Classification Function Research Articles

Seeing the fields through the weeds: introducing the WeedEco R package for comparing past and present arable farming systems using functional weed ecology

The functional ecology of arable weeds provides a way of comparing present-day and past farming regimes. This paper presents the R package WeedEco, an open-source resource which allows users to compare their archaeobotanical dataset against three previously published arable weed models to understand fertility, disturbance or a combination of both. The package provides functions for data organisation, classification and visualisation, allowing users to enter raw archaeobotanical data, obtain trait values from the functional trait dataset, conduct discriminant analysis and plot the results against the relevant present-day model. Using data from the early medieval site of Stafford in the UK, the paper provides a detailed example of the use of the package, demonstrating its different functions, as well as how the results can be interpreted.

The influence of COVID-19 epidemic on the number of orthopaedic surgeries in Japan

BackgroundThere is limited data on the impact of COVID-19 epidemic on the number of orthopaedic surgeries in Japan. MethodsWe conducted a nationwide hospital survey asking for the monthly number of orthopaedic surgeries performed at each facility from January 2019 to June 2021. Those facilities that had performed at least 100 surgeries in 2019 were included for analyses. The facilities were further grouped by prefecture and by hospital characteristics. A brief health economic evaluation was also performed. Risk ratios were compared using univariate analyses with P < 0.05 considered statistically significant. ResultsQuestionnaire was sent to 1988 hospitals with 1671 hospitals (84%) responding. The survey data indicated a total number of orthopaedic surgeries decreased in 2020 compared to 2019 (1,061,541 vs 1,119,955 P < 0.01), and also for the first six months of 2021 compared to the same period in 2019 (530,388 vs 550,378 P < 0.01). In 2020, over 50% of all facilities in nearly all of the prefectures saw a decline in surgical procedures.The risk of incurring more than a 25% decease in the number of surgeries was significantly higher in 2020 for class I designated medical institutions compared to those that were not designated for any types of infectious diseases among the institutions with a tertiary emergency medical center in 2020 (crude risk ratio 2.9: 95% CI 1.2–7.4, p = 0.02) and in 2021 (crude risk ratio 4.7: 95% CI 1 0.9–12.1, p < 0.01).The estimated total nationwide decrease of revenue were in the range of approximately ¥29.2 to ¥116.8 billion per year for orthopaedic surgeries alone. ConclusionThere was a statistically significant decrease in the number of orthopaedic surgeries in Japan. The magnitude of the decline varied by prefectures and hospital characteristics, with the greater impact imposed on medical institutions with higher classification functions. The estimated immediate health economic impact was sizable.

A hybrid neural network model based on optimized margin softmax loss function for music classification

A hybrid neural network model based on optimized margin softmax loss function for music classification

