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4th IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2024

作者： Ho, Chung Lai Deryck Lung, Chung-Horng Mao, Zhengding Carleton University Department of Systems and Computer Engineering Ottawa Canada

ISBN: (纸本)9798350360721

The exponential growth of Internet of Things (IoT) devices highlights the critical need for robust and efficient messaging systems to ensure seamless communication and real-time data exchange. In this research, a thorough comparative analysis was conducted focusing on the real-time data processing architectures of two leading messaging brokers in the IoT domain: Apache Kafka and Message Queuing Telemetry Transport (MQTT) broker. Despite widespread adoption, Kafka and MQTT brokers exhibit uniqueness in their architectures, features, and functionality in real-time data processing scenarios. The study aims to unravel the strengths and weaknesses of each technology within the context of real-time data processing, providing valuable insights to optimize their IoT data workflows. © 2024 IEEE.

关键词： Internet of things

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Pedestrian Detection: An Explainable Approach 23

Pedestrian Detection: An Explainable Approach

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Pang, Hongbo Huang, Changcheng Carleton University Department of Systems and Computer Engineering Ottawa Canada

ISBN: (纸本)9798350374889

In the rapidly advancing field of autonomous technologies, particularly in transportation and surveillance, there is a critical need for accurate and reliable pedestrian detection systems. While deep learning models have achieved remarkable success in this area, their 'black box' nature often concerns their trustworthiness and practical applicability. Our study introduces a modified YOLOv5s model, augmented with a Global Attention Mechanism and enhanced with Gradient-weighted Class Activation Mapping (Grad-CAM) for improved explainability. This approach maintains the high accuracy typical of deep learning models while significantly increasing their transparency. Our modified YOLOv5s architecture is tailored for real-time applications, achieving an inference speed of 9.4 milliseconds on a single NVIDIA A100 GPU. Incorporating Grad-CAM provides a more intuitive interpretation of the model's decisions, potentially increasing user confidence in its predictions. We evaluate our enhanced framework on the CityPersons dataset, demonstrating improvements in precision, recall, and mAP@0.5 compared to the baseline YOLOv5s model. Our results indicate a promising advancement in pedestrian detection, balancing high performance with improved explainability. © 2024 IEEE.

关键词： Deep learning

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COVID-19 Classification Using Pre-Trained Models and Disease Severity Score Masks 1

COVID-19 Classification Using Pre-Trained Models and Disease...

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1st International Conference on Computing, Internet of Things and Microwave systems, ICCIMS 2024

作者： Nehary, Ebrahim A. Rajan, Sreeraman Rossa, Carlos Carleton University Department of Systems and Computer Engineering Ottawa Canada

ISBN: (纸本)9798350351736

The significance of early detection of COVID-19 has been widely acknowledged as a means of reducing its spread and mortality rates among patients. Deep learning techniques for COVID-19 classification based on ultrasound (US) data have been extensively employed. However, detecting COVID-19 based on US images continues to be challenging primarily due to limited datasets with noisy and low-resolution images. This study investigates methods to enhance classification performance by incorporating disease severity score masks while training pretrained models enhanced with self-attention mechanisms. The disease severity scores range from 0 for healthy lung tissue to 1 for initial signs of abnormality, and 2 and 3 for advanced pathological artifacts. These masks and their corresponding US images are employed as inputs to pre-trained models for feature extraction. Subsequently, features extracted from the masks are utilized to recalibrate features obtained from US images using self-attention mechanisms. The proposed method achieves classification accuracy of 95.4, 90.4, 95%, 83%, and 92% when using pre-trained models VGG16, NASNet-Mobile, MobileNet_V2, ResNet50, and Xception, respectively. Further, all pre-trained models yield a low standard deviation of less than 5%. The results demonstrate that incorporating disease severity masks improves the classification performance, thus offering promising techniques for enhancing COVID-19 detection using ultrasound imaging. © 2024 IEEE.

