scholarly journals Faster R-CNN model learning on synthetic images

2020 ◽  
Vol 17 ◽  
pp. 401-404
Author(s):  
Błażej Łach ◽  
Edyta Łukasik
Keyword(s):  
Machine Learning ◽  
Human Resources ◽  
Learning Process ◽  
Training Data ◽  
Mean Average Precision ◽  
Image Size ◽  
Average Precision ◽  
Model Learning ◽  
Synthetic Images

Machine learning requires a human description of the data. The manual dataset description is very time consuming. In this article was examined how the model learns from artificially created images, with the least human participation in describing the data. It was checked how the model learned on artificially produced images with augmentations and progressive image size. The model has achieve up to 3.35 higher mean average precision on syntetic dataset in the training with increasing images resolution. Augmentations improved the quality of detection on real photos. The production of artificially generated training data has a great impact on the acceleration of prepare training, because it does not require as much human resources as normal learning process.

Implementasi Manajemen Mutu Terpadu Dalam Memberikan Layanan Untuk Meningkatkan Prestasi Peserta Didik di SLB YPAC Pangkalpinang

2019 ◽  
Vol 2 (2) ◽  
pp. 265-278
Author(s):  
Diah Rina Miftakhi ◽  
Nurjanah Nurjanah
Keyword(s):  
Quality Management ◽  
Data Collection ◽  
Human Resources ◽  
Learning Process ◽  
School Environment ◽  
Quality Of Services ◽  
Teaching Staff ◽  
Learning Material ◽  
Analysis Model

describe the implementation of an integrated quality management component consisting of the quality of services provided by the school, human resources in teaching, the school environment, and learning process  in SLB YPAC Pangkalpinang.               The method used in this study, namely by using a naturalistic qualitative approach. Data collection is done through observation, interviews, and documentation. The subjects of this study include the principal, teachers, employees, and students. The validity of the data is done by triangulation, and deeper observation. Analysis of the data used is the interactive analysis model of Miles and Huberman through data collection, data reduction, data presentation, and conclusion drawing.              The results showed that: (a) the quality of services to students in SLB YPAC Pangkalpinang had met good service standards. This can be seen from the services in the form of facilities and infrastructure which are quite complete in schools; (b) the quality of human resources in the education process shows good teacher resources. This can be seen from the teacher data which shows that the teaching staff at SLB YPAC Pangkalpinang 95% of educators with S1 qualifications in the field of education; (c) the quality of the environment in SLB YPAC Pangkalpinang is already good. This can be seen from the very strategic location of the school because the location of the school is in the middle of the city so that it is easily accessible by the community; (d) the quality of the learning process carried out by teachers at Pangkal Pinang YPAC SLB is good. This can be seen from the realization of the form of activities through learning planning by preparing lesson plans for each subject, then implementing learning, which includes strategies and methods used by teachers in delivering learning material, and evaluation of learning. Keywords: Integrated quality management, student achievement     ABSTRAK Tujuan dalam melaksanakan penelitian ini  adalah untuk melihat pelaksanaan serta mendeskripsikan implementasi  komponen Manajemen Mutu Terpadu yang terdiri dari kualitas layanan yang diberikan sekolah, sumber daya manusia dalam mengajar, lingkungan sekolah, dan proses pembelajaran di SLB YPAC Pangkalpinang. Metode yang digunakan dalam penelitian ini, yaitu dengan menggunakan pendekatan kualitatif naturalistik. Pengumpulan data dilakukan melalui observasi, wawancara, dan dokumentasi. Subyek penelitian ini antara lain kepala sekolah, guru, pegawai, dan peserta didik. keabsahan data dilakukan dengan triangulasi, dan pengamatan yang lebih mendalam. Analisis data yang digunakan adalah model analisis interaktif Miles dan Huberman melalui kegiatan pengumpulan data, reduksi data, penyajian data, dan penarikan kesimpulan. Hasil penelitian menunjukkan bahwa: (a) mutu layanan terhadap peserta didik di SLB YPAC Pangkalpinang sudah memenuhi standar layanan yang baik. Hal ini dilihat dari layanan yang berupa fasilitas sarana dan prasarana yang sudah cukup lengkap di sekolah; (b) mutu sumber daya manusia dalam proses pendidikan menunjukkan sumber daya guru yang baik. Hal ini dapat dilihat dari data guru yang menunjukkan bahwa tenaga pengajar di SLB YPAC Pangkalpinang 95% pendidik berkualifikasi S1 bidang kependidikan; (c) mutu lingkungan yang ada di SLB YPAC Pangkalpinang sudah baik. Hal ini terlihat dari letak sekolah yang sangat strategis karena lokasi sekolah yang berada di tengah kota sehingga mudah dijangkau oleh masyarakat; (d) mutu proses pembelajaran yang dilakukan oleh guru di SLB YPAC Pangkalpinang sudah baik. Hal ini dapat dilihat dari realisasi bentuk kegiatan melalui perencanaan pembelajaran dengan menyusun RPP setiap mata pelajaran, kemudian pelaksanaan pembelajaran, yang meliputi strategi dan metode yang digunakan guru dalam menyampaikan materi pembelajaran, dan evaluasi pembelajaran.

