Impact Analysis of Potential Induced Degradation on Crystalline Silicon Solar Cell Performance by Correlating Practical Diagnosis with MATLAB Simulation.

Extensive research on fault diagnosis is essential to detect various faults that occur to different photovoltaic (PV) panels to keep PV systems operating at peak performance. Here, we present an impact analysis of potential induced degradation (PID) on the current-voltage (I-V) characteristics of crystalline silicon (c-Si) solar cells. The impact of parasitic resistances on solar cell performance is highlighted and linked to fault and degradation. Furthermore, a Simulink model for a single solar cell is proposed and used to estimate the I-V characteristics of a PID-affected PV cell based on experimental results attributes. The measured data show that the fill factor (FF) drops by approximately 13.7% from its initial value due to a decrease in shunt resistance (Rsh). Similarly, the simulation results find that the fill factor degraded by approximately 12% from its initial value. The slight increase in measured data could be due to series resistance effects which were assumed to be zero in the simulated data. This study links simulation and experimental work to confirm the I-V curve behavior of PID-affected PV cells, which could help to improve fault diagnosis methods.

Assessment and Evaluation of Different Machine Learning Algorithms for Predicting Student Performance.

Student performance is crucial to the success of tertiary institutions. Especially, academic achievement is one of the metrics used in rating top-quality universities. Despite the large volume of educational data, accurately predicting student performance becomes more challenging. The main reason for this is the limited research in various machine learning (ML) approaches. Accordingly, educators need to explore effective tools for modelling and assessing student performance while recognizing weaknesses to improve educational outcomes. The existing ML approaches and key features for predicting student performance were investigated in this work. Related studies published between 2015 and 2021 were identified through a systematic search of various online databases. Thirty-nine studies were selected and evaluated. The results showed that six ML models were mainly used: decision tree (DT), artificial neural networks (ANNs), support vector machine (SVM), K-nearest neighbor (KNN), linear regression (LinR), and Naive Bayes (NB). Our results also indicated that ANN outperformed other models and had higher accuracy levels. Furthermore, academic, demographic, internal assessment, and family/personal attributes were the most predominant input variables (e.g., predictive features) used for predicting student performance. Our analysis revealed an increasing number of research in this domain and a broad range of ML algorithms applied. At the same time, the extant body of evidence suggested that ML can be beneficial in identifying and improving various academic performance areas.

Deep-Learning-Based Approach for Iraqi and Malaysian Vehicle License Plate Recognition.

Recognizing vehicle plate numbers is a key step towards implementing the legislation on traffic and reducing the number of daily traffic accidents. Although machine learning has advanced considerably, the recognition of license plates remains an obstacle, particularly in countries whose plate numbers are written in different languages or blended with Latin alphabets. This paper introduces a recognition system for Arabic and Latin alphabet license plates using a deep-learning-based approach in conjugation with data collected from two specific countries: Iraq and Malaysia. The system under study is proposed to detect, segment, and recognize vehicle plate numbers. Moreover, Iraqi and Malaysian plates were used to compare these processes. A total of 404 Iraqi images and 681 Malaysian images were tested and used for the proposed techniques. The evaluation took place under various atmospheric environments, including fog, different contrasts, dirt, different colours, and distortion problems. The proposed approach showed an average recognition rate of 85.56% and 88.86% on Iraqi and Malaysian datasets, respectively. Thus, this evidences that the deep-learning-based method outperforms other state-of-the-art methods as it can successfully detect plate numbers regardless of the deterioration level of image quality.