Application Of Learning Techniques Research Articles

Current Trends on Deep Learning Techniques Applied in Iron and Steel Making Field: A Review

Current Trends on Deep Learning Techniques Applied in Iron and Steel Making Field: A Review

Comparative Analysis of Various Machine Learning Techniques Applied Towards Intrusion Detection in Computer Networks

Comparative Analysis of Various Machine Learning Techniques Applied Towards Intrusion Detection in Computer Networks

A Review on Machine/Deep Learning Techniques Applied to Building Energy Simulation, Optimization and Management

Given the climate change in recent decades and the ever-increasing energy consumption in the building sector, research is widely focused on the green revolution and ecological transition of buildings. In this regard, artificial intelligence can be a precious tool to simulate and optimize building energy performance, as shown by a plethora of recent studies. Accordingly, this paper provides a review of more than 70 articles from recent years, i.e., mostly from 2018 to 2023, about the applications of machine/deep learning (ML/DL) in forecasting the energy performance of buildings and their simulation/control/optimization. This review was conducted using the SCOPUS database with the keywords “buildings”, “energy”, “machine learning” and “deep learning” and by selecting recent papers addressing the following applications: energy design/retrofit optimization, prediction, control/management of heating/cooling systems and of renewable source systems, and/or fault detection. Notably, this paper discusses the main differences between ML and DL techniques, showing examples of their use in building energy simulation/control/optimization. The main aim is to group the most frequent ML/DL techniques used in the field of building energy performance, highlighting the potentiality and limitations of each one, both fundamental aspects for future studies. The ML approaches considered are decision trees/random forest, naive Bayes, support vector machines, the Kriging method and artificial neural networks. The DL techniques investigated are convolutional and recursive neural networks, long short-term memory and gated recurrent units. Firstly, various ML/DL techniques are explained and divided based on their methodology. Secondly, grouping by the aforementioned applications occurs. It emerges that ML is mostly used in energy efficiency issues while DL in the management of renewable source systems.

Machine Learning Techniques Applied to Development of Flexible Electronic Antireflective Film

Machine Learning Techniques Applied to Development of Flexible Electronic Antireflective Film

Machine Learning Techniques Applied to the Study of Drug Transporters.

With the advancement of computer technology, machine learning-based artificial intelligence technology has been increasingly integrated and applied in the fields of medicine, biology, and pharmacy, thereby facilitating their development. Transporters have important roles in influencing drug resistance, drug-drug interactions, and tissue-specific drug targeting. The investigation of drug transporter substrates and inhibitors is a crucial aspect of pharmaceutical development. However, long duration and high expenses pose significant challenges in the investigation of drug transporters. In this review, we discuss the present situation and challenges encountered in applying machine learning techniques to investigate drug transporters. The transporters involved include ABC transporters (P-gp, BCRP, MRPs, and BSEP) and SLC transporters (OAT, OATP, OCT, MATE1,2-K, and NET). The aim is to offer a point of reference for and assistance with the progression of drug transporter research, as well as the advancement of more efficient computer technology. Machine learning methods are valuable and attractive for helping with the study of drug transporter substrates and inhibitors, but continuous efforts are still needed to develop more accurate and reliable predictive models and to apply them in the screening process of drug development to improve efficiency and success rates.

Current Trends on Deep Learning Techniques Applied in Iron and Steel Making Field: A Review

Current Trends on Deep Learning Techniques Applied in Iron and Steel Making Field: A Review

EIDM: deep learning model for IoT intrusion detection systems

Internet of Things (IoT) is a disruptive technology for the future decades. Due to its pervasive growth, it is susceptible to cyber-attacks, and hence the significance of Intrusion Detection Systems (IDSs) for IoT is pertinent. The viability of machine learning has encouraged analysts to apply learning techniques to intelligently discover and recognize cyber attacks and unusual behavior among the IoTs. This paper proposes an enhanced anomaly-based Intrusion Detection Deep learning Multi-class classification model (EIDM) that can classify 15 traffic behaviors including 14 attack types with the accuracy of 95% contained in the CICIDS2017 dataset. Four state-of-the-art deep learning models are also customized to classify six classes of network traffic behavior. An extensive comparative study in terms of classification accuracy and efficiency metrics is conducted between EIDM and several state-of-the-art deep learning-based IDSs showing that EIDM has achieved accurate detection results.

