International Journal of Data Science

Identification of Influential Nodes in Social Network: Big Data - Hadoop

Rajnish Kumar Kumar — 2024-06-30

Software development and associated data is the most critical factor these days. Currently, people are living in an internet world where data and related artifacts are major sets of information these days. The data is correlated with real-world data. The analysis of large datasets was done as part of the experimental analysis. The dataset for online social media like Facebook and Twitter was taken for the identification of influential nodes. The analysis of the dataset provides an overview and observation of the dataset for Facebook or Twitter. Here, in the current activity, an overview of cloud computing and big data technologies are discussed along with effective methods and approaches to resolve the problem statement. Particularly, big data technologies such as Hadoop provided by Apache for processing and analysis of Gigabyte(GB) or petabyte(PB) scale datasets are discussed for processing data in distributed and parallel data fashion. Here, the processing of large datasets is done by big data technology by implementing Apache Hadoop in online social media.

The Performance of Drought Indices on Maize Production in Northern Nigeria Using Artificial Neural Network Model

Adedayo A. Adepoju — 2024-06-01

Drought is widely known to put the ecosystem at risk. It ensues when there is a major rainfall shortage that causes hydrological discrepancies and alters the land productive structures. The degree of rainfall influences the growth and harvests of maize, particularly where irrigation is not practicable. In some parts of northern Nigeria, rainfall is unpredictable and often lower than the quantity needed for a viable crop. For the detection, classification, and control of drought conditions, drought indices are used. There has been notable progress in the last few years in terms of modelling droughts by utilizing statistical or physical models. Despite the successes documented by most of these approaches; a plain, effective, and well-built statistical model is the artificial neural network (ANN). The use of artificial neural networks (ANN) to evaluate the impact of drought indices on maize output in the 17 northern Nigerian states is presented in this research. For a 25-year period from 1993 to 2018, observed annual data of drought indices, RDI, and the Palmer drought indices, which comprise SCPDSI, SCPHDI, and SCWLPM, as well as maize yield (measured in tonnes) in Northern states of Nigeria. The ANN model was evaluated using several activation functions (sigmoid, hyperbolic tangent, and rectified linear unit), hidden layers (1, 2, and 3), and training sets (70%, 80%, and 90%). The Mean Square Error (MSE) was employed to evaluate each ANN model's performance. In summary, most of the states' lowest mean square errors (MSEs) were generated via RELU. Also, as the training percentage increases, the mean square error increases.

Improving Acute Leukemia Classification through Recursive Feature Elimination and Multilayer Perceptron Analysis of Gene Expression Data

Temitope Ogunbiyi — 2024-06-01

This study presents an approach to improving the classification of acute leukemia subtypes using gene expression data analysis. Leveraging Recursive Feature Elimination (RFE) as a feature selection technique and Multilayer Perceptron (MLP) as the predictive modeling framework, this research aims to identify the most influential genes for distinguishing between Acute Lymphoblastic Leukemia (ALL) and Acute Myeloid Leukemia (AML) cases. RFE systematically ranks and selects the most discriminative gene attributes, while MLP constructs a predictive model based on these attributes. The results demonstrate the effectiveness of this combined approach, achieving precision, accuracy, F1-Score, and recall rates of approximately 99% for leukemia subtype classification. Furthermore, specific genes contributing most to the model's predictive power and shedding light on potential biomarkers for leukemia diagnosis were identified. This research underscores the significance of RFE and MLP in the analysis of gene expression data and their potential impact on clinical decision-making in the field of oncology.

Analyzing of Student Alcohol Consumption and Consequences

Aid Semić — 2024-06-01

Student alcohol consumption is very interesting topic today. It is known that average number of students who consume alcohol today is rapidly increasing. Here is idea to analyze how it affect their studying and grades. To predict the grades of students we have to take a lot of aspect in consideration. This research uses a range of different parameters and machine learning algorithms, such as Exploratory Data Analysis and XGBoost. By using this methodologies and algorithms I have to admit that I didn’t develop the best model for prediction.