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International Conference on Industrial engineering and Applications (ICIEA)

作者： Anas Fattouh Markus Bohlin Daniel Sundmark Division of Computer Science and Software Engineering Mälardalen University Västerås Sweden

A quarry site is used to produce gravel with different granularities based on the customer's needs. The production process consists of different stages that need to be planned in order to achieve high productivity and low cost. During the operation of the quarry site, the operating parameters change, and the site productivity could be affected, therefore, a replanning of the production is needed. This paper presents a real-time optimization model for production planning in a quarry site. The optimization model consists of three modules, a productivity monitoring module, a simulation model, and an optimization algorithm. The productivity monitoring module produces the site productivity based on the current operating parameters of the quarry site. Once the site productivity reaches a specific threshold, the optimization algorithm is run on an updated simulation model to find the optimal values of the site parameters. The proposed real-time optimization model is applied to a real quarry site simulated on a high-fidelity simulator. The case study shows that the proposed optimization model can effectively improve the productivity of the quarry site and hence reduces the wastes and improve environmental sustainability.

关键词： Productivity Analytical models Digital twin Production planning Green products Industrial engineering Real-time systems

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An Intelligent Technique for the Effective Prediction of Parkinson Disease

An Intelligent Technique for the Effective Prediction of Par...

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

作者： Sawera Tariq Madiha Qadeer Attique Ur Rehman Sabeen Javed Tahir Muhammad Ali Azka Mir Suresh Singh Department of Software Engineering University of Sialkot Sialkot Pakistan Department of Computer Science GULF University for Sciences and Technology Mubarak Al-Abdullah Kuwait Division of Research and Development Lovely Professional University Phagwara India KKR and KSR Institute of Technology and Sciences Guntur Andhra Pradesh India

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

Parkinson is a condition of the nervous system that is linked to breakdown of basal ganglia of the brain. This disorder is responsible for affecting the neurological system and other body parts that are nerve-controlled. Its symptoms vary from person to person, however common symptoms include Tremor, Slow Movement and Stiffness in Muscles, Impaired Posture, Speech Changes and Loss of Automatic Movements. The main cause of this disease is not known, that is why it is not curable and has no proven prevention yet. This research concerns the application of machine learning classifiers including KNN, Logistic Regression, Decision Tree, Naive Bayes and Random Forest for the detection of Parkinson's disease. We are using a Parkinson disease dataset using a machine learning algorithm for detection to determine the performance in terms of accuracy. In a nut shell, the Random Forest classifier, without using SMOTE, provided the highest accuracy among the models considered. However, this result and existing results as well are based on imbalanced class. This research is contributing valuably for the convergence of machine learning and healthcare for effective predictions of this disease using Smote for balancing out same dataset classes with accuracy of 98.7%. The results hold significance for future modifications in medical diagnostics for more accurate and effective detection methodologies.

关键词： Logistic regression Accuracy Machine learning algorithms Parkinson's disease Prevention and mitigation Supervised learning Nearest neighbor methods Nervous system Bayes methods Random forests

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Corrigendum to ‘Device-to-device communication in 5G heterogeneous network based on game-theoretic approaches: A comprehensive survey’ [J. Netw. Comput., Volume 238, June 2025, 104152] (Journal of Network and computer Applications (2025) 238, (S1084804525000499), (10.1016/***.2025.104152))

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Journal of Network and computer Applications 2025年 239卷

作者： Ahmad, Rana Zeeshan Rizwan, Muhammad Yousuf, Muhammad Jehanzaib Khan, Mohammad Bilal Almadhor, Ahmad Gadekallu, Thippa Reddy Abbas, Sidra Department of Information Technology University of Sialkot Sialkot Pakistan College of Science and Engineering University of Derby United Kingdom Software Research Institute Athlone Institute of Technology Ireland School of Electronic Engineering Xidian University Xidian China Department of Computer Engineering and Networks College of Computer and Information Science Jouf University Sakaka Saudi Arabia The College of Mathematics and Computer Science Zhejiang A&F University Hangzhou311300 China Division of Research and Development Lovely Professional University Phagwara India Centre of Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India Department of Computer Science COMSATS University Islamabad Sahiwal Pakistan

The authors regret the mistake in affiliation of Author Sidra Abbas. The correct affiliation is as follows: Sidra AbbasDepartment of computer Science, COMSATS University Islamabad, Sahiwal, Pakistansidraabbas@***:sidr...

