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Crafting Customer Segmentation Strategies for Advanced Recommendation System 3

Crafting Customer Segmentation Strategies for Advanced Recom...

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3rd international conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Seethalakshmi, K. Bhagyalakshmi, A. Anitha, G. Sheela, E. Sandeep, Varada Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Tami Nadu Chennai India S.A Engineering College Department of Computer Science and Business Systems Tamil Nadu Chennai India St Peter's Institute of Higher Education and Research Department of Computer Science & Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350369083

With the static nature of recommender systems, it is crucial to take into account the intricate details of customer behaviour to better improve operating efficiencies. This paper is about original methods to augment recommendation frameworks by employing spectral clustering methods, to be more specific. Limited segmentation techniques now cannot catch up with the growing complexity and variety of customer conduct. To address this need, we are proposing a new way in which the spectral clustering technique, a supervised learning technique, can be applied. Revealing in-depth relationships inside customer data will enable an upgrade in the segmentation approach beyond the existing norms. Spectral clustering stands out as a really amazing tool for accomplishing extraordinarily precise customer segments, which help understand the tastes of users on a deeper level. Through discovering separate customer groups, we are aiming for a meaningful contribution in the field of recommendation systems. This project combines the effectiveness of spectral clustering with the convenience of a recommendation system that aims to provide a holistic, meaningful, and user-oriented approach to enhancing recommendation systems in the area of personalised recommendation. Our aim is to create recommendation systems that are not only attuned to the heterogeneity of customer preferences but also adopt new trends in user experience design beforehand. The segmentation is done with K-Means along with the proposed algorithm and the clusters are hence compared and Implementation results shows that spectral clustering is much more efficient than the existing K-Means algorithm © 2024 IEEE.

关键词： Sales

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Investigating the Temporal and Spatial Dynamics in Graph data for Enhancing Accuracy in Traffic Prediction 3

Investigating the Temporal and Spatial Dynamics in Graph Dat...

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3rd IEEE international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Minhas, Deepak Centre for Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350343274

For In this study, we present a comprehensive methodology for traffic prediction that seamlessly integrates linear time series analysis, deep learning techniques, and attention mechanisms. This multifaceted approach is elucidated through three meticulously designed flowcharts, each elucidating a distinct facet of the prediction process. Commencing with the collection of traffic data, it proceeds through a cascade of methods including autoregressive modeling and variational autoregressive techniques. The journey culminates with the application of Support Vector Regression (SVR) and k-Nearest Neighbors (KNN) algorithms. Utilization of deep learning methodologies for traffic prediction. This segment emphasizes the importance of acquiring both spatial and temporal traffic data to encapsulate intricate spatiotemporal patterns. Preprocessing procedures are introduced to make the data amenable to deep learning, encompassing normalization, handling missing values, and data transformation. The application of Convolutional Neural Networks (CNNs) is central to this approach, facilitating the capture of spatial patterns, such as traffic congestion. Furthermore, the integration of ST-ResNet with residual convolution units and the employment of 3D-CNNs for time-space correlations enhance the model's capabilities. The attention process commences by defining queries, keys, and values, followed by the adaptive weighting mechanism. Subsequently, self-attention is integrated, and the Transformer model, renowned for sequence modeling, is harnessed. The comprehensive methodology we present here stands as a robust and adaptable framework for traffic prediction. Its versatility is exemplified through rigorous experimentation, where it consistently outperforms six traditional methods. These empirical results demonstrate the superior predictive capacity of our 'Deep learning Model,' reinforcing its viability for real-world traffic prediction tasks. © 2023 IEEE.

关键词： Time series analysis

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Multi-class Skin Lesion Classification Using intelligent Techniques 3rd

Multi-class Skin Lesion Classification Using Intelligent Tec...

引用

3rd international conference on Advanced Computing and intelligent Technologies, ICACIT 2023

作者： Ranjan, Vibhav Chaurasia, Kuldeep Singh, Jagendra School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9789819719600

This study explores the development and assessment of an advanced skin cancer classification system utilizing deep learning techniques for accurate discrimination among diverse skin cancer types. The primary aim is to construct an effective classification framework based on deep learning that has the capability of distinguishing melanoma, vascular lesions, melanocytic nevus, cutaneous fibromas, benign keratosis, and various carcinomas and skin moles. Evaluation of the system involves comprehensive analysis employing metrics like accuracy, recall, precision, and F1-score using HAM10000 dataset. We utilized pre-trained models, like EfficientNetB0, VGG19, MobileNet, and ResNet50V2 and analyzed the performance for unbalanced and manually balanced dataset for different hyperparameters. The AI-based automatic system serves as a crucial tool in aiding accurate skin cancer diagnosis. It was found that MobileNet and ResNet50V2 performed quite well as compared to other models after dataset augmentation achieving overall accuracy of 84% and average F1-score across classes of 84% for both MobileNet and ResNet50V2. While performance may vary based on image quality, dataset diversity, and population-specific factors, this research highlights the efficacy and potential of deep learning in advancing skin cancer diagnosis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

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An Adaptive Driving Beam System with Compatibility to Different Pixel Matrix Beams 3

An Adaptive Driving Beam System with Compatibility to Differ...

