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Brain Tumor Detection Using Deep Learning(CNN) 6

Brain Tumor Detection Using Deep Learning(CNN)

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6th ieee international conference on Emerging Smart computing and Informatics, ESCI 2024

作者： Patare, Yuvrai Yadav, Sanket Pingale, Rupesh Kavitha, S. School of Computer Engineering MIT Academy of Engineering Pune India

ISBN: (纸本)9798350306613

A brain tumour is a condition in which an accumu-lation of aberrant brain cells leads to *** the brain is covered with the rigid skull, the inner part which is complex and has restricted space would cause many problems. This is very critical disease which causes deaths. Through different reports it has been analyzed that 2400 people die per year due to brain tumor and also it is predicted that, it could be increased by 1.11 percent till 2030. This research focuses on detecting brain tumours with CNN classification techniques. Brain Tumor MRI dataset from Kaggle is used for the study. The study uses five different models to divide the MRI scans into categories such as 'tumour detected' and 'tumour not detected', indicating not only the existence of a tumour but also the specific type of brain tumour that was *** 5 exhibited the highest testing accuracy of 98.63 %, along with a low testing loss of 0.0534, positioning it as the best model. © 2024 ieee.

关键词： CNN data pre-processing Deep Learning Tumor Cells

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Dual Aggregation Convolution for Image Super-Resolution 3

Dual Aggregation Convolution for Image Super-Resolution

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3rd international conference on Cloud computing, Big data Application and Software Engineering, CBASE 2024

作者： Wang, Zhongxun Xie, Zheng School of Physics and Electronic Information Yantai University Shandong China Yantai University Shandong Data Open Innovation Application Laboratory of Smart grid Advanced Technology Shandong China

ISBN: (纸本)9798331506582

In recent years, numerous image super-resolution (SR) methods have been introduced, achieving significant advancements. However, many existing methods employ complex, task-specific operations that, while effective, introduce substantial computational overhead when directly stacked, thereby hampering their practical utility. In response, we introduce DACNet, an efficient SR model that innovatively integrates spatial and channel information through a collaborative framework. Specifically, we alternately apply the Aggregation Spatial Convolution Module (ASCM) and the Aggregation Channel Convolution Module (ACCM) to dynamically extract and integrate multi-level features. Furthermore, we employ a Convolution Compensation Module (CCM) to mitigate local pixel forgetting. Our comprehensive experiments demonstrate that DACNet not only achieves superior performance but also maintains minimal computational demands, making it highly effective in resource-constrained environments. © 2024 ieee.

关键词： Convolution

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data Visualization in Transforming Raw data into Compelling Visual Narratives 2

Data Visualization in Transforming Raw Data into Compelling ...

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2nd ieee international conference on Trends in Quantum computing and Emerging Business Technologies, TQCEBT 2024

作者： Sarkar, Prithu Priya, Yadavalli Devi Patel, Pravina B. Chatterjee Biswas, Prasanta Arigela, Sreenivasulu Sallaram, Sravanthi Amity University Amity School of Communication Kolkata India Computer Science and Engineering Vignan's Institute of Information Technology Duvvada Andhra Pradesh Vishakapatanam India Ganpat University Department of Computer Science Gujarat Mehsana India Cdoe Parul University Vadodara India Mohan Babu University Andhra Pradesh Tirupati India St.Martin's Engineering College Department of Computer Science and Engineering India

ISBN: (纸本)9798350384277

One of the main ways to evaluate information is through visual learning, which traditionally combines textual reading with visual aids like graphical representations. However, separating the visual method of learning to distinguish between text and visuals has been suggested in light of advancements in learning styles. The ability to swiftly create complex visual information and inexpensively distribute it digitally is another way that technology has improved visual presentation. The purpose of this research paper is to conduct a detailed investigation of the Virtualization of data, storytelling, and data Visualization's Narrative, understand how Instruments and methods are used in visualization, and select the appropriate tool for presenting visual data stories and upcoming projects. To address this purpose, this research has reviewed a wide range of different journals and articles to gather information about data visualization, storytelling, and data narratives. Based on the analysis, this research paper finds an all-encompassing perspective on the visual data narrative process, incorporating the phases of discovering insights (data exploration), developing these insights into a tale (creating a story), and sharing this story with others (telling a story). Through the pursuit of these novel research directions, visualizations can facilitate more potent data storytelling. © 2024 ieee.

关键词： Spatio-temporal data

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Soft Real-time Emotion-aware System through Human Physiological Signals using Pervasive Devices 13

Soft Real-time Emotion-aware System through Human Physiologi...

