Analysis of the Application of Machine Learning Algorithm in Spam Detection System: Literature Review

In this chapter, a brief overview of the role and applications of machine learning (ML) algorithms in future wireless cellular networks is presented, more specifically, in the context of self-organizing networks (SONs). SON is a promising and innovative concept, in which future networks are expected to analyze and use historical data in order to improve and adapt themselves to the network conditions. For this to be possible, however, algorithms that are capable of extracting patterns from data and learn from previous actions are necessary. This chapter highlights the utilization and possible applications of ML algorithms in future cellular networks. A brief introduction of ML and SON is presented, followed by an analysis of current state of the art solutions involving ML in SON. Lastly, guidelines on the utilization of intelligent algorithms in SON and future research trends in the area are highlighted and conclusions are drawn.

Application of machine learning in carbon capture and storage: An in-depth insight from the perspective of geoscience

With the evolution of fifth generation (5G) network technologies, network maintenance strategies have become increasingly complex, necessitating the use of predictive analysis enabled by Machine Learning (ML) algorithms. This paper emphasizes exploring how ML algorithms can further enhance predictive maintenance in 5G and future networks. It reviews the current literature on this interdisciplinary topic, identifying key ML models such as Decision Trees, Neural Networks, and Support Vector Machines, and discussing their benefits and limitations. Special attention is given to the methodologies in applying these models, handling of data stages, and the training process. Major challenges in implementing ML in the context of network maintenance, such as data privacy, data gathering, model training, and generalizability, are discussed. Furthermore, the research aims to go beyond predicting maintenance needs to introduce a proactive approach in improving overall network performance and pre-empting potential issues based on ML predictions. The paper also discusses possible future trends including advancements in ML algorithms, Automated Machine Learning (AutoML), Explainable AI, and others. The objective is to provide a comprehensive understanding of the current ML-based predictive maintenance field and outline possibilities for future research. The study finds that the application of ML algorithms continues to show promise in transforming the landscape of network management by improving predictive maintenance and proactive performance enhancement strategies. It remains a challenging yet important area in the context of 5G networks.

The rapid expansion of urban populations, coupled with growing epidemiological evidence that associates extreme temperature events with adverse health outcomes and elevated mortality rates underscores the critical role of Urban Green Areas (UGAs) in delivering ecosystem services that enhance human well-being. Among these services, the air temperature cooling potential (ΔT°C) driven by ecosystem evapotranspiration (ET) represents a key mechanism for mitigating heat-related health risks.This study investigates the capacity of various Machine Learning (ML) algorithms to predict the ΔT°C of UGAs, thereby supporting thermal regulation through ET and highlighting their importance in sustainable urban planning and climate adaptation strategies. We used multiple years of experimental Eddy Covariance (EC) observations of the ET the to train and validate a series of ML algorithms with the objective to simulate the cooling effect of the urban vegetation. A preliminary analysis of predictor variables was conducted to identify and rank their importance using the mean absolute Shapley (Sh) values. Results indicate that incoming shortwave solar radiation (Rg) was the most influential predictor (Sh = 0.45), followed by vapor pressure deficit (VPD, Sh = 0.20), relative humidity (RH, Sh = 0.075), air temperature (AirT, Sh = 0.065), friction velocity (u*, Sh = 0.02), and wind speed (WS, Sh = 0.01). The application of ML algorithms revealed that Bootstrap Aggregation (Bagging) and Least-Squares Boosting (LSBoost) performed best, achieving R² values of 0.89 and 0.83, respectively, during the training phase compared to observed data. Other algorithms, including Neural Networks (NN), Gaussian Process Regression (GPR), and Support Vector Machines (SVM), showed also similar, but slightly lower r2 , with values ranging from 0.80 (NN) to 0.79 (SVM). Ten-fold cross-validation confirmed robust generalization, as model performance remained consistent regardless of the data subset used to compute R² between modeled and observed values. Further evaluation using Taylor diagrams showed that the average normalized standard deviation (σn) and Pearson correlation coefficient of the models were 0.89 (±0.02) and 0.90 (±0.02), respectively, closely matching the observed data.During the testing phase we observed, as expected, a clear reduction of the ML performance compared to the training phase: however, over the three years of the testing phase, RG bagging and LSBOOST have confirmed their superiority, compared to the other algorithms, with an average r2 between observed and simulated data of 0.66 and 0.67 respectively. Discrepancies between predicted and observed ΔT°C during testing were most evident during midday hours, with an average overestimation of 0.31°C (±0.2).Overall, the investigated UGAs demonstrated an average capacity to reduce ambient air temperature during summer by approximately 2°C to 4°C.

