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9th IEEE International Conference on Computational Intelligence and Applications, ICCIA 2024

作者： Tachaapornchai, Abhibhu Kosolsombat, Somkiat Ratanavilisagul, Chiabwoot Data Science and Innovation College of Interdisciplinary Studies Thammasat University Pathum Thani Thailand King Mongkut's University of Technology Faculty of Applied Science Department of Computer and Information Science North Bangkok Thailand

ISBN: (纸本)9798350352214

To build an intelligent intrusion detection system, it is essential to have a suitable and high-quality dataset with a sufficiently large quantity to simulate real-world scenarios. The NSL-KDD dataset is an improved version derived from the previous KDD 99 dataset. In this article, an analysis of the NSL-KDD dataset was conducted to study the efficiency of classification machine learning in detecting abnormalities in network data transmission patterns. The study achieved the highest accuracy by XGBoost method using 14 features important and grid search for hyperparameter tuning and revealing many interesting insights. This research has significant implications and can be further applied in the field of cybersecurity by leveraging machine learning for network intrusion detection systems. © 2024 IEEE.

关键词： Cybersecurity

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Robust Deep Learning Empowered Real Time Object Detection for Unmanned Aerial Vehicles based Surveillance Applications

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Journal of Mobile Multimedia 2023年第2期19卷 451-476页

作者： Ranjith, C. Prasanna Hardas, Bhalchandra M. Khaja Mohideen, M. Syed Nijil Raj, N. Robert, Nismon Rio Mohan, Prakash Faculty in Information Technology Department University of Technology and Applied Sciences Shinas Oman Electronics Engineirng Dept Shri Ramdeobaba College of Enginering and Management Nagpur India Department of Information Technology University of Technology and Applied Science Salalah Oman Department of Computer Science and Engineering Younus College of Engineering and Technology Kollam India Department of Computer Science Christ University Bangalore India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Surveillance is a major stream of research in the field of Unmanned Aerial Vehicles (UAV), which focuses on the observation of a person, group of people, buildings, infrastructure, etc. With the integration of real time images and video processing approaches such as machine learning, deep learning, and computer vision, the UAV possesses several advantages such as enhanced safety, cheap, rapid response, and effective coverage facility. In this aspect, this study designs robust deep learning based real time object detection (RDL-RTOD) technique for UAV surveillance applications. The proposed RDL-RTOD technique encompasses a two-stage process namely object detection and objects classification. For detecting objects, YOLO-v2 with ResNet-152 technique is used and generates a bounding box for every object. In addition, the classification of detected objects takes place using optimal kernel extreme learning machine (OKELM). In addition, fruit fly optimization (FFO) algorithm is applied for tuning the weight parameter of the KELM model and thereby boosts the classification performance. A series of simulations were carried out on the benchmark dataset and the results are examined under various aspects. The experimental results highlighted the supremacy of the RDL-RTOD technique over the recent approaches in terms of several performance measures. © 2022 River Publishers.

关键词： computer vision deep learning image processing object detection Surveillance unmanned aerial vehicles

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PDF Malware Detection Using Machine Learning 9

PDF Malware Detection Using Machine Learning

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9th IEEE International Conference on Computational Intelligence and Applications, ICCIA 2024

作者： Tantikulvichit, Naragorn Kosolsombat, Somkiat Ratanavilisagul, Chiabwoot College of Interdisciplinary Studies Thammasat University Data Science and Innovation Pathum Thani Thailand King Mongkut's University of Technology Faculty of Applied Science Department of Computer and Information Science North Bangkok Thailand

ISBN: (纸本)9798350352214

In today's world, cyber-attacks are on the rise, and PDF files are commonly used as a means of attack. One common type of attack through PDF files is the covert embedding of dangerous malware and tricking users into clicking on malicious links. The victims may have their important data stolen and suffer various forms of damage. Detecting malware embedded in PDF files can help mitigate the harm to users. This project aims to study methods for detecting malware embedded in PDF files using machine learning techniques, with the best-performing model being Gradient Boosting, achieving an accuracy of 99% in 5-fold cross-validation and 97% using 10 features. Using Feature Selection Technique Voting from 3 Models consist of Logistic Regression, Random Forest, Lasso Regression, Each model will choose matching features to use for machine learning analysis. This project approach reduce feature for machine learning to detect PDFMalware. © 2024 IEEE.

