Visual selective image encryption can both improve the efficiency of the image encryption algorithm and reduce the frequency and severity of attacks against data. In this article, a new form of encryption is proposed based on keys derived from Deoxyribonucleic Acid (DNA) and plaintext image. The proposed scheme results in chaotic visual selective encryption of image data. In order to make and ensure that this new scheme is robust and secure against various kinds of attacks, the initial conditions of the chaotic maps utilized are generated from a random DNA sequence as well as plaintext image via an SHA-512 hash function. To increase the key space, three different single dimension chaotic maps are used. In the proposed scheme, these maps introduce diffusion in a plain image by selecting a block that have greater correlation and then it is bitwise XORed with the random matrix. The other two chaotic maps break the correlation among adjacent pixels via confusion (row and column shuffling). Once the ciphertext image has been divided into the respective units of Most Significant Bits (MSBs) and Least Significant Bit (LSBs), the host image is passed through lifting wavelet transformation, which replaces the low-frequency blocks of the host image (i.e., HL and HH) with the aforementioned MSBs and LSBs of ciphertext. This produces a final visual selective encrypted image and all security measures proves the robustness of the proposed scheme.

The evolution of wireless and mobile communication from 0G to the upcoming 5G gives rise to data sharing through the Internet. This data transfer via open public networks are susceptible to several types of attacks. Encryption is a method that can protect information from hackers and hence confidential data can be secured through a cryptosystem. Due to the increased number of cyber attacks, encryption has become an important component of modern-day communication. In this paper, a new image encryption algorithm is presented using chaos theory and dynamic substitution. The proposed scheme is based on two dimensional Henon, Ikeda chaotic maps, and substitution box (S-box) transformation. Through Henon, a random S-Box is selected and the image pixel is substituted randomly. To analyze security and robustness of the proposed algorithm, several security tests such as information entropy, histogram investigation, correlation analysis, energy, homogeneity, and mean square error are performed. The entropy values of the test images are greater than 7.99 and the key space of the proposed algorithm is 2 798. Furthermore, the correlation values of the encrypted images using the the proposed scheme are close to zero when compared with other conventional schemes. The number of pixel change rate (NPCR) and unified average change intensity (UACI) for the proposed scheme are higher than 99.50% and 33, respectively. The simulation results and comparison with the state-of-the-art algorithms prove the efficiency and security of the proposed scheme.

Consumers’ decision-making is complex and diverse in terms of gender. Different social, psychological, and economic factors mold the decision-making preferences of consumers. Most researchers used a variance-based approach to explain consumer decision-making that assumes symmetric relationship between variables. We have collected data from 468 smartwatch users and applied a fuzzy set qualitative comparative analysis (fsQCA) to explain and compare male and female consumers’ decision-making complexity. fsQCA assumes that an asymmetric relationship between variables can exist in the real world, and different combinations of variables can lead to the same output. Results explain that different variables have a core and secondary level of impact on consumer decision-making. Hence, we can not claim that certain factors are significant or insignificant for decision-making. fsQCA results revealed that cost value, performance expectancy, and social influence play a key role in consumers’ buying decisions. This study has contributed to the existing literature by explaining consumer decision-making by applying configuration and complexity theories and identifying unique solutions for both genders. A major contribution to theoretical literature was also made by this research, which revealed the complexity of consumer purchasing decisions made for new products.

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Aerial photography involves capturing images from aircraft and other flying objects, including Unmanned Aerial Vehicles (UAV). Aerial images are used in many fields and can contain sensitive information that requires secure processing. We proposed an innovative new cryptosystem for the processing of aerial images utilizing a chaos-based private key block cipher method so that the images are secure even on untrusted cloud servers. The proposed cryptosystem is based on a hybrid technique combining the Mersenne Twister (MT), Deoxyribonucleic Acid (DNA), and Chaotic Dynamical Rossler System (MT-DNA-Chaos) methods. The combination of MT with the four nucleotides and chaos sequencing creates an enhanced level of security for the proposed algorithm. The system is tested at three separate phases. The combined effects of the three levels improve the overall efficiency of the randomness of data. The proposed method is computationally agile, and offered more security than existing cryptosystems. To assess, this new system is examined against different statistical tests such as adjacent pixels correlation analysis, histogram consistency analyses and its variance, visual strength analysis, information randomness and uncertainty analysis, pixel inconsistency analysis, pixels similitude analyses, average difference, and maximum difference. These tests confirmed its validity for real-time communication purposes.