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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Daghan Dogan Tankut Acarman Mechanical Systems and PCB Design Tubitak Bilgem Kocaeli Turkiye Computer Engineering Dept. Galatasaray University Istanbul Turkiye

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

Face detection is necessary for many different applications: face recognition, image database management, etc. In this study, a face detection algorithm is performed on images based on human skin color, and the detected face image is classified based on gender. In this algorithm, three color spaces (RGB, HSV, YCbCr) are used and face detection is performed by combining different thresholds in these three spaces. Outputs of the algorithm are used as feature vectors for the gender classification. The genders in the images are classified with 100% accuracy using the different classification methods.

关键词： Accuracy Image color analysis Face recognition Nose Mouth Machine learning Skin Vectors Classification algorithms Face detection

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SCHC over DtS-IoT: Performance of SCHC Confirmation Modes in Satellite IoT

SCHC over DtS-IoT: Performance of SCHC Confirmation Modes in...

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IEEE World Forum on Internet of Things (WF-IoT)

作者： Rodrigo Munoz-Lara Sandra Céspedes Marcos Diaz Dept.of Electrical Engineering University of Chile Santiago Chile Dept. of Computer Science & Software Eng. Concordia University Montreal QC Canada

ISBN: (数字)9798350373011

ISBN: (纸本)9798350373028

Static Context Header Compression and Fragmentation (SCHC) is a standard defined as an adaptation layer for supporting IPv6, UDP, and CoAP protocols in low power wide area network (LPWAN) technologies. SCHC has fragmentation modes that use confirmation mechanisms to deliver reliable communications. Direct-to-satellite Internet of Things (DtS-IoT) directly communicates between end devices and the satellite in an IoT environment. If SCHC is implemented in a DtSIoT environment, applications using IPv6 could have global coverage through the Internet of Things (IoT). Unfortunately, it is not known whether SCHC can operate with the current acknowledgement mechanisms in a DtS-IoT environment, and if so, it is not known which one performs better. This work evaluates SCHC acknowledgement modes for ACK-on-Error fragmentation mode, including the new SCHC Compound ACK mode. It determines which mode has the lowest transfer delay for different LoRa data rates and frame error rates in a DtS-IoT environment with LEO satellites.

关键词： Satellites Protocols Low earth orbit satellites Delays Internet of Things Compounds Reliability IP networks Standards Low-power wide area networks

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Machine Learning Approaches To Predict The Stability of Smart Grid

Research Square

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Research Square 2024年

作者： Satu, Shahriare Khan, Imran Dept. of Management Information Systems Noakhali Science and Technology University Noakhali3814 Bangladesh Dept. of Computer Science and Engineering Gono Bishwabidyalay Dhaka1344 Bangladesh

The smart grid is a modern solution to generate, distribute, and use energy effectively and efficiently. Ensuring the stability of the smart grid is critical to guarantee safe and consistent operation. This study proposes a machine learning model to identify smart grid stability more efficiently. To accomplish this, we collected the electrical grid stability dataset from a public machine learning repository and analyzed the correlation of individual features. Different feature selection and transformation techniques were employed to generate subsequent feature sets. Then, various classification techniques were applied to the primary and its subsets to identify the best machine-learning model for predicting smart grid stability more efficiently. The results indicated that MLP showed the highest accuracy (97.06%) for the Z-score transformed data set using Grid Search Optimization. This study demonstrates the potential of using machine learning to predict smart grid stability accurately and efficiently, providing a critical tool for improving the reliability, efficiency, and sustainability of the energy industry. © 2024, CC BY.

关键词： Smart power grids

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Inflation Prediction: A Comparative Study of ARIMA and LSTM Models Across Different Temporal Resolutions

Inflation Prediction: A Comparative Study of ARIMA and LSTM ...

