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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Dael, Fares A. Maidanova, Symbat Shayea, Ibraheem Abitova, Gulnara Seraly, Aigul İzmir Bakirçay University Management Information Systems İzmir Turkey Astana It University Computational and Data Science Astana Kazakhstan Istanbul Technical University Electronic and Communication Engineering Istanbul Turkey Astana It University Intelligent Systems and Cybersecurity Astana Kazakhstan

ISBN: (纸本)9798331505264

The Myers-Briggs Type Indicator (MBTI) is one of the most widely recognized psychological tools for categorizing personality types, often used in various professional and personal development contexts. This study presents a data-driven approach to MBTI personality classification using a range of machine learning models. By leveraging a dataset comprising user responses and linguistic patterns, we aim to predict the MBTI personality types with greater accuracy and reliability. Various models, including Support Vector Machines (SVM), Random Forests, and Gradient Boosting Machines, were evaluated to determine their effectiveness in classifying the 16 MBTI types. Our findings reveal that machine learning models can significantly enhance the predictive accuracy of MBTI classification compared to traditional methods. The Random Forest model, in particular, demonstrated superior performance, achieving an accuracy of [insert specific accuracy here] across the dataset. We also explore the importance of feature selection and data preprocessing in improving model outcomes, highlighting key features that contribute to personality type prediction. The results of this study suggest that a data-driven approach, combined with machine learning techniques, provides a promising avenue for more nuanced and accurate MBTI personality assessments. This approach not only enhances our understanding of personality prediction but also offers practical implications for applications in psychology, human resources, and personal development. © 2024 IEEE.

关键词： Adversarial machine learning

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Emergency Vehicle Detection Using Deep ConvNet2D and Computer Vision 2

Emergency Vehicle Detection Using Deep ConvNet2D and Compute...

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2nd International Conference on Emerging Trends in information Technology and engineering, ic-ETITE 2024

作者： Poongavanam, Elumalaivasan Prathik, A. Munirathinam, T. Kayalvizhi, V. Rajesh, R. Mahavishnu, C. Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai Kattankulathur India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Sona College of Technology Department of Information Technology Salem India School of Computer Science and Engineering and Information Science Presidency University Bangalore India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Shreenivasa Engineering College Department of Computer Science and Engineering Dharmapuri India

ISBN: (纸本)9798350328202

A surveillance system detects emergency vehicles stuck in traffic. This system helps manage traffic because the number of vehicles on the road has been increasing daily for years, causing congestion. This project implements Deep ConvNet2D (Convolutional Network 2D) and Computer Vision emergency vehicle recognition. We propose a CNN-based real-time image processing model for emergency vehicle detection. The signal control unit can be set to terminate the round robin sequence when an emergency vehicle is detected. A CNN trained on Indian ambulance images solves the problem. Tensor Flow, a Python library, was used for training. Our method detects and classifies emergency cars well. Existing systems use ANN algorithm, which is inaccurate and inefficient. The system uses Deep ConvNet2D Algorithm. The proposed real-time system is accurate. The proposed system loads and executes faster than the existing system. The system is efficient, scalable, and enhanced for complex use cases. © 2024 IEEE.

关键词： Real time systems

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A novel myocarditis detection combining deep reinforcement learning and an improved differential evolution algorithm

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1347-1360页

作者： Jing Yang Touseef Sadiq Jiale Xiong Muhammad Awais Uzair Aslam Bhatti Roohallah Alizadehsani Juan Manuel Gorriz Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversiti MalayaKuala LumpurMalaysia Centre for Artificial Intelligence Research(CAIR) Department of Information and Communication TechnologyUniversity of AgderGrimstadNorway Department of Creative Technologies Air UniversityIslamabadPakistan School of Information and Communication Engineering Hainan UniversityHaikouHainanChina Institute for Intelligent Systems Research and Innovation(IISRI) Deakin UniversityWaurn PondsVictoriaAustralia Data Science and Computational Intelligence Institute University of GranadaGranadaSpain

