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2nd International Conference on Electronic and Electrical engineering and Intelligent System, ICE3IS 2022

作者： Octavially, Reynaldi Prama Priyadi, Yudi Widowati, Sri Telkom University Department of Informatics Bandung Indonesia Telkom University Department of Software Engineering Bandung Indonesia

ISBN: (纸本)9781665465410

In this research, extraction is carried out to change the text on an artifact to be processed through Text Mining to process and analyze the text whose results are validated, using the coefficient equation formula. There is a discrepancy between the flow in the Activity Diagram and the steps performed in the Use Case Description. Based on these problems, this study aims to extract an artifact to find similarities between the Activity Diagram and the actions taken in the Use Case Description and perform text analysis using the Text Mining method to obtain similarities, then validation and reliability are carried out. The extraction process is broken down into several documents according to the artifacts used, for Use Case Description, namely: UC01, UC02, UC03, UC04, and UC05. As for the Activity Diagram, namely: AD1, AD2, AD3, AD4, and AD5. The results obtained from this study are the highest similarity between activity diagrams and use case descriptions in sentences UC04 and AD4 with a total similarity of 0.51394086. The highest similarity between words lies in the words 'request' and 'application' with a complete similarity of 0.9231. Kappa's score with the Gwet's AC1 formula using Python programming is 'Less than the Opportunity Agreement' with a number of -008583, while the questionnaire filled out by the Expert is 'Almost perfect' with a score of 0.92246. © 2022 IEEE.

关键词： Extraction

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Object pest detection method based on lightweight SSD-RA algorithm 11

Object pest detection method based on lightweight SSD-RA alg...

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11th International Conference on Networks, Communication and Computing, ICNCC 2022

作者： Li, Shixuan Peng, Hongxing Yuan, Jingqi Software Engineering Institute of Guangzhou China College of Mathematics and Informatics South China Agricultural University China

ISBN: (纸本)9781450398039

In order to detect the types and quantities of pests in rice fields quickly and accurately, a lightweight target pest detection method SSD-RA based on SSD algorithm is proposed. In order to deal with the problems of high missed detection rate, inaccurate positioning, slow detection speed, large number of model parameters and low accuracy of the detection model, the ResNet feature extraction network was introduced and optimized. The first prediction feature layer of SSD was connected to the Conv3-x module of the ResNet network, and all network layers after the Conv3-x module were dropped. The number of parameters of the model is reduced, so that the model is more lightweight, the detection speed is improved, and the redundant features are reduced to ensure the accuracy of the model. In addition, aiming at the characteristics of small target, the structure of prediction feature layer of SSD algorithm is improved, the number of prediction feature layers is adjusted, and the output of underlying feature Conv2-x is connected to the prediction feature layer. The candidate box of each cell in the new prediction feature layer is 6, which accurately divides the boundaries of large, medium and small target boxes. The experimental results show that the mAP of SSD-RA of the improved algorithm in this paper is 84.1%, which is 23.4 percentage points higher than that of the original SSD model. The reasoning time in CPU and GPU environment is 0.056s and 0.009s, which is 0.101s and 0.005s faster than that of the original SSD model, and the model size is 51.9MB. It is reduced to about 7/100 of the original SSD model. Compared with other models, the mAP of SSD-RA is 7.8 and 4.2 percentage points higher than that of EfficientDet and RFCN, respectively. The SSD-RA model is more effective and faster to detecting insect pests and reduces the missed detection rate. © 2022 ACM.

关键词： Forecasting

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Measurement of Similarity between Use Case Description and Sequence Diagram in software Requirement Specification using Text Analysis for Dtrain Application 2

Measurement of Similarity between Use Case Description and S...

