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Depression has become a prevalent mental disorder that significantly affects a person's emotions, behaviors, physical health, ability to perform daily tasks, and ability to maintain healthy relationships. Untreated depression can escalate the risk of suicide, making the situation even worse. Despite an abundance of models previously proposed for forecasting depression, the issue of foretelling the overall number of patients at each administrative level remains under-investigated. Therefore, in this paper, we propose a simple but effective SpatioTemporal Monitoring Framework for National Depression Forecasting (MONDEP). In particular, we analyze national depression statistics data in Thailand as a case study and create prediction models for a real-time depression forecasting system using machine learning and deep learning approaches. In order to forecast the prevalence of depression at various administrative levels, we use the hierarchical structure of depression aggregation. The proposed framework consists of three modules: Data Pre-processing to extract and pre-process the raw data, Exploratory Data Analysis (EDA) to visualize and analyze the data to get insight, and Model Training and Testing to predict future depression cases. The objective of our research is to construct a comprehensive MONDEP framework that utilizes machine learning and deep learning to predict depression profiles at the district and national levels using multivariate time series across various administrative levels. Our study illustrates the considerable association between a spatial-temporal component and demonstrates how depression profiles may be represented by employing lower administrative-level data to estimate the general level of mental health across the nation. Additionally, the best performance across all criteria is obtained when a deep learning model is used to exploit multivariate time series, showing a 13% improvement in MAE measure compared to the SARIMAX baseline. We believe the proposed framework could be used as a point of reference for decision-making regarding the management of depression and has the potential to be incredibly helpful for policymakers in successfully managing mental health services on time.

Comics, particularly Japanese manga, are a powerful medium that blends images and text to convey ideas and encapsulate a unique cultural heritage. Going beyond mere entertainment, manga merges diverse styles and content deeply rooted in Japanese cultural heritage. This study utilizes computer vision analysis, with a specific focus on facial landmark detection, acknowledging the growing significance of technology in analyzing manga images. Through a comprehensive exploration of various methods, the research identifies the extended version of Bidirectional Encoder Representations from Transformers (BERT), BERT Pre-Training of Image Transformers (BEiT), model as a standout performer due to its efficiency and effectiveness. The BEiT model’s success lies in its ability to extract facial features, consequently establishing itself as a go-to solution for landmark detection on manga faces. The outcomes achieved the lowest Failure Rate compared to other landmark detection networks, with a Failure Rate of approximately 9.4% and a Mean Average Error of about 4.6 pixels. Beyond its technical accomplishments, this study carries a cultural significance, contributing to the ongoing narrative of manga in Japan.

Computer scientists often publish their source code accompanying their publications, prominently using code repositories across various domains. Despite the concurrent existence of scholarly articles and their associated official code repositories, explicit references linking the two are often missing. Traditionally, identifying whether scholarly content and code repositories pertain to the same research project requires manual inspection, a time-consuming task. This paper proposes a deep learning-based algorithm for automatically matching scholarly articles with their corresponding official code repositories. Our findings indicate that the most common linking information includes the paper title and BibTeX entries, typically found in the repository’s readme document. In this study, we employed SPECTER for vector embedding of paper and repository metadata. Utilizing these embedding representations with the Light Gradient Boosting Machine (LGBM), our method achieved an F1 score of 0.94. Moreover, combining our best model with a rule-based approach improved performance by 5.31%. This study successfully delineates a connection between academic papers and associated official code repositories, minimizing reliance on explicit bibliographic information in repositories.

Crimes result in not only loss to individuals but also hinder national economic growth. While crime rates have been reported to decrease in developed countries, underdeveloped and developing nations still suffer from prevalent crimes, especially those undergoing rapid expansion of urbanization. The ability to monitor and assess trends of different types of crimes at both regional and national levels could assist local police and national-level policymakers in proactively devising means to prevent and address the root causes of criminal incidents. Furthermore, such a system could prove useful to individuals seeking to evaluate criminal activity for purposes of travel, investment, and relocation decisions. Recent literature has opted to utilize online news articles as a reliable and timely source for information on crime activity. However, most of the crime monitoring systems fueled by such news sources merely classified crimes into different types and visualized individual crimes on the map using extracted geolocations, lacking crucial information for stakeholders to make relevant, informed decisions. To better serve the unique needs of the target user groups, this paper proposes a novel comprehensive crime visualization system that mines relevant information from large-scale online news articles. The system features automatic crime-type classification and metadata extraction from news articles. The crime classification and metadata schemes are designed to serve the need for information from law enforcement and policymakers, as well as general users. Novel interactive spatiotemporal designs are integrated into the system with the ability to assess the severity and intensity of crimes in each region through the novel Criminometer index. The system is designed to be generalized for implementation in different countries with diverse prevalent crime types and languages composing the news articles, owing to the use of deep learning cross-lingual language models. The experiment results reveal that the proposed system yielded 86%, 51%, and 67% F1 in crime type classification, metadata extraction, and closed-form metadata extraction tasks, respectively. Additionally, the results of the system usability tests indicated a notable level of contentment among the target user groups. The findings not only offer insights into the possible applications of interactive spatiotemporal crime visualization tools for proactive policymaking and predictive policing but also serve as a foundation for future research that utilizes online news articles for intelligent monitoring of real-world phenomena.

