Machine Learning Techniques For Prediction Research Articles

Artificial Intelligence and Machine Learning Techniques for Suicide Prediction: Integrating Dietary Patterns and Environmental Contaminants

Artificial Intelligence and Machine Learning Techniques for Suicide Prediction: Integrating Dietary Patterns and Environmental Contaminants

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>

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.

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.

Precision Agriculture using ML for Soil and Weather Prediction

Agriculture is the backbone of many economies and plays a critical role in global food security. However, with growing challenges posed by climate change, erratic weather patterns, and soil degradation, the need for precise and predictive techniques in agriculture has never been more urgent. This paper focuses on the application of machine learning (ML) techniques in soil fertility prediction and weather forecasting, two critical components that can significantly impact agricultural productivity. By analysing soil properties, such as moisture, pH, and nutrient levels, and combining them with accurate weather predictions, our system aims to help farmers make informed decisions regarding crop selection, irrigation scheduling, and pest control. Leveraging algorithms like Naive Bayes for soil classification and Long Short-Term Memory (LSTM) networks for weather prediction, we provide a comprehensive solution for precision agriculture. This system not only enhances productivity but also promotes sustainable agricultural practices by optimizing resource use and reducing wastage

Smart Grid Forecasting with MIMO Models: A Comparative Study of Machine Learning Techniques for Day-Ahead Residual Load Prediction

With the fast expansion of intermittent renewable energy sources in the upcoming smart grids, simple and accurate day-ahead systems for residual load forecasts are urgently needed. Machine learning strategies can facilitate towards drastic cost minimizations in terms of operating-reserves avoidance to compensate the mismatches between the actual and forecasted values. In this study, a multi-input/multi-output model is developed based on artificial neural networks to map the relationship between different predictor inputs, including time indices, weather variables, human activity parameters, and energy price indicators, and target outputs such as wind and photovoltaic generation. While the information flows in only one direction (from the predictor nodes through the hidden layers to the target node), benchmark training algorithms are employed and assessed under different case studies. The model is evaluated under both parametric and non-parametric formulations, namely neural networks and Gaussian process regression. Essential improvements are achieved by enhancing the number of embedded predictors, while superior performance is observed by using Bayesian regularization mechanisms. In terms of mean-error indices and determination coefficient, this opens the pathway towards minimization via Bayesian inference-based approaches in the presence of increased and highly stochastic renewable inputs.

Multi-method machine learning techniques in gold pathfinder elements prediction in central parts of Tanzania using stream sediment geochemical data

Prediction models using machine learning techniques have proven to be a reliable technique in mineral exploration. A combination of these techniques is very robust and reliable in exploration targeting and much dependable as an approach in greenfield. In this study, multi-machine learning methods: random forest (RF), support vector machine (SVM), and artificial neural network (ANN) were employed to conduct a data-driven gold (Au) prospectivity modelling in the Central parts of the Tanzania Craton (TC). A total of 166 samples with Au concentrations from stream sediment samples were considered. Based on the modeling results, the RF model demonstrates superior prediction accuracy compared to the SVM (MSE of 0.89) and ANN models (MSE of 1.21), achieving an MSE of less than 0.82. In terms of overall predictive performance and efficiency, the RF model outperforms other ML models deployed in this research. Therefore, it is deemed the suitable model for gold (Au) prediction in the TC catchments. According to the geological interpretation derived from the model, anomalies in arsenic (As), nickel (Ni), and tungsten (W) now emerge as significant predictors in the quest for gold. This implies that the association of As–Ni–W are potential pathfinder elements in the exploration of gold in the central part of the TC.

