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Weapons Detection for Security and Video Surveillance Using CNN and YOLO-V5s

computers, Materials & Continua 2022年第2期70卷 2761-2775页

作者： Abdul Hanan Ashraf Muhammad Imran Abdulrahman M.Qahtani Abdulmajeed Alsufyani Omar Almutiry Awais Mahmood Muhammad Attique Mohamed Habib Department of Computer Science Shaheed Zulfikar Ali Bhutto Institute of Science and TechnologyIslamabad44000Pakistan Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia College of Applied Computer Science King Saud University(Almuzahmiyah Campus)Riyadh11543Saudi Arabia Department of Computer Science HITEC UniversityTaxila47080Pakistan College of Computing and Informatics Saudi Electronic UniversityRiyadhSaudi Arabia Faculty of Engineering Port Said UniversityPort Fuad CityEgypt

In recent years,the number of Gun-related incidents has crossed over 250,000 per year and over 85%of the existing 1 billion firearms are in civilian hands,manual monitoring has not proven effective in detecting *** is why an automated weapon detection system is *** automated convolutional neural networks(CNN)weapon detection systems have been proposed in the past to generate good ***,These techniques have high computation overhead and are slow to provide real-time detection which is essential for the weapon detection *** models have a high rate of false negatives because they often fail to detect the guns due to the low quality and visibility issues of surveillance *** research work aims to minimize the rate of false negatives and false positives in weapon detection while keeping the speed of detection as a key *** proposed framework is based on You Only Look Once(YOLO)and Area of Interest(AOI).Initially,themodels take pre-processed frames where the background is removed by the use of the Gaussian blur *** proposed architecture will be assessed through various performance parameters such as False Negative,False Positive,precision,recall rate,and F1 *** results of this research work make it clear that due to YOLO-v5s high recall rate and speed of detection are *** reached 0.010 s per frame compared to the 0.17 s of the Faster *** is promising to be used in the field of security and weapon detection.

关键词： Video surveillance weapon detection you only look once convolutional neural networks

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Bluffing the Hackers: Automated Decoy Creation and Real-Time Cyber Deception

Bluffing the Hackers: Automated Decoy Creation and Real-Time...

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International Conference on Signal Processing and Information Security (ICSPIS)

作者： Mohamed Farrag Samir G. Sayed Marwa Zamzam Faculty of Informatics and Computer Science German International University Cairo Egypt EG-CERT National Telecom Regulatory Authority (NTRA) Giza Egypt Faculty of Engineering in Helwan Helwan University Cairo Egypt

ISBN: (数字)9798350368673

ISBN: (纸本)9798350368680

In the digital age, the proliferation of cyber threats necessitates advanced cybersecurity solutions. This paper introduces “Bluff,” an innovative cybersecurity platform designed to enhance intrusion prevention through automated decoy deployment and real-time threat tracking. “Bluff” integrates both backend and frontend components within a single cross-platform executable, simplifying installation and use. Leveraging deception technology, “Bluff” creates strategic decoys that divert cyber attackers, thereby protecting critical systems and gathering valuable threat intelligence. This comprehensive solution not only fortifies defenses but also manipulates adversaries’ perceptions, enabling early threat detection. Through detailed implementation and performance evaluation, we demonstrate “Bluff’s” efficacy in providing a proactive and sophisticated defense mechanism, contributing significantly to the evolving landscape of cybersecurity.

关键词： Performance evaluation computer hacking Prevention and mitigation Information security Signal processing Information age Real-time systems Threat assessment computer crime

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Modelling and Use of Adaptive Control Using Petri Nets 27

Modelling and Use of Adaptive Control Using Petri Nets

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27th International Conference on Knowledge Based and Intelligent Information and engineering Sytems, KES 2023

作者： Balogh, Zoltán Fodor, Kristián Francisti, Jan Drozda, Martin Cavojský, Maroš Department of Computer Science Constantine the Philosopher University in Nitra Nitra949 01 Slovakia Institute of Informatics and Mathematics Slovak University of Technology in Bratislava Bratislava811 07 Slovakia Kándó Kálmán Faculty of Electrical Engineering Óbuda University Budapest Hungary

