Powerful Machine Research Articles

Penerapan Machine Learning Algoritma Regresi Linear Untuk Memprediksi Saham Bank BNI

Indonesia has been growing rapidly, one of which can be seen from the economy and technology in Indonesia, at this time the community is almost entirely using machine power technology as a helper of daily life, and the community has also processed a lot of its finances by way of stock investment, with stock investment, the community believes that stocks are invested safer and more profitable. Shares are securities that show proof of ownership or capital market participation of investors in a company (BNI) and shares have a value that is up and down (volatile). Stocks are very important in a company and stocks are a trigger for rising profits in the company. The rise and fall of stock prices in Indonesia has an adverse effect on companies, especially PT Bank Negara Indonesia (Persero), Tbk. the cause of the rise and fall in stock prices is usually caused by several things, namely the condition and performance of the company, risk, dividends, interest rates, economic conditions, government policies, government issues or other issues, the rate of inflation, supply and demand. Machine learning tools used in predicting stocks, using machine learning, the data obtained is more accurate. Machine learning is an artificial intelligence that can process data that is useful for consideration in making decisions and solving problems. Linear regression algorithm is one of the methods used to predict stock data in Bank Negara Indonesia. Linear regression algorithm tries to model the relationship between two variables by matching the linear equation of the stock data to be studied. One variable is considered the explanatory variable and the other variable is called the dependent variable. Prediction a process for systematically estimating BNI stock data that will appear in the future using data obtained from the past. Thus the company can easily find out the stock data in the future.

Memprediksi Data Saham Bank Mandiri Menggunakan Metode Algoritma Regresi Linear Dengan Bantuan Rapid Miner

Indonesia has been growing rapidly, one of which can be seen from the economy and technology in Indonesia, at this time the community is almost entirely using machine power technology as a helper of daily life, and the community has also processed a lot of its finances by way of stock investment, with stock investment, the community believes that stocks are invested safer and more profitable. A stock can be defined as a mark of participation or ownership of an individual investor or institutional investor or trader on their investment or a certain amount of funds invested in a company. Linear regression algorithm is one of the methods used to predict stock data in Bank Mandiri. Linear regression algorithm tries to model the relationship between two variables by matching the linear equation of the stock data to be studied. One variable is considered the explanatory variable and the other variable is called the dependent variable. Prediction a process for systematically estimating Bank Mandiri stock data that will appear in the future using data obtained from the past. Thus the company can easily find out the stock data in the future.

Rethreading the needle: A novel molecular index of soil health (MISH) using microbial functional genes to predict soil health management

Soil health relies on the actions and interactions of an abundant and diverse biological community. Current soil health assessments rely heavily on a suite of soil biological, chemical, and physical indicators, often excluding molecular information. Soil health is critical for sustainable agricultural production, and a comprehensive understanding of how microbial communities provide ecosystem services can help guide management practices. To explore the role of microbial function in soil health, 536 soil samples were collected from 26 U.S. states, representing 52 different crops and grazing lands, and analyzed for various soil health indicators. The bacterial functional profile was characterized using 16S ribosomal RNA gene sequencing paired with PICRUSt2 to predict metagenome functions. Functional data were used as predictors in eXtreme Gradient Boosting (XGBoost), a powerful machine learning algorithm, and enzymes important to soil health indicators were compiled into a Molecular Index of Soil Health (MISH). The overall MISH score significantly correlated with non-molecular measures of soil health and management practice adoption. Additionally, several new enzymes were identified as potential targets to better understand microbial mediation of soil health. This low-cost, DNA-based approach to measuring soil health is robust and generalizable across climates.

Leveraging machine learning for sustainable integration of renewable energy generation

Long-term economic benefits and sustainability are provided by the integration of renewable energy sources (RESs) into electrical networks. However, because of their intermittent nature and reliance on environmental factors, RESs pose issues in production and consumption balance. Because renewable energy sources like wind and solar are unpredictable, forecasting their output is essential for planning purposes and maintaining grid stability. This thesis focuses on developing effective instruments and algorithms to improve renewable energy generation estimates and handle abnormalities in consumption. These tools and algorithms include maximum power point tracking and machine learning models like random forest (RF), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost). The methods' effectiveness is confirmed by accuracies higher than 80%, which provides speedier and more user-friendly solutions in comparison to the traditional ways. In the end, our effort seeks to offer practical instruments for anticipatory modelling and mitigating intermittentness in renewable energy sources, enabling their assimilation into current power structures to adequately supply energy requirements in a sustainable manner.

