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Utilizing the Power of Residual and Attention Properties with Binary Focal Loss Optimization for Underwater Image Segmentation Using UNet Architecture 7th

Utilizing the Power of Residual and Attention Properties wit...

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7th International Conference on Communication, Device and Networking, ICCDN 2024

作者： George, Geomol Anusuya, S. Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Tamil Nadu Chennai602105 India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Tamil Nadu Chennai602105 India

ISBN: (纸本)9789819764648

Recent developments in deep-sea exploration, environmental surveillance, and ocean research, accurate segmentation of underwater images is essential. This study pursues this goal by exploring underwater image segmentation from a deep learning perspective. It specifically looks at how well the Attention Residual UNet architecture, a more sophisticated version of the U-Net, works with the focal loss technique to achieve accuracy in this crucial task. Using attention mechanisms and residual connections, the Attention Residual UNet architecture, based on the U-Net framework, can capture fine details while maintaining contextual coherence. Model loss and accuracy measures are considered as this study carefully assesses the architecture’s performance. Notably, the model exhibits outstanding accuracy, obtaining 97.23% and 92.76% accuracy on the training and validation sets, respectively. The Jaccard coefficient further demonstrates the model’s efficiency, which measures the intersection between predicted and actual segments and has coefficients for the model and validation sets of 74.31% and 66.51%, respectively. The Mean Intersection over Union (MIoU) statistic, which boasts a value 91.24%, validates the model’s superiority. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Underwater imaging

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Data Driven Methods to Predict Vertical Profiles of the Rails: A Case Study 10

Data Driven Methods to Predict Vertical Profiles of the Rail...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Nugraha, Ade Chandra Supangkat, Suhono Harso Nugraha, Igb Baskara Handoko, Yunendar Aryo Purwadinata, Awan Hermawan Saputro, Dimas Wisnu Bandung State of Polytechnic School of Electrical Engineering and Informatics Bandung Institute of Technology Department of Computer Engineering and Informatics Bandung Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Smart City and Community Innovation Center Bandung Indonesia Bandung Institute of Technology Faculty of Mechanical and Aerospace Engineering Bandung Indonesia PT Kereta Api Indonesia Managing Director of Infrastructure Bandung Indonesia Bandung State of Polytechnic Department of Computer Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9798350339543

Predicting the value of railroad track geometry is needed to reduce the cost and time of rail condition measurements that have been carried out periodically every three months by railroad operators in Indonesia (PT Kereta Api Indonesia/PT KAI). Four track geometry parameters - vertical profile, horizontal alignment, twist, and gauge - are the reference measurements that will be mapped with alternative measurement results using accelerometers installed on regular trains (in-service trains). In this paper, eight prediction algorithms from the machine learning model are applied to map the vertical profile with 73 kilometers of measurement data in the Jakarta to Padalarang railroad segment, precisely on the Jatinegara-Cikampek measuring trip conducted from April 2019 to June 2022. The evaluation performance of the prediction results has been measured with the GadientBoosting Regressor algorithm is the best algorithm with MSE = 0.791, R2 = 0.507 and feature importance on the Train speed and Az attributes. The research can be continued to detect the correlation of three other track geometry parameters referring to the dataset of rail geometry condition measurement results through accelerometers on regular trains (in-service trains). © 2023 IEEE.

关键词： Forecasting

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Toward Utilizing Bidirectional Multi-head Attention Technique for Automatic Correction of Grammatical Errors

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2024年第11期51卷 1886-1897页

作者： Mahmoud, Zeinab Kryvinska, Natalia Abdalsalm, Mohammed Solyman, Aiman Alfatemi, Ali Musyafa, Ahmad School of Computer Science and Technology Wuhan University of Technology China Department of Information Management and Business Systems Comenius University Slovakia Department of Computer Science University of Milan Italy CIS Department Fordham University BronxNY10458 United States Department of Informatics Engineering Pamulang University South Tangerang15310 Indonesia

