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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Jakob Danielsson Marcus Jägemar Moris Behnam Mikael Sjödin Mölardalen University Västeras Sweden Ericsson AB Stockholm Sweden

We discuss how to obtain information of execution characteristics, such as parallelizability and memory utilization, with the final aim to improve the performance and predictability of feature and corner detection algorithms for use in e.g. robotics and autonomous machines. Our aim is to obtain a better understanding of how computer vision algorithms use hardware resources and how to improve the time predictability and execution time of such algorithms when executing on multi-core CPUs. We evaluate a fork-join model applicable to feature detection algorithms and present a method for measuring how well the algorithm performance correlates with hardware resource usage. We have applied our method to the Featured from Accelerated Segment Test (FAST) algorithm. Our characterization of FAST reveals that it is an algorithm with excellent parallelism opportunities, resulting in an almost linear speed-up per core. Our measurements also reveal that the performance of FAST correlates very little with the number of misses in the L1 data cache, L1 instruction cache, data translation lookaside buffer and L2 cache. Thus, the FAST algorithm will not have a negative effect on the execution time when the input data fits in the L2 cache.

关键词： Feature extraction Prediction algorithms Hardware detection algorithms Velocity measurement Monitoring

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An Analysis on Community detection and Clustering algorithms on the Post-Processing of Association Rules

An Analysis on Community Detection and Clustering Algorithms...

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International Joint Conference on Neural Networks

作者： Renan de Padua Lais Pessine do Carmo Solange Oliveira Rezende Veronica Oliveira de Carvalho USP - São Paulo University São Carlos Brazil UNESP - São Paulo State University Rio Claro Brazil

Association rules are widely used to extract patterns from a given database. The association rules are capable of finding correlations among items, making it possible for the user to learn which items are present in the transactions and which of them have a significant correlation. One of the major problems with association rules is that the number of extracted rules usually exceeds the number of transactions present in the database, also surpassing the user's capability to explore the obtained knowledge. To overcome this problem, the post-processing phase was proposed with the objective of directing the user to the rules that potentially have the most interesting knowledge. One of the used approaches is to divide the association rules into groups (or clusters), so that rules behave similarly are on the same group, facilitating the rule set understanding. In the literature, there are some works that uses clustering algorithms to split the rules while some other works use community detection algorithms. As both approaches obtain groups of association rules, but using different premises, different results can be obtained. No study has been done on the differences among clustering and community detection algorithms, which makes the selection of the algorithm hard, once their behavior is not well known in the association rule post-processing phase. This paper presents an analysis on both approaches, aiming to find the differences and the similarities among them, making it easier to select an approach by knowing its behavior.

关键词： Clustering algorithms Itemsets detection algorithms Correlation Data mining Partitioning algorithms

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Community detection Metrics and algorithms in Social Networks

Community Detection Metrics and Algorithms in Social Network...

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International Conference on Secure Cyber Computing and Communication (ICSCCC)

作者： Himansu Sekhar Pattanayak Harsh K Verma A. L. Sangal Department of Computer Scinece and Enggineering Dr B R Ambedkar National Institute of Technology Jalandhar Jalandhar India

ISBN: (纸本)9781538663745;9781538663738

Community detection is one of the key areas of social network analysis. There are various community detection algorithms available in the literature. Numerous community metrics are also available to evaluate the detected communities. In our study, by using synthetic networks, we compare between four well known community metrics, namely; modularity, conductance, coverage and performance. We also compare seven different community detection algorithms based on above mentioned parameters.

关键词： Measurement Social networking (online) detection algorithms Image edge detection Task analysis Organizations Complex networks

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COMPARISON OF ON-LINE CHATTER detection algorithms IN TURNING

COMPARISON OF ON-LINE CHATTER DETECTION ALGORITHMS IN TURNIN...