Prediction of Two Year Survival Following Elective Repair of Abdominal Aortic Aneurysms at A Single Centre Using A Random Forest Classification Algorithm

The decision to electively repair an abdominal aortic aneurysm (AAA) involves balancing the risk of rupture, periprocedural mortality, and life expectancy. Random forest classifiers (RFCs) are powerful machine learning algorithms. The aim of this study was to construct and validate a random forest machine learning tool to predict two year survival following elective AAA repair. All patients who underwent elective open or endovascular repair of AAA from 1 January 2008 to 31 March 2021 were reviewed. They were assessed using the Vascular Surgery Quality Improvement Program pathway involving cardiopulmonary exercise testing, contrast enhanced computerised tomography scan, and multidisciplinary assessment. Patients were followed up for at least two years. A RFC was developed using 70% of the dataset and validated using 30% to predict survival for at least two years following AAA repair. A total of 925 patients (n = 836 men; n = 89 women) underwent elective repair of AAA; 126 (13.6%) died during the first two years; 11 (1.2%) died from periprocedural mortality. Variable importance analysis suggested that anaerobic threshold, pre-operative haemoglobin, maximal O2 consumption, body mass index, risk category, and forced expiratory volume in 1 second - forced vital capacity ratio were the most important contributors to the model. Sensitivity and specificity of the RFC for prediction of two year survival following surgery was 96.7% (95% CI 94.4 - 99%) and 67.1% (95% CI 61 - 72%); overall accuracy: 92.6% (95% CI 88 - 95%) (positive predictive value: 0.93, negative predictive value: 0.80); 10-fold cross validation revealed area under the receiver operator characteristic curve of 0.88. RFCs based on readily available clinical data can successfully predict survival in the first two years following elective repair of AAA. Such information can contribute to the risk benefit assessment when deciding to electively repair AAAs.

Biomarker identification and gene-drug interaction prediction for breast cancer using machine learning algorithms

Abstract Breast cancer (BC) poses a significant worldwide health challenge, necessitating the identification of its molecular origins and the development of possible treatment approaches. In this investigation, we introduce a pioneering methodology integrating genomic data and machine learning algorithms to identify genes associated with BC and their corresponding drugs. Initially, RNA-sequencing data of normal and malignant BC tissues publicly available in the NCBI GEO database were pre-processed using a standard pipeline. Further, machine learning algorithms, such as logistic regression, support vector machine, and random forest, were used to identify the differentially expressed genes (DEGs). The results were validated based on accuracy, sensitivity, specificity, precision, and F-score. Moreover, we identified the drugs corresponding to DEGs using the DepMap database. Our results revealed that genes such as OC90, KLK9, CXCL10, CDRT1, LCN6, GOLGA7B, and ZNF223 were commonly identified by all three machine learning algorithms. We found that the drugs DIHYDROROTENONE, 4-IODO-6-PHENYLPYRIMIDINE, BMS-754807, ARRY- 886, ESTRAMUSTINE-PHOSPHATE, FR-122047, and PIK 93 produced high correlation values on gene-drug interaction. The present study emphasizes the significance of utilizing genetic data and powerful machine learning algorithms to decipher the intricacies of BC biology and expedite the creation of tailored therapeutic approaches.

Unveiling the Secrets of the Mammogram: A Statistical Journey through Machine Learning Approach

Mammograms, once mere X-ray images, are now powerful allies in the fight against breast cancer. Armed with sophisticated statistical image classification techniques, these images reveal hidden clues that can lead to early detection. Beneath the surface of a mammogram lies a complex tapestry of patterns, each one a potential marker of malignancy. By decoding these intricate patterns, algorithms can identify abnormalities that might otherwise go unnoticed. The Mammographic Image Analysis Society (MIAS) has been at the forefront of this revolution, creating a vast database of digital mammograms. From this treasure trove, we've selected 322 images to put our statistical prowess to the test. Using a powerful machine learning tool known as Support Vector Machine (SVM), we've classified these images into seven categories: • Calcifications (CALC): Tiny, hard deposits. • Circumscribed (CIRC) masses: Well-defined lumps. • Speculated (SPIC) masses: Lumps with spiky edges. • Ill-defined (MISC) masses: Irregular, indistinct lumps. • Architectural distortions (ARCH): Changes in the breast's structure. • Asymmetry(s) (ASYM): Differences between the two breasts. • Normal (NORM): Healthy breast tissue. Our goal is to shed light on the intricate process of mammogram classification and share the insights gained from our analysis presented in the user-friendly language and the analysis is carried out using MATLAB.

