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In the ever-evolving world of cricket, the T20 format has captured the imaginations of fans worldwide, intensifying the anticipation for match outcomes with each passing delivery. This study explores the realm of predictive analytics, leveraging the power of machine learning to alleviate the suspense by forecasting T20 cricket match winners before the first ball is bowled. Drawing on a rich dataset encompassing factors such as past team performance and rankings, a diverse ensemble of predictive models, including logistic regression, support vector machine (SVM), random forest, decision tree, and XGBoost, is meticulously employed. Among these, the random forest Classifier emerges as the standout performer, boasting an impressive prediction accuracy rate of 84.06%. To assess the real-world applicability of our predictive framework, a post-case study is conducted, focusing on the high-stakes World Cup T20 matches of 2022, where England emerges as the triumphant team. The dataset underpinning this study is meticulously curated from ESPN CricInfo, ensuring the robustness of our analysis. Moreover, this paper extends its contribution by offering a comprehensive comparative analysis, scrutinizing performance metrics such as accuracy, precision, recall, and the F1-score across benchmark machine learning models for cricket match prediction. This in-depth evaluation not only validates the efficacy of our models but also sheds light on their superior execution time and statistical robustness, further bolstering their utility in the realm of cricket outcome forecasting. [ABSTRACT FROM AUTHOR]

This paper presents forecasting and trend analysis of foreign currency exchange rate in financial market using a hybrid Deep Analytic Network (DAN) technique optimized by a modified water cycle algorithm called Weighted WCA (WWCA) with better generalization capability than the traditional WCA.DAN comprises several stacked KRR (Kernel Ridge Regression) Auto encoders in a multilayer nonlinear regression architecture approach that provides better generalization and accuracy using regularized least squares technique. Further DAN using wavelet kernel function is particularly attractive for its strong data fitting and generalization ability along with its simplified execution procedure, high speed, and better performance achievements in comparison to LSSVM (least squares support vector machine). The output from the DAN is fed to a weighted KRR module to reject noise or the outliers in the noisy data and to make DAN a more robust predictor of the Forex markets, To obtain optimal values of wavelet kernel parameters, a modified metaheuristic water cycle algorithm i.e. the proposed WWCA is utilized. Applications of this new approach to predict forex rate along with trend analysis on three stock markets provide successful results and validate its superiority over some well known approaches like ANN, SVM, Naïve-Bayes, ELM. [ABSTRACT FROM AUTHOR]

This paper presents forecasting and trend analysis of foreign currency exchange rate in financial market using a hybrid Deep Analytic Network (DAN) technique optimized by a modified water cycle algorithm called Weighted WCA (WWCA) with better generalization capability than the traditional WCA.DAN comprises several stacked KRR (Kernel Ridge Regression) Auto encoders in a multilayer nonlinear regression architecture approach that provides better generalization and accuracy using regularized least squares technique. Further DAN using wavelet kernel function is particularly attractive for its strong data fitting and generalization ability along with its simplified execution procedure, high speed, and better performance achievements in comparison to LSSVM (least squares support vector machine). The output from the DAN is fed to a weighted KRR module to reject noise or the outliers in the noisy data and to make DAN a more robust predictor of the Forex markets, To obtain optimal values of wavelet kernel parameters, a modified metaheuristic water cycle algorithm i.e. the proposed WWCA is utilized. Applications of this new approach to predict forex rate along with trend analysis on three stock markets provide successful results and validate its superiority over some well known approaches like ANN, SVM, Naïve-Bayes, ELM. [ABSTRACT FROM AUTHOR]

