Sputum smears quality inspection using an ensemble feature extraction approach

The diagnosis of tuberculosis (TB) is extremely important. Sputum smear microscopy is thought to be the best method available in terms of accessibility and ease of use in resource-constrained countries. In this paper, research was conducted to evaluate the effectiveness of tuberculosis diagnosis by examining, among other things, the underlying causes of sputum smear quality for Ethiopian states such as Tigray, Amahira, and Oromia. However, because it is done manually, it has its limitations. This study proposes a model for sputum smear quality inspection using an ensemble feature extraction approach. The dataset used was recorded and labeled by experts in a regional lab in Bahir Dar, near Felege Hiwot Hospital after being collected from Gabi Hospital, Felege Hiwot Hospital, Adit Clinic and Gondar Hospital, as well as Kidanemihret Clinic in Gondar. We used a controlled environment to reduce environmental influences and eliminate variation. All the data was collected using a smartphone (the standard 15) with a jpg file extension and a pixel resolution of 1,728 × 3,840. Prior to feature extraction, bicubic resizing, and ROI extraction using thresholding was performed. In addition, sequential Gaussian and Gabor filters were used for noise reduction, augmentation, and CLAHE was used for enhancement. For feature extraction, GLCM from the gray label and CNN from the color image were both chosen. Ultimately, when CNN, SVM, and KNN classifiers were used to test both CNN and GLCM features, KNN outperformed them all with scores of 87, 93, and 94% for GLCM, CNN, and a hybrid of CNN and GLCM, respectively. CNN with GLCM outperformed other methods by 0.7 and 0.1% for GLCM and CNN feature extractors using the same classifier, respectively. In addition, the KNN classifier with the combination of CNN and GLCM as feature extractors performed better than existing methods by 1.48%

An energy efficient coverage guaranteed greedy algorithm for wireless sensor networks lifetime enhancement

One of the most significant difficulties in Wireless Sensor Networks (WSNs) is energy efficiency, as they rely on minuscule batteries that cannot be replaced or recharged. In battery-operated networks, energy must be used efficiently. Network lifetime is an important metric for battery-powered networks. There are several approaches to improve network lifetime, such as data aggregation, clustering, topology, scheduling, rate allocation, routing, and mobile relay. Therefore, in this paper, the authors present a method that aims to improve the lifetime of WSN nodes using a greedy algorithm. The proposed Greedy Algorithm method is used to extend the network lifetime by dividing the sensors into a number of disjoint groups while satisfying the coverage requirements. The proposed Greedy algorithm has improved the network lifetime compared to heuristic algorithms. The method was able to generate a larger number of disjoint groups

Headroom-based optimization for placement of distributed generation in a distribution substation

This paper presents a headroom-based optimization for the placement of distributed generation (DG) in a distribution substation. The penetration limits of DGs into the existing distribution substations are often expressed as a function of the feeder’s hosting capacity (headroom). Therefore, it is important to estimate the reliability of the network\u27s operation as well as that of the limits imposed by the power quality standards by evaluating of the hosting capacity (headroom) of the existing distribution feeder substation. This study aims at developing a novel algorithm for positioning a bus with permissible headroom capacity for DG positioning without causing voltage violations but maximizing the active power supply. Since DG increases short-circuit faults, the algorithm is useful for utility companies to select feeder substations that have permissible headroom capacity for DG installation and thus, contributing to reducing high DG penetration in the network. The modeling and optimization were carried out the Power System Software for Engineers (PSS/E) environment using the IEEE 14-bus test system. The results obtained from the case study show that only two (2) feeder substations out of fourteen (14) have the permissible headroom capacity for DG connections

Multivariate sample similarity measure for feature selection with a resemblance model

Feature selection improves the classification performance of machine learning models. It also identifies the important features and eliminates those with little significance. Furthermore, feature selection reduces the dimensionality of training and testing data points. This study proposes a feature selection method that uses a multivariate sample similarity measure. The method selects features with significant contributions using a machine-learning model. The multivariate sample similarity measure is evaluated using the University of California, Irvine heart disease dataset and compared with existing feature selection methods. The multivariate sample similarity measure is evaluated with metrics such as minimum subset selected, accuracy, F1-score, and area under the curve (AUC). The results show that the proposed method is able to diagnose chest pain, thallium scan, and major vessels scanned using X-rays with a high capability to distinguish between healthy and heart disease patients with a 99.6% accuracy

Explainable extreme boosting model for breast cancer diagnosis

This study investigates the Shapley additive explanation (SHAP) of the extreme boosting (XGBoost) model for breast cancer diagnosis. The study employed Wisconsin’s breast cancer dataset, characterized by 30 features extracted from an image of a breast cell. SHAP module generated different explainer values representing the impact of a breast cancer feature on breast cancer diagnosis. The experiment computed SHAP values of 569 samples of the breast cancer dataset. The SHAP explanation indicates perimeter and concave points have the highest impact on breast cancer diagnosis. SHAP explains the XGB model diagnosis outcome showing the features affecting the XGBoost model. The developed XGB model achieves an accuracy of 98.42%

Novel predictive model of cell survival/death related effects of Extracellular Signal-Regulated kinase protein

