imputation using Chain Equations (MICE), MissForest and Regularized Expectation Maximization (RegEM)) are adopted for occlu sion recovery. Results of the study showed improved recognition rates of the study algorithm when the imputation-based recovered faces were used for recognition compared with using their multiple constrained counterparts. However, test faces reconstructed with the MissForest imputation method were more accurately recognized using the FaceNet deep learning algorithm. Furthermore, the study demonstrated that some simple augmentation schemes sufficed to further enhance the performance of the FaceNet model. Specifically, the FaceNet algorithms gave the highest average recognition rates (85.19% and 79.5% for 30% and 40% occlusion levels respectively) under augmentation scheme IV (slight rotations, horizontal flipping, shearing, brightness adjustments, and stretching) using MissForest as the de-occlusion mechanism. The study also found that, no disparity existed in its performance with the choice of either Support Vector Machines (SVM) or City Block (CB) for classification under augmentation scheme IV. The study recommends using the MissForest imputation method in dealing with moderately high occluded test faces with varying expressions to enhance the performance of the FaceNet face recognition model.Item On multiple imputation-based reconstruction of degraded faces and recognition in multiple constrained environments(Scientific African, 2023) Mensah, J.A.; Ocran, E.; Asiedu, L.Recognition of degraded frontal face images acquired under occlusion constraints remain challenging despite the plethora of reconstruction mechanisms. Though recent works have lever aged on some imputation mechanisms in this regard, their robustness in multiple constrained environments may not be guaranteed and may be affected by the choice of pre-processing mechanism. This paper proposes enhancement mechanisms that augment or complement the use of three (3) multiple imputation mechanisms for facial reconstruction in the presence of multiple constraints (10% and 20% occlusions and varying facial expressions). Specifically, we propose the use of a Discrete Cosine Transform-based (DCT) denoising or a Discrete Wavelet based denoising following Histogram Equalization (HE-DWT) of the reconstructed face images prior to recognition. Experimental results showed that the proposed augmented enhancements improved significantly the recognition rates (90.63% & 91.15% and 86.98% & 85.94% for DCT and HE-DWT at 10% and 20% occlusion levels respectively for Missforest de-occluded face images) as compared with DWT in recognizing degraded frontal face images under moderately low levels of occlusions and varying expressions.Item Time series based road traffic accidents forecasting via SARIMA and Facebook Prophet model with potential changepoints(Heliyon, 2023) Agyemang, E.F.; Mensah, J.A.; Ocran, E.; Opoku, E.; Nortey, E.N.N.Road traffic accident (RTA) is a critical global public health concern, particularly in developing countries. Analyzing past fatalities and predicting future trends is vital for the development of road safety policies and regulations. The main objective of this study is to assess the effectiveness of univariate Seasonal Autoregressive Integrated Moving Average (SARIMA) and Facebook (FB) Prophet models, with potential change points, in handling time-series road accident data involving seasonal patterns in contrast to other statistical methods employed by key governmental agencies such as Ghana’s Motor Transport and Traffic Unit (MTTU). The aforementioned models underwent training with monthly RTA data spanning from 2013 to 2018. Their predictive accuracies were then evaluated using the test set, comprising monthly RTA data from 2019. The study employed the Box-Jenkins method on the training set, yielding the development of various tentative time series models to effectively capture the patterns in the monthly RTA data. 𝑆𝐴𝑅𝐼𝑀𝐴 (0, 1, 1) × (1, 0, 0)12 was found to be the suitable model for forecasting RTAs with a log-likelihood value of −266.28, AIC value of 538.56, AICc value of 538.92, BIC value of 545.35. The findings disclosed that the 𝑆𝐴𝑅𝐼𝑀𝐴 (0, 1, 1) × (1, 0, 0)12 model developed outperforms FB-Prophet with a forecast accuracy of 93.1025% as clearly depicted by the model’s MAPE of 6.8975% and a Theil U1 statistic of 0.0376 compared to the FB-Prophet model’s respective forecasted accuracy and Theil U1 statistic of 84.3569% and 0.1071. A Ljung Box test on the residuals of the estimated 𝑆𝐴𝑅𝐼𝑀𝐴 (0, 1, 1) × (1, 0, 0)12 model revealed that they are independent and free from auto/serial correlation. A Box-Pierce test for larger lags also revealed that the proposed model is adequate for forecasting. Due to the high forecast accuracy of the proposed SARIMA model, the study recommends the use of the proposed SARIMA model in the analysis of road traffic accidents in Ghana