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In multi-antenna systems, advanced techniques such as massive multiple-input multiple-output (MIMO), beamforming, and beam selection depend heavily on the accurate acquisition of the channel state. However, pilot contamination (PC) can be a major source of interference which degrades they are performance. Moreover, the severity of PC increases as more pilots are reused between users in the wireless systems. Researchers have shown that PC can be mitigated by using deep learning (DL) approaches. Nevertheless, when minimizing PC, the examination that identifies the applications and factors that distinguish these DL approaches is still limited. This paper reviews these DL approaches and the improvements needed to enhance their performance. Simulation results confirm that DL networks that learn to predict the channels directly have superior performance under PC. [ABSTRACT FROM AUTHOR]

Traffic flow prediction is an essential part of the intelligent transport system. This is the accurate estimation of traffic flow in a given region at a particular interval of time in the future. The study of traffic forecasting is useful in mitigating congestion and make safer and cost-efficient travel. While traditional models use shallow networks, there has been an exponential growth in the number of vehicles in recent times and these traditional machine learning models fail to work in current scenarios. In our paper, we review some of the latest works in deep learning for traffic flow prediction. Many deep learning architectures include Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long ShortTerm Memory (LSTM), Restricted Boltzmann Machines (RBM), and Stacked Auto Encoder (SAE). These deep learning models use multiple layers to extract higher level of features from raw input progressively. The latest deep learning models developed to tackle this very problem are reviewed and due to the complexity of transport networks, this review gives the reader information about how various factors influence these models and what models work best in different scenarios. [ABSTRACT FROM AUTHOR]

In this study, we searched for the latest literature on the use of deep learning applications to combat COVID-19 and these were identified from several search engines including IEEE Xplore, Google Scholar, PubMed, and Scopus. This involved a comprehensive analysis of the studies to identify the challenges associated with the use of deep learning models with a view to highlight the possible future trends in the development of deep learning systems that are efficient and more reliable for the diagnosis of COVID-19 patients. This paper provides information related to the deep learning techniques used to detect COVID-19. This paper discusses the Convolutional Neural Networks’ (CNNs) structure, how to train CNNs, and highlights the different pre-trained models of CNNs that can be used for the detection of COVID-19. This paper explores the latest developments in the diagnosis of COVID-19 using deep learning applications that rely on the use of X-ray images taken from medical imaging samples. A review of the different models developed to facilitate effective diagnosis of COVID-19 provides information regarding the experimental data, the data splitting techniques used, as well as the proposed architecture for detecting COVID-19 and the different evaluation metrics for each model. This paper is a useful resource for medical and technical experts, as it helps them to develop a sound understanding of how deep learning techniques can be harnessed to stop the spread of COVID-19. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy is a common complication of diabetes, that affects blood vessels in the light-sensitive tissue called the retina. It is the most common cause of vision loss among people with diabetes and the leading cause of vision impairment and blindness among working-age adults. Recent progress in the use of automated systems for diabetic retinopathy diagnostics has offered new challenges for the industry, namely the search for a less resource-intensive architecture, e.g., for the development of low-cost embedded software. This paper proposes a comparison between two widely used conventional architectures (DenseNet, ResNet) with the new optimized one (EfficientNet). The proposed methods classify the retinal image as one of 5 class cases based on the dataset obtained from the 4th Asia Pacific Tele-Ophthalmology Society (APTOS) Symposium. [ABSTRACT FROM AUTHOR]

The intelligent monitoring of electric locomotive loading is crucial in unmanned underground systems. A CNN-based monitoring scheme with migration learning was proposed to address efficiency, abnormality, and data acquisition challenges. Locomotive loading datasets are transformed, augmented, and equalized. Our model improves performance and training by modifying the fully connected layer, using optimized learning rate decay and adaptive algorithms. Training in PyTorch, the optimized VGG19-EL migration network achieves 99.85% recognition for 2-classifications, while the optimized RESNET50-EL migration network achieves 97.3% for 10-classifications. Overall, this study proposes a reliable and efficient model for liberating workers and monitoring locomotive loading. [ABSTRACT FROM AUTHOR]

