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检索条件"任意字段=Proceedings of the 5th International Conference on Algorithms, Computing and Systems"

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Application of Bayesian Optimization in Router Port Testing: An Improved Port Scanning Technique 5

Application of Bayesian Optimization in Router Port Testing:...

引用

5th IEEE international conference on Power, Intelligent computing and systems, ICPICS 2023

作者： Yanyan, Long Shanhou, Huang Guangxi Talent International College Guangxi Qinzhou535000 China

ISBN: (纸本)9798350333442

this paper mainly explores the application of Bayesian optimization in router port testing, aiming to improve the efficiency and accuracy of port scanning by constructing a Bayesian optimization model. Since the existing port scanning methods have problems of low efficiency and accuracy, we seek to provide a superior solution through Bayesian optimization. In this process, we first introduce the basic principles of Bayesian optimization, then construct a Bayesian optimization model based on this theory, which takes scanning time and accuracy as optimization goals. In the experimental section, we conduct experiments on two public datasets to verify our method. the experimental results show that compared with traditional methods, the Bayesian optimization method can significantly improve the efficiency and accuracy of port scanning. Especially when the number of scans increases, the advantages of the Bayesian optimization method are more obvious. In general, Bayesian optimization provides a new and effective solution for router port testing and opens up new ideas for future research in the field of network security. © 2023 IEEE.

关键词： Efficiency

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Optimization of industrial robot grasping processes with Q-learning 5

Optimization of industrial robot grasping processes with Q-l...

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5th international conference on Control and Robotics (ICCR)

作者： Belke, Manuel Joeressen, Till Petrovic, Oliver Brecher, Christian Rhein Westfal TH Aachen Lab Machine Tools & Prod Engn Aachen Germany

ISBN: (纸本)9798350307627

Grasping objects in unstructured and dynamic environments remains a challenging task for robotic systems. Traditional grasp planning algorithms often struggle to adapt to the variability and uncertainty present in real-world scenarios. Deep reinforcement learning has emerged as a promising approach for training robotic agents to improve their grasping capabilities. this paper presents a novel grasp refinement strategy that exploits a deep Q-learning algorithm to improve the precision of robotic grasping despite the presence of position and orientation inaccuracies. the proposed strategy focuses on industrial robot grasping processes which are predominantly comprised of a robot with force and torque sensor values and parallel jaw grippers with force and position feedback. Experimental results demonstrate that our RL-based approach increases the grasping accuracy without the presence of complex and high-dimensional tactile sensor input. Moreover, the learned grasping policies exhibit a higher level of robustness against noise in the preceding pose estimation system and object uncertainties.

关键词： machine learning reinforcement learning simulation gripping robot

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Circularly Polarized Two-Port Dielectric Resonator Based Filtering Antenna for mm-wave 5G Communication System 5

Circularly Polarized Two-Port Dielectric Resonator Based Fil...

引用

5th international conference on Smart Electronics and Communication, ICOSEC 2024

作者： Kumar, Abhishek Ahir, Jitendra Gupta, Sanjeev Kumar Rntu Department of Ece Bhopal India Faculty of Engineering and Technology Rntu Bhopal India

ISBN: (纸本)9798331504403

In this research work, a two-port DRA is designed and discussed. Two-port polarized antennas evolved with the main aim of overcoming the principal limitation in modern wireless communication system i.e. restricted available bandwidth and hence the accommodation of large number of simultaneous users is limited. Two port polarized antenna doubles the existing channel capacity by exploiting two polarization states of electromagnetic waves in the form of two distinct communication channels. Stair slot with unequal arm length provides the circular polarization in between 28.5-29.05 GHz. Addition of two unequal stubs at the top of microstrip line degrades the value of gain outside the operating band by an amount to -20 dBi, which makes the radiator as a filtering structure. Random Forest (RF) and XGBoost (XGB) based ML algorithms are utilised to predict the gain value of proposed antenna. Anti-parallel placement of antenna ports (space diversity) and mirror orientation of slots (polarization diversity) helps to get more than 25 dB isolation between the ports. After verification of outcome, it is confirmed that the designed radiator operates in between 28.05-29.3 GHz. Broadsided far-field and good diversity value makes the antenna design employable for mm-wave 5G communication system. © 2024 IEEE.

关键词： Circular polarization

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A systematic review of challenges in Channel estimation for 5G massive MIMO

A systematic review of challenges in Channel estimation for ...

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2024 international conference on Sustainable computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Varshney, Parul Singh, Ritesh Pratap Jain, R.K. Meerut India School of Electrical & Computer Engineering Haramaya Institute of Technology Haramaya University Diredawa Ethiopia

ISBN: (纸本)9798350355338

Massive MIMO is an essential technology with significant promise for 5G and the upcoming generation of telecommunication systems. Moreover, there is a lack of thorough investigation in the available literature that addresses all the critical areas that need to be investigated for such systems. this comprehensive review study investigates and assesses the most recent achievements and covers several research directions related to Massive MIMO systems. the specific area of study that has received significant attention is the development of advanced approaches for estimating the channels in Massive MIMO systems. this paper presents a thorough examination of the latest progress in the development of Massive MIMO systems and the many approaches employed for channel estimation in these systems. Only a select few authors in the field of literature have tackled the topics of Massive MIMO channel estimations and computational models for Massive MIMO systems. To the best of our knowledge, no review article addresses the most recent findings on the estimation of channels for Massive MIMO systems. Hence, the current study seeks to examine the latest algorithms employed in the field, which could be a great asset for researchers. In addition, certain research perspectives are acknowledged. © 2024 IEEE.