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13th ieee international conference on Communication, Networks and Satellite, COMNETSAT 2024

作者： Setiawan, Feri Khowaja, Sunder Ali Bina Nusantara University School of Computing and Creative Arts Jakarta Indonesia Technological University Dublin School of Computing Faculty of Computing Digital and Data Dublin Ireland

ISBN: (纸本)9798350368086

The concept of connected things that involves emotional aspects has been raised as a new research issue known as 'emotional IoT'. One of the challenges is to recognize the emotion in real-time without interfering the user's task or activity. However, most of the existing works on emotion recognition deal with high sampling rate devices and thus need complex arrangement. In this study, we present a framework to recognize four emotions based on human physiological signals using pervasive devices with low sampling rate. The employment of low sampling rate devices may degrade the classification accuracy due to the data offsets or calibration errors. Our study proposes a set of energy deviation features to overcome the accuracy degradation caused by low sampling rate devices. The recursive feature elimination method is applied to avoid overfitting by selecting the most important features and reduce the computational overhead in real-world implementation. Experimental results show that the proposed approach achieves 92.01% and 91.75% accuracy in terms of valence and arousal, respectively. The realization of the proposed approach is done in an end-to-end soft real-time emotion recognition system using smartphone and smartwatch devices. The performance of our system was evaluated under laboratory setup, resulting in 46.3 milliseconds of response time on the average for recognizing emotions. © 2024 ieee.

关键词： Emotion Recognition

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Research on Intelligent Early Warning Algorithm of System Anomaly Detection Using Multi-Modal data Fusion Technology 6

Research on Intelligent Early Warning Algorithm of System An...

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6th ieee international conference on Power, Intelligent computing and Systems, ICPICS 2024

作者： Liu, Hongying School of Engineering Guangzhou College of Technology and Business Guangzhou510850 China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374315

System anomaly detection and early warning are the key technologies to ensure the safe and stable operation of the system in the complex and changeable environment. Traditional anomaly detection methods often rely on a single data source, and it is difficult to capture the multi-dimensional anomaly characteristics of the system. To solve this problem, an intelligent early warning algorithm based on multi-modal data fusion technology is proposed in this paper. The algorithm uses multi-source heterogeneous data, including sensor data, log data, network traffic data, etc., and constructs a multi-modal feature fusion model to realize efficient detection of abnormal behavior of the system. Specifically, we first preprocess and extract features from all kinds of data, and then use deep learning models for feature fusion to build a unified multimodal feature representation. Then, a method combining graph neural network and time series analysis is used to detect and warn the fused features. The experimental results show that the proposed method has higher detection accuracy and lower false positive rate on multiple real world data sets, which has significant advantages over traditional methods. The research results show that multi-modal data fusion technology has broad application prospects in the field of anomaly detection, and provides a new idea for the construction of intelligent and accurate early warning system. © 2024 ieee.

关键词： Deep learning Analytical models Accuracy Time series analysis data integration Telecommunication traffic Predictive models Feature extraction data models Anomaly detection

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Enhancing Path Planning in UAVs: an Improved Goal-Biased RRT Algorithm for complex 3D Environments 3

Enhancing Path Planning in UAVs: an Improved Goal-Biased RRT...

引用

3rd international conference on Artificial Intelligence, Internet of Things and Cloud computing Technology, AIoTC 2024

作者： Ming, Rui Chen, Jinrong College of Computer and Big Data Minjiang Universit Fuzhou350108 China

ISBN: (纸本)9798331530280

This paper addresses the issues of high randomness and low search efficiency in the traditional Rapidly-exploring Random Tree (RRT) algorith m when applied to high-dimensional spaces and complex environments. An improved algorithm is proposed, which incorporates a goal-biased strategy to direct sampling points towards the goal, enhancing the efficiency of path generation. Additionally, fixed sample points that point towards the goal are generated within the exploration space, reducing search randomness. The Euclidean heuristic function is used to calculate the cost of sample points within the exploration space, selecting the point with the minimum cost to form the path, thereby optimizing path quality. Experimental results demonstrate that the improved algorithm outperforms the traditional RRT algorithm in terms of path length, generation time, and the number of explored nodes. This study provides an efficient and reliable solution for path planning in complex three-dimensional environments for unmanned aerial vehicles (UAVs), improving the overall efficiency and reliability of UAV mission execution. © 2024 ieee.

关键词： Random forests

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A GPU-based method for network alignment

A GPU-based method for network alignment

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2024 ieee international conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Cinaglia, Pietro Cannataro, Mario Magna Graecia University of Catanzaro Data Analytics Research Center Department of Health Sciences Italy Magna Graecia University of Catanzaro Data Analytics Research Center Department of Medical and Surgical Sciences Italy

ISBN: (纸本)9798350386226

Network graph models are a powerful tool for handling objects, in terms of interactions and relationships. For instance, in bioinformatics networks are applied for analysing complex biological systems, topologically, as well as for investigating their own homology. In this context, the pairwise network alignment can be performed for producing a set of node-to-node mappings from a source network to a target one. For instance, network alignment can be applied for porting knowledge from a simpler to a more complex system (e.g., a simpler biological organism to a complex one). In this paper, we presented a GPU-based method for the pairwise Local Network Alignment, implemented by using a greedy strategy. It leverages GPU parallelism to accelerate the large-scale computation of a node similarity matrix of interest. Our experimentation showed a relevant improvement in terms of runtime when processing is executed on GPU, in comparison to the traditional CPU computing. Briefly, our method has proven to be an effective solution for the alignment of large networks, especially with dense similarity matrices, thus proving to be an ideal candidate for use in real-world study cases. © 2024 ieee.

