Enhancement Of The Low Contrast Image Using Fuzzy Set Theory

This paper presents a fuzzy grayscale enhancement technique for low contrast image. The degradation of the low contrast image is mainly caused by the inadequate lighting during image capturing and thus eventually resulted in nonuniform illumination in the image

Performances of proposed normalization algorithm for iris recognition

Iris recognition has very high recognition accuracy in comparison with many other biometric features. The iris pattern is not the same even right and left eye of the same person. It is different and unique. This paper proposes an algorithm to recognize people based on iris images. The algorithm consists of three stages. In the first stage, the segmentation process is using circular Hough transforms to find the region of interest (ROI) of given eye images. After that, a proposed normalization algorithm is to generate the polar images than to enhance the polar images using a modified Daugman’s Rubber sheet model. The last step of the proposed algorithm is to divide the enhance the polar image to be 16 divisions of the iris region. The normalized image is 16 small constant dimensions. The Gray-Level Co-occurrence Matrices (GLCM) technique calculates and extracts the normalized image’s texture feature. Here, the features extracted are contrast, correlation, energy, and homogeneity of the iris. In the last stage, a classification technique, discriminant analysis (DA), is employed for analysis of the proposed normalization algorithm. We have compared the proposed normalization algorithm to the other nine normalization algorithms. The DA technique produces an excellent classification performance with 100% accuracy. We also compare our results with previous results and find out that the proposed iris recognition algorithm is an effective system to detect and recognize person digitally, thus it can be used for security in the building, airports, and other automation in many applications

Editorial: Emerging applications of text analytics and natural language processing in healthcare

WOS:001033976100001Text analytics and natural language processing (NLP) have emerged as powerful tools in healthcare, revolutionizing patient care, clinical research, and public health administration. Over the years, as healthcare databases expand exponentially, healthcare providers, pharmaceutical and biotech industries are utilizing both tools to enhance patient outcome

A protocol for Enhanced imaging and Quantification of Cervical Cell Under Scanning electron Microscope

The application of Field Emission Scanning Electron Microscopy and Energy Dispersive X-Ray (FE-SEM/EDX) for the characterization of biological samples can produce promising results for classification purpose. The limitations of the established sample preparation technique of cervical cells for FE-SEM/EDX study that differentiate between normal and abnormal cells prompted the development of a proposed protocol for the preparation of cervical cells. The proposed protocol was conducted by a McDowell-Trump fixative prepared in 0.1M phosphate buffer without osmium tetroxide at 4°C for 2 h in the fixation process. Morphologically, the cervical cells scanned under the FE-SEM/EDX did not present blackening effects, and the structure of the cells was not broken based on the FE-SEM images. Quantitatively, the possible elemental distributions in the cells, such as carbon, nitrogen, oxygen, and sodium, are detected in samples prepared by the proposed protocol. The analysed elements were validated using the Attenuated Total Reflection and Fourier Transform Infrared (ATR/FTIR) spectroscopy. Moreover, by avoiding osmium tetroxide fixation, the time required for sample preparation decreased significantly. This sample preparation protocol can be used for normal and abnormal cervical cells in achieving better results in terms of morphological, detected elemental distribution, and rapid in time

Local Descriptor for Retinal Fundus Image Registration

A feature-based retinal image registration (RIR) technique aligns multiple fundus images and composed of pre-processing, feature point extraction, feature descriptor, matching and geometrical transformation. Challenges in RIR include difference in scaling, intensity and rotation between images. The scale and intensity differences can be minimised with consistent imaging setup and image enhancement during the pre-processing, respectively. The rotation can be addressed with feature descriptor method that robust to varying rotation. Therefore, a feature descriptor method is proposed based on statistical properties (FiSP) to describe the circular region surrounding the feature point. From the experiments on public Fundus Image Registration dataset, FiSP established 99.227% average correct matches for rotations between 0° and 180°. Then, FiSP is paired with Harris corner, scale-invariant feature transform (SIFT), speeded-up robust feature (SURF), Ghassabi's and D-Saddle feature point extraction methods to assess its registration performance and compare with the existing feature-based RIR techniques, namely generalised dual-bootstrap iterative closet point (GDB-ICP), Harris-partial intensity invariant feature descriptor (PIIFD), Ghassabi's-SIFT, H-M 16, H-M 17 and D-Saddle-histogram of oriented gradients (HOG). The combination of SIFT-FiSP registered 64.179% of the image pairs and significantly outperformed other techniques with mean difference between 25.373 and 60.448% (p = <;0.001*)

