Central Kurdish Text-to-Speech Synthesis with Novel End-to-End Transformer Training

Recent advancements in text-to-speech (TTS) models have aimed to streamline the two-stage process into a single-stage training approach. However, many single-stage models still lag behind in audio quality, particularly when handling Kurdish text and speech. There is a critical need to enhance text-to-speech conversion for the Kurdish language, particularly for the Sorani dialect, which has been relatively neglected and is underrepresented in recent text-to-speech advancements. This study introduces an end-to-end TTS model for efficiently generating high-quality Kurdish audio. The proposed method leverages a variational autoencoder (VAE) that is pre-trained for audio waveform reconstruction and is augmented by adversarial training. This involves aligning the prior distribution established by the pre-trained encoder with the posterior distribution of the text encoder within latent variables. Additionally, a stochastic duration predictor is incorporated to imbue synthesized Kurdish speech with diverse rhythms. By aligning latent distributions and integrating the stochastic duration predictor, the proposed method facilitates the real-time generation of natural Kurdish speech audio, offering flexibility in pitches and rhythms. Empirical evaluation via the mean opinion score (MOS) on a custom dataset confirms the superior performance of our approach (MOS of 3.94) compared with that of a one-stage system and other two-staged systems as assessed through a subjective human evaluation.

From A-to-Z Review of Clustering Validation Indices

Data clustering involves identifying latent similarities within a dataset and organizing them into clusters or groups. The outcomes of various clustering algorithms differ as they are susceptible to the intrinsic characteristics of the original dataset, including noise and dimensionality. The effectiveness of such clustering procedures directly impacts the homogeneity of clusters, underscoring the significance of evaluating algorithmic outcomes. Consequently, the assessment of clustering quality presents a significant and complex endeavor. A pivotal aspect affecting clustering validation is the cluster validity metric, which aids in determining the optimal number of clusters. The main goal of this study is to comprehensively review and explain the mathematical operation of internal and external cluster validity indices, but not all, to categorize these indices and to brainstorm suggestions for future advancement of clustering validation research. In addition, we review and evaluate the performance of internal and external clustering validation indices on the most common clustering algorithms, such as the evolutionary clustering algorithm star (ECA*). Finally, we suggest a classification framework for examining the functionality of both internal and external clustering validation measures regarding their ideal values, user-friendliness, responsiveness to input data, and appropriateness across various fields. This classification aids researchers in selecting the appropriate clustering validation measure to suit their specific requirements.

In Search of Excellence: SHOA as a Competitive Shrike Optimization Algorithm for Multimodal Problems

In this paper, a swarm intelligence optimization algorithm is proposed as the Shrike Optimization Algorithm (SHOA). Many creatures living in a group and surviving for the next generation randomly search for food; they follow the best one in the swarm, called swarm intelligence. Swarm-based algorithms are designed to mimic creatures' behaviours, but in multimodal problem competition, they cannot find optimal solutions in some difficult cases. The main inspiration for the proposed algorithm is taken from the swarming behaviours of shrike birds in nature. The shrike birds are migrating from their territory to survive. However, the SHOA mimics the surviving behaviour of shrike birds for living, adaptation, and breeding. Two parts of optimization exploration and exploitation are designed by modelling shrike breeding and searching for foods to feed nestlings until they get ready to fly and live independently. This paper is a mathematical model for the SHOA to perform optimization. The SHOA benchmarked 19 well-known mathematical test functions, 10 from CEC-2019, and 12 from CEC-2022 most recent test functions, a total of 41 competitive mathematical test functions benchmarked and four real-world engineering problems with different conditions, both constrained and unconstrained. The statistical results obtained from the Wilcoxon sum ranking and Fridman test show that SHOA has a significant statistical superiority in handling the test benchmarks compared to competitor algorithms in multi-modal problems. The results for engineering optimization problems show the SHOA outperforms other nature-inspired algorithms in many cases.

