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10th International Conference on Web Research, ICWR 2024

作者： Baradaran, Amir Hossein Bohlouli, Masoumeh Motlagh, Mohammad Reza Jahed Iran University of Science and Technology School of Electrical Engineering Tehran Iran Alzahra University Department of Computer Engineering Tehran Iran Iran University of Science and Technology School of Computer Engineering Tehran Iran

ISBN: (纸本)9798350394986

With the rising prominence of gold as a lucrative investment avenue in Iran, this research delves into predicting the future price of 18-carat gold. In pursuit of this objective, a comprehensive comparison is conducted between two neural network architectures: the Gated Recurrent Unit (GRU) as a single structure and a hybrid model combining Convolutional Neural Network (CNN) and Long Short-Term Memory Neural Network (LSTM). The evaluation criteria employed focus on error metrics to gauge the accuracy of price predictions. Results reveal that the CNN-LSTM hybrid neural network exhibits superior performance, showcasing lower error values in predicting the price of 18-carat gold in Iran. Consequently, the chosen model, CNN-LSTM, is employed to forecast the following day's gold prices, providing valuable insights for investors navigating the Iranian market. This research contributes to the ongoing discourse on gold investment strategies by highlighting the effectiveness of advanced neural network models in enhancing predictive accuracy. © 2024 IEEE.

关键词： Long short-term memory

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Cyber Threat Intelligence in Smart Cities: Bayesian Inference and Energy Optimization in LoRa Networks for Big Data Applications

Cyber Threat Intelligence in Smart Cities: Bayesian Inferenc...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Karras, Aristeidis Theodorakopoulos, Leonidas Karras, Christos Antonopoulou, Hera University of Patras Computer Engineering and Informatics Department Patras Greece University of Patras Department of Management Science and Technology Patras Greece

ISBN: (纸本)9798350362480

In the evolving landscape of smart cities, optimizing energy consumption and enhancing cybersecurity in Internet of Things (IoT) networks are crucial. This study leverages LoRa (Long Range) technology, Bayesian Inference, and Extreme Learning Machines (ELMs) to advance cyber threat intelligence and energy efficiency in large-scale IoT deployments. The proposed algorithms address key challenges within LoRa networks by implementing a novel energy consumption model, kernel-based ELM fine-tuning, Bayesian parameter tuning, and data fusion. Through Bayesian Inference, our approach dynamically adjusts network parameters to optimize packet transmission and collision rates, ultimately reducing power consumption across smart city applications. The Kernel-ELM algorithm fine-tunes LoRa network applications by adapting kernel parameters to the unique demands of IoT environments. Additionally, our Anomaly Detection and Fusion Algorithm (ADFA) integrates data from multiple sources to detect potential cyber threats, enhancing network security. Experimental results validate these algorithms on various datasets, demonstrating improvements in both energy optimization and threat detection. Ultimately, this study provides actionable insights into the deployment of scalable, energy-efficient, and secure IoT networks for smart city infrastructure. © 2024 IEEE.

关键词： Cyber attacks

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Advanced and Secure Data Sharing Scheme with Blockchain and IPFS: A Brief Review 15

Advanced and Secure Data Sharing Scheme with Blockchain and ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Mukhedkar, Moresh Kote, Prajwal Zonde, Mounesha Jadhav, Om Bhasme, Vaibhav Dawande, Nitin A. D Y Patil University School of Engineering and Technology E&tc Engineering Department Pune India D Y Patil University School of Engineering and Technology Computer Engineering Department Pune India

