Cloud computing threats classification model based on the detection feasibility of machine learning algorithms

Spam detection is an evolving issue in line with the increasing volume of data and the evolution of spam techniques. In recent years, the application of machine learning (ML) algorithms has become an effective solution to enhance the accuracy and efficiency of spam detection systems. This study aims to analyze various machine learning algorithms applied in spam detection systems through a literature review. Several popular algorithms used in spam detection include Naive Bayes, Support Vector Machine (SVM), Neural Network, Recurrent Neural Network (RNN), and Transformer-based models. Each algorithm has its strengths and weaknesses that affect its performance in handling spam detection issues, depending on the characteristics of the data and the application requirements. Based on the data obtained, the Naive Bayes algorithm achieved 88% accuracy, 85% precision, 90% recall, and 87% F1-score. In contrast, SVM showed higher results with 93% accuracy, 92% precision, 94% recall, and 93% F1-score. Neural Network reached 96% accuracy, 95% precision, 97% recall, and 96% F1-score, while Recurrent Neural Network (RNN) achieved 95% accuracy, 94% precision, 96% recall, and 95% F1-score. Transformer-based models provided the best results with 97% accuracy, 96% precision, 98% recall, and 97% F1-score. This study adopts a literature analysis method by reviewing various articles and research that discuss the application of these algorithms in spam detection. In conclusion, the selection of the appropriate algorithm should be adjusted to the characteristics of the dataset, the complexity of the problem, and the availability of computational resources, as each algorithm has its own strengths and weaknesses in the context of spam detection.

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.

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 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.

In Mild Cognitive Impairment (MCI), identifying a high risk of conversion to Alzheimer’s Disease Dementia (AD) is a primary goal for patient management. Machine Learning (ML) algorithms are widely employed to pursue data-driven diagnostic and prognostic goals. An agreement on the stability of these algorithms –when applied to different biomarkers and other conditions– is far from being reached. In this study, we compared the different prognostic performances of three supervised ML algorithms fed with multimodal biomarkers of MCI subjects obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Random Forest, Gradient Boosting, and eXtreme Gradient Boosting algorithms predict MCI conversion to AD. They can also be simultaneously employed –with the voting procedure– to improve predictivity. AD prediction accuracy is influenced by the nature of the data (i.e., neuropsychological test scores, cerebrospinal fluid AD-related proteins and APOE ε4, cerebral structural MRI (sMRI) data). In our study, independent of the applied ML algorithms, sMRI data showed the lowest accuracy (0.79) compared to other classes. Multimodal data were helpful in the algorithms’ performances by combining clinical and biological measures. Accordingly, using the three ML algorithms, the highest accuracy (0.90) was reached by employing neuropsychological and AD-related biomarkers. Finally, the feature selection procedure indicated that the most critical variables in the respective classes were the ADAS-Cog-13 scale, the medial temporal lobe and hippocampus atrophy, and the ratio between phosphorylated Tau and Aβ42 proteins. In conclusion, our data support the notion that using multiple ML algorithms and multimodal biomarkers helps make more accurate and solid predictions.

Three-dimensional (3D) printing has become a widely used manufacturing technology, but predicting the mechanical and physical properties of printed parts remains a critical challenge. In particular, tensile strength, hardness, and surface roughness are essential for assessing product quality and reliability. Addressing this gap requires advanced computational approaches. Machine Learning (ML) algorithms have the potential to enhance automation and provide accurate predictions of product quality in 3D printing. The primary objective of this study is to review, classify, and analyze recent research on the application of ML algorithms for predicting mechanical (tensile strength, hardness) and physical (surface roughness) properties in 3D-printed materials. This review aims to identify current research trends, highlight existing gaps in the literature, and propose potential directions for future investigations in this rapidly evolving interdisciplinary field. For this purpose, a literature review was conducted in the Web of Science database using keywords such as “3D printing”, “machine learning”, “tensile strength”, “hardness”, and “roughness. The review systematically examines the material types, ML algorithms, printing parameters, and testing methods reported in the literature, and the frequency of these parameters is visualized through graphs to illustrate research trends. The findings reveal that ML algorithms achieve high accuracy in predicting tensile strength, hardness, and surface roughness, providing valuable insights for process optimization. However, current research still shows limited evidence for direct improvements in efficiency and error reduction, indicating important directions for future work.

