Automatic Classification of Stigmatizing Articles of Mental Illness: The Case of Portuguese Online Newspapers

Artificial intelligence has the potential to transform health care. For that purpose, machine learning (ML) and deep learning (DL) algorithms have been used in the prediction and diagnosis of many diseases. Many people across the world use social media platforms like Twitter, Facebook, Reddit, etc, more often to express their feelings. Mental health has become a big concern after the COVID-19 pandemic and many researchers have applied various ML and DL algorithms to social media data for mental health prediction and analysis. The purpose of this study is to provide a comprehensive analysis of ML and DL algorithms that have been used in the prediction of various mental disorders. This study provides a diverse review of 37 selected research papers. From the selected literature, an accuracy table of ML and DL algorithms for four mental disorders (Depression, Anxiety, Bipolar& ADHA) has been analyzed and created. This study aims to help researchers and practitioners in the future by providing a baseline of the performance of various ML and DL algorithms. Furthermore, we provide a list of publicly available datasets that will act as a knowledge base for future researchers.

This article aims to provide a thorough overview of the use of Artificial Intelligence (AI) techniques in studying the gut microbiota and its role in the diagnosis and treatment of some important diseases. The association between microbiota and diseases, together with its clinical relevance, is still difficult to interpret. The advances in AI techniques, such as Machine Learning (ML) and Deep Learning (DL), can help clinicians in processing and interpreting these massive data sets. Two research groups have been involved in this Scoping Review, working in two different areas of Europe: Florence and Sarajevo. The papers included in the review describe the use of ML or DL methods applied to the study of human gut microbiota. In total, 1109 papers were considered in this study. After elimination, a final set of 16 articles was considered in the scoping review. Different AI techniques were applied in the reviewed papers. Some papers applied ML, while others applied DL techniques. 11 papers evaluated just different ML algorithms (ranging from one to eight algorithms applied to one dataset). The remaining five papers examined both ML and DL algorithms. The most applied ML algorithm was Random Forest and it also exhibited the best performances.

Wind energy has the possibility for bringing out energy in a very constant and sustainable manner being a notable and eligible source. Although, wind energy does include numerous challenges like, the halted asset of wind plants, early investment costs, and therefore, the strain in discovering areas of wind efficiency. The major objective for proposing this work is to determine the power efficiency of wind turbines, which will also aid in the formulation of a proposal to reduce wind turbine maintenance costs. During this research, data analysis of turbine generators is performed on day to day wind speed info using machine learning and deep learning algorithms. A way is put forward by us to support deep learning and machine learning algorithms which can predict different values of power reliably. Hence, the execution of machine and deep learning algorithms are analyzed. For forecasting for a longer term these algorithms may be used for wind generation rate with historical relation to wind speed info. Index Terms: Wind turbine,machine learning algorithm

The World Health Organization (WHO) is revising the ICD-10 classification of mental and behavioural disorders, under the leadership of the Department of Mental Health and Substance Abuse and within the framework of the overall revision framework as directed by the World Health Assembly. This article describes WHO's perspective and priorities for mental and behavioural disorders classification in ICD-11, based on the recommendations of the International Advisory Group for the Revision of ICD-10 Mental and Behavioural Disorders. The WHO considers that the classification should be developed in consultation with stakeholders, which include WHO member countries, multidisciplinary health professionals, and users of mental health services and their families. Attention to the cultural framework must be a key element in defining future classification concepts. Uses of the ICD that must be considered include clinical applications, research, teaching and training, health statistics, and public health. The Advisory Group has determined that the current revision represents a particular opportunity to improve the classification's clinical utility, particularly in global primary care settings where there is the greatest opportunity to identify people who need mental health treatment. Based on WHO's mission and constitution, the usefulness of the classification in helping WHO member countries, particularly low- and middle-income countries, to reduce the disease burden associated with mental disorders is among the highest priorities for the revision. This article describes the foundation provided by the recommendations of the Advisory Group for the current phase of work.

Insolvency prediction is one of the crucial abilities in corporate finance and financial management. It is critical in accounts receivable management, capital budgeting decisions, financial analysis, capital structure management, going concern assessment and co-operation with other companies. The purpose of this paper is to compare the efficiency of selected deep learning and machine learning algorithms trained on a representative sample of Polish companies for the period 2008–2017. In particular, the paper tested the following popular machine learning algorithms: discriminant analysis (DA), logit (L), support vector machines (SVM), random forest (RF), gradient boosting decision trees (GB), neural network with one hidden layer (NN), convolutional neural network (CNN), and naïve Bayes (NB). The research hypotheses evaluated in the paper state that if one has access to a large sample of companies, the most accurate algorithm (first choice) in bankruptcy prediction will be gradient boosting decision trees (H1), random forest (H2) and neural networks (H3) (deep learning) algorithms. The initial hypotheses were formulated based on the practitioners’ opinions regarding the usefulness of various machine learning and artificial intelligence algorithms in bankruptcy prediction. As the results of the research suggest, both deep learning and machine learning algorithms proved to have very comparable efficiency. The new factor introduced in the paper was that the training of the models was carried out on a representative sample of companies (for years 2008–2013) and also the testing phase used a significant number of bankrupt and active companies (validation included a completely different set of companies than those used in the training phase: data were taken from a different time period, 2014–2017, and companies in both sets were also completely different).

