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1.

001-es BibID:BIBFORM122023
035-os BibID:(WoS)000774599800010 (Scopus)85128336939
Első szerző:Abdulsada, Ayad I.
Cím:Privacy preserving scheme for document similarity detection / Ayad I. Abdulsada, Salah Al-Darraji, Dhafer G. Honi
Dátum:2022
ISSN:1300-0632
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Megjelenés:Turkish Journal of Electrical Engineering and Computer Sciences. - 30 : 3 (2022), p. 609-628. -
További szerzők:Al-Darraji, Salah Alshuwaili, Dhafer Gheni Honi (1991) (Informatics)(PhD)
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2.

001-es BibID:BIBFORM121816
035-os BibID:(Scopus)85110330327
Első szerző:Abdulsada, Ayad I.
Cím:Efficient multi-keyword similarity search over encrypted cloud documents / Ayad I. Abdulsada, Dhafer G. Honi, Salah Al-Darraji
Dátum:2021
ISSN:2502-4752 2502-4760
Megjegyzések:Many organizations and individuals are attracted to outsource their data into remote cloud service providers. To ensure privacy, sensitive data should be encrypted before being hosted. However, encryption disables the direct application of the essential data management operations like searching and indexing. Searchable encryption is a cryptographic tool that gives users the ability to search the encrypted data while being encrypted. However, the existing schemes either serve a single exact search that loss the ability to handle the misspelled keywords or multi-keyword search that generate very long trapdoors. In this paper, we address the problem of designing a practical multi-keyword similarity scheme that provides short trapdoors and returns the correct results according to their similarity scores. To do so, each document is translated into a compressed trapdoor. Trapdoors are generated using key based hash functions to ensure their privacy. Only authorized users can issue valid trapdoors. Similarity scores of two textual documents are evaluated by computing the Hamming distance between their corresponding trapdoors. A robust security definition is provided together with its proof. Our experimental results illustrate that the proposed scheme improves the search efficiency compared to the existing schemes. Furthermore, it shows a high level of performance.
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Cloud computing
Multi-keywords ranking
search
Privacy preserving
Searchable encryption
Simhash
Megjelenés:Indonesian Journal of Electrical Engineering and Computer Science. - 23 : 1 (2021), p. 510-518. -
További szerzők:Alshuwaili, Dhafer Gheni Honi (1991) (Informatics)(PhD) Al-Darraji, Salah
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3.

001-es BibID:BIBFORM121813
035-os BibID:(Scopus)85179364062
Első szerző:Al-Ansarry, Suhaib
Cím:An Efficient Path Planning in Uncertainty Environments using Dynamic Grid-Based and Potential Field Methods / Suhaib Al-Ansarry, Salah Al-Darraji, Dhafer Honi
Dátum:2023
ISSN:1814-5892 2078-6069
Megjegyzések:Abstract Path planning is an essential concern in robotic systems, and it refers to the process of determining a safe and optimal path starting from the source state to the goal one within dynamic environments. We proposed an improved path planning method in this article, which merges the Dijkstra algorithm for path planning with Potential Field (PF) collision avoidance. In real-time, the method attempts to produce multiple paths as well as determine the suitable path that's both short and reliable (safe). The Dijkstra method is employed to produce multiple paths, whereas the Potential Field is utilized to assess the safety of each route and choose the best one. The proposed method creates links between the routes, enabling the robot to swap between them if it discovers a dynamic obstacle on its current route. Relating to path length and safety, the simulation results illustrate that Dynamic Dijkstra-Potential Field (Dynamic D-PF) achieves better performance than the Dijkstra and Potential Field each separately, and going to make it a promising solution for the application of robotic automation within dynamic environments
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Robotic
Path Planning
Dijkstra
Potential Field
Static Obstacle
Dynamic Environment
Megjelenés:Iraqi Journal for Electrical and Electronic Engineering. - 19 : 2 (2023), p. 90-99. -
További szerzők:Al-Darraji, Salah Alshuwaili, Dhafer Gheni Honi (1991) (Informatics)(PhD)
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4.