Automatic Grammar Error Correction (GEC) models identify and correct a wide range of grammatical errors. Various strategies have been proposed for GEC, with the Neural Machine Translation (NMT) approach being the most effective. However, NMT-based GEC models with encoder-decoder layers rely heavily on the highest layer, leading to potential inaccuracies. Additionally, during inference, exposure bias can cause the model to substitute previously targeted words with incorrect alternatives. Another challenge is data scarcity. This paper introduces a GEC model leveraging the seq-to-seq Transformer framework, specifically designed for low-resource languages like Arabic. We propose a method to generate noise in the text to create synthetic parallel data, addressing the data constraints. Inspired by Capsule Networks (CapsNet), we incorporate CapsNet in GEC to dynamically aggregate information from multiple layers. In order to mitigate exposure bias, we incorporated a bidirectional training approach and a regularization term using Kullback-Leibler divergence to align left-to-right and right-to-left models. Experiments on two benchmarks demonstrate that our model outperforms current Arabic GEC models, achieving the highest scores. The code is available on GitHub (https://***/Zainabobied/ArabicGEC). © (2024), (International Association of Engineers). All rights reserved.

关键词： Neural machine translation

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Performance Evaluation of Deep Feature in Cross Spectral Face Recognition 11

Performance Evaluation of Deep Feature in Cross Spectral Fac...

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11th International Conference on Information Technology, computer and Electrical engineering, ICITACEE 2024

作者： Meutia, Ernita Dewi Arnia, Fitri Nasaruddin Muharar, Rusdha Oktiana, Maulisa Saddami, Khairun School of Engineering Universitas Syiah Kuala Department of Electrical and Computer Engineering Banda Aceh Indonesia Universitas Syiah Kuala Department of Electrical and Computer Engineering Banda Aceh Indonesia

ISBN: (纸本)9798350389289

Cross-spectral face recognition (CSFR), particularly in the Near Infra-Red (NIR) and visual (VIS) spectrum, presents challenges due to differences in illumination and sensor characteristics. NIR sensors are commonly used in practical applications, such as in nighttime outdoor surveillance systems, due to their ability to capture images in low light conditions and at various standoff distances. This study proposes a simple CSFR method that combines deep learning techniques with photometric normalization using Homomorphic Filtering (HF) to identify subjects in NIR images across different standoff distances. Both NIR and VIS images are filtered to mitigate illumination discrepancies. A deep convolutional neural network (CNN) model is employed to automatically extract discriminative deep features from the normalized images of both spectrums. Four deep CNN models of ResNet-50, DenseNet, EfficientNet, and MobileNetV2 are tested. The performance of the models in feature extraction for CSFR is evaluated on the LDHF (Long Distance Heterogeneous Face) database. The Hamming distance metric is employed to match NIR probe images with VIS gallery images. The experimental findings, expressed in recognition rate RR, demonstrate the effectiveness of the approach in enhancing CSFR accuracy, with MobileNetV2 achieving the highest accuracy of 90% for a short standoff. In addition, HF improves image quality, enhancing the deep learning model's ability to discern image characteristics. © 2024 IEEE.

关键词： Face recognition

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Design of 5G Multi-Frequency Antenna Based on Multi-Objective Sequential Domain Patching

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Progress In Electromagnetics Research C 2024年 145卷 53-61页

作者： Zhu, Wenjian Chen, Jiayi Hu, Panshi Song, Zhi Xue, Yanbing School of Automation and Electrical Engineering Dalian Jiaotong University China School of Computer and Communication Engineering Dalian Jiaotong University Applied Basic Research Project of Liaoning Science and Technology Department Dalian116028 China

This paper introduces a 5G multi-frequency antenna design method based on multi-objective sequential domain patching. By etching helical metamaterials on radiation patches and loading asymmetric electric-inductive-capacitive metamaterials on the transmission line side, the antenna structure is designed and optimized using electromagnetic simulation software, HFSS, and MATLAB. The resulting multi-frequency antenna operates across multiple frequency bands: n1, n41, 3.5G, and 4.9G. To ensure antenna performance and radiation efficiency, the antenna multi-frequency bands are precisely controlled. The experimental results show that the error between each operating frequency band and target frequency bands of the antenna is within 7.2%. Compared with conventional antenna design methods, the use of metamaterials and intelligent algorithms to optimize the structural parameters and loading positions of metamaterials can improve antenna performance while shortening the design cycle. Overall, this research offers novel insights into the design of 5G high-performance antennas. © 2024, Electromagnetics Academy. All rights reserved.

