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designing Convolution Neural Network Architecture by utilizing the Complexity Model of the Dataset 9

Designing Convolution Neural Network Architecture by utilizi...

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9th international conference on Computing for Sustainable Global Development, INDIACom 2022

作者： Kawa, Sajad Ahmad Wani, M Arif University of Kashmir Postgraduate Department of Computer Science Srinagar India

ISBN: (纸本)9789380544441

Convolutional Neural networks (CNN) have been utilized in a wide variety of areas, with a high degree of performance. the design of the CNN has been a general problem addressed mainly by using the experts to design the architecture of the CNN model, which is quite inefficient. In recent times there has been the need to develop the CNN architecture automatically, resulting in multiple approaches that have been utilized for this process. In this paper, we give an overview of the multiple approaches utilized for automatic CNN architecture design and also propose a method to design the architecture, which utilizes the dataset itself for the process of designing the architecture of the CNN. the method shows good performance and in terms of the resources required to generate the architecture has significantly better performance than state-of-the-art models. © 2022 Bharati Vidyapeeth, New Delhi.

关键词： design

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Research on Phonetic Indexing Interface in Xinhua Dictionary Based on Differences of English and Chinese Letters 9th

Research on Phonetic Indexing Interface in Xinhua Dictionary...

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9th international conference on Learning and Collaboration Technologies (LCT) Held as Part of the 24th international conference on Human-computer Interaction (HCII)

作者： Zhao, Zimei Wu, Xumin Liang, Xun Wuhan Univ Technol Wuhan 430070 Hubei Peoples R China Beijing Technol & Business Univ Beijing 100048 Peoples R China

ISBN: (纸本)9783031056574;9783031056567

Xinhua Dictionary is an indispensable reference book in Chinese primary school teaching. However, through observation, it is found that due to the different pronunciations of English and Chinese letters, elementary school students who are beginners in language use the order of English capital letters as the phonetic index order. When using a Chinese character dictionary, phonetic confusion is easy to occur, which affects the retrieval efficiency. Based on the above questions, this paper combines the literature of linguistics and psychology to conduct an in-depth analysis around people, products and usage scenarios. A comparative study of letters and pronunciations that are easily confused between Chinese and English was conducted to explore the regularity of phonetic confusion. Experiment design was carried out on the preliminary summary of confusing phonetic sounds, to evaluate the prevalence of phonetic confusion problems, and to obtain pronunciations with higher confusion frequency. Integrate design elements and propose reasonable design solutions. At the same time, the redesign and usability test of the dictionary sequence retrieval interface are carried out. It is hoped that the research will shed light on the design improvement of user search interfaces in dictionaries and other reference books.

关键词： English and Chinese phonetics Syllable indexing Dictionary retrieval Human-computer interaction

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Applying design of Experiments to Evaluate the Influence of Parameters on the Economic Feasibility of the Eco-Industrial Parks 9th

Applying Design of Experiments to Evaluate the Influence ...

引用

9th EAI international conference on Nature of Computation and Communication, ICTCC 2023

作者： Cao-Van, Kien Faculty of Information Technology Nguyen Tat Thanh University Ho Chi Minh City Viet Nam

ISBN: (纸本)9783031594618

this research employs the design of experimental (DoE) to examine how various parameters impact the economic feasibility and overall satisfaction of enterprises operating within eco-industrial parks (EIPs). A full factorial design is constructed, using economic feasibility and overall satisfaction as response variables, and experimental data is generated by simulating diverse scenarios. Each iteration of the experiment utilizes a single-leader multi-follower (SLMF) game optimization model, focusing on designing water exchange networks within EIPs. the investigation encompasses several parameters in a case study involving ten follower enterprises aiming to minimize their annual operational costs. Concurrently, the EIP authority assumes the leader role with the objective of reducing the collective freshwater consumption of the EIP. Furthermore, this study employs binary logistic and multi-linear regressions to establish causal relationships. these relationships link input parameters with economic feasibility and overall satisfaction of operating businesses within EIPs. Ultimately, the reliability of the DoE methodology is showcased, offering valuable insights into enterprise parameters, EIP design, economic feasibility, and overall satisfaction. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： design of experiments

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Feature Fusion-based Automatic Grading of Color Doppler Flow within Ovarian Masses in Ultrasound Images 23

Feature Fusion-based Automatic Grading of Color Doppler Flow...

