Machine Learning Techniques For Prediction Research Articles

A Review of Theoretical Studies and their Applications in Weak Soil Strength Enhancement using Waste Materials

According to an extensive evaluation of published studies, there is a shortage of research on systematic literature reviews related to machine learning prediction techniques, which includes mostly theoretical studies and methodologies in soil improvement using green waste materials. A literature review suggests that machine learning algorithms are effective at predicting various soil characteristics, including compressive strength, deformations, bearing capacity, California bearing ratio, compaction performance, and stress–strain behavior, geotextiles pullout strength behavior, and soil classification. The current study aims to comprehensively evaluate recent breakthroughs in machine learning algorithms for soil improvement using a systematic procedure known as PRISMA and meta-analysis. Relevant databases, including Web of Science, Science Direct, IEEE, and SCOPUS, were utilized, and the chosen papers were categorized based on: the approach and method employed, year of publication, authors, journals and conferences, research goals, findings and results, and solution and modeling. The review results will advance the understanding of civil and geotechnical designers and practitioners in integrating data for most geotechnical engineering problems

Machine Learning and Deep Learning Techniques for EEG-Based Prediction of Psychiatric Disorders

Early detection of psychiatric disorders as well as efficient treatment are difficult owing to their challenges, which require accurate prediction methods in healthcare. When combined with ML and DL techniques, EEG data promises to yield a promising method for enhancing diagnostic accuracy. In this study, the performance of a wide spectrum of ML and DL techniques for predicting psychiatric disorders from EEG datasets is evaluated and the best choice is found for a particular condition. The study carried an analysis based on public datasets representing diverse psychiatric disorders through systematic analysis. Advanced DL architectures comprising of CNNs and RNNs were compared against the classical traditional ML techniques such as RlForest and Support Vector Machines (SVMs). A comparison between these models was made based on key performance metrics such as accuracy, sensitivity, and specificity. Results showed that DL models, particularly CNNs, excel at feature extraction and classification over traditional ML methods with their highest accuracy of predicting major depressive disorder above 92%. But ML techniques were able to complete faster computationally, in spite of slightly lower predictive accuracy. As DL models excel at capturing complex patterns within EEG data, these findings suggest that there are increased computational demands associated with them. Following that, advanced pattern recognition capabilities associated with DL techniques benefit substantially from the predictive modeling offered by EEG, although their computational efficiency presents as a limitation. This study highlights the importance of hybrid methods combining the best properties of both ML and DL for psychiatric disorders prediction to get improved accuracy and scalability, which is conditioning this generation of safer diagnostic tools for clinical practice .

Rider Cat Optimized Alzheimer’s Disease and MCI Prediction Using Deep Attention BiLSTM and CRDF

The early detection and accurate diagnosis of Alzheimer's Disease (AD) are critical for effective treatment and management. The study proposes a hybrid method based on deep learning and machine learning techniques for the early prediction of AD and Mild Cognitive Impairment (MCI) from Magnetic Resonance Imaging. The method involves skull stripping, Weiner filtering, segmentation using DABiLSTM, feature extraction using DTCWT, and feature selection using RCO. Finally, the CRDF classifier is used to classify the images into five groups. The proposed model achieved an accuracy of 98.58% on the ADNI 2 dataset, suggesting its potential for more efficient and accurate diagnosis of AD and MCI.

A Survey of Machine Learning and Deep Learning Techniques for Lung Cancer Prediction in IoT and Cloud Platform

A Survey of Machine Learning and Deep Learning Techniques for Lung Cancer Prediction in IoT and Cloud Platform

Evaluation of machine learning techniques for real-time prediction of implanted lower limb mechanics.

Accurate prediction of knee biomechanics during total knee replacement (TKR) surgery is crucial for optimal outcomes. This study investigates the application of machine learning (ML) techniques for real-time prediction of knee joint mechanics. A validated finite element (FE) model of the lower limb was used to generate a dataset of knee joint kinematics, kinetics, and contact mechanics. The models were trained on joint alignment data, ligament information, and external boundary conditions. Several predictive algorithms were explored, including linear regression (LRM), multilayer perceptron (MLP), bi-directional long short-term memory (biLSTM), convolutional neural network (CNN), and transformer-based approaches. The performance of these models was evaluated using average normalized root mean squared error (nRMSE). The biLSTM model achieved the highest accuracy, with a significantly lower nRMSE compared to other models. Compared to traditional FE or rigid body dynamics models, these predictive models offered significantly faster prediction speeds, enabling near-instantaneous insights into the TKR system's performance. The small size of the predictive models makes them suitable for deployment on edge devices potentially used in operating rooms. These findings suggest that real-time biomechanical prediction using biLSTM models has the potential to provide valuable feedback for surgeons during TKR surgery. Applications of this work could be applied to provide pre-operative guidance on optimal target implant alignment or given the real-time prediction ability of these models, could also be used intra-operatively after integration of patient-specific intra-op kinematic and soft-tissue information.

