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The timely identification and early prevention of crop diseases are essential for improving production. In this paper, deep convolutional-neural-network (CNN) models are implemented to identify and diagnose diseases in plants from their leaves, since CNNs have achieved impressive results in the field of machine vision. Standard CNN models require a large number of parameters and higher computation cost. In this paper, we replaced standard convolution with depth=separable convolution, which reduces the parameter number and computation cost. The implemented models were trained with an open dataset consisting of 14 different plant species, and 38 different categorical disease classes and healthy plant leaves. To evaluate the performance of the models, different parameters such as batch size, dropout, and different numbers of epochs were incorporated. The implemented models achieved a disease-classification accuracy rates of 98.42%, 99.11%, 97.02%, and 99.56% using InceptionV3, InceptionResNetV2, MobileNetV2, and EfficientNetB0, respectively, which were greater than that of traditional handcrafted-feature-based approaches. In comparison with other deep-learning models, the implemented model achieved better performance in terms of accuracy and it required less training time. Moreover, the MobileNetV2 architecture is compatible with mobile devices using the optimized parameter. The accuracy results in the identification of diseases showed that the deep CNN model is promising and can greatly impact the efficient identification of the diseases, and may have potential in the detection of diseases in real-time agricultural systems.

The stochastic nature of renewable energy sources, especially solar PV output, has created uncertainties for the power sector. It threatens the stability of the power system and results in an inability to match power consumption and production. This paper presents a Convolutional Neural Network (CNN) approach consisting of different architectures, such as the regular CNN, multi-headed CNN, and CNN-LSTM (CNN-Long Short-Term Memory), which utilizes a sliding window data-level approach and other data pre-processing techniques to make accurate forecasts. The output of the solar panels is linked to input parameters such as irradiation, module temperature, ambient temperature, and windspeed. The benchmarking and accuracy metrics are calculated for 1 h, 1 day, and 1 week for the CNN based methods which are then compared with the results from the autoregressive moving average and multiple linear regression models in order to demonstrate its efficacy in making short-term and medium-term forecasts.

Landslide is a devastating natural disaster, causing loss of life and property. It is likely to occur more frequently due to increasing urbanization, deforestation, and climate change. Landslide susceptibility mapping is vital to safeguard life and property. This article surveys machine learning (ML) models used for landslide susceptibility mapping to understand the current trend by analyzing published articles based on the ML models, landslide causative factors (LCFs), study location, datasets, evaluation methods, and model performance. Existing literature considered in this comprehensive survey is systematically selected using the ROSES protocol. The trend indicates a growing interest in the field. The choice of LCFs depends on data availability and case study location; China is the most studied location, and area under the receiver operating characteristic curve (AUC) is considered the best evaluation metric. Many ML models have achieved an AUC value > 0.90, indicating high reliability of the susceptibility map generated. This paper also discusses the recently developed hybrid, ensemble, and deep learning (DL) models in landslide susceptibility mapping. Generally, hybrid, ensemble, and DL models outperform conventional ML models. Based on the survey, a few recommendations and future works which may help the new researchers in the field are also presented.

Short-term load forecasting (STLF) is critical for the energy industry. Accurate predictions of future electricity demand are necessary to ensure power systems’ reliable and efficient operation. Various STLF models have been proposed in recent years, each with strengths and weaknesses. This paper comprehensively reviews some STLF models, including time series, artificial neural networks (ANNs), regression-based, and hybrid models. It first introduces the fundamental concepts and challenges of STLF, then discusses each model class’s main features and assumptions. The paper compares the models in terms of their accuracy, robustness, computational efficiency, scalability, and adaptability and identifies each approach’s advantages and limitations. Although this study suggests that ANNs and hybrid models may be the most promising ways to achieve accurate and reliable STLF, additional research is required to handle multiple input features, manage massive data sets, and adjust to shifting energy conditions.

Usage of effective classification techniques on Magnetic Resonance Imaging (MRI) helps in the proper diagnosis of brain tumors. Previous studies have focused on the classification of normal (nontumorous) or abnormal (tumorous) brain MRIs using methods such as Support Vector Machine (SVM) and AlexNet. In this paper, deep learning architectures are used to classify brain MRI images into normal or abnormal. Gender and age are added as higher attributes for more accurate and meaningful classification. A deep learning Convolutional Neural Network (CNN)-based technique and a Deep Neural Network (DNN) are also proposed for effective classification. Other deep learning architectures such as LeNet, AlexNet, ResNet, and traditional approaches such as SVM are also implemented to analyze and compare the results. Age and gender biases are found to be more useful and play a key role in classification, and they can be considered essential factors in brain tumor analysis. It is also worth noting that, in most circumstances, the proposed technique outperforms both existing SVM and AlexNet. The overall accuracy obtained is 88% (LeNet Inspired Model) and 80% (CNN-DNN) compared to SVM (82%) and AlexNet (64%), with best accuracy of 100%, 92%, 92%, and 81%, respectively.