Since texture is prominent low level feature of an image, most of the image processing and computer vision applications rely on this feature for efficient extraction, retrieval, visualization and classification of the images. Hence, the texture analysis method mainly concentrates on efficient feature extraction and representation of the image. The images captured and analyzed in many of the applications are not in same (or) similar scale, orientation and illumination and also texture has regular, stochastic, periodic, homogeneous (or) inhomogeneous and directional in nature. To address these issues, recent texture analysis method focused on efficient and invariant feature extraction and representation with reduced dimension. Hence this paper proposes a invariant texture descriptor, Locality preserving Rotation Invariant Modified Directional Local Binary Pattern (LRIMDLBP) based on LBP. The classical LBP descriptor is widely used in most of the texture analysis applications due to its simplicity and robustness to illumination changes. However, it does not efficiently capture the discriminative texture information because it uses sign information and ignores the magnitude value of the neighborhood and also suffers from high dimensionality. Hence to improve the performance of LBP, many variants are proposed. Though most of these LBP variants are either geometrical or direction invariant, fails to address the spatial locality and contrast invariance. To address these issues, the proposed LRIMDLBP incorporates spatial locality, contrast and direction information for rotation invariant texture descriptor with reduced dimension. The proposed LRIMDLBP consists of 5 phases: (i) Reference point identification, (ii) Magnitude calculation, (iii) Binary Label computation based on threshold, (iv) Pattern identification in dominant direction and (v) LRIMDLBP code computation. The locality and rotation invariance is achieved by identifying and using reference point in a local neighborhood. The reference point is a dominant pixel whose magnitude is large in the neighborhood excluding center pixel. The spatial locality and rotation invariance is achieved by preserving the weights of LBP dynamically based on the reference point. The proposed method also preserves the direction information of the texture by comparing the magnitude of the pixel in the four dominant directions such as horizontal, vertical, diagonal and anti-diagonal directions. Finally the proposed invariant LRIMDLBP descriptor computes histogram based on decimal pattern value. The proposed LRIMDLBP descriptor results in texture feature with reduced dimension when compared to other LBP variants. The performance of the proposed descriptor is evaluated with large and well known four bench mark texture datasets namely (i) CUReT, (ii) Outex, (iii) KTS-TIPS and (iv) UIUC against three classifiers such as (i). K-Nearest Neighbor (K-NN), (ii). Support Vector Machine (SVM) with Radial Basis Function (RBF) and (iii). Gradient Boosting Classifier (GBC). The intensive experimental result shows that the ensemble based GBC yields superior classification accuracy of 99.38%, 99.43%, 98.67% and 98.82% for the datasets CUReT, Outex, KTH-TIPS and UIUC respectively when compared with other two classifiers and also improves the generalization ability. The proposed LRIMDLBP descriptor achieves approximately 15% more classification accuracy when compared with traditional LBP and also produces 1% to 2.5% more classification accuracy compared with other state of the art LBP variants. [ABSTRACT FROM AUTHOR]

In recent times, driver drowsiness is one of the major reasons for road accidents that leads to severe physical injuries, deaths and significant economic losses. Hence, the existing driver drowsiness detection systems require a countermeasure device for the prevention of sleepiness related accident. This research paper aims to perform drowsiness detection with the help of driver's eye state, head pose, and mouth state information. Initially, the input data were collected from the public drowsy driver database. Then, the Camera Response Model (CRM) was applied to improve the quality of collected data. Also, viola-jones, and Kanade-Lucas-Tomasi (KLT) approaches were used to detect and track the driver's face, eye, and mouth regions from the input video. In this research study, Online Region-Based Active Contour Model (ORACM) algorithm was used to segment the driver's mouth region in order to obtain the threshold value. Successively, feature extraction; Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP) was applied to extract the features from the detected eye region. The extracted features of the eye region were combined with the threshold value of mouth region and head pose angle. After extracting the feature vectors, infinite approach was utilized to choose the relevant feature vectors. Finally, the selected features were classified using Support Vector Machine (SVM) for classifying the stages of drowsiness detection. Simulation outcome illustrated that the proposed system increased the classification accuracy up to 5.52% as related to hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). [ABSTRACT FROM AUTHOR]

In Microarray Data, it is complicated to achieve more classification accuracy due to the presence of high dimensions, irrelevant and noisy data. And also It had more gene expression data and fewer samples. To increase the classification accuracy and the processing speed of the model, an optimal number of features need to extract, this can be achieved by applying the feature selection method. In this paper, we propose a hybrid ensemble feature selection method. The proposed method has two phases, filter and wrapper phase in filter phase ensemble technique is used for aggregating the feature ranks of the Relief, minimum redundancy Maximum Relevance (mRMR), and Feature Correlation (FC) filter feature selection methods. This paper uses the Fuzzy Gaussian membership function ordering for aggregating the ranks. In wrapper phase, Improved Binary Particle Swarm Optimization (IBPSO) is used for selecting the optimal features, and the RBF Kernel-based Support Vector Machine (SVM) classifier is used as an evaluator. The performance of the proposed model are compared with state of art feature selection methods using five benchmark datasets. For evaluation various performance metrics such as Accuracy, Recall, Precision, and F1-Score are used. Furthermore, the experimental results show that the performance of the proposed method outperforms the other feature selection methods. [ABSTRACT FROM AUTHOR]