Transfer learning-assisted 3D deep learning models for knee osteoarthritis detection: Data from the osteoarthritis initiative

Knee osteoarthritis is one of the most common musculoskeletal diseases and is usually diagnosed with medical imaging techniques. Conventionally, case identification using plain radiography is practiced. However, we acknowledge that knee osteoarthritis is a 3D complexity; hence, magnetic resonance imaging will be the ideal modality to reveal the hidden osteoarthritis features from a three-dimensional view. In this work, the feasibility of well-known convolutional neural network (CNN) structures (ResNet, DenseNet, VGG, and AlexNet) to distinguish knees with and without osteoarthritis (OA) is investigated. Using 3D convolutional layers, we demonstrated the potential of 3D convolutional neural networks of 13 different architectures in knee osteoarthritis diagnosis. We used transfer learning by transforming 2D pre-trained weights into 3D as initial weights for the training of the 3D models. The performance of the models was compared and evaluated based on the performance metrics [balanced accuracy, precision, F1 score, and area under receiver operating characteristic (AUC) curve]. This study suggested that transfer learning indeed enhanced the performance of the models, especially for ResNet and DenseNet models. Transfer learning-based models presented promising results, with ResNet34 achieving the best overall accuracy of 0.875 and an F1 score of 0.871. The results also showed that shallow networks yielded better performance than deeper neural networks, demonstrated by ResNet18, DenseNet121, and VGG11 with AUC values of 0.945, 0.914, and 0.928, respectively. This encourages the application of clinical diagnostic aid for knee osteoarthritis using 3DCNN even in limited hardware conditions

Predicting occupational injury causal factors using text-based analytics : A systematic review

Workplace accidents can cause a catastrophic loss to the company including human injuries and fatalities. Occupational injury reports may provide a detailed description of how the incidents occurred. Thus, the narrative is a useful information to extract, classify and analyze occupational injury. This study provides a systematic review of text mining and Natural Language Processing (NLP) applications to extract text narratives from occupational injury reports. A systematic search was conducted through multiple databases including Scopus, PubMed, and Science Direct. Only original studies that examined the application of machine and deep learning-based Natural Language Processing models for occupational injury analysis were incorporated in this study. A total of 27, out of 210 articles were reviewed in this study by adopting the Preferred Reporting Items for Systematic Review (PRISMA). This review highlighted that various machine and deep learning-based NLP models such as K-means, Naïve Bayes, Support Vector Machine, Decision Tree, and K-Nearest Neighbors were applied to predict occupational injury. On top of these models, deep neural networks are also included in classifying the type of accidents and identifying the causal factors. However, there is a paucity in using the deep learning models in extracting the occupational injury reports. This is due to these techniques are pretty much very recent and making inroads into decision-making in occupational safety and health as a whole. Despite that, this paper believed that there is a huge and promising potential to explore the application of NLP and text-based analytics in this occupational injury research field. Therefore, the improvement of data balancing techniques and the development of an automated decision-making support system for occupational injury by applying the deep learning-based NLP models are the recommendations given for future research

Modeling of micro-diaphragm for optical pressure sensor for human artery pulse wave detection / Khairunnisa Hasikin