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International Conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： T Lakshmi Narayanaa R R Skandarsini S. Jhansi Ida S. Rathana Sabapathy P Nanthitha Dept. of Computer Science and Business Systems RMK Engineering College Thiruvallur India Dept. of Information Technology Panimalar Engineering College Chennai India

This research study focuses on the prediction of inflation rates using two distinct models, Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM), tailored to accommodate the temporal resolutions of monthly and quarterly inflation data. While a comprehensive evaluation across varying time horizons was not conducted, the results suggest that the ARIMA model excels in short-term forecasting, as evidenced by its impressive performance on a 2021 holdout dataset. Conversely, the LSTM model displays potential for medium-term predictions. This study underscores the significance of aligning forecasting models with data characteristics, emphasizes the importance of temporal resolution, and discusses potential avenues for improvement, such as multivariate modeling or alternative techniques like Prophet. The findings have broad implications for economists, policymakers, and analysts seeking precise inflation forecasts, shedding light on the nuanced interplay between models and data in the domain of inflation prediction.

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Infrared and terahertz spectrally adaptive filters based on MEMS technologies

Infrared and terahertz spectrally adaptive filters based on ...

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Smart Photonic and Optoelectronic Integrated Circuits 2023

作者： Martyniuk, Mariusz Silva, K.K.M.B. Dilusha Kala, Hemendra Zawierta, Michal Wallace, Vincent P. Walus, Konrad Faraone, Lorenzo Shadrivov, Ilya V. ARC Centre for Transformative Meta-Optical Systems Dept. of Electrical Electronic & Computer Engineering The University of Western Australia 35 Stirling Highway PerthWA6009 Australia Dept. of Physics The University of Western Australia 35 Stirling Highway PerthWA6009 Australia Electrical and Computer Engineering The University of British Columbia Vancouver Canada Nonlinear Physics Centre Research School of Physics and Engineering Australian National University CanberraACT2601 Australia

ISBN: (纸本)9781510659551

Future remote imaging systems promise spectroscopic functionalities extending well beyond the visible wavelengths. This allows real-time spectral information to be gathered from multiple wavelength bands which is highly attractive for numerous remote sensing spectroscopy/imaging applications and aids target recognition. This paper briefly presents a micro-electromechanical systems (MEMS) based electrically tuneable adaptive filter technology developed for the technologically important infrared (IR) bands of the electromagnetic spectrum and reports on the progress towards extension to the significantly longer wavelength THz band. The demonstrated concepts focus on merging MEMS-enabled dynamic modulation with the spectral sensitivity and selectivity of metamaterials, as well as on the possibility of adopting the rapidly evolving 3D printing technologies. © 2023 SPIE.

关键词： MEMS

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An Exploratory Study of ML Sketches and Visual Code Assistants

arXiv

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arXiv 2024年

作者： Gomes, Luís F. Hellendoorn, Vincent J. Aldrich, Jonathan Abreu, Rui Software and Societal Systems Dept. Carnegie Mellon University Pittsburgh United States INESC-ID Faculty of Engineering University of Porto Porto Portugal

This paper explores the integration of Visual Code Assistants in Integrated Development Environments (IDEs). In software engineering, whiteboard sketching is often the initial step before coding, serving as a crucial collaboration tool for developers. Previous studies have investigated patterns in SE sketches and how they are used in practice, yet methods for directly using these sketches for code generation remain limited. The emergence of visually-equipped large language models presents an opportunity to bridge this gap, which is the focus of our research. In this paper, we built a first prototype of a Visual Code Assistant to get user feedback regarding in-IDE sketch-to-code tools. We conduct an experiment with 19 data scientists, most of whom regularly sketch as part of their job. We investigate developers' mental models by analyzing patterns commonly observed in their sketches when developing an ML workflow. Analysis indicates that diagrams were the preferred organizational component (52.6%), often accompanied by lists (42.1%) and numbered points (36.8%). Our tool converts their sketches into a Python notebook by querying an LLM. We use an LLM-as-judge setup to score the quality of the generated code, finding that even brief sketching can effectively generate useful code outlines. We also find a positive correlation between sketch time and the quality of the generated code. We conclude the study by conducting extensive interviews to assess the tool's usefulness, explore potential use cases, and understand developers' needs. As noted by participants, promising applications for these assistants include education, prototyping, and collaborative settings. Our findings signal promise for the next generation of Code Assistants to integrate visual information, both to improve code generation and to better leverage developers' existing sketching practices. Copyright © 2024, The Authors. All rights reserved.

