This work introduces a refined image-processing method explicitly designed to amalgamate raster-scanned images obtained with a compact planar microwave resonator. The resonator tuned for use on the skin provides a dee...
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ISBN:
(数字)9798331510473
ISBN:
(纸本)9798331510480
This work introduces a refined image-processing method explicitly designed to amalgamate raster-scanned images obtained with a compact planar microwave resonator. The resonator tuned for use on the skin provides a deeper field penetration into the dermis layer, compared to the skin impedance measurements. The resonance of the sensor is sensitive to the dielectric properties of the tissues underneath the skin. Healthy and abnormal tissues exhibit different dielectric characteristics. Pixelated images can leverage resonant frequencies and reflection coefficients to enhance contrasts and boundaries to identify the abnormal tissues. We proposed using analytical tools like the structural similarity index to correlate frequency shifts with reflection coefficient magnitudes precisely. Preliminary results suggested the adeptness of the technique at classifying whether the tissue depth surpasses 15 mm and detecting tumor locations with a high accuracy when the image depth does not exceed 15 mm. A compact neural network model was implemented for processing the microwave images when the tumor depth exceeded 15 mm and achieved a high Dice coefficient. The work incorporated simulations to substantiate the novel concept and demonstrated its potential for noninvasive, efficient, and cost-effective subcutaneous imaging in medical applications.
INTRODUCTION: A robust method is proposed in this paper to detect helmet usage in two-wheeler riders to enhance road safety. OBJECTIVES: This involves a custom made dataset that contains 1000 images captured under div...
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Cardiovascular diseases (CVDs) are one of the leading causes of death worldwide, and early identification is crucial to improve the patient’s prognosis. Traditional diagnostic methods for heart diseases are highly de...
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ISBN:
(数字)9798331513894
ISBN:
(纸本)9798331513900
Cardiovascular diseases (CVDs) are one of the leading causes of death worldwide, and early identification is crucial to improve the patient’s prognosis. Traditional diagnostic methods for heart diseases are highly dependent on clinical expertise and can be time-consuming, making machine learning an attractive alternative for improving accuracy to diagnose and support faster decision making. Previously, research has explored various machine learning algorithms, such as decision trees, support vector machines, and neural networks, to improve prediction accuracy. However, many of these models face challenges related to their interpretability and generalization to unexpected data. This paper presents a logistic regression model to predict CVDs based on a dataset of 303 samples, incorporating key demographic, clinical, and diagnostic features. The model achieved an accuracy of $85.12 \%$ in the training data and 81. $97 \%$ in the testing data, demonstrating its effectiveness for binary classification tasks. The study emphasizes the computational efficiency and interpretability of logistic regression, making it appropriate for real-time applications in clinical settings. In conclusion, the results indicate that logistic regression can serve as a reliable tool for early detection of heart disease, and future work can explore dataset expansion, advanced models, and explainable AI techniques to further improve prediction accuracy and model transparency.
In contemporary research, educational data mining (EDM) has become a captivating field for data mining and machine learning experts, focusing on identifying factors influencing students' academic performance and p...
In contemporary research, educational data mining (EDM) has become a captivating field for data mining and machine learning experts, focusing on identifying factors influencing students' academic performance and predicting the likelihood of students dropping out. To uncover these influential factors, feature selection methods are employed, while various machine learning models are used to predict students at risk of underperforming. Filter-based feature selection methods are commonly used in educational data mining due to their efficiency and ability to rank important features affecting academic success. However, because of their independence from classifiers and relying on a fixed threshold or predefined feature count, filter-based methods can sometimes negatively affect model performance. To address this, the present study introduces an optimized chi-square-based feature selection technique that dynamically selects the optimal features for each learning algorithm, ensuring that model performance is not compromised. The effectiveness of five classifiers—k-Nearest Neighbour (k-NN), Decision Tree (DT), Naïve Bayes (NB), Support Vector Machine (SVM), and Logistic Regression (LR)—has been evaluated using three configurations: no feature selection, traditional chi-square feature selection, and proposed optimized chi-square based feature selection. These evaluations were conducted on two distinct student datasets, one from secondary schools (DS1) and another from engineering institutions (DS2). The results demonstrated that the optimized chi-square method consistently improved prediction accuracy across all classifiers. Additionally, a bagging-based ensemble classifier, constructed using the best-performing individual classifier, further enhanced predictive performance. The highest accuracies achieved were 94.62% for DS1 and 96.36% for DS2, outperforming traditional feature selection and ensemble methods. This study presents a scalable, reliable, and stable approach to s
The convergence of medical imaging, computer vision, and orthodontics has made automatic cephalometric landmark detection a pivotal area of research. Accurate cephalometric analysis is crucial in orthodontics, orthogn...
