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11th International Symposium on Computing and Networking Workshops, CANDARW 2023

作者： Ueno, Reo Fujiwara, Akihiro Kyushu Institute of Technology Iizuka Graduate School of Computer Science and Systems Engineering Fukuoka820-8502 Japan

ISBN: (纸本)9798350306941

Membrane computing, which is also known as a P system, is a computational model inspired by the activity of living cells. Several P systems, which work in a polynomial number of steps, have been proposed for solving computationally hard problems. However, most of the proposed algorithms use an exponential number of membranes, and reduction of the number of membranes must be considered in order to make a P system a more realistic *** the present paper, we propose an asynchronous P system using branch and bound for solving the minimum Steiner tree. The proposed P system solves the minimum Steiner tree with n vertices and m edges in O(n2) parallel steps or O(2mn2) sequential *** evaluate the number of membranes used in the proposed P system using experimental simulations. Our experimental results show validity and efficiency of the proposed P system. © 2023 IEEE.

关键词： Membranes

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Correspondence Between SWIR and MWIR Images Using Augmentation and Preprocessing for Registration 38

Correspondence Between SWIR and MWIR Images Using Augmentati...

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38th IEEE Region 10 Conference, TENCON 2023

作者： Inoue, Takeru Kibe, Michiya Miyamoto, Ryusuke Graduate School of Science and Technology Meiji University Dept. of Computer Science 1-1-1 Higashimita Tama-ku Kawasaki Japan School of Science and Technology Meiji University Dept. of Computer Science 1-1-1 Higashimita Tama-ku Kawasaki Japan

ISBN: (纸本)9798350302196

Multispectral sensors are used to ensure visibility in various applications. However, when multiple sensors are used for capturing images, a misalignment may occur between the images taken by each sensor unless special care is taken. To correct such misalignments, image registration based on feature matching is conducted. However, the features captured by each sensor differ, thereby complicating the registration process. In this study, we develop an approach to overcome these challenges and to improve the registration accuracy between short-wave infrared and mid-wave infrared (SWIR and MWIR, respectively) images. First, we compare and validate SiLK, a detector-based feature matching method, and LoFTR, a detector-free feature matching method. The results clearly demonstrate the superior accuracy of LoFTR. Moreover, SWIR and MWIR images exhibit a characteristic color inversion according to Kirchhoff' s law. Therefore, by inverting the color of a SWIR image and aligning the color tone between image pairs, we can improve the matching accuracy. Furthermore, by diversifying the color tones of the training data through augmentation, we can handle the domain gap between SWIR and MWIR images, thereby further enhancing the matching accuracy. © 2023 IEEE.

关键词： Infrared radiation

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Analyzing the Effect of Observer Node Addition Strategy on Bitcoin Double-Spending Attack Detection Using Graph Neural Network 24

Analyzing the Effect of Observer Node Addition Strategy on B...

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24th Asia-Pacific Network Operations and Management Symposium, APNOMS 2023

作者： Kang, Changhoon Woo, Jongsoo Hong, James Won-Ki Department of Computer Science and Engineering POSTECH Korea Republic of Graduate School of Information Technology POSTECH Korea Republic of

ISBN: (纸本)9788995004395

Bitcoin transactions are created through the concept called Unspent Transaction Output (UTXO). Users put their own UTXOs as inputs into a transaction for Bitcoin transfer and create multiple outputs, each specifying the recipient’s wallet address and the amount to be sent. UTXO refers to an output that has not been used as an input for any transaction yet and each UTXO can only be used as an input once. However, attempting to use a UTXO more than once is called a double-spending attack. Although double-spending in Bitcoin is ultimately impossible due to the system structure, it can occur when a transaction is deemed confirmed and off-chain goods or services are provided before sufficient transaction finality is guaranteed. We consider an attempt of double-spending attack when a UTXO used as an input in a transaction for payment exists together with another transaction on the Bitcoin network that uses the same UTXO as an input. In previous research, we randomly deployed observer nodes on the Bitcoin network and proposed a method to detect double-spending attacks using transaction data in the memory pool and a graph neural network model. In this paper, we analyze the impact of adding observer nodes to the Bitcoin network on the performance of graph neural network-based Bitcoin double-spending attack detection. We conducted experiments to examine the performance differences among three strategies for adding observer nodes. However, it was difficult to compare clear differences due to the performance degradation of the model caused by the differences in graph structure between datasets. Therefore, we provide an analysis of the causes and suggestions for improvement. Copyright 2023 KICS.

