Article
Engineering, Civil
Isam Shahrour, Wengang Zhang
Summary: The application of soft computing techniques in TBM tunneling has achieved significant progress in optimizing solutions and reducing costs. Engineers face challenges in selecting the appropriate technique, but recommendations like preliminary analysis, data completion, selection of hidden layers and nodes, use of recurrent neural networks, and hybrid optimization techniques can help overcome these challenges and improve efficiency.
Article
Computer Science, Artificial Intelligence
Tao Zhang, Jiawei Yuan, Yeh-Cheng Chen, Wenjing Jia
Summary: This paper introduces a novel prediction machine based on self-learning generative adversarial network for soft computing application, which collects data through high-precision IoT sensors, solves the crowd prediction problem, can be used to monitor crowd flow in public places, and prevent congestion and traffic jams.
APPLIED SOFT COMPUTING
(2021)
Article
Construction & Building Technology
Panagiotis G. Asteris, Athanasia D. Skentou, Abidhan Bardhan, Pijush Samui, Paulo B. Lourenco
Summary: This study compared conventional soft computing techniques in estimating concrete compressive strength using non-destructive tests, finding that the BPNN model provided the most accurate predictions based on ultrasonic pulse velocity and rebound number values, thus assisting engineers in improving the accuracy of predicting concrete compressive strength during the design phase of civil engineering projects.
CONSTRUCTION AND BUILDING MATERIALS
(2021)
Article
Computer Science, Interdisciplinary Applications
Benyamin Abdollahzadeh, Farhad Soleimanian Gharehchopogh, Seyedali Mirjalili
Summary: Metaheuristics, especially the African Vultures Optimization Algorithm (AVOA), play a crucial role in solving optimization problems, outperforming existing algorithms in standard benchmarks and engineering design problems. The statistical evaluation further confirms the significant superiority of AVOA.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Review
Chemistry, Physical
Moin Ahmed, Yun Zheng, Anna Amine, Hamed Fathiannasab, Zhongwei Chen
Summary: The sales of electric vehicles have been increasing steadily, mainly due to improvements in cost and performance, increased options available to consumers, and growing environmental awareness. However, challenges such as limited driving range, long charging times, and insufficient infrastructure still hinder the rapid and widespread adoption of EVs.
Article
Computer Science, Information Systems
Hatem Ibn-Khedher, Mohammed Laroui, Hassine Moungla, Hossam Afifi, Emad Abd-Elrahman
Summary: The concepts of edge computing and network function virtualization (NFV) can improve network processing and resource allocation. However, the current optimization methods have issues with long computational time and inability to handle large-scale networks. This study proposes the use of deep reinforcement learning (DRL) techniques to enhance the quality of service (QoS) of autonomous vehicles (AVs) and optimize edge resources. The study introduces an optimal virtual edge autopilot placement (OVEAP) algorithm using integer linear programming (ILP), along with an autopilot placement protocol. Extensive simulations and evaluations show that the proposed allocation strategies outperform existing solutions in terms of edge server utilization, allocation time, and successfully allocated autopilots.
Article
Computer Science, Theory & Methods
Haochen Hua, Yutong Li, Tonghe Wang, Nanqing Dong, Wei Li, Junwei Cao
Summary: In recent years, the widespread popularity of the Internet of Things (IoT) has greatly promoted the development of Artificial Intelligence (AI). However, the traditional cloud computing model may face difficulties in independently handling the massive data generated by IoT. In response, the new computing model of Edge Computing (EC) has gained extensive attention. Scholars have found that traditional methods have limitations in enhancing the performance of EC, leading to the exploration of AI as a solution. This article serves as a guide to explore new research ideas in optimizing EC using AI and applying AI to other fields under the EC architecture.
ACM COMPUTING SURVEYS
(2023)
Article
Materials Science, Multidisciplinary
Byungjoon Bae, Yongmin Baek, Jeongyong Yang, Heesung Lee, Charana S. S. Sonnadara, Sangeun Jung, Minseong Park, Doeon Lee, Sihwan Kim, Gaurav Giri, Sahil Shah, Geonwook Yoo, William A. A. Petri, Kyusang Lee
Summary: In order to achieve accurate diagnosis and immunity to viruses, a IGZO-based biosensor field-effect transistor has been developed which can simultaneously detect viral antigens and corresponding antibodies in less than 20 minutes with high accuracy. This system will play a crucial role in preventing global viral outbreaks.
