Mass Surveilance of C. elegans—Smartphone-Based DIY Microscope and Machine-Learning-Based Approach for Worm Detection

The nematode Caenorhabditis elegans (C. elegans) is often used as an alternative animal model due to several advantages such as morphological changes that can be seen directly under a microscope. Limitations of the model include the usage of expensive and cumbersome microscopes, and restrictions of the comprehensive use of C. elegans for toxicological trials. With the general applicability of the detection of C. elegans from microscope images via machine learning, as well as of smartphone-based microscopes, this article investigates the suitability of smartphone-based microscopy to detect C. elegans in a complete Petri dish. Thereby, the article introduces a smartphone-based microscope (including optics, lighting, and housing) for monitoring C. elegans and the corresponding classification via a trained Histogram of Oriented Gradients (HOG) feature-based Support Vector Machine for the automatic detection of C. elegans. Evaluation showed classification sensitivity of 0.90 and specificity of 0.85, and thereby confirms the general practicability of the chosen approach.

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Feature-Based Opinion Mining and Managed Machine Learning with Sentiment Classification Models

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.b4555.129219 ◽

2019 ◽

Vol 9 (2) ◽

pp. 3992-3998

Keyword(s):

Machine Learning ◽

Sentiment Analysis ◽

Language Processing ◽

Opinion Mining ◽

Machine Learning Algorithms ◽

Support Vector ◽

K Nearest Neighbors ◽

Data Intensive ◽

Learning Tasks ◽

Feature Based

Sentiment Analysis is individuals' opinions and feedbacks study towards a substance, which can be items, services, movies, people or events. The opinions are mostly expressed as remarks or reviews. With the social network, gatherings and websites, these reviews rose as a significant factor for the client’s decision to buy anything or not. These days, a vast scalable computing environment provides us with very sophisticated way of carrying out various data-intensive natural language processing (NLP) and machine-learning tasks to examine these reviews. One such example is text classification, a compelling method for predicting the clients' sentiment. In this paper, we attempt to center our work of sentiment analysis on movie review database. We look at the sentiment expression to order the extremity of the movie reviews on a size of 0(highly disliked) to 4(highly preferred) and perform feature extraction and ranking and utilize these features to prepare our multilabel classifier to group the movie review into its right rating. This paper incorporates sentiment analysis utilizing feature-based opinion mining and managed machine learning. The principle center is to decide the extremity of reviews utilizing nouns, verbs, and adjectives as opinion words. In addition, a comparative study on different classification approaches has been performed to determine the most appropriate classifier to suit our concern problem space. In our study, we utilized six distinctive machine learning algorithms – Naïve Bayes, Logistic Regression, SVM (Support Vector Machine), RF (Random Forest) KNN (K nearest neighbors) and SoftMax Regression.

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A feature-based approach to predict hot spots in protein–DNA binding interfaces

Briefings in Bioinformatics ◽

10.1093/bib/bbz037 ◽

2019 ◽

Vol 21 (3) ◽

pp. 1038-1046 ◽

Cited By ~ 7

Author(s):

Sijia Zhang ◽

Le Zhao ◽

Chun-Hou Zheng ◽

Junfeng Xia

Keyword(s):

