Sulaiman Abdullah Alateyah

This article introduces Multimodal Adaptive Patch Embedding with Vision Transformer (MAPE-ViT), a novel approach for RGB-D scene classification that effectively addresses fundamental challenges of sensor misalignment, depth noise, and object boundary preservation. Our framework integrates maximally stable extremal regions (MSER) with wavelet coefficients to create comprehensive patch embedding that capture both local and global image features. These MSER-guided patches, incorporating original pixels and multi-scale wavelet information, serve as input to a Vision Transformer, which leverages its attention mechanisms to extract high-level semantic features. The feature discrimination capability is further enhanced through optimization using the Gray Wolf algorithm. The processed features then flow into a dual-stream architecture, where an extreme learning machine handles multi-object classification, while conditional random fields (CRF) manage scene-level categorization. Extensive experimental results demonstrate the effectiveness of our approach, showing significant improvements in classification accuracy compared to existing methods. Our system provides a robust solution for RGB-D scene understanding, particularly in challenging conditions where traditional approaches struggle with sensor artifacts and noise.

This paper presents an approach for event recognition in sequential images using human body part features and their surrounding context. Key body points were approximated to track and monitor their presence in complex scenarios. Various feature descriptors, including MSER (Maximally Stable Extremal Regions), SURF (Speeded-Up Robust Features), distance transform, and DOF (Degrees of Freedom), were applied to skeleton points, while BRIEF (Binary Robust Independent Elementary Features), HOG (Histogram of Oriented Gradients), FAST (Features from Accelerated Segment Test), and Optical Flow were used on silhouettes or full-body points to capture both geometric and motion-based features. Feature fusion was employed to enhance the discriminative power of the extracted data and the physical parameters calculated by different feature extraction techniques. The system utilized a hybrid CNN (Convolutional Neural Network) + RNN (Recurrent Neural Network) classifier for event recognition, with Grey Wolf Optimization (GWO) for feature selection. Experimental results showed significant accuracy, achieving 98.5% on the UCF-101 dataset and 99.2% on the YouTube dataset. Compared to state-of-the-art methods, our approach achieved better performance in event recognition.

The entire educational model has undergone a significant change as a result of the Internet of Things (IoT)'s introduction to the educational sector. Universities have a significant chance to take advantage of the IoT technology in a variety of ways, including data collection and analysis to improve the educational experience, promote the achievement of learning objectives, and enhance overall school operations. Since the IoT promises, among other things, to improve education process and quality of life, as well as increase resource efficiency and management, the IoT has emerged as a highly debated research topic. Meanwhile, the Internet of things have temptation of using mobile devices and other technology while virtual session start for non-educational purpose, strength is always a reason for weakness. Therefore, student monitoring is a key part of many educational services. Especially during the virtual session. It can reduce the number of students who not completing the course, while increasing the access to education to those who really need these monitoring. In a variety of settings, wireless and mobile technologies are being used to monitor students: Courses and online sessions. The quality and dependability of student monitoring, however, have not been particularly satisfactory due to a number of restrictions, such as Wireless Networks (WN) unforeseen and fragmented coverage of users. In this study, we describe a method for monitoring students that makes use of ad-hoc Wireless Networks (ad-hoc-WN) that may be created on an ongoing basis between wearable and mobile devices. This enables the vital signs’ transmission in both routine and emergency circumstances. In order to make student monitoring through ad-hoc-WN a reality, we clarify a wireless architecture, go over emergency message routing, and bring together numerous relevant technical and non-technical difficulties. The suggested student monitoring method is made with the intention of being trustworthy and doable in the near future.

This research proposes a highly effective soft computing paradigm for estimating the compressive strength (CS) of metakaolin-contained cemented materials. The proposed approach is a combination of an enhanced grey wolf optimizer (EGWO) and an extreme learning machine (ELM). EGWO is an augmented form of the classic grey wolf optimizer (GWO). Compared to standard GWO, EGWO has a better hunting mechanism and produces an optimal performance. The EGWO was used to optimize the ELM structure and a hybrid model, ELM-EGWO, was built. To train and validate the proposed ELM-EGWO model, a sum of 361 experimental results featuring five influencing factors was collected. Based on sensitivity analysis, three distinct cases of influencing parameters were considered to investigate the effect of influencing factors on predictive precision. Experimental consequences show that the constructed ELM-EGWO achieved the most accurate precision in both training (RMSE = 0.0959) and testing (RMSE = 0.0912) phases. The outcomes of the ELM-EGWO are significantly superior to those of deep neural networks (DNN), k-nearest neighbors (KNN), long short-term memory (LSTM), and other hybrid ELMs constructed with GWO, particle swarm optimization (PSO), harris hawks optimization (HHO), salp swarm algorithm (SSA), marine predators algorithm (MPA), and colony predation algorithm (CPA). The overall results demonstrate that the newly suggested ELM-EGWO has the potential to estimate the CS of metakaolin-contained cemented materials with a high degree of precision and robustness.

Visual data mining is a new and effective strategy for dealing with the growing phenomenon of information overload. There is an urgent need for effective visual data mining because the growth of data sets from different domains and sources has made exploring, managing and analyzing vast volumes of data increasingly difficult. Identifying location-based trends and anomalies in data from a numeric output is challenging. The outputs of data mining procedures are often quite difficult to interpret. There is no human input to data exploration or direct involvement in the data mining process. Thus, it is difficult to analyze, explore, understand and view the data. In data mining, massive data sets are clustered using an iterative process that includes human input but data mining algorithms can miss some knowledge and observations. In addition, many users cannot take the appropriate action to address problems or opportunities because the huge amounts of data prevent them from staying aware of what is happening in and around their environments. Visual data mining can help in dealing with information overload and it is effective in analyzing large complex data sets. Visual data mining assists users in gaining a deep visual understanding of data. In the information age, users need to study and observe vast amounts of data to acquire important knowledge, and thus the need for visual and interactive analytical tools is particularly pressing. Visual cluster analysis has long utilized shaded similarity matrices, and this study investigated how they can be used in clustering visualization. We focused on proposing an agglomerative hierarchical clustering method. Ensemble cluster visualization is also presented for handling large data sets. This study proposes the adoption of a shaded similarity matrix to visually cluster knowledge discovered using data mining. Using the technology acceptance model as the measuring tool, we questioned respondents to evaluate the visualization prototype. The findings demonstrated that the visualization was effective and easy to use, and satisfied users.

Healthcare is a critical field of research and equally important for all nations. Providing secure healthcare facilities to citizens is the primary concern of each nation. However, people living in remote areas do not get timely and sufficient healthcare facilities, even in developed countries. During the recent COVID-19 pandemic, many fatalities occurred due to the inaccessibility of healthcare facilities on time. Therefore, there is a need to propose a solution that may help citizens living in remote areas with proper and secure healthcare facilities without moving to other places. The revolution in ICT technologies, especially IoT, 5G, and cloud computing, has made access to healthcare facilities easy and approachable. There is a need to benefit from these technologies so that everyone can get secure healthcare facilities from anywhere. This research proposes a framework that will ensure 24/7 accessibility of healthcare facilities by anyone from anywhere, especially in rural areas with fewer healthcare facilities. In the proposed approach, the patients will receive doorstep treatment from the remote doctor in rural areas or the nearby local clinic. Healthcare resources (doctor, treatment, patient counseling, diagnosis, etc.) will be shared remotely with people far from these facilities. The proposed approach is tested using mathematical modeling and a case study, and the findings confirm that the proposed approach helps improve healthcare facilities for remote patients.