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Text-Independent Algorithm for Source Printer Identification Based on Ensemble Learning

computers, Materials & Continua 2022年第10期73卷 1417-1436页

作者： Naglaa F.El Abady Mohamed Taha Hala H.Zayed Department of Computer Science Faculty of Computers and Artificial IntelligenceBenha University13518Egypt School of Information Technology and Computer Science(ITCS) Nile University12677Egypt

Because of the widespread availability of low-cost printers and scanners,document forgery has become extremely *** or signatures are used to protect important papers such as certificates,passports,and identification *** the origins of printed documents is helpful for criminal investigations and also for authenticating digital versions of a document in today’s *** printer identification(SPI)has become increasingly popular for identifying frauds in printed *** paper provides a proposed algorithm for identifying the source printer and categorizing the questioned document into one of the printer classes.A dataset of 1200 papers from 20 distinct(13)laser and(7)inkjet printers achieved significant identification results.A proposed algorithm based on global features such as the Histogram of Oriented Gradient(HOG)and local features such as Local Binary Pattern(LBP)descriptors has been proposed for printer *** classification,Decision Trees(DT),k-Nearest Neighbors(k-NN),Random Forests,Aggregate bootstrapping(bagging),Adaptive-boosting(boosting),Support Vector Machine(SVM),and mixtures of these classifiers have been *** proposed algorithm can accurately classify the questioned documents into their appropriate printer *** adaptive boosting classifier attained a 96%*** proposed algorithm is compared to four recently published algorithms that used the same dataset and gives better classification accuracy.

关键词： Document forensics source printer identification(SPI) HOG LBP principal component analysis(PCA) bagging AdaBoost

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Applying sentiment analysis in social web for smart decision support marketing

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Journal of Ambient Intelligence and Humanized Computing 2024年第3期15卷 1927-1936页

作者： Wu, Shih-Jung Chiang, Rui-Dong Chang, Han-Chi Department of Innovative Information and Technology Tamkang University New Taipei Taiwan Department of Computer Science and Information Engineering Tamkang University New Taipei Taiwan

Because of the rapid development of communication and service in Taiwan, competition among telecommunication companies has become ever fiercer. Differences in marketing strategy usually become the key factor in keeping existing customers while attracting new ones. Although electronic word-of-mouth (e-WOM) is one of the most important pieces of information to a consumer making a purchase decision, very few articles on opinion mining have discussed and compared the relationship between multifaceted word-of-mouth (WOM) and marketing strategy. In this paper, we use our Chinese opinion-mining system (Wu et al. in J Supercomput 73:2987–3001, 2017) not only to retrieve articles related to 4G and conduct reputation analysis but also to discuss the relation between WOM and marketing strategy. The results show that (1) e-WOM can immediately and directly reflect the results of marketing strategy, and (2) although users are primarily concerned with aspects of price, online speed, and signal quality, for most Taiwanese customers, price is the key in choosing a telecommunication company. Moreover, although this paper used 4G-related articles from June 2014 to June 2015 for analysis, the results are consistent with the Taiwanese telecommunication companies’ current marketing strategy of attracting customers through low pricing. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： Sentiment analysis

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AquaticSafe Sentinel: An Edge-Based IoT-Powered Predictive system for Continuous Dissolved Oxygen Surveillance in Aquaculture 13

AquaticSafe Sentinel: An Edge-Based IoT-Powered Predictive S...

