Fuzzy rank cluster top k Euclidean distance and triangle based algorithm for magnetic field indoor positioning system

The indoor localisation based on indoor magnetic field (MF) has drawn much research attention since they have a range of applications field in science and industry. The position estimation is generally based on the Euclidean distance (ED) between compared data points. Commonly, the state-of-the-art k-nearest neighbour (KNN) algorithm is used to estimate the test point (TP) position by considering the average location of the closest estimated K reference points (RPs). However, the problem of using the KNN algorithm is the fixed K value does not guarantee accurate estimation at every position. In this study, we first optimise the MF RPs database using the clustering method. Each trained RP and other nearby RPs are clustered together at a certain distance. Then, we create a rank cluster algorithm where we match the top 10 ranks RPs with the nearest Euclidean distance to the TP with the RPs cluster. For the proposed fuzzy algorithm, a condition is applied to choose whether the triangle area or average Euclidean algorithm is used to find the final estimated position. Experiments show a localisation accuracy of 5.88 m, which is better than KNN with an improvement of 31 %

A heuristic approach for finding similarity indexes of multivariate data sets

Multivariate data sets (MDSs), with enormous size and certain ratio of noise/outliers, are generated routinely in various application domains. A major issue, tightly coupled with these MDSs, is how to compute their similarity indexes with available resources in presence of noise/outliers - which is addressed with the development of both classical and non-metric based approaches. However, classical techniques are sensitive to outliers and most of the non-classical approaches are either problem/application specific or overlay complex. Therefore, the development of an efficient and reliable algorithm for MDSs, with minimum time and space complexity, is highly encouraged by the research community. In this paper, a non-metric based similarity measure algorithm, for MDSs, is presented that solves the aforementioned issues, particularly, noise and computational time, successfully. This technique finds the similarity indexes of noisy MDSs, of both equal and variable sizes, through utilizing minimum possible resources i.e., space and time. Experiments were conducted with both benchmark and real time MDSs for evaluating the proposed algorithm`s performance against its rival algorithms, which are traditional dynamic programming based and sequential similarity measure algorithms. Experimental results show that the proposed scheme performs exceptionally well, in terms of time and space, than its counterpart algorithms and effectively tolerates a considerable portion of noisy data

Estimated effective lifetime risks of radiation-induced thyroid cancer in Computed Tomography (CT) brain examination

Thyroid is one of the most radiosensitive organs in the human body. Although the scanning range of brain computed tomography (CT) does not include lower neck region, there is possibility for thyroid to be irradiated due to scattered radiation because of its location near to the external beam collimation. The objective of this study was to evaluate effective lifetime risk of radiation-induced thyroid cancer in young adults following brain CT examination. A total of 306 patient data within the age range between 18 and 39 years old were retrospectively analysed. Absorbed dose of the thyroid organ was obtained through the input of data using WAZA- ARI v2. Effective lifetime risk was calculated by multiplying equivalent dose of the thyroid organ with the lifetime attributable cancer risk adapted from Biological Effects in Ionising Radiation (BEIR) Report V11. The effective lifetime risks were recorded as 0.45 ± 0.70 per 100 000 and 0.93 ± 1.52 per 100 000 for single and multiple exposures, respectively. In terms of gender, woman data (0.99 ± 0.76; 1.95 ± 2.15) were found higher as compared to man data (0.13 ± 0.39; 0.35 ± 0.45) for both single and multiple exposure. The percentage difference of effective lifetime risks between single and multiple exposures was up to 107%. The effective lifetime risk noted in this study may be low, however, the long-term risk of cancer development should be considered. This study serves as preliminary reference when revising clinical protocol especially in those involving repeated exposures in young adult patients. Future study should include other radiosensitive organs exploring the effective lifetime risk of radiation induced cancer following CT procedure

Estimated effective lifetime risks of radiation-induced thyroid cancer in computed tomography (CT) brain examination

