Classifying Goliath Grouper (Epinephelus itajara) Behaviors from a Novel, Multi-Sensor Tag

Inertial measurement unit sensors (IMU; i.e., accelerometer, gyroscope and magnetometer combinations) are frequently fitted to animals to better understand their activity patterns and energy expenditure. Capable of recording hundreds of data points a second, these sensors can quickly produce large datasets that require methods to automate behavioral classification. Here, we describe behaviors derived from a custom-built multi-sensor bio-logging tag attached to Atlantic Goliath grouper (Epinephelus itajara) within a simulated ecosystem. We then compared the performance of two commonly applied machine learning approaches (random forest and support vector machine) to a deep learning approach (convolutional neural network, or CNN) for classifying IMU data from this tag. CNNs are frequently used to recognize activities from IMU data obtained from humans but are less commonly considered for other animals. Thirteen behavioral classes were identified during ethogram development, nine of which were classified. For the conventional machine learning approaches, 187 summary statistics were extracted from the data, including time and frequency domain features. The CNN was fed absolute values obtained from fast Fourier transformations of the raw tri-axial accelerometer, gyroscope and magnetometer channels, with a frequency resolution of 512 data points. Five metrics were used to assess classifier performance; the deep learning approach performed better across all metrics (Sensitivity = 0.962; Specificity = 0.996; F1-score = 0.962; Matthew’s Correlation Coefficient = 0.959; Cohen’s Kappa = 0.833) than both conventional machine learning approaches. Generally, the random forest performed better than the support vector machine. In some instances, a conventional learning approach yielded a higher performance metric for particular classes (e.g., the random forest had a F1-score of 0.971 for backward swimming compared to 0.955 for the CNN). Deep learning approaches could potentially improve behavioral classification from IMU data, beyond that obtained from conventional machine learning methods.

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A Deep Learning Approach to Nightfire Detection based on Low-Light Satellite

10.5121/csit.2021.110401 ◽

2021 ◽

Author(s):

Yue Wang ◽

Ye Ni ◽

Xutao Li ◽

Yunming Ye

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Deep Learning ◽

Early Detection ◽

Remote Sensing Image ◽

Experimental Results ◽

Learning Approach ◽

Learning Approaches ◽

Low Light ◽

Conventional Machine

Wildfires are a serious disaster, which often cause severe damages to forests and plants. Without an early detection and suitable control action, a small wildfire could grow into a big and serious one. The problem is especially fatal at night, as firefighters in general miss the chance to detect the wildfires in the very first few hours. Low-light satellites, which take pictures at night, offer an opportunity to detect night fire timely. However, previous studies identify night fires based on threshold methods or conventional machine learning approaches, which are not robust and accurate enough. In this paper, we develop a new deep learning approach, which determines night fire locations by a pixel-level classification on low-light remote sensing image. Experimental results on VIIRS data demonstrate the superiority and effectiveness of the proposed method, which outperforms conventional threshold and machine learning approaches.

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Detection of Malicious Software by Analyzing Distinct Artifacts Using Machine Learning and Deep Learning Algorithms

Electronics ◽

10.3390/electronics10141694 ◽

2021 ◽

Vol 10 (14) ◽

pp. 1694

Author(s):

Mathew Ashik ◽

A. Jyothish ◽

S. Anandaram ◽

P. Vinod ◽

Francesco Mercaldo ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Support Vector ◽

