Article
Agronomy
Yue Pang, Wenbo Yu, Chuanzhong Xuan, Yongan Zhang, Pei Wu
Summary: The mutton sheep breeding industry has shifted from traditional farming methods to a more intelligent approach, making automated sheep face recognition systems essential. This study developed a large-scale benchmark dataset and evaluation protocol for individual sheep recognition algorithms.
Article
Automation & Control Systems
Ion Matei, Maksym Zhenirovskyy, Johan de Kleer, John Maxwell
Summary: We introduce two block coordinate descent algorithms for solving optimization problems with ordinary differential equations (ODEs) as dynamical constraints. In contrast to prior algorithms, ours do not need to implement sensitivity analysis methods to evaluate loss function gradients. The algorithms result from the reformulation of the original problem as an equivalent optimization problem with equality constraints. They are tested on the problem of learning the parameters of the Cucker-Smale model and shown to be faster and more accurate than gradient descent algorithms based on ODE solvers endowed with sensitivity analysis capabilities.
JOURNAL OF MACHINE LEARNING RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Alfonso Rojas-Dominguez, S. Ivvan Valdez, Manuel Ornelas-Rodriguez, Martin Carpio
Summary: By introducing an adaptive sampling method based on importance sampling (IS), the training of deep neural networks (DNNs) is improved. Experimental results show that this method improves both speed and variance without significant impact on classification.
Article
Computer Science, Artificial Intelligence
Kazuki Osawa, Yohei Tsuji, Yuichiro Ueno, Akira Naruse, Chuan-Sheng Foo, Rio Yokota
Summary: This paper proposes a scalable and practical natural gradient descent (SP-NGD) method for large-scale distributed training of deep neural networks. It achieves similar generalization performance to models trained with first-order optimization methods, but with accelerated convergence.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Chemistry, Physical
Wei Xu, Sida Liu, Jiayi Yang, Yan Meng, Shuangshuang Liu, Guobin Chen, Lingjie Jia, Xiuhan Li
Summary: This article introduces a self-powered flexible input panel for multifunctional input detection, including letter recognition, user identification, and digit pattern detection. The input panel has good conformability and stability, making it suitable for wearable human-machine interfaces. By optimizing the design of a convolutional neural network, high classification and identification accuracy are achieved, and potential applications for energy harvesting and real-time digit pattern recognition are proposed.
Article
Computer Science, Artificial Intelligence
Juliana Verga Shirabayashi, Ana Silvia Moretto Braga, Jair da Silva
Summary: Medical diagnostics, product classification, surveillance, and detection of inappropriate behavior are becoming more complex thanks to the development of image analysis methods based on neural networks. In this study, we evaluate state-of-the-art convolutional neural network architectures proposed in recent years to classify driving behavior and driver distractions. Our main goal is to measure the performance of these architectures using only free resources (i.e., free graphic processing unit, open source) and evaluate the extent to which regular users can access this technological evolution.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Engineering, Manufacturing
Joseph Kershaw, Rui Yu, Yuming Zhang, Peng Wang
Summary: This research investigates the application of Machine Learning techniques for real-time welding quality prediction and adaptive welding speed adjustment. Data is collected through monitoring top-side weld pool dynamics and back-side bead formation, with analysis and prediction done using a convolutional neural network. Promising results have been achieved in experimental trials on real-time bead width prediction and adaptive speed adjustment to realize ideal bead width.
JOURNAL OF MANUFACTURING PROCESSES
(2021)
Article
Computer Science, Information Systems
Anyi Zheng, Xiangjin Zeng, Pengpeng Song, Yong Mi, Zhibo He
Summary: This study proposes a face super-resolution method guided by the gradient structure, which uses a sub-network to generate gradient information from low-resolution images and incorporates it as additional information for the entire network. Additionally, a novel upsampling module based on channel attention and pixel attention is designed. Experimental results demonstrate that the network achieves state-of-the-art performance on various evaluation metrics and effectively restores the detailed structure of the images.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Lin Xiang, Xiaoqin Zeng, Shengli Wu, Yanjun Liu, Baohua Yuan
Summary: This paper explores a method of measuring sensitivity by observing corresponding output variation to input perturbation on CNNs, proposing an iterative algorithm to approximate the defined sensitivity and verifying the theoretical sensitivity on the MNIST database. Experimental results show that the theoretical sensitivity is in agreement with the actual output variation and can be used as a quantitative measure for robust network selection.
NEURAL PROCESSING LETTERS
(2021)
Article
Engineering, Multidisciplinary
Wei Wang, Xiang-Gen Xia, Chuanjiang He, Zemin Ren, Tianfu Wang, Baiying Lei
Summary: This paper introduces a noise-robust online convolutional coding model for image representation, which employs an alternating algorithm to tackle the image pursuit and dictionary learning problems. Experimental results demonstrate that the proposed method can generate more meaningful feature representations compared to existing models when the training data is corrupted by Poisson noise.
