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Enhanced Butterfly Optimization Algorithm for Large-Scale Optimization Problems

Journal of Bionic engineering 2022年第2期19卷 554-570页

作者： Yu Li Xiaomei Yu Jingsen Liu Institute of Management Science and Engineering and School of BusinessHenan UniversityKaifeng 475004China School of Business Henan UniversityKaifeng 475004China Institute of Intelligent Network Systems and Software SchoolHenan UniversityKaifeng 475004China

To solve large-scale optimization problems,Fragrance coefficient and variant Particle Swarm local search Butterfly Optimization Algorithm(FPSBOA)is *** the position update stage of Butterfly Optimization Algorithm(BOA),the fragrance coefficient is designed to balance the exploration and exploitation of *** variant particle swarm local search strategy is proposed to improve the local search ability of the current optimal butterfly and prevent the algorithm from falling into local optimality.192000-dimensional functions and 201000-dimensional CEC 2010 large-scale functions are used to verify FPSBOA for complex large-scale optimization *** experimental results are statistically analyzed by Friedman test and Wilcoxon rank-sum *** attained results demonstrated that FPSBOA can better solve more challenging scientific and industrial real-world problems with thousands of ***,four mechanical engineering problems and one ten-dimensional process synthesis and design problem are applied to FPSBOA,which shows FPSBOA has the feasibility and effectiveness in real-world application problems.

关键词： Butterfy optimization algorithm Fragrance coefcient Variant particle swarm local search Large-scale optimization problems Real-world application problems

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TELEX: Two-Level Learned Index for Rich Queries on Enclave-based Blockchain systems

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IEEE Transactions on Knowledge and Data engineering 2025年第7期37卷 4299-4313页

作者： Wu, Haotian Tang, Yuzhe Shen, Zhaoyan Tao, Jun Lin, Chenhao Peng, Zhe The Hong Kong Polytechnic University Department of Industrial and Systems Engineering Hong Kong Syracuse University Department of Electrical Engineering and Computer Science United States Shandong University School of Computer Science and Technology China Southeast University School of Cyber Science and Engineering China Xi'an Jiaotong University Faculty of Electronic and Information Engineering China The Hong Kong Polytechnic University Shenzhen Research Institute Hong Kong

Blockchain has become a popular paradigm for secure and immutable data storage. Despite its numerous applications across various fields, concerns regarding the user privacy and result integrity during data queries persist. Additionally, the need for rich query functionalities to harness the full potential of blockchain data remains an area ripe for exploration. In order to address these challenges, our paper first utilizes a framework based on the Trusted Execution Environment (TEE) and oblivious RAM technique to achieve both privacy and data integrity. To enhance the query efficiency over the entire blockchain, we then devise a two-level learned indexing methodology named TELEX within the TEE for both integer and string keys. We also propose different query processing algorithms for versatile query types, including exact queries, aggregate queries, Boolean queries, and range queries. By implementing the prototype and conducting extensive evaluation, we demonstrate the feasibility and remarkable improvement in efficiency compared to existing solutions. © 1989-2012 IEEE.

关键词： Query languages

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Method for controlling phytoplankton distribution in fresh open water

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International Journal of Environmental Studies 2024年第5期81卷 2130-2147页

作者： Moldasheva, Raushan Ismailova, Aisulu Turmukhanova, Gulnur Mailybayeva, Aiman Yelezhanova, Shynar Zhanuzakova, Zulfiya Zhamangara, Aizhan Department of Information Systems Faculty of Computer Systems and Vocational Education S. Seifullin Kazakh Agrotechnical University Astana Kazakhstan Department of Software Engineering Faculty of Physics Mathematics and Information Technology Atyrau University named after Kh. Dosmukhamedov Atyrau Kazakhstan Department of Computer Science Faculty of Physics Mathematics and Information Technology Atyrau University named after Kh. Dosmukhamedov Atyrau Kazakhstan Laboratory of Palaeobotany Astana Botanical Garden branch of the Institute of Botany and Phytointroduction Astana Kazakhstan