ISBN: (纸本)9798350370249

In the ever-evolving information age, the challenges posed by the exponential growth of data have led to innovative solutions that leverage the power of technology. This paper introduces a comprehensive data sharing platform that seamlessly integrates blockchain and InterPlanetary File System (IPFS) technologies to address the complex issues of data sharing and storage. The approach begins by establishing an alliance blockchain, employing a consensus mechanism rooted in computing power competition to maintain data integrity within the blockchain. Simultaneously, the IPFS system is harnessed for data storage, utilizing distributed storage, file splitting, and splicing techniques. The resultant data sharing platform consists of distinct modules, including blockchain, IPFS, encryption and decryption, and fast retrieval, providing end-to-end data security and accessibility. Furthermore, the paper delves into the practical implementation of this technology by examining a course sharing project that leverages IPFS and Ethereum blockchain. By utilizing IPFS as an access medium, the paper efficiently reduces storage costs and enhances storage efficiency, effectively addressing the challenge of managing extensive course data. The incorporation of Ethereum blockchain technology ensures the elimination of single points of failure and establishes a robust transaction mechanism that incentivizes course users to engage in sharing and enhances course liquidity. This innovative amalgamation of blockchain and IPFS not only resolves the data sharing conundrum but also guarantees secure, decentralized, and efficient data storage and exchange in an era defined by the relentless growth of digital information. © 2024 IEEE.

关键词： blockchain encryption ethereum IPFS security

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An Ensemble Approach using Self-attention based MobileNetV2 for SAR classification 2

An Ensemble Approach using Self-attention based MobileNetV2 ...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Anjali, K.S. Singh, Rimjhim Padam Panda, Manoj Kumar Palaniappan, Kannappan Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Electrical Engineering and Computer Science University of Missouri Columbia United States

The Synthetic Aperture Radar (SAR) image classification has become an essential task in numerous applications. Despite its significance, SAR image classification remains challenging due to the inherent complexity and speckle noise characteristics of SAR data. To address this, an innovative approach is proposed in this study, combining the power of ensemble learning and the efficiency of MobileNetV2 architecture to achieve robust and accurate SAR image classification. The proposed deep learning model consists of two main components: an ensemble learning approach that combines multiple binary classifiers, and the employment of MobilenetV2 architecture with self-attention mechanism as a binary classifier. The ensemble approach uses a max-value selector for predicting final class from the individual classifier output. The MobileNetV2 architecture is finetuned by freezing weights and removing the last four layers. The inclusion of the attention layer within the architecture aids in refining the learning process by focusing on crucial features while disregarding irrelevant background elements in the images. The effectiveness of the model is evaluated using Moving and Stationary Target Acquisition and Recognition (MSTAR) benchmark dataset with accuracy of classification as performance *** model is trained and tested with images captured at various aspect angles. This approach enhances the robustness of the results, making them more applicable to real-world scenarios when compared to prior methods. This study also compares the performance of the ensemble method using MobileNetV2 with state-of-art individual classifiers and different CNN architectures. The proposed ensemble method OVA-MbNN achieves an impressive classification accuracy of 76.63%, surpassing the performance of state-of-the-art classifiers while maintaining robustness. © 2024 Elsevier B.V.. All rights reserved.

关键词： Synthetic aperture radar

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Dielectric Response Analysis Characteristic of Service-Aged XLPE Cables 14

Dielectric Response Analysis Characteristic of Service-Aged ...

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14th International Conference on the Properties and Applications of Dielectric Materials, ICPADM 2024

作者： Thongkong, Patchara Promphanich, Wiboon Udomluksananon, Patt Jeenmuang, Siwakorn Wiangtong, Theerayod Pattanadech, Norasage School of Engineering King Mongkut's Institute of Technology Ladkrabang Department of Electrical Engineering Bangkok Thailand School of Engineering King Mongkut's Institute of Technology Ladkrabang Department of Computer Engineering Bangkok Thailand

ISBN: (纸本)9798350374605

The main insulation used in plastic underground cables is cross-linked polyethylene (XLPE). The XLPE underground cable which has been utilized in service for a long time, has experienced various stresses, or so-called TEAM stresses. Consequently, the XLPE insulation will be degraded over time and lead to insulation properties degradation. This paper represents the dielectric response analysis technique for characterizing the insulation condition of the service-aged XLPE medium voltage underground cable. The dielectric response analysis was performed on the 22 kV XLPE underground cables that have been in service for more than 10 years and were directly buried. The dielectric response analysis measurement consists of a combination of polarization and depolarization current (PDC) measurement in the time-domain and frequency-domain spectroscopy (FDS) measurement. After that, test results were analyzed to evaluate the insulation condition and compare it to the new 22 kV underground cable. The analyzed dielectric response analysis test results will be reported in this paper. © 2024 IEEE.

