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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Zhao, Rong ALDharhani, Ghassan Saleh Ratnavelu, Kuru Thambiayya, Sathiyaraj Jasser, Muhammed Basheer Majeed, Anwar P. P. Abdul Luo, Yang Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur56000 Malaysia Institute of Information Science and Technology Dalian University of Science and Technology Dalian China Faculty of Information Science and Technology UKM University Selangor Bangi43000 Malaysia Institute of Mathematical Sciences University of Malaya Kuala Lumpur56000 Malaysia Institute of Actuarial Science and Data Analytics UCSI University Kuala Lumpur56000 Malaysia Department of Data Science and Artificial Intelligence School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia School of Intelligent Manufacturing Ecosystem Xi’an Jiaotong-Liverpool University Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

Artificial Intelligence is an essential tool for early disease recognition and supporting patient condition monitoring in the future. Timely and exact conclusions about the type of disease are significant for treatment and life extension. A combination of classification and feature selection algorithms can effectively handle complex datasets in the medical field and improve the accuracy of disease diagnosis and treatment. In this paper, we propose a new hybrid feature selection algorithm that uses inconsistency metrics as a filter method for the first step, and then feeds the resulting dataset into a wrapper method, which ultimately results in a reduced dataset. The proposed HFSIM algorithm is tested on five datasets in the medical domain from Kaggle. The obtained feature subsets are verified on three classification algorithms, KNN, LR and RF of machine learning to validate the effectiveness of the proposed algorithm. The experimental results show that the proposed algorithm obtains feature subsets with low dimensionality on most of the datasets and also has high classification accuracy after testing on the classifiers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Customer Segmentation Based on Recency, Frequency, Monetary, Variety and Duration (RFMVD)

Customer Segmentation Based on Recency, Frequency, Monetary,...

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technology Innovation and Its Applications (ICTIIA), International Conference on

作者： Agus Sundari Maya Silvi Lydia Muhammad Anggia Muchtar Department of Data Science and Artificial Intelligence Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

ISBN: (数字)9798350351613

ISBN: (纸本)9798350351620

In banking, maintaining customer retention and customer satisfaction are important. Effective customer segmentation can be a strategic tool to improve customer loyalty and business performance. This research can assist banks in developing marketing strategies to retain customers and improve services based on bill payment transactions. The data that will be used in this study is bill payment transaction data at regional banks in 2023, as for the types of bill payments made by customers, namely electricity, water, telephone payments, and internet bills. The amount of data used is 702,174 rows. This research uses K-Means clustering to group customers based on recency (the last time the customer made a transaction), frequency (the number of customers making transactions), monetary (the nominal amount of money spent by the customer), variety (the number of types of bills paid) and duration (the average duration of time the customer makes the previous transaction until the next transaction) (RFMVD). The results of customer segmentation based on recency, frequency, monetary, variety and duration using K-Means clustering produce 3 customer groups, namely passive customers, loyal customers, and VIP customers. The silhouette score value on customer segmentation using K-Means clustering is 0.7303, which indicates that the grouping is quite good and the number of clusters is correct.

关键词： Time-frequency analysis Technological innovation Electricity Customer satisfaction Banking Telephone sets History Business

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A Convergence Analysis of a Structure-Preserving Gradient Flow Method for the All-Electron Kohn-Sham Model

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Numerical Mathematics(Theory,Methods and Applications) 2023年第3期16卷 597-621页

作者： Yedan Shen Ting Wang Jie Zhou Guanghui Hu School of Mathematics and Information Science Guangzhou UniversityGuangzhouChina Faculty of Science and Technology University of MacaoMacao SARChina Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan UniversityXiangtanChina School of Mathematical Computational Sciences Xiangtan UniversityXiangtanChina Zhuhai UM Science&Technology Research Institute ZhuhaiChina Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications University of MacaoMacao SARChina

