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IEEE Conference on Dependable and Secure Computing

作者： Essam Al Daoud Laith Al Daoud Mahmoud Asassfeh Ala'a Al-Shaikh Ala'a Saeb Al-Sherideh Suha Afaneh Department of Cybersecurity Zarqa University Zarqa Jordan Computer Information System Jordan University of Science and Technology Jordan

ISBN: (数字)9798331540289

ISBN: (纸本)9798331540296

In this study, the enhancement of cybersecurity through the use of transformer models is explored, specifically focusing on the prevention of phishing emails and social media scams. Two modern datasets were utilized: the Phishing_Pot dataset, which provided phishing emails across 19 languages, and a collection of legitimate emails. Additionally, a dataset from Baaz, an Arabic social media platform, was compiled. Four approaches to phishing detection were compared: zero-shot learning with large language models (LLMs), transformers for feature extraction followed by random forest classification, fine-tuning of small language models (SLMs) on newly generated datasets, and an ensemble method combining the top models from the previous approaches. The experiments demonstrated that LLMs such as GPT-4o, Gemini 1.5, and Claude 3 achieved high accuracy, while SLMs showed improved performance post fine-tuning. The ensemble methods can be used to enhance the accuracy of fine-tuned SLM for phishing detection and scam prevention, reaching an accuracy of 98.50% for the first dataset and 95.25% for the second.

关键词： Adaptation models Accuracy Social networking (online) Phishing Prevention and mitigation Zero shot learning Transformers Data models Electronic mail Ensemble learning

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Influence Maximization With Graph Neural Network in Multi-Feature Social Network 24

Influence Maximization With Graph Neural Network in Multi-Fe...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Wang, Yuhao Li, Peng Huang, Weiyi School of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China

ISBN: (纸本)9798350319934

The classical influence maximization (IM) prob-lem aims to select a set of nodes as seed nodes for information propagation. Most related studies focus on one-dimensional information dissemination. However, the diffusion of information should be a multi-dimensional (or multi-feature) diffusion process. The user's judgment of information often depends on the combined judgment of multiple-dimensional information. Based on this, we consider a multi-feature diffusion model. In-formation will be decomposed into multi-dimensional features for diffusion. The user will receive these features and integrate them into a complete message. In addition, we consider seed node selection as a network node classification task. Consid-ering that social networks satisfy a graph structure, we use the node classification technique of graph neural networks. This technique classifies the nodes by the network topology to find the set of influential nodes. We propose a multi-feature influence (MFI) algorithm to improve the performance of the prediction. The set of candidate nodes obtained by classification is used as a benchmark to find the set of nodes with maximum influence. Our experiment results on real datasets show that the proposed algorithm has better algorithmic efficiency than the traditional greedy algorithm and better effectiveness than similar heuristic algorithms. © 2022 IEEE.

关键词： information dissemination

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Peak-Average-Power Ratio Techniques for 5G Waveforms Using D-SLM and D-PTS

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computer systems Science & Engineering 2023年第5期45卷 1199-1210页

作者： Himanshu Sharma karthikeyan Rajagopal G.Gugapriya Rajneesh Pareek Arun Kumar HayaMesfer Alshahrani Mohamed K.Nour Hany Mahgoub Mohamed Mousa Anwer Mustafa Hilal Department of Computer Science&Engineering JECRC UniversityJaipurIndia Centre for Nonlinear Systems Chennai Institute of TechnologyChennaiIndia School of Electronics Engineering Vellore Institute of TechnologyChennaiIndia Department of Electronics and Communication Engineering JECRC UniversityJaipurIndia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Electrical Engineering Department Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Multicarrier Waveform(MCW)has several advantages and plays a very important role in cellular *** generation(5G)MCW such as Non-Orthogonal Multiple Access(NOMA)and Filter Bank Multicarrier(FBMC)are thought to be important in 5G *** Peak to Average Power Ratio(PAPR)is seen as a serious concern in MCW since it reduces the efficiency of amplifier use in the user *** paper presents a novel Divergence Selective Mapping(DSLM)and Divergence Partial Transmission Sequence(D-PTS)for 5G *** is seen that the proposed D-SLM and PTS lower PAPR with low computational *** work highlighted a combination of multi-data block partial transmit schemes along with tone *** this,an overlapping factor is used to determine the number of data blocks for every ***,considering only those data blocks that have minimum signal power,the use of DSLM and DPTS are required to eliminate the segment’s *** results reveal that the suggested hybrid technique proves to be better than the conventional PTS ***,the power saving performance of FBMC and NOMA is compared with the Orthogonal Frequency Division Multiplexing(OFDM)waveform.

