检索结果-内蒙古大学图书馆

On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Rebmann, Adrian Schmidt, Fabian David Glavaš, Goran van der Aa, Han SAP Signavio SAP SE Dietmar-Hopp-Allee 16 Walldorf Baden-Württemberg69190 Germany Center for Artificial Intelligence and Data Science University of Würzburg Sanderring 2 Bavaria Würzburg97070 Germany Faculty of Computer Science University of Vienna Währinger Str. 29 Vienna1090 Austria

Large language models (LLMs) have shown to be valuable tools for tackling process mining tasks. Existing studies report on their capability to support various data-driven process analyses and even, to some extent, that they are able to reason about how processes work. This reasoning ability suggests that there is potential for LLMs to tackle semantics-aware process mining tasks, which are tasks that rely on an understanding of the meaning of activities and their relationships. Examples of these include process discovery, where the meaning of activities can indicate their dependency, whereas in anomaly detection the meaning can be used to recognize process behavior that is abnormal. In this paper, we systematically explore the capabilities of LLMs for such tasks. Unlike prior work, which largely evaluates LLMs in their default state, we investigate their utility through both in-context learning and supervised fine-tuning. Concretely, we define five process mining tasks requiring semantic understanding and provide extensive benchmarking datasets for evaluation. Our experiments reveal that while LLMs struggle with challenging process mining tasks when used out of the box or with minimal in-context examples, they achieve strong performance when fine-tuned for these tasks across a broad range of process types and industries. © 2025, CC BY.

关键词：

ADOD: Adaptive Density Outlier Detection

学校读者我要写书评

暂无评论

ADOD: Adaptive Density Outlier Detection

IEEE International Conference on data Mining (ICDM)

作者： Li Qian Jing Qian Xin Sun Wengang Guo Christian Böhm Institute of Informatics Ludwig Maximilian University of Munich Munich Germany Faculty of Computer Science University of Vienna Vienna Austria Carbon Neutral R&D Center China Railway Hi-Tech Industry Corporation Limited Beijing China Faculty of Data Science City University of Macau Macau China College of Electronic and Information Engineering Tongji University Shanghai China

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

Outlier detection plays a dual role in data analysis: cleansing data to optimize the performance of downstream tasks and identifying potentially rare valuable events or patterns. Proximity-based methods, which are independent of data distribution assumptions, are plagued by parameter selection and performance challenges when handling data with varying densities. This study proposed a novel unsupervised algorithm named Adaptive Density Outlier Detection (ADOD) to address these challenges. The core innovation of ADOD involves two main aspects: adaptive neighborhood boundaries and density consistency scoring. First, instead of relying on a predefined fixed radius, ADOD employs perplexity to calculate the local scale of each data point. It then dynamically adjusts the neighborhood boundaries according to this scale to adapt to data with varying densities. Second, ADOD estimates local density using a mutual neighbor graph and combines the density differences between data points and their neighbors to compute outlier scores, effectively distinguishing outliers that significantly deviate from their surroundings. This study evaluated ADOD on one synthetic and 32 real datasets, and compared it with 14 classical and state-of-the-art algorithms from different categories. Extensive experimental results demonstrated the superior performance of ADOD, achieving the highest average accuracy across ROC, P@N, and AP metrics. This study promotes the development of outlier detection techniques and expands their potential for real-time applications.

关键词： Measurement Technological innovation Accuracy data analysis Sensitivity analysis Heuristic algorithms Memory management Nearest neighbor methods Real-time systems Anomaly detection

A Deep Learning Approach Towards Student Performance Prediction in Online Courses: Challenges Based on a Global Perspective

学校读者我要写书评

暂无评论

A Deep Learning Approach Towards Student Performance Predict...

Information Technology (ACIT)

作者： Abdallah Moubayed MohammadNoor Injadat Nouh Alhindawi Ghassan Samara Sara Abuasal Raed Alazaidah School of Computing and Augmented Intelligence Arizona State University Gilbert Arizona USA Electrical and Computer Engineering Department Lebanese American University Byblos Lebanon Electrical and Computer Engineering Department Western University London Ontario Canada Data Science and Artificial Intelligence Department Faculty of Information Technology Zarqa University Zarqa Jordan Faculty of Science and Information Technology Jadara University Irbid Jordan Computer Science Department Faculty of Information Technology Zarqa University Zarqa Jordan Software Engineering Department Faculty of Information Technology Zarqa University Zarqa Jordan

Analyzing and evaluating students' progress in any learning environment is stressful and time consuming if done using traditional analysis methods. This is further exasperated by the increasing number of students due to the shift of focus toward integrating the Internet technologies in education and the focus of academic institutions on moving toward e-Learning, blended, or online learning models. As a result, the topic of student performance prediction has become a vibrant research area in recent years. To address this, machine learning and data mining techniques have emerged as a viable solution. To that end, this work proposes the use of deep learning techniques (CNN and RNN-LSTM) to predict the students' performance at the midpoint stage of the online course delivery using three distinct datasets collected from three different regions of the world. Experimental results show that deep learning models have promising performance as they outperform other optimized traditional ML models in two of the three considered datasets while also having comparable performance for the third dataset.

