The complexity of chaotic systems, if used in information encryption, can determine the status of security. The paper proposes a novel image encryption scheme that uses chaotic maps and fuzzy numbers for the secure transmission of information. The encryption method combines logistic and sine maps to form the logistic sine map, as well as the fuzzy concept and the Hénon map to form the fuzzy Hénon map, in which these maps are used to generate secure secret keys, respectively. Additionally, a fuzzy triangular membership function is used to modify the initial conditions of the maps during the diffusion process. The encryption process involves scrambling the image pixels, summing adjacent row values, and XORing the result with randomly generated numbers from the chaotic maps. The proposed method is tested against various attacks, including statistical attack analysis, local entropy analysis, differential attack analysis, signal-to-noise ratio, signal-to-noise distortion ratio, mean error square, brute force attack analysis, and information entropy analysis, while the randomness number has been evaluated using the NIST test. This scheme also has a high key sensitivity, which means that a small change in the secret keys can result in a significant change in the encrypted image The results demonstrate the effectiveness of the proposed scheme in ensuring the secure transmission of information.

In today’s digital age, it is crucial to secure the flow of information to protect data and information from being hacked during transmission or storage. To address this need, we present a new image encryption technique that combines the Kronecker xor product, Hill cipher, and sigmoid logistic Map. Our proposed algorithm begins by shifting the values in each row of the state matrix to the left by a predetermined number of positions, then encrypting the resulting image using the Hill Cipher. The top value of each odd or even column is used to perform an xor operation with all values in the corresponding even or odd column, excluding the top value. The resulting image is then diffused using a sigmoid logistic map and subjected to the Kronecker xor product operation among the pixels to create a secure image. The image is then diffused again with other keys from the sigmoid logistic map for the final product. We compared our proposed method to recent work and found it to be safe and efficient in terms of performance after conducting statistical analysis, differential attack analysis, brute force attack analysis, and information entropy analysis. The results demonstrate that our proposed method is robust, lightweight, and fast in performance, meets the requirements for encryption and decryption, and is resistant to various attacks.

Protecting digital data, especially digital images, from unauthorized access and malicious activities is crucial in today’s digital era. This paper introduces a novel approach to enhance image encryption by combining the strengths of the RSA algorithm, homomorphic encryption, and chaotic maps, specifically the sine and logistic map, alongside the self-similar properties of the fractal Sierpinski triangle. The proposed fractal-based hybrid cryptosystem leverages Paillier encryption for maintaining security and privacy, while the chaotic maps introduce randomness, periodicity, and robustness. Simultaneously, the fractal Sierpinski triangle generates intricate shapes at different scales, resulting in a substantially expanded key space and heightened sensitivity through randomly selected initial points. The secret keys derived from the chaotic maps and Sierpinski triangle are employed for image encryption. The proposed scheme offers simplicity, efficiency, and robust security, effectively safeguarding against statistical, differential, and brute-force attacks. Through comprehensive experimental evaluations, we demonstrate the superior performance of the proposed scheme compared to existing methods in terms of both security and efficiency. This paper makes a significant contribution to the field of digital image encryption, paving the way for further exploration and optimization in the future.

An imperative application of artificial intelligence (AI) techniques is visual object detection, and the methods of visual object detection available currently need highly equipped datasets preserved in a centralized unit. This usually results in high transmission and large storage overheads. Federated learning (FL) is an eminent machine learning technique to overcome such limitations, and this enables users to train a model together by processing the data in the local devices. In each round, each local device performs processing independently and updates the weights to the global model, which is the server. After that, the weights are aggregated and updated to the local model. In this research, an innovative framework is designed for real-world object recognition in FL using a proposed Deep Q Network (DQN) based on a Fractional Political–Smart Flower Optimization Algorithm (FP-SFOA). In the training model, object detection is performed by employing SegNet, and this classifier is effectively tuned based on the Political–Smart Flower Optimization Algorithm (PSFOA). Moreover, object recognition is performed based on the DQN, and the biases of the classifier are finely optimized based on the FP-SFOA, which is a hybridization of the Fractional Calculus (FC) concept with a Political Optimizer (PO) and a Smart Flower Optimization Algorithm (SFOA). Finally, the aggregation at the global model is accomplished using the Conditional Autoregressive Value at Risk by Regression Quantiles (CAViaRs) model. The designed FP-SFOA obtained a maximum accuracy of 0.950, minimum loss function of 0.104, minimum MSE of 0.122, minimum RMSE of 0.035, minimum FPR of 0.140, maximum average precision of 0.909, and minimum communication cost of 0.078. The proposed model obtained the highest accuracy of 0.950, which is a 14.11%, 6.42%, 7.37%, and 5.68% improvement compared to the existing methods.

This paper examines the challenges to justice in Tanzania posed by the proliferation of anonymous social media accounts and the dissemination of offensive and harassing content. The study analyzes the legal frameworks governing online content in Tanzania, including the Electronic and Postal Communications Act and the Electronic and Postal Communications (Online Content) Regulation of 2018, and highlights their shortcomings in identifying the publishers of such content. The paper also discusses the Legal and Human Rights Center's challenge to the EPOCA regulation and the authorities and responsible organs identified to deter, adjudicate, and open court processes in dealing with alleged offenses arising from online published content. Ultimately, the paper argues that a better understanding of the hindrances to justice in Tanzania must consider the paucity of legal frameworks governing published and online social media content. The findings of this study have significant implications for policymakers, legal practitioners, and civil society organizations seeking to promote accountability and protect human rights in Tanzania.

This paper examines the challenges to justice in Tanzania posed by the proliferation of anonymous social media accounts and the dissemination of offensive and harassing content. The study analyzes the legal frameworks governing online content in Tanzania, including the Electronic and Postal Communications Act and the Electronic and Postal Communications (Online Content) Regulation of 2018, and highlights their shortcomings in identifying the publishers of such content. The paper also discusses the Legal and Human Rights Center's challenge to the EPOCA regulation and the authorities and responsible organs identified to deter, adjudicate, and open court processes in dealing with alleged offenses arising from online published content. Ultimately, the paper argues that a better understanding of the hindrances to justice in Tanzania must consider the paucity of legal frameworks governing published and online social media content. The findings of this study have significant implications for policymakers, legal practitioners, and civil society organizations seeking to promote accountability and protect human rights in Tanzania.