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Handcrafted and Deep features based Classification of Scoliosis 2

Handcrafted and Deep features based Classification of Scolio...

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2nd International Conference on Digital Futures and Transformative Technologies, ICoDT2 2022

作者： Fatima, Joddat Mohsan, Mashood M. Qaisar, Muhammad Umair Hamza, Muhammad Tahir, Muhammad Zeshan Zaman, Ghazzal Bahria University Dept. of Computer Science Islamabad Pakistan National University of Sciences & Technology Dept. of Computer & Software Engineering Rawalpindi Pakistan

ISBN: (纸本)9781665498197

The Spinal cord acts as the central transmission line connecting the Brain with all other body organs. Vertebrae are 33 uneven bones stacked over one another that holds the whole skeleton structure. Scoliosis is the three-dimensional spinal deformity which commonly occurs during the growing age and erupts before puberty. It is further classified in two Shapes C and S. Our research work has two stages, in first stage we segment out the vertebral column using Mask-RCNN. The segmented column is used for features extraction and in stage two feature based classification is done for normal, C and S shape of scoliosis using AASCE2019 dataset. A comparative study on multiple image classification networks is also conducted and based on results EfficientNet-B4 is selected for formulation of hybrid feature set. The accuracy achieved using Random forest classifier, for handcrafted and deep features was up to 94.32% and 89.66%. Hybrid feature set formulated with combination of deep and handcrafted features attained accuracy up to 94.45%. © 2022 IEEE.

关键词： Decision trees

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SIMULATION EXPLORATION EXPERIENCE (SEE) INTRODUCTORY TUTORIAL

SIMULATION EXPLORATION EXPERIENCE (SEE) INTRODUCTORY TUTORIA...

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2024 Winter Simulation Conference, WSC 2024

作者： Ghorbani, Maziar Anagnostou, Anastasia Abubakar, Nura T. Aatreya, Hridyanshu Curry, Damon Taylor, Simon J.E. Modelling & Simulation Group Dept. of Computer Science Brunel University London Uxbridge United Kingdom Pitch Technologies US OrlandoFL United States

ISBN: (纸本)9798331534202

This paper presents an introductory tutorial based on the Simulation Exploration Experience (SEE) 2024, highlighting a collaborative effort by NASA, SISO and international academic partners to model lunar facilities and habitats through the High-Level Architecture (HLA) for distributed simulations. Focused on federating simulations of lunar infrastructure, this paper outlines methodical steps for creating and executing models that incorporate communication systems and 3D visualizations to support educational and research initiatives in space exploration. Reflecting on SEE 2024’s advancements, the tutorial emphasizes significant progress in using simulation technology to promote innovation and collaboration across various scientific disciplines. This contribution, intended for discussion at the Winter Simulation Conference (WSC) 2024, showcases the role of HLA runtime infrastructure (RTI) in enabling realistic and interoperable simulation environments, enriching the discourse on simulation education. © 2024 IEEE.

关键词： NASA

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A Neural Network Processing Unit Using Adiabatic Quantum-Flux-Parametron Superconducting Technology 11

A Neural Network Processing Unit Using Adiabatic Quantum-Flu...

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11th IEEE Global Conference on Consumer Electronics, GCCE 2022

作者： Wang, Mengmeng Chen, Olivia Yoshikawa, Nobuyuki Yokohama National University Dept. Electrical and Computer Engineering Yokohama Japan Tokyo City University Dept. Computer Science Tokyo Japan

ISBN: (纸本)9781665492324

Adiabatic quantum-flux-parametron (AQFP) is a new superconducting technology developed in recent years. It achieves the highest energy efficiency among the superconducting logic families and has excellent potential for implementing large-scale systems. Convolutional Neural Network (CNN) is one of the representative algorithms of deep learning and has been widely used in many fields, such as computer vision, in recent years. Thus, with the development of neural networks, AQFP will have a broad application prospect in high-performance computing and deep learning. In this study, we propose an AQFP-based processing unit for convolutional computation in CNN. We generate a circuit of the proposed processing unit using AQFP integrated circuit design methodology that contains logic synthesis, placement and routing, simulation and verification. The designed circuit is fabricated by the AIST 10kA/cm2 high-speed standard process (HSTP). © 2022 IEEE.

关键词： Convolution

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Convolutional Neural Network and Capsule Network Fusion for Effective Attrition Classification 12

Convolutional Neural Network and Capsule Network Fusion for ...

