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The Effect of data Augmentation Techniques on Persian Sentiment Analysis 9

The Effect of Data Augmentation Techniques on Persian Sentim...

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9th International Conference on Signal Processing and Intelligent Systems, ICSPIS 2023

作者： Nazarizadeh, Ali Sayyadpour, Minoo Banirostam, Touraj Islamic Azad University Department of Computer Engineering Central Tehran Branch Tehran Iran Kharazmi University Dept. Math and Computer Science Tehran Iran

ISBN: (纸本)9798350369922

The growth of the Internet and increased use of social media have led to the production of a large amount of unstructured data. These data have many variations, such as text, image, video, and audio. Among them, textual data has the largest share. Considerably textual data comprises the viewpoints of users regarding a wide range of services and products. In order to achieve high accuracy in sentiment classification, data Augmentation techniques have been used in this paper, which has significantly increased the volume and variety of data. Seventeen different Deep learning models have been trained on the final dataset. The results show that using the data Augmentation approach has increased the accuracy by more than 6% in Persian sentiment analysis. Methods used in this paper include translating sentences from Farsi to English and vice versa, sentence displacement, replacing synonyms, and data balancing. Also, in this paper, two new Lexicons have been manually created for Persian sentiment analysis. The first is a sentiment lexicon containing 660 positive and negative words, and the second is a lexicon of 390 stop words. Finally, the accuracy obtained using the data Augmentation approach has reached 96.73%, which is sufficient compared to previous research. © 2023 IEEE.

关键词： Sentiment analysis

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Predicting Employee Churn- data Driven Approach for Retention Management

Predicting Employee Churn- Data Driven Approach for Retentio...

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IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： R Jayaraj Safal Mehrotra Harshit Kumar Dept. of Data Science and Business Systems School of computing SRM Institute of Science and Technology Chennai

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510039

Employee churn is a critical challenge faced by organizations across industries, leading to disruptions in productivity and increased costs associated with recruitment and training. In this study, we propose an effective solution leveraging Random Forests to predict employee churn accurately and efficiently. Our approach addresses the inherent complexities of employee turnover prediction while considering resource constraints commonly encountered in practical applications. The proposed model incorporates feature engineering techniques and ensemble learning principles to harness the predictive power of Random Forests. Through a comprehensive evaluation on real-world employee datasets, we demonstrate the superior performance of our model in comparison to existing state-of-the-art methods. Our analysis encompasses various evaluation metrics, including accuracy, precision, recall, and F1-score, showcasing the robustness and reliability of our model across diverse organizational contexts.

关键词： Training Analytical models Accuracy Forestry Predictive models Robustness Complexity theory Churn Random forests Recruitment

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Energy-Optimized Offloading of Delay-Sensitive Tasks in Hybrid Edge-Cloud Computing

Energy-Optimized Offloading of Delay-Sensitive Tasks in Hybr...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Yuan, Haitao Wang, Shen Ma, Yaofei Bi, Jing Yang, Jinhong Zhang, Jia Zhou, MengChu School of Automation Science and Electrical Engineering Beihang University Beijing100191 China College of Computer Beijing University of Technology Beijing100124 China Cssc Systems Engineering Research Institute Beijing China Southern Methodist University Dept. of Computer Science DallasTX75275 United States New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ07102 United States

ISBN: (纸本)9781665410205

Currently, a cloud-edge collaborative system combines almost unlimited storage and computing resources where tasks can be migrated to high-performance servers in edge servers or the cloud. However, resource allocation and task offloading present big challenges due to the competition among mobile devices (MDs) for communication and computing resources of edge servers. Therefore, it is significant to properly offload MDs' tasks to edge servers or the cloud. This work proposes a collaborative edge-cloud architecture, including a centralized cloud, edge servers, and MDs. Then, this work jointly considers computing power, task sizes, computing resources, transmission power of MDs, transmission rates, computing power, transmission power, computing resource of edge servers, and computing resource of the cloud. Considering the abovementioned factors, this work designs a mixed-integer non-linear programming problem. To solve it, a Genetic Simulated annealing-based Particle Swarm Optimization (GSPSO) algorithm is proposed to obtain the best solution. Building upon it, this work proposes an energy-minimized task offloading and resource allocation strategy, thereby minimizing the system's energy consumption while ensuring strict task response time limits. Experimental results show that GSPSO reduces the system's energy by 66.34%, 34.65%, and 4.95% more than particle swarm optimization (PSO), self-adaptive PSO, and Tyrannosaurus optimization. © 2024 IEEE.

