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Big Mart Sales Analysis 6

Big Mart Sales Analysis

引用

6th International Conference on Computational Intelligence and Communication Technologies, CCICT 2024

作者： Kunal Singh, Muskaan Sharma, Harshit Roy, Shweta Agarwal, Kadambri Dept. of computer science and engineering ABES engineering college Ghaziabad India

ISBN: (纸本)9798350374629

Machine learning is a powerful technology that enables computer systems to recognize patterns and make prediction and choices without performing explicit programming. The basic notion behind this technology is to create models and algorithms that can take in input information, predict an outcome using statistical computation, and adjust outputs in response to fresh information. In this research paper, we examine the Big Mart Shopping Centre scenarios in order to forecast sales of various product categories and comprehend the impact of various factors on sales of various product categories. This paper focuses on leveraging machine learning techniques to predict sales for Big Mart with the aim of optimizing inventory management and maximizing revenue. Using a variety of characteristics from a dataset that Big Mart gathered, along with the methodology used to construct a prediction model, very accurate results are produced and these findings can be utilized to make decisions aimed at increasing sales and increase operational efficiency. ©2024 IEEE.

关键词： Sales

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Analyzing and Visualizing Causes of Poverty in India: A Data-Driven Approach Using HPCC Systems 8

Analyzing and Visualizing Causes of Poverty in India: A Data...

引用

8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Skanda, P.R. Shobha, G. Shetty, Jyoti Rv College of Engineering Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331505462

Poverty in India is a complex issue with multiple causes. This paper mainly focuses on three of the main causes for poverty: Education, Unemployment and Consumer Price Index. This study makes use of data received from the National Informatics Center (NIC) of India to predict poverty across the various states of India. Through applying preprocessing techniques and employing the HPCC Systems Visualization bundle, the research shows that Bihar is facing a higher level of poverty, whereas Mizoram exhibits converse trend. Subsequently, the HPCC ML-Core bundle is applied to conduct linear regression analysis for predicting poverty scores. This predictive model demonstrated an accuracy of 97%. Lastly, the difference between a single-node and 8-node cluster is presented for runtime performance which clearly shows that the 8-node cluster has an advantage for similar computational tasks and thus makes it suitable. © 2024 IEEE.

关键词： Visualization

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Enhanced Text-Summarization and Language Simplification:End-to-End Approach for Content Comprehension 1

Enhanced Text-Summarization and Language Simplification:End-...

引用

1st International Conference on Trends in engineering Systems and Technologies, ICTEST 2024

作者： Riza Ayisha, P. Mathew, George Government Engineering College Dept. of Computer Science and Engineering Thrissur India

ISBN: (纸本)9798350307795

Text summarization is the process of shortening a large body of text in a document to form a shorter while preserving its most important information. With the exponential growth of digital data, there is a need for automated text summarization to help users quickly comprehend the content. The use of machine learning techniques, particularly deep learning models, has shown great promise in generating high-quality summaries. Among these models, BERT (Bidirectional Encoder Representations from Transformers) has emerged as a state-of-the-art method for various natural language processing (NLP) tasks, including text summarization. In this paper, we propose to implement a text summarization system using the BERT model. Apart from the normal text summarization, we will be implementing a Language Simplification model that simplifies complex sentence structures or use simpler vocabulary while preserving the original meaning. This model transforms convoluted or verbose sentences into shorter and more straight-forward constructions The paper aims to develop an end-to- end summarization system that takes a long document as input and generates a summary of the most important information contained in the document. For example, like taking the entire news from an online portal and generates it summary. © 2024 IEEE.

关键词： Signal encoding

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Enhanced Fetal Ultrasound Image Segmentation using Spatial Attention Mechanisms with UNet: SAUnet

Enhanced Fetal Ultrasound Image Segmentation using Spatial A...

引用

2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Verma, Harshita Patro, Bdk Rajkiya Engineering College Dept. of Computer Science and Engineering Kannauj India

ISBN: (纸本)9798350376876

Fetal Ultrasound (US) imaging is necessary to prenatal treatment, as it provides information on the health and development of the fetus. Accurate segmenting is essential but poses significant challenges due to the dynamic fetal anatomy. This paper proposes Unet with Spatial Attention (SAUnet), a novel deep learning architecture that integrates attention mechanisms into the Unet framework. Unlike traditional models, SAUnet dynamically emphasizes key anatomical structures while suppressing irrelevant background information, enhancing the model's focus on informative regions within the US images. This targeted approach leads to improved segmentation accuracy and robustness. Our method was evaluated on the HC18 Challenge dataset, achieving a mean absolute difference(±std) of 2.23 ± 2.41 mm in head circumference measure and a mean Dice similarity coefficient of 97.63 ± 1.85%, demonstrating high segmentation accuracy. Additionally, mean difference and Hausdorff distance are 1.06 ± 3.11 mm and 1.42 ± 0.93 mm respectively suggesting robust performance. Incorporation of spatial attention in Unet architecture marks a significant advancement in fetal US image segmentation, offering a lightweight and efficient model suitable for real-time clinical deployment. Benchmark comparisons highlight SAUnet's competitive performance, reinforcing its potential as a state-of-the-art solution for prenatal imaging tasks. © 2024 IEEE.

