Article
Computer Science, Artificial Intelligence
Chen Guo, Heng Tang, Ben Niu, Chang Boon Patrick Lee
Summary: The bacterial foraging optimization algorithm (BFO) is a bio-inspired swarm intelligence optimization algorithm that has attracted wide attention from researchers. By using CiteSpace to construct a knowledge mapping and conducting a thorough literature review, this survey aims to provide a clear outline and useful guidance on the development of BFO.
Article
Computer Science, Information Systems
Kareem Kamal A. Ghany, Amr Mohamed AbdelAziz, Taysir Hassan A. Soliman, Adel Abu El-Magd Sewisy
Summary: This paper proposes a data clustering method called WOATS, which combines Whale Optimization Algorithm (WOA) with Tabu Search (TS). WOATS uses an objective function inspired by partitional clustering to maintain the quality of clustering solutions. Experimental results show that WOATS outperforms other recent SI methods on different datasets, demonstrating its efficiency.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Niu Ben, Duan Qiqi, Wang Hong, Liu Jing
Summary: This paper proposes a simplified BFO algorithm with quorum sensing to address the high computational cost, difficulty in parameter settings, and premature convergence of the original algorithm. By reducing complexity and incorporating bacterial reciprocal behavior, the proposed algorithm demonstrates both effectiveness and efficiency in function optimization.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2021)
Article
Mathematics
Bodo Herzog
Summary: The aim of this article is to establish a stochastic search algorithm for neural networks based on fractional stochastic processes. Fractional stochastic processes, {B-t(H), t = 0}, which generalize a standard Brownian motion, capture different properties in order to simulate real-world phenomena. This approach provides new insights to stochastic gradient descent (SGD) algorithms in machine learning, and convergence properties for fractional stochastic processes are exhibited.
Article
Computer Science, Interdisciplinary Applications
Hang Su, Dong Zhao, Fanhua Yu, Ali Asghar Heidari, Zhangze Xu, Fahd S. Alotaibi, Majdi Mafarja, Huiling Chen
Summary: As science and technology advance, more complex engineering problems emerge, increasing the need for new optimization techniques. The cuckoo search algorithm has been widely used but can no longer meet current optimization requirements. This paper proposes an improved cuckoo search algorithm called CCFCS, which incorporates a crossover optimizer and a decentralized foraging strategy to enhance its search ability and ability to escape local optima. The algorithm's performance is verified through various tests and it shows faster convergence and higher solution quality compared to other algorithms.
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING
(2023)
Article
Multidisciplinary Sciences
Liyun Jia, Tao Wang, Ahmed G. Gad, Ahmed Salem
Summary: In today's data-driven digital culture, there is a demand for optimized solutions that reduce operating expenses and increase productivity. Researchers have been developing feature selection techniques to eliminate unnecessary information from datasets before using machine learning algorithms. This study presents a wrapper feature selection technique based on the sparrow search algorithm (SSA), which has shown better swarm diversity and convergence speed.
SCIENTIFIC REPORTS
(2023)
Article
Computer Science, Artificial Intelligence
Shi Cheng, Lianbo Ma, Hui Lu, Xiujuan Lei, Yuhui Shi
Summary: This paper discusses the importance of using evolutionary computation based optimization methods for solving search-based data analysis problems and provides a comprehensive review of their applications in bioinformatics as an example.
ARTIFICIAL INTELLIGENCE REVIEW
(2021)
Review
Chemistry, Multidisciplinary
Zool Hilmi Ismail, Mohd Ghazali Mohd Hamami
Summary: This study provides a systematic literature review of swarm robotics (SR) strategies for target search problems with environmental constraints, exploring different approaches for handling various levels of environment complexity and summarizing suitable strategies for real-world applications.
APPLIED SCIENCES-BASEL
(2021)
Article
Energy & Fuels
Yasir Muhammad, Muhammad Asif Zahoor Raja, Muhammad Abid Ali Shah, Saeed Ehsan Awan, Farman Ullah, Naveed Ishtiaq Chaudhary, Khalid Mehmood Cheema, Ahmad H. Milyani, Chi-Min Shu
Summary: The protection system plays a vital role in different subsystems of the power network. The research focuses on improving the overall performance by optimizing the operational time of directional over current relays (DOCRs) through a new optimization strategy. The results show that the synergy of fractional calculus, particle swarm optimization (PSO) and gravitational search algorithm (GSA) enhances the performance of the optimizer.
Article
Physics, Multidisciplinary
Tyler S. Barker, Massimiliano Pierobon, Peter J. Thomas
Summary: Information transmission and storage are important concepts in understanding the survival and evolution of biological systems. However, traditional information theory metrics may not be suitable for biological systems, as different pieces of information may have different impacts on survival. This paper explores the discrepancy between classical information theory and biology using a mathematical model and simulation results show that maximizing information efficiency may result in slower growth rates.