FOXANN: A Method for Boosting Neural Network Performance

Artificial neural networks play a crucial role in machine learning and there is a need to improve their performance. This paper presents FOXANN, a novel classification model that combines the recently developed Fox optimizer with ANN to solve ML problems. Fox optimizer replaces the backpropagation algorithm in ANN; optimizes synaptic weights; and achieves high classification accuracy with a minimum loss, improved model generalization, and interpretability. The performance of FOXANN is evaluated on three standard datasets: Iris Flower, Breast Cancer Wisconsin, and Wine. The results presented in this paper are derived from 100 epochs using 10-fold cross-validation, ensuring that all dataset samples are involved in both the training and validation stages. Moreover, the results show that FOXANN outperforms traditional ANN and logistic regression methods as well as other models proposed in the literature such as ABC-ANN, ABC-MNN, CROANN, and PSO-DNN, achieving a higher accuracy of 0.9969 and a lower validation loss of 0.0028. These results demonstrate that FOXANN is more effective than traditional methods and other proposed models across standard datasets. Thus, FOXANN effectively addresses the challenges in ML algorithms and improves classification performance.

Q-FOX Learning: Breaking Tradition in Reinforcement Learning

Reinforcement learning (RL) is a subset of artificial intelligence (AI) where agents learn the best action by interacting with the environment, making it suitable for tasks that do not require labeled data or direct supervision. Hyperparameters (HP) tuning refers to choosing the best parameter that leads to optimal solutions in RL algorithms. Manual or random tuning of the HP may be a crucial process because variations in this parameter lead to changes in the overall learning aspects and different rewards. In this paper, a novel and automatic HP-tuning method called Q-FOX is proposed. This uses both the FOX optimizer, a new optimization method inspired by nature that mimics red foxes' hunting behavior, and the commonly used, easy-to-implement RL Q-learning algorithm to solve the problem of HP tuning. Moreover, a new objective function is proposed which prioritizes the reward over the mean squared error (MSE) and learning time (steps). Q-FOX has been evaluated on two OpenAI Gym environment control tasks: Cart Pole and Frozen Lake. It exposed greater cumulative rewards than HP tuning with other optimizers, such as PSO, GA, Bee, or randomly selected HP. The cumulative reward for the Cart Pole task was 32.08, and for the Frozen Lake task was 0.95. Despite the robustness of Q-FOX, it has limitations. It cannot be used directly in real-word problems before choosing the HP in a simulation environment because its processes work iteratively, making it time-consuming. The results indicate that Q-FOX has played an essential role in HP tuning for RL algorithms to effectively solve different control tasks.

Detection of Auditory Brainstem Response Peaks Using Image Processing Techniques in Infants with Normal Hearing Sensitivity

Introduction: The auditory brainstem response (ABR) is measured to find the brainstem-level peripheral auditory nerve system integrity in children having normal hearing. The Auditory Evoked Potential (AEP) is generated using acoustic stimuli. Interpreting these waves requires competence to avoid misdiagnosing hearing problems. Automating ABR test labeling with computer vision may reduce human error. Method: The ABR test results of 26 children aged 1 to 20 months with normal hearing in both ears were used. A new approach is suggested for automatically calculating the peaks of waves of different intensities (in decibels). The procedure entails acquiring wave images from an Audera device using the Color Thresholder method, segmenting each wave as a single wave image using the Image Region Analyzer application, converting all wave images into waves using Image Processing (IP) techniques, and finally calculating the latency of the peaks for each wave to be used by an audiologist for diagnosing the disease. Findings: Image processing techniques were able to detect 1, 3, and 5 waves in the diagnosis field with accuracy (0.82), (0.98), and (0.98), respectively, and its precision for waves 1, 3, and 5, were respectively (0.32), (0.97) and (0.87). This evaluation also worked well in the thresholding part and 82.7 % correctly detected the ABR waves. Conclusion: Our findings indicate that the audiology test battery suite can be made more accurate, quick, and error-free by using technology to automatically detect and label ABR waves.

