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.

A Comparative Study on p- and n-Type Silicon Heterojunction Solar Cells by AFORS-HET.

Despite the increasing trend of n-type silicon wafer utilization in the manufacturing of high-efficiency heterojunction solar cells due to the superior advantages over p-type counterparts, its high manufacturing cost remains to be one of the most crucial factors, which impedes its market share growth with state-of-the-art silicon heterojunction (SHJ) solar cells demonstrating high conversion efficiencies from various configurations, the prospect of using an n-type wafer is debatable from a cost-efficiency point of view. Hence, a systematic comparison between p- and n-type SHJ solar cells was executed in this work using AFORS-HET numerical software. Front and rear-emitter architectures were selected for each type of wafer with ideal (without defects) and non-ideal (with defects) conditions. For ideal conditions, solar cells with p-type wafers and a front-emitter structure resulted in a maximum conversion efficiency of 28%, while n-type wafers demonstrated a maximum efficiency of 26% from the rear-emitter structure. These high-performance devices were possible due to the optimization of the bandgap and electron-affinity for all passivating and doping layers with values ranging from 1.3 to 1.7 eV and 3.9 to 4 eV, respectively. The correlation between the device structure and the type of wafers as demonstrated here will be helpful for the development of both types of solar cells with comparable performance.

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.

Effectiveness of Artificial Intelligence Models for Cardiovascular Disease Prediction: Network Meta-Analysis.

Heart failure is the most common cause of death in both males and females around the world. Cardiovascular diseases (CVDs), in particular, are the main cause of death worldwide, accounting for 30% of all fatalities in the United States and 45% in Europe. Artificial intelligence (AI) approaches such as machine learning (ML) and deep learning (DL) models are playing an important role in the advancement of heart failure therapy. The main objective of this study was to perform a network meta-analysis of patients with heart failure, stroke, hypertension, and diabetes by comparing the ML and DL models. A comprehensive search of five electronic databases was performed using ScienceDirect, EMBASE, PubMed, Web of Science, and IEEE Xplore. The search strategy was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. The methodological quality of studies was assessed by following the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) guidelines. The random-effects network meta-analysis forest plot with categorical data was used, as were subgroups testing for all four types of treatments and calculating odds ratio (OR) with a 95% confidence interval (CI). Pooled network forest, funnel plots, and the league table, which show the best algorithms for each outcome, were analyzed. Seventeen studies, with a total of 285,213 patients with CVDs, were included in the network meta-analysis. The statistical evidence indicated that the DL algorithms performed well in the prediction of heart failure with AUC of 0.843 and CI [0.840-0.845], while in the ML algorithm, the gradient boosting machine (GBM) achieved an average accuracy of 91.10% in predicting heart failure. An artificial neural network (ANN) performed well in the prediction of diabetes with an OR and CI of 0.0905 [0.0489; 0.1673]. Support vector machine (SVM) performed better for the prediction of stroke with OR and CI of 25.0801 [11.4824; 54.7803]. Random forest (RF) results performed well in the prediction of hypertension with OR and CI of 10.8527 [4.7434; 24.8305]. The findings of this work suggest that the DL models can effectively advance the prediction of and knowledge about heart failure, but there is a lack of literature regarding DL methods in the field of CVDs. As a result, more DL models should be applied in this field. To confirm our findings, more meta-analysis (e.g., Bayesian network) and thorough research with a larger number of patients are encouraged.

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.