Myocarditis is a serious cardiovascular ailment that can lead to severe consequences if not promptly *** is triggered by viral infections and presents symptoms such as chest pain and heart *** detection is crucial for successful treatment,and cardiac magnetic resonance imaging(CMR)is a valuable tool for identifying this ***,the detection of myocarditis using CMR images can be challenging due to low contrast,variable noise,and the presence of multiple high CMR slices per *** overcome these challenges,the approach proposed incorporates advanced techniques such as convolutional neural networks(CNNs),an improved differential evolution(DE)algorithm for pre-training,and a reinforcement learning(RL)-based model for *** this method presented a significant challenge due to the imbalanced classification of the Z-Alizadeh Sani myocarditis dataset from Omid Hospital in *** address this,the training process is framed as a sequential decision-making process,where the agent receives higher rewards/penalties for correctly/incorrectly classifying the minority/majority ***,the authors suggest an enhanced DE algorithm to initiate the backpropagation(BP)process,overcoming the initialisation sensitivity issue of gradient-based methods like back-propagation during the training *** effectiveness of the proposed model in diagnosing myocarditis is demonstrated through experimental results based on standard performance ***,this method shows promise in expediting the triage of CMR images for automatic screening,facilitating early detection and successful treatment of myocarditis.

关键词： classification differential evolution myocarditis reinforcement learning

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data-Driven Strategies for Improving Railway Ticket Demand Forecasting Accuracy 16

Data-Driven Strategies for Improving Railway Ticket Demand F...

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Boltaikhanova, Tomiris Dael, Fares A. Shayea, Ibraheem Leila, Rzayeva Astana It University Computational and Data Science Astana Kazakhstan Izmir Bakirçay University Management Information Systems Izmir Turkey Istanbul Technical University Electronic and Communication Engineering Istanbul Turkey Astana It University Intelligent Systems and Cybersecurity Astana Kazakhstan

ISBN: (纸本)9798331505264

The accurate prediction of railway ticket demand is vital for effective operational planning and resource management in the transportation sector. This study investigates various time series analysis techniques, including ARIMA, SARIMAX, and neural networks such as Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN), to forecast railway ticket demand. Utilizing an extensive dataset of ticket sales spanning several years, we trained and validated these models, evaluating their performance through key metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Demand patterns were represented using Origin-Destination (OD) matrices, where the CNN model was employed to predict the entire OD matrix, while the other models focused on individual OD pairs. The findings reveal that the CNN model outperforms ARIMA, SARIMAX, and LSTM in terms of prediction accuracy, offering a more reliable approach for forecasting demand in railway networks. This study underscores the importance of data-driven strategies in enhancing the precision of demand forecasting, thereby contributing to more informed decision-making and optimized railway operations. © 2024 IEEE.

关键词： Convolutional neural networks

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Machine Learning Approaches to Forecasting Car Prices in the Secondary Market 16

Machine Learning Approaches to Forecasting Car Prices in the...

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Dael, Fares A. Talipov, Daulet Shayea, Ibraheem Kamshat, Asmaganbetova İzmir Bakirçay University Management Information Systems İzmir Turkey Astana It University Computational and Data Science Astana Kazakhstan Istanbul Technical University Electronic and Communication Engineering Istanbul Turkey Astana It University Intelligent Systems and Cybersecurity Astana Kazakhstan

ISBN: (纸本)9798331505264

This study investigates the use of machine learning techniques to predict car prices in the secondary market. Utilizing a comprehensive dataset of used car listings from the United Kingdom, we applied advanced machine learning models, including Random Forest and Neural Networks, to understand the factors influencing car prices and to develop accurate predictive models. Our analysis identified engine size and registration year as key determinants of car prices. The Neural Network model provided highly accurate predictions, closely matching actual prices in the majority of cases. Visual representations of feature importance and prediction errors further elucidate the model's effectiveness. This research demonstrates that machine learning can significantly enhance the accuracy of price predictions in the used car market, offering valuable insights for consumers, dealers, and policymakers. By leveraging these predictive models, stakeholders can make more informed decisions, optimize pricing strategies, and better understand market dynamics. © 2024 IEEE.

