Enhancing the reliability of wind speed forecasting is vital for efficient wind power generation. Given the wind's stochastic nature, preprocessing is crucial to obtain a clean wind speed series. This study introduces an innovative wind speed prediction model that integrates Variational Mode Decomposition (VMD), Symplectic Geometry Mode Decomposition (SGMD), and Long Short-Term Memory (LSTM). The model begins with VMD dividing the series into low- and high-frequency parts, then the SGMD further analyzes the high-frequency segment, and LSTM predicts results based on these components. Collaborative use of VMD and SGMD enables thorough decomposition of intricate wind speed data, while LSTM boosts the model's ability to capture patterns and dependencies. This hybrid model addresses the challenges posed by wind power uncertainty, aiming to efficiently integrate wind energy into power systems. The proposed hybrid model was compared to some benchmark models and outperformed them, reducing MAPE by 58% and RMSE by 31% for Dataset 1, and improving MAPE by 14% and RMSE by 36% for Dataset 2. The results confirm the competitive strength of the proposed strategy. Furthermore, the suggested two-stage decomposition technique demonstrates suitability for the examination of nonlinear characteristics in wind speed patterns.

Improving the reliability of wind speed forecasting is critical for optimizing wind power generation efficiency and grid stability. Accurate predictions enhance operational planning and decision-making, thereby supporting the sustainability and economic viability of wind energy. Given the inherently stochastic and noisy nature of wind, implementing a preprocessing step is essential to obtain more accurate wind speed data. Decomposition techniques are recognized as essential preprocessing, which effectively transform unstable wind speed data into multiple regular components. This study introduced a hybrid wind speed forecasting model that integrates a secondary decomposition algorithm with a Long Short-Term Memory (LSTM) algorithm. For the decomposition part, Wavelet Decomposition (WA) was first used to extract the low-frequency part from the original wind data. Then, the Symplectic Geometry Mode Decomposition(SGMD)decomposes the rest of the high-frequency components. The predictive phase of the model utilizes the LSTM algorithm. Experimental results demonstrate that the proposed secondary decomposition method significantly outperforms single decomposition models. Additionally, the superiority of the proposed hybrid model is evident when compared with other hybrid models. The proposed model demonstrates substantial improvements in prediction accuracy of a utilized dataset by 37%, 13%, and 17% reduction in terms of MAPE, RMSE, and MAE respectively for 1-3 steps of the forecast. Overall, the proposed model provides more accurate and reliable wind speed forecasts compared to other benchmark models.

Wind speed forecasting is crucial for optimizing wind energy systems, enhancing turbine efficiency, ensuring grid stability, and planning energy production. This review critically examines advancements in wind speed forecasting through deep learning algorithms, which surpass traditional statistical methods in handling complex, non-linear, and non-stationary wind speed data. It focuses on various deep learning models, including CNNs, RNNs, LSTMs, and GRUs, and their ability to capture spatial and temporal dependencies. Essential data preprocessing techniques and evaluation metrics like RMSE, MAE, R, R2, and MAPE, are discussed to assess model performance. The review also synthesizes recent case studies demonstrating practical applications. Despite progress, challenges such as data quality, computational demands, overfitting, and model interpretability remain. Future research directions include improving data collection, developing efficient model architectures, enhancing interpretability, and mitigating overfitting. This review provides a concise overview of the current state of deep learning in wind speed forecasting, highlighting key methodologies, challenges, and future research opportunities.

RSA cryptographic algorithm used to encrypt and decrypt the messages to send it over the secure transmission channel like internet. The RSA algorithm is a secure, high quality, public key algorithm. In this paper, a new architecture and modeling has been proposed for RSA public key algorithm, the suggested system uses 1024-bit RSA encryption/decryption for restricted system. The system uses the multiply and square algorithm to perform modular operation. The design has been described by VHDL and simulated by using Xilinx ISE 12.2 tool. The architectures have been implemented on reconfigurable platforms FPGAs. Accomplishment when implemented on Xilinx_Spartan3 (device XC3S50, package PG208, speed -4) which confirms that the proposed architectures have minimum hardware resource, where only 29% of the chip resources are used for RSA algorithm design with realizable operating clock frequency of 68.573 MHz.

The Steganography is an art and science of hiding information by embedding messages within other, seemingly harmless messages and lots of researches are working in it. Proposed system is using AES Algorithm and Lossy technique to overcome the limitation of previous work and increasing the process’s speed. The sender uses AES Algorithm to encrypt message and image, then using LSB technique to hide encrypted data in encrypted message. The receive get the original data using the keys that had been used in encryption process. The proposed system has been implemented in NetBeans 7.3 software uses image and data in different size to find the system’s speed ].

Cryptography algorithms are becoming more necessary to ensure secure data transmission, which can be used in several applications. Increasing use of images in industrial process therefore it is essential to protect the confidential image data from unauthorized access. Advanced Encryption Standard (AES) is a well-known block cipher that has many benefits in data encryption process. In this paper, proposed some modification to the Advanced Encryption Standard (M-AES) to increase and reaching high level security and enhance image encryption. The modification is done by modifying the ShiftRow Transformation. Detailed results in terms of security analysis and implementation are given. Comparing the proposed algorithm with the original AES encryption algorithm shows that the proposed M-AES has more security from the cryptographic view and gives better result of security against statistical attack.