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Optimization Accuracy of Secured Cloud systems Using Deep Learning Model 4

Optimization Accuracy of Secured Cloud Systems Using Deep Le...

引用

4th international conference on intelligent Technologies, CONIT 2024

作者： Hindka, Mayank Texas A&m University-Central Texas MBA-Information Systems United States

ISBN: (纸本)9798350349900

Cloud security is vital in attracting data security and privacy customers. The online attackers disrupt the use of the on-demand services provided by the cloud service providers for their clients. It is distributed in nature for resource sharing over the Internet. Thus, security has become the main agenda for cloud computing usage by cloud customers. As a result, the financial growth of cloud-based organizations is increasing gradually. The critical situation of cloud security is identifying the types of attacks and deciding how to defend such attacks to protect the cloud data from attackers. Numerous methodologies are reviewed to determine the robust security mechanisms for cloud security to protect against DoS or DDoS attacks (by malware attacks, MITM attacks, or other medium attacks). The ML concept has incorporated algorithm conceptions like deep neural networks and quantum neural networks to enhance prediction and protection accuracy. These proposed models not only help to reduce the attack level up to 100% but also increase the trust of cloud users with financial growth to cloud service providers (CSPs). © 2024 ieee.

关键词： Cloud security

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intelligent Method of Predicting the Discount Rate Trend 11

Intelligent Method of Predicting the Discount Rate Trend

引用

11th ieee international conference on intelligent data acquisition and advanced computing systems - Technology and Applications (IDAACS)

作者： Koziuk, Viktor Lipyanina-Goncharenko, Hrystyna West Ukrainian Natl Univ Ternopol Ukraine

ISBN: (纸本)9781665426053

the predictability of National Bank of Ukraine (NBU) monetary policy is an important point to gain credibility as an inflation targeter. Basing on the theoretical and empirical literature the direct tool - expert expectations about up-coming interest rates decision - was chosen as approach on quantitative assessment of NBU's predictability. It is found that generally economic agents do not make a systematic mistake in interest rates expectations. But, predictability is an imperfect that is normal for emerging market countries. Some asymmetries in predictability across interest rates cycle is found. However, economic uncertainty is taken in similar way between experts and policy-makers meaning that transparency policy is generally effective. An intelligent method for predicting the discount rate trend has been developed, based on machine learning methods: ARIMA, LSTM and Prophet. Conducted RMSE Errors, trend: bond rate - ARIMA = 8.15, LSTM = 639 and Prophet = 4.15;expert assessment of the bond rate - ARIMA = 8.18, LSTM = 7.01 and Prophet = 5.99.

关键词： monetary policy predictability central hank credibility expectations transparency uncertainty machine learning ARIMA LSTM Prophet

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AceMiner: Accelerating Graph Pattern Matching using PIM with Optimized Cache System 42

AceMiner: Accelerating Graph Pattern Matching using PIM with...

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42nd international conference on Computer Design

作者： Yan, Liang Lu, Xiaoyang Chen, Xiaoming Xu, Sheng Zou, Xingqi Han, Yinhe Sun, Xian-He Chinese Acad Sci Ctr Intelligent Comp Syst Inst Comp Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China IIT Dept Compute Sci Chicago IL 60616 USA Anhui Normal Univ Sch Comp & Informat Wuhu Peoples R China

ISBN: (纸本)9798350380415;9798350380408

Graph pattern matching (GPM), a critical algorithm for discovering specific patterns within complex structures, is becoming increasingly important in the data-driven world. GPM applications are memory-bound and can be accelerated by memory-centric computing systems, such as processing-inmemory (PIM). However, there are three primary challenges when it comes to accelerating GPM applications with PIM: (1) difficulty in utilizing locality, (2) heavy data movement, and (3) heavy comparison overhead due to pruning. To address these challenges, we propose AceMiner, a framework to accelerate GPM applications with a software and hardware co-design perspective using PIM. In AceMiner, we embed hybridCache, a novel in-DRAM cache system with lower access latency and optimized replacement policy, to leverage the potential locality and reduce data movement in PIM. Additionally, we introduce a comparison unit to address the huge pruning overhead. Experimental results show that AceMiner outperforms the state-of-the-art, achieving speedups of 40.2% and 13.3% over NDMiner and DIMMining respectively, with less energy consumption and design overhead.

关键词： Graph pattern matching Processing-in-memory Cache system

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data-Driven Modeling of WPT systems with Secondary Voltage Control 5

Data-Driven Modeling of WPT Systems with Secondary Voltage C...