关键词： 5G PAPR D-SLM D-PTS

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Deep Transfer Learning Driven Oral Cancer Detection and Classification Model

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computers, Materials & Continua 2022年第11期73卷 3905-3920页

作者： Radwa Marzouk Eatedal Alabdulkreem Sami Dhahbi Mohamed K.Nour Mesfer Al Duhayyim Mahmoud Othman Manar Ahmed Hamza Abdelwahed Motwakel Ishfaq Yaseen Mohammed Rizwanullah Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

Oral cancer is the most commonly occurring‘head and neck cancers’across the *** of the oral cancer cases are diagnosed at later stages due to absence of awareness among *** earlier identification of disease is essential for improved outcomes,Artificial Intelligence(AI)and Machine Learning(ML)models are used in this *** this background,the current study introduces Artificial Intelligence with Deep Transfer Learning driven Oral Cancer detection and Classification Model(AIDTLOCCM).The primary goal of the proposed AIDTL-OCCM model is to diagnose oral cancer using AI and image processing *** proposed AIDTL-OCCM model involves fuzzy-based contrast enhancement approach to perform data *** by,the densely-connected networks(DenseNet-169)model is employed to produce a useful set of deep ***,Chimp Optimization Algorithm(COA)with Autoencoder(AE)model is applied for oral cancer detection and ***,COA is employed to determine optimal parameters involved in AE model.A wide range of experimental analyses was conducted on benchmark datasets and the results were investigated under several *** extensive experimental analysis outcomes established the enhanced performance of AIDTLOCCM model compared to other approaches with a maximum accuracy of 90.08%.

关键词： Oral cancer detection image classification artificial intelligence machine learning deep learning

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Enhanced fireworks algorithm based on particle swarm optimization and reverse learning of small-hole imaging experiment

Enhanced fireworks algorithm based on particle swarm optimiz...

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2022 IEEE International Conference on systems, Man, and Cybernetics, SMC 2022

作者： Jiao, Jiao Li, Jun Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System College of Computer Science and Technology Wuhan China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Since the traditional fireworks algorithm (FWA) is easy to fall into local optimization and has insufficient global optimization capability and low optimization accuracy, this paper proposes the enhanced fireworks algorithm (EFWA-SI) based on particle swarm optimization and small-hole imaging reverse learning. First, a dynamic explosion amplitude mechanism based on the evolution speed of the population is designed. The evolution speed of the population is dynamically adjusted by evaluating the evolution speed of the fitness in each iteration process, and the global and local search information is controlled. Secondly, the small-hole imaging reverse learning strategy combined with the idea of optimal-worst reverse learning is proposed. This strategy increases the diversity of the optimal-seeking positions and improves the ability of the algorithm to jump out of the local optimum;Finally, the current position is updated according to the position and speed of each firework, and the globally optimal fireworks and personally optimal fireworks are updated simultaneously to improve the individual heuristic learning ability. Through the simulation experiments on the CEC2015 test function and comparison with related algorithms, the results show that the algorithm performs better in convergence speed, computational accuracy, and stability. © 2022 IEEE.