For emotion recognition, here the features extracted from prevalent speech samples of Berlin emotional database are pitch, intensity, log energy, formant, mel-frequency ceptral coefficients (MFCC) as base features and power spectral density as an added function of frequency. In these work seven emotions namely anger, neutral, happy, Boredom, disgust, fear and sadness are considered in our study. Temporal and Spectral features are considered for building AER(Automatic Emotion Recognition) model. The extracted features are analyzed using Support Vector Machine (SVM) and with multilayer perceptron (MLP) a class of feed-forward ANN classifiers is/are used to classify different emotional states. We observed 91% accuracy for Angry and Boredom emotional classes by using SVM and more than 96% accuracy using ANN and with an overall accuracy of 87.17% using SVM, 94% for ANN. [ABSTRACT FROM AUTHOR]

This paper presents the development of a new low rank robust kernel ridge regression (KRR) classifier for power quality (PQ) disturbance pattern recognition. It is well known that the kernel methods are used extensively for regression and classification problems using support vector machines (SVM) by mapping the original nonlinear data into a high-dimensional space thereby increasing the generalization performance, and accuracy of classification. On the other hand, the nonlinear kernel ridge regression approach is known for its simple implementation, fast processing speed and accuracy in comparison to the widely used least square support vector machine (LS-SVM). However, for large scale training patterns, the size of kernel matrix becomes large thereby the execution time becomes prohibitive. Besides, the presence of noise and outliers in the data affects the accuracy of the conventional KRR classifier. This paper, therefore, attempts to develop a dimensionally reduced but robust KRR classifier by choosing a random set of support vectors from the training subset to reduce substantially the training time at the cost of a slight loss in accuracy. Further, the KRR algorithm is modified by using a new objective function to minimize the mean and variance of the error to provide robustness and accuracy. For applying this new classifier to power quality disturbance events three relatively new signal processing techniques like the Short-time modified Hilbert Transform (STMHT), Morphological filters, and Fourier kernel S-transform are used to extract the relevant features from the data samples. The simulation results imply that the proposed methods have a higher recognition rate compared with other established techniques such as ELM and Poly SVM while classifying the PQ disturbances. A PC integrated hardware assembly has been used to verify the PQ events classification in real-time. [ABSTRACT FROM AUTHOR]

In this work, an efficient scheme has been proposed for the computer-aided detection of the wide-spread disease diabetes. This scheme involves certain data mining techniques for the purpose of detecting the chances of diabetes by looking into a patient's medical record. This work attempts to classify the nature of diabetes (Type-I and Type-II) as well. It also tries to determine the level of risk associated presently with the affected patient. Four different algorithms namely decision tree, Naive Bayes, support vector machine (SVM), and Adaboost-M1 have been used for the purpose of labeling the records as either diabetic or non-diabetic. A comparison strategy is then followed to adopt the best scheme among these through the voting expert. The proposed work gives satisfactory diagnosis result when compared to the ground-truth data. Overall accuracy rate of 95% is achieved through k-fold cross-validation (k = 10) method. Comparison of the proposed work with other state-of-the-art schemes has also been performed that favors the said work. [ABSTRACT FROM AUTHOR]

A new smoothing approach for the implicit Lagrangian twin support vector regression is proposed in this paper. Our formulation leads to solving a pair of unconstrained quadratic programming problems of smaller size than in the classical support vector regression and their solutions are obtained using Newton-Armijo algorithm. This approach has the advantage that a system of linear equations is solved in each iteration of the algorithm. Numerical experiments on several synthetic and real-world datasets are performed and, their results and training time are compared with both the support vector regression and twin support vector regression to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The paper proposes a new approach for Time frequency analysis using modified time-time transform (TT-transform) for recognizing non-stationary power signal disturbance patterns. The TT-transform is derived from the well known S-transform (ST) and uses a new window function with its width inversely proportional to the frequency raised to a power 'c', varying between 0 and 1. The power disturbance signals after being processed by the TT-transform yields features, which are used for automatic recognition of disturbances; with the help of kernel based support vector machine (SVM) algorithm. Further to improve the classification performance of the TT-SVM based pattern recognizer, a differential evolution optimization algorithm (DEOA) is used. Several test cases are provided to prove the significant improvement in recognition, accuracy and drastic reduction of support vectors. [ABSTRACT FROM AUTHOR]

