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Publishing Bangladesh Socio-economic Data as Linked and Multidimensional Data

Publishing Bangladesh Socio-economic Data as Linked and Mult...

引用

International Conference on computer and information Technology (ICCIT)

作者： Rudra Pratap Deb Nath Amrit Bhattacharjee Dept. of Computer Science and Engineering Big Data Information and Knowledge Engineering (BIKE) Lab University of Chittagong Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Inspired by the vision of achieving a ‘Smart Bangladesh,’ the Bangladesh Bureau of Statistics consistently endeavors to manage and disseminate socio-economic data on the web. However, the existing datasets face several challenges: 1) they are presented in an unsustainable, read-only, static, and aggregated format, 2) they lack adherence to the Findability, Accessibility, Interoperability, and Reusability (FAIR) principles, and 3) they are not conducive to the interactive analysis and integration with other data sources. In this paper, we propose a comprehensive solution by outlining a step-by-step process for constructing a knowledge graph specifically tailored to the Bangladesh census data. The resulting knowledge graph is presented using the Resource Description Framework (RDF) model, enriched with multidimensional semantics, facilitating Online Analytical Processing (OLAP)-like analysis and aligning with the FAIR and linked data principles. It has a size of 6.31 GB, is linked to 772 external resources, and contains approximately 39 million RDF triples. Our evaluation focuses on assessing both the integration process and the quality of the generated knowledge graph in terms of FAIR principles and OLAP operability.

关键词： Publishing Soft sensors Linked data Semantics Knowledge graphs Resource description framework Socioeconomics information technology Interoperability Faces

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Max-Min Fair 3D Trajectory Planning for Solar-Powered UAV-Assisted Data Collection

Max-Min Fair 3D Trajectory Planning for Solar-Powered UAV-As...

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2022 IEEE/CIC International Conference on Communications in China, ICCC 2022

作者： Sun, Chao Xiong, Xinxuan Ni, Wei Ohtsuki, Tomoaki Wang, Xin Fudan University Dept. of Communication Science and Engineering Shanghai China Data61 Business Unit Sydney Australia Keio University Dept. of Information and Computer Science Yokohama Japan

ISBN: (数字)9781665484800

ISBN: (纸本)9781665484800

This paper presents a new solar-powered fixed-wing unmanned aerial vehicle (UAV)-assisted data collection technique, where a fixed-wing UAV harvests solar energy to fly and collect data from smart devices (SDs). The minimum of the data upload-ed from any of the SDs is maximized by optimizing the UAV's three-dimensional (3D) trajectory. The key idea is that we develop variable substitution and successive convex approximation (SCA) techniques to convexify the nonconvex data transmission, UAV energy consumption and mobility constraints, as well as the nonconvex energy harvesting constraint. The proposed algorithm guarantees a locally optimal solution satisfying the Karush-Kuhn-Tucker (KKT) conditions. Another important aspect is that an effective set of approximation parameters are identified for accurate approximation of the solar energy harvesting at relatively low altitudes, contributing to the design of efficient 3D trajectories balancing energy harvesting and data collection. Numerical results show that the 3D flight increases uploaded data by 59% as compared to the two-dimensional (2D) flight. © 2022 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Detection of Brain Tumor Using a Deep Learning Approach

Detection of Brain Tumor Using a Deep Learning Approach

引用

Artificial Intelligence and Data engineering (AIDE), International Conference on

作者： Arun Kumar S Anand Kumar M Robin Rohit Vincent Anusha M S Uttam Upendra Hegde N Nauman Pasha Dept. of Computer Studies Symbiosis Institute of Computer Studies and Research (SICSR) Pune India Dept. of Information Science and Engineering Networks & IoT Lab Presidency University Bangaluru India Dept. of Computer Science and Engineering Bio-Intelligence Lab Presidency University Bangaluru India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