关键词： Channel state information

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Quantitative Mathematical Analysis of Character's Actions 5

Quantitative Mathematical Analysis of Character's Actions

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5th international conference on Control systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2023

作者： Matytcina, Marina S. Chekulai, Igor V. Mukhartova, Darya A. Prokhorova, Olga N. Kuchmistyy, Vladislav A. Lipetsk State Technical University Foreign Languages Department Lipetsk Russia Belgorod State National Research University The Institute of Cross-Cultural Communications and International Relations Belgorod Russia Belgorod State National Research University Belgorod Russia

ISBN: (纸本)9798350309126

this paper discusses the portrait of a character in terms of describing the appearance, as well as the characteristic body movements, facial expressions, gestures, eye and facial expressions from quantitative analysis perspective. the above mentioned traits are determined to characterize the character's actions from the point of view of its "dynamism"/ "staticity". © 2023 IEEE.

关键词： character's actions dynamism numerical data quantitative mathematical analysis staticity

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11th international Symposium on Applied computing for Software and Smart systems, ACSS 2024

11th International Symposium on Applied Computing for Softwa...

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11th international Symposium on Applied computing for Software and Smart systems, ACSS 2024

ISBN: (纸本)9789819797615

the proceedings contain 19 papers. the special focus in this conference is on Applied computing for Software and Smart systems. the topics include: CCD Array Spectrometer-Based FBG Bragg Wavelength Detection Using ANN Algorithm;performance Assessment of Sequential Models for Solar Radiation Forecasting Over Varying Forecast Horizon;advancing Smartphone Sensor-Based Keystroke Dynamics for Implicit and Active Authentication: Addressing Challenges and Enhancing Usability Control;a Prototype for Peer-to-Peer Transient Microservices with Wi-Fi Direct;comparison of the Effectiveness of Face Recognition algorithms in Terms of Photo Distortion Level;multi-class Histology Image Analysis Using Handcrafted Texture and Colour Features for Breast Abnormality Classification;fvFc-Net: Forged Video Frame Classification Network;semi-automated Ground Truth Generation System for Bangla Offline Handwritten Text Documents;SWFEM: Sparse Weighted Fine-Tuned Ensemble Model for Leaf Disease Detection;a Comparative Study of Hospital Length of Stay Prediction of Indoor Patients for Admission, Post Admission, and Discharge Stage Data Using Machine Learning algorithms;BEN-RS-ANN: An Innovative Approach for Revealing Emotion from Bengali Text with Exposure to Polysemy Resolution;Flood Detection in UAV Images of Urban Area Using Machine Learning and Deep Learning Techniques;supervised Classification Approach for Precise Cell Type Identification Improves Single Cell Data Analysis;quality-Driven Web Service Selection: Machine Learning Based Approach;contextual Correlation Inference in Multi-fleet Robotic systems;risk Assessment in Agile Software Development;Adaptive Cyber Defence: Leveraging GANs for Simulating and Mitigating Advanced Network Attacks in IoT Environments.

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Toward a Deep Neural Network System for Machining Chatter Monitoring of thin Walled Workpieces during Turning Process 24

Toward a Deep Neural Network System for Machining Chatter Mo...

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5th international conference on Artificial Intelligence in Electronics Engineering (AIEE)

作者： Mehdi, Kamel Monka, Peter Pavol Monkova, Katarina Univ Tunis UT Engn Natl High Sch Tunis ENSIT Mech Prod & Energet Lab LMPE 5 Ave Taha HusseinPB 56 Bab Mnara Tunis 1008 Tunisia Tech Univ Kosice Fac Mfg Technol Sturova 31 Presov 08001 Slovakia

ISBN: (纸本)9798400716850

Nowadays, intelligent monitoring systems during machining operation are increasingly developed. So, as manufacturing machines are increasingly equipped with sensors and communication capabilities, and thanks to artificial intelligence models based on machine learning (ML) approaches, there is significant potential to further improve the knowledge of conditions of manufacturing machines and processes, reduce operational downtime, improve the level of automation and product quality and faster response to ever-changing customer demands. In this paper we present an approach based on STFT and Deep Neural Network (DNN) for chatter monitoring during turning process of thin walled workpieces. the STFT was employed to convert cutting force signal to time frequency image as the input of the DNN to achieve high classification accuracy. Experiments are conducted on datasets collected by measuring cutting force signals during turning process of several thin walled workpieces made of steel and aluminum alloys under different cutting conditions. Simulation results shows that the proposed methodology successfully classifies the stability of the cutting process with over 96% accuracy.

