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International Symposium on Electronics and Telecommunications (ISETC)

作者： Layth Rafea Hazim Oguz Ata Dept. of Electrical and Computer Engineering Altinbas University Istanbul Turkey College of Computer Science and Mathematics Tikrit University Tikrit Iraq Dept. of Software Engineering Altinbas University Istanbul Turkey

ISBN: (数字)9798350390865

ISBN: (纸本)9798350390872

AI-generated content impersonating human writing is an issue that has gained attention as AI spreads its wings. This particular study serves as a comparison between the existing Logistic Regression and Feedforward Neural Networks (FNNs) by employing sentence-BERT-appended models and RoBERTa in the determination of authenticity in a given text. A dataset that was balanced between human-written and AI-generated texts was utilized. Techniques like tokenization and normalization were first used, followed by feature extraction using transformer-based models. Cross-validation and confusion matrix analysis that used measures such as accuracy, precision, recall, F1 score, and ROC AUC were included to guarantee the models’ robustness. The hybrid RoBERTa-FNN model that was deposed challengers was the most outstanding model in respect of precision and recall, and the highest accuracy (99.95%) was obtained as mentioned in the data. The improved performance serves as a proof of how effectively RoBERTa uses its embeddings to represent context on the fine-grained level required for this kind of text classification. This work is a stepping stone to the creation of strong AI text detection systems, besides our advancements to the knowledge of models and embedding performance with respect to text classification. The results lay emphasis on the selection of model configuration and the embedding technique, as they are the key factors in achieving the best results in practical applications.

关键词： Training Logistic regression Ethics Accuracy Text categorization Writing Transformers Data models Feedforward neural networks Artificial intelligence

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Enhance Energy Using Bio-Inspired Algorithms in Manet: An Overview

Enhance Energy Using Bio-Inspired Algorithms in Manet: An Ov...

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Electrical engineering and Automatic Control (ICEEAC), International Conference on

作者： Abdelmalek Djihene Boumedjout Amal Kies Ali dept. Computer science USTO Oran Algeria dept. Systems Engineering ENPO Maurice Audin Oran Algeria

ISBN: (数字)9798350349740

ISBN: (纸本)9798350349757

The limited power availability in Mobile Ad hoc Networks (MANETs) poses a fundamental challenge, as node instability and the lack of power supply contribute to reduced network lifetime, impacting MANET performance. In recent years, there has been a growing interest in exploring Artificial Intelligence approaches, particularly bio-inspired algorithms, for MANET routing due to their adaptability and robustness. These approaches have shown promise in minimizing energy consumption, reducing overhead, and improving overall network efficiency. This paper provides an overview of enhancing energy efficiency using bio-inspired algorithms through the application of various meta-heuristic approaches in MANET routing. This paper provides a synthesis of existing research on bio-inspired algorithms in MANET routing, highlighting their effectiveness in addressing energy-related challenges. Furthermore, the paper outlines potential future research directions, including the ex- ploration of hybrid algorithms and the integration of machine learning techniques to further optimize energy efficiency in MANETs.

关键词： Machine learning algorithms Heuristic algorithms Metaheuristics Machine learning Routing Ad hoc networks Energy efficiency

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Towards an Automatic Construction of Simulation Scenarios: A Systematic Review 23

Towards an Automatic Construction of Simulation Scenarios: A...

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Proceedings of the Winter Simulation Conference

作者： Christopher W. H. Davis Antonie J. Jetter Philippe J. Giabbanelli Microsoft USA Dept. of Engineering & Technology Management Portland State University USA Dept. of Computer Science & Software Engineering Miami University Oxford USA

ISBN: (纸本)9798350369663

A predictive simulation is built on a conceptual model (e.g., to identify relevant constructs and relationships) and serves to estimate the potential effects of 'what-if' scenarios. Developing the conceptual model and plausible scenarios has long been a time-consuming activity, often involving the manual processes of identifying and engaging with experts, then performing desk research, and finally crafting a compelling narrative about the potential futures captured as scenarios. Automation could speed-up these activities, particularly through text mining. We performed the first review on automation for simulation scenario building. Starting with 420 articles published between 1995 and 2022, we reduced them to 11 relevant works. We examined them through four research questions concerning data collection, extraction of individual elements, connecting elements of insight and (degree of automation of) scenario generation. Our review identifies opportunities to guide this growing research area by emphasizing consistency and transparency in the choice of datasets or methods.