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Machine learning models for automatic labeling: A systematic literature review 15

Machine learning models for automatic labeling: A systematic...

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15th International Conference on software Technologies, ICSOFT 2020

作者： Fredriksson, Teodor Bosch, Jan Olsson, Helena Holmstrm Department of Computer Science and Engineering Division of Software Engineering Chalmers University of Technology Gothenburg Sweden Department of Computer Science and Media Technology Malm University Malm Sweden

ISBN: (纸本)9789897584435

Automatic labeling is a type of classification problem. Classification has been studied with the help of statistical methods for a long time. With the explosion of new better computer processing units (CPUs) and graphical processing units (GPUs) the interest in machine learning has grown exponentially and we can use both statistical learning algorithms as well as deep neural networks (DNNs) to solve the classification tasks. Classification is a supervised machine learning problem and there exists a large amount of methodology for performing such task. However, it is very rare in industrial applications that data is fully labeled which is why we need good methodology to obtain error-free labels. The purpose of this paper is to examine the current literature on how to perform labeling using ML, we will compare these models in terms of popularity and on what datatypes they are used on. We performed a systematic literature review of empirical studies for machine learning for labeling. We identified 43 primary studies relevant to our search. From this we were able to determine the most common machine learning models for labeling. Lack of unlabeled instances is a major problem for industry as supervised learning is the most widely used. Obtaining labels is costly in terms of labor and financial costs. Based on our findings in this review we present alternate ways for labeling data for use in supervised learning tasks. Copyright © 2020 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

关键词： Program processors

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Improving WiFi Fingerprint Database With Irregular Distribution of Reference Points Using Interpolation Methods

Improving WiFi Fingerprint Database With Irregular Distribut...

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International Conference on Advanced Technologies for Communications, ATC

作者： Trung Vu-Thanh Jing He Ninh Duong-Bao Seon-Woo Lee Luong Nguyen Thi Khanh Nguyen-Huu College of Computer Science and Electronic Engineering Hunan University Changsha China Faculty of Mathematics and Informatics Dalat University Dalat Vietnam Division of Software Hallym University Chuncheon Korea Faculty of Information Technology Dalat University Dalat Vietnam Department of Electronics and Telecommunications Dalat University Dalat Vietnam

ISBN: (数字)9798350353983

ISBN: (纸本)9798350353990

Using WiFi Fingerprinting for indoor positioning is becoming increasingly popular due to its cost-effectiveness. However, to ensure the system can operate with high accuracy and stability, this technique requires a burden of data collection tasks from a dense distribution of reference points (RPs). To decrease the cost of data collection, as well as to improve the WiFi fingerprint database with irregular distribution of RPs, this study applies and compares different interpolation methods. Evaluating these methods in a public dataset including multiple sub-datasets with irregular distributions of RPs, the results highlight the Linear Barycentric interpolation (LB) as the most favorable method due to its low complexity and stable performance. The LB-based fingerprint database achieves an average interpolation error of less than 3 dBm compared to the original one. When applying the interpolated database to positioning tasks, it can help reduce acquisition costs by up to 50% while improving positioning accuracy by up to 11%. The study reveals the feasibility of interpolation in optimizing WiFi fingerprint data collection, especially in large and complex environments where regular distribution of RPs cannot be deployed.