Dengue fever modelling in endemic locations is critical to reducing outbreaks and improving vector-borne illness control. Early projections of dengue are a crucial tool for disease control because of the unavailability of treatments and universal vaccination. Neural networks have made significant contributions to public health in a variety of ways. In this paper, we developed a deep learning modelling using random forest (RF) that helps to extract the features of the dengue fever from the text datasets. The proposed modelling involves the data collection, preprocessing of the input texts, and feature extraction. The extracted features are studied to test how well the feature extraction using RF is effective on dengue datasets. The simulation result shows that the proposed method achieves higher degree of accuracy that offers an improvement of more than 12% than the existing methods in extracting the features from the input datasets than the other feature extraction methods that have laid a foundation for Artificial Neural Network (ANN) model efficacy test. Further, the study reduces the errors associated with feature extraction that is 10% lesser than the other existing methods and this shows the efficacy of the model. [ABSTRACT FROM AUTHOR]

The spread of Coronavirus Disease-2019 worldwide necessitates the development of accurate identification methods and the determination of genetic relatedness. The result of genomic methods involving nucleotide alignment informed the considerations of several alignment-free techniques for virus detection. This paper presents a genomic sequence identification model, developed based on Genomic Signal Processing (GSP), deep learning, and genomic datasets of Coronavirus 2 (SARS-CoV-2), Severe Acute Respiratory Syndrome CoV (SARS-CoV), and Middle East Respiratory Syndrome CoV (MERS-CoV). Our results showed that the Z-Curve images for the three viral strains depicted high visual similarities in texture and color, thus making it difficult to differentiate the strains by visual inspection. However, the homogeneity distance showed that SARS-CoV-2 is closer to SAR-CoV than MERS-CoV. Following a validation accuracy of 98.33%, it became clear that Z-Curve images for MERS-CoV, SARS-CoV and SARS-CoV-2 have distinct features after transformation by the Convolutional Neural Network (CNN) classifier. The divergence in texture and color reflects genetic variation among the strains, which is too insignificant for differentiation via visual inspection. Our results showed that higher layers of CNN amplify aspects of input images that are critical for discrimination, thereby confirming the importance of deep learning and GSP in accurate viral detection. [ABSTRACT FROM AUTHOR]

Smart cards, integrated circuit cards, are frequently used to pay transportation fees in many countries such as the United States, the European Union, Korea and others. Smart card transaction histories in public transportation have been used in making diverse traffic policies. Unfortunately, data preprocessing is required to organize records to extract useful information since the raw data contain artifacts and incomplete information. The paired data, i.e. a boarding and its associated alighting, are one of the key pieces of information required to yield the city traffic demand. However, it is difficult to gather the paired data mainly because the card tagging policies and data collecting methods vary from city to city and from institution to institution. The broken trip links between the boarding and its associated alighting information are the most frequent incomplete data in the smart card histories. Although huge budgets are spent on annual traffic surveys in many countries, there are no accurate Smart card data analysis methods that cover all city cases due to high data complexity and limited base information. Among these two restrictions, limited information is more difficult to resolve because it has a large codomain, thousands of stops, for inference under tiny samples. It gives handicaps of applying well-known high-performance approaches such as deep learning and probability analysis. We propose a reconstruction method by a customized clustering algorithm based on coordinates of transactions-occurred stops, user trip histories and actual vehicle movement trajectories for incomplete trip pairs. The proposed method is derived from evaluating each passenger's historical clusters and trip patterns. In a blind test 79.1% accuracy is achieved in predicting missing alighting stops while conventional methods reach only 42.3% accuracy. [ABSTRACT FROM AUTHOR]

Demand forecasting, a crucial aspect of anticipating future customer needs, involves using historical data to predict trends. With the rise of artificial intelligence (AI), companies are increasingly turning to machine learning algorithms to enhance accuracy in forecasting compared to traditional methods. This article delves into the application of machine learning algorithms in demand forecasting, specifically within the realm of supply chain management, addressing both long-term (4-5 years) and short-term (3-4 months) scenarios. The primary focus is on improving prediction accuracy by employing feature selection algorithms and various machine learning and deep learning approaches. Utilizing diverse algorithms, such as time series, traditional machine learning, and advanced deep learning techniques, the study aims to forecast demand for different timeframes. Evaluation metrics like Mean Absolute Percentage Error (MAPE) and Mean Percentage Error (MPE) are employed to assess the effectiveness of each model. The primary goal is to pinpoint the most effective algorithm tailored to a specific dataset. This empowers companies to make well-informed decisions and enhance their supply chain operations by leveraging precise demand forecasts. The results of this study hold the potential to empower decision-makers and practitioners by enhancing their forecasting capabilities. By integrating both forecast periods a more comprehensive and robust supply chain strategy is ensured. [ABSTRACT FROM AUTHOR]

