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Advancements in Computing (ICAC), International Conference on

作者： R.G.V Saranga A.H.N. Jayasinghe U.A.S. Divyantha S.W.L.P de Silva N.H.P. Ravi Supunya Swarnakantha P. K. Suriya Kumari Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

The study aims to develop a mobile application for young children to learn Sinhala letters, shapes, colors, and storytelling incorporating machine learning models to evaluate and enhance educational activities. With the rise of online education during the COVID-19 pandemic, the familiarity of children with mobile devices provides an opportunity to create an engaging and educational experience. The application will teach Sinhala letters using object images, allowing children to upload their own images for feedback. It also includes a feature for children to practice writing letters and analyze their progress. Also, the application introduces colors and shapes in Sinhala, encouraging children to draw and track their improvement. Additionally, the application aims to generate stories in Sinhala to improve children's creativity and thinking knowledge. This research addresses a critical gap in existing Sinhala learning applications by integrating machine learning for activity assessment, promising to significantly impact and improve early language education for children.

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Exploring the role of project status information in effective code smell detection

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Cluster Computing 2025年第1期28卷 1-18页

作者： Alkharabsheh, Khalid Alawadi, Sadi Crespo, Yania Taboada, José A. Software Engineering Department Prince Abdullah bin Ghazi Faculty of Information and Communication Technology Al-Balqa Applied University Al-Salt19117 Jordan School of Computer Science Blekinge Tekniska Högskola Blekinge Tekniska Högskola Blekinge Karlskrona37179 Sweden Departamento de Informática Escuela de Ingeniería Informática Universidad de Valladolid Paseo de Belén 15 Valladolid47011 Spain Department of Electronics and Computing Universidad de Santiago de Compostela Santiago15782 Spain

Repairing code smells detected in the code or design of the system is one of the activities contributing to increasing the software quality. In this study, we investigate the impact of non-numerical information of software, such as project status information combined with machine learning techniques, on improving code smell detection. For this purpose, we constructed a dataset consisting of 22 systems with various project statuses, 12,040 classes, and 18 features that included 1935 large classes. A set of experiments was conducted with ten different machine learning techniques by dividing the dataset into training, validation, and testing sets to detect the large class code smell. Feature selection and data balancing techniques have been applied. The classifier’s performance was evaluated using six indicators: precision, recall, F-measure, MCC, ROC area, and Kappa tests. The preliminary experimental results reveal that feature selection and data balancing have poor influence on the accuracy of machine learning classifiers. Moreover, they vary their behavior when utilized in sets with different values for the selected project status information of their classes. The average value of classifiers performance when fed with status information is better than without. The Random Forest achieved the best behavior according to all performance indicators (100%) with status information, while AdaBoostM1 and SMO achieved the worst in most of them (> 86%). According to the findings of this study, providing machine learning techniques with project status information about the classes to be analyzed can improve the results of large class detection. © The Author(s) 2024.

关键词： software quality

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Construction of PCOS Prediction Model Based on BP Neural Network

Construction of PCOS Prediction Model Based on BP Neural Net...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Xianqing Hu Amit Yadav Asif Khan Abhishek Pratap Sah Sami Azam School of Computer and Software Chengdu Neusoft University Chengdu China Faculty of Science and Technology Charles Darwin University NT Australia Department of Computer Application Integral University Lucknow India School of Management Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Polycystic ovary syndrome (PCOS), a common endocrine-metabolic disorder affecting about 10-13% of women during reproductive age worldwide, often leads to irregular menstruation, infertility, obesity, and long-term health risks such as diabetes and cardiovascular disease. In this paper, clinical data from PCOS patients and healthy control group were collected from ten hospitals containing 37 key indicators such as age, weight, BMI, menstrual cycle, etc. BP (Back Propagation) Neural Network prediction models were constructed and compared using the collected data and the cross-validation method was used for parameter tuning. It was found that BP N eural Network performed particularly well on the test set, and both demonstrated high prediction accuracy and generalization ability which provided strong evidence for the early identification and early valuable intervention opportunities for PCOS patients.

关键词： Training Adaptation models Accuracy Biological system modeling Computational modeling Neural networks Predictive models Data models Robustness Tuning

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Deep Learning-Based E-Learning Solution for Identifying and Bridging the Knowledge Gap in Primary Education

Deep Learning-Based E-Learning Solution for Identifying and ...