关键词： Adversarial machine learning

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Sports Events Recognition Using Multi Features and Deep Belief Network

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computers, Materials & Continua 2024年第10期81卷 309-326页

作者： Bayan Alabdullah Muhammad Tayyab Yahay AlQahtani Naif Al Mudawi Asaad Algarni Ahmad Jalal Jeongmin Park Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science Applied CollegeKing Khalid UniversityAbha61421Saudi Arabia Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

In the modern era of a growing population,it is arduous for humans to monitor every aspect of sports,events occurring around us,and scenarios or *** recognition of different types of sports and events has increasingly incorporated the use of machine learning and artificial *** research focuses on detecting and recognizing events in sequential photos characterized by several factors,including the size,location,and position of people’s body parts in those pictures,and the influence around those *** approaches utilized,here are feature descriptors such as MSER(Maximally Stable Extremal Regions),SIFT(Scale-Invariant Feature Transform),and DOF(degree of freedom)between the joint points are applied to the skeleton ***,for the same purposes,other features such as BRISK(Binary Robust Invariant Scalable Keypoints),ORB(Oriented FAST and Rotated BRIEF),and HOG(Histogram of Oriented Gradients)are applied on full body or *** integration of these techniques increases the discriminative nature of characteristics retrieved in the identification process of the event,hence improving the efficiency and reliability of the entire *** extracted features are passed to the early fusion and DBscan for feature fusion and *** deep belief,network is employed for *** results demonstrate a separate experiment’s detection average recognition rate of 87%in the HMDB51 video database and 89%in the YouTube database,showing a better perspective than the current methods in sports and event identification.

关键词： Machine learning silhouettes extremal regions joint points scalable keypoints

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Random Forest Pruning Techniques: A Recent Review

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Operations Research Forum 2023年第2期4卷 43页

作者： Manzali, Youness Elfar, Mohamed Laboratory of Applied Physics Computer Science and Statistics Faculty of Sciences Dhar El Mahraz Sidi Mohamed Ben Abdellah University Fez Morocco

Random forest is one of the most used machine learning algorithms since its high predictive performance. However, many studies criticize it for the fact that it generates a large number of trees, which requires important storage space and a significant learning time. In addition, the final model induced by RF may contain redundant trees and others that do not contribute to the prediction that may even disadvantage performance. This is why many researchers try to reduce the number of trees in a forest called forest pruning. This article presents a study of the pruning work of random forest classifiers, explains in detail the operating principle of each technique, and cites their advantages and disadvantages. Finally, it compares their classification performance in terms of accuracy, speed of learning, and complexity. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Ensemble method Pruning techniques Random forest Tree selection

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Time Sensitive Networking via 5G - An Overview and Recent Advances 28

Time Sensitive Networking via 5G - An Overview and Recent Ad...

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28. VDE ITG-Fachtagung Mobilkommunikation: Technologien und Anwendungen - 28th VDE ITG Conference on Mobile Communication: Technologies and Applications

作者： Kahmann, Felix Muslim, Anas Bin Tönjes, Ralf Islam, Syed Tasnimul Bauschert, Thomas Faculty of Engineering and Computer Science Hochschule Osnabrück - University of Applied Sciences Osnabrück Germany Faculty of Electrical Engineering and Information Technology Chemnitz University of Technology Chemnitz Germany