In[Dai et al.,***.18(4)(2020)],a structure-preserving gradient flow method was proposed for the ground state calculation in Kohn-Sham density functional theory,based on which a linearized method was developed in[Hu et al.,EAJAM.13(2)(2023)]for further improving the numerical *** this paper,a complete convergence analysis is delivered for such a linearized method for the all-electron Kohn-Sham ***,the convergence,the asymptotic stability,as well as the structure-preserving property of the linearized numerical scheme in the method is discussed following previous works,while spatially,the convergence of the h-adaptive mesh method is demonstrated following[Chen et al.,***.12(2014)],with a key study on the boundedness of the Kohn-Sham potential for the all-electron Kohn-Sham *** examples confirm the theoretical results very well.

关键词： Kohn-Sham density functional theory gradient flow model structure-preserving linear scheme convergence analysis

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Motion-STUDiO : Motion Style Transfer Utilized for Dancing Operation by Considering Both Style and Dance Features

Motion-STUDiO : Motion Style Transfer Utilized for Dancing O...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Yuki Era Ren Togo Keisuke Maeda Takahiro Ogawa Miki Haseyama Graduate School of Information Science and Technology Hokkaido University Faculty of Information Science and Technology Hokkaido University Data-Driven Interdisciplinary Research Emergence Department Hokkaido University

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

In this paper, we propose motion style transfer utilized for dancing operation (Motion-STUDiO). Conventional motion style transfer methods depend on training datasets constructed from the pair of motion data and their style labels, and they have difficulties in transforming styles of dancing because of the dancing’s complexity and diversity. In the proposed method, we separately pre-train networks for style and dance information based on labeled non-dance data and unlabeled dance data. Then we train these networks based on the reflection of style and the validity of dance to preserve their knowledges. Experimental results show that the proposed method can improve the transfer performance by separately pre-training networks for style and dance information. The video of our paper can be found at https://***/file/d/1L_9sqJothxv4TYl_P-rXBKCZ5xFFwtJK/view

关键词： Training Humanities Transforms Reflection Complexity theory Consumer electronics

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Quality Analysis of Shopee Seller Portal by Using Category Recommendation System Approach 19

Quality Analysis of Shopee Seller Portal by Using Category R...

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19th International Conference on ICT and Knowledge Engineering, ICT and KE 2021

作者： Thwe, Yamin Tungkasthan, Anucha Jongsawat, Nipat RMUTT Data and Information Science Faculty of Science and Technology Pathum Thani Thailand

ISBN: (纸本)9781665432122

Consumers have shifted away from traditional transactions to online shopping as a result of the COVID-19 pandemic, digital social life, and staying at home. This has a significant impact on the volume of transactions for several ecommerce platforms, including Shopee, which must continue to improve customer satisfaction. One way to accomplish this is to improve the accuracy of the automatic category recommendation, which currently contains a significant number of errors made by the seller. The Shopee data Scraper was used to collect data, and two qualitative analyses, namely content and thematic analysis, were used to determine how many instances of automatic category recommendation fraud were committed by the *** to the content analysis, there is a 29 percent error in product category selection. Meanwhile, 75.1 percent of products should be classified separately but are instead classified as 'others.' Following that, 72.7 percent of product titles contain the same words as existing *** this research, we managed to get an analysis of the percentage of errors in the automatic category recommendation mechanism from the Shopee platform which causes sellers to place their products in the wrong category so that they can be used for suggestions for improvement or further research. © 2021 IEEE.

