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47th International Conference on Telecommunications and Signal Processing, TSP 2024

作者： Ustubioglu, Beste Dincer, Samet Ustubioglu, Arda Ulutas, Guzin Karadeniz Technical University Computer Engineering Trabzon Turkey Trabzon University Management Information Systems Trabzon Turkey

ISBN: (纸本)9798350365597

Complex forgeries such as deepfakes, which are very popular today, as well as very simple but effective manipulation techniques, the production of which does not require the use of deep networks such as GAN, are still practiced. Audio splicing forgery, which combines multiple speech segments from different recordings of a person to alter the content of a speech recording, is one of these manipulation techniques and presents a great challenge to audio forgery. In this paper, a novel audio splicing detection method based on ArCapsN et architecture is proposed. The proposed method consists of two stages. In the first stage, the audio file given as input is converted into a cochleagram image. In the second stage, features are extracted from the cochleagram images with EfficientN et and the ArCapsN et is trained with these features. As a result of the training, the audio files given as a test are labelled as forged/original. The proposed method is tested on the database created by us using the TIMIT database. Our model gives the baseline metrics precision, recall and Fl metrics by 98.59%, 97.99%, and 98.29% on the dataset, respectively. © 2024 IEEE.

关键词： Digital forensics

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Hybrid Fuzzy Data Aggregation and Optimization-Based Routing for Energy Efficiency in Heterogeneous Wireless Sensor Networks

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Informatica (Slovenia) 2024年第20期48卷 95-106页

作者： Alhijaj, Asaad A. Marzook, Ali K. Hussein, Dheyaa Mezaal Alkenani, Jawad Department of Computer Information Systems College of Computer Science and Information Technology University of Basrah Basrah Iraq University of Basrah College of Engineering Department of Electrical Engineering Basrah Iraq University of Technology Office of the President of the University Baghdad Iraq Department of Computer Science Shatt Al-Arab University College Basra Iraq

Wireless sensor networks (WSNs) are networks with many sensor nodes that are utilized for various purposes, including the military and medical. In hazardous circumstances, precise data aggregation and routing are essential, and the energy consumption of the sensors needs to be closely controlled. Nonetheless, there is a significant chance of redundant data because of external factors and nearby sensors. A multitude of information can be found in large datasets, some of it unnecessary and others useful. This redundancy negatively impacts performance in terms of redundant transmission and computing costs. However, data aggregation might help a network get rid of unnecessary data. In this work, we present a hybrid protocol called fuzzy data aggregation with fuzzy spider monkey optimization routing protocol (FDA-FSMORP) that represents an intelligent approach to collecting sensor data in HWSNs considering energy consumption. The results indicated that the suggested method beat in minimizing data latency our approach reduced energy consumption by 73% using energy more effectively when compared to our simulated outcomes. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Sensor nodes

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Transparent Machine Vision Techniques for Facial Emotion Detection 5

Transparent Machine Vision Techniques for Facial Emotion Det...

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5th International Conference on Electronics and Sustainable Communication systems, ICESC 2024

作者： Onanyang, Isaac Katamba, Harun Arnold Kasozi, Carlos Mark Gahwera, Tumusiime Andrew Nakibuule, Rose Hellen, Nakayiza Marvin, Ggaliwango Makerere University Department of Computer Science Kampala Uganda Makerere University Department of Information Systems Kampala Uganda Muni University Department of Computer and Information Science Arua Uganda

