Nowadays E-commerce plays a major role in a business organization. People prefer online shopping rather than offline shopping which helps them to purchase their product from anywhere around the world through mobile ph...
Nowadays E-commerce plays a major role in a business organization. People prefer online shopping rather than offline shopping which helps them to purchase their product from anywhere around the world through mobile phones and laptop. Online shopping websites help people by saving time and product order can be done easily by clicking the product. Online shopping websites are built using the Single Page Application (SPA) framework and the objective of this research is to find the customer preference product prediction by tracking the frequent clicks of the product by the customer. By tracking the clicks of customer we can find their product choice and helps retailer to add the products according to user preference.
The Computational Visual Media(CVM)conference series is intended to provide a major international forum for exchanging novel research ideas and significant computational methods that either underpin or apply visual **...
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The Computational Visual Media(CVM)conference series is intended to provide a major international forum for exchanging novel research ideas and significant computational methods that either underpin or apply visual *** primary goal is to promote cross-disciplinary research to amalgamate aspects of computergraphics,computer vision,machine learning,image and video processing,visualization and geometric *** main topics of interest to CVM include classification,composition,retrieval,synthesis,cognition and understanding of visual media(e.g.,images,videos,3D geometry).
This paper presents a summary of the Masked Face Recognition Competitions (MFR) held within the 2021 International Joint Conference on Biometrics (IJCB 2021). The competition attracted a total of 10 participating team...
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Launched in 2013, LivDet-Iris is an international competition series open to academia and industry with the aim to assess and report advances in iris Presentation Attack Detection (PAD). This paper presents results fr...
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To detect the objects around an autonomous vehicle is very essential to operate safely. This paper presents to detect and classify the objects for assisting autonomous driving. In autonomous driving systems, the task ...
To detect the objects around an autonomous vehicle is very essential to operate safely. This paper presents to detect and classify the objects for assisting autonomous driving. In autonomous driving systems, the task of object detection itself is one of the most important prerequisites to autonomous navigation. Deep learning one of the computer vision tasks, perform object detection very effectively than compared to earlier methods and this project is to detect the objects like vehicles, persons, traffic lights, etc. In this work, an approach to object detection in deep learning that makes the bounding box for an image to predict is explored. Object detection is the method of detecting the objects present in a given image. Apart from detecting the number of objects present in an image it also specifies in which location that object is present in the image. The objects are detected by means of bounding boxes. In the existing system algorithm like Convolutional Neural Network (CNN) using Resnet-50 were used to detect the objects like vehicles, persons, traffic lights separately. The problem identified here is, in the existing system the camera is fixed in a particular place and it detects objects only if the objects come into the camera frames. It is not detecting both objects and lanes simultaneously when the autonomous vehicle is in motion at any location. To overcome these problems, in the proposed system object detection is performed by mounting camera in front of the moving vehicle. You Only Look Once (YOLO) V3 Algorithm is used for the process of object detection. Compared to earlier detection approaches YOLO V3 shows improvement in detection accuracy. It provides good feature extraction and detection in large-scale. The proposed YOLO al gorithm has better average precision value for detecting all objects than compared to existing CNN using Resnet-50. In addition YOLO V3 algorithm performs lane detection apart from object detection.
In India, It is very necessary to make normal cities as smart cities near future. So, Develop a smart city is one of the most recent fields in today's digital era to researches. Machine to Machine (M2M ) communica...
In India, It is very necessary to make normal cities as smart cities near future. So, Develop a smart city is one of the most recent fields in today's digital era to researches. Machine to Machine (M2M ) communication is the latest innovative-fangled type of communiqué that enables full automation with the help of integrated applications to make a normal city to a smart city. Many devices are connected in a smart city to monitoring many tasks and it is a very challenging task to integrate and manage these connected devices. Interconnection of several devices and machines using the internet to form an M2M Area Network (M2MAN) provides controlling and monitoring smart applications like smart-metering, weather, environment, home, air quality, water supply with quality management, power supply with bill monitoring, traffic, and healthcare, etc. under M2M communication and integrated single level umbrella structured applications. Out of several M2M monitoring and controlling applications towards smart cities, few key applications are presented in this paper with their basic approach, workflow, and concerning issues and used technologies for the implementation with the brief growth analysis.
Discussions related to the Digital Banking (DB) transformation has become the main issues in the industry nowadays. Digital disruption has changed the way peoples do business and perform transactions. However, the ban...