Machine Learning ◽

Dna Binding ◽

Hot Spots ◽

Binding Free Energy ◽

Hot Spot ◽

Solvent Accessible Surface Area ◽

Support Vector ◽

Independent Test ◽

Feature Based ◽

Binding Interfaces

Abstract DNA-binding hot spot residues of proteins are dominant and fundamental interface residues that contribute most of the binding free energy of protein–DNA interfaces. As experimental methods for identifying hot spots are expensive and time consuming, computational approaches are urgently required in predicting hot spots on a large scale. In this work, we systematically assessed a wide variety of 114 features from a combination of the protein sequence, structure, network and solvent accessible information and their combinations along with various feature selection strategies for hot spot prediction. We then trained and compared four commonly used machine learning models, namely, support vector machine (SVM), random forest, Naïve Bayes and k-nearest neighbor, for the identification of hot spots using 10-fold cross-validation and the independent test set. Our results show that (1) features based on the solvent accessible surface area have significant effect on hot spot prediction; (2) different but complementary features generally enhance the prediction performance; and (3) SVM outperforms other machine learning methods on both training and independent test sets. In an effort to improve predictive performance, we developed a feature-based method, namely, PrPDH (Prediction of Protein–DNA binding Hot spots), for the prediction of hot spots in protein–DNA binding interfaces using SVM based on the selected 10 optimal features. Comparative results on benchmark data sets indicate that our predictor is able to achieve generally better performance in predicting hot spots compared to the state-of-the-art predictors. A user-friendly web server for PrPDH is well established and is freely available at http://bioinfo.ahu.edu.cn:8080/PrPDH.

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Morphological Neuroimaging Biomarkers for Tinnitus: Evidence Obtained by Applying Machine Learning

Neural Plasticity ◽

10.1155/2019/1712342 ◽

2019 ◽

Vol 2019 ◽

pp. 1-11

Author(s):

Yawen Liu ◽

Haijun Niu ◽

Jianming Zhu ◽

Pengfei Zhao ◽

Hongxia Yin ◽

...

Keyword(s):

Machine Learning ◽

Healthy Subjects ◽

Morphological Changes ◽

Gray Matter Volume ◽

Brain Regions ◽

Classification Model ◽

Middle Temporal Gyrus ◽

Support Vector ◽

Svm Classifier ◽

Neuroimaging Biomarkers

According to previous studies, many neuroanatomical alterations have been detected in patients with tinnitus. However, the results of these studies have been inconsistent. The objective of this study was to explore the cortical/subcortical morphological neuroimaging biomarkers that may characterize idiopathic tinnitus using machine learning methods. Forty-six patients with idiopathic tinnitus and fifty-six healthy subjects were included in this study. For each subject, the gray matter volume of 61 brain regions was extracted as an original feature pool. From this feature pool, a hybrid feature selection algorithm combining the F-score and sequential forward floating selection (SFFS) methods was performed to select features. Then, the selected features were used to train a support vector machine (SVM) model. The area under the curve (AUC) and accuracy were used to assess the performance of the classification model. As a result, a combination of 13 cortical/subcortical brain regions was found to have the highest classification accuracy for effectively differentiating patients with tinnitus from healthy subjects. These brain regions include the bilateral hypothalamus, right insula, bilateral superior temporal gyrus, left rostral middle frontal gyrus, bilateral inferior temporal gyrus, right inferior parietal lobule, right transverse temporal gyrus, right middle temporal gyrus, right cingulate gyrus, and left superior frontal gyrus. The accuracy in the training and test datasets was 80.49% and 80.00%, respectively, and the AUC was 0.8586. To the best of our knowledge, this is the first study to elucidate brain morphological changes in patients with tinnitus by applying an SVM classifier. This study provides validated cortical/subcortical morphological neuroimaging biomarkers to differentiate patients with tinnitus from healthy subjects and contributes to the understanding of neuroanatomical alterations in patients with tinnitus.

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A machine learning–based framework for analyzing car brand styling

Advances in Mechanical Engineering ◽

10.1177/1687814018784429 ◽

2018 ◽

Vol 10 (7) ◽

pp. 168781401878442 ◽

Cited By ~ 3

Author(s):

Baojun Li ◽

Ying Dong ◽

Zhijie Wen ◽

Mingzeng Liu ◽

Lei Yang ◽

...

