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1st International Conference on Computing and Emerging Technologies, ICCET 2023

作者： Uallah, Naeem Khan, Javed Ali Raza, Asaf Shah, Syed Yaqub Assam, Muhammad Arshad, Hasna Department of Software Engineering University of Engineering and Technology Taxila Taxila Pakistan Department of Software Engineering University of Science and Technology Bannu Bannu Pakistan Department of Computer Science HITEC University Taxila Pakistan

ISBN: (纸本)9783031776199

Many countries are severely affected by COVID-19, and massive efforts are required to mitigate the terrible impacts of the COVID-19 global epidemic. To stop COVID-19 from spreading widely and minimize COVID-19 patient treatment issues, early discovery of the virus in its early stages is essential. Conventional methods for COVID-19 detection are time-consuming, cumbersome, unreliable, and yield an incorrect diagnosis. Most deep learning (DL) methods for COVID-19 identification described in the past only support one data type (chest radiographs or CT scan images). Examining unusual patterns in various data types may yield high classification performance. For this purpose, we use two image datasets: chest radiographs and chest computer tomography (CT). This research study proposed an excellent COVID-19 detection strategy based on the Resnet-18 deep convolutional neural network (CNN) to timely and efficiently identify COVID-19 spread. Also, we used each image data type to conduct extensive Covid-19 detection experiments with the proposed DL model. We performed a trinary classification task (normal, covid-19, and pneumonia) using chest radiographs and a binary classification (COVID-19 and Normal) using CT scan images. We validated the performance of the proposed approach on two standard Kaggle datasets, i.e., the COVID-19 Radiography Database and the SARS-CoV-2 CT scan dataset. The Resnet model's detection accuracy of 99.42% using chest radiographs (CRs) and 100% using CT scans for COVID-19 detection indicates its efficacy in detecting COVID-19. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： COVID-19

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Intelligent Spatial Anomaly Activity Recognition Method Based on Ontology Matching

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computers, Materials and Continua 2025年第3期83卷 4447-4476页

作者： Longgang Zhao Seok-Won Lee The Knowledge-intensive Software Engineering (NiSE) Research Group Department of Artificial Intelligence Ajou University Suwon City 16499 Republic of Korea Department of Software and Computer Engineering Ajou University Suwon City 16499 Republic of Korea

This research addresses the performance challenges of ontology-based context-aware and activity recognition techniques in complex environments and abnormal activities, and proposes an optimized ontology framework to improve recognition accuracy and computational efficiency. The method in this paper adopts the event sequence segmentation technique, combines location awareness with time interval reasoning, and improves human activity recognition through ontology reasoning. Compared with the existing methods, the framework performs better when dealing with uncertain data and complex scenes, and the experimental results show that its recognition accuracy is improved by 15.6% and processing time is reduced by 22.4%. In addition, it is found that with the increase of context complexity, the traditional ontology inference model has limitations in abnormal behavior recognition, especially in the case of high data redundancy, which tends to lead to a decrease in recognition accuracy. This study effectively mitigates this problem by optimizing the ontology matching algorithm and combining parallel computing and deep learning techniques to enhance the activity recognition capability in complex environments.

关键词： Context awareness activity recognition ontological reasoning complex context anomaly detection

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Critical Role of Individual Cell Temperature Monitoring in Mitigating Thermal Runaway and Reducing Accelerated Degradation in Lithium-Ion Batteries

Critical Role of Individual Cell Temperature Monitoring in M...

