Jafreezal Jaafar’s research while affiliated with Universiti Teknologi Petronas and other places

Detecting outliers within MetOcean datasets is crucial for identifying extreme weather conditions to facilitate informed decision-making whilst various outlier detection approaches and methods are available to be selected. However, choosing the most appropriate one requires an in-depth understanding of their capabilities and this poses a great challenge to the novice within the domain. Therefore, this study is proposed to empirically compare these approaches and provide insights on their performance when dealing with large MetOcean data. The comparison is driven by three outlier detection approaches using the SEAFINE dataset: Extreme Value Analysis (EVA), Functional Data Analysis (FDA), and Unsupervised Learning (UL). A framework is designed to systematically conduct the comparative analysis, which involves five phases: data preprocessing, model training, outlier detection, parameter estimation, and evaluation. This analysis provides insights into the performance of each outlier detection method for each approach and the comparison between them. Overall, the results indicate that the EVA approach exhibits the fastest average execution time compared to the FDA and UL. Within the EVA approach, Peak-Over-Threshold is the most effective, closely aligning the distribution of extreme values with the estimated distribution. Functional Boxplot emerges as the optimal method for FDA, capable of detecting outliers far from the median. Cluster-Based Local Outlier Factor (CBLOF) is the most effective UL method, predicting outliers close to the expected total, with high Excess-Mass values and fast execution.

Industries are generating massive amounts of data due to increased automation and interconnectedness. As data from various sources becomes more available, the extraction of relevant information becomes crucial for understanding complex systems’ behavior and performance. The growing volume and complexity of time-series data in diverse industries have created a demand for effective anomaly detection methods. Detecting anomalies in multivariate time-series data presents unique challenges, such as the presence of multiple correlated variables and intricate relationships among them. Traditional approaches often fall short in detecting anomalies, making deep learning methods a promising solution. This review article provides a comprehensive analysis of different deep learning techniques for anomaly detection in time-series data, examining their applicability across various industries and discussing the associated challenges. The article emphasizes the significance of deep learning in detecting anomalies and offers valuable insights to inform decision-making processes. Furthermore, it proposes recommendations for model developers, advocating for the development of hybrid models that combine different deep learning techniques and the exploration of attention mechanisms in Recurrent Neural Networks (RNNs). These recommendations aim to overcome the challenges associated with deep learning-based anomaly detection in multivariate time-series data.

An Equity Market Neutral Portfolio (EMNP) safeguards a safe portfolio concerning exposure to pertinent market benchmarks. Although it is possible to efficiently solve the problem of EMNP optimization through linear programming techniques, combining the risk budget constraint of risky assets with other constraints of EMNP where there is no market exposure, leveraging, or portfolio beta makes it challenging to resolve this problem via conventional approaches directly. This study aims to propose a novel technique to solve the problem of constrained optimization of EMNP via differential evolution strategies involving multiple crossovers (Exponential as well as binomial together with the Hall of Fame). The suggested automated technique enables portfolio managers to select the portfolio with the highest potential return. Monitoring the optimal combination of evolutionary techniques also confirms the results’ consistency. Therefore, impending outcomes were chosen depending on the optimal balance of portfolio returns and risk. This analysis includes Nifty50’s monthly stock prices.

Prediction of machine failure is challenging as the dataset is often imbalanced with a low failure rate. The common approach to handle classification involving imbalanced data is to balance the data using a sampling approach such as random undersampling, random oversampling, or Synthetic Minority Oversampling Technique (SMOTE) algorithms. This paper compared the classification performance of three popular classifiers (Logistic Regression, Gaussian Naïve Bayes, and Support Vector Machine) in predicting machine failure in the Oil and Gas industry. The original machine failure dataset consists of 20,473 hourly data and is imbalanced with 19945 (97%) ‘non-failure’ and 528 (3%) ‘failure data’. The three independent variables to predict machine failure were pressure indicator, flow indicator, and level indicator. The accuracy of the classifiers is very high and close to 100%, but the sensitivity of all classifiers using the original dataset was close to zero. The performance of the three classifiers was then evaluated for data with different imbalance rates (10% to 50%) generated from the original data using SMOTE, SMOTE-Support Vector Machine (SMOTE-SVM) and SMOTE-Edited Nearest Neighbour (SMOTE-ENN). The classifiers were evaluated based on improvement in sensitivity and F-measure. Results showed that the sensitivity of all classifiers increases as the imbalance rate increases. SVM with radial basis function (RBF) kernel has the highest sensitivity when data is balanced (50:50) using SMOTE (Sensitivitytest = 0.5686, Ftest = 0.6927) compared to Naïve Bayes (Sensitivitytest = 0.4033, Ftest = 0.6218) and Logistic Regression (Sensitivitytest = 0.4194, Ftest = 0.621). Overall, the Gaussian Naïve Bayes model consistently improves sensitivity and F-measure as the imbalance ratio increases, but the sensitivity is below 50%. The classifiers performed better when data was balanced using SMOTE-SVM compared to SMOTE and SMOTE-ENN.

Traditional techniques for identifying disease genes and disease modules involve high-cost clinical experiments and unpredictable time consumption for analysis. Network-based computational approaches usually focus on the systematic study of molecular networks to predict the associations between diseases and genes. The random walk-based method is a network-based approach that utilises biological networks for analysis. As the random walk models efficiently capture the complex interplay among molecules in diseases, it is extensively applied in biological problem-solving based on networks. Despite their comprehensive employment, the fundamentals of random walk and overall background may not be fully understood, leading to misinterpretation of results. This review aims to cover the fundamental knowledge of random walk models for biological network analysis. This study reviewed diffusion-based random walk methods for disease gene prediction and disease module identification. The random walk-based disease gene prediction methods are categorised into node classification and link prediction tasks. This study details the advantages and limitations of each method. Finally, the potential challenges and research directions for future studies on random walk models are highlighted.