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Phase Fraction Calculation in Multiphase Steel Microstructures Through Semantic Segmentation 10

Phase Fraction Calculation in Multiphase Steel Microstructur...

引用

10th International Conference on Applied System Innovation, ICASI 2024

作者： Swain, Bishal Ranjan Cheoi, Kyung Joo Ko, Jaepil Kumoh National Institute of Technology Dept. of Computer & Ai Convergence Engineering Gumi Korea Republic of Chungbuk National University Dept. of Computer Science Cheonju Korea Republic of

ISBN: (纸本)9798350394924

The characterization of a material is defined by its transformation kinetics which is measured using the fraction of volume transformed. In common situations, a polycrystalline parent phase is transformed into various sub-phase constituent regions based on the desired material property. Characterization of such multiphase regions include accurate identification of phase and calculation of the phase fraction. To this end, we propose using semantic segmentation to identify and calculate the phase fraction in steel microstructures. We assign each pixel in an image to accomplish both phase identification and segmentation which we perform using enhanced ELU-Net coupled with improved combined loss function. Though scalable experiments we demonstrate more accurate phase fraction calculation via superior semantic segmentation by achieving an overall 8.6% performance increase over baseline with mean phase fraction error of 5.69%. © 2024 IEEE.

关键词： Semantic Segmentation

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Cricket shot classification using Deep Learning 2

Cricket shot classification using Deep Learning

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Kallur, Shanta Thotad, Puneeth N. Yaligar, Suman Kle Institute of Technology Affiliated With Visvesvaraya Technological University Dept. of Computer Science & Engineering Hubballi580027 India Kle Institute of Technology Affiliated With Visvesvaraya Technological University Dept. of Master of Computer Applications Hubballi580027 India K.L.E. Institute of Technology Dept. of Computer Science and Engineering Hubballi India Visvesvaraya Technological University Belagavi India

ISBN: (纸本)9798350388916

Cricket is a popular sport that involves various batting techniques and shot types. Accurate and real-time detection of cricket shots can provide priceless visual images for coaches, players, and analysts. This study proposes an artificial intelligence-based approach for cricket shot detection aimed at automatically recognizing and classifying different shots played by batsmen. Images are extracted and pre-processed to enhance their quality. The pre-processed images extract key features like bat trajectory, player movement, and batsman position. A training dataset is used to enable shot classification, consisting of labeled samples of different cricket shots. Neural network models, such as a convolutional neural network, VGG16, VGG19, are trained using an image set to learn the patterns and characteristics of each shot type. The model is then employed to classify shots in real time for predicting the corresponding shot type based on the selected features. The proposed approach is evaluated on a diverse dataset of cricket match images, encompassing a broad extent of shots played by professional batsmen. Execution metrics such as accuracy, precision, recall, and F1-score are used to assess the effect of the shot detection system. A VGG16 model in cricket shot prediction of the dataset yielded an impressive accuracy of 95.81%. The solution establishes the efficiency of the planned approach in accurately detecting and classifying cricket shots in real-time. The developed system has the potential to enhance cricket coaching, player performance analysis, and broadcast coverage by providing valuable insights into the shot selection and execution of batters. © 2024 IEEE.

关键词： Convolutional neural networks

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High Gain, E-Shaped Microstrip Antenna with Two Identical Slits for 5G Applications in the 60 GHz Band 3

High Gain, E-Shaped Microstrip Antenna with Two Identical Sl...

引用

3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Ali, Ala'a N. S. Gaid, AbdulGuddoos S. A. Saeed, Mamoon M. Saeed, Rashid A. Hawash, Burkan Taiz University Faculty of Engineering & Information Technology Dept. of Communications & Computer Taiz Yemen Faculty of Engineering Dept. Communications & Electronics Engineering Sana'a Yemen College of Computers & Information Technology Taif University Dept. of Computer Engineering Taif21944 Saudi Arabia Faculty of Information Science & Technology Selangor Malaysia

ISBN: (纸本)9798350305333

This article presents a simple, high-gain, E-shaped antenna suitable for 5G applications in the unlicensed 60 GHz band, characterized by high absorption and path losses. In order to optimize the reflection coefficient performance, enhance the gain, and adjust the resonance frequency of the proposed antenna, two identical slits are incorporated into the front edge of the radiating patch. The proposed structure is built on a Rogers RT/Duroid 5880 dielectric substrate, characterized by a thickness of 0.118 mm, a dielectric constant of 2.2, and a tangent loss of 0.0009. The optimized design with a compact size of 3.8 x 6 x 0.118 mm3 achieves an impedance bandwidth ( © 2023 IEEE.

