scholarly journals Deteksi dan Klasifikasi Merek Mobil untuk Penentuan Iklan Billboard Menggunakan Convolution Neural Network

2020 ◽  
Vol 7 (4) ◽  
pp. 701
Author(s):  
Windra Swastika ◽  
Ardian Kurniawan ◽  
Hendry Setiawan
Keyword(s):  
Rapid Development ◽  
Input Image ◽  
Image Size ◽  
Car Detection ◽  
Use Of Technology ◽  
Input Size ◽  
Optimization Function ◽  
The Right ◽  
Billboard Advertising

<p class="Abstrak">Dunia periklanan di Indonesia saat ini memiliki perkembangan yang sangat pesat. Hal ini dibuktikan dengan semakin bertambah banyaknya media periklanan yang diciptakan, salah satunya adalah iklan billboard pada jalan raya. Iklan billboard ini memiliki kelemahan, yaitu materi atau konten dari iklan yang ditampilkan tidak dapat berubah-ubah, dengan demikian maka target dari periklanan tidak bisa tertuju pada konsumen yang tepat. Untuk mengatasi masalah tersebut maka dibutuhkan pemanfaatan teknologi untuk mendukung keefektifan kinerja dari iklan billboard. Pada penelitian ini dibuat sebuah sistem yang dapat mendeteksi mobil dan mengenali merek dari mobil yang terdeteksi, sehingga materi iklan dapat berubah sesuai dengan merek mobil yang dikenali oleh sistem. Untuk deteksi pada mobil digunakan metode You Only Look Once (YOLO) dan untuk klasifikasi pada merek mobil digunakan metode MiniVGGNet. Proses latih dilakukan dengan menggunakan 1100 buah gambar dan terdapat 11 macam merek mobil yang dapat diklasifikasikan. Dari pengujian yang dilakukan, didapatkan akurasi akhir 93% pada deteksi mobil. Untuk klasifikasi dari merek mobil dilakukan pengujian dengan fungsi optimasi Adam dengan ukuran masukan gambar 64x64 piksel. Untuk akurasi terbaik yang didapatkan adalah 60%.</p><p class="Abstrak"><em><br /></em></p><p class="Abstrak"><strong><em>Abstract</em></strong></p><p class="Abstract"><em>The world of advertising in Indonesia today has a very rapid development. This is proven by the increasing number of advertising media created, one example is billboard advertising on the highway. Billboard advertising has a weakness, namely the material or the content of the ads displayed cannot change, therefore the target of advertising cannot be directed at the right consumer. To overcome this problem, the use of technology is needed to support the effectiveness of billboard advertising. In this study a system was created which is can detect the car and recognize the brand of the car detected, so the advertising material can change according to the brand of the car that is recognized by the system. For the detection of cars, using You Only Look Once (YOLO) method and for the classification of car brands, using MiniVGGNet method. The training process is carried out using 1100 pictures and there are 11 kinds of car brands that can be classified. From the tests performed, 93% final accuracy was found in car detection. The classification of the car brand was tested with Adam optimization functions with an image input size of 64x64 pixels. For the best accuracy obtained is 60% using the Adam optimization function with the input image size of 64x64 pixels.</em></p><p class="Abstrak"><strong><em><br /></em></strong></p>

scholarly journals Artificial Intelligence Framework for Efficient Detection and Classification of Pneumonia Using Chest Radiography Images

2021 ◽  
Author(s):  
Ali Mohammad Alqudah ◽  
Shoroq Qazan ◽  
Ihssan S. Masad
Keyword(s):  
Artificial Intelligence ◽  
Input Image ◽  
Image Size ◽  
X Ray ◽  
Softmax Classifier ◽  
Non Invasive ◽  
Efficient Detection ◽  
X Ray Imaging ◽  
Chest X Ray