ISBN: (纸本)9798350379945

Emotions are very important when it comes to collective understanding among people. In recent times, emotion recognition has emerged as a crucial facet within the realm of effective computing. Many physiological signals that could be used to notice emotions, getting facial expression images is one of the most natural and cheapest approaches. While humans find recognizing facial expressions for emotion detection effortless, the same task presents a unique challenge for computer algorithms devoid of empathy. Having this question in mind, we want to use machine vision based techniques for getting a better result regarding emotions detection. In this study, facial expression recognition (FER) was classified using the Vision Transformers(ViT), Convulational Neural Networks(CNN), ResNet, VGG16 and VGG19 models. The experimental outcomes show case that our models have the ability of being successfully used in practical settings having been trained on the FER + dataset consisting of 35,887 gray-scale images of the 8 classes, with each image having a resolution of 48×48 pixels. Experimental findings indicate that the Vision Transformers were most effective with an accuracy of 83. Additionally, the CNN model achieved an accuracy of 78 while the VGG19, VGG16 and ResNet achieved accuracy of 72, 71 and 63 respectively. Additionally we applied explainability techniques such as Lime, Integrated gradients and layerwise propagation on each model so as to understand what the model focuses on so as to classify an expression and Lime turned out to be the best XAI technique for our models. © 2024 IEEE.

关键词： Machine vision

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Phishing Attacks Detection Using EnsembleMachine Learning Algorithms

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computers, Materials & Continua 2024年第7期80卷 1325-1345页

作者： Nisreen Innab Ahmed Abdelgader Fadol Osman Mohammed Awad Mohammed Ataelfadiel Marwan Abu-Zanona Bassam Mohammad Elzaghmouri Farah H.Zawaideh Mouiad Fadeil Alawneh Department of Computer Science and Information Systems College of Applied SciencesAlmaarefa UniversityDiriyahRiyadh13713Saudi Arabia Applied College King Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Management Information Systems College of Business AdministrationKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Computer Science Faculty of Computer Science and Information TechnologyJerash UniversityJerash26110Jordan Department of Business Intelligence and Data Analysis Faculty of Financial Sciences and BusinessIrbid National UniversityIrbid21110Jordan Faculty of Information Technology Ajloun National UniversityAjloun26767Jordan

Phishing,an Internet fraudwhere individuals are deceived into revealing critical personal and account information,poses a significant risk to both consumers and web-based *** indicates a persistent rise in phishing ***,these fraudulent schemes are progressively becoming more intricate,thereby rendering them more challenging to ***,it is imperative to utilize sophisticated algorithms to address this *** learning is a highly effective approach for identifying and uncovering these harmful *** learning(ML)approaches can identify common characteristics in most phishing *** this paper,we propose an ensemble approach and compare it with six machine learning techniques to determine the type of website and whether it is normal or not based on two phishing *** that,we used the normalization technique on the dataset to transform the range of all the features into the same *** findings of this paper for all algorithms are as follows in the first dataset based on accuracy,precision,recall,and F1-score,respectively:Decision Tree(DT)(0.964,0.961,0.976,0.968),Random Forest(RF)(0.970,0.964,0.984,0.974),Gradient Boosting(GB)(0.960,0.959,0.971,0.965),XGBoost(XGB)(0.973,0.976,0.976,0.976),AdaBoost(0.934,0.934,0.950,0.942),Multi Layer Perceptron(MLP)(0.970,0.971,0.976,0.974)and Voting(0.978,0.975,0.987,0.981).So,the Voting classifier gave the best *** in the second dataset,all the algorithms gave the same results in four evaluation metrics,which indicates that each of them can effectively accomplish the prediction ***,this approach outperformed the previous work in detecting phishing websites with high accuracy,a lower false negative rate,a shorter prediction time,and a lower false positive rate.

关键词： Social engineering attacks phishing attacks machine learning security artificial intelligence

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Improving the Efficiency of Smart Grid Scheduling: A Linear Regression Model with Meta Job information 2

Improving the Efficiency of Smart Grid Scheduling: A Linear ...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Al-Najjar, Hazem Al-Rousan, Nadia College Of Information Technology University Of Petra Department Of Computer Science Amman Jordan German Jordianian University Faculty Of Electrical Engineering And Information Systems Department Of Computer Engineering Jordan