MobileNet-Yolo Based wildlife detection model: A case study in yunnan tongbiguan nature reserve, China

2021 ◽  
pp. 1-11
Author(s):  
Tingting Zhao ◽  
Xiaoli Yi ◽  
Zhiyong Zeng ◽  
Tao Feng
Keyword(s):  
Real Time ◽  
Nature Reserve ◽  
Complex Model ◽  
Training Data ◽  
Mean Average Precision ◽  
Detection Accuracy ◽  
High Definition ◽  
Average Precision ◽  
Data Set ◽  
Real Time Detection

YTNR (Yunnan Tongbiguan Nature Reserve) is located in the westernmost part of China’s tropical regions and is the only area in China with the tropical biota of the Irrawaddy River system. The reserve has abundant tropical flora and fauna resources. In order to realize the real-time detection of wild animals in this area, this paper proposes an improved YOLO (You only look once) network. The original YOLO model can achieve higher detection accuracy, but due to the complex model structure, it cannot achieve a faster detection speed on the CPU detection platform. Therefore, the lightweight network MobileNet is introduced to replace the backbone feature extraction network in YOLO, which realizes real-time detection on the CPU platform. In response to the difficulty in collecting wild animal image data, the research team deployed 50 high-definition cameras in the study area and conducted continuous observations for more than 1,000 hours. In the end, this research uses 1410 images of wildlife collected in the field and 1577 wildlife images from the internet to construct a research data set combined with the manual annotation of domain experts. At the same time, transfer learning is introduced to solve the problem of insufficient training data and the network is difficult to fit. The experimental results show that our model trained on a training set containing 2419 animal images has a mean average precision of 93.6% and an FPS (Frame Per Second) of 3.8 under the CPU. Compared with YOLO, the mean average precision is increased by 7.7%, and the FPS value is increased by 3.

scholarly journals Algorithm for auto annotation of scanned documents based on subregion tiling and shallow networks

2021 ◽  
Author(s):  
Komuravelli Prashanth ◽  
Kalidas Yeturu
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
High Performance ◽  
Performance Metrics ◽  
Training Data ◽  
Mean Average Precision ◽  
Average Precision ◽  
Squared Error Loss Function ◽  
Scanned Documents