The proliferation of Android malware has become a significant concern in the cyber security landscape. Traditional signature-based detection methods are no longer effective against the rapidly evolving malware threats. Machine Learning (ML) and Deep Learning (DL) algorithms have emerged as a promising solution for Android malware classification and detection. This study aims to investigate the effectiveness of various ML and DL algorithms for Android malware detection. This work analyses the performance of several algorithms, including Support Vector Machines (SVM), Random Forest (RF), and Recurrent Neural Network (RNN). From this analysis, reveals the DL algorithms, particularly RNN, outperform traditional ML algorithms in terms of accuracy, precision, and recall. This finding suggests that the DL algorithm and selects the features can provide an effective solution for Android malware classification and detection. The results of this study can be used to develop a robust and efficient Android malware detection system, which can help protect against the increasing threats of mobile malware. Overall, this study demonstrates the potential of ML and DL algorithms in detecting Android malware and provides insights into the development of effective detection systems.

Recognition of Radix Bupleuri origin using laser-induced breakdown spectroscopy (LIBS) combined with deep learning and machine learning algorithms

Purpose–This study explores the feasibility and effectiveness of utilizing a deep learning algorithm integrated into an AI robot to provide mental health support. Method–The research employs deep learning techniques and machine learning algorithms to develop an AI-powered robot capable of understanding and responding to human emotions and mental health needs. The algorithm is trained on a diverse dataset of mental health-related information, including text, audio, and visual inputs, to enhance its comprehension and response capabilities. Results–Initial testing of the AI robot demonstrates promising results in its ability to accurately recognize and respond to various emotional cues and mental health states exhibited by users. The deep learning algorithm enables the robot to adapt and personalize its interactions based on individual preferences and needs, enhancing its effectiveness as a mental health support tool. Conclusion–Integrating deep learning algorithms into AI robots holds significant potential for revolutionizing mental health support services. By leveraging advanced technologies, such as natural language processing and computer vision, these robots can provide personalized and accessible assistance to individuals experiencing mental health challenges. Recommendations–Future research should focus on expanding the dataset used for training the deep learning algorithm to encompass a broader range of cultural and demographic backgrounds. Additionally, efforts should be made to enhance the interpretability and transparency of the AI system to foster trust and acceptance among users and healthcare professionals. Practical Implications–The development of AI-powered robots for mental health support has practical implications for healthcare providers, policymakers, and individuals seeking assistance. These technologies have the potential to supplement existing mental health services and improve access to care, by seeking help for mental health concerns. Keywords–deep learning algorithm, mental health support, artificial intelligence (ai) robot, emotional recognition, personalized interaction

Wind energy being a notable and eligible source, has the possibility for bringing out energy in a very constant and sustainable manner. However, wind energy does include numerous challenges like, the halted asset of wind plants, early investment costs, and the strain in discovering areas of wind efficiency. The major objective for proposing this work is to determine the power efficiency of wind turbines, which also aids in the formulation of a proposal to reduce wind turbine maintenance costs. During this research, data analysis of turbine generators is performed on day-to-day wind speed info using machine learning and deep learning algorithms. A way is put forward to support deep learning and machine learning algorithms which can predict different values of power reliably. Hence, the execution of machine and deep learning algorithms are analyzed. For forecasting for a longer term, these algorithms may be used for wind generation rate with historical relation to wind speed info. Moreover, the application of deep and machine learning-based models is place distinct to that of model-trained places. This data analysis demonstrates that in unspecified geographies of wind plants, these sets of algorithms could be successfully implied by utilizing the base location model. The entire project focuses on wind turbine generators and includes the use of data visualization of data analytics to analyze the data and detect the factors that influence wind power generation. With the support of previous data output, wind power is anticipated using both machine learning and deep learning models, where different datasets are used for training and testing. This adds to the uniqueness of this work.

Meeting the UN Sustainable Development Goals for mental health: why greater prioritization and adequately tracking progress are critical.