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13th IEEE International Conference on Communication systems and Network Technologies, CSNT 2024

作者： Taghizadeh, Seyedreza Fardarani, Zahra Nooraninejad Shiraz University Engineering and Information Technology Department of Computer Science Shiraz Iran

ISBN: (纸本)9798350305463

Aquaculture stands as a flourishing industry, serving as a significant financial resource for many. However, it encounters various challenges such as environmental degradation and disease outbreaks. Consequently, maintaining water quality in aquaculture systems becomes imperative. Dissolved oxygen (DO), a key indicator of oxygen availability for fish respiration, emerges as a crucial measure of water quality. In this research, we propose AquaticSafe Sentinel, an innovative IoT-backed predictive system designed for edge-based continuous monitoring of DO and real-time alerting in aquaculture. Our two-phase framework utilizes both edge computing and cloud computing to swiftly and reliably predict DO levels based on environmental and biological characteristics. Phase one involves making ensemble forecasts of DO levels at the cloud layer using a combination of three machine learning approaches, namely Linear Regression, Random Forest, and AdaBoost. Then phase two involves employing the results of Linear Regression at the edge of the network to make real-time decisions about DO levels, and triggering alerts if necessary. Subsequently, the system awaits a more refined conclusion from the combination technique at the cloud layer. We conduct a comparative analysis with various baseline methods, demonstrating that our framework surpasses them in terms of prediction accuracy and computing efficiency. Additionally, we illustrate how our framework effectively minimizes false alerts, achieving a balance between speed and accuracy to deliver timely solutions. © 2024 IEEE.

关键词： Edge computing

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Enhancing the Efficiency of Navigation systems: Dijkstra Algorithm Optimization Through Integration of Douglas-Peucker Line Simplification Technique 23

Enhancing the Efficiency of Navigation Systems: Dijkstra Alg...

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8th International Conference on Sustainable information Engineering and technology, SIET 2023

作者： Haq, Muhammad Yasir Anshari Sianturi, Riswan Septriayadi Pinandito, Aryo Informatics Engineering Department Universitas Brawijaya Faculty of Computer Science Indonesia Information System Department Universitas Brawijaya Faculty of Computer Science Indonesia

ISBN: (纸本)9798400708503

Smartphones have grown popular in the current digital era among people. Map-based applications are one of many that are used daily by people to acquire information about specific locations. Google Maps and Waze are two popular examples of map-based applications for travel and navigation, as they provide detailed information while the user navigates from one point to another. One of the most commonly used algorithms in map-based applications is the Dijkstra algorithm. Although the Dijkstra algorithm can determine the shortest route between two points, there is an issue when the selected route involves a large number of data points that need to be processed. The more data points involved, the longer it takes to do a route calculation in the Dijkstra algorithm. This can lead to a degradation in system performance, especially on smartphones with limited resources. To address this issue, the Douglas-Peucker algorithm is proposed as a solution to reduce the number of points involved in the calculation using the line simplification technique. Line simplification means reducing unnecessary points that are considered unimportant. This study suggested that integrating the Douglas-Peucker line simplification technique before Dijkstra could improve the system performance more than the system without integrating line simplification in terms of reducing both memory usage and the time required to compute the solution. © 2023 ACM.

关键词： Navigation systems

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An Intrusion Detection system for Multiclass Classification Across Multiple Datasets in Industrial IoT Using Machine Learning and Neural Networks Integrated with Edge Computing 2

An Intrusion Detection System for Multiclass Classification ...

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2nd International Conference on Data, information and Computing science, CDICS 2024

作者： Zhukabayeva, Tamara Ahmad, Zulfiqar Karabayev, Nurdaulet Baumuratova, Dilaram Ali, Mushtaq Faculty of Information Technology L.N. Gumilyov Eurasian National University Astana Kazakhstan Department of Computer Science and Information Technology Hazara University Mansehra21300 Pakistan

ISBN: (纸本)9781643685762

With the rapid expansion of industrial IoT (IIoT), maintaining robust cybersecurity is essential for the smooth operation of industrial processes. Industrial environments require adaptive solutions to effectively mitigate evolving cyber threats and protect sensitive operations. This research aims to improve the cybersecurity of industrial IoT environments. The research intends to design and implement an adaptive and real-time intrusion detection system with edge computing integration that improves the reliability of the operations in industrial IoT. We incorporated machine learning approaches to classify cyber threats using XGBoost and Deep Neural Networks (DNN). A comparative analysis of results obtained from two datasets shows that the XGBoost model was slightly more accurate than the DNN model, with an accuracy of 79% for dataset D1 and approximately 99.42% for data set D2. This analysis also clearly demonstrates the usefulness of these machine learning approaches and the need to select a model depending on the requirements for detecting particular attacks. Confusion matrix analysis shows that both models have several advantages in terms of recognizing different types of cyber threats. © 2025 The Authors.

