Wyniki wyszukiwania - BazTech

1

Electronic Toll Collector Framework

Mujeeb Abdul, Ahmed Nisar, Arshed Husnain, Khan Farhan Ajmal

Advances in Science and Technology. Research Journal

|

2022

|

Vol. 16, no 1

294--302

EN

Manual toll collection systems are obsolete due to time, fuel, and pollution issues and need to be replaced by new and better alternatives. Traditionally, governments have always employed people to collect toll, but the manual labor isn’t much effective when it comes to monitoring and efficiency. We took this problem and researched out an effective solution i.e., “Electronic Toll Collector Framework” which is a framework mainly for collection and monitoring of the toll fees collected by the toll plazas in the vicinity of metropolitan cities like Lahore or Karachi. The software can generate toll tax based on vehicle type. Additionally, it can also generate daily/monthly/yearly revenue reports. The framework can serve other purposes like monitoring of vehicles (by the law enforcement agencies) and generation of analytics. It can also serve as a backbone for the government departments who are having a hard time monitoring the revenue generated by the employers. There are two operational modes of the framework (partly manual and automatic). The partly manual approach uses TensorFlow backend, and the automatic approach uses Yolov2 backend. This work will be helpful in guiding future research and practical work in this domain.

2

Analiza wydajności bibliotek uczenia maszynowego

Kędziora Ewa Justyna, Maksim Grzegorz Krzysztof

Journal of Computer Sciences Institute

|

2021

|

Vol. 20

230--236

PL

W artykule zaprezentowane zostały wyniki analizy wydajności bibliotek uczenia maszynowego. Badania oparte zostały na narzędziach ML.NET i TensorFlow. Przeprowadzona analiza bazowała na porównaniu czasu działania bibliotek podczas wykrywania obiektów na zbiorach zdjęć, przy użyciu sprzętu o różnych parametrach. Biblioteką, zużywającą mniejsze zasoby sprzętowe, okazała się TensorFlow. Nie bez znaczenia okazał się wybór platformy sprzętowej oraz możliwość użycia rdzeni graficznych, mających wpływ na zwiększenie wydajności obliczeń.

EN

The paper presents results of performance analysis of machine learning libraries. The research was based on ML.NET and TensorFlow tools. The analysis was based on a comparison of running time of the libraries, during detection of objects on sets of images, using hardware with different parameters. The library, consuming fewer hardware resources, turned out to be TensorFlow. The choice of hardware platform and the possibility of using graphic cores, affecting the increase in computational efficiency, turned out to be not without significance.

3

Towards a new deep learning algorithm based on GRU and CNN : NGRU

Atassi Abdelhamid, el Azami Ikram

Journal of Automation Mobile Robotics and Intelligent Systems

|

2020

|

Vol. 14, No. 4

45--47

EN

This paper describes our new deep learning system based on a comparison between GRU and CNN. Initially we start with the first system which uses Convolutional Neural Network (CNN) which we will compare with the second system which uses Gated Recurrent Unit (GRU). And through this comparison we propose a new system based on the positive points of the two previous systems. Therefore, this new system will take the right choice of hyper-parameters recommended by the authors of both systems. At the final stage we propose a method to apply this new system to the dataset of different languages (used especially in socials networks).

4

BCT Boost Segmentation with U-net in TensorFlow

Wieczorek Grzegorz, Antoniuk Izabella, Kurek Jarosław, Świderski Bartosz, Kruk Michał, Pach Jakub, Orłowski Arkadiusz

Machine Graphics and Vision

|

2019

|

Vol. 28, No. 1/4

25--34

EN

In this paper we present a new segmentation method meant for boost area that remains after removing the tumour using BCT (breast conserving therapy). The selected area is a region on which radiation treatment will later be made. Consequently, an inaccurate designation of this region can result in a treatment missing its target or focusing on healthy breast tissue that otherwise could be spared. Needless to say that exact indication of boost area is an extremely important aspect of the entire medical procedure, where a better definition can lead to optimizing of the coverage of the target volume and, in result, can save normal breast tissue. Precise definition of this area has a potential to both improve the local control of the disease and to ensure better cosmetic outcome for the patient. In our approach we use U-net along with Keras and TensorFlow systems to tailor a precise solution for the indication of the boost area. During the training process we utilize a set of CT images, where each of them came with a contour assigned by an expert. We wanted to achieve a segmentation result as close to given contour as possible. With a rather small initial data set we used data augmentation techniques to increase the number of training examples, while the final outcomes were evaluated according to their similarity to the ones produced by experts, by calculating the mean square error and the structural similarity index (SSIM).

5

Zastosowanie sieci neuronowych do analizy opinii konsumenckich

Mysan Roman, Loichuk Ivan, Plechawska-Wójcik Małgorzata

Journal of Computer Sciences Institute

|

2019

|

Vol. 13

310--314

PL

Niniejszy artykuł przedstawia analizę możliwości zastosowania sieci neuronowych do klasyfikacji danych tekstowych w postaci komentarzy. Ponadto przedstawiono wyniki badania dwóch metod optymalizacji sieci neuronowej: Adam i Gradientu. Celem pracy jest przeprowadzenie badań zachowania się sieci neuronowej w zależności od zmiany parametrów oraz ilości danych użytych do nauczania sieci neuronowej. Na potrzeby realizacji tego celu utworzona została aplikacja testowa korzystająca z sieci neuronowej w celu wyświetlenia ogólnej oceny obiektu noclegowego na podstawie dodanych opinii użytkowników.

EN

This paper presents an analysis of the possibilities of using neural networks to classify text data in the form of comments. Moreover, results of research of two neural network optimization methods: Adam and Gradient are presented. The aim of the work is to conduct research on the behavior of the neural network depending on the change of parameters and the amount of data used to teach the neural network. To achieve the goal, a test application was created. It uses a neural network to display the overall assessment of the accommodation facility based on the added user feedback.