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2023 International Conference on Data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Senthil Murugan, K.R. Murugan, K. Aravintha Kumar, M. Deepalakshmi, R. Jesson Daniel, J. Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9798350348910

Pneumonia is one of the most common and serious respiratory infections. It occurs due to inflammation of alveoli and air sacs filled with other substances like pus and mucus. It affects the lungs and makes it delicate to breathe. Colorful pathogens can cause pneumonia. Numerous algorithms have surfaced to describe pneumonia. In this paper, we will see one similar effective and accurate algorithm that detects pneumonia by using casketX-Ray images(CXR). This paper introduces an LSTM model that uses deep learning and Convolutional Neural Networks (CNNs) to detect pneumonia. The model is also suitable to separate bacterial and viral pneumonia. A similar bracket will be veritably helpful for croakers. The results of our work show that we're suitable to separate COVID pneumonia and bacterial pneumonia. As COVID-19 pneumonia is contagious, we can take preventive measures fluently with the proposed system. Our system provides a delicacy of 93.72 percent and perceptivity of 92.80 percent. The deep literacy model that we achieved made for better perfection. © 2023 IEEE.

关键词： SARS

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Sales Forecasting using SARIMAX for B2C

Sales Forecasting using SARIMAX for B2C

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2023 International Conference on Data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Senthil Murugan, K.R. Prathiba Selvi, R. Dheeksha, S. Deepalakshmi, R. Paul Steve Mithun, B. Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9798350348910

Sales forecasting is the process of predicting future sales and revenue for a business or organization. It involves using historical sales data, market analysis, and various quantitative and qualitative methods to make educated estimates about future sales performance. The primary goal of sales forecasting is to provide valuable insights and information that can guide business decisions, resource allocation, and strategic planning. Accurate sales predictions enable businesses to avoid overstocking or understocking situations, optimizing their inventory levels to meet customer demand while minimizing carrying costs. In this paper, we will see about one such efficient and accurate algorithm that forecast the sales based on the historical values and its seasonality. A SARIMAX model has been proposed in this paper which is the seasonal version of the ARIMA model to predict the future sales. The model is also enables the business people to view the sales based on its periodicity. Such classification will be very helpful for business people to identify the sales for the particular period or month. A graph is plotted for the forecasted value based in the inputs from the individual. This helps the individual to avoid the overstocking and understocking of goods. Our system provides rmse value of 337.5 whereas the mape value as 6.97. The error rate of the system is comparatively low. © 2023 IEEE.

关键词： Sales

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Diagnosis of compressive strength of cement based material using deep learning technique

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Materials Today: Proceedings 2022年 69卷 1402-1407页

作者： Rajendran, Sasikumar Matheshwaran, P. Deepa, K. Department of Computer Science and Engineering K.Ramakrishnan College of Engineering Trichy India Department of Computer Science and Engineering K.Ramakrishnan College of Technology Trichy India Department of Computer Science and Engineering M. Kumarasamy College of Engineering Karur India

Although the widespread usage of mortars in construction over the last few decades, there is no published method for estimating their strength based on the mix parameters of the mortars themselves. This is owing to the fact that the mortar's compressive forte is very nonlinear in response to the mixture's components. Researchers in this research examined how artificial intelligence might be used to forecast compressive strength for cement-based mortars. In previous studies, artificial networks have been used to map the mechanical possessions of cement-based materials. Overfitting and the difficulty to explain decisions are some of the key drawbacks of this machine learning technique, which have been documented in the literature. This study therefore analyses if deep learning may be used to make predictions. Proposed methodology may accurately estimate mortar's strength when compared to experimental results, according to the results obtained from this study. As expected, machine learning predicted mortar compressive strength more accurately than provided model, however it was discovered during verification. To solve the problem of mortar compressive strength, the built deep learning model has been presented as the best predicting procedure. © 2022

关键词： Mortar

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Outlier detection in maize field using Isolation Forest: A one-class classifier 1

Outlier detection in maize field using Isolation Forest: A o...