APPLIED MATHEMATICAL MODELLING
(2021)
Article
Computer Science, Artificial Intelligence
Kensuke Nakamura, Stefano Soatto, Byung-Woo Hong
Summary: BCSC is a stochastic first-order optimization algorithm that adds a cyclic constraint to the selection of data and parameters, resulting in higher accuracy in image classification. It effectively limits the impact of outliers in the training set and provides better generalization performance within the same number of update iterations.
Article
Computer Science, Information Systems
Bhavana Tiple, Manasi Patwardhan
Summary: The number of music enthusiasts is growing rapidly, leading to increased importance of emotion recognition models. In this research, a novel music emotion recognition system is developed by inter-linking pre-processing, feature extraction, and classification steps to improve accuracy. Experimental results show that the proposed system achieves an accuracy of 94.55% and outperforms other algorithms.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Automation & Control Systems
David Holzmueller, Ingo Steinwart
Summary: This study proves that two-layer (Leaky)ReLU networks initialized by the widely used method proposed by He et al. (2015) and trained using gradient descent on a least-squares loss are not universally consistent. In certain cases, the network can only find a bad local minimum and essentially performs linear regression, even for non-linear target functions.
JOURNAL OF MACHINE LEARNING RESEARCH
(2022)
Article
Energy & Fuels
Yuhao Nie, Ahmed S. Zamzam, Adam Brandt
Summary: The study aims to address the imbalance in sky image datasets for PV output prediction, showing that resampling and data augmentation can effectively enhance model performance for now-casting tasks but have limited impact on forecasting tasks.
Article
Computer Science, Artificial Intelligence
Grega Vrbancic, Vili Podgorelec
Summary: Utilizing an ensemble method based on stochastic gradient descent with warm restarts (SGDRE) can address the issues of generalization, dataset size, and time complexity in classifying childhood pneumonia using chest X-ray images. Experimental results demonstrate a significant improvement in accuracy and competitive performance compared to baseline methods.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Mathematics
Jeewon Park, Oladayo S. Ajani, Rammohan Mallipeddi
Summary: Recently, optimization-based energy disaggregation algorithms have gained significance as they require less data for training compared to pattern-based algorithms. However, the performance of these algorithms depends on the problem formulation, including objective functions and constraints. In this study, the energy disaggregation problem is formulated as a constrained multi-objective problem and solved using a constrained multi-objective evolutionary algorithm. The proposed formulation is compared to three high-performing formulations in the literature, and the results show improvements in both appliance-level and overall energy disaggregation.
Article
Computer Science, Artificial Intelligence
Saykat Dutta, M. Sri Srinivasa Raju, Rammohan Mallipeddi, Kedar Nath Das
Summary: This paper proposes a decomposition-based multi-objective optimization algorithm that effectively solves problems with discontinuous or degenerated Pareto fronts. It also introduces an adaptive mating selection strategy to further improve the algorithm's performance.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Abhishek Kumar, Bablesh Kumar Jha, Swagatam Das, Rammohan Mallipeddi
Summary: Conventional power flow algorithms are ineffective in droop-regulated islanded microgrids due to inaccurate assumptions about constant slack bus voltage and system operating frequency. This study proposes a novel constrained optimization formulation to solve the power flow problem in islanded microgrids, considering non-linear and linear constraints for power balance and various modes of Distributed Generation units. The proposed optimization algorithm, called SS-NR (Spherical Search with Newton-Raphson based repair), outperforms state-of-the-art algorithms in convergence and accuracy, as demonstrated through experimental comparisons and validation with other power flow tools.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Amin Jalali, Minho Lee
Summary: This study proposes an adversarial Lagrangian integrated contrastive embedding (ALICE) method for small-sized datasets. The method demonstrates improved accuracy and training convergence through pre-trained adversarial transfer. It also investigates an adversarial integrated contrastive model with various augmentation techniques and incorporates multi-objective augmented Lagrangian multipliers to encourage low-rank and sparsity.
Article
Plant Sciences
Oladayo S. Ajani, Esther Aboyeji, Rammohan Mallipeddi, Daniel Dooyum Uyeh, Yushin Ha, Tusan Park
Summary: Optimal sensor location methods are crucial for achieving a sensor profile that meets pre-defined performance criteria and minimal cost. This study presents a genetic programming-based optimal sensor placement approach for greenhouse monitoring and control, starting with a reference micro-climate condition obtained by aggregating measurements from 56 sensors. The results demonstrate high correlations and low error values, indicating the effectiveness of the proposed model.
