Emotion detection from the text

Inspired by natural language processing techniques, we here introduce Mol2vec, which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Like the Word2vec models, where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that point in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing the vectors of the individual substructures and, for instance, be fed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pretrained once, yields dense vector representations, and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as a reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment-independent and thus can also be easily used for proteins with low sequence similarities.

BackgroundThe rapid growth of the biomedical literature makes identifying strong evidence a time-consuming task. Applying machine learning to the process could be a viable solution that limits effort while maintaining accuracy.ObjectiveThe goal of the research was to summarize the nature and comparative performance of machine learning approaches that have been applied to retrieve high-quality evidence for clinical consideration from the biomedical literature.MethodsWe conducted a systematic review of studies that applied machine learning techniques to identify high-quality clinical articles in the biomedical literature. Multiple databases were searched to July 2020. Extracted data focused on the applied machine learning model, steps in the development of the models, and model performance.ResultsFrom 3918 retrieved studies, 10 met our inclusion criteria. All followed a supervised machine learning approach and applied, from a limited range of options, a high-quality standard for the training of their model. The results show that machine learning can achieve a sensitivity of 95% while maintaining a high precision of 86%.ConclusionsMachine learning approaches perform well in retrieving high-quality clinical studies. Performance may improve by applying more sophisticated approaches such as active learning and unsupervised machine learning approaches.

Twitter is an important platform for many activities such as communication, promotion and news. Twitter is starting to clamp down on removing content and accounts whose sole purpose is to spread disinformation at scale. The increasing number of bots on Twitter has led to a significant circulation of malicious content. It has the power of manipulating and influencing people negatively. Hence bot detection and finding their extent of influence is of prime importance. This research has used varied approaches for the detection of bots; a supervised machine learning approach which makes use of a unique feature called the bot score to determine the bot probability of a user. Secondly, using an unsupervised machine learning approach, users were divided into various clusters based on their activity. Furthermore, to assess the influence of users in hashtag manipulation, this research analyzed various categories of users in a hashtag and made promising conclusions.

The aim of this study was to examine the risk factors for pharyngeal carriage of meningococci in third-level students using an unsupervised machine learning approach. Data were gathered as part of meningococcal prevalence studies conducted by the Irish Meningitis and Sepsis Reference Laboratory (IMSRL). Pharyngeal swab cultures for meningococcal carriage were taken from each student once they had completed a single-page anonymous questionnaire addressing basic demographics, social behaviors, living arrangements, vaccination, and antibiotic history. Data were analyzed using multiple correspondence analysis through a machine learning approach. In total, 16,285 students who had a pharyngeal throat swab taken returned a fully completed questionnaire. Overall, meningococcal carriage rate was 20.6%, and the carriage of MenW was 1.9% (n = 323). Young Irish adults aged under 20 years and immunized with the meningococcal C vaccine had a higher MenW colonization rate (n = 171/1260, 13.5%) compared with non-Irish adults aged 20 years or older without the MenC vaccine (n = 5/81, 6%, chi-square = 3.6, p = .05). Unsupervised machine learning provides a useful technique to explore meningococcal carriage risk factors. The issue is very complex, and asked risk factors only explain a small proportion of the carriage. This technique could be used on other conditions to explore reasons for carriage.

BackgroundThe main challenge in the management of indeterminate incidentally discovered adrenal tumours is to differentiate benign from malignant lesions. In the absence of clear signs of invasion or metastases, imaging techniques do not always precisely define the nature of the mass. The present pilot study aimed to determine whether radiomics may predict malignancy in adrenocortical tumours.MethodsCT images in unenhanced, arterial, and venous phases from 19 patients who had undergone resection of adrenocortical tumours and a cohort who had undergone surveillance for at least 5 years for incidentalomas were reviewed. A volume of interest was drawn for each lesion using dedicated software, and, for each phase, first-order (histogram) and second-order (grey-level colour matrix and run-length matrix) radiological features were extracted. Data were revised by an unsupervised machine learning approach using the K-means clustering technique.ResultsOf operated patients, nine had non-functional adenoma and 10 carcinoma. There were 11 patients in the surveillance group. Two first-order features in unenhanced CT and one in arterial CT, and 14 second-order parameters in unenhanced and venous CT and 10 second-order features in arterial CT, were able to differentiate adrenocortical carcinoma from adenoma (P < 0.050). After excluding two malignant outliers, the unsupervised machine learning approach correctly predicted malignancy in seven of eight adrenocortical carcinomas in all phases.ConclusionRadiomics with CT texture analysis was able to discriminate malignant from benign adrenocortical tumours, even by an unsupervised machine learning approach, in nearly all patients.