Enhancing Sustainability System Forecasts with Modern Artificial Intelligence (AI) Techniques: An Investigation in Beijing, China" is the title of the research that delves into the possibility of using cutting-edge AI methods to improve the accuracy of climate change models. The study's primary goal is to enhance the accuracy of climate predictions by using artificial intelligence techniques such as deep learning networks and machine learning algorithms, as conventional climate models fail to adequately represent complicated, non-linear climate systems. The research delves into the difficulties of predicting weather factors including temperature, precipitation, and air quality in the Beijing area, where pollution and fast urbanisation cause a great deal of climatic fluctuation. More precise risk assessments, enhanced decision-making for adaptation and mitigation plans, and enhanced modelling of future climatic scenarios are all possible outcomes of applying AI technologies to massive amounts of meteorological data. In light of Beijing's specific environmental circumstances, this study showcases the effective use of AI in climate research, showing how AI has the ability to transform predictive modelling and guide better climate policy. Global economic losses of more than $500 billion have been caused by climate change, which is already a significant hazard. It is harming both urban and natural systems. As AI draws on a wealth of online resources to provide timely recommendations grounded on reliable climate change forecasts, it has the potential to alleviate some of these problems. Energy efficiency, carbon sequestration and storage, transportation, grid management, building design, transportation, precision agriculture, industrial processes, reducing deforestation, resilient cities, and recent research and applications of artificial intelligence in climate change mitigation are highlighted in this review.

Digital image processing has witnessed a significant transformation, owing to the adoption of deep learning (DL) algorithms, which have proven to be vastly superior to conventional methods for crop detection. These DL algorithms have recently found successful applications across various domains, translating input data, such as images of afflicted plants, into valuable insights, like the identification of specific crop diseases. This innovation has spurred the development of cutting-edge techniques for early detection and diagnosis of crop diseases, leveraging tools such as convolutional neural networks (CNN), K-nearest neighbour (KNN), support vector machines (SVM), and artificial neural networks (ANN). This paper offers an all-encompassing exploration of the contemporary literature on methods for diagnosing, categorizing, and gauging the severity of crop diseases. The review examines the performance analysis of the latest machine learning (ML) and DL techniques outlined in these studies. It also scrutinizes the methodologies and datasets and outlines the prevalent recommendations and identified gaps within different research investigations. As a conclusion, the review offers insights into potential solutions and outlines the direction for future research in this field. The review underscores that while most studies have concentrated on traditional ML algorithms and CNN, there has been a noticeable dearth of focus on emerging DL algorithms like capsule neural networks and vision transformers. Furthermore, it sheds light on the fact that several datasets employed for training and evaluating DL models have been tailored to suit specific crop types, emphasizing the pressing need for a comprehensive and expansive image dataset encompassing a wider array of crop varieties. Moreover, the survey draws attention to the prevailing trend where the majority of research endeavours have concentrated on individual plant diseases, ML, or DL algorithms. In light of this, it advocates for the development of a unified framework that harnesses an ensemble of ML and DL algorithms to address the complexities of multiple plant diseases effectively.

The cognitive impairment known as dementia affects millions of individuals throughout the globe. The use of machine learning (ML) and deep learning (DL) algorithms has shown great promise as a means of early identification and treatment of dementia. Dementias such as Alzheimer’s Dementia, frontotemporal dementia, Lewy body dementia, and vascular dementia are all discussed in this article, along with a literature review on using ML algorithms in their diagnosis. Different ML algorithms, such as support vector machines, artificial neural networks, decision trees, and random forests, are compared and contrasted, along with their benefits and drawbacks. As discussed in this article, accurate ML models may be achieved by carefully considering feature selection and data preparation. We also discuss how ML algorithms can predict disease progression and patient responses to therapy. However, overreliance on ML and DL technologies should be avoided without further proof. It’s important to note that these technologies are meant to assist in diagnosis but should not be used as the sole criteria for a final diagnosis. The research implies that ML algorithms may help increase the precision with which dementia is diagnosed, especially in its early stages. The efficacy of ML and DL algorithms in clinical contexts must be verified, and ethical issues around the use of personal data must be addressed, but this requires more study.

2 - Artificial intelligence techniques: Machine learning and deep learning algorithms

The main objective of this research is to analyze and compare the performance of machine learning (ML) and deep learning (DL) algorithms in detecting online hate speech. Therefore, Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), Convolution Neural Network (CNN), Recurrent Neural Network_Long Short-Term Memory (RNN_LSTM), BERT (Bidirectional Encoder Representations from Transformers), and Distil BERT algorithms have been explored and analyzed in this research. This research has applied the dataset on hate speech which was developed by Andry Samoshyn which is publicly available in Kaggle. ML algorithms and DL algorithms have got good scores in accuracy. In ML, SVM, RF, and LR have got top accuracy values. In DL algorithms, RNN_LSTM, Distil BERT, and BERT have performed well in accuracy. Based on F-measurement, DL classifiers have outperformed ML algorithms. Distil BERT has obtained the highest F-measurement scores. When we compare the overall performances, DL is performed well rather than ML in detecting hate speech. Especially transformer-based models of DL are more efficient than other DL and ML algorithms.KeywordsHate speechMachine learningDeep learning TwitterAnd performance comparison