关键词： gpu graph local network alignment network alignment parallel computing

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A High-Performance Multicore Testing Platform for Multi-Scenario Applications 17

A High-Performance Multicore Testing Platform for Multi-Scen...

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17th ieee international conference on Solid-State and Integrated Circuit Technology, ICSICT 2024

作者： Ling, Zipeng Zhuo, Tianshu Yang, Zhuoyuan Ye, Jinhong Han, Jun Zhang, Jingtao State Key Laboratory of Integrated Chips and Systems Shanghai201203 China ZTE Corporation Shenzhen518057 China

ISBN: (纸本)9798350361834

Cloud computing and high-performance data centers, as key pillars of computing infrastructure, face the challenges of rapidly growing network loads and high concurrency requests. Traditional data centers use modern processors to handle high bandwidth concurrent requests, and for the vast majority of network loads, do not require complex cache optimization strategies and adopt streaming processing, so there is no issue of cache consistency. This article proposes a high throughput multi-core platform for network processing, which includes input and output engines to complete data transmission and reception, as well as independent memory space and scheduler modules for each core. For the processing of network packets, due to the frequent switching of processor cores in different scenarios, the multi-core platform designed in this article can complete the scheduling and adaptation of processor cores for various application testing scenarios. The final experimental results show that the multi-core platform proposed in this article can efficiently process network packet processing requests in different scenarios simultaneously, achieving high data throughput. The platform that integrates four input/output engines and four PE cores can achieve a data throughput of 1.97Gbit/s in the scenario of forwarding packets. © 2024 ieee.

关键词： data centers

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DASIV: Directional Acoustic Sensing based Intelligent Vehicle Interaction System

DASIV: Directional Acoustic Sensing based Intelligent Vehicl...

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2024 ieee international Performance, computing, and Communications conference, IPCCC 2024

作者： Zhao, Dinghua Zhou, Juntao Ding, Dian Lu, Yu Li, Yijie Yang, Hang Chen, Yi-Chao Xue, Guangtao Shanghai Jiao Tong University Department of Computer Science and Engineering China Governance and Web3 Shanghai Key Laboratory of Trusted Data Circulation China

ISBN: (纸本)9798350367942

With the increase in motor vehicles, more convenient and accurate interactions are expected while retaining a high standard of safe driving. However, complex and dynamic vehicle environments challenge sensing tasks such as breathing monitor and hand gesture recognition. In this paper, we propose DASIV, which utilizes the highly directional nature of ultrasonic signals to achieve fine-grained directional acoustic sensing in vehicle environments. Due to air nonlinearity, the system enables synchronized directional acoustic communication to transmit information (e.g., navigation) to the driver without affecting other passengers. By optimizing the frequency of the Frequency Modulated Continuous Wave (FMCW) signals, DASIV avoids mutual interference between the sensing and communication signals and achieves breathing detection and hand gesture recognition for the driver. Specifically, the system extracts breathing-induced weak thoracic bullying through the signal phase, captures and analyses breathing patterns using bandpass and Gaussian filters, and develops a breathing model. Then, the system defines 10 interaction hand gestures to meet daily interaction needs, uses spectral features to mine complex and fast hand movement features, and proposes a hand gesture recognition model. Extensive experiments in real environments show that DASIV achieves high-precision breathing monitor (Pearson correlation coefficient of 0.89) and hand gesture recognition (Precision of 91.7%). © 2024 ieee.

关键词： Palmprint recognition

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data Management Over Cloud Edge Continuum in the Context of AC3 29

Data Management Over Cloud Edge Continuum in the Context of ...

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29th ieee international Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2024

作者： Amaxilatis, Dimitrios Klonidis, Dimitrios Ksentini, Adlen Moulos, Vrettos Sengupta, Souvik Tsironis, Nikolaos Verikoukis, Christos Spark Works Ltd. Galway Ireland Ubitech Ltd. Athens Greece Eurecom Sophia-Antipolis France University of Piraeus Piraeus Greece IONOS Se Karlsruhe Germany University of Patras and ISI/ATHENA Patras Greece

ISBN: (纸本)9798350377644

The proliferation of Internet of Things (IoT) tech-nologies and the exponential growth of data have necessitated advanced data management solutions that can operate across distributed architectures. The AC3 project introduces a data Management Platform as a Service (DMPaaS), designed to address these challenges by providing a robust, scalable, and flexible platform bridging cloud and edge computing environments. This paper explores the core components of the AC3 DMPaaS, empha-sizing its dynamic data catalog, efficient data management processes, and the seamless integration of data connectors that facil-itate data movement across the cloud-edge continuum. Through advanced metadata management, real-time data processing, and a hybrid cloud-edge data framework, the AC3 DMPaaS supports complex data operations and analytics, ensuring data integrity and consistency. This comprehensive overview establishes the foundational elements of the AC3 project's DMPaaS, showcasing its significant advantages for enterprises leveraging IoT data across diverse computing landscapes. © 2024 ieee.

关键词： Cloud platforms

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