关键词： Graph neural networks

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Evaluation of Machine Learning on Smart Home Data for Prediction of Electrical Energy Consumption

Evaluation of Machine Learning on Smart Home Data for Predic...

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2023 International Conference on computer science, Information technology and Engineering, ICCoSITE 2023

作者： Widianto, Mochammad Haldi Gunawan, Alexander Agung Santoso Heryadi, Yaya Budiharto, Widodo Bina Nusantara University BINUS Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia School of Computer Science Computer Science Department Bina Nusantara University Jl. K.H.Syahdan No. 9 Palmerah Kemanggisan Jakarta11480 Indonesia

ISBN: (纸本)9798350320954

Electrical energy consumption is always increasing, and this causes the supply of electrical energy to be increased to compensate. One solution is to predict electricity energy consumption using Artificial Intelligence (AI) technology in Smart Homes. Several studies' solutions for predicting electrical energy consumption usually focused only on performance but rarely evaluated Machine Learning (ML) by correlation for feature selection and utilized interpretability model. This study uses an ML model for predicting utilization (Linear Regression, Decision Tree, Random Forest, and XGBoost). Then, Feature Selection utilizes correlation to choose the best feature. After that, the interpretability model utilizes Local Interpretable Model-agnostic Explanations (LIME). The results show that XGBoost has the best Root Mean Squared Error (RMSE) value (0.318) with a percentage of the number of train and test data (90/10). After that, by eliminating features that correlate with 0.01, XGBoost improves with an increase of (0.018) to become (0.3). Then from LIME. This work also gets positive feature from XGBoost such as: "Furnance, Well dan Living Room". © 2023 IEEE.

关键词： Forecasting

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Hi-Le and HiTCLe: Ensemble Learning Approaches for Early Diabetes Detection Using Deep Learning and Explainable Artificial Intelligence

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IEEE Access 2024年 12卷 66516-66538页

作者： Shaheen, Ifra Javaid, Nadeem Alrajeh, Nabil Asim, Yousra Aslam, Sheraz Comsats University Islamabad Department of Computer Science Islamabad44000 Pakistan National Yunlin University of Science and Technology International Graduate School of Artificial Intelligence Yunlin Douliu64002 Taiwan King Saud University College of Applied Medical Sciences Department of Biomedical Technology Riyadh11633 Saudi Arabia Limassol3036 Cyprus

Diabetes is a metabolic disease caused by the body's failure to use insulin or break down meals correctly. Every year, an alarming number of new cases of diabetes are recorded. A poor lifestyle and an unfavorable environment are the two main causes of diabetes. If it is not treated at early stages, it becomes a lifelong disease and further leads to failure of important organs such as the kidneys, heart, eyes, and so on. This danger can be decreased with timely and precise identification. Deep Learning (DL) is the best method for illness prediction, as demonstrated by recent developments in DL for clinical use. We have proposed two ensemble learning approaches: blending and hybrid by using the Diabetes Prediction Dataset (DPD), which is a highly imbalanced dataset. The number of diabetic patients in it are 8500 whereas, the number of non-diabetic individuals are 91500. To overcome the class imbalance problem, a Proximity-Weighted Synthetic Oversampling (ProWSyn) technique is implemented. We have proposed a hybrid of highway and LeNet model, named Hi-Le, for early and accurate diabetes detection. Hi-Le model achieves an accuracy of 94%, a F1-Score of 96%, precision score of 94% and recall of 95% and beats its individual models in terms of accuracy, F1-Score, precision and recall. We have also proposed a blending model named HiTCLe using Highway, LeNet, and a Temporal Convolutional Network (TCN) to detect and predict diabetes at an early stage. HiTCLe performs best, beats its individual models, highway, TCN and LeNet, and achieves an accuracy score of 94% and a F1-Score of 94%, whereas individual models achieve an accuracy score between 89% and 91% on 10 epochs. To validate models' results, we have implemented K-Fold Cross Validation (K-FCV). Also, to know the features contributions, we have implemented Shapley Additive eXplanations (SHAP) post processing technique. Both ensemble learning models outperform their individual models in term of accurate diabetes detection

关键词： Forecasting

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Development of Detection Lung Disease Stand Alone System Based on Convolutional Neural Network (CNN) 7

Development of Detection Lung Disease Stand Alone System Bas...

