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检索条件"机构=dept. of Computer Computer Systems Engineering"

共 66560 条记录，以下是391-400 订阅

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LTP Performance Evaluation Based on PHY Characteristics for Earth-Moon Satellite Links 10

LTP Performance Evaluation Based on PHY Characteristics for ...

引用

10th IEEE Information Technology International Seminar, ITIS 2024

作者： Ehile, Israel Ehile Muratov, Alisher Kizilirmak, Refik Caglar Nazarbayev University Dept. of Electrical and Computer Engineering Astana Kazakhstan

ISBN: (纸本)9798331521295

As the growth in satellite applications and the Internet is projected to extend beyond Earth into the solar system, satellite networks operating beyond Earth's orbit are becoming a major point of interest. These satellite networks are expected to implement the Delay Tolerant Networking (DTN) architecture, which is specifically designed for networks with frequent intermittency, large delays, and high packet loss. The Licklider Transmission Protocol (LTP) is a specialized transport layer protocol used in space internetworking, providing reliability through retransmissions of the lost packets, with lower overhead compared to Transmission Control Protocol (TCP). This paper presents an analysis of LTP performance for satellite-to-satellite Earth-to-Moon links, based on the physical layer parameters of these radio links. Specifically, we defined the link budget for such links and, using two different modulation methods, calculated the segment error probability (SER). Given the SER, LTP performance was evaluated in terms of transmission time for varying block sizes. Our results show that a lower modulation order leads to a lower SER, which in turn reduces transmission time and increases throughput. © 2024 IEEE.

关键词： Budget control

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Machine Learning - Driven Approaches for Detection and Analysis of Phishing attacks: A Robust Framework for Cybersecurity 7

Machine Learning - Driven Approaches for Detection and Analy...

引用

7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Sarvan, N.D.S.S. Sai Ram Reddy, Venkata Jain, Darshan Rakesh, Guntupalli Anguraj, Dinesh Kumar Kl University Dept. of Computer Science Engineering Guntur India

ISBN: (纸本)9798350359299

Phishing is the attempt to trick someone into disclosing confidential and private information by using internet communication, such as bank account details, passwords, credit/debit card details, and usernames. The accuracy of foreseeing such phishing assaults can be substantially enhanced through utilizing these algorithms. The primary objective of this research work is to enhance internet security by providing a dependable and productive method for detecting phishing websites. To achieve this, a machine learning-based approach was employed, utilizing various models trained on a dataset of phishing website attributes. The study evaluated a range of machine learning algorithms and classifiers, including Naive Bayes, Support Vector Machine, K-Nearest Neighbors, Gradient Boosting, Decision Tree, Random Forest, Bagging, Voting, and AdaBoost Classifiers. These evaluation parameters are used to determine the efficiency of the machine learning models including F1 Score, Accuracy, Precision, and Recall values. By training the dataset with key variables from credible websites, the Bagging Classifier algorithm demonstrated a substantial improvement, achieving an accuracy rate of 98.4% resulting in a significant enhancement in phishing website detection compared to other methods. © 2024 IEEE.

关键词： Support vector machines

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Monitoring Post-Earthquake Search and Rescue Operations through UAVs Vision: Teams, Equipment and Structural Damage Detection 8

Monitoring Post-Earthquake Search and Rescue Operations thro...

引用

8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024

作者： Kiratli, Rabia Eroglu, Alperen Necmettin Erbakan University Dept. of Computer Engineering Konya Turkey

ISBN: (纸本)9798331531492

With the development of unmanned aerial vehicles and deep learning-based computer vision algorithms, there is an increasing interest in using these technologies together in managing pre- and post-natural disaster operations which are serious crises. This study focuses on enhancing search and rescue efforts and crisis management, particularly in time-critical and high-accuracy tasks, especially for earthquakes. It addresses the detection of teams, equipment, and structural damage by analyzing a dataset comprising images captured by UAVs from various lighting conditions, altitudes, and viewing angles using YOLO algorithms. Comparative results between the latest YOLOv8 algorithm and YOLOv5 are presented, showing an accuracy rate of 88% mAP, along with high speed, which will enable critical tasks in search and rescue operations to be carried out effectively with sufficient equipment and accurate identification of needs. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Mobile Gateways for LoRa End-Devices: Support Connectivity on the Move 29

Mobile Gateways for LoRa End-Devices: Support Connectivity o...