<div>There are millions of scanned documents worldwide in around 4 thousand languages. Searching for information in a scanned document requires a text layer to be available and indexed. Preparation of a text layer requires recognition of character and sub-region patterns and associating with a human interpretation. Developing an optical character recognition (OCR) system for each and every language is a very difficult task if not impossible. There is a strong need for systems that add on top of the existing OCR technologies by learning from them and unifying disparate multitude of many a system. In this regard, we propose an algorithm that leverages the fact that we are dealing with scanned documents of handwritten text regions from across diverse domains and language settings. We observe that the text regions have consistent bounding box sizes and any large font or tiny font scenarios can be handled in preprocessing or postprocessing phases. The image subregions are smaller in size in scanned text documents compared to subregions formed by common objects in general purpose images. We propose and validate the hypothesis that a much simpler convolution neural network (CNN) having very few layers and less number of filters can be used for detecting individual subregion classes. For detection of several hundreds of classes, multiple such simpler models can be pooled to operate simultaneously on a document. The advantage of going by pools of subregion specific models is the ability to deal with incremental addition of hundreds of newer classes over time, without disturbing the previous models in the continual learning scenario. Such an approach has distinctive advantage over using a single monolithic model where subregions classes share and interfere via a bulky common neural network. We report here an efficient algorithm for building a subregion specific lightweight CNN models. The training data for the CNN proposed, requires engineering synthetic data points that consider both pattern of interest and non-patterns as well. We propose and validate the hypothesis that an image canvas in which optimal amount of pattern and non-pattern can be formulated using a means squared error loss function to influence filter for training from the data. The CNN hence trained has the capability to identify the character-object in presence of several other objects on a generalized test image of a scanned document. In this setting some of the key observations are in a CNN, learning a filter depends not only on the abundance of patterns of interest but also on the presence of a non-pattern context. Our experiments have led to some of the key observations - (i) a pattern cannot be over-expressed in isolation, (ii) a pattern cannot be under-xpressed as well, (iii) a non-pattern can be of salt and pepper type noise and finally (iv) it is sufficient to provide a non-pattern context to a modest representation of a pattern to result in strong individual sub-region class models. We have carried out studies and reported \textit{mean average precision} scores on various data sets including (1) MNIST digits(95.77), (2) E-MNIST capital alphabet(81.26), (3) EMNIST small alphabet(73.32) (4) Kannada digits(95.77), (5) Kannada letters(90.34), (6) Devanagari letters(100) (7) Telugu words(93.20) (8) Devanagari words(93.20) and also on medical prescriptions and observed high-performance metrics of mean average precision over 90%. The algorithm serves as a kernel in the automatic annotation of digital documents in diverse scenarios such as annotation of ancient manuscripts and hand-written health records.</div>

scholarly journals Improving the Quality of Positive Datasets for the Establishment of Machine Learning Models for pre-microRNA Detection

2017 ◽  
Vol 14 (2) ◽  
Author(s):  
Müşerref Duygu Saçar Demirci ◽  
Jens Allmer
Keyword(s):  
Machine Learning ◽  
Training Data ◽  
Quality Data ◽  
Virus Infections ◽  
High Quality ◽  
Disease States ◽  
Positive Data ◽  
Mirna Detection ◽  
Post Transcriptional Regulation

AbstractMicroRNAs (miRNAs) are involved in the post-transcriptional regulation of protein abundance and thus have a great impact on the resulting phenotype. It is, therefore, no wonder that they have been implicated in many diseases ranging from virus infections to cancer. This impact on the phenotype leads to a great interest in establishing the miRNAs of an organism. Experimental methods are complicated which led to the development of computational methods for pre-miRNA detection. Such methods generally employ machine learning to establish models for the discrimination between miRNAs and other sequences. Positive training data for model establishment, for the most part, stems from miRBase, the miRNA registry. The quality of the entries in miRBase has been questioned, though. This unknown quality led to the development of filtering strategies in attempts to produce high quality positive datasets which can lead to a scarcity of positive data. To analyze the quality of filtered data we developed a machine learning model and found it is well able to establish data quality based on intrinsic measures. Additionally, we analyzed which features describing pre-miRNAs could discriminate between low and high quality data. Both models are applicable to data from miRBase and can be used for establishing high quality positive data. This will facilitate the development of better miRNA detection tools which will make the prediction of miRNAs in disease states more accurate. Finally, we applied both models to all miRBase data and provide the list of high quality hairpins.