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2nd International Conference on Electronic and Electrical engineering and Intelligent System, ICE3IS 2022

作者： Yanis, Rifqi Zaidan Irsyad Priyadi, Yudi Puspitasari, Shinta Yulia Telkom University Department of Informatics Bandung Indonesia Telkom University Department of Software Engineering Bandung Indonesia

ISBN: (纸本)9781665465410

Dtrain application is built by implementing software documents, one of which is the software Requirement Specification (SRS). In compiling software documents, there is a mismatch between one document element with other elements. Therefore, this study will measure the similarity between the Use Case Description (UCD) on the SRS case study Dtrain with Sequence Diagrams (SD) to determine how similar or compatible the two SRS elements are. This study aims to measure the similarity of UML artifacts between UCD and SD contained in a software development document. The suitability of the two artifacts can be done by analyzing the text in the description (UCD) and object (SD) sections. Based on the results and discussion, this study resulted in the following. First, a study that finds level of similarity between UCD and the SD by comparing the step results performed an extraction on the UCD and the extraction of messages and actors on the SD. Second, there are five use cases in the Dtrain application, so the UCD and SD are each 5. Third, the highest similarity value results are found in SD 1 and SD 5, with a similarity value of 0.712. Fourth, the results of the validation value using algorithm code to get a kappa score of 0.331. Fifth, the results of the validation obtained from results of the expert questionnaire got a value of 0.8905. © 2022 IEEE.

关键词： Extraction

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Securing the Silver Surfers: Cybersecurity Awareness Among Older Adults 19

Securing the Silver Surfers: Cybersecurity Awareness Among O...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Ahmad Nawi, Haslinda Sutan Omar, Siti Fatimah Basaruddin, Suzana Info. Science & Computing Department Management and Science University Selangor Malaysia Software Engineering Section UniKL MIIT Kuala Lumpur Malaysia Informatics & Analytics Section UniKL MIIT Kuala Lumpur Malaysia

ISBN: (纸本)9798331507817

This research investigates the perceptions of cybersecurity among older adults in Malaysia, aged 60 years and above, who are reliant on technology for basic activities. The data from 331 participants showed that 30.8% were online for 4-8 hours daily, while 3.3% were completely disconnected. Interestingly, more than half of the respondents were unaware of cybersecurity threats, especially malware and ransomware, which put their data in jeopardy. While there is some awareness related to unauthorized access and computer fraud, knowledge gaps remain significant, and effective educational interventions are thus needed. Informal, self-directed learning appears to be a widespread way of acquiring skills in this age group, often supplemented by community workshops and peer networks. Given that almost half of the older adults spend considerable time online, improving their digital skills in tandem with their support systems is an important goal for enhancing their online experiences. It indicates the need for a twin approach: enhancing cybersecurity awareness with specialized educational programs, alongside developing flexible opportunities for informal learning. Educational institutions and relevant policies are encouraged to prioritize building hands-on, specific cybersecurity education for seniors. Also, user interface design considering cybersecurity-related issues can make using the technological environment as user-friendly and secure as possible. These strategies will contribute to creating an inclusive digital society, where older adults are confident and safe in using digital technologies while continuously contributing to their personal and professional development. © 2025 IEEE.

关键词： Economic and social effects

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Reconstruct and match: out-of-distribution robustness via topological homogeneity 24

Reconstruct and match: out-of-distribution robustness via to...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Chaoqi Chen Luyao Tang Hui Huang College of Computer Science and Software Engineering Shenzhen University School of Informatics Xiamen University

ISBN: (纸本)9798331314385

Since deep learning models are usually deployed in non-stationary environments, it is imperative to improve their robustness to out-of-distribution (OOD) data. A common approach to mitigate distribution shift is to regularize internal representations or predictors learned from in-distribution (ID) data to be domain invariant. Past studies have primarily learned pairwise invariances, ignoring the intrinsic structure and high-order dependencies of the data. Unlike machines, humans recognize objects by first dividing them into major components and then identifying the topological relation of these components. Motivated by this, we propose Reconstruct and Match (REMA), a general learning framework for object recognition tasks to endow deep models with the capability of capturing the topological homogeneity of objects without human prior knowledge or fine-grained annotations. To identify major components from objects, REMA introduces a selective slot-based reconstruction module to dynamically map dense pixels into a sparse and discrete set of slot vectors in an unsupervised manner. Then, to model high-order dependencies among these components, we propose a hypergraph-based relational reasoning module that models the intricate relations of nodes (slots) with structural constraints. Experiments on standard benchmarks show that REMA outperforms state-of-the-art methods in OOD generalization and test-time adaptation settings.

