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37th Australasian Joint Conference on Artificial Intelligence, AJCAI 2024

作者： Qayyum, Abdul Mazher, Moona Lee, Angela Solis-Lemus, Jose A. Razzak, Imran Niederer, Steven A. National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Turing Research and Innovation Cluster: Digital Twins The Alan Turing Institute London United Kingdom Department of Computer Science University College London London United Kingdom School of Computer Science and Engineering University of New South Wales Sydney Australia

ISBN: (纸本)9789819603503

Unlike Right Atrium (RA), Left Atrium (LA) presents distinctive challenges, including much thinner myocardial walls, complex and irregular morphology, as well as diversity in individual’s structure, making off-the-shelf methods designed for the Left Ventricle (LV) may not work in the context of the left atrium. To overcome aforementioned challenges, we are the first to present comprehensive technical workflow designed for 4D registration modeling to automatically analyze LA motion using high-resolution 3D Cine MR images. We integrate segmentation network and 4D registration process to precisely delineate LA segmentation throughout the full cardiac cycle. Additionally, an image 4D registration network is employed to extract LA displacement vector fields (DVFs). Our findings show the potential of proposed end to end framework in providing clinicians with novel regional biomarkers for left atrium motion tracking and deformation, carrying significant clinical implications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image segmentation

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Optimized Fractional-Order PID Controller for Temperature Control of CSTH: A Comparative Study with PID Cascade Controller

Optimized Fractional-Order PID Controller for Temperature Co...

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2025 International Conference on Energy, Power, Environment, Control and Computing, ICEPECC 2025

作者： Alaswadi, Abdulaziz F. Alyazidi, Nezar M. AL-Aswadi, Fatima N. Ahmed, Amjed Abbas Department of Control and Instrumentation Engineering King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia Interdisciplinary Center of Smart Mobility and Logistics King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia Institute of Computer Science & Digital Innovation (ICSDI) UCSI University Kuala Lumpur 56000 Malaysia Faculty of Computer Science and Engineering Hodeidah University Al Hudaydah Yemen Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Department of Computer Techniques Engineering Imam AlKadhum College (IKC) Baghdad Iraq

ISBN: (纸本)9781837243235

Controlling the temperature in chemical reactors, heating furnaces, distillation columns, and reboilers for circulating heating fluids is crucial across all industries. This project is a simulation-based study where both PID-cascade and fractional order PID (FO-PID) controllers were used to control the temperature of water in the continuously stirred tank heater system. The simulation was designed by using SIMULINK. Initially, the work reproduces results from a previous study that evaluated the performance of PID cascade controller applied for controlling the temperature of continuous stirred tank heater. Building on this foundation, a novel fractional-order PID (FO-PID) controller was designed and its parameters were optimized using the Whale Optimizer Algorithm (WOA). Results showed that the FO-PID outperforms the PID-cascade controller in terms of settling time and percentage overshoot. This study demonstrates significant advancements in process innovation and optimization, supporting sustainable industrial systems. The FO-PID controller in comparison to the Cascade PID controller reduced the percentage overshoot by approximately 97%, reduced settling time by 87% and maintained comparable steady-state error. © The Institution of engineering & Technology 2025.

关键词： FO-PID Mathematical Modeling PID-cascade Whale Optimizer Algorithm

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Estimation Analysis of Edge and Line Detection Methods in Digital Image Processing 3

Estimation Analysis of Edge and Line Detection Methods in Di...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Garg, Prateek Yadav, Rakesh Kumar Devaraj Verma, C. Nirmala, D. Sable, Nilesh P. Murari, Krishn Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Ramnagar562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Vivekananda Global University Department of Civil Engineering Jaipur India