This paper presents a processed phytoplankton dataset from Lake Zerendi and other Kokshetau Upland lakes, Kazakhstan. The study aimed to develop an automated method for controlling the distribution of phytoplankton in fresh open water using a gradient descent approach with the search for a starting point for research. The paper also presents the reconstructed biomass indicators for changes in water temperature, with model calibration based on results from 5 years of surface sampling. A special feature of the proposed computer data processing is the cubic spline interpolation, which allows a higher calculation accuracy. This method reduces the sample size required, providing a comprehensive picture of the condition of the infrastructure and allowing for a more efficient response to emerging ecosystem issues. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Phytoplankton

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Scalable and Low-Cost Remote Lab Platforms: Teaching Industrial Robotics Using Open-Source Tools and Understanding Its Social Implications 16th

Scalable and Low-Cost Remote Lab Platforms: Teaching Indust...

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16th International Conference on Social Robotics, ICSR + AI 2024

作者： Kumar, Amit Jose, Jaison Jain, Archit Kulkarni, Siddharth Arya, Kavi Centre for Systems and Control Indian Institute of Technology Bombay Mumbai India Embedded Real-Time Systems / e-Yantra Lab Indian Institute of Technology Bombay Mumbai India Computer Science and Engineering Department Indian Institute of Technology Bombay Mumbai India

ISBN: (纸本)9789819635214

With recent advancements in industrial robots, educating students in new technologies and preparing them for the future is imperative. However, access to industrial robots for teaching poses challenges, such as the high cost of acquiring these robots, the safety of the operator and the robot, and complicated training material. This paper proposes two low-cost platforms built using open-source tools like Robot Operating System (ROS) and its latest version ROS 2 to help students learn and test algorithms on remotely connected industrial robots. Universal Robotics (UR5) arm and a custom mobile rover were deployed in different life-size testbeds, a greenhouse, and a warehouse to create an Autonomous Agricultural Harvester System (AAHS) and an Autonomous Warehouse Management System (AWMS). These platforms were deployed for a period of 7 months and were tested for their efficacy with 1,433 and 1,312 students, respectively. The hardware used in AAHS and AWMS was controlled remotely for 160 and 355 hours, respectively, by students over a period of 3 months. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Costs

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A Method for Image De-Hazing for Vision Based Applications

A Method for Image De-Hazing for Vision Based Applications

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2024 International Conference on Computational Intelligence for Green and Sustainable Technologies, ICCIGST 2024

作者： Santhosh Krishna, B.V. Senthil Anandhi, A. Sharmila, G. Ramya, D. Balaji, L. Pasupulati, Baburao New Horizon College of Engineering Computer Science and Engineering Karnataka Bangalore India Karpagam College of Engineering Anna University Computer Science and Engineering Tamilnadu Coimbatore India Sri Eshwar College of Engineering Computer Science and Business Systems Tamilnadu Coimbatore India Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Electronics and Communication Engineering Tamilnadu Chennai India Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Electrical & Electronics Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350381887

The acquired images deteriorate in hazy or foggy circumstances, reducing the fidelity of the color, contrast, and visibility records. The atmospheric particles attenuate, scatter the source radiations are to be blame for this picture degradation. The corruption force relies upon assorted situations having a variable density of environmental particles, their frequency and distance from obtaining gadget. Existing picture dehazing strategies for apparent band pictures are either founded on earlier suspicion to reproduce the transmission map or utilized a learning system to straightforwardly gauge the dehazed picture. As of late, execution examination of existing well known picture dehazing techniques utilizing ghostly dim pictures are acted in which chose frequency groups from various for thickness levels are to be utilized for correlations. The correlation results are shown execution debasement of existing strategies with frequency groups determination and haze thickness levels. In this review, we plan a compelling ghastly and earlier based picture dehazing and upgrade network showing better execution when contrasted with existing strategies while utilizing phantom. Dim pictures from variable frequency groups and haze thickness levels. Our SPIDE-NET comprises of two organizations: 1) otherworldly Picture De-hazing Organization, which is prepared on multi-ghastly murky pictures between 450 nm and 720 nm, and exploits fluctuating constrictions in various frequency groups. 2) Multiscale Earlier based picture De-hazing Organization utilizes multi-scale dull channel and variety lessening priors on picture trios chose from a multiotherworldly foggy picture data set. The suggested approach is a CNN network in the encoder-decoder manner that combines data from both SID-Net and MPDNet by sharing a typical decoder stage. The SHIA dataset was used to create the suggested network, which was then assessed at various haze thickness levels. In comparison to well-known prior learning

关键词： Demulsification

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Intelligent Energy Management Energy Storage systems Using Machine Learning

Intelligent Energy Management Energy Storage Systems Using M...

