Predicting academic performance of students from VLE big data using deep learning models
پیش بینی عملکرد علمی دانش آموزان از داده های بزرگ VLE با استفاده از مدل های یادگیری عمیق-2020
The abundance of accessible educational data, supported by the technology-enhanced learning platforms, provides opportunities to mine learning behavior of students, addressing their issues, optimizing the educational environment, and enabling data-driven decision making. Virtual learning environments complement the learning analytics paradigm by effectively providing datasets for analysing and reporting the learning process of students and its reflection and contribution in their respective performances. This study deploys a deep artificial neural network on a set of unique handcrafted features, extracted from the virtual learning environments clickstream data, to predict at-risk students providing measures for early intervention of such cases. The results show the proposed model to achieve a classification accuracy of 84%–93%. We show that a deep artificial neural network outperforms the baseline logistic regression and support vector machine models. While logistic regression achieves an accuracy of 79.82%–85.60%, the support vector machine achieves 79.95%–89.14%. Aligned with the existing studies - our findings demonstrate the inclusion of legacy data and assessment-related data to impact the model significantly. Students interested in accessing the content of the previous lectures are observed to demonstrate better performance. The study intends to assist institutes in formulating a necessary framework for pedagogical support, facilitating higher education decision-making process towards sustainable education.
Keywords: Learning analytics | Predicting success | Educational data | Machine learning | Deep learning | Virtual learning environments (VLE)
Detection and classification of bruises of pears based on thermal images
تشخیص و طبقه بندی کبودی گلابی بر اساس تصاویر حرارتی-2020
The detection and classification of bruises of pears based on thermal images have been investigated. A simple thermal imaging system in the long-wavelength ranges (8–14 μm) assembledμwith hot air equipment was constructed to capture cleaner images. Higher velocity and temperature of the air reduced the time required to obtain a clean image, but the images were not sufficient able to discriminate the slight and invisible variation of bruises over consecutive days. The grey-level co-occurrence matrix of the thermal images were analysed, and the slight differences in the pears over consecutive days were presented in the form of a line chart. A traditional deep learning algorithm commonly used in classification of big data sets was modified to one suitable for classification of a small sample data set of thermasl images (3246 samples were used as the training data set and 1125 were used as a test data set) collected from 300 pears over 10 days. The best test prediction accuracy obtained was 99.25%.
Keywords: Detection and classification | Thermal images | Grey-level co-occurrence matrix | Deep learning
Therapy-driven Deep Glucose Forecasting
پیش بینی گلوکز عمیق درمان محور-2020
The automatic regulation of blood glucose for Type 1 diabetes patients is the main goal of the artificial pancreas, a closed-loop system that exploits continue glucose monitoring data to define an optimal insulin therapy. One of the most successful approaches for developing the artificial pancreas is the model predictive control, which exhibits promising results on both virtual and real patients. The performance of such controller is highly dependent on the reliability of the glucose–insulin model used for prediction purpose, which is usually implemented with classic mathematical models. The main limitation of these models consists in the difficulties of modeling the physiological nonlinear dynamics typical of this system. The availability of big amount of in silico and in vivo data moved the attention to new data-driven methods which are able to easily overcome this problem. In this paper we propose Deep Glucose Forecasting, a deep learning approach for forecasting glucose levels, based on a novel, two-headed Long-Short Term Memory implementation. It takes in input the previous values obtained through continue glucose monitoring, the carbohydrate intake, the suggested insulin therapy and forecasts the interstitial glucose level of the patient. The proposed architecture has been trained on 100 virtual adult patients of the UVA/Padova simulator, and tested on both virtual and real patients. The proposed solution is able to generalize to new unseen data, outperforms classical population models and reaches performance comparable to classical personalized models when fine-tuning is exploited on real patients.
Keywords: Diabetes | Forecasting | Prediction | Deep learning | LSTM
Highway crash detection and risk estimation using deep learning
تشخیص تصادف بزرگراه و تخمین ریسک با استفاده از یادگیری عمیق-2020
Crash Detection is essential in providing timely information to traffic management centers and the public to reduce its adverse effects. Prediction of crash risk is vital for avoiding secondary crashes and safeguarding highway traffic. For many years, researchers have explored several techniques for early and precise detection of crashes to aid in traffic incident management. With recent advancements in data collection techniques, abundant real-time traffic data is available for use. Big data infrastructure and machine learning algorithms can utilize this data to provide suitable solutions for the highway traffic safety system. This paper explores the feasibility of using deep learning models to detect crash occurrence and predict crash risk. Volume, Speed and Sensor Occupancy data collected from roadside radar sensors along Interstate 235 in Des Moines, IA is used for this study. This real-world traffic data is used to design feature set for the deep learning models for crash detection and crash risk prediction. The results show that a deep model has better crash detection performance and similar crash prediction performance than state of the art shallow models. Additionally, a sensitivity analysis was conducted for crash risk prediction using data 1-minute, 5-minutes and 10-minutes prior to crash occurrence. It was observed that is hard to predict the crash risk of a traffic condition, 10 min prior to a crash.
