Machine Learning Techniques For Prediction Research Articles

Brain-implantable needle-type CMOS imaging device enables multi-layer dissection of seizure calcium dynamics in the hippocampus

Objective: Current neuronal imaging methods mostly use bulky lenses that either impede animal behavior or prohibit multi-depth imaging. To overcome these limitations, we developed a lightweight lensless biophotonic system for neuronal imaging, enabling compact and simultaneous visualization of multiple brain layers. Approach: Our developed ‘CIS-NAIST’ device integrates a micro-CMOS image sensor, thin-film fluorescence filter, micro-LEDs, and a needle-shaped flexible printed circuit. With this device, we monitored neuronal calcium dynamics during seizures across the different layers of the hippocampus and employed machine learning techniques for seizure classification and prediction. Main results: The CIS-NAIST device revealed distinct calcium activity patterns across the CA1, molecular interlayer, and dentate gyrus. Our findings indicated an elevated calcium amplitude activity specifically in the dentate gyrus compared to other layers. Then, leveraging the multi-layer data obtained from the device, we successfully classified seizure calcium activity and predicted seizure behavior using Long Short-Term Memory and Hidden Markov models. Significance: Taken together, our ‘CIS-NAIST’ device offers an effective and minimally invasive method of seizure monitoring that can help elucidate the mechanisms of temporal lobe epilepsy.

Predictive mapping of surface roughness in turning of hardened AISI 4340 using carbide tools

This study presents a novel approach to predict surface roughness in the hard turning of AISI 4340 steel using carbide tools, aimed to develop a comprehensive predictive map. The hypothesis that surface roughness can be accurately predicted using a linear regression model was tested and confirmed. Experimental results showed surface roughness in the range of 1.946 to 5.636 microns. Statistical analysis revealed a normal distribution of surface roughness data with linear regression as the best-fit model, significantly determined by feed rate and explaining 98.41% of the variance. Machine learning validated this model, achieving high prediction accuracy (R² = 96.91%, MSE = 0.058, RMSE = 0.242). The innovative predictive map, created using a full factorial design, demonstrated a strong agreement between predicted and validated values. This work highlights the potential of integrating statistical and machine learning techniques for precise surface roughness prediction, recommending industrial validation to enhance machining productivity.

Forecasting Customer Actions: Exploring Machine Learning Techniques for Behavior Analysis and Prediction

Modern modelling of today’s consumer behaviour is highly dependent on data mining which mines customers’ data in order to answer specific questions in time. This complexity and uncertainty relating to the consumer be-haviour makes it difficult for us to predict. Therefore, selection of appropri-ate methods is very important. Predictive models can be designed so as to identify specific group or individual actions and thus making them useful marketing tools. However, many models are overly simplistic for they leave out some of the essential factors hence leading to inaccurate forecasting out-comes. Consequently, instead of concentrating on customer behaviour-firm capital structure relationships only a strong association rule mining model should be created using online store data that effectively estimates customer response. Knowing their business clients is crucial when it comes to profitability by aligning the company’s earnings with their concerns. Artificial intelligence (AI) optimizes product location by clustering techniques targeting the right customers. It examines customer behaviour and buying trends utilizing Kaggle’s client membership card records such as Customer ID, age, gender, annual income, spending score among others. This includes basic market analytics based on demographic information like age or income split into groups, however big data superiority over traditional methods cannot be underestimated including machine learning applications such as Customer Seg-mentation Automated Methodology (CSAM), k-means clustering for user segmentation among others. This approach facilitates focused advertising campaigns and development and launch of new products aimed at particular consumer sections.

Chronic Kidney Disease Prediction Using Machine Learning

Abstract: Chronic kidney disease (CKD) is a progressive condition in which the kidneys lose their ability to function effectively over time. Individuals with hypertension, diabetes, or a family history of CKD are at increased risk, emphasizing the importance of early detection for effective intervention and management. Recent research has focused on employing machine learning techniques, including Ant Colony Optimization (ACO) and Support Vector Machine (SVM) classifiers, to predict CKD presence using a minimal set of features. This study aims to optimize predictive accuracy through advanced machine learning methodologies, facilitating timely and targeted healthcare interventions for at-risk individuals. By analyzing relevant clinical data, predictive models developed in this research offer promising avenues for early identification of CKD, enabling proactive disease management strategies. The integration of machine learning techniques in CKD prediction not only enhances predictive accuracy but also contributes to advancing personalized healthcare. Early detection allows for the implementation of preventive measures and personalized treatment plans, ultimately improving patient outcomes and reducing the burden on healthcare systems. The proposed methodology seeks to optimize predictive accuracy, aiding in timely and targeted healthcare interventions.

