Data security and data hiding have been studied throughout history. Studies show that steganography and encryption methods are used together to hide data and avoid detection. Large amounts of data hidden in the cover multimedia distort the image, which can be detected in visual and histogram analysis. The proposed method will solve two major drawbacks of the current methods: the limitation imposed on the size of the data to be hidden in the cover multimedia and low resistance to steganalysis after stego-operation. In the proposed method, plaintext data are divided into fixed-sized bits whose corresponding matching bits’ indices in the cover multimedia are accumulated. Thus, the hidden data are composed of the indices in the cover multimedia, causing no change in it, thus enabling considerable amounts of plaintext to be hidden. The proposed method also has high resistance to known steganalysis methods because it does not cause any distortion to the cover multimedia. The test results show that the performance of the proposed method outperforms similar conventional stenographic techniques. The proposed Ozyavas–Takaoglu–Ajlouni (OTA) method relieves the limitation on the size of the hidden data, and hidden data is undetectable by steganalysis because it is no longer embedded in the cover multimedia.

The design and implementation of a multi-stage PID (MS-PID) controller for non-inertial referenced UAVs are highly complex. Symmetrical multirotor UAVs are unstable systems, and it is thought that the kinematics of the symmetrical UAV rotor, such as the quadrotor and hexacopter resembles the kinematics of an inverted pendulum. Several researchers have investigated the structure and design of PID controllers for high-order systems during the last decade. The designs were always concerned with the enhanced response, robustness, model reduction and performance of PID controllers. An accurate tuning process of such a controller depends on the engineer’s experience level. This is due to the number of variables and hyperparameters tuned during the process. An adaptive genetic algorithm (AGA) is utilized to optimize the MS-PID controllers for controlling the quadrotor in this study. The proposed method optimizes the offline-planned approach, providing several possibilities for adapting the controllers with various paths and or varying weather conditions. The MS-PID parameters are optimized in parallel, as every PID controller affects the other controller’s behavior and performance. Furthermore, the proposed AGA generates new chromosomes for “new solutions” by randomly developing new solutions close to the previous best values, which will prevent any local minima solution. This study intends to investigate the design and development of a highly tuned robust multi-stage PID controller for a symmetrical multirotor UAV. The work presents a model for a non-referenced inertial frame multirotor UAV (quadcopter). Once the model is defined, a robust multi-stage PID controller for the non-inertial referenced frame symmetrical multirotor UAV is designed, tuned, and tested. A genetic algorithm (GA) will be used to tune the MS-PID controller. Finally, the performance comparison between the proposed and conventional methods is presented. The results show that the proposed method provides stability improvement, better transient response, and power consumption.

Steganography is a subject of study that has been used to hide information throughout history. In cryptology science, the information to be hidden is encrypted. Both study subjects are widely used in information security and protection. In our study, digital image steganography, which is one of the application areas of steganography, was developed and applied to hide text in the selected images. While doing this, the low bands that will hide the data using the discrete Haar wavelet transform of the images obtained first. The text to be hidden is encrypted with the one-time-pad algorithm. The key used for the encryption is transmitted to the receiver using a transmission layer based on a Highly Secured Information Exchange Algorithm. The algorithms use a randomly generated key pool maintained by both the transmitter and the receiver. A key is selected from the pool randomly by generating a random key start point for every message. The pool size and the randomness are critical factors in guaranteeing no key repetition, which is a requirement for a one-time-pad. The ciphertext and the key starting point indicator are hidden in the low bands of the pictures by utilizing the least significant bit method. The optimal pixel adjustment process was applied to the pre-stego-images, this resulted in an improvement in the results. The results obtained in this study are compared against the pre-optimal pixel adjustment process results and the results obtained through peer studies. The test results show that the proposed method outperformed all the methods in terms of peak signal-to-noise ratio, structural similarity index metric, mean absolute error, mean consequential error and the encryption key security.

The optimization of PID controllers for Continuous Stirred Tank Reactors (CSTRs) is critical for ensuring stable and efficient chemical processing under varying operational conditions and external disturbances. This study presents a novel approach that integrates advanced tuning techniques, including Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and their hybrid combinations with a Machine Learning (ML) surrogate model, to improve PID controller performance in disturbed environments. A unique evolutionary framework is employed, where populations of both controllers and plants are co-evolved to handle the most challenging plant models. An adversarial testing approach is utilized to evaluate the best-tuned controller against the four most difficult CSTR plants, with disturbances such as feed concentration and temperature fluctuations. The results demonstrate that both GA and PSO, when enhanced with the ML surrogate model, effectively tune PID controllers to manage disturbances, with the GA tuned controller achieving faster convergence and the PSO tuned controller showing greater robustness. Additionally, the hybrid ML surrogate model significantly improved control performance and disturbance rejection. The findings highlight the ability of the ML_GA and ML_PSO controllers to maintain stability and accuracy across a range of challenging conditions, providing a robust solution for optimizing control systems in nonlinear dynamic environments. This research contributes to the field of process control, showcasing the potential of combining evolutionary algorithms with machine learning surrogate models for adaptive, resilient control strategies in complex chemical systems.

Accurate monthly streamflow prediction is essential for effective water-resource planning and basin-scale hydrological management. This study develops and evaluates a hybrid wavelet–machine learning framework for one-month-ahead streamflow prediction in the Karamandere Creek Basin, Türkiye. Multiresolution features derived from Discrete Wavelet Transform using Daubechies (db4) and Symlet (sym8) wavelets were integrated with five supervised learning algorithms, including Decision Tree, Linear Regression, Random Forest, Extra Trees, and Support Vector Regression. Daily streamflow, precipitation, and temperature observations collected from January 2005 to September 2020 were aggregated into 189 monthly records. After constructing the one-month lagged predictor and future streamflow target, the final supervised dataset consisted of 187 prediction cases. The cases were chronologically divided into 151 development observations and 36 independent test observations covering October 2017 to September 2020. Model development incorporated expanding-window walk-forward validation, while predictive performance was evaluated using the coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE). Across the independent test period, wavelet-derived representations improved predictive performance compared with corresponding raw-input configurations for all evaluated algorithms. Random Forest combined with sym8-derived features achieved the highest performance (R² = 0.92, RMSE = 1.72 m³/s, and MAE = 0.95 m³/s), followed by Extra Trees (R² = 0.91). Support Vector Regression exhibited the largest relative improvement, increasing its test R² from 0.65 with raw predictors to 0.88 with sym8-derived features. Although sym8-based representations provided better performance than db4 under the evaluated experimental conditions, this finding should be interpreted as evidence specific to the tested wavelet configurations rather than general superiority of the Symlet family. The results are limited by the single-basin application, relatively short monthly record, and absence of multi-site external validation. Further investigations using larger datasets, additional catchments, and broader wavelet comparisons are required to assess the transferability of the proposed framework.