关键词：

An artificial intelligence-based decision support system for early and accurate diagnosis of Parkinson's Disease

学校读者我要写书评

暂无评论

Decision Analytics Journal 2024年 10卷

作者： T.R., Mahesh V., Vinoth Kumar Bhardwaj, Rajat Khan, Surbhi B. Alkhaldi, Norah Victor, Nancy Verma, Amit Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore India School of Information Technology and Engineering VIT University Tamil Nadu Vellore India Department of Computer Science and Engineering RV University Bengaluru India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Department of Data Science School of Science Engineering and Environment University of Salford Manchester United Kingdom Department of Computer Science King Faisal University Saudi Arabia School of Information Technology and Engineering Vellore Institute of Technology Tamilnadu Vellore India University Centre for Research and Development Department of Computer Science Chandigarh University Punjab Gharuan Mohali India

People with Parkinson's Disease (PD) might struggle with sadness, restlessness, or difficulty speaking, chewing, or swallowing. A diagnosis can be challenging because there is no specific PD test. It is diagnosed by doctors using a neurological exam and a medical history. This study proposes several Machine Learning (ML) algorithms to predict PD. These ML algorithms include K-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting algorithms (XGBoost), and their ensemble methods using publicly available PD dataset with 195 instances. The ML algorithms are used to predict and classify PD using homogeneous XGBoost ensemble techniques with reduced amount of entropy. Synthetic Minority Oversampling Technique (SMOTE) is utilized to handle imbalanced data, and 10-fold cross-validation is employed for evaluation. The results show that the homogeneous XGBoost-Random Forest outperforms other ML methods with 98% accuracy and Matthew's correlation coefficient value 0.93. © 2023 The Authors

关键词： Artificial intelligence Diagnosis and classification Entropy Machine learning Parkinson's Disease Predictive analytics

IoT for Water Quality Categorization

学校读者我要写书评

暂无评论

IoT for Water Quality Categorization

International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Hermantoro Sastrohartono Andreas Wahyu Krisdiarto Arief Ika Uktoro Reza Rahutomo Teddy Suparyanto Bens Pardamean Agricultural Engineering Department Faculty of Agricultural Technology Institute of Agriculture STIPER Yogyakarta Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

In agricultural water research, the adoption of Internet of Things (IoT) technology has emerged as a pivotal approach for large-scale data collection. Water availability in the context of water quality is very important, both for domestic and industrial purposes. For domestic purposes, drinking water and bathing water are separated. Meanwhile, for the palm oil industry, boiler filler is differentiated from additional process water (dilution water). Water quality parameters can be assessed from turbidity and Total Dissolve Solid (TDS). Measurements using measuring instruments separately and repeatedly require significant energy, time, and costs. This research was conducted with the primary objective of presenting a novel method for categorizing water quality with the approach of IoT sensor technology. The research methodology entailed the utilization of an integrated IoT water sensors system in conjunction with manual water categorization. The methods consist of (1) system design, (2) design and installation of sensor and IoT-based microcontrollers, and (3) accuracy and precision testing compared with laboratory measurements. The precision of the integrated IoT water sensors was assessed through a dedicated sensor precision test, resulting in an accuracy rate of 94.4% for the turbidity sensor and 97.5% for the TDS sensor. Notably, this approach successfully discriminated drinking water with valid categorization, while other water types, including groundwater, water with tea, and water with coffee, yielded null categorization results.

关键词：

Cyclic Quantum Teleportation of Two-Qubit Entangled States by using Six-Qubit Cluster State and Six-Qubit Entangled State

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Slaoui, A. El Kirdi, M. Ahl Laamara, R. Alabdulhafith, M. Chelloug, S.A. Abd El-Latif, A.A. LPHE-Modeling and Simulation Faculty of Sciences Mohammed V University in Rabat Rabat Morocco Centre of Physics and Mathematics CPM Faculty of Sciences Mohammed V University in Rabat Rabat Morocco Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia EIAS Data Science Lab College of Computer and Information Sciences Center of Excellence in Quantum and Intelligent Computing Prince Sultan University Riyadh11586 Saudi Arabia Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom32511 Egypt

Cyclic quantum teleportation schemes requires at least the existence of three collaborators acting all as senders and receivers of quantum information, each one of them has an information to be transmitted to the next neighbour in a circular manner. Here, new cyclic quantum teleportation scheme is proposed for perfectly transmitting cyclically three arbitrary unknown two-qubit states (α, β and γ) among the three collaborators. In this scheme, Alice can send to Bob the quantum information contained in her two-qubit state α and receive from Charlie the quantum information contained in the two-qubit state in his possession γ and similarly, Bob can transmit to Charlie the quantum information contained in his two-qubit state β through a quantum channel of twelve-qubit state consisting of a six-qubit cluster state and a six-qubit entangled state by sequentially and cyclically performing Bell state measurements. Subsequently, each one of the three participants can afterwards retrieve his own desired two-qubit state using classical channel and by performing appropriate unitary Pauli operators and we have shown that our proposed scheme performs efficiently. © 2024, CC0.