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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Mohit Sharma Akash Samanta William Locke Sheldon Williamson Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa Canada

ISBN: (数字)9798331516116

ISBN: (纸本)9798331516123

Thermal runaway in lithium-ion batteries (LIBs) is a critical risk, potentially leading to fires and explosions. This phenomenon can be triggered by overcharging, short-circuiting, physical damage, manufacturing defects, and overheating. Effective mitigation of thermal runaway relies on precise and close monitoring of individual cells within the battery pack, a task often complicated by the complexity and cost of implementing temperature sensors on each cell in automotive applications. This study demonstrates the importance of individual cell temperature monitoring by intentionally including an unhealthy cell (C#12) in a 14-cell LIB 21700 module equipped with an automotive-grade battery management system (BMS) from NXP®. Experimental results reveal that despite efficient BMS balancing, the weak cell exhibited a significantly higher temperature rise, with a final temperature difference of 6°C and a voltage difference of over 200 mV compared to healthy cells. These findings underscore the potential for thermal failure and runaway if individual cell temperatures are not closely monitored. Additionally, reliance on voltage-based control alone can lead to suboptimal battery pack utilization, as evidenced by a 5% capacity loss due to the weak cell. This research highlights the necessity of monitoring the temperature of each cell to prevent thermal runaway and ensure efficient battery performance.

关键词： Temperature measurement Temperature sensors Lithium-ion batteries Degradation Accuracy Automotive applications Battery management systems Voltage control Monitoring Thermal degradation

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A Predictive Model for Early Effort Estimation of Mobile Games

A Predictive Model for Early Effort Estimation of Mobile Gam...

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International Conference on Advancements in Computational Sciences (ICACS)

作者： Ahrar Javed Ali Afzal Malik Department of Software Engineering National University of Computer and Emerging Sciences Lahore Pakistan

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

Mobile games are a popular, cheap, and convenient source of entertainment. However, the increasing complexity of these games is making it more difficult to thoroughly plan, monitor, and control mobile game development projects. Possessing the ability to accurately estimate the development effort for these projects early in their life cycle is expected to make the jobs of their managers much easier. The objective of this study was to build a calibrated and validated early effort prediction model for mobile games developed using the Unity game engine. First, we identified factors that potentially influence development effort in these projects by conducting an industrial survey. Once these factors were identified, we gathered information about these factors and actual development effort for more than 100 real mobile games developed by different game studios. Then, we performed a simple linear regression (SLR) analysis to rank these factors with respect to their individual influence on mobile game development effort. Finally, we used the games data collected earlier to build, assess, and validate a forward step-wise multiple linear regression (FSMLR)-based model for early effort prediction of such mobile games. The MMRE value of this model is 0.136 and its PRED (25) value is $85.98 \%$. These values indicate that this model has reasonably good estimation accuracy. It can be a useful tool in the arsenal of managers interested in effectively planning their mobile game development projects early in the life cycle.

关键词： Surveys Accuracy Linear regression Estimation Entertainment industry Games Predictive models Planning Monitoring Land mobile radio

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PoolKANNeXt: A new pooling-based Kolmogorov Arnold convolutional neural network

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Alexandria engineering Journal 2025年 128卷 144-152页

作者： Lv, Fangxing Wei, Qing Huang, Yuwen Tuncer, Turker Dogan, Sengul Özyurt, Fatih School of Computer Heze University Heze274015 China Department of Digital Forensics Engineering Technology Faculty Firat University Elazig Turkey Department of Software Engineering Engineering Faculty Firat University Elazig Turkey

The Kolmogorov–Arnold Network (KAN) is a new-generation neural network. It provides an alternative to multilayer perceptrons (MLPs). PoolFormer showed that pooling alone can mix features efficiently. We propose PoolKANNeXt, a CNN that merges the KAN structure with pooling-based feature mixing. This design targets high accuracy with a low number of the learnable parameters. We evaluated PoolKANNeXt on six image datasets, five biomedical and one general (CIFAR-10). The model comprises four stages. In the stem stage, a ConvNeXt-style patchify block converts each 224 × 224 × 3 image into a 56 × 56 × 96 tensor. In the main stage, average pooling first mixes local features;then two parallel 3 × 3 convolutions—one followed by GELU and the other by Swish—extract complementary representations, and a 1 × 1 convolution scales the combined output and adds it back to the input via a residual connection. In the downsampling stage, strided 3 × 3 convolutions halve spatial dimensions and double the channel count. In the output stage, global average pooling produces a feature vector that feeds into a softmax classifier. PoolKANNeXt achieved over 90 % accuracy on all datasets and reached 99.43 % on CIFAR-10, ranking among the top five models on that benchmark. PoolKANNeXt offers a lightweight yet powerful architecture. Its innovative combination of pooling and dual-activation KAN blocks yields strong performance across diverse tasks. The design is scalable and adaptable to larger or more complex datasets. © 2025 The Authors