Handwritten character recognition (HCR) is a critical component of diverse applications, especially in multilingual regions such as India. Despite technological advancements, recognizing handwritten characters remains challenging because of variations in writing style, shape and linguistic nuances. This study presents an efficient HCR system tailored for Indian languages, such as Tamil and Telugu scripts, to address these challenges. The proposed nearest quad-tree (NQT) classifier enhances recognition accuracy while ensuring computational efficiency. This novel approach, a light-weight design, combines local feature-level decisions with a global decision-making process, thereby making it suitable for devices with limited computational resources. Key contributions include the emphasis of the NQT classifier on efficiency and lightweight design, applicability to regional languages and usability in resource-constrained settings. This study utilized three datasets from the Tamil, Devanagari and Telugu languages to demonstrate the exceptional performance of the NQT classifier in character recognition through the minimization of misclassification errors. The classifier exhibited consistently low misclassification error rates across a diverse range of characters, outperforming existing deep learning models. Notably, the NQT classifier achieved an overall accuracy, precision, recall and F1-measure of 96%, 95%, 97% and 96%, respectively, across the three datasets, surpassing the performance of popular transfer learning models. Furthermore, a comprehensive computational efficiency analysis highlighted the superior efficiency of the NQT classifier in terms of training time, CPU and GPU utilization and memory requirements, underscoring its potential for practical applications in resource-constrained environments. [ABSTRACT FROM AUTHOR]

The liver is an important and multifunctional human organ. Early and accurate diagnosis of a liver tumor can save lives. Computed Tomography (CT) images provide comprehensive information for liver tumor diagnosis using feature extraction techniques. These extracted features help classify liver tumors using Machine Learning (ML) and Deep Learning (DL) methods. For this research, twelve 1hundred CT images were acquired from the Radiology Department of Nishter Medical University & Hospital. The noise was removed using Gabor Filter after converting CT images into grayscale. Image quality was enhanced by adopting Histogram Equalization (HE), and finally, the Image's edges and boundaries were improved using a smoothening and sharpening algorithm. Preprocessed images were forwarded to extract six features: Histogram, Run-length, Co-occurrence, Autogressive, Gradient, and Wavelet Transform. A major focus of this research is to evaluate that ML methods produced good accuracy using already extracted features while DL Algorithms could not produce better results. Firstly, ML methods such as Decision Tree (DT), Random Forest (RF), Boost, and Support Vector Machine (SVM) are deployed using an already extracted feature list containing all six features. It was observed that DT, RF, Boost, and SVM produced 96.5%, 99.6%, 99.7%, and 98.0% classification accuracy. After that, DL Algorithms such as Neural Networks (NN), Long-Short Term Memory (LSTM), Bi-Directional Long Short Term Memory (Bi-LSTM), and Convolutional Neural Networks (CNN) were deployed. The results showed that NN, LSTM, Bi-LSTM, and CNN produced 50.0%, 53.0%, 54.0%, and 54.0% accuracy respectively. To validate the major focus of this research, finally, Pre-trained DL Algorithms such as Residual Network 50 (Resnet50), Visual Geometry Group 16 (VGG16) and LSTM + CNN were deployed. Results showed that Resnet50, VGG16, and LSTM + CNN attained 78.0%, 88.0%, and 97.0% accuracy respectively. Hence, ML methods performed better using already extracted features, while DL Algorithms could not produce promising results on these extracted features. [ABSTRACT FROM AUTHOR]

Before using concrete for a particular purpose, its strength must be determined because its physical properties vary depending on the type of supplementary cementitious material (SCMs). Initially, researchers anticipated it with statistical processes; more recently, they started implementing deep learning (DL) and machine learning (ML) models. This research employed ensemble machine learning (EML) approaches to predict the compressive strength (CS) of concrete developed for ground-granulated blast furnace slag (GGBFS) along with Alccofine 1203 (AF). Ensemble regressions of Random Forest Regression (RFR) and AdaBoost Regression (ABR) were considered for model prediction using the Jupyter Notebook. The models were created based on the outcome of the compressive strength under the 90 experimental conditions. Moreover, a comparative study was performed between the RFR and ABR to benchmark the proposed model against a given combination of features and CS. The experimental conditions were optimized using a genetic algorithm (GA) after the model was created by ANN. With the two EML algorithms, the smallest errors, along with the largest coefficient of determination, were observed in the RFR algorithm. It was recognized that the optimized value of CS is 48.606 MPa, and their optimized process elements such as cement, alccofine, GGBS, water, and days were 261.506 kg/m³, 0.055 kg/m³, 52.468 kg/m³, 141.007 L, and 27.999 days, respectively. [ABSTRACT FROM AUTHOR]