关键词： Qubits

A Machine Vision Approach for Classification of Skin Cancer Using Hybrid Texture Features

学校读者我要写书评

暂无评论

Computational Intelligence and Neuroscience 2022年第0期2022卷 4942637页

作者： Zareen, Syeda Shamaila Guangmin, Sun Li, Yu Kundi, Mahwish Qadri, Salman Qadri, Syed Furqan Ahmad, Mubashir Khan, Ali Haider Faculty of Information Technology Beijing University of Technology Beijing100124 China Department of Informatics University of Leicester Leicester United Kingdom Department of Computer Science Muhammad Nawaz Shareef University of Agriculture Multan66000 Pakistan Research Center for Healthcare Data Science Zhejiang Lab Hangzhou311121 China Faculty of Computer Science and Technology University of Lahore Sargodha Pakistan Department of Computer Science School of Systems & Technology University of Management and Technology Lahore Pakistan

The main purpose of this study is to observe the importance of machine vision (MV) approach for the identification of five types of skin cancers, namely, actinic-keratosis, benign, solar-lentigo, malignant, and nevus. The 1000 (200 × 5) benchmark image datasets of skin cancers are collected from the International Skin Imaging Collaboration (ISIC). The acquired ISIC image datasets were transformed into texture feature dataset that was a combination of first-order histogram and gray level co-occurrence matrix (GLCM) features. For the skin cancer image, a total of 137,400 (229 × 3 x 200) texture features were acquired on three nonover-lapping regions of interest (ROIs). Principal component analysis (PCA) clustering approach was employed for reducing the dimension of feature dataset. Each image acquired twenty most discriminate features based on two different approaches of statistical features such as average correlation coefficient plus probability of error (ACC + POE) and Fisher (Fis). Furthermore, a correlation-based feature selection (CFS) approach was employed for feature reduction, and optimized 12 features were acquired. Furthermore, a classification algorithm naive bayes (NB), Bayes Net (BN), LMT Tree, and multilayer perception (MLP) using 10 K-fold cross-validation approach were employed on optimized feature datasets and the overall accuracy achieved by MLP is 97.1333%. © 2022 Syeda Shamaila Zareen et al.

关键词： computer vision

Disentangling degree and tie strength heterogeneity in egocentric social networks

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Heydari, Sara Iñiguez, Gerardo Kertész, János Saramäki, Jari Department of Computer Science Aalto University School of Science Aalto00076 Finland Faculty of Information Technology and Communication Sciences Tampere University Tampere33720 Finland Department of Network and Data Science Central European University Vienna1100 Austria Centro de Ciencias de la Complejidad Universidad Nacional Autonóma de México Ciudad de México04510 Mexico Complexity Science Hub Vienna1080 Austria

The structure of personal networks reflects how we organise and maintain social relationships. The distribution of tie strengths in personal networks is heterogeneous, with a few close, emotionally intense relationships and a larger number of weaker ties. Recent results indicate this feature is universal across communication channels. Within this general pattern, there is a substantial and persistent inter-individual variation that is also similarly distributed among channels. The reason for the observed universality is yet unclear-one possibility is that people's traits determine their personal network features on any channel. To address this hypothesis, we need to compare an individual's personal networks across channels, which is a non-trivial task: while we are interested in measuring the differences in tie strength heterogeneity, personal network size is also expected to vary a lot across channels. Therefore, for any measure that compares personal networks, one needs to understand the sensitivity with respect to network size. Here, we study different measures of personal network similarity and show that a recently introduced alter-preferentiality parameter and the Gini coefficient are equally suitable measures for tie strength heterogeneity, as they are fairly insensitive to differences in network size. With these measures, we show that the earlier observed individual-level persistence of personal network structure cannot be attributed to network size stability alone, but that the tie strength heterogeneity is persistent too. We also demonstrate the effectiveness of the two measures on multichannel data, where tie strength heterogeneity in personal networks is seen to moderately correlate for the same users across two communication channels (calls and text messages). Copyright © 2024, The Authors. All rights reserved.

关键词： Communication channels (information theory)

Stochastic orderings between two finite mixture models with inverted-Kumaraswamy distributed components

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Bhakta, Raju Kundu, Pradip Kayal, Suchandan Alizadeh, Morad Department of Mathematics National Institute of Technology Rourkela Odisha Rourkela769008 India School of Computer Science and Engineering XIM University Odisha Bhubaneswar India Department of Statistics Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr75169 Iran

In this paper, we consider two finite mixture models (FMMs), with inverted-Kumaraswamy distributed components’ lifetimes. Several stochastic ordering results between the FMMs have been obtained. Mainly, we focus on three different cases in terms of the heterogeneity of parameters. The usual stochastic order between the FMMs have been established when heterogeneity presents in one parameter as well as two parameters. In addition, we have also studied ageing faster order in terms of the reversed hazard rate between two FMMs when heterogeneity is in two parameters. For the case of heterogeneity in three parameters, we obtain the comparison results based on reversed hazard rate and likelihood ratio orders. The theoretical developments have been illustrated using several examples and *** Codes 60E15, 90B25 Copyright © 2023, The Authors. All rights reserved.

关键词： Stochastic systems