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作者： YuYu Zheng HaoXuan Huang JunMing Chen School of Optoelectronic Information and Computer Engineering University of Shanghai for Science and Technology Department of Computing and Software McMaster University Department of Mathematics University of CaliforniaLos Angeles

Nowadays,people developed various convolutional neural network(CNN) based models for computer *** famous models,such as GoogLeNet,Residual Network(ResNet),Visual Geometry Group(VGG),and You Only Look Once(YOLO),have different architecture and *** which model to use may be a troublesome problem for those just starting to study image *** solve this problem,we introduce the GoogLeNet,ResNet-18,and VGG-16 models,comparing their architecture,features,and *** we give our suggestions based on the test results to help beginners choose a suitable *** conducted experiments to train and test GoogLeNet,ResNet-18,and VGG-16 on the Cifar-100 datasets with the same *** on the test results(test accuracy,average test loss,training loss),we analyze the figures for trends,key points,increase rate,and other *** we combine the architecture of each model to make our *** experimental results show that ResNet-18 can be a good choice when training the model with the Cifar-100 datasets because it performs well after training and has a low time *** Net-18 also has the fastest convergence *** would be the second choice because it functions similarly to ResNet-18 and is even ***,training GoogLeNet is a time-consuming *** is not recommended in this experiment because it has the worst performance and similar training complexity compared with ResNet-18.

关键词： GoogLeNet ResNet VGG Image Classification Architecture Comparison

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R-TAP: A Cloud-Based Multi-Region Road Traffic Accident Severity Prediction Model

R-TAP: A Cloud-Based Multi-Region Road Traffic Accident Seve...

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IEEE Industrial Electronics and Applications Conference (IEACon)

作者： Albe Bing Zhe Chai Bee Theng Lau Mark Kit Tsun Tee Chris McCarthy Faculty of Engineering Computing & Science Swinburne University of Technology Sarawak Campus Kuching Malaysia Department of Computer Science & Software Engineering Swinburne University of Technology Melbourne Australia

ISBN: (数字)9798350382976

ISBN: (纸本)9798350382983

The tremendous impact of road traffic accidents (RTAs) remains a global concern, especially in low- and middle-income countries (LMICs). While most LMICs have limited budget to afford advanced traffic monitoring systems, it is necessary to develop feasible solutions for enhancing road safety in these regions. Recent studies have extensively explored the applicability of machine learning (ML) models to predict RTA severity in different regions. However, there is limited exploration of training and evaluating the performance of ML model from a multi-region perspective. Further investigation is also necessary to formulate a cost-effective implementation strategy for LMICs to deploy ML models for real-time prediction. Hence, this paper contributes by proposing R-TAP, which deploys a TabNet-based model on the cloud for multi-region RTA severity predictions. A web application is also developed to receive inputs and visualise the prediction results. The TabNet model is trained on a multi-region dataset consisting of 42 features, produced by combining the individual RTA datasets collected from Australia, Brazil, Ethiopia, New Zealand, the UK, and the US. Evaluation with random forest and multinomial logistic regression showed that TabNet can achieve performance comparable to random forest. It also has the highest area under the receiver operating characteristics at 0.8857. R-TAP is also simulated using testing samples as real-time input, with an overall execution time of 4.104s per prediction. It is considered satisfactory and is expected to be useful for emergency responders to make informed decisions. This solution is expected to support LMICs in their roadmap towards enhanced road safety. Future research is essential to improve the predictive power and include datasets from more regions for a more inclusive RTA severity prediction.