SENTIMENT ANALYSIS TECHNOLOGY FOR USER FEEDBACK SUPPORT IN E-COMMERCE SYSTEMS BASED ON MACHINE LEARNING

Context. The interaction between a company and its target audience has been studied for centuries. From the very beginning of commercial relations, the relationship between the service provider and the recipient has been valued almost above all else. Trade is built on trust and respect. The image of an entrepreneur is often more important than the product he sells. For hundreds of years, the relationship between the merchant and the buyer, the entrepreneur and the client has not lost its importance, and in the era of mass digitalization, the quality of the relationship between the company and the target audience of different sizes and professional feedback support with clients often start the success of e-business. To provide these additional tools and information technologies to help businessmen monitor e-business development opportunities in a specific location, as well as establish feedback with users through social networks and mass media. Obtaining such tools will significantly expand the vision of market opportunities for ebusiness, it will clarify which of them make sense to invest in, and which ones are not worth paying time for. Also see what idea has the future and what business model needs to be implemented/maintained/developed for the rapid development of territorial/interregional e-business. It will also help to understand which levers have the greatest effect for business changes: what not to touch, and what policies to change to ensure high speed in the implementation of the plan based on the analysis of relevant research results, for example, to receive: direct feedback from customers, the dynamics of changes in overall satisfaction or interest of the target audience and advantages/disadvantages from users using NLP analysis; support for the development of e-business in relation to the location of their enterprise and the best directions; – graphs of business development (improvement/deterioration) depending on the content of comments.&#x0D; Objective of the study is to develop information technology to support the development of e-business by analyzing business locations, processing feedback from users, analyzing and classifying customer feedback in real time from social networks: Twitter, Reddit, Facebook and others using deep learning and Natural methods. Language Processing of Ukrainian-speaking and Englishspeaking texts.&#x0D; Method. NLP-methods were used to analyze the opinions of users and customers. Among the methods of implementing the main functions of English-language news classification, the following machine learning methods are used: naive Bayesian classifier, logistic regression, and the method of support vectors. The Naive Bayes algorithm was used to classify Ukrainian-language user feedback, as it performs well on small amounts of data, is easy to train and operate, and works well with text data. Naive Bayes classifier is a very good option for our system and considering that the number of responses in the dataset is smaller compared to the averages.&#x0D; Results. A machine learning model was developed for the analysis and classification of Ukrainian- and English-language reviews from users of e-commerce systems.&#x0D; Conclusions. The created model shows excellent classification results on test data. The overall accuracy of the sentimental model for the analysis of Ukrainian-language content is quite satisfactory, 92.3%. The logistic regression method coped best with the task of analyzing the impact of English-language news on the financial market, which showed an accuracy of 75.67%. This is certainly not the desired result, but it is the largest indicator of all considered. The support vector method (SVM) coped somewhat worse with the task, which showed an accuracy of 72.78%, which is a slightly worse result than the one obtained thanks to the logistic regression method. And the naïve Bayesian classifier method did the worst with the task, which achieved an accuracy of 71.13%, which is less than the two previous methods.

Unconventional T cells in brain homeostasis, injury and neurodegeneration.

The interaction between peripheral immune cells and the brain is an important component of the neuroimmune axis. Unconventional T cells, which include natural killer T (NKT) cells, mucosal-associated invariant T (MAIT) cells, γδ T cells, and other poorly defined subsets, are a special group of T lymphocytes that recognize a wide range of nonpolymorphic ligands and are the connection between adaptive and innate immunity. Recently, an increasing number of complex functions of these unconventional T cells in brain homeostasis and various brain disorders have been revealed. In this review, we describe the classification and effector function of unconventional T cells, review the evidence for the involvement of unconventional T cells in the regulation of brain homeostasis, summarize the roles and mechanisms of unconventional T cells in the regulation of brain injury and neurodegeneration, and discuss immunotherapeutic potential as well as future research goals. Insight of these processes can shed light on the regulation of T cell immunity on brain homeostasis and diseases and provide new clues for therapeutic approaches targeting brain injury and neurodegeneration.

Flexible loss functions for binary classification in gradient-boosted decision trees: An application to credit scoring

This paper introduces new flexible loss functions for binary classification in Gradient-Boosted Decision Trees (GBDT) that combine Dice-based and cross-entropy-based losses and offer link functions from either a generalized extreme value (GEV) or exponentiated exponential logistic (EEL) distribution. Testing 27 different GBDT models using XGBoost on a Freddie Mac mortgage loan database showed that the choice of the loss function is useful. Specifically, when the class imbalance ratio (IR) is less than 99, using a skewed GEV distribution-based link function in XGBoost enhances discriminatory power and classification accuracy while retaining a simple model structure, which is particularly important in credit scoring applications. In cases where class imbalances are severe, typically between IRs of 99 and 200, we found that an advanced loss function, which is composed of a symmetric hybrid loss function and a link derived from a positively skewed EEL distribution, outperforms other XGBoost variants. Based on our findings, the accuracy improvements of these proposed extensions result in lower misclassification costs, which are especially evident when IR is below 99, which results in higher profitability for the business. Furthermore, the study highlights the transparency associated with GBDT, which is also an integral component of financial applications. Researchers and practitioners can use these insights to create more accurate and discriminative machine learning models, with possible extensions to other GBDT implementations and machine learning techniques that take into account loss functions. The source code for the proposed approach is publicly available at https://github.com/jm-ml/flexible-losses-for-binary-classification-with-GBDT.