Keyword(s):

Machine Learning ◽

Classification Accuracy ◽

Expert Knowledge ◽

Classification Problem ◽

Group Behavior ◽

Direct Perception ◽

Support Vector ◽

Machine Learning Method ◽

Feature Encoding ◽

Feature Based

To avoid the requirement of expert knowledge in conventional methods for car styling analysis, this article proposes a machine learning–based method which requires no expert-engineered features for car frontal styling analysis. In this article, we aim to identify the group behaviors in car styling such as the degree of brand styling consistency among different automakers and car styling patterns. The brand styling consistency is considered as a group behavior in this article and is formulated as a brand classification problem. This classification problem is then solved by a machine learning method based on the PCANet for automatic feature encoding and the support vector machine for feature-based classification. The brand styling consistency can thus be measured based on the classification accuracy. To perform the analysis, a car frontal styling database with 23 brands is first built. To present discovered brand styling patterns in classification, a decoding method is proposed to map salient features for brand classification to original images for revelation of salient styling regions. To provide a direct perception in brand styling characteristics, frontal styling representatives of several brands are present as well. This study contributes to efficient identification of brand styling consistency and visualization of brand styling patterns without relying on expert experience.

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TF-IDF Feature-Based Spam Filtering of Mobile SMS Using Machine Learning Approach

10.20944/preprints202109.0251.v1 ◽

2021 ◽

Author(s):

Syed Md. Minhaz Hossain ◽

Khaleque Md. Aashiq Kamal ◽

Anik Sen ◽

Iqbal H. Sarker

Keyword(s):

Machine Learning ◽

Power Efficiency ◽

Large Scale ◽

Computational Time ◽

Support Vector ◽

Short Message ◽

Ve Bayes ◽

Message Service ◽

Area Under Roc Curve ◽

Feature Based

Short Message Service (SMS) is becoming the secure medium of communication due to large-scale global coverage, reliability, and power efficiency. As person--to--person (P2P) messaging is less secure than application-to-person (A2P) messaging, anyone can send a message, leading to the attack. Attackers mistreat this opportunity to spread malicious content, perform harmful activities, and abuse other people, commonly known as spam. Moreover, such messages can waste a lot of time, and important messages are sometimes overlooked. As a result, accurate spam detection in SMS and its computational time are burning issues. In this paper, we conduct six different experiments to detect SMS spam from the dataset of 5574 messages using machine learning classifiers such as Multinomial Naïve Bayes (MNB) and Support Vector Machine (SVM), considering variations of \textit{Term Frequency-- Inverse Document Frequency (TF--IDF)} features for exploring the trade-off among accuracy, F1-score and computational time. The experiments achieve the best result of the accuracy of 98.50\%, F1--score of 98\%, and area under roc curve (AUC) of 0.97 for multinomial naïve bayes classifier with TF--IDF after stemming.

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Hybrid diffusion tensor imaging feature-based AD classification

Journal of X-Ray Science and Technology ◽

10.3233/xst-200771 ◽

2020 ◽

pp. 1-19

Author(s):

Lan Deng ◽

Yuanjun Wang

Keyword(s):

Machine Learning ◽

Diffusion Tensor ◽

Pearson Correlation ◽

Brain Networks ◽

Support Vector ◽

Detection Accuracy ◽

Detection Model ◽

Learning Classifier ◽

Diffusion Tensor Images ◽

Feature Based

BACKGROUND: Effective detection of Alzheimer’s disease (AD) is still difficult in clinical practice. Therefore, establishment of AD detection model by means of machine learning is of great significance to assist AD diagnosis. OBJECTIVE: To investigate and test a new detection model aiming to help doctors diagnose AD more accurately. METHODS: Diffusion tensor images and the corresponding T1w images acquired from subjects (AD = 98, normal control (NC) = 100) are used to construct brain networks. Then, 9 types features (198×90×9 in total) are extracted from the 3D brain networks by a graph theory method. Features with low correction in both groups are selected through the Pearson correlation analysis. Finally, the selected features (198×33, 198×26, 198×30, 198×42, 198×36, 198×23, 198×29, 198×14, 198×25) are separately used into train 3 machine learning classifier based detection models in which 60% of study subjects are used for training, 20% for validation and 20% for testing. RESULTS: The best detection accuracy levels of 3 models are 90%, 98% and 90% with the corresponding sensitivity of 92%, 96%, and 72% and specificity of 88%, 100% and 94% when using a random forest classifier trained with the Shortest Path Length (SPL) features (198×14), a support vector machine trained with the Degree Centrality features (198×33), and a convolution neural network trained with SPL features, respectively. CONCLUSIONS: This study demonstrates that the new method and models not only improve the accuracy of detecting AD, but also avoid bias caused by the method of direct dimensionality reduction from high dimensional data.

