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Exploring Convolutional Neural Networks for Fingerprint Damage Detection and Classification

Exploring Convolutional Neural Networks for Fingerprint Dama...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Swathi, R. Kansal, Vipashi Shyamala Valsalan, Prajoona Vekariya, Vipul Taqui, Syed Noeman Prasad v Potluri Siddhartha Institute of Technology Department of Electrical and Electronics Engineering Andhra Pradesh Vijayawada521108 India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India Government Polytechnic for Women Department of Electronics and Communication Engineering Karnataka Ramannagara562159 India Dhofar University Department of Electrical and Computer Engineering Dhofar Salalah Oman Parul Institute of Engineering and Technology Parul University Department of Computer Science and Engineering Post Limda Gujarat Waghodia391760 India Saveetha School of Engineering Simts Saveetha University Department of Vlsi Microelectronics Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350375237

This study proposes a novel approach to fingerprint classification using Deep Convolutional Neural Networks (CNNs), leveraging datasets from prominent sources like the National Institute of Standards and Technology (NIST), Fingerprint Verification Competition (FVC), and the SHUFFLE database. The research involves a systematic pipeline, beginning with data collection and preprocessing to create a comprehensive dataset that represents real-world variations in fingerprint patterns. A tailored CNN architecture is designed to capture hierarchical features essential for accurate classification, trained on the amalgamated dataset using state-of-the-art optimization algorithms and loss functions. The model's performance is rigorously evaluated across diverse fingerprint patterns using separate datasets from NIST, FVC, and SHUFFLE, showcasing its superiority and adaptability compared to existing benchmark methods. Additionally, the study addresses privacy and ethical considerations associated with biometric data usage, ensuring the model's reliability and applicability in various fingerprint recognition applications. © 2024 IEEE.

关键词： Database systems

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Community Capacity Building: A Renewable Energy Workshop as an Investment in Sustainable Businesses in the Galapagos Islands 12

Community Capacity Building: A Renewable Energy Workshop as ...

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12th Annual IEEE Global Humanitarian Technology Conference, GHTC 2022

作者： Villavicencio, Viviana Urquizo, Javier Singh, Pritpal Lansdale, David Martin, Cesar Villanova University Department of Electrical and Computer Engineering VillanovaPA United States Beyond Chacay Foundation Founder and President Quito Ecuador ESPOL Polytechnic University Electrical and Computer Engineering Department Guayaquil Ecuador

ISBN: (数字)9781665450973

ISBN: (纸本)9781665450973

With renewable energy penetration rapidly increasing worldwide in response to climate change concerns, efforts must also consider teaching and educating communities about its principles and how they can take advantage of these resources. This paper aims to show how building local capacity in renewable energy and developing a renewable energy program can provide education and training to students and youth in the Galapagos Islands. The goal will be to train the youth, starting with basic electrical principles and culminating with how to design and install renewable energy systems on the islands and build productive enterprises using these resources. The program was designed to reach the youth in the Galapagos Archipelago. High school students and young people interested in renewable energy were the primary target recruitment for the program. Twenty Santa Cruz Island students participated in the program's first iteration. The design and implementation of workshops are described in this paper. Two surveys were conducted before and after the workshop to evaluate the knowledge improvement related to renewable energy and off-grid PV solar systems. The survey results show that the participants initially had a limited understanding of renewable energy technologies. Thus, educating the community about renewable energy and solar PV systems contributed to promoting environmental awareness and building capacity towards clean and sustainable electricity generation and solar energy resources in the Galapagos Islands. © 2022 IEEE.

关键词： Students

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Modular Multilevel Converters-Part I: Modeling Based on State-Plane Analysis 48

Modular Multilevel Converters-Part I: Modeling Based on Stat...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Hsieh, Yi-Hsun Lee, Fred C. Virginia Polytechnic Institute and State University The Bradley Department of Electrical and Computer Engineering BlacksburgVA United States

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

Modular multilevel converter is deemed the solution for high-voltage high-power applications. The introduction of capacitor to each arm leads to complicated power flow and thus complicated control scheme. Without a proper control, this often results in large circulating energy. The state plane analysis identified the two types of circulating energies, one related to the power difference between input and output, and the other related to the internal power swapping. Due to their orthogonal nature, a coordinate transformation is proposed, which leads to a decoupled equivalent circuit model. The proposed model suggests two control laws, aiming at eliminating both types of circulating energy. © 2022 IEEE.

