Forecasting wind power based on an improved al-Biruni Earth radius metaheuristic optimization algorithm

Wind power forecasting is pivotal in optimizing renewable energy generation and grid stability. This paper presents a groundbreaking optimization algorithm to enhance wind power forecasting through an improved al-Biruni Earth radius (BER) metaheuristic optimization algorithm. The BER algorithm, based on stochastic fractal search (SFS) principles, has been refined and optimized to achieve superior accuracy in wind power prediction. The proposed algorithm is denoted by BERSFS and is used in an ensemble model’s feature selection and optimization to boost prediction accuracy. In the experiments, the first scenario covers the proposed binary BERSFS algorithm’s feature selection capabilities for the dataset under test, while the second scenario demonstrates the algorithm’s regression capabilities. The BERSFS algorithm is investigated and compared to state-of-the-art algorithms of BER, SFS, particle swarm optimization, gray wolf optimizer, and whale optimization algorithm. The proposed optimizing ensemble BERSFS-based model is also compared to the basic models of long short-term memory, bidirectional long short-term memory, gated recurrent unit, and the k-nearest neighbor ensemble model. The statistical investigation utilized Wilcoxon’s rank-sum and analysis of variance tests to investigate the robustness of the created BERSFS-based model. The achieved results and analysis confirm the effectiveness and superiority of the proposed approach in wind power forecasting

Learning to rank using privileged information

Many computer vision problems have an asymmetric distribution of information between training and test time. In this work, we study the case where we are given additional information about the training data, which however will not be available at test time. This situation is called learning using privileged information (LUPI). We introduce two maximum-margin techniques that are able to make use of this additional source of information, and we show that the framework is applicable to several scenarios that have been studied in computer vision before. Experiments with attributes, bounding boxes, image tags and rationales as additional information in object classification show promising results

Two-stream deep learning architecture-based human action recognition

Human action recognition (HAR) based on Artificial intelligence reasoning is the most important research area in computer vision. Big breakthroughs in this field have been observed in the last few years; additionally, the interest in research in this field is evolving, such as understanding of actions and scenes, studying human joints, and human posture recognition. Many HAR techniques are introduced in the literature. Nonetheless, the challenge of redundant and irrelevant features reduces recognition accuracy. They also faced a few other challenges, such as differing perspectives, environmental conditions, and temporal variations, among others. In this work, a deep learning and improved whale optimization algorithm based framework is proposed for HAR. The proposed framework consists of a few core stages i.e., frames initial preprocessing, fine-tuned pre-trained deep learning models through transfer learning (TL), features fusion using modified serial based approach, and improved whale optimization based best features selection for final classification. Two pre-trained deep learning models such as InceptionV3 and Resnet101 are fine-tuned and TL is employed to train on action recognition datasets. The fusion process increases the length of feature vectors; therefore, improved whale optimization algorithm is proposed and selects the best features. The best selected features are finally classified using machine learning (ML) classifiers. Four publicly accessible datasets such as Ut-interaction, Hollywood, Free Viewpoint Action Recognition using Motion History Volumes (IXMAS), and centre of computer vision (UCF) Sports, are employed and achieved the testing accuracy of 100%, 99.9%, 99.1%, and 100% respectively. Comparison with state of the art techniques (SOTA), the proposed method showed the improved accuracy

A NOVEL AND HYBRID WHALE OPTIMIZATION WITH RESTRICTED CROSSOVER AND MUTATION BASED FEATURE SELECTION METHOD FOR ANXIETY AND DEPRESSION

Introduction: Anxiety and depression are two leading human psychological disorders. In this work, several swarm intelligence- based metaheuristic techniques have been employed to find an optimal feature set for the diagnosis of these two human psychological disorders. Subjects and Methods: To diagnose depression and anxiety among people, a random dataset comprising 1128 instances and 46 attributes has been considered and examined. The dataset was collected and compiled manually by visiting the number of clinics situated in different cities of Haryana (one of the states of India). Afterwards, nine emerging meta-heuristic techniques (Genetic algorithm, binary Grey Wolf Optimizer, Ant Colony Optimization, Particle Swarm Optimization, Artificial Bee Colony, Firefly Algorithm, Dragonfly Algorithm, Bat Algorithm and Whale Optimization Algorithm) have been employed to find the optimal feature set used to diagnose depression and anxiety among humans. To avoid local optima and to maintain the balance between exploration and exploitation, a new hybrid feature selection technique called Restricted Crossover Mutation based Whale Optimization Algorithm (RCM-WOA) has been designed. Results: The swarm intelligence-based meta-heuristic algorithms have been applied to the datasets. The performance of these algorithms has been evaluated using different performance metrics such as accuracy, sensitivity, specificity, precision, recall, f-measure, error rate, execution time and convergence curve. The rate of accuracy reached utilizing the proposed method RCM-WOA is 91.4%. Conclusion: Depression and Anxiety are two critical psychological disorders that may lead to other chronic and life-threatening human disorders. The proposed algorithm (RCM-WOA) was found to be more suitable compared to the other state of art methods