Intelligent algorithms have excellent performance in managing large databases and mining effective knowledge, and many researchers have introduced these algorithms into research to improve the performance of book management systems. This article introduced the application of machine learning algorithms in electronic book database management systems. A linear classifier (linear regression algorithm is one of machine learning algorithms) was used to analyze the empirical loss value. This paper studied the application of machine learning algorithm in the database management of library special collection resources, aiming to optimize the management of library special collection resources database through machine learning algorithm, and provided students with better experience of using special collection resources. This paper verified the effectiveness of the machine learning algorithm from three aspects: the retrieval time before and after the special resource database, the number of crashes in a month and the number of library staff. The average time of the five sets using the machine learning algorithm was about 150% faster than the average search time of the five sets of special resource databases. The number of crashes after using machine learning algorithm was 2, 1, 1, 2, 1, which was much lower than that of special resource database. After using the machine learning algorithm, the five groups of workers were 7, 5, 9, 6, and 4, respectively, far fewer than the five groups of special collectors. Finally, by comparing the linear regression model, Support vector machine model, and random forest model, it was found that the accuracy of the linear regression model reached 98.2%, an increase of 8.2% compared to the random forest model, the precision rate reached 96.7%, and the recall rate reached 98.2%. 97.8%, F1 value reached 97.5%.It can be seen from the experimental data that the machine learning algorithm plays a good role in the database management of the library special collection resources.

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.

Liver diseases represent a major health burden worldwide. Machine learning (ML) algorithms have been extensively used to diagnose liver disease. This study accordingly aims to employ various individual and integrated ML algorithms on distinct liver disease datasets for evaluating the diagnostic performances, to integrate dimensionality reduction method with the ML algorithms for analyzing variation in results, to find the best classification model and to analyze the merits and demerits of these algorithms. KNN and PCA-KNN emerged to be the top individual and integrated models. The study also concluded that one specific algorithm can't show best results for all types of datasets and integrated models not always perform better than the individuals. It is observed that no algorithm is perfect and performance of an algorithm totally depends on the dataset type and structure, its number of observations, its dimensions and the decision boundary.

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.

Data set preprocessing is a critical step for the successful application of machine learning algorithms in classification tasks. Even though we rely on learning algorithms to pinpoint the optimal decision boundaries in the feature space by properly detecting latent relationships among the input features, their performance is often bounded by the discriminative power of the available features. Therefore, much effort has been devoted to developing preprocessing methods that are capable of transforming the input data with the final goal of aiding the machine learning algorithm in building high-quality classification models. One such a method is feature construction, which is a flexible preprocessing procedure that exploits linear and nonlinear transformations of the original feature space in an attempt to capture useful information that is not explicit in the original data. Since the task of feature construction can be modelled as a heuristic search in the space of novel latent features, this paper investigates an evolutionary approach for performing such a task, namely grammatical evolution (GE). In our proposed approach, GE is employed for building an extra novel feature from the available input data in order to maximize the predictive performance of the learning algorithm in training data. Results show that many interesting implicit relationships are indeed found by the evolutionary approach, improving the performance of two well-known decision-tree induction algorithms.

Applying hybrid machine learning algorithms to assess customer risk-adjusted revenue in the financial industry