Malware Analysis ◽

Learning Approaches ◽

Dynamic Features ◽

System Calls ◽

Prevention Methods ◽

Structural Aspects

Malware is one of the most significant threats in today’s computing world since the number of websites distributing malware is increasing at a rapid rate. Malware analysis and prevention methods are increasingly becoming necessary for computer systems connected to the Internet. This software exploits the system’s vulnerabilities to steal valuable information without the user’s knowledge, and stealthily send it to remote servers controlled by attackers. Traditionally, anti-malware products use signatures for detecting known malware. However, the signature-based method does not scale in detecting obfuscated and packed malware. Considering that the cause of a problem is often best understood by studying the structural aspects of a program like the mnemonics, instruction opcode, API Call, etc. In this paper, we investigate the relevance of the features of unpacked malicious and benign executables like mnemonics, instruction opcodes, and API to identify a feature that classifies the executable. Prominent features are extracted using Minimum Redundancy and Maximum Relevance (mRMR) and Analysis of Variance (ANOVA). Experiments were conducted on four datasets using machine learning and deep learning approaches such as Support Vector Machine (SVM), Naïve Bayes, J48, Random Forest (RF), and XGBoost. In addition, we also evaluate the performance of the collection of deep neural networks like Deep Dense network, One-Dimensional Convolutional Neural Network (1D-CNN), and CNN-LSTM in classifying unknown samples, and we observed promising results using APIs and system calls. On combining APIs/system calls with static features, a marginal performance improvement was attained comparing models trained only on dynamic features. Moreover, to improve accuracy, we implemented our solution using distinct deep learning methods and demonstrated a fine-tuned deep neural network that resulted in an F1-score of 99.1% and 98.48% on Dataset-2 and Dataset-3, respectively.

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Analysis of the Nosema Cells Identification for Microscopic Images

Sensors ◽

10.3390/s21093068 ◽

2021 ◽

Vol 21 (9) ◽

pp. 3068

Author(s):

Soumaya Dghim ◽

Carlos M. Travieso-González ◽

Radim Burget

Keyword(s):

Neural Network ◽

Machine Learning ◽

Image Processing ◽

Deep Learning ◽

The Other ◽

Support Vector ◽

Learning Approaches ◽

Microscopic Images ◽

Trained Neural Network ◽

Nosema Disease

The use of image processing tools, machine learning, and deep learning approaches has become very useful and robust in recent years. This paper introduces the detection of the Nosema disease, which is considered to be one of the most economically significant diseases today. This work shows a solution for recognizing and identifying Nosema cells between the other existing objects in the microscopic image. Two main strategies are examined. The first strategy uses image processing tools to extract the most valuable information and features from the dataset of microscopic images. Then, machine learning methods are applied, such as a neural network (ANN) and support vector machine (SVM) for detecting and classifying the Nosema disease cells. The second strategy explores deep learning and transfers learning. Several approaches were examined, including a convolutional neural network (CNN) classifier and several methods of transfer learning (AlexNet, VGG-16 and VGG-19), which were fine-tuned and applied to the object sub-images in order to identify the Nosema images from the other object images. The best accuracy was reached by the VGG-16 pre-trained neural network with 96.25%.

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A Study on the Evolution of Ransomware Detection Using Machine Learning and Deep Learning Techniques

IoT ◽

10.3390/iot1020030 ◽

2020 ◽

Vol 1 (2) ◽

pp. 551-604

Author(s):

Damien Warren Fernando ◽

Nikos Komninos ◽

Thomas Chen

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Learning Approach ◽

Learning Approaches ◽

Learning Techniques ◽

New Directions ◽

Encryption Schemes ◽

Malware Evolution ◽

Number Of Citations ◽

Research Studies

This survey investigates the contributions of research into the detection of ransomware malware using machine learning and deep learning algorithms. The main motivations for this study are the destructive nature of ransomware, the difficulty of reversing a ransomware infection, and how important it is to detect it before infecting a system. Machine learning is coming to the forefront of combatting ransomware, so we attempted to identify weaknesses in machine learning approaches and how they can be strengthened. The threat posed by ransomware is exceptionally high, with new variants and families continually being found on the internet and dark web. Recovering from ransomware infections is difficult, given the nature of the encryption schemes used by them. The increase in the use of artificial intelligence also coincides with this boom in ransomware. The exploration into machine learning and deep learning approaches when it comes to detecting ransomware poses high interest because machine learning and deep learning can detect zero-day threats. These techniques can generate predictive models that can learn the behaviour of ransomware and use this knowledge to detect variants and families which have not yet been seen. In this survey, we review prominent research studies which all showcase a machine learning or deep learning approach when detecting ransomware malware. These studies were chosen based on the number of citations they had by other research. We carried out experiments to investigate how the discussed research studies are impacted by malware evolution. We also explored the new directions of ransomware and how we expect it to evolve in the coming years, such as expansion into IoT (Internet of Things), with IoT being integrated more into infrastructures and into homes.

