Classification Model For Analysis Research Articles

Enhancing accident cause analysis through text classification and accident causation theory: A case study of coal mine gas explosion accidents

Accident analysis is a crucial aspect of accident prevention, and natural language processing (NLP) techniques can efficiently be applied to analyze the causes of accidents. However, existing analysis methods primarily rely on text clustering and lack the application of accident causation theories, leading to a lack of specific accident cause analysis results. Combining text classification techniques with accident causation theories is an effective approach to address this issue. In this study, we integrated text classification techniques with accident causation theories and utilized coal mine gas explosion accidents as an example. We constructed a corpus, trained a BERT model, and evaluated its performance to obtain a text classification model for accident cause analysis. The results indicated that the BERT model-based text classification algorithm had an accuracy and macro-average F1 value of 0.9878 and 0.7792, respectively, significantly outperforming the control model. The application of this approach demonstrated that combining accident causation theories with text classification techniques for accident cause analysis can improve the efficiency of accident analysis while ensuring the richness of details in analyzing accident causes. By efficiently analyzing a large number of accident cases, this approach can provide a data foundation for data-driven accident prevention and technical support for integrated accident prevention.

Individual- vs. Multiple-Objective Strategies for Targeted Sentiment Analysis in Finances Using the Spanish MTSA 2023 Corpus

Multitarget sentiment analysis extracts the subjective polarity of text from multiple targets simultaneously in a given context. This approach is useful in finance, where opinions about different entities affect the target differently. Examples of possible targets are other companies and society. However, typical multitarget solutions are resource-intensive due to the need to deploy multiple classification models for each target. An alternative to this is the use of multiobjective training approaches, where a single model is capable of handling multiple targets. In this work, we propose the Spanish MTSACorpus 2023, a novel corpus for multitarget sentiment analysis in finance, and we evaluate its reliability with several large language models for multiobjective training. To this end, we compare three design approaches: (i) a Main Economic Target (MET) detection model based on token classification plus a multiclass classification model for sentiment analysis for each target; (ii) a MET detection model based on token classification but replacing the sentiment analysis models with a multilabel classification model; and (iii) using seq2seq-type models, such as mBART and mT5, to return a response sequence containing the MET and the sentiments of different targets. Based on the computational resources required and the performance obtained, we consider the fine-tuned mBART to be the best approach, with a mean F1 of 80.300%.

IGO_CM: An Improved Grey-Wolf Optimization Based Classification Model for Cyber Crime Data Analysis Using Machine Learning

IGO_CM: An Improved Grey-Wolf Optimization Based Classification Model for Cyber Crime Data Analysis Using Machine Learning

Adaptive sentiment analysis using multioutput classification: a performance comparison.

The primary objective of this research is to create a multi-output classification model for sentiment analysis through the combination of 10 algorithms: BernoulliNB, Decision Tree, K-nearest neighbor, Logistic Regression, LinearSVC, Bagging, Stacking, Random Forest, AdaBoost, and ExtraTrees. In doing so, we aim to identify the optimal algorithm performance and role within the model. The data utilized in this study is derived from customer reviews of cryptocurrencies in Indonesia. Our results indicate that LinearSVC and Stacking exhibit a high accuracy (90%) compared to the other eight algorithms. The resulting multi-output model demonstrates an average accuracy of 88%, which can be considered satisfactory. This research endeavors to innovate in adaptive sentiment analysis classification by developing a multi-output model that utilizes a combination of 10 classification algorithms.