Application of machine learning techniques for churn prediction in the telecom business

The telecom business generates a significant amount of data on a daily basis due to its massive client base. Acquiring a fresh client base is more expensive than retaining existing customers, whereas churn refers to customers transitioning from one company to another within a specified timeframe. Telecom managers and analysts investigate why customers cancel their subscriptions and analyze the behavior patterns of customers who have stopped using the services. In this work employs categorization methodologies to determine the instances of leave subscriptions and gathers the rationales behind client leave subscriptions in the telecommunications sector. The primary objective of this work is to examine various machine learning algorithms necessary for creating customer churn prediction (CP) models and identifying the reasons for churn. This work aims to provide retention strategies and plans to address churn. This work utilizes machine learning (ML) technique such as random forests (RF) to collect and classify client data for leave subscriptions. These results compare with other ML algorithm such as support vector machines (SVM), gradient boosting (GB), Extreme Gradient Boosting (XGBoost), and light gradient boosting machines (LGBM), The business model provides a practical analysis of customer churn data, enabling accurate forecasts of customers likely to churn. This allows business management to take timely action to prevent churn and minimize profit loss. In this work obtains an accuracy of 98.1 % by utilizing the random forest classifier for churn prediction. The classifier matrix has obtained a precision of 92.8 % and a recall factor of 92.7 %, resulting in an overall accuracy of 95.6 %. Similarly, our research endeavors enhance churn prediction, encompass additional business domains, and furnish prediction models to retain their current consumers, improve customer service, and efficiently prevent churn.

Permissioned blockchain network for proactive access control to electronic health records

BackgroundAs digital healthcare services handle increasingly more sensitive health data, robust access control methods are required. Especially in emergency conditions, where the patient’s health situation is in peril, different healthcare providers associated with critical cases may need to be granted permission to acquire access to Electronic Health Records (EHRs) of patients. The research objective of this work is to develop a proactive access control method that can grant emergency clinicians access to sensitive health data, guaranteeing the integrity and security of the data, and generating trust without the need for a trusted third party.MethodsA contextual and blockchain-based mechanism is proposed that allows access to sensitive EHRs by applying prognostic procedures where information based on context, is utilized to identify critical situations and grant access to medical data. Specifically, to enable proactivity, Long Short Term Memory (LSTM) Neural Networks (NNs) are applied that utilize patient’s recent health history to prognose the next two-hour health metrics values. Fuzzy logic is used to evaluate the severity of the patient’s health state. These techniques are incorporated in a private and permissioned Hyperledger-Fabric blockchain network, capable of securing patient’s sensitive information in the blockchain network.ResultsThe developed access control method provides secure access for emergency clinicians to sensitive information and simultaneously safeguards the patient’s well-being. Integrating this predictive mechanism within the blockchain network proved to be a robust tool to enhance the performance of the access control mechanism. Furthermore, the blockchain network of this work can record the history of who and when had access to a specific patient’s sensitive EHRs, guaranteeing the integrity and security of the data, as well as recording the latency of this mechanism, where three different access control cases are evaluated. This access control mechanism is to be enforced in a real-life scenario in hospitals.ConclusionsThe proposed mechanism informs proactively the emergency team of professional clinicians about patients’ critical situations by combining fuzzy and predictive machine learning techniques incorporated in the private and permissioned blockchain network, and it exploits the distributed data of the blockchain architecture, guaranteeing the integrity and security of the data, and thus, enhancing the users’ trust to the access control mechanism.

Employing machine learning techniques for prediction of micronutrient supplementation status during pregnancy in East African Countries

Micronutrient deficiencies, known as “hidden hunger” or “hidden malnutrition,” pose a significant health risk to pregnant women, particularly in low-income countries like the East Africa region. This study employed eight advanced machine learning algorithms to predict the status of micronutrient supplementation among pregnant women in 12 East African countries, using recent demographic health survey (DHS) data. The analysis involved 138,426 study samples, and algorithm performance was evaluated using accuracy, area under the ROC curve (AUC), specificity, precision, recall, and F1-score. Among the algorithms tested, the random forest classifier emerged as the top performer in predicting micronutrient supplementation status, exhibiting excellent evaluation scores (AUC = 0.892 and accuracy = 94.0%). By analyzing mean SHAP values and performing association rule mining, we gained valuable insights into the importance of different variables and their combined impact, revealing hidden patterns within the data. Key predictors of micronutrient supplementation were the mother’s education level, employment status, number of antenatal care (ANC) visits, access to media, number of children, and religion. By harnessing the power of machine learning algorithms, policymakers and healthcare providers can develop targeted strategies to improve the uptake of micronutrient supplementation. Key intervention components involve enhancing education, strengthening ANC services, and implementing comprehensive media campaigns that emphasize the importance of micronutrient supplementation. It is also crucial to consider cultural and religious sensitivities when designing interventions to ensure their effectiveness and acceptance within the specific population. Furthermore, researchers are encouraged to explore and experiment with various techniques to optimize algorithm performance, leading to the identification of the most effective predictors and enhanced accuracy in predicting micronutrient supplementation status.