scholarly journals Cumulative Gains Model Quality Metric

2009 ◽  
Vol 2009 ◽  
pp. 1-14 ◽  
Author(s):  
Thomas Brandenburger ◽  
Alfred Furth
Keyword(s):  
Learning Process ◽  
Entire Range ◽  
Area Under The Curve ◽  
Decision Threshold ◽  
Model Quality ◽  
Model Learning ◽  
Quality Metric ◽  
Wide Range ◽  
Gains Model

This paper proposes a more comprehensive look at the ideas of KS and Area Under the Curve (AUC) of a cumulative gains chart to develop a model quality statistic which can be used agnostically to evaluate the quality of a wide range of models in a standardized fashion. It can be either used holistically on the entire range of the model or at a given decision threshold of the model. Further it can be extended into the model learning process.

scholarly journals Inovasi Perguruan Tinggi Raharja Dalam Era Disruptif Menggunakan Metodologi iLearning

2019 ◽  
Vol 13 (1) ◽  
pp. 23
Author(s):  
Untung Rahardja ◽  
Ninda Lutfiani ◽  
Arini Dwi Lestari ◽  
Edward Boris P Manurung
Keyword(s):  
Higher Education ◽  
Critical Thinking ◽  
Human Resources ◽  
Technological Innovation ◽  
Learning Process ◽  
Learning System ◽  
Learning Method ◽  
Human Relations ◽  
The Face

Rapidly technological advancements have led to the emergence of a disruptive era, namely the innovation theory that was initiated by newcumbent, the publication of which threatened incumbent. The effect of this disruptive is a fundamentally significant and widespread technological innovation that changes the way human relations in various heresies is no exception to higher education. When viewed from a quantitative perspective, the growth is quite severe. However, if it is related to its quality, its development is worrying. Therefore, higher education must compete to change the learning system by following disruptive patterns in order to improve the quality of learning that will improve the quality of human resources. In this study there are 2 (two) methods, The results of this study present the readiness of Raharja University in the face of the disruptive era through iLearning. Where in the learning process includes 3 (three) things, called Rinfo, iDu and iMe.With this learning method, students become more innovative and critical thinking.

scholarly journals Machine Learning Techniques for Hypoglycemia Prediction: Trends and Challenges

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 546
Author(s):  
Omer Mujahid ◽  
Ivan Contreras ◽  
Josep Vehi
Keyword(s):  
Machine Learning ◽  
Diabetic Patient ◽  
Life Quality ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Diabetic Patients ◽  
Learning Approaches ◽  
Prediction Horizon ◽  
Learning Techniques

(1) Background: the use of machine learning techniques for the purpose of anticipating hypoglycemia has increased considerably in the past few years. Hypoglycemia is the drop in blood glucose below critical levels in diabetic patients. This may cause loss of cognitive ability, seizures, and in extreme cases, death. In almost half of all the severe cases, hypoglycemia arrives unannounced and is essentially asymptomatic. The inability of a diabetic patient to anticipate and intervene the occurrence of a hypoglycemic event often results in crisis. Hence, the prediction of hypoglycemia is a vital step in improving the life quality of a diabetic patient. The objective of this paper is to review work performed in the domain of hypoglycemia prediction by using machine learning and also to explore the latest trends and challenges that the researchers face in this area; (2) Methods: literature obtained from PubMed and Google Scholar was reviewed. Manuscripts from the last five years were searched for this purpose. A total of 903 papers were initially selected of which 57 papers were eventually shortlisted for detailed review; (3) Results: a thorough dissection of the shortlisted manuscripts provided an interesting split between the works based on two categories: hypoglycemia prediction and hypoglycemia detection. The entire review was carried out keeping this categorical distinction in perspective while providing a thorough overview of the machine learning approaches used to anticipate hypoglycemia, the type of training data, and the prediction horizon.

scholarly journals Design of a Fracture Detection System based on Deep Program in a Convolutional Neural Network