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3rd IEEE international conference on Information Technology, Big data and Artificial Intelligence, ICIBA 2023

作者： Zhou, Shiying Liang, Guiyou Guan, Zhongxu Zheng, Hongli Yang, Huikai Zhang, Hang Wu, Bin China Faw Group Corporation Changchun130011 China Jilin University College of Electronic Science and Engineering Changchun130012 China

ISBN: (纸本)9781665490788

The Adaptive Driving Beam(ADB) is an intelligent driving assistance technique which is developed in recent years. The main function of ADB system is to automatically detect vehicles on the road by the intelligent camera, and forms a shadow area for the the target vehicle located in front of the vehicle which is using high beams. ADB system can help drivers to use high beams properly. The shadow area created by the ADB system can avoid generating dazzling to the drivers, hence the driving safety at night can be greatly improved. Considering the compatibility of the ADB technique, matrix LED systems with different pixel setting are discussed under an identical ADB control strategy. The angle relationships between the shadow area and the different matrix LED settings are analyzed and established respectively. Filtering method is introduced to smooth the output angles of the shadow area, which is used to eliminate the flickering of the LEDs. The hardware structure and software diagram of the ADB system are also discussed. © 2023 IEEE.

关键词： Light emitting diodes

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Browser compatibility performance detection method under virtual cloud technology 3

Browser compatibility performance detection method under vir...

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3rd international conference on Electronic Information engineering, Big data, and Computer Technology, EIBDCT 2024

作者： Li, Qiang Liu, Yongqing Liang, Yi Cai, Qingyuan Xing, Chaoming State Grid Information and Telecommunication Group Co. Ltd. Beijing102209 China Fujian Yirong Information Technology Co. Ltd. Fuzhou350003 China

ISBN: (纸本)9781510680449

With the rapid development of cloud computing and virtualization technology, more and more applications are being deployed and run in cloud environments, and compatibility issues between different browsers are becoming increasingly prominent. A browser compatibility performance detection method based on virtual cloud technology is proposed to address this issue. Using virtual cloud technology for environmental simulation, building a prediction model based on machine learning technology, predicting compatibility issues under different browser versions through this model, obtaining browser compatibility test data, and discovering patterns, trends, and patterns. Finally, build a browser compatibility performance test based on the Linux operating system to obtain the test results. The experimental results show that the detection results of this method have a high recall rate and a low false alarm rate. By increasing the number of test cases, it covers more situations and scenarios, improving the comprehensiveness of browser compatibility performance detection results. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Machine learning

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Compensation method for metal defect detection based on deep learning and ultrasonic wave 3

Compensation method for metal defect detection based on deep...

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3rd international conference on Advanced Manufacturing Technology and Manufacturing Systems, ICAMTMS 2024

作者： Wu, Chunjie Ma, Zaisheng Ding, Yong Fu, Junlei Wu, Xinjia Zhejiang Fangyuan Test Group Co. Ltd. Hangzhou310018 China Zhejiang Fangyuan Metal Materials Test Co. Ltd. Hangzhou310018 China

ISBN: (纸本)9781510681804

Ultrasonic nondestructive testing plays a crucial role in evaluating the integrity of metal structures. When there are defects in the tested workpiece, there will be reflection, refraction, diffraction, and other behaviors of sound waves. By analyzing the ultrasonic echo signal, the location and depth of defects can be identified. In previous ultrasonic defect detection, due to changes in the ultrasonic probe, the identified defects may change, affecting the accuracy of defect discrimination. This paper proposes a method for detecting the depth of metal cracks based on deep learning. This method combines finite element models and experimental analysis data to compensate for the depth of metal cracks, improving the accuracy of defect identification. Finally, an ultrasonic experimental study on metal components was conducted to verify the effectiveness of this method. © 2024 SPIE.

