Seyed Mohammad Hossein Hasheminejad

dblp:95/8719 · DBLP profile ↗
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12ranked-venue papers
3as first author
6since 2021 · last 2025
0000-0002-7357-7906ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 7 · 3 first-author · 2 since 2021Artificial intelligence and machine learning · 3 · 2 since 2021Graphics, computer vision, multimedia, augmented reality and games · 2 · 2 since 2021
YearPublicationVenuePosition
2025 DiSA-CF: A distance-integrated self-attention model for collaborative filtering in web service recommendation
Masoumeh Alinia, Seyed Mohammad Hossein Hasheminejad
Expert Syst. Appl.2
2024 Deep learning semantic image synthesis: a novel method for unlimited capacity, high noise resistance coverless video steganography
Zeinab Torabi Jahromi, Seyed Mohammad Hossein Hasheminejad, Seyed Vahab Shojaedini
Multim. Tools Appl.2
2024 Correction to: Deep learning semantic image synthesis: a novel method for unlimited capacity, high noise resistance coverless video steganography
Zeinab Torabi Jahromi, Seyed Mohammad Hossein Hasheminejad, Seyed Vahab Shojaedini
Multim. Tools Appl.2
2023 A two-phase gene selection method using anomaly detection and genetic algorithm for microarray data
Motahare Akhavan, Seyed Mohammad Hossein Hasheminejad
Knowl. Based Syst.2
2023 Software design pattern selection approaches: A systematic literature review
abstract
Abstract Software design patterns have a considerable impact on the software development life cycle. Design pattern (DP) is a reliable and reusable solution provided by software experts to obtain quality software design. However, due to the large number of design patterns, selecting the appropriate one is quite difficult. To overcome this difficulty, several approaches with different methods have been presented to suggest the appropriate DP. Despite conducting a number of studies that have explored some aspects of this field, such as design pattern selection tools and techniques, there is a need for a deeper understanding, analysis, classification, and thorough review of the design pattern selection process. So far, no systematic review of design pattern selection approaches is available. This paper aims to classify existing approaches, provide several criteria for comparing approaches, analyze each one, and identify and analyze the most important elements in this field, including open issues, data sets, and so on. The present investigation paper will help future research to employ the existing approaches taking into account the specification of each one and it also raises awareness about the approaches used in previous research and their potential limitations.
Amene Naghdipour, Seyed Mohammad Hossein Hasheminejad, Roghayeh Barmaki
Softw. Pract. Exp.2
2023 Implications of semi-supervised learning for design pattern selection
Amene Naghdipour, Seyed Mohammad Hossein Hasheminejad
Softw. Qual. J.2
2019 Software component identification and selection: A research review
abstract
Summary Nowadays, with the development of software reuse, software developers are paying more attention to component‐related technologies, which have been mostly applied in the development of large‐scale complex applications to enhance the productivity of software development and accelerate time to market. Component‐based software development is well acknowledged as a methodology, which establishes the reusability of software and reduces the development cost effectively. Two crucial problems in component‐based software development are component identification and component selection. The main purpose of this paper is to provide a reference point for future research by categorizing and classifying different component identification and component selection methods and emphasizing their respective strengths and weaknesses. We hope that it can help researchers find the current status of this issue and serve as a basis for future activities.
Shabnam Gholamshahi, Seyed Mohammad Hossein Hasheminejad
Softw. Pract. Exp.2
2015 CCIC: Clustering analysis classes to identify software components
Seyed Mohammad Hossein Hasheminejad, Saeed Jalili
Inf. Softw. Technol.1
2015 Automated software design using ant colony optimization with semantic network support
Vali Tawosi, Saeed Jalili, Seyed Mohammad Hossein Hasheminejad
J. Syst. Softw.3
2014 An evolutionary approach to identify logical components
Seyed Mohammad Hossein Hasheminejad, Saeed Jalili
J. Syst. Softw.1
2013 Dynamic clustering using combinatorial particle swarm optimization
Hamid Masoud, Saeed Jalili, Seyed Mohammad Hossein Hasheminejad
Appl. Intell.3
2012 Design patterns selection: An automatic two-phase method
Seyed Mohammad Hossein Hasheminejad, Saeed Jalili
J. Syst. Softw.1