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Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Somayeh Davar Thomas Fevens Department of Computer Science and Software Engineering Concordia University Montréal Canada

ISBN: (数字)9798350387179

ISBN: (纸本)9798350387186

The segmentation of magnetic resonance imaging (MRI) is an important task in medical imaging, particularly for brain MRIs, where accurate segmentation of anatomical structures, often challenged by uneven shapes and fuzzy boundaries of tumours, is difficult to achieve. Thus, automating reliable segmentation of the region of interest (ROI) is essential in medical imaging. This work introduces an automated approach for brain tumour segmentation, leveraging a Deep Convolutional Neural Network (CNN) with a focus on effectively segmenting brain tumours within T1-weighted contrast-enhanced MRI (CE-MRI) images. The proposed method is first performed on the classified images to localize the tumour regions of interest(ROIs). In the next stage, the algorithm contours the concentrated tumour boundary for the segmentation process, which contains the network and attention module, both spatial and channel. To evaluate the overall system's performance, precision, recall, Jaccard index, and dice similarity coefficient (DSC) were calculated, where we achieved 0.8757, 0.8804, 0.8624, and 0.8995, respectively. Our approach demonstrates promising results compared to previous methods using the same database.

关键词： Image segmentation Databases Shape Magnetic resonance imaging System performance Convolutional neural networks Indexes

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IoT Devices Authentication Using Artificial Neural Network

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computers, Materials & Continua 2022年第2期70卷 3701-3716页

作者： Syed Shabih Ul Hasan Anwar Ghani Ikram Ud Din Ahmad Almogren Ayman Altameem Department of Computer Science and Software Engineering International Islamic University Islamabad45000Pakistan Department of Information Technology The University of Haripur22620HaripurPakistan Chair of Cyber Security Department of Computer ScienceCollege of Computer and Information SciencesKing Saud UniversityRiyadh11633Saudi Arabia Chair of Cyber Security Department of Natural and Engineering SciencesCollege of Applied Studies and Community ServicesKing Saud UniversityRiyadh11543Saudi Arabia

User authentication is one of the critical concerns of information *** tend to use strong textual passwords,but remembering complex passwords is hard as they often write it on a piece of paper or save it in their mobile *** passwords are slightly unprotected and are easily *** attacks include dictionary,shoulder surfing,and brute *** passwords overcome the shortcomings of textual passwords and are designed to aid memorability and ease of *** paper proposes a Process-based Pattern Authentication(PPA)system for Internet of Things(IoT)devices that does not require a server to maintain a static password of the login *** server stores user’s information,which they provide at the time of registration,i.e.,the R-code and the symbol,but the P-code,i.e.,the actual password,will change with every login attempt of *** this scheme,users may draw a pattern on the basis of calculation from the P-code and Rcode in the PPA pattern,and can authenticate themselves using their touch dynamic behaviors through Artificial Neural Network(ANN).The ANN is trained on touch behaviors of legitimate users reporting superior performance over the existing *** experimental purposes,PPA is implemented as a prototype on a computer system to carry out experiments for the evaluation in terms of memorability and *** experiments show that the system has an effect of 5.03%of the False Rejection Rate(FRR)and 4.36%of the False Acceptance Rate(FAR),respectively.

关键词： Implicit authentication behavioral authentication artificial neural network processed pattern authentication

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Confusion Matrix Explainability to Improve Model Performance: Application to Network Intrusion Detection

Confusion Matrix Explainability to Improve Model Performance...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Elyes Manai Mohamed Mejri Jaouhar Fattahi Department of Computer Science and Software Engineering Laval University Québec Canada

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

High-performance Machine Learning (ML) models are indispensable in cybersecurity due to the need for real-time threat detection, scalability in handling large datasets, and the ability to recognize complex patterns and evolving threats. These models should reduce false positives and negatives, adapt to dynamic environments, and enable automated response mechanisms. This paper introduces an innovative methodology aimed at improving the performance and interpretability of ML models in binary classification, with a distinct emphasis on network intrusion detection. The proposed approach centers on an in-depth analysis of the confusion matrix, utilizing its insights to enhance model performance. We test our methodology on the UNSW-NB15 network intrusion dataset. We managed to improve almost all metrics with an increase in accuracy from 81.92% to 89.6%, recall from 76.42% to 89.22%, and F1 score from 82.51% to 89.76%, with the potential to obtain more improvements.

关键词： Measurement Adaptation models Accuracy Scalability Network intrusion detection Machine learning Threat assessment Real-time systems Pattern recognition Information technology

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YOLOv5 for Automatic License Plate Recognition in Smart Cities 4th

YOLOv5 for Automatic License Plate Recognition in Smart Citi...