Keywords: Crash detection | Crash prediction | Deep learning
Identification of animal individuals using deep learning: A case study of giant panda
شناسایی فردی حیوانی با استفاده از یادگیری عمیق: یک مطالعه موردی از پاندا غول پیکر-2020
Giant panda (Ailuropoda melanoleuca) is an iconic species of conservation. However, long-term monitoring of wild giant pandas has been a challenge, largely due to the lack of appropriate method for the identification of target panda individuals. Although there are some traditional methods, such as distance-bamboo stem fragments methods, molecular biological method, and manual visual identification, they all have some limitations that can restrict their application. Therefore, it is urgent to explore a reliable and efficient approach to identify giant panda individuals. Here, we applied the deep learning technology and developed a novel face-identification model based on convolutional neural network to identify giant panda individuals. The model was able to identify 95% of giant panda individuals in the validation dataset. In all simulated field situations where the quality of photo data was degraded, the model still accurately identified more than 90% of panda individuals. The identification accuracy of our model is robust to brightness, small rotation, and cleanness of photos, although large rotation angle (> 20°) of photos has significant influence on the identification accuracy of the model (P < 0.01). Our model can be applied in future studies of giant panda such as long-term monitoring, big data analysis for behavior and be adapted for individual identification of other wildlife species.
Keywords: Deep learning | convolutional neural network | Individual identification | Giant panda
An empirical case study on Indian consumers sentiment towards electric vehicles: A big data analytics approach
یک مطالعه موردی تجربی در مورد احساسات مصرف کنندگان هندی نسبت به وسایل نقلیه برقی: یک رویکرد تحلیل داده های بزرگ-2020
Today, climate change due to global warming is a significant concern to all of us. Indias rate of greenhouse gas emissions is increasing day by day, placing India in the top ten emitters in the world. Air pollution is one of the significant contributors to the greenhouse effect. Transportation contributes about 10% of the air pollution in India. The Indian government is taking steps to reduce air pollution by encouraging the use of electric vehicles. But, success depends on consumers sentiment, perception and understanding towards Electric Vehicles (EV). This case study tried to capture the feeling, attitude, and emotions of Indian consumers towards electric vehicles. The main objective of this study was to extract opinions valuable to prospective buyers (to know what is best for them), marketers (for determining what features should be advertised) and manufacturers (for deciding what features should be improved) using Deep Learning techniques (e.g Doc2Vec Algorithm, Recurrent Neural Network (RNN), Convolutional Neural Network (CNN)). Due to the very nature of social media data, big data platform was chosen to analyze the sentiment towards EV. Deep Learning based techniques were preferred over traditional machine learning algorithms (Support Vector Machine, Logistic regression and Decision tree, etc.) due to its superior text mining capabilities. Two years data (2016 to 2018) were collected from different social media platform for this case study. The results showed the efficiency of deep learning algorithms and found CNN yield better results in-compare to others. The proposed optimal model will help consumers, designers and manufacturers in their decision-making capabilities to choose, design and manufacture EV.
Keywords: Electric vehicles | Deep learning | Big data | Sentiment analysis | India
A hybrid deep learning model for efficient intrusion detection in big data environment
یک مدل یادگیری عمیق ترکیبی برای تشخیص نفوذ موثر در محیط داده های بزرگ-2020
The volume of network and Internet traffic is expanding daily, with data being created at the zettabyte to petabyte scale at an exceptionally high rate. These can be character- ized as big data, because they are large in volume, variety, velocity, and veracity. Security threats to networks, the Internet, websites, and organizations are growing alongside this growth in usage. Detecting intrusions in such a big data environment is difficult. Various intrusion-detection systems (IDSs) using artificial intelligence or machine learning have been proposed for different types of network attacks, but most of these systems either cannot recognize unknown attacks or cannot respond to such attacks in real time. Deep learning models, recently applied to large-scale big data analysis, have shown remarkable performance in general but have not been examined for detection of intrusions in a big data environment. This paper proposes a hybrid deep learning model to efficiently detect network intrusions based on a convolutional neural network (CNN) and a weight-dropped, long short-term memory (WDLSTM) network. We use the deep CNN to extract mean- ingful features from IDS big data and WDLSTM to retain long-term dependencies among extracted features to prevent overfitting on recurrent connections. The proposed hybrid method was compared with traditional approaches in terms of performance on a publicly available dataset, demonstrating its satisfactory performance.