Minimum Recall-Based Loss Function for Imbalanced Time Series Classification

This paper deals with imbalanced time series classification problems. In particular, we propose to learn time series classifiers that maximize the minimum recall of the classes rather than the accuracy. Consequently, we manage to obtain classifiers which tend to give the same importance to all the classes. Unfortunately, for most of the traditional classifiers, learning to maximize the minimum recall of the classes is not trivial (if possible), since it can distort the nature of the classifiers themselves. Neural networks, in contrast, are classifiers that explicitly define a loss function, allowing it to be modified. Given that the minimum recall is not a differentiable function, and therefore does not allow the use of common gradient-based learning methods, we apply and evaluate several smooth approximations of the minimum recall function. A thorough experimental evaluation shows that our approach improves the performance of state-of-the-art methods used in imbalanced time series classification, obtaining higher recall values for the minority classes, incurring only a slight loss in accuracy.

A novel cost function for nuclei segmentation and classification in imbalanced histopathology data-sets.

Cancer is a major global health problem, causing millions of deaths yearly. Histopathological analysis plays a crucial role in detecting and diagnosing various types of cancer, enabling an accurate diagnosis to inform targeted treatment planning, allowing for better cancer staging, and ultimately improving prognosis. We aim to detect cancer earlier, which can ultimately help reduce mortality rates and enhance patients' quality of life. However, detecting and classifying rare cells is a key challenge for pathologists and researchers. Many histopathological data-sets contain imbalanced data, with only a few instances of rare cells whose unique morphological structures can impede early diagnosis efforts. Our model, SPNet, a spatially aware convolutional neural network, addresses this problem by employing a spatial data balancing technique, enhancing the classification of rare nuclei by 21.8 %. Since nuclei often cluster and exhibit patterns of the same class, SPNet's novel cost function targets spatial regions, resulting in a 1.9 % increase in the F1 classification of rare class types within the CoNSeP dataset. When integrated with a ResNet50-SE encoder, SPNet increases the mean F1 score for classifying all nuclei in the CoNSeP dataset by 4.3 %, compared to the benchmark set by the state-of-the-art HoVer-Net model. The potential integration of SPNet into existing medical devices could allow us to streamline diagnostic processes and minimise false negatives.

Discussion on structure classification and regulation function of histone deacetylase and their inhibitor.

Epigenetic regulation of genes through posttranslational regulation of proteins is a well-explored approach for disease treatment, particularly in cancer chemotherapy. Histone deacetylases have shown significant potential as effective drug targets in therapeutic studies aiming to restore epigenetic normality in oncology. Besides their role in modifying histones, histone deacetylases can also catalyze the deacetylation of various nonhistone proteins and participate in the regulation of multiple biological processes. This paper provides a review of the classification, structure, and functional characteristics of the four classes of human histone deacetylases. The increasing abundance of structural information on HDACs has led to the gradual elucidation of structural differences among subgroups and subtypes. This has provided a reasonable explanation for the selectivity of certain HDAC inhibitors. Currently, the US FDA has approved a total of six HDAC inhibitors for marketing, primarily for the treatment of various hematological tumors and a few solid tumors. These inhibitors all have a common pharmacodynamic moiety consisting of three parts: CAP, ZBG, and Linker. In this paper, the structure-effect relationship of HDAC inhibitors is explored by classifying the six HDAC inhibitors into three main groups: isohydroxamic acids, benzamides, and cyclic peptides, based on the type of inhibitor ZBG. However, there are still many questions that need to be answered in this field. In this paper, the structure-functional characteristics of HDACs and the structural information of the pharmacophore model and enzyme active region of HDAC is are considered, which can help to understand the inhibition mechanism of the compounds as well as the rational design of HDACs. This paper integrates the structural-functional characteristics of HDACs as well as the pharmacophore model of HDAC is and the structural information of the enzymatic active region, which not only contributes to the understanding of the inhibition mechanism of the compounds, but also provides a basis for the rational design of HDAC inhibitors.