关键词： Cable sheathing

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A Vulnerability Detection Method for Smart Contracts Based on Dynamic Meta Optimizer

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IEEE Internet of Things Journal 2025年第13期12卷 24904-24915页

作者： He, Daojing Gong, Wei Chan, Sammy Harbin Institute of Technology School of Computer Science and Technology Shenzhen China Jiangxi University of Science and Technology School of Information Engineering Jiangxi Ganzhou China City University of Hong Kong Department of Electrical Engineering Hong Kong Hong Kong

With the increasingly complex blockchain technology environment and emerging security threats, the detection and prevention of vulnerabilities in blockchain smart contracts have become crucial for ensuring the healthy development of blockchain technology and avoiding substantial economic losses. Recently developed vulnerability detection methods for smart contracts suffer from the drawbacks of insufficient feature extraction and inadequate multitask detection. This paper proposes a multifaceted learning model, DLR, based on a dual-loop architecture of meta learning to address these issues by adopting model-agnostic meta-learning techniques. This model employs a syntax analyzer for targeted feature extraction, with graph information used as a supporting tool during the initial stage of feature engineering. Combined with an improved optimizer algorithm in the inner loop, the model can effectively learn and adjust based on the specific requirements of each task. In the outer loop, the model achieves efficient learning rate adjustment for multi-task learning by integrating the adaptive learning rate of the Adam optimizer with a global adjustment using simulated annealing strategy, thereby enhancing performance across diverse tasks. Experimental results demonstrate significant improvements in detection accuracy over state-of-the-art methods for three types of vulnerabilities, with our method achieving detection accuracies of 94.40%, 93.36%, and 94.33% for reentrancy, timestamp dependence, and integer overflow vulnerabilities, respectively. © 2014 IEEE.

关键词： Contrastive Learning

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Forensic Eye: Convolutional Neural Network-based Crime Scene Object Detection System 2

Forensic Eye: Convolutional Neural Network-based Crime Scene...

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2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Sudha, I. Vadivel, M. Lakshmiramana, P. Kalpana, N. Kumar, Nellore Manoj Vinoth, R. Saveethe University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science Tamilnadu Chennai India Excel Engineering College Department of Information Technology Komarapalayam Namakkal India Madanapalle Institute of Technology & Science Department of Computer Science & Technology Andhra Pradesh India Psna College of Engineering and Technology Department of Computer Science and Engineering India Saveetha School of Engineering Department of Mathematics Tamilnadu Chennai India Rmk College of Engineering and Technology Department of Computer Science and Engineering Puduvoyal India

ISBN: (纸本)9798350364699

Forensic investigations rely heavily on the accurate detection and analysis of crime related objects present at crime scenes. Traditional methods of object detection often involve manual labor and are error prone process to human error. In recent years, Convolutional Neural Networks (CNNs) have shown remarkable success in various computer vision tasks, including in object detection. In this article, we introduce ForensicEye, a novel CNN-based crime scene object detection system designed to assist forensic investigators in identifying the objects related to the crime. It utilizes a state-of-the-art CNN architecture trained on a large dataset of crime scene images, enabling robust detection even in challenging lighting and environmental conditions. We evaluated the performance of the ForensicEye on benchmark datasets with various preprocessing techniques, demonstrating its superior accuracy and efficiency compared to traditional methods. Our proposed model achieved the accuracy of 96.2%, precision of 97.9%, Recall of 96.9%, F1-score of 97.4%. © 2024 IEEE.