ISBN: (纸本)9798350319125

This paper provides an estimation evaluation of the area and line detection methods in virtual photo processing. This analysis evaluates the performance of numerous facet and line detection algorithms in phrases of facet/line first-rate, accuracy, computational complexity, and robustness to noise in the context of artificial and actualinternational snapshots. Mainly, it discusses the overall performance of 4 edge detection methods: Roberts, Prewitt, Sobel, and Canny;and 3 line detection techniques: Hough remodel, Linear Estimation, and Probabilistic Hough remodel. as compared to the classical algorithms, the Probabilistic Hough rework algorithm is determined to have the excellent accuracy and robustness to noise. Furthermore, a contrast of computational complexities shows that the Hough transform has the bottom complexity and computational time, even as the Linear Estimation algorithm has the best complexity and computational time. The primary outcome of this study is that the brink/line first-rate, accuracy, computational complexity, and robustness are very dependent on the sort of entered snapshots and the respective selected parameters. © 2023 IEEE.

关键词： comparison computational performance Probabilistic Transform

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An Automated Approach for Analysing Students Feedback Using Sentiment Analysis Techniques 5th

An Automated Approach for Analysing Students Feedback Using...

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5th Mediterranean Conference on Pattern Recognition and Artificial Intelligence, MedPRAI 2021

作者： Sadriu, Shpetim Nuci, Krenare Pireva Imran, Ali Shariq Uddin, Imran Sajjad, Muhammad Faculty of Computer Science and Engineering University for Business and Technology Pristina Kosovo Gjøvik2815 Norway Department of Computer Science Islamia College University Peshawar Peshawar25000 Pakistan

ISBN: (纸本)9783031041112

Conducting and evaluating continuous student feedback is essential for any quality enhancement cell (QEC) within an education institution. Students’ feedback based on their personal opinions can play a vital role in ensuring quality education. However, students’ subjective opinions are often ignored due to time constraints or a lack of adequate analysis strategies. Therefore, to automate the quality assurance process, two classification models (i.e., based on Monkey learn API and SentiWord using TextBlob) are proposed to analyze students’ feedback data. The results shows that the model employing MonkeyLearn performs nearly 22% points better than the Textblob on the Albanian language dataset obtained from 114 students’ responses, achieving 72.12% accuracy. © 2022, Springer Nature Switzerland AG.

关键词： Sentiment analysis

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Weighted Mask Recurrent-Convolutional Neural Network based Plant Disease Detection using Leaf Images 7

Weighted Mask Recurrent-Convolutional Neural Network based P...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Sharmila, R. Kamalitta, R. Moorthy Singh, Devesh Pratap Chauhan, Amit Acharjee, Purnendu Bikash Dhanalakshmi Srinivasan Engineering College Department of MCA Tamilnadu Perambalur India Information Technology K.Ramakrishnan College of Engineering Tamilnadu Trichy India Electronics Engineer Production Dhaksha Unmanned System Pvt Ltd Tamilnadu Chennai India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttrakhand Dehradun India Department of Life Sciences Karnataka Bengaluru India CHRIST University Faculty Member Computer Science Maharashtra Pune India

ISBN: (纸本)9798350397253

Large losses in output, money, and quality/quantity of agricultural goods are incurred due to plant diseases. Seventy percent of India's GDP is tied to the agricultural sector, thus protecting plants from diseases is crucial. For this reason, it is important to keep an eye on plants from the moment they sprout. The usual approach for this omission is naked eye inspection, which is more time-consuming, costly, and requires significant skill. Thus, automating the method for detecting diseases is necessary to speed up this process. It is imperative that image processing methods be used in the creation of the illness detection system. Disease detection involves a number of processes, including Weighted Mask R-CNN, GLCM feature extraction, Multi-thresholding image pre-processing, and K means image segmentation classification. The weighted Mask R-CNN outperforms the standard RNN, the Mask R-CNN, and the CNN in terms of accuracy and recall in analytical trials by a significant margin. © 2023 IEEE.

关键词： Recurrent neural networks

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Predicting Career Trajectories of Information System Graduates Using AdaBoost Algorithm 9

Predicting Career Trajectories of Information System Graduat...