Article
Computer Science, Artificial Intelligence
Lei Zhao, Rui Li, Jianda Han, Jianlei Zhang
Summary: This article proposes a framework for multidifferent-target search in unknown environments based on swarm intelligence. The framework introduces the idea of distributed model predictive control in the target search method and enhances the robot's path prediction ability using a hierarchical prediction strategy. Compared to existing methods, this strategy significantly improves the functionality and success rate of multidifferent-target search in unknown complex obstacle environments. The article also presents two effective efforts to reduce computational complexity and speed up decision making.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Article
Optics
Max Segel, Szymon Gladysz
Summary: Modal control is a useful tool in adaptive optics for reducing controllable degrees of freedom and optimizing gain. Cross-correlations of Zernike polynomials can lead to correction errors, but the proposed optimal modal decomposition method avoids this issue by using statistically independent functions for blind correction. The algorithm's performance is analyzed in both static and dynamic simulated turbulence conditions.
Article
Computer Science, Information Systems
Gianni D'Angelo, Francesco Palmieri
Summary: Genetic algorithms have shown effectiveness in solving real-world optimization problems, especially when combined with gradient-descent technique. The hybrid algorithm proposed in this work aims to improve the efficiency of GAs in finding global optimal solutions by utilizing the gradient-descent capability for local searching. Experimental results demonstrate competitiveness in solution precision and resource efficiency compared to other complex approaches.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Shitu Singh, Jagdish Chand Bansal
Summary: This paper proposes a new variant of the Grey wolf optimizer (GWO) called Mutation-driven Modified Grey wolf optimizer (MDM-GWO), which improves the performance of the conventional GWO by introducing a new search mechanism, modified control parameter, mutation-driven scheme, and greedy approach of selection. The proposed MDM-GWO is evaluated on standard benchmark problems and real-world engineering design problems, and the results show its superiority over other algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Automation & Control Systems
Yizhi Wu, Fei Xie, Lei Huang, Rui Sun, Jiquan Yang, Qiang Yu
Summary: This paper proposes a path planning algorithm that does not require a prior global map. It combines convolutional evaluation and gradient first search methods to enable the robot to collect environmental information and perform path planning simultaneously, significantly improving the efficiency of path planning.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Meera Ramadas, Ajith Abraham
Summary: Air pollution is a global issue that can cause major health hazards. Satellite remote sensing is an effective way to monitor the atmosphere and improve understanding of complex images through clustering and segmentation techniques. The novel DiDE algorithm showed superior outcomes compared to traditional approaches, and its application in multi-level thresholding significantly reduced computational delay and improved image quality.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Chemistry, Analytical
Bhaskar Kapoor, Bharti Nagpal, Praphula Kumar Jain, Ajith Abraham, Lubna Abdelkareim Gabralla
Summary: This paper proposes a hybrid optimization-controlled ensemble classifier to automatically analyze EEG signal dataset for epileptic seizure prediction, combining signal processing and machine learning. The proposed technique shows high accuracy, sensitivity, and specificity in early seizure prediction.
Article
Computer Science, Information Systems
E. Rajalakshmi, R. Elakkiya, V. Subramaniyaswamy, L. Prikhodko Alexey, Grif Mikhail, Maxim Bakaev, Ketan Kotecha, Lubna Abdelkareim Gabralla, Ajith Abraham
Summary: A novel vison-based hybrid deep neural net methodology is proposed in this study for recognizing Indian and Russian sign gestures. The proposed framework aims to establish a single framework for tracking and extracting multi-semantic properties, such as non-manual components and manual co-articulations. By using a 3D deep neural net with atrous convolutions for spatial feature extraction, attention-based Bi-LSTM for temporal and sequential feature extraction, modified autoencoders for abstract feature extraction, and a hybrid attention module for discriminative feature extraction, the proposed sign language recognition framework yields better results than other state-of-the-art frameworks.
Article
Computer Science, Information Systems
S. U. Aswathy, P. P. Fathimathul Rajeena, Ajith Abraham, Divya Stephen
Summary: Lung malignancy, one of the most common types of cancer worldwide, was studied in this research. The study focused on the multifaceted nature of lung cancer diagnosis and proposed a method using nanotechnology for precise segmentation of lesions in nano-CT images. The results showed high accuracy and precision in tumor classification and segmentation.
Article
Computer Science, Artificial Intelligence
Benkuan Cui, Kun Ma, Leping Li, Weijuan Zhang, Ke Ji, Zhenxiang Chen, Ajith Abraham
Summary: Despite the benefits provided by the Internet and social media, the proliferation of fake news has had negative effects on society and individuals. This paper proposes a Chinese fake news detection model using a Third-order Text Graph Tensor and Information Propagation Network. Data augmentation and a novel text graph tensor representation are employed to address the challenges of feature sparsity and capturing context information. The model outperforms existing methods in fake news detection according to experimental results on four public datasets.
APPLIED INTELLIGENCE
(2023)
Article
Multidisciplinary Sciences
Mincheol Shin, Mucheol Kim, Geunchul Park, Ajith Abraham
Summary: High-performance computing supports advancements in various scientific disciplines by providing computing power and insights. This paper proposes an adaptive variable sampling model for performance analysis in high-performance computing environments. The model automatically selects optimal variables for performance prediction without requiring expert knowledge. Experiments show that the model improves speed by at least 24.25% and up to 58.75% without sacrificing accuracy.