Abstract BackgroundChest diseases are serious health problems that threaten the lives of people. The early and accurate diagnosis of such diseases is very crucial in the success of their treatment and cure. Pneumonia is one of the most widely occurred chest diseases responsible for a high percentage of deaths especially among children. So, detection and classification of pneumonia using the non-invasive chest x-ray imaging would have a great advantage of reducing the mortality rates.ResultsThe results showed that the best input image size in this framework was 64 64 based on comparison between different sizes. Using CNN as a deep features extractor and utilizing the 10-fold methodology the propose artificial intelligence framework achieved an accuracy of 94% for SVM and 93.9% for KNN, a sensitivity of 93.33% for SVM and 93.19% for KNN and a specificity of 96.68% for SVM and 96.60% for KNN.ConclusionsIn this study, an artificial intelligence framework has been proposed for the detection and classification of pneumonia based on chest x-ray imaging with different sizes of input images. The proposed methodology used CNN for features extraction that were fed to two different types of classifiers, namely, SVM and KNN; in addition to the SoftMax classifier which is the default CNN classifier. The proposed CNN has been trained, validated, and tested using a large dataset of chest x-ray images contains in total 5852 images.

scholarly journals Artificial Intelligence Framework for Efficient Detection and Classification of Pneumonia Using Chest Radiography Images

2020 ◽  
Author(s):  
Ali Mohammad Alqudah ◽  
Shoroq Qazan ◽  
Ihssan S. Masad
Keyword(s):  
Artificial Intelligence ◽  
Input Image ◽  
Image Size ◽  
X Ray ◽  
Softmax Classifier ◽  
Non Invasive ◽  
Efficient Detection ◽  
X Ray Imaging ◽  
Chest X Ray

Abstract BackgroundChest diseases are serious health problems that threaten the lives of people. The early and accurate diagnosis of such diseases is very crucial in the success of their treatment and cure. Pneumonia is one of the most widely occurred chest diseases responsible for a high percentage of deaths especially among children. So, detection and classification of pneumonia using the non-invasive chest x-ray imaging would have a great advantage of reducing the mortality rates.ResultsThe results showed that the best input image size in this framework was 64 64 based on comparison between different sizes. Using CNN as a deep features extractor and utilizing the 10-fold methodology the propose artificial intelligence framework achieved an accuracy of 94% for SVM and 93.9% for KNN, a sensitivity of 93.33% for SVM and 93.19% for KNN and a specificity of 96.68% for SVM and 96.60% for KNN.ConclusionsIn this study, an artificial intelligence framework has been proposed for the detection and classification of pneumonia based on chest x-ray imaging with different sizes of input images. The proposed methodology used CNN for features extraction that were fed to two different types of classifiers, namely, SVM and KNN; in addition to the SoftMax classifier which is the default CNN classifier. The proposed CNN has been trained, validated, and tested using a large dataset of chest x-ray images contains in total 5852 images.

scholarly journals Robust Cherry Tomatoes Detection Algorithm in Greenhouse Scene Based on SSD

Agriculture ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 160
Author(s):  
Ting Yuan ◽  
Lin Lv ◽  
Fan Zhang ◽  
Jun Fu ◽  
Jin Gao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Detection Algorithm ◽  
Input Image ◽  
Detection Methods ◽  
Single Shot ◽  
Image Size ◽  
Growth Difference ◽  
Input Size ◽  
Base Network ◽  
Cherry Tomatoes

The detection of cherry tomatoes in greenhouse scene is of great significance for robotic harvesting. This paper states a method based on deep learning for cherry tomatoes detection to reduce the influence of illumination, growth difference, and occlusion. In view of such greenhouse operating environment and accuracy of deep learning, Single Shot multi-box Detector (SSD) was selected because of its excellent anti-interference ability and self-taught from datasets. The first step is to build datasets containing various conditions in greenhouse. According to the characteristics of cherry tomatoes, the image samples with illumination change, images rotation and noise enhancement were used to expand the datasets. Then training datasets were used to train and construct network model. To study the effect of base network and the input size of networks, one contrast experiment was designed on different base networks of VGG16, MobileNet, Inception V2 networks, and the other contrast experiment was conducted on changing the network input image size of 300 pixels by 300 pixels, 512 pixels by 512 pixels. Through the analysis of the experimental results, it is found that the Inception V2 network is the best base network with the average precision of 98.85% in greenhouse environment. Compared with other detection methods, this method shows substantial improvement in cherry tomatoes detection.

scholarly journals Camera Assisted Roadside Monitoring for Invasive Alien Plant Species Using Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6126
Author(s):  
Mads Dyrmann ◽  
Anders Krogh Mortensen ◽  
Lars Linneberg ◽  
Toke Thomas Høye ◽  
Kim Bjerge
Keyword(s):  
Plant Species ◽  
High Speed ◽  
Input Image ◽  
Image Size ◽  
Alien Plant Species ◽  
Deep Convolutional Neural Networks ◽  
Alien Plant ◽  
Lupinus Polyphyllus ◽  
Invasive Alien Plant