关键词： Codes (symbols)

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A Study of the Ecological Dynamics of Batumi, on the Black Sea Coast, Analyzing Environmental Factors and Their Impact

A Study of the Ecological Dynamics of Batumi, on the Black S...

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International Black Sea Conference on Communications and Networking (BlackSeaCom)

作者： Lily Petriashvili Irine Khomeriki Mzia Kiknadze Tamar Lominadze Nona Otkhozoria Dept. of Program Engineering Georgian Technical University Tbilisi Georgia Dept. of Interdisciplinary Informatics Georgian Technical University Tbilisi Georgia Dept. of Computer Engineering Georgian Technical University Tbilisi Georgia Dept. of Information Systems Georgian Technical University Tbilisi Georgia Dept. of Microprocessor and Measurement Systems Georgian Technical University Tbilisi Georgia

ISBN: (数字)9798350351859

ISBN: (纸本)9798350351866

This paper studies the complex ecological dynamics of Batumi on the Black Sea coast, through adetailed analysis of environmental factors and their impact. We use Business Intelligence (BI) methods to explore the complex interactions between natural and human impacts on the local ecosystems. By using data-focused approaches, including statistical analysis and predictive modeling, we aim to distinguish patterns and trends in ecological dynamics. This study aspires to make clear the complex relationships between environmental factors such as climate change, pollution levels, habitat degradation, and biodiversity loss, and their impacts on the coastal ecosystem of Batumi. Through this study, we aim to provide valuable information on sustainable management practices and conservation measures to protect the ecological integrity of this important coastal region.

关键词： Sea coast Pollution Statistical analysis Ecosystems Habitats Sea measurements Predictive models

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SWIN and Vision Transformer-Driven Crack Detection in the Al Qattara Oasis, UAE: Towards Sustainable Infrastructure Management

SWIN and Vision Transformer-Driven Crack Detection in the Al...

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IEEE/ACM International Symposium on Big Data Computing (BDC)

作者： Luqman Ali Medha Mohan Ambali Parambil Muhammed Swavaf Fady Alnajjar Hamad Aljassmi Adriaan De Man Dept. Computer Science and Software Engineering Emirates Centre for Mobility Research United Arab Emirates University Al Ain UAE Dept. Computer Science and Software Engineering United Arab Emirates University Al Ain UAE Dept. Civil and Enviromental Engieering Engieering Emirates Centre for Mobility Research Al Ain UAE Dept. Tourism and Heritage United Arab Emirates University Al Ain UAE

ISBN: (数字)9798350367300

ISBN: (纸本)9798350367317

Heritage sites are central to the cultural identity and historical narrative of a community. The Al Qattara Oasis, located in the United Arab Emirates (UAE), illustrates this function well. This study addresses the pressing need to preserve and maintain built heritage by employing modern technological solutions for infrastructure upkeep. Specifically, it focuses on crack detection, a critical aspect of ensuring the structural integrity of heritage buildings. Utilizing data collected from various structures across the UAE, obtained through handheld cameras, the effectiveness of Vision Transformers (ViTs) and Swin Transformers in identifying cracks within Al Qattara is assessed. The research thoroughly evaluates models, with particular attention to different input patch sizes. Through systematic experimentation and analysis over 100 epochs, ViT models with a patch size 16 exhibit significant promise in crack detection. Notably, the ViT-16 model achieves good performance metrics, including training and validation accuracies of 85% and (82%) respectively, and correctly classifies 791 out of 957 cracks and 772 out of 903 non-cracks. In contrast, Swin Transformers show lower validation accuracies (70-74%) and higher misclassification rates. The outcomes underscore the potential of ViT models in enhancing infrastructure maintenance efforts within heritage sites like Qattara Oasis. By combining advanced technology with a profound respect for historical preservation, this study aims to contribute to the sustainable conservation and protection of cultural heritage, ensuring that future generations can continue to appreciate the enduring legacy of sites such as Qattara Oasis.

