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machine learning for Detecting Noisy Neighbours in 5G Networks 7th

Machine Learning for Detecting Noisy Neighbours in 5G Networ...

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7th international conference on Smart Trends in computing and Communications, SmartCom 2023

作者： Srivastava, Satish Surendra Makh, Sanjay Rodge, P.R. Shivajirao S Jondhale College of Engineering Dombivali East Mumbai India Sant Gadagebaba University Maharashtra Amaravati India

ISBN: (纸本)9789819908370

the structure of 5G networks is predicted to become more dynamic as well as unpredictable than that of present networks. Network Functions (NF) will cease to exist firmly tied through the hardware on which they run with the arrival of Network Function Virtualization (NFV), posing new issues in network administration. A noisy neighbour is a term widely used in NFV architecture to describe circumstances in which an application's performance suffers because a few of the assets it requires are consumed through other apps on the same cloud node. Such circumstances are difficult to identify using simple procedures, necessitating the employment of complex methodologies to NFV infrastructure management. We show how machine learning (ML) approaches may be utilised to sense such occurrences in this work. We demonstrate that conventional models intended for automatic classification can sense the noisy neighbour phenomena with greater than 90% correctness in a basic scenario using data gathered at actual NFV infrastructure. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Network function virtualization

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Detecting Biodegradable and Non-biodegradable Image Using CNN and Deep learning 1

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7th international conference on Intelligent computing and Optimization, ICO 2023

作者： Ripa, Tanjina Akter Ahmed, Mahade Akter, Khadija Sheikh, Md. Shahadat Anik Jennifer, Sanjeda Sara Shamim, Mahbub Hasan Reza, Ahmed Wasif Department of Computer Science and Engineering East West University Dhaka1212 Bangladesh

ISBN: (数字)9783031733185

ISBN: (纸本)9783031733178

To distinguish between biodegradable and non-biodegradables, different methodologies and models are implemented in this study using image processing. We experimented with Convolutional Neural Networks (CNN), U-Net, and Support Vector machines (SVM). CNN outperformed the other models obtaining a training accuracy of 0.9397, which is approximately 94%, and a validation accuracy of 0.9165, which can be rounded off to 92%. SVM on the other hand obtained an accuracy of 92%. We assessed our model's accuracy and our approach was able to predict well whether the object is biodegradable or not based on the input of a single image. To make sure that it can recognize the primary objective from a single image, we went through our models extensively. the identification, tracking, sorting, and processing of biodegradable and non-biodegradable items might be aided by our model when it will be improved. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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Hybrid Method for Named Entity Recognition in Kumauni Language Using machine learning 12th

Hybrid Method for Named Entity Recognition in Kumauni Langua...

引用

12th international conference on soft computing for Problem Solving, SocProS 2023

作者： Pant, Vinay Kumar Sharma, Rupak Kundu, Shakti Delhi NCR Campus Modinagar India Delhi NCR Campus Modinagar India Haryana Gurugram122413 India

ISBN: (纸本)9789819731794

Named Entity Recognition (NER) identified entities in a text according to specified rules. machine interpretation, question answering, and automated summarization are among NER's numerous NLP applications. Language barriers make identifying people harder. they recognized words having various meanings or uses in other formulations. Writing similar words might be tough. Finding unlabeled terms is difficult. To mitigate these challenges in this research, a novel fusion chain model for NER in Kumauni Language using machine learning (ML) is proposed. A fusion of Convolution Neural Network (CNN) with Bidirectional Long- and Short-Term Memory (LSTM) and Conditional Random Field (CRF) is utilized as the proposed model for training and testing in which Support Vector machine (SVM) is used for tagging. Finally, for measuring the performance of the proposed model, the performance metrics are calculated. the results show that the precision, recall, and F-measure of the proposed model with skip gam embedding are 76, 75, and 75.5%, and with GloVe embedding the precision, recall, and F-measure of the proposed model are 78, 62, and 69.08%, in both the cases, the proposed model performs better than other conventional approaches. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Embeddings

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Role of RPA & AI in Optimizing Network Field Services 7

Role of RPA & AI in Optimizing Network Field Services

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7th IEEE international conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023

作者： Yendluri, Dileep Kumar Ponnala, Jainath Tatikonda, Reshmi Kempanna, M. thatikonda, Ramya Bhuvanesh, A. University of the Cumberlands Computer Science United States Jawaharlal Technological University Ece Hyderbad India University of the Central Missouri Computer Information Systems Warrensburg United States Bangalore Institute of Technology Department of Artificial Intelligence and Machine Learning Karnataka Bengaluru India University of the Cumberlands Information Technology Williamsburg United States Psn College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Tirunelveli India