关键词： Prediction models

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Opinion Dynamics Induced by Agents with Particular Goal

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Journal of systems science & Complexity 2022年第6期35卷 2319-2335页

作者： LI Zhenpeng TANG Xijin HONG Zhenjie School of Electronics and Information Engineering(School of Data Science) Taizhou UniversityTaizhou 318000China Academy of Mathematics and Systems Science Chinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China Wenzhou University Wenzhou 325035China

The authors investigate the opinion dynamics in a setting where some special agents induce public opinions towards their desired direction,with Particular Goal(PG agents for short)to manipulate *** on the bounded confidence model,the authors find PG agents can significantly improve the level of *** authors also study how opinion pattern is influenced by varying the model in terms of changing the network structure,different parameters,and PG agents choosing *** authors conduct the comparison of model results with empirical data from online social *** authors hope the study may shade a light on public opinion control and regulation.

关键词： Collective pattern complex networks opinion formation social influence

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Cross-cultural contextualisation for recommender systems

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Journal of Ambient Intelligence and Humanized Computing 2024年第2期15卷 1659-1670页

作者： Hong, Minsung An, Sojung Akerkar, Rajendra Camacho, David Jung, Jason J. Big Data Research Group Western Norway Research Institute Vestlandsforsking Sogndal6851 Norway Department of Computer Science and Engineering Chung-Ang University 84 Heukseok-ro Heukseok-dong Dongjak-gu Seoul Korea Republic of Information Systems Department Technical University of Madrid Madrid28031 Spain

Cultural Heritage (CH) domain is rapidly moving from traditional heritage sites into smart cultural heritage environment through various technologies. As one of the important technologies in the smart space, Recommender systems (RSs) have been widely utilised to personalised services and matching visitors’ goals and behaviours. Whereas, cultural difference is often considered a barrier to technology transfer or adoption. However, few studies focus on how the cultural factor influences recommendation despite cultural difference largely affects user preferences in the RSs. Furthermore, existing researches have mainly analysed evaluation results of their recommendation to reveal cultural differences, rather than utilising the cross-cultural factors into RSs. In this paper, we propose a novel concept of cross-cultural contextualisation and a model to compute the cross-cultural factor affecting users (countries or cultures) preferences by using matrix factorisation and clustering techniques. In addition, we discuss how to apply the model to RSs in CH domain through cross-domain recommendation techniques. Note that the two computational techniques were used to analyse cross-cultural factors which impact to user preferences, rather than to recommend items. In other words, the proposed model and computing results capable of utilisation into the other RSs as well as various research fields. Results of experiments with a real-world dataset showed effectiveness of the proposed model and supported that there is cultural difference influencing users’ rating behaviours. Furthermore, a systematic analysis of dataset and the experimental results presented that individual users could be considered as country-wise groups for the model to analyse the cross-cultural factors. © Springer-Verlag GmbH Germany, part of Springer Nature 2019.

关键词： Recommender systems

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Wearable Vibration Device for Supporting Postural Stability based on Proprioceptive Stimulation 46

Wearable Vibration Device for Supporting Postural Stability ...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Endo, Keigo Kadone, Hideki Hassan, Modar Hirokawa, Masakazu Suzuki, Kenji University of Tsukuba Artificial Intelligence Labotaroty Tsukuba Japan University of Tsukuba Center for Innovative Medicine and Engineering Tsukuba Japan University of Tsukuba Institute of Systems and Information Engineering Tsukuba Japan Nec Corporation Data Science Laboratories Kawasaki Japan

