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Application of a Nine-Variate Prediction Ellipsoid for Normalized data and Machine learning Algorithms for Keystroke Dynamics Recognition 11

Application of a Nine-Variate Prediction Ellipsoid for Norma...

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11th international Research conference Information Technology and Implementation - Workshop: intelligent Systems and Security, IT and I-WS 2024: ISS

作者： Prykhodko, Sergiy Trukhov, Artem Admiral Makarov National University of Shipbuilding Heroes of Ukraine Ave. 9 Mykolaiv54007 Ukraine Odesa Polytechnic National University Shevchenko Ave. 1 Odesa65044 Ukraine

Keystroke dynamics recognition is a crucial element in enhancing security, enabling personalized user authentication, and supporting various identity verification systems. this study offers a comparative analysis of a nine-variate prediction ellipsoid for normalized data and machine learning algorithms specifically, autoencoder, isolation forest, and one-class support vector machine for keystroke dynamics recognition. Traditional methods often assume a multivariate normal distribution. However, real-world keystroke data typically deviate from this assumption, negatively impacting model performance. To address this, the dataset was normalized using the multivariate Box-Cox transformation, allowing the construction of a decision rule based on a nine-variate prediction ellipsoid for normalized data. the study also includes outlier removal during preprocessing, further improving the model's performance. the results revealed that the application of the Box-Cox transformation significantly enhanced both the accuracy and robustness of the prediction ellipsoid. Although all models demonstrated strong performance, the nine-variate prediction ellipsoid for normalized data consistently outperformed the machine learning algorithms. the study highlights the importance of careful feature selection and multivariate normalizing transformations in keystroke dynamics recognition. Future studies could benefit from broader datasets that include a wider range of user behaviors, such as variations in environmental factors and longer key sequences. © 2024 Copyright for this paper by its authors.

关键词： Adversarial machine learning

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Quantum Gate Count Analysis 11

Quantum Gate Count Analysis

引用

11th IEEE international conference on intelligent Computing and Information Systems, ICICIS 2023

作者： Ghoniem, Omar Elsayed, Hatem Soubra, Hassan German University in Cairo Computer Science and Engineering Cairo Egypt OTH Regensburg Digital Built Environment Group Regensburg Germany ECE-Ecole Centrale d'Electronique Computer Engineering Lyon France

ISBN: (纸本)9798350322101

In recent years, interest in quantum computers has grown significantly. While commercial quantum computers are not available yet, both private and public sector investment into developing quantum computers has developed considerably. One of the key challenges in quantum computing is optimizing the performance of the quantum hardware devices. Different factors can be put into consideration regarding this point, including the number of gates used by the processors to execute different quantum computations and operations. this paper focuses on developing quantum hardware metrics that can be used to measure the performance of a quantum processor in terms of number of quantum gates needed to execute different quantum algorithms. the proposed metrics have been implemented and experimental evaluations have been conducted on various IBM back-end Quantum Computers and using 4 different algorithms: a 5-Qubit entangled circuit algorithm, a 5-Qubit implementation of Grover's quantum search algorithm, a 5-Qubit implementation of Simon's quantum algorithm and the 7-Qubit implementation of Steane's error correction quantum algorithm. In addition, quantitative data on the number of quantum gates used by quantum processors to execute the different quantum algorithms were gathered, which enabled the analysis of how the availability and compatibility of different gate sets affect the overall number of quantum gates needed in the execution of the different quantum algorithms. © 2023 IEEE.

关键词： Logic gates

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Optimization Model for Reactive Voltage Control in Active Distribution Networks Based on Transformer Based Deep Reinforcement learning 11

Optimization Model for Reactive Voltage Control in Active Di...