This study introduces a deep learning-based method for classifying brain tumors using a pre-trained VGG19 convolutional neural network (CNN). By leveraging transfer learning, we adapted the VGG19 model with custom fully connected layers and trained it on MRI images to differentiate between tumor and non-tumor cases. The model’s architecture includes dropout layers to minimize overfitting and enhance generalization while maintaining the robust feature extraction capabilities of the VGG19 base. We developed a Flask web application interface to make the model accessible via web-based user input, allowing users to upload MRI images and receive immediate predictions on tumor presence. Preliminary results show that proposed model can accurately classify brain tumor images with high accuracy, highlighting the potential of deep learning in automating brain tumor diagnostics. This tool could significantly aid clinicians in making rapid and accurate diagnoses, ultimately improving patient outcomes.

关键词： Deep learning Adaptation models Accuracy Magnetic resonance imaging Transfer learning Brain tumors Predictive models Brain modeling Convolutional neural networks Reliability

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LiberationKG: A Knowledge Graph of Bangladesh Liberation War from Newspaper Articles

LiberationKG: A Knowledge Graph of Bangladesh Liberation War...

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International Conference on computer and information Technology (ICCIT)

作者： Mohammad Fahim Foisal Rudra Pratap Deb Nath Nasrin Sultana Dept. of Computer Science and Engineering Big Data Information and Knowledge Engineering (BIKE) Lab University of Chittagong Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

The Bangladesh Liberation War of 1971 remains one of the most significant and emotional events in the country’s history. In today’s digital world, historical narratives are at risk of being distorted by disinformation and the spread of false news. Newspaper articles from unbiased sources provide a reliable overview of the situation. However, while unstructured texts are easily understandable for humans, they are not readily interpretable by machines. To address this, we present Libera-tionKG, a knowledge graph of the Bangladesh Liberation War, generated from reputable sources such as Time Magazine and The New York Times. LiberationKG is created by producing a total of 12,291 Resource Description Framework (RDF) triples from the mentioned sources using a Large Language Model and constructs from RDFS, OWL, and *** vocabularies. Its development involves advanced natural language processing techniques, RDF triple validation and optimization, and linking to external resources. LiberationKG provides an organized representation of knowledge and an efficient system for information extraction. With 80% accuracy, LiberationKG is linked to 521 external resources and adheres to FAIR principles, ensuring interoperability and data accessibility, making it a valuable digital resource for preserving this pivotal historical event.

关键词： Vocabulary Large language models Pipelines OWL Knowledge graphs Resource description framework Natural language processing Reliability Optimization Interoperability

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Integrating Random Forest and Support Vector Regression Models for Optimized Energy Consumption Evaluation in Cloud Computing Data Centers 3

Integrating Random Forest and Support Vector Regression Mode...

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3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Shaik, Nazeer Kumar, B. Vinoth Bhati, Anita Kumar, Sandeep Singh, Jagendra Sahu, Dinesh Prasad SRIT-Autonomous Department of Computer Science Engineering Anantapur India Ksr Institute for Engineering and Technology Dept. of Electronics and Communication Engineering Tiruchengode India Jecrc University Department of Computer Applications Jaipur India Raj Kumar Goel Institute of Technology Department of Information Technology Ghaziabad India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798350342338

This study proposes a novel approach for energy consumption evaluation in cloud computing data centers. The immense energy consumption of these data centers presents significant environmental and economic challenges. Traditional evaluation methods often fail to provide accurate estimations due to the complex dynamics of cloud computing environments. The research introduces an integrated model, combining Random Forest Regression and Support Vector Regression, along with a Gradient Boosting Regressor for iterative training of regression parameters. We measure the model's performance using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE), replacing traditional relative deviation measures. The proposed approach aims to provide more accurate and efficient energy consumption evaluations, offering potential benefits in energy management, operational costs reduction, and minimizing environmental impact. © 2023 IEEE.

关键词： Cloud Computing Energy Consumption Evaluation Gradient Boosting Regressor Random Forest Regression Support Vector Regression

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Device Identification using Network Traces in a Smart Home IoT Network

Device Identification using Network Traces in a Smart Home I...

