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Abstract Cervical cancer is one of the most dangerous malignancies in women. Prolonged survival times are made possible by breakthroughs in early recognition and efficient treatment of a disease.The existing methods are lagging on finding the important attributes to predict the survival outcome. The main objective of this study is to find individuals with cervical cancer who are at greater risk of death from recurrence by predicting the survival.A novel approach in a proposed technique is Triangulating feature importance to find the important risk factors through which the treatment may vary to improve the survival outcome.Five algorithms Support vector machine, Naive Bayes, supervised logistic regression, decision tree algorithm, Gradient boosting, and random forest are used to build the concept. Conventional attribute selection methods like information gain (IG), FCBF, and ReliefFare employed. The recommended classifier is evaluated for Precision, Recall, F1, Mathews Correlation Coefficient (MCC), Classification Accuracy (CA), and Area under curve (AUC) using various methods. Gradient boosting algorithm (CAT BOOST) attains the highest accuracy value of 0.99 to predict survival outcome of recurrence cervical cancer patients. The proposed outcome of the research is to identify the important risk factors through which the survival outcome of the patients improved.

Rumor data classification in social media seems essential research due to its dependency on digital communications, and this rumor data makes social media unstable. In Natural Language Processing, word embedding, feature extraction, and classification techniques are challenging in rumor detection research. In this research, two phases of feature extraction techniques are involved for performing word embedding and deep feature extraction. In the first phase, Bidirectional Encoder Representations from Transformers with the Optimized Convolutional Neural Network (BERT-OPCNN) techniques are integrated, and in the second phase, the fastText with information gain of an ant colony optimization technique (FIAC) is proposed. Finally, the vectors formed using BERT-OPCNN and the FIAC embedding model are classified using a customized Bi-LSTM. The experiment is computed and compared with existing techniques for balanced and unbalanced datasets. The results evaluation shows that our proposed FIAC embedding with BERT-OPCNN outperforms all other existing techniques using the customized Bi-LSTM classifier.

Skin lesions are localized regions of the skin exhibiting atypical growth or appearance and can be either benign or malignant. Detecting and classifying these lesions accurately is crucial for early diagnosis and effective treatment of skin cancer. However, existing approaches for classifying cancerous and non-cancerous skin lesions from dermoscopic images are limited in capturing the appropriate features and also susceptible to overfitting in imbalanced datasets. This work aims to improve skin cancer classification by enhancing feature representation in dermoscopic images. A hybrid classification system is proposed, combining two tracks: the Swin Transformer and the Dense Group Shuffle Non-Local Attention (DGSNLA) Network, which integrates DenseNet169, Group Shuffle Depth-wise blocks (GSDW), and an enhanced non-local attention block (ENLA). This deep feature fusion approach effectively aggregates global and local features, resulting in more accurate feature representation and improved classification performance. To the best of our knowledge, this is the first study to combine a transformer with a custom CNN for skin cancer classification. The proposed network achieves an accuracy of 94.21%, an F1-score of 96.64%. a precision of 96.62%, and a recall of 96.25% on the HAM10000 dataset.

Three-dimensional (3D) object classification is crucial in various applications, including autonomous driving, robotics, and augmented reality, where precise detection of objects in 3D space is required. Traditional techniques for 3D object classification encompass voxel-based representations, multi-view projections, and point cloud analysis. However, these approaches can be computationally intensive, and might not capture complete structure of the underlying object. This research introduces a novel lightweight model for 3D object classification based on point clouds that achieves higher accuracy while requiring minimal computing overhead. The proposed system incorporates adaptive sampling, an Attentive-Convolution Feature block, a Dualpath feature processing block, and a non-local attention mechanism to enhance feature representation and classification performance. To our knowledge, this is the first study to propose a lightweight TnetLight module as a transformation network that aligns and converts input point clouds, capturing subtle geometric variations and highlighting differences between objects to enhance shape recognition and differentiation accuracy. Additionally, the model integrates an Attentive-Convolution Feature extraction block that combines local geometric features with global contextual information, enhancing the network’s ability to capture both detailed and broader characteristics for better differentiation between objects with slight variations. The network was evaluated on the ModelNet10 dataset, achieving an accuracy of 94.4%.

Environmental micro-organisms (EM) play a crucial role in ecosystems, contributing to nutrient cycling, bioremediation and, supporting overall biodiversity. The identification and classification of these microbes are of significant importance for environmental monitoring and management. However, traditional detection methods of EM detection are labour-intensive, time-consuming and, require extensive material resources. Deep learning (DL) offers a more efficient and accurate solution to this problem by automating the detection and classification of EMs. This research introduces a novel network for EM classification using Dense network, Efficient Hierarchical Channel Refinement (EHCR) block, Dilated Adaptive Shuffled-Attention (DASA) block and Neighbourhood Attention (NA). The DenseNet-169 architecture is modified to integrate the EHCR and DASA blocks, allowing the model to focus on semi-local feature extraction for improved feature representation. NA efficiently targets relevant regions within the extracted features, enhancing EM classification performance. Additionally, the model incorporates Grad-CAM-based Explainable Artificial Intelligence (XAI) to improve interpretability, providing visual insights into the decision-making of the model. The performance of the proposed network surpassed state-of-the-art architectures and achieved an accuracy of 82.74% on the EMDS-6 dataset.