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Emotion analysis of Arabic tweets using deep learning approach

Journal Of Big Data ◽

10.1186/s40537-019-0252-x ◽

2019 ◽

Vol 6 (1) ◽

Author(s):

Massa Baali ◽

Nada Ghneim

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Media Analysis ◽

Machine Learning Algorithms ◽

Learning Approach ◽

Learning Approaches ◽

Social Media Analysis ◽

Deep Convolutional Neural Networks ◽

Emotion Analysis ◽

Classification Tasks

Abstract Nowadays, sharing moments on social networks have become something widespread. Sharing ideas, thoughts, and good memories to express our emotions through text without using a lot of words. Twitter, for instance, is a rich source of data that is a target for organizations for which they can use to analyze people’s opinions, sentiments and emotions. Emotion analysis normally gives a more profound overview of the feelings of an author. In Arabic Social Media analysis, nearly all projects have focused on analyzing the expressions as positive, negative or neutral. In this paper we intend to categorize the expressions on the basis of emotions, namely happiness, anger, fear, and sadness. Different approaches have been carried out in the area of automatic textual emotion recognition in the case of other languages, but only a limited number were based on deep learning. Thus, we present our approach used to classify emotions in Arabic tweets. Our model implements a deep Convolutional Neural Networks (CNN) trained on top of trained word vectors specifically on our dataset for sentence classification tasks. We compared the results of this approach with three other machine learning algorithms which are SVM, NB and MLP. The architecture of our deep learning approach is an end-to-end network with word, sentence, and document vectorization steps. The deep learning proposed approach was evaluated on the Arabic tweets dataset provided by SemiEval for the EI-oc task, and the results-compared to the traditional machine learning approaches-were excellent.

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Sentiment Analysis of Lithuanian Texts Using Traditional and Deep Learning Approaches

Computers ◽

10.3390/computers8010004 ◽

2019 ◽

Vol 8 (1) ◽

pp. 4 ◽

Cited By ~ 4

Author(s):

Jurgita Kapočiūtė-Dzikienė ◽

Robertas Damaševičius ◽

Marcin Woźniak

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Sentiment Analysis ◽

Short Term Memory ◽

Machine Learning Techniques ◽

Support Vector ◽

Learning Approaches ◽

Full Dataset ◽

Learning Techniques ◽

Long Short Term Memory

We describe the sentiment analysis experiments that were performed on the Lithuanian Internet comment dataset using traditional machine learning (Naïve Bayes Multinomial—NBM and Support Vector Machine—SVM) and deep learning (Long Short-Term Memory—LSTM and Convolutional Neural Network—CNN) approaches. The traditional machine learning techniques were used with the features based on the lexical, morphological, and character information. The deep learning approaches were applied on the top of two types of word embeddings (Vord2Vec continuous bag-of-words with negative sampling and FastText). Both traditional and deep learning approaches had to solve the positive/negative/neutral sentiment classification task on the balanced and full dataset versions. The best deep learning results (reaching 0.706 of accuracy) were achieved on the full dataset with CNN applied on top of the FastText embeddings, replaced emoticons, and eliminated diacritics. The traditional machine learning approaches demonstrated the best performance (0.735 of accuracy) on the full dataset with the NBM method, replaced emoticons, restored diacritics, and lemma unigrams as features. Although traditional machine learning approaches were superior when compared to the deep learning methods; deep learning demonstrated good results when applied on the small datasets.