关键词： Training Visualization Receivers Predictive models Road traffic Road safety Real-time systems Random forests Accidents Testing

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A reinforcement learning and predictive analytics approach for enhancing credit assessment in manufacturing

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Decision Analytics Journal 2025年 15卷

作者： Razaque, Abdul Beishenaly, Aliya Kalpeyeva, Zhuldyz Uskenbayeva, Raisa Nikolaevna, Moldagulova Aiman Department of Software Engineering & AI School of Computing Gachon University Seongnam-si South Korea Department of Software Engineering Institute of Automation and Information Technologies Satbayev University Kazakhstan Department of Computer Engineering International Information Technology University (IITU) Almaty Kazakhstan

The fundamental issue with a credit system for manufacturers and importers of commodities is inefficient credit assessment. Traditional techniques frequently produce inaccurate risk assessments and credit scores, resulting in financial losses for lenders, missing business growth possibilities, and less favorable client conditions. To overcome this issue, a comprehensive credit assessment scoring system should be implemented to increase importers’ confidence. The article proposes a predictive-based reinforcement learning (PRL) model to help manufacturers and importers acquire more accurate and dependable credit scores while avoiding default risk. Furthermore, the proposed PRL model enhances decision-making, system efficiency, and risk-tolerant financial conditions. To attain these cutting-edge objectives, the proposed PRL model combines three algorithms. Algorithm 1 collects and aggregates data to indicate areas for improvement if credit scoring is poor. Algorithm 2 uses reinforcement learning to validate and enhance bank scores. Algorithm 3 focuses on predictive modeling for bank scoring, ensuring that the credit decision-making system is operational and constantly improving. Furthermore, reinforcement learning leverages the features from local interpretable model-agnostic explanations (LIME) and shapely additive explanations (SHAP) to generate locally reliable explanations and attribute the contribution of each feature for determining the output of the model. The Python platform tests the proposed PRL to achieve the objectives. Based on the results, The PRL model markedly enhances credit assessment precision, achieving an accuracy of over 99.5%, which outstrips current methodologies such OCLA (96.12%), PSML (84.12%), and EMPCC (91.67%). Furthermore, the PRL model augments leverage ratios, rising from 2.75% in 2015 to 3.36% in 2024.5, and increases accounts receivable turnover from 4.38% in 2015 to 7.4% in 2024.5, surpassing alternative credit evaluation methodologi

关键词： Financial analytics Intelligent credit systems Machine learning Manufacturing Predictive analytics Reinforcement learning

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Empirical Insights into Product Security Challenges and Practices in software Development

Empirical Insights into Product Security Challenges and Prac...

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Frontiers of Information Technology (FIT)

作者： Saima Rafi Muhammad Azeem Akbar Arif Ali Khan School of Computing Engineering & the Built Environment Edinburgh Napier University Scotland Software Engineering Department Lappeenranta-Lahti University of Technology Lappeenranta Finland M3S Empirical Software Engineering Research Unit University of Oulu Oulu Finland

ISBN: (数字)9798331510503

ISBN: (纸本)9798331510510

Ensuring product security in software development is crucial, encompassing hardware, firmware, and holistic protection measures. With rising cyber threats, organizations face challenges in safeguarding software products, often viewing security as a bottleneck. To address these issues, we conducted an empirical study with 22 practitioners across various industries, exploring challenges and practices in product security. Our findings highlight both the applicability of security practices and the new challenges in their implementation. This research contributes valuable insights, enabling organizations to proactively address security concerns and develop robust strategies for protecting their software products.

关键词： Industries Organizations software Hardware Security software measurement Protection Information technology software development management Microprogramming

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Amplifying the Voice of Blacks in computing

Amplifying the Voice of Blacks in Computing

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Black Issues in computing Education (BICE)

作者： Cheryl Swanier Cheryl Seals Dale-Marie Wilson Manning College of Information and Computing Sciences University of Massachusetts USA Computer Science and Software Engineering Auburn University USA Department of Software and Information Systems University of North Carolina at Charlotte USA