Application of machine learning techniques for warfarin dosage prediction: a case study on the MIMIC-III dataset.

Warfarin, a commonly prescribed anticoagulant, poses significant dosing challenges due to its narrow therapeutic range and high variability in patient responses. This study applies advanced machine learning techniques to improve the accuracy of international normalized ratio (INR) predictions using the MIMIC-III dataset, addressing the critical issue of missing data. By leveraging dimensionality reduction methods such as principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE), and advanced imputation techniques including denoising autoencoders (DAE) and generative adversarial networks (GAN), we achieved significant improvements in predictive accuracy. The integration of these methods substantially reduced prediction errors compared to traditional approaches. This research demonstrates the potential of machine learning (ML) models to provide more personalized and precise dosing strategies that reduce the risks of adverse drug events. Our method could integrate into clinical workflows to enhance anticoagulation therapy in cases of missing data, with potential applications in other complex medical treatments.

Data-driven analysis of climate impact on tomato and apple prices using machine learning.

Machine learning has been used in various areas, but there are few studies on price prediction for agricultural products. Here, a machine learning technique for the price prediction of tomato and apple fruits was attempted based on environment and price data for 12 years. The goal of this study is to discover 1) how much can we accurately predict the product prices with the environmental factors and 2) how much each environmental factor affects to the product prices. This study assumes that the environmental factors directly affect crop growth and thus indirectly determine fruit production and accompanying price. In addition, it is assumed that there are two kinds of time lags, between the change in the environmental factors and their effects on the crop growth, and between the change in the crop growth and its effect on the price. In the process, machine learning techniques were used instead of econometric models commonly used in agricultural economics. The relationship between the environmental factors and fruit price with varying time lags in data-driven manner using long short-term memory (LSTM) was modeled in this study. The study empirically revealed that there are suitable time lags between the environmental factors and fruit price in the price prediction, and taking these time lags into the prediction improves the accuracy. Moreover, the importance of each of the environmental factors on the price using shapely additive explanations (SHAP) was demonstrated though this study, which assists the decision-making process in agriculture against the climate change.

A Comparative Study of Supervised Machine Learning Techniques for Water Leakage Prediction in Drill and Blast Excavated Water Tunnels in the Himalayan Region

In the Himalayan region, drill and blast (DB) method excavated water tunnels often pass through complex geological rock mass conditions which were formed with frequent tectonic movements. Due to these tectonic movements, the rock mass conditions in the Himalayas are highly faulted, folded, jointed, sheared, and fractured. The geological formations are the pathway for the water ingress and leakage out in the water tunnel. This water leakage in the tunnel causes a complicated geological hazard, which significantly increases tunnel instability and can lead to a delay in completion time and finally increase the cost of the tunnel project. Therefore, an efficient water ingress/leakage prediction model is essential to mitigate these challenges. In this research, various field data such as rock mass properties, topography, and permeability data sets were collected from the Nilgiri-II Hydroelectric project water tunnel. These real-time field datasets have been used for comprehensive assessment and to predict the water ingress/leakage in the water tunnel by using four supervised machine learning (ML) approaches such as Support Vector Regression (SVR), Decision Tree (DT) regression, K-Nearest Neighbors (KNN) and Random Forest (RF) regression models. It was observed that the KNN shows the best regression performance of 93% followed by RF of 92%, DT of 86%, and SVR of 66%. Therefore, all these machine learning approaches show good performance in predicting water ingress/leakage based on real field data except the SVR model.