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1st IEEE International Conference on Networking and Communications, ICNWC 2023

作者： Michael, J Justina Thenmozhi, M. SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350336009

In agriculture, manual weed identification remains a challenge for farmers and agriculturalists. Recent technologies address the problem of weeds through several methods among which outlier detection is one powerful technique. Outlier detection is an effective weed identification method because it builds the model only by training the positive samples, hence known as one-class classification (OCC). In the context of agriculture, all crops except the target crop are considered to be weeds. This makes OCC a suitable method to identify weeds. In this work, we build OCC-IF-WD (One-Class Classification using Isolation Forest for Weed Detection) model that treats the target class as an inlier and all other classes as outliers. To train this model, an original dataset from Kaggle is used. Isolation Forest, which is a one-class classifier was used to build the model. OCC-IFWD recognizes maize as an inlier and all its associated weeds as outliers. The model was trained with 1082 images. The model was tested against various categories of outliers to examine its performance level. The results show the highest accuracy of 90.2% when the model is tested to differentiate between maize (inlier) and blackgrass (outlier). Also, a precision of 87%, recall of 94%, and Fl-score of 91% were recorded from the model. © 2023 IEEE.

关键词： Crops

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An Exploratory Assessment on Text Summarization 2

An Exploratory Assessment on Text Summarization

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Nishitha, P. Sarika, T. Vijayakumari, E.N. Vuyyuru, Asha Silparaj, M. Mohanty, Jnyana Ranjan Mlr Institute of Technology Department of Assistant Computer Science and Information Technology Hyderabad India Mlr Institute of Technology Department of Computer Science and Information Technology Hyderabad India Mlr Institute of Technology Department of Computer Science Engineering Hyderabad India Hyderabad India Hyderabad501510 India Kalinga Institute of Industrial Technology University India

ISBN: (纸本)9798350370188

Text summarization involves creating a short, concise summary that captures the salient points of the source text. We'll demonstrate a synthesis process based on the use of trainable machine learning algorithms with a collection of features taken straight from the source text. There are two groups of these features: statistics-also referred to as extractive summarization-based on how frequently different textual components occur and linguistics also known as abstractive summarization - based on morphological and syntactic analysis of documents. In this paper, we show how BERT can be usefully applied in text summarization and provide a common framework for abstraction models. We also demonstrate that a fine-tuning method can further improve the quality of the generated summaries. © 2024 IEEE.

关键词： Syntactics

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A Deep Echo State Network With Scaling Factor Activation Functions for Fault Diagnosis of Electrical Drive Systems

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7874-7884页

作者： Gong, Yuanpeng Jiang, Yulian Cheng, Chao Chen, Hongtian Wang, Shenquan Changchun University of Technology College of Electrical and Electronic Engineering Changchun130012 China Changchun University of Technology School of Computer Science and Engineering Changchun130012 China Shanghai Jiao Tong University Department of Automation Shanghai200240 China

Deep echo state networks (Deep-ESNs) play an important role in fault diagnosis. However, due to its limitation in the iterative process of dealing with nonlinear data, the accuracy of fault diagnosis is relatively low. In order to improve the fault diagnosis accuracy of electrical drive systems, this article proposes a novel Deep-ESN based on the synergistic effect of golden jackal optimization (GJO), variational mode decomposition (VMD), and scaling factor activation function, called GVSD-ESN. The main work of this study contains: 1) the novel scaling factor activation function is proposed to solve the gradient vanishing problem in the Deep-ESN model;2) GJO is used to solve high-dimensional optimization problems of VMD;3) the power spiral curve is proposed to optimize the position update equation of GJO, which solves the problem of falling into the local optimal;and 4) adding a sparse regularization layer between reservoirs can enhance the class definition of GVSD-ESN. Finally, the effectiveness of the proposed method is verified in electrical drive systems. © 2015 IEEE.

