Mohammad Fathian

Breast cancer remains a global health challenge, and early detection is crucial for improving survival rates. However, traditional biomarker detection methods in machine learning face challenges such as high false positives, large gene datasets, and limited sample sizes. These challenges are exacerbated by the limitations of traditional differential evolution techniques, which struggle with scalability and effectiveness in high-dimensional, complex problems. This study introduces Ensemble-Based Logical Binary QANA (LBQANA_En), an improved differential evolution variant tailored for large-scale global optimization in gene expression analysis. By integrating six gene expression datasets, LBQANA_En overcomes the constraints of small sample sizes and the complexities inherent in gene expression data. Inspired by the quantum navigation of migratory birds and leveraging logical operators like XOR and OR, LBQANA_En outperforms other binary QANA variants in biomarker detection. The algorithm successfully identifies key biomarkers, including LPL, LEP, CD36, CDC20, TOP2A, and EZH2, significantly improving breast cancer detection accuracy and reducing false positives, achieving an impressive F1 score of 98.958%. These biomarkers provide critical insights into important pathways, such as AMPK and PPAR signaling, setting a new benchmark in computational biology and bioinformatics research, and paving the way for advancements in diagnostic techniques and medical science.

Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks are among the most lethal cyber threats in this world, which make an online service unavailable to its legitimate users by overwhelming the service provider’s resources. Regarding the importance of online services in a human's life, researchers have been working on techniques to detect and mitigate these kinds of attacks. Machine-learning models showed acceptable performance in DDoS detection. Hence, in this paper, we developed a machine-learning model for classifying network traffic and detecting DDoS attacks using a unique approach to pre-process the data. The most innovative aspect of our work is deploying our developed machine-learning model into an online real-time DDoS detection system and testing it under real DDoS attacks. Implementing and testing a DDoS detection system that can work outside of a dataset and can be used against real threats was the missing part of other similar works that were done in this paper. The model on offline data and the system under real attacks both showed great performance in detecting attack traffic with accuracies of 99.99% and 95.30%, respectively, and proved they can effectively be used against DDoS attacks.

The prevalence of insurance fraud in the auto industry poses significant financial challenges and undermines customer trust. Despite the application of machine learning methods to reduce these losses, current literature lacks effective tuned algorithms for detecting fraud in insurance claims. To address this gap, this study proposes an ensemble-based method with a weighted voting strategy for auto insurance fraud detection. The study uses the Binary Quantum-Based Avian Navigation Optimizer Algorithm (BQANA) to optimize the hyperparameters of Support Vector Machines (SVM), Random Forest (RF), and XGBoost classifiers, which are combined into an ensemble. To address the dataset’s imbalance, random undersampling was applied to create five legitimate-to-fraudulent claim ratios: A:A, 1:1, 2:1, 4:1, and 8:1. The performance of BQANA was compared with Genetic Algorithms and Simulated Annealing for hyperparameter tuning. The results indicate that the ensemble model with BQANA-optimized hyperparameters outperforms other methods, particularly at a 1:1 ratio, achieving 99.94% Accuracy, 98.93% Precision, 100% Recall, and a 99.46% F1-score. These metrics surpass those obtained without optimization or with traditional tuning methods. This research highlights the efficacy of the BQANA algorithm in optimizing hyperparameters for classification models. By combining these optimized classifiers into an ensemble, the study significantly enhances predictive accuracy in car insurance fraud detection, offering notable improvements over conventional methods.

Objectives Identifying stable co-occurring symptoms in breast cancer (BC) patients during chemotherapy can improve symptom management and the treatment process. This study examines symptom cluster stability in Iranian BC patients receiving chemotherapy and evaluates stability across three-time points within each cluster. Methods This study collected data from three-time points: initial chemotherapy commencement, 2½ months postdiagnosis, and 5 months postdiagnosis. The research used exploratory factor analysis (EFA) in combination with hierarchical cluster analysis (HCA) and K means clustering to address research questions. Results In the initial clustering step, EFA identified five clusters with high consistency across three-time points. The first cluster showed depression, anxiety and irritability, while the second cluster was characterised by sexual interest and pain. The third cluster was associated with diarrhoea and vomiting. In the second step, we obtained the HCA item output and two clusters of K means clustering that recorded depression and anxiety symptoms over time. Vomiting, dry mouth, sexual interest, worrying and numbness were observed during the first and second points, but the frequency has decreased since then. Conclusions Cancer’s psychological and physiological symptoms, including depression, anxiety, digestive and hormonal issues, remain stable throughout the disease. Palliative care centres can improve patients’ quality of life and treatment process by addressing persistent symptoms.

Predictive Process Monitoring (PPM) techniques leverage incomplete execution traces and historical event logs to predict outcomes, activities, and remaining time in ongoing processes. Accurately predicting process remaining time benefits process managers, enabling proactive decisions. A prediction model’s effectiveness extends beyond accuracy, emphasizing timely predictions. Despite the continuous nature of time, the prevailing emphasis on regression-based approaches has overshadowed the untapped potential of classification-based methods. This study aims to perform a comparative analysis of Classification-Based PPM (CB-PPM) models and Regression-Based PPM (RB-PPM) models. The focus is on predicting remaining time in various processes, considering accuracy, offline execution time (model training), and online execution time (real-time predictions) as key evaluation criteria. We aim to assess the impact of model configuration on the performance of the prediction models. To accomplish this, our methodology includes designing experiments and implementing 136 PPM models on ten real-world datasets. These models configured with various combinations of four bucketing methods, five encoding methods, and eight prediction algorithms. The TOPSIS analysis highlights that the CB-PPM method is utilized in 90% of the most suitable models, whereas the RB-PPM method is found in only 10% of these models. The hypothesis testing results confirm that the CB-PPM method surpasses the RB-PPM method, significantly enhancing the accuracy of remaining time prediction. While the CB-PPM method has a higher online execution time, there is no observed increase in offline execution time. Furthermore, this study emphasizes the dataset-dependent nature of model configurations, underscoring that a single configuration may not universally apply to all datasets.

This study contributes to the literature by introducing the role of “location information sharing strategy” in optimizing the closed-loop supply chain (CLSC) performance. In the investigated CLSC, one manufacturer collects the used products through two collectors. The collectors may face collection disruptions and adopt the location information sharing strategy to overcome the disruptions. More precisely, the collector who faces disruption shares the customers’ location information with its rival to collect the used products. On the other hand, the manufacturer invests in the corporate social responsibility effort, which has bilateral effects on the forward and reverse flows. The products are sold through online and retail channels. The problem is first formulated in decentralized and centralized settings. Afterward, a coordination mechanism is developed to maximize profitability. The data from the case study is used to evaluate the performance of the proposed structures. The results indicate that the developed contract improves profitability and sustainability. Moreover, it is shown that the location information sharing strategy can be an efficient strategy for managers to overcome collection disruptions.