Multi-Transfer Learning Techniques for Detecting Auditory Brainstem Response

The assessment of the well-being of the peripheral auditory nerve system in individuals experiencing hearing impairment is conducted through auditory brainstem response (ABR) testing. Audiologists assess and document the results of the ABR test. They interpret the findings and assign labels to them using reference-based markers like peak latency, waveform morphology, amplitude, and other relevant factors. Inaccurate assessment of ABR tests may lead to incorrect judgments regarding the integrity of the auditory nerve system; therefore, proper Hearing Loss (HL) diagnosis and analysis are essential. To identify and assess ABR automation while decreasing the possibility of human error, machine learning methods, notably deep learning, may be an appropriate option. To address these issues, this study proposed deep-learning models using the transfer-learning (TL) approach to extract features from ABR testing and diagnose HL using support vector machines (SVM). Pre-trained convolutional neural network (CNN) architectures like AlexNet, DenseNet, GoogleNet, InceptionResNetV2, InceptionV3, MobileNetV2, NASNetMobile, ResNet18, ResNet50, ResNet101, ShuffleNet, and SqueezeNet are used to extract features from the collected ABR reported images dataset in the proposed model. It has been decided to use six measures accuracy, precision, recall, geometric mean (GM), standard deviation (SD), and area under the ROC curve to measure the effectiveness of the proposed model. According to experimental findings, the ShuffleNet and ResNet50 models' TL is effective for ABR to diagnose HL using an SVM classifier, with a high accuracy rate of 95% when using the 5-fold cross-validation method.

BCDDO: Binary Child Drawing Development Optimization

A lately created metaheuristic algorithm called Child Drawing Development Optimization (CDDO) has proven to be effective in a number of benchmark tests. A Binary Child Drawing Development Optimization (BCDDO) is suggested for choosing the wrapper features in this study. To achieve the best classification accuracy, a subset of crucial features is selected using the suggested BCDDO. The proposed feature selection technique's efficiency and effectiveness are assessed using the Harris Hawk, Grey Wolf, Salp, and Whale optimization algorithms. The suggested approach has significantly outperformed the previously discussed techniques in the area of feature selection to increase classification accuracy. Moderate COVID, breast cancer, and big COVID are the three datasets utilized in this study. The classification accuracy for each of the three datasets was (98.75, 98.83%, and 99.36) accordingly.

GOOSE Algorithm: A Powerful Optimization Tool for Real-World Engineering Challenges and Beyond

This study proposes the GOOSE algorithm as a novel metaheuristic algorithm based on the goose's behavior during rest and foraging. The goose stands on one leg and keeps his balance to guard and protect other individuals in the flock. The GOOSE algorithm is benchmarked on 19 well-known benchmark test functions, and the results are verified by a comparative study with genetic algorithm (GA), particle swarm optimization (PSO), dragonfly algorithm (DA), and fitness dependent optimizer (FDO). In addition, the proposed algorithm is tested on 10 modern benchmark functions, and the gained results are compared with three recent algorithms, such as the dragonfly algorithm, whale optimization algorithm (WOA), and salp swarm algorithm (SSA). Moreover, the GOOSE algorithm is tested on 5 classical benchmark functions, and the obtained results are evaluated with six algorithms, such as fitness dependent optimizer (FDO), FOX optimizer, butterfly optimization algorithm (BOA), whale optimization algorithm, dragonfly algorithm, and chimp optimization algorithm (ChOA). The achieved findings attest to the proposed algorithm's superior performance compared to the other algorithms that were utilized in the current study. The technique is then used to optimize Welded beam design and Economic Load Dispatch Problem, three renowned real-world engineering challenges, and the Pathological IgG Fraction in the Nervous System. The outcomes of the engineering case studies illustrate how well the suggested approach can optimize issues that arise in the real-world.

VAPI: Vectorization of Algorithm for Performance Improvement

This study presents the vectorization of metaheuristic algorithms as the first stage of vectorized optimization implementation. Vectorization is a technique for converting an algorithm, which operates on a single value at a time to one that operates on a collection of values at a time to execute rapidly. The vectorization technique also operates by replacing multiple iterations into a single operation, which improves the algorithm's performance in speed and makes the algorithm simpler and easier to be implemented. It is important to optimize the algorithm by implementing the vectorization technique, which improves the program's performance, which requires less time and can run long-running test functions faster, also execute test functions that cannot be implemented in non-vectorized algorithms and reduces iterations and time complexity. Converting to vectorization to operate several values at once and enhance algorithms' speed and efficiency is a solution for long running times and complicated algorithms. The objective of this study is to use the vectorization technique on one of the metaheuristic algorithms and compare the results of the vectorized algorithm with the algorithm which is non-vectorized.