The current study presents a novel multi-criteria decision making (MCDM) approach to address decision analysis with T-spherical fuzzy data, whose weights of criteria are fully unknown. To serve the purpose, we design some generalized operational laws, namely Frank operational laws for T-spherical fuzzy numbers (T-SFNs) using Frank t-norm and t-conorm. Then, based on the proposed operations, a range of T-spherical fuzzy aggregation operators is developed to aggregate T-spherical fuzzy information efficiently. Also, discuss their particular cases and desirable properties are well proved. Next, we propound the T-spherical fuzzy entropy measure and its capability to fulfil the required properties. Then it is further used for criteria weight determination in the proposed aggregation based MCDM approach. In addition, a descriptive example is provided for viewing the applicability of the established approach. Lastly, the superiority and validity of this approach are highlighted by parameter analysis and comparative analysis.

In this study, a novel technique using a hyper chaotic dynamical system and DNA computing has been designed with high plaintext sensitivity. In order to reduce cost, a selection procedure using tent map has been employed for generating different key streams from the same chaotic data obtained from the iterations of chaotic dynamical system. After separating the three channels from the input color image, they are both confused and diffused. First of all, these channels are diffused on a decimal level. Then they are permuted. Further, DNA encoding is performed upon these channels. Moreover, DNA level diffusion is performed to further increase the degree of randomness in the image. Lastly, the DNA encoded image is converted into decimal to get the final cipher image. Both the experimental results and security analysis strongly demonstrate the robustness of the proposed scheme. A comparison of the proposed scheme has also been made with other recently developed schemes to show that this scheme outperforms the others in terms of computational cost, time and memory efficiency. Additionally, with the large key space, the proposed scheme can resist any brute force, plaintext and statistical attacks, therefore it is a good fit for the real world applications of the image security.

Conflicts occur naturally in the real world at all levels of society, individually, in groups or society as a whole. Almost all the existing conflict resolution models are unilateral in their decision‐making process. They do not consider the actions of the involved parties simultaneously. Therefore, in this paper, we aim to design a novel conflict resolution model based on game‐theoretic rough sets by constructing a game between all the concerned parties (players), computing the payoff of different strategies and classifying them following equilibrium rules. The proposed model yields more realistic and accurate results as it explores all possibilities and is flexible in determining different threshold values relative to the complexities of real‐life problems. Three real‐life conflict situations are solved with the proposed model, and a comprehensive analysis is done to validate the effectiveness of the proposed approach.

Decision making is the key component of people’s daily life, from choosing a mobile phone to engaging in a war. To model the real world more accurately, probabilistic linguistic term sets (PLTSs) were proposed to manage a situation in which several possible linguistic terms along their corresponding probabilities are considered at the same time. Previously, in linguistic term sets, the probabilities of all linguistic term sets are considered to be equal which is unrealistic. In the process of decision making, due to the vagueness and complexity of real life, an expert usually hesitates and unable to express its opinion in a single term, thus making it difficult to reach a final agreement. To handle real life scenarios of a more complex nature, only membership linguistic decision making is unfruitful; thus, some mechanism is needed to express non-membership linguistic term set to deal with imprecise and uncertain information in more efficient manner. In this article, a novel notion called probabilistic hesitant intuitionistic linguistic term set (PHILTS) is designed, which is composed of membership PLTSs and non-membership PLTSs describing the opinions of decision makers (DMs). In the theme of PHILTS, the probabilities of membership linguistic terms and non-membership linguistic terms are considered to be independent. Then, basic operations, some governing operational laws, the aggregation operators, normalization process and comparison method are studied for PHILTSs. Thereafter, two practical decision making models: aggregation based model and the extended TOPSIS model for PHILTS are designed to classify the alternatives from the best to worst, as an application of PHILTS to multi-attribute group decision making. In the end, a practical problem of real life about the selection of the best alternative is solved to illustrate the applicability and effectiveness of our proposed set and models.