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9th international conference on Computing and Artificial Intelligence, ICCAI 2023

作者： Wang, Xu Zhu, Linlin Tao, Guowei Ma, Xin School of Control Science and Engineering Shandong University Shandong Jinan China Department of Ultrasound Qilu Hospital Jinan China

ISBN: (纸本)9781450399029

Ovarian cancer is the gynecological malignant tumor with the highest mortality. the early diagnosis still faces challenges. With the rapid development of computer-aided diagnosis technology, the demand for application in the medical industry has greatly increased. According to the Ovarian-adnexal Reporting and Data System (O-RADS), we propose an automatic grading approach for color Doppler flow within ovarian masses. After separating the color Doppler flow from the original image, the features such as histogram of oriented gradient (HOG), gray-level co-occurrence matrix (GLCM), and their fused features are extracted. the stratified 10-fold cross-validation is adopted to train the support vector machine (SVM) and k-nearest neighbor (KNN) classifiers. Experiments on a dataset with 570 ovarian color Doppler ultrasound images generate an average accuracy of 92.64%, a recall of 90.30%, a specificity of 97.75%, and a macro_F1 of 90.07%. the results show that the fused feature of GLCM and HOG can better grade the blood flow compared with other features. KNN has superior performance over SVM for this task. the color score of color Doppler flow within ovarian masses combined with the B-mode ultrasound images can ensure the integrity of key information while avoiding redundancy. the study lays the foundation for the development of a subsequent O-RADS grading system for ovarian masses. © 2023 Copyright held by the owner/author(s).

关键词： computer aided diagnosis

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Predicting Anger Proneness Using Deep Learning Techniques 9

Predicting Anger Proneness Using Deep Learning Techniques

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9th IEEE international conference on Computing, Engineering and design, ICCED 2023

作者： Adhinegoro, Ammar Yaafi Adiputra, Ida Bagus Gede Purwa Manik Tionando, Vincent Colin Setiawan, Karli Eka Hasani, Muhammad Fikri Bina Nusantara University Computer Science Department Jakarta Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350370126

High levels of anger proneness can affect the individuals and others around them in the society. Anger proneness or the likelihood of experiencing anger in response to certain stimuli or situations is influenced by a variety of factors, including personality traits, life experiences, and environmental factors. Accurately predicting the level of anger proneness in individuals can help mitigate a variety of risks, including relationship and mental health problems. In this paper, we tried to create a model that is capable of accurately predicting levels of anger proneness based on one's answers to a personality questionnaire. To do so, we utilized a deep learning algorithm namely SAINT (Self-Attention and Intersample Attention Transformer), alongside with other three machine learning and deep learning algorithms as the comparisons, LSTM (Long Short-Term Memory), GRU (Gated Recurrent Unit), and XGBoost (EXtreme Gradient Boosting). the four models were trained with the same dataset and the same data preprocessing methods. All the four algorithms couldn't manage to produce any models with satisfactory performances, with the highest performing model produced using the state-of-the-art machine learning classifier XGBoost only achieving 55.38% accuracy. Multiple data preprocessing methods such as resampling and feature selection with the help of XGBoost were applied to the dataset with negligible effects to the models' performances. the results lead us to the conclusion that the dataset contains too many inconsistencies in its rows, as can be fairly expected from a dataset based on individuals' responses to an online personality questionnaire. the inconsistencies affected all the models' ability to find the patterns in between rows and columns and compromise their resulting efficacies to do the inferences. © 2023 IEEE.

关键词： Long short-term memory

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Comparison of Machine Learning Algorithms for Diabetes Classification 9

Comparison of Machine Learning Algorithms for Diabetes Class...