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International Symposium on Flexible Automation (ISFA)

作者： Vinh Nguyen Melkote, Shreyes Deshamudre, Amar Khanna, Maneesh Walker, Dan Georgia Inst Technol 801 Ferst Dr Atlanta GA 30332 USA Siemens Energy Inc 5101 Westinghouse Blvd Charlotte NC 28273 USA

ISBN: (纸本)9781509034673

Dynamic instability in the form of chatter is a highly undesirable phenomenon that occurs during machining, resulting in poor surface finish and reduced tool life. Though analytical models exist for identifying conditions that avoid chatter, they do not account for process uncertainties or they require extensive cutting tests. Therefore, an on-line chatter detection algorithm is required to detect chatter before irreversible damage to the cutting tool or workpiece occurs. This paper evaluates the performance of three chatter detection algorithms in detecting chatter in the turning process with the eventual goal of developing embedded sensor-based process monitoring automation. Cutting force data are gathered from a number of turning scenarios with varying workpiece geometries. Of the chatter detection algorithms evaluated, spectral analysis is found to be the most robust and capable of detecting dynamic instability before tool or workpiece damage occurs.

关键词： Force Correlation Heuristic algorithms Feeds Turning detection algorithms Dynamics

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Disease detection in Grape Cultivation Using Strategically Placed Cameras and Machine Learning algorithms With a Focus on Powdery Mildew and Blotches

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IEEE ACCESS 2024年 12卷 139505-139523页

作者： Hesham Khan, Kashif Aljaedi, Amer Ishtiaq, Muhammad Shakeel Imam, Hassan Bassfar, Zaid Jamal, Sajjad Shaukat RMIT Univ Sch Comp Technol Melbourne Vic 3000 Australia Univ Tabuk Coll Comp & Informat Technol Tabuk 71491 Saudi Arabia Riphah Int Univ Dept Biomed Engn Islamabad 44000 Pakistan King Khalid Univ Coll Sci Dept Math Abha 61413 Saudi Arabia

Grape cultivation faces various challenges, such as pests, management, fertilizer quality, and diseases caused by bacteria, fungi, and viruses. Notably, powdery mildew and blotches are significant diseases with different features, necessitating an accurate detection system to minimize crop losses. While traditional methods involve capturing images of diseased leaves, this research proposes a smart approach using deep learning and machine learning algorithms to analyze images taken by strategically placed cameras on farms. The research aims to design a system capable of detecting diseases that can provide information relevant to decisions, alert farmers, and allow authorized actions. Employing artificial intelligence algorithms such as support vector machines (SVM), convolutional neural networks (CNN), decision trees (DT), Naive Bayes (NB), and random forest (RF), the proposed model classifies datasets of powdery mildew, blotches, and healthy leaves when using augmented and histogram-oriented gradient (HOG) preprocessing. Following the classification of affected and healthy leaves, a stacking algorithm is used to select the optimal algorithm that provides the highest level of accuracy. The experimental results and analysis reveal that the CNN classifier outperforms others, achieving an accuracy of 96.1%. When transfer learning and fine tuning are applied to the CNN-based model, the accuracy of the model increases by 1.4% and 3.1%, respectively. SVM classification also provides a suitable level of accuracy of 96.0% for HOG augmented data.

关键词： Accuracy Deep learning Classification algorithms Convolutional neural networks Feature extraction Plant diseases Plants (biology) Crops Farming Machine learning detection algorithms Grape cultivation disease detection image processing machine learning deep learning

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A Situation Based Predictive Approach for Cybersecurity Intrusion detection and Prevention Using Machine Learning and Deep Learning algorithms in Wireless Sensor Networks of Industry 4.0

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IEEE ACCESS 2024年 12卷 34800-34819页

作者： Al-Quayed, Fatima Ahmad, Zulfiqar Humayun, Mamoona Jouf Univ Coll Comp & Informat Sci Dept Comp Sci Sakaka 72388 Saudi Arabia Hazara Univ Dept Comp Sci & Informat Technol Mansehra 21300 Pakistan Jouf Univ Coll Comp & Informat Sci Dept Informat Syst Sakaka 72388 Saudi Arabia

