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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Medhi, Shrabani Boruah, Rituraj Baruah, Sagar Pratim Das, Hiranya Kumar Girijananda Chowdhury University Dept. of Computer Science and Engineering Guwahati India Girijananda Chowdhury Institute of Management and Technology Dept. of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798331300579

Air pollution is a global issue of concern. Accurate prediction of the air quality index (AQI) plays a crucial role in understanding pollution patterns and implementing effective mitigation strategies. This research paper presents a comprehensive study on the application of ensemble learning techniques for AQI prediction, aiming to enhance prediction accuracy and provide insights into the driving factors behind pollution levels. The primary objective is to conduct a comparative analysis of homogenous ensemble method, namely, bagging in conjunction with individual base models, such as multiple linear regression, support vector regression, decision trees, and artificial neural networks. In total we have created 4 non-ensembled models and 4 ensembled models. Additionally, the research investigates the importance of different features in AQI prediction models, aiming to identify the key factors influencing air pollution. Hyperparameter tuning is done to optimize the models. © Grenze Scientific Society, 2024.

关键词： Prediction models

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Predictive Modeling of Cryptocurrency Prices Using Machine Learning Algorithms 15

Predictive Modeling of Cryptocurrency Prices Using Machine L...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Mahmud, Tanjim Akter, Tahmina Chakma, Rishita Barua, Tikle Hossain, Mohammad Shahadat Andersson, Karl Dept. of Computer Science and Engineering Rangamati Science and Technology University Rangamati4500 Bangladesh Dept. of Computer Science and Engineering Port City International University Chittagong Bangladesh Dept. of Computer Science and Engineering University of Chittagong Chittagong4331 Bangladesh Cybersecurity Laboratory Luleå University of Technology Luleå97187 Sweden

ISBN: (纸本)9798350370249

Cryptocurrencies, such as Bitcoin, Binance, Ethereum, FTX, and XRP, are decentralized digital assets known for their volatile nature and potential as investment instruments. Accurate price prediction is crucial for informed investment decisions. This study explores the feasibility of various modeling techniques on diverse data structures and features for predicting the prices of these cryptocurrencies. We utilize daily and high-frequency price data to classify and predict prices using deep learning and machine learning techniques, including LSTM, Bi-LSTM, GRU, linear regression, and SGD regression. Our findings indicate that daily price projections achieve an accuracy of 0.99, outperforming more complex deep learning and machine learning models. Compared to benchmark results, our approach demonstrates superior performance, with the highest scores achieved by the applied statistical methods and advanced algorithms. This research highlights the effectiveness of deep learning and machine learning models in cryptocurrency price forecasting, offering a foundation for further exploration in the field and emphasizing the significance of sample size in predictive modeling. ©2024 IEEE.

关键词： Adversarial machine learning

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Evaluating the Performance of Machine Learning Models in Handwritten Signature Verification

Evaluating the Performance of Machine Learning Models in Han...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Akter, Tahmina Akter, Mst. Sharmin Mahmud, Tanjim Chakma, Rishita Hossain, Mohammad Shahadat Andersson, Karl Port City International University Dept. of Computer Science and Engineering Chittagong Bangladesh Rangamati Science and Technology University Dept. of Computer Science and Engineering Rangamati4500 Bangladesh University of Chittagong Dept. of Computer Science and Engineering Chittagong4331 Bangladesh Luleå University of Technology Cybersecurity Laboratory Luleå97187 Sweden

ISBN: (纸本)9798350361537

Handwritten signatures remain a widely used method for personal authentication in various official documents, including bank checks and legal papers. The verification process is often labor-intensive and time-consuming, necessitating the development of efficient methods. This study evaluates the performance of machine learning models in handwritten signature verification using the ICDAR 2011 Signature and CEDAR datasets. The investigation involves preprocessing, feature extraction using CNN architectures, and optimization techniques. The most effective models undergo a rigorous evaluation process, followed by classification using supervised ML algorithms, such as linear SVM, random forest, logistic regression, and polynomial SVM. The results indicate that the VGG16 architecture, optimized with the Adam optimizer, achieves the satisfactory performance metrics. This study demonstrates the potential of ML methodologies to enhance the efficiency and accuracy of signature verification, offering a robust solution for document authentication. © 2024 IEEE.