关键词： Radiation efficiency

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DigiPramaan: Blockchain Based Certificate Management System 8

DigiPramaan: Blockchain Based Certificate Management System

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8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Swami, Prathamesh Vhatkar, Kapil Ambekar, Yash Singh, Kartikey Kaware, Yashovardhan Dr. D. Y. Patil Institute of Technology Dept. of Computer Engineering Pune India

ISBN: (纸本)9798350391770

With more people taking online courses and earning certifications, there are great new opportunities to improve skills. But, this digital trend has also led to a problem with fake certificates. To address this problem, we propose a robust certificate management system utilizing blockchain technology and smart contracts. Our system comprises three key components: certificate creation, authenticity verification, and a user-friendly digital storage solution. Blockchain technology is at the core of our system, ensuring that each certificate is immutable and trustworthy. We utilize cryptographic hashes as unique identifiers for each certificate, which detect any tampering by resulting in a different hash. Additionally, our system offers secure digital storage based on cloud technology, allowing users to efficiently manage and access their certificates. We also emphasize features for certificate revocation and updates, enhancing the system's security and adaptability. In conclusion, our blockchain based system offers a comprehensive and secure solution to the issue of fake certificates in the digital era. © 2024 IEEE.

关键词： Blockchain

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LPCC Based Music Genrefication Using Hybrid Computational Model 7

LPCC Based Music Genrefication Using Hybrid Computational Mo...

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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Sul, Astha Sarda, Harshit Billa, Aishwarya Mishra, Tusar Kanti School of Computer Science and Engineering Vellore Institute of Technology Vellore India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Dept. of Computer Science and Engineering Manipal India

ISBN: (纸本)9798350304268

This work deals with spontaneous music genrefication through computational models which in the recent times has been gaining importance rapidly. Through these hybrid computational models implemented users get an enhanced level of satisfaction when their choice of music files genre with least latency is gained. The paper includes the use of LPCC attributes to obtain the features of the music files along with robust classification models such as neural networks. For this purpose, one thousand audio files are taken as the sample set. The input audio file is pre-processed and suitable LPCC features are computed and stored into final vector. The pool of vectors of numerous audio samples are finally utilized to train a neural model. The proposed work trains the model for ten very distinct categories of music such as hip-hop, jazz, metal, pop, blues, classical, country, disco, reggae, and rock. Further, a comparison is also made between all the classifiers such as SVM, ANN and random forest (RF). Comparatively the best accuracy rate of 85.53\% has been achieved for the proposed work that validates its effectiveness. © 2023 IEEE.

关键词： ANN gtzan dataset LPCC Music Genre

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Offensive Message Spam Detection in Social Media Using Long Short-Term Memory 3

Offensive Message Spam Detection in Social Media Using Long ...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Sivakumar, M. Abishek, S.A. Karthik, N. Vanitha, J. National Institute of Technology Dept. of Electrical and Electronics Engineering Puducherry India National Institute of Technology Dept. of Computer Science Engineering Puducherry India National Institute of Technology Puducherry India E.G.S.Pillay Engineering College Nagapattinam India

ISBN: (纸本)9798331518592

In this world, online communication has become an essential part in our daily lives. Almost everyone uses social media platforms to communicate with the whole world. But this also leads to a major problem: cyberbullying. This paper proposes a system to detect users who spam offensive messages repeatedly and ban them using natural language processing and machine learning. The system would be beneficial to service providers, social media platforms, and individuals, allowing us to strive for a world of harmony. The amalgamation of NLP, Big Data, and ML approaches presents a robust and efficient solution for offensive message spam detection. The findings of this project will shed light on the potential of these technologies to curb the proliferation of offensive content, thereby fostering a healthier and more respectful online communication ecosystem. This paper delves on offensive spam detection using the LSTM model, and the obtained MSE of this model is 12.58. © 2024 IEEE.

关键词： Social networking (online)

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Multi-Access Edge Computing Using Intelligent Mobile User Resource Allocation In 6G 4

Multi-Access Edge Computing Using Intelligent Mobile User Re...

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4th IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer engineering, MI-STA 2024

作者： Saeed, Mamoon M. Saeed, Rashid A. Mokhtar, Rania A. Gaid, AbdulGuddoos S. A. Ahmed, Zeinab E. Elbasheir, Mohammed S. Faculty of Engineering Department of Communications and Electronics Engineering Yemen Taif University Faculty of Engineering Department of Electrical and Electronic Engineering Altaif Saudi Arabia College of Computers and Information Technology Taif University Department of Computer Engineering Taif21944 Saudi Arabia Taiz University Faculty of Engineering & Information Technology Dept. of Communication & Computer Engineering Taiz Yemen International Islamic University Malaysia Department of Electrical and Computer Malaysia School of Electronic Engineering Sudan University of Science and Technology Khartoum Sudan

ISBN: (纸本)9798350372632

Edge computing plays a crucial role in the advancement of Beyond 5G (B5G) and 6G networks. The substantial growth in network traffic and computational requirements necessitates the implementation of multi-access edge computing (MEC) as a viable solution. MEC aims to bring the capabilities of cloud computing closer to end-users by integrating them into the radio access network (RAN). This research introduces an optimal approach to connect mobile users with MEC hosts and access points, effectively distributing radio and computing resources to individual mobile users. The objective is achieved by lowering the total user transmit power while abiding by latency constraints in both the communication and computation phases, using the Grey Wolf Optimization technique (GWO). A comparison to alternative smart techniques demonstrates the effectiveness of the suggested approach © 2024 IEEE.