ISBN: (纸本)9783800763832

Time Sensitive Networking (TSN) is a promising and widely used Ethernet based technology for precise time-aware networking. It enables low latency communication, prioritization of traffic, and replicated and precise machine to machine communication. While Industry 4.0 have exactly these requirements, wide coverage is also needed, which can be difficult with the Ethernet based TSN. Thus, the integration of the 5G and TSN emerges as a solution for Industry 4.0’s ultra reliable low latency and strictly timed communication requirements. 5G’s Release 16 and 17 aims to provide TSN functionality through 5G networks as a service. This paper investigates various approaches regarding how 5G and TSN can be integrated to preserve ultra reliability, low latency, and precisely timed TSN data streams as they navigate through the 5G network. It also focuses on major TSN components, how 5G plans to handle each components’ requirements, and what are the challenges to reach the final TSN-5G integrated solution. Results of private 5G network testings are also presented for easier understanding of the challenges faced in the TSN-5G integration. © 2024 Mobilkommunikation: Technologien und Anwendungen - 28. VDE ITG-Fachtagung. All rights reserved.

关键词： Ethernet

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Constructing Varied and Attractive Shortlists from Databases: A Group Decision Approach 7th

Constructing Varied and Attractive Shortlists from Databas...

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7th Seminar on Quantitative Methods of Group Decision Making, 2021

作者： Ramsey, David M. Mariański, Aleksander Department of Computer Science and Systems Engineering Faculty of Computer Science and Telecommunications Wroclaw University of Science and Technology Wroclaw Poland Department of Applied Computer Science Faculty of Computer Science and Telecommunications Wroclaw University of Science and Technology Wroclaw Poland

ISBN: (纸本)9783662665961

Thanks to the Internet, basic information can be found about a very large number of offers at very little cost. Many sites give basic information about real estate offers, including price, size, location and number of rooms. Information of this kind is sufficient to assess whether an offer is potentially attractive or not, but does not suffice in making an ultimate decision. The number of real estate offers in a city may be very large. In such a case, it can be beneficial to use an automatic procedure to first eliminate offers that do not satisfy the basic criteria of an individual or family and then construct a shortlist of varied and potentially attractive offers. This article recalls an algorithm that derives such a shortlist for an individual searcher. The novelty of this article lies in adapting this algorithm to scenarios in which a group decision is made. A practical example based on real estate offers in Warsaw is used to illustrate the algorithm. © 2022, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Software engineering

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Dataset of Large Gathering Images for Person Identification and Tracking

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computers, Materials & Continua 2023年第3期74卷 6065-6080页

作者： Adnan Nadeem Amir Mehmood Kashif Rizwan Muhammad Ashraf Nauman Qadeer Ali Alzahrani Qammer H.Abbasi Fazal Noor Majed Alhaisoni Nadeem Mahmood Faculty of Computer and Information System Islamic University of MadinahMadinah42351Saudi Arabia Department of Computer Science and Information Technology Sir Syed University of Engineering and TechnologyKarachi75300Pakistan Department of Computer Science Federal Urdu University of ArtsScience&TechnologyIslamabad45570Pakistan Department of Physics Federal Urdu University of ArtsScience&TechnologyKarachi75300Pakistan James Watt School of Engineering University of GlasgowGlasgowG128QQUK Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science University of KarachiKarachi75270Pakistan

This paper presents a large gathering dataset of images extracted from publicly filmed videos by 24 cameras installed on the premises of Masjid Al-Nabvi,Madinah,Saudi *** dataset consists of raw and processed images reflecting a highly challenging and unconstraint *** methodology for building the dataset consists of four core phases;that include acquisition of videos,extraction of frames,localization of face regions,and cropping and resizing of detected face *** raw images in the dataset consist of a total of 4613 frames obtained fromvideo *** processed images in the dataset consist of the face regions of 250 persons extracted from raw data images to ensure the authenticity of the presented *** dataset further consists of 8 images corresponding to each of the 250 subjects(persons)for a total of 2000 *** portrays a highly unconstrained and challenging environment with human faces of varying sizes and pixel quality(resolution).Since the face regions in video sequences are severely degraded due to various unavoidable factors,it can be used as a benchmark to test and evaluate face detection and recognition algorithms for research *** have also gathered and displayed records of the presence of subjects who appear in presented frames;in a temporal *** can also be used as a temporal benchmark for tracking,finding persons,activity monitoring,and crowd counting in large crowd scenarios.

