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International Conference on Informatics and Computing (ICIC)

作者： Nurul Hidayat R. Rizal Isnanto Department of Informatics Jenderal Soedirman University Purwokerto Indonesia Department of Computer Engineering Diponegoro University Semarang Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Image detection plays a vital role in digital image processing and artificial intelligence, with applications ranging from security surveillance to autonomous vehicles and medical image analysis. This study employs a qualitative approach to review the performance of image detection algorithms, focusing on deep learning methods such as YOLO, SSD, and CNN. Through computational experiments using quantitative data, this research provides performance comparisons based on accuracy, precision, recall, and computational efficiency. The results highlight YOLO’s superior performance in terms of both accuracy ($\mathbf{9 2} .5 \%$) and inference speed (75.2 fps), making it suitable for real-time applications. This study contributes by addressing the gap in balancing computational efficiency and adaptability for real-world applications.

关键词： YOLO Deep learning Machine learning algorithms Accuracy Computational modeling Real-time systems Inference algorithms Computational efficiency detection algorithms Edge computing

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Performance Analysis of Learning-based Intent Drift detection algorithms in Next Generation Networks

Performance Analysis of Learning-based Intent Drift Detectio...

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IEEE Latin-American Conference on Communications, LATINCOM

作者： Chukwuemeka Muonagor Mounir Bensalem Admela Jukan Technical University of Braunschweig Germany

ISBN: (数字)9798331521110

ISBN: (纸本)9798331521127

Intent-Based Networking (IBN) is a known concept for enabling the autonomous configuration and self-adaptation of networks. One of the major issues in IBN is maintaining the applied intent due to the effects of drifts over time, which is the gradual degradation in the fulfillment of the intents, before they fail. Despite its critical role to intent assurance and maintenance, intent drift detection was largely overlooked in the literature. To fill this gap, we propose a learning-based intent drift detection algorithm for predictive maintenance of intents and analyze its performance by applying various unsupervised known learning techniques available as open-source (Affinity Propagation, DBSCAN, Gaussian Mixture Models, Hierarchical clustering, K-Means clustering, OPTICS, One-Class SVM). We apply these techniques for intent-drift detection and analyze them comparatively on their efficiency in detecting drifts. The results show that DBSCAN is the most efficient model for detecting the intent drifts. The worst performance is measured by the Affinity Propagation model, reflected in its poorest accuracy and latency values. To the best of our knowledge, this is the first work to address the problem of intent drift detection, and analyze its efficiency for intent maintenance.

关键词： Support vector machines Affinity propagation Machine learning Optics Maintenance Performance analysis detection algorithms Next generation networking Gaussian mixture model Predictive maintenance

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Real-Time Pipe Burst Localization in Water Distribution Networks Using Change Point detection algorithms

Real-Time Pipe Burst Localization in Water Distribution Netw...

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International Conference on Emerging Trends in Networks and Computer Communications (ETNCC)

作者： Takudzwa Mzembegwa Clement N Nyirenda Dept. of Computer Science University of the Western Cape Cape Town South Africa

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

Change point detection (CPD) has proved to be an effective tool for detecting drifts in data and its use over the years has become more pronounced due to the vast amount of data and IoT devices readily available. This study analyzes the effectiveness of Cumulative Sum (CUSUM) and Shewhart Control Charts for identifying the occurrence of abrupt pressure changes for pipe burst localization in Water Distribution Network (WDN). Change point detection algorithms could be useful for identifying the nodes that register the earliest and most drastic pressure changes with the aim of detecting pipe bursts in real-time. TSN et, a Python package, is employed in order to simulate pipe bursts in a WDN. The pressure readings are served to the pipe burst localization algorithm the moment they are available for real-time pie burst localization. The performance of the pipe burst localization algorithm is evaluated using a key metric such as localization accuracy under different settings to compare its performance when paired with either CUSUM or Shewhart. Results show that the pipe burst localization algorithm has an overall better performance when paired with CUSUM. Although, it does show great accuracy for both CPD algorithms when pressure readings are being continuously made available without a big gap between time steps. The proposed approach however still needs further experiments on different WDNs to assess the performance and accuracy of the algorithm on real-world WDN models.

关键词： Location awareness Measurement Accuracy Computational modeling Distribution networks Real-time systems Registers Transient analysis detection algorithms Optimization

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A Review of Air-Tightness detection algorithms Based on Object detection

A Review of Air-Tightness Detection Algorithms Based on Obje...

