Artificial intelligence (AI) is a well-known and reliable technology that enables a machine to simulate human behavior. While the major theme of AI is to make a smart computer system that thinks like a human to solve awkward problems, machine learning allows a machine to automatically learn from past information without the need for explicit programming. In this analysis, we aim to derive the idea of Aczel–Alsina aggregation operators based on an intuitionistic fuzzy soft set. The initial stage was the discovery of the primary and critical Aczel–Alsina operational laws for intuitionistic fuzzy soft sets. Subsequently, we pioneer a range of applicable theories (set out below) and identify their essential characteristics and key results: intuitionistic fuzzy soft Aczel–Alsina weighted averaging; intuitionistic fuzzy soft Aczel–Alsina ordered weighted averaging; intuitionistic fuzzy soft Aczel–Alsina weighted geometric operators; and intuitionistic fuzzy soft Aczel–Alsina ordered weighted geometric operators. Additionally, by utilizing certain key information, including intuitionistic fuzzy soft Aczel–Alsina weighted averaging and intuitionistic fuzzy soft Aczel–Alsina weighted geometric operators, we also introduce the theory of the weighted aggregates sum product assessment method for intuitionistic fuzzy soft information. This paper also introduces a multi-attribute decision-making method, which is based on derived operators for intuitionistic fuzzy soft numbers and seeks to assess specific industrial problems using artificial intelligence or machine learning. Finally, to underline the value and reasonableness of the information described herein, we compare our obtained results with some pre-existing information in the field. This comparison is supported by a range of numerical examples to demonstrate the practicality of the invented theory.

DevOps (“development and operations”) is a dominant and collaborative organizational effort to computerize simultaneously the supply of a software strategy with a theme to enhance software features. The usage of the DevOps technique is not an easy task as there is various vagueness involved with it. The main goal of this analysis is to employ the Frank t-norm and t-conorm (which is an accommodating class of norms and conorms and due to the parameters the Frank t-norm and t-conorm become more flexible in the combining the data and appropriate to address the real-life issues) in the environment of bipolar complex fuzzy theory to handle the DevOps procedures. Moreover, we define Frank’s operational laws and their appropriate result employing BCF information. Then, we establish, the bipolar complex fuzzy (BCF) Frank weight averaging (BCFFWA), BCF Frank ordered weight averaging (BCFFOWA), BCF Frank hybrid weight averaging (BCFFHWA), BCF Frank weight geometric (BCFFWG), BCF Frank ordered weight geometric (BCFFOWG), BCF Frank hybrid weight geometric (BCFFHWG) operators and assess certain properties and results. Furthermore, we find the major factors that positively influence the DevOps technique in software organizations. In the presence of the prevailing information, nineteen factors were diagnosed. The diagnosed features were moreover authorized by intellectuals via hypothetical information. Finally, BCF analytical hierarchy process (BCFAHP) technique is invented to prioritize the classification success features. The final ranking mentioned that “DevOps security pipeline”, “use system orchestration”, and “attempt matrix organization and transparency” features are the beneficial ranked success features for the valuable utilization of DevOps techniques. Finally, we compare the diagnosed operators with various existing theories to improve the worth of the invented approaches.

Software reliability growth models (SRGMs) are widely used for predicting the reliability of software systems during testing and debugging. Selecting the most appropriate SRGM solutions is a multi-criteria decision-making (MCDM) problem that is very difficult when criteria are imprecise or ambiguous. The paper introduces a novel approach to SRGM selection which relies on q-rung orthopair fuzzy sets (q-ROFS) and the compromise ranking method of VIKOR. New q-rung orthopair fuzzy (q ROF) entropy and divergence measures are proposed for criteria weights assignment and to select superior SRGMs. The VIKOR method is then applied on the q-ROF decision matrix to identify the optimal compromise SRGM solution. This approach provides a systematic framework for handling subjective criteria and modeling uncertainty during SRGM selection. The proposed MCDM methodology is illustrated on the example of a case study involving four common SRGMs evaluated on the four different criteria. Results are demonstrated to be in line with the latest q-rung measures which provide more accurate results than the previous intuitionistic fuzzy methods. The q-ROF VIKOR approach provides the software teams with a more robust information base for the reliability growth decision-making process. At the end of this manuscript, we do the comparison of the proposed theory with certain prevailing concepts to reveal the dominance and supremacy of this work. Whereas yet there are some expected limitations of the proposed work for instance it can’t be helpful in the generalized structures of q-ROFS.

Data sources are the raw information that is required for analysis and modeling in data sciences. They assist data scientists in making proper conclusions, proving hypotheses, and making rational decisions. It is always preferable if analytical results can be obtained from multiple reliable sources. Thus, it is essential to assess such data sources in data sciences about various critical characteristics and factors. Nevertheless, the application of all-inclusive multi-attribute decision-making methodologies for the selection of data sources for integration has not received adequate attention in the existing literature and research. Thus, this article explains a new multi-attribute decision-making method through the model of the complex hesitant fuzzy rough set, which is the complex hesitant fuzzy rough multi-attribute decision-making method. This methodology would handle the evaluated values of attributes that have uncertainty, hesitancy, and roughness altogether. Besides, this study starts several properties of complex hesitant fuzzy rough sets and develops several aggregation operators in the framework of complex hesitant fuzzy rough set and their properties. Subsequently, a case study of data source selection in data science is explained to explain the relevance of the developed multi-attribute decision-making framework in data sciences. Finally, the comparison of the devised theory with prevailing theories is interpreted.

Pattern recognition is the computerized identification of shapes, designs, and reliabilities in information. It has applications in information compression, machine learning, statistical information analysis, signal processing, image analysis, information retrieval, bioinformatics, and computer graphics. Similarly, a medical diagnosis is a procedure to illustrate or identify diseases or disorders, which would account for a person’s symptoms and signs. Moreover, to illustrate the relationship between any two pieces of intuitionistic hesitant fuzzy (IHF) information, the theory of generalized dice similarity (GDS) measures played an important and valuable role in the field of genuine life dilemmas. The main influence of GDS measures is that we can easily obtain a lot of measures by using different values of parameters, which is the main part of every measure, called DGS measures. The major influence of this theory is to utilize the well-known and valuable theory of dice similarity measures (DSMs) (four different types of DSMs) under the assumption of the IHF set (IHFS), because the IHFS covers the membership grade (MG) and non-membership grade (NMG) in the form of a finite subset of [0, 1], with the rule that the sum of the supremum of the duplet is limited to [0, 1]. Furthermore, we pioneered the main theory of generalized DSMs (GDSMs) computed based on IHFS, called the IHF dice similarity measure, IHF weighted dice similarity measure, IHF GDS measure, and IHF weighted GDS measure, and computed their special cases with the help of parameters. Additionally, to evaluate the proficiency and capability of pioneered measures, we analyzed two different types of applications based on constructed measures, called medical diagnosis and pattern recognition problems, to determine the supremacy and consistency of the presented approaches. Finally, based on practical application, we enhanced the worth of the evaluated measures with the help of a comparative analysis of proposed and existing measures.