Skin cancer is a dangerous form of cancer that develops slowly in skin cells. Delays in diagnosing and treating these malignant skin conditions may have serious repercussions. Likewise, early skin cancer detection has been shown to improve treatment outcomes. This paper proposes DeepMetaForge, a deep-learning framework for skin cancer detection from metadata-accompanied images. The proposed framework utilizes BEiT, a vision transformer pre-trained as a masked image modeling task, as the image-encoding backbone. We further propose merging the encoded metadata with the derived visual characteristics while compressing the aggregate information simultaneously, simulating how photos with metadata are interpreted. The experiment results on four public datasets of dermoscopic and smartphone skin lesion images reveal that the best configuration of our proposed framework yields 87.1% macro-average F1 on average. The empirical scalability analysis further shows that the proposed framework can be implemented in a variety of machine-learning paradigms, including applications on low-resource devices and as services. The findings shed light on not only the possibility of implementing telemedicine solutions for skin cancer on a nationwide scale that could benefit those in need of quality healthcare but also open doors to many intelligent applications in medicine where images and metadata are collected together, such as disease detection from CT-scan images and patients’ expression recognition from facial images.

Over the last ten years, the field of dermatology has experienced significant advancements through the utilization of artificial intelligence (AI) technologies. The adoption of such technologies is multifaceted, encompassing tasks such as screening, diagnosis, treatment, and prediction of treatment outcomes. The majority of prior systematic reviews in this domain were centered on medical dermatology, with the aim of detecting and managing serious skin diseases such as skin cancer. However, the adoption of AI in cosmetic dermatology, which focuses on improving skin conditions for cosmetic purposes, has not been comprehensively reviewed. Therefore, the objective of this systematic review article is to analyze the existing and recent research revolving around applications of AI in the field of cosmetic dermatology. The study encompasses articles published between 2018 and 2023, where a total of 63 publications are deemed relevant based on the established inclusion criteria, divided into five categories based on utilization domains, namely cosmetic product development, skin assessment, skin condition diagnosis, treatment recommendation, and treatment outcome prediction. This systematic review article provides not only valuable insights for researchers interested in exploring new research areas related to aesthetic medicine but also applicable guidance for practitioners seeking to implement AI technologies to address real-world challenges in cosmetic services.

Self-harm pertains to actions of self-inflicted poisoning or injury that lead to either non-fatal injuries or death, irrespective of the individual’s intention. Self-harm incidents not only cause loss to individuals but also incur a negative impact on the nation’s economy. Studies have demonstrated an increase in trends of self-harm that are correlated with the emergence of technological advancements and swift urban expansion in developing countries. The capacity to nowcast and forecast national-level patterns of self-harm trends could be imperative to policymakers and stakeholders in the public health sector, as it would enable them to implement prompt measures to counteract the underlying factors or avert these projected calamities. Prior research has utilized historical data to predict self-harm trends at the population level in various nations using conventional statistical forecasting methods. However, in some countries, such historical statistics may be challenging to obtain or insufficient for accurate prediction, impeding the ability to comprehend and project the national self-harm landscape in a timely manner. This paper proposes FAST, a framework designed to forecast self-harm patterns at the national level by analyzing mental signals obtained from a large volume of social media data. These signals serve as a proxy for real-world population mental health that could be used to enhance the forecastability of self-harm trends. Specifically, language-agnostic language models are first trained to extract different mental signals from collected social media messages. Then, these signals are aggregated and processed into multi-variate time series, on which the time-delay embedding algorithm is applied to transform into temporal embedded instances. Finally, various machine learning regressors are validated for their forecastability. The proposed method is validated through a case study in Thailand, which utilizes a set of 12 mental signals extracted from tweets to forecast death and injury cases resulting from self-harm. The results show that the proposed method outperformed the traditional ARIMA baseline by 43.56% and 36.48% on average in terms of MAPE on forecasting death and injury cases from self-harm, respectively. As far as current understanding permits, our research represents the initial exploration of utilizing aggregated social media information for the purposes of nowcasting and forecasting trends of self-harm on a nationwide scale. The results not only provide insight into improved forecasting techniques for self-harm trends but also establish a foundation for forthcoming social-network-driven applications that hinge on the capacity to predict socioeconomic factors.