关键词： Convolutional neural networks

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Trauma-Informed Insights from Co-Design of Self-Disclosure Robots with Domestic Abuse Survivors 20

Trauma-Informed Insights from Co-Design of Self-Disclosure R...

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20th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2025

作者： Morris, Nyomi Carrine Williams, Tom Jelen, Ben Colorado School of Mines MIRRORLab GoldenCO United States Rose-Hulman Institute of Technology Department of Computer Science and Software Engineering Terre HauteIN United States

ISBN: (纸本)9798350378931

Survivors of domestic abuse face significant challenges securing recovery resources, such as housing, mental health care, and social connections. Accessing these resources requires survivors to disclose their status as survivors of domestic abuse;a process that can be traumatic and emotionally burdensome. In this work, we consider how social robots might help domestic abuse survivors to address challenges surrounding self-disclosure. To do so, we conducted a three-session co-design workshop involving 8 participants from a domestic abuse shelter. Our results provide three key contributions: (1) new insights into the benefits and barriers to self-disclosure;(2) new insights into the ways that social robots can help survivors to better achieve those benefits and overcome those barriers before, during, and after disclosure, including key design recommendations associated with each of these phases;and (3) observations into how a trauma-informed computing perspective may enable more effective design work in Human-Robot Interaction. © 2025 IEEE.

关键词： Social robots

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An LLM-Based Framework for Synthetic Data Generation

An LLM-Based Framework for Synthetic Data Generation

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IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Mandeep Goyal Qusay H. Mahmoud Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa ON Canada

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

The demand for high-quality datasets is rapidly increasing across sectors such as healthcare, finance, and cybersecurity, yet challenges like data scarcity and privacy concerns persist. To address this, we introduce a framework for synthetic data generation that empowers users to create realistic datasets while maintaining privacy. The framework leverages fine-tuned Large Language Models (LLMs) and differential privacy techniques, including IBM's diffprivlib, to generate synthetic data that replicates real-world patterns without exposing sensitive information. A proof-of-concept platform has been constructed to facilitate seamless data generation and augmentation, making it particularly useful in scenarios where original datasets are inaccessible, scarce, or privacy-restricted. The platform supports the creation of datasets across five key categories, employing advanced methods to preserve data integrity while ensuring compliance with stringent privacy standards. By combining cutting-edge AI technologies with robust privacy-preserving techniques, this framework offers a practical solution for researchers and professionals seeking reliable synthetic data to drive innovation in data-sensitive fields.

关键词： Technological innovation Differential privacy Large language models Data integrity Finance Medical services Data models Reliability Standards Synthetic data

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Medical Image Classification with KAN-Integrated Transformers and Dilated Neighborhood Attention

arXiv

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arXiv 2025年

作者： Manzari, Omid Nejati Asgariandehkordi, Hojat Koleilat, Taha Xiao, Yiming Rivaz, Hassan Tehran Iran Department of Electrical and Computer Engineering Concordia University Montreal Canada Department of Computer Science and Software Engineering Concordia University Montreal Canada