TorchSISSO: A PyTorch-based implementation of the sure independence screening and sparsifying operator for efficient and interpretable model discovery

Symbolic regression (SR) is a powerful machine learning approach that searches for both the structure and parameters of algebraic models, offering interpretable and compact representations of complex data. Unlike traditional regression methods, SR explores progressively complex feature spaces, which can uncover simple models that generalize well, even from small datasets. Among SR algorithms, the Sure Independence Screening and Sparsifying Operator (SISSO) has proven particularly effective in the natural sciences, helping to rediscover fundamental physical laws as well as discover new interpretable equations for materials property modeling. However, its widespread adoption has been limited by performance inefficiencies and the challenges posed by its FORTRAN-based implementation, especially in modern computing environments. In this work, we introduce TorchSISSO, a native Python implementation built in the PyTorch framework. TorchSISSO leverages GPU acceleration, easy integration, and extensibility, offering a significant speed-up and improved accuracy over the original. We demonstrate that TorchSISSO matches or exceeds the performance of the original SISSO across a range of tasks, while dramatically reducing computational time and improving accessibility for broader scientific applications.

Automated Forensic Examination of Virtual Assets Using XGBoost

The issues of identification of virtual assets and digital currencies have become more acute in recent years because of increased numbers of transactions, their increased volumes, and complexity in forensic examination. This paper is devoted to a new method of the automated forensic investigation of virtual assets with the help of a powerful machine learning algorithm called XGBoost. XGBoost has very high speed and it is quite useful when dealing with large amounts of data and used for finding patterns that may suggest unlawful operations. The given framework is based on XGBoost by identifying the transactions using their features activities, amount, frequencies, volumes and addresses. Through training the model that is given the historical data, the model is, therefore, able to separate normal and suspicious transactions and alert the decision-making process for further review. Network analysis integration takes the system capability to a new level by enabling the identification of intricate transaction characteristics and the interconnectedness of addressing. Aside from adding automation to the forensic examinations to make it more efficient and accurate, such a procedure assists in identifying the developments of virtual asset transactions. The results show the effectiveness of the XGBoost in supporting the advance of forensic investigators helping them to meet the growing roles that cybercriminals portray in today’s digital asset markets.

Permutation-equivariant quantum convolutional neural networks

Abstract The Symmetric group S n manifests itself in large classes of quantum systems as the invariance of certain characteristics of a quantum state with respect to permuting the qubits. Subgroups of S n arise, among many other contexts, to describe label symmetry of classical images with respect to spatial transformations, such as reflection or rotation. Equipped with the formalism of geometric quantum machine learning, in this study we propose the architectures of equivariant quantum convolutional neural networks (EQCNNs) adherent to S n and its subgroups. We demonstrate that a careful choice of pixel-to-qubit embedding order can facilitate easy construction of EQCNNs for small subgroups of S n . Our novel EQCNN architecture corresponding to the full permutation group S n is built by applying all possible QCNNs with equal probability, which can also be conceptualized as a dropout strategy in quantum neural networks. For subgroups of S n , our numerical results using MNIST datasets show better classification accuracy than non-equivariant QCNNs. The S n -equivariant QCNN architecture shows significantly improved training and test performance than non-equivariant QCNN for classification of connected and non-connected graphs. When trained with sufficiently large number of data, the S n -equivariant QCNN shows better average performance compared to S n -equivariant QNN . These results contribute towards building powerful quantum machine learning architectures in permutation-symmetric systems.

Random forest models reveal academic and financial factors outweigh demographics in predicting completion of a year-round veterinary program.

