Think Positive: Towards Twitter Sentiment Analysis from Scratch

Recommending music based on a user’s music preference is a way to improve user listening experience. Finding the correlation between the user data (e.g., location, time of the day, music listening history, emotion, etc.) and the music is a challenging task. In this paper, we propose an emotion-aware personalized music recommendation system (EPMRS) to extract the correlation between the user data and the music. To achieve this correlation, we combine the outputs of two approaches: the deep convolutional neural networks (DCNN) approach and the weighted feature extraction (WFE) approach. The DCNN approach is used to extract the latent features from music data (e.g., audio signals and corresponding metadata) for classification. In the WFE approach, we generate the implicit user rating for music to extract the correlation between the user data and the music data. In the WFE approach, we use the term-frequency and inverse document frequency (TF-IDF) approach to generate the implicit user ratings for the music. Later, the EPMRS recommends songs to the user based on calculated implicit user rating for the music. We use the million songs dataset (MSD) to train the EPMRS. For performance comparison, we take the content similarity music recommendation system (CSMRS) as well as the personalized music recommendation system based on electroencephalography feedback (PMRSE) as the baseline systems. Experimental results show that the EPMRS produces better accuracy of music recommendations than the CSMRS and the PMRSE. Moreover, we build the Android and iOS APPs to get realistic data of user experience on the EPMRS. The collected feedback from anonymous users also show that the EPMRS sufficiently reflect their preference on music.

This paper presents the approach of the GTI Research Group to SemEval-2016 task 4 on Sentiment Analysis in Twitter, or more specifically, subtasks A (Message Polarity Classification), B (Tweet classification according to a two-point scale) and D (Tweet quantification according to a two-point scale). We followed a supervised approach based on the extraction of features by a dependency parsing-based approach using a sentiment lexicon and Natural Language Processing techniques.

In this paper we report our works for SemEval-2014 Sentiment Analysis in Twitter evaluation challenge. This is the first time we attempt for this task, and our submissions are based on supervised machine learning algorithm. We use Support Vector Machine for both the tasks, viz. contextual polarity disambiguation and message polarity classification. We identify and implement a small set of features for each the tasks, and did not make use of any external resources and/or tools. The systems are tuned on the development sets and finally blind evaluation is performed on the respective test set, which consists of the datasets of five different domains. Our submission for the first task shows the F-score values of 76.3%, 77.04%, 70.91%, 72.25% and 66.32% for LiveJournal2014, SMS2013, Twitter2013, Twitter2014 and Twitter2014Sarcasm datasets, respectively. The system developed for the second task yields the F-score values of 54.68%, 40.56%, 50.32%, 48.22% and 36.73%, respectively for the five different test datasets.

The feasibility of localizing, segmenting, and classifying individual cells in multi-channel confocal microscopy images is highly dependent on image quality. In certain applications, with good image quality, the segmentation can be trivial and accomplished via thresholding, watershed, or a collection of other well-established and studied heuristics; however, at the limit of poor image quality and complex image features, these techniques fail. It is at this limit that deep convolutional neural network (DCNN) approaches excel. Our research studies the interaction of individual immune cells and their shape changes relative to inflammatory immune reactions1 using multi-channel immunofluorescence imaging of renal biopsies from patients with inflammatory kidney disease. We present here a deep learning methodology for application to nuclear and cell membrane immunofluorescent stains to automatically segment and classify multiple T-cell and dendritic cell types. With both T-cells and dendritic cells segmented, we are able to study how T-cells with different surface antigens change shape with proximity to dendritic cells. Shape changes are seen when T-cells move close to dendritic cells and interact. We use a sliding window, max-filtering DCNN to segment and classify 3 cell types from 6 image stains channels within a single DCNN. This DCNN maintains images at original resolution throughout the network using dilated convolutions and max-filtering in place of max pooling layers. In addition, we use 3D convolution kernels with two spatial dimensions and one channel dimension. This allows us to output a multi-class binary classification based on multichannel data at the original image resolution. We trained and validated the network across 24 patients with 8,572 segmented cells. Our results demonstrate a mean Dice-Sorensen score of 0.78 +/- 0.18, a mean classification sensitivity of 0.76, and a mean classification specificity of 0.75 across all 3 segmented cell types.