ISBN: (纸本)9798350371499

In this study, we present a wearable device for local vibration stimulation aimed to support postural stability in balance-compromised persons. Experiments were conducted with 15 young and 9 middle-aged adults without lower limb disabilities in a standing posture with vibration stimulation. The results showed a decrease in the mean velocity of the center-of-pressure sway in the standing posture during vibration presentation. In middle-aged and older adults, the rate of increase in the postural stability index was higher than that in the absence of vibration when the standing balance test was repeated while vibration was presented. These results suggest that the vibration device used as the proprioceptive stimulation may support the user's postural stability. © 2024 IEEE.

关键词： Fall prevention Local vibration stimulation Posture stabilization Proprioception

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Detecting Questions in Online Communities: A Machine Learning Approach 16

Detecting Questions in Online Communities: A Machine Learnin...

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Omarova, Dilnaz Dael, Fares A. Shayea, Ibraheem Abitova, Gulnara Sailaukhanov, Eldos Computational and Data Science Astana It University Astana Kazakhstan Management Information Systems İzmir Bakιrçay University Izmir Turkey Electronic and Communication Engineering Istanbul Technical University Istanbul Turkey Intelligent Systems and Cybersecurity Astana It University Astana Kazakhstan

ISBN: (纸本)9798331505264

The proliferation of online forums and communities has greatly facilitated knowledge sharing and user support but has also introduced the significant challenge of managing redundant and semantically similar questions. Traditional keyword-based methods have proven inadequate in addressing this issue due to the inherent complexities of natural language, where the same idea can be expressed in numerous ways. This study investigates the use of advanced machine learning algorithms - Logistic Regression, Random Forest, and Gradient Boosting (XGBoost) - to detect semantically similar questions. By employing the Quora Question Pairs dataset, the performance of these models is evaluated using metrics such as accuracy, precision, recall, and F1-score. This research not only provides a comparative analysis of these machine learning models but also suggests a framework for improving information retrieval and user experience in online forums. The study highlights the potential for future integration of deep learning models and advanced semantic understanding techniques to further enhance the detection of semantically similar questions. © 2024 IEEE.

关键词： Adversarial machine learning

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Discovering Frequency Bursting Patterns in Temporal Graphs 39

Discovering Frequency Bursting Patterns in Temporal Graphs

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39th IEEE International Conference on data engineering, ICDE 2023

作者： Zhang, Qianzhen Guo, Deke Zhao, Xiang Yuan, Long Luo, Lailong National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory China National University of Defense Technology Laboratory for Big Data and Decision China Nanjing University of Science and Technology China

ISBN: (纸本)9798350322279

A frequency bursting pattern (FBP) in temporal graphs represents some interaction behavior that accumulates its frequency at the fastest rate. Mining FBPs is essential to early warning of emergencies. However, existing studies on frequency-based pattern mining in graphs do not consider the temporal information and bursting features of a subgraph pattern. As a result, they may not provide effective and efficient mining algorithms for FBP discovery. In this paper, we study the problem of discovering top-k FBPs in temporal graphs. We present a novel model, referred to as maximal (m, θ)-bursting pattern, to describe FBPs in a temporal graph, which is a subgraph with a size larger than m that accumulates its frequency at the fastest rate during a time interval of length no less than θ. A naive solution for top-k FBPs discovery is to use the best-first search algorithm, where the burstiness threshold changes as more patterns are mined. However, this method will result in huge search space since we need to check every possible time interval for a candidate pattern in the temporal graph. To tackle this problem, we devise an online top-k framework in which k candidate results are maintained from the initial timestamp to the end in the temporal graph. Under the new framework, we further conceive two optimization strategies by exploiting incremental subgraph matching and Evolutionary Game Theory to boost the performance. Extensive experiment results on five real temporal graphs show that our algorithm has higher efficiency, effectiveness and scalability. © 2023 IEEE.

关键词： Game theory

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