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International Conference on Emerging Technologies, ICET

作者： Ebtihaj Abdullah Waseem Ullah Khan Safdar Nawaz Khan Marwat Salman Ahmed Dept. of Computer Systems Engg University of Engineering & Technology Peshawar Pakistan Faculty of Computer Science and Engg GIK Institute of Engineering Sciences & Technology Swabi Pakistan

ISBN: (数字)9798350379587

ISBN: (纸本)9798350379594

In an age of information and digital communication, social media platforms are becoming essential spaces where individuals can voice their thoughts, exchange ideas, and take part in discussions on a variety of subjects. High quality and high value datasets of such ideas and discussions in textual and formatted form are fundamental to the research aspects of Artificial Intelligence and data-science. In this paper, we present a dataset of indirect harassment. There are approximately 10,700 tweets with binary labels. The labels were assigned by a team of researchers collectively. The dataset contains approximately 19 percent positive indirect harassment labels and 81 percent negative indirect harassment labels. The data is useful for training and running machine and deep learning models to detect indirect and direct harassment. The ease in understanding the data for researchers and other peers is also very crucial. The corpus mentioned in this paper is easy to understand via the use of a binary label. The data is simplistic in nature because there are only two columns with one being the text and the other being the indirect label. This work was necessary because it was a component of a larger project that also needed this kind of dataset.

关键词： Deep learning Training Social networking (online) Oral communication Data science Digital communication Information age Data models

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Transforming Cybersecurity in the Digital Era: The Power of AI 2

Transforming Cybersecurity in the Digital Era: The Power of ...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Mahfuri, Mahmoud Ghwanmeh, Sameh Almajed, Rsha Alhasan, Waseem Salahat, Mohammed Lee, Jin Hie Ghazal, Taher M. International University For Science And Technology Computer And Information Technology Department Damascus Syria American University In The Emirates Department Of Computer Scienc Dubai United Arab Emirates School Of Computer Science And Informatics De Montfort University Dubai Dubai United Arab Emirates College Of Engineering And Technology University Of Science And Technology Of Fujairah Fujairah United Arab Emirates Miracle Clinic Seoul Korea Republic of Khalifa University Centre For Cyber Physical Systems Computer Science Department United Arab Emirates Center For Cyber Security Faculty Of Information Science And Technology Selangor Bangi43600 Malaysia

ISBN: (纸本)9798350394962

In the midst of the digital revolution of the 21st century, cybersecurity has come to be a primary social situation, requiring revolutionary and Innovative solution. To find the proper answer for this pressing demand, AI (AI) has developed as an innovative catalyst that has fundamentally transformed the cybersecurity landscape. The power of AI is exemplified by its ability to process and interpret large and heterogeneous cybersecurity data sets optimizing critical functions such as threat detection, asset prioritization, and vulnerability management beyond human capabilities swiftly and accurately. This transformation is redefining our cybersecurity approach. This paper presents a full exploration of AI's extreme influence on cybersecurity, delving deep into how AI tools not only enhance but often surpass human-mediated processes. By unraveling the intricacies of integrating AI into the realm of cybersecurity, we vividly illustrate AI's potential to anticipate, detect, and proactively mitigate cyber threats, empowering organizations to bolster their digital security. However, it's vital to recognize the essential difficulties of AI. We underline the critical necessity for non-stop human administration and involvement to confirm that cybersecurity processes are in proportion and powerful. Additionally, we mention the probable ethical concerns and underscore the importance of strong authority structures to accelerate accountable and transparent AI usage in cybersecurity. This paper elucidates how AI is revolutionizing cybersecurity techniques, contributing to a more secure and greater secure digital future. It additionally lays the base for current investigation and discourse on the role of AI in cybersecurity - a conversation of growing importance in our unexpectedly advancing digital age. © 2024 IEEE.

关键词： Cybersecurity

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

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

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Cyber Resilience (ICCR), International Conference on

作者： Hazem Al-Najjar Nadia Al-Rousan Department of Computer science College of Information Technology University of Petra Amman Jordan Department of Computer Engineering Faculty of electrical Engineering and information systems German Jordianian University

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

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.

关键词： Measurement Processor scheduling Computational modeling Linear regression Predictive models Prediction algorithms Smart grids

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Decision Tree-Based IoT Botnet Attack Detection

Decision Tree-Based IoT Botnet Attack Detection

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Cyber Resilience (ICCR), International Conference on

作者： Hazem Al-Najjar Nadia Al-Rousan Department of Computer Science College of Information Technology University of Petra Amman Jordan Department of Computer Engineering Faculty of Electrical Engineering and Information Systems German Jordanian University

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

This paper investigated the predictive capabilities of three decision tree models for IoT botnet attack prediction using packet information while minimizing the number of predictors. The study employed three decision tree models (C5, CHAID, and Random Forest) and two additional models (Logistic Regression and Bayesian Network) for comparison purposes. The IoT botnet attack dataset comprised various devices, mainly two types of attacks (Gafgyt and Mirai) and 23 feature engineering variables. Simulation results from all models showed that Accuracy, Precision, Recall, and False Omission Rate (FOR) values are approximately one, with an F1 score of around 0.5. CHAID and C5 models outperform other models in predicting IoT botnet attacks, as they are developed using only four and two variables, respectively. These results demonstrated that decision tree models with fewer variables can perform better than models that utilize all predictors.