Skin cancer is one of the most dangerous types of cancer due to its immediate appearance and the possibility of rapid spread. It arises from uncontrollably growing cells, rapidly dividing cells in one area of the body, invading other bodily tissues, and spreading throughout the body. Early detection helps prevent cancer progress from reaching critical levels, reducing the risk of complications and the need for more aggressive treatment options. Convolutional neural networks (CNNs) revolutionize skin cancer diagnosis by extracting intricate features from images, enabling an accurate classification of lesions. Their role extends to early detection, providing a powerful tool for dermatologists to identify abnormalities in their nascent stages, ultimately improving patient outcomes. This study proposes a novel deep convolutional neural network (DCNN) approach to classifying skin cancer lesions. The proposed DCNN model is evaluated using two unbalanced datasets, namely HAM10000 and ISIC-2019. The DCNN model is compared with other transfer learning models, including VGG16, VGG19, DenseNet121, DenseNet201, and MobileNetV2. Its performance is assessed using four widely used evaluation metrics: accuracy, recall, precision, F1-score, specificity, and AUC. The experimental results demonstrate that the proposed DCNN model outperforms other deep learning (DL) models that utilized these datasets. The proposed DCNN model achieved the highest accuracy with the HAM10000 and ISIC-2019 datasets, reaching 98.5%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$98.5\\%$$\\end{document} and 97.1%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$97.1\\%$$\\end{document}, respectively. These experimental results show how competitive and successful the DCNN model is in overcoming the problems caused by class imbalance and raising skin cancer classification accuracy. Furthermore, the proposed model demonstrates superior performance, particularly excelling in terms of accuracy, compared to other recent studies that utilize the same datasets, which highlights the robustness and effectiveness of the proposed DCNN.

WiDet: Wi-Fi based device-free passive person detection with deep convolutional neural networks

In this paper, we propose a deep learning convolutional neural network (CNN) approach to classify arrhythmia based on the time interval of the QRS complex of the ECG signal. The ECG signal was denoised using multiple filters based on the Pan Tompkins algorithm. QRS detection has been done using Pan Tompkins Algorithm. Then the QRS complex is identified using local peaks based technique inside the layers of the Convolutional Neural Network where the repeated application of the same filter to our input results in a map of activations called a feature map, indicating the locations and strength of a detected feature in an input which in our case is the changes in the q-s time interval. Based on the R-R time interval, Heart rate variability (HRV) was computed, and Poincare plot was generated. Instead of using raw ECG signal to train the CNN, we used the feature extracted from ECG signal obtained from Physionet database to train the CNN and map the pattern changes for different classes of diseases. The classifier was then used to classify the test input as either or normal, tachyarrhythmia or intracardiac atrial fibrillation. Data acquisition, ECG data pre-processing and CNN classifier are the several methods that are involved for the classification of several arrhythmias.

We present, in this paper, our contribution in SemEval2017 task 4 : Sentiment Analysis in Twitter , subtask A: Message Polarity Classification , for En-glish and Arabic languages. Our system is based on a list of sentiment seed words adapted for tweets. The sentiment relations between seed words and other terms are captured by cosine similarity between the word embedding representations (word2vec). These seed words are extracted from datasets of annotated tweets available online. Our tests, using these seed words, show significant improvement in results compared to the use of Turney and Littman's (2003) seed words, on polarity classification of tweet messages.

This paper describes the system that was submitted to SemEval2015 Task 10: Sentiment Analysis in Twitter. We participated in Subtask B: Message Polarity Classification. The task is a message level classification of tweets into positive, negative and neutral sentiments. Our model is primarily a supervised one which consists of well designed features fed into an SVM classifier. In previous runs of this task, it was found that lexicons played an important role in determining the sentiment of a tweet. We use existing lexicons to extract lexicon specific features. The lexicon based features are further augmented by tweet specific features. We also improve our system by using acronym and emoticon dictionaries. The proposed system achieves an F1 score of 59:83 and 67:04 on the Test Data and Progress Data respectively. This placed us at the 18 th position for the Test Dataset and the 16 th position for the Progress Test Dataset.

Diabetic Retinopathy (DR) is eye disease caused through the retinal damage induced through long-term diabetes mellitus illness. In the initial stage, DR has minor vision difficulties or no symptoms but it outputs in vision loss if it is not treated early. However, overfitting occurs when a model learns noise or specific features from a small dataset which leading to poor generalization. To solve this issue, this research proposed an Otsu Multi-level Adaptive Thresholding with Deep Convolution Neural Network (OMAT+DCNN) approach for DR segmentation and classification. The Contrast Limited Adaptive Histogram Equalization (CLAHE) is used for preprocessing input image which enhances the contrast level and image visibility without amplifying noise. The OMAT is used for segmenting the affected regions and ResNet50 is used for extracting Deep Learning (DL) features. Then, relevant features are selected through Multi-Verse Optimizer (MVO) and DCNN is used for classification. The OMAT improves segmentation accuracy by considering multiple thresholds which adopts to local intensity variations within the image, even when the dataset is limited. DCNN capturing intricate patterns and spatial hierarchies within images and its deep architecture allows to learn complex representations which leads to enhance accuracy. The parameters like dice coefficient, Intersection over Union (IoU), Mean IoU (MIoU), precision, accuracy, f1-score and sensitivity are used for assessing performance. The OMAT+DCNN reaches 0.993 of accuracy when compared to existing techniques such as Convolutional Transformer Network (CTNet) and Residual Attention Network (RAN).