Review
Energy & Fuels
Shahryar Zare, A. R. Tavakolpour-saleh, A. Aghahosseini, M. H. Sangdani, Reza Mirshekari
Summary: This paper discusses the application of soft computing methods in the optimization and design of Stirling engines, including genetic algorithms, particle swarm optimization, fuzzy logic, and artificial neural networks. These soft computing methods can effectively address the main concerns of researchers, and optimizing parameters can improve the performance of Stirling engines.
Article
Computer Science, Artificial Intelligence
Wenxi Wang, Huansheng Ning, Feifei Shi, Sahraoui Dhelim, Weishan Zhang, Liming Chen
Summary: This article introduces the new developments in theoretical research and practical applications of social computing, particularly under the influence of artificial intelligence. By combining human intelligence and AI, H-AI shows significant advantages in dealing with social problems.
IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Tie Li, Junyou Yang, Dai Cui
Summary: This paper introduces several artificial-intelligence-based algorithms for performance optimization control of industrial system communication networks, avoiding the requirement of system models, and demonstrates the effectiveness of these algorithms in a benchmark microgrid system.
PHYSICAL COMMUNICATION
(2021)
Article
Materials Science, Multidisciplinary
Bai Sun, Tao Guo, Guangdong Zhou, Shubham Ranjan, Yixuan Jiao, Lan Wei, Y. Norman Zhou, A. Yimin Wu
Summary: Synaptic devices, such as synaptic memristors and synaptic transistors, have the potential to revolutionize traditional data storage and computing methods by enabling high-performance super-parallel computing through neuromorphic computing. This review focuses on the applications of synaptic devices in artificial intelligence, covering topics such as circuit theory, material selection, and future applications.
MATERIALS TODAY PHYSICS
(2021)
Article
Computer Science, Information Systems
Shreshth Tuli, Giuliano Casale, Nicholas R. Jennings
Summary: In recent years, deep learning models have gained widespread usage in both industry and academia. However, deploying large-scale neural networks on resource-constrained mobile edge computing platforms, such as surveillance and healthcare domains, is challenging due to compute and memory limitations. To address this issue, a promising solution is to split resource-intensive neural networks into lightweight disjoint components for distributed processing. The current approaches involve semantic and layer-wise splitting, but lack an intelligent algorithm for decision-making and optimal placement of modular splits. To tackle this, this work proposes SplitPlace, an AI-driven online policy that uses Multi-Armed-Bandits to choose between layer and semantic splitting strategies based on service deadline demands. SplitPlace uses decision-aware reinforcement learning for efficient and scalable computing by placing such neural network split fragments on mobile edge devices. It also adapts to volatile environments through fine-tuning. Experimental results show that SplitPlace significantly improves the state-of-the-art in terms of average response time, deadline violation rate, inference accuracy, and total reward by up to 46, 69, 3, and 12 percent respectively.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2023)
Article
Computer Science, Hardware & Architecture
Gongyuan Li, Bohan Liu, He Zhang
Summary: With the widespread use of artificial intelligence in various aspects of human life, AI systems have become the driving force behind the digital economy. This study analyzed seven normative documents and four secondary studies to identify 17 quality attributes of trustworthy AI.
Article
Optics
Aakif Anjum, A. A. Shaikh, Nilesh Tiwari
Summary: Laser micro-machining is attracting attention from industries and researchers due to its wide range of processability and material flexibility with micro-scale accuracy. However, there is a lack of consideration for process variables and soft computing approaches in micro-scale sectors. This study presents a framework for investigating the micro-milling capabilities of PMMA using various input parameters and evaluates different soft computing approaches for predicting depth, surface roughness, and kerf width. The gaussian process regression model achieves the highest accuracy for depth, surface roughness, and kerf width prediction.