关键词： Mobile edge computing

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Energy-Efficient and Latency-Optimized Computation Offloading with Improved MOEA for Industrial Internet of Things

Energy-Efficient and Latency-Optimized Computation Offloadin...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhai, Jiahui Bi, Jing Yuan, Haitao Yang, Jinhong Zhang, Jia Zhou, MengChu College of Computer Beijing University of Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Cssc Systems Engineering Research Institute Beijing China Southern Methodist University Dept. of Computer Science DallasTX75275 United States New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ07102 United States

ISBN: (纸本)9781665410205

The unprecedented prosperity of the industrial Internet of Things has thoroughly facilitated the transition from traditional manufacturing towards intelligent manufacturing. In industrial environments, resource-constrained industrial equipments (IEs) often fail to meet the diverse demands of numerous compute-intensive and latency-sensitive tasks. Mobile edge computing has emerged as an innovative paradigm for lower latency and energy consumption for IEs. However, computational offloading and coordinating of multiple IEs with diverse task types and multiple edge nodes in industrial environments poses challenges. To address this challenge, we propose a multi-task approach encompassing scientific and concurrent workflow tasks to achieve energy-efficient and latency-optimized computation offloading. Furthermore, this work designs an improved Quantum Multi-objective Grey wolf optimizer with Manta ray foraging and Associative learning (QMGMA) to optimize multi-task computation offloading. Comprehensive experiments demonstrate the superior efficiency and stability of QMAGA compared to state-of-the-art algorithms in balancing latency and energy consumption. QMAGA improves average inverse generation distance and average spacing by 37% and 31% on average than multi-objective grey wolf optimizer, non-dominated sorting genetic algorithm II, and multi-objective multi-verse optimization, proving the convergence and diversity of its non-dominated solutions. © 2024 IEEE.

关键词： Smart manufacturing

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Analyzing and Forecasting Financial Services using Packet Transform through Wavelet Transitions 2

Analyzing and Forecasting Financial Services using Packet Tr...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Manjunathan, N. Prabaharan, G. Prathik, A. Jaganathan, D. Manikandan, N.K. Muthulingam, S. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Dept. of Cse Avadi Chennai India Madanapalle Institute of Technology & Science Department of Artificial Intelligence India Alliance University Dept. of Cse Bangalore India

ISBN: (纸本)9798350368413

Financial analysis plays a pivotal role in understanding market trends and making informed investment decisions. This research leverages the power of Wavelet Packet Transform (WPT) to extract valuable insights from financial time series data. By applying WPT to financial data, complex patterns and trends can be uncovered, enabling more accurate forecasting and analysis. The study utilizes Plotly's interactive line charts to visualize these insights effectively. The proposed approach has demonstrated exceptional performance, achieving an accuracy rate of 99.5%. This high level of accuracy empowers stakeholders and financial advisors to navigate the dynamic financial landscape with greater confidence. By providing deeper insights into market behavior, this research contributes to improved decision-making and risk management in the financial industry. © 2024 IEEE.

关键词： Decentralized finance

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BlockShield: A TPM-Integrated Blockchain-Based Framework for Shielding Against Deepfakes 32

BlockShield: A TPM-Integrated Blockchain-Based Framework for...

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32nd IFIP/IEEE International Conference on Very Large Scale Integration, VLSI-SoC 2024

作者： Bathalapalli, Venkata K. V. V. Kumar, Aakarshan Mohanty, Saraju P. Kougianos, Elias Yanambaka, Venkata P. University of North Texas. Dept. of Computer Science and Engineering United States University of North Texas. Texas Academy of Mathematics and Science United States University of North Texas Dept. of Electrical Engineering United States School of the Sciences Texas Woman's University United States

ISBN: (纸本)9798331539672

The increasing threat to individual privacy and personalized digital content on social media posed by Deepfakes has highlighted the importance for a secure and reliable multimedia content integrity mechanism. In this paper a novel Blockchain driven hardware secure video attestation scheme, BlockShield is proposed for Deepfake mitigation. The proposed system includes a novel approach that ensures digital content traceability and privacy using Blockchain smart contracts and TPM's digital signature mechanism. The proposed work explores the scope of hardware-assisted security for Deepfake mitigation through a hardware TPM working together with Blockchain for enhanced digital media protection and sharing. The proposed work is experimentally validated and presented which validates the scope of hardware-assisted and blockchain integrated Deepfake mitigation framework. © 2024 IEEE.

关键词： Differential privacy

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Breaking Tester Stereotypes: who is testing and why it matters 24

Breaking Tester Stereotypes: who is testing and why it matte...

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Proceedings of the 37th International BCS Human-computer Interaction Conference

作者： Isabel Evans Chris Porter Mark Micallef Dept. of Computer Information Systems Faculty of ICT University of Malta Msida Malta Dept. of Computer Science

Testing is essential for successful delivery of software solutions and is not always performed by specialist testers. In earlier studies, we noted a wide diversity in the backgrounds of the testers participating in these studies, prompting our research question: Who is Testing? We conducted a qualitative study of over 70 industry testers, covering testers from multiple countries and domains, with information about their backgrounds, hobbies, roles and characteristics. We show testers are from a wide range of backgrounds, with differing needs, characteristics, and problem-solving preferences. Their roles in software projects are multi-faceted, requiring a high cognitive skill level. We discuss how to break stereotyping and best support diversity in testers’ backgrounds and *** consider whether software testers are different from other software practitioners, and how understanding tester personas helps support of testers and testing.

