ISSN:2320-9151 Impact Factor:3.5

Volume 11, Issue 6, June 2023 Edition - IEEE-SEM Journal Publication

A Review on Educational Administration and ManagementPDF

Jalila S. Lala

Education management and administration are fields of study to pursue and a type of career a professional can work within. Professionals in this field not only have a passion for education, but they also want to be decision-makers for matters of policy, curriculum, and personnel. Educational administration gets into the role of setting up of goals of education, review, feedback and evaluation. Education management on the other hand is the function that coordinates and directs the human resources to meet the goals and objectives of the institution by using the available resources effectively.

A systematic Approach to Fraud Detection on automated Banking using Machine learning Technics.PDF

Tuleun Terhemen Daniel,Md Mehedi Hassan, Alexey Nikolaevich Nazarov, Abdulrashid Saadat

Internet banking has a greater impact on every nation economy but if not properly handled can cause the downfall of the economy of a whole country or a continent at large. Hence, there is every need for researchers in the field of data science to be up to date in order to track any malicious attack or any attempt that will lead to tempering with the holistic nature of legitimacy of our financial transactions on the internet. The objective of this paper is to find the patterns of transactions performed and help algorithms to learn those patterns by identifying the fraudulent transactions and flag them. The algorithm is built to identify and prevent fraudulent activities on the banking website to ensure a safe and trustworthy online experience for the customers. Thereby developing a robust and accurate system that can identify and prevent fraudulent activities in online payment transaction. In order to achieve this we selected some strong and interesting machine learning algorithms where a python programing language is used to train the dataset and great results were obtain. All our selected seven algorithms perform excellent with a greater competitive accuracy between XGBoost and Random forest. Finally, Random forest is considered the best model with the accuracy of 100.

Enhanced Detection of Heart Diseases Using Data Mining TechniquesPDF

Reem Reda Mohamed 1 , Ahmed I.B ElSeddawy 2 , Mona Mohamed Nasr3

Data mining techniques nowadays is considered to be reliable tool on health sector, specifically in diagnosing and detecting different diseases. In this paper, we shed the light on the detection of heart disease, seeking to reduce the development of the disease into serious and complicated situation, and to minimize the costly health care. We used fifteen important attributes that considered to be essential assessment factors to any cardiologist, such as Age, Sex, Cp, Trest bps, Chol, etc. By using these attributes, we predict the possibility of having heart disease at an early stage to group of individuals, in reach to know the expected number of heart patients, and help decision-makers take the procedure of early treatment steps and recovery, which will achieve more efficiency of both diseases prediction and medical treatment services. We used a data set from two resources, UCI university AI archive, and a survey that built on group of people responses. By using RapidMiner Studio, a framework of three different classification algorithms was used, to determine the best accuracy among them. The results show that the best accuracy are Random Forest (RF) Algorithm, k-Nearest Neighbor (K-NN) Algorithm, and Decision tree (DT) Algorithm respectively. Random forest accuracy was 91.71%, and a Cluster K-mean Algorithm was used, and the result of Avg. within centroid distance: 0.250.