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Failure Prediction for Scientific Workflows Using Nature-Inspired Machine Learning Approach

Intelligent Automation & Soft Computing 2023年第4期36卷 223-233页

作者： S.Sridevi Jeevaa Katiravan Department of Computer Science and Engineering Velammal Engineering CollegeChennai600066India

Scientiﬁc workﬂows have gained the emerging attention in sophisti-cated large-scale scientiﬁc problem-solving *** pay-per-use model of cloud,its scalability and dynamic deployment enables it suited for executing scientiﬁc workﬂow *** the cloud is not a utopian environment,failures are inevitable that may result in experiencingﬂuctuations in the delivered *** a single task failure occurs in workﬂow based applications,due to its task dependency nature,the reliability of the overall system will be affected *** rather than reactive fault-tolerant approaches,proactive measures are vital in scientiﬁc workﬂ*** work puts forth an attempt to con-centrate on the exploration issue of structuring a nature inspired metaheuristics-Intelligent Water Drops Algorithm(IWDA)combined with an efﬁcient machine learning approach-Support Vector Regression(SVR)for task failure prognostica-tion which facilitates proactive fault-tolerance in the scheduling of scientiﬁc workﬂow *** failure prediction models in this study have been implemented through SVR-based machine learning approaches and the precision accuracy of prediction is optimized by IWDA and several performance metrics were evaluated on various benchmark workﬂ*** experimental results prove that the proposed proactive fault-tolerant approach performs better compared with the other existing techniques.

关键词： Failure prediction intelligent water drops support vector regression proactive fault-tolerance scientiﬁc workﬂows precision accuracy resource provisioning

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Nonlinear Filtering With Sample-Based Approximation Under Constrained Communication:Progress, Insights and Trends

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1539-1556页

作者： Weihao Song Zidong Wang Zhongkui Li Jianan Wang Qing-Long Han IEEE the State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering ScienceCollege of Engineering Peking University the Department of Computer Science Brunel University London the School of Aerospace Engineering Beijing Institute of Technology the School of Science Computing and Engineering Technologies Swinburne University of Technology

The nonlinear filtering problem has enduringly been an active research topic in both academia and industry due to its ever-growing theoretical importance and practical *** main objective of nonlinear filtering is to infer the states of a nonlinear dynamical system of interest based on the available noisy measurements. In recent years, the advance of network communication technology has not only popularized the networked systems with apparent advantages in terms of installation,cost and maintenance, but also brought about a series of challenges to the design of nonlinear filtering algorithms, among which the communication constraint has been recognized as a dominating concern. In this context, a great number of investigations have been launched towards the networked nonlinear filtering problem with communication constraints, and many samplebased nonlinear filters have been developed to deal with the highly nonlinear and/or non-Gaussian scenarios. The aim of this paper is to provide a timely survey about the recent advances on the sample-based networked nonlinear filtering problem from the perspective of communication constraints. More specifically, we first review three important families of sample-based filtering methods known as the unscented Kalman filter, particle filter,and maximum correntropy filter. Then, the latest developments are surveyed with stress on the topics regarding incomplete/imperfect information, limited resources and cyber ***, several challenges and open problems are highlighted to shed some lights on the possible trends of future research in this realm.

关键词： Communication constraints maximum correntropy filter networked nonlinear filtering particle filter sample-based approximation unscented Kalman filter

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NURTURE An Analytical Model that Supports Healthy Relationship between Parents and Children Using Machine Learning 6

NURTURE An Analytical Model that Supports Healthy Relationsh...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Saraswat, Laxmi Singh, Simar Preet Yadav, Venus Sharma, Ritika Chauhan, Shweta ABES Enginering College Department of Computer Science Ghaziabad India Bennett University Department of Computer Science Engineering Greater Noida India

