Power Grid Contingency Analysis with Machine Learning: A Brief Survey and Prospects

Growth in system complexity increases the need for automated techniques dedicated to different log analysis tasks such as Log-based Anomaly Detection (LAD). The latter has been widely addressed in the literature, mostly by means of a variety of deep learning techniques. However, despite their many advantages, that focus on deep learning techniques is somewhat arbitrary as traditional Machine Learning (ML) techniques may perform well in many cases, depending on the context and datasets. In the same vein, semi-supervised techniques deserve the same attention as supervised techniques since the former have clear practical advantages. Further, current evaluations mostly rely on the assessment of detection accuracy. However, this is not enough to decide whether or not a specific ML technique is suitable to address the LAD problem in a given context. Other aspects to consider include training and prediction times as well as the sensitivity to hyperparameter tuning, which in practice matters to engineers. In this paper, we present a comprehensive empirical study, in which we evaluate a wide array of supervised and semi-supervised, traditional and deep ML techniques w.r.t. four evaluation criteria: detection accuracy, time performance, sensitivity of detection accuracy and time performance to hyperparameter tuning. Our goal is to provide much stronger and comprehensive evidence regarding the relative advantages and drawbacks of alternative techniques for LAD. The experimental results show that supervised traditional and deep ML techniques fare similarly in terms of their detection accuracy and prediction time on most of the benchmark datasets considered in our study. Moreover, overall, sensitivity analysis to hyperparameter tuning with respect to detection accuracy shows that supervised traditional ML techniques are less sensitive than deep learning techniques. Further, semi-supervised techniques yield significantly worse detection accuracy than supervised techniques.

Unmanned aerial vehicles (UAVs) are increasingly being integrated into the domain of precision agriculture, revolutionizing the agricultural landscape. Specifically, UAVs are being used in conjunction with machine learning techniques to solve a variety of complex agricultural problems. This paper provides a careful survey of more than 70 studies that have applied machine learning techniques utilizing UAV imagery to solve agricultural problems. The survey examines the models employed, their applications, and their performance, spanning a wide range of agricultural tasks, including crop classification, crop and weed detection, cropland mapping, and field segmentation. Comparisons are made among supervised, semi-supervised, and unsupervised machine learning approaches, including traditional machine learning classifiers, convolutional neural networks (CNNs), single-stage detectors, two-stage detectors, and transformers. Lastly, future advancements and prospects for UAV utilization in precision agriculture are highlighted and discussed. The general findings of the paper demonstrate that, for simple classification problems, traditional machine learning techniques, CNNs, and transformers can be used, with CNNs being the optimal choice. For segmentation tasks, UNETs are by far the preferred approach. For detection tasks, two-stage detectors delivered the best performance. On the other hand, for dataset augmentation and enhancement, generative adversarial networks (GANs) were the most popular choice.

Chemical heuristics have been fundamental to the advancement of chemistry and materials science. These heuristics are typically established by scientists using knowledge and creativity to extract patterns from limited datasets. Machine learning offers opportunities to perfect this approach using computers and larger datasets. Here, we discuss the relationships between traditional heuristics and machine learning approaches. We show how traditional rules can be challenged by large-scale statistical assessment and how traditional concepts commonly used as features are feeding the machine learning techniques. We stress the waste involved in relearning chemical rules and the challenges in terms of data size requirements for purely data-driven approaches. Our view is that heuristic and machine learning approaches are at their best when they work together.

Neutral-to-ground voltage (NTGV) abnormalities in secondary distribution systems (SDS) pose significant power quality (PQ) challenges, including safety hazards, power losses, and equipment damage. Despite their importance, these abnormalities remain relatively understudied. Accurate classification of NTGV events is crucial for effective mitigation strategies. Existing research primarily relies on machine learning (ML) models trained on manually extracted features from simulated or real-world signals. This paper introduces a novel end-to-end deep learning approach that leverages Gate Recurrent Units (GRU) to bypass manual feature extraction, directly utilizing real-world signals from three NTGV event categories: ground fault, lightning strike, and normal conditions. This is first time that GRU has been used for NTGV classification using raw data. The model's generalizability is assessed through 5-fold cross-validation. A comparative analysis with baseline models and traditional ML techniques demonstrates the proposed model's superior performance and computational efficiency due to its ability to directly process raw data.

