GA Based PHOG-PCA Feature Weighting for On-RoadVehicle Detection

Histopathological analysis is important for detection of the breast cancer (BC). Computer-aided diagnosis and detection systems are developed to assist the radiologist in the diagnosis process and to relieve the patient from unnecessary pain. In this study, a computer-aided diagnosis system for the early detection of benign and malignant breast cancer is proposed. The proposed system consists of feature extraction, feature ensemble, and classification stages. Various preprocessing steps such as grayscale conversion, noise filtering, and image resizing are employed on the input histopathological images. The local texture descriptors namely Local Binary Pattern (LBP), Frequency Decoded LBP (FDLBP), Binary Gabor Pattern (BGP), Local Phase Quantization (LPQ), Binarized Statistical Image Features (BSIF), CENsus TRansform hISTogram (CENTRIST), and Pyramid Histogram of Oriented Gradients (PHOG) are employed for feature extraction from the histopathologic images. The obtained features are then concatenated for the construction of the ensemble of the features. Three classifiers namely Support Vector Machines (SVM), K-nearest neighbor (KNN), and Neural Networks (NN) are used in the detection of the BC and the classification accuracy score is used for performance evaluation. A dataset called BreaKHis is used in the studies. There are 9109 microscopic pictures in BreaKHis, with 2480 benign samples and 5429 malignant samples. During the collection of the data, 82 patients' breast tumor tissues were envisioned using various magnification factors such as 40X, 100X, 200X, and 400X. The accuracy score is used to assess the acquired findings. The results show that the proposed method has the potential to use accurate BC detection.

The focus is made to develop predictor models for wind speed prediction employing the support vector machine neural models. Basically, support vector machines (SVM) is employed as classifiers, but this contribution models variant of SVM to act as predictors. A developed model of linear support vector machine (LSVM) and proximal support vector machine (PSVM) is proposed to carry out the wind speed prediction using the available real time wind farm data. In developed PSVM predictor, it is modeled for both linear PSVM predictor and non-linear PSVM predictor. The difference between the developed linear and non-linear PSVM predictor models lies in their applicability of kernel functions to perform effective wind speed prediction. The prediction application is implemented for the set of wind farm data with a wind mill height of 50 m in a manner to minimize the mean square error. The training process of the neural network algorithmic flow is done with the developed LSVM, L-PSVM (Linear PSVM) and N-PSVM (nonlinear PSVM) for predicting the wind speed in renewable energy systems. Results computed are compared with the other types of predictors to prove the effectiveness of the proposed variants of SVM predictors. The simulated results presents the effectiveness of the proposed predictors for the real time wind farm data and the applicability of the predictors for the considered datasets.

Support Vector Machine (SVM) is a widely used supervised learning algorithm known for its robustness, efficiency, and theoretical reliability. This study analyzes the performance of linear kernel SVMs on real-world binary classification tasks from healthcare and social behavior domains. Two datasets were used: a heart disease dataset with 14 clinical features, and a social network advertisement dataset with demographic features like age and salary. Both were pre-processed using normalization, label encoding, and train-test splitting, and implemented in Python using scikit-learn. Evaluation metrics included accuracy, precision, recall, F1-score, and confusion matrix. The heart disease model achieved 83.3% accuracy, while the social network model reached 76.7%, showing linear SVM’s effectiveness on structured, moderately sized datasets. The study also covers core SVM concepts like support vectors, hyperplanes, and margin maximization. Soft margin classification was used to handle noisy data, and hyperparameter tuning (e.g., the regularization parameter C) improved performance. In conclusion, linear SVM is still a strong baseline for binary classification due to its simplicity, speed, and interpretability. Future work may explore multi-class problems, kernel comparisons, and ensemble methods to expand its applicability.

This paper introduces a joint feature of Fourier histograms of oriented gradients (FHOG) and local binary pattern (LBP) for vehicle detection in aerial images. Both of them are rotation invariant, so any rotation angle of vehicle in aerial images can be easily detected. A linear support vector machine (SVM) classifier is then trained over the joint feature vectors for the final vehicle detection. We evaluate our method on a public dataset and compare with some state-of-theart algorithms, the proposed joint feature outperforms them in detecting small targets in complicated backgrounds.

Specific characteristics of the functional near infrared spectroscopy (fNIRS) of the hemodynamic response may represent the brain cortical activity levels during mental arithmetic tasks. In this paper, we use hemodynamic response signals of the prefrontal cortex, acquired by a 4-channel fNIRS system to identify the difficulty level of an arithmetic task. To this end, twelve temporal features and several classification methods are used. In addition, most discriminating features are identified by principle component analysis (PCA) method. Experimental results show that the highest accuracy rate of 92.2% is achieved by a linear Support Vector Machine (SVM) classifier. They also show that skewness and total area of the signal from the 3 cm channel on the left prefrontal lobe are the most discriminating features.