关键词： Convolutional neural networks

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A Hybrid Approach to Dynamic Thread Allocation Driven by Workload and Task Prioritization

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SN computer Science 2025年第3期6卷 1-19页

作者： Abirami, K. Harini, N. Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham Coimbatore India

In today’s rapidly changing world, cloud service providers face numerous challenges in managing resources and meeting customer demands. To address these challenges, cloud service providers should prioritize the tasks and allocate threads efficiently. By allocating threads efficiently, service providers can optimize resource utilization and reduce response times and job completion times which in turn improves customer satisfaction. We propose a hybrid model that integrates the threshold-driven thread Balancer (TDTB) for dynamic thread allocation with a Customer Retention Model (CRM) to prioritize tasks based on customer behaviour. Our model initiates the establishment of a thread pool configured with a dynamic threshold based on workload (on-demand requests). Concurrently, a queue to handle incoming customer requests is set up alongside a CRM priority queue designed for task prioritization. Our model calculates Job duration and completion times for n priority classes. Crown consumers experience shorter durations and completion times compared to other priority classes. Tasks are completed in descending order of priority. The results of thorough experimentation clearly show that crown consumer tasks are highly satisfied. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Customer segmentation Response time Service differentiation Thread allocation Throughput Workload

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Enhancing Belief Propagation Decoding of Polar Codes: A Reinforcement Learning Approach

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IEEE Communications Letters 2025年第6期29卷 1285-1289页

作者： Moradi, Mohsen Habib, Salman Mitchell, David G. M. Northeastern University Department of Electrical & Computer Engineering BostonMA02115 United States New Mexico State University Klipsch School of Electrical and Computer Engineering Las CrucesNM88003 United States Arizona State University School of Electrical Computer and Energy Eng TempeAZ85287 United States

Short block-length polar-like codes showcase exceptional error correction performance (ECP) using sequential decoding or successive cancellation list decoding with a large list size. However, achieving a high level of reliability with these methods involves high-latency decoding. To meet the growing demand for low-latency communication with acceptable complexity, belief propagation (BP) decoding emerges as an attractive option, although its ECP is known to fall short of those high-latency alternatives. In this paper, we propose an enhanced BP decoding approach for polar codes, leveraging reinforcement learning (RL) to optimize the message-passing schedule. Moreover, we investigate the design of the polar code rate profile and corresponding Tanner graph representation to enhance the benefits of RL. Numerical results demonstrate a performance gain of more than 1 dB for polar codes with a length of 128 and a rate of 0.5 compared to conventional BP decoding alone at high Eb/N0 values, demonstrating the promise of the proposed approach. © 1997-2012 IEEE.

关键词： Decoding

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A Yet Powerful Routing Protocol for Vehicular Ad-hoc Networks (VANETs) Utilizing Software Defined Network (SDN) and Fog Computing 8

A Yet Powerful Routing Protocol for Vehicular Ad-hoc Network...

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8th International Conference on Information Technology, Information Systems and Electrical engineering, ICITISEE 2024

作者： Darabkh, Khalid A. Al-Mistarihi, Mamoun F. Al-Maaitah, Mera Ismail School of Engineering The University of Jordan Computer Engineering Department Amman11942 Jordan Jordan University of Science and Technology Faculty of Engineering Electrical Engineering Department Irbid22110 Jordan

ISBN: (纸本)9798350368970

In this article, we propose a novel protocol that employs different technologies together, such as Software Defined Network (SDN) for the purpose of offering scalability, programmability, and global network information, and Fog computing for the reason of providing location services that meet V ANET standards. Additionally, we propose a novel cluster head selection algorithm for the purpose of enhancing the efficiency of the protocol by considering three different dimensions, namely, lifetime, average distance, and signal-to-interference noise ratio. The novelty of this proposed algorithm comes from reducing the number of control overheads, reducing long-distance communications, and reducing packet loss due to collisions and contentions inside each cluster, respectively. The proposed protocol has been assessed using N s3 simulator whereas its results are so promising and worth publishing. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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