Machine Learning Techniques Applied to the Harmonic Analysis of Railway Power Supply

Harmonic generation in power system networks presents significant issues that arise in power utilities. This paper describes a machine learning technique that was used to conduct a research study on the harmonic analysis of railway power stations. The research was an investigation of a time series whose values represented the total harmonic distortion (THD) for the electric current. This study was based on information collected at a railway power station. In an electrified substation, measurements of currents and voltages were made during a certain interval of time. From electric current values, the THD was calculated using a fast Fourier transform analysis (FFT) and the results were used to train an adaptive ANN—GMDH (artificial neural network–group method of data handling) algorithm. Following the training, a prediction model was created, the performance of which was investigated in this study. The model was based on the ANN—GMDH method and was developed for the prediction of the THD. The performance of this model was studied based on its parameters. The model’s performance was evaluated using the regression coefficient (R), root-mean-square error (RMSE), and mean absolute error (MAE). The model’s performance was very good, with an RMSE (root-mean-square error) value of less than 0.01 and a regression coefficient value higher than 0.99. Another conclusion from our research was that the model also performed very well in terms of the training time (calculation speed).

Upaya Meningkatkan Hasil Belajar Bahasa Indonesia Pada Materi Keterampilan Menulis Karangan Narasi Menggunakan Teknik Meneruskan Cerita Bagi Siswa Kelas XI SMK Negeri 2 Garut

The skills of writing narrative essays for Class XI students of SMK Negeri 2 Garut are still low. Therefore, it is necessary to use learning techniques that can increase student motivation and achievement, namely by telling stories. The purpose of this study was to describe the improvement in writing narrative essay skills and changes in the behavior of Class XI students at SMK Negeri 2 Garut in writing narrative essays using the storytelling method. This type of research is classroom action research (CAR). Classroom action research was carried out in four stages, namely: (1) planning, (2) action, (3) observation or observation, and (4) reflection. This study uses two variables, namely the skill of writing narrative essays and the technique of telling stories. Data collection was divided into two stages, namely cycle I and cycle II using test and non-test techniques with the application of learning techniques. The tests used were action tests in the form of assignments to write narrative essays, while the non-test techniques used were observations, journals, interviews, and photo documentation. This study uses qualitative and quantitative data analysis techniques. The results of this study indicate an increase in narrative essay writing skills with storytelling techniques for Class XI students of SMKN 2 Garut. The average value in the first cycle stage is 67.8. in cycle II to 79.43. The results of non-test data analysis showed an increase in the behavior of Class XI students at SMKN 2 Garut in a positive direction. Students became more enthusiastic about learning to write news texts.

A Robust Chronic Kidney Disease Classifier Using Machine Learning

Clinical support systems are affected by the issue of high variance in terms of chronic disorder prognosis. This uncertainty is one of the principal causes for the demise of large populations around the world suffering from some fatal diseases such as chronic kidney disease (CKD). Due to this reason, the diagnosis of this disease is of great concern for healthcare systems. In such a case, machine learning can be used as an effective tool to reduce the randomness in clinical decision making. Conventional methods for the detection of chronic kidney disease are not always accurate because of their high degree of dependency on several sets of biological attributes. Machine learning is the process of training a machine using a vast collection of historical data for the purpose of intelligent classification. This work aims at developing a machine-learning model that can use a publicly available data to forecast the occurrence of chronic kidney disease. A set of data preprocessing steps were performed on this dataset in order to construct a generic model. This set of steps includes the appropriate imputation of missing data points, along with the balancing of data using the SMOTE algorithm and the scaling of the features. A statistical technique, namely, the chi-squared test, is used for the extraction of the least-required set of adequate and highly correlated features to the output. For the model training, a stack of supervised-learning techniques is used for the development of a robust machine-learning model. Out of all the applied learning techniques, support vector machine (SVM) and random forest (RF) achieved the lowest false-negative rates and test accuracy, equal to 99.33% and 98.67%, respectively. However, SVM achieved better results than RF did when validated with 10-fold cross-validation.

Machine Learning Techniques Applied to the Development of a Fall Risk Index for Older Adults

Machine Learning Techniques Applied to the Development of a Fall Risk Index for Older Adults

Modeling Health Data Using Machine Learning Techniques Applied to Financial Management Predictions

Health management has steadily improved in performance and accuracy using IT technology. Hospitals and health institutions hold an enormous number of data in their software applications, which can be used with Big Data methodologies to extract useful information. One of the most challenging aspects of health institutional management is financial management; billing prediction is a key aspect to maintain a predictable service level for patients, avoiding unpleasant surprises and anticipating treasury management. Using patient data from public patient databases and applying a machine learning approach, this article offers a model that helps to make more precise and detailed financial plans.