The Detection of State of Mental Health using Recurrent Neural Networks

In the recent days, the number of people affected by Mental Depression Disorder (MDD) is on the rise with age, occupation related stress levels and several other factors. Depression has been identified as the main cause behind various diseases in individuals. In most cases, mental depression disorder is diagnosed with the help of counselling given by psychiatrists. However, even after the counselling and clinical diagnosis, the symptoms of depression persist. Social stigma associated with depression results in reluctance on the part of individuals to consult psychiatrists to diagnose mental illness. Also the existing techniques or methods do not guarantee accurate prediction of the level of depression. In order to overcome these problems, a new emotional model is designed to analyze the depression in individuals. A set of questionnaires called Personal Survey Questionnaire (PSQ) is framed to collect responses from the tweeters to understand about their mindset and depression level. Based on the PSQ answers, E-Ranking is calculated and compared with the polarity value generated by the PSQ answers. The performance of the proposed questionnaire-based model is compared with seven existing model based on parameters such as estimate and P-Value. Finally, the Recurrent Neural Network (RNN) is combined with Rule Based model (RB) to define the level and symptoms of depression. The blended RNN is compared with NLP process (Nature Language Processing) and it is proved that the Hybrid RNN and RB models give the best classification model for depression analysis.

Parameter Tuned Machine Learning Based Emotion Recognition on Arabic Twitter Data

Arabic is one of the most spoken languages across the globe. However, there are fewer studies concerning Sentiment Analysis (SA) in Arabic. In recent years, the detected sentiments and emotions expressed in tweets have received significant interest. The substantial role played by the Arab region in international politics and the global economy has urged the need to examine the sentiments and emotions in the Arabic language. Two common models are available: Machine Learning and lexicon-based approaches to address emotion classification problems. With this motivation, the current research article develops a Teaching and Learning Optimization with Machine Learning Based Emotion Recognition and Classification (TLBOML-ERC) model for Sentiment Analysis on tweets made in the Arabic language. The presented TLBOML-ERC model focuses on recognising emotions and sentiments expressed in Arabic tweets. To attain this, the proposed TLBOML-ERC model initially carries out data pre-processing and a Continuous Bag Of Words (CBOW)-based word embedding process. In addition, Denoising Autoencoder (DAE) model is also exploited to categorise different emotions expressed in Arabic tweets. To improve the efficacy of the DAE model, the Teaching and Learning-based Optimization (TLBO) algorithm is utilized to optimize the parameters. The proposed TLBOML-ERC method was experimentally validated with the help of an Arabic tweets dataset. The obtained results show the promising performance of the proposed TLBOML-ERC model on Arabic emotion classification.

Optimized Classification Model for Biomedical Data Analysis

In recent years, medical data analysis has become a well-known research topic in healthcare in recent years. Smart healthcare will use a new generation of information technologies, like artificial intelligence, the Internet of Things, cloud computing, and big data, to transform the conventional medical system in an all-around way, making healthcare highly effective, more personalized, and more convenient to support healthcare practitioners in their decision-making. Due to the huge amount of medical data, appropriate clustering methods have been proven useful to implement an efficient medical data classification process. In addition, the development of machine learning models effectively aids the classification process. With this motivation, this paper focuses on a unit-wide search algorithm with an optimal support vector machine model for medical data analysis. The purpose of the proposed model is to classify health data using clustering and classification models. In addition, the proposed technique includes a clustering method for medical data, which helps to improve the classification performance. In addition, the model used examines accumulated medical data to perform the classification process. In addition, an optimization algorithm is used to improve the classification results of medical data. Extensive benchmarking and a wide range of simulations can highlight the promising performance of the research technique.

Retraction Note to: Multiclass normalized clustering and classification model for electricity consumption data analysis in machine learning techniques

Retraction Note to: Multiclass normalized clustering and classification model for electricity consumption data analysis in machine learning techniques

Analisis Sentimen Pengguna Indihome dengan Metode Klasifikasi Support Vector Machine (SVM)

Indonesia Digital Home (IndiHome) is a communication service provider from PT Telekomunikasi Indonesia (Telkom) that provides several communication and data service packages such as internet, home telephone and cable television (Usee TV & IP TV) which implements copper and fiber pptic cable services. Currently, IndiHome is implementing a 100% fiber service replacement for all customers in order to produce high data speeds and more reliable services. However, the fact is that fiber optic services often receive complaints from customers due to weather and other factors. It was recorded that in 2020 internet users in Indonesia reached 196.7 million people or 73.7% million of the population and around 51.2% were social media users (Kompas.com, 2020). One of the social media with 6.43 million active users is Twitter. Twitter then became a medium for channeling opinions regarding a service, including the services provided by Indihome. Based on this, a method is needed, namely sentiment analysis to understand whether the opinion is negative or positive. The Support Vector Machine (SVM) is used to create a classification model for sentiment analysis of IndiHome service users' opinions on Twitter with an accuracy of 91.3%.