Gish: a novel activation function for image classification

Gish: a novel activation function for image classification

Penerapan Data Mining dalam Implementasi Algoritma K-Means Clustering untuk Pelanggan Potensial pada Koperasi Simpan Pinjam

Apart from that, there are efforts to provide for the needs of its members as well as financial assistance for education, health and there are also concessions needed by the members. By conducting this customer cluster, it will help the company determine its potential customers so that it can implement the right marketing strategy for each type of existing customer, and will certainly provide benefits for the company in increasing the quality and loyalty of customers towards the company. Data mining has functions, namely prediction, description, classification and clustering functions. Data mining also has many methods for its application, one of these methods is K-Means. The K-Means Clustering algorithm can be implemented in grouping potential customers, especially in savings and loan cooperatives. Based on the data sampling used, the data can be grouped into 2 (two) clusterings.

Developing a new integrated advanced driver assistance system in a connected vehicle environment

Advanced driver assistance systems (ADASs) can effectively enhance driving and safety performance. Due to the inherent limitations of in-vehicle technologies concerning information sharing, existing studies mainly focus on demonstrating the effectiveness of onboard sensor-based individual ADAS functions rather than their collaborative effectiveness. Thanks to the emerging connected vehicle (CV) technologies, it is viable to physically realize the collaboration and coordination between different ADAS functions. This study aims to synthesize seven ADAS functions into an integrated advanced driving assistance system (iADAS) in a CV environment. The seven ADAS functions include omni-directional collision warning, lane-change warning, curve speed warning, emergency event notification, car-following guidance, identification of variable speed limits, and information services. The activation indicators and activation conditions of these ADAS functions are derived based on a single local coordinate system. This derivation has considered the real-time motion states of the ego and surrounding vehicles. Different ADAS functions are classified based on their roles in accident reduction, traffic efficiency enhancement, and driver convenience improvement. Afterwards, their priority of releasing information is set by considering the classification and primary functionality. Finally, the effectiveness of the iADAS is validated in field tests. Testing results reveal that the iADAS helps reduce rear-end collisions and prevent rollovers or sideslips on curved roads. Furthermore, younger drivers respond faster with higher driving stability regarding lateral collision warnings. Young, well-educated, and low-risk taking drivers maintain a short but safe time headway to the leading vehicle.

Human pose estimation and action recognition for fitness movements

Human pose estimation and action recognition have important applications in the areas of security, medicine and sports. In this work, we propose an improved YOLOv7-Pose algorithm to solve the problem of human posture and action recognition for fitness movements in various scenarios. For the algorithm based on YOLOv7-Pose, we add the function of classification to the original network. And we introduce Coordinate Attention to the network to improve the model’s ability to identify important features of human skeletal joints and action classifications. The improved ConvNeXt network structure is introduced to replace the CBS convolution kernel of the original model to improve the accuracy of human key point detection and action classification of the model. We optimise the spatial pyramid pool structure, which can reduce the loss function and accelerate the convergence rate of the model. We adopt EIOU as the regression function of the target detection frame to improve the accuracy of the coordinate regression. Experiments show that the improved YOLOv7-Pose has an mAP of 95.9% on a homemade test set of fitness actions, which is 5.4% higher than HRNet, with a 4.2% improvement over the original YOLOv7 algorithm, suggesting that the accuracy of action recognition and key point estimation has significantly improved.

Deep Learning for Zero-day Malware Detection and Classification: A Survey

Zero-day malware is malware that has never been seen before or is so new that no anti-malware software can catch it. This novelty and the lack of existing mitigation strategies make zero-day malware challenging to detect and defend against. In recent years, deep learning has become the dominant and leading branch of machine learning in various research fields, including malware detection. Considering the significant threat of zero-day malware to cybersecurity and business continuity, it is necessary to identify deep learning techniques that can somehow be effective in detecting or classifying such malware. But so far, such a comprehensive review has not been conducted. In this article, we study deep learning techniques in terms of their ability to detect or classify zero-day malware. Based on our findings, we propose a taxonomy and divide different zero-day resistant, deep malware detection and classification techniques into four main categories: unsupervised, semi-supervised, few-shot, and adversarial resistant. We compare the techniques in each category in terms of various factors, including deep learning architecture, feature encoding, platform, detection or classification functionality, and whether the authors have performed a zero-day evaluation. We also provide a summary view of the reviewed papers and discuss their main characteristics and challenges.

