International Journal of Formal Sciences: Current and Future Research Trends

Artificial Intelligence-A Key Note Biomarker in Drug Discovery and Development Running Title: ARTIFICIAL INTELLIGENCE

Madhavi Nimmathota, Merlin T Babu, S Tanusri, N Aishwarya, Rama Rao T — Sun, 10 Dec 2023 00:00:00 +0000

The application of artificial intelligence (AI) has recently accelerated in several societal domains, with the pharmaceutical sector leading the way. This paper highlights the useful applications of AI in the pharmaceutical industry, including medication discovery and development, drug repurposing, and pharmaceutical quality improvement. Among others, productivity, clinical trials, etc., have reduced the workload of people and expedited the completion of goals. Crosstalk about the methods and instruments used to enforce AI, ongoing problems, and solutions to them is also explored, as is the role of AI in terms of efficiency, accuracy, and speed. AI has the potential to revolutionize the drug discovery process. However, the availability of high-quality data, addressing ethical issues, and using AI effectively all depend on an understanding of the limitations of AI-based techniques. This article discusses the advantages of AI in this field, along with potential solutions. The suggested barriers to the situation are the addition of data, comprehensible AI, and the incorporation of the potential benefits of AI in the pharmaceutical industry, as well as how it compares to conventional experimental methods. This paper underlines the potential of artificial intelligence in drug discovery and gives details about the possibilities for attaining.

Modeling of the Microsoft Stock Prices Using Machine Learning and Classical Models: Identification of Optimal Model for Application

Fri, 01 Dec 2023 00:00:00 +0000

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.

A Review on Housing Policies and their Reflections on Real Estate Values in the Case of Turkey

Pelin Yiğit — Thu, 26 Oct 2023 00:00:00 +0000

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.

Theoretical Review on Future Financing and Investment in Nigeria

Jane Uchechi Ukangwa — Sun, 21 Jan 2024 00:00:00 +0000

Little is known about Future Financing and investments, as only a small number of surveys exist. This paper analyzed future finance and investments in our country of origin and covered current practices and motivations, obstacles to future finance and investment, addressing obstacles and other incentive mechanisms, and preferences for future finance and investment. Most countries strive to achieve high investment because of its literature acknowledged advantages as a tool of economic growth. Nigeria though faced by the problem of saving investment gap has one of its principal objectives under the new democratic dispensation as “the towards growth sustenance”. The result clearly shown that investment financing has a positive and strong relationship with economic growth in Nigeria, from the findings some of the problems of investment financing in Nigeria that were identified are the issues of Inadequate macroeconomic framework and policy inconsistencies, Low level of domestic savings, and Low return on investment. Therefore, the research recommended that government should pursue strong macroeconomic policies, improve economic efficiency, and increase public investment towards human capital development and improve infrastructures in the country to enhance productivity and efficiency.

Architecture of Deep Learning Algorithms in Image Classification: Systematic Literature Review

Vincent Mbandu Ochango Ochango, John Gichuki Ndia — Fri, 10 Nov 2023 00:00:00 +0000

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.

DDOS (Distributed Denial of Service) Attack Detection and Mitigation Using Statistical and Machine Learning Methods in SDN (Software-Defined Networking)

Ahmed Fadel Abd Ali — Fri, 13 Jan 2023 00:00:00 +0000

This study focuses on addressing the growing threat of Distributed Denial of Service (DDoS) attacks in Software-Defined Networking (SDN) environments. DDoS(Distributed Denial of Service attacks can cause significant disruption to network services by overwhelming target systems with a flood of malicious traffic. To combat this, we propose a novel approach that combines statistical and machine learning methods for the detection and mitigation of DDoS(Distributed Denial of Service attacks in SDN .To implement the detection and mitigation system, we design and deploy a comprehensive framework within the SDN infrastructure [1].

Proposal of the Land Valuation Process in Vietnam According to the Rules of the Market when the State Acquires Land

Tran Cong Lap — Sat, 27 Jan 2024 00:00:00 +0000

When the State acquires land, the price of land use rights is valued by compensation, which is one of the essential bases for guaranteeing land users. Resolution No. 18/NQ-TW also clearly states that land valuation must have a method valued according to market principles. Regulations on valuing specific land prices in the 2013 Land Law, amended and supplemented in 2018, have also revealed limitations related to failing to ensure specialization, objectivity, fairness, participation in supervision, and access to information of people whose land is acquired. The article proposes to develop a land valuation process in Vietnam according to market principles when the State acquires land to ensure a balance of benefits for those whose land is acquired.