Comparative Analysis of Machine Learning Models to Predict Common Vulnerabilities and Exposure

Predicting Common Vulnerabilities and Exposures (CVE) is a challenging task due to the increasing complexity of cyberattacks and the vast amount of threat data available. Effective prediction models are crucial for enabling cybersecurity teams to respond quickly and prevent potential exploits. This study aims to provide a comparative analysis of machine learning techniques for CVE prediction to enhance proactive vulnerability management and strengthening cybersecurity practices. The supervised machine learning model which is Gaussian Naive Bayes and unsupervised machine learning models that utilize clustering algorithms which are K-means and DBSCAN were employed for the predictive modelling. The performance of these models was compared using performance metrics such as accuracy, precision, recall, and F1-score. Among these models, the Gaussian Naive Bayes achieved an accuracy rate of 99.79%, and outperformed the clustering-based machine learning models in effectively determining the class labels or results of the data it was trained on or tested against. The outcome of this study will provide a proof of concept to Cybersecurity Malaysia, offering insights into the CVE model.

Comparing machine learning techniques for neonatal mortality prediction: insights from a modeling competition.

Predicting mortality risk in neonatal intensive care units (NICUs) is challenging due to complex, variable clinical and physiological data. Machine learning (ML) offers potential for more accurate risk stratification. To compare the performance of various ML models in predicting NICU mortality using a team-based modeling competition. We conducted a modeling competition with five neonatologist-led teams applying ML techniques-logistic regression, CatBoost, neural networks, random forest, and XGBoost-to a shared dataset from over 6,000 NICU admissions. The dataset included static demographic and clinical variables, alongside daily samples of heart rate and oxygen saturation. Each team developed models to predict mortality risk at baseline and within 7 days. Models were evaluated using the area under the receiver operator characteristic curve (AUC). Results were presented at a national meeting, where an audience poll ranked models before AUC results were revealed. The audience favored the most complex model (CNN) for real-world application, though logistic regression achieved the highest AUC on test data. Teams employed varied feature selection, tuning, and evaluation strategies. Logistic regression outperformed more complex models, highlighting the importance of selecting modeling methods based on data characteristics, interpretability, and expertise rather than model complexity alone. By demonstrating that model complexity does not necessarily equate to better predictive performance, this research encourages the careful selection of modeling approaches.

Utilizing advanced machine learning techniques for accurate prediction of oxygen quantity in gas-fired boiler combustion to enhance environmental pollution control

Utilizing advanced machine learning techniques for accurate prediction of oxygen quantity in gas-fired boiler combustion to enhance environmental pollution control

Energy-efficient Cloud Infrastructure for IoT Device Management: A Comprehensive Analysis of Edge-Cloud Workload Distribution Strategies

The rapid expansion of the Internet of Things (IoT) has led to significant increases in data traffic and computational demands on cloud infrastructures, raising concerns about energy consumption in data centers. This article explores innovative approaches to creating energy-efficient cloud infrastructures for managing IoT device fleets through optimized workload distribution between edge devices and cloud resources. It proposes implementing energy-aware algorithms that dynamically determine the optimal location for data processing based on energy consumption, latency requirements, and workload characteristics. The system employs advanced machine learning techniques for workload prediction and resource allocation, demonstrating substantial improvements in energy efficiency while maintaining high-performance standards. Case studies in smart cities, agricultural monitoring, and transportation networks validate the effectiveness of this approach in real-world scenarios. The results indicate that intelligent workload distribution across edge and cloud platforms can significantly reduce energy consumption while enhancing system performance and operational efficiency, providing a sustainable pathway for large-scale IoT deployments.

Advancements in Machine Learning and Deep Learning Techniques for Crop Yield Prediction: A Comprehensive Review

Agriculture is the crucial pillar and basic building block of our nation. Agriculture plays a key role as the major source of revenue for our nation. Farming is the primary financial source of India. Abrupt environmental changes affect crop yield prediction. Unpredictable climate changes, lack of water resources, deficiency of nutrients, depletion of soil fertility, unbalanced irrigation systems, and conventional farming techniques are the major causes of crop yield prediction. Today, AI, the use of machine learning, and deep learning techniques provide an achievable solution to improve crop yields. The key intent of the survey is to accurately predict and improve crop yield by combining agricultural statistics with machine learning and deep learning models. To accomplish this, we have surveyed the optimization algorithms implemented in conjunction with the Random Forest and Cat Boost models. A survey made across multiple databases to determine the effectiveness of crop yield prediction and analysis was performed on the included articles. The survey results show that a hybrid CNN DNN and RNN model with optimization algorithms outperforms the other existing traditional models.