The issue of modeling holds significant importance for the applicability of control theory, particularly at higher control levels where the verbal description of control objects becomes increasingly crucial. However, a notable disadvantage of such verbal descriptions is the inherent ambiguity and vagueness of the obtained results. This article aims to address this challenge by proposing an approach that utilizes Petri nets (PN) to model and control a specific component within an intelligent home. Additionally, the Simulink modeling tool and Petri net analysis tools are introduced as aids in the modeling process. The article focuses on creating a model that accurately represents the temperature regulation system in the home, considering the constant rise in energy consumption and costs. Furthermore, the transfer of the model to the Simulink environment using IF THEN rules is demonstrated, allowing for an evaluation of its performance. Through the comprehensive application of this approach, the aim is to optimize energy usage and reduce costs within household environments. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Matlab Petri nets Simulation Smart homes

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Real-Time Driver Sleepiness Detection and Classification Using Fusion Deep Learning Algorithm 5th

Real-Time Driver Sleepiness Detection and Classification Usi...

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5th International Conference on Recent Innovations in Computing, ICRIC 2022

作者： Rajawat, Anand Singh Goyal, S.B. Bhaladhare, Pawan Bedi, Pradeep Verma, Chaman Florin-Emilian, Țurcanu Candin, Mihaltan Traian School of Computer Science and Engineering Sandip University Nashik India Faculty of Information Technology City University Malaysia Petaling Jaya Malaysia Galgotias University Greater Noida India Faculty of Informatics University of Eötvös Loránd Budapest1053 Hungary Department of Building Services Faculty of Civil Engineering and Building Services Gheorghe Asachi Technical University of Iasi Jassy700050 Romania Faculty of Building Services Cluj-Napoca Technical University of Cluj-Napoca Cluj-Napoca400114 Romania

ISBN: (纸本)9789811998751

In the past few years, driving while tired has become one of the main causes of car accidents. This kind of dangerous driving can cause serious injuries or even death and huge financial losses. The face, a significant part of the body, can send many different messages. When a person is tired, their facial expressions, like how often they blink their eyes, differ from when they are alert. This is very clear when it comes to drivers. In this study, a fusion deep learning algorithm (FDLA) is used to determine if a driver is tired and how tired they are in real time. This algorithm uses video footage to determine how tired a driver is based on how often they blink and how long their eyes are closed. We are making a new face-tracking algorithm because the ones that are already out there aren’t good enough. This will make face tracking more accurate. Also, a new way to find problems in facility areas has been made, and it is based on seventy essential spots. After that, these parts of the driver’s face can be used to determine how he or she is feeling. When the eyes and lips are used together, a driver care solution can tell if the driver is getting tired. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Learning algorithms

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The Effect of Digital Marketing on Micro, Small and Medium Enterprise in Indonesia 1

引用

International Conference on Applied informatics and Media Design, AIMD 2019

作者： Sasongko, Alvin Igo Widjaja, Gregorius Christian Theodore, Jason Afriliana, Nunik Matsuo, Tokuro Gaol, Ford Lumban School of Information System Bina Nusantara University Jakarta Indonesia Faculty of Engineering and Informatics Universitas Multimedia Nusantara Tangerang Indonesia Computer Science Department Bina Nusantara University Jakarta Indonesia Graduate School of Industrial Technology Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (数字)9783031307690

ISBN: (纸本)9783031307683

Digital Marketing is a product marketing strategy using digital media and internet networks. Nowadays, digital marketing is a necessity for companies that thrive in the digital world. It greatly affects the growth of MSME (Micro, Small, Medium Enterprises) especially when it comes to increasing their sales performance by enhancing their brand awareness through various media or instruments such as a Facebook ad, Instagram advertising, Search Engine Optimization, and Search Engine Marketing. This research is aimed to measure how much impact of digital marketing utilization into Micro, Small, Medium enterprises. On the other hand, it also provides an understanding on what are the best digital marketing method to be utilized in the current situation. The method we used in this paper is quantitative. Data were collected through various questions constructed in an electronic questionnaire. This research provides a reference of digital marketing strategies for Micro, Small and Medium business owners. The result shows that digital marketing has a huge impact on the growth of Micro, Small and Medium Enterprise from increasing sales, increasing brand awareness, and gaining customers based on the data collected from both customer and Micro, Small, and Medium Enterprise players in Indonesia. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Digital storage