Comparative Study on Stock Movement Prediction Using Hybrid Deep Learning Model

Applying machine learning techniques in stock market prediction has evolved significantly, with deep learning methodologies gaining prominence. Conventional algorithms such as Linear Regression and Neural Networks initially dominated but struggled to capture complex temporal dependencies in financial data. Recent research has explored deep learning architectures like LSTM and CNN and hybrids such as CNN-LSTM and LSTM-CNN, showcasing promising results. However, there's a gap in research comparing these models across different datasets, particularly in predicting stock movements. This study addresses this gap by conducting a comparative analysis of deep learning and hybrid models for stock movement prediction in the Indonesian banking sector. The evaluation based on RMSE and MAE reveals that the LSTM-CNN hybrid consistently outperforms other models, showcasing its versatility and accuracy across different data characteristics. Then, exploration through hyperparameter tuning demonstrates the criticality of parameter selection in optimizing model performance. These findings contribute to advancing predictive modeling in financial markets, offering valuable insights for investors, analysts, and policymakers. Further research in hyperparameter tuning and model optimization holds promise for enhancing accuracy and reliability in stock price prediction.

A Machine Learning Technique for Prediction of Cold Spray Additive Manufacturing Input Process Parameters to Achieve a Desired Spray Deposit Profile

A Machine Learning Technique for Prediction of Cold Spray Additive Manufacturing Input Process Parameters to Achieve a Desired Spray Deposit Profile

Composite activity type and stride-specific energy expenditure estimation model for thigh-worn accelerometry

BackgroundAccurately measuring energy expenditure during physical activity outside of the laboratory is challenging, especially on a large scale. Thigh-worn accelerometers have gained popularity due to the possibility to accurately detect physical activity types. The use of machine learning techniques for activity classification and energy expenditure prediction may improve accuracy over current methods. Here, we developed a novel composite energy expenditure estimation model by combining an activity classification model with a stride specific energy expenditure model for walking, running, and cycling.MethodsWe first trained a supervised deep learning activity classification model using pooled data from available adult accelerometer datasets. The composite energy expenditure model was then developed and validated using additional data based on a sample of 69 healthy adult participants (49% female; age = 25.2 ± 5.8 years) who completed a standardised activity protocol with indirect calorimetry as the reference measure.ResultsThe activity classification model showed an overall accuracy of 99.7% across all five activity types during validation. The composite model for estimating energy expenditure achieved a mean absolute percentage error of 10.9%. For running, walking, and cycling, the composite model achieved a mean absolute percentage error of 6.6%, 7.9% and 16.1%, respectively.ConclusionsThe integration of thigh-worn accelerometers with machine learning models provides a highly accurate method for classifying physical activity types and estimating energy expenditure. Our novel composite model approach improves the accuracy of energy expenditure measurements and supports better monitoring and assessment methods in non-laboratory settings.

Comparative Analysis of Machine Learning Techniques for Water Consumption Prediction: A Case Study from Kocaeli Province.

This study presents a comparative analysis of various Machine Learning (ML) techniques for predicting water consumption using a comprehensive dataset from Kocaeli Province, Turkey. Accurate prediction of water consumption is crucial for effective water resource management and planning, especially considering the significant impact of the COVID-19 pandemic on water usage patterns. A total of four ML models, Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Machines (SVM), and Gradient Boosting Machines (GBM), were evaluated. Additionally, optimization techniques such as Particle Swarm Optimization (PSO) and the Second-Order Optimization (SOO) Levenberg-Marquardt (LM) algorithm were employed to enhance the performance of the ML models. These models incorporate historical data from previous months to enhance model accuracy and generalizability, allowing for robust predictions that account for both short-term fluctuations and long-term trends. The performance of each model was assessed using cross-validation. The R2 and correlation values obtained in this study for the best-performing models are highlighted in the results section. For instance, the GBM model achieved an R2 value of 0.881, indicating a strong capability in capturing the underlying patterns in the data. This study is one of the first to conduct a comprehensive analysis of water consumption prediction using machine learning algorithms on a large-scale dataset of 5000 subscribers, including the unique conditions imposed by the COVID-19 pandemic. The results highlight the strengths and limitations of each technique, providing insights into their applicability for water consumption prediction. This study aims to enhance the understanding of ML applications in water management and offers practical recommendations for future research and implementation.

