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Remaining Cycle Time Prediction in Business Processes with TPOT Regression for Automated machine learning 4th

Remaining Cycle Time Prediction in Business Processes with T...

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4th international conference on Novel and Intelligent Digital Systems, NiDS 2024

作者： Bousdekis, Alexandros Kaftantzis, Savvas theodoropoulou, Georgia Miaoulis, Georgios Department of Informatics and Computer Engineering University of West Attica Athens Greece

ISBN: (纸本)9783031733437

Process mining allows the extraction of useful information from the event logs and historical data of business processes. Predictive business process monitoring is a sub-field of process mining and deals with runtime methods aimed at generating predictive models about various objectives related to a process instance by taking advantage of machine learning (ML) and deep learning algorithms. However, despite the large amount of research works dealing with predictions about next activity prediction and remaining time of running process instances, the objective of cycle time prediction has been overseen although it can provide higher level insights targeted the strategic planning. At the same time, Automated machine learning (AutoML) has not been investigated in the predictive business process monitoring domain in order to automate the time-consuming process of designing and optimizing ML pipelines. therefore, motivated by its promising results in other domains, we propose an approach for remaining cycle time prediction in business processes based on AutoML, and specifically on the Tree-Based Pipeline Optimization Tool (TPOT) method. the results demonstrate that the proposed approach achieves high accuracy, while it minimizes human intervention in the configuration of ML pipelines. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Deep learning

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Towards Scalable and Efficient Client Selection for Federated Object Detection 26

Towards Scalable and Efficient Client Selection for Federate...

引用

26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Constantinou, George You, Suya Shahabi, Cyrus Univ Southern Calif Integrated Media Syst Ctr Los Angeles CA 90089 USA Army Res Lab Adelphi MD USA

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Various computer vision techniques based on deep neural networks have been proposed to detect objects accurately and fast. However, due to the privacy, security and communication bandwidth restrictions of diverse participating parties, it is sometimes prohibitive to train such models on a centralized machine. Federated learning (FL) provides a promising solution to learn a model from decentralized data. Despite the advances in FL, the diversity of client regions in which they operate and the Non-IID nature of the crowdsourced datasets reduces the accuracy of object detection models significantly. In this paper, we introduce a novel FL object detection system to efficiently train models with heterogeneous client datasets. We propose lightweight client selection methods to learn object detection models faster. Our client selection methods based on the object data distribution at clients achieves up to 74% reduction in required federated rounds compared to conventional approaches. We further extend this method by leveraging the metadata of the training images (e.g., location, direction, depth), to select clients which maximize the coverage of diverse geographical regions. We report on extensive experiments with real datasets.

关键词： Training Deep learning Privacy Computer vision Federated learning Neural networks Object detection

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Legal Forensics System Based on data mining 4

Legal Forensics System Based on Data Mining

引用

4th international conference on machine learning and Big data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Dai, Hongrui Law school of Chongqing University Chongqing400044 China Law school of Yunnan Minzu University Kunming650504 China

the existing forensics systems are developing in a more and more perfect direction in terms of forensic steps, legal constraints, audit supervision, etc., but they are still not fully applicable to legal forensics. A general legal forensics system must have a complete forensics process to ensure the smooth progress of the forensics work. One of the purposes of legal forensics is to use legal means to bring criminals to justice. Only by ensuring the legality of the evidence can it be accurately identified case, and therefore also to ensure the legality of the evidence collection process. In order to design a more powerful legal forensics system, this paper introduces a data mining algorithm to extract valuable information from massive data through association rules to improve the efficiency of forensics. At the same time, this paper also proposes three coding schemes for the integrity of the evidence object, and compares the forensic effects of the three schemes. © 2024 the Authors.

关键词： Digital forensics

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Deep Neural Network-Based Prediction of B-Cell Epitopes for SARS-CoV and SARS-CoV-2: Enhancing Vaccine Design through machine learning 4

Deep Neural Network-Based Prediction of B-Cell Epitopes for ...