Thyroid is one of the most radiosensitive organs in the human body. Although the scanning range of brain computed tomography (CT) does not include lower neck region, there is possibility for thyroid to be irradiated due to scattered radiation because of its location near to the external beam collimation. The objective of this study was to evaluate effective lifetime risk of radiation-induced thyroid cancer in young adults following brain CT examination. A total of 306 patient data within the age range between 18 and 39 years old were retrospectively analysed. Absorbed dose of the thyroid organ was obtained through the input of data using WAZA- ARI v2. Effective lifetime risk was calculated by multiplying equivalent dose of the thyroid organ with the lifetime attributable cancer risk adapted from Biological Effects in Ionising Radiation (BEIR) Report V11. The effective lifetime risks were recorded as 0.45 ± 0.70 per 100 000 and 0.93 ± 1.52 per 100 000 for single and multiple exposures, respectively. In terms of gender, woman data (0.99 ± 0.76; 1.95 ± 2.15) were found higher as compared to man data (0.13 ± 0.39; 0.35 ± 0.45) for both single and multiple exposure. The percentage difference of effective lifetime risks between single and multiple exposures was up to 107%. The effective lifetime risk noted in this study may be low, however, the long-term risk of cancer development should be considered. This study serves as preliminary reference when revising clinical protocol especially in those involving repeated exposures in young adult patients. Future study should include other radiosensitive organs exploring the effective lifetime risk of radiation induced cancer following CT procedure

Hyperparameter tuning and pipeline optimization via grid search method and tree-based AutoML in breast cancer prediction

Automated machine learning (AutoML) has been recognized as a powerful tool to build a system that automates the design and optimizes the model selection machine learning (ML) pipelines. In this study, we present a tree-based pipeline optimization tool (TPOT) as a method for determining ML models with significant performance and less complex breast cancer diagnostic pipelines. Some features of pre-processors and ML models are defined as expression trees and optimal gene programming (GP) pipelines, a stochastic search system. Features of radiomics have been presented as a guide for the ML pipeline selection from the breast cancer data set based on TPOT. Breast cancer data were used in a comparative analysis of the TPOT-generated ML pipelines with the selected ML classifiers, optimized by a grid search approach. The principal component analysis (PCA) random forest (RF) classification was proven to be the most reliable pipeline with the lowest complexity. The TPOT model selection technique exceeded the performance of grid search (GS) optimization. The RF classifier showed an outstanding outcome amongst the models in combination with only two pre-processors, with a precision of 0.83. The grid search optimized for support vector machine (SVM) classifiers generated a difference of 12% in comparison, while the other two classifiers, naïve Bayes (NB) and artificial neural network—multilayer perceptron (ANN-MLP), generated a difference of almost 39%. The method’s performance was based on sensitivity, specificity, accuracy, precision, and receiver operating curve (ROC) analysis

Impact of image contrast enhancement on stability of radiomics feature quantification on a 2D mammogram radiograph

The present work aimed to evaluate the reproducibility of radiomics features derived from manual delineation and semiautomatic segmentation after enhancement using the Contrast Limited Adaptive Histogram Equalization (CLAHE) and Adaptive Histogram Equalization (AHE) techniques on a benign tumor of two-dimensional (2D) mammography images. Thirty mammogram images with known benign tumors were obtained from The Cancer Imaging Archive (TCIA) datasets and were randomly selected as subjects. The samples were enhanced for semiautomatic segmentation sets using the Active Contour Model in MATLAB 2019a before analysis by two independent observers. Meanwhile, the images without any enhancement were segmented manually. The samples were divided into three categories: (1) CLAHE images, (2) AHE images, and (3) manual segmented images. Radiomics features were extracted using algorithms provided by MATLAB 2019a software and were assessed with a reliable intra-class correlation coefficient (ICC) score. Radiomics features for the CLAHE group (ICC = 0.890 ± 0.554, p 0.05). Features in all three categories were more robust for the CLAHE compared to the AHE and manual groups. This study shows the existence in variation for the radiomics features extracted from tumor region that are segmented using various image enhancement techniques. Semiautomatic segmentation with image enhancement using CLAHE algorithm gave the best result and was a better alternative than manual delineation as the first two techniques yielded reproducible descriptors. This method should be applicable for predicting outcomes in patient with breast cancer