Convolutional networks, transformers, hybrid models, and Mamba-based architectures have demonstrated strong performance across various medical image classification tasks. However, these methods were primarily designed to classify clean images using labeled data. In contrast, real-world clinical data often involve image corruptions that are unique to multi-center studies and stem from variations in imaging equipment across manufacturers. In this paper, we introduce the Medical Vision Transformer (MedViTV2), a novel architecture incorporating Kolmogorov-Arnold Network (KAN) layers into the transformer architecture for the first time, aiming for generalized medical image classification. We have developed an efficient KAN block to reduce computational load while enhancing the accuracy of the original MedViT. Additionally, to counteract the fragility of our MedViT when scaled up, we propose an enhanced Dilated Neighborhood Attention (DiNA), an adaptation of the efficient fused dot-product attention kernel capable of capturing global context and expanding receptive fields to scale the model effectively and addressing feature collapse issues. Moreover, a hierarchical hybrid strategy is introduced to stack our Local Feature Perception and Global Feature Perception blocks in an efficient manner, which balances local and global feature perceptions to boost performance. Extensive experiments on 17 medical image classification datasets and 12 corrupted medical image datasets demonstrate that MedViTV2 achieved state-of-the-art results in 27 out of 29 experiments with reduced computational complexity. MedViTV2 is 44% more computationally efficient than the previous version and significantly enhances accuracy, achieving improvements of 4.6% on MedMNIST, 5.8% on NonMNIST, and 13.4% on the MedMNIST-C benchmark. Our code is available at https://***/Omid-Nejati/*** © 2025, CC BY.

关键词： Medical imaging

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Digitally Controlled Misalignment-Tolerant Inductive Power Transfer System with Adaptive Hybrid Compensation for CC/CV Charging of E-Scooter

Digitally Controlled Misalignment-Tolerant Inductive Power T...

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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Niranjan Shrestha V. S. R. Varaprasad Oruganti Sheldon Williamson Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa Canada

ISBN: (数字)9798331516116

ISBN: (纸本)9798331516123

This paper presents a digitally controlled Inductive power transfer (IPT) system for charging Electric scooters (E-scooters), utilising an adaptive hybrid compensation network design. This system ensures constant current (CC) and constant voltage (CV) charging, with misalignment. It leverages the advantages of double-sided inductor-capacitor-capacitor (DS-LCC) and LCC-series (LCC-S) topologies, which respectively provide CC and CV output. A Type-II digital anti-windup PI control method with a selectable compensation network is introduced, allowing both CC and CV modes to operate with zero voltage switching (ZVS) under misalignment conditions. This approach reduces system losses and enhances the stability and efficiency of the IPT system. A 270W/85-kHz IPT-based E-scooter charger was designed and simulated in the MATLAB/Simulink environment, and an experimental prototype was developed to evaluate the performance of the proposed charger as per SAE J2954 standards. Testing under three different coupling conditions perfect alignment, 5 cm misalignment, and 10 cm misalignment demonstrated that the output parameters remained constant across all conditions, confirming the effectiveness of the proposed control technique.

关键词： Couplings Adaptive systems Stability criteria Motorcycles Zero voltage switching Hybrid power systems Topology Voltage control Standards Testing

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Towards Collaborative Simultaneous Localization and Mapping: A Survey of the Current Research Landscape

Field Robotics

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Field Robotics 2025年 2卷 971-1000页

作者： Pierre-Yves Lajoie Benjamin Ramtoula Fang Wu Giovanni Beltrame Department of Computer and Software Engineering Polytechnique Montréal Canada Oxford Robotics Institute Department of Engineering Science University of Oxford United Kingdom Desay SV Automotive Singapore

Motivated by the tremendous progress, we witnessed in recent years, this paper presents a survey of the scientific literature on the topic of Collaborative Simultaneous Localization and Mapping (C-SLAM), also known as multi-robot SLAM. With fleets of self-driving cars on the horizon and the rise of multi-robot systems in industrial applications, we believe that Collaborative SLAM will soon become a cornerstone of future robotic applications. In this survey, we introduce the basic concepts of C-SLAM and present a thorough literature review. We also outline the major challenges and limitations of C-SLAM in terms of robustness, communication, and resource management. We conclude by exploring the area's current trends and promising research avenues.

关键词： Robots Simultaneous localization and mapping Robot kinematics Robot sensing systems Collaboration Surveys Multi-robot systems Sensors Market research Trajectory

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