Remotely sensed air temperature data from NASA POWER are widely used in regions with scarce climatic observations, particularly for agricultural applications such as calculating crop water requirements. This study employed a suite of machine learning (ML) algorithms to correct biases in NASA POWER air temperature outputs, including multiple support vector regression (SVR) variants—Linear SVR, Quadratic SVR, Cubic SVR, Fine Gaussian SVR, Medium Gaussian SVR, Coarse Gaussian SVR—and ensemble decision tree models: bagged trees (BGT) and boosted trees (BT). The objective of this study was to assess the ability of different ML algorithms to reduce biases in NASA POWER air temperature data, with the broader goal of identifying the most suitable ML method for air temperature bias correction in Nigeria. For this analysis, we used daily air temperature records from seven meteorological stations across diverse regions of Nigeria. The performance of NASA POWER minimum and maximum air temperature datasets was evaluated using standard error metrics. Subsequent application of ML algorithms significantly improved data accuracy: the normalized root mean square error (NRMSE) of the corrected outputs was mostly below 10%, indicating excellent predictive performance when ML was integrated. Among the SVR variants tested, Fine Gaussian SVR consistently yielded the best prediction results. This finding suggests that Fine Gaussian SVR is a robust tool for enhancing the reliability of air temperature data—critical for improving the accuracy of crop water requirement calculations in regions where in-situ air temperature observations are limited.

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.

Machine learning algorithms are designed to identify efficiently and to predict accurately patterns within multivariate data. They provide analysts computational tools to aid predictive modelling and the interpretation of interactions between data and the phenomena under investigation. The analysis of large volumes of disparate multivariate geospatial data using machine learning algorithms therefore offers great promise to industry and research in the geosciences. Geoscience data are frequently characterised by a restriction in the number and distribution of direct observations, irreducible noise in these data and a high degree of intraclass variability and interclass similarity. The choice of machine learning algorithm, or algorithms and the details of how algorithms are applied must therefore be appropriate to the context of geoscience data. With this knowledge, I aim to employ machine learning as a means of understanding the spatial distribution of complex geological phenomena. I conduct a rigorous and comprehensive comparison of machine learning algorithms, representing the five general machine learning strategies, for supervised lithology classification applications. I also develop and test a novel method for obtaining robust estimates of the uncertainty associated with machine learning algorithm categorical predictions. The insights gained from these experiments leads to the further development and comparison of new methods for the incorporation of spatial-contextual information into machine learning supervised classifiers. In using machine learning algorithms for geoscience applications, I have developed bestpractice methodologies that address the challenges facing geoscientists for geospatial supervised classification. Guidelines are established that detail the preparation and integration of disparate spatial data, the optimisation of trained classifiers for a given application and the robust statistical and spatial evaluation of outputs. I demonstrate, through a case study in a region that is prospective for economic mineralisation, the combination of supervised and unsupervised machine learning algorithms for the critical appraisal of pre-existing geological maps and formulation of meaningful interpretations of geological phenomena. The experiments conducted as part of my research confirm the efficacy of machine learning algorithms to generate accurate geological maps representing a variety of terranes. I identify and explore key aspects of the spatial and statistical istributions of geoscience data that affect machine learning algorithm performance. My research clearly identifies Random Forests‚Äöv묢 as a good first-choice algorithm for the prediction of classes representing lithologies using commonly available multivariate geological and geophysical data. Furthermore, Random Forests prediction uncertainty is shown to be closely related to ambiguous and/or erroneous classifications and, thus provides a practical means of indicating variable levels of confidence. Spatial-contextual information is best incorporated into machine learning supervised classifiers via the pre-processing of input variables and/or the post-regularisation of classifications. My findings indicate that a trade-off between optimal predictive models and interpretable explanatory models exists, whereby, intuitively interpretable models are not necessarily the most accurate. The practical application of machine learning algorithms requires the implementation of three key stages: (1) data pre-processing; (2) algorithm training; and (3) prediction evaluation. This methodology provides the foundation for generating accurate and geologically meaningful predictions with minimal user intervention and assists in the formulation of robust interpretations of complex geological phenomena. For example, classifications obtained by Random Forests are useful for critically appraising interpreted geological maps. Clusters produced by Self-Organising Maps indicate the presence of discrete, spatially contiguous and geologically significant sub-classes within individual lithological units, which represent regions of contrasting primary composition and alteration styles. My results may be widely applied to a broad range of practical geoscience challenges such as ore deposit targeting, geo-hazard risk assessment, engineering and construction projects, hydrological and environmental modelling and ecological studies. The applications of machine learning algorithms detailed in this thesis align well with state-of-the-art Big Data online infrastructure and virtual laboratories currently emerging in Australia.