The purpose of this study was to develop random forest classifier models (a type of supervised machine learning algorithm) that could (1) predict students who will or will not complete the DVM degree requirements and (2) identify the top predictors for academic success and completion of the DVM degree. The study utilized Ross University School of Veterinary Medicine student records from 2013 to 2022. Twenty-four variables encompassing demographic (eg, age, race), academic (eg, grade point average), and financial aid (eg, outstanding balances) data were assessed in 11 cross-validated random forest machine learning models. One model was built assessing all years of data and 10 individual models were developed for each enrollment year to compare how the top predictors of success varied among the years. Consistently, only academic and financial factors were identified as being features of importance (predictors) in all models. Demographic factors such as race were not important for predicting student success. All models performed very well to excellently based on multiple performance metrics including accuracy, ranging from 96.1% to 99%, and the areas under the receiver operating characteristic curves, ranging from 98.1% to 99.9%. The random forest algorithm is a powerful machine learning prediction model that performs well with veterinary student academic records and is customizable such that variables important to each veterinary school's student population can be assessed. Identifying predictors of success as well as at-risk students is essential for providing targeted curricular interventions to increase retention and achieve timely completion of a DVM degree.

Revolutionizing Cardio Vascular Disease Diagnosis Through Lasso Regression Model

Cardiovascular diseases (CVDs) are among the most prevalent and fatal health conditions globally, necessitating early and accurate diagnosis to mitigate risk and enhance treatment outcomes. Traditional diagnostic methods often rely on a combination of clinical assessments and static risk models, which can be constrained by their inability to handle the complexity and interrelationships of multiple risk factors. In this research, we propose a novel approach to revolutionizing cardiovascular disease diagnosis through the application of the Lasso (Least Absolute Shrinkage and Selection Operator) regression model, a powerful machine learning technique for feature selection and predictive modeling. The Lasso regression model is particularly advantageous for high-dimensional medical datasets, where multiple clinical features may be correlated. By applying Lasso, we aim to address two critical challenges in cardiovascular disease prediction: identifying the most relevant predictors from large and complex datasets and reducing model over-fitting, which is common when working with a large number of co-variates. Our results demonstrated that the Lasso model significantly improves prediction accuracy when compared to traditional logistic regression and other machine learning models. Lasso regression has the potential to revolutionize cardiovascular disease diagnosis by providing a data-driven, efficient, and interpretable solution that bridges complex medical datasets with practical clinical decision-making. Keywords: Cardio vascular diseases (CVD), Machine learning models, Losso regression model.

Machine learning unveils key Redox signatures for enhanced breast Cancer therapy

BackgroundBreast cancer remains a leading cause of mortality among women worldwide, necessitating innovative prognostic models to enhance treatment strategies.MethodsOur study retrospectively enrolled 9,439 breast cancer patients from 12 independent datasets and single-cell data from 12 patients (64,308 cells). Moverover, 30 in-house clinical cohort were collected for validation. We employed a comprehensive approach by combining ten distinct machine learning algorithms across 108 different combinations to scrutinize 88 pre-existing signatures of breast cancer. To affirm the efficacy of our developed model, immunohistochemistry assays were performed. Additionally, we investigated various potential immunotherapeutic and chemotherapeutic interventions.ResultsThis study introduces an Artificial Intelligence-aided Redox Signature (AIARS) as a novel prognostic tool, leveraging machine learning to identify critical redox-related gene signatures in breast cancer. Our results demonstrate that AIARS significantly outperforms existing prognostic models in predicting breast cancer outcomes, offering a robust tool for personalized treatment planning. Validation through immunohistochemistry assays on samples from 30 patients corroborated our results, underscoring the model’s applicability on a wider scale. Furthermore, the analysis revealed that patients with low AIARS expression levels are more responsive to immunotherapy. Conversely, those exhibiting high AIARS were found to be more susceptible to certain chemotherapeutic agents, including vincristine.ConclusionsOur study underscores the importance of redox biology in breast cancer prognosis and introduces a powerful machine learning-based tool, the AIARS, for personalized treatment strategies. By providing a more nuanced understanding of the redox landscape in breast cancer, the AIARS paves the way for the development of redox-targeted therapies, promising to enhance patient outcomes significantly. Future work will focus on clinical validation and exploring the mechanistic roles of identified genes in cancer biology.