001-es BibID:BIBFORM121055
035-os BibID:(WOS)001053763200001 (Scopus)85163554647
Első szerző:Al-Ansarry, Suhaib
Cím:Bi-Directional Adaptive Probabilistic Method With a Triangular Segmented Interpolation for Robot Path Planning in Complex Dynamic-Environments / Suhaib Al-Ansarry, Salah Al-Darraji, Asmaa Shareef, Dhafer G. Honi, Francesca Fallucchi
Dátum:2023
ISSN:2169-3536
Megjegyzések:Path planning is a fundamental aspect of mobile robots and autonomous systems. Methods of path planning are used in robotics to create a path for a robot or autonomous system to follow from a starting position to a goal one while avoiding obstacles and satisfying any additional conditions. There are many different methods to plan the path, including probabilistic methods, heuristics-based approaches, and optimization-based methods. In this paper, we introduce a novel path planning method called Dynamic Adaptive RRT-connect with Triangular Segmented Interpolation. Our approach aims to enhance the conven- tional Rapidly-exploring Random Tree (RRT) algorithms by incorporating an Adaptive-RRT strategy. This strategy involves selecting a random node as a new node to augment the exploration of the tree, thereby improving its coverage of the search space. Furthermore, we employ a Bi-directional scheme to further enhance the convergence time and cost of our method. By exploring the search space from both the initial and goal configurations simultaneously, we exploit the advantages of a two-way search, potentially resulting in more efficient and optimized paths. To improve the quality of the generated paths, our method leverages the Triangular Segmented Interpolation (TSI) technique. TSI helps in reducing the path length and increasing its smoothness by interpolating between the configurations in a triangular segmented manner, resulting in more natural and feasible trajectories. Moreover, considering the dynamic nature of the environment, our method operates within the framework of the Dynamic Window Approach (DWA). By adapting to the changing environment, our approach effectively avoids dynamic obstacles and navigates the robot or system through complex and unpredictable scenarios. We have conducted extensive experiments in various environments to evaluate the performance of our proposed method. The results demonstrate that our approach outperforms the individual RRT and RRT-connect algorithms in terms of computation time (reduced by 90-80%), cost (reduced by 82-63%), and path length (shortened by 17-12%). Additionally, our method exhibits efficient obstacle avoidance capabilities, enabling successful navigation in dynamic environments.
Tárgyszavak:idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Autonomous system
dynamic obstacles
interpolation
probabilistic methods
robot path planning
Megjelenés:IEEE Access. - 11: (2023), p. 87747 - 87759. -
További szerzők:Al-Darraji, Salah Shareef, Asmaa Alshuwaili, Dhafer Gheni Honi (1991) (Informatics)(PhD) Fallucchi, Francesca
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5.

001-es BibID:BIBFORM121815
035-os BibID:(WoS)000913143400001 (Scopus)85119048409
Első szerző:Al-Darraji, Salah
Cím:Employee Attrition Prediction Using Deep Neural Networks / Salah Al-Darraji, Dhafer G. Honi, Francesca Fallucchi, Ayad I. Abdulsada, Romeo Giuliano, Husam A. Abdulmalik
Dátum:2021
ISSN:2073-431X
Megjegyzések:Decision-making plays an essential role in the management and may represent the most important component in the planning process. Employee attrition is considered a well-known problem that needs the right decisions from the administration to preserve high qualified employees. Interestingly, artificial intelligence is utilized extensively as an efficient tool for predicting such a problem. The proposed work utilizes the deep learning technique along with some preprocessing steps to improve the prediction of employee attrition. Several factors lead to employee attrition. Such factors are analyzed to reveal their intercorrelation and to demonstrate the dominant ones. Our work was tested using the imbalanced dataset of IBM analytics, which contains 35 features for 1470 employees. To get realistic results, we derived a balanced version from the original one. Finally, cross-validation is implemented to evaluate our work precisely. Extensive experiments have been conducted to show the practical value of our work. The prediction accuracy using the original dataset is about 91%, whereas it is about 94% using a synthetic dataset.
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
deep learning
machine learning
attrition prediction
Megjelenés:Computers. - 10 : 11 (2021), p. 1-11. -
További szerzők:Alshuwaili, Dhafer Gheni Honi (1991) (Informatics)(PhD) Fallucchi, Francesca Abdulsada, Ayad I. Giuliano, Romeo Abdulmalik, Husam A.
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6.

001-es BibID:BIBFORM122022
Első szerző:Alshuwaili, Dhafer Gheni Honi (Informatics)(PhD)
Cím:Privacy Preserving Image Matching Scheme with Aggregated Local Descriptors / Dhafer G. Honi, Husam A. Abdulmalik, Ayad I. Abdulsada, Salah Al-Darraji
Dátum:2022
Tárgyszavak:Műszaki tudományok Informatikai tudományok előadáskivonat
könyvrészlet
Megjelenés:IEEE Conference Publication. - p. 1-7. -
További szerzők:Abdulmalik, Husam A. Abdulsada, Ayad I. Al-Darraji, Salah
Internet cím:Intézményi repozitóriumban (DEA) tárolt változat
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