Ahmed Alkhayyat’s research while affiliated with Alhamd Islamic University and other places

The use of machine learning to analyze sentiments has attained considerable interest in the past few years. The task of analyzing sentiments has becfigome increasingly important and challenging. Due to the specific attributes of this type of data, including length of text, spelling errors, and abbreviations, unconventional methods and multiple steps are required for effectively analyzing sentiment in such a complex environment. In this research, two distinct word embedding models, GloVe and Word2Vec, were utilized for vectorization. To enhance the performance long short-term memory (LSTM), the model was optimized using the amended dwarf mongoose optimization (ADMO) algorithm, leading to improvements in the hyperparameters. The LSTM–ADMO achieved the accuracy values of 97.74 and 97.47 using Word2Vec and GloVe, respectively on IMDB, and it could gain the accuracy values of 97.84 and 97.51 using Word2Vec and GloVe, respectively on SST-2. In general, it was determined that the proposed model significantly outperformed other models, and there was very little difference between the two different word embedding techniques.

Speech recognition-based applications increased and developed as a result of artificial intelligence’s rapid growth,particularly Machine Learning, which play a crucial role in many aspects of daily life, such as applications related tohuman-computer interaction, and natural language processing. The complexity and diversity of speech signals provideschallenges in maximizing the rate of accuracy and efficiency of speech recognition systems. Hyperparameter tuningis a crucial step in machine learning that has a significant role in optimizing the performance and generalization bydetermining the optimal values for the model’s hyperparameters. This paper employed the recently developed WARStrategy optimization algorithm for optimizing the features related to the speech signal and tuning the hyperparametersof machine learning typical models for accurate and rapid speech recognition. Two types of features are extracted fromthe speech signal including the spectral feature using the Mel-Frequency Cepstral Coefficients (MFCCs) technique andthe statistical features. Afterward these features are optimized using the WAR Strategy optimization algorithm to obtainthe optimum features set that describe the speech signal important information. Finally, the hyperparameters of sixclassical machine learning models are tuned to serve as newly designed classifiers in the final classification phase of theproposed system. Three different language speech datasets are used to evaluate the proposed system (i.e. English, Arabic,Malaysian) to prove the high generalization property of the proposed system. The obtained recognition accuracy thatwas ranging from 98.38% to 100% in a training time between 0.001 to 19.8 second demonstrate the high effectivenessof the proposed speech recognition system in dealing with the many obstacles facing the recognition of speech signalwithin high accuracy, low resources requirements, and minimum training time.

Human iris’ identification is a constantly developing technology and it has it’s own significant in many commonplace applications such as financial sector, identity verification, evidence analysis, law enforcement, and security standards. Several obstacles face the recognition of the iris and the high variation in its captured image is one the most highly affected that is brought on by many factors including aging, illumination, and occlusion. Furthermore, there are some issues with the computing time and complexity of systems concerned in recognizing iris that require attention. In this research, a proposed Iris recognition system that can show a high recognition accuracy and a reduced time is presented. The Corona Virus Optimization Algorithm is a sophisticated bioinspired algorithm that serves as the foundation for the suggested system. The main objective of the suggested approach is to increase the iris identification accuracy rate by fi-ne-tuning the hyperparameter of six conventional Machine Learning models and selecting as well refining the most useful features. Four versions of Iris Image Database known as of CASIA (i.e., 1.0, 2.0, 3.0, 4.0), have been employed to test the system. The evaluation experiment outcomes findings proven the system’s efficiency in catching the high recognition accuracy in uncontrolled environments when compared to current methods. This is accomplished in a through a recognition time ranging from 1564.16 to 13.97 milliseconds, requiring extraordinarily little processing complexity and effort to attain 94%–100% accuracy.

This study introduces an innovative optimization algorithm called Lionfish Search (LFS) technique, which is inspired by the visual predator Lionfish, in which it is specifically imitating their hunting tactics. The suggested algorithm considers several parameters that influence the hunting behaviour of lionfish, such as visual acuity, mobility, striking success, and prey swallowing potential. Furthermore, this study examines the influence of the physiological traits of the lionfish and their relationship with environmental factors. The novel search algorithm has shown enhanced performance and efficiency, particularly in scenarios where the integration of visual cues and intricate hunting strategies is vital. The suggested LFS method was evaluated using 20 well‐known single‐modal and multi‐modal mathematical functions to analyse its different characteristics. The LFS method has shown remarkable efficacy in both exploration and exploitation, effectively reducing the likelihood of being trapped in local optima. Additionally, it has a rapid convergence capacity, particularly in the realm of large‐scale global optimization. Comparisons were made between the LFS algorithm, and 10 other prominent algorithms mentioned in the literature. The proposed LFS metaheuristic algorithm outperformed the others on almost all of the examined functions, demonstrating a statistically significant advantage. Moreover, the positive results found in three practical optimization situations demonstrate the effectiveness of the LFS in accomplishing problem‐solving tasks that have limited and unknown search areas.

A novel Synchronized Spatiotemporal Linear Discriminant Model –Iris (SSLDMNet-Iris,) a deep learning architecture is introduced in this work which is designed to address the challenges associated with iris recognition under varying environments, such as occlusion, variations in eye pupil dilation, and lower image quality. This has been implemented by integrating multi-scale convolutional feature extraction with synchronized temporal modeling through Gated Recurrent Units (GRUs), the proposed SSLDMNet-Iris model effectively can catch both intricate texture details and global spatial patterns related to the iris. Additionally, the model utilizes Fisher’s Linear Discriminant (FLD) for features extraction and optimizing the separation between classes while minimizing intra-class variance, thereby raising recognition accuracy. Comprehensive experiments conducted on seven benchmark datasets (i.e., CASIA Iris 1.0, CASIA Iris 2.0, CASIA Iris 3.0, CASIA Iris 4.0, IITD, UBIRIS, MMU), and exhibit a promising accuracy rate where, the SSLDMNet-Iris surpassing traditional models like VGG16, AlexNet, and ResNet. Notably, SSLDMNet-Iris attains 100% accuracy on CASIA Iris 1.0, CASIA Iris 2.0, and MMU datasets, while maintaining high computational efficiency with a reduced processing time. These results highlight the robustness and versatility of SSLDMNet-Iris, making it an ideal candidate for real-time iris recognition applications.