Silencing eavesdroppers: SVM unveiling physical layer threats

According to statistics from the Global Burden of Cancer Study (Globocon) of the World Health Organization (WHO), cancer, particularly breast cancer, is a severe health issue in Indonesia with 68,858 new cases and 22,000 deaths recorded in 2020. Ultrasonography (USG) technology is acknowledged as one of the potentials to support early detection, which is vital in reducing mortality from breast cancer. This study focuses on classifying ultrasound images using the Support Vector Machine (SVM) algorithm, GLCM feature extraction, Min-Max normalization, and Mutual Information with SelectKBest Feature Selection. From several experiments using the SVM algorithm with various combinations of parameter values that have been set and different Tests, namely using a Train/Test Split with a proportion of 80/20 and K-Fold Cross Validation, it shows that the SVM algorithm is capable of classifying ultrasound images of breast cancer. into two categories (Benign Tumor and Malignant Tumor) with the same maximum accuracy of 79% after applying the SMOTE Balancing Data technique or without using the Balancing Data technique. As a result, the Support Vector Machine (SVM) algorithm has the potential to be an effective model for identifying breast cancer ultrasound images, both on data from the original set that has not been balanced and data from the set that has been balanced.

Aim: prediction of coronary disease using novel support vector machine and comparing its accuracy with logistic regression algorithm. Materials and methods: Two social affairs are proposed for predicting the accuracy (%) of coronary disease. To be explicit, the novel supports vector machine and logistic regression algorithms. Here we take 20 samples each for appraisal and compare. The sample size was calculated using G power with pretest power at 80% and the alpha of 0.05 value. Result: The logistic regression gives better precision (87.82%) than the novel support vector machine (SVM) accuracy (81.30%). Thus the real significance of logistic regression is better than novel support vector machine algorithms. Conclusion: From the result, it might be gathered that logistic regression helps in expecting the coronary sickness with more accuracy to appear differently in relation to novel support vector machine algorithms.

In order to improve the accuracy of the prediction model of lung tuberculosis, a prediction model of lung tuberculosis based on grayscale mixed nucleus AR-SVM model is proposed in the Thesis. First, early warning model of the incidence trend of lung tuberculosis was constructed by using support vector machine (SVM) algorithm which includes two circumstances. One is that there is no extreme risk preference; the other is that there is extreme risk preference. And the searching was conducted in the optimum sorting process in training set based on SVM algorithm; Secondly, aimed at the problem that early warning of extreme risk preference of SVM model is easily out of order in the prediction of incidence trend of lung tuberculosis, data record of incidence trend of lung tuberculosis was preprocessed by using improved SVM model and SVM algorithm was improved by polynomial kernel SVM, which achieved improvement of prediction performance of autoregression model of sample data; finally, the effectiveness of algorithm is verified by positive analysis.

In this study, a data-driven fault diagnosis method was developed for solid oxide fuel cell (SOFC) systems. First, the complete experimental data was obtained following the design of the SOFC system experiments. Then, principal component analysis (PCA) was performed to reduce the dimensionality of the obtained experimental data. Finally, the fault diagnosis algorithms were designed by support vector machine (SVM) and BP neural network to identify and prevent the reformer carbon deposition and heat exchanger rupture faults, respectively. The research results show that both SVM and BP fault diagnosis algorithms can achieve online fault identification. The PCA + SVM algorithm was compared with the SVM algorithm, BP algorithm, and PCA + BP algorithm, and the results show that the PCA + SVM algorithm is superior in terms of running time and accuracy, the diagnosis accuracy reached more than 99%, and the running time was within 20 s. The corresponding system optimization scheme is also proposed.

Dilihat dari sisi konsumen, ada berbagai aspek yang mempengaruhi konsumen untuk membeli suatu kendaraan bermotor. Hal ini karena masing-masing orang memiliki minat dan kebutuhan yang berbeda. Oleh karena itu, tujuan dari penelitian ini adalah untuk mengetahui kelompok umur dan profesi apa saja yang menjadi pelanggan potensial di SPS Motor Honda Lombok Timur. Metode penelitian yang digunakan adalah metode analitik deskriptif, dimana peneliti mengambil data-data penjualan sepeda motor dari SPS Motor Honda Lombok Timur, yang selanjutnya diolah dan dianalisis menggunakan software Rapidminer 5. Hasil analisis data tersebut selanjutnya dideskripsikan dengan detail mengenai kelompok pelanggan potensial. Berdasarkan analisis yang telah dilakukan, dapat ditarik kesimpulan bahwa kelompok yang termasuk dalam pelanggan potensial dari SPS Motor Honda Lombok Timur adalah kelompok dengan kriteria umur berkisar 33-46 tahun yang berprofesi sebagai wiraswasta dan petani, Hal ini disebabkan karena, pelanggan tersebut memiliki penghasilan dengan usia produktif. Sedangkan kelompok pelanggan yang tidak potensial ada 2 irisan yaitu yang pertama apabila kategori usia produktif tapi tidak memiliki penghasilan dengan rentang umur 17-32 tahun dengan profesi pelajar dan wiraswasta. Kedua yaitu, apabila kategori usia tidak produktif dengan rentang usia 47-73 tahun dan tidak memiliki penghasilan dengan profesi petani dan wiraswata.DOI : 10.29408/jit.v2i2.1447

