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W-Net Plus: Dnn for Spatial Saliency Prediction in Videos 1

W-Net Plus: Dnn for Spatial Saliency Prediction in Videos

引用

1st IEEE International Conference on Innovations in High Speed Communication and Signal Processing, IHCSP 2023

作者： Sharma, Gaurav Singh, Maheep Kumain, Sandeep Chand National Institute of Technology Dept. of Computer Science Engineering Srinagar India

ISBN: (纸本)9798350345957

Deep neural networks (DNNs) have been employed recently to identify saliency in images and videos. Different models have been suggested to predict spatiotemporal as well as temporal and spatial saliency. In this study, a model called W-net Plus is proposed for predicting spatial saliency in videos. The newly proposed model not only translates information from the encoder to the decoder but also from the decoder to the encoder, and it is inspired by U -net architecture. The proposed model has been evaluated by the author(s) on various data sets, and they have obtained noteworthy findings for the F - Max, structural similarity (S-measure), and Mean Absolute Error (MAE) measurement parameters, demonstrating its effectiveness and survivability. No stage of the training involves the utilization of transfer learning in the work. © 2023 IEEE.

关键词： Deep neural networks

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Delta-γ Modeling of Wireless System Resilience in Multipath Fading and Interference Environments 7

Delta-γ Modeling of Wireless System Resilience in Multipath...

引用

7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Upadhyay, Deepak Upadhyay, Abhay Sharma, Kunj Bihari Gupta, Mridul Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India Madhav Institute of Technology & Science Deemed University Centre for Internet of Things Gwalior India

ISBN: (纸本)9798350350067

In this paper, we pursue the resilience of wireless communication systems in multipath fading and interference environments utilizing Delta- γ modelling. Furthermore, fading phenomena such as Rayleigh/Rician/Nakagami channels can be simulated to examine the impact on important performance metrics like SNR, BER and throughput. Different types of interference scenarios, covering co-channel and adjacent channel interferences are also studied to understand impact on system capacity as well as reliability. This article investigates the use of adaptive modulation techniques, multiple-input multiple-output (MIMO) strategies and advanced interference management schemes by extensive *** results illustrate the importance of adaptive strategies in overcoming fading and interference which leads to an increase in system resilience as well as a robust communication link. Metrics for the outage probability and mean time to failure (MTTF) can inform how reliable systems are across operational scenarios. This work reconciles theory with practice to provide a systematic way of controlling the operational parameters in wireless networks under varying conditions. These lessons will be crucial in steering the design of forthcoming wireless systems that must more reliably satisfy a broader set of application requirements. © 2024 IEEE.

关键词： Multipath fading

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Feature Level Ensemble Learning Technique for Cervical Cancer Cell Classification 7

Feature Level Ensemble Learning Technique for Cervical Cance...

引用

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

作者： Bhuta, Nishi Jadhav, Tejas Shinde, Swati Gaikwad, Aaishwarya Jangale, Param Pimpri Chinchwad College of Engineering Dept. of Computer Science Pune India

ISBN: (纸本)9798350304268

The early detection of abnormal cells with Pap smear screening is a commonly used strategy for preventing cervical cancer, which is a major public health concern. In this study, we use the SIPaKMeD dataset to present an ensemble learning and multi - model ML learning strategy based on DL and ML for the categorization of cancerous cells in cervical cancer. Here we have use feature-level ensemble learning. ResNet, DenseNet, EfficientNet, and MobileNet were four pre-trained deep learning models that we utilised to extract features from the cervical cell pictures. These features were concatenated to create a feature vector, which was then classified using five base classifiers: Support Vector Machines, K-Nearest Neighbours (KNN), Random Forest (RF), Decision Trees (DT), and Logistic Regression (LR). The combined features were then applied to each classifier individually. The highest accuracy of 95% was achieved by the LR. The accuracy of the individual basic classifiers was 94,.4% (SVM), 87.2% (KNN), 76.5% (RF), and 74.6% (DT), respectively. Our suggested method has the potential to increase the precision of aberrant cell classification in Pap smear-based cervical cancer screening © 2023 IEEE.