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Automatic Hate Speech Detection in English-Odia Code Mixed Social Media Data Using Machine Learning Techniques

Applied Sciences ◽

10.3390/app11188575 ◽

2021 ◽

Vol 11 (18) ◽

pp. 8575

Author(s):

Sudhir Kumar Mohapatra ◽

Srinivas Prasad ◽

Dwiti Krishna Bebarta ◽

Tapan Kumar Das ◽

Kathiravan Srinivasan ◽

...

Keyword(s):

Machine Learning ◽

Social Media ◽

Hate Speech ◽

Learning Algorithm ◽

Machine Learning Techniques ◽

Mixed Data ◽

Support Vector ◽

Speech Detection ◽

Detection Model ◽

Feature Based

Hate speech on social media may spread quickly through online users and subsequently, may even escalate into local vile violence and heinous crimes. This paper proposes a hate speech detection model by means of machine learning and text mining feature extraction techniques. In this study, the authors collected the hate speech of English-Odia code mixed data from a Facebook public page and manually organized them into three classes. In order to build binary and ternary datasets, the data are further converted into binary classes. The modeling of hate speech employs the combination of a machine learning algorithm and features extraction. Support vector machine (SVM), naïve Bayes (NB) and random forest (RF) models were trained using the whole dataset, with the extracted feature based on word unigram, bigram, trigram, combined n-grams, term frequency-inverse document frequency (TF-IDF), combined n-grams weighted by TF-IDF and word2vec for both the datasets. Using the two datasets, we developed two kinds of models with each feature—binary models and ternary models. The models based on SVM with word2vec achieved better performance than the NB and RF models for both the binary and ternary categories. The result reveals that the ternary models achieved less confusion between hate and non-hate speech than the binary models.

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A Deep Learning Analysis Reveals Nitrogen-Doped Graphene Quantum Dots Damage Neurons of Nematode Caenorhabditis elegans

Nanomaterials ◽

10.3390/nano11123314 ◽

2021 ◽

Vol 11 (12) ◽

pp. 3314

Author(s):

Hongsheng Xu ◽

Xinyu Wang ◽

Xiaomeng Zhang ◽

Jin Cheng ◽

Jixiang Zhang ◽

...

Keyword(s):

Quantum Dots ◽

Nervous System ◽

Deep Learning ◽

Morphological Changes ◽

Graphene Quantum Dots ◽

Nematode Caenorhabditis Elegans ◽

Nitrogen Doped ◽

C Elegans ◽

Nitrogen Doped Graphene ◽

Doped Graphene

Along with the rapidly increasing applications of nitrogen-doped graphene quantum dots (N-GQDs) in the field of biomedicine, the exposure of N-GQDs undoubtedly pose a risk to the health of human beings, especially in the nervous system. In view of the lack of data from in vivo studies, this study used the nematode Caenorhabditis elegans (C. elegans), which has become a valuable animal model in nanotoxicological studies due to its multiple advantages, to undertake a bio-safety assessment of N-GQDs in the nervous system with the assistance of a deep learning model. The findings suggested that accumulated N-GQDs in the nematodes’ bodies damaged their normal behavior in a dose- and time-dependent manner, and the impairments of the nervous system were obviously severe when the exposure dosages were above 100 μg/mL. When assessing the morphological changes of neurons caused by N-GQDs, a quantitative image-based analysis based on a deep neural network algorithm (YOLACT) was used because traditional image-based analysis is labor-intensive and limited to qualitative evaluation. The quantitative results indicated that N-GQDs damaged dopaminergic and glutamatergic neurons, which are involved in the neurotoxic effects of N-GQDs in the nematode C. elegans. This study not only suggests a fast and economic C. elegans model to undertake the risk assessment of nanomaterials in the nervous system, but also provides a valuable deep learning approach to quantitatively track subtle morphological changes of neurons at an unbiased level in a nanotoxicological study using C. elegans.

