International Journal of Formal Sciences: Current and Future Research Trends 2023-10-22T10:01:01+00:00 Prof. Feras Fares Open Journal Systems <p style="text-align: justify;">The International Journal of Social Sciences: Current and Future Research Trends (IJSSCFRT) is an open access International Journal for scientists and researchers to publish their scientific papers in Social Sciences related fields. IJSSCFRT plays its role as a refereed international journal to publish research results conducted by researchers.</p> <p>This journal accepts scientific papers for publication after passing the journal's double peer review process within 4 weeks. For detailed information about the journal kindly check <a title="About the Journal" href="">About the Journal</a> page. </p> <p style="text-align: justify;">All IJSSCFRT published papers in Social Sciences will be available for scientific readers for free; no fees are required to download published papers in this international journal.</p> <p style="text-align: justify;"> </p> A Review on Housing Policies and their Reflections on Real Estate Values in the Case of Turkey 2023-10-13T13:14:54+00:00 Pelin Yiğit <p>In this study, the housing sector will be examined with a financial approach, based on the housing policies in Turkey and the housing policies implemented in the planned and unplanned period after the proclamation of the Republic. In this study, housing finance systems applied in the world were examined and then an attempt was made to develop a financing model suitable for Turkey. The applicability of the model in question to real life was examined with a feasibility project. In the study, based on the definition of the concept of housing, the cost factors affecting the sector and the size of the sector in fixed capital investments are mentioned.</p> 2023-10-26T00:00:00+00:00 Copyright (c) 2023 International Journal of Formal Sciences: Current and Future Research Trends Modeling of the Microsoft Stock Prices Using Machine Learning and Classical Models: Identification of Optimal Model for Application 2023-10-20T21:53:01+00:00 Chrysogonus Chinagorom Nwaigwe Desmond Chekwube Bartholomew Emmanuel Chigozie Umeh Godwin Onyeka Nwafor Ibrahim Adamu Simplicius Chidiebere Oguguo <p>In this study, Microsoft stock price was modeled using two traditional time series models and two machine learning models for reliable predictions of the future behavior of the stock prices and more gainful investment. Model metrics such as AIC, BIC, Log-likelihood, RMSE, and confidence set test were the basis for comparison of the models. The results showed that the GARCH model outperformed the ARIMA and Support Vector Regression models while the Long Short-Time Memory –Recurrent model outperformed the GARCH model. Forecasts from the Long Short-Time Memory were made and found to be highly reliable. The results of the forecast also showed an uptrend movement up to a price of around $275 from November 2023 to January 2024. In conclusion, the LSTM-RNN is capable of accurately tracking and forecasting movements of volatile stock prices and is preferred over the other models considered in this study.</p> 2023-12-01T00:00:00+00:00 Copyright (c) 2023 International Journal of Formal Sciences: Current and Future Research Trends Architecture of Deep Learning Algorithms in Image Classification: Systematic Literature Review 2023-10-22T10:01:01+00:00 Vincent Mbandu Ochango Ochango John Gichuki Ndia <p>The number of data points predicted correctly out of the total data points is known as accuracy in image classification models. Assessment of the accuracy is very important since it compares the correct images to the ones that have been classified by the image classification models. Image classification accuracy is a challenge since image classification models classify images to the class they don’t belong to hence there is an inaccurate relationship between the predicted class and the actual class which results in a low model accuracy score. Therefore, there is a need for a model that can classify the images with the highest accuracy. The paper presents image classification models together with the feature extraction methods used to classify maize disease images. The researcher used an augmented maize leaf disease dataset obtained from the Kaggle website. Features are extracted from maize disease images and passed to the machine learning classification algorithm to identify the possible disease based on the features detected using the feature extraction method. The maize disease images used include images of common rust, leaf spot, and northern leaf blight and healthy images. An evaluation was done for the feature extraction methods and the outcomes revealed Histogram of Oriented Gradients performed best with classifiers compared to KAZE and Oriented FAST and rotated BRIEF. The experimental outcome also indicated that the Artificial Neural Network model had the highest accuracy of 0.82 compared to Logistic Regression, K-Nearest Neighbors, Random Forest, Linear Support Vector Classifier, Decision Tree, and Support Vector Machine.</p> 2023-11-10T00:00:00+00:00 Copyright (c) 2023 International Journal of Formal Sciences: Current and Future Research Trends