Keywords: Big data | Intrusion | detection Deep learning | Convolution neural network | Weight-dropped long short-term memory | network
Identifying non-O157 Shiga toxin-producing Escherichia coli (STEC) using deep learning methods with hyperspectral microscope images
شناسایی اشرشیا کولی تولید کننده سم غیر شیتا Sh157 با استفاده از روشهای یادگیری عمیق با تصاویر میکروسکوپ فوق قطبی-2020
Non-O157 Shiga toxin-producing Escherichia coli (STEC) serogroups such as O26, O45, O103, O111, O121 and O145 often cause illness to people in the United States and the conventional identification of these “Big-Six” are complex. The label-free hyperspectral microscope imaging (HMI) method, which provides spectral “fingerprints” information of bacterial cells, was employed to classify serogroups at the cellular level. In spectral analysis, principal component analysis (PCA) method and stacked auto-encoder (SAE) method were conducted to extract principal spectral features for classification task. Based on these features, multiple classifiers including linear discriminant analysis (LDA), support vector machine (SVM) and soft-max regression (SR) methods were evaluated. Different sizes of datasets were also tested in search for the suitable classification models. Among the results, SAE-based classification models performed better than PCA-based models, achieving classification accuracy of SAE-LDA (93.5%), SAE-SVM (94.9%) and SAE-SR (94.6%), respectively. In contrast, classification results of PCA-based methods such as PCA-LDA, PCA-SVM and PCA-SR were only 75.5%, 85.7% and 77.1%, respectively. The results also suggested the increasing number of training samples have positive effects on classification models. Taking advantage of increasing dataset, the SAE-SR classification model finally performed better than others with average accuracy of 94.9% in classifying STEC serogroups. Specifically, O103 serogroup was classified with the highest accuracy of 97.4%, followed by O111 (96.5%), O26 (95.3%), O121 (95%), O145 (92.9%) and O45 (92.4%), respectively. Thus, the HMI technology coupled with SAE-SR classification model has the potential for “Big-Six” identification.
Keywords: Foodborne bacteria | Classification | Food safety | Machine learning | Stacked auto-encoder | Optical method
Urban big data fusion based on deep learning: An overview
تلفیق داده های بزرگ شهری مبتنی بر یادگیری عمیق: یک مرور کلی-2020
Urban big data fusion creates huge values for urban computing in solving urban problems. In recent years, various models and algorithms based on deep learning have been proposed to unlock the power of knowledge from urban big data. To clarify the methodologies of urban big data fusion based on deep learning (DL), this paper classifies them into three categories: DL-output-based fusion, DL-input-based fusion and DL-double-stage-based fusion. These methods use deep learning to learn feature representation from multi-source big data. Then each category of fusion methods is introduced and some examples are shown. The difficulties and ideas of dealing with urban big data will also be discussed.
Keywords: Urban computing | Big data | Data fusion | Deep learning
A novel deep learning method based on attention mechanism for bearing remaining useful life prediction
یک روش جدید یادگیری عمیق مبتنی بر مکانیسم توجه برای تحمل پیش بینی عمر مفید باقیمانده-2020
Rolling bearing is a key component in rotation machine, whose remaining useful life (RUL) prediction is an essential issue of constructing condition-based maintenance (CBM) system. However, recent data-driven approaches for bearing RUL prediction still require prior knowledge to extract features, construct health indicate (HI) and set up threshold, which is inefficient in the big data era. In this paper, a pure data-driven method for bearing RUL prediction with little prior knowledge is proposed. This method includes three steps, i.e., features extraction, HI prediction and RUL calculation. In the first step, five band-pass energy values of frequency spectrum are extracted as features. Then, a recurrent neural network based on encoder–decoder framework with attention mechanism is proposed to predict HI values, which are designed closely related with the RUL values in this paper. Finally, the final RUL value can be obtained via linear regression. Experiments carried out on the dataset from PRONOSTIA and comparison with other novel approaches demonstrate that the proposed method achieves a better performance.
Keywords: Remaining useful life prediction | Recurrent neural network | Attention mechanism