ISBN: (纸本)9798350394962

In order to forecast the run time of the jobs that were submitted, this research provides two linear regression prediction models that include continuous and categorical factors. A continuous predictor is built using the number of CPUs, average CPU speed, and memory size, whereas a categorical predictor is built using the user ID, group ID, and executable ID. According to the findings, the prediction rates for categorical and continuous predictors are 61% and 1%, respectively. sixty times better than the earlier models, which used continuous variables as a foundational model to determine task complexity and weight. The effectiveness of the categorical predictor in enhancing a job scheduling problem is then evaluated by combining it with three suggested job scheduling strategies. The suggested algorithms incorporated metrics - predicted run time, waiting time, and resource requirement - to select the smallest jobs. According to the results, Algorithm 3 performs better than earlier models in both performance metrics. The variation in total execution time and average waiting time is 1.14 to 1.76 and 4.5, respectively compared to previous models, Additionally, Algorithms 1 and 2 demonstrating superior performance in every scenario. © 2024 IEEE.

关键词： Grid computing

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Development of Key Performance Indicators in e-Learning Implementation in Higher Education Institutions 8

Development of Key Performance Indicators in e-Learning Impl...

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8th International Conference on information Management and Technology, ICIMTech 2023

作者： Sardjono, Wahyu Kristin, Desi Maya Putra, Gustian Rama Binus Graduate Program - Master of Information Systems Management Bina Nusantara University Information Systems Management Department Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Pakuan University Computer Science Study Program Faculty of Mathematics & Natural Sciences Bogor Indonesia

ISBN: (纸本)9798350326093

Companies or organizations usually use key performance indicators (KPI) as indicators to determine their performance. The company's performance achievements are reflected in the set indicators that will describe the overall performance, as well as higher education institutions can also set key performance indicators, to see their performance achievements. The use of e-learning as a tool to support teaching and learning processes to be more effective and efficient is an example of which performance needs to be measured. This article discusses how the process of developing key performance indicators is carried out through the stages of designing questionnaires and distributing questionnaires to students, data is processed using the method of factor analysis to obtain the main indicators determining performance and the method of regression analysis is also used to build performance models. obtain KPI determinants, and then develop KPI models. The results show that there are indicators of sharing knowledge, managing knowledge, understanding knowledge, and utilization knowledge as performance measurement indicators. © 2023 IEEE.

关键词： Regression analysis

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Enhanced In-Flight Connectivity for Urban Air Mobility via LEO Satellite Networks 30

Enhanced In-Flight Connectivity for Urban Air Mobility via L...

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30th IEEE International Conference on Telecommunications, ICT 2024

作者： Biswas, Karnika Ghazzai, Hakim Khanfor, Abdullah Sboui, Lokman CEMSE Division Thuwal Saudi Arabia College of Computer Science and Information Systems Najran University Computer Science Department Najran Saudi Arabia University of Québec Systems Engineering Department Montréal Canada

ISBN: (纸本)9798350356694

Urban Air Mobility (UAM) is the envisioned future of inter-city aerial transportation. This paper presents a novel, in-flight connectivity link allocation method for UAM, which dynamically switches between terrestrial cellular and Low Earth Orbit (LEO) satellite networks based on real-time conditions. Our approach prefers cellular networks for cost efficiency, switching to LEO satellites under poor cellular conditions to ensure continuous UAM connectivity. By integrating real-time metrics like signal strength, network congestion, and flight trajectory into the selection process, our algorithm effectively balances cost, minimum data rate requirements, and continuity of communication. Numerical results validate minimization of data-loss while ensuring an optimal selection from the set of available above-threshold data rates at every time sample. Furthermore, insights derived from our study emphasize the importance of hybrid connectivity solutions in ensuring seamless, uninterrupted communication for future urban aerial vehicles. © 2024 IEEE.