Industry 4.0 is fundamentally based on networked systems. Real-time communication between machines, sensors, devices, and people makes it easier to transmit the data needed to make decisions. Informed decision-making is empowered by the comprehensive insights and analytics made possible by this connectedness in conjunction with information transparency. Industry 4.0-based wireless sensor networks (WSNs) are an integral part of modern industrial operations however, these networks face escalating cybersecurity threats. These networks are always vulnerable to cyber-attacks as they continuously collect data and optimize processes. Increased connections make people more susceptible to cyberattacks, necessitating the use of strong cybersecurity measures to protect sensitive data. This study proposes a predictive framework intended to intelligently prioritize and prevent cybersecurity intrusions on WSNs in Industry 4.0. The proposed framework enhances the cybersecurity of WSNs in Industry 4.0 using a multi-criteria approach. It implements machine-learning and deep-learning algorithms for cybersecurity intrusion detection in WSNs of Industry 4.0 and provides prevention by assigning priorities to the threats based on the situation and nature of the attacks. We implemented three models, i.e., Decision Tree, MLP, and Autoencoder, as proposed algorithms in the framework. For multidimensional classification and detection of cybersecurity intrusions, we implemented Decision Tree and MLP models. For binary classification and detection of cybersecurity intrusions in WSNs of Industry 4.0, we implemented Autoencoder model. Simulation results show that the Decision Tree model provides an accuracy of 99.48%, precision of 99.49%, recall of 99.48%, and F1 score of 99.49% in the detection and classification of cybersecurity intrusions. The MLP model provides an accuracy of 99.52%, precision of 99.5%, recall of 99.5%, and F1 score of 99.5% in the detection and classification of cybersecuri

关键词： Wireless sensor networks Computer security Fourth Industrial Revolution Sensors Intrusion detection Wireless communication Cyberattack detection algorithms Predictive models Machine learning Deep learning Cybersecurity WSN detection prediction intrusions machine learning and deep learning

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A framework for analyzing community detection algorithms

A framework for analyzing community detection algorithms

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IEEE Students' Technology Symposium (TechSym)

作者： Anupam Biswas Bhaskar Biswas Department of Computer Science & Engineering Indian Institute of Technology (BHU) Varanasi India

Evaluation of community detection algorithms is very important to ensure both accuracy and quality of identified communities. Measuring quality incorporates edges, while measuring accuracy involves node labels. Due to this fundamental difference between accuracy and quality, often the evaluation process confronts with the issues such as trade-off between the two. In addition, real world networks such as social networks are of unknown structure and complex. Accuracy of communities detected with any algorithm for these networks cannot be measured due to unavailability of ground truth. In such cases, the algorithms are certainly more likely to predict accurate communities that show higher inclination towards accuracy in the networks where ground truths are available. In this paper, a framework is proposed to analyze Relative Inclination Towards accuracy (RITA) of a set of community detection algorithms. The RITA analysis gives an intuition about how likely an algorithm would produce accurate communities in the networks where ground truth is not available. Moreover, the RITA analysis overcomes trade-off between accuracy and quality by incorporating both into a common platform. Results on variety of networks show the competency of proposed framework in dealing with the trade-off during analysis.

关键词： Measurement detection algorithms Algorithm design and analysis Dolphins Social network services Prediction algorithms Image edge detection

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Instantaneous P- and T-wave detection: Assessment of three ECG fiducial points detection algorithms

Instantaneous P- and T-wave detection: Assessment of three E...

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International Workshop on Wearable and Implantable Body Sensor Networks (BSN)

作者： Heike Leutheuser Stefan Gradl Lars Anneken Martin Arnold Nadine Lang Stephan Achenbach Bjoern M. Eskofier Digital Sports Group Universität Erlangen-Ntirnberg (FAU) Germany Department of Cardiology University Hospital Erlangen Germany Fraunhofer Institute for Integrated Circuits IIS Erlangen Germany