A new hybrid ensemble machine-learning model for severity risk assessment and post-COVID prediction system.

Starting from December 2019, the COVID-19 pandemic has globally strained medical resources and caused significant mortality. It is commonly recognized that the severity of SARS-CoV-2 disease depends on both the comorbidity and the state of the patient's immune system, which is reflected in several biomarkers. The development of early diagnosis and disease severity prediction methods can reduce the burden on the health care system and increase the effectiveness of treatment and rehabilitation of patients with severe cases. This study aims to develop and validate an ensemble machine-learning model based on clinical and immunological features for severity risk assessment and post-COVID rehabilitation duration for SARS-CoV-2 patients. The dataset consisting of 35 features and 122 instances was collected from Lviv regional rehabilitation center. The dataset contains age, gender, weight, height, BMI, CAT, 6-minute walking test, pulse, external respiration function, oxygen saturation, and 15 immunological markers used to predict the relationship between disease duration and biomarkers using the machine learning approach. The predictions are assessed through an area under the receiver-operating curve, classification accuracy, precision, recall, and F1 score performance metrics. A new hybrid ensemble feature selection model for a post-COVID prediction system is proposed as an automatic feature cut-off rank identifier. A three-layer high accuracy stacking ensemble classification model for intelligent analysis of short medical datasets is presented. Together with weak predictors, the associative rules allowed improving the classification quality. The proposed ensemble allows using a random forest model as an aggregator for weak repressors' results generalization. The performance of the three-layer stacking ensemble classification model (AUC 0.978; CA 0.920; F1 score 0.921; precision 0.924; recall 0.920) was higher than five machine learning models, viz. tree algorithm with forward pruning; Naïve Bayes classifier; support vector machine with RBF kernel; logistic regression, and a calibrated learner with sigmoid function and decision threshold optimization. Aging-related biomarkers, viz. CD3+, CD4+, CD8+, CD22+ were examined to predict post-COVID rehabilitation duration. The best accuracy was reached in the case of the support vector machine with the linear kernel (MAPE = 0.0787) and random forest classifier (RMSE = 1.822). The proposed three-layer stacking ensemble classification model predicted SARS-CoV-2 disease severity based on the cytokines and physiological biomarkers. The results point out that changes in studied biomarkers associated with the severity of the disease can be used to monitor the severity and forecast the rehabilitation duration.

ANALISIS SENTIMEN PADA TWEET TERKAIT VAKSIN COVID-19 MENGGUNAKAN METODE SUPPORT VECTOR MACHINE

Covid-19 is a disease that has been declared a global pandemic since March 2020. One of the challenges in dealing with the current Covid-19 pandemic is the widespread doubts about the use of vaccines, even though vaccination is one of the most successful ways to deal with infectious disease outbreaks. Vaccine hesitancy can be observed, among others, from public sentiment or perception on social media, one of them is Twitter. The existence of social media can affect the absorption of information received by a person, in this case social media is also a medium for anti-vaccine propaganda which can result in a decrease in public confidence in the Covid-19 vaccine. This study aims to develop a classification model using the Support Vector Machine (SVM) method for sentiment analysis of Tweet related to the Covid-19 vaccine. Several previous studies have conducted sentiment analysis related to Covid-19, but this research specifically conducts sentiment analysis on the topic of the Covid-19 vaccine so that data preparation and model configuration will be different. This study also uses the Design Science Research Methodology (DSRM) for research as a whole before focusing on the use of the SVM method. The results of the study consist of an algorithm for creating data sets and a classification model for sentiment analysis that can be used to determine public perceptions of the issue of Covid-19 vaccination. This study also compares the use of unigram and bigram tokenization. Based on the results obtained, the average value of each aspect of the evaluation measurement is higher when the bigram tokenization is used. Although higher, the value obtained has an insignificant difference in the range of 0.6% - 0.7%. In the evaluation results using unigram and bigram tokenization, the highest scores for all aspects of measurement, namely accuracy, recall, f-measure, and precision were 84%.