关键词： 5G mobile communication systems

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Analysis on Crop Yield Prediction using various Ensemble Methods 8

Analysis on Crop Yield Prediction using various Ensemble Met...

引用

8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Hariyani, Gulshan Singh, Aniket Patil, Prathamesh Kothari, Virag Javale, Deepali Dr. Vishwanath Karad MIT-World Peace University Dept. of Computer Engineering and Technology Pune India

ISBN: (纸本)9798350391770

Agriculture is a crucial facet of human existence as it fulfils the primary and fundamental requirement of sustenance, namely food. Therefore, it is incumbent upon us, as human beings, to cultivate and enhance our agricultural methodologies. Crop Yield Prediction is a method that enables farmers to determine the most suitable crops to produce in specific seasons based on historical data, prior to harvesting. This research examines the prediction of crop productivity in three distinct states of India. The states are Bihar, Jharkhand, and Odisha. We employ many ensemble methods, namely Random Forest, XGB Regression, and Stacking Regression, in which we combine Random Forest and XGB Regression. Next, compute the results by evaluating L1 and L2 using LASSO and Ridge Regression. Ultimately, XGB Regression yields a more precise outcome in comparison to the other two methods. © 2024 IEEE.

关键词： Agricultural economics

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Exploring CNN Architectures for Seizure Detection from MRI during Pregnancy 3

Exploring CNN Architectures for Seizure Detection from MRI d...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Nayak, Geetanjali Padhy, Neela Madhab Mishra, Tusar Kanti Giet Unviersity Dept. of Computer Science and Engineering Odisha Gunupur India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Computer Science and Engineering Karnataka Manipal India

ISBN: (纸本)9798350375190

Deep Learning (DL) represents a subset that has revolutionized traditional machine learning. Unlike earlier methods relying on handcrafted feature extraction, DL automates both feature extraction and classification. This innovation has significantly advanced applications in various medical domains, including the diagnosis of epileptic seizures. This research provides a comparative overview of studies concentrating on automated epileptic seizure detection using leading convolutional neural networks (CNN) architectures. It explores diverse architectures proposed for the automatic diagnosis of epileptic seizures through MRI, encompassing an analysis of rehabilitation systems utilizing DL. The proposed work investigates the application of four primitive CNN architectures on the identification of onset of seizure. The proposed architectures are validated through benchmark MRI samples. Experimental evaluation is made on samples of pregnant females. Overall rate of accuracy stands at 85%, 80%, 90%, and 95% respectively. © 2024 IEEE.

关键词： Network architecture

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Enhancing Power Quality in Distribution Systems through Bi-directional Solid-State Transformers 3

Enhancing Power Quality in Distribution Systems through Bi-d...

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3rd IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2024

作者： Ghambirlou, Khaled Esamil SH Abosnina, Adel Ali Moschopoulos, Gerry Engineering Western University Dept. Electrical and Computer London Canada College of Electrical and Electronics Technology Benghazi Libya

ISBN: (纸本)9798331540319

Power quality issues, including voltage sags, swells, harmonic distortion, oscillatory voltage transients, etc. can degrade equipment performance, leading to inefficiencies and costly disruptions. Studying these issues and introducing solutions are essential for maintaining the reliability and efficiency of modern distribution systems. This paper examines and contrasts the benefits of a bidirectional three-stage solid-state transformer (SST) in mitigating power quality issues to traditional transformers. SST introduces reactive power support to improve the system quality by controlling active and reactive power. This paper investigates key power quality issues-voltage sag, swell, harmonic distortion, and oscillatory transients-arising from capacitor bank energizing, focusing on the novel application of solid-state transformers (SSTs) in mitigating these effects. Additionally, it examines SST-based fault isolation across various fault types (three-phase, double line-to-ground, and line-to-ground). Several simulations have been performed using the MATLAB/Simulink platform to highlight the benefits of the Solid-State Transformer (SST) over conventional transformers and cascade multiple-active-bridge SST (CMABSST) types. The proposed SST employs active and reactive power control to alleviate these effects, allowing the SST controller to manage reactive power effectively and provide the required support that makes the system stable and reliable. © 2024 IEEE.

关键词： Distribution transformers

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