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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]