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An Evolving Ensembles Method Based on Genetic programming and Whale Optimization Algorithm for Imbalanced Datasets 4

An Evolving Ensembles Method Based on Genetic Programming an...

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4th international conference on artificial Intelligence and Electromechanical Automation, AIEA 2023

作者： Ji, Jingchao Li, Xiaobo School of Computer Science and Technology Zhejiang Normal University Jinhua321004 China

ISBN: (纸本)9781510666412

Genetic programming (GP) algorithm has been successfully applied to classification tasks for many years, with a long history and remarkable effects. It is worth noting that GP tends to evolve biased classifiers when it is used for class imbalanced datasets. Biased classifiers are often unreliable and will seriously affect the classification performance. In addition, when GP is used to train the model to adapt to the high-dimensional imbalanced datasets, it is easy to be affected by the majority class and ignore the existence of the minority class. therefore, classification tasks based on GP in high-dimensional and imbalanced datasets need to be further studied. In this paper, we propose a GP approach for imbalance classification tasks with the aim of improving the classification performance and minimizing the time consumption, named HGPWOA. A multi-objectivization fitness function is adopted to solve the problem, supplemented by Whale Optimization Algorithm (WOA) for feature selection, and then feature augmentation strategy is adopted to improve the classification efficiency. the method was tested on 5 imbalanced datasets. the experimental results show that this method is superior to the traditional classification algorithms, which provides a new way to study the class imbalance problems. © 2023 SPIE.

关键词： Genetic programming

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GPU Acceleration of the Enhanced Jaya Optimization Algorithm for Solving Large Systems of Nonlinear Equations 4th

GPU Acceleration of the Enhanced Jaya Optimization Algorit...

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4th international conference on Numerical Computations: theory and Algorithms, NUMTA 2023

作者： Silva, Bruno Guerreiro Lopes, Luiz Doctoral Program in Informatics Engineering University of Madeira Madeira Island Funchal Portugal Regional Secretariat for Education Science and Technology Regional Government of Madeira Funchal Portugal Faculty of Exact Sciences and Engineering University of Madeira Madeira Island Funchal9020-105 Portugal

ISBN: (纸本)9783031812408

this paper presents a GPU-based parallel implementation of the Enhanced Jaya (EJAYA) algorithm for solving large-scale systems of nonlinear equations. EJAYA is a gradient-free metaheuristic optimization scheme based on the population-based parameter-less Jaya algorithm that has recently been shown to be capable of solving nonlinear equation systems, a class of challenging problems that are difficult to solve by traditional approaches, particularly as systems become larger. the proposed parallel algorithm was implemented using the Julia programming language on a high-performance GeForce RTX 3090 GPU with 10 496 CUDA cores and 24 GB GDDR6X VRAM and was tested using a set of difficult scalable nonlinear equation system problems. the obtained results, with average speedups up to 122.25x, evidenced the capability of the proposed GPU-based parallel algorithm to meet the computational demands of the important class of problems under consideration. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Optimization algorithms

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Cryptic Fusion: Advancing Text Steganography via Pixel Manipulation & ASCII Integration 4

Cryptic Fusion: Advancing Text Steganography via Pixel Manip...

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4th IEEE Global conference for Advancement in Technology, GCAT 2023

作者： ul Mosaher, Quazi Saad Hasan, Mousumi Department of Computer Science & Engineering Bangladesh Army International University of Science & Technology Cumilla Bangladesh

ISBN: (纸本)9798350305258

Unlocking Secrets in Plain Sight: A Cutting-Edge Text Steganography Approach Exploiting Pixel Manipulation and ASCII Fusion. Steganography, an ancient art of concealed communication, has evolved into a science of imperceptible messaging, hiding data within seemingly innocent messages. In this groundbreaking paper, we delve into a novel technique of concealing text messages within images, wielding the power of pixel manipulation and the binary form of ASCII values. Our innovative methodology empowers us to encode individual characters seamlessly into single pixels, leading to the creation of encrypted stego-images. these masterfully crafted images have undergone a series of transformative and quality measurement techniques, meeting all the essential criteria of steganography. the prime goal of our research is to develop a robust steganography method that ensures unparalleled confidentiality during communication, while simultaneously maximizing payload capacity. By enabling the transmission of vast amounts of confidential data through compact cover pictures, our approach not only accelerates communication but also optimizes bandwidth utilization, revolutionizing the way secrets are safeguarded and exchanged. © 2023 IEEE.

