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2023 Asia-Pacific Conference on Image Processing, Electronics and computers, IPEC 2023

作者： Sun, Chen Tian, Xiaolin Macau University of Science and Technology Faculty of Innovation Engineering School of Computer Science and Engineering China

Lunar domes have always been one of the important windows to understand lunar volcanic activities, but traditional geological dome identification methods are costly. This study attempts to establish an automatic identification method for lunar volcanic domes through FSOD (Few-Shot Object Detection). Since our previous research has been trying to automatically identify lunar volcanic hills using traditional recognition algorithms, our team will try Few-Shot Object Detection this time. In this study, the researchers first obtained the dome coordinates from the list of known lunar domes and intercepted the data this research need from the corresponding coordinates on the DEM moon images. Subsequently, this research used the existing data to train six traditional recognition algorithms and FSOD and compared their performance to verify the feasibility of this study. This research use AP50 and AP75 to evaluate the performance of the model. Finally, this research found that the AP50 of FSOD can reach 0.76, and the AP75 can reach 0.43. However, although FSOD does perform better than other traditional identification methods, this research believe its accuracy is still far from our expectations. Admittedly, compared with YOLO v5, the performance of FSOD has not been significantly improved, but this study still verifies that it is feasible to apply FSOD to the identification of lunar volcanic domes. © Published under licence by IOP Publishing Ltd.

关键词： Object detection

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An Extensive Study of Frequent Mining Algorithms for Colossal Patterns 3rd

An Extensive Study of Frequent Mining Algorithms for Colossa...

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3rd International Conference on computer Vision, High Performance Computing, Smart Devices, and Networks, CHSN 2022

作者： Sreenivasula Reddy, T. Sathya, R. Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Annamalai Nagar Tamil Nadu Chidambaram608002 India

ISBN: (纸本)9789819966899

During the last decade of research, a lot of focus has been placed on the subject of frequent pattern mining (FPM). A profitable data set with a large sum of transactions and only a few items in each transaction has been used to develop numerous FPM algorithms. Because of the rise of bioinformatics, a new sort of data set called a high-dimensional data set has emerged, with fewer transactions but a greater sum of elements in each. The execution time of classical algorithms grows with deal length. High-dimensional data sets can’t be processed by existing algorithms. But when applied to large data sets with a lot of dimensions, mining algorithms generate a huge amount of data, much of which is useless to scientists because of the little and medium-sized patterns they include. As a way to lessen the number of output patterns for mining patterns, colossal pattern mining is discussed. Since small and mid-sized patterns aren’t mined, mining algorithms for enormous patterns run faster. In this work, an extensive study of colossal patterns, existing mining algorithms with its drawback is mentioned. The definitions of FPM, high utility mining and relation of colossal patterns with others are also explained. PatternFusion is the first algorithm, which is developed for colossal patterns that is described briefly in this work. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Data mining

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Using Ensemble Learning, A Cosine Similarity-Based Model for Detecting Security Anomalies in Software-Defined Networks 20

Using Ensemble Learning, A Cosine Similarity-Based Model for...

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20th CSI International Symposium on Artificial Intelligence and Signal Processing, AISP 2024

作者： Samadzadeh, Mohammadreza Zahedi, Mohammad Hadi Farahani, Elham University of Tehran Department of Electrical and Computer Engineering Tehran Iran K. N. Toosi University of Technology Faculty of Industrial Engineering Information Technology Department Tehran Iran Sharif University of Technology Department of Computer Engineering Tehran Iran

