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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Al-Attabi, Kassem Rethik, Kumar Ranjusha, J.P. Sindhuja, N. Yadav, Sanjay Shivakalyan, B. The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Science Engineering Punjab Mohali India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Aditya Engineering College Department of Computer Science & Engineering Surampalem India Ies College of Technology Department of Computer Science & Engineering M.P. Bhopal India Saveetha Institute of Medical and Technical Sciences Department of CSE-AI Tamil Nadu Chennai India

ISBN: (纸本)9798350390759

This research examines the feasibility of using LSTM networks for financial prediction in frameworks such as the ERP system with an objective of enhancing budgetary prediction. Simple forecasting techniques do not take into account certain dynamics within the data that are non-linear, hence result in small forecast accuracy. Autoencoders can be employed to extract such features and LSTM networks capable of handling sequential data and long term dependencies could be used to anticipate the occurrence of emergencies. The process includes data culling that is composed of extensive datasets, data pre-processing, LSTM model development and training, and ERP system implementation. The findings illustrate that by extending the number of timesteps in LSTM models, it offers more reliability compared to conventional methods that help to improve decision-making and forecasting in finance. It is possible to upgrade them from time to time and make the models as accurate and as current as possible. These specific findings corroborate the overall proposition that discrete advancements in ML algorithms can converge to radically reshape a key area of ERP systems - financial forecasting. © 2024 IEEE.

关键词： Data accuracy

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TC-ShadowGAN: A Target-Consistency Generative Adversarial Network for Unpaired Shadow Removal

TC-ShadowGAN: A Target-Consistency Generative Adversarial Ne...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Tan, Chao Feng, xin Chen, Bin Long, Jianwu College of Computer Science and Engineering Chongqing University of Technology Chongqing China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Deep learning based shadow removal aims to learn a mapping that translates shadow image from shadow domain to non-shadow domain. In this work, we develop a sim-ple yet effective target-consistency generative adversarial net-work (TC-ShadowGAN) for the task of unpaired shadow re-moval without any additional label for supervision. Com-pared with the popular bidirectional mapping based methods, TC-ShadowGAN targets to directly learn a one-side mapping to translate shadow images into shadow-free ones. With the proposed target-consistency constraint that is designed to connect a couple of GAN-based sub-networks, the corre-lations between shadow image and the output shadow-free image, and the realness of recovered shadow-free image are strictly confined. Extensive quantitative and qualitative evalu-ation experiments results show that TC-ShadowGAN outper-forms the state-of-the-art unpaired shadow removal methods by 14.9% in terms of FID and 31.5% in terms of KID on the unpaired shadowremoval dataset. The code isavailable at https://***/chaotan/TC-GAN. © 2022 IEEE.

关键词： Generative adversarial networks

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Data-driven Methods for Quality Prediction of Aluminum Alloy Ingots 19

Data-driven Methods for Quality Prediction of Aluminum Alloy...

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19th IEEE International Conference on Networking, Sensing and Control, ICNSC 2022

作者： Yao, Minghao xiao, Wanyi Zhao, Wei Liu, Shixin School of Computer Science and Technology Xi'an Jiaotong University Xi'an China Foreign Studies College Northeastern University Shenyang China School of Automation and Electrical Engineering Shenyang Ligong University Shenyang China College of Information Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9781665472432

The quality of ingots greatly affects the yield and performance of aluminum alloy products and is closely related to the process parameters. This work proposes a data-driven method to predict the quality of ingots. The original data are collected from a practical process of an aluminum alloy production system. To improve data quality, some data preprocessing steps, including prior feature processing, missing value processing, and outlier processing, are performed. Then, XGBoost and other classification methods are used to establish the data model revealing the relationship between the process parameters and the quality of an ingot. Finally, a genetic algorithm is designed to optimize the feature selection process. The experimental results show that the proposed method has good performance and can predict the quality of the ingot well based on the optimized process features. © 2022 IEEE.

关键词： Genetic algorithms

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Enhanced Lung Cancer Prediction Using Ensemble Machine Learning Algorithms 2

Enhanced Lung Cancer Prediction Using Ensemble Machine Learn...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Krishna Kumar, V. Saravana Balaji, B. Sabarmathi, K.R. Najat Ahmed, Ahmed Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India University Kazakhstan Almaty Department of Computing De Montfort Kazakhstan Bannari Amman Institute of Technology Dept of Information Technology Sathyamangalam India Lebanese French University Department of Computer Engineering Erbil Iraq

ISBN: (纸本)9798331534950

Lung cancer is one among critical diseases and numerous people are suffering from this disease. Lung cancer is not only caused by smoking cigarettes, pipes or cigars;other risk factors include exposure to second-hand smoke (passive smoking), air pollution, radon, familial history of lung cancer and asbestos as a pollutant in the atmosphere. Predicting lung cancer in an earlier stage can help more lung cancer survivors live longer. The classification and prediction accuracy are not so high in the existing methods. In this work, the proposed Lung Cancer prediction model uses Machine learning algorithms with a lung cancer dataset of sixteen attributes for better classification of lung cancer which includes few etiological factors responsible for lung cancer along with regular factors such as Smoking, Yellow Fingers, Anxiety, Peer Pressure, Chronic diseases, etc., Classification accuracy is boosted with Ensemble techniques like Bagging, Boosting and Voting © 2024 IEEE.