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Chaos Based Pixel Scrambling Algorithm Based On Corner Traversal

Chaos Based Pixel Scrambling Algorithm Based On Corner Trave...

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International Artificial Intelligence and Data Processing Symposium (IDAP)

作者： Cemile İnce Kenan İnce Davut Hanbay Computer Engineering Dept Inönü University Malatya Türkiye Software Engineering Dept. Inönü University Malatya Türkiye

ISBN: (数字)9798331531492

ISBN: (纸本)9798331531508

Lightweight image encryption has become a critical area of cryptography, especially for resource-constrained devices like those used in the Internet of Things (IoT). Chaotic maps, known for their sensitivity to initial conditions, ergodicity, and seemingly random behavior, are commonly used in image encryption algorithms. These maps are employed in either a standalone or combined fashion during the diffusion or confusion stages of the encryption process. The high correlation between pixels in image data necessitates specialized algorithms. Pixel scrambling, or confusion, is a fundamental step in image encryption. It involves rearranging pixel positions to disrupt the correlation between neighboring pixel values. This study enhances the Corner Traversal algorithm, a pixel scrambling technique, by using chaotic maps to determine the algorithm parameters at each layer. This modification increases the nonlinearity of the encryption process. The results demonstrate that these improvements lead to a more secure encryption scheme compared to the original Corner Traversal algorithm. The performance of the enhanced algorithm is evaluated by comparing it to the original version.

关键词： Performance evaluation Chaos Correlation Sensitivity Data processing Encryption Internet of Things Artificial intelligence Random sequences

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Efficient Design for Multi-user Downlink Beamforming with Reconfigurable Intelligent Surface

arXiv

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arXiv 2023年

作者： Ebrahimi, Mohammad Dong, Min The Dept. of Electrical Computer and Software Engineering Ontario Tech University ON Canada

This paper considers downlink multi-user transmission facilitated by a reconfigurable intelligent surface (RIS). First, focusing on the multi-group multicast beamforming scenario, we develop a fast and scalable algorithm for the joint base station (BS) and RIS beamforming optimization to minimize the transmit power subject to the user quality-of-service (QoS) constraints. By exploring the structure of this QoS problem, we show that the joint beamforming optimization can be naturally decomposed into a BS multicast beamforming QoS problem and an RIS passive multicast beamforming max-min-fair (MMF) problem. We propose an alternating multicast beamforming (AMBF) algorithm to solve the two subproblems alternatingly. For the BS QoS subproblem, we utilize the optimal multicast beamforming structure to obtain the BS beamformers efficiently. Furthermore, we reformulate the challenging RIS MMF subproblem and employ a first-order projected subgradient algorithm (PSA), which yields closed-form updates. The computational complexity of the AMBF algorithm grows linearly with the number of RIS elements and BS antennas. We further show that the AMBF approach is also an efficient method for the RIS-assisted downlink multi-user unicast beamforming problem, providing semi-closed-form updates. Next, we study the MMF problem for the RIS-assisted downlink beamforming design and propose a PSA-based fast algorithm to compute the BS and RIS beamforming solutions with closed-form updates per iteration, leading to a highly computationally efficient solution. Simulation results show the efficacy of our proposed algorithms in both performance and computational cost compared to other alternative methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Beamforming

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Effective Feature Extraction Framework to Improve Network Intrusion Detection System 9

Effective Feature Extraction Framework to Improve Network In...

