scholarly journals Geo-Landmark Recognition and Detection

2021 ◽  
Vol 10 (7) ◽  
pp. 95-98
Author(s):  
Nishika Manira* ◽  
Swelia Monteiro ◽  
Tashya Alberto ◽  
Tracy Niasso ◽  
Supriya Patil
Keyword(s):  
Mobile Applications ◽  
Historical Data ◽  
Image Data ◽  
Recognition System ◽  
Physical World ◽  
Retrieval Task ◽  
Landmark Recognition ◽  
Mobile Data ◽  
Photo Collections ◽  
Efficient Recognition

The widespread use of smartphones and mobile data in the present-day society has exponentially led to the interaction with the physical world. The increase in the amount of image data in web and mobile applications makes image search slow and inaccurate. Landmark recognition, an image retrieval task, faces its challenges due to the uncommon structure it possesses, such as, buildings, cathedrals, castles or museums. These are shot from various angles which are often different from each other, for instance, the exterior and interior of a landmark. This paper makes use of a Convolutional Neural Networks (CNN) based efficient recognition system that serves in navigation, to organize photo collections, identify fake reports and unlabeled landmarks from historical data. It identifies landmarks correctly from a variety of images taken at different viewpoints as well as distances. An appropriate CNN architecture helps to provide the best solution for the currently selected dataset.

scholarly journals Mobile Applications for Knowledge Workers and Field Workers

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Stefan Stieglitz ◽  
Christoph Lattemann ◽  
Tobias Brockmann
Keyword(s):  
Data Storage ◽  
Mobile Applications ◽  
Mobile Apps ◽  
Knowledge Workers ◽  
Competitive Advantages ◽  
Mobile Business ◽  
Mobile Data ◽  
Individual Enterprise ◽  
Employees Satisfaction ◽  
Field Workers

In recent years, the diffusion of mobile applications (mobile apps) has risen significantly. Nowadays, mobile business apps are strongly emerging in business, enhancing productivity and employees’ satisfaction, whilst the usage of customized individual enterprise apps is still an exception. Standardized business apps enable basic functionalities, for example, mobile data storage and exchange (e.g., Dropbox), communication (e.g., Skype), and other routine processes, which support mobile workers. In addition, mobile apps can, for example, increase the flexibility of mobile workers by easing the access to firm’s information from outside the enterprise and by enabling ubiquitous collaboration. Hence, mobile apps can generate competitive advantages and can increase work efficiency on a broad scale. But mobile workers form no coherent group. Our research reveals, based on two case studies, that they can be clustered into two groups: knowledge workers and field workers. Knowledge workers and field workers fulfill different tasks and work in different environments. Hence, they have different requirements for mobile support. In this paper we conclude that standardized mobile business apps cannot meet the different requirements of various groups of mobile workers. Task- and firm-specific (individualized) requirements determine the specification, implementation, and application of mobile apps.

scholarly journals Iris Segmentation

2018 ◽  
Vol 7 (2.5) ◽  
pp. 77
Author(s):  
Anis Farihan Mat Raffei ◽  
Rohayanti Hassan ◽  
Shahreen Kasim ◽  
Hishamudin Asmuni ◽  
Asraful Syifaa’ Ahmad ◽  
...  
Keyword(s):  
Iris Recognition ◽  
Image Data ◽  
Recognition System ◽  
Environmental Condition ◽  
Segmentation Method ◽  
Iris Segmentation ◽  
Transform Method ◽  
Circular Hough Transform ◽  
Segmentation Methods

The quality of eye image data become degraded particularly when the image is taken in the non-cooperative acquisition environment such as under visible wavelength illumination. Consequently, this environmental condition may lead to noisy eye images, incorrect localization of limbic and pupillary boundaries and eventually degrade the performance of iris recognition system. Hence, this study has compared several segmentation methods to address the abovementioned issues. The results show that Circular Hough transform method is the best segmentation method with the best overall accuracy, error rate and decidability index that more tolerant to ‘noise’ such as reflection.  

scholarly journals Ion-Imprinted Polymer-Based Receptors for Sensitive and Selective Detection of Mercury Ions in Aqueous Environment

2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
Maimoona Yasinzai ◽  
Ghulam Mustafa ◽  
Nazia Asghar ◽  
Ikram Ullah ◽  
Muhammad Zahid ◽  
...  
Keyword(s):  
Detection Limit ◽  
Metal Ions ◽  
Recognition System ◽  
Aqueous Environment ◽  
Mercury Ions ◽  
Polymeric Systems ◽  
Interdigital Electrodes ◽  
Ion Imprinted ◽  
Ion Imprinted Polymers ◽  
Efficient Recognition

Interdigital electrodes (IDE) coated with ion-imprinted polymers (IIP) as recognition materials have been tested for screening and ion quantification. For screening of receptors, three polymer systems based on styrene (Sty), N-vinylpyrrolidone (NVP), and Sty-co-NVP were examined to identify an efficient recognition system for mercury ions in an aqueous environment. Results showed that all these polymeric systems can detect analyte even in very low concentration, that is, 10 ppm. Ion-imprinted polystyrene system proved to be an ideal receptor for detecting mercury ions in solution with a detection limit of 2 ppm. The sensitivity of ion-imprinted copolymeric system was further enhanced by making its composite with graphene oxide, and estimated detection limit of composite system was around 1 ppm. Ion- imprinted Sty-co-NVP graphene composite-based sensor system exhibits 2 to 5 times higher sensor response towards templated analyte in comparison to other polymer-based sensor systems. Moreover, the composite-based sensor shows very low or negligible response to competing metal ions with similar or different oxidation states such as Zn, Mg, Na, and As metal ions.