关键词： Interpolation Costs Accuracy Databases Fingerprint recognition Data collection Reliability Computational complexity Wireless fidelity Floors

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Enhancing Sovereign Allocation of Resources in Cloud Milieus Using a Causal Dilated Geometric Algebra Approach for Dynamic Scalability

Enhancing Sovereign Allocation of Resources in Cloud Milieus...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Chandrasekhar Rao Katru Sathish Srinivasan Mohan Kumar T G Sunita Yadwad G. Satish Ramya Maranan Lead Software Engineer Indian Land SC USA Oracle Cloud Infrastructure Division Machine Learning & AI Development San Francisco Bay Area California USA Department of Information Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru Karnataka Computer science and Engineering Faculty of Engineering and Technology Parul institute of Engineering and Technology Parul University Post Limda Gujarat Department of Electrical and Electronics Engineering Sree Dattha Institute of Engineering and Science Sheriguda Telangana Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Cloud environments utilize availability and usage of resources as crucial components in order to produce scalability, reliability and low cost. Such resource capabilities require autonomous systems that can handle these resources dynamically in spite of fluctuating workloads and service delivery requirements. Static models of resource management suffer from scalability and inability to react on the fly and accurately predict the demand thus causing Service Level Agreement (SLA) breaches and affecting the Quality of Service (QoS) as well as energy consumption profile. Advanced and elaborative cloud workloads require an intelligent system that can analyse the temporal and spatial patterns without impacting the overall system's performance significantly. Inspired by these challenges, this research proposes a Causal Dilated Geometric Algebra Convolutional Transformer Network (CATNet) along with the Prairie-Dog Optimizer (P-DogO), referred to as CAT-DogNet to learn and predict the dynamic scalability. CATNet integrates Causal Dilated Convolutional Networks in time domain for temporal structure capturing and Geometric Algebra Transformer for spatial resource coding. P-DogO adjusts weight parameters and losses for resource allocation, enhancing them depending on a work environment. A set of comprehensive assessments demonstrate a 0.85% SLA violation rate, average response time of less than 10ms, and overall 12.3% less energy consumption than baseline methods. The system had favourable predictability rates over 99.4% for the resource demand estimate. CAT-DogN et presents a sound and agnostic architecture for autonomous resource allocation in cloud environment, which indeed extend the state of art by considering flexibility, power consumption and QoS compliance.

关键词： Cloud computing Energy consumption Algebra Scalability Quality of service Transformers Real-time systems Resource management Time factors Service level agreements

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Transformer-based Compound Correlation Miner for Smart Home Anomaly Detection

Transformer-based Compound Correlation Miner for Smart Home ...

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IEEE International Conference on Cloud Networking (CLOUDNET)

作者： Andrew D’Angelo Chenglong Fu Xiaojiang Du Paul Ratazzi Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA Department of Software and Information Systems University of North Carolina at Charlotte Charlotte NC USA Information Warfare Division Air Force Research Laboratory Rome NY USA

IoT-enabled smart homes can be double-edged swords. On one side, the convenience and efficiency brought about by smart device integrations can improve the standard of living dramatically. However, on the other side, the prevalent interconnectivity among home devices can drastically increase the potential risk of attack. Therefore, in order to reduce the possibility of a successful attack we propose a complex correlation-based anomaly detection system powered by intricate two-to-one correlations. Through the use of a state-of-the-art transformer model, we present a novel correlation mining mechanism that leverages the power of attention weights to develop an understanding of the underlying correlations that exist between IoT events in a smart home environment. Using this knowledge, we use a special validation algorithm to verify 52 two-to-one correlations in our system. Furthermore, we simulate four distinct attack scenarios and attain an average detection accuracy and recall of 96.59% and 97.38% respectively. Our results indicate that our method is effective at identifying a range of IoT attacks and successfully demonstrates the capabilities of IoT correlations.

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AI-LCE: Adaptive and Intelligent Life Cycle engineering by applying digitalization and AI methods-An emerging paradigm shift in Life Cycle engineering 28

AI-LCE: Adaptive and Intelligent Life Cycle Engineering by a...

