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This paper reviews the work in the areas of machine learning applications for energy management in smart buildings, 5G technology’s role in smart energy management, and the use of machine learning algorithms in microgrid energy management systems. The first area focuses on the adaptability of building-integrated energy systems to unpredictable changes through AI-initiated learning processes and digital twins. The second area explores the impact of 5G technology on smart buildings, particularly in Singapore, emphasizing its role in facilitating high-class services and efficient functionalities. The third area delves into the application of various machine learning algorithms, such as supervised and unsupervised learning, in managing and monitoring microgrids. These broad areas collectively offer a comprehensive understanding of how machine learning can revolutionize energy management systems in smart buildings, making them more efficient, adaptable, and sustainable.

Summary: The rapid growth of Wi‐Fi networking and intelligent systems, fueled by the emergence of the Internet of Things (IoT), has enabled significant transformative advancements. The imminent arrival of sixth‐generation (6G) wireless networks offers more opportunities as fifth‐generation (5G) wireless networks improve IoT connections. This study explores the potential advantages of 6G technology and IoT networks, focusing on edge intelligence, reconfigurable smart surfaces, Terahertz communications, and blockchain integration. The study examines potential implementation challenges and highlights the potential impact of 6G on IoT device usage. This research outlines potential applications of 6G in many sectors like the internet of vehicles, healthcare, autonomous driving, unmanned aerial vehicles, satellite television, and industrial IoT. This research elaborates on specific research topics related to 6G‐IoT technologies to enhance understanding and practical applicability. [ABSTRACT FROM AUTHOR]

Efficiently scheduling heterogeneous cloud workloads in modern computing environments is a formidable challenge. Managing diverse resource needs, intricate task dependencies, and optimizing resource allocation are key concerns in this context. To address these challenges, this paper introduces a novel approach that combines the Advantage Actor-Critic (AAC) and Proximal Policy Optimization (PPO) models. AAC accurately estimates task dependencies, while PPO optimizes task scheduling operations. The proposed model leverages essential parameters such as CPU utilization, memory requirements, storage demands, network bandwidth, task deadlines, and task dependencies. The results of experiments conducted demonstrate the efficacy of the proposed approach. It achieves a 4.9% reduction in makespan, indicating faster task completion, an 8.5% increase in resource utilization, showcasing improved resource efficiency, a 2.5% reduction in energy requirements, extending the cloud deployment lifespan, and a 4.9% increase in the deadline hit ratio, highlighting the model's proficiency in meeting task deadlines. In conclusion, the novel scheduling approach, integrating AAC and PPO models, offers substantial advantages over existing methods. Precise task dependency estimation and optimized scheduling lead to enhanced performance metrics, including reduced makespan, augmented resource utilization, increased throughput, and improved deadline adherence. These findings have promising implications for real-world applications, underscoring the solution's potential impact on contemporary computing environments. [ABSTRACT FROM AUTHOR]

Multimodal medical image fusion is a perennially prominent research topic that can obtain informative medical images and aid radiologists in diagnosing and treating disease more effectively. However, the recent state-of-the-art methods extract and fuse features by subjectively defining constraints, which easily distort the exclusive information of source images. To overcome these problems and get a better fusion method, this study proposes a 2D data fusion method that uses salient structure extraction (SSE) and a swift algorithm via normalized convolution to fuse different types of medical images. First, salient structure extraction (SSE) is used to attenuate the effect of noise and irrelevant data in the source images by preserving the significant structures. The salient structure extraction is performed to ensure that the pixels with a higher gradient magnitude impact the choices of their neighbors and further provide a way to restore the sharply altered pixels to their neighbors. In addition, a Swift algorithm is used to overcome the excessive pixel values and modify the contrast of the source images. Furthermore, the method proposes an efficient method for performing edge-preserving filtering using normalized convolution. In the end,the fused image are obtained through linear combination of the processed image and the input images based on the properties of the filters. A quantitative function composed of structural loss and region mutual data loss is designed to produce restrictions for preserving data at feature level and the structural level. Extensive experiments on CT-MRI images demonstrate that the proposed algorithm exhibits superior performance when compared to some of the state-of-the-art methods in terms of providing detailed information, edge contour, and overall contrasts. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Due to its potential to produce substantial returns and reduce risks, stock price prediction has garnered a lot of attention in the financial markets. OBJECTIVES: A comparison of neural network models for stock price prediction is presented in this research report. METHODS: Through this study, I aim to compare, on the basis of the precision and accuracy, the performance of different neural network models for stock price prediction. LSTM model along with RNN model accuracy in predicting the next day's stock price i.e., which model can predict closest to the actual value. RESULTS: It is found that LSTM works better than RNN in predicting a value closer to the actual open price stock value. CONCLUSION: A comparison between the models shows LSTM is the more accurate model. [ABSTRACT FROM AUTHOR]

Dealing with imbalanced data is a common challenge in machine learning, where one class has significantly fewer examples than another. Successfully addressing this challenge requires careful consideration of the data, algorithm, and evaluation metrics to ensure that the model accurately predicts the minority class. In this study, we present a hybrid approach called Siamese‐HYNAA, which combines a Siamese network and a population‐based optimizer hypercube natural aggregation algorithm (HYNAA) to generate candidate solutions for augmenting the minority class. We collected 10 imbalanced datasets ranging from 1.81 to 8.78 imbalanced ratios and built solution pairs based on correctly predicted candidate solutions using support vector machine (SVM). We then fed these solutions to the Siamese network, which employs a one‐shot learning approach to improve predictions with fewer candidate solutions. However, we found that SVM predicted only a small number of minority class samples accurately, prompting us to optimize the number of candidate solution pairs using HYNAA to generate more synthetic samples for the Siamese network. We evaluated our proposed strategy against basic SMOTE and our previous work, SMOTE‐PSOEV, using various performance measures, including ROC‐AUC learning curves, sensitivity, specificity, accuracy, Characteristic stability index, balanced accuracy, F1‐score, informedness, markedness, and execution time. Our results indicate that Siamese‐HYNAA generates promising results for imbalanced data. [ABSTRACT FROM AUTHOR]