A Systematic Literature Review of the Current Status and Future Prospects of Machine Learning Methods and Techniques Applied to Novel Drug Discovery

Drug development is the hardest phase for the pharmaceutical industry because it is extremely costly and time consuming. Though, due to the growing demand to produce safe and innovative medicines faster and more cost-effectively, the scientific community changed its objective into enhancing the lead identification and the lead optimization at the early discovery phase. This could be achieved using recent intelligent technologies that allow virtual screening as well as quantitative structure-activity relationship (QSAR) modeling to define the possible relationships between chemical compounds and biological activities. Among recent technologies, artificial intelligence (AI) has been introduced as a powerful solution to address problems related to drug discovery and development. In particular, machine learning (ML) has been meaningfully instrumental in the production of new drug candidates. In this work, we review the fundamental principles of machine learning algorithms, study and discuss their application and current issues in drug development.

Machine Learning Techniques Applied to Identify the Two-Phase Flow Pattern in Porous Media Based on Signal Analysis

The development of flow pattern identification technology for gas–liquid two-phase flow in porous media is of great significance to engineering research and production. In this paper, a high accuracy identification method for two-phase flow pattern in porous media is proposed with Machine learning techniques. The gas–liquid two-phase flow patterns and corresponding differential pressure signals in porous beds with particle diameters of 1.5 mm, 3 mm, and 6 mm are obtained through visual experiments. Three time domain characteristic parameters (Mean, Standard deviation, and Range) are calculated by a statistical method, while the EMD energy spectrum of the signal is obtained by empirical mode decomposition. Based on these parameters, machine learning technology, including support vector machine (SVM) and BP neural network, are employed to identify the flow pattern. Four flow pattern identification models are trained based on SVM and BP neural network, with accuracies of 94.77%, 93.4%, 96.08%, and 91.5%. Furthermore, the three models with good performance are integrated by integrated learning technology. An integrated identification model of gas–liquid two-phase flow pattern in porous media with an overall accuracy of 98.04% is finally obtained.

A Systematic Mapping Study on Machine Learning Techniques Applied for Condition Monitoring and Predictive Maintenance in the Manufacturing Sector

Background: Today’s production facilities must be efficient in both manufacturing and maintenance. Efficiency enables the company to maintain the required output while reducing production effort or costs. With the increasing interest in process automation and the Internet of things since Industry 4.0 was introduced, such shop floors are growing in complexity. Every component of the production needs to be continuously monitored, which is the basis for predictive maintenance (PdM). To predict when maintenance is needed, the components’ conditions are monitored with the help of a condition monitoring (CM) system. However, this task is difficult for human employees, as the monitoring and analysis is very demanding. To overcome this, machine learning (ML) can be applied to ensure more efficient production. Methods: This paper aims to investigate the application of ML techniques for CM and PdM in the manufacturing sector. For this reason, a systematic mapping study (SMS) is conducted in order to structure and classify the current state of research and identify potential gaps for future investigation. Relevant literature was considered between January 2011 and May 2021. Results: Based on the guidelines for SMSs and previously defined research questions, existing publications are examined and a systematic overview of the current state of the research domain is provided. Conclusions: Techniques such as reinforcement learning and transfer learning are underrepresented, but increasingly attracting more attention. The findings of this study suggest that the most promising results belong to the applications of hybrid ML methods, where a set of methods are combined to build a more powerful model.

Machine Learning Techniques Applied for Exploring Heart Disease and Classifying Stages through ECG Signal

Electrocardiogram (ECG) has given crucial data concerning various heart attack criterion of the human heart this examination can be the key goal of the research for the detection and prevention of cardiac circumstances frighten. The proposed method using machine learning techniques for classifying and analyzing ECG signal processing and this research mainly developed for early discovery of heart diseases and also the prediction of stages level. The dataset was utilized as a person ECG signal of Heart Database which was taken from the UCI repository of Machine learning dataset vault. In this paper, a simple algorithm is presented for to discover R-peaks automatically from a single lead digital ECG data. The proposed method detecting the time interval of the ECG signal from the R-peaks level next level with the double squared difference signal is used to localize the region of QRS which is the time interval between the binary data. this method consists of different stages of sorting from the raw data for reducing nosier signal, threshold a difference signal of ECG by analyzing the time interval of QRS, and finally a comparison of relative magnitude to detect the region of interval processing to analyze accuracy result. The proposed research novel machine learning techniques of the multi-module neural network system (MMNNS) is used to analyze the imbalance problem form the ECG signal classification if the wave was abnormal then the user of dataset patients will be affected by heart diseases. Using the time interval varies from the range of QRS then it analyzes the abnormal of ECG by MMNNS algorithm to define the classification result and finally analyze the stages. If the patients were presented an abnormal ECG signal compared with a normal ECG signal wave graph then they were affected by heart diseases if whether they patients were affected then finally classify the stages of heart diseases whether they are predictable/unpredictable stages. The examination result is completed among two strategies on the better accuracy premise and effective training time of the process.