Machine Learning Techniques for Spatio-Temporal Air Pollution Prediction to Drive Sustainable Urban Development in the Era of Energy and Data Transformation

Sustainable urban development in the era of energy and digital transformation is crucial from a societal perspective. Utilizing modern techniques for analyzing large datasets, including machine learning and artificial intelligence, enables a deeper understanding of historical data and the efficient prediction of future events based on data from IoT sensors. This study conducted a multidimensional historical analysis of air pollution to investigate the impacts of energy transformation and environmental policy and to determine the long-term environmental implications of certain actions. Additionally, machine learning (ML) techniques were employed for air pollution prediction, taking spatial factors into account. By utilizing multiple low-cost air sensors categorized as IoT devices, this study incorporated data from various locations and assessed the influence of neighboring sensors on predictions. Different ML approaches were analyzed, including regression models, deep neural networks, and ensemble learning. The possibility of implementing such predictions in publicly accessible IT mobile systems was explored. The research was conducted in Krakow, Poland, a UNESCO-listed city that has had long struggle with air pollution. Krakow is also at the forefront of implementing policies to prohibit the use of solid fuels for heating and establishing clean transport zones. The research showed that population growth within the city does not have a negative impact on PMx concentrations, and transitioning from coal-based to sustainable energy sources emerges as the primary factor in improving air quality, especially for PMx, while the impact of transportation remains less relevant. The best results for predicting rare smog events can be achieved using linear ML models. Implementing actions based on this research can significantly contribute to building a smart city that takes into account the impact of air pollution on quality of life.

A Comprehensive Survey on Machine Learning and Deep Learning Techniques for Crop Disease Prediction in Smart Agriculture

Diseases caused by bacteria, fungi, and viruses are a problem for many crops. Farmers have challenges when trying to evaluate their crops daily by manual inspection across all forms of agriculture. Also, it is difficult to assess the crops since they are affected by various environmental factors and predators. These challenges can be addressed by employing crop disease detection approaches using artificial intelligence-based machine learning and deep learning techniques. This paper provides a comprehensive survey of various techniques utilized for crop disease prediction based on machine learning and deep learning approaches. This literature review summarises the contributions of a wide range of research works to the field of crop disease prediction, highlighting their commonalities and differences, parameters, and performance indicators. Further, to evaluate, a case study has been presented on how the paradigm shift will lead us to the design of an efficient learning model for crop disease prediction. It also identifies the gaps in knowledge that are supposed to be addressed to forge a path forward in research. From the survey conducted, it is apparent that the deep learning technique shows high efficiency over the machine learning approaches, thereby preventing crop loss.

Advanced Machine Learning Techniques for Corrosion Rate Estimation and Prediction in Industrial Cooling Water Pipelines.

This paper presents the results of a study on data preprocessing and modeling for predicting corrosion in water pipelines of a steel industrial plant. The use case is a cooling circuit consisting of both direct and indirect cooling. In the direct cooling circuit, water comes into direct contact with the product, whereas in the indirect one, it does not. In this study, advanced machine learning techniques, such as extreme gradient boosting and deep neural networks, have been employed for two distinct applications. Firstly, a virtual sensor was created to estimate the corrosion rate based on influencing process variables, such as pH and temperature. Secondly, a predictive tool was designed to foresee the future evolution of the corrosion rate, considering past values of both influencing variables and the corrosion rate. The results show that the most suitable algorithm for the virtual sensor approach is the dense neural network, with MAPE values of (25 ± 4)% and (11 ± 4)% for the direct and indirect circuits, respectively. In contrast, different results are obtained for the two circuits when following the predictive tool approach. For the primary circuit, the convolutional neural network yields the best results, with MAPE = 4% on the testing set, whereas for the secondary circuit, the LSTM recurrent network shows the highest prediction accuracy, with MAPE = 9%. In general, models employing temporal windows have emerged as more suitable for corrosion prediction, with model performance significantly improving with a larger dataset.