ISBN: (数字)9798350372915

ISBN: (纸本)9798350372922

This workshop is designed to intentionally share experiences and connect participants to their greatest asset, their voice. There is power in your voice. Engaging interaction, thought-provoking storytelling, and authentic truth telling will open the way for understanding the value of harnessing voice power when navigating professionally and personally. Despite diversity, equity, and inclusion efforts, stated commitments to diversity, the demographic characteristics of the professoriate and industry look remarkably like the faculty and professionals of 50 years ago for Blacks in computing. Blacks in computing constantly face discrimination in the form of microaggressions and white fragility. This discrimination has not translated to greater Black faculty and professional representation in their career advancement. Instead, the health of both faculty and professionals are being adversely impacted, as they are experiencing insomnia, anxiety, depression, emotional distress, and physical exhaustion. This disruption of norms has a negatively impacted the productivity of both Black men and women at work and can lead to their demotion and dismissal. Many times, these struggles are not shared, and their voices are silenced. This workshop primarily addresses how Blacks in computing can use their voice to advance their careers and utilize proven voice positioning strategies to harness their voice power and to maximize professional and personal wellbeing. These voice strategies will facilitate both Black men and women in their career advancement, skill development, and preparation for entry into their next career level. A failure to address these challenges has negative implications for both academia and industry

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Predicting Modalities of Dyslexic Students using Neuro-Linguistic Programming to Enhance Learning Method

Predicting Modalities of Dyslexic Students using Neuro-Lingu...

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2022 IEEE International Conference on Data Science and Information System, ICDSIS 2022

作者： Choube, Gaurav Dudhmande, Gauri Rahul Pushparaj, Jagalingam Anand, Christopher Suresh, Shilpa Software Engineer Optum Global Solutions Pvt Ltd Hyderabad India Software Engineer Intel Technology Pvt Ltd Aurangabad India Vellore Institute of Technology School of Computer Science and Engineering Vellore India McMaster University Department of Computing and Software Ontario Canada Manipal Institute of Technology Department of Mechatronics Manipal India

ISBN: (数字)9781665498012

ISBN: (纸本)9781665498012

Dyslexia causes difficulty in reading, writing and learning. The children at a tender age have always suffered due to dyslexia. Dyslexia deceives student's perception and makes it difficult in the process of learning. In this paper, machine learning techniques like multi-layer perceptron, Decision tree and Gaussian NB approaches were implemented for the prediction of modalities. To enhance the learning approach for the students suffering from dyslexia, the predicted modalities can be adopted. The sampled data was trained, and the target labels were classified into three classes as visual, auditory, and kinesthetic. The data was processed and fed into multi-layer perceptron, decision tree and naive bayes machine learning algorithms using scikit-learn. Confusion matrix was used to evaluate the performance measure of the algorithms. It was observed that models achieved accuracy of 81.41% for MLP Classifier, 63.82% for Decision tree and 79.25% for Naive bayes. The best result was achieved by MLP Classifier. © 2022 IEEE.

关键词： Decision trees

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Improving Network Threat Detection by Knowledge Graph, Large Language Model, and Imbalanced Learning

arXiv

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arXiv 2025年

作者： Zhang, Lili Zhu, Quanyan Ray, Herman Xie, Ying Hewlett Packard Enterprise United States Department of Electrical and Computer Engineering New York University United States College of Computing and Software Engineering Kennesaw State University United States

Network threat detection has been challenging due to the complexities of attack activities and the limitation of historical threat data to learn from. To help enhance the existing practices of using analytics, machine learning, and artificial intelligence methods to detect the network threats, we propose an integrated modelling framework, where Knowledge Graph is used to analyze the users’ activity patterns, Imbalanced Learning techniques are used to prune and weigh Knowledge Graph, and LLM is used to retrieve and interpret the users’ activities from Knowledge Graph. The proposed framework is applied to Agile Threat Detection through Online Sequential Learning. The preliminary results show the improved threat capture rate by 3%-4% and the increased interpretabilities of risk predictions based on the users’ activities. Copyright © 2025, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Review of Inference Time Prediction Approaches of DNN: Emphasis on Service robots with cloud-edge-device architecture

Review of Inference Time Prediction Approaches of DNN: Empha...