ISBN: (纸本)9798350304480

The mental health of children aged 6 to 15 is a vital concern, given that not all youngsters find it easy to confide in parents regarding issues like school bullying and emotional struggles. This research delves into the complex problem of children's hesitancy to share problems and proposes strategies for addressing this within the evolving landscape of modern families. The current context is marked by a rise in dual-income households and nuclear families, leading to reduced parent-child time and weakened relationships. Amidst this, the study underscores the importance of optimizing the limited time parents and children have together. Recognizing timeconstraints, the research introduces a novel approach: enhancing the impact of existing time. By equipping parents with insights into children's moods, they can tailor interactions accordingly. If a child feels down, a day of attention, affection, and understanding can be provided, while happier days may require less parental involvement. The study promotes cultivating a friendly parental approach, aiming for stronger parent-child bonds. As parents adapt to children's emotional needs, a more open and communicative relationship is likely to develop. Ultimately, this research contributes to improving children's mental health within the bounds of contemporary family dynamics. © 2023 IEEE.

关键词： Ada boost CNN Decision Tree ECG EMG Emotions EOG GBDT GSR HRV KNN Linear Regression LSTM MLP MMC RF SK Learn SVM

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A feature-enhanced multiscale attention approach for automated hand bone segmentation

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Multimedia Tools and Applications 2024年第16期84卷 1-21页

作者： Nagaraju, Y. Venkatesh Thanu Shree Yadav, P.R. Vaishnavi, A. Tejashree, S.V. Department of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore University Karnataka Bengaluru560001 India

In the medical field, comprehensive analysis of bone structures is paramount for assessing skeletal health and diagnosing conditions. X-ray imaging serves as a cornerstone in bone age evaluation and the fabrication of implants. In addition to clinical applications, automated bone structure analysis aids students in studying anatomy and radiology by providing accurate and consistent representations. This automation has the potential to significantly reduce processing time and enhance students’ understanding of bone structures and their variations, thereby improving their understanding and training in the medical field. This study presents the Adaptive Multiscale Expansion Segmentation Model (AMESM), a lightweight encoder-decoder-based multiclass semantic segmentation model for segmenting hand bones. The proposed method assists physicians in tasks such as growth analysis, hand movement assessment, and implant design. The model incorporates a Multi-Scale Attention Block that improves feature representation and segmentation accuracy. The experimental results demonstrate promising performance, with a Mean Intersection over Union (mIoU) of 88.47% and a Dice Score of 93.86%. The comprehensive analysis confirms the model’s ability to achieve accurate segmentation while remaining computationally efficient. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Automation

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The Societal Implications of False Positives Using Machine Learning 6th

The Societal Implications of False Positives Using Machine L...

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6th International Conference on Recent Innovations in Computing, ICRIC 2023

作者： Dubey, Anurag Mishra, Aryan Tayal, Divyansh Yadav, Aarush Jamshed, Aatif Department of IT ABES Engineering College AKTU Lucknow India Department of Computer Science KIET Group of Institutions Delhi-NCR Ghaziabad India

ISBN: (纸本)9789819734412

The article aims to investigate the societal consequences of false positive detection through a systematic review leveraging ML (machine learning) techniques. It seeks to assess the impact of inaccurate detections on various aspects of society, such as healthcare, security, and privacy, to inform better decision-making and policy development. The study employs a comprehensive dataset encompassing diverse sources and topics to train and validate the model's efficacy. Results showcase promising outcomes, indicating that sentiment analysis can effectively contribute to the identification of misleading information. The proposed approach not only demonstrates robust performance in distinguishing between genuine and fake news but also provides insights into the emotional underpinnings of deceptive content. This research contributes to the evolving field of fake news detection, offering a nuanced perspective that aligns sentiment analysis with the imperative of fostering a more informed and resilient information landscape. The purpose of this work is to propose a notion and methods for identifying misinformation. The process of gathering news items and then utilizing SVM (SVM(Support Vector Machine)) to categorize them as either authentic or fake is accomplished through the utilization of ML (machine learning) and natural language-processing processes. Comparing the results of the proposed model with those of the existing models is the purpose of this comparison. With an accuracy rating of 93.6%, the model that was provided is performing efficiently and accurately identifying the outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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Automatic American sign language prediction for static and dynamic gestures using KFM-CNN

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Soft Computing 2024年第20期28卷 11703-11715页