Breast cancer is a frequent cancer that develops when normal cells in the breast transform into malignant cells. Breast cancer can arise from glandular tissue, muscular tissue, or fatty tissue in the breast. Many variables contribute to the risk of breast cancer, including genetics, environmental exposure, food, and lifestyle. Breast cancer should be detected early through breast self-examination, regular clinical evaluation, and mammography to identify any abnormal changes, In recent years, early detection of breast cancer in women has emerged as a beacon of hope and a pivotal point in the treatment of this dangerous disease, and its timely identification has become paramount. Modern advancements in technology, especially artificial intelligence algorithms, have played a vital role in developing systems that facilitate automated disease detection, diagnosis, rapid response, and a reduced risk of fatalities. This paper delves into a comparative study of various machine learning (ML) techniques, namely logistic regression (LR), support vector machines (SVM), linear SVM, Gaussian Naive Bayes (GNB), and artificial neural networks (ANNs). The evaluation metrics used in this study are accuracy and elapsed time. The results show that Gaussian Naive Bayes achieved the highest accuracy of 94.07% in just 0.005495 seconds, outperforming SVM (91.85%), linear SVM (90.19%), logistic regression (87.04%), and ANN (37.04%). These findings highlight the potential of Gaussian Naive Bayes in aiding the early detection of breast cancer, leading to more effective and timely interventions, ultimately improving patient outcomes. Keywords: Breast Cancer, Machine learning (ML), Logistic Regression (LR), Support Vector Machine (SVM), Linear SVM, Gaussian Naive Bayes (GNB) and Artificial Neural Networks (ANNs).

Construction accident narrative classification: An evaluation of text mining techniques

This study compares the performance of local binary patterns (LBP) and scale invariant feature transform (SIFT) with support vector machines (SVM) in automatic classification of discrete facial expressions. Facial expression recognition is a multiclass classification problem and seven classes; happiness, anger, sadness, disgust, surprise, fear and comtempt are classified. Using SIFT feature vectors and linear SVM, 93.1% mean accuracy is acquired on CK+ database. On the other hand, the performance of LBP-based classifier with linear SVM is reported on SFEW using strictly person independent (SPI) protocol. Seven-class mean accuracy on SFEW is 59.76%. Experiments on both databases showed that LBP features can be used in a fairly descriptive way if a good localization of facial points and partitioning strategy are followed.

Beam string structure (BSS) is introduced as a new type of hybrid prestressed string structures. The composition and mechanics features of BSS are discussed. The main principles of wavelet packet transform (WPT), principal component analysis (PCA) and support vector machine (SVM) have been reviewed. WPT is applied to the structural response signals, and feature vectors are obtained by feature extraction and PCA. The feature vectors are used for training and classification as the inputs of the support vector machine. The method is used to a single one-way arched beam string structure for damage detection. The cable prestress loss and web members damage experiment for a beam string structure is carried through. Different prestressing forces are applied on the cable to simulate cable prestress loss, the prestressing forces are calculated by the frequencies which are solved by Fourier transform or wavelet transform under impulse excitation. Test results verify this method is accurate and convenient. The damage cases of web members on the beam are tested to validate the efficiency of the method presented in this study. Wavelet packet decomposition is applied to the structural response signals under ambient vibration, feature vectors are obtained by feature extraction method. The feature vectors are used for training and classification as the inputs of the support vector machine. The structural damage position and degree can be identified and classified, and the test result is highly accurate especially combined with principle component analysis.

There is a growing body of knowledge indicating long-lasting residual electroencephalography (EEG) abnormalities in concussed athletes that may persist up to 10-year postinjury. Most often, these abnormalities are initially overlooked using traditional concussion assessment tools. Accordingly, premature return to sport participation may lead to recurrent episodes of concussion, increasing the risk of recurrent concussions with more severe consequences. Sixty-one athletes at high risk for concussion (i.e., collegiate rugby and football players) were recruited and underwent EEG baseline assessment. Thirty of these athletes suffered from concussion and were retested at day 30 postinjury. A number of task-related EEG recordings were conducted. A novel classification algorithm, the support vector machine (SVM), was applied as a classifier to identify residual functional abnormalities in athletes suffering from concussion using a multichannel EEG data set. The total accuracy of the classifier using the 10 features was 77.1%. The classifier has a high sensitivity of 96.7% (linear SVM), 80.0% (nonlinear SVM), and a relatively lower but acceptable selectivity of 69.1% (linear SVM) and 75.0% (nonlinear SVM). The major findings of this report are as follows: 1) discriminative features were observed at theta, alpha, and beta frequency bands, 2) the minimal redundancy relevance method was identified as being superior to the univariate t -test method in selecting features for the model calculation, 3) the EEG features selected for the classification model are linked to temporal and occipital areas, and 4) postural parameters influence EEG data set and can be used as discriminative features for the classification model. Overall, this report provides sufficient evidence that 10 EEG features selected for final analysis and SVM may be potentially used in clinical practice for automatic classification of athletes with residual brain functional abnormalities following a concussion episode.