HYBRYDOWY, BINARNY ALGORYTM WOA OPARTY NA TRANSMITANCJI STOŻKOWEJ DO PROGNOZOWANIA DEFEKTÓW OPROGRAMOWANIA

Reliability is one of the key factors used to gauge software quality. Software defect prediction (SDP) is one of the most important factors which affects measuring software's reliability. Additionally, the high dimensionality of the features has a direct effect on the accuracy of SDP models. The objective of this paper is to propose a hybrid binary whale optimization algorithm (BWOA) based on taper-shape transfer functions for solving feature selection problems and dimension reduction with a KNN classifier as a new software defect prediction method. In this paper, the values of a real vector that represents the individual encoding have been converted to binary vector by using the four types of Taper-shaped transfer functions to enhance the performance of BWOA to reduce the dimension of the search space. The performance of the suggested method (T-BWOA-KNN) was evaluated using eleven standard software defect prediction datasets from the PROMISE and NASA repositories depending on the K-Nearest Neighbor (KNN) classifier. Seven evaluation metrics have been used to assess the effectiveness of the suggested method. The experimental results have shown that the performance of T-BWOA-KNN produced promising results compared to other methods including ten methods from the literature, four types of T-BWOA with the KNN classifier. In addition, the obtained results are compared and analyzed with other methods from the literature in terms of the average number of selected features (SF) and accuracy rate (ACC) using the Kendall W test. In this paper, a new hybrid software defect prediction method called T-BWOA-KNN has been proposed which is concerned with the feature selection problem. The experimental results have proved that T-BWOA-KNN produced promising performance compared with other methods for most datasets.Niezawodność jest jednym z kluczowych czynników stosowanych do oceny jakości oprogramowania. Przewidywanie defektów oprogramowania SDP (ang. Software Defect Prediction) jest jednym z najważniejszych czynników wpływających na pomiar niezawodności oprogramowania. Dodatkowo, wysoka wymiarowość cech ma bezpośredni wpływ na dokładność modeli SDP. Celem artykułu jest zaproponowanie hybrydowego algorytmu optymalizacji BWOA (ang. Binary Whale Optimization Algorithm) w oparciu o transmitancję stożkową do rozwiązywania problemów selekcji cech i redukcji wymiarów za pomocą klasyfikatora KNN jako nowej metody przewidywania defektów oprogramowania. W artykule, wartości wektora rzeczywistego, reprezentującego indywidualne kodowanie zostały przekonwertowane na wektor binarny przy użyciu czterech typów funkcji transferu w kształcie stożka w celu zwiększenia wydajności BWOA i zmniejszenia wymiaru przestrzeni poszukiwań. Wydajność sugerowanej metody (T-BWOA-KNN) oceniano przy użyciu jedenastu standardowych zestawów danych do przewidywania defektów oprogramowania z repozytoriów PROMISE i NASA w zależności od klasyfikatora KNN. Do oceny skuteczności sugerowanej metody wykorzystano siedem wskaźników ewaluacyjnych. Wyniki eksperymentów wykazały, że działanie rozwiązania T-BWOA-KNN pozwoliło uzyskać obiecujące wyniki w porównaniu z innymi metodami, w tym dziesięcioma metodami na podstawie literatury, czterema typami T-BWOA z klasyfikatorem KNN. Dodatkowo, otrzymane wyniki zostały porównane i przeanalizowane innymi metodami z literatury pod kątem średniej liczby wybranych cech (SF) i współczynnika dokładności (ACC), z wykorzystaniem testu W. Kendalla. W pracy, zaproponowano nową hybrydową metodę przewidywania defektów oprogramowania, nazwaną T-BWOA-KNN, która dotyczy problemu wyboru cech. Wyniki eksperymentów wykazały, że w przypadku większości zbiorów danych T-BWOA-KNN uzyskała obiecującą wydajność w porównaniu z innymi metodami