Fertility preferences significantly influence population dynamics and reproductive health outcomes, particularly in low-resource settings, such as Somalia, where high fertility rates and limited healthcare infrastructure pose significant challenges. Understanding the determinants of fertility preferences is critical for designing targeted interventions. This study leverages machine learning (ML) algorithms and Shapley Additive extensions (SHAP) to identify key predictors of fertility preferences among reproductive-aged women in Somalia. This cross-sectional study utilized data from the 2020 Somalia Demographic and Health Survey (SDHS), encompassing 8,951 women aged 15–49 years. The outcome variable, fertility preference, was dichotomized as either desire for more children or preference to cease childbearing. Predictor variables included sociodemographic factors, such as age, education, parity, wealth, residence, and distance to health facilities. Seven ML algorithms were evaluated for predictive performance, with Random Forest emerging as the optimal model based on metrics such as accuracy, precision, recall, F1-score, and the Area Under the Receiver Operating Characteristic Curve (AUROC). SHAP was employed to interpret the model by quantifying the feature contributions. The SHAP analysis identified the most influential predictors of fertility preferences as age group, region, number of births in the last five years, number of children born, marital status, wealth index, education level, residence, and distance to health facilities. Specifically, age group was the most significant feature, followed by region and number of births in the last five years. Women aged 45–49 years and those with higher parity were significantly more likely to prefer no additional children. Distance to health facilities has emerged as a critical barrier, with better access being associated with a greater likelihood of desiring more children. The Random Forest model demonstrated superior performance, achieving an accuracy of 81%, precision of 78%, recall of 85%, F1-score of 82%, and AUROC of 0.89. SHAP analysis provided interpretable insights, highlighting the nuanced interplay of sociodemographic factors. This study underscores the potential of ML algorithms and SHAP in advancing our understanding of fertility preferences in low-resource settings. By identifying critical sociodemographic determinants, such as age group, region, number of births in the last five years, number of children born, marital status, wealth index, education level, residence, distance to health facilities, and employment status, these findings offer actionable insights to inform evidence-based reproductive health interventions in Somalia. Future research should expand the application of ML to longitudinal data and incorporate additional cultural and psychosocial predictors to enhance the robustness and applicability of this model.

The disease popularly known as Alzheimer’s disease (AD), which causes cognitive impairment, is extensively distributed throughout the world. The lack of a cure or strategy to stop the growth of the disease notwithstanding, it is one of the most extensively studied illnesses in medicine and healthcare. However, patients with AD have a variety of treatment options to select from, including pharmaceuticals or non-drug alternatives, to help them maintain their quality of life. Over time, patients will need to be treated differently based on how far into the disease process they are. In this regard, early detection and classification of the illness stages can have a substantial impact on the treatment of symptoms. Early detection of Alzheimer’s disease is critical for the analysis of patient medications and the selection of appropriate specialists. The goal of this project is to provide a comprehensive comparison of the techniques used in the detection and classification of Alzheimer’s disease. In medical research, machine learning algorithms can be used to uncover facts, particularly in disease prediction. Support vector machines (SVM), decision trees, Bayes classifiers, K-nearest neighbors (KNN), gradient boosting, and other machine learning algorithms are used to diagnose a variety of diseases and conditions. The application of machine learning algorithms can result in the quick and highly accurate prediction and classification of this disease. This project helps in analyses of different machine learning techniques and algorithms that are used to predict and classify and provides an extensive accuracy comparison of the machine learning techniques used for the prediction and classification of Alzheimer’s disease.

Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): This research project is funded by Tuscany Region Background/Introduction Electrocardiograms (ECGs) are rapidly moving from analog to digital versions. Consequently, a series of automatic analyses of standard 12-lead ECGs are attracting interest for their ability to support clinicians in the automatic recognition of specific features associated with different cardiac diseases [2]. Artificial Intelligence applications and Machine Learning (ML) algorithms have gained much attention in the last years for their ability to figure out patterns from data independently, without being explicitly taught rules. Peculiar features define the ECGs of patients with Brugada Syndrome (BrS); however, ambiguities still exist for the correct diagnosis of BrS and discrimination with respect to other pathologies. Purpose The BrAID (Brugada syndrome and Artificial Intelligence applications to Diagnosis) project aims to develop an innovative system for diagnosing Type 1 BrS based on ECG pattern recognition through the application of ML algorithms. In this work, an application of Echo State Networks (ESN), a type of Recurrent Neural Network (RNN), for the diagnosis of BrS from ECG is presented. Methods After approval from the Local Ethical Committees, 12-lead ECGs were obtained in patients enrolled in 5 Centers diagnosed with typical spontaneous Type 1 pattern (coved) (group A, 81 patients). Baseline ECG was also collected in patients undergoing the ajmaline test, classified as positive (group B, 37 patients) or negative (group C, 14 patients) according to test results. 174 patients with no clinical and familial history of arrhythmias were considered controls (group D). Data were collected from 4 beats extracted from the ECGs as input to the ESN. The datasets obtained in the different groups were used for the ESN model’s training and assessment (testing) through a double cross-validation approach. Results As shown in Table 1, the performances using three leads (V1, V2, V3) or V2 only were compared. The algorithm performance was assessed in all the datasets (group A+B+C+D) and in spontaneous BrS (group A) and controls (group D). A good accuracy (79.21%) was seen when the three leads were considered for groups A and D only; the best test set accuracy (80.20%) was obtained in the case in which V2 only was used as input in all the datasets. Conclusion(s) In this work, a novel system for diagnosing Type 1 BrS using an ESN approach was developed. Our preliminary results show that this ML model is able to detect ECG patterns associated with Type 1 BrS with good and comparable accuracy both when three leads (79.21% ) or V2 only (80.20%) were analyzed. The future availability of larger datasets could improve the model performance, increasing the ESN potentialities as a clinical support system tool to be used in everyday clinical practice. Table 1. The accuracy, specificity, and sensitivity reported for each dataset group are obtained through double cross-validation.

ObjectiveEnhanced technology in computer and internet has driven scale and quality of data to be improved in various areas including healthcare sectors. Machine Learning (ML) has played a pivotal role in efficiently analyzing those big data, but a general misunderstanding of ML algorithms still exists in applying them (e.g., ML techniques can settle a problem of small sample size, or deep learning is the ML algorithm). This paper reviewed the research of diagnosing mental illness using ML algorithm and suggests how ML techniques can be employed and worked in practice.MethodsResearches about mental illness diagnostic using ML techniques were carefully reviewed. Five traditional ML algorithms-Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Random Forest, Naïve Bayes, and K-Nearest Neighborhood (KNN)-frequently used for mental health area researches were systematically organized and summarized.ResultsBased on literature review, it turned out that Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Random Forest, Naïve Bayes, and K-Nearest Neighborhood (KNN) were frequently employed in mental health area, but many researchers did not clarify the reason for using their ML algorithm though every ML algorithm has its own advantages. In addition, there were several studies to apply ML algorithms without fully understanding the data characteristics.ConclusionResearchers using ML algorithms should be aware of the properties of their ML algorithms and the limitation of the results they obtained under restricted data conditions. This paper provides useful information of the properties and limitation of each ML algorithm in the practice of mental health.