关键词： Metamaterials

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Analytical solutions of simplified modified Camassa-Holm equation with conformable and M-truncated derivatives:A comparative study

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Journal of Ocean engineering and Science 2024年第3期9卷 240-250页

作者： Ismail Onder Melih Cinar Aydin Secer Mustafa Bayram Department of Mathematical Engineering Yildiz Technical UniversityIstanbulTurkey Graduate School of Natural and Aplied Sciences Yildiz Technical UniversityIstanbulTurkey Department of Computer Engineering Biruni UniversityIstanbulTurkey

This paper extracts some analytical solutions of simplified modified Camassa-Holm(SMCH)equations with various derivative operators,namely conformable and M-truncated derivatives that have been recently *** SMCH equation is used to model the unidirectional propagation of shallowwater *** extended rational sine−cosine and sinh−cosh techniques have been successfully implemented to the considered equations and some kinds of the solitons such as kink and singular have been *** have checked that all obtained solutions satisfy the main equations by using a computer algebraic ***,some 2D and 3D graphical illustrations of the obtained solutions have been *** effect of the parameters in the solutions on the wave propagation has been examined and all figures have been *** derived solutions may contribute to comprehending wave propagation in shallow ***,the solutions might help further studies in the development of autonomous ships/underwater vehicles and coastal zone management,which are critical topics in the ocean and coastal engineering.

关键词： Modified Camassa-Holm equation Conformable derivative M-truncated derivative Extended rational sine−cosine technique Extended rational sinh−cosh technique Exact solutions

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A Modification of SynthEnsemble: A Different Approach to the Fusion of Diverse Classifiers for Automated Thoracic Disease Diagnosis 31

A Modification of SynthEnsemble: A Different Approach to the...

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31st National and 9th International Iranian Conference on Biomedical engineering, ICBME 2024

作者： Vahedi, Reyhane Moshiri, Behzad University of Tehran School of Electrical and Computer Engineering Tehran Iran University of Waterloo Department of Electrical and Computer Engineering WaterlooON Canada

ISBN: (纸本)9798331529710

Accumulated Chest X-Ray images have widely been used in the task of automating the diagnosis of thoracic diseases. Multiple studies on using single classifiers for this task and also some studies on the fusion of classifiers for the same purpose have been previously done and all have reached promising results. On furthering the classifier fusion approach, we have implemented a variation of methods including those used in the SynthEnsemble [1] paper on the ChestX-ray14 dataset [2], while focusing on the balance of classifier diversity and accuracy and using the same pre-trained single classifiers from SynthEnsemble as grounds for comparison. We have conducted all the same decision fusion methods, such as weighted averaging and using stochastic global optimization methods for classifier weight optimization, and have also employed new and additional methods, such as various heuristic and intelligent weight optimization methods, with regards to optimizing the number of base classifiers for reducing the computational cost and tuning the accumulated diversity of the fused network. The proposed approach, in summary, is a comparison between a brute-force based global heuristic search as a classifier weight optimizer, and genetically inspired stochastic weight optimizations added to an ordered weighted averaging fusion method, complemented with minimizing the set of base classifiers in accordance to their accuracy-diversity trade-off. Throughout this study, we use various metrics of model evaluation through all of which we have reached better or at least similar performance results while lowering the diversity of the set of chosen classifiers, reducing the number of base classifiers from six to four, and decreasing the execution time by a notable 98.7%. Even notwithstanding the improved performance of our proposed fusion methods, which is greatly dependent on the choice of base classifiers and is strictly limited to those used in [1] for accurate comparison, we can draw the

关键词： Stochastic models

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Focusing on your subject:Deep subject-aware image composition recommendation networks

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Computational Visual Media 2023年第1期9卷 87-107页

作者： Guo-Ye Yang Wen-Yang Zhou Yun Cai Song-Hai Zhang Fang-Lue Zhang BNRist Department of Computer Science and TechnologyTsinghua UniversityBeiing 100084China School of Engineering and Computer Science Victoria University of WellingtonWellington 6012New Zealand

