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6th International Conference on Machine Vision and Applications, ICMVA 2023

作者： Don, Duleep Rathgamage Aygun, Ramazan Karakaya, Mahmut School of Data Science and Analytics College of Computing and Software Engineering Kennesaw State University MariettaGA30060 United States Department of Computer Science College of Computing and Software Engineering Kennesaw State University MariettaGA30060 United States

ISBN: (纸本)9781450399531

Small object detection is one of the most challenging problems in computer vision. Algorithms based on state-of-the-art object detection methods such as R-CNN, SSD, FPN, and YOLO fail to detect objects of very small sizes. In this study, we propose a novel method to detect very small objects, smaller than 8×8 pixels, that appear in a complex background. The proposed method is a multistage framework consisting of an unsupervised algorithm and three separately trained supervised algorithms. The unsupervised algorithm extracts ROIs from a high-resolution image. Then the ROIs are upsampled using SRGAN, and the enhanced ROIs are detected by our two-stage cascade classifier based on two ResNet50 models. The maximum size of the images used for training the proposed framework is 32×32 pixels. The experiments are conducted using rescaled German Traffic Sign Recognition Benchmark dataset (GTSRB) and downsampled German Traffic Sign Detection Benchmark dataset (GTSDB). Unlike MS COCO and DOTA datasets, the resulting GTSDB turns out to be very challenging for any small object detection algorithm due to not only the size of objects of interest but the complex textures of the background as well. Our experimental results show that the proposed method detects small traffic signs with an average precision of 0.332 at the intersection over union of 0.3. © 2023 ACM.

关键词： Object detection

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Enhancing Classification Algorithm Recommendation in Automated Machine Learning: A Meta-Learning Approach Using Multivariate Sparse Group Lasso

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Computer Modeling in engineering & Sciences 2025年第2期142卷 1611-1636页

作者： Irfan Khan Xianchao Zhang Ramesh Kumar Ayyasamy Saadat M.Alhashmi Azizur Rahim School of Software Dalian University of TechnologyDalian116620China Faculty of Information and Communication Technology Universiti Tunku Abdul RahmanKampar31900Malaysia College of Computing and Informatics University of SharjahSharjah27272United Arab Emirates Department of Computer Systems Engineering University of Engineering and Applied SciencesSwat19200Pakistan

The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods have become impractical due to their resource *** Machine Learning(AutoML)systems automate this process,but often neglect the group structures and sparsity in meta-features,leading to inefficiencies in algorithm recommendations for classification *** paper proposes a meta-learning approach using Multivariate Sparse Group Lasso(MSGL)to address these *** method models both within-group and across-group sparsity among meta-features to manage high-dimensional data and reduce multicollinearity across eight meta-feature *** Fast Iterative Shrinkage-Thresholding Algorithm(FISTA)with adaptive restart efficiently solves the non-smooth optimization *** validation on 145 classification datasets with 17 classification algorithms shows that our meta-learning method outperforms four state-of-the-art approaches,achieving 77.18%classification accuracy,86.07%recommendation accuracy and 88.83%normalized discounted cumulative gain.

关键词： Meta-learning machine learning automated machine learning classification meta-features

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Research on Authorization Verification Methods for Message Signatures Within Multi-Level Groups 1

Research on Authorization Verification Methods for Message S...

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1st International Conference on Intelligent Manufacturing and Cloud computing, ICIMCC 2024

作者： Zhang, Hu Zhang, Jiarui Zhang, Han Undergraduate Software Engineering Class of 2021 Hefei University Hefei China Department of Information Engineering Yantai Institute of Technology Yantai China Beijing Academy of Blockchain and Edge Computing Beijing China

ISBN: (纸本)9781643685779

With the widespread adoption of digital signature,a variety of specialized digital signature methods have been developed. However, the need for authorization verification within hierarchical group structures has not garnered sufficient attention. This research has addressed this issue. Initially, the problem was delineated and formally articulated;subsequently, encryption and decryption algorithms that incorporate authorization sharing were designed, along with a description of the code;finally, the methods were validated through experimentation. The outcomes demonstrate that the proposed approach is reliable and stable, capable of effectively accommodating the authorization verification of signed messages within multi-tiered, large-scale group communications. This addresses the shortfall of current digital signature technologies, which struggle to authorize multiple verifiers within hierarchical group structures, by supplementing and refining a method for the authorization verification of signed messages among users in multi-level groups. © 2025 The Authors.