Government policy regarding the discourse on the use of renewable energy in electricity, this discourse is widely discussed in the community, especially on social media twitter. The public's response to the implementation of the use of renewable energy varies, there are positive, negative and neutral responses to this government policy. Sentiment analysis is part of Machine Learning which aims to identify responses in the form of text. The data used in this study amounted to 1,367 tweets. The purpose of this study is to determine the sentiment analysis of government discourse related to the use of renewable energy using an optimisation-based Support Vector Machine (SVM) algorithm approach. This research involves several stages including data collection, data pre-processing, experiments and modelling and evaluation. The data is divided into 3 classes, 120 positive, 1221 neutral and 26 negative. In this research, there are five optimisation models used namely Forward Selection, Backward Elimination, Optimised Selection, Bagging and AdaBoost. The results obtained are the use of Optimised Selection (OS) optimisation with the Support Vector Machine (SVM) algorithm obtained an increase in accuracy from 93% to 96%. The increase in the use of SVM using selection optimization obtained the highest increase, because other optimization techniques only reached 1% and 2% of the original results using the SVM algorithm, namely the accuracy value of 93% to 96% (high accuracy). From the research that has been done, it is certainly important to understand public sentiment towards renewable energy policies, especially renewable energy electricity, the hope is that this research will become a reference for the government.

For a commodity, in addition to its quality, its external package is also very essential. This paper briefly introduced the intelligent support vector machine (SVM) algorithm for color design of paper packaging. The features were extracted from photos of packages using scale-invariant feature transform (SIFT), and the intelligent algorithm was trained and tested using photos of paper packaging for ceramic products collected at the ceramic crafts market as a sample set. Two paper package schemes designed in this study were used for further test. The SVM algorithm was compared with the back-propagation (BP) algorithm and the convolutional neural network (CNN) algorithm. The results showed that the three intelligent algorithms could evaluate the color design of paper packages, but the SVM algorithm was more accurate than the BP and CNN algorithms in evaluating the imagery of color design, both for the samples collected in the craft market and for the paper packaging scheme designed in this paper.

The main problem in producing high accuracy software defect prediction is if the data set has an imbalance class and dichotomous characteristics. The imbalanced class problem can be solved using a data level approach, such as resampling methods. While the problem of software defects predicting if the data set has dichotomous characteristics can be approached using the classification method. This study aimed to analyze the performance of the proposed software defect prediction method to identify the best combination of resampling methods with the appropriate classification method to provide the highest accuracy. The combination of the proposed methods first is the resampling process using oversampling, under-sampling, or hybrid methods. The second process uses the classification method, namely the Support Vector Machine (SVM) algorithm and the Logistic Regression (LR) algorithm. The proposed, tested model uses five NASA MDP data sets with the same number attributes of 37. Based on the t-test, the < = 0.0344 < 0.05 and the > = 3.1524 > 2.7765 which indicates that the combination of the proposed methods is suitable for classifying imbalanced class. The performance of the classification algorithm has also improved with the use of the resampling process. The average increase in AUC values using the resampling in the SVM algorithm is 17.19%, and the LR algorithm is at 7.26% compared to without the resampling process. Combining the three resampling methods with the SVM algorithm and the LR algorithm shows that the best combining method is the oversampling method with the SVM algorithm to software defects prediction in imbalanced class with an average accuracy value of 84.02% and AUC 91.65%.

Traction machine is the power source of traction elevator, through fault diagnosis of traction machine can effectively reduce the occurrence of elevator safety accidents. In view of the problems of weak fault characteristics of traction machine and the influence of the vibration signal by the transmitting path, which lead to the poor diagnosis effect, a vibration signal acquisition scheme and fault diagnosis method of elevator traction machine based on improved Complementary Ensemble Empirical Mode Decomposition (CEEMD) and Support Vector Machines (SVM) Algorithm are proposed. Firstly, the vibration signal acquisition scheme of traction machine is designed by finite element simulation of vibration response of traction machine by Abaqus. A fault diagnosis method based on improved CEEMD and SVM Algorithm is proposed to solve the problem of weak fault feature and poor fault diagnosis effect of traction machine. The improved CEEMD method introduces the integrated fault factors to calculate the fault feature correlation degree of each IMF component, and sorts them according to the integrated fault factors from large to small, the Intrinsic Mode Functions (IMF) with larger integrated fault factors is selected and its time-frequency characteristic is calculated to construct the feature vector, and then the tractor fault is identified and classified by SVM. Finally, the effect and recognition rate of this method and the unimproved CEEMD method are compared by fault injection test. Experimental results show that the CEEMD method based on integrated fault factors can effectively extract the fault features of the tractor and improve the recognition rate of the tractor fault diagnosis.