关键词： ASCII Value Cover Image Encrypted Image Image Processing Mean Squared Error (MSE) Peak to Signal Ratio (PSNR) Steganography Stego-Image Structure Similarity Index(SSIM)

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Transforming GP-CNN Tree Search Into Trainable Architectures for Image Classification

Transforming GP-CNN Tree Search Into Trainable Architectures...

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2024 IEEE international conference on Systems, Man, and Cybernetics, SMC 2024

作者： Sun, Feng Ke, Yan Gong, Yue-Jiao Li, Yun Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518110 China School of Computer Science and Engineering South China University of Technology Guangzhou China i4AI Ltd London WCIN3AX United Kingdom

ISBN: (纸本)9781665410205

Data-efficient image classification poses a challenge in achieving effectiveness with limited data, as evidenced by the current methods based on convolutional neural networks (CNNs) and genetic programming (GP). Existing works employing these two methods encounter limitations, such as a lack of flexibility and an inability to effectively explore the latent features of the data. To tackle these challenges, this paper introduces a genetic programming method for data-efficient image recognition, leveraging novel function sets, terminal sets, and program structures. this method transforms tree-based data structures in GP into trainable CNN architectures. Further, by employing block structures instead of single operations in the search space, the search space is reduced and the stability of the search structures enhanced. Comparative experiments with state-of-the-art neural network methods and GP-based methods on data-efficient classification datasets validate the GP-CNN method offering higher performance. © 2024 IEEE.

关键词： Genetic programming

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Mixed Integer Linear programming Based Large Neighborhood Search Approaches for the Directed Feedback Vertex Set Problem 9th

Mixed Integer Linear Programming Based Large Neighborhood Se...

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9th international conference on Metaheuristics and Nature Inspired Computing (META)

作者： Bresich, Maria Varga, Johannes Raidl, Guenther R. Limmer, Steffen TU Wien Inst Log & Computat Vienna Austria Honda Res Inst Europe GmbH D-63073 Offenbach Germany

ISBN: (纸本)9783031692567;9783031692574

A directed feedback vertex set (DFVS) of a directed graph is a subset of vertices whose removal makes the graph acyclic. Finding a DFVS of minimum cardinality is the goal of the directed feedback vertex set problem, an NP-hard combinatorial optimization problem. We first consider two mixed integer linear programming (MILP) models for this problem, which, when solved with Gurobi, are effective on graphs of small to medium complexity but do not scale well to large instances. Aiming at better scalability and higher robustness over a large variety of graphs, we investigate a large neighborhood search (LNS) in which a destroy operator removes randomly chosen nodes from an incumbent DFVS and one of the MILP models is used for repair. Regarding the destroy operator, finding a best degree of destruction is challenging. A main contribution lies in proposing several selection strategies for this parameter as well as a strategy for choosing the more promisingMILP model for repair. We evaluate the performance of the MILP models and different LNS variants on benchmark instances and compare the approaches to each other as well as to state-of-the-art procedures. Results show that our LNS variants yield clearly better solutions on average than standalone MILP solving. Even though our approaches cannot outperform the state-of-the-art, we gain valuable insights on beneficially configuring such a MILP-based LNS.

关键词： Large neighborhood search Mathematical programming Mixed integer linear programming Directed feedback vertex set problem

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AI-Generated programming Solutions: Impacts on Academic Integrity and Good Practices 16

AI-Generated Programming Solutions: Impacts on Academic Inte...

