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Optimizing User Satisfaction and Streamlining Operations in Online Retail Using cnn algorithm 2

Optimizing User Satisfaction and Streamlining Operations in ...

2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Rayavel, P. Sendhil Kumar, K.S. Sarah Rachel, S. Sushmitha, K. Akalya, P. Sri Sairam Institute of Technology Chennai India Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350306415

The rising trend of online shopping has become increasingly prevalent in today's generation. With the convenience of browsing and purchasing products from the comfort of one's own home, it has reshaped the way people shop. In this digital age, having a dedicated shopping app has become a necessity for businesses looking to tap into this growing market. These apps offer seamless navigation, personalized recommendations, and secure payment options, catering to the preferences and expectations of today's tech-savvy consumers. As the world continues to embrace e-commerce, the need for user-friendly shopping apps remains essential for both consumers and businesses alike. Furthermore, this online shopping app incorporates advanced technologies, such as image recognition and augmented reality, to enhance the product exploration process. Customers can use their smartphones to scan product images or QR codes, allowing for a three-dimensional visualization of the chosen items and a deeper understanding of their features and assembly. Additionally, the app introduces a collaborative shopping feature that enables users to create shared wish lists and make group purchases, fostering social engagement and simplifying the gift-buying process. This innovative online shopping application not only caters to the evolving needs and preferences of modern consumers but also empowers retailers to offer a more immersive and efficient shopping experience. In a rapidly evolving e-commerce landscape, this app stands as a testament to the ongoing digital transformation of the retail industry. © 2024 IEEE.

关键词： 3D Simulation cnn algorithm Image blending LSTM

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cnn algorithm for plant classification in deep learning

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Materials Today: Proceedings 2021年 46卷 3684-3689页

作者： G. Valarmathi S.U. Suganthi V. Subashini R. Janaki R. Sivasankari S. Dhanasekar ECE Sri Sairam Institute of Technology Chennai 600044 India ECE Sri Eshwar College of Engineering Coimbatore 641202 India

The prior methodology of characterizing the plants for dependent on surface based order and another strategy depends on KNN classifier. This paper presents qualities examination of plants utilizing picture preparing methods for robotized vision framework utilized at horticultural field. In farming examination, the programmed plant attributes recognition is fundamental one in observing huge field. The proposed dynamic framework uses picture content portrayal and regulated classifier sort of neural organization. This will naturally distinguish the plant species when we import its picture as info. Picture preparing strategies for this sort of choice investigation includes pre-processing and characterization stage. At Processing, an info picture will be resized and commotion expulsion procedure is applied. At definite stage the neural organization orders the pictures as farming plant, harmful plant and therapeutic plant separately. At that point it will show the attributes of each plant.

关键词： Plant classification kNN algorithm SVM classifier Feature extraction cnn algorithm

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Bone Fracture Detection System using cnn algorithm

Bone Fracture Detection System using CNN Algorithm

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International Conference on Intelligent Computing and Control Systems (ICCS)

作者： Sinthura, Siva S. Prathyusha, Y. Pranusha, Y. Harini, K. Poojitha, B. BVRIT HYDERABAD Coll Engn Women Dept Elect & Commun Engn Hyderabad India

ISBN: (纸本)9781538681138

Identification of faults through computer-based techniques is a growing trend these days in all fields. A highly responsive system is characterized by two key features of quick detection and being highly accurate through leverage of modern techniques and efficient utilization of resources. Break in a bone or bone fracture is the result of excess external force beyond the threshold of what the bone can withstand. Canny Edge detection is an image processing methodology to detect the bone fracture through efficient use of automated fracture detection and overwhelms the noise removal problem. In today's world there are several methodologies available for edge detection like Sobel, Canny, Log, Prewitt, and Robert. However, these techniques are plagued with key shortcomings like a lack of capability to perform multiresolution analysis that result in inability to detect minor details during analysis. The other key shortcoming of the techniques is that though they work fine with high resolution and high-quality images, but can't work as well with noisy images due to theirinherent lack of ability to distinguish between edges and noise components [4]. The method being proposed overcomes over these problems using cnn algorithm. The results from the simulations done reveal that the proposed method is much more efficient mechanism to perform edge detection at aggregate scales. The proposed method has also proved to be resilient enough to extract the necessary information and do the processing needed on key portions of the images and handle noise in a much better manner than the currently available edge detectors