关键词： Network intrusion

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Review of Recent Trends in the Hybridisation of Preprocessing-Based and Parameter Optimisation-Based Hybrid Models to Forecast Univariate Streamflow

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computer Modeling in Engineering & sciences 2024年第1期138卷 1-41页

作者： Baydaa Abdul Kareem Salah L.Zubaidi Nadhir Al-Ansari Yousif Raad Muhsen Department of Civil Engineering University of MaysanMaysan57000Iraq Department of Civil Engineering Wasit UniversityWasit52001Iraq College of Engineering University ofWarith Al-AnbiyaaKarbala56001Iraq Department of Civil Environmental and Natural Resources Engineering Lulea University of TechnologyLulea97187Sweden Department of Computer Science Faculty of Computer Science and Information TechnologyUniversiti Putra MalaysiaSerdang43400Malaysia

Forecasting river flow is crucial for optimal planning,management,and sustainability using freshwater *** machine learning(ML)approaches have been enhanced to improve streamflow *** techniques have been viewed as a viable method for enhancing the accuracy of univariate streamflow estimation when compared to standalone *** researchers have also emphasised using hybrid models to improve forecast ***,this paper conducts an updated literature review of applications of hybrid models in estimating streamflow over the last five years,summarising data preprocessing,univariate machine learning modelling strategy,advantages and disadvantages of standalone ML techniques,hybrid models,and performance *** study focuses on two types of hybrid models:parameter optimisation-based hybrid models(OBH)and hybridisation of parameter optimisation-based and preprocessing-based hybridmodels(HOPH).Overall,this research supports the idea thatmeta-heuristic approaches precisely improveML ***’s also one of the first efforts to comprehensively examine the efficiency of various meta-heuristic approaches(classified into four primary classes)hybridised with ML *** study revealed that previous research applied swarm,evolutionary,physics,and hybrid metaheuristics with 77%,61%,12%,and 12%,***,there is still room for improving OBH and HOPH models by examining different data pre-processing techniques and metaheuristic algorithms.

关键词： Univariate streamflow machine learning hybrid model data pre-processing performance metrics

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Performance Analysis of Energy and Secure Efficient Routing for Wireless Sensor Networks

Performance Analysis of Energy and Secure Efficient Routing ...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Hussein, Zainab Mohammed Najim, Ali Hamza Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisha Hassan Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

Wireless Sensor Networks (WSNs) are essential for various applications, but their architecture makes them vulnerable to attacks. While traditional security methods like authentication and encryption offer some protection, they often fall short against specific node attacks. This paper introduces the Secure Energy Aware Routing (SEAR) strategy, which uses trust-based assessments to exclude malicious nodes while optimizing energy consumption. SEAR factors in node trust, hop time, and residual energy to ensure reliable data transmission through trustworthy nodes. MATLAB simulations on networks of varying sizes (100-500) compare SEAR with existing methods (EAD and EASER), demonstrating SEAR's superior performance. Results show significant improvements in packet delivery ratio (13-16%), network throughput (19-24 %), energy consumption (32-36%), packet loss (16-190/0), and end-to-end latency (21-26%), proving SEAR's effectiveness in enhancing WSN security, energy efficiency, and performance. © 2024 IEEE.