Unveiling DNA Sequences: A Comparison of Machine Learning and Deep Learning Techniques for Prediction

<div align="center"><table width="590" border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="387"><p>DNA is the biological macromolecule unit that carries the information of all protein, amino acid sequences. With the help of this protein sequence, we explore the mutated gene and disease-causing mutated genomic pattern. Currently, the progression of genomic innovation is the source of DNA arrangement information developing at a dangerous rate—external factors have stimulated the volume of research into DNA genomes. Initially, the development process of DNA sequencing is accomplished with the support of the Database, data structures, and sequence similarity. The method is capable of extracting a particular property in DNA. We employ the deep learning algorithm to pull out protein sequences' features. The DNA sequence is classified based on the in-build protein structures extracted into the Fasta file. Therefore, the DNA sequence of E.coli with 106 data sets and 57 nucleotides is tested experimentally. Finally, we compared the results with the existing decision tree algorithm, KNN- Classification, random forest, and neural networks. The deep learning algorithm yields higher efficiency of 98% compared to other machine learning algorithms. This highlights the potential of deep learning in genomics research and its ability to yield superior results in classifying DNA sequences.</p></td></tr></tbody></table></div>

GAN-based synthetic time-series data generation for improving prediction of demand for electric vehicles

Demand forecasting is essential for any business to grow and manage its different business activities. With the basic needs of customers, it is hard to predict the future demand using traditional techniques. The popular approach to overcoming the difficulties faced by startup businesses is to use machine learning techniques for demand prediction. Another constraint to train machine learning algorithms for accurate prediction requires a considerable amount of data. For a new startup business, vast data acquisition is a very problematic issue. To overcome the data scarcity problem data enhancement techniques are mainly used for expanding the existing data. Synthetic data generation to balance the existing data which can lead to increased prediction model accuracy. In this study at the beginning, we found that the accuracy of the proposed clustering-based ensemble regression model was bad because of the small size of the data. To overcome this issue, we proposed a modified Conditional Wasserstein Generative Adversarial Network with a Gradient Penalty (CWGAN-GP) for generating synthetic time-series data according to the original data distribution. This generated synthetic data was further added to the original data to train the model and additionally enhanced the demand prediction accuracy for shared electric kickboards. Improved performance was noticed after the model was trained with combined data. Using a range of evaluation measures and graphical representations, we evaluated the performance of our approach against that of other ensemble models. For the production of synthetic data, our GAN model converged more quickly than other GAN models and solved the mode collapse problem. We have contrasted our suggested approach with other cutting-edge models. This study can be helpful for companies to meet the user’s demand for a better quality of service.

Assessing the Effectiveness of Machine Learning Techniques for Silver Price Prediction: A Comparative Study

Silver is considered an important asset in terms of economic indicators and a valuable investment asset in terms of the markets. Therefore, determining silver prices is critically important for both national economies and investors. However, the non-stationary and non-linear nature of silver prices makes predicting price movements challenging. The methods used for predicting silver prices must be suitable for capturing these volatile and complex behavioral characteristics. The silver market can be influenced by other commodities and investment assets. Factors affecting silver prices, such as gold prices, Brent crude oil prices, the US Dollar index, the VIX index, and the S&P 500 index, can play a significant role. In this context, these variables have been used as inputs for predicting silver prices in the study. Three different models have been developed to predict the prices one, two, and three days ahead. These models have been predicted using four different machine learning methods: linear regression, support vector regression (SMOReg), k-nearest neighbors (k-NN), and random forest (RF). The results show that the random forest and k-NN methods exhibit the highest performance. The random forest achieves the highest accuracy in the first two models, while k-NN excels in the third model. Linear regression and SMOReg methods are less successful compared to the others. Consequently, it can be concluded that random forest and k-NN methods can be preferred for long-term predictions, and that these results may provide valuable insights, especially for investors and decision-makers.

