Decision-making approach based on bipolar complex fuzzy Hamacher power aggregation operators for classifying computer forensics

Purpose Gathering, analyzing and securing electronic data from various digital devices for use in legal or investigative procedures is the key process of computer forensics. Information retrieved from servers, hard drives, cellphones, tablets and other devices is all included in this. This article tackles the challenging problem of how to prioritize different kinds of computer forensics and figure out which kind is most useful in cases of cybercrime, fraud, theft of intellectual property, harassment and espionage. Design/methodology/approach Therefore, we first introduce enhanced versions of Hamacher power aggregation operators (AOs) within the framework of bipolar complex fuzzy (BCF) sets. These include BCF Hamacher power averaging (BCFHPA), BCF Hamacher power-weighted averaging (BCFHPWA), BCF Hamacher power-ordered weighted averaging (BCFHPOWA), BCF Hamacher power geometric (BCFHPG), BCF Hamacher power-weighted geometric (BCFHPWG) and BCF Hamacher power-ordered-weighted geometric (BCFHPOWG) operators. Employing the devised AOs, we devise a technique of decision-making (DM) for dealing with DM dilemmas with the BCF set (BCFS). Findings We prioritize different types of computer forensic by taking artificial data in a numerical example and getting the finest computer forensic. Further, by this example, we reveal the applicability of the proposed theory. This work provides a more elaborate and versatile procedure for classifying computer forensics with dual aspects of criteria and extra fuzzy information. It allows for better and less biased DM in the more intricate digital investigations, which may lead to better DM and time-saving in real-life forensic scenarios. To demonstrate the significance and impression of the devised operators and techniques of DM, they are compared with existing ones. Originality/value This research is the first to combine Hamacher and power AOs in BCFS for computer forensics DM. It presents new operators and a DM approach that is not encountered in the existing literature and is specifically designed to deal with the challenges and risks associated with the classification of computer forensics. The framework’s capacity to accommodate bipolar criteria and extra fuzzy information is a major development in the field of digital forensics and decision science.