Tirana Noor Fatyanosa

Objectives: Hypertension, commonly known as high blood pressure, is a prevalent and serious condition affecting a significant portion of the adult population globally. It is a chronic medical issue that, if left unaddressed, can lead to severe health complications, including kidney problems, heart disease, and stroke. This study aims to develop a feature selection model using the XGBoost algorithm to identify specific single nucleotide polymorphisms (SNPs) as biomarkers for detecting hypertension risk. Methods: We propose using the high dimensionality of genetic variations (i.e., SNPs) to build a classifier model for prediction. In this study, SNPs were used as markers for hypertension in patients. We utilized the OpenSNP dataset, which includes 19,697 SNPs from 2,052 samples. Extreme gradient boosting (XGBoost) is an ensemble machine learning method employed here for feature selection, which incrementally adjusts weights in a series of steps. Results: The experimental results identified 292 SNPs that exhibited high performance, with an F1-score of 98.55%, precision of 98.73%, recall of 98.38%, and overall accuracy of 98%. This study provides compelling evidence that the XGBoost feature selection method outperforms other representative feature selection methods, such as genetic algorithms, analysis of variance, chi-square, and principal component analysis, in predicting hypertension risk, demonstrating its effectiveness. Conclusions: We developed a model for predicting hypertension using the SNPs dataset. The high dimensionality of SNP data was effectively managed to identify significant features as biomarkers using the XGBoost feature selection method. The results indicate high performance in predicting the risk of hypertension.

The Internet of Things (IoT) infrastructure exhibits numerous security vulnerabilities which have led to an increasing number of cyber-attacks. This problem has led to the development of Intrusion Detection Systems (IDSs) explicitly tailored for detecting cyber-attacks on IoT infrastructure, particularly IDSs that utilize machine learning models. To achieve high-performance machine learning models, the training data is pivotal. However, IoT infrastructure has limited computational resources, especially CPU, memory, and power. One solution is reducing the amount of data by selecting only certain features that have a significant impact on the quality of the machine learning models. This study proposes a novel hybrid framework to select the important features to build an efficient IDS model for IoT using the XGBoost algorithm. The performance of the proposed framework was rigorously tested using the network TON-IoT dataset, which is specifically designed to develop IDSs for IoT infrastructure. The experimental results show that our proposed framework yields machine-learning models with optimal performance while conserving computational resources. Through the implementation of our framework, we achieve a substantial reduction in features by 78.95%, coupled with a 3.18% Macro F1-score improvement. Furthermore, the framework also yields significant reductions in training, validation, and testing durations by 53.25%, 41.94%, and 47.06%, respectively. Also, it yields a model with impressive reductions in memory usage by 81.82%.

Time series datasets collected from marine sensors inevitably undergo missing data problems. This cause unreliable sensor data to assist the decision-making process. Many methods are offered to impute missing values. However, selecting the best imputation method is not a trivial task, as it usually requires domain expertise and several trial-and-error iterations. Furthermore, when imputations are carried out in a careless way, it generates a high error factor that can lead stakeholders to wrong assumptions. This paper provides a systematic approach that is able to extract characteristics of underlying data and, based on it, recommends the less error-prone imputation method. We evaluate our proposed method using nine real-world vessel datasets. In total, we generated 3859 data samples consisting of 17 inputs and 1 target feature. Experimental results show that the proposed approach is capable of obtaining a weighted F1-Score of 92.6%. Additionally, when compared with the application of careless selected imputation methods, our work is able to gain up to 86% on the average imputation score, with the worst case gain being of 5%. We empirically demonstrate that the proposed approach is efficient when selecting the best imputation methods.

Hyperparameters and architecture greatly influence the performance of convolutional neural networks (CNNs); therefore, their optimization is important to obtain the desired results. One of the state-of-the-art methods to achieve this is the use of neuroevolution that utilizes a genetic algorithm (GA) to optimize a CNN. However, the GA is often trapped into a local optimum resulting in premature convergence. In this study, we propose an approach called the “diversity-guided genetic algorithm-convolutional neural network (DGGA-CNN)” that uses adaptive parameter control and random injection to facilitate the search process by exploration and exploitation while preserving the population diversity. The alternation between exploration and exploitation is guided by using an average pairwise Hamming distance. Moreover, the DGGA fully handles the architecture of the CNN by using a novel finite state machine (FSM) combined with three novel mutation mechanisms that are specifically created for architecture chromosomes. Tests conducted on suggestion mining and twitter airline datasets reveal that the DGGA-CNN performs well with valid architectures and a comparison with other methods demonstrates its capability and efficiency.

Phrase table combination in pivot approaches can be an effective method to deal with low-resource language pairs. The common practice to generate phrase tables in pivot approaches is to use standard symmetrization, i.e., grow-diag-final-and. Although some researchers found that the use of non-standard symmetrization could improve bilingual evaluation understudy (BLEU) scores, the use of non-standard symmetrization has not been commonly employed in pivot approaches. In this study, we propose a strategy that uses the non-standard symmetrization of word alignment in phrase table combination. The appropriate symmetrization is selected based on the highest BLEU scores in each direct translation of source–target, source–pivot, and pivot–target of Kazakh–English (Kk–En) and Japanese–Indonesian (Ja–Id). Our experiments show that our proposed strategy outperforms the direct translation in Kk–En with absolute improvements of 0.35 (a 11.3% relative improvement) and 0.22 (a 6.4% relative improvement) BLEU points for 3-gram and 5-gram, respectively. The proposed strategy shows an absolute gain of up to 0.11 (a 0.9% relative improvement) BLEU points compared to direct translation for 3-gram in Ja–Id. Our proposed strategy using a small phrase table obtains better BLEU scores than a strategy using a large phrase table. The size of the target monolingual and feature function weight of the language model (LM) could reduce perplexity scores.