Nowadays, cybersecurity has grown into a more significant and difficult sci-entific issue. The recognition of threats and attacks meant for knowledge and safety on the internet is growing harder to detect. Since cybersecurity guar-antees the privacy and security of data sent via the Internet, it is essential, while also providing protection against malicious attacks. Encrypt has grown into an answer that has become an essential element of information security systems. To ensure the security of shared data, including text, images, or videos, it is essential to employ various methods and strategies. This study delves into the prevalent cryptographic methods and algorithms utilized for prevention and stream encryption, examining their encoding techniques such as advanced encryption standard (AES), Blowfish, Twofish, Salsa20, and ChaCha20. The primary objective of this research is to identify the optimal times and throughputs (speeds) for data encryption and decryption processes. The methodology of this study involved selecting five distinct types of images to compare the outcomes of the techniques evaluated in this research. The as-sessment focused on processing time and speed parameters, examining visual encoding and decoding using Java as the primary platform. A comparative analysis of several symmetric key ciphers was performed, focusing on handling large datasets. Despite this limitation, comparing different images helped evaluate the techniques' novelty. The results showed that ChaCha20 had the best average time for both encryption and decryption, being over 50% faster than some other algorithms. However, the Twofish algorithm had lower throughput during testing. The paper concludes with findings and suggestions for future improvements.

Cloud computing has transformed data storage and processing by offering on-demand resources and global accessibility. However, this convenience introduces significant security risks due to the reliance on third-party services, raising concerns about data confidentiality and integrity. This research proposes a hybrid encryption model that combines the high-speed ChaCha20 algorithm for data encryption with the Elliptic Curve Diffie-Hellman (ECDH) protocol for secure key exchange. The model ensures robust data protection in Cloud environments by generating a ChaCha20 key, encrypting it with ECDH, and securely storing encrypted key fragments in the cloud for later reassembly and decryption. This approach enhances security during data transmission and storage while mitigating the common vulnerabilities found in single-algorithm solutions. The study evaluates and compares the performance of ChaCha20 with ECDH against Rivest-Shamir-Adleman (RSA) with advanced encryption standard (AES) and Blowfish with Elliptic-Curve Cryptography (ECC). The results show that ChaCha20 with ECDH provides the fastest encryption time of 2ms and a key generation time of 15.8ms, with moderate memory usage. By contrast, RSA with AES is slower but offers consistent memory usage, while Blowfish with ECC balances speed and memory efficiency. The proposed hybrid model outperforms traditional encryption methods in both speed and security, making it suitable for modern cloud applications requiring scalability and high performance. Future research could focus on optimizing this model for resource-constrained environments, such as IoT and mobile.

The global rise in cardiovascular disease (CVD) cases underscores the critical need for accurate and early diagnostic solutions. This study introduces a robust machine learning (ML) framework for predicting CVD risk by integrating two large, feature-identical datasets containing clinical and biological indicators along with patient history. Seven classification algorithms – logistic regression, random forest (RF), support vector machine (SVM), Gaussian naive Bayes (GNB), gradient boosting (GB), K-nearest neighbors, and decision tree (DT) – were employed. A stratified sampling strategy was used to ensure balanced class distribution, and model performance was further validated using k-fold cross-validation to enhance robustness and generalizability. The datasets, sourced from the UCI repository, were pre-processed and evaluated using metrics such as accuracy, precision, F1-score, log loss, and error rate, with performance further assessed using confusion matrices. Results revealed that ensemble models, particularly RF and DT, achieved optimal performance with 100% accuracy, while stratification significantly improved the outcomes of SVM, GNB, and GB. The integration of datasets, stratified sampling, and k-fold validation effectively enhanced model reliability while minimizing overfitting. These findings highlight the potential of ML to support early CVD diagnosis and lay the groundwork for future research on hybrid models and real-world clinical applications.

Nowadays, cybersecurity has grown into a more significant and difficult scientific issue. The recognition of threats and attacks meant for knowledge and safety on the internet is growing harder to detect. Since cybersecurity guarantees the privacy and security of data sent via the Internet, it is essential, while also providing protection against malicious attacks. Encrypt has grown into an answer that has become an essential element of information security systems. To ensure the security of shared data, including text, images, or videos, it is essential to employ various methods and strategies. This study delves into the prevalent cryptographic methods and algorithms utilized for prevention and stream encryption, examining their encoding techniques such as advanced encryption standard (AES), Blowfish, Twofish, Salsa20, and ChaCha20. The primary objective of this research is to identify the optimal times and throughputs (speeds) for data encryption and decryption processes. The methodology of this study involved selecting five distinct types of images to compare the outcomes of the techniques evaluated in this research. The assessment focused on processing time and speed parameters, examining visual encoding and decoding using Java as the primary platform. A comparative analysis of several symmetric key ciphers was performed, focusing on handling large datasets. Despite this limitation, comparing different images helped evaluate the techniques' novelty. The results showed that ChaCha20 had the best average time for both encryption and decryption, being over 50% faster than some other algorithms. However, the Twofish algorithm had lower throughput during testing. The paper concludes with findings and suggestions for future improvements.