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16th international conference on Computer Supported Education, CSEDU 2024

作者： Tang, Chung Man Ng, Vanessa S.C. Leung, Henry M.F. Yuen, Joe C.H. School of Computing and Information Science Anglia Ruskin University CambridgeCB1 1PT United Kingdom School of Engineering and Applied Science Aston University BirminghamB4 7ET United Kingdom

ISBN: (纸本)9789897586972

As AI-powered chatbots, notably ChatGPT, gain widespread popularity, their integration into academic settings raises concerns about preserving academic integrity. Students increasingly employ these chatbots to generate answers for assignments and, notably, programming problems. Existing countermeasures, such as plagiarism checkers equipped with AI writing detection capabilities, struggle to detect AI-generated computer programs. To thoroughly examine this challenge, we conducted an experiment, presenting diverse programming problems to ChatGPT. Alarming findings revealed its remarkable proficiency in generating correct solutions across various topics, complexities of problems, and programming languages. To explore the implications, we engaged a focus group of programming teachers, resulting in the identification of key practices and strategies to respond to AI-generated work. these insights provide valuable guidance for educators seeking to maintain integrity while adapting to the evolving role of AI in education. Copyright © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： E-learning

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Ocular artefact Removal from Electroencephalogram Signals: A Review 4

Ocular Artefact Removal from Electroencephalogram Signals: A...

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4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Babeetha, S. Sridhar, S.S. College of Engineering and Technology SRM Institute of Science and Technology Department of Computing Technologies Tamilnadu India

ISBN: (纸本)9798350306118

Electroencephalogram (EEG) is widely utilized since it provides information on brain activity without causing any harm to the subject. EEG's great sensitivity makes it vulnerable to artefacts from both the environment and the brain. Blinks and eye movement, head movement, pulse, and so on may all generate artefacts in EEG recordings, which is the primary source of poor signal quality. artefacts complicate the analysis of EEG data and may lead to erroneous conclusions about brain activity because they overlap with the signal collected from the brain. the creation of algorithms to detect and eliminate artefacts from EEG signals is crucial for obtaining such precise and trustworthy signal information. In this study, we take a look at the state of the art in removing artefacts caused by blinks and eye movements from EEG data. However, conventional artefact removal methods cannot eradicate all noise and will result in data loss due to the indiscriminate elimination of contaminated parts. the research demonstrates that combining conventional with other approaches is widely utilized because it may enhance artefact removal. the most up-To-date method for eliminating artefacts is an automated one that uses machine learning and deep neural networks. © 2023 IEEE.

关键词： Electroencephalography

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Empowering Farmers with AI: Federated Learning of CNNs for Wheat Diseases Multi-Classification 4

Empowering Farmers with AI: Federated Learning of CNNs for W...

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4th international conference for Emerging Technology, INCET 2023

作者： Mehta, Shiva Kukreja, Vinay Vats, Satvik Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350335750

Higher agricultural outputs are required due to the rising worldwide population, shifting nutritional preferences, and growing demand for food and basic materials for the industry. However, the farming sector confronts several difficulties, such as climate change and a rise in the severity of production risks, which have had a detrimental effect on food output. Crop production forecast algorithms have been created using machine learning and deep learning techniques to handle this issue. Traditional machine learning techniques, however, are less effective because many characteristics related to meteorological data, earth data, and agricultural management data are dispersed and isolated to specific organization computers or smart farming devices. Using about 9,876 pictures, a collaborative learning CNN method for wheat disease identification is suggested. the suggested method used the federated averaging technique to train the model on laterally dispersed datasets across various client devices. the suggested method beat current state-of-the-art models for detecting wheat illness, according to the findings of our experiments, obtaining high accuracy, precision, recall, and F1 score. the suggested method shows how federated learning can enhance machine learning models in a distributed environment. It can also be applied to other agricultural uses, such as crop forecast, soil analysis, and insect detection. © 2023 IEEE.