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7th International Conference of computer and Informatics Engineering, IC2IE 2024

作者： Liewarnata, Edgar Caesar Mariana, Yosica Tan, Sofyan Liawatimena, Suryadiputra Astuti, Winda Bina Nusantara University Binus Aso School of Engineering Automotive and Robotics Program Computer Engineering Department Jakarta11480 Indonesia Bina Nusantara University Binus Aso School of Engineering Product Design Program Industrial Engineering Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Master of Computer Science Automotive and Robotics Program Computer Engineering Department Binus Aso School of Engineering Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331505530

Lung disease, especially Tuberculosis (TBC), placed the highest death rate in Indonesia. Tuberculosis (TB) in Indonesia is ranked second after India. Therefore, it is important to reduce or early detection of the lung disease, to prevent this disease and speed up handling. The system can recognize the disease lung identification, and the system applied as standalone system. In this work, the Convolutional Neural Network (CNN) approach for identifying diseases lung identification is proposed. The Mel Frequency Cepstral Coefficient (MFCC) applied to process the stethoscope sounds which will used as input to the CNN. The performance of the proposed system has been investigated and resulted. The accuracy of 99% and 98%, for training and testing accuracy respectively. Furthermore, the system accurately detects lung diseases identification. © 2024 IEEE.

关键词： Lung cancer

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Pyrolysis characteristics and dynamics analysis of a coal mine roadway conveyor belt

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Journal of Thermal Analysis and Calorimetry 2023年第11期148卷 4823-4832页

作者： Wang, Weifeng Liu, Hanfei Yang, Bo Zhang, Duo Lyu, Huifei Song, Xianming Shu, Chi-Min School of Safety Science and Engineering Xi’an University of Science and Technology Shaanxi Xi’an710054 China Postdoctoral Research Mobile Station in Mining Engineering Xi’an University of Science and Technology Shaanxi Xi’an710054 China Postdoctoral Program Xi’an University of Science and Technology 58 Yanta Mid. Rd. Shaanxi Xi’an710054 China Mine Rescue Team Yankuang Group Ltd. Co. Shandong Jining273500 China Graduate School of Engineering Science and Technology National Yunlin University of Science and Technology Yunlin Douliou64002 Taiwan

Coal mine conveyor-belt fires account for numerous mine disasters. Accurate prediction of burning degree is one of the critical factors in preventing and controlling conveyor-belt fires. The sparse qualitative smoke-detection method is challenging to meet the precise early warning of belt fire. Therefore, the Tangkou mine transportation belt was used to analyze TG-DTG curve change late, gas generation law, conversion rate, and apparent activation energy, and other factors at four heating rates via TG-FTIR experiments. The seven characteristic temperatures and their thresholds during belt combustion were experimentally determined, and the process was divided into three stages (I, II, III). The process mainly generated CH4, CO, CO2, and other gases, and the temperature range of various gases was identified. The conversion degree rate of each stage was positively correlated with the temperature. In Stages I and II, activation energy increased with the conversion rate and then decreased. Rate and temperature corresponding to the maximum activation energy were determined. Dynamic compensation effect of the belt combustion process was found. The reactions that could occur at each stage were inferred based on the above-mentioned characteristics;these findings were essential to intelligent comprehensive monitoring and early warning of conveyor-belt fire in a mine laneway. © 2022, Akadémiai Kiadó, Budapest, Hungary.

关键词： Coal

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Implementation of Face Patterns and Smile Recognition for Intelligent Class Attendance Systems 6

Implementation of Face Patterns and Smile Recognition for In...