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12th IEEE International Conference on Advanced Computing, ICoAC 2023

作者： Praveen, R. Pabitha, P. Sakthi, V. Madhavi, S. Vijay Sai, R. Anna University Mit Campus Chromepet Dept. of Computer Technology Chennai600 044 India K. S. Rangasamy College of Technology Dept. of Computer Science and Engineering Tiruchengode637 215 India

ISBN: (纸本)9798350318210

In today's rapidly evolving corporate landscape, employee attrition has emerged as a critical challenge for organizations aiming to sustain a skilled and motivated workforce. This paper introduces a novel approach to address the complex task of employee attrition classification by leveraging the combined power of Convolutional Neural Networks (CNNs) and Capsule Networks. The proposed fusion of CNNs and Capsule Networks capitalizes on their distinct strengths in feature extraction and capturing hierarchical relationships within data, respectively. By integrating these cutting-edge deep learning architectures, we present a holistic framework that enhances the accuracy and interpretability of attrition prediction models. Our experiments on real-world employee attrition datasets demonstrate the superior performance of the CNN and Capsule Network fusion approach compared to traditional methods. The fusion model achieves not only higher classification accuracy but also provides valuable insights into the underlying factors contributing to attrition. © 2023 IEEE.

关键词： Convolutional neural networks

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Software Development for Fuzzy Logic-Based Pipe Condition Prediction

Software Development for Fuzzy Logic-Based Pipe Condition Pr...

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Pipelines 2022 Conference

作者： Xu, Hao Dept. of Computer Science Virginia Tech BlacksburgVA United States

ISBN: (数字)9780784484289

ISBN: (纸本)9780784484289

Water pipe condition models have been important for water infrastructure management systems. Practitioners are in need of a robust and reliable condition rating system to help with the decision making related to water pipe infrastructure management. Over the past decades, researchers have developed a variety of condition models, among which fuzzy logic models are an essential category. Previous researchers have confirmed the advantages of fuzzy logic models over weighted factor models. However, there are still some shortcomings in the existing fuzzy logic models, such as the limited coverage of pipe types, limited piloting with utilities, use of rarely available variables, etc. The objective of this study is to develop a fuzzy logic software system for a variety of pipes, including metallic, cementitious, and plastic pipes, making use of structural, operational, and environmental variables. The fuzzy logic model together with a management plan will aid in the prioritization of future inspection and maintenance practices. © Pipelines 2022: Planning and Design - Proceedings of Sessions of the Pipelines 2022 Conference.

关键词： Fuzzy logic

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AI-Driven Traffic Congestion Management: A Predictive Analytics Approach for Smart Cities 3

AI-Driven Traffic Congestion Management: A Predictive Analyt...

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3rd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2025

作者： Rathore, Saurabh Pratap Singh Farhaoui, Yousef Aniebonam, Ebuka Emmanuel Nagpal, Tapsi Thanuja, M. Kaushik, Priyanka Icapsr Dept. of Management & Research New Delhi India Moulay Ismail University Dept. of Computer Science Errachidia Morocco University Marshall Dept. of Business MN United States Lingayas Vidyapeeth Dept. of Computer Science & Engineering Haryana India Sai Vidya Institute of Technology India Chandigarh University Dept. of Cse (AIML) CSE-APEX Chandigarh India

ISBN: (纸本)9798331521691

Rapid urbanization, increased vehicle usage, and the growth of developing cities have resulted in severe traffic congestion, leading to economic losses, environmental harm, and a decline in quality of life. Traditional traffic management systems tend to be reactive, focusing on addressing traffic flow issues after they occur, rather than proactively managing them. To tackle this challenge, this paper proposes an AI-driven system designed to predict and manage traffic congestion. The system leverages continuous traffic data from IoT devices, such as images, GPS, and inductive loop sensors, to monitor real-time traffic conditions. Traffic predictions are made using a combination of CNN and LSTM networks. Additionally, Geographic Information System (GIS) technology is integrated for spatial analysis and dynamic traffic control, enabling real-time traffic light adjustments and providing drivers with alternative routes to avoid congestion. Preliminary experiments show that intelligent traffic management systems can significantly improve traffic flow and reduce congestion in large cities. The paper also explores challenges related to data privacy, scalability, and future advancements, including the integration of self-driving vehicles and the application of reinforcement learning. This system offers a practical solution for alleviating congestion and optimizing urban transportation systems. © 2025 IEEE.

关键词： AI-based Traffic Management Geographic Information System (GIS) IoT Sensors Long Short-Term Memory (LSTM) Networks Real-time Data Processing Traffic Congestion

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Efficient Fund Tracking system using Blockchain and GraphDB 7

Efficient Fund Tracking system using Blockchain and GraphDB

引用

7th International Conference on Image Information Processing, ICIIP 2023

作者： Kaushal, Arpit Nadda, Shiveen Modi, Praveen Jaypee University of Information Technology Dept. of Computer Science And Engineering And Information Technology H.P Solan India

ISBN: (纸本)9798350371406

Powerful Asset Tracking System is a cutting edge arrangement that utilizes the force of blockchain innovation and Graph Database to give a vigorous and secure resource following stage. Conventional asset global positioning frameworks frequently face difficulties connected with straightforwardness, security and effectiveness. Notwithstanding, this framework conquers these limits by exploiting the blockchain and its decentralized and permanent nature, as well as the adaptability and versatility of diagram information bases. By incorporating blockchain innovation, the framework guarantees a straightforward and unapproved recording of asset exchanges. Every exchange is safely put away in blocks that structure a chain of information that can't be changed or erased. This increases responsiveness and disposes of the requirement for middle man, decreasing expenses and handling time. © 2023 IEEE.