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2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023

作者： Xiang, Tian Meng, Qiwei Zhang, Ji Zhang, Beibei Song, Wei Xie, Anhuan Gu, Jason Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Research Center for Intelligent Robotics Hangzhou311100 China The Chinese University of Hong Kong T Stone Robotics Institute Department of Mechanical and Automation Engineering Hong Kong Research Institute of Intelligent Computing Zhejiang Laboratory Research Center for Intelligent Computing Software Hangzhou311100 China Dalhousie University Electrical and Computer Engineering Halifax Canada

ISBN: (纸本)9798350325706

In recent years, the global robot market has witnessed substantial growth, particularly in the domain of service robots. Despite their expanding presence, service robots encounter limitations when operating autonomously in unstructured environments, primarily due to their constrained computational capacities. As a solution, the fusion of cloud and edge computing resources becomes imperative to expedite task inference and enhance scenario perception capabilities. The integration of cloud-edge-device models holds significant promise in bolstering the operational efficiency of robots. This entails the dynamic partitioning of intricate robotic tasks, executed collaboratively across cloud, edge, and device resources. In this landscape, deep neural network (DNN) models play a pivotal role in facilitating a wide array of robotic tasks. The inference time for each layer of a DNN model in actual deployment, emerges as a critical determinant in the model's partitioning strategy. It also serves as an important metric influencing the model's suitability for a specific hardware platform. This article presents an overview of recent advancements in predicting inference and training time of DNN models, summarizes the related methods, and finally discusses the challenges in this field and the research that can be studied in the future. © 2023 IEEE.

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Cybersecurity Risks Quantification in the Internet of Things 7

Cybersecurity Risks Quantification in the Internet of Things

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7th IEEE International Conference on Recent Advances and Innovations in engineering, ICRAIE 2022

作者： Alagappan, Annamalai Baptist Andrews, Leo John Sampath Kumar, V. Raj, Raymon Antony Sarathkumar, D. Botho University Faculty of Engineering and Technology Department of Network and Infrastructure Management Gaborone Botswana Botho University Faculty of Computing Department of Software Engineering Botswana Grant Thornton Plot 50370 Acument Park Fair Grounds Gaborone Botswana Kongu Engineering College Department of Electrical and Electronics Engineering Tamilnadu India

ISBN: (纸本)9781665489102

A dynamic platform, the Internet of Things uses data, which in turn invites cybersecurity risks. In a dynamic and complicated world, many organisations are putting more of their attention into understanding cyber threats and attempting to quantify their exposure. This paper highlights and goes into great detail on standard cyber risk assessment methods while focusing on the assessment of cybersecurity threats in IoT and its vectors through a risk-based approach employing machine learning algorithms. Based on the statistical research, it can be determined that the CAQ model utilising J48 classifies at 94 percent, while the Bayesian method performs in evaluating possible risks at 82 percent. The Bayes algorithm also aids in estimating complicated risks coming from various sources, aids in comprehending how risk variables originate and connect in an IoT environment, and aids in determining what controls are necessary for quantifying and reducing them. © 2022 IEEE.

关键词： Cybersecurity

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Socially-Critical software Systems: Is Extended Regulation Required? 1

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29th European Systems, software and Services Process Improvement, EuroSPI 2022

作者： Dagg, Niall Kostick, Conor Fallon, James O’Neill, Alex Yilmaz, Murat Messnarz, Richard Clarke, Paul M. School of Computing Dublin City University Dublin Ireland Department of Computer Engineering Gazi University Ankara Turkey ISCN the International Software Consulting Network Graz Austria Lero the Science Foundation Ireland Research Center for Software Limerick Ireland

ISBN: (数字)9783031155598

ISBN: (纸本)9783031155581

Data has become a prevailing aspect of our daily lives, becoming ever more present since the beginning of the 21st century. It is a commodity in today’s world and the amount of data being produced has increased enormously. One of the major ways data is produced and collected is from the use of websites and web-based applications. This data is later used for many different purposes. This paper presents findings from a multivocal literature review, exploring the methods of how this data is collected, what the data is used for once it has been collected, the ethics of data and its collection, and the future of data collection. Among the possible futures, we introduce the concept of socially-critical applications, where data harvesting in web-based applications might require pre-market disclosure and evaluation by notified bodies (instructed by regulation) as a means to break the existing cycle of technology companies outpacing under-resourced and ill-equipped regulators. Rather than regulators continually falling short of enacting laws to satisfy the common good, a new class of socially-critical application could be created in law to permit pre-market evaluation of applications (or versions of applications) that could undermine or interrupt the common good. © 2022, Springer Nature Switzerland AG.

关键词： Websites

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