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Adaptive Image Enhancement Model for the Robot Vision System 14

Adaptive Image Enhancement Model for the Robot Vision System

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14th International Scientific and Practical Conference on Environment. Technology. Resources, ETR 2023

作者： Smelyakov, Kyrylo Savulioniene, Loreta Chupryna, Anastasiya Sakalys, Paulius Sandrkin, Denys Software engineering department Kharkiv National University of Radio Electronics Kharkiv Ukraine Faculty of electronics and informatics Vilniaus Kolegija/Higher Education Institution Vilnius Lithuania

ISBN: (纸本)9781713874881

Robotics is one of the important trends in the current development of science and technology. Most modern robots and drones have their own vision system, including a video camera, which they use to take digital photos and video streams. These data are used to analyze the situation in the robot's camera field of view, as well as to determine a real-time robot's behavior algorithm. In this regard, the novelty of the paper is special polynomial mathematical model and method for adaptive gradational correction of a digital image. The proposed model and method make it possible to independently adjust to brightness scales and image formats and optimally perform gradational image correction in various lighting conditions. Thus, ensuring the efficiency of the entire subsequent cycle of image analysis in the robot's vision system. In addition, the paper presents the results of numerous experiments of such gradational correction for images of various classes, as well as conditions of reduced and increased levels of illumination of the field of view objects. Conclusions and recommendations are given regarding the practical application of the proposed model and method. © 2023 Kyrylo Smelyakov, Anastasiya Chupryna, Denys Sandrkin, Loreta Savulioniene, Paulius Sakalys.

关键词： Image enhancement

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Predicting Student Attrition: A Case Study of the University of Namibia Bachelor of Accounting (Chartered Accountancy) Programme

Predicting Student Attrition: A Case Study of the University...

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2023 International Conference on Emerging Trends in Networks and Computer Communications, ETNCC 2023

作者： Nakale, Samuel Nghidengwa Amugongo, Lameck Mbangula Namibia University of Science and Technology Department of Informatics Journalism & Media Technology Windhoek Namibia Namibia University of Science and Technology Department of Software Engineering Windhoek Namibia

ISBN: (纸本)9798350314878

Understanding the reasons behind student attrition is essential to developing retention strategies. In this study, we developed models to predict student attrition for the Chartered Accountancy programme at the University of Namibia (UNAM). Three hundred and eighty-nine records of accounting students were collected from the UNAM student database. We applied five classification machine learning algorithms: logistic regression (LR), support vector machines (SVM), k-nearest neighbors (k-NN), decision trees (DT) and Gaussian Naive Bayes (GNB) to identify patterns in student attrition. After, accuracy, specificity, sensitivity, F-measure and Area Under Curve were computed to measure the performance of each model. Finally, Shapley additive explanations (SHAP) was used to determine how variables influenced model prediction. The LR, k-NN, DT, SVM and GNB achieved an accuracy of 80.3%, 73.8%, 82%, 80.3% and 77%, respectively. Overall, the DT classifier performed the best at predicting student attrition. We found that academic performance in year 1 core modules: financial accounting A and financial accounting B were the most important features, on average contributing the most to the predicted probability of dropping out. We have demonstrated that it is feasible to predict students who are likely to abandon their studies while in their first year. The major difference between our study and previous studies is that we do not focus on traditional studied areas (science, technology, engineering and mathematics). In this study, we developed predictive models to identify student attrition in the bachelor of accounting program in a Namibian university. © 2023 IEEE.

关键词： Forecasting

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Enhancing software Cost Estimation Using Feature Selection and Machine Learning Techniques

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Computers, Materials & Continua 2024年第12期81卷 4603-4624页

作者： Fizza Mansoor Muhammad Affan Alim Muhammad Taha Jilani Muhammad Monsoor Alam Mazliham Mohd Su’ud Department of Software Engineering FAST-National University of Computer&Emerging SciencesKarachi75030Pakistan Faculty of Engineering Science and Technology IQRA UniversityKarachi72500Pakistan Department of Computer Science Bahria UnivesityKarachi74800Pakistan Faculty of Computing Riphah International UniversityIslamabad44600Pakistan Faculty of Computing and Informatics Multimedia University(MMU)Cyberjaya63100SelangorMalaysia

software cost estimation is a crucial aspect of software project management,significantly impacting productivity and *** research investigates the impact of various feature selection techniques on software cost estimation accuracy using the CoCoMo NASA dataset,which comprises data from 93 unique software projects with 24 *** applying multiple machine learning algorithms alongside three feature selection methods,this study aims to reduce data redundancy and enhance model *** findings reveal that the principal component analysis(PCA)-based feature selection technique achieved the highest performance,underscoring the importance of optimal feature selection in improving software cost estimation *** is demonstrated that our proposed method outperforms the existing method while achieving the highest precision,accuracy,and recall rates.