引用

29th IEEE International Workshop on computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2024

作者： Moysiadis, Vasileios Skoutas, Dimitrios N. Skianis, Charalabos Lagkas, Thomas Argyriou, Vasileios Sarigiannidis, Panagiotis University of the Aegean Dept. of Information and Communication Systems Engineering Samos83200 Greece International Hellenic University Dept. of Computer Science Greece University of Western Macedonia Dept. of Electrical and Computer Engineering Kozani Greece Kingston University Dept. of Networks and Digital Media Kingston upon Thames United Kingdom

ISBN: (纸本)9798350377644

This paper proposes a method enabling mobile LoRa gateways to support mobile end devices. The approach utilizes trilateration and similar approaches based on RSSI to estimate the position of end devices and specify the movement of the gateways to maintain contact. The evaluation of the proposed method tests various parameters such as speed and number of end devices, and it shows that it is able to retain contact even with speeds up to 12 mps. Moreover, the evaluation shows that the ADR-MIN algorithm performs better in this scenario than the default ADR. © 2024 IEEE.

关键词： Gateways (computer networks)

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Enhancing LoRaWAN Security: Protection Against Bit Flipping Attacks in IoT Networks 17

Enhancing LoRaWAN Security: Protection Against Bit Flipping ...

引用

17th International Conference on Security of Information and Networks, SIN 2024

作者： Dave, Jay Choudhury, Nikumani Havishteja, Dantu Revanth, Katuri Bits Pilani Dept. of Computer Science & Information Systems Hyderabad Campus India

ISBN: (纸本)9798331509736

The prominence of the Internet of Things (IoT) has surged in recent years due to tech-nological advancements, increased connectivity, and the widespread adoption of smart devices. Currently, IoT solutions are pervasive across various sectors, including smart homes, wearable devices, health-care, transportation, and industrial automation. The Long Range Wide Area Network (LoRaWAN) is a wireless communication protocol designed to provide reliable connection over long distances, spanning many kilometers, using low-power and wide-area networks (LPWANs) often used in IoT applications. However, LoRaWAN is vulnerable to bit-flipping attacks, where transmitted data is intercepted and maliciously altered by flipping specific bits in the message payload. Such attacks have the potential to undermine the trustworthiness and dependability of the system. In this paper, we propose a novel security enhancement for LoRaWAN. Our scheme incorporates a hash digest along with the end device's payload, enabling the application server to detect any malicious alterations in the received content using the hash value. We analyze the security of the proposed scheme against bit-flipping attacks and measure its performance through experiments in a real testbed. Our observations indicate that the proposed mecha-nism not only secures communication against these attacks but also incurs minimal overhead in terms of power consumption, transmission overhead, and latency. © 2024 IEEE.

关键词： Network security

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Cyberbullying Detection and Severity Classification Using Bi-LSTM 9

Cyberbullying Detection and Severity Classification Using Bi...

引用

9th International Conference on Communication and Electronics systems, ICCES 2024

作者： Devi, K. Nirmala Rajasekar, Vani Jayanthi, P. Balasubramani, Kavin Kandasamy, Kaviya Gowrisankar, Keerthana Kongu Engineering College Dept. of Computer Science Erode India

ISBN: (纸本)9798350377972

Cyberbullying has grown to be a significant issue in the internet age, affecting individuals of all ages, particularly teens. It involves using internet channels to harass, threaten, or disparage people, often with lingering emotional and psychological repercussions. In social media platforms cyber bullying is becoming more and more common, which has raised the need for efficient tools to identify and remove harmful content. Based on the frequency of objectionable words, a system was created for this research to categorize tweets into four groups: neither, low, medium, or high. A Kaggle dataset was first cleaned and prepared by pre-processing with Keras and Natural Language Toolkit (NLTK). The offending severity level of the tweets was determined using a fuzzy logic with a triangular membership function. Several models were trained using that categorized datasets like Bidirectional Long Short-Term Memory (Bi-LSTM), Bidirectional Encoder Representations from Transformers (BERT-Base) and DistilBERT. Among those models Bi-LSTM showed a high degree of accuracy with a precision score of 95.52%, recall of 95.48%, accuracy score of95.48%, and F1 score of 95.49%. © 2024 IEEE.

关键词： Deep learning

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Spatial Data Discovery Using Small Language Model 5

Spatial Data Discovery Using Small Language Model

引用

5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Thakur, Sujit Kumar Tyagi, Neha Amity University Dept. of Computer Science & Engineering Noida India

ISBN: (纸本)9798350383522

The ubiquitous use of data in a plethora of use cases and different organizational dependence on it for various insight has made data a valuable resource across business and scientific domains. Data has attained much more significance than oil itself, unlike the British mathematician Clive Humby's quote in 2006 'data is the new oil'. Every business and processes generate data and with the advent of AI and machine learning, all types of data have valuable use case attached. This also ensued a series of initiatives to clean, augment and provide data as data-products. This has given rise to businesses and solutions using inhouse data or data purchased from a dedicated data provider. Data consumers often face the challenge of inefficient data product discovery. More frequently than not they require active support from data product experts to point them to right dataset. The semantic meaning of the attributes and keywords of a data product is also heavily domain oriented. Data providers find it easy to update and augment metadata of a data set with details more feasible solution than modifying the dataset itself with attribute rename or adding composite attribute. The expected next step is that the metadata be self-explanatory enough to help in data cataloguing, but often we find consuming data scientist looking for help from data experts to make sense of the data in the context of the consumer's use case. This research seeks to analyse if the role of a data expert can be reduced and a finetuned small language model can help a data consuming data scientist in discovering the right data product, based on the metadata of the data product. Overall, this research contributes to the advancement of data discovery techniques and highlights the potential of leveraging language models to streamline and democratize access to valuable data resources, thereby empowering organizations to make more informed and data-driven decisions. © 2024 IEEE.