Support Vector Machines (SVMs) is a popular and highly performing classification model with a very good generalization ability for many classification applications. This method uses kernels to classify data that are not linearly separable. The added complexity of the kernels that map data to a higher dimensional space degrade the SVM classifier performance when dealing with large datasets. Moreover, classifying a dataset by choosing an appropriate kernel and finding the best set of its parameters is challenging. Failing in this process can easily cause to an overfit problem.;In this thesis we propose the Piece-wise Linear SVM (PWLSVM) using MagKmeans clustering to address the complexity and computational cost of SVMs. We use a linear SVM to overcome the complexity of dealing with the kernels, and a MagKmeans clustering to cluster the data into balanced groups. MagKmeans which is a supervised technique clusters equal number of each class in one group. It ensures that a linear SVM has balanced training samples for each class and can attain an accurate model.;The detailed mathematical formulation and modeling of the proposed Distributed MagKmeans (D-MagKmeans) is presented. The algorithm uses a Distributed MagKmeans clustering approach to transform the PWLSVM to Distributed Piece-wise Linear SVM (D-PWLSVM). The proposed D-MagKmeans clustering approach makes the MagKmeans clustering work in distributed network by only passing the centroid of each cluster in one node only to its one-hop neighbors. This feature of the D-Magkmeans makes our approach appropriate for a distributed processing and decision making while maximizing privacy and minimizing the communication overload.;The proposed algorithm was validated using four datasets in terms of dimensions on the features and the number of samples. Pima Indian Diabetes, with 768 samples and 8 features, is the smallest dataset of the four. We also examined Abalone, with 4177 samples and 8 features, Waveform, with 5000 samples and 22 features, and EHarmony, with over half million samples and 116 features. The results reveal that a reasonable trade-off is required when dealing with a large dataset. The results also illustrate that PWLSVM and D-PWLSVM outperform SVMs on a relatively large dataset, such as EHarmony, Abalone, and Waveform.

In this paper, we present an evaluation of face recognition algorithm in video surveillance system using multi-class Support Vector Machine (SVM). We also show how the variation of sizes (due to distance) and intensity of training images (due to camera hardware) can improve the face recognition rate. The system starts by detecting the face regions using AdaBoost training algorithm. Holistic approach is chosen to extract features from the detected face regions as this approach is the simplest. After the feature vectors are generated, multi-class SVM is used for classification step. SVM is chosen as the classifier as it is a low samples, high dimensional classifier. As the training and test set, we use SCface database in which the images were taken in uncontrolled indoor environment using five video surveillance cameras of various qualities. Based on our study using only linear kernel multi-class SVM, it is shown that distance of approximately 2.6m gives the best results, and the training and test images have to be from similar intensity cameras although surveillance camera will have different quality of images.

A Linear Support Vector Machine (LSVM) is based on determining an optimum hyperplane that separates the data into two classes with a maximum margin. LSVM typically has higher classification accuracy for linearly separable data than nonlinearly separable data. For this type of data, Support Vector Selection and Adaptation (SVSA) method was developed that uses the support vectors obtained by LSVM and adapts them with respect to training dataset. However, the SVSA's classification performance may not be very satisfactory for linearly separable data in comparison to LSVM depending on the dataset used. In this paper, a hybrid model was presented that combines the results of LSVM and the SVSA efficiently. The main idea of the hybrid model is to utilize the performance of LSVM with the SVSA as LSVM model is already available during implementation of the SVSA. The method which uses the proposed hybrid model is called the Hybrid Support Vector Selection and Adaptation method (HSVSA). In order to show the effectiveness of the proposed model, one real multispectal and two hyperspectral dataset were experimented with the SVSA, LSVM, nonlinear SVM and the HSVSA in the classification. The results showed that when LSVM performs better than the SVSA, the HSVSA achieves LSVM's performance with the hybrid model, and vice versa.

Histogram of oriented gradient (HOG) feature has been widely used in vehicle detection. In this paper, a modified version of HOG is proposed by introducing compass gradient into the HOG calculation. Three different versions of the modified HOG features are used as an input for linear and nonlinear support vector machine (SVM). The modified HOG variants proved to have better classification performance than that of the standard HOG. The classification results of modified HOG and nonlinear SVM are compared to the classification results of YOLO object detector. Finally, a vehicle detection system based on the best performing classifiers is introduced.

Abstract: A significant challenge in women’s health is posed by Polycystic Ovary Syndrome (PCOS) because it is a difficult disorder to comprehend and exhibits various symptoms. Thisresearch project suggests the creation of a computerized prediction scheme for PCOS using machine learning algorithms and bioinformatics tools. The aim of this system is to give personalized risk assessment to women thus enabling early detection, and proactive management of PCOS. It combines other intelligible probabilities about PCOS with the use of user provided health data with Machine Learning models such as Random Forest, Logistic Regression, Support Vector Machine, Naive Bayes, Radial SVM, Linear SVM and KNeighbours Classifier. Additionally, it provides intuitive predictions regarding PCOS likelihood through ML models, which are based on Random Forest, Logistic Regression, Support Vector Machine (SVM), Naive Bayes (NB), Radial SVM (RSVM), Linear SVM (LSVM) and KNeighbours Classifier (KNC). By way of thorough assessment as well as comparison within these models the system intends to improve precision together with reliability during prediction of PCOS. The major objective is therefore to enable timely intervention along with individualized healthcare strategies that will promote better health outcomes for all women.