关键词： Software Testing

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Lung Nodule Segmentation and Classification Using Conv-Unet Based Deep Learning

Lung Nodule Segmentation and Classification Using Conv-Unet ...

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2023 IEEE International Conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Akash, V. Monish Sai, R. Amith, K.B. Jayanthi, M.G. Kannadaguli, Prashanth Cambridge Institute of Technology Dept. of Artificial Intelligence and Machine Learning Bangalore India Cambridge Institute of Technology Dept. of Information Science and Engineering Bangalore India Cambridge Institute of Technology Dept. of Computer Science and Engineering Bangalore India Dhaarini Academy of Technical Education Bangalore India

ISBN: (纸本)9798350300826

This paper proposes a Convolutional U-Net architecture, a variation of the standard U-Net architecture for the segmentation of lung nodules and classification using Deep learning on computerized Tomography (CT) scans. The Primary steps employed are Preprocessing, Segmentation and Classification of nodules. In the preprocessing step, the lung region is segmented using techniques such as normalization, median filtering, Kmeans clustering, morphological and thresholding operations to extract lung Region of Interest (ROI) and nodule masks. The Conv-Unet design adds more convolutional layers to the standard U -Net architecture to help capture complicated patterns and boundaries of lung nodules for more accurate segmentation. Categorization of the segmented lung nodules is done using a CNN network on the LIDC-IDRI, and LUNA16 dataset. Overall, this model achieves a dice score of 62% and classification accuracy of 82% displaying appropriate performance in comparison with other variations of the U-Net architecture. © 2023 IEEE.

关键词： Convolution

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A Survey on Deepfake Video Detection Techniques Using Deep Learning 2

A Survey on Deepfake Video Detection Techniques Using Deep L...

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2nd International Conference on Next Generation Intelligent Systems, ICNGIS 2022

作者： Das, Athirasree Viji, K.S Angel Sebastian, Linda College of Engineering M-Tech Scholar Dept. of Computer Science and Engg Kerala Kidangoor India Assistant Professor College of Engineering Dept. of Computer Science and Engg Kerala Kidangoor India

ISBN: (纸本)9781665467926

Deep learning is an effective technology that has been widely used in different ways. 'DeepFake' videos are generated using deep learning technology called generative adversarial network where the videos are created with swaped faces in a video, altered facial expressions, change of gender, creation of fake video content and altered facial features. Fake videos are used in the situations like extortion of money, terrorism events or create political agitation. The deepfake technologies are used for positive purposes, such as film-making and virtual reality. The good quality results from deepfakes are very hard to recognised with people eyes. Many deep learning techniques are built to detect deepfake content in images and videos. A comparative study of the performance of various deepfake videos detection models in the deep learning is preseted in this paper. Convolutional Neural Network (CNN) models include ResNet, VGG16, Efficientnet etc.. Recurrent Neural Network (RNN) model includes Long Short-Term Memory LSTM. © 2022 IEEE.

关键词： Support vector machines

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Federated Learning for Real-Time Decentralized Smile Detection in Virtual Reality Environments

Federated Learning for Real-Time Decentralized Smile Detecti...

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Metaverse Computing, Networking and Applications (MetaCom), IEEE International Conference on

作者： Salabat Khan Anwar Ghani Syed Shehreyar Ali Naqvi Murad Ali Khan Muhammad Faseeh Do Hyeun Kim Muhammad Sharif Dept. of Computer Engineering Big Data Research Center Jeju National University South Korea Dept. of Computer Engineering Jeju National University South Korea Dept. of Computer Science Comsats University Islamabad Pakistan

ISBN: (数字)9798331515997

ISBN: (纸本)9798331516000

Facial expression detection is paramount in real-world applications, including patient monitoring, photo selection, and facial protection within the metaverse and virtual reality environments. Among various expressions, detecting smiles poses a significant challenge. Traditional methods relying on low-level face descriptors are being superseded by advancements leveraging Convolutional Neural Networks (CNNs) to enhance smile detection performance. In this research, we introduce and implement three CNN architectures tailored for smile detection from facial images. Our proposed model incorporates a fully convolutional neural network featuring two deep convolutional layers dedicated to smile detection. Additionally, pre-trained LeNet and DenseNet CNNs are employed for this task. The effectiveness of our approach is demonstrated through extensive evaluations of the Hromada dataset, encompassing images with and without smiles within the virtual reality environment. Moreover, we delve into detailed discussions on the practicality and efficacy of CNNs in facial expression detection, explicitly focusing on smile detection in real-time scenarios. Our experiments are conducted within a federated learning framework, enabling model inference and optimization for privacy preservation and scalability of the smile detection system in large-scale virtual reality applications. Through this decentralized approach, we address challenges related to real-time detection, model inference, and collaboration in the metaverse.

关键词： Training Solid modeling Federated learning Metaverse Scalability Collaboration Virtual reality Real-time systems Convolutional neural networks Faces

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