Human following is a very useful task in the robotics industry. With modern compact-sized robots, there is a demand for further investigated computer-vision solutions that can perform effectively on them. A well-known deep learning model along this line of thought is the MobileNet-Ssd, an object detection model renowned for its resource-constrained usage. Available in popular frameworks like TensorFlow and PyTorch, this model can be of great use in deployments on robotic applications. This research attempts to investigate the MobileNet-Ssd model in order to evaluate its suitability for stand-alone object detection on a Raspberry Pi. To determine the effect of input size on the model, the model's performance has been investigated with speed in frames-per-second across different input sizes on both CPU and GPU-powered devices. To evaluate the model's effectiveness in the human following task, a Raspberry Pi-based robot was designed leveraging the tracking-by-detection approach with TensorFlow-Lite. Furthermore, the model's performance was evaluated using PyTorch while the model's inputs were adjusted, and the results were compared to those of other state-of-the-art models. The investigation revealed that, despite its modest speeds, the model outperforms other noteworthy models in PyTorch and is an ideal choice when working with Raspberry Pi using TensorFlow-Lite. [ABSTRACT FROM AUTHOR]

Automatic type identification of sub-axial spine fractures is of prime importance for orthopaedicians to reduce image interpretation time and increase patient care time. But identifying fracture types is challenging due to imbalanced datasets. In this work, CT scan images of fractured spine has been collected from a Tertiary Care hospital and extended Deep Convolutional Generative Adversarial Network (DCGAN) architecture is developed for generating spine fracture images that overcomes the imbalanced dataset problem. These enhanced dataset are clinically evaluated with Two Visual Turing Tests (VTTs): the first test to "identify real and generated images" and second test to determine "type of fractures in the generated images." The first VTT demonstrates that generated images of fractures are realistic and that even spine surgeons have difficulty in distinguishing them from real. The second VTT demonstrates that fracture lines are clearly visible in the generated images. The VTT results are analyzed using Fleiss Kappa statistical techniques to determine the inter-observer reliability of spine surgeons' clinical evaluation. The results showed high interobserver agreement for "type identification" in the generated images. The clinically evaluated generated images are provided to the proposed ensemble based type identification model, which outperformed other models in type identification. [ABSTRACT FROM AUTHOR]

Lung Cancer is a major cancer in the world and specifically India. Histopathological examination of tumorous tissue biopsy is the gold standard method used to clinically identify the type, sub-type, and stage of cancer. Two of the most prevalent forms of lung cancer: Adenocarcinoma & Squamous Cell Carcinoma account for nearly 80% of all lung cancer cases, which makes classifying the two subtypes of high importance. Proposed in this study is a data pre-processing pipeline for the H&E-stained lung biopsy images along with a customized EfficientNetB3-based Convolutional Neural Network employing spatial attention, trained on a public three-class lung cancer histopathological image dataset. The pre-processing pipeline employed before training, validation and testing helps enhance features of the histopathological images and removes biases due to stain variations for increased model robustness. The usage of a pre-trained CNN helps the deep learning model generalize better with the pre-trained weights, while the attention mechanism On three-fold validation, the classifier bagged accuracies of 0.9943 ± 0.0012 and 0.9947 ± 0.0018 and combined F1-Scores of 0.9942 ± 0.0042 and 0.9833 ± 0.0216 over the validation and testing data respectively. The high performance of the model combined with its computational efficiency could enable easy deployment of our model without necessitating infrastructure overhaul. [ABSTRACT FROM AUTHOR]

Alzheimer's disease is a gradual neurodegenerative condition affecting the brain, causing a decline in cognitive function by progressively damaging nerve cells over time. While a cure for Alzheimer's remains elusive, the detection of Alzheimer's disease (AD) through brain biomarkers is crucial to impede its advancement. High-resolution structural MRI scans, particularly T1-weighted images, are commonly used in Alzheimer's detection. These images provide detailed information about the brain's structure, allowing researchers and clinicians to identify abnormalities. Our study employs a deep learning methodology using T1-weighted MRI images for a binary classification task--distinguishing between AD and normal/healthy control (NC). The volumetric convolutional neural network model is deployed on pre-processed images and validated on MIRIAD datasets, achieving an impressive accuracy of 97%, surpassing other network models. Addressing the challenge of limited datasets for deep learning models, we incorporated various augmentation techniques such as rotation and rescaling, resulting in outstanding model accuracy and effective discerning between Alzheimer's disease and normal controls. [ABSTRACT FROM AUTHOR]

