Today, fog and cloud computing environments can be used to further develop the Internet of Things (IoT). In such environments, task scheduling is very efficient for executing user requests, and the optimal scheduling of IoT task requests increases the productivity of the IoT-fog-cloud system. In this paper, a hybrid meta-heuristic (MH) algorithm is developed to schedule the IoT requests in IoT-fog-cloud networks using the Aquila Optimizer (AO) and African Vultures Optimization Algorithm (AVOA) called AO_AVOA. In AO_AVOA, the exploration phase of AVOA is improved by using AO operators to obtain the best solution during the process of finding the optimal scheduling solution. A comparison between AO_AVOA and methods of AVOA, AO, Firefly Algorithm (FA), particle swarm optimization (PSO), and Harris Hawks Optimization (HHO) according to performance metrics such as makespan and throughput shows the high ability of AO_AVOA to solve the scheduling problem in IoT-fog-cloud networks.

The complexity and high dimensions of big data sonar, as well as the unavoidable presence of unwanted signals such as noise, clutter, and reverberation in the environment of sonar propagation, have made the classification of big data sonar one of the most interesting and applicable topics for active researchers in this field. This paper proposes the use of the Grasshopper Optimization Algorithm (GOA) to train Multilayer Perceptron Artificial Neural Network (MLP-NN) and also to select optimal features in big data sonar (called GMLP-GOA). GMLP-GOA hybrid classifier first extracts the features of experimental sonar data using MFCC. Then, the most optimal features are selected using GOA. In the last step, MLP-NN trained with GOA is used to classify big data sonar. To evaluate the performance of GMLP-GOA, this classifier is compared with MLP-GOA, MLP-GWO, MLP-PSO, MLP-ACO, and MLP-GSA classifiers in terms of classification rate, convergence rate, local optimization avoidance power, and processing time. The results indicated that GMLP-GOA achieved a classification rate of 98.12% in a processing time of 3.14 s.

This research examines whether ultrasonic waves can enhance the hydrolysis, stability, and dewatering of activated sludge from raw urban wastewater. Sampling and physical examination of the activated sludge that was returned to the aeration pond were carried out using ultrasonic waves that were guided at frequencies of 30 and 50 kHz for periods of 0.5, 1, 3, 5, 10, 15, and 30 min. Various tests, including volatile suspended solids, inorganic solids, volatile solids, sludge resistant time, capillary suction time, total suspended solids, total solids, and volatile soluble solids, were carried out to advance further the processes of hydrolysis, stabilization, and dehydration of samples. According to the observations, the volatile soluble solids at a frequency of 30 kHz and $t=15$ min were raised by 72%. The capillary suction time of 30 and 50 kHz in 1 min demonstrated a drop of 29 and 22%, respectively. It is crucial to consider that, at 10 min and the frequency of 50 kHz, the greatest efficiency was found. The 30 kHz and 1 min yielded the optimum sludge dewatering conditions. Finally, artificial neural networks (ANN) are utilized to propose predictive models for concentration, and the results were also very accurate ($\mathrm{M}\mathrm{A}\mathrm{E}=1.37\%$). Regarding the computational costs, the ANN took approximately 5% of the time spent on experiments.

This study presents the development and evaluation of a deep learning-based optical character recognition (OCR) model specifically designed for recognizing Old Turkic script. Utilizing a convolutional neural network (CNN), the project aimed to achieve high classification accuracy across a dataset comprising 38 distinct Old Turkic characters. To enhance the model’s robustness and generalization capabilities, sophisticated data augmentation techniques were employed, generating 760 augmented images from the original 38 characters. The model was rigorously trained and validated, achieving an overall ac- curacy of 96.34%. Evaluation metrics such as precision, recall, and F1-scores were systematically analyzed, showing superior performance in most classes while identifying areas for further optimization. The results underscore the effectiveness of CNN architectures in specialized OCR tasks, demonstrating their potential in preserving and digitizing historical scripts. This study not only advances the field of document analysis and OCR but also contributes to the digital preservation and accessibility of ancient scripts.