关键词： COVID-19

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Author profiling from Romanized Urdu text using transfer learning models

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Neural Computing and Applications 2025年第6期37卷 4455-4470页

作者： Ali, Abid khan, Muhammad Sohail Khan, Muhammad Amin Khan, Sajid Ullah Khan, Faheem Department of Computer Software Engineering University of Engineering & Technology Mardan KPK Mardan23200 Pakistan Islamabad H-11/4 Islamabad44000 Pakistan Department of Information Systems College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Department of Computer Engineering Gachon University Seongnam-Si Korea Republic of

This research concentrates on author profiling using transfer learning models for classifying age and gender. The investigation encompassed a diverse set of transfer learning techniques, including Roberta, BERT, ALBERT, Distil BERT, Distil Roberta, ELECTRA, and XLNet. Through meticulous evaluation using metrics such as the Matthews Correlation Coefficient, Accuracy, Precision, Recall, and F1 Score, the study examined the efficacy of these models. The curated dataset was divided for gender and age tasks, resulting in robust gender prediction with the XLNet model and age prediction with the BERT model. Notably, the XLNet model achieved the highest MCC (0.7946), Accuracy (0.8957), Precision (0.8992), Recall (0.8957), and F1 Score (0.8958) values in gender classification, while the BERT model excelled in age prediction with an MCC of (0.7338), Accuracy of (0.8220), Precision of (0.8324), Recall of (0.8220), and F1 Score of (0.8243). Visualized outcomes provide valuable insights into the model’s performance nuances, paving the way for their practical implementation. This research offers novel contributions to author profiling tasks, bridging the gap between theory and real-world applications. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Contrastive Learning

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DDoS Flood Detection and Mitigation using SDN and Network Ingress Filtering - an Experiment Report 4

DDoS Flood Detection and Mitigation using SDN and Network In...

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4th IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2024

作者： Marleau, Sebastien Rahman, Prianna Lung, Chung-Horng Carleton University Department of Systems and Computer Engineering Ottawa Canada

ISBN: (纸本)9798350360721

Distributed Denial of Service (DDoS) attacks are a common security threat to overwhelm a target server. IP source spoofing is a common approach used for DDoS attacks. It is claimed that all large DDoS attacks require IP spoofing and around 20% of the Internet still allows IP spoofing. DDoS attacks can also be launched much more easily using low-cost Internet of Things (IoT) devices. DDoS attacks originating from IoT devices have increased 5-fold in a year. Software-defined networking (SDN) has been proposed as a new paradigm to reduce complexity. However, security issues are still challenging for SDN, as DDoS attacks on the controller could fail the entire network. A mechanism that can detect and mitigate DDoS attacks on SDN is crucial. Ingress filtering has been adopted to reduce the probability of exploiting a network to launch an attack. This, among other things, mandates the dropping of packets if the source IP address of incoming packets is inconsistent with the configured one. Best Current Practice 38 (BCP 38) is a network ingress filtering technique to prevent source IP spoofing. By validating incoming packets, their IP addresses, and possibly other attributes at the ingress devices, it provides a first line of defense against suspicious traffic. We present an approach to DDoS detection and mitigation using BCP 38 and SDN. To evaluate the effectiveness of the proposed approach, a simple method was designed to detect and mitigate DDoS floods using BCP 38 and SDN. Experiments were conducted using Mininet and the Ryu controller. The result demonstrated that the proposed approach corresponded to DDoS attacks with IP spoofing efficiently and effectively. © 2024 IEEE.

关键词： Controllers

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Analysis of Statistical and Deep Learning Techniques for Temperature Forecasting

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Recent Advances in computer Science and Communications 2024年第2期17卷 49-65页

作者： Kruthika, Sriram Ganesan Rajasekaran, Umamaheswari Alagarsamy, Malini Sharma, Vandana Department of Computer Science and Business Systems Thiagarajar College of Engineering Tamil Nadu Madurai India NCR Delhi India