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9th IEEE international conference on Computing, Engineering and design, ICCED 2023

作者： Irmawan, Frederick Nathan Muliawan, Nicholas Hans Angky, Edbert Valencio Setiawan, Karli Eka Hasani, Muhammad Fikri Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350370126

Diabetes is a chronic disease that influences how the human body turns food into energy and affects a significant portion of the global population. Diabetes prediction plays a major role in helping diagnose diabetes, which can help prevent complications and improve patient outcomes. this paper presents a machine learning approach for predicting the onset of diabetes using clinical and demographic data. the dataset used is from the Pima Indian Diabetes Database (PIDD), which was gathered from the National Institute of Diabetes and Digestive and Kidney Diseases. this dataset contains various variables such as pregnancies, glucose, blood pressure, skin thickness, insulin, body mass index (BMI), diabetes pedigree function, and age. Some preprocessing methods, such as oversampling and removing zero values to impossible data, was done to the dataset in order to balance and improve the input data. then the paper compares four different machine learning algorithms, which are the K-Nearest Neighbors Algorithm (KNN), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Random Forest (RF). these models are then built to be predictive. the model's performance is assessed using a number of metrics, including the F1-score, accuracy, precision, and recall. the Random Forest algorithm works better than other models, according to the results by achieving an accuracy of 89.5%, a precision of 97%, a recall of 83%, and an f1-score of 89%. this study shows the potential of machine learning approaches in diabetes prediction as well as the efficacy of the suggested preprocessing strategy which hopefully help clinicians identify individuals at risk of developing diabetes for early intervention. © 2023 IEEE.

关键词： Forecasting

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design and Implementation of a Collaborative Idea Evaluation System 9th

Design and Implementation of a Collaborative Idea Evaluation...

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9th international conference on HCI in Business, Government and Organizations (HCIBGO) Held as Part of the 24th international conference on Human-computer Interaction (HCII)

作者： Hermann, Andreas Univ Munster European Res Ctr Informat Syst ERCIS Munster Germany

ISBN: (纸本)9783031055447;9783031055430

Digital transformation is a complex endeavor with unforeseen pitfalls. However, small and medium-sized enterprises (SMEs) seem to struggle with digital transformation. To address this problem, governments have started initiatives, i.e., publicly-funded support units, with the purpose to support digital transformation through facilitating idea management in SMEs. Studies suggest that the application of (IT-based) idea evaluation systems could impact the digital transformation success of SMEs. However, contributions addressing the interplay of support units and SMEs in the context of idea evaluation are scarce. therefore, this article addresses the following problems (1) SMEs require external support in the context of digital transformation, and (2) idea evaluation is often improperly executed. Bringing together these areas leads to the objective to design and implement a collaborative idea evaluation system. the system development follows the methodological pathway for design science research. Observations from seven small-scale projects with SMEs complemented with selected literature inform the system design. Two focus group discussions serve as an evaluation of the system. the results are a set of design features and an interaction concept. Finally, a prototypical web application puts the conceptual design into action to tackle the identified practical problem.

关键词： Idea management Software design Digital transformation Idea evaluation Small and medium-sized enterprises Publicly-funded support units

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A comparative study of deep learning algorithms for image-based classification of hyperpigmented skin disease 9th

A comparative study of deep learning algorithms for image-ba...

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9th international conference on computer Science and Computational Intelligence, ICCSCI 2024

作者： Ranuh, I. Gusti Bagus Ramadha Saverian Sanjoto, Marvellino Christian Zakiyyah, Alfi Yusrotis Meiliana Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mathematics Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