scholarly journals CFDIL: A Context-aware Feature Deep Interaction Learning for  App Recommendation

2021 ◽  
Author(s):  
Qingbo Hao ◽  
Ke Zhu ◽  
Chundong Wang ◽  
Peng Wang ◽  
Xiuliang Mo ◽  
...  
Keyword(s):  
Mobile Applications ◽  
Historical Data ◽  
Contextual Information ◽  
Rapid Development ◽  
User Preferences ◽  
Mobile Internet ◽  
Context Aware ◽  
Interaction Data ◽  
Learning Potential ◽  
The One

Abstract The rapid development of Mobile Internet has spa-wned various mobile applications (apps). A large number of apps make it difficult for users to choose apps conveniently, causing the app overload problem. As the most effective tool to solve the problem of app overload, the app recommendation has attracted extensive attention of researchers. Traditional recommendation methods usually use historical data of apps used by users to explore their preferences, and then make an app recommendation list for users. Although the traditional app recommendation methods have achieved certain results, the performance of app recommendation still needs to be improved due to the following two reasons. On the one hand, it is difficult to construct traditional app recommendation models when facing with the sparse user-app interaction data. On the other hand, contextual information has a large impact on users’ app usage preferences, which is often overlooked by traditional app recommendation methods. To overcome the aforementioned problems, we proposed a Context-aware Feature Deep Interaction Learning (CFDIL) method to explore user preferences, and then perform app recommendation by learning potential user-app relationships in different contexts. The novelty of CFDIL is as follows: (1) CFDIL incorporates contextual features into users' preferences modeling by constructing a novel user and app feature portrait. (2) The problem of data sparsity is effectively solved by the use of dense user and app feature portraits, as well as the tensor operations for label sets. (3) CFDIL trains a new deep network structure, which can make accurate app recommendation using the contextual information and attribute information of users and apps. We applied CFDIL on three real datasets and conducted extensive experiments, which showed that CFDIL outperformed the benchmark method.

scholarly journals Automated Bird Species Recognition System Based on Image Processing and Svm Classifier

2021 ◽  
Vol 12 (2) ◽  
pp. 351-356
Author(s):  
Chandra. B, Et. al.
Keyword(s):  
Image Processing ◽  
Species Recognition ◽  
Bird Species ◽  
Image Data ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Svm Classifier ◽  
Data Set ◽  
Learning Techniques ◽  
Processing Techniques

Here, in this study we can learn about Bird species recognition. In forest areas cameras are fixed at various locations which capture images periodically. From those images the birds living in such dense forest areas can be identified. It would be useful if we can able to classify the species of birds with the help of those images. But that is not an easy task because of the variations in the light effects, illumination and camera viewpoints. So we need to involve image processing techniques for preprocessing the captured image and also deep learning techniques are to be implemented for classifying the images. For classification purpose training is to be done with the help of image data set. Here we propose a method of discriminating birds by means of the ratio of the distance between eye and beak to that of the beak width. By combining this mythology with image processing and SVM classification technique a new bird species recognition algorithm is proposed. The proposed new methodology will improve the accuracy in classifying.

scholarly journals Pengenalan Jenis Kayu Berdasarkan Citra Makroskopik Menggunakan Metode Convolutional Neural Network

2021 ◽  
Vol 7 (2) ◽  
Author(s):  
Eko Yudhi Prastowo
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Convolutional Neural Network ◽  
Building Materials ◽  
Raw Materials ◽  
Image Data ◽  
Recognition System ◽  
Fiber Direction ◽  
Processed Products ◽  
The Right

Until now, wood has an irreplaceable function. Building materials, shipping, furniture, sports equipment, carvings and handicrafts using wood. Indonesia has more than 4,000 types of wood, so choosing the right wood is a challenge because choosing the wrong type of wood can make the quality of processed products decline and not as expected. In addition, proper identification of timber can also prevent illegal logging, especially on certain types of wood which are now increasingly scarce. Recognition to wood by looking directly is a difficult thing for ordinary people to do and can only be done by a wood expert, so it is necessary to find a method of recognizing wood that can be used by people independently. One method that can be used to identify type of wood is image processing based on characteristics of wood which include color, fiber direction and texture. This paper will describe recognition of wood-based image processing using Convolutional Neural Network (CNN) method. This method is derived from Neural Networks with addition of an extraction layer feature, which can reduce free parameters that are not needed by the system. Wood image data used in this study are four types of wood that are often used as raw materials for making houses and furniture, namely Glugu, Teak, Sengon and Waru. Results of this study were able to recognize four types of wood with an accuracy of 95% in 600 epochs/iteration, so that it can be used as a simple, easy and inexpensive wood recognition system.

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