关键词： Semantic Segmentation

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Homogenous Ensemble Learning for Air Quality Index Prediction 15

Homogenous Ensemble Learning for Air Quality Index Predictio...

引用

15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Medhi, Shrabani Boruah, Rituraj Baruah, Sagar Pratim Das, Hiranya Kumar Girijananda Chowdhury University Dept. of Computer Science and Engineering Guwahati India Girijananda Chowdhury Institute of Management and Technology Dept. of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798331300579

Air pollution is a global issue of concern. Accurate prediction of the air quality index (AQI) plays a crucial role in understanding pollution patterns and implementing effective mitigation strategies. This research paper presents a comprehensive study on the application of ensemble learning techniques for AQI prediction, aiming to enhance prediction accuracy and provide insights into the driving factors behind pollution levels. The primary objective is to conduct a comparative analysis of homogenous ensemble method, namely, bagging in conjunction with individual base models, such as multiple linear regression, support vector regression, decision trees, and artificial neural networks. In total we have created 4 non-ensembled models and 4 ensembled models. Additionally, the research investigates the importance of different features in AQI prediction models, aiming to identify the key factors influencing air pollution. Hyperparameter tuning is done to optimize the models. © Grenze Scientific Society, 2024.

关键词： Prediction models

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Forecasting Stock Price by LSTM-CNN Hybrid Model and Compares Deep Learning Models 1

Forecasting Stock Price by LSTM-CNN Hybrid Model and Compare...

引用

1st IEEE International Conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Arora, Aditi Upadhyay, Yashaswi Shukla, Satvik Saket Abes Engineering College Dept. of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350377002

The The accurate forecasting of stock prices holds significant importance in devising effective trading strategies and making informed decisions about stock transactions. The inherent volatility in stock prices, driven by factors like geopolitical tensions, corporate earnings, and commodity costs, underscores the need for precise predictions. Domestic factors such as central bank policies, governmental decisions, inflation, and global market uncertainties also trigger stock price fluctuations. Estimating this volatility is a complex challenge for traders. A novel approach is presented in this research, proposing a hybrid model that integrates two distinct algorithms - Long Short-Term Memory-Convolutional Neural Network (LSTM-CNN). This model harnesses diverse data representations from stock time series to enhance price prediction accuracy. Comparative analyses are performed with alternative models like LSTM (Long Short-Term Memory), CNN (Convolutional Neural Networks) and Naïve Bayes to ascertain the most effective prediction paradigm with minimal error. The study demonstrates that combining temporal and image features extracted from the same dataset diminishes prediction errors compared to using these features independently. The hybrid model outperforms singular prediction models, highlighting a high enhancement. Among the individual models, the LSTM model exhibits superior performance in terms of prediction accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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Utilizing the U-Net Network for Segmenting the Left Ventricle from Cine-MRI Scans 4

Utilizing the U-Net Network for Segmenting the Left Ventricl...

引用

4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Malhotra, Sonal Thapliyal, Siddhant Aggarwal, Priyanshi Kumar, Mukesh 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

ISBN: (纸本)9798350387490

In this research, we studied the U-Net segmentation model for identifying and demonstrating addresses related to cardiac left ventricle contour in cine-MRI images. Efficacy of the model is evaluated across different stages such as preprocessing, training, validation and robustness testing along with comparisons to conventional methods and other deep learning architectures. But the preprocessing includes standardized pixel intensities, and by data augmentation using rotation, flip method or scaling techniques. The training and validation stages clearly show the robust consolidation of our model with little overfitting, guaranteeing decent performance on different datasets. The developed model is robust by passing the test against noise/ artifacts, and providing a very good segmentation in extreme situations so that there are no accuracy outliers represented. Comparative analyses show better sensitivity-specificity trade-offs and precision-recall characteristics versus other methods. In general, a U-Net model developed here works well for cardiac MRI analysis and can be used as an effective tool to help automatic LV segmentation accurate and efficient in clinical applications which will improve diagnosis of heart diseases from imaging points. © 2024 IEEE.