Invasive alien plant species (IAPS) pose a threat to biodiversity as they propagate and outcompete natural vegetation. In this study, a system for monitoring IAPS on the roadside is presented. The system consists of a camera that acquires images at high speed mounted on a vehicle that follows the traffic. Images of seven IAPS (Cytisus scoparius, Heracleum, Lupinus polyphyllus, Pastinaca sativa, Reynoutria, Rosa rugosa, and Solidago) were collected on Danish motorways. Three deep convolutional neural networks for classification (ResNet50V2 and MobileNetV2) and object detection (YOLOv3) were trained and evaluated at different image sizes. The results showed that the performance of the networks varied with the input image size and also the size of the IAPS in the images. Binary classification of IAPS vs. non-IAPS showed an increased performance, compared to the classification of individual IAPS. This study shows that automatic detection and mapping of invasive plants along the roadside is possible at high speeds.

scholarly journals A classification of organizational interventions to enable detachment from work

2020 ◽  
Author(s):  
Jana Mattern
Keyword(s):  
Best Practices ◽  
Academic Research ◽  
Work Hours ◽  
Negative Effects ◽  
Work Related ◽  
Organizational Interventions ◽  
Use Of Technology ◽  
Common Framework ◽  
The Right

Negative effects of extensive connectivity to work through excessive use of technology have yielded discussions about the right to disconnect for employees. Organizations are beginning to introduce interventions that aim at enabling their employees to detach from work (i.e., refrain from work-related thoughts and activities during non-work hours). However, there is limited academic research on how organizations should introduce interventions that lead to a successful disconnection of their employees. Based on an interdisciplinary literature review and reports on companies’ best practices, this study proposes a classification of organizational interventions based on the level, target, and mechanism of the intervention. I include the theory of psychological detachment to propose a measurement of the success of an intervention. The classification provides researchers and practitioners with a common framework to develop and evaluate interventions aimed at fostering employees’ disconnection from work.

Awareness and Decision Making in Professional Ethics

2000 ◽  
Vol 5 (1) ◽  
pp. 19-27 ◽  
Author(s):  
Ronny Swain
Keyword(s):  
Decision Making ◽  
Professional Ethics ◽  
Ethical Decision Making ◽  
Code Of Ethics ◽  
Professional Autonomy ◽  
Ethical Awareness ◽  
Ethical Decisions ◽  
The Right ◽  
Aid Decision

The paper describes the development of the 1998 revision of the Psychological Society of Ireland's Code of Professional Ethics. The Code incorporates the European Meta-Code of Ethics and an ethical decision-making procedure borrowed from the Canadian Psychological Association. An example using the procedure is presented. To aid decision making, a classification of different kinds of stakeholder (i.e., interested party) affected by ethical decisions is offered. The author contends (1) that psychologists should assert the right, which is an important aspect of professional autonomy, to make discretionary judgments, (2) that to be justified in doing so they need to educate themselves in sound and deliberative judgment, and (3) that the process is facilitated by a code such as the Irish one, which emphasizes ethical awareness and decision making. The need for awareness and judgment is underlined by the variability in the ethical codes of different organizations and different European states: in such a context, codes should be used as broad yardsticks, rather than precise templates.

INTENSITAS ASUPAN GIZI DALAM MENGEMBANGKAN KESEHATAN ANAK USIA 5-6 TAHUN DI TK UMMI ERNI DESA PEMATANG JOHAR

2019 ◽  
Vol 7 (2) ◽  
Author(s):  
Reka Indriani ◽  
Mesiono Mesiono ◽  
Sapri Sapri
Keyword(s):  
Rapid Development ◽  
Pure Water ◽  
Student Parents ◽  
Interview Method ◽  
Descriptive Research ◽  
Children Nutrition ◽  
Children Health ◽  
Class B ◽  
Quantitative Descriptive ◽  
The Right