关键词： Large crowd gatherings a dataset of large crowd images highly uncontrolled environment tracking missing persons face recognition activity monitoring

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Renewable Energy Efficiency of Unmanned Aerial Vehicles Operating with Wireless Sensor Networks and Mobile Ad Hoc Networks 2

Renewable Energy Efficiency of Unmanned Aerial Vehicles Oper...

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2nd International Conference on Smart Technologies for Power and Renewable Energy, SPECon 2024

作者： Ahmad, Wakeel Ahmed, Sarfaraz Ahmad, Adeel Siddiqui, Shams Tabrez Khamruddin, Mohammad Khan, Haneef Jazan University College of Applied Industrial Tech Deptartment of Electrical Engineering Technology Jazan Saudi Arabia Jazan University College of Computer Science and Information Technology Dept. of Cnet Jazan Saudi Arabia Aligarh Muslim University Faculty of Science Department of Computer Science U.P Aligarh India Jazan University College of Computer Science and Information Technology Department of Computer Science Jazan Saudi Arabia

ISBN: (纸本)9798350383041

The amalgamation of Unmanned Aerial Vehicles (UAVs) into Wireless Sensor Networks (WSNs) and Mobile Ad Hoc Networks (MANETs) provides significant prospects to improve network capabilities. This study examines the energy renewable efficiency and performance of UAVs operating in WSNs and MANETs. The assessment considers a variety of elements that influence energy consumption, including the possibility of integrating renewable energy, energy management measures, and the impact on overall network performance. This seeks to provide insights into optimizing energy usage and improving the operating efficiency of UAVs in these dynamic network situations through extensive analysis and evaluation. A special emphasis is placed on the management of mobility, the optimization of energy usage, and the effectiveness of routing protocols. The paper explores existing protocols such as LEACH, AODV and OLSR highlighting their benefits and challenges for UAV's. A comparative analysis evaluates adapted protocols based on metrics such as energy efficiency, latency, and reliability. © 2024 IEEE.

关键词： Wireless sensor networks

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Engineering Education Using Serious Games: The Current State, Trends and Future Using a Bibliometric Study 1

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International Symposium on Automatic Control and Emerging Technologies, ACET 2023

作者： Sabri, Zineb Remaida, Ahmed Benyoussef, Abdellaoui Qostal, Aniss Sellamy, Khadija Fakhri, Youssef Moumen, Aniss Laboratory of Computer Science Faculty of Science Ibn Tofail University Kenitra Morocco Engineering Sciences Laboratory National School of Applied Science Ibn Tofail University Kenitra Morocco S.I.G.E.R Laboratory Faculty of Science and Technology Fez Morocco

ISBN: (数字)9789819701261

ISBN: (纸本)9789819701254

There is a global concern about using serious games in multiple fields with numerous publications, especially in Education. Conducting a bibliometric analysis can be insightful to depict and analyze the current state, trends, and methods most used in serious games for engineering education to provide knowledge upgrades in this research area. Therefore, this bibliometric study used the Web of science database to select the most relevant documents, VOSviewer and Bibliometrix to conduct the analysis. The results obtained in this bibliometric study highlight that researchers are currently interested in exploiting serious games worldwide. On the one hand, the most advanced countries like USA, England, Germany, and others are the top countries focusing on using games in educational fields. On the other hand, the trend topics related to serious games are indeed applied in the educational engineering area and computer science. Based on the network and thematic map, these two pillars are fundamental and must be developed more. Finally, the top relevant articles reviewed indicate that computer engineering is the most used discipline in serious games. Most of the articles used quantitative or mixed methods as research design. The most used game genres are Simulation, and web-based platforms are reported commonly in serious games in engineering education. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Serious games

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