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International Research Conference on Smart Computing and Systems engineering (SCSE)

作者： D.P.H Arunoda S.R Walpola S.M.I Piumira A.D.M.P Athukorala Thusithanjana Thilakarathna Sanjeevi Chandrasiri Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

Educational teaching apps are primarily available in app stores to educate students in various contexts. Lack of educational resources, physical and mental health conditions, and poverty cause some students to skip school and move on to the next school grade without completing the course content of the previous grade. Most of the available apps focus on specific content to cover. The Smart Primary Education Tutor (SPET) teaching app specifically focuses on the missed content by analyzing their knowledge gap and providing lessons to cover the missed content. The main objective of SPET is to develop a methodology to identify the gap in student knowledge and fill the knowledge gap by teaching using smart techniques. SPET is determined to identify students’ interactions (attention, emotions) with the system to identify students’ ability to use the learning tool, identifying gaps in students’ knowledge levels compared to their actual grades using activities and voice-based technologies, teaching to cover the knowledge gap by providing engaging activities and lessons and evaluating students by conducting a final assessment and analyze students’ knowledge and performance obtained through the system. Students between the ages of 5 and 8 are targeted in the community to apply. The solution embeds deep learning-based models including attention classification models using head posture estimation, facial expression recognition, and eye gaze estimation, speech recognition models to identify provided verbal answers, handwriting recognition models to evaluate student performance, and smart teaching. The child emotion recognition model achieved 93% accuracy. The Attention span evaluation model achieved 85% accuracy. The handwritten numerical and English character data recognition model which detects answers for the final assessment paper achieved 85% percent of accuracy.

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A novel three-phase primary-side control topology for high-power IPT system

A novel three-phase primary-side control topology for high-p...

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Wireless Power Week (WPW)

作者： Zhihao He Duleepa Thrimawithana Martin Neuburger Grant Covic Department of Electrical Computer and Software Engineering University of Auckland Auckland New Zealand Faculty of Mobility and Technology Esslingen University of Applied Sciences Goppingen Germany

There is a recognised need for developing multiphase inductive power transmission (IPT) systems to shorten the charging time in the wireless charging field. Researchers have not treated the converter needed to drive the multi-phase coils in much detail. This paper proposes a three-phase integrated boost multilevel converter (TP-IBMC) based on a multi-phase IPT system. There are advantages of multi-level converters and the boost function in TP-IBMC. Using a digitized modulation scheme, it can output a square wave voltage with variable amplitude to regulate the power in the primary. The TP-IBMC includes three bridge arms (a, b, c), each of which contains a DC inductor to achieve energy storage and multiple sub-modules to realize variable levels. The number of sub-modules can be increased or decreased. This paper establishes a three-phase IPT model considering the mutual inductance between phases and discusses the capacitor voltage balancing strategy. A primary-side control IPT simulation model compliant with the SAE J2954 WPT 3 standard is built to verify that TP-IBMC can regulate output power through a digitised modulation scheme, under the fluctuations of coupling factor (i.e., 0.14 - 0.31) and battery voltage (i.e., 280 V - 420 V).

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Modif-SegUnet: Innovatively Advancing Liver Cancer Diagnosis and Treatment through Efficient and Meaningful Segmentation of 3D Medical Images

Modif-SegUnet: Innovatively Advancing Liver Cancer Diagnosis...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Chenhang Wang Yujian Jin Jiayi Liang YuHan Yang Shanling Nie Yang Hai Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Faculty of Engineering The University of Sydney Sydney Australia

Hepatocellular Carcinoma (HCC) holds a record of high incidence and severe global harm. In tasks of liver cancer segmentation based on 3D medical images, the majority of methods have endeavored to enhance the 3D U-net by integrating the latest modules from the field of computer Vision (such as the transformer), often overlooking the distinct characteristics of liver components. We introduced a novel deep learning architecture, Modif-SegUnet, to circumvent this limitation. This architecture extends the U-net model by incorporating a 3D-modifiable attention module, thereby fostering a heightened focus on distinguishing between normal liver sections and lesions. Furthermore, Modif-SegUnet ingeniously amalgamates the 3D-modifiable attention module with the Modif-transformer block, enabling efficient capture of relevant and valuable full-text information in CT images that contain liver or tumor regions. We subjected the proposed Modif-SegUnet to evaluation on the Liver Tumor Segmentation benchmark dataset. Experimental outcomes indicate that our methodology surpasses state-of-the-art approaches in liver tumor segmentation, suggesting potential pathways for advancing the diagnosis and treatment of liver cancer.

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A-REA: Relation-Aware Graph Convolutional Network Entity Alignment Via a Differential Adjacency Matrix

A-REA: Relation-Aware Graph Convolutional Network Entity Ali...

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Image Processing, Electronics and computers (IPEC), Asia-Pacific Conference on

作者： Yuemei Dang Chen Qiu Feng Gao Jinguang Gu Lihua Zhang Ghazaleh Arman School of Computer Science and Technology Wuhan University of Science and Technology Wuhan Hubei China Software College East China Jiaotong University Nanchang Jiangxi China Faculty of Information Technology and Computer Engineering Urmia University of Technology Urmia Iran

Entity Alignment is the task of seeking entities with the same semantics from distinct knowledge graphs (KGs). Researchers have recently proposed Graph Convolutional Network (GCN) based approaches to achieve this task. Most existing GCN-based methods embed all the relations into an identical value, making the underlying loss on the diversity of the relationship. Relation semantics of entities and relation structure have benefitted the entity alignment task in the monolingual scenarios. This paper proposes a Relation-Aware Graph Convolutional Network with a Differential Adjacency Matrix (A-REA), which exploits the relation-specific semantics and structural information of entities in the KGs to promote the entity representations. The experiments on DBP-15K and DWY-100K datasets, including three cross-lingual subsets and two cross-knowledge graph sub-datasets, reveal that our approach leads to advance in entity alignment task in both the cross-lingual and cross-knowledge KGs. Discriminating the relation shows the effective signal for entity alignment, especially for the schema heterogeneity of different KGs.