<p><em>When children are in a process of growth and rapid development, parents and young people should pay atantion to the health and health of children so that the children can grow and develop according to their age.The purpose of this research is to identify: (1). The children nutrition 5-6 years old, (2). The children health development 5-6 years old, (3). The alternative to protect children health. This research is a quantitative descriptive research. The participants of this research which are include the principal, teacher, and the student parents at class B who is 5-6 years old. In process of collecting the data the researcher used interview method, observation, and documentation. From the research we can conclude 1).Nutrition or food that often given to the children is just four healthy five perfect foods, 2).The children in TK Ummi are the children who have healtiness, 3). The alternative that can be commited to protect the children health is do the practice, make the children common to throw the rubbish in the right place,  check the children nail, stock the pure water, set many dustbins and make a common to wash their hand before eating.</em></p>

ORGANIZATION OF INTERNAL CONTROL IN A REMOTE WORK ENVIRONMENT

2020 ◽  
Vol 3 (8) ◽  
pp. 73-79
Author(s):  
NGUYEN THI HA MY ◽  
Keyword(s):  
Control System ◽  
Work Environment ◽  
Internal Control ◽  
Business Processes ◽  
Rapid Development ◽  
Remote Work ◽  
Use Of Technology ◽  
Internal Control System ◽  
The Russian Federation ◽  
Ministry Of Finance

With the rapid development and widespread use of technology, business processes are being transformed. One of the consequences of the implementation of technologies into the business is the partial transition to remote work, which made it necessary to reflect the corresponding changes in the internal control system (IC). The article is devoted to the analysis of the main shortcomings identified during the transition to the remote mode, in response to which measures are proposed to adapt the IC to the conditions of remote work. Identifies the following areas for improvement of the internal control system. In response to the identified areas successful practical examples are analyzed and potential measures are proposed in the context of the elements identified in the COSO conceptual framework and methodological documents of the Ministry of Finance of the Russian Federation.

The Chemistry Of Antibiofilm Phytocompounds.

2020 ◽  
Vol 20 ◽  
Author(s):  
Dibyajit Lahiri ◽  
Moupriya Nag ◽  
Sayantani Garai ◽  
Rina Rani Ray
Keyword(s):  
Antimicrobial Activity ◽  
Antibiotic Resistance ◽  
Antibiotic Therapy ◽  
Antimicrobial Agents ◽  
Chemical Nature ◽  
Rapid Development ◽  
Wide Spread ◽  
Chronic Infections ◽  
Therapeutic Uses

: Phytocompounds are long known for their therapeutic uses due to their competence as antimicrobial agents. The antimicrobial activity of these bioactive compounds manifests their ability as an antibiofilm agent and is thereby proved to be competent to treat the wide spread of biofilm-associated chronic infections. Rapid development of antibiotic resistance in bacteria has made the treatment of these infections almost impossible by conventional antibiotic therapy, which forced in the switch over to the use of phytocompounds. The present overview deals with the classification of the huge array of phytocompounds according to their chemical nature, detection of their target pathogen, and elucidation of their mode of action.

scholarly journals A Stochastic Model for Block Segmentation of Images Based on the Quadtree and the Bayes Code for It

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 991
Author(s):  
Yuta Nakahara ◽  
Toshiyasu Matsushima
Keyword(s):  
Computational Cost ◽  
Block Size ◽  
Input Image ◽  
Generative Model ◽  
Image Size ◽  
Variable Block ◽  
General Data ◽  
The Difference ◽  
Segmentation Of Images ◽  
Target Data

In information theory, lossless compression of general data is based on an explicit assumption of a stochastic generative model on target data. However, in lossless image compression, researchers have mainly focused on the coding procedure that outputs the coded sequence from the input image, and the assumption of the stochastic generative model is implicit. In these studies, there is a difficulty in discussing the difference between the expected code length and the entropy of the stochastic generative model. We solve this difficulty for a class of images, in which they have non-stationarity among segments. In this paper, we propose a novel stochastic generative model of images by redefining the implicit stochastic generative model in a previous coding procedure. Our model is based on the quadtree so that it effectively represents the variable block size segmentation of images. Then, we construct the Bayes code optimal for the proposed stochastic generative model. It requires the summation of all possible quadtrees weighted by their posterior. In general, its computational cost increases exponentially for the image size. However, we introduce an efficient algorithm to calculate it in the polynomial order of the image size without loss of optimality. As a result, the derived algorithm has a better average coding rate than that of JBIG.

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