关键词： Crops

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Harnessing Bidirectional Learning for Enhanced Student Performance Prediction Model 4

Harnessing Bidirectional Learning for Enhanced Student Perfo...

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4th international conference on Educational Technology, ICET 2024

作者： Zhang, Leyao Li, Yangyang Xiao, Xiong He, Xiuling Peng, Zhipin Central China Normal University National Engineering Research Center for E-Learning Wuhan China Central China Normal University Faculty of Artificial Intelligence in Education Wuhan China

ISBN: (纸本)9798350376944

Online learning behaviors significantly impact academic performance. Studies have shown that predicting student performance through learning behaviors can identify students' propensity to fail, helping teachers intervene in time to prevent dropouts. the rapid development of online education generates data characterized by high latitude, dynamics, and nonlinearity, posing a challenge to performance prediction modeling. However, most existing methods utilize unidirectional neural network structures, disregarding the joint influence of forward and backward associations of learning on student performance. Additionally, methods for predicting student performance often use characteristics such as demographics, learning behaviors, and assessments of learning. However, not all characteristic behaviors are closely related to academic performance, which emphasizes the importance of carefully selecting characteristics. this study presents the proposed Bidirectional Performance Prediction Model (BPPM), which includes the Feature Distiller and Random-Mask-BERT model. the Feature Distiller and Random-Mask mechanisms are used to enhance the model's bidirectional learning ability. Experimental verification shows that the BPPM can successfully predict student performance and outperform other baseline models. Finally, this study used the mRMR feature selection algorithm to eliminate redundant features and designed ablation experiments. It has been demonstrated that the use of Feature Selection, Feature Distiller, and Random-Mask mechanisms plays an irreplaceable role in improving the predictive performance of the model. © 2024 IEEE.

关键词： Prediction models

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Cost-Effectiveness Analysis of Solar and Wind Power Generation in Microgrid Systems: A Case Study of Changbin Industrial Zone 4

Cost-Effectiveness Analysis of Solar and Wind Power Generati...

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4th IEEE international conference on Recent Advances in Systems science and engineering, RASSE 2024

作者： I-Hua, Chung Chin-Pao, Chu Ming-Jun, Zhang Master's Program in Offshore Wind Energy Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan

ISBN: (数字)9798331528263

ISBN: (纸本)9798331528263

the study centers on a comprehensive cost-effectiveness analysis of solar and wind power generation within the microgrid system located in the Changbin Industrial Zone. this region, characterized by favorable solar irradiance and wind conditions, is an ideal location for developing and implementing renewable energy systems. through extensive simulations, we examined the impact of temperature fluctuations on photovoltaic (PV) module efficiency and the influence of varying wind turbine heights on energy output. Our findings revealed that temperature plays a crucial role in determining the performance of PV modules, with higher temperatures leading to a noticeable decline in efficiency. Conversely, wind power generation is significantly affected by the height of wind turbines, where increased height results in enhanced energy capture due to stronger and more consistent wind speeds at elevated altitudes. Furthermore, the study underscores the importance of considering seasonal variations, as these can significantly influence the overall efficiency and reliability of renewable energy systems within a microgrid. By optimizing wind turbine heights and incorporating strategies to mitigate the effects of temperature fluctuations on solar panels, the study demonstrates that it is possible to significantly improve the overall power generation efficiency of the microgrid system. Improving efficiency reduces the cost per kilowatt-hour of electricity generated, thereby enhancing the economic viability of renewable energy projects. the insights gained from this analysis are expected to provide valuable guidance for future renewable energy planning, particularly in regions with similar climatic and geographical conditions. Such strategic planning is essential for maximizing the return on investment and ensuring the long-term sustainability of renewable energy systems. © 2024 IEEE.

关键词： Windmill

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