Analysis of the Principle and Applications of Four-stroke Engines in Transportation

Abstract. Four-stroke engines a type of internal combustion engine (IC engine) that can turn the heat provided from the fuel to work, and they are mainly classified as two groups, which are gasoline engines and diesel engines. Four-stroke engines can be applied to high power machines because of its high thermal efficiency. In four-stroke engines, the piston reaches the dead center four times in the cylinder to complete a cycle. This cycle includes the taking in of gas, the compression, the ignition, the expansion and the exhaustion of gas. In gasoline engine, this cycle is called the Otto cycle, and in diesel engine, this cycle is called the Diesel cycle. The greatest difference between these two engines is that they burn different types of fuel. In that way, the procedure of converting the heat to work is different. To help this cycle performs better economically and more efficiently, additional attachments such as hybrid engine and computer assistance are used. This research summarizes and analyses the application of four-stroke engines in land transportation, in aviation and in marine transportation, and this article gives a possible prediction to the future trend of the technology advancements of engines in transportation based on the analyses.

Exploring Diversity and Abundance of Stingless Bee using Clustering Approach

Stingless bees are paramount in food chain as they are important pollinators of field crops. Recent studies revealed that these bees are seriously threatened by climate change and rapid urbanization across the world. It is thus important to study the relationship between the stingless bee’s diversity and the characteristics of the locations they inhibit. At the same time, clustering algorithms is a powerful machine learning approach in exploring unsupervised data. Consequently, this study aims to explore the stingless bee diversity in Malaysia through hierarchical, k-means and DBSCAN clustering. The dataset of this study consists of individual stingless bees collected from 12 locations. It comprises 14 environmental features, 3 physical characteristics, 35 species count, 12 genera counts and 3 diversity-and-abundance weights. A four-stage methodology is employed in the study. The results show that DBSCAN effectively groups data into clusters that are well-defined, but the results are less informative. In contrast, hierarchical and k-means clustering are found producing results that provide clearer insights, with hierarchical clustering delivering notably richer results.

AI-driven canine cataract detection: a machine learning approach using support vector machine

ABSTRACT The pivotal role of the Support Vector Machine becomes evident in addressing the impact of aging and genetics on canine cataract development. Timely detection, crucial for preventing illness in dogs, is facilitated by Artificial Intelligence. This proves especially beneficial for veterinarians grappling with detection challenges. Within the realm of Artificial Intelligence, the subset of Machine Learning emerges as a promising avenue for tackling intricate tasks. The current study introduces a model for grading canine cataracts through images captured on standard mobile phone cameras. Notably, the proposed system centers around harnessing the power of the Support Vector Machine, a potent Machine Learning algorithm tailored for classifying (grading) cataracts based on images. Rigorous evaluation, employing K-fold Cross-validation, validates the images’ reliability. Both binary-class and multi-class experimentation contribute to the dataset. Impressively, the accuracy rates for the trained classification model stand at 83% (without Cross-validation) and 81% (with Cross-validation for K = 10), underscoring the robustness of the approach.

Controller design and optimal sizing of battery energy storage system for frequency regulation in a multi machine power system

Frequency regulation is one of the key components needed to keep the power grid stable and reliable in the case of an imbalance between generation and load. This study looks at several control techniques for Battery Energy Storage Systems (BESSs) to keep the frequency stable in the power system during generation/load disruptions. This research aims to build several BESS controllers, including the proportional-integral (PI), proportional integral derivative (PID), and Tilt-Integral Derivative (TID) controllers. Also, the BESS controller parameters are optimized and compared by using metaheuristics based particle swarm optimization (PSO) and the BAT algorithm. However, for practical power systems with high MVA ratings, the size of the battery energy storage systems has to be increased considerably to offset frequency deviations. Additionally, by utilizing PSO to reduce the frequency deviations within prescribed limits, this work presents the optimal BESS sizing for multimachine power systems. Furthermore, the research study examines the impact of BESS connected to various voltage levels, such as LV, MV, and HV subgrids. It also examines BESS's role in frequency management, which is an area of expertise that needs further investigation and is anticipated to have a big influence on the frequency stability of the system. Time domain simulations are carried out, which shows that the PSO based controller design is capable of stabilizing the system frequency with superior performance as well as the BESS's capability to participate effectively in frequency regulation. Additionally, this paper also implements the experimental verification through Hardware-in-the-Loop (HIL) simulations to validate the performance and robustness of the proposed control system. The WSCC 9-bus test system is used to demonstrate the BESS's efficacy. The system is modelled and simulated using the DIgSILENT PowerFactory software.