作者： Thushara, A. Hani, Reymond Hakkim Baisil Mukundan, Manu Computer Science and Engineering TKM College of Engineering Kollam APJ Abdul Kalam Technological University Thiruvananthapuram India

For human–computer interaction, one of the most important tools is Sign Language Recognition in which one of the significant research topics is static Hand Gesture (HG) and dynamic Hand Gesture Recognition (HGR) of American Sign Language (ASL). However, recognizing them was challenging owing to improper contour models. Thus, this paper proposes a new model for ASL Static and dynamic HGR utilizing Deep Learning (DL). Primarily, the videos from the dataset are collected and transformed into image frames and then given to noise filtering utilizing an Alternative Window Size-Lone Diagonal Sorting Algorithm. The RGB noises are removed in noise filtering and given to Canopy Algorithm-based Minibatch K-Means Clustering clustering of static and dynamic gesture images. After that, by utilizing the YCbCr Palm point and Finger speed-based Threshold in the Region seed Grow Segmentation algorithm, the clustered images are segmented that segments both palm and fingers. Then, the features are extracted, and these features are given to the Kohnen Feature Mapping-based CNN classifier. From the Classifier, HG-recognized character outcomes are obtained. In a software environment, the novel model is implemented;also, for exhibiting the proposed technique’s superiority, the outcomes are analogized with the prevailing approaches. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： K-means clustering

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MAG-Net: Multi-fusion network with grouped attention for retinal vessel segmentation

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Mathematical Biosciences and engineering 2024年第2期21卷 1938-1958页

作者： Jiang, Yun Chen, Jie Yan, Wei Zhang, Zequn Qiao, Hao Wang, Meiqi College of Computer Science and Engineering Northwest Normal University Lanzhou730070 China

Retinal vessel segmentation plays a vital role in the clinical diagnosis of ophthalmic diseases. Despite convolutional neural networks (CNNs) excelling in this task, challenges persist, such as restricted receptive fields and information loss from downsampling. To address these issues, we propose a new multi-fusion network with grouped attention (MAG-Net). First, we introduce a hybrid convolutional fusion module instead of the original encoding block to learn more feature information by expanding the receptive field. Additionally, the grouped attention enhancement module uses high-level features to guide low-level features and facilitates detailed information transmission through skip connections. Finally, the multi-scale feature fusion module aggregates features at different scales, effectively reducing information loss during decoder upsampling. To evaluate the performance of the MAG-Net, we conducted experiments on three widely used retinal datasets: DRIVE, CHASE and STARE. The results demonstrate remarkable segmentation accuracy, specificity and Dice coefficients. Specifically, the MAG-Net achieved segmentation accuracy values of 0.9708, 0.9773 and 0.9743, specificity values of 0.9836, 0.9875 and 0.9906 and Dice coefficients of 0.8576, 0.8069 and 0.8228, respectively. The experimental results demonstrate that our method outperforms existing segmentation methods exhibiting superior performance and segmentation outcomes. © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://***/licenses/by/4.0)

关键词： Convolution

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A Novel Optimized Deep Convolutional Neural Network for Efficient Seizure Stage Classification

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computers, Materials & Continua 2024年第12期81卷 3903-3926页

作者： Umapathi Krishnamoorthy Shanmugam Jagan Mohammed Zakariah Abdulaziz S.Almazyad K.Gurunathan Department of Biomedical Engineering KIT-Kalaignarkarunanidhi Institute of TechnologyCoimbatore641402India Department of Computer Sciences and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai600062India Department of Computer Sciences and Engineering College of Applied ScienceKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Science and Engineering Mount Zion College of Engineering and TechnologyPudukkottai622507India