关键词： Training Measurement computer vision Accuracy Systematics Pressing Transformers Maintenance Cultural differences Protection

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Intrusion Detection System (IDS) of Multiclassification IoT by using Pipelining and an Efficient Machine Learning

Intrusion Detection System (IDS) of Multiclassification IoT ...

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International Conference on engineering and Emerging Technologies (ICEET)

作者： Layth Rafea Hazim Abdulrahman Ahmed Jasim Oguz Ata Muhammad Ilyas College of Computer Science and Mathematics Tikrit University Tikrit Iraq Dept. of Electrical and Computer Engineering Altinbas University Istanbul Turkey College of Engineering Al-Iraqia University Baghdad Iraq Dept. of Software Engineering Altinbas University Istanbul Turkey Dept. of Computer Engineering Altinbas University Istanbul Turkey

ISBN: (数字)9798350316926

ISBN: (纸本)9798350316933

The Internet of Things (IoT) has quickly advanced and been incorporated into many different fields. With the use of IoT technology, gadgets can receive, process, and send data automatically. IoT has been rapidly accepted in many important fields since it makes life easier and increases service quality, yet it still faces significant privacy and security problems. An Intrusion Detection System (IDS) could be implemented as a security feature to protect IoT networks from a variety of cyberattacks. This study suggests using IDS to defend against a wide range of cyberattacks on IoT systems. The suggested approach makes use of the Multi-layer Perceptron (MLP) as well as Extra Trees (ExT) as efficient algorithms of classification. Also, the study uses the pipeline to put together several cross-validated phases while selecting various parameters to increase the detection rate. One dataset is utilized for evaluating and analyzing the performance outcomes so as to validate the efficiency of the suggested IDS approach. The evaluation findings show that the suggested IDS methods may greatly increase detection performance results concerning accuracy rate, precision, F1-score, and recall while also improving detection efficiency.

关键词： Measurement Privacy Correlation Redundancy Intrusion detection Machine learning Security

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LoRaWAN Scheduling Mechanism for 6G-Based LEO Satellite Communications

LoRaWAN Scheduling Mechanism for 6G-Based LEO Satellite Comm...

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IEEE Conference on Vehicular Technology (VTC)

作者： Abhijeet Manoj Varma Nikumani Choudhury Jay Dave Anakhi Hazarika Moustafa M. Nasralla Dept. of Computer Science & Information Systems Birla Institute of Technology & Science Pilani Hyderabad India Dept. of Electrical & Electronics Engineering Birla Institute of Technology & Science Pilani Hyderabad India Dept. of Communications & Networks Engineering Smart Systems Engineering Lab Prince Sultan University Riyadh Saudi Arabia

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

As the Internet of Things (IoT) is poised to become a global phenomenon, it is imperative to schedule the transmissions of IoT devices effectively and in a fair way. Leveraging Long Range (LoRa) technology, we can achieve transmissions that consume minimal power while covering vast distances, aligning with the requirements of IoT devices. However, the proximity of multiple devices within the same area often leads to packet interference and collisions. To address this, our study introduces a pioneering scheduling method utilizing a constellation of Low Earth Orbit (LEO) satellites to manage and streamline the transmission of data from End Devices (EDs). This method employs two LEO satellites: the first satellite assigns the sequence for EDs to dispatch their packets, and the second collects these packets in the predetermined sequence before forwarding them to the LoRa Network Server (LNS). For urgent (URG) communications, EDs can alert the first satellite, which then coordinates with the LNS to schedule these priority transmissions. The LNS generates a schedule that is relayed to the second satellite, informing EDs with URG packets of their specific transmission times and channels. This scheduling approach is designed to optimize channel usage effectively while accommodating the transmission of urgent data.

关键词： 6G mobile communication Schedules Vehicular and wireless technologies Satellites Scheduling algorithms Low earth orbit satellites LoRaWAN

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