Clinical information extraction for lower-resource languages and domains with few-shot learning using pretrained language models and prompting

Abstract A vast amount of clinical data are still stored in unstructured text. Automatic extraction of medical information from these data poses several challenges: high costs of clinical expertise, restricted computational resources, strict privacy regulations, and limited interpretability of model predictions. Recent domain adaptation and prompting methods using lightweight masked language models showed promising results with minimal training data and allow for application of well-established interpretability methods. We are first to present a systematic evaluation of advanced domain-adaptation and prompting methods in a lower-resource medical domain task, performing multi-class section classification on German doctor’s letters. We evaluate a variety of models, model sizes (further-pre)training and task settings, and conduct extensive class-wise evaluations supported by Shapley values to validate the quality of small-scale training data and to ensure interpretability of model predictions. We show that in few-shot learning scenarios, a lightweight, domain-adapted pretrained language model, prompted with just 20 shots per section class, outperforms a traditional classification model, by increasing accuracy from $48.6\%$ to $79.1\%$ . By using Shapley values for model selection and training data optimization, we could further increase accuracy up to $84.3\%$ . Our analyses reveal that pretraining of masked language models on general-language data is important to support successful domain-transfer to medical language, so that further-pretraining of general-language models on domain-specific documents can outperform models pretrained on domain-specific data only. Our evaluations show that applying prompting based on general-language pretrained masked language models combined with further-pretraining on medical-domain data achieves significant improvements in accuracy beyond traditional models with minimal training data. Further performance improvements and interpretability of results can be achieved, using interpretability methods such as Shapley values. Our findings highlight the feasibility of deploying powerful machine learning methods in clinical settings and can serve as a process-oriented guideline for lower-resource languages and domains such as clinical information extraction projects.

Sentiment classification for insider threat identification using metaheuristic optimized machine learning classifiers

This study examines the formidable and complex challenge of insider threats to organizational security, addressing risks such as ransomware incidents, data breaches, and extortion attempts. The research involves six experiments utilizing email, HTTP, and file content data. To combat insider threats, emerging Natural Language Processing techniques are employed in conjunction with powerful Machine Learning classifiers, specifically XGBoost and AdaBoost. The focus is on recognizing the sentiment and context of malicious actions, which are considered less prone to change compared to commonly tracked metrics like location and time of access. To enhance detection, a term frequency-inverse document frequency-based approach is introduced, providing a more robust, adaptable, and maintainable method. Moreover, the study acknowledges the significant impact of hyperparameter selection on classifier performance and employs various contemporary optimizers, including a modified version of the red fox optimization algorithm. The proposed approach undergoes testing in three simulated scenarios using a public dataset, showcasing commendable outcomes.

Enhancing photovoltaic cell design with multilayer sequential neural networks: A study on neodymium-doped ZnO nanoparticles

Multilayer sequential neural networks, a powerful machine learning model, demonstrate the ability to learn intricate relationships between input features and desired outputs. This study focuses on employing such models to design photovoltaic cells. Specifically, neodymium (Nd)-doped ZnO nanoparticles (NPs) were utilized as a photoanode for fabricating dye-sensitized solar cells (DSSCs). A natural dye extracted from Spinacia oleracea was employed, while two types of electrolytes, liquid and gel (polyethylene glycol-based), were used for comparative analysis. Extensive material characterization of the photoanode highlights the impact of Nd content on the physicochemical properties of ZnO. Notably, when the doped photoanode and gel electrolyte were combined, a substantial 110% improvement in power conversion efficiency (PCE) was achieved. Building on these findings, the machine learning model in this research accurately predicts the current-voltage (I-V) curve values for such photoanodes, with an impressive accuracy of 98%. Additionally, the model illuminates the significance of variables like crystal distortion, texture coefficient, and doping concentration, underscoring their importance in the context of photovoltaic cell design.