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ISBN:
(纸本)9781728126289
Discussions related to the Digital Banking (DB) transformation has become the main issues in the industry nowadays. Digital disruption has changed the way peoples do business and perform transactions. However, the bankers still found many problems when performing DB transformation. Main issues on DB transformation are that many banks still assume that digital transformation is about workflows and systems rather than focus on customer experience. Nowadays, Artificial Intelligence (AI) and Big Data Analytics (BDA) have risen and played as an important role in the new banking era. The recent trend of AI and BDA enable banking to be more customer-centric based on data driven. Personalization service becoming an important strategy for leveraging the existing customer engagement, and attracting potential customer become new customers. This study explores the application of AI and BDA in banking for leveraging customer experience. This study used literature review and interviews to gather the data. We interview more than some persons in Indonesia banking industry to get the insight on the implementation of AI and BDA in Indonesia. The paper reveals best practices of the global banking and Indonesian banking, in the implementation of AI & BDA. The contributions of this study are proposed enterprise architecture and recommended digital innovation in AI and BDA that enables banking institutions to leverage customer experiences.
Research in the fields of machine learning and intelligent systems addresses essential problem of developing computer algorithms that can deal with huge amounts of data and then utilize this data in an intellectual wa...
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Research in the fields of machine learning and intelligent systems addresses essential problem of developing computer algorithms that can deal with huge amounts of data and then utilize this data in an intellectual way to solve a variety of real-world problems. In many applications, to interpret data with a large number of variables in a meaningful way, it is essential to reduce the number of variables and interpret linear combinations of the data. Principal Component Analysis (PCA) is an unsupervised learning technique that uses sophisticated mathematical principles to reduce the dimensionality of large datasets. The goal of this paper is to provide a complete understanding of the sophisticated PCA in the fields of machine learning and data dimensional reduction. It explains its mathematical aspect and describes its relationship with Singular Value Decomposition (SVD) when PCA is calculated using the covariance matrix. In addition, with the use of MATLAB, the paper shows the usefulness of PCA in representing and visualizing Iris dataset using a smaller number of variables.
Deep learning belongs to the field of artificial intelligence, where machines perform tasks that typically require some kind of human intelligence. Deep learning tries to achieve this by drawing inspiration from the l...
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Deep learning belongs to the field of artificial intelligence, where machines perform tasks that typically require some kind of human intelligence. Deep learning tries to achieve this by drawing inspiration from the learning of a human brain. Similar to the basic structure of a brain, which consists of (billions of) neurons and connections between them, a deep learning algorithm consists of an artificial neural network, which resembles the biological brain structure. Mimicking the learning process of humans with their senses, deep learning networks are fed with (sensory) data, like texts, images, videos or sounds. These networks outperform the state-of-the-art methods in different tasks and, because of this, the whole field saw an exponential growth during the last years. This growth resulted in way over 10,000 publications per year in the last years. For example, the search engine PubMed alone, which covers only a sub-set of all publications in the medical field, provides already over 11,000 results in Q3 2020 for the search term 'deep learning', and around 90% of these results are from the last three years. Consequently, a complete overview over the field of deep learning is already impossible to obtain and, in the near future, it will potentially become difficult to obtain an overview over a subfield. However, there are several review articles about deep learning, which are focused on specific scientific fields or applications, for example deep learning advances in computer vision or in specific tasks like object detection. With these surveys as a foundation, the aim of this contribution is to provide a first high-level, categorized meta-survey of selected reviews on deep learning across different scientific disciplines and outline the research impact that they already have during a short period of time. The categories (computer vision, language processing, medical informatics and additional works) have been chosen according to the underlying data sources (image,
作者：
Apurv GargBhartendu SharmaRijwan KhanScholar
ABES Institute of Technology Ghaziabad Uttar Pradesh – 201009 India Professor
Department of Computer Science and Engineering ABES Institute of Technology Ghaziabad Uttar Pradesh – 201009 India
Machine Learning (ML), which is one of the most prominent applications of Artificial Intelligence, is doing wonders in the research field of study. In this paper machine learning is used in detecting if a person has a...
Machine Learning (ML), which is one of the most prominent applications of Artificial Intelligence, is doing wonders in the research field of study. In this paper machine learning is used in detecting if a person has a heart disease or not. A lot of people suffer from cardiovascular diseases (CVDs), which even cost people their lives all around the world. Machine learning can be used to detect whether a person is suffering from a cardiovascular disease by considering certain attributes like chest pain, cholesterol level, age of the person and some other attributes. Classification algorithms based on supervised learning which is a type of machine learning can make diagnoses of cardiovascular diseases easy. Algorithms like K-Nearest Neighbor (KNN), Random Forest are used to classify people who have a heart disease from people who do not. Two supervised machine learning algorithms are used in this paper which are, K-Nearest Neighbor (K-NN) and Random Forest. The prediction accuracy obtained by K-Nearest Neighbor (K-NN) is 86.885% and the prediction accuracy obtained by Random Forest algorithm is 81.967%.
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