关键词： Errors

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A Fast Multi-Object Tracking Method using DDR Descriptor 19

A Fast Multi-Object Tracking Method using DDR Descriptor

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19th International Conference on ICT and Knowledge Engineering, ICT and KE 2021

作者： Leewan, Kasama Jongsawat, Nipat Tungkasthan, Anucha Faculty of Science and Technology RMUTT Data and Information Science Pathum Thani Thailand

ISBN: (纸本)9781665432122

In the visual intelligence system, the two main processes for visual analysis are object detection and object tracking. At present, the first process is overcome by using the YOLO-based frameworks. It shows very good results as the object detection tool using deep learning technique. However, many researchers realize that there are lots of obstacles and challenges in the second process. Therefore, the objective of this paper is to propose the Distance, Direction, and Aspect Ratio (DDR) method that can be used to improve the effectiveness of object tracking process. The main contribution of this paper is to present the technique that achieves greater than Deep SORT algorithm in term of speed for tracking objects. The experimental results show that our proposed method can be used to track object more accurately compared to a Deep SORT algorithm but consume less processing time than a Deep SORT algorithm at a frame rate, 25 frames per second on average. © 2021 IEEE.

关键词： Object recognition

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An Interpretation Method for Fault Diagnosis in Rolling Bearings: Integrating Whale Optimization Algorithm and SqueezeNet Model

An Interpretation Method for Fault Diagnosis in Rolling Bear...

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2023 IEEE International Conference on Electrical, Automation and Computer Engineering, ICEACE 2023

作者： Gu, Wenyu Zhu, Zhehao Xu, Wenxin College of Big Data and Information Engineering Guizhou University Guizhou China School of Mathematical Sciences Beijing Normal University Beijing China Faculty of Science and Technology University of Macau Macao China

ISBN: (纸本)9798350309614

The bearing is a commonly encountered element in mechanical systems, and the adoption of a precise and efficient methodology for diagnosing bearing faults can greatly enhance the operational safety and stability of the system. This study introduces an innovative methodology to tackle the problem of diagnosing faults in bearing vibration signal features that are weak and pose challenges. The method proposed in this study entails the utilization of an enhanced SqueezeNet network, which exhibits the ability to interpret features for the purpose of detecting bearing faults in images. The initial aim is to perform preprocessing on the original vibration signal data acquired from the bearing. The process entails converting the initial signal in the time domain into a two-dimensional representation known as a time-frequency map. In order to replicate the noise conditions found in the real working environment, a deliberate introduction of a noise signal is performed during this transformation, with a signal-to-noise ratio varying between 10 and 20. Following a comprehensive comparative analysis of different deep learning models, the SqueezeNet model has been selected due to its exceptional antinoise capabilities and superior accuracy. The progressive grid method is utilized for the purpose of optimization, aiming to analyze the fluctuations in network accuracy. The objective of this study is to examine the relationship between network accuracy and changes in hyperparameters. The SqueezeNet model, which has undergone fine-tuning, is subsequently employed in combination with diverse swarm optimization algorithms. Based on a comparative analysis of the loss function iteration curves, the whale algorithm has been selected as the preferred optimization algorithm. The aforementioned algorithm is subsequently utilized within the framework of resolving the fault detection issue. The experiment also demonstrates the model's feature extraction process by employing the t-SNE visualiza

关键词： bearing faults class activation mapping SqueezeNet t-SNE visualization method WOA

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Artificial intelligence based machine learning algorithm for prediction of cancer in female anatomy

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Multimedia Tools and Applications 2024年第16期84卷 1-27页

作者： Ghantasala, G S Pradeep Hung, Bui Thanh Chakrabarti, Prasun R, Sathiyaraj Pellakuri, Vidyullatha Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Karnataka Bangalore India Data Science Laboratory Faculty of Information Technology Industrial University of Ho Chi Minh city Ho Chi Minh City Viet Nam Deputy Provost ITM SLS Baroda University Gujarat Vadodara India Department of CSE GITAM School of Technology GITAM University Bengaluru India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur India