Hierarchical pathology screening for cervical abnormality.

Cervical smear screening is an imaging-based cancer detection tool, which is of pivotal importance for the early-stage diagnosis. A computer-aided screening system can automatically find out if the scanned whole-slide images (WSI) with cervical cells are classified as "abnormal" or "normal", and then alert pathologists. It can significantly reduce the workload for human experts, and is therefore highly demanded in clinical practice. Most of the screening methods are based on automatic cervical cell detection and classification, but the accuracy is generally limited due to the high variation of cell appearance and lacking context information from the surroundings. Here we propose a novel and hierarchical framework for automatic cervical smear screening aiming at the robust performance of case-level diagnosis and finding suspected "abnormal" cells. Our framework consists of three stages. We commence by extracting a large number of pathology images from the scanned WSIs, and implementing abnormal cell detection to each pathology image. Then, we feed the detected "abnormal" cells with corresponding confidence into our novel classification model for a comprehensive analysis of the extracted pathology images. Finally, we summarize the classification outputs of all extracted images, and determine the overall screening result for the target case. Experiments show that our three-stage hierarchical method can effectively suppress the errors from cell-level detection, and provide an effective and robust way for cervical abnormality screening.

Automated detection of European wild mammal species in camera trap images with an existing and pre-trained computer vision model

The use of camera traps is a nonintrusive monitoring method to obtain valuable information about the appearance and behavior of wild animals. However, each study generates thousands of pictures and extracting information remains mostly an expensive, time-consuming manual task. Nevertheless, image recognition and analyzing technologies combined with machine learning algorithms, particularly deep learning models, improve and speed up the analysis process. Therefore, we tested the usability of a pre-trained deep learning model available on the TensorFlow hub–FasterRCNN+InceptionResNet V2 network applied to images of ten different European wild mammal species such as wild boar (Sus scrofa), roe deer (Capreolus capreolus), or red fox (Vulpes vulpes) in color as well as black and white infrared images. We found that the detection rate of the correct region of interest (region of the animal) was 94%. The classification accuracy was 71% for the correct species’ name as mammals and 93% for the correct species or higher taxonomic ranks such as “carnivore” as order. In 7% of cases, the classification was incorrect as the wrong species’ name was classified. In this technical note, we have shown the potential of an existing and pre-trained image classification model for wildlife animal detection, classification, and analysis. A specific training of the model on European wild mammal species could further increase the detection and classification accuracy of the models. Analysis of camera trap images could thus become considerably faster, less expensive, and more efficient.

An optimal support vector machine based classification model for sentimental analysis of online product reviews

In present days, recent developments in data analytics take place that allows the identification of underlining trends through effective computational models. In several e-commerce and social platform, massive number of online product reviews is posted by end users that significantly help the developers with priceless insight while designing the products. This paper presents a new cluster based classification model for online product reviews. The presented model comprises several processes. Initially, support vector machine (SVM) based classification model is applied to classify the product reviews. Then, confusion matrix is generated to consider the possibilities of every consumer purchasing the product. Next, K-means clustering technique is applied to cluster the available data into two groups. At the next stage, sentimental analysis approach is employed to extracting the features. Finally, fuzzy based soft set theory is applied to determine the possibility of the customer to purchase the product effectively. The experimental validation of the presented model takes place on ipod dataset. The simulation outcome pointed out the superior characteristics of the presented model under several aspects.

