One of the prominent challenges encountered in real-world data is an imbalance, characterized by unequal distribution of observations across different target classes, which complicates achieving accurate model classifications. This survey delves into various machine learning techniques developed to address the difficulties posed by imbalanced data. It discusses data-level methods such as oversampling and undersampling, algorithm-level solutions including ensemble learning and specific algorithm adjustments, cost-sensitive algorithms, and hybrid strategies that combine multiple approaches. Moreover, this paper emphasizes the crucial role of evaluation methods like Precision, F1 Score, Recall, G-mean, and AUC in measuring the effectiveness of these strategies under imbalanced conditions. A detailed review of recent research articles helps pinpoint persistent gaps in generalizability, scalability, and robustness across these methods, underscoring the necessity for ongoing improvements. The survey seeks to offer an extensive overview of current approaches that improve the efficiency and effectiveness of machine learning models dealing with imbalanced datasets, thus equipping researchers with the insights needed to develop robust and effective models ready for real-world application.

The depletion of natural resources and the intermittence of renewable energy resources have pressed the need for a hybrid microgrid, combining the benefits of both AC and DC microgrids, minimizing the overall deficiency shortcomings and increasing the reliability of the system. The hybrid microgrid also supports the decentralized grid control structure, aligning with the current scattered and concentrated load scenarios. Hence, there is an increasing need to explore and reveal the integration, optimization, and control strategies regarding the hybrid microgrid. A comprehensive study of hybrid microgrid’s performance parameters, efficiency, reliability, security, design flexibility, and cost-effectiveness is required. This paper discusses major issues regarding the hybrid microgrids, the integration of AC and DC microgrids, their security and reliability, the optimization of power generation and load management in different scenarios, the efficient management regarding uncertainty for renewable energy resources, the optimal placement of feeders, and the cost-effective control methodologies for the hybrid microgrid. The major research areas are briefly explained, aiming to find the research gap that can further improve the performance of the grid. In light of the recent trends in research, novel strategies are proposed that are found most effective and cost-friendly regarding the hybrid microgrid. This paper will serve as a baseline for future research, comparative analysis, and further development of novel techniques regarding hybrid microgrids.

The growth of cloud computing is hindered by concerns about privacy and security. Despite the widespread use of network intrusion detection systems (NIDS), the issue of false positives remains prevalent. Furthermore, few studies have approached the intrusion detection problem as a time series issue, requiring time series modeling. In this study, we propose a novel technique for the early detection of intrusions in cloud computing using time series data. Our approach involves a method for Feature Selection (FS) and a prediction model based on the Facebook Prophet model to assess its efficiency. The FS method we propose is a collaborative feature selection model that integrates time series analysis techniques with anomaly detection, stationary, and causality tests. This approach specifically addresses the challenge of misleading connections between time series anomalies and attacks. Our results demonstrate a significant reduction in predictors employed in our prediction model, from 70 to 10 predictors, while improving performance metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), Median Absolute Percentage Error (MdAPE), and Dynamic Time Warping (DTW). Furthermore, our approach has resulted in reduced training, prediction, and cross-validation times of approximately 85%, 15%, and 97%, respectively. Although memory consumption remains similar, the utilization time has been significantly reduced, resulting in substantial resource usage reduction. Overall, our study presents a comprehensive methodology for effective early detection of intrusions in cloud computing based on time series anomalies, employing a collaborative feature selection model and the Facebook Prophet prediction model. Our findings highlight the efficiency and performance improvements achieved through our approach, contributing to the advancement of intrusion detection techniques in the context of cloud computing security.

Ransomware attacks pose significant security threats to personal and corporate data and information. The owners of computer-based resources suffer from verification and privacy violations, monetary losses, and reputational damage due to successful ransomware assaults. As a result, it is critical to accurately and swiftly identify ransomware. Numerous methods have been proposed for identifying ransomware, each with its own advantages and disadvantages. The main objective of this research is to discuss current trends in and potential future debates on automated ransomware detection. This document includes an overview of ransomware, a timeline of assaults, and details on their background. It also provides comprehensive research on existing methods for identifying, avoiding, minimizing, and recovering from ransomware attacks. An analysis of studies between 2017 and 2022 is another advantage of this research. This provides readers with up-to-date knowledge of the most recent developments in ransomware detection and highlights advancements in methods for combating ransomware attacks. In conclusion, this research highlights unanswered concerns and potential research challenges in ransomware detection.

This research presents a groundbreaking approach in aerial image analysis by integrating the Real-Time Detection and Recognition (RT-DETR-X) model with the Slicing Aided Hyper Inference (SAHI) methodology, utilizing the VisDrone-DET dataset. Aimed at enhancing the efficiency of drone technology across a spectrum of applications, including water conservancy, geological exploration, and military operations, this study focuses on harnessing the real-time, end-to-end object detection capabilities of RT-DETR-X. Characterized by its high-speed and high-accuracy performance, particularly in UAV aerial photography, RT-DETR-X demonstrates a remarkable 54.8% Average Precision (AP) and 74 frames per second (FPS), surpassing similar models in both speed and accuracy. The research thoroughly examines the VisDrone-DET dataset, which encompasses a diverse range of small targets in UAV aerial photography scenes. Covering 10 distinct categories, the dataset provides a robust platform for rigorous model testing. The study emphasizes the utilization of the original image dataset for comprehensive training and evaluation, alongside the practical implementation of the SAHI method for enhanced detection of small-scale objects. Through an in-depth exploration of the model’s performance in various scenarios and a detailed analysis of the environmental setup, this paper underscores the impact of integrating RT-DETR with the SAHI approach. The findings reveal significant progress in drone detection technologies, offering a holistic framework for effective and efficient aerial surveillance. The integration not only boosts the model’s detection accuracy but also opens new avenues for advanced image analysis in UAV applications.

Cardiovascular disease (CVD) is a prevalent and serious condition causing a significant global mortality rate. According to the World Health Organization (WHO), in 2022, CVD claimed the lives of approximately 19.1 million people, accounting for 33% of global fatalities. ECG is widely used for automatic detection of CVD using traditional machine learning; however, it is usually difficult to select optimal features. Addressing this issue, a scalable machine learning-based architecture is proposed for early CVD detection based optimal feature selection. This architecture aims to revolutionize healthcare by enabling timely diagnosis and treatment, reducing CVD-related fatalities. Comprising data collection, storage, and processing components, the system employs machine learning classifiers to predict patients’ heart conditions. Initially feature are extracted from ECG signals then feature selection techniques like FCBF, MrMr, and relief, along with PSO-optimization are used to select optimal features. Extra Tree and Random Forest classifiers trained on the selected features have achieved notable performance rates with accuracy of 100% respectively. Furthermore, a comparison of the proposed method with state of the art on both small and large dataset is provided. The proposed system holds potential to revolutionize patient care and substantially lower CVD-related mortality, enhancing the quality of life for affected individuals. In summary, this architecture offers a promising solution to the pressing issue of CVD and paves the way for advanced healthcare systems.