Medical image segmentation is a crucial stage in computer vision and image processing to help the later-stage diagnosis process become more accurate. Because medical image segmentation, such as X-ray, can extract tissue, organs, and pathological structures. However, medical image processing, primarily in the segmentation process, has significant challenges regarding feature representation. Because medical images have different characteristics than other images related to contrast, blur, and noise. This study proposes the use of lung segmentation on chest X-ray images based on deep learning with the FCA-Net (Fully Convolutional Attention Network) architecture. In addition, attention modules, namely spatial attention and channel attention, are added to the Res2Net encoder so that it is expected to be able to represent features better. This research was conducted on chest X-ray images from Qatar University contained in the Kaggle repository. A chest x-ray image measuring 256 × 256 pixels and as many as 1500 images were then divided into 10% testing data and 90% training data. The training data will then be processed in K-Fold Cross validation from K = 2 until K = 10. The experiment was conducted with scenarios that used spatial attention, channel attention, and a combination of spatial and channel attention. The best test results in this study were using a variety of spatial attention and channel attention in the division of K-Fold with a value of K = 5 with a DSC (Dice Similarity Coefficient) value in the testing data of 97.24% and IoU (Intersection over Union) in the testing data of 94.66%. This accuracy result is better than the UNet++, DeepLabV3+, and SegNet architectures. [ABSTRACT FROM AUTHOR]

In this study, several machine learning approaches are used for the prediction of the unconfined compressive strength (UCS) of polypropylene-stabilised soft soil. This research work generates new data and applies several machine learning algorithms for the analysis of UCS. Fifty-two samples are in our generated data. In our generated data, five input features are used: Column Reinforcement Type, Column Diameter, Area replacement ratio, Column Penetration Ratio and Max_Deviator Stress. On the other hand, the output consists of three target stress class. Our experimental result shows that Random Forest (RF) provides good prediction result of unconfined compressive test (UCT) and that is satisfied. RF model gets result of mean absolute error of 0.0625, mean square root error of 0.0625, root mean sqrt error of 0.2500, r² value of 0.8942 and accuracy of 0.9375. In addition, the sequential model got training loss of 0.2535, training accuracy of 0.9024, validation loss of 0.4056 and validation accuracy: 0.9091. The results showed that the suggested RF and sequential model performs excellently in predicting the UCS of stabilised soft soil with polypropylene. Our technique is more practical and time-consuming than arduous laboratory work. In the future, we will do the experiment with various soft soil characteristics to develop high-performing machine and deep learning models. [ABSTRACT FROM AUTHOR]

Person re-identification (ReID) seeks to identify the same individual across different cameras by matching their corresponding images. The current ReID datasets are limited in size and diversity, especially in terms of clothing changes, making traditional techniques vulnerable to appearance variations. Further, current approaches rely heavily on appearance features for discrimination, which is unreliable when a person's appearance changes. We hypothesize that the ReID accuracy can be enhanced by training the ReID model on a large volume of diversified training data and combining multiple features for discrimination. We use the image channel shuffling data augmentation method, producing a large volume of diversified training data. Also, a two-stream visual and spatial-temporal method is proposed to learn the feasible features for appearance change scenarios. The appearance features obtained from the visual stream are combined with spatiotemporal information to discriminate between two people. The proposed approach is evaluated for its robustness on short-term and on long-term datasets. The presented two-stream approach outperforms earlier methods by achieving Rank1 accuracy of 98.6% on Market1501, 95.52% on DukeMTMC-reID, 76.21% on LTCC, and 91.76% on VC-Clothes, respectively. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has caused more than 200 million infected cases and 4 million deaths across the world. The pandemic has triggered a massive epidemic, with a significant effect on the health and lives of many people worldwide. Early detection of this disease is very important for maintaining social well-being. Generally, the RT-PCR test is a diagnosis method used for the detection of the COVID-19, yet it is not the only reliable diagnostic tool. In this study, we discuss the image-based modalities for the detection of coronavirus utilizing Deep Learning methodology, which is one of the most innovative technologies today and has proven to be an efficient solution for a number of medical conditions. Coronavirus affects the respiratory tract of individuals. One of the best ways is to identify this disease from chest radiography images. Early research demonstrated unique anomalies in chest radiographs of covidpositive patients. By using Deep Learning Multi-layered networks, we classified the chest images as covid positive or negative. The proposed model uses the dataset of patients infected with Coronavirus, in which the radiologist indicated multilobar involvements in the chest X-rays. A total of 6500 images have been considered for the study. The convolutional network (CNN) model was trained and a validation accuracy of 94% is obtained. [ABSTRACT FROM AUTHOR]