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Sentiment Analysis and Topic Modeling on Tweets about Online Education during COVID-19

Applied Sciences ◽

10.3390/app11188438 ◽

2021 ◽

Vol 11 (18) ◽

pp. 8438

Author(s):

Muhammad Mujahid ◽

Ernesto Lee ◽

Furqan Rustam ◽

Patrick Bernard Washington ◽

Saleem Ullah ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Online Education ◽

Sentiment Analysis ◽

Topic Modeling ◽

Support Vector ◽

Learning Approaches ◽

Learning Models ◽

E Learning ◽

Machine Learning Models

Amid the worldwide COVID-19 pandemic lockdowns, the closure of educational institutes leads to an unprecedented rise in online learning. For limiting the impact of COVID-19 and obstructing its widespread, educational institutions closed their campuses immediately and academic activities are moved to e-learning platforms. The effectiveness of e-learning is a critical concern for both students and parents, specifically in terms of its suitability to students and teachers and its technical feasibility with respect to different social scenarios. Such concerns must be reviewed from several aspects before e-learning can be adopted at such a larger scale. This study endeavors to investigate the effectiveness of e-learning by analyzing the sentiments of people about e-learning. Due to the rise of social media as an important mode of communication recently, people’s views can be found on platforms such as Twitter, Instagram, Facebook, etc. This study uses a Twitter dataset containing 17,155 tweets about e-learning. Machine learning and deep learning approaches have shown their suitability, capability, and potential for image processing, object detection, and natural language processing tasks and text analysis is no exception. Machine learning approaches have been largely used both for annotation and text and sentiment analysis. Keeping in view the adequacy and efficacy of machine learning models, this study adopts TextBlob, VADER (Valence Aware Dictionary for Sentiment Reasoning), and SentiWordNet to analyze the polarity and subjectivity score of tweets’ text. Furthermore, bearing in mind the fact that machine learning models display high classification accuracy, various machine learning models have been used for sentiment classification. Two feature extraction techniques, TF-IDF (Term Frequency-Inverse Document Frequency) and BoW (Bag of Words) have been used to effectively build and evaluate the models. All the models have been evaluated in terms of various important performance metrics such as accuracy, precision, recall, and F1 score. The results reveal that the random forest and support vector machine classifier achieve the highest accuracy of 0.95 when used with Bow features. Performance comparison is carried out for results of TextBlob, VADER, and SentiWordNet, as well as classification results of machine learning models and deep learning models such as CNN (Convolutional Neural Network), LSTM (Long Short Term Memory), CNN-LSTM, and Bi-LSTM (Bidirectional-LSTM). Additionally, topic modeling is performed to find the problems associated with e-learning which indicates that uncertainty of campus opening date, children’s disabilities to grasp online education, and lagging efficient networks for online education are the top three problems.

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Utilização de técnicas de Machine Learning e de Deep Learning para a predição de casos de internações causadas por dengue em municípios da Paraíba

10.5753/ercemapi.2021.17914 ◽

2021 ◽

Author(s):

Ewerthon Dyego de Araújo Batista ◽

Wellington Candeia de Araújo ◽

Romeryto Vieira Lira ◽

Laryssa Izabel de Araújo Batista

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Random Forest ◽

Convolutional Neural Network ◽

Support Vector Regression ◽

Multilayer Perceptron ◽

Support Vector

Dengue é um problema de saúde pública no Brasil, os casos da doença voltaram a crescer na Paraíba. O boletim epidemiológico da Paraíba, divulgado em agosto de 2021, informa um aumento de 53% de casos em relação ao ano anterior. Técnicas de Machine Learning (ML) e de Deep Learning estão sendo utilizadas como ferramentas para a predição da doença e suporte ao seu combate. Por meio das técnicas Random Forest (RF), Support Vector Regression (SVR), Multilayer Perceptron (MLP), Long ShortTerm Memory (LSTM) e Convolutional Neural Network (CNN), este artigo apresenta um sistema capaz de realizar previsões de internações causadas por dengue para as cidades Bayeux, Cabedelo, João Pessoa e Santa Rita. O sistema conseguiu realizar previsões para Bayeux com taxa de erro 0,5290, já em Cabedelo o erro foi 0,92742, João Pessoa 9,55288 e Santa Rita 0,74551.

