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Social media platforms such as Twitter and Facebook have become popular channels for people to record and express their feelings, opinions, and feedback in the last decades. With proper extraction techniques such as sentiment analysis, this information is useful in many aspects, including product marketing, behavior analysis, and pandemic management. Sentiment analysis is a technique to analyze people's thoughts, feelings and emotions, and to categorize them into positive, negative, or neutral. There are many ways for someone to express their feelings and emotions. These sentiments are sometimes accompanied by sarcasm, especially when conveying intense emotion. Sarcasm is defined as a positive sentence with underlying negative intention. Most of the current research work treats them as two distinct tasks. To date, most sentiment and sarcasm classification approaches have been treated primarily and standalone as a text categorization problem. In recent years, research work using deep learning algorithms have significantly improved performance for these standalone classifiers. One of the major issues faced by these approaches is that they could not correctly classify sarcastic sentences as negative. With this in mind, we claim that knowing how to spot sarcasm will help sentiment classification and vice versa. Our work has shown that these two tasks are correlated. This paper proposes a multi-task learning-based framework utilizing a deep neural network to model this correlation to improve sentiment analysis's overall performance. The proposed method outperforms the existing methods by a margin of 3%, with an F1-score of 94%. [ABSTRACT FROM AUTHOR]

Intraoperative neuromonitoring (IONM) has been used to help monitor the integrity of the nervous system during spine surgery. Transcranial motor-evoked potential (TcMEP) has been used lately for lower lumbar surgery to prevent nerve root injuries and also to predict positive functional outcomes of patients. There were a number of studies that proved that the TcMEP signal's improvement is significant towards positive functional outcomes of patients. In this paper, we explored the possibilities of using a machine learning approach to TcMEP signal to predict positive functional outcomes of patients. With 55 patients who underwent various types of lumbar surgeries, the data were divided into 70 : 30 and 80 : 20 ratios for training and testing of the machine learning models. The highest sensitivity and specificity were achieved by Fine KNN of 80 : 20 ratio with 87.5% and 33.33%, respectively. In the meantime, we also tested the existing improvement criteria presented in the literature, and 50% of TcMEP improvement criteria achieved 83.33% sensitivity and 75% specificity. But the rigidness of this threshold method proved unreliable in this study when different datasets were used as the sensitivity and specificity dropped. The proposed method by using machine learning has more room to advance with a larger dataset and various signals' features to choose from. [ABSTRACT FROM AUTHOR]

Digitalization is revolutionizing our way of life and catalyzing the transformation into smart city. Intelligent Transportation System (ITS) being an indispensable component of smart city leverages massive amount of collected information to improve traffic efficiency, thereby creating a safer and comfortable commuting environment for the users. One of the most important tasks in ITS is vehicle classification which aims to find out the vehicle identity, including vehicle segment, automobile maker, model, etc. In this article, we first present the vehicle classification taxonomy branched based on the nature of input data. We subsequently investigate diverse area of sensor-based vehicle classification followed by image-based vehicle classification which cover both the conventional and emerging techniques in a comprehensive manner. The methodologies together with the corresponding strengths and potential weaknesses are elucidated so that it serves as an invaluable reference for vehicle classification related applications in the future. More importantly, we express our views on future research direction with the intention to accelerate the development of vehicle classification field. In contrast to previous works, we aim to cover wide spectrum of vehicle classification methodologies in this review to provide more clarity when it comes to selecting a solution that suits individual need. They include both sensor-based and image-based vehicle classification for VTR, VLR and VMMR. We employ exhaustive coverage approach for the former to identify the extant solutions that are built upon various kinds of sensing technologies and they are further collated according to the installation methods. For image-based methods, we screen for the works that have been central to both pre- and post-deep learning era. The featured works either address the shortcoming of previous works in image domain or present novel concept to advance the classification performance. [ABSTRACT FROM AUTHOR]