关键词： Deep learning

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Gaze Detection and Text Extraction from Images using CNN with DBSCAN-based Filter Design 11

Gaze Detection and Text Extraction from Images using CNN wit...

引用

11th IEEE International Conference on Advances in Computing and Communications, ICACC 2024

作者： Jose, Jithin Ramanujan, Ajeesh College Of Engineering Trivandrum Dept. Of Computer Science And Engineering Trivandrum India

ISBN: (纸本)9798350379136

Gaze detection and text extraction are pivotal technologies in the domain of human-computer interaction and computer vision, enabling applications such as assistive technologies, user interface optimization, and automated content analysis. This paper presents a novel method that identifies the user's gaze direction and extracts the necessary portion of text, which constitutes the user's region of interest, using a Convolutional Neural Network (CNN) integrated with a spatial attention mechanism. A Density-Based Spatial Clustering of Applications with Noise (DBSCAN)-based filter is incorporated into the system to remove textual noise from the extracted image, thereby improving the model's accuracy and reducing computation time. The proposed method demonstrates reduced dependency on the CNN compared to previous approaches, resulting in enhanced real-time operation capability and lower latency. Computational optimization and real-time performance were thoroughly evaluated, showing that our system not only achieves high accuracy in gaze detection and text extraction but also maintains efficient processing speeds suitable for dynamic applications. This advancement offers significant improvements for applications in assistive technology, educational tools, and interactive media, where precise and efficient gaze detection and text extraction are critical. © 2024 IEEE.

关键词： Convolutional neural networks

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A Review of Disease Detection in Sugarcane Using Machine Learning and Deep Learning Techniques 15

A Review of Disease Detection in Sugarcane Using Machine Lea...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Pavan, Undavalli Chandra Pateriya, R.K. Tomar, Deepak Singh Singh, Surendra Maulana Azad National Institute of Technology Dept. of computer science & engineering Bhopal India National Institute of Technology Dept. of computer science & engineering Uttarakhand India

ISBN: (纸本)9798350370249

Agriculture has been an active source of food and economic growth, but it faces significant challenges from diseases and climate change. In Indonesia, sugarcane production is severely impacted by viral infections such as Sugarcane mosaic virus (SCMV) and Sugarcane streak mosaic virus (SCSMV). These infectious diseases pose a threat to the world's sugarcane industry, resulting in significant crop losses in nations like Brazil and India, along with other viruses like the Fiji Disease Virus (FDV), and Sugarcane Yellow Leaf Virus (SCYLV). For such diseases to be managed and performance to be maintained, early detection is essential. Disease detection is accomplished by both conventional and cutting-edge approaches, such as molecular, serological, and imaging methods. However, the advancements in Machine learning, Deep learning and computational image processing algorithms have demonstrated significant potential in the early and precise diagnosis of disease. In this study, different methods used for machine learning (ML) and deep learning (DL) for plant disease diagnosis were reviewed and it highlights the need of high-quality datasets, various pre-processing techniques, robust extraction of features, and efficient classification techniques. It also discussed the issues with data collection, symptom variability, over-fitting, and imbalances in classes, along with potential avenues for further advancement in diagnosing diseases systems. © 2024 IEEE.

关键词： challenges deep learning detection hybrid models machine learning Plant diseases

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Ethereum Powered E-Tendering System

Ethereum Powered E-Tendering System

引用

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

作者： Ananthajothi, K. Jaisuriya, P.K. Haribala, S. Rajalakshmi Engineering College Dept. Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350352689

The use of information technology to trade information, deliver services, and other things is becoming more and more commonplace in the era of technological advancement. Tendering is one such area where the public and private sectors collaborate. E-tendering is a procurement technique that uses the internet and digital technologies to automate and simplify the whole bidding or tendering process. This makes it easier for the bidders to browse through all of the open tenders and select one to proceed with. But it frequently results in a number of challenges that both bids and bidders must deal with. The system is inefficient because of scale and transparency problems brought on by its centralized architecture. Additionally, the system frequently shares information about tenders with other outside organizations, which compromises the confidentiality and integrity of the tenders. To manage these issues, a decentralized method is used in the deployment of the Blockchain e-tendering system. Consequently, the procurement procedures will have the opportunity to leverage the potential of blockchain technology to enhance security and transparency. This research aims to provide an electronic tendering system that is transparent, liquidate, and safe. It will manage tender allocation and use smart contracts to automate the underlying processes with the least amount of human interaction possible. © 2024 IEEE.

关键词： Smart contract

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