关键词： abnormal cell classification cervical cancer deep learning Ensemble learning feature extraction Multi- model learning Pap smear screening pre-trained models

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A Federated Learning Monitoring Tool for Self-Driving Car Simulation 37

A Federated Learning Monitoring Tool for Self-Driving Car Si...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Lee, Taejoon Mun, Hyunsu Lee, Youngseok Dept. of Computer Science and Engineering Chungnam National University Korea Republic of

ISBN: (纸本)9781577358800

We propose CARLA-FLMon, which can monitor the progress of running federated learning (FL) training in the open-source autonomous driving simulation software, CARLA. The purpose of CARLA-FLMon is to visually present the status and results of federated learning training, and to provide an extensible FL training environment with which FL training can be performed repeatedly with updated learning strategies through analysis. With CARLA-FLMon, we can determine what factors have positive or negative influences on learning by visualizing training data. Then, we can optimize the parameters of the FL training model to improve the accuracy of FL. With preliminary experiments of CARLA-FLMon on lane recognition, we demonstrate that CARLA-FLMon can increase the overall accuracy from 80.33% to 93.82% by identifying lowly-contributing clients and excluding them. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Open systems

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Handwritten trip sheet to digital conversion using OCR 1

Handwritten trip sheet to digital conversion using OCR

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1st IEEE International Conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems, ICAECIS 2023

作者： Sanjana, N. Krishnatejaswi, S. Vinayak, C. Deepamala, N. Shobha, G. RV College of Engineering Dept. of Computer Science & Engineerin Bengaluru India

ISBN: (纸本)9798350348057

Neural Networks have promising potential at recognizing handwritten texts. In this paper, neural networks have been utilized for converting the handwritten trip sheet into digital format. A trip sheet is a written record containing the details of a trip. It is always easier to maintain records in a digitized format rather than in a physical form where there are chances of the documents being lost or damaged. Converting them into digital format solves all these problems making it easier & safer to organize and work on the data. The model proposed here is based on a combination of EasyOCR and Regular Expressions that help convert the data on a trip sheet into a CSV format. The proposed model works by splitting the image into various boxes by using the intersection of the lines and tries to recognize the characters present in that space separately. All of the recognized texts are then stored individually and can be later converted into various formats. The proposed model was tested on many handwritten trip sheets that provided favorable results with 85% accuracy. © 2023 IEEE.

关键词： Optical character recognition

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Systematic Approach to Analyze The Avast IOT-23 Challenge Dataset For Malware Detection Using Machine Learning 18

Systematic Approach to Analyze The Avast IOT-23 Challenge Da...

引用

18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Jahangir, Muhammad Talha Wakeel, Muhammad Asif, Humza Ateeq, Ahsan Mns University of Engineering and Technology Dept. of Computer Science Multan Pakistan

ISBN: (纸本)9798350328172

The integration of the Internet of Things (IoT) becomes more ingrained in our lives, it has gained significant importance for manufacturing companies. By enabling real time monitoring of machine status, evaluating product quality, and observing environmental conditions within factories. IoT empowers managers to make well considered decisions that reduce risks and alleviate losses. However, the growing reliance on IoT devices and services has also given rise to concerns about security and safety due to possible anomalies within IoT networks. Swiftly identifying and resolving these anomalies are crucial to prevent potential harm or losses. The proposed method introduces an innovative approach that leverages machine learning and deep learning techniques for the rapid classification of malicious software attacks on IoT networks. The central goal of this research is to conduct a thorough examination of the Avast IoT-23 dataset with the aim of pinpointing the most effective algorithm in terms of performance and efficiency. In this proposed methodology, the Decision Tree (DT) algorithm stands out as the most efficient and effective choice due to its high accuracy and low time complexity cost. © 2023 IEEE.

关键词： Internet of things

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An Introduction of Explainable Artificial Intelligence to Winter Sports Performance Analysis

An Introduction of Explainable Artificial Intelligence to Wi...

引用

2023 IEEE International Workshop on Sport, Technology and Research, STAR 2023

作者： Odong, Lawrence A. Bouquet, Paolo University of Trento Dept. of Information Engineering and Computer Science Trento Italy

ISBN: (纸本)9798350316056

In recent years, the utilization of machine learning techniques has revolutionized performance analysis in the field of sports. However, a crucial drawback of these algorithms is their inherent black box nature, which hinders stakeholders' ability to comprehend the underlying rationale behind their decisions. This lack of transparency presents challenges for coaches, trainers, and athletes who heavily rely on analysis outcomes to shape their strategies and training approaches. To address this limitation, this paper introduces the concept of Explainable Artificial Intelligence (XAI) to sports performance analysis. We employ SHAP (SHapley Additive exPlanations), a model-agnostic explainable method, to provide a clear and interpretable representation of the contribution of different features towards the final model prediction. Ski jumping is chosen as the initial test case to illustrate the effectiveness of this approach. © 2023 IEEE.