引用

4th international Signal Processing, Communications and Engineering Management conference, ISPCEM 2024

作者： Shi, Xinyu Tao, Yixin Lin, Shih-Chi College of Agronomy and Biotechnology Southwest University Chongqing China Accounting/Finance Department Heritage Provider Network NorthridgeCA United States Epocrates Athenahealth Inc. AustinTX United States

ISBN: (纸本)9798331528676

the accurate prediction of B-cell epitopes is critical for guiding vaccine development against infectious diseases, including SARS and COVID-19. this study explores the use of a deep neural network (DNN) model to predict B-cell epitopes for SARS-CoV and SARS-CoV-2, leveraging a dataset that incorporates essential protein and peptide features. Traditional sequence-based methods often struggle with large, complex datasets, but deep learning offers promising improvements in predictive accuracy. Our model employs regularization techniques, such as dropout and early stopping, to enhance generalization, while also analyzing key features, including isoelectric point and aromaticity, which influence epitope recognition. Results indicate an overall accuracy of 82% in predicting COVID-19 negative and positive cases, with room for improvement in detecting positive samples. this research demonstrates the applicability of deep learning in epitope mapping, suggesting that such approaches can enhance the speed and precision of vaccine design for emerging pathogens. Future work could incorporate structural data and diverse viral strains to further refine prediction capabilities. ©2024 IEEE.

关键词： COVID-19

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Hardware-Efficient Ultrasonic Entrance Counting: Comparing Different machine learning Approaches 26

Hardware-Efficient Ultrasonic Entrance Counting: Comparing D...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Langer, Tim Waschneck, Bernd Partzsch, Johannes Kelber, Florian Mayr, Christian Infineon Technol Dresden GmbH Co KG Dresden Germany Fraunhofer Inst Integrated Circuits Div Engn Adapt Syst Dresden Dresden Germany Tech Univ Dresden Chair Highly Parallel VLSI Syst & Neuro Microelec Dresden Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

In this work, the classification of walking direction based on ultrasonic signals has been examined for entrance counting. Feed-forward and recurrent neural network architectures as well as simpler machine learning techniques have been investigated and compared with classical signal processing techniques. Using only a single ultrasonic receiver, the focus was set on the development of a hardware-efficient system concept. Different ultrasonic measurement methods in time and frequency domain have been compared with the perspective of a holistic energy optimization. the analysis of the system's hardware efficiency was completed by an estimation of algorithmic latency, energy and storage consumption based on the arithmetic of the classification algorithms. All algorithms showed an estimated energy consumption of less than 10 mu J for a single inference on a state-of-the-art implementation of an ARM (R) Cortex (R) M4F micro-controller, which was found to be negligible compared to the energy of the measurement principle. Compared to other sensor types and multi-sensor systems, a state-of-the-art test accuracy of 99.72% could be achieved for differentiating between the two entrance directions of a present person and the absence of a person.

关键词： Entrance Counting Ultrasound machine learning Edge Computing Low Power TinyML Smart Buildings

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Localization of False data Injection Attacks in Smart Grids: A Deep learning Framework Based on SAE-CNN 4

Localization of False Data Injection Attacks in Smart Grids:...

引用

4th international conference on Electrical Engineering and Control Science, IC2ECS 2024

作者： Jiang, Wenlong Gao, Wengen Zhu, Guangyao Sun, Wanjun Anhui Polytechnic University School of Electrical Engineering Wuhu China Hangzhou Jibao Electric Group Co. Ltd Hangzhou China

ISBN: (纸本)9798331510107

the widespread adoption of smart grids has greatly enhanced grid operation and management through automation, real-time monitoring, and data collection capabilities. However, the open and distributed architecture of smart grids exposes them to various cyber threats, particularly False data Injection Attacks (FDIA). FDIAs manipulate sensor data to disrupt grid operations, potentially causing erroneous state estimations and flawed scheduling decisions, which can compromise grid security and stability. To address this challenge, this paper introduces SAE-CNN, a hybrid method combining Sparse Autoencoder (SAE) and Convolutional Neural Network (CNN) for effective detection and localization of false data injection attacks. the approach begins with a sparse autoencoder for feature learning, extracting anomalous patterns from grid operation data to enable efficient compression and anomaly detection. the extracted features are then fed into a convolutional neural network, leveraging its strong spatial pattern recognition capabilities to accurately pinpoint the sources of false data injections. Experimental results demonstrate that the SAE-CNN method achieves high accuracy and robustness in identifying attack nodes, significantly enhancing the smart grid's defense against false data injection attacks. © 2024 IEEE.