In the field of meteorology, temperature forecasting is a significant task as it has been a key factor in industrial, agricultural, renewable energy, and other sectors. High accuracy in temperature forecasting is needed for decision-making in advance. Since temperature varies over time and has been studied to have non-trivial long-range correlation, non-linear behavior, and seasonal variability, it is important to implement an appropriate methodology to forecast accurately. In this paper, we have reviewed the performance of statistical approaches such as AR and ARIMA with RNN, LSTM, GRU, and LSTM-RNN Deep Learning models. The models were tested for short-term temperature forecasting for a period of 48 hours. Among the statistical models, the AR model showed notable performance with a r2 score of 0.955 for triennial 1 and for the same, the Deep Learning models also performed nearly equal to that of the statistical models and thus hybrid LSTM-RNN model was tested. The hybrid model obtained the highest r2 score of 0.960. The difference in RMSE, MAE and r2 scores are not significantly different for both Statistical and Vanilla Deep Learning approaches. However, the hybrid model provided a better r2 score, and LIME explanations have been generated for the same in order to understand the dependencies over a point forecast. Based on the reviewed results, it can be concluded that for short-term forecasting, both Statistical and Deep Learning models perform nearly equally. © 2024 Bentham Science Publishers.

关键词： Decision making

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Arrhythmia Detection by Using Chaos Theory with Machine Learning Algorithms

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computers, Materials & Continua 2024年第6期79卷 3855-3875页

作者： Maie Aboghazalah Passent El-kafrawy Abdelmoty M.Ahmed Rasha Elnemr Belgacem Bouallegue Ayman El-sayed Math and Computer Science Department Faculty of ScienceMenoufia UniversityShebin El-komEgypt College of Engineering Computer Science DepartmentEffat UniversityJeddahKingdom of Saudi Arabia Department of Computer Engineering College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia Computer Science and Engineering Department Faculty of Electronic EngineeringMenoufia UniversityShebin El-komEgypt Climate Change Information Center and Expert Systems Agriculture Research CenterGizaEgypt

Heart monitoring improves life ***(ECGs or EKGs)detect heart *** learning algorithms can create a few ECG diagnosis processing *** first method uses raw ECG and time-series *** second method classifies the ECG by patient *** third technique translates ECG impulses into Q waves,R waves and S waves(QRS)features using richer *** ECG signals vary naturally between humans and activities,we will combine the three feature selection methods to improve classification accuracy and *** using all three approaches have not been examined till *** researchers found that Machine Learning(ML)techniques can improve ECG *** study will compare popular machine learning techniques to evaluate ECG *** algorithms—Support Vector Machine(SVM),Decision Tree,Naive Bayes,and Neural Network—compare categorization *** plus prior knowledge has the highest accuracy(99%)of the four ML *** characteristics failed to identify signals without chaos *** 99.8%classification accuracy,the Decision Tree technique outperformed all previous experiments.

关键词： ECG extraction ECG leads time series prior knowledge and arrhythmia chaos theory QRS complex analysis machine learning ECG classification

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Innovative Mechanical Ventilation Control for Enhanced Indoor Air Quality and Energy Efficiency

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Energy engineering 2025年第3期122卷 861-883页

作者： Giovanni Miracco Francesco Nicoletti Vittorio Ferraro Dimitrios Kaliakatsos Department of Mechanical Energy and Management Engineering(DIMEG)University of CalabriaCosenza87036Italy Department of Computer ModellingElectronics and Systems Engineering(DIMES)University of CalabriaCosenza87036Italy

Indoor air quality(IAQ)is often overlooked,yet a poorly maintained environment can lead to significant health issues and reduced concentration and productivity in work or educational *** study presents an innovative control system for mechanical ventilation specifically designed for university classrooms,with the dual goal of enhancing IAQ and increasing energy *** classrooms with distinct construction characteristics were analyzed:one with exterior walls and windows,and the other completely *** each classroom,a model was developed using DesignBuilder software,which was calibrated with experimental data regarding CO_(2) concentration,temperature,and relative humidity *** proposed ventilation system operates based on CO_(2) concentration,relative humidity,and potential for free heating and *** addition,the analysis was conducted for other locations,demonstrating consistent energy savings across different climates and environments,always showing an annual reduction in energy *** demonstrate that mechanical ventilation,when integrated with heat recovery and free cooling strategies,significantly reduces energy consumption by up to 25%,while also maintaining optimal CO_(2) levels to enhance comfort and air *** findings emphasize the essential need for well-designed mechanical ventilation systems to ensure both psychophysical well-being and IAQ in enclosed spaces,particularly in environments intended for extended occupancy,such as ***,this approach has broad applicability,as it could be adapted to various building types,thereby contributing to sustainable energy management practices and promoting healthier indoor *** study serves as a model for future designs aiming to balance energy efficiency with indoor air quality,especially relevant in the post-COVID era,where the importance of indoor air quality has become more widely recognized.