AbstractComputational modelling is a technique for modelling and solving real-world problems by utilising computing to provide solutions. This paper presents a novel predictive model of cell survival/death-related effects of Extracellular Signal-Regulated Kinase Protein. The computational model was designed using Neural Networks and fuzzy system. Three hundred ERK samples were examined using ten different concentrations of three input proteins: EGF, TNF, and insulin. Based on the different concentrations of input proteins and different samples of ERK protein, adjustment Anderson darling (AD) statistics for multiple distribution functions were computed considering different test such as visual test, Pearson correlation coefficient, and uniformity tests. The results reveal that utilising different concentrations and samples, values such as 7.55 AD and 18.4 AD were obtained using the Weibull distribution function for 0 ng/ml of TNF, 100 ng/ml of EGF, and 0 ng/mL of insulin concentrations. The model was validated by predicting the various ERK protein values that fall within the observed range. The proposed model agrees with the deterministic model, which was developed using difference equations

An image feature selection approach for dimensionality reduction based on kNN and SVM for AkT proteins

Communication triggered for cell survival/apoptosis is achieved by three different input proteins. In this paper, we have considered the heat map image that shows 11 different proteins for the HT carcinoma cells which helps in cell survival/apoptosis. Based on the introduction and integration of an algorithm in the classification model, feature selection was divided into three main categories namely: filtering method (FM), wrapper method (WM), and Embedded Method (EM). After applying the feature selection (FS) algorithm, we obtained 7 different marker proteins but out of these proteins, this paper concentrates on only one of them, the AkT which is used for classification using k-nearest neighbour (kNN) classifier and support vector machine (SVM) classifier for calculating predicted mean, standard deviation ratio, and correlation. For kNN, we have used different distance approaches (Euclidean, city block), while for SVM, linear, polynomial, RBF and sigmoid kernels are used for Tier 1 and Tier 2. Results with linear Tier 1 using SVM and Euclidean distance outperform other methods. An accuracy of 76.9% and 84.6% was obtained using the kNN and SVM classifiers respectively with GLDS features. The results obtained gave a better performance when compared with the result of other research papers

Load flow and contingency analysis for transmission line outage

In recent years, power systems have been pushed to operate above their limits due to the increase in the demand for energy supply and its usage. This increase is accompanied by various kinds of obstructions in power transmission systems. A power system is said to be secured when it is free from danger or risk. Power systems security deals with the ability of the system to withstand any contingencies without any consequences. Contingencies are potentially harmful disturbances which occur during the steady state operation of a power system. Load flow constitutes the most important study in a power system for planning, operation, and expansion. Contingency selection is performed by calculating two kinds of performance indices; an active performance index (PIP) and reactive power performance index (PIV) for a single transmission line outage. In this paper, with the help of the Newton Raphson method, the PIP and PIV were calculated with DIgSILENT Power Factory simulation software and contingency ranking was performed. Based on the load flow results and performance indexes, the Ethiopian Electric Power (EEP) North-West region network is recommended for an upgrade or the reactive power or series compensators should be constructed on the riskiest lines and substations

Investigation of Approximate Mode Shape and Transition Velocity of Pipe Conveying Fluid in Failure Analysis

© The Author(s) 2022. This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/)Structures dynamic characteristics and their responses can change due to variations in system parameters. With modal characteristics of the structures, their dynamic responses can be identified. Mode shape remains vital in dynamic analysis of the structures. It can be utilized in failure analysis, and the dynamic interaction between structures and their supports to circumvent abrupt failure. Conversely, unlike empty pipes, the mode shapes for pipes conveying fluid are tough to obtain due to the intricacy of the eigenvectors. Unfortunately, fluid pipes can be found in practice in various engineering applications. Thus, due to their global functions, their dynamic and failure analyses are necessary for monitoring their reliability to avert catastrophic failures. In this work, three techniques for obtaining approximate mode shapes (AMSs) of composite pipes conveying fluid, their transition velocity and relevance in failure analysis were investigated. Hamilton’s principle was employed to model the pipe and discretized using the wavelet-based finite element method. The complex modal characteristics of the composite pipe conveying fluid were obtained by solving the generalized eigenvalue problem and the mode shapes needed for failure analysis were computed. The proposed methods were validated, applied to failure analysis, and some vital results were presented to highlight their effectiveness.Peer reviewe

Nonzero Staircase Modulation Scheme for Switching DC-DC Boost Converter

This paper presents a novel modulation scheme known as the nonzero staircase modulation scheme for switching DC-DC boost converters. This modulation scheme generates two distinct pulse trains/firing signals when a 50 Hz nonzero staircase modulating signal is compared with a 1.5 kHz triangular wave signal. Unlike the conventional modulation schemes, the proposed novel modulation scheme provides two distinctive trains of pulse-width modulated signals for mitigating low and high harmonics. It also possesses 0.56% total harmonic distortions (THD) of the output voltage waveform system, a power output of 4591 W, and THD of 1.12% in the DC-DC boost converter system. It has a simple design and low power loss of 209 W. The proposed scheme enables the single switch boost DC-DC converter used to have an efficiency of 96%. The proposed scheme can be applied in single switch or double switch boost DC-DC converter based-hospital equipment