关键词： Training Logistic regression Botnet Predictive models Bayes methods Internet of Things Decision trees

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Design and Development of a Fixed-Wing Tail-Sitter Tethered VTOL Drone with Custom Autopilot and Robust Control Techniques 16th

Design and Development of a Fixed-Wing Tail-Sitter Tether...

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16th APCA International Conference on Automatic Control and Soft Computing, CONTROLO 2024

作者： Safaee, Ahmad Aguiar, A. Pedro Santos, Filipe Neves Moreira, Antonio Paulo SYSTEC-ARISE Research Center for Systems and Technologies Faculty of Engineering University of Porto Porto4200-465 Portugal INESC-TEC-Institute for Systems and Computer Engineering Technology and Science CRIIS-Centre for Robotics in Industry and Intelligent Systems Porto4200-465 Portugal INESC-TEC-Institute for Systems and Computer Engineering and DEEC/FEUP - Department of Electrical and Computer Engineering Faculty of Engineering of the University of Porto Porto4200-465 Portugal

ISBN: (纸本)9783031817236

The need for aerial platforms capable of sustained operation is critical in fields such as surveillance, agricultural monitoring, disaster response, and temporary telecommunication systems. Traditional unmanned aerial vehicles (UAVs) are hampered by limited battery capacity and inefficient energy utilization. This paper introduces an autonomous vertical flight drone tethered to a ground-based platform, which overcomes these limitations. The drone is designed to optimize its position and energy usage by harnessing wind currents, functioning similarly to a kite when conditions permit. This is enabled by a custom-designed autopilot system comprising both hardware and software tailored for this application. We detail the model identification process for three vertical take-off and landing (VTOL) drone systems, employing authentic data that measures lateral positional errors from the tether. Robust mixed sensitivity controllers are developed and implemented. The effectiveness of these controllers is validated through simulations showing their capability to handle uncertainties. Furthermore, the practical application and robust performance of the system are confirmed by a real-world case study, highlighting its potential in real operational environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： VTOL/STOL aircraft

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The Impact of Class Weight Optimization on Improving Machine Learning Outcomes in Identifying COVID-19 Specific ECG Patterns

The Impact of Class Weight Optimization on Improving Machine...

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17th International Joint Conference on Biomedical engineering systems and Technologies, BIOSTEC 2024

作者： Khan, Sara Ismail, Walaa N. Alsalamah, Shada Mohamed, Ebtesam Alsalamah, Hessah A. Department of Information Systems and Technology Management George Washington University WashingtonDC United States Management Information Systems Department College of Business Al Yamamah University Riyadh Saudi Arabia Information Systems Department King Saud University Riyadh Saudi Arabia Faculty of Computer Science Minia University Minia Egypt Computer Engineering Department College of Engineering and Architecture Al Yamamah University Riyadh Saudi Arabia

The Covid-19 pandemic has resulted in 550 million cases and 6.3 million fatalities, with the virus severely affecting the lungs and cardiovascular system. A study utilizes a VGG16 model adapted for a 12-Lead ECG Image database to assess the disease's impact on cardiovascular health. The research addresses the challenge of data imbalance by experimenting with different training approaches: using balanced datasets, imbalanced datasets, and class weight adjustments for imbalanced datasets. These models are designed for a three-class multiclass classification of ECG images: Abnormal, Covid-19, and Normal categories. Performance evaluations, including accuracy scores, confusion matrices, and classification reports, show promising results. The model trained on a balanced dataset achieved a 90% accuracy rate. When trained on an imbalanced dataset, the accuracy dropped to 82%. However, with class weight adjustments, the accuracy rebounded to 87%. The study proves that the adapted VGG16 model can effectively handle both balanced and imbalanced datasets. Further testing and enhancements can be carried out using additional datasets, making it a valuable tool for understanding the cardiovascular implications of Covid-19. © 2024 by SCITEPRESS–science and Technology Publications, Lda.

关键词： COVID-19

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SDSFusion: A Semantic-Aware Infrared and Visible Image Fusion Network for Degraded Scenes

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IEEE Transactions on Image Processing 2025年 34卷 3139-3153页

作者： Chen, Jun Yang, Liling Yu, Wei Gong, Wenping Cai, Zhanchuan Ma, Jiayi China University of Geosciences School of Automation Wuhan 430074 China Automation for Complex Systems Hubei Key Laboratory of Advanced Control and Intelligent Wuhan 430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan 430074 China China University of Geosciences Faculty of Engineering Wuhan 430074 China Macau University of Science and Technology School of Computer Science and Engineering Macau Macao Wuhan University Electronic Information School Wuhan 430072 China

A single-modal infrared or visible image offers limited representation in scenes with lighting degradation or extreme weather. We propose a multi-modal fusion framework, named SDSFusion, for all-day and all-weather infrared and visible image fusion. SDSFusion exploits the commonality in image processing to achieve enhancement, fusion, and semantic task interaction in a unified framework guided by semantic awareness and multi-scale features and losses. To address the disparity between infrared and visible images in degraded scenes, we differentiate modal features in a unified fusion model. Unlike existing joint fusion methods, we propose an adversarial generative network that refines the reconstruction of low-light images by embedding fused features. It provides feature-level brightness supplementation and image reconstruction to refine brightness and contrast. Extensive experiments in degraded scenes confirm that our approach is superior to state-of-the-art approaches in visual quality and performance, demonstrating the effectiveness of interaction improvement. © 1992-2012 IEEE.