This thesis presents the development of a micro-diaphragm for an optical pulse pressure sensor using Micro-electro-mechanical Systems (MEMS) technology. Modelling of the micro-diaphragm was conducted and the comparison of its performance was simulated for silicon nitride and polyirnide materials. In addition. diaphragm's radius and thickness were varied to further analyze the performance of the micro-diaphragm. There are three design parameters that affecting the micro-diaphragm performance namely diaphragms radius. thickness and material. Thus, an optimization analysis using Taguchi method was done to attain significant design parameters that give the best micro-diaphragm performance. Findings indicated that the best performance of the micro-diaphragm was obtained when the micro-diaphragm achieved high deflection and sensitivity as well as low resonance frequency. Simulation results have concluded that these performances are achieved when the diaphragm's radius and thickness are large and small respectively. Furthermore, the Taguchi method verified that the optimized design parameters radius of 90~-tm, thickness of 4~-tm and polyimide material have successfully achieved the best micro-diaphragm performance. In addition, the selected design parameters have been proven to provide an adequate sensitivity to detect the pulse pressure on human's radial artery

Flexible Sector Detector-Based Mismatch Supply Voltage in Direct Torque Control Doubly Fed Induction Machine: An Experimental Validation

Direct Torque Control (DTC) of Induction Motors (IMs) is popular in motor drive applications because of its robust and simple control structure. The IM winding can be controlled on both sides using dual inverter technique which more effective for Electric Vehicle (EV) with a greater number of voltage vectors. However, the battery performance of the dual inverter will deteriorate unevenly on both sides, resulting in fluctuating voltages for the EV system. This will lead to the generation of distorted stator currents and a significant droop in the stator flux, which in turn can increase the total harmonic distortion (THD) in the system. Additionally, the performance of torque may not be able to regulate effectively. This paper examines the effect of unstable voltage on voltage vector mapping performance with tilted angles and proposes new sector definitions based on voltage ratio conditions. Moreover, the proposed sector for each predefined voltage ratio is tested under three-speed conditions. The proposed technique effectiveness is validated through hardware experiments using a dSPACE 1104 controller and retuning the stator current for proper waveform. This approach improves the stator current waveform, improves stator flux droop, enhances torque regulation and minimizes the THD in the DTC system

3D Efficient Multi-Task Neural Network for Knee Osteoarthritis Diagnosis Using MRI Scans: Data From the Osteoarthritis Initiative

Deep learning, particularly Convolutional Neural Networks, has demonstrated effectiveness in computer-aided diagnosis applications, including knee osteoarthritis analysis. Two of the most common tasks done in medical imaging are segmentation and classification tasks. This research investigates the feasibility of multi-task models for volumetric analysis using Magnetic Resonance Imaging scans in knee osteoarthritis diagnosis, while considering computational efficiency. In order to leverage the correlation between segmentation and classification tasks, two 3D multi-task models, OA&#x005F;MTL (Osteoarthritis&#x005F;Multi-Task Learning) and RES&#x005F;MTL (Residual&#x005F;Multi-Task Learning) models are developed to simultaneously segment knee structures and classify knee osteoarthritis incidence. The performance of the multi-task models is evaluated against single-task baseline models and other existing convolutional neural network models using a total of eight different performance metrics, while comparing the computational complexity among the models. Experimental results demonstrate that multi-task model leverages the information of segmentation task to improve the classification performance. OA&#x005F;MTL is a multi-task model that incorporates an encoder-decoder architecture, residual modules, and depthwise separable convolutions for enhanced performance. OA&#x005F;MTL achieves superior performance for classification tasks with an accuracy score of 0.825, and a comparable segmentation DSC score of 0.915. OA&#x005F;MTL achieves a favorable trade-off between computational complexity and model performance. The contribution of this work includes an approach that simultaneously performs knee structure segmentation and osteoarthritis classification in 3D MRI, which addresses the need for efficient models in the field of medical imaging, specifically on computationally challenging 3D medical imaging applications