关键词： Authorization

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Web Application to predict Skin Disease: An Approach Using Deep Learning Algorithm 6

Web Application to predict Skin Disease: An Approach Using D...

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6th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Thayalan, Eelanila Arumugavel, Ayingaran Ganeshan, Sharujan Dalaxshan, Makenthiran Fernando, Harinda Rajapakse, Kaushalya Department of Information Technology Faculty of computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Systems Engineering Faculty of computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Science and Software Engineering Faculty of computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350394634

The Disease Prediction System revolutionizes healthcare with advanced machine learning techniques for early detection of skin diseases, notably focusing on skin cancer. Through image processing and Transfer Learning, it excels in identifying potential malignancies, enabling timely interventions. Its capabilities extend to benign tumors and growth disorders, utilizing the ResNet50 Model for precise identification. Additionally, it predicts skin reactions associated with various dermatological conditions, such as Urticaria Hives and Warts, leveraging the efficientnetB0 Model. Integration of the VGG16 Model enhances diagnostic accuracy for inflammatory skin conditions. This holistic approach prioritizes patient-centric care, leveraging diverse datasets and intricate pattern recognition in medical images. The system's proactive nature embodies personalized solutions for early detection, timely intervention, and improved patient outcomes. Its versatility and accuracy underscore itstransformative potential in healthcare delivery. By harnessing diverse datasets and recognizing intricate patterns within medical images, it heralds a new era of personalized healthcare solutions. In essence, the Disease Prediction System exemplifies the transformative potential of machine learning in healthcare, ensuring the highest standards of diagnostic accuracy and efficacy, while prioritizing patient well-being and quality oflife. © 2024 IEEE.

关键词： Deep learning

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Motivational factors and Challenges in the adoption of latest digital technology in educational institutes: A thematic analysis

Motivational factors and Challenges in the adoption of lates...

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2023 International Conference on ENTERprise Information Systems, CENTERIS 2023 - International Conference on Project MANagement, ProjMAN 2023 - International Conference on Health and Social Care Information Systems and Technologies, HCist 2023

作者： Gulzar, Maryam Smolander, Kari Ali, Arshad Naqvi, Bilal Department of Software Engineering LUT University Lappeenranta53851 Finland FAST School of Computing National University of Computer and Emerging Sciences Lahore54770 Pakistan

Information technology infrastructure (ITI) has gone through an exponential change due to the rapid evolution of digital technologies during the past few years. It is therefore relevant to identify the factors that push organizations towards the adoption of new technologies. This paper considers a specific case study of the higher education institutions (HEI) to present (i) the motivational factors that push HEIs toward the adoption of new technologies, and (ii) the key challenges HEIs face in the adoption of these technologies. The data was gathered after conducting 17 open-ended interviews with practitioners working at HEIs in Finland and Pakistan. Data analysis identified 10 motivational factors and 41 challenges faced by HEI in the adoption of the latest technologies. Furthermore, the data was further classified to identify broad themes and categories representing the challenges and motivation factors. © 2024 The Author(s). Published by Elsevier B.V.

关键词： Challenges IT infrastructure Motivational factors Technology adoption

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From User Stories to Domain Models: Recommending Relationships between Entities

From User Stories to Domain Models: Recommending Relationshi...