关键词： Global optimization

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IntelliCross: Adaptive Pedestrian Crossing system 5

IntelliCross: Adaptive Pedestrian Crossing System

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5th International Conference on Advanced Research in Computing, ICARC 2025

作者： Dissanayake, Udayanga Weerasekara, Dinusha Sumanasekara, Hansika Ishara, Dodamgoda Wijesiri, Pipuni Moonamaldeniya, Menaka Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer System Engineering Sri Lanka Sri Lanka Institute of Information Technology Department of Computing Kandy Uni Sri Lanka

ISBN: (纸本)9798331530983

Urban traffic management at pedestrian crossings presents considerable issues, such as pedestrian safety, congestion, and effective prioritizing of emergency vehicles. Traditional traffic signal systems are frequently static, unable to respond to real-time changes in pedestrian flow, vehicle density, and environmental variables. To overcome these issues, an IoT-based adaptive pedestrian crossing system, "IntelliCross,"is presented. The system detects emergency vehicle sirens using sound sensors and automatically adjusts pedestrian signals to green to prioritize emergency vehicle passage, resulting in faster response times and shorter delays. Furthermore, machine learning algorithms alter signal timings based on real-time pedestrian counts and vehicle density, assuring smooth traffic flow and pedestrian safety. Vulnerable pedestrians, such as the elderly and disabled, are accommodated by dynamically extending green light durations to ensure safe crossing. The technology also includes real-time meteorological data, such as rain, to extend green light durations and improve pedestrian safety. IntelliCross, by combining IoT sensors with machine learning, offers a scalable and cost-effective solution for improving urban traffic management, closing crucial gaps in present systems, and contributing to the development of smart cities. Public surveys demonstrate considerable support for systems that prioritize emergency vehicles while also assuring pedestrian safety, proving the system's ability to revolutionize urban traffic infrastructure. © 2025 IEEE.

关键词： Pedestrian safety

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Deep Complex-valued Convolutional Learning for Waveform OFDM Receiver Design

Deep Complex-valued Convolutional Learning for Waveform OFDM...

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IEEE Conference on Wireless Communications and Networking

作者： Jiequ Ji Nam Phuong Tran Zehui Xiong Kun Zhu Tony Q. S. Quek Department of Information System and Design Singapore University of Technology Design Singapore College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Orthogonal frequency division multiplexing (OFD-M) has been widely used in modern communication networks. Notice that OFDM typically relies on (inverse) Discrete Fourier Transform (DFT/IDFT) for processing its waveforms. In this context, we propose a deep learning-based OFDM receiver that uses a deep complex-valued convolutional neural network (DC-CNN) to recover the information bit stream from synchronized time-domain signals without relying on DFT/IDFT. Specifically, a learned linear transform is designed to utilize the cyclic prefix (CP) of OFDM waveforms instead of DFT/IDFT, which presents the ability of DCCNN for complex communication waveforms. To improve the convergence of the training model for the DCCNN-based receiver, a novel transfer learning scheme is developed to train channel equalization and demodulation in two phases. In addition, both the DCCNN equalizer and DCCNN demodulator are trained and tested at different SNRs for Rayleigh fading and noise, and a mixed multiple fading channel model with various delay spreads is utilized to smooth the training loss. Simulation results suggest that our developed DCCNN channel estimator outperforms conventional estimators such as least square (LS), linear minimum mean square error (LMMSE) and low-rank approximation of LMMSE (ALMMSE) in multipath Rayleigh fading models with varying Doppler spreads and delay spreads.

关键词： Training OFDM Transfer learning Channel estimation Receivers Rayleigh channels Delays Synchronization Time-domain analysis Demodulation

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Low Energy Consumption and Time Deterministic Energy-saving Workflow Task Scheduling Algorithm based on DVFS 9

Low Energy Consumption and Time Deterministic Energy-saving ...