Fusion of diverse data sources for flood extent mapping and risk assessment in Sindh: A comparative study of inundation mapping approaches

Banana plants (Musa spp.) are one of the leading horticultural commodities in Indonesia that have high economic value and play an important role in national food security. However, banana productivity often decreases due to attacks by various diseases such as Sigatoka, Cordana, and Pestalotiopsis infections that can spread quickly. Early detection of these diseases is crucial to prevent greater losses. This study aims to develop a banana plant disease detection system based on digital image processing with the Support Vector Machine (SVM) algorithm. The research method includes the stages of banana leaf image acquisition, pre-processing using color segmentation, color and texture feature extraction, and disease type classification with the SVM algorithm. The test results show that the developed system is able to recognize banana leaf diseases with an accuracy of 97.8%, precision of 97%, and recall of 98%. These findings prove that the application of digital image processing and the SVM algorithm is effective in detecting banana plant diseases. This system is expected to be a fast, efficient, and accurate diagnostic tool for farmers to increase the productivity and quality of banana harvests.

Here, a brain tumor classification method using the support vector machine (SVM) algorithm by utilizing discrete wavelet transform (DWT) transformation and feature extraction of gray-level co-occurrence matrix (GLCM) and local binary pattern (LBP) has been implemented using the magnetic resonance imaging (MRI) image belong to the low-grade glioma (LGG) or high-grade glioma (HGG) group. SVM algorithm used as a classification method has been widely used in research that raises the topic of classification. Through the formation of a hyperplane between 2 data classes, the SVM algorithm can be said to be a reliable method but does not require complicated computations. The DWT transformation is intended to provide clearer feature details from the MRI image, so that when the feature extraction algorithm is applied, it is expected that the extracted features will differ between benign tumor MRI images and malignant tumor MRI images. In 1 level DWT using high-low (HL) sub-band yield the highest specificity, sensitivity, and accuracy than using 3 levels using HL or low-high (LH) sub-band in LGG MRI image.Compared with another research, our proposed method is slightly better in terms of accuracy to classify the brain tumor image with achieved the accuracy of 98.6486%.

In this study, a method that recognizes the sound of hail is proposed for a system designed to minimize the damage caused by hail to vehicles. The designed system uses signal processing and machine learning. The sounds received by a microphone in the vehicle were converted into frequency space and the kernel density estimation of the frequency values occurring in a certain time interval (approximately 2 seconds) was obtained. This is based on the prediction that the histogram of the frequency of hail falling on the car can have a defining characteristic. In this context, it has been designed to create a two-class machine learning problem, including full sound samples and ambient sound samples. A solution to the machine learning problem was sought with the Support Vector Machines (SVM) algorithm. The SVM algorithm was chosen due to its simplicity and fast working dynamics. While learning is offline in the SVM algorithm, testing is done online. Related software was implemented using MATLAB. In experimental studies, we collected a dataset where almost 500 hail sound segments were used and similarly 400 ambient sound segments were collected. A hold out cross validation approach with various split ratio values are used. It has been seen that the proposed method predicts hail sounds with 92.22% accuracy when the hold out cross validation ration is 90% and 10%.

Digital image recognition based on Fractional-order-PCA-SVM coupling algorithm

Abstract. Hartoni, Siregar VP, Wouthuyzen S, Agus SB. 2021. Object based classification of benthic habitat using Sentinel 2 imagery by applying with support vector machine and random forest algorithms in shallow waters of Kepulauan Seribu. Biodiversitas 23: 514-520. Benthic habitats have very high complexity and are home to many types of aquatic organisms. Benthic habitats have various functions, including habitat for flora and fauna, sediment traps, nursery areas, and foraging areas for aquatic fauna that are susceptible to damage due to human activities or natural factors. Therefore, more accurate spatial information is needed. The purpose of this study was to examine the ability of object-based classification techniques for mapping shallow waters benthic habitats using Sentinel 2A imagery. The two classification algorithms used are support vector machine (SVM) and random forest (RF). The input image layer (IIL) used for classification is the natural color band (Band 432). The results showed that the SVM and RF classification algorithms could classify eight classes of benthic habitats. The overall accuracy (OA) of the SVM algorithm is 65%, while the RF accuracy is 67%, with kappa values of 0.59 and 0.60, respectively. The significant test applied to Sentinel 2 images with SVM and RF algorithms for benthic habitats has a Z test value of-0.41. These results indicate that the classification results between the SVM and RF algorithms are not significantly different.