关键词： Circuit simulation

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Modular Multilevel Converters-Part II: Control Based on Decoupled Equivalent Circuit Model 48

Modular Multilevel Converters-Part II: Control Based on Deco...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Hsieh, Yi-Hsun Lee, Fred C. Virginia Polytechnic Institute and State University The Bradley Department of Electrical and Computer Engineering BlacksburgVA United States

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

Since there are less control means than state variables to be controlled, the modular multilevel converter is an overly-constrained system. The minimum circulating energy was unknown, and the control implementation to address this challenge was vague. In Part I, two types of circulating energy with orthogonal nature are identified though state-space analysis: one related to instantaneous input and output power unbalance, and the other related to internal power swapping. The corresponding decoupled equivalent circuit based on the proposed coordinate transformation was presented, which leads to two control laws aiming at eliminating each type of circulating energy. In Part II, the convoluted multi-state-variable is clearly explained using the proposed equivalent circuit model. The two control laws and the state planes are utilized to evaluate the effectiveness of the existing control methods. © 2022 IEEE.

关键词： Equivalent circuits

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Comparative Study of Machine Learning and Holt-Winters Exponential Smoothing Models for Prediction of CPI's Seasonal Data 2

Comparative Study of Machine Learning and Holt-Winters Expon...

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2nd International Conference on Software engineering and Information Technology, ICoSEIT 2024

作者： Afrah, Ashri Shabrina Sari, Nur Fitriyah Ayu Tunjung Utama, Shoffin Nahwa Holle, Khadijah Fahmi Hayati Lestandy, Merinda Sintiya, Endah Septa Rizdania Universitas Islam Negeri Maulana Malik Ibrahim Malang Department of Informatics Engineering Malang Indonesia Universitas Muhammadiyah Malang Department of Electrical Engineering Malang Indonesia State Polytechnic of Malang Information Technology Department Malang Indonesia University of Pgri Wiranegara Computer Science Department Pasuruan Indonesia

ISBN: (纸本)9798350317503

Inflation is one of the factors influencing price stability. Inflation affects people's purchasing power and impacts their decisions as economic actors. Consumer Price Index (CPI) is one of the factors used by economists to measure the inflation or deflation in a country. This research focuses on comparing the prediction results of the CPI for the Education Goods and Services using the Holt's Winters Exponential Smoothing and Machine Learning Methods, namely Long Short-Term Memory (LSTM), Extreme Learning Machine (ELM), Ridge, and Least Absolute Shrinkage and Selection Operator (LASSO). The data used is univariate data on the CPI for the Education Goods and Services in Malang City in 1996-2013, which was obtained from the publication of the Statistics Indonesia (BPS), entitled 'Malang City in Figures' which was published in 1997-2014. The results of this research show that the Ridge Method produces the smallest Mean Absolute Percentage Error (MAPE) value compared to other Machine Learning Methods and the Multiplicative Holt-Winters Exponential Smoothing Method, with MAPE= 2.10723%%. Machine Learning models that have been simulated have very good accuracy values, with MAPE values © 2024 IEEE.

关键词： Forecasting

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Development of Hybridized Solar and Biogas Power Plant for Agricultural Applications

Development of Hybridized Solar and Biogas Power Plant for A...