Photo composition is one of the most important factors in the aesthetics of *** a popular application,composition recommendation for a photo focusing on a specific subject has been ignored by recent deep-learning-based composition recommendation *** this paper,we propose a subject-aware image composition recommendation method,SAC-Net,which takes an RGB image and a binary subject window mask as input,and returns good compositions as crops containing the *** model first determines candidate scores for all possible coarse cropping *** crops with high candidate scores are selected and further refined by regressing their corner points to generate the output recommended cropping *** final scores of the refined crops are predicted by a final score regression *** existing methods that need to preset several cropping windows,our network is able to automatically regress cropping windows with arbitrary aspect ratios and *** propose novel stability losses for maximizing smoothness when changing cropping windows along with view *** results show that our method outperforms state-of-the-art methods not only on the subject-aware image composition recommendation task,but also for general purpose composition *** also have designed a multistage labeling scheme so that a large amount of ranked pairs can be produced *** use this scheme to propose the first subject-aware composition dataset SACD,which contains 2777 images,and more than 5 million composition ranked *** SACD dataset is publicly available at https://***/SACD/.

关键词： subject-aware image composition image cropping deep learning recommendation

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Plugged-in electric vehicle-assisted demand response strategy for residential energy management

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Energy informatics 2023年第1期6卷 6页

作者： Alfaverh, Khaldoon Alfaverh, Fayiz Szamel, Laszlo Department of Electric Power Engineering Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics P.O.B. 91 Budapest1521 Hungary School of Engineering and Computer Science University of Hertfordshire HatfieldAL10 9AB United Kingdom

Demand response (DR) management systems are a potentially growing market due to their ability to maximize energy savings by allowing customers to manage their energy consumption at times of peak demand in response to financial incentives from the electricity supplier. Successful execution of a demand response program requires an effective management system where the home energy management system (HEMS) is a promising solution nowadays. HEMS is developed to manage energy use in households and to conduct the management of energy supply, either from the grid or the alternative energy sources like solar or wind power plants. With the increase of vehicle electrification, in order to achieve a more reliable and efficient smart grid (SG), cooperation between electric vehicles (EVs) and residential systems is required. This cooperation could involve not only vehicle to grid (V2G) operation but a vehicle to home (V2H) too. V2H operation is used to transfer the power and relevant data between EVs and residential systems. This paper provides an efficient HEMS enhanced by smart scheduling and an optimally designed charging and discharging strategy for plugged-in electric vehicles (PEVs). The proposed design uses a fuzzy logic controller (FLC) for smart scheduling and to take the charging (from the grid)/discharging (supply the household appliances) decision without compromising the driving needs. Simulations are presented to demonstrate how the proposed strategies can help to reduce electricity costs by 19.28% and 14.27% with 30% and 80% state of charge (SOC) of the PEV respectively compared to the case where G2V operation only used along with the photovoltaic (PV) production, improve energy utilization by smoothing the energy consumption profile and satisfy the user’s needs by ensuring enough EV battery SOC for each planned trip. © 2023, The Author(s).

关键词： Energy utilization

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Detection of Aphasia Using Speech Characteristics: A Comparative Analysis of Learning Models 5th

Detection of Aphasia Using Speech Characteristics: A Compara...

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5th International Conference on Computing and Network Communications, CoCoNet 2023

作者： Surisetty, Hima Varshini Gottimukkala, Sarayu Varma Vekkot, Susmitha Gupta, Deepa Alotaibi, Yousef A. M. Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham Bengaluru India Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh11451 Saudi Arabia

ISBN: (纸本)9789819747108

Aphasia is one of the brain disorders that affects the part of the brain that is responsible for communication. This work deals with the detection of aphasia from speech features. The features extracted from the speech samples are MFCC, mel spectrogram, zero crossing rate, chroma, energy, spectral centroid, spectral roll-off, fundamental frequency and utterance duration. These different speech features were then ranked based on the SHapley Additive exPlanations (SHAPs), and the most important features were retained in an optimized feature set that was given to various machine learning models like support vector machine and decision trees and deep learning models like LSTM, LSTM with GRU, Bi-LSTM and Bi-LSTM with GRU. These models were then evaluated based on their train and test set accuracy, F1 score, recall, precision, plots of training loss and validation loss and confusion matrix. Based on the analysis of the performance metrics, Bi-LSTM with GRU was found to be the best model among all the models used. The model’s performance was evaluated using metrics like accuracy, F1 score, recall and precision when trained and tested with the optimal feature set which were 87.5%, 79.7%, 90% and 75%, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Support vector machines

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