Researchers at many different institutions around the world study voltage and current waveform distortions in power networks using a variety of techniques. Due to the uncontrolled growing number of nonlinear devices supplied by electrical energy, more severe supraharmonic pollution has been observed. A bibliometric analysis of the topic development between 2013 and 2022 is presented in the paper. Using the selected search tools, a comparative review of articles published in the last three years was conducted. It organizes the existing knowledge about supraharmonic pollution generated by nonlinear devices and identifies current research challenges associated with the spread of these disturbances in electrical networks. The most frequently discussed topics by researchers are those that deal with the level of emissions generated by supraharmonic sources and their effects on components of the power system. The second most prominent research direction is the detection, measurement, analysis, and severity evaluation of supraharmonic pollution. Finally, the authors discuss areas of study related to the topic that offers perspectives for future research. The impact of high-frequency component pollution generated by nonlinear loads on emissions intentionally designed to carry communications signals through electrical networks needs to be explored under various power supply conditions.

The paper presents a power-quality analysis in the utility low-voltage network focusing on harmonic currents’ pollution. Usually, to forecast the modern electrical and electronic devices’ contribution to increasing the current total harmonic distortion factor (THDI) and exceeding the regulation limit, analyses based on tests and models of individual devices are conducted. In this article, a composite approach was applied. The performance of harmonic currents produced by sets of devices commonly used in commercial and residential facilities’ nonlinear loads was investigated. The measurements were conducted with the class A PQ analyzer (FLUKE 435) and dedicated to the specialized PC software. The experimental tests show that the harmonic currents produced by multiple types of nonlinear loads tend to reduce the current total harmonic distortion factor (THDI). The changes of harmonic content caused by summation and/or cancellation effects in total current drawn from the grid by nonlinear loads should be a key factor in harmonic currents’ pollution study. Proper forecasting of the level of harmonic currents injected into the utility grid helps to maintain the quality of electricity at an appropriate level and reduce active power losses, which have a direct impact on the price of electricity generation.

Various plant diseases are major threats to agriculture. For timely control of different plant diseases in effective manner, automated identification of diseases are highly beneficial. So far, different techniques have been used to identify the diseases in plants. Deep learning is among the most widely used techniques in recent times due to its impressive results. In this work, we have proposed two methods namely shallow VGG with RF and shallow VGG with Xgboost to identify the diseases. The proposed model is compared with other hand-crafted and deep learning-based approaches. The experiments are carried on three different plants namely corn, potato, and tomato. The considered diseases in corns are Blight, Common rust, and Gray leaf spot, diseases in potatoes are early blight and late blight, and tomato diseases are bacterial spot, early blight, and late blight. The result shows that our implemented shallow VGG with Xgboost model outperforms different deep learning models in terms of accuracy, precision, recall, f1-score, and specificity. Shallow Visual Geometric Group (VGG) with Xgboost gives the highest accuracy rate of 94.47% in corn, 98.74% in potato, and 93.91% in the tomato dataset. The models are also tested with field images of potato, corn, and tomato. Even in field image the average accuracy obtained using shallow VGG with Xgboost are 94.22%, 97.36%, and 93.14%, respectively.

This Special Issue was intended as a forum to advance research and apply machine-learning and data-mining methods in order to facilitate the development of modern electric power systems, grids and devices, smart grids and protection devices, as well as to develop tools for more accurate and efficient power system analysis [...]

The radial distribution networks are designed for unidirectional power flows and are passive in nature. However, with the penetration of Distributed Generation (DG), the power flow becomes bidirectional and the network becomes active. The integration of DGs into distribution network creates many issues with: system stability, protection coordination, power quality, islanding, proper placement and sizing etc. Among these issues, the two most significant are optimal sizing and placement of DGs and their protection coordination in utility network. The proper coordination of relays with high penetration of DGs placed at optimal location increases the availability and reliability of the network during abnormal operating conditions. This research addresses most of the available methods for efficient sizing and placement of DGs in distribution system (numerical, analytical and heuristic) as well as the developed protection coordination techniques for utility networks in the presence of DGs (Artificial Intelligence (AI), adaptive and non-adaptive, multi-agent, hybrid). This paper indicates the possible research gaps and highlights the applications possibilities and methods’ limitations in the area of DGs.