A comprehensive study of machine learning techniques for log-based anomaly detection

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.

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.

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.

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

Over the years, communication through Short Message Service (SMS) has been a primary tool for mobile subscribers. SMS has varied applications in health, industry, finances, education and social networking among others. The growth of mobile devices and SMS usage has consequently increased the attack surface for cyber-criminals culminating to the proliferation of malicious activities introduced using SMS spam, phishing, spyware, malware etc. Ham messages are normal messages people trade with one another and are usually not unwanted by the recipient, while spam messages are unsolicited junk and redundant messages that may be sent to a large number of people at once and are usually unwanted. Various spam detection models have been developed using various traditional machine and deep learning techniques. However, most studies where comparison between deep and traditional machine learning algorithms is done, have unfortunately omitted K-Nearest Neighbors and Support Vector Machine (SVM) which are empirically deemed as the most popular traditional machine learning algorithms. In this study, therefore, we develop a deep learning model based on Recurrent Neural Network (RNN) for Spam and Ham SMS classification and compare its performance against SVM model for the same University of California (UCl) SMS dataset. The results show that RNN has a slightly higher training and validation accuracy of 0.98 compared to SVM at 0.94, however, the false positive rate of SVM is marginally lower. Exploring application of deep learning with better optimization algorithms such as RNN improves accuracy, reduces computational complexities i.e. memory consumption and speed, and thus minimizing false positive rates. For future work, we suggest the use of varied performance metrics to validate the model in a distributed dataset environment.

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.

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.

We briefly review previous applications of machine learning (ML) in power grid analyses and introduce our ongoing effort toward developing a generative-adversarial (GA) model for fast and reliable grid contingency analyses. According to our review, the persisting limitation of traditional ML techniques in grid analyses is the need for an exhaustive amount of training data for model generalization and accurate predictions. GA models overcome this limitation by first learning true data distribution from a small training set, from which new samples assimilating true data are generated with some variations. Subsequently, GA models can transfer learn or super-generalize with increased accuracy, that is, accurately predict n − (k + 2) contingencies from a small n − k training set and generated n − (k + 1) data. The joint effort between Idaho National Lab and Florida State University strives to develop a zero-shot and deep learning-based contingency analysis tool, named Smart Contingency Analysis Neural Network (SCANN), by leveraging the aforementioned advantages of GA models. The basic architecture of SCANN stems from the Latent Encoding of Atypical Perturbations network combined with an adversarial network, and it is designed to generate imbalanced power flow data from learned true data distributions for prediction purposes. Here we also introduce the abstract concept of resilience-chaos plots, a new resilience characterization tool proposed to complement SCANN by aiding in the assessment of large amounts of high-order contingency predictions.

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.

A novel approach for water quality classification based on the integration of deep learning and feature extraction techniques

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.

The growth of Hyperspectral Image (HSI) analysis is due to technology advancements that enable cameras to collect hundreds of continuous spectral information of each pixel in an image. HSI classification is challenging due to the large number of redundant spectral bands, limited training samples and non-linear relationship between the collected spatial position and the spectral bands. Our survey highlights recent research in HSI classification using traditional Machine Learning techniques like kernel-based learning, Support Vector Machines, Dimension Reduction and Transform-based techniques. Our study also digs into Deep Learning (DL) techniques that involve the usage of Autoencoders, 1D, 2D and 3D-Convolutional Neural Networks to classify HSI. From the comparison, it is observed that DL-based classification techniques outperform ML-based techniques. It has also been observed that spectral-spatial HSI classification outperforms pixel-by-pixel classification because it incorporates spectral signatures and spatial domain information. The performance of ML and DL-based classification techniques has been reviewed on commonly used land cover datasets like Indian Pines, Salinas valley and Pavia University.

In the recent years, machine learning and deep learning techniques are being applied on brain data to study mental health. The activation of neurons in these models is static and continuous-valued. However, a biological neuron processes the information in the form of discrete spikes based on the spike time and the firing rate. Understanding brain activities is vital to understand the mechanisms underlying mental health. Spiking Neural Networks are offering a computational modelling solution to understand complex dynamic brain processes related to mental disorders, including depression. The objective of this research is modeling and visualizing brain activity of people experiencing symptoms of depression using the SNN NeuCube architecture. Resting EEG data was collected from 22 participants and further divided into groups as healthy and mild-depressed. NeuCube models have been developed along with the connections across different brain regions using Synaptic Time Dependent plasticity (STDP) learning rule for healthy and depressed individuals. This unsupervised learning revealed some distinguishable patterns in the models related to the frontal, central and parietal areas of the depressed versus the control subjects that suggests potential markers for early depression prediction. Traditional machine learning techniques, including MLP methods have been also employed for classification and prediction tasks on the same data, but with lower accuracy and fewer new information gained.

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.