关键词： Variational mode decomposition

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Stock Price Prediction Using Machine Learning 1

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11th International Conference on Recent Trends in Computing, ICRTC 2022

作者： Piyush Amarjeet Sharma, Anubhav Kumar, Sunil Ansari, Nighat Naaz Department of Computer Science and Engineering Meerut Institute of Engineering and Technology Meerut India

ISBN: (数字)9789811988257

ISBN: (纸本)9789811988240

Stock price prediction is the methodology of predicting the future value of a company stock. It is strenuous task to buy stock or invest in set of goods/assets;the financial market’s fast transformation makes it very difficult for prediction of future value of assets with high accuracy. Machine learning is the phenomenon of teaching computers to perform tasks that would normally need human intelligence, and is a major topic in scientific research at present. This article tries to develop a model for predicting future stock market rates by using the "Long Short Term Memory" model also called LSTM algorithm along with deep learning algorithms which include DENSE, DROP OUT, and SEQUENTIAL. In this work, we have considered mainly five factors that are open, close, low, high, and volume. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Financial markets

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A Generalized Additive Decomposition Based Deep Neural Network Architecture for Time Series Forecasting Problems 6

A Generalized Additive Decomposition Based Deep Neural Netwo...

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6th International Conference on Electrical Information and Communication technology, EICT 2023

作者： Tanvin, Asnuva Rahman, Sazia Hashem, M.M.A. Khulna University of Engineering & Technology Department of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798350370836

Multivariate time series forecasting is a significant area of research across many domains such as finance, sales, crime prediction, electricity consumption, road occupancy prediction etc. The availability of large multivariate data along with the existence of spatial and temporal short-term and long-term patterns in them make this task a difficult one. In this study, we propose a new idea of a general framework having a multi modular architecture which is built only using deep learning algorithms to perform multi-step forecasting of multivariate time series data. We preprocessed time series data by decomposing it into seasonal, trend and residual components using additive decomposition technique. Then we separately fed each component into independent modules that utilize recurrent deep learning methods such as Stacked GRU, Self attention based LSTM and LSTM encoder decoder architecture. The final result is the summation of the forecast results of all three components. The framework is tested on three benchmark dataset on which it performed encouragingly well. © 2023 IEEE.

关键词： Forecasting

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Machine Learning Approaches and Analysis for Bangla Music Genre Classification 5

Machine Learning Approaches and Analysis for Bangla Music Ge...

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5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Rokib, Raisa Tasnimul Hasan, S.M. Ani, Fahim Hossain Saha Joy, Sajib Kumar Shah, Faisal Muhammad Ahsanullah University of Science and Technology Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350394290

The challenge of classifying the genres of Bangla music has been given a machine learning based technique in this study. This paper has explored the use of feature extraction techniques for selecting highly co-related features and evaluated the performance of gradient boosting classifiers, CatBoost, and XGBoost, on a comprehensive Bangla music dataset. Our paper's proposed method achieves remarkable accuracy scores of 74% using CatBoost and XGBoost. This study emphasizes how machine learning approaches have enormous promise to address the difficult job of classifying genres in Bangla music. By using advanced feature extraction techniques and powerful gradient boosting classifiers,the proposed method were able to achieve promising results in this challenging task. The implications of the work can be extended to the development of intelligent music rec-ommendation systems and automated music tagging, facilitating enhanced user experiences in exploring and discovering Bangla music genres. As the field of machine learning continues to evolve, this research contributes valuable insights and avenues for future advancements in music genre classification and related domains. © 2023 IEEE.

关键词： Feature extraction

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Classification of Indicators Based on Morbidity and Mortality

Classification of Indicators Based on Morbidity and Mortalit...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Deepak Kumar, A. Jayakumar, Shruti Balamurali, A. Mithun, P. Janagiraman, S. Rajesh Kanna, R. St. Joseph's Institute of Technology Department of Computer Science and Engineering Chennai India SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai Ramapuram India

ISBN: (纸本)9798350319200

In our evolving world, human vulnerability to diseases remains a critical concern, with an overwhelming volume of health data generated by applications. Managing this massive and complex big data requires advanced processing methods. This is particularly evident in medicine, where predicting outcomes is vital for patient care [15]. The intricate and variable nature of healthcare data presents challenges, necessitating the development of sophisticated algorithms and tools. These advancements have the potential to revolutionize disease prevention, personalized treatment, and healthcare [17] system optimization. Enhancing analytical capabilities is urgent, enabling healthcare professionals to uncover valuable patterns that improve patient care [8]. Efforts are underway to efficiently analyze medical big data [11], promising a brighter future for healthcare. Ultimately, of all the algorithms used, Decision Tree analysis proves to be the most effective approach after enhancing using bagging classifier. © 2023 IEEE.

关键词： Bagging Classifier Big data Decision Tree Machine Learning

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