Article
Environmental Sciences
Hao Chen, Yuheng Liang, Xing Meng
Summary: An unmanned aerial vehicle (UAV) path planning method is proposed to obtain more building surface information with fewer images, utilizing an opposition-based learning artificial bee colony (OABC) algorithm. A target information entropy ratio model based on observation angles is proposed to evaluate the obtained information, considering the observation angle constraints under different conditions. The experiments conducted on real residential buildings show that the proposed method effectively reduces the number of images needed for 3D reconstruction while maintaining comparable accuracy, significantly reducing operation time and improving efficiency.
Article
Computer Science, Artificial Intelligence
Songyi Xiao, Wenjun Wang, Hui Wang, Zhikai Huang
Summary: This paper proposes a new multi-objective artificial bee colony algorithm called ROMOABC, which is based on reference point and opposition. Experimental results on multiple benchmark functions demonstrate that ROMOABC achieves competitive convergence and diversity.
INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION
(2022)
Article
Engineering, Multidisciplinary
Omur Sahin, Bahriye Akay, Dervis Karaboga
Summary: Testing object-oriented software is challenging due to various properties like classes, inheritance, states, behavior, association, and polymorphism. Search-based testing methods like ABC algorithm can automatically generate test cases to optimize coverage goals. Use of archive in ABC algorithm improves convergence and coverage for software testing.
ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH
(2021)
Article
Computer Science, Information Systems
Syed Sarmad Ali, Jian Ren, Kui Zhang, Ji Wu, Chao Liu
Summary: The software industry has grown rapidly, making software development crucial for multinational corporations. Effective software development is necessary due to the increasing demand for complex systems and limited resources. Errors in job estimating often lead to project failure, as underestimating costs can exceed the budget. Researchers propose the use of ensemble models for better estimation results in software development projects. A heterogeneous ensemble effort estimation (EEE) model combining standalone models was proposed and validated using benchmark datasets and industry use cases, outperforming standalone models in accuracy.
Article
Computer Science, Artificial Intelligence
Chunfeng Wang, Pengpeng Shang, Peiping Shen
Summary: This paper presents a novel ABC algorithm based on Bayesian estimation (BEABC) to improve the performance of the original ABC algorithm. By replacing the selection probability with a probability calculated by Bayesian estimation and designing a directional guidance mechanism, BEABC achieves better results in single-objective, multi-objective, and real-world optimization problems.
COMPLEX & INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Xu Chen, Hugo Tianfield, Wenli Du
Summary: This paper introduces a novel bee-foraging learning PSO (BFL-PSO) algorithm with three different search phases, showing very competitive performance in terms of solution accuracy.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Information Systems
K. Sridevi, Md Abdul Saifulla
Summary: Software Defined Networking (SDN) is a popular paradigm in modern networking, and distributed SDN is an emerging area due to scalability and reliability issues with a single controller. This paper proposes a mechanism for load balancing in the control plane of SDN using Artificial Bee Colony (ABC) optimization. The experiments demonstrate that the proposed model LBABC achieves efficient results and avoids unnecessary migrations compared to existing models.
PEER-TO-PEER NETWORKING AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Halcyon Davys Pereira de Carvalho, Roberta Fagundes, Wylliams Santos
Summary: The study aimed to identify variables influencing estimation accuracy in software engineering, using the ELM model for effort estimation and comparing it with literature models to determine that ELM provided the best results.
Article
Computer Science, Artificial Intelligence
Anupama Kaushik, Prabhjot Kaur, Nisha Choudhary, Priyanka
Summary: This study aims to improve the effort prediction accuracy of Analogy-Based Estimation (ABE) by proposing a solution function called Stacking Regularization in Analogy-Based Software Effort Estimation (SABE). The results suggest that SABE shows promising performance on various evaluation criteria.
Article
Computer Science, Artificial Intelligence
Yeou-Ren Shiue, Gui-Rong You, Chao-Ton Su, Hua Chen
Summary: A new ensemble learning approach, ELBAD, based on balanced accuracy and diversity using a two-phase artificial bee colony (ABC) algorithm, is proposed to balance the accuracy and diversity of ensemble learners. Experimental results show that ELBAD significantly outperforms other popular ensemble learning algorithms on multiple datasets.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Information Systems
Xuemei Jiang, Yanjie Song, Lining Xing
Summary: This paper proposes a multi-population evolutionary algorithm for the mission planning problem of earth observation satellites. A mixed-integer programming model and a dual-population artificial bee colony algorithm are introduced to achieve efficient task planning and optimization.
Article
Physics, Multidisciplinary
Chayapol Chaiyanan, Keiji Iramina, Boonserm Kaewkamnerdpong
Summary: The way people learn will be crucial for the development of the educational system in the future, utilizing technology and incorporating it into learning processes can produce better learners. Implicit learning, commonly seen in young children, allows individuals to grasp underlying rules without consciously seeking or understanding them.
Article
Business
Suyash Shukla, Sandeep Kumar
Summary: Machine learning approaches are gaining recognition in software effort estimation research as they can demonstrate complex relationships between software effort and other attributes. There is a need for systematic literature review to discuss the applicability of ML techniques in object-oriented project effort estimation. Different ML techniques have shown better performance than classical models in these works.
IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
(2022)
Article
Computer Science, Information Systems
Huynh Thai Hoc, Radek Silhavy, Zdenka Prokopova, Petr Silhavy
Summary: This study aims to improve the accuracy of effort estimation by using ensemble, deep learning, and transfer learning techniques. The results show that combining regression models with Random Forest as the final regressor and XGBoost and Histogram Gradient Boost as prior generators yields more accurate effort estimation than other combinations. The study highlights the potential of transfer learning in the deep learning method, which exhibits superior performance over the ensemble approach.
Article
Computer Science, Interdisciplinary Applications
Peng Shao, Ying Liang, Guangquan Li, Xing Li, Le Yang
Summary: Optimization technologies are facing challenges due to the increasing size and complexity of problems. To address this issue, a new global optimization model called birefringence learning (BRL) is proposed, which is inspired by the birefringence phenomenon. The model is applied to the artificial bee colony algorithm (ABC) to enhance its global optimization performance. Experimental results show that the BRL-based ABC algorithm achieves higher accuracy and faster convergence compared to other well-established algorithms, validating the effectiveness of the BRL model for global optimization.
MATHEMATICS AND COMPUTERS IN SIMULATION
(2023)
Article
Computer Science, Artificial Intelligence
Preeti Gupta, Tarun Kumar Sharma, Deepti Mehrotra, Ajith Abraham
NEURAL COMPUTING & APPLICATIONS
(2019)
Article
Computer Science, Interdisciplinary Applications
Tarun Kumar Sharma, Millie Pant
JOURNAL OF COMPUTATIONAL SCIENCE
(2017)
Article
Computer Science, Artificial Intelligence
Tarun K. Sharma, Ajith Abraham
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING
(2020)
Article
Computer Science, Artificial Intelligence
Tarun K. Sharma
Summary: This study introduces the Butterfly Optimization Algorithm (BOA) and its modified variant, Bidirectional Butterfly Optimization Algorithm (BBOA). By incorporating bidirectional search and greedy selection of direction, BBOA accelerates the convergence rate of BOA while also demonstrating competitiveness in solving optimization problems.
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING
(2021)
Article
Computer Science, Information Systems
Himanshu Gupta, Saurav Kumar, Drishti Yadav, Om Prakash Verma, Tarun Kumar Sharma, Chang Wook Ahn, Jong-Hyun Lee
Summary: The study utilized the SIRD model to predict the development trend of COVID-19 in India, with optimization techniques like PSO, G, and PS for parameter estimation. Results indicated that PSO and G+PS+G methods were more effective, and India has passed the peak period of the pandemic with a high recovery rate of 81%.
Article
Computer Science, Artificial Intelligence
Priya Mathur, Amit Kumar Gupta, Deepak Panwar, Tarun Kumar Sharma
Summary: This study focuses on using machine learning algorithms to estimate the specific heat capacity (SHC) of blended nanofluids. The SVR-GA model showed the best predictive accuracy among the models tested. It can be used for quick and reliable prediction of the SHC of blended nanofluids.
Article
Computer Science, Artificial Intelligence
Sumit Singh Dhanda, Brahmjit Singh, Poonam Jindal, Tarun Kumar Sharma, Deepak Panwar
Summary: Healthcare is a top concern for countries worldwide, and Internet of Medical Things (IoMT) can address the vulnerability of health infrastructure and limited healthcare resources through its wide range of medical services and applications. The deployment of 6G technology will further enhance the potential of IoMT.
Article
Computer Science, Information Systems
Siddhi Jain, Rahul Sahni, Tuneer Khargonkar, Himanshu Gupta, Om Prakash Verma, Tarun Kumar Sharma, Tushar Bhardwaj, Saurabh Agarwal, Hyunsung Kim
Summary: This study proposes an automatic system in the form of a smartphone application (E-crop doctor) for disease detection on paddy leaves, with the ability to suggest pesticides to farmers. The results show that YOLOv4 tiny outperforms YOLOv3 tiny by achieving a significantly higher mAP of 97.36%.
Article
Engineering, Multidisciplinary
Tarun Kumar Sharma, Divya Prakash
INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT
(2020)
Article
Engineering, Multidisciplinary
Katyayani Kashyap, Tarun K. Sharma, Jitendra Rajpurohit
INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT
(2020)
Article
Computer Science, Artificial Intelligence
Tarun K. Sharma, Jitendra Rajpurohit
INTERNATIONAL JOURNAL OF APPLIED METAHEURISTIC COMPUTING
(2019)
Proceedings Paper
Computer Science, Artificial Intelligence
Vaishali, Tarun K. Sharma, Ajith Abraham, Jitendra Rajpurohit
PROCEEDINGS OF THE EIGHTH INTERNATIONAL CONFERENCE ON SOFT COMPUTING AND PATTERN RECOGNITION (SOCPAR 2016)
(2018)
Article
Engineering, Multidisciplinary
Preeti Gupta, Deepti Mehrotra, Tarun Kumar Sharma
INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT
(2018)
Article
Engineering, Multidisciplinary
Tarun Kumar Sharma, Millie Pant
INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT
(2018)
Article
Engineering, Multidisciplinary
Tarun Kumar Sharma, Preeti Gupta
INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT
(2018)