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2024 International Conference on Intelligent systems for Cybersecurity, ISCS 2024

作者： Govindasamy, Chinnaraj Dayana, T.R Nisha Adkar, Siddhika Dilip Prakash, D. Shyam Gupta, A. Shilpa Trivedi, Sanjeev Kumar Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamilnadu Chennai602105 India Vels Institute of Science Technology and Advance Studies Department of Computer Science Chennai India School of Computer Science Engineering and Applications D y Patil International University Akurdi India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore India Department of Electrical and Electronics Engineering Aurora's Technological Research Institute Uppal Hyderabad India Khwaja Moinuddin Chishti Language University Faculty of Engineering and Technology Department of Electronics Lucknow India

ISBN: (纸本)9798350375237

A nevertheless-emerging generation called cloud computing permits customers to pay for services on a usage-based foundation. Internet-primarily based IT offerings are supplied through cloud computing, at the same time as virtualization enables the availability of PC sources. The muse of cloud computing is the information center, which is made up of networked computers, cables, electricity components, and different components that host and shop corporate records. With cloud facts centers, high overall performance has continually been the most critical concern, yet it compromises strength utilization. The key hassle is to lessen power consumption at the same time as preserving provider nice and performance a good way to stability device performance and strength intake. A detailed grasp of strength use styles within the cloud environment is needed for our suggested technique. We look at power consumption tendencies and reveal how, through the use of the right optimization standards based on our strength intake models, we can keep more strength in cloud records facilities. All through the prediction section, tablet optimization, which has a 97 percent accuracy rate, permits this era to provide extra correct future price forecasts. © 2024 IEEE.

关键词： Machine learning

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Leveraging Smart Video-Based Learning to Enhance Student Engagement and Collaboration 6

Leveraging Smart Video-Based Learning to Enhance Student Eng...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Serasinghe, Chanukya Perera, Nashali Samarasinghe, Devindu Muthusinghe, Madusha Wijesundara, Malitha Sri Lanka Institute of Information Technology Dept. of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Humanities and Sciences Malabe Sri Lanka

ISBN: (纸本)9798331517878

The COVID-19 pandemic has expedited the shift in education to online learning, which has exposed shortcomings in virtual learning environments' personalization and engagement. This research addresses these issues by developing a web application designed to enhance student engagement and learning efficiency. The system has gamified collaboration features, video chapter segmentation, and personalized video recommendation system. The methodology comprises models for generating chapter titles and for precise video transcription, and machine learning algorithms for tailored suggestions depending on user input. Interactive Questions and Answers modules and real-time synchronized video playback are included. The results demonstrate how the system's personalized content, interactive tests, and teamwork tools increase user engagement and comprehension. The efficacy of personalized material and real-time synchronization in enhancing learning results is discussed. This innovative approach offers a comprehensive solution to the challenges of online education, creating a more immersive and interactive learning experience. © 2024 IEEE.

关键词： Federated learning

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A Machine Learning Approach for Predicting Microsatellite Instability using RNA-seq

A Machine Learning Approach for Predicting Microsatellite In...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Simões, Miguel Pereira, Tania Silva, Francisco MacHado, José Carlos Oliveira, Hélder P. University of Porto Inesc Tec - Institute for Systems and Computer Engineering Technology and Science Feup - Faculty of Engineering Porto Portugal Inesc Tec - Institute for Systems and Computer Engineering Technology and Science Porto Portugal University of Porto Inesc Tec - Institute for Systems and Computer Engineering Technology and Science Fcup - Faculty of Science Porto Portugal University of Porto Ipatimup - Institute of Molecular Pathology and Immunology i3S - Instituto de Investigação e Inovação em Saúde Fmup - Faculty of Medicine Porto Portugal