OPTICS AND LASER TECHNOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Hassan Sayyaadi, Ali Sadollah, Anupam Yadav, Neha Yadav
JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE
(2019)
Article
Computer Science, Artificial Intelligence
Anupam Yadav, Ali Sadollah, Neha Yadav, J. H. Kim
NEURAL COMPUTING & APPLICATIONS
(2020)
Article
Computer Science, Artificial Intelligence
Indu Bala, Anupam Yadav
NEURAL COMPUTING & APPLICATIONS
(2020)
Article
Computer Science, Artificial Intelligence
Anita, Anupam Yadav
APPLIED SOFT COMPUTING
(2020)
Article
Computer Science, Artificial Intelligence
Anita, Anupam Yadav, Nitin Kumar
EXPERT SYSTEMS WITH APPLICATIONS
(2020)
Article
Computer Science, Artificial Intelligence
Anita Sajwan, Anupam Yadav
Summary: This article studies the convergence and stability analysis of the artificial electric field algorithm (AEFA) and proposes boundary conditions for the convergence of particle positions. The coefficient boundaries for different oscillation behaviors are discussed. The theoretical findings are validated through solving benchmark optimization problems.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Dikshit Chauhan, Anupam Yadav
Summary: This article introduces a population-based optimization technique called Artificial Electric Field Algorithm (AEFA) and its binary version. Theoretical and experimental studies show that the proposed binary versions have high efficiency and optimization ability in solving discrete optimization problems.
EVOLUTIONARY INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Dikshit Chauhan, Anupam Yadav
Summary: This article proposes an adaptive artificial electric field algorithm (iAEFA) which embeds a comprehensive learning strategy into AEFA. The algorithm utilizes a novel adaptive approach for developing a better learning strategy and has shown a stronger potential to discover better candidate solutions. The objective is to develop an efficient optimizer for continuous optimization problems.
Article
Computer Science, Information Systems
Dikshit Chauhan, Anupam Yadav
Summary: This article proposes a multilevel hierarchical artificial electric field algorithm with competitive and collaborative strategies (PAEFA) to optimize the performance of population-based optimization algorithms. The algorithm constructs a multilevel structure and implements a collaborative mechanism to enhance the diversity and performance of the population. Extensive experiments demonstrate that PAEFA outperforms state-of-the-art algorithms in terms of accuracy, statistical results, and convergence speed, validating its adaptability and effectiveness.
INFORMATION SCIENCES
(2023)
Article
Multidisciplinary Sciences
Deepika Khurana, Anupam Yadav, Ali Sadollah
Summary: This article proposes a method called multi-objective Neural Network Algorithm to solve multi-objective optimization problems. The proposed method shows good performance in solving difficult multi-objective optimization problems.
Article
Computer Science, Artificial Intelligence
Indu Bala, Anupam Yadav
Summary: A new niching strategy named "Niching Comprehensive Learning Gravitational Search algorithm" is proposed in this study to solve complex problems with multiple solutions. The algorithm efficiently explores the search space without trapping in local optima and locates all possible global optima. CLGSA algorithm successfully solves multimodal problems and the Reactive Power Dispatch problem with significant accuracy.
EVOLUTIONARY INTELLIGENCE
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Indu Bala, Anupam Yadav
HARMONY SEARCH AND NATURE INSPIRED OPTIMIZATION ALGORITHMS
(2019)
Article
Computer Science, Artificial Intelligence
Guiliang Gong, Jiuqiang Tang, Dan Huang, Qiang Luo, Kaikai Zhu, Ningtao Peng
Summary: This paper proposes a flexible job shop scheduling problem with discrete operation sequence flexibility and designs an improved memetic algorithm to solve it. Experimental results show that the algorithm outperforms other algorithms in terms of performance. The proposed model and algorithm can help production managers obtain optimal scheduling schemes considering operations with or without sequence constraints.
SWARM AND EVOLUTIONARY COMPUTATION
(2024)
Article
Computer Science, Artificial Intelligence
Daniel Molina-Perez, Efren Mezura-Montes, Edgar Alfredo Portilla-Flores, Eduardo Vega-Alvarado, Barbara Calva-Yanez
Summary: This paper presents a new proposal based on two fundamental strategies to improve the performance of the differential evolution algorithm when solving MINLP problems. The proposal considers a set of good fitness-infeasible solutions to explore promising regions and introduces a composite trial vector generation method to enhance combinatorial exploration and convergence capacity.
SWARM AND EVOLUTIONARY COMPUTATION
(2024)