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5th ieee international conference on Power, intelligent computing and systems, ICPICS 2023

作者： Wu, Xiaorui Xiao, Jing Wu, Ning Gong, Wenlan Mo, Yuhong Duan, Chanzhen Electric Power Research Institute of Guangxi Power Grid Co. Ltd Southern Power Grid Corporation Wireless Power Transmission Joint Laboratory Nanning China School of Automation Chongqing University Chongqing China

ISBN: (纸本)9798350333442

Dynamic models are of great importance for the analysis, design, and control of wireless power transfer (WPT) systems. This study concentrates on the WPT system with active rectifiers on the secondary side, and uses a data-driven modeling method to construct a correlation between the load voltage and the phase shift angle associated with the active rectifier. The data-driven modeling method only requires sampling the system's input and output signals, eliminating the necessity for a complete understanding of the system's internal operational mechanisms. The model obtained by this modeling method can not only be used for system dynamic analysis, but also provide reference for controller design. Finally, the effectiveness of the model obtained by this method was verified through simulation on the MATLAB platform. © 2023 ieee.

关键词： Energy transfer

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Enhanced Traffic Collision Avoidance System Using Single Photon LiDAR in Aviation Operations

Enhanced Traffic Collision Avoidance System Using Single Pho...

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2024 international conference on Information Technology and computing, ICITCOM 2024

作者： El Messaoudi, Imane El Allami, Nissrine Alj, Yassine Salih School of Science and Engineering Al Akhawayn University Ifrane Morocco

ISBN: (纸本)9798350379839

This paper introduces a novel approach to enhance aviation safety by integrating Single-photon Light Detection and Ranging (LiDAR) technology into the existing Traffic Collision Avoidance systems (TCAS). This integration aims to address the limitations of current TCAS by providing precise 3D imaging over extensive distances. A study of data fusion using the Kalman filter algorithm is presented for TCAS and LiDAR sensors. The results indicate successful superposition for a moving 2D object. Additionally, design considerations, and cost and power analyses for the proposed system are discussed. A simulation in Python using iterative closest point (ICP) shows that comprehensive 3D point cloud acquisition is attainable. Hence, the system has the potential to enhance the safety of the aviation industry, by potentially reducing mid-air collisions (MAC). © 2024 ieee.

关键词： data fusion

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Marker-less Kendo Motion Prediction Using High-speed Dual-camera System and LSTM Method

Marker-less Kendo Motion Prediction Using High-speed Dual-ca...

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ieee/ASME international conference on advanced intelligent Mechatronics (AIM)

作者： Cao, Yongpeng Yamakawa, Yuji Univ Tokyo Inst Ind Sci 4-6-1 KomabaMeguro Ku Tokyo 1538505 Japan

ISBN: (数字)9781665413084

ISBN: (纸本)9781665413084

Kendo is becoming popular worldwide nowadays. This research aims to develop a high-speed vision system to capture Kendo motions clearly and predict the intent attacking target of the trainee. We proposed a method to increase the joints detecting frequency by combining the joints tracking algorithm with OpenPose. For motion prediction, we applied LSTM method to achieve real-time predicting. In the end, we got 66% accuracy for basic Kendo moves prediction.

关键词： Training Target tracking Tracking Machine vision Training data Real-time systems Libraries

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Improved Hierarchical Agglomerative Clustering Based Multivariate Gaussian Outlier Detection for Education data 4

Improved Hierarchical Agglomerative Clustering Based Multiva...

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4th international conference on Ubiquitous computing and intelligent Information systems, ICUIS 2024

作者： Sivasankari, S. Sukumaran, S. Erode Arts and Science College Tamilnadu Erode India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529635

data Mining is the procedure of analyzing dataset in any field to have meaningful patterns or relationship from the variables. Outliers refer to the data that has special insight and valuable information which deviates from the rest that can be in the form Local or Global. Noise and Outliers are used interchangeably based. Precisely Noise data refers to the erroneous data and the Outliers implies deviated data. This work proposes Cluster based Outlier Detection method on Education data to filter the Outliers that implies the students or learners who are deviated or considered extraordinary from the rest of the students. The existing Outlier detection method has computational complexity and more processing time. In order to handle these issues, this work initially applies Edited Nearest Neighbor method to filter the global Outliers, then implements Cluster based Outlier Detection method to filter local Outliers. For clustering, the existing Hierarchical Agglomerative method is improved with Fuzzy Partition values continued with Flat Clustering to split the Dendrogram, which is the common output form of Hierarchical Clustering. Furthermore, the work calculates Multivariate Gaussian Outlier Score in order to filter the local Outliers in each clusters formed by Flat clusters. The proposed method is implemented in Rapidminer tool, assessed with the appropriate clustering evaluation measures. The proposed method proves its efficiency by having less Cluster Density, Sum of Squares and Processing Time while compared with the existing benchmark methods. © 2024 ieee.