Symmetric LINEX loss twin support vector machine for robust classification and its fast iterative algorithm

Twin support vector machine (TSVM) is a practical machine learning algorithm, whereas traditional TSVM can be limited for data with outliers or noises. To address this problem, we propose a novel TSVM with the symmetric LINEX loss function (SLTSVM) for robust classification. There are several advantages of our method: (1) The performance of the proposed SLTSVM for data with outliers or noise can be improved by using the symmetric LINEX loss function. (2) The introduction of regularization term can effectively improve the generalization ability of our model. (3) An efficient iterative algorithm is developed to solve the optimization problems of our SLTSVM. (4) The convergence and time complexity of the iterative algorithm are analyzed in detail. Furthermore, our model does not involve loss function parameter, which makes our method more competitive. Experimental results on synthetic, benchmark and image datasets with label noises and feature noises demonstrate that our proposed method slightly outperforms other state-of-the-art methods on most datasets.

Asymmetric gradient penalty based on power exponential function for imbalanced data classification

Model bias is a tricky problem in imbalanced data classification. An asymmetric gradient penalty method is proposed based on the power exponential function to alleviate this. The methodology integrates a power exponential function as a moderator into the cross-entropy loss of the negative samples, driving the model to focus on hesitant samples while ignoring easy and singular samples. The rationality of the algorithm is explored from the gradient point of view, and it is demonstrated that the approach improves focal loss and asymmetric focal loss. Then, the imbalanced data classification experiments were deployed on MNIST, CIFAR10, CIFAR100, and Caltech101, respectively. For binary classification, datasets with several imbalance ratios constituted by varying the sample size of the majority class and minority class are included in the experiments. In the multi-category classification experiments, we discuss imbalanced datasets with only a single majority category and those with several majority categories and examine step-imbalance datasets and linear-imbalance datasets. The results reveal that the proposed method exhibits competitiveness on various imbalanced datasets and better robustness on high imbalance ratio datasets. Finally, the approach is deployed on the pulsar candidate dataset HTRU, and the state-of-the-art results are yielded. Our code is available at https://github.com/gzmtzly/GPPE.

AUC margin loss for limited, imbalanced and noisy medical image diagnosis – a case study on CheXpert5000

Abstract The AUC margin loss is a valuable loss function for medical image classification as it addresses the problems of imbalanced and noisy labels. It is used by the current winner of the CheXpert competition. The CheXpert dataset is a large dataset (200k+ images), however datasets in the range of 1k-10k medical datasets are much more common. This raises the question if optimizing AUC margin loss also is effective in scenarios with limited data.We compare AUC margin loss optimization to binary cross-entropy on limited, imbalanced and noisy CheXpert5000, a subset of CheXpert dataset. We show that AUC margin loss is beneficial for limited data and considerably improves accuracy in the presence of label noise. It also improves out-of-box calibration.

Real-Time Early Safety Warning for Personnel Intrusion Behavior on Construction Sites Using a CNN Model

The high number of annual safety accidents and casualties reflects the problems of slow detection of safety accidents and untimely early warnings in current construction safety management, and China urgently needs new methods and technologies to improve the safety management efficiency of the construction industry. However, there are fewer achievements in the use of new technologies for intelligent construction safety management, and most of the research focuses on intrusion detection and specific event alarms, which cannot be well implemented for systematic early warning functions. Based on the existing research and the characteristics of early warning scenarios, this study introduces the convolutional neural network (CNN) to build a video image recognition and classification model to give early safety warnings for intrusion behavior in hazard areas of construction and demonstrates the warning effect and accuracy with practical cases. First, it clarifies the early warning demand information, such as the attributes of construction personnel and hazard areas. Then, the construction model is realized by multi-scale hierarchical feature extraction mapping, the Softmax classification function, and the argmax function. Finally, from the empirical analysis, it can be seen that an early safety warning based on the CNN model has an accurate ability to identify the intrusion behavior of construction site personnel, which can reduce the probability of construction safety accidents to a certain extent, and provide enlightenment for further realization of intelligent construction sites.