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9th International Conference on Wireless and Telematics, ICWT 2023

作者： Kurniawan, Rahmad Gusti, Siska Kurnia Sukamto Syamsudhuha Abdillah, Rahmad Universitas Riau Faculty of Mathematics and Natural Sciences Department of Computer Science Pekanbaru Indonesia Universitas Islam Negeri Sultan Syarif Kasim Riau Department of Informatics Engineering Pekanbaru Indonesia Universitas Riau Faculty of Mathematics and Natural Sciences Department of Mathematics Pekanbaru Indonesia

ISBN: (纸本)9798350305029

The undergraduate unemployment rate in Indonesia is increasing, necessitating universities to produce quality graduates. In alignment with global technological advancements, universities are committed to enhancing the quality of their graduates through curriculum evaluation. In this study, we investigated the department of Information Systems at the University of Riau and developed an alum career analysis system using the AdaBoost algorithm. The data used in the study was collected via an online survey from graduates. Relevant features were selected from features to preprocess the data. The experiments conducted using the SAMME.R boosting method, with a learning rate of 0.1, 100 estimators, and cross-validation on the five-fold sampling method, resulted in the highest accuracy of 83.3%. The decision tree of the AdaBoost algorithm explains the factors influencing alums to pursue careers in their field, with English proficiency, learning methods, alum cohorts, and alum's activeness in job searching being the main determinants of success in their careers. The alum career analysis system proved reliable for evaluating the curriculum and producing quality graduates in line with technological advancements. Future work includes expanding the system to include graduates from other departments and implementing findings to improve the curriculum. © 2023 IEEE.

关键词： Curricula

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Tracking changes using Kullback-Leibler divergence for the continual learning

Tracking changes using Kullback-Leibler divergence for the c...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Basterrech, Sebastian Wozniak, Michal VŠB-Technical University of Ostrava Faculty of Electrical Engineering and Computer Science Ostrava Czech Republic Wroclaw University of Science and Technology Department of Systems and Computer Networks Wroclaw Poland

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Recently, continual learning has received a lot of attention. One of the significant problems is the occurrence of concept drift, which consists of changing probabilistic characteristics of the incoming data. In the case of the classification task, this phenomenon destabilizes the model's performance and negatively affects the achieved prediction quality. Most current methods apply statistical learning and similarity analysis over the raw data. However, similarity analysis in streaming data remains a complex problem due to time limitation, non-precise values, fast decision speed, scalability, etc. This article introduces a novel method for monitoring changes in the probabilistic distribution of multi-dimensional data streams. As a measure of the rapidity of changes, we analyze the popular Kullback-Leibler divergence. During the experimental study, we show how to use this metric to predict the concept drift occurrence and understand its nature. The obtained results encourage further work on the proposed methods and its application in the real tasks where the prediction of the future appearance of concept drift plays a crucial role, such as predictive maintenance. © 2022 IEEE.

关键词： Forecasting

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Optimal Control of Heterogeneous-Susceptible-Exposed-Infectious-Recovered-Susceptible Malware Propagation Model in Heterogeneous Degree-Based Wireless Sensor Networks

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Journal of Donghua University(English Edition) 2022年第3期39卷 220-230页

作者： ZHANG Hong SHEN Shigen WU Guowen CAO Qiying XU Hongyun School of Computer Science and Technology Donghua University Shanghai 201620 China Department of Computer Science and Engineering Shaoxing University Shaoxing 312000 China Faculty of Business Information Shanghai Business School Shanghai 201400 China

Heterogeneous wireless sensor networks(HWSNs)are vulnerable to malware propagation,because of their low configuration and weak defense ***,an optimality system for HWSNs is developed to suppress malware propagation in this ***,a heterogeneous-susceptible-exposed-infectious-recovered-susceptible(HSEIRS)model is proposed to describe the state dynamics of heterogeneous sensor nodes(HSNs)in ***,the existence of an optimal control problem with installing antivirus on HSNs to minimize the sum of the cumulative infection probabilities of HWSNs at a low cost based on the HSEIRS model is proved,and then an optimal control strategy for the problem is derived by the optimal control ***,the optimal control strategy based on the HSEIRS model is transformed into corresponding Hamiltonian by the Pontryagin’s minimum principle,and the corresponding optimality system is ***,the effectiveness of the optimality system is validated by the experimental simulations,and the results show that the infectious HSNs will fall to an extremely low level at a low cost.