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Science, engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Bukar Goni Kasim Hussaini Abatcha Geidam Goni Ahmad Habu Computer Engineering Technology Department Mai Idris Alooma Polytechnic Geidam Electrical/Electronic Engineering Department Federal Polytechnic Damaturu Department of Electrical Electronic Engineering College Of Agriculture Science and Technology Gujba

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

As the cost of conventional fuel rises, the need for sustainable and cost-effective energy solutions becomes paramount, particularly in advancing agricultural practices. This paper introduces a Hybridized Solar-Biogas Power System designed for agricultural applications, addressing the increasing demand for automated systems to enhance productivity. The system employs a 5KVA hybrid inverter, 48V- 400AH battery backup, and a 6.5hp biogas generator, utilizing cow dung as the biogas source. Integration with an automated controller and switchover system ensures continuous power supply, even during evenings, nights, and periods of reduced sunlight. The biogas component, utilizing cow dung, demonstrates comparable running speeds to a petroleum engine, attesting to its optimal performance. This innovative hybrid system not only provides a sustainable alternative for agricultural power but also aligns with sustainable development goals (SDGs) by reducing fuel costs, emissions, and dependence on nonrenewable energy sources. As a potential solution for powering agricultural automation, this system has the capability to significantly reduce operational costs, enhance productivity, and contribute to a greener and more sustainable future.

关键词： Productivity Costs Power supplies Green products Cows Biogas Hybrid power systems

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Design and Analysis of the Characteristics of a Brushless Permanent Magnet Motor for Critical Drive 36

Design and Analysis of the Characteristics of a Brushless Pe...

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36th International Conference on electrical Drives and Power Electronics, EDPE 2023

作者： Korkosz, Mariusz Krzywdzinska-Kornak, Krystyna Parfianowicz, Kamil Prokop, Jan Shchur, Ihor Rzeszow University of Technology Faculty of Electrical and Computer Engineering Rzeszow Poland Lviv Polytechnic National University Department of Electric Mechatronics and Computerized Electromechanical Systems Lviv Ukraine

ISBN: (纸本)9798350322750

The paper concerns the analyzis of the characteristics of a brushless permanent magnet motor for critical drive. To increase the level of redundancy, a multichannel power supply is provided for the analyzed motor. At the motor design stage, a provision was made for dual-channel or single-channel operation with balanced magnetic pull, or quadchannel, triple-channel, dual-channel, and single-channel operation with the possibility of unbalanced magnetic pull appearing. The results of numerical tests of the analyzed structure are included. Selected tests were carried out under laboratory conditions to verify the design assumptions. The prototype design was shown to tolerate single-channel operation with balanced magnetic pull, enabling continuous operation. In the case of single-channel operation with unbalanced magnetic pull, it is only possible to operate within a limited time range while maintaining the required output power. © 2023 IEEE.

关键词： Permanent magnets

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Evolution of Artificial Intelligence and Machine Learning in Betterment of Healthcare Services 1

Evolution of Artificial Intelligence and Machine Learning in...

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1st International Conference on Healthcare Innovations, Software and engineering Technologies, HISET 2024

作者： Abu Eid, Aws Ismail Maula, Shabana Wagh, Aparna Vyas, Vibha Patil, Prakash Sridhar, P. Arab Open University Faculty of Computing Studies Amman Jordan Jaywant College of Engineering and Polytechnic Department of Computer Science Engineering Karad India Jaywant College of Management Karad India College of Engineering Pune Department of Electronics and Telecommunication Pune India Department of Radio-Diagnosis Karad India Institute of Aeronautical Engineering Department of Electrical and Electronics Engineering Hyderabad India

ISBN: (纸本)9798350360707

Artificial Intelligence usually comprises a system that is composed of hardware and software. Machine learning is a subset of artificial intelligence (AI). The study discovered that the environment and contemporary technology on the current healthcare services have created new demands, which has led to the formation of these five categories of research. By matching eligible patients with clinical trials, AI technologies in the healthcare industry may also be beneficial for medical research. There are numerous other fascinating uses for AI in the biomedical field. It is evident that AI is becoming more and more relevant in the field of biomedicine. AI technologies may be able to assist in addressing significant health issues, but its potential may be constrained by the caliber of the health data that is now available and by the fact that AI cannot exhibit certain human traits, like empathy. Numerous ethical and societal concerns are brought up by the use of AI, many of which are also brought up by the use of data and healthcare technologies more generally. © 2024 IEEE.