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Joint of REFSQ-2023 Workshops, Doctoral Symposium, Posters and Tools Track and Journal Early Feedback, REFSQ-JP 2023

作者： Bragilovski, Maxim Dalpiaz, Fabiano Sturm, Arnon Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Israel Department of Information and Computing Sciences Utrecht University Netherlands

User stories are a common notation for expressing requirements, especially in agile development projects. While user stories provide a detailed account of the functional requirements, they fail to deliver a holistic view of the domain. As such, they can be complemented with domain models that not only help gain this comprehensive view, but also serve as a basis for model-driven development. We focus on the task of recommending relationships between entities in a domain model, assuming that these entities were previously extracted from a user story collection either manually or through an automated tool. We investigate whether an approach based on supervised machine learning can recommend essential relationships in a domain model more accurately than state-of-the-art rule-based methods. Based on a collection of datasets that we manually labeled and a set of 32 features we engineered, we train a machine learning model by using a random forest classifier. The results indicate that our approach has higher precision and F1-score than the baseline rule-based methods. Our findings provide preliminary evidence of the suitability of using machine learning to support the development of domain models, especially in recommending relationships between related entities. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Supervised learning

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The Progression of Competencies and Dispositions: How do Maturing software Engineers Compare? 54

The Progression of Competencies and Dispositions: How do Mat...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Clear, Tony Clear, Alison Auckland University of Technology Department of Computer Science and Software Engineering Auckand New Zealand School of Computing Eastern Institute of technology Auckland New Zealand

ISBN: (纸本)9798350351507

This research paper describes and extends the outcomes from an in- depth study investigating the difference in the expected skills requirements from junior software engineers to senior software engineers, and reflections on the findings from that study. It is a given that senior software engineers have more experience and skills than junior software engineers. However, a focus on their differing competencies and dispositions provides an enhanced mechanism for comparison. Gaps were identified in assessing 'professional knowledge' as categorized by the IEEE/ACM computing Curriculum Overview Report (CC2020), and in assessing 'dispositions'. It appeared that the specific scenario of comparing the expected competencies between junior and senior software engineers, tested the framework for assessing competencies developed in the CC2020 project and applied in its mapping to the IEEE/ACM Computer Science (CS2013) approved curriculum. In this study into the difference between Junior and Senior software Engineers, an initial review of relevant literature was conducted. The review found that research analyzing job requirements for software engineers of different levels was limited;'experience' as a keyword was seldom mentioned;and a common distinction was made between 'soft' and 'hard' skills - the latter being skills that were 'technical', such as programming languages, frameworks, libraries, and tools, whereas soft skills referred to skills such as personality traits, attitudes, and teamwork skills. In our extension of that work the notion of soft skills was unpacked into professional skills and dispositions. The process of mapping from the CC2020 competency framework to the CS2013 curriculum had deliberately modelled how to represent a competency-based rather than a knowledge-based curriculum. The critical deficiency identified here was the limitation imposed by adopting a skills framework based on the cognitive taxonomy, and thereby unwittingly omitting the crucial compani

关键词： Mapping

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Advanced Mechanisms of Perception in the Digital Hide and Seek Game Based on Deep Learning 5

Advanced Mechanisms of Perception in the Digital Hide and Se...

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5th IEEE International Conference on Smart Systems and Technologies, SST 2022

作者： Livada, Časlav Hodak, David Department of Software Engineering Faculty of Electrical Engineering Computing and Information Osijek Croatia Faculty of Electrical Engineering Computing and Information Osijek Croatia

ISBN: (数字)9781665482158

ISBN: (纸本)9781665482158

The goal of this paper is to demonstrate the power of machine learning and deep learning combined with powerful tools used in creating virtual simulations. This is made possible by the Unity Game Engine, a software used for the development of 2D and 3D video games and simulations, and the ML-agents plugin, which contains algorithms for training intelligent agents. The basic principles and algorithms of Deep Learning are described. Agent uses these methods to perceive the environment and solve certain tasks. In this paper, a simulation is created in which the agent tries to find hidden objects using machine learning. When the agent finds an item, a new location for the item is randomly generated. It must also complete the task in a specified amount of time. With the right implementation and parameters, the agent could successfully complete the task. © 2022 IEEE.