RETRACTED ARTICLE: Multiclass normalized clustering and classification model for electricity consumption data analysis in machine learning techniques

RETRACTED ARTICLE: Multiclass normalized clustering and classification model for electricity consumption data analysis in machine learning techniques

An integrated approach for network traffic analysis using unsupervised clustering and supervised classification

Traffic analysis is a significant task to control the network traffic in heterogeneous manner. The unsupervised learning system fails to expand the supervised classification model for network analysis. Several data mining techniques employed to classify the network traffic pattern using unsupervised learning approach. However, the continuous evaluation of network traffic on multi-dimensional data is a difficult task. In order to solve this issue, an integrated K-means unsupervised clustering and supervised C4.5 Classification (KUC-SC) technique is designed. At first, K means unsupervised learning algorithms is applied in KUC-SC technique to form a 'k' number of clusters. After that, C4.5 is used to classify the data whether it is real-time or non-real-time traffic through the construction of decision-tree. An experimental result shows that the KUC-SC technique obtains better performance in terms of classification accuracy, classification time, true positive rate and communication overhead compared to the state-of-the-art works.

An integrated approach for network traffic analysis using unsupervised clustering and supervised classification

Traffic analysis is a significant task to control the network traffic in heterogeneous manner. The unsupervised learning system fails to expand the supervised classification model for network analysis. Several data mining techniques employed to classify the network traffic pattern using unsupervised learning approach. However, the continuous evaluation of network traffic on multi-dimensional data is a difficult task. In order to solve this issue, an integrated K-means unsupervised clustering and supervised C4.5 Classification (KUC-SC) technique is designed. At first, K means unsupervised learning algorithms is applied in KUC-SC technique to form a 'k' number of clusters. After that, C4.5 is used to classify the data whether it is real-time or non-real-time traffic through the construction of decision-tree. An experimental result shows that the KUC-SC technique obtains better performance in terms of classification accuracy, classification time, true positive rate and communication overhead compared to the state-of-the-art works.

C5.0: Advanced Decision Tree (ADT) classification model for agricultural data analysis on cloud

Agriculture plays a crucial role in India’s economy and around 70% of people earn their income through it and also provides large employment opportunities. The technological advancement has led to remarkable achievements in developing Agricultural based software applications to get faster information. But, many farmers are still applying the traditional methods of farming and hence the result of productivity becomes very low. Agriculture prediction process for organic and inorganic farms is an open issue and it depends upon weather, soil fertility, water, seasons and commodity prices, etc. Soil fertility factor is paramount important to maintain the crop growth and increase the production. The soil fertility levels help the farmers to identify the deficiencies in the soil, namely nutrient content, soil type, pH value, EC (Electrical Conductivity) value and soil texture and to choose the right crops to increase the production. In this work, as a novelty, the soil fertility level is predicted by analyzing the Virudhunagar District Soil information and recommendations are offered for crop selection and sowing by using C5.0: Advanced Decision Tree (ADT) classifier algorithm. Using this technique, an Android-based mobile phone applications named as Design of Smart Information System (DSIS) application has been developed. The proposed application activates the Global Positioning System (GPS) to identify the user location. The performance of proposed model is analysed and it is compared with the existing classification model for agricultural data.

Multi-modal multi-layered topic classification model for social event analysis

In this paper, we pay attention to reveal the event topics and track the evolutionary trend of social event and a novel probabilistic topic model is proposed. The Multi-modal Multi-layered Topic Classification Model (tm_MMC) for Social Event Analysis has the capacity for revealing visual and non-visual topics, by jointly modeling the textual and visual information while simultaneously learning and predicting the multi-layered category labels. In order to track the evolutionary trends of the topics online, tm_MMC uses topic intensity and heritability to incrementally build an up-to-date model. To evaluate the effectiveness of our model, we experiment using a collected data, and compare the results with those of other traditional models. The results demonstrate the effectiveness and advantages of our model against several state-of-the-art methods.

Editorial

Editorial