The advent of IoT technology and the increase in wireless networking devices has led to an enormous increase in network attacks from different sources. To maintain networks as safe and secure, the Intrusion Detection System (IDS) have become very critical. Intrusion Detection Systems (IDS) are designed to protect the network by identifying anomaly behaviors or improper uses. Intrusion Detection systems provide more meticulous security functionality than access control barriers by detecting attempted and successful attacks at the end-point or within the network. Intrusion prevention systems are the next logical step to this approach as they can take real-time action against breaches. To have an accurate IDS, detailed visibility is required into the network traffic. The intrusion detection system should be able to detect inside the network threats as well as access control breaches. IDS has been around for a very long time now. These traditional IDS were rules and signature-based. Though they were able to reduce false positives they were not able to detect new attacks. In today's world due to the growth of connectivity, attacks have increased at an exponential rate and it has become essential to use a data-driven approach to tackle these issues. In this paper, the KDD data set was used to train the unsupervised machine learning algorithm called Isolation Forest. The data set is highly imbalanced and contains various attacks such as DOS, Probe, U2R, R2L. Since this data set suffers from a redundancy of values and class imbalance, the data preprocessing will be performed first and also used unsupervised learning. For this network traffic based anomaly detection model isolation forest was used to detect outliers and probable attacks the results were evaluated using the anomaly score.

Background: The objective of this study was to characterize patients with hyponatremia at hospital admission into clusters using an unsupervised machine learning approach, and to evaluate the short- and long-term mortality risk among these distinct clusters. Methods: We performed consensus cluster analysis based on demographic information, principal diagnoses, comorbidities, and laboratory data among 11,099 hospitalized adult hyponatremia patients with an admission serum sodium below 135 mEq/L. The standardized mean difference was utilized to identify each cluster’s key features. We assessed the association of each hyponatremia cluster with hospital and one-year mortality using logistic and Cox proportional hazard analysis, respectively. Results: There were three distinct clusters of hyponatremia patients: 2033 (18%) in cluster 1, 3064 (28%) in cluster 2, and 6002 (54%) in cluster 3. Among these three distinct clusters, clusters 3 patients were the youngest, had lowest comorbidity burden, and highest kidney function. Cluster 1 patients were more likely to be admitted for genitourinary disease, and have diabetes and end-stage kidney disease. Cluster 1 patients had the lowest kidney function, serum bicarbonate, and hemoglobin, but highest serum potassium and prevalence of acute kidney injury. In contrast, cluster 2 patients were the oldest and were more likely to be admitted for respiratory disease, have coronary artery disease, congestive heart failure, stroke, and chronic obstructive pulmonary disease. Cluster 2 patients had lowest serum sodium and serum chloride, but highest serum bicarbonate. Cluster 1 patients had the highest hospital mortality and one-year mortality, followed by cluster 2 and cluster 3, respectively. Conclusion: We identified three clinically distinct phenotypes with differing mortality risks in a heterogeneous cohort of hospitalized hyponatremic patients using an unsupervised machine learning approach.

Among kidney transplant recipients, Black patients continue to have worse graft function and reduced patient and graft survival. Better understanding of different phenotypes and subgroups of Black kidney transplant recipients may help the transplant community to identify individualized strategies to improve outcomes among these vulnerable groups. To cluster Black kidney transplant recipients in the US using an unsupervised machine learning approach. This cohort study performed consensus cluster analysis based on recipient-, donor-, and transplant-related characteristics in Black kidney transplant recipients in the US from January 1, 2015, to December 31, 2019, in the Organ Procurement and Transplantation Network/United Network for Organ Sharing database. Each cluster's key characteristics were identified using the standardized mean difference, and subsequently the posttransplant outcomes were compared among the clusters. Data were analyzed from June 9 to July 17, 2021. Machine learning consensus clustering approach. Death-censored graft failure, patient death within 3 years after kidney transplant, and allograft rejection within 1 year after kidney transplant. Consensus cluster analysis was performed for 22 687 Black kidney transplant recipients (mean [SD] age, 51.4 [12.6] years; 13 635 men [60%]), and 4 distinct clusters that best represented their clinical characteristics were identified. Cluster 1 was characterized by highly sensitized recipients of deceased donor kidney retransplants; cluster 2, by recipients of living donor kidney transplants with no or short prior dialysis; cluster 3, by young recipients with hypertension and without diabetes who received young deceased donor transplants with low kidney donor profile index scores; and cluster 4, by older recipients with diabetes who received kidneys from older donors with high kidney donor profile index scores and extended criteria donors. Cluster 2 had the most favorable outcomes in terms of death-censored graft failure, patient death, and allograft rejection. Compared with cluster 2, all other clusters had a higher risk of death-censored graft failure and death. Higher risk for rejection was found in clusters 1 and 3, but not cluster 4. In this cohort study using an unsupervised machine learning approach, the identification of clinically distinct clusters among Black kidney transplant recipients underscores the need for individualized care strategies to improve outcomes among vulnerable patient groups.