关键词： Reinforcement learning

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Towards Edge computing for 6G Internet of Everything: Challenges and Opportunities 1

Towards Edge Computing for 6G Internet of Everything: Challe...

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Alawadhi, Abdulwadood Almogahed, Abdullah Azrag, Etidal Universiti Utara Malaysia School of Computing Kedah Malaysia Taiz University Faculty of Engineering and Information Technology Department of Software Engineering Taiz Yemen

ISBN: (纸本)9781665472753

The Internet of Everything (IoE)-based smart services are becoming more prominent as a result of the growing demands placed on wireless networks. Despite 5G having the ability to serve a wide range of IoE-based applications, they are unable to fully satisfy the needs of the newest intelligent systems. Therefore, 6G is the next generation for the IoT, IoE, and cellular networks, which aim to significantly improve smart services quality, such as maximum throughput and reduced latency. The number of internet-connected smart devices grows exponentially on a daily basis, resulting in Big Data. Several other cloud-based implementations use data centers as centralized servers for handling data collected by edge devices. This model places ever-increasing demands on computational infrastructure and communication, with unavoidable consequences for Experience and Quality-of-Service. By attempting to obtain cloud abilities closer to end users, edge computing is a novel technology which enables the development for 6G by attempting to overcome typical cloud weaknesses such as high latency and a lack of security. The purpose of this paper is to discuss challenges and opportunities in edge computing technology in 6G IoE. © 2023 IEEE.

关键词： Internet of Everything

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Unmasking Fake News with Emotional Cues: A Comparative Study of Sentiment Reasoning Models 4

Unmasking Fake News with Emotional Cues: A Comparative Study...

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4th International Conference on Advanced Research in computing, ICARC 2024

作者： Abishethvarman, Vadivel Banujan, Kuhaneswaran Kumara, B.T.G.S. Ravikumar, Nirubikaa Sabaragamuwa University of Sri Lanka Department of Computing and Information Systems Belihuloya Sri Lanka Australia Sabaragamuwa University of Sri Lanka Department of Software Engineering Belihuloya Sri Lanka

ISBN: (纸本)9798350384864

Discerning truth from deception requires innovative tools in an era besieged by misinformation. Fake news is a form of misleading information delivered as reliable news. It is mainly made up of intentionally manipulating public opinion, decision bias, and exploiting general credulity. Various supervised learning-based fake news detection and classifications only focus on the semantic values due to their subjectivity in the current context. Sentiment analysis is also a common interpretation mechanism of information mining. This research explores the role of sentiment analysis in the context of fake news detection. Sentiment analysis, a facet of natural language processing, delves into the nuanced sentiments within the text, unveiling hidden narratives and differentiating between authentic reporting and carefully crafted deception. The study employs various sentiment reasoning models, including VADER, TextBlob, and Roberta, focusing on comparative analysis. The VADER model, complemented by a Bag of Words approach, emerges as the most effective, outperforming, compared to TextBlob and RoBERTa. Especially BERT sentiment reasoner model underperformed with uneven distribution in this fake news domain. The best VADER sentiments are taken for the transitions of sequential neural network architectures such as Simple Neural Network (SNN), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) featuring Long Short-Term Memory (LSTM). The LSTM model captures intricate patterns, dependencies, and contextual information. Evaluation metrics, encompassing accuracy, precision, recall, and F1 score, provide a comprehensive understanding of models' performance. The RNN LSTM performed well compared to the other sequential models. It primely emphasised the multifaceted landscape of sentiment analysis, presenting a robust strategy for combating the proliferation of fake news in an information-abundant age. Combining Sentiment Reasoning in VADER and RNN LSTM presents a rob

关键词： Sentiment analysis

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