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Unveiling the Digital Fingerprints: Analysis of Internet attacks based on website fingerprints

arXiv

引用

arXiv 2024年

作者： Rexha, Blerim Musa, Arbena Vishi, Kamer Martiri, Edlira University of Prishtina Faculty of Electrical and Computer Engineering Kodra e Deillit pn Prishtina1000 Serbia University of Oslo Department of Informatics Oslo Norway University of Tirana Faculty of Economics Tirana Albania

Parallel to our physical activities our virtual presence also leaves behind our unique digital fingerprints, while navigating on the Internet. These digital fingerprints have the potential to unveil users’ activities encompassing browsing history, utilized applications, and even devices employed during these engagements. Many Internet users tend to use web browsers that provide the highest privacy protection and anonymization such as Tor. The success of such privacy protection depends on Tor feature to anonymize end user IP address and other metadata that constructs the website fingerprint. In this paper, we show that using the newest machine learning algorithms an attacker can deanonymize Tor traffic by applying such techniques. In our experimental framework, we establish a baseline and comparative reference point using a publicly available dataset from Universidad Del Cauca, Colombia. We capture network packets across 11 days, while users navigate specific web pages, recording data in .pcapng format through the Wireshark network capture tool. Excluding extraneous packets, we employ various machine learning algorithms in our analysis. The results show that the Gradient Boosting Machine algorithm delivers the best outcomes in binary classification, achieving an accuracy of 0.8363. In the realm of multi-class classification, the Random Forest algorithm attains an accuracy of 0.6297. © 2024, CC BY.

关键词： Anonymity

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Optimizing Convolutional Neural Network by Hybridized Elephant Herding Optimization Algorithm for Magnetic Resonance Image Classification of Glioma Brain Tumor Grade

Optimizing Convolutional Neural Network by Hybridized Elepha...

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2021 Zooming Innovation in Consumer Technologies Conference, ZINC 2021

作者： Bezdan, Timea Milosevic, Stefan Venkatachalam, K. Zivkovic, Miodrag Bacanin, Nebojsa Strumberger, Ivana Singidunum University Faculty of Informatics and Computing Belgrade Serbia Vit University School of Computer Science and Engineering Bhopal India

ISBN: (纸本)9781665404174

Gliomas belong to the group of the most frequent types of brain tumors. For this specific type of brain tumors, in its beginning stages, it is extremely complex to get the exact diagnosis. Even with the works from the most experienced doctors, it will not be possible without magnetic resonance imaging, which aids to make the diagnosis of brain tumors. In order to create classification of the images, to where the class of glioma belongs to, for achieving superior performance, convolutional neural networks can be used. For achieving high-level accuracy on the image classification, the convolutional network hyperparameters' calibrations must reach a very accurate response of high accuracy results and this task proves to take up a lot of computational time and energy. Proceeding with the proposed solution, in this scientific research paper a metaheuristic method has been proposed to automatically search and target the near-optimal values of convolutional neural network hyperparameters based on hybridized version of elephant herding optimization swarm intelligence metaheuristics. The hybridized elephant herding optimization has been incorporated for convolutional neural network hyperparameters' tuning to develop a system for automatic and instantaneous image classification of glioma brain tumors grades from the magnetic resonance imaging. Comparative analysis was performed with other methods tested on the same problem instance an results proved superiority of approach proposed in this paper. © 2021 IEEE.

关键词： Image classification

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Deep Learning Based Topic Classification for Sensitivity Assignment to Personal Data 6

Deep Learning Based Topic Classification for Sensitivity Ass...