Implementing machine learning techniques for customer retention and churn prediction in telecommunications

This review paper explores the application of machine learning techniques in predicting customer churn and enhancing customer retention within the telecommunications industry. The paper begins by discussing the significance of customer churn, its causes, and the limitations of traditional churn prediction methods. It then delves into machine learning algorithms, including decision trees, support vector machines, and ensemble methods. It highlights their effectiveness in handling large and complex datasets typical of the telecom sector. The discussion extends to the challenges faced in data quality, model selection, implementation, and ethical considerations in using customer data for predictive analytics. The paper also compares machine learning models with traditional methods, emphasizing the advantages of scalability, accuracy, and real-time processing. Furthermore, it identifies potential innovations, such as improved data integration, interpretable models, and personalized retention strategies. Finally, the paper reflects on future trends, predicting the growing role of AI and machine learning in telecommunications, particularly in customer service automation and network optimization. The review underscores the importance of adopting machine learning to reduce churn and improve customer retention while considering the field's ethical implications and future opportunities. Keywords: Customer Churn Prediction, Machine Learning, Telecommunications, Customer Retention, Predictive Analytics, AI in Telecom

Machine Learning Techniques for Medicinal Leaf Prediction and Disease Identification

Trees have been a crucial component in humans' lives for hundreds of years, providing food, shelter, and medicine. Some trees have a lot of medicinal properties that cure many diseases. In the old days, Ayurvedic methods were popular for various treatments, but nowadays, the demand for foreign medicine is increasing gradually, which also has side effects. This paper addresses this issue by deciding on the medical conditions corresponding to a symptom and predicting an herb leaf that can be treated it using some modern machine learning techniques. We have used three machine learning methods to accomplish this goal: Multinomial Naive Bayes, Gradient Boosting and Random Forest. These techniques were then used to assess the symptoms and decide the name of the disease and which leaf is appropriate for medicine. The highest accuracy (92%) was produced by the Multinomial Naive Bayes algorithm, thereby showing its capability to predict the right medicinal leaf based on given symptoms. The results show that machine learning algorithms, especially Multinomial Naive Bayes, can identify diseases and recommend suitable medicinal leaves. This approach holds promise for integrating traditional Ayurvedic knowledge with modern technology to offer alternative treatments with potentially fewer side effects.

Comparative analysis of machine learning techniques for accurate prediction of unfrozen water content in frozen soils

Unfrozen water content (UWC) plays a critical role in determining the thermal, hydraulic, and mechanical properties of frozen soils. Existing empirical, semi-empirical, and theoretical models for UWC estimation have limitations in terms of accuracy as well as generalizability. To address these challenges, the present study explored the application of six machine learning techniques to predict UWC in frozen soils: Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), K-Nearest Neighbors (KNN), Support Vector Regression (SVR), and Backpropagation Neural Network (BPNN). Considering the UWC hysteresis phenomenon between the freezing and thawing processes, experimental UWC data collected from the literature were separated into two sub-datasets: freezing branch dataset (FBD) and thawing branch dataset (TBD). Based on that, a comprehensive framework integrating Bayesian optimization and 10-fold cross-validation was established to optimize the six models' hyperparameters and to evaluate their performance. The results highlighted significant variations in the predictive capability among the six machine learning models, with ensemble methods (i.e., RF, XGBoost, LightGBM) generally demonstrating superior accuracy. Feature importance analysis, robustness checks, and uncertainty quantification further elucidated the strengths and limitations of each model. The present study provides profound insights into the selection and application of machine learning models for accurately modeling the properties of frozen soils for cold regions science and engineering.