ISBN: (纸本)9798350343144

the paper investigates incorporating and implementing RPA and AI technologies within NFS to improve efficiency and boost service quality. Robotic Process Automation enables the streamlining of repetitive processes. It simplifies work processes and releases personnel for more critical projects. On the contrary, AI strengthens NFS providers via data-informed decision-making. Additionally, it facilitates anticipatory maintenance and preemptive network administration. the document explores particular RPA and cognitive computing instances, including AI-powered network issue resolution and AI-driven preventive maintenance for the network hardware. Furthermore, it emphasizes the advantages and constraints of adopting robotic process automation and artificial intelligence within the NFS framework. It considers aspects including data handling, growth potential, and moral implications. Prospects for the future related to RPA and AI within the NFS domain are also analyzed. It foresees self-governing network management, sophisticated predictive analytics, and merging with IoT and edge computing technologies. the article highlights the revolutionary capability of robotic process automation and artificial intelligence in transforming the field service sector, aiming for increased efficiency, reliability, and a customer-centric approach. It emphasizes embracing these technologies to keep up in the dynamic business sector. © 2023 IEEE.

关键词： Decision making

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Wheat, Rice and Corn Yield Prediction for Jammu District Using machine learning Techniques 7th

Wheat, Rice and Corn Yield Prediction for Jammu District Usi...

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7th international conference on Information and Communication Technology for Intelligent Systems, ICTIS 2023

作者： Gandotra, Sakshi Chhikara, Rita Dhull, Anuradha The NorthCap University Gurugram India

ISBN: (纸本)9789819940394

Timely prediction of agricultural produce has always been a significant estimator for various stakeholders in agricultural economy. High computing capacity coupled with machine learning techniques has led humans to harness the potential of data sets to address challenges in agriculture sector. In pursuit to identify the most appropriate machine learning model for wheat, rice and corn yield prediction for Jammu district of J&K, we present the performance of machine learning models of Long Short-Term Memory, Gated Recurrent Unit, Bi directional long short-term memory and classic DNN along with the baseline models of Support Vector Regression, Random Forest Regressor and Ensemble method of boosting (adaboost). Environmental data set comprising temperature, rainfall, humidity, solar radiation, basic sunshine hours for the span of year 2009–2019 has been employed for this study. Yield and environment data set have been procured from Directorate of Agriculture and Sher—e—Kashmir—Agricultural University of Science and Technology (SKAUST) Jammu respectively. Results show the long short-term memory outperforms the other evaluated models with low values for loss metrics of RMSE, MAE, MAPE of 0.30, 0.20 and 0.23 respectively. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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Book Genre Classification System Using machine learning Approach: A Survey 5th

Book Genre Classification System Using Machine Learning Appr...

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5th international conference on soft computing and Signal Processing, ICSCSP 2022

作者： Sethy, Abhisek Rout, Ajit Kumar Gunda, Chandra Routhu, Sai Kiran Kallepalli, Srinu Garbhapu, Geetha Department of Information Technology GMR Institute of Technology Srikakulam Rajam India Department of Computer Science & Engineering Silicon Institute of Technology Bhubaneswar India

ISBN: (纸本)9789811986680

the amount of complicated texts and documents that require a deeper understanding of machine learning techniques has expanded rapidly in recent decades. Several machine learning approaches have shown exceptional results in NLP. Complex models and non-linear data correlations help these learning systems function well. It is difficult for academics to discover appropriate text classification structures, designs, and procedures. Manually reading and classifying a book's category would be tedious. It takes a long time for a language beginner to absorb the complete text in order to discern its genre. Natural Language Processing (NLP), which is commonly used nowadays, can help overcome this issue. It includes text classification and summarization. the book synopsis is input to the machine learning algorithms, which output the genre. this review includes text feature extraction, dimensionality reduction, algorithms, and evaluation. Finally, the limitations of each strategy are assessed, as well as their practicality. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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the Impacts of Covid-19 on Education and Educational Institutions 7th

The Impacts of Covid-19 on Education and Educational Institu...

引用

7th international conference on Intelligent computing and Optimization, ICO 2023

作者： Deepak, T.J. Goyal, S.B. Visveswararao, P. Venishri, P. Pushpanjali Selangor Shah Alam Malaysia Faculty of Information Technology City University Petaling Jaya46100 Malaysia Director R & D REVA University Bangalore India Brickfields Asia College Selangor Malaysia SRM Dental College Tamil Nadu Chennai India

ISBN: (纸本)9783031733239

the COVID -19 pandemic outbreak has forced online teaching and learning to be the principal teaching format in education sector worldwide. One of the distressing fears about online learning is whether this method is effective, specifically when compared to face-to-face classes. COVID has enforced the worldwide lock down creating very bad effect on the student’s life. Learners stopped traveling to Schools/Colleges/Universities and all educational activities were halted. the eruption of COVID -19 has demonstrated us that change is unavoidable. It has succeeded as a catalyst for the educational institutions to grow and select for platforms with tools, which have not been utilized before. the education sector has been fighting to survive the COVID-19 crises with a different approach to tackle the challenges to eliminate the threat of the deadly pandemic. this paper highlights both the positive and negative impacts of COVID-19 are discussed. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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Fault Diagnosis Methods of Deep Convolutional Dynamic Adversarial Networks 7th

Fault Diagnosis Methods of Deep Convolutional Dynamic Advers...