Article
Engineering, Electrical & Electronic
Ankit Rajpal, Subodh Kumar, Neeraj Kumar Sharma, Ajith Abraham, Anurag Mishra, Naveen Kumar
Summary: This paper proposes a chest X-ray image watermarking scheme (CXRmark) using an online sequential reduced kernel extreme learning machine (OS-RKELM). The scheme segments the lung area into the region of non-interest (RONI) and region of interest (ROI) using U-Net, and modulates the approximation coefficients using OS-RKELM with different embedding strengths for ROI and RONI. Experimental results on 461 CXR images demonstrate that CXRmark outperforms other schemes in terms of perceptual quality and robustness.
CIRCUITS SYSTEMS AND SIGNAL PROCESSING
(2023)
Review
Automation & Control Systems
Shreyas Gawde, Shruti Patil, Satish Kumar, Pooja Kamat, Ketan Kotecha, Ajith Abraham
Summary: Industry 4.0 is the era of smart manufacturing, which relies heavily on machinery. Maintaining critical rotating machines is the top priority for engineers to minimize unplanned shutdowns and increase their useful life. This paper aims to provide a systematic literature review on the data-driven approach for multi-fault diagnosis of industrial rotating machines, highlighting the foundational work, comparative study, major challenges, and research gaps in this field.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Automation & Control Systems
Alicia Passah, Samarendra Nath Sur, Ajith Abraham, Debdatta Kandar
Summary: Research in artificial intelligence focuses on teaching machines to understand and interpret visual data in the field of computer vision. Through the use of digital images, deep learning models, and synthetic aperture radar (SAR), machines can properly recognize and classify items and respond accordingly. This paper presents a survey on different techniques and architectures proposed for SAR image applications, covering target detection and recognition models, analyzing their techniques and performances. It provides novel discussions, comparisons, and observations, highlighting the advantages and disadvantages of different approaches to inspire future research and suggest potential directions for hybrid models.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Debolina Paul, Saptarshi Chakraborty, Swagatam Das
Summary: Principal component analysis (PCA) is widely used for data visualization, denoising, and dimensionality reduction, but it is sensitive to outliers and may fail to detect low-dimensional structures. This article proposes a PCA method called MoMPCA based on the Median of Means (MoM) principle, which is computationally efficient and achieves optimal convergence rates. The method is robust to outliers and does not make assumptions about them. The efficacy of the proposed method is demonstrated through simulations and real data applications.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Information Systems
Gargi Joshi, Ananya Srivastava, Bhargav Yagnik, Mohammed Hasan, Zainuddin Saiyed, Lubna A. Gabralla, Ajith Abraham, Rahee Walambe, Ketan Kotecha
Summary: Web Information Processing (W.I.P.) has had a significant impact on modern society as many people rely on the internet for information. Social Media platforms provide both a means of disseminating information and a breeding ground for misinformation. Machine learning models have been used to detect misinformation, but the development of generalized and explainable detectors remains a challenge. Integrating domain adaptation and explainable A.I. approaches can address these challenges.
Article
Computer Science, Information Systems
Mayur Wankhade, Chandra Sekhara Rao Annavarapu, Ajith Abraham
Summary: Sentiment classification is a crucial task in natural language processing. This research investigates the impact of text preprocessing techniques on sentiment classification and proposes a novel framework called CBMAFM that leverages the synergistic power of CNN and BiLSTM through a multi-attention fusion mechanism. The framework preserves both local and global context dependencies, resulting in improved performance compared to other state-of-the-art methods.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Behzad Saemi, Ali Asghar Rahmani Hosseinabadi, Azadeh Khodadadi, Seyedsaeid Mirkamali, Ajith Abraham
Summary: The task scheduling problem in Mobile Cloud Computing (MCC) is a difficult problem to solve, and this study proposes a non-dominated multi-objective strategy based on the Harris Hawks Optimization (HHO) technique to address this issue. By comparing with other algorithms, it is found that the proposed method performs better in terms of job completion time and energy savings.
Article
Computer Science, Information Systems
Deepali Arun Bhanage, Ambika Vishal Pawar, Ketan Kotecha, Ajith Abraham
Summary: This paper proposes a semantic log analysis model that utilizes three log features to capture the essence of the log message. By employing the BERT pre-trained model and an attention-based OLSTM classifier, the proposed model is able to detect failures in different infrastructures. The evaluation results demonstrate that the system delivers improved and stable results across various IT infrastructures.
Article
Computer Science, Information Systems
Poria Pirozmand, Ali Asghar Rahmani Hosseinabadi, Maedeh Jabbari Chari, Faezeh Pahlavan, Seyedsaeid Mirkamali, Gerhard-Wilhelm Weber, Summera Nosheen, Ajith Abraham
Summary: In this study, a discrete metaheuristic method called D-PFA is proposed to efficiently solve the Traveling Salesman Problem (TSP). By comparing and validating with other algorithms, the proposed method has shown to be more competitive and resilient in solving TSP.