关键词： cnn algorithm edge detection canny

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Research on the UBI Car Insurance Rate Determination Model Based on the cnn-HVSVM algorithm

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IEEE ACCESS 2020年 8卷 160762-160773页

作者： Yan, Chun Wang, Xindong Liu, Xinhong Liu, Wei Liu, Jiahui Shandong Univ Sci & Technol Coll Math & Syst Sci Qingdao 266590 Peoples R China Beijing Inst Petrochem Technolog Dept Math & Phys Beijing 102617 Peoples R China Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Peoples R China

With the support of Internet of Vehicles technology, UBI (Usage Based Insurance) car insurance rate determination has certain guiding significance for achieving accurate pricing of car insurance rates and satisfying the personalized needs of users. Based on the cnn (Convolutional Neural Networks) algorithm and SVM (Support Vector Machine) algorithm, this paper establishes a rating model for UBI car insurance rates. The model first performs a series of operations such as convolutions, pooling and nonlinear activation function mapping using the cnn algorithm so that it can extract the features from the driving behavior data of UBI customers. Then, it introduces the Hull Vector to optimize the operating efficiency of the SVM algorithm. The HVSVM (Hull Vector Support Vector Machine) algorithm classifies customers according to their driving behavior, and thus obtains UBI customer car insurance rate grades. Therefore, this paper proposes a UBI car insurance rate grade determination model based on the cnn-HVSVM algorithm. The empirical results of the model show that the cnn-HVSVM algorithm has higher discrimination accuracy in the risk rating process of UBI customer driving behavior than the cnn algorithm, BP neural network algorithm and SVM algorithm;and when dealing with large training sets, it has a speed advantage over the cnn-SVM algorithm. Furthermore, it is easy to realize in the process of establishing the UBI car insurance rate determination model and it has good robustness, which can adapt to diverse data sets, thus achieving better results in the car insurance rate determination process. Therefore, the cnn-HVSVM model can predict the grade of UBI car insurance users more accurately and efficiently, and the prediction results are more consistent with the actual situation, which has strong applicability and flexibility. The UBI car insurance premium rate determination model based on the cnn-HVSVM algorithm can determine driver behavior more fairly and reasonably,

关键词： Insurance Automobiles Support vector machines Industries Prediction algorithms Big Data cnn algorithm cnn-HVSVM algorithm rate grade judgment SVM algorithm UBI car insurance

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An Image-Based Technique for Measuring Droplet Size Distribution: The Use of cnn algorithm

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JOURNAL OF DISPERSION SCIENCE AND TECHNOLOGY 2016年第10期37卷 1444-1452页

作者： Farzi, G. A. Nejad, A. Parsian Hakim Sabzevari Univ Dept Mat & Polymer Engn Fac Engn Sabzevar Iran Hakim Sabzevari Univ Dept Mech Engn Fac Engn Sabzevar Iran

A challenging task in measuring droplet size is the ability to perform in-situ droplet size distribution analysis on multiphase fluids in their native states in the undisturbed environment. In this study, an inline two-dimensional low cost-high accuracy technique is presented for continuous measurement of spherical or non-spherical droplets in emulsions using image processing. The characteristic of the droplets is evaluated and the describe drop size distributions in different ranges is determined. This droplet size determination algorithm is based on both cellular neural networks and linear matrix inequality. Our main work focuses on the performance of the proposed methodology for exploring the dynamical evolution of such droplet size distributions by in-situ measurement. Moreover, the results were compared with those obtained using laser diffraction analyzer technique. It was proved that this method can efficiently characterize the quality of dispersed phase by determining droplet size distribution.