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9th IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2023

作者： Mv, Hari Vinayak Jarin, T. Raja, Kiruba Thangam Jyothi Engineering College APJ Abdul Kalam Technological University Department of ECE Thrissur India Jyothi Engineering College APJ Abdul Kalam Technological University Department of EEE Thrissur India School of CSE and Information Systems Vellore Institute of Technology Dept. of Software and Systems Engineering Vellore India

ISBN: (纸本)9798350319651

A network intrusion detection system (NIDS) is a crucial component of a robust cybersecurity strategy. Its primary purpose is to continuously monitor network traffic and detect suspicious or malicious activity that could be indicative of a cyberattack or unauthorized network access. The effectiveness of NIDS depends heavily on the techniques we use to boost the classification accuracy of intrusion and minimize the computational difficulty while performing training and testing. The high volume of network traffic combined with its large number of features will increase the classification time. With the recent emergence of deep learning techniques, scientists have shown interest in learning dataset features, followed by the classification of intrusions. This study offers a novel method for extracting high-dimensional features from input data by employing a stacked sparse autoencoder. Simple machine learning models are then built using the remaining low-dimensionality features. Simulations were conducted, and the efficacy of binary and multiclass classifications was verified. The proposed method exceeds most of the other existing approaches in terms of performance. © 2023 IEEE.

关键词： Feature extraction

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Sustainability of Machine Learning-based Android Malware Detection Using API calls and Permissions 5

Sustainability of Machine Learning-based Android Malware Det...

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5th IEEE International Conference on Artificial Intelligence and Knowledge engineering, AIKE 2022

作者： Cho, Woosang Lee, Hojun Han, Sangchul Hwang, Youngsup Cho, Seong-Je Dept. of Software Science Dankook University Gyeonggi-do Yongin-si16890 Korea Republic of Dept. of Computer Engineering Konkuk University Chungju Korea Republic of Dept. of Computer Science and Engineering Sun Moon University Asan Korea Republic of Dept. of Sofware Science Dankook University Yongin Korea Republic of

ISBN: (数字)9781665471206

ISBN: (纸本)9781665471206

As the Android platform and malicious apps continue to evolve, most existing Android malware detection techniques using machine learning are turning out to be unsustainable. In this paper, we propose machine learning-based Android malware detection techniques which uses both API calls and permissions as a feature set. These features are complementary and are often used to detect malicious apps. We first analyze whether a 'yearly dataset-based trained classifier' (YDataC) is sustainable or not. The 'yearly dataset-based trained classifier' refers to the classifier that learns from 80% of the dataset of a specific year from 2014 to 2021, and is tested with 20% of the datasets of every year between 2014 and 2021. Through experiments, we discovered that the classification rate has dropped significantly since 2019, and something big has changed. Therefore, the 'yearly dataset-based trained classifier' is judged to be unsustainable. Next, we present and evaluate two incremental learning methods for gradual training: an incrementally trained Random Forest (RF) and an incrementally trained Neural Network (NN). Evaluation results show that two incremental learning classifiers have better sustainability than the 'yearly dataset-based trained classifier'. The incrementally trained RF has better sustainability than the incrementally trained NN in terms of given metrics such as f1 and AUT (Area under Time). © 2022 IEEE.

关键词： Android malware

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Deep Reinforcement Learning Algorithms for Option Hedging

arXiv

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arXiv 2025年

作者： Neagu, Andrei Godin, Frédéric Kosseim, Leila Dept. of Computer Science and Software Engineering Concordia University Montréal Canada Dept. of Mathematics & Statistics Concordia University Montréal Canada

Dynamic hedging is a financial strategy that consists in periodically transacting one or multiple financial assets to offset the risk associated with a correlated liability. Deep Reinforcement Learning (DRL) algorithms have been used to find optimal solutions to dynamic hedging problems by framing them as sequential decision-making problems. However, most previous work assesses the performance of only one or two DRL algorithms, making an objective comparison across algorithms difficult. In this paper, we compare the performance of eight DRL algorithms in the context of dynamic hedging;Monte Carlo Policy Gradient (MCPG), Proximal Policy Optimization (PPO), along with four variants of Deep Q-Learning (DQL) and two variants of Deep Deterministic Policy Gradient (DDPG). Two of these variants represent a novel application to the task of dynamic hedging. In our experiments, we use the Black-Scholes delta hedge as a baseline and simulate the dataset using a GJR-GARCH(1,1) model. Results show that MCPG, followed by PPO, obtain the best performance in terms of the root semi-quadratic penalty. Moreover, MCPG is the only algorithm to outperform the Black-Scholes delta hedge baseline with the allotted computational budget, possibly due to the sparsity of rewards in our environment. © 2025, CC BY.