关键词： Decision trees

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Deep Learning-Driven Cloud Intrusion Detection: Internet of Things and Cloud Networks 1

Deep Learning-Driven Cloud Intrusion Detection: Internet of ...

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1st International Conference on Advances in computer science, electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Moon, Iffath Tanjim Islam Mondal, Md. Nazrul Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798331518578

In this paper, we have utilized deep learning approaches to detect cloud intrusion for the Internet of Things (IoT). The emerging growth of IoT and cloud environments has revolutionized many industries by allowing real-time data sharing and storage. Cloud platforms offer the essential infrastructure for managing the massive volumes of data produced by IoT devices. However, due to several flaws, including insufficient encryption, insecure APIs, and weak authentication, intruders can exploit the vulnerabilities to interrupt services and obtain unauthorized access. Therefore, deep learning can be an effective solution to detect cloud intrusion in IoT. In this research, we have developed individual convolutional neural network (CNN) and long short-term memory (LSTM) models, along with a hybrid CNN-LSTM model for multiclass classification and binary classification to detect cloud intrusion. In addition, we have examined which models perform the best for cloud intrusion detection for IoT among the individual and hybrid models. The proposed models are effective in detecting cloud intrusion, as CNN can extract spatial features, and LSTM can capture temporal dependencies from network traffic data. Furthermore, we have utilized one of the most recent and comprehensive datasets for IoT, the ToN-IoT dataset, containing real-world data. In multiclass classification, the hybrid CNN-LSTM has given the best performance, achieving an accuracy of 97.65%, precision of 97.72%, recall of 97.65%, f1 score of 97.67%, and AUC of 99.93%. In binary classification, all CNN, LSTM, and hybrid CNN-LSTM have demonstrated high performance, where LSTM has achieved the highest accuracy of 99.97%. Moreover, we have evaluated the performance of multiclass classification for detecting nine distinct kinds of attacks. In this instance, CNN has shown better performance in detecting backdoor attacks. Thus, this research can make the IoT platform more secure by efficiently detecting cloud intrusion. © 2025

关键词： Network intrusion

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IMPROVED INDOOR FORMALDEHYDE MONITORING SYSTEM USING ENHANCED SELF-DEVELOPED CIRCUITS AND LOCAL OUTLIER FACTOR ALGORITHM

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: electrical engineering and computer science 2022年第2期84卷 137-152页

作者： Li, Bin Zhong, Kai Niu, Yongyue Cui, Peng Xue, Liang Ji, Nan Wei, Dahui Dept. of Electrical and Computer Engineering Jilin Jianzhu University China

An indoor distributed formaldehyde monitoring system has been developed with multiple self-developed sensor nodes and wireless communication circuits. Local outlier factor algorithm has been adopted to increase the accuracy of the system. In addition, software has been developed based on LabVIEW for data receiving, data storing, data displaying and remote accessing. Experiments were carried out to evaluate the performance of this improved system. Compared to our previous work, the proposed system exhibits some improvements in performance of formaldehyde sensing ability, software function and quality of wireless communication due to the enhancement of hardware and software in the system. © 2022, Politechnica university of Bucharest. All rights reserved.

关键词： Remote control

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Mutiphysics Analysis for Electromagnetics-Structural Deformation-Circuit Coupled Problem of Electromagnetic Expulsive De-icing System

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Transactions of the Korean Institute of electrical Engineers 2022年第11期71卷 1599-1607页

作者： Kang, Sung-Jun Lee, Jun-Sung Rho, Seung-Eun Park, Jong-Oh Jung, Eun-Chae Park, Il-Han Dept. of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

One of the ice protection systems for flights above medium altitudes is the electromagnetic explusive deicing system(EEDS). EEDS is composed of a coil which is structurally deformed by electromagnetic repulsion of electric current supplied by external circuit, a complex multiphysics phenomenon requiring electromagnetic, structural, and circuit analysis simultaneously. In this paper, we suggest electromagnetic(EM)-structural deformation-circuit coupled lumped parameter system for the analysis of EEDS. The results of the numerical analysis using the coupled lumped parameter system is compared with the results of measuring experiment using the EEDS prototype, which showed high concordance in current, electromagnetic force, and displacement. The coupled lumped parameter system enables bi-directional analysis which takes into account the interdependence between the different systems. Furthermore, it can significantly reduce the computational cost, which enables complex performance evaluation on various design variables and saves time and cost in the initial design steps. Copyright © The Korean Institute of electrical Engineers.