Analysis of causal effects of 137Cs deposition on 137Cs concentrations in trees after the Fukushima accident using machine learning

In so far as everyone needs food, farming helps to provide for this basic requirement. Despite making up only 15.4% of the country's GDP, the agriculture sector is crucial to India since it creates a large number of job possibilities. In addition to providing useful information, the use of machine learning is improving farming by preventing or minimizing losses. Machine learning includes situations in which a machine may learn and make choices based on predetermined criteria. It may be classed as an area of artificial intelligence (AI). Artificial Neyral Networks (ANN), one of hottest artificial intelligence approaches of the present day, are extended by deep learning, a popular and contemporary data analysis tool in the last ten years. As the world's population rises, agriculture becomes more and more significant.This article discusses modern machine learning applications in agriculture and how they address issues before during, and after agricultural production.Numerous deep learning and machine learning methods, databases performances were reviewed in the publications. They grouped the approaches they looked at according to the most popular techniques, the kinds of data used, the performance metrics, and the results of these analyses.To detect tomato leaf diseases, machine learning techniques and image processing are used, albe it their accuracy is lower. Initially, tomato leaf diseases are automatically identified utilizing traditional image processing and machine learning techniques, yet the accuracy is lower.

Infertility significantly affects the quality of life on social and psychological levels and is estimated to expand in the coming years. In vitro fertilization is the applied answer of modern medicine to the ever-rising problem of low fertility in economically developed countries. Designated experts base their decision on selecting the most suitable embryo for transfer in the uterus by reviewing blastocysts images. Therefore, subjectivity and erroneous judgement can influence the progress of the whole fertilization process since no repeatable criteria exist to characterize the quality of each embryo. Towards the quantization of the visual content of ‘wannabe babies’ embryos, a comparative study between traditional machine and deep learning techniques is conducted in this paper. The utilization of a novel unsupervised segmentation scheme for the separation of trophectoderm and inner cell mass area provides a significant boost to the performance of traditional machine learning techniques. Moreover, an explainability technique that is based on the information retrieved by the Fisher Vector's generative model provides the necessary connection between the visual stimuli and the predicted results. The classification results of the proposed methodology are comparable with state-of the-art deep learning techniques and are accompanied by corresponding visual explanations that reveal the inner workings of each model and provide useful insight concerning the predictions' validity.

Communication-based train control (CBTC) systems are automated train control systems based on continuous and bidirectional train-ground communication. As one of the critical subsystems of the safety-demanding CBTC systems, the Data Communication System (DCS) is physically exposed to the environment. DCS faults may lead to delayed trains, stranded passengers, suspension of service, or even catastrophic losses of lives or assets. Therefore, it is quite desirable to identify the DCS faults and accordingly take effective maintenance measures. Most fault classification works are based on traditional Machine Learning (ML) techniques, such as Decision Tree (DT) and Bayesian Network. A substantial amount of fault information can be lost when we manually extract features from the system logs. This paper proposes a Deep Hybrid Learning (DHL) method to identify the DCS faults. A pre-trained Chinese Bidi-rectional Encoder Representation from Transformers (BERT) Deep Learning (DL) model is used to extract fault features from raw text logs automatically, and several traditional ML models are used to classify the system faults by the extracted features. Our study highlights the advantages of the DHL model in DCS fault diagnosis tasks. The intense simulation results illustrate that the proposed DHL model achieves substantial accuracy compared with traditional ML algorithms.

The aim of this review is to present an up-to-date overview of the application of machine learning methods in heart failure including diagnosis, classification, readmissions and medication adherence. Recent studies have shown that the application of machine learning techniques may have the potential to improve heart failure outcomes and management, including cost savings by improving existing diagnostic and treatment support systems. Recently developed deep learning methods are expected to yield even better performance than traditional machine learning techniques in performing complex tasks by learning the intricate patterns hidden in big medical data. The review summarizes the recent developments in the application of machine and deep learning methods in heart failure management.

Predicting student academic performance has been an important research topic in Educational Data Mining (EDM) which uses machine learning and data mining techniques to explore data from educational settings. However measuring academic performance of students is challenging since students academic performance hinges on diverse factors. The interrelationship between variables and factors for predicting performance participate in complicated nonlinear ways. Traditional data mining and machine learning techniques may not be applied directly to these types of data and problems. In this study we develop a classification model to predict student performance using Deep Learning which automatically learns multiple levels of representation. We pre-train hidden layers of features layerwisely using an unsupervised learning algorithm sparse auto-encoder from unlabeled data, and then use supervised training for finetuning the parameters. We train model on a relatively large real world students dataset, and the experimental results show the effectiveness of the proposed method which can be applied into academic pre-warning mechanism.