Brain signal analysis from electroencephalogram(EEG)recordings is the gold standard for diagnosing various neural disorders especially epileptic *** signals are highly chaotic compared to normal brain signals and thus can be identified from EEG *** the current seizure detection and classification landscape,most models primarily focus on binary classification—distinguishing between seizure and non-seizure *** effective for basic detection,these models fail to address the nuanced stages of seizures and the intervals between *** identification of per-seizure or interictal stages and the timing between seizures is crucial for an effective seizure alert *** granularity is essential for improving patient-specific interventions and developing proactive seizure management *** study addresses this gap by proposing a novel AI-based approach for seizure stage classification using a Deep Convolutional Neural Network(DCNN).The developed model goes beyond traditional binary classification by categorizing EEG recordings into three distinct classes,thus providing a more detailed analysis of seizure *** enhance the model’s performance,we have optimized the DCNN using two advanced techniques:the Stochastic Gradient Algorithm(SGA)and the evolutionary Genetic Algorithm(GA).These optimization strategies are designed to fine-tune the model’s accuracy and ***,k-fold cross-validation ensures the model’s reliability and generalizability across different data *** and validated on the Bonn EEG data sets,the proposed optimized DCNN model achieved a test accuracy of 93.2%,demonstrating its ability to accurately classify EEG *** summary,the key advancement of the present research lies in addressing the limitations of existing models by providing a more detailed seizure classification system,thus potentially enhancing the effectiveness of real-time seizure prediction and management systems in clinic

关键词： Bonn EEG dataset cross-validation genetic algorithm batch normalization seizure classification stochastic gradient

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IntelliCrop: Major Cropping Pattern Prediction using Machine Learning Techniques 7

IntelliCrop: Major Cropping Pattern Prediction using Machine...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Attavar, Aditi Manohar, Akshatha Leela Himoshra, D. Harshiya Banu, B. Pooja, M.R. Vidyavardhaka College of Engineering Computer Science And Engineering Mysuru India Vidyavardhaka College of Engineering Department of Computer Science And Engineering Mysuru India

ISBN: (纸本)9798350359299

The sustainable livelihoods of farmers in India hinge upon effective crop management and optimal yield. However, the existing manual system for crop selection is time-consuming, relies heavily on experience, and often leads to suboptimal yields and financial losses. To address this challenge, we propose an automated cropping pattern prediction system that leverages advanced machine learning techniques. Our system aims to empower farmers by providing insights into the diverse range of crops suitable for their specific land. By understanding the intricate relationships between different crop types, farmers can make informed decisions to maximize yield quality. The absence of a robust system for identifying cropping patterns results in random crop choices, leading to yield fluctuations. This study focuses on developing a system that predicts the optimal crop priority, considering various factors such as land characteristics, soil conditions, and climate data. This prioritization minimizes losses due to suboptimal crop combinations. The machine learning algorithms that are used include Naïve Bayes, and Eclat, which aim at disseminating information from the data at once, will enable the agricultural departments to work effectively. This approach allows for a more informed and effective decision-making process in agricultural practices. © 2024 IEEE.

关键词： Forecasting

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An Unified PSO and Fuzzy Satisfaction Framework for Power Flow Optimization

An Unified PSO and Fuzzy Satisfaction Framework for Power Fl...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Chatterjee, Vedatrayee Roy, Dhiman Maitra, Arnab Ghosh, Soubhik Wania, Rumsha Waqia Asansol Engineering College Computer Science & Engineering Asansol India Bengal College of Engineering & Technology Computer Science & Engineering Durgapur India

ISBN: (纸本)9798350390759

This study introduces a new method for multi-objective optimization (MOO) in electrical power systems, aiming to minimise emission release, power losses and fuel costs concurrently. Diverging from traditional techniques that primarily target cost reduction, the proposed Power Flow Optimization (PFO) framework combines Fuzzy Satisfaction Maximization strategy with Particle Swarm Optimization (PSO), acknowledging the vagueness in decision-makers' objectives. By formulating the optimization problem as a fuzzy satisfaction maximization problem, the method aims to find optimal trade-off solutions that balance conflicting objectives. Testing the PFO on a standard IEEE 30-bus system with six generators demonstrates its effectiveness in providing solutions that optimize both actual and imaginary powers while considering fuel cost, emission reduction, and power loss, thereby offering valuable insights for efficient power system operation. © 2024 IEEE.

关键词： Load flow optimization

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