BackgroundDespite the interest in machine learning (ML) algorithms for analyzing real-world data (RWD) in healthcare, the use of ML in predicting time-to-event data, a common scenario in clinical practice, is less explored. ML models are capable of algorithmically learning from large, complex datasets and can offer advantages in predicting time-to-event data. We reviewed the recent applications of ML for survival analysis using RWD in healthcare.MethodsPUBMED and EMBASE were searched from database inception through March 2023 to identify peer-reviewed English-language studies of ML models for predicting time-to-event outcomes using the RWD. Two reviewers extracted information on the data source, patient population, survival outcome, ML algorithms, and the Area Under the Curve (AUC).ResultsOf 257 citations, 28 publications were included. Random survival forests (N = 16, 57%) and neural networks (N = 11, 39%) were the most popular ML algorithms. There was variability across AUC for these ML models (median 0.789, range 0.6–0.950). ML algorithms were predominately considered for predicting overall survival in oncology (N = 12, 43%). ML survival models were often used to predict disease prognosis or clinical events (N = 27, 96%) in the oncology, while less were used for treatment outcomes (N = 1, 4%).ConclusionsThe ML algorithms, random survival forests and neural networks, are mainly used for RWD to predict survival outcomes such as disease prognosis or clinical events in the oncology. This review shows that more opportunities remain to apply these ML algorithms to inform treatment decision-making in clinical practice. More methodological work is also needed to ensure the utility and applicability of ML models in survival outcomes.

In recent years, machine learning technology has made great improvements in social networks applications such as social network recommendation systems, sentiment analysis, and text generation. However, it cannot be ignored that machine learning algorithms are vulnerable to adversarial examples, that is, adding perturbations that are imperceptible to the human eye to the original data can cause machine learning algorithms to make wrong outputs with high probability. This also restricts the widespread use of machine learning algorithms in real life. In this paper, we focus on adversarial machine learning algorithms on social networks in recent years from three aspects: sentiment analysis, recommendation system, and spam detection, We review some typical applications of machine learning algorithms and adversarial example generation and defense algorithms for machine learning algorithms in the above three aspects in recent years. besides, we also analyze the current research progress and prospects for the directions of future research.

Predictive modelling of mineral prospectivity using satellite remote sensing and machine learning algorithms

The application of machine learning (ML) and remote sensing (RS) in soil and water conservation has become a powerful tool. As analytical tools continue to advance, the variety of ML algorithms and RS sources has expanded, providing opportunities for more sophisticated analyses. At the same time, researchers are required to select appropriate technologies based on the research objectives, topic, and scope of the study area. In this paper, we present a comprehensive review of the application of ML algorithms and RS that has been implemented to advance research in soil and water conservation. The key contribution of this review paper is that it provides an overview of current research areas within soil and water conservation and their effectiveness in improving prediction accuracy and resource management in categorized subfields, including soil properties, hydrology and water resources, and wildfire management. We also highlight challenges and future directions based on limitations of ML and RS applications in soil and water conservation. This review aims to serve as a reference for researchers and decision-makers by offering insights into the effectiveness of ML and RS applications in the fields of soil and water conservation.