In this paper a finite Newton iterative method of solution for solving the implicit Lagrangian Support Vector Regression (SVR) formulation has been proposed. Unlike solving a quadratic programming problem for the case of the standard SVR the solution of the proposed method is obtained by solving a system of linear equations at each iteration of the algorithm. For the linear or nonlinear SVR the finite termination of the proposed method has been established. The algorithm converges from any starting point and does not need any optimization packages. Experiments have been performed on a number of interesting synthetic and real-world datasets. The results obtained by the proposed method are compared with the standard SVR. Similar or better generalization performance of the proposed method clearly demonstrates its effectiveness and applicability. [ABSTRACT FROM AUTHOR]

The pattern recognition applications like speech recognition, text classification and image recognition result in the solution of multi-class problems. Multi-class problems are reduced into several two class problems using the Machine Learning techniques such as Neural Networks and Support Vector Machines. We propose a hybrid approach for the design of output codes for multi-class pattern recognition problems. This approach has the advantage of taking into account the different aspects that are relevant for a code matrix to achieve good performance. Conventionally, code matrix is designed based on either the features of the problem or the features of the code matrix. The proposed work, focused on designing a new code matrix based on both the features of the problem and code matrix. This model aims at developing a hybrid version of ECOC and adaptive Recursive ECOC with BBO to achieve maximum classification accuracy and minimum computational time. Validation of the results has been performed using non-parametric statistical tests. The statistical results demonstrate that the evolving output codes through BBO provide a general-purpose method for improving the performance of base learners for real world multi-class pattern recognition problems. [ABSTRACT FROM AUTHOR]

A novel framework of indirect adaptive H_{&infty;} control method based on least squares support vector machines (LS-SVM) is proposed for a class of uncertain nonlinear systems with unavailable states and external disturbance. In this method, a state observer is designed to estimate the system states, and the LS-SVM is employed to approximate unknown nonlinear dynamics of the systems. The proposed adaptive H_{&infty;} control is used to attenuate the effect on the tracking error caused by LS-SVM approximation errors and external disturbance. The parameters of the controller are self-tuned according to the adaptive law derived by using Lyapunov stability theory. The asymptotic stability of the proposed close-loop control system is proved. For evaluating the proposed method, numerical simulations of a chaotic system and an inverted pendulum system are presented. The simulation results verified its effectiveness and feasibility. [ABSTRACT FROM AUTHOR]

This paper proposes a content-based recommendation method tuned for multiple content domains. Most content-based recommender systems use content attributes (e.g. description of content and category of content) to represent items and user profiles. This raises the problem that when recommending content from new content domain, the recommendation quality will be poor until the item profile representation is updated through the input of a sufficient quantity of contents in the new content domain. In this paper, to develop an item profile representation that is independent of the contents, we propose a method that uses common categories to represent item profiles; it focuses on the real world activities (tasks) that attract the user's interest. Concretely, we present a light-weight task-model that covers a wide variety of tasks and yields item profiles. We also create a recommendation algorithm by incorporating the proposed profile representation into a statistical SVM(Support Vector Machine) based recommendation algorithm. Finally, we conduct a user test and the results of this test show 9% higher user evaluation scores of content recommendations compared to an existing content-based recommendation algorithm using term-based profile representation. This shows the effectiveness of our user-centered profile representation approach. [ABSTRACT FROM AUTHOR]

The major drawback of Support Vector Machines (SVMs) consists of the training time, which is at least quadratic to the number of data. Among the multitude of approaches developed to alleviate this limitation, several research works showed that mixtures of experts can drastically reduce the runtime of SVMs. The mixture employs a set of SVMs each of which is trained on a sub-set of the original dataset while the final decision is evaluated throughout a gater. The present work proposes a new support vector mixture in which Sugeno's fuzzy integral is used as a gater to remove the time complexity induced by conventional gaters such as artificial neural networks. Experiments conducted on standard datasets of optical character and face recognition reveal that the proposed approach gives a significant reduction of the runtime while keeping at least the same accuracy as the SVM trained over the whole dataset. [ABSTRACT FROM AUTHOR]

The key challenge in kernel based learning algorithms is the choice of an appropriate kernel and its optimal parameters. Selecting the optimal degree of a polynomial kernel is critical to ensure good generalisation of the resulting support vector machine model. In this paper we propose Bayesian and Laplace approximation methods to estimate the polynomial degree. A rule based meta-learning approach is then proposed for automatic polynomial kernel and its optimal degree selection. The new approach is constructed and tested on different sizes of 112 datasets with binary class as well as multi class classification problems. An extensive computational evaluation of these methods is conducted, and rules are generated to determine when these approximation methods are appropriate. [ABSTRACT FROM AUTHOR]