Images acquired in underwater environments are usually affected by light absorption and scattering. These are the two phenomena that reduce the clarity of images that are captured in these environments. These factors cause low contrast and anamorphic colour diffusion. To tackle these issues, we propose an optimized low contrast enhancement scheme. The main thrust of this paper borders on enhancement of underwater image contrast by preserving the brightness level. The approach is termed Fuzzy-Histogram Equalisation Optimised for Brightness Preservation (FHEOBP) technique, where a combination of fuzzy and classical histogram equalisation techniques is employed towards the enhancement of the contrast of images from underwater scene. The scheme is optimized using teaching-learning-based optimisation technique that is built into the algorithm. The proposed FHEOBP filter shows improved performance over Local Histogram Equalisation (LHE) and Global Histogram Equalisation (GHE) as it has a higher luminance distortion index value than those of LHE and GHE. This translates into a better image details preservation. In fact, the computed luminance distortion indices for optimised FHEOBP are 16.4%, 28.3% and 20.1%, respectively higher than those of the corresponding GHE, in the same test images utilised for performance evaluation. Between the optimised and non-optimised FHEOBP, luminance distortion figures of optimised FHEOBP are 8%, 6.8% and 9.8% higher than those of the equivalent non-optimised FHEOBP in the test image data set.

Most algorithms of data compression were developed with English language as target text syntax. However, this paper approaches the problem of Yor?b? text files compression via the use of Discrete Wavelet Transform (DWT) and Huffman algorithm. Text files in Yor?b? language syntax are first converted into signal format that are then decomposed using DWT. The decomposed ASCII code representation of the text files are subsequently encoded using Huffman algorithm. Twenty different variants of DWTs taken from four families of wavelet filters (Haar, Daubechies, Symlets and bi-orthogonal) are considered to select the optimal DWT for Yor?b? text files compression. Furthermore, experiments are carried out in the proposed compression scheme with six different Yor?b? text files extracted from the open sources as input data sets. It is found that out of the twenty variants of DWT investigated, sym6 gives the best output for effective Yor?b? text files compression, due to its relatively high compression ratio, high compression factor and lowest compression error. Thus, sym6 as a wavelet transform is suitable for lossy text compression algorithm meant for Yor?b? language syntax text files.

In the recent decades, the use of artificial intelligence (AI) technology in decision making has continued to gain popularity in many disciplines including finance, marketing, insurance, engineering, and medicine to mention but a few, however, their applications have been very limited in the residential rental property market. The aim of the current paper is to compare the performance of four selected ML classifiers including Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR) with a view to choosing the best classifier appropriate for rental applications screening. A total of 724 data samples of the residential rental applications were obtained from the databases of 53 Estate Surveyors and Valuers (property managers), licensed to practice in the Lagos Metropolitan property market, Nigeria. The collected data were subdivided into training and testing datasets representing 70% and 30% respectively, and were analyzed using Python 3. The data were also used in determining the respective classification power of the classifiers using eight performance metrics such as recall/sensitivity, specificity, Type I error, Type II errors, and precision, others include F1 score, F1 adjusted measure, Mathew's Correlation Coefficient and area under the curve (AUC). The results reveal among others that the performance of all the four selected classifiers was good and satisfactory. However, DT outperformed the other classifiers in detecting true positive and false positive, while SVM achieved a better result than other classifiers in detecting false negative (Type II). As revealed in the study, the empirical comparison among different algorithms suggests that no single classifier is best for all learning tasks. The models provide cost and time-saving inputs for property investors, property management professionals, and policymakers.