Categorising the existing irradiance based blind control occupant behavior models (BC-OBMs) using unsupervised machine learning approach: A case of office building in India

Sentence tokenization is the foundational step in natural language processing to analyze the sentence. Apart from others, main causes which make the sentence tokenization difficult are quotation marks and the multipurpose usage of punctuation marks especially dot “.”. In this paper, a framework has proposed for sentence tokenization for running text in Gujarati language using statistical unsupervised machine learning approach and rule-based approach.

A database of physical and chemical properties from one-hundred different jet fuels was used to create supervised Machine Learning (ML) models that predict Derived Cetane Number (DCN) as well as an unsupervised Self Organizing Map (SOM) of these conventional fuels. The best supervised ML models predict jet fuel DCN with just over one CN average error. The two dominant physical properties in the determination of DCN are fuel density and T50. The various models agree with increases in n-alkanes leading to higher DCN values, while increases in cyclo-aromatic content leading to decreases in DCN. The prediction of eleven newer Sustainable Aviation Fuel (SAF) DCNs showed at best errors of 3 to 5 DCN which corresponds to a 5 to 10 percent prediction error. There were two SAF anomalies with much greater DCN prediction errors (over-prediction), as these two fuels are heavily branched iso-alkanes with very low DCNs. An unsupervised ML approach was also used to characterize fuel similarity. The unsupervised SOM created and trained with the conventional jet fuels was then used to evaluate the SAF fuels. Two of the SAFs which had DCNs similar to the baseline database fuel mean had Quantization Errors (QEs) double the average QE of the database fuels. The two low DCN iso-alkane branched SAF fuels had very high QEs and thus were correctly determined by the SOM ML based approach as most dissimilar to the conventional jet fuels. This unsupervised ML approach shows promise for detecting new fuel differences early in the fuel evaluation process.

Chaotic tumbleweed optimization algorithm with stacked deep learning based cyberattack detection in industrial CPS environment

The purpose of this paper is to reveal how social network marketing (SNM) can affect consumers’ purchase behavior (CPB). We used the combination of structural equation modeling (SEM) and unsupervised machine learning approaches as an innovative method. The statistical population of the study concluded users who live in Hungary and use Facebook Marketplace. This research uses the convenience sampling approach to overcome bias. Out of 475 surveys distributed, a total of 466 respondents successfully filled out the entire survey with a response rate of 98.1%. The results showed that all dimensions of social network marketing, such as entertainment, customization, interaction, WoM and trend, had positively and significantly influenced consumer purchase behavior (CPB) in Facebook Marketplace. Furthermore, we used hierarchical clustering and K-means unsupervised algorithms to cluster consumers. The results show that respondents of this research can be clustered in nine different groups based on behavior regarding demographic attributes. It means that distinctive strategies can be used for different clusters. Meanwhile, marketing managers can provide different options, products and services for each group. This study is of high importance in that it has adopted and used plspm and Matrixpls packages in R to show the model predictive power. Meanwhile, we used unsupervised machine learning algorithms to cluster consumer behaviors.

Natural hazards, such as flooding, have been negatively impacting developed and emerging economies alike. The effects of floods are more prominent in countries of the Global South, where large parts of the population and infrastructure are insufficiently protected from natural hazards. From this scope, a lot of effort is required to mitigate these impacts by continuously providing new and more reliable tools to aid in mitigation and preparedness, during or after a flood event. Flood mapping followed by damage assessment plays an important role in all these stages. In this work we investigate a new dataset provided by DrivenData Labs based on Sentinel-1 (S1) imagery (VH, VV imagery and labels) to help map floods in the city of Beira in Mozambique. Exploiting Google Earth Engine (GEE), we deployed supervised and unsupervised machine learning (ML) methods on a dataset comprising imagery from 13 countries worldwide. We first mapped the floods country-by-country including Mozambique. This first part was helpful to understand the sensitivity of each method when applied to data from different regions and with different polarizations. We then trained the supervised model globally (in all 13 countries) and used it to predict floods in Beira. To assess the accuracy of the experiments we used the intersection over the union (IoU) metric, results of which we compared with the benchmark IoU achieved by the winner in the DrivenData competition for flood mapping in 2021. The implementation of unsupervised and supervised ML using VH and VV+VH produced satisfactory results, and showed to be better than using VV imagery; in Cambodia and Bolivia with VH polarization yielded IoUs values ranging from 0.819 to 0.856 which is above the benchmark (0.8094). The predictions in Beira using VH imagery yielded IoU of 0.568, which is a reasonable outcome. The proposed approach is a reliable alternative for flood mapping, especially in Mozambique due to its low cost and time effectiveness as even with unsupervised approaches, relatively high-quality results are yielded in near real-time. Finally, we used Sentinel-2 (S2) imagery for a land cover classification to perform damage assessment in Beira and integrated population data from Beira to enhance the quality the results. The results show that 20% of agricultural area and about 10% of built up area were flooded. Flooded built up area includes highly populated neighborhoods such as Chaimite and Ponta Gea that are located in the center of the city.

Supplementary material to "Towards the construction of regional marine radiocarbon calibration curves: an unsupervised machine learning approach"