关键词： adversarial generative network image fusion Multi-modal fusion semantic awareness

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On the Potential of Using ERP Business and System Data for Fraud Detection

On the Potential of Using ERP Business and System Data for F...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Schnepf, Jonas Vetter, Paula Temel, Tarik Scheuermann, Bernd Schmidt-Thieme, Lars University of Applied Sciences Karlsruhe Faculty of Management Science and Engineering Karlsruhe Germany Institute of Computer Science University of Hildesheim Information Systems and Machine Learning Lab Hildesheim Germany

ISBN: (纸本)9781665480451

Enterprise Resource Planning (ERP) systems are used to support and to control the business processes of a company or organization. Such systems integrate the data across the entire company into a complete system that is capable of enhancing the key operations in virtually any department. Commonly, running an ERP system helps companies operate more efficiently, however, this also leads to problems. Employees are able to enrich themselves through insider knowledge or by exploiting incomplete or incorrect permission settings. This is referred to as occupational fraud. Since ERP systems keep records of all executed business activities and log system events and permission checks, they provide a variety of different data sources that can be used to detect occupational fraud. This paper reviews existing literature in the area of fraud detection and fraud cases based on ERP data including business data and system data. Using the most widespread ERP system from SAP, the potential and the suitability of the various data sources with respect to fraud detection is examined. © 2022 IEEE.

关键词： Enterprise resource planning

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Interpretable SHAP-Driven Machine Learning for Accurate Fault Detection in Software engineering 5th

Interpretable SHAP-Driven Machine Learning for Accurate Fau...

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5th Joint International Conference on AI, Big Data and Blockchain, ABB 2024

作者： Kassaymeh, Sofian Rjoub, Gaith Dssouli, Rachida Bentahar, Jamal Almobydeen, Shahed Bassam Faculty of Information Technology Aqaba University of Technology Aqaba Jordan Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Department of Computer Science 6G Research Center Khalifa University Abu Dhabi United Arab Emirates

ISBN: (纸本)9783031731501

In order to design and develop secure and reliable software, accurate prediction of software errors is crucial. The complex, nonlinear relationship between software features and bugs that occur during development despite precautions and measures taken to prevent them makes it difficult for traditional empirical models to predict these bugs with any degree of accuracy. An integration between the Decision Tree (DT) model and the SHAP (Shapley Additive exPlanations) technique was developed in this work with the aim of predicting software faults and providing informative explanations of the expected results. The synergistic advantages of integrating DT and SHAP allow the creation of an accurate, efficient, and fully interpretable technique. In order to make the process visible and reliable, SHAP provides a global explanation of how features of the developed software affect quality and a local explanation of how features contribute to each prediction. The tendency of software features to influence software quality was revealed by feature dependence analysis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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Peridynamic-based modeling of elastoplasticity and fracture dynamics

Peridynamic-based modeling of elastoplasticity and fracture ...

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作者： Wang, Haoping Wang, Xiaokun Xu, Yanrui Zhang, Yalan Yao, Chao Guo, Yu Ban, Xiaojuan School of Intelligence Science and Technology University of Science and Technology Beijing Beijing China Faculty of Science and Engineering University of Groningen Groningen Netherlands School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Beijing Advanced Innovation Center for Materials Genome Engineering School of Intelligence Science and Technology University of Science and Technology Beijing Beijing China Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education University of Science and Technology Beijing Beijing China Institute of Materials Intelligent Technology Liaoning Academy of Materials Shenyang China

This paper introduces a particle-based framework for simulating the behavior of elastoplastic materials and the formation of fractures, grounded in Peridynamic theory. Traditional approaches, such as the Finite Element Method (FEM) and Smoothed Particle Hydrodynamics (SPH), to modeling elastic materials have primarily relied on discretization techniques and continuous constitutive model. However, accurately capturing fracture and crack development in elastoplastic materials poses significant challenges for these conventional models. Our approach integrates a Peridynamic-based elastic model with a density constraint, enhancing stability and realism. We adopt the Von Mises yield criterion and a bond stretch criterion to simulate plastic deformation and fracture formation, respectively. The proposed method stabilizes the elastic model through a density-based position constraint, while plasticity is modeled using the Von Mises yield criterion within the bond of particle paris. Fracturing and the generation of fine fragments are facilitated by the fracture criterion and the application of complementarity operations to the inter-particle connections. Our experimental results demonstrate the efficacy of our framework in realistically depicting a wide range of material behaviors, including elasticity, plasticity, and fracturing, across various scenarios. © 2024 John Wiley & Sons Ltd.

关键词： Elastoplasticity

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