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International Conference on Advanced Computer Theory and Engineering, ICACTE

作者： Wenyun Wang Zhenglong Ding Yingying Li Zhigeng Pan School of Electronic and Information Engineering Anhui Jianzhu University Hefei China School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing China

ISBN: (数字)9798331539887

ISBN: (纸本)9798331539894

The problem of air-tightness detection in industry has become the center of attention, and it is very important to introduce object detection for automatic and accurate detection of leakage locations. This paper is a review article. First, the typical models of object detection is introduced. Second, based on object detection, we analyzed air-tightness testing from the aspects of data enhancement, optimized feature representation, feature fusion, new backbone networks and training strategies, increased attention mechanisms, enhanced generalization and lightweight. Third, the existing air-tightness detection data sets and evaluation indexes based on object detection are introduced. Finally, we made a conclusion and proposed the outlook.

关键词： Training Industries Attention mechanisms Reviews Data enhancement Object detection Feature extraction Indexes detection algorithms Testing

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An Exploratory Study of Periodicity detection algorithms for Energy Theft Prevention in Smart Grids 8

An Exploratory Study of Periodicity Detection Algorithms for...

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IEEE PES/IAS PowerAfrica Conference

作者： Ayogu, Ikechukwu I. Ogu, Reginald E. Fed Univ Technol Owerri Dept Comp Sci Owerri Nigeria Fed Univ Technol Owerri Dept Software Engn Owerri Nigeria

ISBN: (纸本)9781665403115

The sophistry evolved by dishonest consumers to thwart consumption measurement systems continues to increase in scope and dimension such that the utilitarian value of power sectors across the world is diminished as a result. This paper proposes a hybrid model for efficient periodicity analysis without incurring the computational overload that is often experienced with the Dynamic Time Warping (DTW) algorithm. Autocorrelation function was used to screen candidate periods from an FFT-generated periodogram to obtain a single, accurate period for use by the DTW algorithm to determine the similarity or otherwise of each of the data instance to its shifted version. The resulting, segregated data, either normal or theft, was then employed to construct machine learning models for electricity theft detection. A Decision tree classifier (DTC), two tree-based ensembles - Random Forest (RFC) and Extremely randomized tree classifiers (ETC) and an SVM model were trained on two separate categories of data- daily and mean weekly consumption data. The results show that the periodicity detection approach was promising. The ETC model produced the highest AUC of 95 % and 98 respectively on the daily and mean weekly data.

关键词： Anomaly detection Time series Classification algorithms Supervised learning Periodicity detection Intelligent systems Internet of Things Pattern recognition detection algorithms

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Evaluation of Duplicate detection algorithms: From Quality Measures to Test Data Generation 37

Evaluation of Duplicate Detection Algorithms: From Quality M...

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37th IEEE International Conference on Data Engineering (IEEE ICDE)

作者： Panse, Fabian Naumann, Felix Univ Hamburg Hamburg Germany Univ Potsdam Hasso Plattner Inst Potsdam Germany

ISBN: (纸本)9781728191843

Duplicate detection identifies multiple records in a dataset that represent the same real-world object. Many such approaches exist, both in research and in industry. To investigate essential properties of duplicate detection algorithms, such as their result quality or runtime behavior, they must be executed on suitable test data. The quality evaluation requires that these test data are labeled, constituting a ground truth. Correctly labeled, sizable, and real or at least realistic test datasets, however, are not easy to obtain, creating an obstacle for the advancement of research. In this tutorial, we present common methods to evaluate duplicate detection algorithms and to generate labeled test data. We close with a discussion of open problems.

关键词： Industries Runtime Conferences Tutorials Data engineering Object recognition detection algorithms

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Performance Comparison of Dysgraphia detection algorithms

Performance Comparison of Dysgraphia Detection Algorithms

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Gunjan Goyal Sidhanth Karwal Rakesh Garg Amity School of Engineering and Technology Amity University Uttar Pradesh Noida India

This research mainly aims on the comparative analysis of existing popular machine learning algorithms for Dysgraphia detection such as Naive Bayes, KNN (K-Nearest Neighbours), SVM (Support Vector Machine), Decision tree, Random Forest. The evaluation of these algorithms is done on four measure performance measure namely accuracy, precision, F1 score and recall. The comparative statics obtained from the present study depict that although these algorithms are capable to detect dysgraphia upto some extent still there arises a need to develop more effective algorithms for the same at early stages.