Web Application for Company Stock Price Prediction

This study contributes to predicting stock prices. In this paper, we have provided a novel machine learning and deep learning technique for stock prediction, which capitalizes on the complex emotional patterns of investors that can be extracted from Twitter data. By joining two separate datasets, such as Twitter sentiments and the associated stock prices, we have applied complex algorithms and evaluated this method, which classifies a user’s sentiment into a positive and negative one. It was possible to address the complexity of investor behavior within the highly unstable stock market environment. One of the most important problems that are encountered by developers is gaining the possibility to properly capture stock price fluctuations in the temporal dimension. It implies the necessity to utilize such reliable evaluation metrics as precision-recall factors for classification purpose and root mean squared error for regression purpose. by using these metrics, the performance of the predictive model can be evaluated in detail, which allows to draw meaningful permanent conclusions within the context of how well it reflects the complex relationship between investor sentiment and market dynamics. The results of the evaluation conducted as part of the process of validating the proposed hypothesis show that, indeed, the predictive accuracy improves significantly from the use of machine learning and deep learning algorithms if paired with Twitter users’ emotional sentiment data. This combination enhances the predictive capacity of the model and provides meaningful insights into how the changes in stock prices are impacted by the sentiment of investors. Moreover, in the process of this study, the explanation and detail consideration of the algorithms for feature engineering and data processing were provided, which stands crucial for increasing the predictive model’s accuracy The study has a valuable implication for both the financial analysis and technical spheres due to the resolution of these methods and presents a model for further research activities in stock prediction. To conclude, this interdisciplinary strategy that combines sentiment analysis and stock prediction not only advances our understanding of the investor behavior but also brings the opportunity for novel applications in the emerging field at the juncture of technology and finance. As a result, the current research sheds light on the intricate connection between the dynamics of the stock market and investor sentiment, thus offering new insights that can better inform predictive modeling research in the financial industry.

Development and trends in research on hypertension and atrial fibrillation: A bibliometric analysis from 2003 to 2022.

This study aimed to comprehensively analyze research related to hypertension and atrial fibrillation, 2 common cardiovascular diseases with significant global public health implications, using bibliometric methods from 2003 to 2022. From the Web of Science Core Collection database, literature on the theme of hypertension and atrial fibrillation was retrieved. Subsequently, comprehensive bibliometric analyses were conducted across multiple dimensions utilizing software tools such as VOSviewer, Citespace, Pajek, Scimago Graphica, and ClusterProfiler. These analyses encompassed examinations of the literature according to country/region, institution, authors, journals, citation relationships, and keywords. It revealed an increasing interest and shifting focus in research over the years. The analysis covered 7936 relevant publications, demonstrating a gradual rise in research activity regarding hypertension combined with atrial fibrillation over the past 2 decades, with a stable growth trend in research outcomes. Geographically, Europe and the Americas, particularly the United States, have shown the most active research in this field, while China has also gained importance in recent years. Regarding institutional contributions, internationally renowned institutions such as the University of Birmingham and the Mayo Clinic have emerged as core forces in this research direction. Additionally, Professor Lip Gregory, with his prolific research output, has stood out among numerous scholars. The American Journal of Cardiology has become a primary platform for publishing research related to hypertension and atrial fibrillation, highlighting its central role in advancing knowledge dissemination in this field. The research focus has shifted from exploring the pathophysiological mechanisms to investigating the treatment of complications and risk factors associated with hypertension and atrial fibrillation. Future research will focus on in-depth exploration of genetic and molecular mechanisms, causal relationship exploration through Mendelian randomization studies, and the application of machine learning techniques in prediction and treatment, aiming to promote the development of precision medicine for cardiovascular diseases. In conclusion, this study provides a comprehensive overview of the developmental trajectory of research on hypertension and atrial fibrillation, presenting novel insights into trends and future research directions, thus offering information support and guidance for research in this crucial field of cardiovascular medicine.