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6th International Conference on computer and Informatics Engineering, IC2IE 2023

作者： Miftakhurrokhmat Suparyanto, Teddy Elwirehardja, Gregorius Natanael Karimah, Shofiyati Nur Pardamean, Bens Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Universitas Amikom Yogyakarta Faculty of Computer Science Department of Informatics Yogyakarta Indonesia Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350345162

Students 'attendance in class is one important success parameter in face-to-face learning processes. Conventional attendance systems, such as paper-based attendance sheets or identity card systems, require a long time in the manual recapitulation process. Without additional verifications, even computer vision-based methods are prone to fraudulent practices by the students instead of gaining their excitement and attention in a class. To stimulate students' attention in a class, this work designs an intelligent class attendance system, in which facial pattern and smile recognition are implemented by using the latter as an additional task-based verification to reduce the risks of fake attendance. For the face recognition module, this pilot study used FaceNet as a feature extractor combined with SVM for classification, whereas the Haar cascade algorithm is used for recognizing smiles. This face recognition pipeline was implemented as a service installed on minicomputers or Internet of Things (IoT) devices in each classroom and connected to an IP camera. Every recorded attendance will be sent as a notification to a mobile application for students that requires their active participation to confirm it with a smiling self-photo. The proposed pipeline obtained 92.86% accuracy on the test data, and 66.67% accuracy when evaluated in a real-life simulation setting through the implemented system. The lower accuracy in the simulation indicated that further improvements are indispensable, especially since the model obtained 28.57% False Negative Rate. Future studies will need to acquire more data and experiment with more efficient methods of attendance verification. © 2023 IEEE.

关键词： class attendance convolutional neural network deep learning face recognition smile

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Data collection and talent demand analysis of recruitment website based on principal component analysis 5

Data collection and talent demand analysis of recruitment we...

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5th International Conference on computer Information science and Artificial Intelligence, CISAI 2022

作者： Fan, Jihui Yuan, Fengshan Department of Graduate School St.Paul University Philippines Tuguegarao Cagayan Philippines School of Computer Science and Engineering Guangzhou Institute of Science and Technology Guangzhou China

ISBN: (纸本)9781510662520

The talent structure has been flattened, from the pyramid talent structure to the olive talent structure. Jobs are more complicated, industries are upgraded from low-end to high-end, and the situation faced by jobs is more complex. Talent specification compounding, industry understanding, data thinking, programming ability, innovative thinking and comprehensive problem-solving ability. With the rapid development of science and technology, the demand for social talents is constantly changing, and the big data wave is coming,How to match the talent training of the school with the talent demand of the society. This paper uses the crawler technology to obtain data from the recruitment website, through data collection, cleaning, and data preprocessing, and finally carries out data analysis and visual display. © 2023 SPIE.

关键词： Big data

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Exploring the Potential of Discrete Chaotic Evolution Algorithm for Combinatorial Optimization

Exploring the Potential of Discrete Chaotic Evolution Algori...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Ding, Yi Meng, Xiang Pei, Yan Li, Jianqiang Graduate School of Computer Science and Engineering University of Aizu Fukushima Aizuwakamatsu Japan Beijing University of Technology Faculty of Information Beijing100124 China

ISBN: (纸本)9781665410205

We propose an extension of the chaotic evolution algorithm into the discrete domain to address combinatorial optimization problems. In this study, we leverage the discrete chaotic evolution algorithm to tackle the Traveling Salesman Problem (TSP) for assessment purposes. The chaotic evolution algorithm exploits the ergodicity of chaos to facilitate the search process within the optimization algorithm. It incorporates a mathematical mechanism into the iterative evolution process, simulating ergodic motion within a search space based on a simple principle. To manage the discrete mutation operation within the chaotic evolution algorithm, we introduce a specifically designed chaotic operation. This operation is tailored for its application in solving combinatorial optimization problems. The chaotic sequence plays a crucial role in determining the mutation location. Our evaluation involves the comparison of our proposed discrete chaotic evolution algorithm with the outcomes of the simulated annealing algorithm and the tabu search algorithm. The assessment serves to demonstrate and validate that the discrete chaotic evolution algorithm yields superior optimization performance within the discrete domain. © 2024 IEEE.

关键词： Optimization algorithms

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