Artificial intelligence (AI) proves decisive in today's rapidly developing society and is a motive force for the evolution of financial technology. As a subdivision of artificial intelligence research, machine learning (ML) algorithm is extensively used in all aspects of the daily operation and development of the supply chain. Using data mining, deductive reasoning, and other characteristics of machine learning algorithms can effectively help decision-makers of enterprises to make more scientific and reasonable decisions by using the existing financial index data. At present, globalization uncertainties such as COVID-19 are intensifying, and supply chain enterprises are facing bankruptcy risk. In the operation process, practical tools are needed to identify and opportunely respond to the threat in the supply chain operation promptly, predict the probability of business failure of enterprises, and take scientific and feasible measures to prevent a financial crisis in good season. Artificial intelligence decision-making technology can help traditional supply chains to transform into intelligent supply chains, realize smart management, and promote supply chain transformation and upgrading. By applying machine learning algorithms, the supply chain can not only identify potential risks in time and adopt scientific and feasible measures to deal with the crisis but also strengthen the connection and cooperation between different enterprises with the advantage of advanced technology to provide overall operation efficiency. On account of this, the paper puts forward an artificial intelligence-based corporate financial-risk-prevention (FRP) model, which includes four stages: data preprocessing, feature selection, feature classification, and parameter adjustment. Firstly, relevant financial index data are collected, and the quality of the selected data is raised through preprocessing; secondly, the chaotic grasshopper optimization algorithm (CGOA) is used to simulate the behavior of grasshoppers in nature to build a mathematical model, and the selected data sets are selected and optimized for features. Then, the support vector machine (SVM) performs classification processing on the quantitative data with reduced features. Empirical risk is calculated using the hinge loss function, and a regular operation is added to optimize the risk structure. Finally, slime mould algorithm (SMA) can optimize the process to improve the efficiency of SVM, making the algorithm more accurate and effective. In this study, Python is used to simulate the function of the corporate business finance risk prevention model. The experimental results show that the CGOA-SVM-SMA algorithm proposed in this paper achieves good results. After calculation, it is found that the prediction and decision-making capabilities are good and better than other comparative models, which can effectively help supply chain enterprises to prevent financial risks.

Machine learning (ML) is a subdivision of artificial intelligence in which the machine learns from machine-readable data and information. It uses data, learns the pattern and predicts the new outcomes. Its popularity is growing because it helps to understand the trend and provides a solution that can be either a model or a product. Applications of ML algorithms have increased drastically in G.I.S. and remote sensing in recent years. It has a broad range of applications, from developing energy-based models to assessing soil liquefaction to creating a relation between air quality and mortality. Here, in this paper, we discuss the most popular supervised ML models (classification and regression) in G.I.S. and remote sensing. The motivation for writing this paper is that ML models produce higher accuracy than traditional parametric classifiers, especially for complex data with many predictor variables. This paper provides a general overview of some popular supervised non-parametric ML models that can be used in most of the G.I.S. and remote sensing based projects. We discuss classification (Naïve Bayes (NB), Support Vector Machine (SVM), Random Forest (RF), Decision Trees (DT)) and regression models (Random Forest (RF), Support Vector Machine (SVM), Linear and Non-Linear) here. Therefore, the article can be a guide to those interested in using ML models in their G.I.S. and remote sensing based projects.

The screening and diagnosis of osteoporosis in patients with type 2 diabetes mellitus (T2DM) based on bone mineral density remains challenging because of the limited availability and accessibility of dual-energy X-ray absorptiometry. We aimed to develop and validate models to predict the risk of osteoporosis in postmenopausal women with T2DM based on machine learning (ML) algorithms. This retrospective study included 303 postmenopausal women with T2DM. To develop prediction models for osteoporosis, we applied nine ML algorithms combined with demographic, clinical, and laboratory data. The least absolute shrinkage and selection operator were used to perform feature selection. We used the bootstrap resampling technique for model training and validation. To test the performance of the models, we calculated indices including the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, calibration curve, and decision curve analysis. Furthermore, we conducted fivefold cross-validation for parameter optimization and model validation. Feature importance was assessed using the SHapley additive explanation (SHAP). We identified 10 independent predictors as the most valuable features. An AUROC of 0.616-1.000 was observed for nine ML algorithms. The extreme gradient boosting (XGBoost) model exhibited the best performance, outperforming conventional risk assessment tools and registering 0.993 in the training set, 0.798 in the validation set, and 0.786 in the test set for fivefold cross-validation. Using SHAP, we found that the explanatory variables contributed to the model and their relationship with osteoporosis occurrence. Furthermore, we developed a user-friendly tool for calculating the risk of osteoporosis. With the integration of demographic and clinical risk factors, ML algorithms can accurately predict osteoporosis. The XGBoost model showed ideal performance. With the incorporation of these models in the clinic, patients may benefit from early osteoporosis diagnosis and treatment.