As one of the 21st Century skills, fostering leadership competencies in college students has been a focus of research in the behavioral education field. While leaders are expected to have general leadership traits such as communication and resource management, leading teams in particular expertise areas requires additional skills specifically tailored to the tasks involved. For example, in information technology (IT), the roles of leaders are different from those in other non-IT fields due to the characteristics of IT projects. IT leaders, in particular, are required to have not just managerial and interpersonal skills but also necessary technical and practical capabilities. Understanding the key elements that contribute to the emergence and development of student leaders necessitates the ability to assess and recognize leadership in college students. However, current approaches for measuring leadership qualities entail administering questionnaires, which have coverage constraints and may mislabel future leaders if the survey is administered too early in a student’s educational phase, when their leadership talents may not have emerged yet. Addressing this problem, this paper introduces LAIGA, a machine learning-based framework for automatically and passively assessing and identifying leaders in college students from their academic profiles and behavior in the learning management system (LMS). The proposed technique can capture students’ actions that indicate general leadership qualities and technical competence, as evidenced by their academic performances. Specifically, we address two problems: leadership assessment and leader identification tasks. A case study of senior-year college students in the IT major is used to validate the proposed method, yielding 5.87% MAPE in the leadership assessment task and 83.2% F1 in the leadership identification task. Furthermore, the results also show that the proposed method can forecast leadership assessment and identification after graduation, knowing as little as students’ information during their first year of study. The findings of this study not only shed light on the ability to use machine learning technologies to automatically assess and identify leadership in college students but also allow educational organizations to understand factors that help develop leadership skills and design mechanisms that promote the growth of student leaders. The proposed method is highly generalizable to other fields of study as long as students’ grades and LMS activities are available.

Abstract The explosion of online information with the recent advent of digital technology in information processing, information storing, information sharing, natural language processing, and text mining techniques has enabled stock investors to uncover market movement and volatility from heterogeneous content. For example, a typical stock market investor reads the news, explores market sentiment, and analyzes technical details in order to make a sound decision prior to purchasing or selling a particular company’s stock. However, capturing a dynamic stock market trend is challenging owing to high fluctuation and the non-stationary nature of the stock market. Although existing studies have attempted to enhance stock prediction, few have provided a complete decision-support system for investors to retrieve real-time data from multiple sources and extract insightful information for sound decision-making. To address the above challenge, we propose a unified solution for data collection, analysis, and visualization in real-time stock market prediction to retrieve and process relevant financial data from news articles, social media, and company technical information. We aim to provide not only useful information for stock investors but also meaningful visualization that enables investors to effectively interpret storyline events affecting stock prices. Specifically, we utilize an ensemble stacking of diversified machine-learning-based estimators and innovative contextual feature engineering to predict the next day’s stock prices. Experiment results show that our proposed stock forecasting method outperforms a traditional baseline with an average mean absolute percentage error of 0.93. Our findings confirm that leveraging an ensemble scheme of machine learning methods with contextual information improves stock prediction performance. Finally, our study could be further extended to a wide variety of innovative financial applications that seek to incorporate external insight from contextual information such as large-scale online news articles and social media data.

Ethereum, the second-largest cryptocurrency after Bitcoin, has attracted wide attention in the last few years and accumulated significant transaction records. However, the underlying Ethereum network structure is still relatively unexplored. Also, very few attempts have been made to perform link predictability on the Ethereum transactions network. This paper presents a Detailed Analysis of the Ethereum Network on Transaction Behavior, Community Structure, and Link Prediction (DANET) framework to investigate various valuable aspects of the Ethereum network. Specifically, we explore the change in wealth distribution and accumulation on Ethereum Featured Transactional Network (EFTN) and further study its community structure. We further hunt for a suitable link predictability model on EFTN by employing state-of-the-art Variational Graph Auto-Encoders. The link prediction experimental results demonstrate the superiority of outstanding prediction accuracy on Ethereum networks. Moreover, the statistic usages of the Ethereum network are visualized and summarized through the experiments allowing us to formulate conjectures on the current use of this technology and future development.