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Optimizing Results in Aerial Images through Post-Processing Techniques on YOLOv7 23

Optimizing Results in Aerial Images through Post-Processing ...

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12th International Symposium on Information and Communication technology, SOICT 2023

作者： Vo, Nguyen D. Nguyen, Thu M. Nguyen, Triet H.M. Nguyen, Khang Multimedia Communications Laboratory University of Information Technology Vietnam National University Ho Chi Minh City Ho Chi Minh city Viet Nam Faculty of Computer Science University of Information Technology Vietnam National University Ho Chi Minh City Ho Chi Minh city Viet Nam Faculty of Software Engineering University of Information Technology Vietnam National University Ho Chi Minh City Ho Chi Minh city Viet Nam

ISBN: (纸本)9798400708916

Object detection in aerial images has garnered significant attention from the research community in recent years. The challenges posed by small objects, diverse orientations, and complex backgrounds have spurred extensive research efforts. In this paper, we focus on object detection in a YOLOv7-based framework, to address the issue of overlapping predicted regions, we introduce and apply the Soft-NMS (Non-Maximum Suppression) technique, a post-processing method known for its effectiveness in improving detection accuracy in such scenarios. Soft-NMS adjusts bounding box scores based on the extent of overlap, allowing for more accurate localization of objects in densely populated regions. Furthermore, we present comprehensive experimental results to validate the efficacy of our proposed approach. The analyses encompass a thorough evaluation on the UCAS Aerial Object Detection (UCAS AOD) dataset, comprising over 1500 aerial images captured from diverse perspectives. Our method has demonstrated an improvement in object detection performance, particularly in scenarios with closely positioned objects. The proposed framework showcases its ability to handle complex aerial scenes with higher precision and recall rates compared to conventional methods. © 2023 ACM.

关键词： Object detection

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CIXG: A Comprehensive Approach to Driver Gene Identification and Causal Interpretation

CIXG: A Comprehensive Approach to Driver Gene Identification...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yawen Liu Yiwen Chen Shanling Nie Hai Yang Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Center for Continuing and Lifelong Education National University of Singapore Faculty of Engineering The University of Sydney Australia

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

With the ongoing advancements in science and technology and the increasing research focus on cancer-related issues, there has been a proliferation of omics-related resources for in-depth analysis and exploration. This burgeoning volume and complexity of biological data have fostered the integration of machine-learning techniques into biology. As a result, numerous machine-learning strategies have been established to identify driver mutations. Yet, many of these strategies produce complex models, complicating comprehension and thereby clouding the impact of input features on the resulting predictions. Our analysis presented the CIXG framework, which integrates a driver gene prediction module using XGBoost with a causality interpretation module anchored on CXPlain. This architecture enables quantifying each input feature’s contribution to the prediction outcome and ensures precise predictions of driver genes. When benchmarked against the state-of-the-art (SOTA) method, CIXG demonstrated superior accuracy in pinpointing driver genes across pan-cancer studies and within the 32 specific cancer types. Importantly, our results underscored that mutation features chiefly influence CIXG’s predictive prowess, with additional support from other omics features.

关键词： Accuracy Biological system modeling Cause effect analysis Machine learning Predictive models Benchmark testing Complexity theory Bioinformatics Cancer

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Uniqueness Proof of Optimal Power Management Strategies for Energy Storage Dynamic Models

SSRN

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SSRN 2024年

作者： Goldstein-Tweg, Tom Ginzburg-Ganz, Elinor Belikov, Juri Levron, Yoash The Andrew and Erna Viterbi Faculty of Electrical and Computer Engineering Technion—Israel Institute of Technology Haifa3200003 Israel Department of Software Science Tallinn University of Technology Akadeemia tee 15a Tallinn12618 Estonia

The current paper belongs to a class of research works that aim to find analytic and semi-analytic solutions to optimal power flow problems that involve storage systems. The main contribution of this work is a rigorous proof for the uniqueness of the ``shortest-path" optimal solution, which is a cornerstone in this class of algorithms, and relies upon a graphical design procedure presented in an earlier work. The proof builds upon five consequential lemmas, each provides a different characteristic of the optimal solution. These characteristics are then merged to prove the uniqueness of the optimal solution, which is given by the shortest path of the generated energy within bounds. The presented proof provides not only a sound theoretical foundation for this class of algorithms, but may also help develop additional analytic solutions for more complex optimal control problems involving storage systems. © 2024, The Authors. All rights reserved.

关键词： Power management

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