Webology ◽  
2021 ◽  
Vol 18 (2) ◽  
pp. 509-518
Author(s):  
Payman Hussein Hussan ◽  
Syefy Mohammed Mangj Al-Razoky ◽  
Hasanain Mohammed Manji Al-Rzoky
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Bone Fractures ◽  
Detection System ◽  
Training Data ◽  
Learning Models ◽  
Fracture Detection ◽  
Data Set ◽  
Final Fracture

This paper presents an efficient method for finding fractures in bones. For this purpose, the pre-processing set includes increasing the quality of images, removing additional objects, removing noise and rotating images. The input images then enter the machine learning phase to detect the final fracture. At this stage, a Convolutional Neural Networks is created by Genetic Programming (GP). In this way, learning models are implemented in the form of GP programs. And evolve during the evolution of this program. Then finally the best program for classifying incoming images is selected. The data set in this work is divided into training and test friends who have nothing in common. The ratio of training data to test is equal to 80 to 20. Finally, experimental results show good results for the proposed method for bone fractures.

scholarly journals IMPROVING THE QUALITY OF LEARNING IN IMMIGRATION POLYTECHNICS THROUGH IMMIGRATION MANAGEMENT INFORMATION SYSTEM FACILITIES DIRECTORATE GENERAL OF IMMIGRATION

2019 ◽  
Vol 1 (2) ◽  
pp. 1-8
Author(s):  
Fakhri Hafiz-AM
Keyword(s):  
Human Resources ◽  
Learning Process ◽  
Management Information System ◽  
Research Method ◽  
Human Resources Development ◽  
Management Information ◽  
Electronic Learning ◽  
Directorate General ◽  
Immigration Management

Immigration Polytechnic is an official university under the Ministry of Law and Human Rights wich produces immigration cadres who have integrity and intellectuality to carry out their duties as immigration officer. The Human Resources Development Agency is a forum for produce student from the Immigration Polytechnic. On the other side, Directorate General of Immigration has a system called the Sistem Informasi Manajemen Keimigrasian (SIMKIM). The problem that will be to describe how the learning process at the Immigration Polytechnic is currently and how to utilize the existing system to support learning at the Immigration Polytechnic. The application of the research method used is descriptive qualitative research method, namely by explaining or describing an object of research by means of observation and interviews with related sources. Based on the research results, it can be concluded that the learning process at the Immigration Polytechnic goes through several stages and also uses Teaching, Training, and Nurturing patterns that can improve the quality of Cadets. And also the Immigration Polytechnic has utilized several systems that are very helpful for learning, including Sistem Pengaduan Taruna (SIPENA), Sistem Informasi Akademik (SIAKAD), Electronic Learning Immigration System (ELIPS), dan juga Immigration Exam (IM-EXAM).

scholarly journals Use of Methods Everyone Is A Teacher Here To Improve Geography Learning Outcomes

2021 ◽  
Vol 2 (2) ◽  
pp. 194-199
Author(s):  
Miftahul Jannah
Keyword(s):  
Human Resources ◽  
Learning Outcomes ◽  
Learning Process ◽  
Geography Education ◽  
State Education ◽  
The Government ◽  
Student Center ◽  
Life On Earth

Education plays an important role to realize the government's efforts in educating the nation's life and ensuring the survival of the nation and the State. Education is also a long-term investment for the government and a vehicle to improve and develop the quality of human resources. The purpose of Geography education in schools in general is that students are expected to be able to understand concepts about the symptoms of nature and life on earth as well as interactions between humans and their environment that are closely related to the aspects of space and time. The research on the application of Everyone Is A Teacher Here Method in Geography learning in class XI IPS 1 SMAN 1 Rengat Barat is classroom action research, which is a study conducted by the teacher himself to improve the learning process that is his responsibility. The use of everyone is a Teacher Here method learning can improve students' learning outcomes in Geography learning in Grade XI IPS 1 SMAN 1 Rengat Barat, also make the teacher's activity in delivering student center-oriented materials significantly improved.

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