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International Symposium on Advanced Networks and Telecommunication Systems (ANTS)

作者： Jwngfu Brahma Rakesh Matam Nikumani Choudhury Dept. of Computer Science & Engineering Indian Institute of Information Technology Guwahati India Dept. of Computer Science & Information Systems Birla Institute of Technology & Science Hyderabad India

Device identification is a crucial aspect of securing networks, particularly in the context of the Internet of Things (IoT), where a vast variety of devices are interconnected. Recently, there has been significant research on developing techniques for identifying IoT devices based on their unique characteristics, called as device fingerprints. These techniques use machine learning algorithms, which can effectively learn and classify devices based on their features. However, device identification remains a challenging task due to the diversity of IoT devices and the constant evolution of their characteristics. This paper presents our attempt at device identification by analyzing distinct device characteristics observed during network communication and using different machine learning techniques.

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Ultra-Wideband Pattern Reconfigurable Antenna for Base station Applications

Ultra-Wideband Pattern Reconfigurable Antenna for Base stati...

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India Council International Subsections Conference (INDISCON), IEEE

作者： Shalini Bhickta Naveen Jaglan Amit Kumar Dept. of Electronics and Communication Engineering Jaypee University of Information Technology Waknaghat Solan India Dept. of Computer Science & Engineering and Information Technology Jaypee University of Information Technology Waknaghat Solan India

ISBN: (数字)9798350376753

ISBN: (纸本)9798350376760

The proposed work aims at 5G base station antenna communication applications. The Reconfigurable Antenna is built with an idea of parasitic loaded elements in antenna. The proposed design has a zig-zag S-shape structure with a driven Monopole antenna located along the central axis and a parasitic structure enfolding around the monopole. Reconfiguration is accomplished by adjusting the PIN diodes bias voltages. The 2.44-9.46 GHz frequency range i.e., the Ultra-Wide-Band frequency spectrum is used at which this reconfigurable antenna is designed to operate. This paper has three radiation pattern which are obtained by two PIN diodes. Beam-steering capability in the elevation plane is achieved i.e., θ (0°-180°). The outcome shows the design’s potential and its wide range concurrent applications for base station antennas.

关键词： Base stations PIN photodiodes 5G mobile communication Ultra wideband antennas Loaded antennas Antenna radiation patterns

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An Improved YOLOv5 Detection Method for Strip Surface Defect 28

An Improved YOLOv5 Detection Method for Strip Surface Defect

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28th International Conference on Automation and Computing, ICAC 2023

作者： Liu, Yefeng Yu, Longfei Zhang, Qichun School of Automation and Electrical Dept. Liaoning Key Laboratory of Information Physical Fusion and Intelligent Cnc Machine Tool Manufacturing Shenyang Institute of Technology Dept. Fushun China Shenyang Ligong University Dept. School of Automation and Electrica Engineering China University of Bradford Bradford Dept. Department of Computer Science United Kingdom

ISBN: (纸本)9798350335859

Traditional steel surface defect detection methods have issues such as low detection accuracy and efficiency. It is suggested a technique based on enhanced YOLOv5 steel surface fault identification. In order to improve the network's capacity to handle feature information, this study suggests adding SE attention mechanisms to the YOLOv5s network's Backbone, Neck, and SPPF and replacing the C3 structure at the end of the Backbone with C3TR. To achieve network model lightweight, it is proposed to replace the Neck part in the YOLOv5s model and the convolution in SPPF with Ghost convolution. Then, to improve network feature fusion, extend the improved SPPF to the Neck part. Introduce the SPD convolution in the Backbone section to reduce the information loss caused by the neural network's convolution. The tested mAP is 75.3%, which is 5.3% higher than the original model, 24.6% higher than the SSD method, and 4.6% higher than the YOLOv6 algorithm after the upgraded model has been trained on the NEU-DET dataset. The observed FPS is 81.3, which is 6.3 more than the relative literature algorithm and 18.0 more than the SSD algorithm. © 2023 IEEE.