关键词： cnn algorithm droplet size emulsion image processing

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Recommendation algorithm Integrating cnn and Attention System in Data Extraction

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Computers, Materials & Continua 2023年第5期75卷 4047-4063页

作者： Yang Li Fei Yin Xianghui Hui College of Information and Management Science Henan Agricultural UniversityZhengzhou450046China

With the rapid development of the Internet globally since the 21st century,the amount of data information has increased *** helps improve people’s livelihood and working conditions,as well as learning ***,data extraction,analysis,and processing have become a hot issue for people from all walks of *** recommendation algorithm still has some problems,such as inaccuracy,less diversity,and low *** solve these problems and improve the accuracy and variety of the recommendation algorithms,the research combines the convolutional neural networks(cnn)and the attention model to design a recommendation algorithm based on the neural network *** the text convolutional network,the input layer in cnn has transformed into two channels:static ones and non-static ***,the self-attention system focuses on the system so that data can be better processed and the accuracy of feature extraction becomes *** recommendation algorithm combines cnn and attention system and divides the embedding layer into user information feature embedding and data name feature extraction *** obtains data name features through a convolution ***,the top pooling layer obtains the length *** attention system layer obtains the characteristics of the data *** results show that the proposed recommendation algorithm that combines cnn and the attention system can perform better in data extraction than the traditional cnn algorithm and other recommendation algorithms that are popular at the present *** proposed algorithm shows excellent accuracy and robustness.

关键词： Data extraction recommendation algorithm cnn algorithm attention model

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Stock Return Prediction Based on Ensemble and cnn algorithms 24

Stock Return Prediction Based on Ensemble and CNN Algorithms

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Proceedings of the 2024 2nd International Conference on Internet of Things and Cloud Computing Technology

作者： Linlin Li Shaohua Zhou Yuxue Wang Department of Mathematics Northeast Petroleum University Daqing China

ISBN: (纸本)9798400710148

Although traditional rolling training methods are widely used for model performance evaluation and optimization, they often lead to data leakage during the training process, which affects the model's reliability and effectiveness. To address this issue, this paper proposes the use of ensemble models based on XGBoost and LightGBM, along with a cnn model, to predict future stock returns while avoiding the use of future data during rolling training, thereby effectively preventing data leakage. Using daily trading data from the constituent stocks of the CSI 300 Index, a multi-factor model is constructed based on various technical indicators (such as exponential moving average, standard deviation, linear regression slope, relative strength index, and Williams %R) for stock return prediction. Since return calculation involves the closing prices of the next five trading days, the traditional LightGBM multi-factor model experienced data leakage during rolling training. To solve this, ensemble models and the cnn model were introduced to predict the returns for the last five days of each rolling training window using the return data from the previous 252 days, effectively preventing data leakage. Backtesting results show that the cumulative return of the ensemble model increased to 193.54%, demonstrating higher stability and predictive power across different backtesting periods, outperforming the traditional and cnn models in key metrics such as annualized return, Sharpe ratio, and maximum drawdown.

关键词： cnn algorithm

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Estimating and mapping tailings properties of the largest iron cluster in China for resource potential and reuse: A new perspective from interpretable cnn model and proposed spectral index based on hyperspectral satellite imagery

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2025年 139卷

作者： Lei, Haimei Bao, Nisha Yu, Moli Cao, Yue Northeastern Univ Coll Resources & Civil Engn Shenyang Peoples R China Shenyang Urban Construct Univ Dept Basic Teaching Shenyang Peoples R China North Informat Control Res Acad Grp Co Ltd Nanjing Peoples R China

Iron tailings are crystalline powders predominantly composed of iron (Fe) and silicon dioxide (SiO2). Spatially characterizing the physical and chemical properties of iron tailings is greatly important for optimal utilization and proper disposal of tailings. Visible-near infrared-shortwave infrared (VIS-NIR-SWIR;350-2500 nm) spectroscopy offers a rapid, non-destructive, and cost-effective method for quantitatively analyzing tailings properties. This study aimed to quantify and map the spatial distribution of total Fe (TFe) and SiO2 contents of tailings dams at the largest iron cluster in China using laboratory spectra and GF-5 hyperspectral images. A total of 230 samples were collected from the surface of 11 tailings dams and scanned by a VIS-NIR-SWIR reflectance spectrometer in the laboratory. A novel spectral index was developed through a multi-objective programming methodology. This novel index utilizes band ratios to identify the optimal combination of spectral bands that show a strong correlation with concentrations of TFe and SiO2. Simultaneously, it minimizes the impact of moisture content and particle size variations in surface tailings. In addition, the partial least squares regression (PLSR), random forest (RF) and convolutional neural network (cnn) algorithms based on laboratory spectra were used to calibrate spectral information with associated tailing properties. The contribution of wavelength in the calibration modeling process by calculating SHaply Additive exPlanations (SHAP) values. According to the results, the reflectance spectra were negatively correlated to TFe content and positively correlated to SiO2 content. The three-band spectral index (TBI) calculated by R827/(R900 x R2200) correlated best to TFe with the correlation coefficient (r) of 0.87, while the R2397/(R776xR900) correlated best to SiO2 with r of 0.70. It also minimized the effect of particle size and moisture content on the reflectance spectra of tailings properties. The cnn algorit