Machine Learning-Based Identification of Mating Type and Metalaxyl Response in Phytophthora infestans Using SSR Markers.

Phytophthora infestans is the causal agent of late blight in potato. The occurrence of P. infestans with both A1 and A2 mating types in the field may result in sexual reproduction and the generation of recombinant strains. Such strains with new combinations of traits can be highly aggressive, resistant to fungicides, and can make the disease difficult to control in the field. Metalaxyl-resistant isolates are now more prevalent in potato fields. Understanding the genetic structure and rapid identification of mating types and metalaxyl response of P. infestans in the field is a prerequisite for effective late blight disease monitoring and management. Molecular and phenotypic assays involving molecular and phenotypic markers such as mating types and metalaxyl response are typically conducted separately in the studies of the genotypic and phenotypic diversity of P. infestans. As a result, there is a pressing need to reduce the experimental workload and more efficiently assess the aggressiveness of different strains. We think that employing genetic markers to not only estimate genotypic diversity but also to identify the mating type and fungicide response using machine learning techniques can guide and speed up the decision-making process in late blight disease management, especially when the mating type and metalaxyl resistance data are not available. This technique can also be applied to determine these phenotypic traits for dead isolates. In this study, over 600 P. infestans isolates from different populations-Estonia, Pskov region, and Poland-were classified for mating types and metalaxyl response using machine learning techniques based on simple sequence repeat (SSR) markers. For both traits, random forest and the support vector machine demonstrated good accuracy of over 70%, compared to the decision tree and artificial neural network models whose accuracy was lower. There were also associations (p < 0.05) between the traits and some of the alleles detected, but machine learning prediction techniques based on multilocus SSR genotypes offered better prediction accuracy.

Correction to: Advanced model based machine learning technique for early stage prediction of ankylosing spondylitis under timely analysis with featured textures

Correction to: Advanced model based machine learning technique for early stage prediction of ankylosing spondylitis under timely analysis with featured textures

Just-in-Time crash prediction for mobile apps

Just-In-Time (JIT) defect prediction aims to identify defects early, at commit time. Hence, developers can take precautions to avoid defects when the code changes are still fresh in their minds. However, the utility of JIT defect prediction has not been investigated in relation to crashes of mobile apps. We therefore conducted a multi-case study employing both quantitative and qualitative analysis. In the quantitative analysis, we used machine learning techniques for prediction. We collected 113 reliability-related metrics for about 30,000 commits from 14 Android apps and selected 14 important metrics for prediction. We found that both standard JIT metrics and static analysis warnings are important for JIT prediction of mobile app crashes. We further optimized prediction performance, comparing seven state-of-the-art defect prediction techniques with hyperparameter optimization. Our results showed that Random Forest is the best performing model with an AUC-ROC of 0.83. In our qualitative analysis, we manually analysed a sample of 642 commits and identified different types of changes that are common in crash-inducing commits. We explored whether different aspects of changes can be used as metrics in JIT models to improve prediction performance. We found these metrics improve the prediction performance significantly. Hence, we suggest considering static analysis warnings and Android-specific metrics to adapt standard JIT defect prediction models for a mobile context to predict crashes. Finally, we provide recommendations to bridge the gap between research and practice and point to opportunities for future research.

Machine Learning for Water Quality Index Forecasting

This study aims to forecast water quality in the Tumkur district, Karnataka state, India, to increase pollution levels. Various machine learning techniques, including support vector machines, regression trees, linear regression, and neural networks, are employed. The Water Quality Index (WQI) is determined using parameters such as total hardness, pH, alkalinity, turbidity, chloride, dissolved solids, and conductivity. The dataset is split into training and testing sets (80:20) to assess model performance. Support Vector Machines and Linear Regression outperform other models, achieving R2 values of 0.96 and 0.99 for training and testing, respectively. This research underscores the importance of advanced machine learning techniques for accurate water quality prediction, crucial for effective pollution reduction strategies in the region.