An analysis of machine learning models for sentiment analysis of Tamil code-mixed data

Breast cancer, the most prevalent malignant tumor among women, poses a significant threat to patients' physical and mental well-being. Recent advances in early screening technology have facilitated the early detection of an increasing number of breast cancers, resulting in a substantial improvement in patients' overall survival rates. The primary techniques used for early breast cancer diagnosis include mammography, breast ultrasound, breast MRI, and pathological examination. However, the clinical interpretation and analysis of the images produced by these technologies often involve significant labor costs and rely heavily on the expertise of clinicians, leading to inherent deviations. Consequently, artificial intelligence(AI) has emerged as a valuable technology in breast cancer diagnosis. Artificial intelligence includes Machine Learning(ML) and Deep Learning(DL). By simulating human behavior to learn from and process data, ML and DL aid in lesion localization reduce misdiagnosis rates, and improve accuracy. This narrative review provides a comprehensive review of the current research status of mammography using traditional ML and DL algorithms. It particularly highlights the latest advancements in DL methods for mammogram image analysis and offers insights into future development directions.

ObjectiveEnhanced technology in computer and internet has driven scale and quality of data to be improved in various areas including healthcare sectors. Machine Learning (ML) has played a pivotal role in efficiently analyzing those big data, but a general misunderstanding of ML algorithms still exists in applying them (e.g., ML techniques can settle a problem of small sample size, or deep learning is the ML algorithm). This paper reviewed the research of diagnosing mental illness using ML algorithm and suggests how ML techniques can be employed and worked in practice.MethodsResearches about mental illness diagnostic using ML techniques were carefully reviewed. Five traditional ML algorithms-Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Random Forest, Naïve Bayes, and K-Nearest Neighborhood (KNN)-frequently used for mental health area researches were systematically organized and summarized.ResultsBased on literature review, it turned out that Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Random Forest, Naïve Bayes, and K-Nearest Neighborhood (KNN) were frequently employed in mental health area, but many researchers did not clarify the reason for using their ML algorithm though every ML algorithm has its own advantages. In addition, there were several studies to apply ML algorithms without fully understanding the data characteristics.ConclusionResearchers using ML algorithms should be aware of the properties of their ML algorithms and the limitation of the results they obtained under restricted data conditions. This paper provides useful information of the properties and limitation of each ML algorithm in the practice of mental health.

Employee promotion is an important aspect of the human resource management process. Due to different factors, it refers to the automatic improvement among the employees in an organization. Promoting employees from the lower level to the higher level brings a feeling of satisfaction among the employees. It improves their job satisfaction and motivation by providing more significant income, status, and responsibilities. By building up loyalty, promotion reduces employee attrition. Thus, it is difficult to accurately decide, whether an employee should or should not be promoted based on their current and past performance. So, human resource management does research about promotion, because there are a limited number of research about the finding of employee promotion prediction in the existing studies. First, to find the reasons for employee promotion, we need to analyze the research study for finding the factors which are related to the promotion. The aim of this research study is to implement an employee promotion prediction framework using machine learning. A modified AdaBoost classifier is used for automatic promotion prediction, and six machine learning techniques for instance, Support Vector Machine (SVM), Logistic Regression (LR), Artificial Neural Network (ANN), Random Forest (RF), XGBoost (XGB), and AdaBoost are applied in performance comparison. Through a complex assessment process, the performance of these supervised machine learning algorithms for predicting employee advancement is analyzed using assessment metrics on the employees' evaluation dataset for promotion prediction. The Artificial Neural Network (ANN) and AdaBoost model provide better results on this dataset than all traditional machine learning techniques. Finally, Our proposed modified AdaBoost approach outperformed all other methods evaluated with an accuracy of 95.30%.

Predictive maintenance is increasingly important in rotating machinery to prevent unexpected failures, reduce downtime, and improve operational efficiency. This study compares the efficacy of traditional machine learning (ML) and deep learning (DL) techniques in diagnosing bearing faults under varying load and speed conditions. Two classification tasks were conducted: a simpler three-class task that distinguishes healthy bearings, inner race faults, and outer race faults, and a more complex nine-class task that includes faults of varying severity in the inner and outer races. In this study, the machine learning algorithm ensemble bagged trees, achieved maximum accuracies of 93.04% for the three-class and 87.13% for the nine-class classifications, followed by neural network, SVM, KNN, decision tree, and other algorithms. For deep learning, the CNN model, trained on scalograms (time–frequency images generated by continuous wavelet transform), demonstrated superior performance, reaching up to 100% accuracy in both classification tasks after six training epochs for the nine-class classifications. While CNNs take longer training time, their superior accuracy and capability to automatically extract complex features make the investment worthwhile. Consequently, the results demonstrate that the CNN model trained on CWT-based scalogram images achieved remarkably high classification accuracy, confirming that deep learning methods can outperform traditional ML algorithms in handling complex, non-linear, and dynamic diagnostic scenarios.