ISBN: (纸本)9781509030880

Arrhythmia detection algorithms require the exact and instantaneous detection of fiducial points in the ECG signal. These fiducial points (QRS-complex, P- and T-wave) correspond to distinct cardiac contraction phases. The performance evaluation of different fiducial points detection algorithms require the existence of large databases (DBs) encompassing reference annotations. Up to last year, P- and T-wave annotations were only available for the QT DB. This was addressed by Elgendi et al. who provided P- and T-wave annotations to the MIT-BIH arrhythmia DB. A variety of ECG fiducial points detection algorithms exists in literature, whereas, to the best knowledge of the authors, we could not identify any single-lead algorithm ready for instantaneous P- and T-wave detection. In this work, we present three P- and T-wave detection algorithms: a revised version for QRS detection using line fitting capable to detect P- and T-wave, an expeditious version of a wavelet based ECG delineation algorithm, and a fast naive fiducial points detection algorithm. The fast naive fiducial points detection algorithm performed best on both DBs with sensitivities ranging from 73.0% (P-wave detection, error interval of ± 40 ms) to 89.4% (T-wave detection, error interval of ± 80 ms). As this algorithm detects a wave event in every search window, it has to be investigated how this affects arrhythmia detection algorithms. The reference Matlab implementations are available for download to encourage the development of high-accurate and automated ECG processing algorithms for the integration in daily life using mobile computers.

关键词： Electrocardiography detection algorithms Heart beat Event detection Databases MATLAB Real-time systems

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Abnormal Electricity detection of Users Based on Improved Canopy-Kmeans and Isolation Forest algorithms

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IEEE ACCESS 2024年 12卷 99110-99121页

作者： Wang, Jianyuan Li, Xiaoyao Northeast Elect Power Univ Key Lab Modern Power Syst Simulat & Control & Rene Minist Educ Jilin 132012 Peoples R China

Aiming at the existing user abnormal electricity consumption detection methods that have the problem of difficult classification of user similar electricity consumption patterns, this paper proposes an unsupervised isolation forest abnormal electricity consumption detection model based on the Canopy-Kmeans algorithm with weighted density improvement. To start, we propose a composite parameter analysis method for user electricity consumption patterns, volatility, trends, and correlations using Irish smart meter data. This method involves joint data cleaning, interpolation, and feature construction. Additionally, principal component analysis is introduced to fuse features across layers and reduce dimensionality in user electricity consumption. Subsequently, we introduce the weighted density improvement Canopy-Kmeans clustering algorithm. This algorithm determines the K value and clustering centers using the maximum weight product method, based on definitions of sample density, average intra-class sample distance, and inter-class distance in the multilayer fusion feature data. Finally, we propose a fusion mechanism of weighted density improvement Canopy-Kmeans and isolation forest algorithms to jointly construct a model for detecting abnormal power usage based on multilayer fusion feature data analysis. The results demonstrate that multilayer fusion feature parameters vary in size and discretization among different user types, enabling classification of users with diverse electricity consumption patterns. Moreover, the anomaly detection model based on multilayer fusion feature data analysis improves accuracy rates, recall rates, and F1 scores compared to other algorithms.

关键词： Electricity Clustering algorithms Classification algorithms Anomaly detection Forestry Prediction algorithms Feature extraction detection algorithms Energy consumption Unsupervised learning Abnormal detection of electricity consumption by users Canopy-Kmeans algorithm isolation forest algorithm unsupervised learning weighted density

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Health Care Fraud detection with Community detection algorithms

Health Care Fraud Detection with Community Detection Algorit...

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International Conference on Smart Computing (SMARTCOMP)

作者： Aryya Gangopadhyay Song Chen Inf. Syst. Univ. of Maryland Baltimore County MD USA Information Systems University of Maryland Baltimor County

ISBN: (纸本)9781509008995

Fraud detection is interesting research topic and it not only needs data mining techniques but also needs a lot of inputs from domain experts. In health care claims, relationships between physicians and patients form complex communities structures and these communities could lead to potential fraud discoveries. Traditionally, researchers have focused on clustering physicians and patients and tried to find the suspicious communities. In this paper, we studied and discussed different types of relationships and focus on small but exclusive relationships that are suspicious and may indicate potential health care frauds. We developed two algorithms to detect these small and exclusive communities. These algorithms can be applied to larger dataset and are highly scalable. We tested these algorithms with a set of synthesized datasets. These synthesized datasets were created to resemble the real health care claims datasets and used to test the fraud detection algorithms. The test results show the these algorithms are very efficient and can evaluate the communities structures of 50,000 providers in about 1 minute.

关键词： Clustering algorithms Matrix converters detection algorithms Insurance Algorithm design and analysis Drugs

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