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6th International Conference on computer Science and engineering, UBMK 2021

作者： Yayık, Apdullah Apik, Hasan Tosun, Ayşe Mobildev R&D Istanbul Turkey Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey

ISBN: (纸本)9781665429085

This study aims to understand non-linear relations in Turkish textual contents to predict their topic with the help of machine learning models and to discuss contributions of the models to compliance with Personal Data Protection Rule (PDPR). Since the exponential growth of concerns in personal data processing, it has been a necessity to know the topic of the textual contents to interpret their usage in which environment they are located. The topic of the document is a piece of inclusive information presented together with all the data in its content, by this reason the categories of personal data defined in the PDPR may vary due to semantic impacts from its environment in which it is being processed. Many experiments are conducted with the fasttext model employing logistic regression and deep bidirectional transformers (BERT) models having attention layers. Model performances and statistical post-hoc test results on the model predictions are analyzed, then model deployment together with the industrial usage are discussed. As a result, a fasttext model having a macro-averaged F1-measure of 94.73 ± 0.67% is created and integrated into production efficiently. © 2021 IEEE

关键词： Forecasting

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Automated Recognition of Structures in Scanning Electron Microscopy Images Using Specialized Algorithms in MATLAB and Python: An Overview 16

Automated Recognition of Structures in Scanning Electron Mic...

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16th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2024

作者： Marascu, Valentina Mihailescu, Marius Iulian Nita, Stefania Loredana Rogobete, Marius 'Spiru Haret' University Scientific Research Center in Mathematics and Informatics Faculty of Engineering and Computer Science Bucharest Romania National Institute for Laser Plasma and Radiation Physics Low Temperature Plasma Department Bucharest Romania 'Ferdinand i' Military Technical Academy Department of Computers and Cybersecurity Bucharest Romania Telematics Harman International Bucharest Romania

ISBN: (纸本)9798350371154

Applied physics serves as the bridge connecting theoretical principles with their practical implementations in everyday situations. informatics plays a crucial role in this subject because of its comprehensive and intricate capacity to simulate and forecast physical phenomena. This research highlights the indispensability of computer-based algorithms for analyzing the Scanning Electron Images of the obtained materials, particularly materials synthesized by laboratory plasma. Herein, the conducted research highlights the advantages of image processing via Matlab and Python algorithms, in order to analyze the synthesized structures. This approach can offer perspectives on physical processes by employing image processing techniques. The approaches and future directions were identified. © 2024 IEEE.

关键词： Python

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Evaluation of Three Different Operators for Object Highlighting in Medical RGB Images: Canny, Roberts, and LoG in Independent Color Spaces 25

Evaluation of Three Different Operators for Object Highlight...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Ababneh, Jafar Abu-Jassar, Amer Abuowaida, Suhaila Liubchenko, Valentyn Lyashenko, Vyacheslav Zarqa University Faculty of Information Technology Cyber Security Department Zarqa Jordan University of Business and Technology College of Engineering Jeddah21448 Saudi Arabia Amman Arab University College of of Computer Sciences and Information Amman Jordan Al Al-Bayt University Faculty of Information Technology Department of Computer Science Al-Mafraq Jordan Kharkiv National University of Radio Electronics Department of Informatics Kharkiv Ukraine Kharkiv National University of Radio Electronics Department of Media Systems and Technology Kharkiv Ukraine

ISBN: (纸本)9798331540012

The research presents a comparative analysis of the selection of objects of interest in color medical cytology images. For these purposes, the individual edge detection operators studied are Canny, Roberts, and LoG. The comparison contrasts the standard method of assessing color photographs with a technique that incorporates supplementary information. Data from individual color channels, derived from the breakdown of a color image in RGB format, serve as supplementary information. The following individual cases for potential examination are emphasized: Image processing with a complete level of reuse of edge detection operators;with a partial level of reuse of edge detection operators. The results are displayed through an array of tables, graphs, and images. Metrics of visual perception of images are utilized for comparative analysis. Compared with traditional edge detection methods, the proposed approach yields better results, which can exceed the estimates of the classical approach by 20%. © 2024 IEEE.

关键词： Color photography

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