Exploring Machine Learning Techniques for Accurate Prediction of Methane Hydrate Formation Temperature in Brine: A Comparative Study

AbstractAccurate estimation of formation conditions plays a pivotal role in effectively managing various processes related to hydrates, including flow assurance, deep-water drilling, and hydrate-based technology development. The formation temperature of methane hydrates in the presence of brine greatly affects the efficacy and accuracy of these processes. This work presents a comprehensive and novel comparative analysis of nine distinct machine learning models for accurate prediction of formation temperatures of methane hydrate. This study investigated the application of major machine learning (ML) algorithms including multiple linear regression (MLR), long short-term memory (LSTM), radial basis function (RBF), support vector machine (SVM), artificial neural network (ANN), gradient boosting regression (GBR), gradient process regression (GPR), random forest (RF), and K-nearest neighbor (KNN). The model accuracy was validated against a large dataset comprising of over 1000 data points with diverse range of salt concentrations. In this regard, model accuracies were compared using several metrics including R2, ARD, and AARD. The experimental results exhibited KNN algorithm to be fast-converging, accurate, and consistent over the entire range of data points with an R2 score of 0.975 and AARD of 0.385%. The results enable efficient and accurate temperature estimation with ML algorithms for multiple hydrate-related processes.

Revolutionizing Traffic Management: Advanced Machine Learning Techniques for Accurate Traffic Flow Prediction

In modern urban¬¬¬¬¬ transportation systems, efficient traffic flow prediction is of paramount importance to optimize traffic management, reduce congestion, and improve the overall commuting experience. This research report examines the application of state-of-the-art machine learning algorithms to accurately predict traffic flow patterns. By leveraging historical traffic data, weather conditions, time of day, and various other relevant features, our proposed model exhibits significant predictive capabilities. We study the effectiveness of various machine learning techniques, such as neural networks, decision trees, and ensemble methods, in capturing the complex dynamics of traffic flows. Through extensive experiments and validation using real datasets, we demonstrate the superiority of our approach compared to traditional methods. Ultimately, this research will contribute to the further development of intelligent transportation systems, paving the way for more efficient and sustainable urban mobility solutions

Translating Test Responses to Images for Test-termination Prediction via Multiple Machine Learning Strategies

Failure diagnosis is a software-based data-driven procedure. Collecting an excessive amount of fail data not only increases the overall test cost but can also potentially reduce diagnostic resolution. Test-termination prediction is thus proposed to dynamically determine which failing test pattern to terminate testing, producing an amount of test data that is sufficient for an accurate diagnosis analysis. In this work, we describe a set of novel methods utilizing advanced machine learning techniques for efficient test-termination prediction. To implement this approach, we first generate images representing failing test responses from failure-log files. These images are then used to train a multi-layer convolutional neural network (CNN) incorporating a residual block. The trained CNN model leverages the images and known diagnostic results to determine the optimal test-termination strategy within the testing process, ensuring efficient and high-quality diagnosis. In addition to the integration of test response-to-image translation, our approach harnesses two cutting-edge learning strategies to enhance fail data and boost performance in subsequent tasks. The first strategy is transfer learning, which utilizes sample-label information from one circuit to guide the decision of whether to continue or stop testing for another circuit lacking labels. The second strategy involves the use of a generative deep model to generate fail data in the form of synthetic images. This technique increases the modeling effectiveness by expanding the volume of training samples. Experimental results conducted on actual failing chips and standard benchmarks validate that our proposed method surpasses existing approaches. Our method creates opportunities to harness the power of recent advances in machine learning for improving test and diagnosis efficiency.