there are growing numbers of significant skin disorders, including skin pigmentation. It states that skin color is determined by the amount of melanin produced by the body. the two main categories of skin pigmentation are hyperpigmentation, in which pigment seems to overflow, and hypopigmentation, in which pigment appears to decrease. However, many skin conditions share characteristics, making it difficult for dermatologists to correctly early diagnose their patients. Consequently, the accurate detection of skin disorders and the diagnosis of dermatoscopy pictures can be greatly aided by machine learning and deep learning approaches. the most effective deep learning technique for picture identification was investigated to diagnose hyperpigmented skin diseases. YOLO, DenseNet201, GoogLeNet, InceptionResNetV2, and MobileNet were among the pretrained models used to classify four most common hyperpigmented skin disorders. Using assessment metrics like accuracy and AUC (Area Under Curve), it was determined which deep learning method would work best for creating a clinical diagnostic system. the study analyzed the accuracy rates of five pretrained models, including GoogleNet, MobileNet, DenseNet201, InceptionResNetV2, and YOLO, after 50 iterations. Using metrics such as accuracy and Area Under the Curve (AUC), the study evaluated the models' performance on a small dataset split into 80% for training and 20% for testing. Training accuracy rates were 93.8%, 100%, 100%, 98.77%, and 97.43%, respectively, while test accuracy rates were 87.18%, 79.49%, 87.18%, 89.74%, and 97.56%. DenseNet201 showed strong performance, particularly for cafe-au-lait spots (CS), melasma (ML), and nevi (MN), but struggled with congenital nevus (CN). Despite DenseNet201's strong traditional CNN capabilities and generalization, YOLO emerged as the top model due to its stable accuracy and AUC values, as confirmed by confusion matrices. While these models show promise as diagnostic tools for dermatolog

关键词： Dermatology

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A Case Study in Playful Facilitation for designing Embodied Experiences 9th

A Case Study in Playful Facilitation for Designing Embodied ...

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9th international conference on design, Learning and Innovation-DLI

作者： Font, Jose O'Bryan, Mikaela Alvarez, Alberto Robinson, Raquel Malmo Univ Game Lab Dept Comp Sci & Media Technol Malmo Sweden IT Univ Copenhagen Copenhagen Denmark

ISBN: (纸本)9783031856624;9783031856631

Embodied design is fostering relevant advancements in novel design methods for learning and innovation. Nevertheless, practice and research show that teaching and facilitating embodied design is a challenging task. this paper presents a playful approach for conducting embodied design sessions, which adapts existing card-game mechanics to improve the accessibility and usability of existing embodied design toolboxes during a design session. Our approach hypothesizes that card-game mechanics can address usual pitfalls common to card-based facilitation toolkits by adding structure, guidelines, and playfulness. We also envision that such toolboxes, that have become popular in the research in participatory and co-design contexts, can seamlessly incorporate card-game mechanics like the ones we propose. We describe the setting of the design session, as well as present and discuss the results obtained from observation and participant questionnaires. Finally, we also outline the next steps for designing an embodied workshop in sustainability based on the results from this study. We strive to analyze our proposed method by its capacity to blend into the structure and materials of a card-based toolbox, eliciting the playfulness embedded in the leveraged card-game mechanics to implicitly provide support and guidance through the session, to both facilitators and participants, without disrupting the flexible and open flow of an movement-based design session.

关键词： Movement-based design game-based design design facilitation method cards

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computer Data Based on Domino Application System design and Implementation of Interface Analysis Experiment 23

Computer Data Based on Domino Application System Design and ...

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9th international conference on Frontiers of Educational Technologies, ICFET 2023

作者： Jun, Zheng Wei, Huang Ming, Li Xiao Department of Computer Science Guizhou Police College Guiyang550005 China Information Engineering Department Guizhou Light Industry Vocational and Technical College Guizhou Guiyang550001 China Department of Planning Guizhou Police College Guizhou Guiyang550005 China

ISBN: (纸本)9798400707353

In the construction of enterprise informatization based on Domino application systems, solving the problem of data sharing among various systems has become the key to achieving a unified platform for such enterprises. this article uses XML files as an intermediate carrier for structured data, implementing a domino based application system and data interaction between various systems. For unstructured data, it is transmitted to other systems in the form of attachments and identified in the XML file. the delivery method adopts FTP service delivery to meet the loosely coupled architecture, reducing overall complexity and dependencies, making the application environment more agile, adapting to changes faster, and reducing risks. © 2023 ACM.

关键词： Electronic data interchange

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