Detecting major depressive disorder presence using passively-collected wearable movement data in a nationally-representative sample

Protective measures against small unmanned aerial vehicles (UAVs) are vital from a national security perspective. As a result, the importance of surveillance systems that automatically identify and classify low radar cross section (RCS) aerial targets increases. In this work, an indigenously developed continuous wave (CW) (X-band: 10 GHz) radar is used to build a diversified “DIAT- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> SAT” dataset comprising 4849 micro-Doppler signature images of five different small aerial targets. We also proposed a transfer learning-based deep convolutional neural network (DCNN) approach for classifying low RCS aerial targets. We demonstrated the classification accuracy of 95% and 97%, with VGG16 and VGG19 as feature extractors, respectively, with minimal false-negative and -positive results. The open-field experimental classes covered in this work are: 1) a two-blade rotor; 2) a three-short-blade rotor; 3) a three-long-blade rotor; 4) a quadcopter; 5) a bionic bird; and 6) a two-blade-rotor and bionic bird. We also observed a good classification accuracy (>97%) when more than one target is operated simultaneously.

Diabetic retinopathy (DR) is one of the primary causes of preventable blindness, highlighting the necessity for accurate and automated retinal screening systems. Manual diagnosis through fundus image inspection is time-consuming and prone to subjective interpretation, particularly in regions with limited access to ophthalmic specialists. This study presents a deep convolutional neural network (CNN) approach based on ResNet50 architecture with fine-tuning for multi-class classification of retinal diseases. The proposed model was developed using the APTOS 2019 Blindness Detection dataset, consisting of 3,662 fundus images categorized into five levels of DR severity. A robust preprocessing pipeline, including illumination correction, contrast enhancement, normalization, and extensive data augmentation, was implemented to improve image quality and balance the dataset. The network was trained using the Adam optimizer with a learning rate of 1×10?? and categorical cross-entropy loss for 30 epochs under an 80:20 train–validation split. Experimental evaluation demonstrated high performance with 92.4% accuracy, 0.91 precision, 0.92 recall, 0.91 F1-score, and an AUC of 0.95, outperforming baseline CNN and VGG16 models. Furthermore, Grad-CAM visualization confirmed that the model accurately localized critical retinal regions associated with microaneurysms, hemorrhages, and exudates, enhancing interpretability and clinical trust. The proposed ResNet50-based framework provides an explainable, efficient, and reliable solution for automated diabetic retinopathy detection, supporting large-scale tele-ophthalmology and early diagnosis applications in medical imaging

Although the left ventricle (LV) is commonly assessed in current clinical practice, the assessment of the right ventricle (RV) also plays an important role in the diagnosis of cardiovascular disease. RV failure has numerous causes, including pulmonary hypertension, myocardial infarction, and congenital heart disease. However, assessment of the RV is more challenging than the LV due to its complex shape and thin walls. This study proposes an automated approach to delineate the RV from magnetic resonance imaging (MRI) scans using a deep convolutional neural network approach. The proposed method uses nnU-Net, a self-adapting framework based on the U-Net neural network approach for the segmentation of the RV from short and long axis MRI images at the end-systolic and end-diastolic phases of the heart. The proposed neural network models were trained using the datasets provided by Multi-Disease, Multi-View & Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge hosted by MICCAI 2021 conference. The quantitative evaluations were performed by the challenge organizers on a test set consisting of MRI scans acquired from 160 patients where the images and ground truth were blinded to the challenge participants. The proposed method yielded an overall Dice metric of \(92.47\%\) with \(92.73\%\) and \(91.71\%\) for short and long axis images, respectively. The corresponding Hausdorff distance values were 9.08 mm, 10.05 mm, and 6.16 mm, respectively.KeywordsCardiac magnetic resonance imagingDeep convolutional neural networksRight ventricleImage segmentation

This paper describes SiTAKA, our system that has been used in task 4A, English and Arabic languages, Sentiment Analysis in Twitter of SemEval2017. The system proposes the representation of tweets using a novel set of features, which include a bag of negated words and the information provided by some lexicons. The polarity of tweets is determined by a classifier based on a Support Vector Machine. Our system ranks 2nd among 8 systems in the Arabic language tweets and ranks 8th among 38 systems in the English-language tweets.