ISBN: (纸本)9798350383942

Currently, Software-Defined Network (SDN) is as one of the most commonly used network architectures. It requires using a software-based, centralized controller to communicate with the underlying network hardware to control the flow of the traffic through the network. This helps achieve easy, flexible, and integrated control and management of the entire network. In SDNs, the controller is the sole entity that monitors the entire network and is accountable for traffic management based on its comprehensive understanding of the network. Software-Defined Networks (SDNs) rely on a centralized controller for network management, yet this centralized control exposes vulnerabilities, leaving networks susceptible to disruptive attacks. To fortify SDN security, this article presents an innovative approach that merges machine learning and deep learning techniques through ensemble learning. Our method introduces several key innovations. Firstly, it employs ensemble learning, combining multiple models to enhance predictive capability. Secondly, the utilization of cosine similarity-based classification enables the grouping of attacks with varying degrees of similarity, refining the distinction between attack types. Thirdly, the approach includes customized classifiers, trained specifically for distinct attack classes, optimizing detection accuracy for each type. Finally, the K-Nearest Neighbor (KNN) algorithm is applied for final classification of new data samples, improving precision in identifying attack types. With an exceptional accuracy rate of 99.91%, our method surpasses the performance of analogous studies. By amalgamating these innovations, our approach establishes a robust framework for detecting security anomalies in SDNs, reinforcing network integrity and reliability. © 2024 IEEE.

关键词： Learning systems

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OpenAI-Powered NLP for SQL Generation: Enhancing Database Access for Non-Technical Users 10

OpenAI-Powered NLP for SQL Generation: Enhancing Database Ac...

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10th International Conference on Digital Arts, Media and technology and 8th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2025

作者： Nilsiam, Yuenyong Khammaw, Piyaphiphat Nuchitprasitchai, Siranee Faculty of Engineering King Mongkut's University of Technology North Bangkok Department of Electrical and Computer Engineering Bangkok Thailand Faculty of Information Technology and Digital Innovation King Mongkut's University of Technology North Bangkok Department of Information Technology Bangkok Thailand

ISBN: (纸本)9798331543273

Database accessibility can be challenging for users without technical expertise, particularly when generating SQL queries to retrieve information. This study addresses this gap by developing an OpenAI-powered system designed to automate SQL command generation, enabling non-expert users to interact with databases using natural language. Implemented as a web application, the system was evaluated by expert users, assessing both performance in query accuracy and user satisfaction. Results indicate high usability and satisfaction scores, with the system effectively enhancing accessibility to database functions, thus providing a user-friendly solution for database management. © 2025 IEEE.

关键词： Expert systems

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Auto-Handover Gateway LoRaWAN based on Autonomous Vehicles 10

Auto-Handover Gateway LoRaWAN based on Autonomous Vehicles

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10th IEEE Information technology International Seminar, ITIS 2024

作者： Purnama, Iwan Pane, Rahmadani Sooai, Adri Gabriel Edi, Firman Chamid, Ahmad Abdul Adi, Puput Dani Prasetyo Universitas Labuhanbatu Faculty of Science and Technology Department of Information Technology Labuhanbatu Indonesia Universitas Katolik Widya Mandira Faculty of Computer Science Department of Computer Engineering NTT Kupang Indonesia Batam Institute of Technology Faculty of Industrial Technology Department of Engineering Management Batam City Indonesia Muria Kudus University Department of Informatics Engineering Kudus Indonesia Department of Telecommunication Bandung Indonesia

ISBN: (纸本)9798331521295

This article presents a simulation study on the use of LoRaWAN technology in autonomous vehicles. The research focuses on developing an Auto-Handover Gateway for Autonomous Vehicles, utilizing LoRaWAN parameters such as bit rates, spreading factor (SF), packet loss, and received signal strength. These parameters are tested in simulations of autonomous vehicle movements, which include both regular and random motion patterns. The aim is to assess the performance of LoRaWAN in different scenarios and its impact on communication in autonomous vehicle systems. The simulation results show several key trends. As the vehicle moves further, the spreading factor (SF) increases, and bit rates tend to decrease, indicating a drop in communication performance over longer distances. The research also identifies that the gateway with the closest proximity to the vehicle is selected, both in random and non-random movement scenarios. Additionally, packet loss increases as the distance between the vehicle and the gateway grows, highlighting the challenges of maintaining reliable communication over long distances. These findings provide insights into optimizing LoRaWAN parameters for real-time communication in autonomous vehicles, contributing to the development of intelligent vehicle networks. © 2024 IEEE.