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7th international conference on soft computing in Data Science (SCDS)

作者： Hai, Tao Zhang, Fuhao Qiannan Normal Univ Nationalities Sch Comp & Informat Duyun 558000 Guizhou Peoples R China Baoji Univ Arts & Sci Sch Comp Sci Baoji 721007 Peoples R China Key Lab Complex Syst & Intelligent Optimizat Guiz Duyun 558000 Guizhou Peoples R China

ISBN: (纸本)9789819904044;9789819904051

A DCDAN is proposed for intelligent fault diagnosis to address the issue that it is easy to obtain a large amount of labeled fault-type data in a laboratory environment but difficult or impossible to obtain a large amount of labeled data under actual working conditions. this method transfers the fault diagnosis knowledge acquired in the laboratory environment to the actual engineering equipment, obtains more comprehensive fault information by fusing the time domain and frequency domain data, employs the residual network to deeply extract fault features in the feature extraction layer, and makes use of the extracted fault features to improve fault diagnosis. To achieve unsupervised transfer learning, the marginal distributions and conditional probability distributions of the source and target domains are aligned bymaximizing the domain classification loss, while the failure classification ofmechanical equipment is achieved byminimizing the class prediction loss. the experimental results demonstrate that this model has a high classification accuracy in the unlabeled target data set and can effectively solve the problem of the lack of labels in the data set, i.e., realize intelligent mechanical fault diagnosis, under certain conditions.

关键词： Transfer diagnosis Deep learning Adversarial network Unsupervised learning

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Optimizing Binary Classification Performance in Neural Networks through Simulation: A Comparative Study of Activation Functions 7th

Optimizing Binary Classification Performance in Neural Netwo...

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7th international conference on Intelligent computing and Optimization, ICO 2023

作者： Boateng, Alexander Aidoo, Eric Nimako Maposa, Daniel Odoom, Christopher Owusu, Samuel Adjei Department of Biostatistics University of the Free State Bloemfontein South Africa School of Mathematical and Computer Sciences Heriot-Watt University Dubai United Arab Emirates Department of Statistics and Operations Research University Limpopo Polokwane South Africa Department of Mathematics and Statistics University of Massachusetts AmherstMA United States Department of Mathematics and Statistics Youngstown State University YoungstownOH United States

ISBN: (纸本)9783031733239

Binary classification using Artificial Neural Networks (ANN) is a fundamental problem in machine learning, and the choice of activation functions plays a vital role in determining the performance of the model. this study investigates the performance of various activation functions for binary classification tasks using neural networks across multiple datasets. the results show that ReLU and Tanh, compared to Logistic, consistently excel in accuracy, precision, recall, F1-Score, and AUC-ROC, making them versatile choices for diverse tasks. However, Identity exhibits variable performance, highlighting the need for careful activation function selection based on task specifics. Additionally, the study emphasizes the impact of the dataset size on activation function performance, with ReLU and Tanh offering consistency across varying data volumes. Practitioners can leverage these insights to optimize neural network designs, improving model efficacy in binary classification tasks. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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Federated Ensemble Algorithm Based on Deep Neural Network 7th

Federated Ensemble Algorithm Based on Deep Neural Network

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7th international conference on soft computing in Data Science (SCDS)

作者： Wang, Dan Wang, Ting Qiannan Normal Univ Nationalities Sch Math & Stat Duyun 558000 Guizhou Peoples R China Baoji Univ Arts & Sci Sch Comp Sci Baoji 721007 Peoples R China Key Lab Complex Syst & Intelligent Optimizat Guiz Duyun 558000 Guizhou Peoples R China

ISBN: (纸本)9789819904044;9789819904051

In the realm of multi-source privacy data protection, federated learning is now one of the most popular study topics. When the data being used is not local, its architecture has the ability to train a common model that can satisfy the needs of many parties. On the other hand, there are circumstances in which local model parameters are challenging to incorporate and cannot be used for security purposes. As a result, a federated ensemble algorithm that is based on deep learning has been presented, and both deep learning and ensemble learning have been used within the context of federated learning. Using the various integrated algorithms that integrate local model parameters, which improve the accuracy of the model and take into account the security of multi-source data, the accuracy of the local model can be improved by optimizing the parameters of the local model. this in turn improves the accuracy of the local model. the results of the experiments show that, in comparison to conventional multi-source data processing technology, the accuracy of the algorithm in the training model for the MNIST dataset, the digits dataset, the letter dataset, and thewine dataset is improved by 1%, 8%, 1%, and 1%, respectively, and the accuracy is guaranteed. Additionally, accuracy is guaranteed. It also improves the security of data and models that come from more than one source, which is a very useful feature.

关键词： Federated learning Federated ensemble algorithm Deep neural network model Ensemble algorithm Deep learning

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