A Simple and Sustainable Exercise to Enhance Student Self-Reflection on Error-Making, Focus Support, and Guide Curricular Design

Problem: Self-reflection is a critical component of professional development and clinical practice, but medical students’ ability to self-reflect is typically limited. While inadequate self-reflection impacts future clinical decision-making, it may also adversely impact current learning through an inability to identify learning-behavior deficits. This may be exacerbated by common use of multiple-choice questions (MCQ) where incorrect responses provide less insight than other measures for students, faculty, or academic support. To address this, an Error Reflection Method (ERM) was developed to help students focus on ‘why’ they got an MCQ wrong rather than ‘what’ they got wrong, thereby promoting self-reflection and a learning-focus on assessment. Understanding students’ learning-behavior deficits could also enrich engagement with academic support services and guide curricular design. Intervention: The ERM is a list of 10 common types of exam errors that were either ‘test-taking’ (unwitting) errors or ‘learning-behavior’ errors that reflected learning deficits. The ERM is simple, transferable, and sustainable, allowing longitudinal and regular monitoring of individual and collective error-making to focus support and guide curricular development. Context: Undergraduate medical students at the Virginia Tech Carilion School of Medicine, USA, used the ERM in formative assessment review sessions in pre-clinical years to select an error type that best described the cause of each incorrect response. Impact: Initial findings suggest the ERM is robust and associated with improved student performance and curricular development. Analysis of 3,775 student-identified errors showed the error types in the ERM described 96% of errors students made. Learning-behavior errors were more common (76%), but surprisingly, 19% were test-taking errors, allowing academic support to focus on test-taking skills in a population previously thought of as consummate test-takers. The most common error type reported was ‘the content looked familiar but I couldn’t answer the question’ (32%); which we suggest is consistent with shallow learning. This finding has helped steer recent curricular development toward active and applied learning techniques. Lessons Learned: By formally and regularly identifying learning deficits, students may be more capable of addressing them and improve summative exam performance. As well as focusing academic support, understanding common student errors has been useful in guiding curricular design and content delivery. Further potential of the ERM may be realized in faculty development and directing assessment culture toward a learning focus.

Artificial Neural Networks and Deep Learning Techniques Applied to Radar Target Detection: A Review

Radar target detection (RTD) is a fundamental but important process of the radar system, which is designed to differentiate and measure targets from a complex background. Deep learning methods have gained great attention currently and have turned out to be feasible solutions in radar signal processing. Compared with the conventional RTD methods, deep learning-based methods can extract features automatically and yield more accurate results. Applying deep learning to RTD is considered as a novel concept. In this paper, we review the applications of deep learning in the field of RTD and summarize the possible limitations. This work is timely due to the increasing number of research works published in recent years. We hope that this survey will provide guidelines for future studies and applications of deep learning in RTD and related areas of radar signal processing.

Nowcasting Extreme Weather with Machine Learning Techniques Applied to Different Input Datasets

Nowcasting Extreme Weather with Machine Learning Techniques Applied to Different Input Datasets

Detecting Pine Trees Damaged by Wilt Disease Using Deep Learning Techniques Applied to Multi-Spectral Images

Pine wilt disease (PWD) is responsible for significant damage to East Asia’s pine forests, including those in Korea, Japan, and China. Preventing the spread of wilt disease requires early detection and removal of damaged trees. This paper proposes a method of detecting disease-damaged pines using ortho-images corrected from 5-band multi-spectral images captured by unmanned aviation vehicles. The proposed method relies on a ResNet18 backbone network connected to a modified DenseNet module, classifies the 5-band multispectral (RGB, NIR, Red_Edge) ortho-image patches, and visualizes the results as a heat map. The patch-based classifier was retrained with hard negative examples, after which it achieved 98.66% accuracy, an improvement over the 96.0% accuracy associated with the same method applied to RGB images. The resulting heat map reflects the approximate distribution, and movement of the disease. Disease locations are also predicted by local maximums in the heat map. When the distance between a ground truth and the predicted location is less than visible distance, e.g. about 5m, it is counted as a correct detection. The proposed detection which consists of heat map generation followed by localization achieves Recall of 93.39%, Precision of 88.26%, and F1-score of 90.75%.