关键词： Ultrasonic testing

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Machine learning in Student Health - A Review 3

Machine Learning in Student Health - A Review

引用

3rd international conference on Issues and Challenges in intelligent Computing Techniques, ICICT 2022

作者： Gurusamy, Deivanai Chakrabarti, Prasun Chakkaravarthy, Midhun Lincoln University College Faculty of Computer Science and Multimedia Malaysia Itm Sls Baroda University Department of Computer Science and Engineering India

ISBN: (纸本)9781665482684

Students' health is an important research topic today because they are the cornerstone of our society. Researchers have used various technological breakthroughs to address schoolchildren's and college/university students' health issues, and machine learning is now frequently employed. However, to understand the efficacy of machine learning and progress in student health research, a concise review of the influence of machine learning on student health is required, which the paper provides. The paper's primary objective is to examine which of the students' health concerns are efficiently addressed by machine learning algorithms and the outcomes of the approaches. The paper also discusses what leads students to perform poorly in schools, colleges, and universities and if machine learning will improve student health in the future. The review findings can be helpful to researchers in moving forward with unaddressed health-related problems and other solutions concerning the student's health. © 2022 IEEE.

关键词： Students

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Construction and Application of Machine learning Algorithm in Mental Health Teacher Competency Model 1

引用

3rd EAI international conference on Application of Big data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2023

作者： Wang, Minxin Zhang, Xiaoying Xu, Jianbo Wuhan Business University Hubei Wuhan430000 China College of Foreign Languages Xiangnan University Hunan Chenzhou423000 China

ISBN: (数字)9783031631399

ISBN: (纸本)9783031631382

The role of machine learning algorithms in constructing mental health teacher competency models is very important, but there is a problem of low construction accuracy. Standard competency models are poorly constructed and cannot solve many aspects of psychoanalysis. Therefore, this paper proposes a machine learning algorithm and a mental health teacher competency model. Firstly, the competency theory is used to classify mental health teachers, and the competency construction scheme is selected according to the mental health teaching standards. Then, a building set is formed according to the competency criteria, and the competency parameters are iteratively judged. MATLAB simulations show that in mental health teachers, machine learning algorithms can improve competency accuracy and shorten construction Time, the results were better than the standard competency model. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： MATLAB

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Analysis and Classification of Restaurants Based on Rating with XGBoost Model 3

Analysis and Classification of Restaurants Based on Rating w...

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3rd international conference on Issues and Challenges in intelligent Computing Techniques, ICICT 2022

作者： Dixit, Anuj Kumar Nair, Rekha R Babu, Tina School of Engineering Dayananda Sagar University Department of Computer Applications Bengaluru India School of Engineering Dayananda Sagar University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665482684

The restaurant business is one of the most competitive and the need for restaurants is growing daily. Bangalore is a foodie's paradise, boasting cuisines from all over the world. Hence, this research work focuses the classification of restaurants on the basis of rate with XGBoost model. The Exploratory data Analysis(EDA) with different graphs provides an analysis of the data before classification. Prior to EDA the data sets are cleaned with various steps to increase the accuracy of the visualization and classification. The research was performed using Zomato data set for restaurants in a specific locality(Bangalore). data Visualization techniques helped to analyze food culture, trends and patterns. This research describes a model for understanding the elements that influence restaurant ratings. Predictive analytics and machine learning together with a variety of tools and methodologies, help in predicting restaurant ratings. The model in this research is developed using multiple supervised techniques, with the most efficient algorithm being evaluated. The classification XGBoost model provided an accuracy of 98.07 %. The outcome of the work assists new restaurants in selecting on their menu, cuisine, theme, prices, geographic location, and so on, consequently enhancing business. The rate and location details helps people to select the restaurants for the dining. © 2022 IEEE.

关键词： Predictive analytics

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Performance prediction of densely deployed WLANs using Graph Isomorphism Networks 3

Performance prediction of densely deployed WLANs using Graph...

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3rd international conference on Electronics Technology and Artificial Intelligence, ETAI 2024

作者： Mohan, Rajasekar Navali, Anish Dhruva Chaitanya, G. Gagandeep, H.S. Patil, Neelan S. PES University Bangalore India

ISBN: (纸本)9781510683143

As the usage of wireless devices has increased exponentially, deployments of IEEE 802.11 WLANs has been significantly increasing and hence is bound to cause higher interference and reduced throughputs, particularly in dense-deployments. Though a network simulator like Komondor captures comprehensive deployment details of WLANs to provide accurate performance metrics, the high compute time and the concomitant complexity of such simulators cannot be disregarded. Deep learning techniques prove to be the ideal alternative to be able to quickly compute the throughput of a WLAN deployment with reduced compute time and complexity. In this paper we implement a Graph Isomorphism Network (GIN) for predicting the throughputs of BSSs in dense deployments. The regression model is trained on the data obtained from the Komondor network simulator for a several comprehensive set of deployments. As the topology of the deployments mimic a graph network, the capability of GIN is explored for mapping the deployments and to accurately predict the throughput of a densely deployed WLAN. © 2024 SPIE.

关键词： Deep learning

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