关键词： monitoring cutting force turning process stability prediction thin walled workpiece SFTF deep neural network

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2020 IEEE 5th international conference on Cloud computing and Big Data Analytics, ICCCBDA 2020

2020 IEEE 5th International Conference on Cloud Computing an...

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5th IEEE international conference on Cloud computing and Big Data Analytics, ICCCBDA 2020

ISBN: (纸本)9781728160245

the proceedings contain 103 papers. the topics discussed include: online purchase behavior prediction and analysis using ensemble learning;research on deep learning based code generation from natural language description;power consumption forecast of energy-intensive enterprises based on power marketing business data;a fast and accurate face alignment method based on deep learning on mobile device;time series segmentation of flood flow based on bi-LG-LSTM neural network;analysis of vessel anomalous behavior based on Bayesian recurrent neural network;operation framework of the command information system based on big data analysis;and a cooperative vehicles selection method based on privacy-preserving driving routes comparison for urban scenarios.

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Combination of Boruta's Algorithm and Lasso Logistic Regression in Establishing Blood-Based Biomarkers for Alzheimer's Disease 5

Combination of Boruta's Algorithm and Lasso Logistic Regress...

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5th international conference on Artificial Intelligence and Data Sciences, AiDAS 2024

作者： Abdullah, Mohammad Nasir Wah, Yap Bee School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi Mara Tapah Campus Perak Tapah35400 Malaysia School of Information Technology Unitar International University Tierra Crest Jalan SS6/3 SS6 Selangor Petaling Jaya47301 Malaysia

ISBN: (纸本)9798331528553

Alzheimer's disease (AD) is the leading neurodegenerative disorder worldwide, significantly impacting morbidity, disability, and mortality rates. this study aims to enhance the early diagnosis of AD by identifying blood-based biomarkers using Boruta's algorithm and lasso logistic regression. the Gene Expression Omnibus (GEO) dataset GSE140829 identified 47,008 potential biomarkers, which preprocessing reduced to 22,372. Boruta's algorithm further narrowed these to 21 significant biomarkers. Lasso logistic regression was applied to this balanced dataset, resulting in six key biomarkers: CST7, DPAGT1, DZIP1, MOSC1, TACC3, and YDJC. the final model, validated with a C-index of 0.763, demonstrated good discriminative power and clinical utility. this research offers a robust method for predicting AD, potentially improving early diagnosis and guiding treatment decisions. © 2024 IEEE.

关键词： Logistic regression

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MyDAS Corpus: Malay Social Media Texts for Detecting Depression, Anxiety, and Stress on Facebook 5

MyDAS Corpus: Malay Social Media Texts for Detecting Depress...

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5th international conference on Artificial Intelligence and Data Sciences, AiDAS 2024

作者： Ahmad, Zaaba Mohamed, Azlinah Conway, Mike Zakaria, Rozanizam Teo, Noor Hasimah Ibrahim Maskat, Ruhaila College of Computing Informatics & Mathematics Universiti Teknologi Mara Cawangan Perak Kampus Tapah Perak Tapah Malaysia Ysl Strategic Alliance Selangor Shah Alam Malaysia Centre for Digital Transformation of Health The University of Melbourne MelbourneVIC Australia Kulliyah of Medicine International Islamic University Malaysia Department of Psychiatry Pahang Kuantan Malaysia College of Computing Informatics & Mathematics Universiti Teknologi Mara Cawangan Melaka Kampus Jasin Melaka Merlimau Malaysia College of Computing Informatics & Mathematics Universiti Teknologi Mara Selangor Shah Alam Malaysia

ISBN: (纸本)9798331528553

the application of Natural Language Processing (NLP) in mental health monitoring has significantly expanded;however, the specific challenges of interpreting Depression, Anxiety, and Stress (DAS) in Malay language social media texts have not been adequately addressed. this gap underscores the need for NLP solutions that are sensitised to the linguistic and cultural specificities of Malay-speaking populations. this study develops and validates a specialised Malay language corpus from social media content, targeting DAS. Utilising a hybrid ground truth strategy that integrates self-reports with expert assessments, the research offers methodological refinements in the analysis of Malay linguistic patterns and the deployment of machine learning classifiers to efficiently identify mental health indicators. the paper reviews existing methodologies, outlines a novel corpus development strategy, and discusses classifier performance. the Decision Tree classifier achieved the highest F1 score of 0.75, followed by the Support Vector Machine (SVM) with an F1 score of 0.73, and Random Forest with 0.70. Multinomial Naive Bayes (MNB) and K-Nearest Neighbors (KNN) demonstrated lower performances with F1 scores of 0.55 and 0.52 respectively. Comprehensive analyses using bi-gram networks and t-SNE visualisations explore the nuanced linguistic indicators of mental health states, culminating in a discussion of the implications for future NLP applications in mental health monitoring. © 2024 IEEE.

关键词： Support vector machines

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