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Sentiment Analysis using Feature Based Support Vector Machine – A Proposed Method

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b1463.0982s1119 ◽

2019 ◽

Vol 8 (2S11) ◽

pp. 3671-3676 ◽

Cited By ~ 1

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Vital Role ◽

Support Vector ◽

Features Selection ◽

Linguistic Features ◽

Good Tool ◽

Business Decisions ◽

The People ◽

Feature Based

Business decisions for any service or product depend on sentiments by the people. The mood of people towards any event, service and product are expressed in sentiments. The text sentiment contains different linguistic features of sentence. A sentiment sentence also contains other features which are playing a vital role in deciding the polarity of sentiments.The features like duplication of sentiment, unknown emotics may change the polarity of sentiment.If features selection is proper one can extract better sentiments for decision making. A directed preprocessing will feed filtered input to any machine learning approach. Support vector machine proved as a good tool of machine learning for better sentiment analysis.Better use of parts os speech (POS) folled by guided preprocessing and evaluation will provide less errorus polarity of sentiments

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Comparative Analysis of Machine Learning Methods to Predict Growth of F. sporotrichioides and Production of T-2 and HT-2 Toxins in Treatments with Ethylene-Vinyl Alcohol Films Containing Pure Components of Essential Oils

Toxins ◽

10.3390/toxins13080545 ◽

2021 ◽

Vol 13 (8) ◽

pp. 545

Author(s):

Eva María Mateo ◽

José Vicente Gómez ◽

Andrea Tarazona ◽

María Ángeles García-Esparza ◽

Fernando Mateo

Keyword(s):

Machine Learning ◽

Vinyl Alcohol ◽

Petri Dish ◽

Toxin Production ◽

Support Vector ◽

Fungal Culture ◽

Ethylene Vinyl Alcohol ◽

Copolymer Films ◽

Control Growth ◽

Increasing Temperature

The efficacy of ethylene-vinyl alcohol copolymer films (EVOH) incorporating the essential oil components cinnamaldehyde (CINHO), citral (CIT), isoeugenol (IEG), or linalool (LIN) to control growth rate (GR) and production of T-2 and HT-2 toxins by Fusarium sporotrichioides cultured on oat grains under different temperature (28, 20, and 15 °C) and water activity (aw) (0.99 and 0.96) regimes was assayed. GR in controls/treatments usually increased with increasing temperature, regardless of aw, but no significant differences concerning aw were found. Toxin production decreased with increasing temperature. The effectiveness of films to control fungal GR and toxin production was as follows: EVOH-CIT > EVOH-CINHO > EVOH-IEG > EVOH-LIN. With few exceptions, effective doses of EVOH-CIT, EVOH-CINHO, and EVOH-IEG films to reduce/inhibit GR by 50%, 90%, and 100% (ED50, ED90, and ED100) ranged from 515 to 3330 µg/culture in Petri dish (25 g oat grains) depending on film type, aw, and temperature. ED90 and ED100 of EVOH-LIN were >3330 µg/fungal culture. The potential of several machine learning (ML) methods to predict F. sporotrichioides GR and T-2 and HT-2 toxin production under the assayed conditions was comparatively analyzed. XGBoost and random forest attained the best performance, support vector machine and neural network ranked third or fourth depending on the output, while multiple linear regression proved to be the worst.

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