关键词： Timing circuits

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K-Band QPSK PMCW Radar with Digital I/Q Constellation Phase Compensation and I/Q Imbalance Correction

K-Band QPSK PMCW Radar with Digital I/Q Constellation Phase ...

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2025 IEEE Topical Conference on Wireless Sensors and Sensor Networks, WiSNeT 2025

作者： Thompson, Derek Li, Changzhi Dept. of Electrical and Computer Engineering Texas Tech University LubbockTX United States

ISBN: (纸本)9798331507336

This paper presents a novel K-Band Quadrature Phase Shift Keying (QPSK) phase-modulated continuous-wave (PMCW) radar front end architecture with digital I/Q constellation phase compensation and I/Q channel imbalance correction. Although QPSK is a very common form of digital phase modulation, it is rarely reported for PMCW radar systems, especially those designed on printed circuit board (PCB) technology. A MATLAB simulation platform is also developed and presented to help verify the benefits of QPSK PMCW radar for range detection. © 2025 IEEE.

关键词： MATLAB

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An Extension of Interior Point Differential Dynamic Programming for Optimal Control Problems with Second-Order Conic Constraints

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Transactions of the Korean Institute of electrical Engineers 2022年第11期71卷 1666-1672页

作者： Kim, Min-Gyeom Kim, Kwang-Ki Dept. of Electrical and Computer Engineering Inha University Korea Republic of

This paper presents the second-order conic IPDDP (SOC-IPDDP) that modifies interior point differential dynamic programming (IPDDP) to explicitly handle second-order conic constraints. Differential dynamic programming (DDP) has been widely used to solve nonlinear optimal control problems in control and robotics. However, DDP has a big drawback that it cannot handle hard constraints and constraints are usually encoded softly as parts of the cost functions. To overcome this constraint-handling limitation of DDP, interior point differential dynamic programming (IPDDP) is proposed to deal with hard constraints using the interior point method. In this paper, we extend IPDDP to efficiently take care of second-order conic constraints by exploiting their algebraic structures. For demonstration, the performances of SOC-IPDDP are compared with IPDDP in the planar rocket landing optimal control problem. It is shown that the proposed SOC-IPDDP has a comparable convergence rate to the existing IPDDP and is less sensitive to the changes in the parameters of the cost function. Copyright © The Korean Institute of electrical Engineers.

关键词： Cost functions

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Nonlinear HF Propagation in the Ionosphere above HAARP

Nonlinear HF Propagation in the Ionosphere above HAARP

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2025 United States National Committee of URSI National Radio science Meeting, USNC-URSI NRSM 2025

作者： Burch, Harrison R. Moore, Robert C. University of Florida Dept. of Electrical & Computer Engineering GainesvilleFL32611 United States

ISBN: (纸本)9781946815200

The nature of HF radio wave propagation through the lower ionosphere has significant impact on scientific efforts such as ELF/VLF wave generation and ionospheric sounding, as well as operational efforts e.g. over-the-horizon radar and satellite-to-ground communications. Historical interpretation of propagation within the lower ionosphere centers on D-region absorption models, but observations of polar mesospheric echoes in HF and VHF radars and anomalous absorption in HF cross-modulation experiments highlight the potential for other interactions to impact HF radio wave propagation in the lower ionosphere. © 2025 USNC-URSI.

关键词： D region

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Inverter Control of Scheme for IEEE1547-2018 Category3 in Fuel Cell Systems

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Transactions of the Korean Institute of electrical Engineers 2022年第1期71卷 90-97页

作者： Won, Su-Yeon Lee, Kyo-Beum Dept. of Electrical and Computer Engineering Ajou University Korea Republic of

This paper presents an inverter control scheme for fuel cell systems in IEEE1547-2018 Category3. The current configuration of Grid-Connected fuel cell system has limitations Category3's LVRT (Low Voltage Ride Through) reponse, additional inverter are needed. The inverter operates in an abnormal state where the grid voltage is unstable, and normally supplies the voltage to BOP (Balance of Plant) of the fuel cell system. Simulated verification of the inverter control scheme was performed to change the voltage source to the BOP from grid to inverter during abnormal conditions. © 2022 Korean Institute of electrical Engineers. All rights reserved.

关键词： Fuel cells

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