COMPARATIVE ANALYSIS OF REGRESSION MODELS FOR STOCK PRICE PREDICTION: LINEAR, SUPPORT VECTOR, POLYNOMIAL, AND LASSO

This paper investigates the performance of various regression-based machine learning techniques for short-term stock price prediction. Four regression algorithms – linear regression, support vector regression (SVR), polynomial regression and LASSO regression – were applied to Apple Inc’s historical price data for two-years ending on October 1st, 2024, to predict the next day’s closing stock price. Technical indicators such as moving average, relative strength index (RSI) and lag features were included in the regressions to improve prediction performance. Evaluation metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R²) were employed to assess the models’ performance. Results show that linear regression and LASSO regression were the most performant models, reaching R² values of more than 0.95, with minimal error values. While SVR yielded the poorest results when accounting for all metrics. Overall, the study highlights the predictive power of simpler regression models over more complex ones in stock price predictions and offers recommendations for model selection.

Assessment of Supervised Machine Learning Techniques for Predicting Groundwater Availability in Mexican Aquifers

Groundwater overexploitation is a global problem. In Mexico, 653 aquifers provide 39.1% of water for consumptive use. The National Water Commission manages this resource, but predicting aquifer availability is challenging, and the number of aquifers in deficit has increased. Physical models can address this issue but require extensive resources, whereas supervised learning algorithms offer a less resource-demanding alternative. This study evaluates four machine learning techniques for groundwater availability prediction: support vector machine regression, M5' model trees, random forests (RFR), and artificial neural networks. The models were trained using climatological, land use, and concession data from 1997 to 2015 and tested with data from 2018 and 2020. Random forests performed the best, showing a high correlation coefficient and low RMSE errors. The prediction accuracy for the availability state was 81.24% for 2018 and 76.79% for 2020. Thus, RFR can effectively predict short-term water availability, aiding sustainable aquifer management.

Optimizing Breast Cancer Prediction by Implementing Feature Selection with Principal Component Analysis

Breast cancer is a major global health concern, where early detection plays a pivotal role in improving patient outcomes. Advances in machine learning (ML) offer significant potential in enhancing the accuracy of cancer diagnosis, leading to more effective treatment strategies. This study explores the application of machine learning techniques for breast cancer prediction, utilizing a dataset collected from cancer hospitals in Pune, Maharashtra, India. The data includes clinical and diagnostic variables, such as lifestyle factors, hereditary background, and cancer stages. The primary focus of this research work is to evaluate the impact of dimensionality reduction using Principal Component Analysis (PCA) on the performance of several machine learning classifiers. By reducing the dimensionality of the dataset, the study aims to improve model interpretability, computational efficiency, and predictive accuracy. The classifiers are evaluated both with and without PCA to determine the optimal approach for breast cancer classification. The results indicate that PCA significantly enhances model performance in terms of accuracy, efficiency, and generalizability, offering a more streamlined approach to breast cancer prediction. This research work suggests that incorporating dimensionality reduction into machine learning workflows can provide valuable support for early diagnosis and personalized treatment in breast cancer care.

A spatially explicit multi-hazard framework for assessing flood, landslide, wildfire, and drought susceptibilities

Sustainable development goals require evaluating vulnerabilities and examining natural and climatic hazards for effective planning that reduces their impact on economic, social, and developmental efforts. Key hazards like floods, landslides, wildfires, and droughts have significantly affected terrestrial ecosystems and human societies, emphasizing the importance of comprehending these hazards. This study aimed to predict and spatially map multi-hazard, identifying historical and potential risks to inform sustainable development and construction programs that mitigate risks and promote resilience. A 34-year drought magnitude map was generated using long-term data, and ensemble and individual machine learning techniques were used to produce maps of flood, landslide, and wildfire hazards in a northwest region of Iran. Results demonstrated that ensemble learning models outperformed individual models, with the top-performing models being the weighted average (WA) of the two best models, random forest, extreme gradient boosting, WA models with over 80% accuracy, and WA incorporating all models, respectively. The CART model performed best among individual models. Variable importance analysis revealed that slope and precipitation were crucial factors for identifying high-hazard landslide areas, distance from waterways, vegetation cover, and topographic humidity index emerged as the most crucial factor for identifying flood hazard areas, while vegetation, rainfall, and proximity to roads significantly impacted wildfire hazard. The multi-hazard map produced by our study indicated that about 30% of the study area was highly and very highly susceptible to floods, landslides, wildfires, and droughts and the hazards mitigation efforts should be primarily directed to these specific portions of the study area. Our study underscored the importance of integrating long-term data and machine learning techniques in multi-hazard prediction and mapping, ultimately guiding mitigation efforts and promoting resilience in the face of natural and climatic hazards.