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Airline Delay Prediction using Machine Learning and Deep Learning Techniques

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b4047.079220 ◽

2020 ◽

Vol 9 (2) ◽

pp. 1049-1054

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Random Forest ◽

Short Term Memory ◽

Research Work ◽

Machine Learning Algorithms ◽

Support Vector ◽

Maximum Delay ◽

Learning Techniques ◽

Delay Prediction

In this paper, we have tried to predict flight delays using different machine learning and deep learning techniques. By using such a model it can be easier to predict whether the flight will be delayed or not. Factors like ‘WeatherDelay’, ‘NASDelay’, ‘Destination’, ‘Origin’ play a vital role in this model. Using machine learning algorithms like Random Forest, Support Vector Machine (SVM) and K-Nearest Neighbors (KNN), the f1-score, precision, recall, support and accuracy have been predicted. To add to the model, Long Short-Term Memory (LSTM) RNN architecture has also been employed. In the paper, the dataset from Bureau of Transportation Statistics (BTS) of the ‘Pittsburgh’ is being used. The results computed from the above mentioned algorithms have been compared. Further, the results were visualized for various airlines to find maximum delay and AUC-ROC curve has been plotted for Random Forest Algorithm. The aim of our research work is to predict the delay so as to minimize loses and increase customer satisfaction.

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Prediction of Chemotherapy Response of Osteosarcoma Using Baseline 18F-FDG Textural Features Machine Learning Approaches with PCA

Contrast Media & Molecular Imaging ◽

10.1155/2019/3515080 ◽

2019 ◽

Vol 2019 ◽

pp. 1-7 ◽

Cited By ~ 3

Author(s):

Su Young Jeong ◽

Wook Kim ◽

Byung Hyun Byun ◽

Chang-Bae Kong ◽

Won Seok Song ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Fdg Pet ◽

Image Texture ◽

Support Vector ◽

Chemotherapy Response ◽

Learning Approach ◽

Textural Features ◽

Response To Chemotherapy ◽

Machine Learning Approach

Purpose. Patients with high-grade osteosarcoma undergo several chemotherapy cycles before surgical intervention. Response to chemotherapy, however, is affected by intratumor heterogeneity. In this study, we assessed the ability of a machine learning approach using baseline 18F-fluorodeoxyglucose (18F-FDG) positron emitted tomography (PET) textural features to predict response to chemotherapy in osteosarcoma patients. Materials and Methods. This study included 70 osteosarcoma patients who received neoadjuvant chemotherapy. Quantitative characteristics of the tumors were evaluated by standard uptake value (SUV), total lesion glycolysis (TLG), and metabolic tumor volume (MTV). Tumor heterogeneity was evaluated using textural analysis of 18F-FDG PET scan images. Assessments were performed at baseline and after chemotherapy using 18F-FDG PET; 18F-FDG textural features were evaluated using the Chang-Gung Image Texture Analysis toolbox. To predict the chemotherapy response, several features were chosen using the principal component analysis (PCA) feature selection method. Machine learning was performed using linear support vector machine (SVM), random forest, and gradient boost methods. The ability to predict chemotherapy response was evaluated using the area under the receiver operating characteristic curve (AUC). Results. AUCs of the baseline 18F-FDG features SUVmax, TLG, MTV, 1st entropy, and gray level co-occurrence matrix entropy were 0.553, 0538, 0.536, 0.538, and 0.543, respectively. However, AUCs of the machine learning features linear SVM, random forest, and gradient boost were 0.72, 0.78, and 0.82, respectively. Conclusion. We found that a machine learning approach based on 18F-FDG textural features could predict the chemotherapy response using baseline PET images. This early prediction of the chemotherapy response may aid in determining treatment plans for osteosarcoma patients.

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