引用

11th China international conference on Electricity Distribution, CICED 2024

作者： Wenhai, Hao Ru, Duan Wei, Xue Xiaofei, Kang Yulong, Yang Co. Ltd Baotou China

ISBN: (纸本)9798350368345

Aiming at the challenges of insufficient data feature extraction, limited model generalization ability, and low computational efficiency in traditional deep learning methods for reactive voltage control (VRC) in active distribution networks (ADN), a Transformer based Deep Reinforcement learning (DRL) VRC for AND model is proposed. Firstly, the necessity of VRC in active distribution networks based on ADN coordinated control and its optimized operation mode was analyzed. then, by introducing a transformer into the traditional DRL model, a VRC optimization model based on transformer was designed to effectively improve ADN performance. Finally, a comparative analysis was conducted between the proposed method and three other methods under the same database conditions through numerical examples. the experimental results show that under the same experimental conditions, the proposed method has the smallest root mean square error and the highest correlation coefficient, with the highest degree of data fitting. the minimum value is 0.0289, and the maximum correlation coefficient is 0.996, which meets the requirements of VRC in ADN. Its performance is superior to other comparative methods. © 2024 IEEE.

关键词： Distribution transformers

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automated Student Emotion Analysis During Online Classes Using Convolutional Neural Network 1

引用

11th international conference on Soft Computing for Problem Solving, SocProS 2022

作者： Mukherjee, Sourish Suhakar, Bait Yash Kamma, Samhitha Barukula, Snehitha Agarwal, Purab Singh, Priyanka Department of Computer Science and Engineering SRM University-AP Amaravati522502 India

ISBN: (数字)9789811965258

ISBN: (纸本)9789811965241

In non-verbal communication, facial emotions play a very crucial role. Facial recognition can be useful in various ways, such as understanding people better and using the collected data in various fields. In an e-learning platform, students’ facial expressions determine their comprehension levels. Students’ facial emotions can have a favorable or unfavorable impact on their academic performance. As a result, instructors need to create a positive, emotionally secure classroom environment to optimize student learning. In this paper, a novel Facial Emotion Recognition for improving our understanding of students during e-learning is proposed. Suggested model detects different students’ facial emotions such as anger, disgust, fear, happiness, sadness, surprise, and neutral and utilizing them for better teaching and learning during a lecture in an e-learning platform. Convolutional neural networks (CNNs) have been used for detecting facial emotions of students in e-learning platforms, and the proposed model shows an outcome of test accuracy of 67.5%. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Students

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Efficient Modeling and Calibration of Multi-Electrode Stimuli for Epiretinal Implants 11

Efficient Modeling and Calibration of Multi-Electrode Stimul...

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11th international IEEE EMBS conference on Neural engineering (IEEE/EMBS NER)

作者： Vasireddy, Praful K. Gogliettino, Alex R. Brown, Jeff B. Vilkhu, Ramandeep S. Madugula, Sasi S. Phillips, A. J. Mitra, Subhasish Hottowy, Pawel Sher, Alexander Litke, Alan Shah, Nishal P. Chichilnisky, E. J. Stanford Univ Stanford CA 94305 USA AGH Univ Sci & Technol Krakow Poland Univ Calif Santa Cruz Santa Cruz CA 95064 USA

ISBN: (纸本)9781665462921

Epiretinal implants are designed to restore visual function by direct stimulation of retinal ganglion cells (RGCs) with a multi-electrode array. However, the efficacy of presentday epiretinal implants is limited by indiscriminate, simultaneous activation of many RGCs of different types that normally convey distinct visual information, resulting in highly unnatural visual signals. Even with high-density multi-electrode arrays in a laboratory setting, single-electrode stimulation often cannot target an individual cell selectively. A possible solution is to use spatially patterned multi-electrode stimulation. Previous studies have demonstrated enhanced selectivity using such stimuli, but a general framework for characterizing the responses of RGCs to stimulation by multiple electrodes has yet to be realized. Here we propose a generalizable model for the response of a neuron to multi-electrode stimuli, and demonstrate that it can be used to systematically and efficiently improve the selectivity of epiretinal stimulation. the efficacy of the model and of multi-electrode stimuli is validated using three-electrode triplet stimulation. the model assumes that there are several possible activation sites on each cell where a spike can originate, and that the probability of activation at each site is determined by a weighted sum of the currents applied on the electrodes. the multi-site model provides excellent fits to experimental data as well as an interpretable mechanism of multi-electrode activation. Additionally, since the model has few parameters, it permits efficient closed-loop calibration. An optimal experimental design is presented and validated for quickly learning the model parameters and identifying multi-electrode stimuli that are more selective than can be achieved with single-electrode stimuli. the model and closed-loop algorithm could potentially be used to optimize the selectivity of other neural interfaces.