关键词： Indoor air quality mechanical ventilation system innovative control system energy efficiency Energy-Plus simulation

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Deep Learning Based Side-Channel Attack Detection for Mobile Devices Security in 5G Networks

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1012-1026页

作者： Amjed A.Ahmed Mohammad Kamrul Hasan Ali Alqahtani Shayla Islam Bishwajeet Pandey Leila Rzayeva Huda Saleh Abbas Azana Hafizah Mohd Aman Nayef Alqahtani Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Imam Alkadhim University College Department of Computer Techniques EngineeringBaghdad 10066Iraq Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Department of Networks and Communications Engineering College of Computer Science and Information SystemsNajran UniversityNajran 61441Saudi Arabia Institute of Computer Science and Digital Innovation UCSI UniversityKuala Lumpur 56000Malaysia Department of Intelligent Systems and Cyber Security Astana IT UniversityAstana 20000Kazakstan Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinah 42353Saudi Arabia Department of Electrical Engineering College of EngineeringKing Faisal UniversityAl-Hofuf 31982Saudi Arabia

Mobile devices within Fifth Generation(5G)networks,typically equipped with Android systems,serve as a bridge to connect digital gadgets such as global positioning system,mobile devices,and wireless routers,which are vital in facilitating end-user communication ***,the security of Android systems has been challenged by the sensitive data involved,leading to vulnerabilities in mobile devices used in 5G *** vulnerabilities expose mobile devices to cyber-attacks,primarily resulting from security ***-permission apps in Android can exploit these channels to access sensitive information,including user identities,login credentials,and geolocation *** such attack leverages"zero-permission"sensors like accelerometers and gyroscopes,enabling attackers to gather information about the smartphone's *** underscores the importance of fortifying mobile devices against potential future *** research focuses on a new recurrent neural network prediction model,which has proved highly effective for detecting side-channel attacks in mobile devices in 5G *** conducted state-of-the-art comparative studies to validate our experimental *** results demonstrate that even a small amount of training data can accurately recognize 37.5％of previously unseen user-typed ***,our tap detection mechanism achieves a 92％accuracy rate,a crucial factor for text *** findings have significant practical implications,as they reinforce mobile device security in 5G networks,enhancing user privacy,and data protection.

关键词： Fifth Generation(5G)networks smartphone information leakage Side-Channel Attack(SCA) deep learning

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LookHere: Vision Transformers with Directed Attention Generalize and Extrapolate 38

LookHere: Vision Transformers with Directed Attention Genera...

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Fuller, Anthony Kyrollos, Daniel G. Yassin, Yousef Green, James R. Department of Systems and Computer Engineering Carleton University OttawaON Canada

High-resolution images offer more information about scenes that can improve model accuracy. However, the dominant model architecture in computer vision, the vision transformer (ViT), cannot effectively leverage larger images without finetuning - ViTs poorly extrapolate to more patches at test time, although transformers offer sequence length flexibility. We attribute this shortcoming to the current patch position encoding methods, which create a distribution shift when extrapolating. We propose a drop-in replacement for the position encoding of plain ViTs that restricts attention heads to fixed fields of view, pointed in different directions, using 2D attention masks. Our novel method, called LookHere, provides translation-equivariance, ensures attention head diversity, and limits the distribution shift that attention heads face when extrapolating. We demonstrate that LookHere improves performance on classification (avg.↑ 1.6%), against adversarial attack (avg.↑ 5.4%), and decreases calibration error (avg.↓ 1.5%) - on ImageNet without extrapolation. With extrapolation, LookHere outperforms the current SoTA position encoding method, 2D-RoPE, by 21.7% on ImageNet when trained at 2242 px and tested at 10242 px. Additionally, we release a high-resolution test set to improve the evaluation of high-resolution image classifiers, called ImageNet-HR. © 2024 Neural information processing systems foundation. All rights reserved.

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