关键词： heterogeneous wireless sensor network(HWSN) malware propagation optimal control Pontryagin’s minimum principle

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An Application of Fuzzy ANOVA on Field of Agricultural Machinery

An Application of Fuzzy ANOVA on Field of Agricultural Machi...

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Intelligent and Fuzzy Systems - Intelligence and Sustainable Future Proceedings of the INFUS 2023 Conference

作者： Gökkuş, Zeynep Şentürk, Sevil Yildiz, Taner Cevher, Elçin Yeşiloğlu Vocational School Computer Technologies Department Kastamonu University Kastamonu Turkey Faculty of Science Department of Statistics Eskişehir Technical University Eskişehir Turkey Faculty of Agriculture Department of Agricultural Machinery and Technologies Engineering Ondokuz Mayıs University Samsun Turkey

ISBN: (纸本)9783031397769

ANOVA is a statistical analysis test that addresses the significance of variances in the means of different populations. If the data includes imprecise or vagueness in the applied sciences, the classical ANOVA cannot be the solution. In which cases, fuzzy ANOVA based on fuzzy observations ought to be used and are particularly more suitable for such problems in contrast to ANOVA, Fuzzy ANOVA (FANOVA) uses fuzzy numbers and is a generalization of ANOVA. In recent years, however;particularly, fuzzy statistical methods have been put into use for the designing process of agricultural machinery. ANOVA for the studies that have been put out regarding literature, there is not a study present that uses the FANOVA method. Therefore, in this study, span, which is one of the foremost factors to be taken into consideration for the design of an agricultural machine to be developed for harvesting grainy products, on the max force that can be applied to a chestnut was evaluated and the results were assessed. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Population statistics

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Parkinson’s Detection Using RNN-Graph-LSTM with Optimization Based on Speech Signals

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computers, Materials & Continua 2022年第7期72卷 871-886页

作者： Ahmed S.Almasoud Taiseer Abdalla Elfadil Eisa Fahd N.Al-Wesabi Abubakar Elsafi Mesfer Al Duhayyim Ishfaq Yaseen Manar Ahmed Hamza Abdelwahed Motwakel Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversityMahayil62529Saudi Arabia Department of Information Systems-Girls Section King Khalid UniversityMahayil62529Saudi Arabia Department of Computer Science College of Science and Artsat MahayilKing Khalid UniversityMahayil62529Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahSaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversityMahayil62529Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj62529Saudi Arabia

Early detection of Parkinson’s Disease(PD)using the PD patients’voice changes would avoid the intervention before the identification of physical *** machine learning algorithms were developed to detect PD ***,these ML methods are lack in generalization and reduced classification performance due to subject *** overcome these issues,this proposed work apply graph long short term memory(GLSTM)model to classify the dynamic features of the PD patient speech *** proposed classification model has been further improved by implementing the recurrent neural network(RNN)in batch normalization layer of GLSTM and optimized with adaptive moment estimation(ADAM)on network hidden *** consider the importance of feature engineering,this proposed system use Linear Discriminant analysis(LDA)for dimensionality reduction and SparseAuto-Encoder(SAE)for extracting the dynamic speech *** on the computation of energy content transited from unvoiced to voice(onset)and voice to voiceless(offset),dynamic features are *** PD datasets is evaluated under 10 fold cross validation without sample *** proposed smart PD detection method called RNN-GLSTM-ADAM is numerically experimented with persistent phonations in terms of accuracy,sensitivity,and specificity andMatthew correlation *** evaluated result of RNN-GLSTM-ADAM extremely improves the PD detection accuracy than static feature based conventional ML and DL approaches.

关键词： Dimensionality reduction LDA feature extraction auto encoder classification recurrent neural network LSTM and optimization

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