关键词： Convolutional neural networks

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the Face Sync - Smart Attendance System 4

The Face Sync - Smart Attendance System

引用

4th international conference on Expert Clouds and Applications, ICOECA 2024

作者： Asha, P. Bekkem, Chakradhar Reddy Priya, Y Soni Mathivanan, G. Grace, L. K. Joshila Srinivasan, N. Sathyabama Institute of Science and Technology Computer Science and Engineering Chennai India

ISBN: (纸本)9798350385793

the existing attendance marking in various institutions, including schools and offices, is fraught with various challenges. Traditional methods, such as manual data collection through individual calls, are time-consuming and prone to errors. However, advancements in technology offer automated solutions that streamline the process and ensure accurate record-keeping. In this context, this study proposes the implementation of 'the Face Sync-Smart Attendance Marking System, ' which leverages cutting-edge technologies for face identification and analysis. Developed using deep learning algorithms, this system effectively recognizes faces by analyzing gathered data. the recorded attendance data is then transmitted to the host via email for further processing. the proposed system is implemented using Python programming language and utilizes libraries such as CV2, Face recognition, and Smtplib to achieve its functionality. this innovative approach promises to revolutionize attendance management, enhancing efficiency and reliability across various organizational settings. © 2024 IEEE.

关键词： Adversarial machine learning

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Analysis of Bangla Transformation of Sentences Using machine learning 1

引用

4th international conference on Deep learning, Artificial Intelligence and Robotics, ICDLAIR 2022

作者： Das, Rajesh Kumar Sammi, Samrina Sarkar Kobra, Khadijatul Ajmain, Moshfiqur Rahman khushbu, Sharun Akter Noori, Sheak Rashed Haider Daffodil International University Dhaka1341 Bangladesh

ISBN: (数字)9783031303968

ISBN: (纸本)9783031303951

In many languages, various language processing tools have been developed. the work of the Bengali NLP is getting richer day by day. Sentence pattern recognition in Bangla is a subject of attention. Additionally, our motivation was to work on implementing this pattern recognition concept into user-friendly applications. So, we generated an approach where a sentence (sorol, jotil and jougik) can be correctly identified. Our model accepts a Bangla sentence as input, determines the sentence construction type, and outputs the sentence type. the most popular and well-known six supervised machine learning algorithms were used to classify three types of sentence formation: Sorol Bakko (simple sentence), Jotil Bakko (complex sentence) and Jougik Bakko(compound sentence). We trained and tested our dataset, which contains 2727 numbers of data from various sources. We analyzed our dataset and got accuracy, precision, recall, f1-score and confusion matrix. We get the highest accuracy with the decision tree classifier, which is 93.72%. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Natural language processing systems

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Plant Disease Detection Using machine learning Approaches: A Review 4th

Plant Disease Detection Using Machine Learning Approaches: A...

引用

4th international conference on data, Engineering, and Applications, IDEA 2022

作者： Saraf, Puja Dipak Patil, Jayantrao Bhaurao Department of Computer Engineering R. C. Patel Institute of Technology Shirpur425405 India

ISBN: (纸本)9789819700363

Agriculture plays an important role in the country’s development, and it is the key source of food for the population of the world. But in developing countries like India, Pakistan and Nepal, the condition of the agriculture sector is getting worse day by day. there are many reasons behind it, but one of the reasons is the degeneration of crops by pests and disease. Agriculture solely depends on the biological health of the seeds, crops, information on weather conditions soils and biotic features liable for the crop diseases. Robust diagnostic methods are accomplished by dispensation and scrutinizing large quantities of records near get additional consistent material and ample more accurate predictions remain vital. Deep learning, image processing, soft computing and data mining are likely to play a vital role in recognizing plant diseases. this paper aims to review existing studies and investigations using the recent practices to solve a variety of agricultural problems. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Song, Danming Gao, Yipeng Yan, Junkai Sun, Wei Zheng, Wei-Shi Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Sun Yat Sen Univ Sch Elect & Informat Technol Guangzhou Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Self-supervised contrastive learning methods have shown promising transferability in pretraining by maximizing the mutual information between two cropped regions as views from the same image. In order to effectively extract mutual information between views, the cropped regions need to be the same instance as prior hypothesis. However, the data collected in general scenes usually have multiple instances, so the two cropped regions probably contain different instances which will mislead the contrastive learning process. In this paper, we make the first attempt to exploit the spatial position relationships of the two cropped regions in self-supervised contrastive learning with images that include multiple instances. then, we propose an effective method called Space-correlated Contrastive learning (SpaceCL). Specifically, given two randomly cropped regions as contrastive pairs from the same image, we implement self-supervised contrastive learning by optimizing a space correspondence contrastive similarity loss. As a result, our method achieves state-of-the-art performance and remarkably outperforms other counterparts when pretrained on the COCO dataset of which images contain multiple instances. Experiments show our method outperforms ReSim with 2.6%AP on PASCAL VOC object detection, 0.8%AP(bb) and 0.6%AP(mk) on COCO object detection and instance segmentation, 1.3%AP(mk) on Cityscapes instance segmentation.

关键词： learning systems Representation learning Image segmentation Semantics Object detection data mining Task analysis

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