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9th IEEE International Conference on Cyber Security and Cloud Computing and 8th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2022

作者： Liu, Xun Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System College of Computer Science and Technology Wuhan China

ISBN: (数字)9781665480666

ISBN: (纸本)9781665480666

With the development of streaming media, soft real-time system in today's life could participate in the use of more extensive areas. The use frequency was also increasing. Consequently, modern processors were equipped with software control mechanisms such as DVFS (Dynamic Voltage Frequency Scaling) to allow operating systems to meet required performance while reducing power consumption. Therefore, we propose a task scheduling algorithm combined DVFS technology and time deterministic cyclic scheduling to achieve energy saving effect. First, the algorithm needed to minimize the preemption between tasks to reduce latency, so we created a buffer to save periodic tasks to avoid preemption. Second, to reduce the computational cost of the scheduling scheme, a scheduling template were designed to perform tasks. In this paper, the scheduling of periodic tasks, task scheduling would be designed when the task scheduling template would be fixed length. Besides, this algorithm supported that task could adopt appropriate voltage and frequency through DVFS technology in idle time under the condition of satisfying task dependence. Experimental analysis showed that the proposed algorithm could effectively reduce the system energy consumption while ensuring the completion of the task. © 2022 IEEE.

关键词： Scheduling

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Mining Alarm Association Rules in Optical Transmission Networks Using a Statistical Approach 24

Mining Alarm Association Rules in Optical Transmission Netwo...

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作者： Deng, Miao Li, Peng Liu, Yu Zhu, Rongjing Zhang, Yujie School of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China

ISBN: (纸本)9798350319934

Due to the interconnection between devices in the optical transmission network (OTN), when one device fails and generates alarm information, a large number of other invalid alarms are generated in OTN. How to mining association rules in alarm data is an important problem. In this paper, we investigate the association rules mining problem based on a statistical method. First, we remove the chattering alarm in preprocess to reduce data redundancy. Second, we propose a statistics-based association rule mining algorithm. We use a sliding time window to scan the alarm data, and we exploit the matrix to record the corresponding results. According to the matrix, we calculate the support and confidence of association rules. Compared with other traditional association rule mining algorithms, the proposed algorithm does not need to set the minimum support artificially, and the obtained association rules have strong causality. Through extensive experiments on syn-thetic and real OTN alarm datasets, we verify the effectiveness of our proposed algorithm and the correctness of the obtained association rules. © 2022 IEEE.

关键词： Association rules

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An Integrated Approach to Robotic Object Grasping and Manipulation

arXiv

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arXiv 2024年

作者： Ahmed, Owais Huzaifa, M. Areeb, M. Khan, Hamza Ali Students B.E Computer & Information System Engineering NED University OF Engineering and Technology Karachi Pakistan

In response to the growing challenges of manual labor and efficiency in warehouse operations, Amazon has embarked on a significant transformation by incorporating robotics to assist with various tasks. While a substantial number of robots have been successfully deployed for tasks such as item transportation within warehouses, the complex process of object picking from shelves remains a significant challenge. This project addresses the issue by developing an innovative robotic system capable of autonomously fulfilling a simulated order by efficiently selecting specific items from shelves. A distinguishing feature of the proposed robotic system is its capacity to navigate the challenge of uncertain object positions within each bin of the shelf. The system is engineered to autonomously adapt its approach, employing strategies that enable it to efficiently locate and retrieve the desired items, even in the absence of pre-established knowledge about their placements. Crucially, the robotic system operates with a paramount emphasis on autonomy. The intricate interplay of algorithms, control mechanisms, and sensor fusion empowers the robot to execute the entire object picking task without human intervention. This unfaltering commitment to autonomy is a pivotal step towards revolutionizing warehouse operations, potentially paving the way for unprecedented levels of efficiency and productivity. This project serves as a testament to the intersection of robotics, computer vision, and artificial intelligence in tackling a complex challenge within the realm of modern logistics. The envisioned robotic system represents a significant advancement in autonomous object picking technology, holding the promise of transforming conventional warehousing practices. As the fusion of cutting-edge technology and logistical innovation unfolds, the outcomes of this endeavor have the potential to redefine the future of warehouse operations and automation within the industry. © 2024, CC BY.

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