关键词： Packet loss

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Avoid Suspicious Route of Blackhole Nodes in MANET’s:Using A Cooperative Trapping

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computer Systems Science & engineering 2023年第5期45卷 1901-1915页

作者： Abdllkader Esaid Mary Agoyi Department of Computer Engineering Faculty of EngineeringCyprus International UniversityNicosiaCyprus Information Technology Department School of Applied SciencesCyprus International UniversityNicosiaCyprus

Mobile Ad hoc Network(MANET)is decentralized wireless network and can communicate without existing infrastructure in many *** is vulnerable to various attacks that affect its performance such as blackhole *** attacker,inject fault routing information to persuade the source node to select the path with malicious node as the shortest *** eliminate malicious nodes from launching any collaborative attack.A cooperative Trapping Approach(CTA)was proposed based on modifying Ad-hoc On-demand Distance Vector(AODV)routing protocol and trapping the malicious nodes by responding to the trap request *** approach aims to eliminate and rule out both single and collaborative malicious blackhole nodes from any *** approach realizes a backward tracking mechanism to perform the elimination *** proposed algorithm(CTA)was executed using NS-2 network *** performance metrics that has been considered to evaluate the performance of the proposed algorithm such as throughput,end to end delay,packet delivery ratio,and consuming *** experimental results have shown the performance metrics of the proposed approach outperformed other state of at algorithms.

关键词： AODV Blackhole MANET Malicious node

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A Secure and Efficient Scheme for Mutual Authentication for Integrity of Exchanged Data in IoMT 28

A Secure and Efficient Scheme for Mutual Authentication for ...

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28th International computer Conference, computer Society of Iran, CSICC 2023

作者： Faraji, Mahmoud Reza Shahriari, Hamid Nikooghadam, Mahdi Amirkabir University of Technology Tehran Polytechnic Faculty of Computer Engineering Computer Science Department Tehran Iran

ISBN: (纸本)9798350338195

The emergence of pandemic diseases like Covid-19 in recent years has made it more important for Internet of Medical Things (IoMT) environments to build contact between patients and doctors in order to control their health state. Patients will be able to send their healthcare data to the cloud server of the medical service provider in remote medical environments through sensors connected to their smart devices, such as watches or smartphones. However, patients' worries surrounding their data privacy protection are still present. In order to ensure the security and privacy of patients' healthcare data in remote medical environments, a number of different schemes have been proposed by researchers. However, these schemes have not been able to take all security requirements into account. Consequently, in this study, we have proposed a secure and effective protocol to safeguard the privacy of patients' medical data when it is sent to the server. This protocol entails two components: mutual authentication of the patient and the server of the medical service provider, as well as the integrity of the exchanged data. Also, our scheme satisfies security requirements and is resistant to well-known attacks. Following this, we used the Scyther tool to formally analyze our proposed scheme. The results showed that the scheme is secure, and in the section on performance analysis, we demonstrated that the proposed scheme performs better than comparable schemes. © 2023 IEEE.

关键词： Authentication

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Enhanced and Efficient Gated Recurrent Unit and Long Short-Term Memory Architectures for Detecting False Information

Enhanced and Efficient Gated Recurrent Unit and Long Short-T...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Raman, Ramakrishnan Kumar, Vikram Sanjay, C.P. Rabadiya, Dhaval Patre, Smruti Meenakshi, R. India SRV Media Pvt. Ltd. India It & Business Analytics India Parul Institute of Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology India India Chennai Institute of Technology Computer Science and Engineering India