关键词： Blockchain

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Coordinated Sampling in SDNs with Dynamic Flow Rates

Coordinated Sampling in SDNs with Dynamic Flow Rates

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International Conference on Network and Service Management

作者： Soroosh Esmaeilian Mahdi Dolati Sogand Sadrhaghighi Majid Ghaderi Dept. of Computer Science University of Calgary Calgary Canada School of Computer Science IPM Tehran Iran

ISBN: (数字)9783903176669

ISBN: (纸本)9798331505158

Traffic sampling has become an indispensable tool in network management. While there exists a plethora of sampling systems, they generally assume flow rates are stable and predictable over a sampling period. Consequently, when deployed in networks with dynamic flow rates, some flows may be missed or under-sampled, while others are over-sampled. This paper presents the design and evaluation of dSamp, a network-wide sampling system capable of handling dynamic flow rates in Software-Defined Networks (SDNs). The key idea in dSamp is to consider flow rate fluctuations when deciding on which network switches and at what rate to sample each flow. To this end, we develop a general model for sampling allocation with dynamic flow rates, and then design an efficient approximate integer linear program called APX that can be used to compute sampling allocations even in large-scale networks. To show the efficacy of dSamp for network monitoring, we have implemented APX and several existing solutions in ns-3 and conducted extensive experiments using model-driven simulations. Our results indicate that, by considering dynamic flow rates, APX outperforms the existing solutions by up to 10% in sampling more flows at a given sampling rate.

关键词： Fluctuations Computational modeling Dynamic scheduling Approximation algorithms Resource management Software defined networking Computational complexity Monitoring

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Fine-Grained Dynamic Hardware Resource Usage Measurement for Distributed Key-Value Stores

Fine-Grained Dynamic Hardware Resource Usage Measurement for...

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IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA)

作者： Shuyi Zhang Jiakun Zhang Yongkun Li Dept. of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (数字)9798331530808

ISBN: (纸本)9798331530815

In the era of big data, distributed key-value stores are widely used in various fields. These systems efficiently store and manage massive amounts of data by distributing data across different. It also splits data into multiple fine-grained shards for management purposes. However, the minimum unit of hardware resource usage perception is the node because it is difficult to measure the hardware resource usage of the shard since it is a logical structure, and the corresponding data is scattered in different locations in the nodes. This inconsistency in granularity leads to challenging hardware resource usage management while the unit of management is shard. To solve this problem, we propose a new hardware resource usage measurement framework FDHM, which integrates a measurement model based on the shard and node attributes and a dynamic attribute update mechanism to achieve fine-grained and dynamic measurement. We implemented a lightweight integration of FDHM on TiKV and tested it. Experimental results show that FDHM significantly improves the accuracy of hardware resource usage measurement compared with existing methods. And we also verified its benefits in resource management through a case study.

关键词： Cloud computing Accuracy Distributed databases Big Data Dynamic scheduling Hardware Resource management

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The Use of Time and Frequency Features in Finger Movements Based on Electromyogram Recording

The Use of Time and Frequency Features in Finger Movements B...

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2022 International Conference for Advancement in Technology, ICONAT 2022

作者： Atmaji, Catur Lelono, Danang Harjoko, Agus Dharmawan, Andi Universitas Gadjah Mada Doctoral Program in Computer Science Yogyakarta Indonesia Universitas Gadjah Mada Dept. of Computer Science and Electronics Yogyakarta Indonesia

ISBN: (纸本)9781665425773

Human movement is a direct result of brain commands to muscles. The muscle activity can be analyzed with an electromyograph device and produce a recording in the form of an electromyogram or EMG signal. There are two recording methods, namely intramuscular EMG and surface EMG or sEMG which are easier and more convenient to implement but have lower resolution. Analysis of the EMG record data will be better if the right method is chosen, one of which is the selection of feature extraction methods. This study performs basic finger motion classification with a limited number of electrodes, namely 4 electrodes. The purpose of this research is to compare the variation of features in the time or frequency domain that will be used for classification using artificial neural networks (ANN) and long short-term memory (LSTM). Data acquisition was carried out with the Ganglion Board device on 6 subjects aged 20-22 years who conducted experiments with two kinds of movements, namely opening and holding all fingers alternately. The results of this study indicate that the use of time domain features for classification with ANN produces better accuracy than LSTM. This can happen because the duration of the movement in this study is quite short, which is only for two seconds. The results of the use of the frequency domain feature show that the use of LSTM produces better accuracy, especially on the mean power and median frequency characteristics. © 2022 IEEE.

关键词： Frequency domain analysis

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