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Normal and abnormal sample [6]
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Artificial Intelligence and Machine Learning algorithms were used to identify the coronavirus (COVID-19) from X-ray photos of the chest. The authors propose a model for early coronavirus detection based on image filtering strategies and a hybrid feature selection model in this analysis. Traditional statistical and machine learning methods are used...
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... The value of k is typically an odd number; therefore, the approach of the classification doesn't have same distance. Euclidean distance is used for determining the distance between neighbors [16]. ...
... The value of k is typically an odd number; therefore, the approach of the classification doesn't have same distance. Euclidean distance is used for determining the distance between neighbors [16]. ...
The use of Chest radiograph (CXR) images in the examination and monitoring of different lung disorders like infiltration, tuberculosis, pneumonia, atelectasis, and hernia has long been known. The detection of COVID-19 can also be done with CXR images. COVID-19, a virus that results in an infection of the upper respiratory tract and lungs, was initially detected in late 2019 in China's Wuhan province and is considered to majorly damage the airway and, thus, the lungs of people afflicted. From that time, the virus has quickly spread over the world, with the number of mortalities and cases increasing daily. The COVID-19 effects on lung tissue can be monitored via CXR. As a result, This paper provides a comparison regarding k-nearest neighbors (KNN), Support-vector machine (SVM), and Extreme Gradient Boosting (XGboost) classification techniques depending on Harris Hawks optimization algorithm (HHO), Salp swarm optimization algorithm (SSA), Whale optimization algorithm (WOA), and Gray wolf optimizer (GWO) utilized in this domain and utilized for feature selection in the presented work. The dataset used in this analysis consists of 9000 2D X-ray images in Poster anterior chest view, which has been categorized by using valid tests into two categories: 5500 images of Normal lungs and 4044 images of COVID-19 patients. All of the image sizes were set to 200×200 pixels. this analysis used several quantitative evaluation metrics like precision, recall, and F1-score.
... In order to study the influence of each feature on the fall risk assessment as well as trying to optimize our future results, we looked at calculating feature importance using Random Forest as it had been proven effective in similar papers like in the work of Larkman et al. [45] where Random Forest is used to discover biomarkers for early diagnosis of diseases or the work of Yadav [44] that uses RF to select features in order to predict heart diseases, among others [43] [42]. Feature importance was calculated by measuring how much the performance of the model decreased when a particular feature was randomly permuted. ...
Falls are a leading cause of unintentional trauma-related deaths worldwide, and a significant contributor to elderly dependence. To address this, the goal of this project was to predict recurrent falls in the older population using machine learning techniques, with the aim of reducing the number of falls and their consequences.To achieve this, a dataset obtained from Getafe University Hospital’s Geriatric Falls Unit was used (obtained from the Hospital’s Electronic Health Records). This extensive dataset was one the key strengths of our work. Feature extraction was performed through natural language processing, which recognized pre-defined patterns and helped build the profiles of the 304 older adults who composed the dataset. The proposed data system was comprised of four main blocks: the senior’s profile and environment, clinical information and tests carried out in the hospital, medications, and different diseases they presented. Using the extracted attributes and data from those 304 older adults, this project compared the performance of various machine learning techniques in their ability to classify older adults between future fallers and non fallers. Training different models and ensembles and comparing the results, we obtained that Bagging with Random Forest as base model is the best classifier, predicting accurately 75.8% of the data with 70.0% sensitivity and 80.5% specificity. Ultimately, this research project aimed at setting the first stone to a larger study that could help monitoring older adults and obtaining dynamic and automatic predictions of falls.
... Thus, consistency and persistence of the model can be observed. The performance of the classification models is evaluated based on confusion matrix since the confusion matrix is more widely used to assess the efficiency of a machine learning classification model [75]. The number of correct and incorrect outputs in a classification problem are summarised and compared with the training model. ...