关键词： Support vector machines Machine learning algorithms Decision trees Sustainable development detection algorithms Random forests Diseases

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Development of Lane and Zebra Crossing Marker detection algorithms

Development of Lane and Zebra Crossing Marker Detection Algo...

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Device Intelligence, Computing and Communication Technologies, (DICCT), International Conference on

作者： Vivek Chamoli R Gowri V P Dubey Jyoti Kandpal Jyoti Joshi Divesh Kumar Dept. of Electronics & Communication Engineering Graphic Era Hill University Dehradun India Dept. of Comupter Science & Engineering Graphic Era Hill University Dehradun India

This research involves using an object recognition system and an ancillary image sensor to detect lane markers and zebra crossings on a roadway in a vehicle. To perform this detection, these steps have been followed: take a picture of the scene, digitize it, normalize it, define a search area within it, look for lane and zebra markers within it. The Lane Tracking for Driving System was created to assist drivers in making lane departure decisions, to lessen concentration breaks, and to avoid accidents while driving. To offer a way to detect a specific traffic violation—namely, stopping on a zebra crossing at a traffic signal rather than following behind it—and to offer a workaround. In the proposed work, systems are implemented and improved using adaptive algorithms. MATLAB’s image processing toolbox is used to design and implement the proposed algorithm.

关键词： Image sensors Image processing Adaptive algorithms Communications technology Object recognition detection algorithms Vehicles

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Designing Heavy-Hitter detection algorithms for Programmable Switches

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IEEE-ACM TRANSACTIONS ON NETWORKING 2020年第3期28卷 1172-1185页

作者： Ben Basat, Ran Chen, Xiaoqi Einziger, Gil Rottenstreich, Ori Harvard Univ Sch Engn & Appl Sci Cambridge MA 02138 USA Princeton Univ Dept Comp Sci Princeton NJ 08544 USA Ben Gurion Univ Negev Dept Comp Sci IL-84105 Beer Sheva Israel Technion IL-3200003 Haifa Israel

Programmable network switches promise flexibility and high throughput, enabling applications such as load balancing and traffic engineering. Network measurement is a fundamental building block for such applications, including tasks such as the identification of heavy hitters (largest flows) or the detection of traffic changes. However, high-throughput packet processing architectures place certain limitations on the programming model, such as restricted branching, limited capability for memory access, and a limited number of processing stages. These limitations restrict the types of measurement algorithms that can run on programmable switches. In this paper, we focus on the Reconfigurable Match Tables (RMT) programmable high-throughput switch architecture, and carefully examine its constraints on designing measurement algorithms. We demonstrate our findings while solving the heavy hitter problem. We introduce PRECISION, an algorithm that uses Partial Recirculation to find top flows on a programmable switch. By recirculating a small fraction of packets, PRECISION simplifies the access to stateful memory to conform with RMT limitations and achieves higher accuracy than previous heavy hitter detection algorithms that avoid recirculation. We also evaluate each of the adaptations made by PRECISION and analyze its effect on the measurement accuracy. Finally, we suggest two algorithms for the hierarchical heavy hitters detection problem in which the goal is identifying the subnets that send excessive traffic and are potentially malicious. To the best of our knowledge, our work is the first to do so on RMT switches.

关键词： Pipelines Throughput Memory management detection algorithms Task analysis Approximation algorithms Software defined networking measurement

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A Survey on the detection algorithms for False Data Injection Attacks in Smart Grids

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IEEE TRANSACTIONS ON SMART GRID 2020年第3期11卷 2218-2234页

作者： Musleh, Ahmed S. Chen, Guo Dong, Zhao Yang Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia

Cyber-physical attacks are the main substantial threats facing the utilization and development of the various smart grid technologies. Among these attacks, false data injection attack represents a main category with its widely varied types and impacts that have been extensively reported recently. In addressing this threat, several detection algorithms have been developed in the last few years. These were either model-based or data-driven algorithms. This paper provides an intensive summary of these algorithms by categorizing them and elaborating on the pros and cons of each category. The paper starts by introducing the various cyber-physical attacks along with the main reported incidents in history. The significance and the impacts of the false data injection attacks are then reported. The concluding remarks present the main criteria that should be considered in developing future detection algorithms for the false data injection attacks.

关键词： Smart grids detection algorithms Explosions Security Pipelines Generators Malware Cyber-physical attacks data-driven detection algorithms false data injection machine learning model-based detection algorithms smart grid state estimation stealth attacks

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