关键词： Machine learning software cost estimation PCA hyper parameter feature selection

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Comparison of Potential Road Accident Detection Algorithms for Modern Machine Vision System 14

Comparison of Potential Road Accident Detection Algorithms f...

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14th International Scientific and Practical Conference on Environment. Technology. Resources, ETR 2023

作者： Byzkrovnyi, Oleksandr Savulioniene, Loreta Smelyakov, Kyrylo Sakalys, Paulius Chupryna, Anastasiya Software engineering department Kharkiv National University of Radio Electronics Kharkiv Ukraine Faculty of electronics and informatics Vilniaus Kolegija/Higher Education Institution Vilnius Lithuania

ISBN: (纸本)9781713874881

Nowadays the robotics is relevant development industry. Robots are becoming more sophisticated, and this requires more sophisticated technologies. One of them is robot vision. This is needed for robots which communicate with the environment using vision instead of a batch of sensors. These data are utilized to analyze the situation at hand and develop a real-time action plan for the given scenario. This article explores the most suitable algorithm for detecting potential road accidents, specifically focusing on the scenario of turning left across one or more oncoming lanes. The selection of the optimal algorithm is based on a comparative analysis of evaluation and testing results, including metrics such as maximum frames per second for video processing during detection using robot’s hardware. The study categorises potential accidents into two classes: danger and not-danger. The Yolov7 and Detectron2 algorithms are compared, and the article aims to create simple models with the potential for future refinement. Also, this article provides conclusions and recommendations regarding the practical implementation of the proposed models and algorithm. © 2023 Oleksandr Byzkrovnyi, Kyrylo Smelyakov, Anastasiya Chupryna, Loreta Savulioniene, Paulius Sakalys.

关键词： Machine learning

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Improving the Performance of Classification via Clustering on the Students’ Academic Performance using Stacking Algorithm 1

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14th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2022, and the 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2022

作者： Yamasari, Yuni Ahmad, Rafif Aydin Tjahyaningtijas, Hapsari Peni Agustin Qoiriah, Anita Rochmawati, Naim Prihanto, Agus Department of Informatics Universitas Negeri Surabaya Surabaya Indonesia Independent Software Developer Surabaya Indonesia Department of Electrical Engineering Universitas Negeri Surabaya Surabaya Indonesia

ISBN: (数字)9783031275241

ISBN: (纸本)9783031275234

The students’ academic performance is crucial since it is linked to the success of educational institutions. Consequently, a high-performance model in this area is necessary. Ensemble learning is a viable strategy for performance enhancement. This study examines the applicability of ensemble learning-stacking on the academic performance of students. Base learners are Naive Bayes and CN2 Rule Induction, whereas Logistic Regression is a meta-learner. Our research analyzes two student datasets, namely Data1 and Data2, to assess the model's dependability. In addition, the model evaluation is conducted using two methods: cross-validation and percentage split. The experimental results show that the accuracy of stacking_Data1 and stacking_Data2 dominates among the other models. Stacking_Data1 and stacking_Data2 achieve the highest accuracy levels of about 91.1% and 94.8% on the cross-validation technique and about 84.92% and 92.56% on the percentage split technique, respectively. Furthermore, these results indicate that the implementation of a stacking algorithm can improve the performance of a single learning model, as indicated by an increase in the level of accuracy of Naive Bayes_Data1, CN2 RI_Data1, Naive Bayes_Data2, and CN2 RI_Data2 in the cross-validation technique of 17.4%, 18.4%, 13.1%, and 10.5%, respectively. Using the percentage split approach, the accuracy level increases by 16.7%, 15.7%, 14.2%, and 18.9% for Naive Bayes_Data1, CN2 RI_Data1, and Naive Bayes_Data2, respectively. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Students

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