Ganoderma boninense (G. boninense) infection reduces the productivity of oil palms and causes a severe threat to the palm oil industry. Early detection of G. boninense is vital since there is no effective treatment to stop the continuing spread of the disease unless ergosterol, a biomarker of G. boninense can be detected. There is yet a non-destructive and in-situ technique explored to detect ergosterol. Capability of NIR to detect few biomarkers such as mycotoxin and zearalenone (ZEN) has been proven to pave the way an effort to explore NIR's sensitivity towards detecting ergosterol, as discussed in this paper. A compact hand-held NIR with a measurement range of 900–1700 nm is utilized by scanning the leaves of three oil palm seedlings inoculated with G. boninense while the other three were non-inoculated from 16-weeks-old to 32-weeks-old. Significant changes of spectral reflectance have been notified occur at the wavelength of ∼1450 nm which reflectance of infected sample is higher 0.2–0.4 than healthy sample which 0.1–0.19. The diminishing of the spectral curve at approximately 1450 nm is strongly suspected to happened due to the loss of water content from the leaves since G. boninense attacks the roots and causes the disruption of water supply to the other part of plant. However, a few overlapped NIRs' spectral data between healthy and infected samples require for further validation which chemometric and machine learning (ML) classification technique are chosen. It is found the spectra of healthy samples are scattered on the negative sides of PC-1 while infected samples tend to be on a positive side with large loading coefficients marked significant discriminatory effect on healthy and infected samples at the wavelength of 1310 and 1452 nm. A PLS regression is used on NIR spectra to implement the prediction of ergosterol concentration which shows good corelation of R = 0.861 between the ergosterol concentration and oil palm NIR spectra. Four different ML algorithms are tested for prediction of G. boninense infection: K-Nearest Neighbour (kNN), Naïve Bayes (NB), Support Vector Machine (SVM) and Decision Tree (DT) are tested which depicted DT algorithm achieves a satisfactory overall performance with high accuracy up to 93.1% and F1-score of 92.6% compared to other algorithms. High accuracy shows the capability of the classification model to correctly predict the G. boninense detection while high F1-score indicates that the classification is able to validate the detection of G. boninense correctly with low misclassification rate. The result represents a significant step in the development of a non-destructive and in-situ detection system which validated by both chemometric and machine learning (ML) classification techniques.
... It utilizes the method of decreasing its neighbouring nodes in order to select only one output node (winner node). Only the winning neurons or neurons with the lowest value that receive a weight update are in the layer's output value [3]. Several studies utilizing LVQ have been effectively implemented in the classification process, such as majors based on mathematics, physics, chemistry, and biology, or historical values, geography, and sociology, to assist schools in identifying majors, whether science or social [4]. ...
This study aims to use data from 57 patients at Rantauprapat Hospital to train a Neural Network using a quantization learning vector method for the categorization of ear, nose, and throat disorders. The input factors were fever, tiredness, nausea, breathing pain, sore throat, hearing loss, allergies, chills and sweating, and thick and transparent mucus. The factors studied were ear canal infections, pharyngitis of the neck, throat, nose, and sinusitis. The findings revealed that ten neurons with an objective value of 0.01 in the learning rate range of 0.01 - 0.05 resulted in categorizing snoring, nose, and ear disorders, including the input layer. The MATLAB program is utilized in this approach, with an average accuracy of 67 per cent and a mean square error of 0.2.
... This research resulted in SVM being more reliable than KNN. SVM produces a precision value of 97%, while KNN produces a precision value of 86% [5]. * Corresponding author: elvira.wahyuni@uii.ac.id ...
COVID-19 has become a pandemic spread to nearly all countries in the world. This virus has caused many deaths. Screening using a chest X-ray is an alternative to find out positive COVID-19 patients. Chest X-ray is advantageous because every hospital must have an X-ray device so that hospitals do not need additional equipment to detect COVID-19-positive patients. This study aims to compare the machine learning models of Naive Bayes, Decision Tree, K-Nearest Neighbor, and Logistic Regression to predict COVID-19 positive patients. The stages of the research carried out by this study are the Pre-process stage, feature extraction, and classification. The results showed that the Naïve Bayes classification method got the highest performance with an accuracy of 95.24%.
... Penelitian ini menghasilkan SVM lebih handal dari pada KNN. SVM menghasilkan nilai presisi sebesar 97%, sedangkan KNN menghasilkan nilai presisi sebesar 86% [5]. ...
COVID-19 sudah menjadi pandemi yang menyebar ke hampir semua negara di dunia. Virus ini telah menyebabkan banyak kematian. Screening menggunakan rontgen dada menjadi salah satu alternatif untuk mengetahui pasien positif COVID-19. Rontgen dada memiliki keunggulan karena setiap rumah sakit pasti memiliki alat rontgen sehingga rumah sakit tidak memerlukan alat tambahan untuk mendeteksi pasien positif COVID-19. Penelitian ini bertujuan untuk membandingkan model machine learning Naive Bayes, Decision Tree, K-Nearest Neighbor, dan Logistic Regression untuk memprediksi pasien positif COVID-19. Penelitian ini menghasilkan Naive Bayes merupakan model yang paling baik untuk menyelesaikan permasalahan ini. Naive Bayes mendapatkan nilai akurasi tertinggi sebesar 95,24%, tidak ada False Negative (Tidak ada kesalahan dalam prediksi pasien negatif COVID-19), dan memiliki F1-Score tertinggi sebesar 95%.