关键词： Lung cancer

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PathUp: Patch-wise Timestep Tracking for Multi-class Large Pathology Image Synthesising Diffusion Model 24

PathUp: Patch-wise Timestep Tracking for Multi-class Large P...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Li, Jingxiong Zheng, Sunyi Zhu, Chenglu Sun, Yuxuan Chen, Pingyi Shui, Zhongyi Zhang, Yunlong Li, Honglin Yang, Lin Zhejiang University College of Computer Science and Technology Hangzhou China Westlake University School of Engineering Hangzhou China Westlake University Hangzhou China Zhejiang University Hangzhou China

ISBN: (纸本)9798400706868

In digital pathology, cancer lesions are identified by analyzing the spatial context within pathology images. Synthesizing such complex spatial context is challenging as pathology whole slide images typically exhibit high resolution, low inter-class variety, and are sparsely labeled. To address these challenges, we propose PathUp, a novel diffusion model tailored for the synthesis of multi-class high-resolution pathology images. Our approach includes a latent space patch-wise timestep tracking, which helps to generate high-quality images without tiling artifacts. Pathology knowledge is integrated through our patho-align. The robust generation of lesion subtypes and scale information is ensured by introducing a feature entropy loss. The effectiveness of our method is evaluated through extensive experiments, supplemented by assessments from human experts, demonstrating the authenticity of the synthetic data produced. Furthermore, we highlight the potential utility of our generated images as an augmentation method, thereby enhancing the performance of downstream tasks such as cancer subtype classification. © 2024 ACM.

关键词： Classification (of information)

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Recent Evolutionary Computing Algorithms and Industrial Applications: A Review 15th

Recent Evolutionary Computing Algorithms and Industrial Appl...

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15th International Conference on Genetic and Evolutionary Computing, ICGEC 2023

作者： Chu, Shu-Chuan Dao, Thi-Kien Ha, Thi-Minh-Phuong Ngo, Truong-Giang Nguyen, Trong-The College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China University of Information Technology Ho Chi Minh City700000 Viet Nam Vietnam National University Ho Chi Minh City700000 Viet Nam Information Technology Center Hanoi Law University Hanoi Viet Nam Faculty of Computer Science and Engineering Thuyloi University Hanoi Viet Nam

ISBN: (纸本)9789819700677

Evolutionary computing algorithms have gained significant attention in recent years due to their ability to solve complex optimization problems in various domains. This paper provides a comprehensive review of recent advancements in evolutionary computing algorithms and their industrial applications. The objective is to analyze the state-of-the-art evolutionary computing algorithms and assess their effectiveness in addressing real-world challenges in different industrial sectors. The paper also discusses the key challenges and future directions for the integration of evolutionary computing algorithms in industrial settings. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Optimization

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Improving Sentiment Classification on Restaurant Reviews Using Deep Learning Models 2

Improving Sentiment Classification on Restaurant Reviews Usi...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Patil, Ratna Nitin Singh, Yadvendra Pratap Rawandale, Shitalkumar Adhar Singh, Sofia Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology Pune411048 India Computer Science and Engineering Department Manipal University Jaipur303007 India Department of Mechanical Engineering Pimpri Chinchwad College of Engineering and Technology Pune411044 India School of Engineering and Technology Amity University Noida201301 India

Natural Language processing (NLP) includes the task of classifying texts. The proposed paper uses the ability and power of machine learning and deep learning techniques for sentiment analysis on restaurant reviews. With the increasing use of social media, classification of reviews has significant role in the development of public opinion. This work has extensive applications from client opinion study, market research and social media monitoring. The machine learning algorithms and NLP techniques are used classifying reviews and the results are rigorously analysed. It was noted that results obtained from logistic regression and multinomial naïve bayes algorithms are better than other machine learning models. Hence these two algorithms are selected to optimize further. After tuning the hyperparameters by grid search technique, the best accuracy obtained by logistic regression and multinomial naive bayes on validation dataset was 89.9% and 89.6% respectively. Deep learning architectures were also utilized for reviews classification. The accuracy obtained by Convolutional Neural Net (CNN) and Bi-Directional Long-short-term-memory (Bi-LSTM) was 89% and 90% respectively. © 2024 Elsevier B.V.. All rights reserved.

关键词： Sentiment analysis

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Detection of anomalies in networks by implementing and analyzing deep learning-based classifiers

Detection of anomalies in networks by implementing and analy...