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4th Joint International Conference on Deep Learning, Big Data and Blockchain, DBB 2023

作者： Raza, Abir Badidi, Elarbi Badidi, Basma Al Zahmi, Sarah Department of Computer Science and Software Engineering College of Information Technology UAE University P.O. Box 15551 Al-Ain United Arab Emirates

ISBN: (纸本)9783031423161

Recognition and extraction of license plate information from still images or videos are the basis of modern traffic and security systems. Automatic License Plate Recognition (ALPR) is transforming public safety and transportation in many ways. Such license plate recognition systems enable advanced solutions for toll roads, offer significant operational cost savings through automation, and even open up new market opportunities, such as license plate readers mounted on police vehicles. In this work, we implement license plate recognition based on convolutional neural networks to achieve high accuracy. You Only Look Once (YOLO) algorithm is employed for detection, while, an Optical Character Recognition (OCR) technique is used for text recognition. We use four variants of the YOLOv5 for license plate detection and the EasyOCR for license plate recognition. The results show that YOLOv5x (extra-large) achieves a Mean Average Precision (mAP) of nearly 82% in detecting vehicles and license plates in an image. Furthermore, the recognition of the letters on license plates is achieved with a confidence score of nearly 65%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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Modeling and Analysis for the Propagation of DDoS Attacks in Microgrid System

Modeling and Analysis for the Propagation of DDoS Attacks in...

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2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, 2023 International Conference on Pervasive Intelligence and Computing, 2023 International Conference on Cloud and Big Data Computing, 2023 International Conference on Cyber science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Xiong, Lei Xu, Qichao Fang, Dongfeng Zeng, Hui School of Mechatronic Engineering and Automation Shanghai University Shanghai China California Polytechnic State University Department of Computer Science and Software Engineering San Luis ObispoCA United States

ISBN: (纸本)9798350304602

With the development and progress of electric power technology and information technology, microgrid has become an important and indispensable part of smart grid. The cyber-security of microgrids has a significant impact on the stability and security of smart grids. Distributed denial-of-service (DDoS) attack as one of the common types of cyberattacks in microgrids has formed a considerable threat to the network security of microgrids. In a DDoS event, attackers first use malware to infect few nodes in the microgrid and the infected nodes can generate large-scale malware propagation in the system. These infection lays the foundation for the flood attacks. Therefore, this paper establishes a model to analyse the DDoS attack propagation in microgrid. Firstly, this paper establishs a dynamic model based on the epidemiological theory to describe the propagation dynamics of DDoS attack in microgrid. Then we go on to compute the attack threshold $(R_{0})$ of the model and prove the stability of equilibrium point. Finally we carry out the experimental simulation based on the model, and the experimental results show that the proposed model can reflect the propagation of DDoS attack in microgrid effectively. © 2023 IEEE.

关键词： Denial-of-service attack

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BoQ: A Place is Worth a Bag of Learnable Queries

BoQ: A Place is Worth a Bag of Learnable Queries

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Amar Ali-bey Brahim Chaib-draa Philippe Giguère Department of Computer Science and Software Engineering Université Laval Québec Canada

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

In visual place recognition, accurately identifying and matching images of locations under varying environmental conditions and viewpoints remains a significant challenge. In this paper, we introduce a new technique, called Bag-of-Queries (BoQ), which learns a set of global queries, designed to capture universal place-specific attributes. Unlike existing techniques that employ self-attention and generate the queries directly from the input, BoQ employ distinct learnable global queries, which probe the input features via cross-attention, ensuring consistent information aggregation. In addition, this technique provides an inter-pretable attention mechanism and integrates with both CNN and Vision Transformer backbones. The performance of BoQ is demonstrated through extensive experiments on 14 large-scale benchmarks. It consistently outperforms current state-of-the-art techniques including NetVLAD, MixVPR and EigenPlaces. Moreover, despite being a global re-trieval technique (one-stage), BoQ surpasses two-stage re-trieval methods, such as Patch-NetVLAD, TransVPR and R2Former, all while being orders of magnitude faster and more efficient. The code and model weights are publicly available at https:/***/amaralibey/Bag-of-Queries.

关键词： computer vision Codes Buildings Lighting Benchmark testing Transformers Eigenvalues and eigenfunctions

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Self-Adapting Design and Maintenance of Multi-Model Databases 22

Self-Adapting Design and Maintenance of Multi-Model Database...

