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Evolution of Deep Quantum Learning Models Based on Comprehensive Survey on Effective Malware Identification and Analysis
Published in Thiruselvan Subramanian, Archana Dhyani, Adarsh Kumar, Sukhpal Singh Gill, Artificial Intelligence, Machine Learning and Blockchain in Quantum Satellite, Drone and Network, 2023
S. Poornima, Thiruselvan Subramanian
In previous sections, malware detection and analysis were discussed in detail. Various techniques such as instruction embedding, register assigning, code obfuscations, and opcode embedding were also discussed. This section focuses on a comprehensive analysis of multiple datasets implemented via ML and DL techniques. ML and DL employ their own set of algorithms to detect and analyze malwares. They include static analysis and dynamic analysis mechanisms to validate the dataset attributes. By considering the ML and DL technique, this section represents a detailed review of existing ML and DL tools and techniques along with pros and cons. Moreover, report generated from variant behavior, instruction opcodes, Application Programming Interface (API) calls, and opcode validation feature set is considered for legitimating the malicious samples. In summary, the literature review of malware analysis via ML and DL is represented by considering the following parameters.
AI Programming Languages and Tools
Published in Adrian A. Hopgood, Intelligent Systems for Engineers and Scientists, 2021
The empty brackets at the end of the first line of the function definition signify that setup takes no parameters. As in the definition of accept, let is used to declare a local variable and to give it an initial value of nil, that is, the empty list. Then dolist is used to consider each element of materials_database in turn and to assign it to the local variable material. Each successive value of material is a list comprising the polymer name, its type, and lists of properties and values. Our aim is to extract from it a list comprising a name and type only, and to collect all such two-element lists together into the list shortlist. The Lisp function cons adds an element to a list, and it can therefore be used to build up shortlist.
Decoding Common Machine Learning Methods
Published in Himansu Das, Jitendra Kumar Rout, Suresh Chandra Moharana, Nilanjan Dey, Applied Intelligent Decision Making in Machine Learning, 2020
Srinivasagan N. Subhashree, S. Sunoj, Oveis Hassanijalilian, C. Igathinathane
Most of these applications used software packages that are available for different ML methods in the form of black-box modules in various programming languages, such as Scikit-learn and TensorFlow in Python (Pedregosa et al., 2011; Abadi et al., 2015; Sarkar et al., 2018); nnet, and caret in R (Ramasubramanian and Singh, 2017; Kuhn, 2019); and Statistic and ML toolbox in MATLAB (Martinez and Martinez, 2015). All of these packages are great for training, developing, and testing ML models, but they come with their pros and cons. Python and R (R Core Team, 2019) are preferred among industry, since they are free, open source, and work cross-platform; while MATLAB, a commercial software that, as well as requiring an annual subscription, is commonly used in academia.
Project Portfolio Reliability: A Bayesian Approach for LeAgile Projects
Published in Engineering Management Journal, 2022
Sagar Chhetri, Dongping Du, Susan Mengel
We conducted an in-depth review of the existing literature to understand further the possibility of establishing an applied mathematical model for complex information project/portfolio systems. The existing research in LeAgile project systems mainly focused on identifying risk factors, continuous improvement factors, complexity aspects, pros and cons, definitions, acceptance of agile or lean, and causes of failures (Budzier, 2011; Cline, 2015; De França et al., 2017; Discenza & Forman, 2007; Fitzgerald & Stol, 2014; Hong et al., 2014; Krancher et al., 2018; Schwaber & Beedle, 2002; Shehzad et al., 2017; Suomalainen et al., 2015; Wang et al., 2012). However, none of the studies have attempted to provide practical and convenient applied mathematical models for engineering managers to implement learning and reduce these challenges. A practical and comprehensive tool for the measurement of complexity and performance of modern projects is still missing in the IS literature and in practice (Mirza & Ehsan, 2017).
Lean management framework for improving maintenance operation: development and application in the oil and gas industry
Published in Production Planning & Control, 2021
Wenchi Shou, Jun Wang, Peng Wu, Xiangyu Wang
There are potential limitations to this study. First, the framework was tested by using a single representative case study. The selected case for validating the proposed framework was relatively simple because only a repetitive process was investigated. Future studies should address this issue. Second, the lean management framework developed should be evaluated and tested with more case studies in the oil and gas industry. This would help evaluate its wider applicability as well as create best practices for future implementation of lean initiatives in TAM. A wider study provides explanations on how the performance of TAM operation can be improved through the application of the lean theory and the relevant lean tools. It is suggested that the selection of lean production tools and techniques for project-based production context should be done with a comprehensive analysis. The review gives a general understanding of the features, pros, and cons of the selection and implementation of lean tools and techniques for improving the process efficiency in a TAM project. The development of lean applications in the oil and gas industry can be leveraged by identifying the critical factors that affect the selection process of appropriate lean production tools and techniques.
An integrated approach for automated physical architecture generation and multi-criteria evaluation for complex product design
Published in Journal of Engineering Design, 2019
Ruirui Chen, Yusheng Liu, Hongri Fan, Jianjun Zhao, Xiaoping Ye
Case-based reasoning refers to solving problems, evaluating solutions, explaining abnormal situations and understanding new situations by using old cases or experiences (Peng 2007). In this study, case-based mapping is inspired by case-based reasoning. A case is represented as shown in Figure 4. Here, a function corresponds to the ‘problem’ item, and a component corresponds to the ‘solution’ item. There exists additional information (the ‘situation’ item) in the case, which consists of the pros and cons of the solution (component) in solving (realising) the problem (function) in the previous products. When a component that is found in case-based mapping is expected to be used in the product, the information that is contained in the ‘situation’ item of its case can be used as a reference to decide whether the component should be used.