关键词： GF-5 hyperspectral satellite imagery Iron tailings VIS-NIR-SWIR spectroscopy cnn algorithm SHAP values

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Liquid Filling Quality Control System for Beverage Industry Using Image Processing (cnn) 24

Liquid Filling Quality Control System for Beverage Industry ...

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24th International Conference on Control, Automation and Systems

作者： Chatchapol, B. Autanan, W. Anan, W. Rajamangala Univ Technol Lanna Fac Elect Engn Dept Elect Engn Chiang Mai 50300 Thailand Rajamangala Univ Technol Lanna Fac Engn Dept Elect Engn & Automat Control Syst Chiang Mai Thailand Chiang Mai Univ Fac Engn Robot Engn & AI Chiang Mai 50200 Thailand

ISBN: (纸本)9798331517939;9788993215380

The beverage industry has been leveraging automation to enhance production efficiency. An essential advancement involves the use of AI and image processing to ensure the high quality of products. Image processing systems employ sophisticated algorithms to meticulously inspect specific aspects of the production process (liquid level, lid sealing, text print). The cnn algorithm is primarily utilized to scrutinize water bottle images to verify their quality. Maintaining consistent production speed poses a challenge, underscoring the importance of examining the image processing system from various perspectives. The research proposed implementing a system that oversees liquid filling quality through an image processing algorithm, a camera module, and conveyor belt speed control to address. This system monitors liquid levels and bottle capping while the production line is operational. On average, the system achieves a 98.89 percent accuracy rate in detecting liquid levels and a 100 percent accuracy rate in sealing the lids. Furthermore, the conveyor speed control can adjust automatically to detect variations in product movement speed.

关键词： Beverage Liquid Filling Image Processing cnn algorithm

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Automatic Attendance Management System Using cnn 4

Automatic Attendance Management System Using CNN

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4th International Conference on Artificial Intelligence and Signal Processing

作者： Sreenivasu, S. V. N. Rajani, Ch. Rani, Birlangi Usha Dasaradha, Arangi Rafi, Shaik Narasaraopeta Engn Coll Dept CSE Narasaraopet 522601 Andhra Pradesh India Aditiya Inst Technol & Management Autonomous Dept Informat Technol Tekkali Andhra Pradesh India Aditiya Inst Technol & Management Autonomous Dept CSM Tekkali Andhra Pradesh India

ISBN: (纸本)9798350350661;9798350350654

Facial recognition technology plays a crucial role in various applications, from enhancing security at banks and organizations to streamlining attendance tracking in public gatherings and educational institutions. Traditional methods of attendance marking, such as signatures, names, and biometrics, can be time-consuming and error-prone. To address these challenges, a smart attendance system is proposed, leveraging Deep Learning, Convolutional Neural Networks (cnn), and the OpenCV library in Python for efficient face detection and recognition. The system utilizes advanced algorithms, including Eigen faces and fisher faces, to recognize faces accurately. While deep learning models excel with large datasets, they may not perform optimally with few samples. By comparing input faces with images in the dataset, the system automatically updates recognized names and timestamps into a CSV file, which is then sent to the respective organization's head. Additionally, the system allows users to upload a single photo or a group photo, and it returns matched photos as output using a cnn. This feature enhances the system's flexibility and usability, providing users with a convenient way

关键词： cnn algorithm OpenCV Attendance Tracking Deep Learning Dataset Preparation

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