Existing and emerging breast cancer detection technologies and its challenges: a review

Breast cancer is the most leading cancer occurring in women and is a significant factor in female mortality. Early diagnosis of breast cancer with Artificial Intelligent (AI) developments for breast cancer detection can lead to a proper treatment to affected patients as early as possible that eventually help reduce the women mortality rate. Reliability issues limit the current clinical detection techniques, such as Ultra-Sound, Mammography, and Magnetic Resonance Imaging (MRI) from screening images for precise elucidation. The capability to detect a tumor in early diagnosis, expensive, relatively long waiting time due to pandemic and painful procedure for a patient to perform. This article aims to review breast cancer screening methods and recent technological advancements systematically. In addition, this paper intends to explore the progression and challenges of AI in breast cancer detection. The next state of the art between image and signal processing will be presented, and their performance is compared. This review will facilitate the researcher to insight the view of breast cancer detection technologies advancement and its challenges

Effect of different signal weighting function of magnetic field using KNN for indoor localization

The present work aimed to investigate the signal weighting function based on magnetic field (MF) models to obtain accurate location estimates for indoor positioning system. We compare the state-of-the-art Euclidean distance and three proposed different signal weighting function namely actual weight, square weight and square root weight which used to estimate location using MF. Additionally, the effect of signal weighting function is investigated further using multiple k value of K nearest neighbor (KNN) algorithm. According to the results, the square root weighting function have low position error of 8.156 m than Euclidean distance with improvement of 5.5%. We also found that the use of (k = 5) of KNN for square weight of my distance measure give the lowest mean estimation error of 7.188 m

Analysis of multiple prediction techniques of received signal strength to reduce surveying effort in indoor positioning

Received Signal Strength is the measure of attenuation of electromagnetic signals emitted by the access point, reaching the receiver after traveling some distance. This work used the attenuation of Wireless Local Area Network signals propagated through the air for the purpose of indoor positioning. Previous research had shown some problems such as indoor mapping requires human effort and are time-consuming. Furthermore, received signal strength for different indoor conditions may vary such that constant calibration and new acquisition for unknown indoor locations is required. An approach to reduce manual acquisition is by employing prediction algorithms. In this work, an analysis on prediction techniques used predict the RSS is analyzed in the context on indoor positioning. First, to determine the optimum training size for the models, the models are given different training size. Then the models are evaluated based on the similarity of signal pattern predicted and the error between the predicted signal and real signal. In conclusion, the random function model showed best estimation for signal for most of the tested signal received at certain distances from the transmitter. The optimum training size found for all the prediction models are 1100 out of 1200 data. It is also found that for a very noisy data set, the minimum training size for best result are at 900 out of 1200. Bayesian Support Vector Regression outperforms other models in terms of root mean square error

Effectiveness of Post-Mortem Computed Tomography (PMCT) in comparison with conventional autopsy: a systematic review

Background: With the advancement of technology, Computed Tomography (CT) scan imaging can be used to gain deeper insight into the cause of death. Aims: The purpose of this study was to perform a systematic review of the efficacy of Post- Mortem Computed Tomography (PMCT) scan compared with the conventional autopsies gleaned from literature published in English between the year 2009 and 2016. Methodology: A literature search was conducted on three databases, namely PubMed, MEDLINE, and Scopus. A total of 387 articles were retrieved, but only 21 studies were accepted after meeting the review criteria. Data, such as the number of victims, the number of radiologists and forensic pathologists involved, causes of death, and additional and missed diagnoses in PMCT scans were tabulated and analysed by two independent reviewers. Results: Compared with the conventional autopsy, the accuracy of PMCT scans in detecting injuries and causes of death was observed to range between 20% and 80%. The analysis also showed that PMCT had more advantages in detecting fractures, fluid in airways, gas in internal organs, major hemorrhages, fatty liver, stones, and bullet fragments. Despite its benefits, PMCT could also miss certain important lesions in a certain region such as cardiovascular injuries and minor vascular injuries. Conclusion: This systematic review suggests that PMCT can replace most of the conventional autopsies in specific cases and is also a good complementary tool in most cases