ISBN: (纸本)9798350359688

In the digital era, the rapid dissemination of false information has emerged as a formidable challenge, undermining the credibility of online platforms and posing a threat to informed public discourse. Addressing this issue, our research introduces innovative enhancements to Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) architectures, aimed at bolstering the efficiency and accuracy of false information detection mechanisms. Leveraging the inherent strengths of GRU and LSTM models in capturing temporal dependencies within data, our proposed modifications incorporate adaptive learning rates and a novel hybrid attention mechanism. These enhancements facilitate a more nuanced understanding of the contextual and stylistic nuances characteristic of false information, thereby significantly improving the models' predictive performance. Through extensive experimentation on a diverse dataset comprising various forms of digital content, our findings reveal that the enhanced GRU and LSTM architectures outperform existing models in terms of detection accuracy, while simultaneously reducing computational overhead. This advancement in deep learning techniques represents a pivotal step towards more reliable and efficient false information detection, with potential applications spanning across social media platforms, news outlets, and beyond. Our research not only contributes to the academic discourse on natural language processing and misinformation studies but also offers practical solutions for safeguarding the integrity of information in the digital landscape. © 2024 IEEE.

关键词： Long short-term memory

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Comparative Analysis of MEMS Tuning Fork Gyroscope Designs with Microlever Amplification Mechanism for Enhanced Sensitivity and Robustness 6

Comparative Analysis of MEMS Tuning Fork Gyroscope Designs w...

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6th International Conference on Robotics and Automation in Industry, ICRAI 2024

作者： Bashir, Uzair Javaid, Faraz Ali, Osama Arshad, Adeel Rehman, Masood Ur Department of Electrical and Computer Engineering Islamabad Pakistan Department of Mechatronics Engineering Islamabad Pakistan Superior University Faculty of Engineering and Technology Lahore Pakistan

ISBN: (纸本)9798350355703

This paper presents a comparative analysis of two MEMS Tuning Fork Gyroscope (TFG) designs employing microlever amplification mechanisms for sensitivity amplification in the sense mode. An input frame added in microlever amplification frame to increase the robustness of MEMS gyroscope. Design A incorporates a non-anchored input frame in microlever, while Design B features an anchored input frame in microlever. Frequency response analysis and modal analysis were performed using CoventorMEMS+ to evaluate the impact of input frame of microlever on undesired mode suppression, mechanical sensitivity, and robustness. The results indicate that anchoring the microlever in Design B significantly reduces quadrature displacement arise because of environmental linear acceleration in the sense mode while maintaining comparable amplified sensitivity to Design A. Design B offers enhanced stability and robustness, making it a more suitable candidate for precision applications such as autonomous vehicle navigation systems. © 2024 IEEE.

关键词： Gyroscopes

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Interaction graph learning of line cascading failure in power networks and its statistical properties

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Energy Informatics 2023年第1期6卷 17页

作者： Ghasemi, Abdorasoul de Meer, Hermann Kantz, Holger Faculty of Computer Engineering K. N. Toosi University of Technology Tehran Iran Faculty of Computer Science and Mathematics Passau University Passau Germany Max Planck Institute for the Physics of Complex Systems Dresden Germany

We consider line failure cascading in power networks where an initial random failure of a few lines leads to consecutive other line overloads and failures before the system settles in a steady state. Such cascades are rooted in non-obvious, long-range, and higher-order couplings among the lines’ flows induced by physical constraints on the network. Failure interaction graph encodes which and to what extent other lines in a networked system are affected after each line failure and can help to predict the final state after an initial disturbance. We perform data analytics on the final lines’ steady states of cascade trajectories to infer a specific line’s state given the states of others. We use a generative model to reconstruct possible steady states, and a predictive model aims to predict the probability of each line’s failures after the initial failure as a regression problem. The generative model uses regularized pseudolikelihood estimator to infer interaction weights by solving the inverse Ising problem and deploys Glauber dynamics to generate steady states. The discriminative model uses boosted trees to efficiently learn over training and predict over test data the state of each line as a target finding an appropriate subset of other lines’ states as explanatory variables. We analyze the degree distribution of the corresponding interaction graphs to study the number of other components affected by each line failure (out-degree) or the number of lines that affect the state of a given line (in-degree). Both models show that the in-degree follows a power-law distribution. Finally, we discuss the possible application of the interaction graph for early link removal to mitigate the failure-cascading consequences. © 2023, The Author(s).

关键词： Forecasting

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