FRONTIERS IN PLANT SCIENCE
(2023)
Article
Computer Science, Artificial Intelligence
Bishal Karmakar, Abhishek Kumar, Rammohan Mallipeddi, Dong-Gyu Lee
Summary: CMA-ES is an effective evolutionary algorithm for solving complex optimization problems, but it suffers from the computational burden of unstable matrix decomposition. This paper proposes an improved evolution path by using first-order exponential approximation to replace the costly covariance matrix decomposition, and incorporating the Heaviside function for mutation matrix update to control mutation step size. The proposed xSCMA-ES framework outperforms existing CMA-ES algorithms on various benchmark tests and a hybrid active power filter design problem in minimizing harmonic distortions.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Engineering, Electrical & Electronic
Kyoungho Hwang, Junwon Lee, Amin Jalali, Minho Lee
Summary: Air conditioner consumers expect their products to work well without any problem. Consumers encounter problems if the amount of refrigerant in the air conditioner is insufficient. Therefore, we propose a novel deep learning approach that predicts the amount of refrigerant in advance. Our approach differs from others, as it is not limited to specific types of air conditioners and is applicable to all types.
IEEE TRANSACTIONS ON CONSUMER ELECTRONICS
(2023)
Article
Computer Science, Artificial Intelligence
Zhuji Yang, Abhishek Kumar, Rammohan Mallipeddi, Dong-Gyu Lee
Summary: This paper presents the Spherical Search (SS) algorithm based on hyper spherical search methodology, and extends its application to constrained optimization problems. The results show that this algorithm has good exploration capability for solving constrained problems and performs better than other algorithms on multiple test problems.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Review
Green & Sustainable Science & Technology
Member Joy Usigbe, Senorpe Asem-Hiablie, Daniel Dooyum Uyeh, Olayinka Iyiola, Tusan Park, Rammohan Mallipeddi
Summary: Agricultural production systems play a crucial role in providing the world's food, fuel, and fiber supplies. Modern technologies powered by artificial intelligence (AI) can help address challenges such as climate change and resource depletion. This comprehensive review explores the potential of AI-based technologies in enhancing resilience, sustainability, and climate-smart agriculture in agricultural production systems.
ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY
(2023)
Article
Mathematics
Oladayo S. Ajani, Member Joy Usigbe, Esther Aboyeji, Daniel Dooyum Uyeh, Yushin Ha, Tusan Park, Rammohan Mallipeddi
Summary: This paper proposes a method to predict micro-climates by using fixed location sensors, avoiding the cost and inconvenience of moving sensors, and achieves a high level of correlation.
Article
Computer Science, Artificial Intelligence
Safaa Abdullahi Moallim Mohamud, Amin Jalali, Minho Lee
Summary: In this study, a novel encoder-decoder cycle (EDC) framework is proposed for tackling challenging problems such as visual question answering (VQA) and visual relationship detection (VRD). EDC is inspired by the perception-action cycle in human learning process and considers the understanding of visual features as perception and answering questions as an action. The framework mimics the mechanism of introspection by comprehending and refining visual features and performs cyclic decoding of visual and language features to generate answer features. Evaluation on multiple datasets demonstrates the superiority of the proposed framework over state-of-the-art models.
PATTERN RECOGNITION
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Edukondalu Chappidi, Alok Singh, Rammohan Mallipeddi
Summary: Given a set of potential sites, the DACLP aims to find the minimum number of facilities that can be located on these sites while maintaining a minimum distance requirement between them. This problem is closely related to the ACLP, which seeks to maximize the number of facilities with the same distance requirement. The DACLP is an NP-hard problem and has significant implications in various real-world applications. In this paper, two intelligent optimization approaches, GA and DDE, are proposed as the first heuristics to solve the DACLP, and their effectiveness is demonstrated through extensive testing.
DISTRIBUTED COMPUTING AND INTELLIGENT TECHNOLOGY, ICDCIT 2023
(2023)
Article
Thermodynamics
Ehsanolah Assareh, Seyed Sajad Mousavi Asl, Neha Agarwal, Mehrdad Ahmadinejad, Amin Jalali, Moonyong Lee
Summary: This paper presents a thermodynamics modeling, economic assessment, and optimization of a multigenerational module that utilizes compressed-air energy storing and polymer electrolyte membrane electrolysis with methane and hydrogen fuels, with a case study focused on Munich, Germany. The researchers selected the optimal scenario and evaluated the performance and cost of the module using multi-objective optimization.
THERMAL SCIENCE AND ENGINEERING PROGRESS
(2023)
Article
Computer Science, Information Systems
Arjun Ghosh, Nanda Dulal Jana, Swagatam Das, Rammohan Mallipeddi
Summary: This study proposes a two-phase evolutionary framework, TPEvo-CNN, for automatically designing CNN models for medical image classification. The framework utilizes differential evolution to determine the number of layers of the CNN architecture and genetic algorithm to fine-tune the hyperparameters. Experimental results demonstrate the superiority of the proposed framework in medical image classification tasks compared to existing CNN models.
Article
Computer Science, Information Systems
Kanchan Keisham, Amin Jalali, Jonghong Kim, Minho Lee
Summary: This study proposes a new few-shot learning method for temporal action localization in long videos. The method utilizes a multi-level encoder cosine-similarity alignment module to capture the alignment of visual information, and incorporates cosine similarity in Transformer encoder layers to emphasize refined features. By adopting an episodic-based training scheme, it learns the alignment of similar video snippets and adapts to novel classes at test time.
INFORMATION SCIENCES
(2023)
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