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28th CIRP Conference on Life Cycle engineering, LCE 2021

作者： Sakao, Tomohiko Funk, Peter Matschewsky, Johannes Bengtsson, Marcus Ahmed, Mobyen Uddin Division of Environmental Technology and Management Department of Management and Engineering Linköping University LinköpingSE-581 83 Sweden Division of Intelligent Future Technologies School of Innovation Design and Engineering Mälardalen University VästeråsSE-122 20 Sweden Operations Volvo Construction Equipment Eskilstuna Sweden Division of Computer Science and Software Engineering School of Innovation Design and Engineering Mälardalen University VästeråsSE-122 20 Sweden

This paper presents a vision for a much-needed paradigm shift in Life Cycle engineering (LCE), which is termed Adaptive and Intelligent Life Cycle engineering (AI-LCE). To do so, interdisciplinary analysis of literature in domains of AI and LCE is performed. Needed concepts and methods are described: key enabling technologies are Artificial Intelligence (AI), the Internet of Things, and data lakes, which are becoming cost-effective and increasingly implemented in industry. Both artificial and human intelligence are used in combination. Its advantages compared with the conventional LCE include shorter time for changing activities in the lifecycle and the accuracy of changes made. © 2021 Elsevier B.V.. All rights reserved.

关键词： Internet of things

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MedFACT: Modeling Medical Feature Correlations in Patient Health Representation Learning via Feature Clustering

arXiv

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arXiv 2022年

作者： Ma, Xinyu Chu, Xu Wang, Yasha Yu, Hailong Ma, Liantao Tang, Wen Zhao, Junfeng Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China Department of Computer Science Tsinghua University Beijing China School of Computer Science Peking University Beijing China National Engineering Research Center of Software Engineering Peking University Beijing China Division of Nephrology Peking University Third Hospital Beijing China

In healthcare prediction tasks, it's essential to exploit the correlations between medical features and learn better patient health representations. Existing methods try to estimate feature correlations only from data, or increase the quality of estimation by introducing task-specific medical knowledge. However, such methods either are difficult to estimate the feature correlations due to insufficient training samples, or cannot be generalized to other tasks due to reliance on specific knowledge. There are medical researches revealing that not all the medical features are strongly correlated. Thus, to address the issues, we expect to group up strongly correlated features and learn feature correlations in a group-wise manner to reduce the learning complexity without losing generality. In this paper, we propose a general patient health representation learning framework MedFACT. We estimate correlations via measuring similarity between temporal patterns of medical features with kernel methods, and cluster features with strong correlations into groups. The feature group is further formulated as a correlation graph, and we employ graph convolutional networks to conduct group-wise feature interactions for better representation learning. Experiments on two real-world datasets demonstrate the superiority of MedFACT. The discovered medical findings are also confirmed by literature, providing valuable medical insights and explanations. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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CRC-Model for Word Attributes Classification in Chinese Diagnostic Report 10th

CRC-Model for Word Attributes Classification in Chinese Diag...

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10th International Conference on Frontier Computing, FC 2020

作者： Zhang, Jitong Jiang, Huiyan Huang, Liangliang Pei, Yan Sino-Dutch Biomedical and Information Engineering College Northeastern University Shenyang110819 China Software College Northeastern University Shenyang110819 China Computer Science Division The University of Aizu Fukushima Aizu-Wakamatsu965-8580 Japan

ISBN: (纸本)9789811601149

Chinese diagnostic report has enormous potential in doctor coaching and text structuring. With the development of natural language processing, the tasks on Chinese diagnostic report have attracted more and more scholars’ attentions. These tasks contain classifying words in texts as word attributes classification task. The deep learning methods, e.g., convolutional neural network (CNN) and recurrent neural network (RNN), can capture features through word embedding in diagnostic report and have shown a powerful performance to solve name entity recognition (NER) tasks. Therefore, this paper introduces a new method named convolution recurrent classification model (CRC-Model), which can achieve better performance than previous methods. The proposed method is sufficiently compared with three state-of-the-art NER models and two variants of the proposed method, which can obtain a total F1 of 94.33%. Meantime, in terms of training efficiency, the proposed method shortens the 25% training time compared with other methods. The experimental results and comparisons demonstrate that the CRC-Model does not only achieve better results than other methods, but also improve training efficiency in some extent. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolution

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