关键词： Adversarial machine learning

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Graph Coordinates and Conventional Neural Networks - An Alternative for Graph Neural Networks

Graph Coordinates and Conventional Neural Networks - An Alte...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Qin, Zheyi Paffenroth, Randy Jayasumana, Anura P. Colorado State University Electrical and Computer Engineering Fort Collins United States Worcester Polytechnic Institute Department of Data Science Program WorcesterMA United States

ISBN: (纸本)9798350324457

Graph-based data present unique challenges and opportunities for machine learning. Graph Neural Networks (GNNs), and especially those algorithms that capture graph topology through message passing for neighborhood aggregation, have been a leading solution. However, these networks often require substantial computational resources and may not optimally leverage the information contained in the graph's topology, particularly for large-scale or complex *** propose Topology Coordinate Neural Network (TCNN) and Directional Virtual Coordinate Neural Network (DVCNN) as novel and efficient alternatives to message passing GNNs, that directly leverage the graph's topology, sidestepping the computational challenges presented by competing algorithms. Our proposed methods can be viewed as a reprise of classic techniques for graph embedding for neural network feature engineering, but they are novel in that our embedding techniques leverage ideas in Graph Coordinates (GC) that are lacking in current *** results, benchmarked against the Open Graph Benchmark Leaderboard, demonstrate that TCNN and DVCNN achieve competitive or superior performance to message passing GNNs. For similar levels of accuracy and ROC-AUC, TCNN and DVCNN need far fewer trainable parameters than contenders of the OGBN Leaderboard. The proposed TCNN architecture requires fewer parameters than any neural network method currently listed in the OGBN Leaderboard for both OGBN-Proteins and OGBN-Products datasets. Conversely, our methods achieve higher performance for a similar number of trainable parameters. These results hold across diverse datasets and edge features, underscoring the robustness and generalizability of our methods. By providing an efficient and effective alternative to message passing GNNs, our work expands the toolbox of techniques for graph-based machine learning. A significantly lower number of tunable parameters for a given evaluation metric makes TCNN and DVCNN especiall

关键词： graph analytics graph coordinate graph embedding green AI network embedding

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Enhancing Intrusion Detection in IoT Environments: An Advanced Ensemble Approach Using Kolmogorov-Arnold Networks

Enhancing Intrusion Detection in IoT Environments: An Advanc...

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2024 International Symposium on Networks, computers and Communications, ISNCC 2024

作者： Amouri, Amar Al Rahhal, Mohamad Mahmoud Bazi, Yakoub Butun, Ismail Mahgoub, Imad Abu Dhabi Polytechnic Abu Dhabi United Arab Emirates College of Computer & Information Science King Saud University Riyadh Saudi Arabia Florida Atlantic University Department of Electrical Engineering and Computer Science Boca RatonFL United States

ISBN: (纸本)9798350364910

In recent years, the evolution of machine learning techniques has significantly impacted the field of intrusion de-tection, particularly within the context of the Internet of Things (IoT). As IoT networks expand, the need for robust security measures to counteract potential threats has become increasingly critical. This paper introduces a hybrid Intrusion Detection System (IDS) that synergistically combines Kolmogorov-Arnold Networks (KANs) with the XGBoost algorithm. Our proposed IDS leverages the unique capabilities of KANs, which utilize learnable activation functions to model complex relationships within data, alongside the powerful ensemble learning techniques of XGBoost, known for its high performance in classification tasks. This hybrid approach not only enhances the detection accuracy but also improves the interpretability of the model, making it suitable for dynamic and intricate IoT environments. Experimental evaluations demonstrate that our hybrid IDS achieves an impressive detection accuracy exceeding 99 % in dis-tinguishing between benign and malicious activities. Additionally, we were able to achieve F1-scores, precision, and recall that are exceeding 98%. Furthermore, we conduct a comparative analysis against traditional Multi-Layer Perceptron (MLP) networks, assessing performance metrics such as Precision, Recall, and F1-score. The results underscore the efficacy of integrating KANs with XGBoost, highlighting the potential of this innovative approach to significantly strengthen the security framework of IoT networks. © 2024 IEEE.

关键词： Network intrusion

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