关键词： Sports

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A Bridgless Stacked Switches-Based AC-DC Converter with Reduced Storage Capacitances and Low-Frequency Voltage Ripple for HV EV Systems

A Bridgless Stacked Switches-Based AC-DC Converter with Redu...

引用

2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Derakhshan, Siamak Lam, John York University Dept. of Electrical Engineering and Computer Science Toronto Canada

ISBN: (纸本)9798350316445

This paper presents a fully soft-switched single-stage stacked switches-based AC-DC converter with significantly reduced storage capacitances and low-frequency voltage ripple for High Voltage (HV) Electric Vehicle (EV) application. In the proposed converter, a front-end duty ratio closed-loop control system based on the input current equation provides a high-quality sinusoidal input current. Moreover, a dual feedback control loops in both the primary and secondary side of the converter are responsible to minimize the low-frequency DC-link and output voltage oscillations, eliminating the need for bulky DC-link and output storage capacitors and enhancing the reliability, lifetime, and cost-effectiveness of the converter. Moreover, the output voltage of the converter is regulated through a Variable Frequency (VF) control algorithm. The performance of the proposed converter is verified through a 1kW, 120Vrms/800Vdc, 150kHz proof-of-concept hardware prototype. © 2023 IEEE.

关键词： closed-loop control low capacitance size minimum low-frequency ripple Soft-switching

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Study on Deep Learning Models for Human Pose Estimation and its Real Time Application 6

Study on Deep Learning Models for Human Pose Estimation and ...

引用

6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Jangade, Jyoti Babulal, Kanojia Sindhuben Central University of Jharkhand Dept. of Computer Science and Engineering Ranchi India

ISBN: (纸本)9798350346961

In computer vision, human pose estimation details the posture of the person's body structure that can be Kinematic, Planer, and Volumetric in an image or video. However, pose detection is often critical to be driven by distinct human actions. Thus, this survey report analysis the recent progression of the bottom-up and top-down human pose evaluation models. This survey report focuses on 2D and 3D skeleton-based human pose detection from the captured Red Green Blue(RGB) images. We have condensed the performance of the recent pose recognition, tracking, and detection techniques that utilize pose estimation from colour images as captured and then exhibit room for much more refinement in this domain. In this paper, scrutinize the study of human pose estimation models like 2d and 3d HPE for identify human movements such as running, dancing, sport so on and recent computer vision-based advances. This study has included various methods for detecting in two and three dimensions. This paper summarises the deep learning models for HPE, dataset, and challenges.. © 2023 IEEE.

关键词： Three dimensional computer graphics

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An Advanced Ensemble Directionality Pattern (EDP) based Block Ensemble Neural Network (BENN) Classification Model for Face Recognition System

An Advanced Ensemble Directionality Pattern (EDP) based Bloc...

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2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2022

作者： Harshitha, C.J. Bharathi, R.K. Jss Science and Technology University Dept. of Computer Applications Mysuru India

ISBN: (纸本)9781665483162

Developing the face detection and recognition system is one of the most demanding and challenging tasks in the recent days. For this purpose, there are different types of image processing techniques have been developed in the conventional works, which are mainly focusing on detecting the facial images under occlusions. Still, it limits with the major problems of high complexity in algorithm design, does not have the ability to handle large dimensional data, requires more time consumption for training the model, and increased misclassification results. Thus, this paper intends to develop an efficient face recognition system for detecting the occluded faces by implementing an advanced image processing techniques using COFW-100 dataset which includes 507 occluded images. Here, the Gaussian filter technique is utilized to improve the overall quality of original image by suppressing the noise/artifacts. After that, an Ensemble Directionality Pattern (EDP) extraction technique is applied to extract the texture features from the normalized image by estimating the similarity between the pixel intensity in different angles of projection plane. It is mainly used to obtain the clear features of the person in the cell of each image frame and, this type of feature extraction helps to increase the accuracy of overall classification system. Then, the Block Ensemble Neural Network (BENN) classification model is deployed to accurately detect the occluded faces by processing the image with separate patterns of features. During experimentation, the performance of both existing and proposed techniques is validated and compared by using various evaluation metrics. Then, the results show that the proposed technique outperforms the other approaches by accurately detecting the face based on the image patterns with separate blocks. © 2022 IEEE.

关键词： Face recognition

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