Breast and cervical cancers account for more than 85 percent of all cancer-related fatalities in developing nations, according to the World Cancer Research Fund. As a result, breast and cervical cancer have become one of the leading causes of mortality among women worldwide. This field is still in its infancy, with only a few studies in gynaecology and computer science looking into the detection of breast and cervical cancer. According to the researchers, medical records and early testing from individuals with breast and cervical cancer will be used in this study to determine the prognosis of those suffering from the diseases. To assess our cervical cancer predictions, we employed machine learning models such as Optimized Hybrid Ensemble Classifier (OHEC), which were trained on patient behavior and variables revealed to be associated with patient behavior. The datasets in this study have a substantial number of missing values, and the distribution of those values has been altered as a function of the missing values. OHEC classifier performance has been shown to improve when the number of features is reduced and the problem of high-class imbalance is resolved, because the accuracy, sensitivity, and specificity of the classifier, as well as the number of false positives, were used to demonstrate the success of feature selection in the suggested model's predictive analysis. This has been demonstrated through the use of numerous tests involving categorization challenges. The study underscores the critical significance of early detection and prognosis in combating breast and cervical cancers, which remain leading causes of mortality worldwide. Through the utilization of machine learning models like the OHEC, the authors have demonstrated the potential for improved predictive accuracy and clinical outcomes. The findings highlight the importance of addressing challenges such as missing data and class imbalance in enhancing the performance of predictive models for effective

关键词： Diseases

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LESaET: Low-Dimensional Embedding Method for Link Prediction Combining Self-Attention and Enhanced-TuckER

LESaET: Low-Dimensional Embedding Method for Link Prediction...

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

作者： Ding, Lichao Zhao, Jing Lu, Kai Hao, Zenghao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9781665410205

Knowledge graphs (KGs) provide a structured representation of the real world through entity-relation triples. However, current KGs are often incomplete, typically containing only a small fraction of all possible facts. This involves inferring missing content from existing information, a task known as link prediction. Existing methods in the field of link prediction struggle with controlling the dimensionality of embedding vectors or suffer from overly complex models. In order to tackle these challenges, we introduce a method in this paper, named Low-Dimensional Embedding Method for Link Prediction Combining Self-Attention and Enhanced-TuckER (LESaET). LESaET leverages both self-attention mechanisms and tensor factorization to learn expressive contextual-enhanced representations of KGs. Specifically, LESaET employs the multi-head self-attention mechanism of Transformer as an encoder to capture the mutual information between entities and relationships, and utilizes Enhanced-TuckER as a decoder, ultimately achieving expressive low-dimensional embeddings for link prediction tasks. LESaET demonstrates competitive performance compared to advanced methods on standard datasets. © 2024 IEEE.

关键词： Prediction models

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Implementation of Road Segmentation Using U-Net Model on Single Board Computer 9

Implementation of Road Segmentation Using U-Net Model on Sin...

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9th International Conference on Computer, Control, Informatics and Its Applications: Digital Transformation Towards Sustainable Society for Post Covid-19 Recovery, IC3INA 2022

作者： Prakasa, Esa Zhafran, Dary Astharini, Dwi Al Azhar University of Indonesia Faculty of Science and Technology Jakarta Indonesia Research Center for Data and Information Sciences BRIN Bandung Indonesia

ISBN: (纸本)9781450397919

Technological developments in the era of globalization, several companies are competing in the field of artificial intelligence by developing autonomous drive systems. Training and road segmentation testing in this study were carried out using deep learning with the U-Net architecture method. The advantage of this method over other methods is that U-Net retains the full context of the input image. The road segmentation algorithm is implemented using Python programming. The algorithm is then executed on a single board computer. The parameters sought are the first accuracy with a value of 91.65 %, the second precision with a value of 75.53 %, the third recall with a value of 93.19 %, the fourth F1-Score with a value of 82.99 %, and the last IOU with a value of 71.36 %. The live segmentation algorithm can still detect roads, but some scenes involving objects are not included in the road segmentation process. This is due to several factors such as confusing light conditions, blur, shadows, and colors. Live segmentation using RGB frame produces an average FPS value of 0.30, and without RGB produces an average FPS value of 1.82. © 2022 ACM.

关键词： Roads and streets

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