EBSD observations were conducted on the damaged materials obtained by interrupted creep tests and interrupted creep-fatigue tests for 304 austenitic stainless steel for boiler tube use in fossil power plants, and the shapes of crystal grains extracted from KAM maps and GOS maps were approximated by ellipses. Furthermore, a damage evaluation system has been developed with a neural network, which uses the information obtained by elliptic approximation as parameters. As a result, it was quantitatively found that as creep and creep-fatigue damage progress, crystal grains become elongated toward the load axis direction. Ensemble learning showed the best classification accuracy using the 20 learners obtained by changing the rank of the relative frequency of KAM. The damage evaluation system in this study was able to estimate the damage rates with a classification accuracy of 98.33% for creep test materials and 97.50% for creep-fatigue test materials using information from one of crystal grains in the EBSD image. Therefore, the system with the neural network developed in this study is effective for evaluating creep and creep-fatigue damage for 304 austenitic stainless steel. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has emerged as a major frontier in healthcare and finds broad range of applications. It has the potential to revolutionize current procedures of disease diagnosis and treatment, thus influencing the clinical practice. Artificial intelligence (AI) in ophthalmology, primarily concentrates on diagnostic and treatment pathways for eye conditions such as cataract, glaucoma, age-related macular degeneration (MDA) and diabetic retinopathy (DR). The purpose of this article is to systematically review the existing state of literature on the various AI techniques and its applications in the diagnosis and treatment of eye diseases and conduct an in-depth enquiry to identify the challenges in accurate detection, pre-processing of data, monitoring and assessment through various AI algorithms. The results suggest that all AI models proposed reduce the detection time considerably. The potential limitations and challenges in the development and application play a significant role in clinical practice. There is a need for the development of AI-assisted technologies that shall consider the clinical implications based on experience and guided by patient-centred healthcare principles. The diagnostic models should assist ophthalmologists on making quick and accurate decisions in determining the progression of various ocular diseases. [ABSTRACT FROM AUTHOR]

The electrical power system comprises of several complex interrelated and dynamic elements, that are usually susceptible to electrical faults. Due to their critical impacts, faults on the electrical power system in the secondary distribution network should be immediately detected, classified, and urgently cleared. Several studies have endeavored to determine appropriate methods for electrical power systems faults detection and classifications using a mathematical approach, expert systems, and normal artificial neural network-integrated with Supervisory Control and Data Acquisition (SCADA) and Phasor Measurement Units (PMU) systems as the sensing element. However, limited studies have explored the application of deep learning approaches in fault detection and classifications. In this study, several deep learning approaches were compared including Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Feed Forward Neural Network (FFNN), and Artificial Neural Network (ANN) to determine the appropriate approach for implementation. The simulation results have shown that the RNN deep learning approach is efficient in detecting and classifying faults in the electrical secondary distribution network, whilst the accuracy increases as the complexity increases. The study takes advantage of the developments in sensors and the Internet of Things (IoT) technologies to capture and preprocess data along with the secondary distribution network. The research used the challenge-driven education approach where Tanzania Electric Supply Company Limited (TANESCO) was the case study and source of the training data. [ABSTRACT FROM AUTHOR]

Based on statistics from the World Nuclear Association, Kazakhstan has the highest uranium production in the world. Most of the uranium in the country is mined via in-situ leaching and the accurate classification of lithologic composition using electric logging data is economically crucial for this type of mining. In general, this classification is done manually, which is both inefficient and erroneous. Information technology tools, such as predictive analytics with Supervised Machine Learning (SML) algorithms and Artificial Neural Networks (ANN) models, are nowadays widely used to automate geophysical processes, but little is known about their application for uranium mines. Previous experiments showed an ANN accuracy of about 60% in the task of lithological interpretation of logging data. To determine the upper limit of the accuracy of machine learning algorithms in such task and for indirect assessment of the experts' influence, a digital borehole model was developed. This made it possible to generate a complete set of data avoiding subjective expert assessments. Using these data, the work of various ML algorithms, both simple (kNN) and deep learning models (LSTM), was evaluated. [ABSTRACT FROM AUTHOR]