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26th International Database Engineered Applications Symposium, IDEAS 2022

作者： Holubova, Irena Koupil, Pavel Lu, Jiaheng Department of Software Engineering Charles University Prague Czech Republic Department of Computer Science University of Helsinki Helsinki Finland

ISBN: (纸本)9781450397094

Multi-model data is organised in various mutually interlinked formats and models, often with contradictory features. In addition, its structure may change over time, and its size can grow to the extremes of Big Data. In terms of research and practical processing, this creates one of the most complex challenges of effective data management. As it is not humanly possible to handle such a complex task manually, in this vision paper, we focus on the area of automatic management of dynamic multi-model Big Data. We envision a framework capable of accepting different levels of user input and different types of data, queries, changes, and propagation strategies and ensuring the preservation of adequate and efficient data access. © 2022 ACM.

关键词： Information management

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The Application of a Synonyms Dictionary Using Bi-LSTM in Bangla for Better Approximation 2

The Application of a Synonyms Dictionary Using Bi-LSTM in Ba...

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2nd International Conference on Information and Communication Technology for Sustainable Development, ICICT4SD 2023

作者： Hossain, M Iffat Saha, Utsha Billa, Arefa Software Engineer BJIT Ltd. Dhaka Bangladesh North Dakota State University Department of Computer Science Fargo United States Florida State University Department of Electrical and Computer Engineering Tallahassee United States

ISBN: (纸本)9798350358667

With the increasing internet usage, the volume of computerized data in Bangla is also growing exponentially. This vast repository of unstructured web data has various potential applications in Natural Language Processing (NLP). However, research in Bangla NLP faces significant challenges, including language complexity and limited availability of tools and datasets. These factors result in a lower accuracy rate when traditional methods used for English NLP are applied to Bangla. Specifically, the Bangla language has a rich lexicon of root words and characters, leading to an increase in out-of-vocabulary words when dealing with unseen texts. This paper proposes a novel method that employs a synonym dictionary to mitigate this problem. By converting unseen words into their synonyms that are recognized by the model, the method significantly reduces out-of-vocabulary errors. Our approach achieved an accuracy rate of 97.4%, compared to 95.3% without using the synonym dictionary, and had a loss value of 0.96. This methodology holds promise as an effective tool for solving classification problems in Bangla NLP. © 2023 IEEE.

关键词： Long short-term memory

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Street Light Cum Garbage System Optimal Design Based on IOT 2

Street Light Cum Garbage System Optimal Design Based on IOT

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Rajesh, G. Deepa, J. Ezilarasan, M.R. Syed Fiaz, A. Siva Rama Lingham, N. Jagannathan, J. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Information Technology Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Department of Software and Systems Engineering Vellore India

ISBN: (纸本)9798350328202

Street lights currently use more energy than other types of lighting because of an inefficient mechanism that makes the bulbs use a lot of electricity. The suggested approach uses various sensors on intelligent streetlights to keep an eye on and manage the lights. It has sensors for measuring temperature, brightness, and power that control dimming levels and keep track of status. One Zigbee network serves as the connection point for these lights. In the modification, a cloud- and IoT-based installation was made. Through Zigbee -based data transmission, sensor values are recorded on a distant server known as the Cloud. Additionally, it lowers electricity theft. We include a trash can notice in addition to this street light idea. When the garbage can is whole, a machine automatically notifies the business as people approach the sun, and the brightness of the street lights changes or is increased. © 2024 IEEE.

关键词： Light emitting diodes

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Implementation of multicloud strategies for healthcare organisations to avoid cloud sprawl

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International Journal of Cloud Computing 2022年第5-6期11卷 529-536页

作者： Jayanthiladevi, A. Ayoobkhan, Mohamed Uvaze Ahamed ThamaraiSelvi, R. Jimmy, Laishram Mishra, Piyush Robert, Nismon Rio Industry Relations and International Research Collaborations Srinivas University Karnataka India Software Engineering New Uzbekistan University Tashkent Uzbekistan Department of MCA Bishop Heber College Trichy India Department of Computer Science and Engineering Manipur Technical University Government Polytechnic Campus Manipur Imphal India Department of Computer Engineering and Applications GLA University Uttar Pradesh Mathura India Department of Computer Science Christ University Bengaluru India

Healthcare organisations are being overwhelmed by data, devices and apps, and disjointed multiple cloud services. Well-heeled multicloud can provide a unified cloud model that provides greater control and scalability at reduced costs. Healthcare multicloud is turning into an appealing path for associations to manage the blast of advanced healthcare information in digital health, internet of things, associated gadgets and healthcare applications. As more human services associations grasp distributed computing, they are progressively going to a blend of open, private, and hybrid cloud administrations and foundation. In fact most of the healthcare service organisations plan to utilise different cloud vendors over the upcoming years. While having multiple cloud vendors can give organisations more flexibility and redundancy, managing multiple clouds can be a challenge. It can lead to cloud sprawl, unauthorised cloud use known as shadow IT, disjointed cloud solutions, inefficiencies, and waste. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Health care

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