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2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Anitha, Cuddapah Aswal, Upendra Singh Chaithra, M.H. Dange, Jyoti Barve, Amit Maranan, Ramya Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College School Of Computing Department Of Computer Science And Engineering Andhra Pradesh Tirupati517102 India Graphic Era Deemed To Be University Department Of Computer Science & Engineering Uttarakhand Dehradun248002 India Reva University Department Of Computer Science And Engineering Karnataka Bangalore560064 India Atharva College Of Engineering Department Of Information Technology Malad West Maharashtra Mumbai400095 India Parul University Parul Institute Of Technology Faculty Of Engineering And Technology Department Of Computer Science And Engineering P.O.Limda Gujarat 391760 India Saveetha School Of Engineering Simats Department Of Research And Innovation Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350352689

With the increasing complexity and sophistication of cyber threats, the demand for effective anomaly detection mechanisms in network security has grown substantially. This research focuses on the utilization of deep learning (DL) to detect anomalies in networks, implementing and analyzing three distinct models of deep learning-based classifiers. The classifiers include a binary classification model, a multiclass classification model with proportionality classification, and a multiclass classification model with exclusion strategies. The efficacy of these models is evaluated through cross-validation runs and testing on data from a database parallel to that of its training. The binary classification model serves as the baseline, distinguishing between normal and anomalous network behavior. The multiclass classification model with proportionality classification extends the binary approach by categorizing anomalies into multiple classes with consideration for their proportional representation. On the other hand, the multiclass classification model with exclusion strategies refines anomaly detection by explicitly excluding certain patterns from classification, enhancing the model's specificity. The research methodology involves the development of deep learning-based classifiers, the collection of network data for training, and rigorous evaluation through cross-validation techniques. Additionally, the models are tested on a separate dataset to assess their generalization capabilities in real-world scenarios. The outcomes of this research contribute to the understanding of DL applications in network anomaly detection, providing insights into the strengths and limitations of different classifier strategies. © 2024 IEEE.

关键词： Classification (of information)

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Power Optimization for Low Transmission Delay in Software Defined Data Center Networks

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IEEE Transactions on Mobile Computing 2025年第7期24卷 5716-5730页

作者： Zhao, Yong Cao, Jiannong Wang, xingwei Li, Fuliang He, Qiang Liu, xiaojie University of Electronic Science and Technology of China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Laboratory of Intelligent Collaborative Computing Chengdu611731 China Hong Kong Polytechnic University Department of Computing Hung Hom Hong Kong Northeastern University College of Computer Science and Engineering Shenyang110819 China Northeastern University College of Medicine and Biological Information Engineering Shenyang110819 China Pengcheng Laboratory Shenzhen518066 China

Software-Defined Data Center Networks (SDDCNs) utilizes Software Defined Networking (SDN) as a network architecture to achieve highly flexible, programmable, and automated management of Data Center Networks (DCNs). The high energy consumption of DCNs remains a persistent and significant challenge. Thus, the energy saving is crucial and imperative for DCNs. Current energy-efficient solutions primarily rely on flow consolidation and dynamic device sleeping techniques to reduce energy consumption. However, these approaches often yield long Flow Completion Time (FCT), potentially resulting in violations of service-level agreements, particularly for delay-sensitive applications. In this paper, we formulate the problem of minimizing the power consumption in SDDCNs as key objective while ensuring timely FCT for delay-sensitive applications. To solve this problem, we first introduce the Active Network Generation (ANG) approach, which generates a minimal active subnet with the least number of active devices while meeting the current traffic demand. Subsequently, we propose two algorithms based on the type of applications: the Delay-Tolerant Flow Route (DTFR) algorithm for delay-tolerant applications and the Delay-Sensitive Flow Route (DSFR) algorithm for delay-sensitive applications. Simulation results demonstrate that our propose solution achieves an energy-saving rate of up to 67.77% and significantly reduces FCT compared to benchmark solutions. © 2002-2012 IEEE.

关键词： Energy efficiency

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Analyzing the Tweets of the Patients During the COVID-19 Pandemic Using Machine Learning Techniques 1

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7th International Conference on Internet of Things and Connected Technologies, ICIoTCT 2022

作者： Shanmukh, Routhu Roy, Rita Chekuri, Kavitha Rao, Rowthu Lakshmana Mukherjee, Subhodeep Department of Computer Science and Engineering Centurion University of Technology and Management Andhra Pradesh Vizianagaram India Department of Computer Science and Engineering GITAM Institute of Technology Andhra Pradesh Visakhapatnam India Department of Computer Science and Engineering Raghu Engineering College Andhra Pradesh Visakhapatnam India Andhra Pradesh Visakhapatnam India

ISBN: (数字)9789811997198

ISBN: (纸本)9789811997181

Text classification is also known as text categorization. Using natural language processing, the required text can be analyzed and classified into various texts depending on the features. The purpose of this study is to measure the length of the characters of every patient tweet regarding COVID. In this COVID data set, linear regression is accurate. The input data were pre-processed, extracted, vectorized and classified using machine learning techniques like the implementation of linear regression, the performance of gradient descent in linear regression, logistic regression, random forest, k nearest neighbors, and polynomial regression. The experiment uses a corona data set with different column elements for text classification. The accuracy varies from one algorithm to the other depending on the data set related to the algorithm. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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