Penelitian ini bertujuan untuk merancang dan membuat sebuah aplikasi untuk keamanan pesan. Pesan teks atau SMS (Short Message Service) adalah salah satu cara komunikasi yang populer. Namun, keamanan pesan seringkali menjadi perhatian utama pengguna. Oleh karena itu, penelitian ini bertujuan untuk mengembangkan aplikasi keamanan pesan teks dengan menggunakan algoritma kriptografi ElGamal berbasis Android. Aplikasi yang dibangun akan mengenkripsi pesan teks sebelum dikirim, sehingga hanya penerima yang dimaksud yang dapat membaca pesan tersebut. Penggunaan algoritma ElGamal dipilih karena fitur keamanannya yang kuat dan sulit untuk diretas. Penelitian ini juga akan membandingkan keamanan aplikasi yang dibangun dengan aplikasi pesan teks biasa. Hasil penelitian menunjukkan bahwa aplikasi yang dibangun lebih aman dalam mengirim dan menerima pesan teks. Penggunaan aplikasi ini diharapkan dapat memberikan keamanan tambahan bagi pengguna dalam bertukar pesan teks.
Penelitian ini bertujuan untuk merancang sistem Pendaftaran dan Monitoring Pelayanan Jasa Notaris dan PPAT pada Kantor Notaris dan PPAT Sujatmoko,S.H. Metode yang digunakan dalam penelitian ini adalah penelitian normatif penelitian hukum terhadap data sekunder berupa penelitian kepustakaan (Library Research). Langkah-langkah dalam penelitian ini adalah: pertama perancangan yang akan dibuat yaitu sistem alur kerja Perancangan Aplikasi Sistem Pendaftaran dan Monitoring Pelayanan Jasa Notaris dan PPAT ataupun membuat rancangan pemodelan flowchart sistem, kemudian akan membuat rancangan tampilan interface ataupun tampilan antarmuka user maupun pengguna. Hasil akhir dari penelitian ini berupa aplikasi sistem pendaftaran dan monitoring pelayanan jasa Notaris dan PPAT untuk kantor Notaris & PPAT SUJATMOKO Rantau Prapat yang siap diimplementasikan. Implementasi program yang dihasilkan pada penelitian ini diharapkan bisa mengatasi permasalahan yang ada dan dapat memberikan pelayanan yang lebih baik
Medical images play a crucial role in the classification of various diseases and conditions. One of the imaging modalities is X-rays which provide valuable visual information that helps in the identification and characterization of various medical conditions. Chest radiograph (CXR) images have long been used to examine and monitor numerous lung disorders, such as tuberculosis, pneumonia, atelectasis, and hernia. COVID-19 detection can be accomplished using CXR images as well. COVID-19, a virus that causes infections in the lungs and the airways of the upper respiratory tract, was first discovered in 2019 in Wuhan Province, China, and has since been thought to cause substantial airway damage, badly impacting the lungs of affected persons. The virus was swiftly gone viral around the world and a lot of fatalities and cases growing were recorded on a daily basis. CXR can be used to monitor the effects of COVID-19 on lung tissue. This study examines a comparison analysis of k-nearest neighbors (KNN), Extreme Gradient Boosting (XGboost), and Support-Vector Machine (SVM) are some classification approaches for feature selection in this domain using The Moth-Flame Optimization algorithm (MFO), The Grey Wolf Optimizer algorithm (GWO), and The Glowworm Swarm Optimization algorithm (GSO). For this study, researchers employed a data set consisting of two sets as follows: 9,544 2D X-ray images, which were classified into two sets utilizing validated tests: 5,500 images of healthy lungs and 4,044 images of lungs with COVID-19. The second set includes 800 images, 400 of healthy lungs and 400 of lungs affected with COVID-19. Each image has been resized to 200x200 pixels. Precision, recall, and the F1-score were among the quantitative evaluation criteria used in this study.