关键词： neural prosthetics closed-loop active learning

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intelligent Integration of Diversified Retirement Information Based on Feature Weighting 5th

Intelligent Integration of Diversified Retirement Informatio...

引用

5th EAI international conference on Multimedia Technology and Enhanced learning, ICMTEL 2023

作者： Wang, Ye Zhang, Yuliang Wuxi Vocational Institute of Commerce Wuxi214153 China

ISBN: (纸本)9783031505737

When carrying out intelligent integration of retirement information, the existing data processing and analysis architecture can no longer meet the current requirements for the storage and processing of massive text data, which reduces the efficiency and accuracy of intelligent integration of diversified retirement information. therefore, a feature-weighted intelligent integration method of multi-element retirement information is proposed. Use the packet capture mechanism to collect diversified retirement information samples, and complete the pre-processing of initial retirement information through information filtering, normalization and other steps. intelligent calculation and distribution of diversified retirement information weights, extraction of mutual information, information gain and other characteristics of diversified retirement information, use of feature weighting algorithm to determine the type of retirement information, and complete the intelligent integration of diversified retirement information. through the performance test experiment, it is concluded that compared with the traditional integration method, the integrity coefficient of the retirement information integration result obtained by the optimization design method has increased by 2.4%, and the information redundancy coefficient has been effectively controlled. It is applied to the retrieval of retirement information, effectively improving the retrieval speed of information. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Information filtering

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Robust automated Event Detection from Machine-learning Analysis of Network data 28th

Robust Automated Event Detection from Machine-Learning Analy...

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28th international conference on Knowledge Based and intelligent information and engineering Systems, KES 2024

作者： Rodda, Matthew Chambers, Alexander Rohl, Alexander Defence Science and Technology Group Department of Defence Australia

Visibility of network communications is critical for security analysts to understand, monitor, and secure computer systems. this visibility is difficult to achieve in the context of modern industrial control systems (ICSs) however, as these may contain a multitude of unique sensors, actuators, and controllers;each communicating via bespoke and diverse network protocols. We are the first to show that machine-learning algorithms, trained with logs of observable (and irregular) system events, can serve as accurate black-box network-monitoring tools for unknown protocols, and are robust to mislabelling and non-uniform timing lag. Using a newly-curated ICS analogue dataset, available here, we analyze a suite of classical and deep-learning models for an event-detection task, showing that decision trees can achieve F1macro = 99.9% with perfectly labeled training data, gradient-boosted decision-tree ensembles can achieve F1macro = 99.0% with mislabeled training data, and a 1D-CNN-LSTM architecture can achieve F1macro = 92.1% with non-uniform timing lag. We validate these techniques on existing ICS and operational technology datasets, achieving F1macro = 99.0%. Our results are a starting point for future work to enhance existing intrusion-detection systems and forensic analyses. © 2024 the Authors.

关键词： Deep Packet Inspection Event Detection Network data Classification

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A Robust EfficientNet Architecture for Brain Tumor Classification and Identification Using MRI Image 11

A Robust EfficientNet Architecture for Brain Tumor Classific...

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11th international conference on intelligent Systems and Embedded Design, ISED 2023

作者： Pathak, Manisha Chaudhary, Deevesh Sharma, Kratika Sharma, Akhilesh K Gupta, Ashish Sharma, Brij Kishore Mits Dept. of Cse Gwalior India Manipal University Jaipur Dept. of Data Science & Engineering Jaipur India