• The proposed CVAE-based method surpassed classical data balancing methods (SMOTE & Adasyn). • A Stack Predictor for Heart Disease (SPFHD) is proposed for heart disease detection. • The SHAP framework is used to interpret the features that impact the SPFHD output. • A two-step statistical test verifies the performance disparity between the models. Worldwide, cardiovascular disease is the leading cause of death. Based on clinical data, a Machine Learning (ML) system can detect cardiac disease in its early stages, which enables a reduction in mortality rates. However, imbalanced and high dimensionality data have been a persistent challenge in ML, impeding accurate predictive data analysis in many real-world applications, such as the detection of cardiovascular disease. To address this, computational methods targeting heart disease detection have been developed. However, their performance is still inadequate. Hence, this study presents a new stack predictor for the heart disease model (termed SPFHD). SPFHD employs five common tree-based ensemble learning algorithms as base models for heart disease detection. In addition, the predictions from the base models are integrated using a support vector machine algorithm to enhance the accuracy of heart disease detection. A new conditional variational autoencoder (CVAE) based method is developed to overcome the imbalance issue, which performs better than the conventional balancing methods. Finally, the SPFHD model is tuned by Bayesian optimization. The results show that the proposed SPFHD model outperforms the state-of-art methods over four datasets achieving higher f1-score of 4.68 %, 4.55 %, 2 %, and 1 % for HD clinical, Z-Alizadeh Sani, Statlog, and Cleveland, respectively. Moreover, this new framework offers vital interpretations which assist in understanding model success by leveraging the powerful SHapley Additive explanation (SHAP) algorithm. This highlights the most significant attributes for detecting heart disease and overcoming the limitations of current 'Black-box' methods that cannot reveal causal relationships between features. [ABSTRACT FROM AUTHOR]

Driver distraction is one of the main causes of fatal traffic accidents. Therefore, the ability to detect driver inattention is essential in building a safe yet intelligent transportation system. Currently, the available driver distraction detection systems are not widely available or limited to specific class actions. Various research efforts have approached the problem through different techniques, including the usage of intrusive sensors, which are not feasible for mass production. Most of the work in early 2010s used traditional machine learning approaches to perform the detection task. With the emergence of deep learning algorithms, many research has been conducted to perform distraction detection using neural networks. Furthermore, most of the work in the field is conducted under simulation or lab environment, and did not validate the proposed system under naturalistic scenario. Most importantly, the research efforts in the field could be further subdivided into many subtasks. Thus, this paper aims to provide a comprehensive review of approaches used to detect driving distractions through various methods. We review all recent papers from 2014–2021 and categorized them according to the sensors used. Based on the reviewed articles, a simplified framework to visualize the detection flow, starting from the used sensors, collected data, measured data, computed events, inferred behaviour, and finally its inferred distraction type is proposed. Besides providing an in-depth review and concise summary of various published works, the practicality and relevancy of driver distraction detection towards increasing vehicle automation are discussed. Further, several open research challenges and provide suggestions for future research directions are provided. We believe that this review will remain helpful despite the development towards a higher level of vehicle automation. • An overview of usage of machine learning in driver distraction detection is provided. • Deep learning outperforms traditional machine learning in driver distraction detection. • A framework for driver distraction detection is introduced. • Research gap and future research for driver distraction detection is discussed. [ABSTRACT FROM AUTHOR]

Object detection in aerial images has been an active research area thanks to the vast availability of unmanned aerial vehicles (UAVs). Along with the increase of computational power, deep learning algorithms are commonly used for object detection tasks. However, aerial images have large variations, and the object sizes are usually small, rendering lower detection accuracy. Besides, real-time inferencing on low-cost edge devices remains an open-ended question. In this work, we explored the usage of state-of-the-art deep learning object detection on low-cost edge hardware. We propose YOLO-RTUAV, an improved version of YOLOv4-Tiny, as the solution. We benchmarked our proposed models with various state-of-the-art models on the VAID and COWC datasets. Our proposed model can achieve higher mean average precision (mAP) and frames per second (FPS) than other state-of-the-art tiny YOLO models, especially on a low-cost edge device such as the Jetson Nano 2 GB. It was observed that the Jetson Nano 2 GB can achieve up to 12.8 FPS with a model size of only 5.5 MB. [ABSTRACT FROM AUTHOR]

The removal of shadows from images is a classic problem in computer vision, aiming to restore the lighting in shadowed areas, thereby reducing the information interference and loss caused by the presence of shadows. In recent years, numerous excellent shadow removal algorithms have emerged, particularly with the rapid development of deep learning technology, which has disrupted traditional physics-based approaches and significantly improved the effectiveness of shadow removal. In this paper, we conduct a comprehensive survey of shadow removal methods published from 2017 to the present. We first introduce background knowledge about image shadow removal, providing detailed explanations of both physics-based and learning-based shadow removal methods. We analyze and compare these algorithms from both quantitative and qualitative perspectives, reassessing all models that provided open-source result sets according to uniform criteria. Additionally, we introduce commonly used datasets and evaluation metrics in the field. Finally, we discuss applications of shadow removal in specific scenarios, along with research challenges and opportunities in this domain. • Surveys taxonomy of recently image shadow removal techniques, datasets and performance metrics. • Systematic quantitative evaluations are summarized. • The impacts, applications and importance of shadow removal are explored. • Challenges and future direction are explored and analyzed. [ABSTRACT FROM AUTHOR]