ISBN: (纸本)9798350337488

Microsatellite Instability (MSI) is an important biomarker in cancer patients, showing a defective DNA mismatch repair system. Its detection allows the use of immunotherapy to treat cancer, an approach that is revolutionizing cancer treatment. MSI is especially relevant for three types of cancer: Colon Adenocarcinoma (COAD), Stomach Adenocarcinoma (STAD), and Uterus corpus endometrial cancer (UCEC). In this work, learning algorithms were employed to predict MSI using RNA-seq data from The Cancer Genome Atlas (TCGA) database, with a focus on the selection of the most informative genomic features. The Multi-Layer Perceptron (MLP) obtained the best score (AUC = 98.44%), showing that it is possible to exploit information from RNA-seq data to find relevant relationships with the instability levels of microsatellites (MS). The accurate prediction of MSI with transcription data from cancer patients will help with the correct determination of MSI status and adequate prescription of immunotherapy, creating more precise and personalized patient care. At the genetic level, the study revealed a high expression of genes related to cell regulation functions, and a low expression of genes responsible for Mismatch Repair functions, in patients with high instability. © 2023 IEEE.

关键词： Artificial Neural Networks Cancer Immunotherapy Microsatellite Instability Prediction RNA-seq Data

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Tolerant Tracking Control Protocol for PMSM Based on Policy Iteration Algorithm and Fault Compensation 16th

Tolerant Tracking Control Protocol for PMSM Based on Polic...

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16th International Conference on Social Robotics, ICSR + InnoBiz 2024

作者： Yan, Shuya Li, Xiaocong Qian, Huaming Ma, Jun Al Mamun, Abdullah College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin150001 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Guangzhou China Information Science and Technology Eastern Institute of Technology Ningbo China

ISBN: (纸本)9789819611508

In robotics applications, ensuring reliable performance in the presence of actuator faults is essential for maintaining system safety and reliability. This paper presents a tolerant tracking control method for permanent magnet synchronous motors (PMSMs) based on adaptive dynamic programming and fault compensation. The method simultaneously considers tracking accuracy and energy consumption through a policy iteration algorithm. In the optimality analysis of the algorithm, more relaxed conditions are provided to demonstrate that the performance function can converge to a near-optimal value within a finite number of iterations. In practical implementation, an actor-critic network is used to approximate the performance function and control protocol, alongside a fault detection mechanism based on an expanded time horizon, which achieves fault detection from arbitrary initial values. The effectiveness of the proposed algorithm is verified using a high-fidelity PMSM model in Simulink. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Permanent magnets

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Optimizing Depression Prediction in Older Adults: A Comparative Study of Feature Extraction and Machine Learning Models

Optimizing Depression Prediction in Older Adults: A Comparat...

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2024 International Conference on Control, Automation and Diagnosis, ICCAD 2024

作者： Javeed, Ashir Anderberg, Peter Ghazi, Ahmad Nauman Javeed, Asim Dallora, Ana Luiza Berglund, Johan Sanmartin Blekinge Institute of Technology Department of Health Blekinge Karlskrona37179 Sweden Blekinge Institute of Technology Department of Software Engineering Blekinge Karlskrona37179 Sweden Times Institute Department of Computer Science Multan60650 Pakistan

ISBN: (纸本)9798350361025

Depression emerged as a major public health concern in older adults, and timely prediction of depression has become a difficult problem in medical informatics. The latest studies have attentiveed on feature transformation and selection for better depression prediction. In this study, we assess the performance of various feature extraction algorithms, including principal component analysis (PCA), independent component analysis (ICA), locally linear Embedding (LLE), and t-distributed stochastic neighbor embedding (TSNE). These algorithms are combined with machine learning (ML) classifier algorithms such as Gaussian Naive Bayes (GNB), Logistic Regression (LR), K-nearest-neighbor (KNN), and Decision Tree (DT) to enhance depression prediction. In total, sixteen automated integrated systems are constructed based on the above-mentioned feature extraction methods and ML classifiers. The performance of all of these integrated models is assessed using data from the Swedish National Study on Aging and Care (SNAC). According to the experimental results, the PCA algorithm combined with the Logistic Regression (LR) model provides 89.04% depression classification accuracy. As a result, it is demonstrated that the PCA is a more suitable feature extraction method for depression data than ICA, LLE, and TSNE. © 2024 IEEE.

关键词： Feature extraction

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