关键词： Convolution

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Enhancing Hyperledger Fabric Security with Lightweight Post-Quantum Cryptography and National Cryptographic Algorithms

Enhancing Hyperledger Fabric Security with Lightweight Post-...

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Open Innovations Association (FRUCT), Conference of

作者： Vinay Kumar Kasula Sarath Babu Rakki Rajkumar Banoth Dept. Information Technology University of the Cumberlands Williamsburg KY USA Dept. of Computer Science and Engineering JNTUH University College of Engineering Hyderabad TG India Dept. of Computer Science University of Texas at San Antonio San Antonio TX USA

ISBN: (数字)9789526524634

ISBN: (纸本)9798331595425

Hyperledger Fabric is a scalable and modular consortium blockchain platform designed for enterprise applications, where cryptographic algorithms play a fundamental role in ensuring data security and integrity. However, the native Fabric framework lacks support for national cryptographic standards, necessitating the integration of secure cryptographic mechanisms. This study proposes an enhanced approach to embedding national cryptographic algorithms into the Fabric platform. First, Graph-based Dependency Analysis is employed to investigate the interaction logic among Fabric components and the invocation of cryptographic functions, facilitating an efficient integration strategy. Next, the Lightweight Post-Quantum Cryptography (L-PQC) framework, which enhances resistance to quantum threats while maintaining computational efficiency, is utilized to integrate SM2, SM3, and SM4 cryptographic algorithms into Fabric’s Blockchain Cryptographic Service Provider (BCCSP) module. Subsequently, a Microservices-Based Cryptography Integration mechanism is designed to establish seamless mapping between Fabric’s cryptographic function calls and the national cryptographic algorithm interfaces, ensuring compatibility and interoperability. Finally, the implementation is evaluated using Blockchain Simulation Environments, where a fabric-gm consortium blockchain instance is deployed to validate the correctness and efficiency of the embedded cryptographic modules. Comparative analysis with the original Fabric platform reveals that the enhanced system introduces a 2.5% increase in network startup time, a 1.8× rise in transaction latency, and a 7.5% increase in dynamic certificate generation time, while maintaining operational performance within acceptable limits. The proposed integration strategy ensures secure and efficient cryptographic support within Hyperledger Fabric, making it resilient to emerging cryptographic threats.

关键词： Resistance Technological innovation Quantum computing Distributed ledger Heuristic algorithms System performance Fabrics Blockchains Cryptography Standards

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DCP: a TCP-Inspired Method for Online Domain Adaptation under Dynamic Data Drift

DCP: a TCP-Inspired Method for Online Domain Adaptation unde...

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IEEE International Symposium on World of Wireless Mobile and Multimedia Networks (WoWMoM)

作者： Alessandro Buratto Marco Levorato Leonardo Badia Dept. of Information Engineering (DEI) University of Padova Italy Donald Bren School of Information and Computer Science Irvine California

ISBN: (数字)9798350394665

ISBN: (纸本)9798350394672

Mobile computing faces challenges due to the resource constraints of mobile devices, such as limited computing power, energy, and connectivity. These limitations hinder the use of high-complexity classifiers and wireless transmissions. To address this issue, we propose a novel collaboration paradigm between mobile devices and edge servers, where the edge server assists the mobile devices by dynamically retraining a low-complexity classifier to adapt to temporal changes in data distribution. We propose a novel approach called drift control protocol (DCP) which is inspired by TCP congestion control mechanism. DCP aims to strike a balance between low-complexity classifier retraining frequency and communication costs with the edge server. It adjusts the update rate of the classifier on the mobile device based on distribution drift characteristics and controls the number of input samples sent to the edge server to improve accuracy. We evaluate and study different versions of DCP using synthetic and real datasets We demonstrate that DCP keeps the error bound, while reducing the burden of the communication cost by 90% for the mobile nodes, which makes our proposal suitable for online domain adaptation.

关键词： Wireless communication Costs Protocols Multimedia computing Mobile handsets Mobile nodes Servers

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