Detection of post-COVID-19-related pulmonary diseases in X-ray images using Vision Transformer-based neural network

Objective:Computer methods related to the diagnosis of COVID-19 disease have progressed significantly in recent years. Chest X-ray analysis supported by artificial intelligence is one of the most important parts of the diagnosis. Unfortunately, there is no digital tool dedicated to post-acute pulmonary changes related to COVID-19 and modern diagnostic tools are needed. Methods:This paper introduces a novel neural network architecture for chest X-ray analysis, which consists of two parts. The first is an Inception architecture that captures global features, and the second is a combination of Inception modules and a Vision Transformer network to analyze the local features. Considering that several diseases can occur in X-ray images together, a specific loss function for multilabel classification was applied — asymmetric loss function (ASL), which we modified for our purpose. In contrast to other works, we focus only on the subgroup of 9 diseases from the chestX-ray14 dataset, which can appear as a consequence of COVID-19. Results:This work proves the effectiveness of the proposed neural network architecture combined with the asymmetric loss function on post-COVID-related diseases. The results were compared with several well-known classification architectures, such as VGG19, DenseNet121, EfficientNetB4, InceptionV3 and ResNet101. According to the results, the proposed method outperforms the mentioned models with AUC – 0.819, accuracy – 0.736, sensitivity – 0.7683, and specificity – 0.7221. Significance:Our work is the first one, which focuses on the diagnosis of post-COVID-19 related pulmonary diseases from X-ray images that uses deep learning. The proposed neural network reaches better accuracy than existing well-known architectures.

Arabic ChatGPT Tweets Classification Using RoBERTa and BERT Ensemble Model

ChatGPT OpenAI, a large-language chatbot model, has gained a lot of attention due to its popularity and impressive performance in many natural language processing tasks. ChatGPT produces superior answers to a wide range of real-world human questions and generates human-like text. The new OpenAI ChatGPT technology may have some strengths and weaknesses at this early stage. Users have reported early opinions about the ChatGPT features, and their feedback is essential to recognize and fix its shortcomings and issues. This study uses the ChatGPT tweets Arabic dataset to automatically find user opinions and sentiments about ChatGPT technology. The dataset is preprocessed and labeled using the TextBlob Arabic Python library into positive, negative, and neutral tweets. Despite extensive works for the English language, languages like Arabic are less studied regarding tweet analysis. Existing literature about Arabic tweet sentiment analysis has mainly focused on machine learning and deep learning models. We collected a total of 27,780 unstructured tweets from Twitter using the Tweepy SNscrape Python library using various hash-tags such as # Chat-GPT, #OpenAI, #Chatbot, Chat-GPT3, and so on. To enhance the model’s performance and reduce computational complexity, unstructured tweets are converted into structured and normalized forms. Tweets contain missing values, URL and HTML tags, stop words, punctuation, diacritics, elongations, and numeric values that have no impact on the model performance; hence, these increase the computational cost. So, these steps are removed with the help of Python preprocessing libraries to enhance text quality and consistency. This study adopts Transformer-based models such as RoBERTa, XLNet, and DistilBERT that automatically classify the tweets. Additionally, a hybrid transformer-based model is proposed to obtain better results. The proposed hybrid model is developed by combining the hidden outputs of the RoBERTA and BERT models using a concatenation layer, then adding dense layers with “Relu” activation employed as a hidden layer to create non-linearity and a “softmax” activation function for multiclass classification. They differ from existing state-of-the-art models due to the enhanced capabilities of both models in text classification. Hybrid models combine the different models to make accurate predictions and reduce bias and enhanced the overall results, while state-of-the-art models are incapable of making accurate predictions. Experiments show that the proposed hybrid model achieves 96.02% accuracy, 100% precision on negative tweets, and 99% recall for neutral tweets. The performance of the proposed model is far better than existing state-of-the-art models.