关键词： Hierarchical clustering

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Digital Application Akelius as a Blended Learning Tool

Digital Application Akelius as a Blended Learning Tool

引用

ieee international Workshop on intelligent data acquisition and advanced computing systems: Technology and Applications

作者： Zhannura Manapbayeva Dinara Aliyeva International Information Technology University Almaty Kazakhstan

The article deals with modern approaches to the problem of blended learning, which are based on different criteria for organizing the learning environment depending on the expected learning outcomes. The advantages of blended teaching Russian as a foreign language are creating an individual learning trajectory, taking into account the level of language proficiency, the changing social environment (students are children of migrants), and an inclusive environment. The authors demonstrate the possibilities of using the Akelius digital application for teaching Russian as a foreign language, based on the experience of using it in a blended learning format, possible ways of using the Akelius application in the educational process when teaching language literacy in combination with traditional forms of education. The article focuses on the advantages of this application, built on the principle of a communicative-activity approach, which involves situational learning of a foreign language and is in the nature of inclusion of everyday communication in the language environment. The role of the teacher in the organization of the educational process when learning a new language in conditions of blended learning has also been noted. The authors offer modern teaching technologies that contribute to the transformation of the learning environment into a creative process of socialization of migrant children; demonstrate a lesson model with the use of the Akelius digital application.

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Construction of Anti-Terrorism Intelligence Graph Based on DIKW Framework 25

Construction of Anti-Terrorism Intelligence Graph Based on D...

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25th ieee international conferences on High Performance computing and Communications, 9th international conference on data Science and systems, 21st ieee international conference on Smart City and 9th ieee international conference on Dependability in Sensor, Cloud and Big data systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Gao, Peng Yuan, Haoran Liu, Yanfei Mao, Bowen Duan, Yucong Wang, Zhenhua Gansu Police Vocational College Gansu China Chongqing City Science and Technology College Artificial Intelligence and Big Data Academy Chongqing China Tianjin University Chongqing Police College Department of Intelligence and Computing Department of Infor Security Chongqing China Tianjin University Department of Intelligence and Computing Tianjin China School of Computer Science and Technology Hainan University Haikou China Chongqing Police College Department of Tactics Chongqing China

ISBN: (纸本)9798350330014

'Construction of anti-Terrorism Intelligence Graph Based on the DIKW Framework' simplifies the process of handling decentralized and diverse sources of intelligence in counter-terrorism. It uses the DIKW model to consolidate and structure data for better efficiency. It creates a data graph to illustrate relationships, making data more accessible. This data is then used to form an information graph, connecting data with relevant knowledge. From this, intelligent models are extracted, aiding in counter-terrorism prevention and prediction. This approach emphasizes transforming data into intelligence and knowledge hierarchically to improve counter-terrorism efforts. © 2023 ieee.

关键词： Terrorism

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BandX : An intelligent IoT-band for Human Activity Recognition based on TinyML 23

BandX : An Intelligent IoT-band for Human Activity Recogniti...

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24th international conference on Distributed computing and Networking (ICDCN)

作者： Saha, Bidyut Samanta, Riya Ghosh, Soumya K. Roy, Ram Babu Indian Inst Technol Kharagpur Kharagpur W Bengal India

ISBN: (纸本)9781450397964

Human Activity Recognition (HAR) is used in several human-centric real-world situations. In many learning-based systems, sensors capture situational data and transfer it to adjacent computing devices for processing and analysis. This concerns user data privacy and network availability, as well as increasing response latency and unnecessary power consumption. To address these, we developed a device BandX that classifies human activities from wearable sensors using an on-device deep learning inference mechanism on a microcontroller unit (MCU) based on TinyML. BandX performs with an accuracy of around 94% and reduces significant network traffic in comparison to computational offloading methods. We have also designed a framework that manages BandX sensor/inference data and provides a logical layer for user activity-level inferences. This framework includes an interactive user interface that lets people track their activity patterns and other health statistics.

关键词： TinyML On-Device Inference data acquisition Human Activity Recognition Wearable IoT

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