Carpal tunnel syndrome (CTS) is a prevalent medical condition resulting from compression of the median nerve in the hand, often caused by overuse or age-related factors. In this study, a total of 160 patients participated, including 80 individuals with CTS presenting varying levels of severity across different age groups. Numerous studies have explored the use of machine learning (ML) and deep learning (DL) techniques for CTS diagnosis. However, further research is required to fully leverage the potential of artificial intelligence (AI) technology in CTS diagnosis, addressing the challenges and limitations highlighted in the existing literature. In our work, we propose a novel approach for CTS diagnosis, prediction, and monitoring disease progression. The proposed framework consists of three main layers. Firstly, we employ three distinct DL models for CTS diagnosis. Through our experiments, the proposed approach demonstrates superior performance across multiple evaluation metrics, with an accuracy of 0.969%, precision of 0.982%, and recall of 0.963%. The second layer focuses on predicting the cross-sectional area (CSA) at 1, 3, and 6 months using ML models, aiming to forecast disease progression during therapy. The best-performing model achieves an accuracy of 0.9522, an R2 score of 0.667, a mean absolute error (MAE) of 0.0132, and a median squared error (MdSE) of 0.0639. The highest predictive performance is observed after 6 months. The third layer concentrates on assessing significant changes in the patients’ health status through statistical tests, including significance tests, the Kruskal-Wallis test, and a two-way ANOVA test. These tests aim to determine the effect of injections on CTS treatment. The results reveal a highly significant reduction in symptoms, as evidenced by scores from the Symptom Severity Scale and Functional Status Scale, as well as a decrease in CSA after 1, 3, and 6 months following the injection. SHAP is then utilized to provide an understandable explanation of the final prediction. Overall, our study presents a comprehensive approach for CTS diagnosis, prediction, and monitoring, showcasing promising results in terms of accuracy, precision, and recall for CTS diagnosis, as well as effective prediction of disease progression and evaluation of treatment effectiveness through statistical analysis.

Carpal tunnel syndrome (CTS) is a clinical disease that occurs due to compression of the median nerve in the carpal tunnel. The determination of the severity of carpal tunnel syndrome is essential to provide appropriate therapeutic interventions. Machine learning (ML)-based modeling can be used to classify diseases, make decisions, and create new therapeutic interventions. It is also used in medical research to implement predictive models. However, despite the growth in medical research based on ML and Deep Learning (DL), CTS research is still relatively scarce. While a few studies have developed models to predict diagnosis of CTS, no ML model has been presented to classify the severity of CTS based on comprehensive clinical data. Therefore, this study developed new classification models for determining CTS severity using ML algorithms. This study included 80 patients with other diseases that have an overlap in symptoms with CTS, such as cervical radiculopathysasas, de quervian tendinopathy, and peripheral neuropathy, and 80 CTS patients who underwent ultrasonography (US)-guided median nerve hydrodissection. CTS severity was classified into mild, moderate, and severe grades. In our study, we aggregated the data from CTS patients and patients with other diseases that have an overlap in symptoms with CTS, such as cervical radiculopathysasas, de quervian tendinopathy, and peripheral neuropathy. The dataset was randomly split into training and test data, at 70% and 30%, respectively. The proposed model achieved promising results of 0.955%, 0.963%, and 0.919% in terms of classification accuracy, precision, and recall, respectively. In addition, we developed a machine learning model that predicts the probability of a patient improving after the hydro-dissection injection process based on the aggregated data after three different months (one, three, and six). The proposed model achieved accuracy after six months of 0.912%, after three months of 0.901%, and after one month 0.877%. The overall performance for predicting the prognosis after six months outperforms the prediction after one and three months. We utilized statistics tests (significance test, Spearman’s correlation test, and two-way ANOVA test) to determine the effect of injection process in CTS treatment. Our data-driven decision support tools can be used to help determine which patients to operate on in order to avoid the associated risks and expenses of surgery.

Edge computing is a distributed computing paradigm that involves processing data at or near the edge of the internet of things (IoT) network, instead of centralized server. This makes the cyber-attacks increasingly sophisticated, and traditional security measures become no longer sufficient to protect against them. Concurrently, privacy concerns arise when sensitive data is involved in Edge computing applications containing confidential information. In this paper, we propose a privacy-preserved federated learning (FL) approach for cyber-attack detection in edge based IoT ecosystem. A novel lightweight convolutional Transformer network (LCT) network is designed to precisely identify cyber-attacks though learning attack patterns from IoT traffics in local edge devices, where model is personalized though fine-tuning. The privacy of model and data is preserved in our system via incorporating differential privacy and secure aggregation during the cooperative training process on edge devices. We evaluate our proposed approach on a real-world dataset of network traffic (NSL-KDD) containing various types of attacks, and the experimental results show that our personalized FL approach outperforms traditional FL in terms of detection accuracy. We also show that our approach is effective in handling non-stationary data and adapting to changes in the network environment.

Among spatial information applications, SpatialHadoop is one of the most important systems for researchers. Broad analyses prove that SpatialHadoop outperforms the traditional Hadoop in managing distinctive spatial information operations. This paper presents a Two Dimensional Priority R-Tree (2DPR-Tree) as a new partitioning technique in SpatialHadoop. The 2DPR-Tree employs a top-down approach that effectively reduces the number of partitions accessed to answer the query, which in turn improves the query performance. The results were evaluated in different scenarios using synthetic and real datasets. This paper aims to study the quality of the generated index and the spatial query performance. Compared to other state-of-the-art methods, the proposed 2DPR-Tree improves the quality of the generated index and the query execution time.

SpatialHadoop could handle spatial data operations in a low partitioning execution time compared to the traditional Hadoop. However, developing an efficient and an accurate partitioning algorithm is still a research field opened to many researchers. Confidently, this paper proposes a Minimum Boundary Rectangle-aware Priority R-Tree (MBR-aware PR-Tree) as an enhanced partitioning algorithm applicable at SpatialHadoop. Compared to state-of-art partitioning algorithms, our proposed algorithm outperforms them in terms of query execution time, file size, number of partitions, indexing time, and number of returned objects. The experimental results show superiority of our algorithm which have been confirmed for both spatial range query and k-nearest-neighbour query through evaluating the performance in different scenarios using a real dataset.