关键词： Air mobility

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Enhancing Secure Development in Globally Distributed Software Product Lines: A Machine Learning-Powered Framework for Cyber-Resilient Ecosystems

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computers, Materials & Continua 2024年第6期79卷 5031-5049页

作者： Marya Iqbal Yaser Hafeez Nabil Almashfi Amjad Alsirhani Faeiz Alserhani Sadia Ali Mamoona Humayun Muhammad Jamal University Institute of Information Technology PMAS-Arid Agriculture UniversityRawalpindiPakistan Department of Software Engineering College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Department of Computer Science College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Department of Computer Engineering&Networks College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Department of Information Systems College of Computer and Information SciencesJouf UniversitySakakaAl Jouf72388Saudi Arabia Department of Mathematics Pir Mehr Ali Shah Arid Agriculture UniversityRawalpindiPakistan

Embracing software product lines(SPLs)is pivotal in the dynamic landscape of contemporary software ***,the flexibility and global distribution inherent in modern systems pose significant challenges to managing SPL variability,underscoring the critical importance of robust cybersecurity *** paper advocates for leveraging machine learning(ML)to address variability management issues and fortify the security of *** the context of the broader special issue theme on innovative cybersecurity approaches,our proposed ML-based framework offers an interdisciplinary perspective,blending insights from computing,social sciences,and ***,it employs ML for demand analysis,dynamic feature extraction,and enhanced feature selection in distributed settings,contributing to cyber-resilient *** experiments demonstrate the framework’s superiority,emphasizing its potential to boost productivity and security in *** digital threats evolve,this research catalyzes interdisciplinary collaborations,aligning with the special issue’s goal of breaking down academic barriers to strengthen digital ecosystems against sophisticated attacks while upholding ethics,privacy,and human values.

关键词： Machine Learning variability management cybersecurity digital ecosystems cyber-resilience

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Enhancing Image Security with Rossler Attractor-Based Encryption 2

Enhancing Image Security with Rossler Attractor-Based Encryp...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Al-Najjar, Hazem Al-Rousan, Nadia University Of Petra Faculty Of Information Technology Department Of Computer Science Amman Jordan German Jordanian University Faculty Of Electrical Engineering And Information Systems Department Of Computer Engineering Jordan

ISBN: (纸本)9798350394962

we introduced image encryption algorithms with high sensitivity, such that even a single alteration in a plain-text image would result in a complete transformation of the ciphered image. The first algorithm employed pixel values in a substitution approach, whereas the second algorithm utilized pixel values to generate chaotic function keys. Additionally, we employed a Rossler chaotic function with pixel data to generate chaotic values for the shuffling and substitution processes. After subjecting these algorithms to rigorous testing using standard metrics, our experiments revealed the efficiency of the algorithms in image encryption. Notably, even a minute change in the original image resulted in a vastly different ciphered image. These proposed algorithms underscored the effectiveness of integrating chaotic functions with pixel information, significantly enhancing image security. © 2024 IEEE.

关键词： Cryptography

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History of Artificial Lighting, From Fire to LED [History]

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IEEE INDUSTRY APPLICATIONS MAGAZINE 2025年第2期31卷 10-25页

作者： Canale, Laurent Hamady, Mohamad S & C Electric Company Chicago Illinois USA Research Engineer CNRS LAPLACE Laboratory UMR 5213 Paul Sabatier University Toulouse Cedex 9 France Computer Systems Department Faculty of Computer and Information Sciences Ain Shams University Cairo Cairo Egypt

Having become so present and accessible, artificial light has been forgotten in the same way as the air we breathe. It is obvious and is only noticed by its often-exceptional absence. Its omnipresence is such that it goes to excess, resulting in light pollution;environmental impacts;impacts on fauna and flora;and, even more serious, impacts on human health. But it has also been an essential driving force in the evolution of humanity. From the first traces of the mastery of fire as a light source up to the "electronic" light of LEDs, we will travel through more than 500,000 years of history of lighting technologies and more particularly focus on the last 150 years, which have ended with the LED revolution.

关键词： Force Flora Fauna Light emitting diodes Artificial light History Light sources

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