关键词： MATLAB

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An Automated Real-Time Face Mask Detection system Using Transfer Learning with Faster-RCNN in the Era of the COVID-19 Pandemic

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computers, Materials & Continua 2022年第5期71卷 4151-4166页

作者： Maha Farouk S.Sabir Irfan Mehmood Wafaa Adnan Alsaggaf Enas Fawai Khairullah Samar Alhuraiji Ahmed S.Alghamdi Ahmed A.Abd El-Latif Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversitySaudi Arabia Centre for Visual Computing Faculty of Engineering and InformaticsUniversity of BradfordBradfordU.K Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahP.O.Box 23713Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahSaudi Arabia Department of Mathematics and Computer Science Faculty of ScienceMenoufia University32511Egypt

Today,due to the pandemic of COVID-19 the entire world is facing a serious health *** to the World Health Organization(WHO),people in public places should wear a face mask to control the rapid transmission of *** governmental bodies of different countries imposed that wearing a face mask is compulsory in public ***,it is very difficult to manually monitor people in overcrowded *** research focuses on providing a solution to enforce one of the important preventative measures of COVID-19 in public places,by presenting an automated system that automatically localizes masked and unmasked human faces within an image or video of an area which assist in this outbreak of *** paper demonstrates a transfer learning approach with the Faster-RCNN model to detect faces that are masked or *** proposed framework is built by fine-tuning the state-of-the-art deep learning model,Faster-RCNN,and has been validated on a publicly available dataset named Face Mask Dataset(FMD)and achieving the highest average precision(AP)of 81%and highest average Recall(AR)of 84%.This shows the strong robustness and capabilities of the Faster-RCNN model to detect individuals with masked and un-masked ***,this work applies to real-time and can be implemented in any public service area.

关键词： COIVD-19 deep learning faster-RCNN object detection transfer learning face mask

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A globally shared resource paradigm for encoded storage systems in the public cloud

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Fundamental Research 2024年第3期4卷 642-650页

作者： Zhiyue Li Guangyan Zhang Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Beijing National Research Center for Information Science and Technology Tsinghua UniversityBeijing 100084China

Public clouds favor sharing of storage resources,in which many tenants acquire bandwidth and storage capacity from a shared storage *** provide high availability,data are often encoded to provide fault tolerance with low storage *** this,efficiently organizing an encoded storage system for shared I/Os is critical for application *** is usually hard to achieve as different applications have different stripe configurations and fault tolerance *** this paper,we first study the block trace from the Alibaba cloud,and find that I/O patterns of modern applications prefer the resource sharing *** on this,we propose a globally shared resource paradigm for encoded storage system in the public *** globally shared resource paradigm can provide balanced load and fault tolerance for numerous disk pool sizes and arbitrary application stripe ***,we demonstrate with two case studies that our theory can help address the device-specific problems of HDD and SSD RAID arrays with slight modifications:comparing the existing resource partition and resource sharing methods,our theory can promote the rebuild speed of the HDD RAID arrays by 2.5,and reduce the P99 tail latency of the SSD arrays by up to two orders of magnitude.

关键词： Public cloud Encoded storage Load balancing RAID reconstruction Tail latency

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Design and Implementation of Distribution Well Safety Monitoring system Based on ZigBee 4

Design and Implementation of Distribution Well Safety Monito...

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4th IEEE International Conference on Electronic technology, Communication and information, ICETCI 2024

作者： Zhao, Zhenguo Dalian Neusoft University of Information Department of Computer Science and Technology Dalian China

ISBN: (数字)9798350361643

ISBN: (纸本)9798350361643

Distribution wells in buildings are the blind spots for security monitoring and are prone to safety accidents. In order to make up for the shortcomings of security monitoring, this paper reduces the workload of manual inspections of distribution wells, and reduces the probability of accidents caused by distribution wells, and short distance wireless communication technology ZigBee was used to network the Internet of Things perception layer. DTU was used for network layer access, and MQTT protocol was adopted for message subscription and publication by the application layer. We designed and implemented a distribution well security monitoring system, which used data fusion from multiple sensors to determine whether the system was safe and to decide whether to alarm. The system makes up for the shortcomings of the fire monitoring system. Signals from the distribution well safety monitoring system can be extended and connected to the fire monitoring system, expanding the monitoring range of fire monitoring. © 2024 IEEE.

关键词： Zigbee

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