关键词： Learning algorithms

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CoML: Machine Learning based approach for COVID-19 related Suicidal Ideation detection

CoML: Machine Learning based approach for COVID-19 related S...

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Intelligent systems and computer Vision (ISCV)

作者： Salah Bouktif Akib Mohi Ud Din Khanday Ali Ouni Dept. of Computer Science and Software Engineering CIT United Arab Emirates University Al Ain UAE Dept. of Software Engineering and IT École de Technologie Supérieure University of Quebec Montreal Canada

ISBN: (数字)9798350350180

ISBN: (纸本)9798350350197

The pervasive use of online social media platforms has changed the communication habits and subsequently the content and the frequency of the human exchanges. People share their thoughts on social media more frequently and freely. In particular and due to the societal stigma, people find less obstacle to discuss mental health problems on social media compared to face-o-face discussion. Mining social media for the sake of analysing suicidal intention is an attractive and at the same time challenging task especially during COVID-19 era. The main objective is to leverage machine learning to classify the social media user suicidal behavior while considering the impact of COVID-19 circumstances. We propose a rigorous feature engineering technique based on TF/IDF and Bag of Words. Machine learning classifiers and ensemble models are compared to find that the Neural Network(NN) classifier outperforms the benchmarks with a precision of 94%, a Recall of 94%, an F1 Score of 94% , and an overall accuracy of 94%. In the future, more feature selection techniques and deep learning can be used.

关键词： COVID-19 Accuracy Social networking (online) Neural networks Mental health Media Feature extraction

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USDOT Tier-1 University Transportation Center for Advancing Cybersecurity Research and Education 1

USDOT Tier-1 University Transportation Center for Advancing ...

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1st IEEE International Conference on Mobility, Operations, Services and Technologies, MOST 2023

作者： Zhang, Yunpeng Jack Wang, Kailai Song, Lingguang Lendasse, Amaury Song, Houbing Herbert Han, Zhu Verma, Rakesh Silva, Arlei Rubio-Medrano, Carlos Li, Zhixia Richard Zhang, Guohui University of Houston Dept. of Information Science Technology HoustonTX United States University of Houston Dept. of Construction Management HoustonTX United States University of Maryland Baltimore County Dept. of Information Systems BaltimoreMD United States University of Houston Dept. of Electrical & Computer Engineering HoustonTX United States University of Houston Dept. of Computer Science HoustonTX United States Rice University Dept. of Computer Science HoustonTX United States Texas A&m University-Corpus Christi Dept. of Computer Science Corpus ChristiTX United States University of Cincinnati Dept. of Civil and Architectural Engineering and Construction Management CincinnatiOH United States University of Hawai'i at Mānoa Dept. Civil and Environmental Engineering HonoluluHI United States

ISBN: (纸本)9798350319958

The Transportation Cybersecurity Center for Advanced Research and Education (CYBER-CARE) is a US Department of Transportation (USDOT) Tier-1 University Transportation Center (UTC) funded in 2023. CYBER-CARE primarily focuses on the USDOT statutory research priority area of 'Reducing Transportation Cybersecurity Risks.' CYBER-CARE aims to establish a fundamental knowledge basis and explore advanced theory to mitigate the impacts of large-scale cyberattacks on transportation infrastructure and connected and automated vehicle (CAV) systems. The research projects at CYBER-CARE will develop conceptual frameworks, construct comprehensive datasets, explore novel analytical approaches, support the implementation of public policies and infrastructure investments, and build a high-quality industry workforce through education. All CYBER-CARE research projects can be organized into four thrusts: CAV cybersecurity, transportation data security, advanced traffic management system (ATMS) cybersecurity, and next-generation transportation cybersecurity systems. In addition, CYBER-CARE will accelerate industry collaborations, foster new technologies, and provide professionals with the skills and opportunities needed to become successful leaders in their fields. Notably, as CYBER-CARE will prioritize engagement with underrepresented minorities, these communities stand to benefit from professional development training in transportation cybersecurity. © 2023 IEEE.

关键词： Technology transfer

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