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The convergence of medical imaging, computer vision, and orthodontics has made automatic cephalometric landmark detection a pivotal area of research. Accurate cephalometric analysis is crucial in orthodontics, orthognathic and maxillofacial surgery for diagnosis, treatment planning, and monitoring craniofacial growth. In this research study, a multi-branch fused feature extraction network titled CephTransXnet is proposed to automatically predict landmark coordinates from cephalometric radiographs. The initial sequential branch enhances discriminative local feature learning and feature extraction through parallel feature fusion by integrating Convolved Pooled Normalized (CPNB) and Gradient Optimized Multi-Path Bottleneck (GMBB) blocks with Channel and Spatial Attention (CSATM) module. The Swin Transformer (STB) branch efficiently handles long-range dependencies and extracts global features in cephalometric radiographs. The multi-branch fused features along with features from skip connections of CPNB and GMBB blocks are concatenated using a Coordinate Attention module (CoATM) to captures the positional relationships between various landmark features. A Landmark Discriminative Deviation Factor (LDDF) is determined by applying the Neighborhood Rough Set (NRS) approach to analyse the surrounding features of each landmark by considering spatial relationships or similarity measures between the landmarks and neighboring regions. The Spatial Pyramid Pooling (SPPL) layer incorporated in the final phase of CephTransXnet model extracts multi-scale features by pooling over sub-regions of varying sizes, enabling the network to capture both local and global context for precise cephalometric landmark identification. The CephTransXnet framework achieved an average Successful Detection Rates (SDRs) of 88.71 % and 79.05 % in 2 mm using the 2015 International Symposium on Biomedical Imaging (ISBI) grand challenge dental X-ray analysis dataset. The effectiveness of the CephTransXnet mod
The wholly wholesale transition into remote and hybrid workspaces is characteristic of modern-day work settings. As more and more firms adopt such flexible models, it is important to note, their implications on employ...
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ISBN:
(数字)9798350357509
ISBN:
(纸本)9798350357516
The wholly wholesale transition into remote and hybrid workspaces is characteristic of modern-day work settings. As more and more firms adopt such flexible models, it is important to note, their implications on employee wellness and productivity. This paper uses machine learning techniques in order to understand the effects of mental distancing and hybridization on various key results such as the mental state of the employees, balance between their work and private life, separation from the society, as well as productivity levels. With a sample size of 5000 employees collect from kaggle, we determine the advantages and disadvantages these work models present. In other regards, geographical larger shifts create practical benefits including greater flexibility and autonomy plus productivity. However, such dramatic shifts create higher work-related stress, greater social tension and isolation, as well as challenges with workplace culture. AI, or more precisely machine learning, uncovers concepts in relation to work models in this paper, fico that enable firms to better personate their constituents and the workforce. Reasonable conclusions could be drawn so as flexible working arrangements benefit in reducing work-life conflict however improved organizational structure within the companies is needed to have such benefits due. Mental health as one of the resources, alongside technology will assist in such transition. This paper is a step forward for HR practitioners.
Unpredictability in illumination conditions, similar illness symptoms, skewed datasets, processing expense, and the challenge of determining infections at their onset are some of the challenges in plant leaf disease d...
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The quick growth of e-commerce has brought attention to how important efficient recommendation systems are to enhancing user experience and accomplishing business objectives. This paper investigates the effectiveness ...
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Measuring clock skew of devices over a network fully relies on the offsets, the differences between sending and receiving times. Offsets that shape a thick line are the most ideal one as their slope is directly the cl...
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Behavior prediction is a fundamental task in intelligent consumer electronics. Its goal is to predict potential future behaviors based on known behaviors, playing a crucial role in understanding user intentions and op...
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