ISBN: (纸本)9798350318159

the most common method for classifying brain tumors is by using biopsy, but this procedure is only carried out after surgery in which a tiny piece of brain tissue is takeout as well as analyzed under an electronic microscope to see a tumor. Since technology has advanced, particularly in ML and AI, we are now able to identify and categorize tumors without undergoing surgery by feeding the machine's classification engine the current data from various MRI images. By doing our study, we present a approach for the identification of tumors utilizing EfficientNet, an architecture that has already been trained utilizing the transfer learning methodology. this study emphasizes on the EfficientNet-B2, EfficientNet-B3, as well as EfficientNet-B4 algorithms from the group of EfficientNet approach. the suggested approach makes use of thresholds to enhance the dataset for more effective accuracy as well as data augmentation to improve the variety of brain images in the data in addition to the previously-trained approach to enhance the effectiveness of training an improved the model. Because it utilizes the compound coefficient to scale all three dimensions of an image with a fixed value of ratio, depth, width, and resolution - EfficientNet relatives of models outperform earlier CNN architecture, according to preliminary results. the outcomes also showed that scaling the previous approaches, enabled the model to capture complex features, improving the accuracy of the model. © 2023 IEEE.

关键词： Tumors

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Identification and Detection of Leaf Miner, Pests Infestation in Cucurbitaceae Family in Real Time Infield Scenarios using YOLOv5s Object Detection Model 18

Identification and Detection of Leaf Miner, Pests Infestatio...

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18th INDIAcom;11th international conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Keerthi Prasad, M.A. Shobha Rani, N. Sangamesha, M.A. Vinay, K.V. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science Mysuru India The National Institute of Engineering Department of Chemistry Mysuru India The National Institute of Engineering Department of Computer Science Mysuru India

ISBN: (纸本)9789380544519

this study contributes to technological developments in precision agriculture by developing a YOLOv5sbased model with a high F1 score and mAP, highlighting its potential for real-world use in wax gourd plants. In precision agriculture, early disease identification is crucial for effective harvesting. the present research focuses on the creation and evaluation of a YOLOv5s-based model for detecting pests and leaf miner infections in wax gourd plants in real time infield conditions at various stages of plant growth. Using a dataset of over 2000 samples collected in a variety of in-field settings, the model was trained for 200 epochs with a low learning rate (0.00001) and a batch size of 32. the achieved F1 score of 0.96 demonstrates the model's remarkable combination of precision and recall, ensuring precise disease detection. Furthermore, a mean Average Precision (mAP) greater than 0.45 demonstrates the model's proficiency in precise leaf localization and overall detection ability. the model's ability to detect numerous occurrences of disease in different leaves simultaneously is a crucial strength, reflecting the complexities common in real-world agricultural scenarios. the dataset's quantity and diversity help the model be robust and generalizable across diverse infection types and growth stages. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： crop health monitoring data-driven agriculture object detection precision agriculture Smart agriculture sustainable farming

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Design of UAV-Based Information Acquisition and Environmental Monitoring System for Dangerous Goods Warehouse 11th

Design of UAV-Based Information Acquisition and Environmenta...

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11th international conference on Traffic and Transportation Studies, ICTTS 2024

作者： Yao, Jiachen Gao, Xinyu Liu, Huaqiong Department of Transportation and Logistics Engineering Shandong Jiaotong University Jinan250300 China

ISBN: (纸本)9789819796434

Due to the special nature of the dangerous goods warehouse itself, for safety reasons, the monitoring requirements for the dangerous goods storage environment are more stringent than those for ordinary warehouses. However, the internal environment of the dangerous goods warehouse is highly hazardous to human beings, prone to personal injury and death accidents, and not suitable for personnel on-site task operations. this paper intends to combine the characteristics of UAV space flight flexibility, easy to carry ancillary equipment, as well as the wireless transmission of data from wireless sensor networks, low power consumption and other characteristics, the application of UAV technology, Internet of things technology, research and design a set of UAV cargo information collection and environmental monitoring system solutions suitable for dangerous goods warehouses, to carry out real-time recording and monitoring of a variety of environmental parameters for the dangerous goods in the storage of the warehouse, and realize the intelligent collection of dangerous goods information and real-time monitoring and management of environmental parameters in dangerous goods warehouses, thereby improving the safety of dangerous goods storage, and at the same time improving the efficiency of storage inventory and inspection and the intelligent level of environmental monitoring. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Zigbee

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