Key Performance Indicators Detection Based Data Mining

One of the most prosperous domains that Data mining accomplished a great progress is Food Security and safety. Some of Data mining techniques studies applied several machine learning algorithms to enhance and traceability of food supply chain safety procedures and some of them applying machine learning methodologies with several feature selection methods for detecting and predicting the most significant key performance indicators affect food safety. In this research we proposed an adaptive data mining model applying nine machine learning algorithms (Naive Bayes, Bayes Net Key -Nearest Neighbor (KNN), Multilayer Perceptron (MLP), Random Forest (RF), Support Vector Machine (SVM), J48, Hoeffding tree, Logistic Model Tree) with feature selection wrapper methods (forward and backward techniques) for detecting food deterioration’s key performance indicators. In conclusion the proposed model applied effectively and successfully detected the most significant indicators for meat safety and quality with the aim of helping farmers and suppliers for being sure of delivering safety meat for consumer and diminishing the cost of monitoring meat safety

Risk Assessment Approaches in Banking Sector –A Survey

Prediction analysis is a method that makes predictions based on the data currently available. Bank loans come with a lot of risks to both the bank and the borrowers. One of the most exciting and important areas of research is data mining, which aims to extract information from vast amounts of accumulated data sets. The loan process is one of the key processes for the banking industry, and this paper examines various prior studies that used data mining techniques to extract all served entities and attributes necessary for analytical purposes, categorize these attributes, and forecast the future of their business using historical data, using a model, banks\u27 business, and strategic goals

Credit Card Fraud Detection Using Machine Learning Techniques

This is a systematic literature review to reflect the previous studies that dealt with credit card fraud detection and highlight the different machine learning techniques to deal with this problem. Credit cards are now widely utilized daily. The globe has just begun to shift toward financial inclusion, with marginalized people being introduced to the financial sector. As a result of the high volume of e-commerce, there has been a significant increase in credit card fraud. One of the most important parts of today\u27s banking sector is fraud detection. Fraud is one of the most serious concerns in terms of monetary losses, not just for financial institutions but also for individuals. as technology and usage patterns evolve, making credit card fraud detection a particularly difficult task. Traditional statistical approaches for identifying credit card fraud take much more time, and the result accuracy cannot be guaranteed. Machine learning algorithms have been widely employed in the detection of credit card fraud. The main goal of this review intends to present the previous research studies accomplished on Credit Card Fraud Detection (CCFD), and how they dealt with this problem by using different machine learning techniques

Trichogenic Silver-Based Nanoparticles for Suppression of Fungi Involved in Damping-Off of Cotton Seedlings

Mycogenic silver nanoparticles (AgNPs) produced by some biocontrol agents have shown the ability to inhibit the growth of numerous plant pathogenic fungi, which may be a unique method of disease management. This study describes the extracellular production of AgNPs by Trichoderma harzianum. The size, shape, charge, and composition of the AgNPs were subsequently studied by UV-visible spectroscopy, DLS, zeta potential, TEM, SEM, and EDX, among other methods. The AgNPs had sizes ranging from 6 to 15 nm. The antifungal activities of bio-synthesized AgNPs and two commercial fungicides (Moncut and Maxim XL) were tested against three soil-borne diseases (Fusarium fujikuroi, Rhizoctonia solani, and Macrophomina phaseolina). Cotton seedling illnesses were significantly reduced under greenhouse settings after significant in vitro antifungal activity was documented for the control of plant pathogenic fungi. The use of biocontrol agents such as T. harzianum, for example, may be a safe strategy for synthesizing AgNPs and using them to combat fungus in Egyptian cotton