关键词： data discovery finetuning llama2 small llm spatial data

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An LPWAN-Based Framework for Food Supply Chain Management Using Blockchain and Federated Multi-Task Learning 8

An LPWAN-Based Framework for Food Supply Chain Management Us...

引用

8th International Conference on Smart Cities, Internet of Things and Applications, SCIoT 2024

作者： Navidimehr, Mehrnoosh Khalilabad, Ali Mojahed Amintoosi, Haleh Allahbakhsh, Mohammad Ferdowsi University of Mashhad Dept. Computer Engineering Mashhad Iran

ISBN: (纸本)9798350374674

The escalating global population has amplified concerns regarding the provision of high-quality food. In response to this issue, various food supply chain monitoring systems have been developed. This paper introduces an innovative approach that combines blockchain technology with a multi-trust package-based model to enhance the reliability and efficiency of supply chain interactions. Our proposed framework includes a Low-Power Wide-Area Network (LPWAN) IoT setup beside methodologies to enhance sensor power consumption. It employs Hyperledger Fabric for the secure and immutable recording of data. Furthermore, the framework utilizes a federated multi-task learning approach for anomaly detection and the prediction of time-series sensory data, thereby fostering robust trust relationships among supply chain participants. © 2024 IEEE.

关键词： Blockchain

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Real- Time Hand Gesture Recognition for Home Automation: A YOLOv8-Based Approach with Identity Verification and Low-Resource Hardware Implementation 21

Real- Time Hand Gesture Recognition for Home Automation: A Y...

引用

21st International Multi-Conference on systems, Signals and Devices, SSD 2024

作者： Hussein, Shayda Khalid Ahmed, Abdullah Salam Kul, Zeynep Ashir, Abubakar M. Tishk International University Dept. of Computer Engineering Erbil Iraq

ISBN: (纸本)9798350374131

The article introduces an approach for real-time sign language detection tailored for home automation applications. The proposed model utilizes the YOLOv8 (You Only Look Once) architecture for efficient and accurate sign detection while incorporating face recognition for identity verification. The fusion of sign language detection and identity verification enhances the system's usability in secure and personalized home automation environments. To address the challenge of deploying this system on low-resource hardware such as the Raspberry Pi, a unique approach is taken. The model leverages a combination of low-resolution and high-resolution images, optimizing the trade-off between accuracy and computational efficiency. This design choice ensures the feasibility of real-time sign language detection on resource-constrained devices commonly found in home automation setups. Furthermore, the system implements a frame skipping technique to reduce the frame rate, maintaining real-time responsiveness while further alleviating computational demands. This feature enhances the adaptability of the proposed model to diverse hardware configurations, making it suitable for a broad range of applications within the context of home automation. Impressive and consistent results were obtained at both experimental and implementation stages. It shows that this systems precision accuracy goes up to 97%. © 2024 IEEE.

关键词： Object detection

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Enhancing Doodling Experience Using Generative Adversarial Networks 2

Enhancing Doodling Experience Using Generative Adversarial N...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Ashtagi, Rashmi Rathi, Devanshu Handoo, Ripul Purohit, Rishi Madhavaswala, Abbas Nogamawala, Mohammad Vishwakarma Institute of Technology Dept. Of Computer Engineering Pune India

ISBN: (纸本)9798350388916

Interacting with AI systems solely through textual prompts often encounters limitations due to the challenges of expressing complex visual ideas in text. This paper introduces an advanced AI system that interprets and visualizes users' sketches by employing state-of-the-art Generative Adversarial Networks (GANs), such as pix2pix, SketchGAN, DCGAN, and ESRGAN. This system not only significantly enhances real-time interactions but also shows exceptional proficiency in image editing tasks, thus facilitating more intuitive communication between humans and AI. By enabling rapid and accurate visualizations, this technology substantially streamlines and improves design workflows in the architecture and fashion industries. The advancements fostered by this system contribute significantly towards achieving greater capabilities akin to Artificial General Intelligence, enhancing creative processes and production efficiencies. © 2024 IEEE.

关键词： Generative adversarial networks

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