Boosting enabled efficient machine learning technique for accurate prediction of crop yield towards precision agriculture

Due to the limited availability of natural resources, it is essential that agricultural productivity keep pace with population growth. Despite unfavorable weather circumstances, this project's major objective is to boost production. As a consequence of technological advancements in agriculture, precision farming as a way for enhancing crop yields is gaining appeal and becoming more prevalent. When it comes to predicting future data, machine learning employs a number of methods, including the creation of models and the acquisition of prediction rules based on past data. In this manuscript, we examine various techniques to machine learning, as well as an automated agricultural yield projection model based on selecting the most relevant features. For the purpose of selecting features, the Grey Level Co-occurrence Matrix method is utilised. For classification, we make use of the AdaBoost Decision Tree, Artificial Neural Network (ANN), and K-Nearest Neighbour (KNN) algorithms. The data set that was used in this study is simply a compilation of information about a variety of topics, including yield, pesticide use, rainfall, and average temperature. This data collection consists of 33 characteristics or qualities in total. The crops soya beans, maze, potato, rice, paddy, wheat, and sorghum are included in this data collection. This data collection was made possible through the collaboration of the Food and Agriculture Organisation (FAO) and the World Data Bank, both of which make their data available to the public. The AdaBoost decision tree has achieved the highest level of accuracy possible when used to anticipate agricultural yield. Both the accuracy rate and the recall rate are quite high at 99 percent.

Study on the use of different machine learning techniques for prediction of concrete properties from their mixture proportions with their deterministic and robust optimisation

The construction industry relies so heavily on concrete that it's crucial to precisely forecast and optimize the strength and workability of concrete mixtures, while reducing costs as much as possible. For this objective, this study tries to predict and optimize the compressive strength and workability (slump) of concrete by using deterministic and robust optimization approaches, so as to determine the optimum concrete mixture proportions, while minimizing cost. Specifically, strength and slump were predicted based on concrete mixture proportions with five different machine learning techniques—support vector machine (SVM), artificial neural network (ANN), fuzzy inference system (FIS), adaptive fuzzy inference system (ANIS), and genetic expression programming (GEP), based on a dataset comprising two hundred concrete mixtures, which has various levels of key ingredients, including cement, water, fine aggregate, coarse aggregate, and size of coarse aggregate, along with their associated measures of strength and workability. These ingredients were used as input parameters, while compressive strength and slump (representing workability) served as output parameters for each mix proportion. Experimental investigations were conducted on fifteen distinct concrete mixes to validate the performance of the five networks, finding that ANFIS can yield the best results both for training and validation. This study provides valuable insights for predicting concrete properties and optimizing concrete mixture proportions, thus helping to maximize strength and workability while minimizing costs.

Machine Learning Techniques for Gender Recognition and Age Prediction Using Digital Images of Human Dentition

In today's digital age, the utilization of digital medical imagery is on the rise, since the increased computational power that enables the development of more advanced machine learning techniques. We have implemented a system capable of determining the gender and estimating the age of individuals based on digital images of their teeth. Teeth, being a robust and relatively unchanging part of the human body, serve as an ideal source for such analysis. By examining Orthopantomogram images, our system systematically extracts gender and age-related information. Developed using the Python programming language, our application is particularly valuable in forensic contexts, where traditional prediction methods can be time-consuming, often taking days. In contrast, our model delivers results in under a minute. We employed the Random Forest (RF) Classifier algorithm to deploy the prediction model, as it effectively addresses the overfitting issue associated with growing input sample sizes. With this approach, we have achieved an impressive accuracy rate of 94% in gender and age prediction.

Predictive machine learning in optimizing the performance of electric vehicle batteries: Techniques, challenges, and solutions

AbstractThis research paper explores the importance of optimizing the performance of electric vehicle (EV) batteries to align with the rapid growth in EV usage. It uses predictive machine learning (ML) techniques to achieve this optimization. The paper covers various ML methods like supervised, unsupervised, and deep learning (DL) and ways to measure their effectiveness. Significant battery performance factors, such as state of charge (SoC), state of health (SoH), state of function (SoF), and remaining useful life (RUL), are discussed, along with methods to collect and prepare data for accurate predictions. The paper introduces an operation research model for optimizing the performance of EV Batteries. It also looks at challenges unique to battery systems and ways to overcome them. The study showcases ML models' ability to predict battery behavior for real‐time monitoring, efficient energy use, and proactive maintenance. The paper categorizes different applications and case studies, providing valuable insights and forward‐looking perspectives for researchers, practitioners, and policymakers involved in improving EV battery performance through predictive ML.This article is categorized under: Technologies &gt; Classification Fundamental Concepts of Data and Knowledge &gt; Explainable AI Technologies &gt; Machine Learning