Due to its potential use in evaluating mental health and enhancing patient care, emotion recognition using physiological signals, such as EEG, ECG, and EMG, has drawn a lot of attention in the healthcare industry. However, better neural network architecture and training parameter optimization is needed to achieve accurate and dependable emotion classification. This research aims to investigate the impact of various configurations of neural network architectures, proposed multimodal fusion network and training parameters on the classification performance of emotion recognition systems utilizing EEG, ECG, and EMG signals. Nine experiments were conducted, each representing a unique combination of hyperparameters, including EEG filters, kernel sizes, pooling sizes, ECG filters, EMG filters, LSTM units, dropout rates, epochs, and batch sizes. The experiments were designed to systematically explore the effects of these hyperparameters on classification accuracy, robustness, and computational efficiency. The experiments revealed that the choice of hyperparameters significantly influences the performance of emotion recognition systems. Proposed model achieved higher average accuracies, with Experiment 3 exhibiting the highest accuracy of 87.83%. However, the model also incurred higher computational costs in terms of training time and memory usage. Additionally, experiments with higher dropout rates demonstrated a slight decrease in accuracy compared to those with lower dropout rates, emphasizing the importance of balancing regularization and model complexity. The findings underscore the importance of fine-tuning hyperparameters and selecting appropriate neural network architectures to optimize the performance of emotion recognition systems. Advanced deep learning techniques and physiological signal processing, healthcare providers can develop robust and accurate emotion recognition systems to enhance patient care and support personalized interventions. Further research may focus on exploring novel architectures and refining training strategies to continue improving the efficacy and efficiency of AI-driven emotional intelligence solutions in healthcare delivery.

Wireless sensor network (WSN) models pose substantial security vulnerabilities since most WSNs are deployed in unattended hostile environments. This research focuses on denial-of-service (DoS) attack detection, a crucial problem in WSN security. Previous research has mostly focused on offline machine learning algorithms, which require long-term data collection and cannot continually adapt to new data. Online learning is thus more suitable for detecting DoS attacks in WSN due to the benefit of continuous improvement with fresh data. Nevertheless, existing online DoS attack detection algorithms do not take internal and external data interference into consideration. Thus, noisy data might have a negative effect on the performance of the model. Moreover, the data includes features that are redundant or unnecessary for the classification. Hence, the selection of proper features not only decreases computational time but also improves the performance of the model. This study proposes an online-learning-based approach for detecting DoS attacks in WSN. Specifically, a feature selection method is proposed to identify the most appropriate attributes for the training process. Furthermore, a noise-tolerant online passive-aggressive multi-class classifier is also developed. The performance of our proposed method is investigated in terms of accuracy, precision, recall, and F1-score, and it proves to be competitive.

In today's digital era, the rise of hate speech has emerged as a critical concern, driven by the rapid information-sharing capabilities of social media platforms and online communities. As the internet expands, the proliferation of harmful content, including hate speech, presents considerable obstacles in ensuring a secure and inclusive online environment. In response to this challenge, researchers have embraced machine learning and deep learning methods to create automated systems that can effectively detect hate speech and conduct sentiment analysis, offering potential solutions to address this pressing issue. This survey article provides a comprehensive overview of recent advancements in hate speech detection and sentiment analysis using machine learning and deep learning models. We present an in-depth analysis of various methodologies and datasets employed in this domain. Additionally, we explore the unique challenges faced by these models in accurately identifying and classifying hate speech and sentiment in online text. Finally, we outline areas where more study is needed and suggest potential new avenues for exploration in the field of hate speech identification and sentiment analysis. Using the results of this survey, we hope to encourage the development of more effective machine learning and deep learning-based solutions to curb hate speech and promote a more inclusive online environment.

Abstract Pipelines are observed one of the economic modes of transport for transporting oil, gas, and water between various locations. Most of the countries in the world transport petroleum and other flammable products through underground pipelines. The underground and aboveground pipelines are facing various damages due to corrosion, dents, and ruptures due to the environment and operational fluid conditions. The danger of leaks and accidents increases as a result of these damages. Pipelines must be evaluated on a regular basis to make sure they are fit for transmission. By evaluating the effects of damages and the possibility of catastrophic failures using a variety of techniques, pipeline integrity is controlled. Applying the relative risk scoring (RRS) technique, pipeline failures are predicted. One of the probabilistic techniques used to forecast risk based on an impartial assessment is machine learning. With different parameters like corrosion, leakage, materials, atmosphere, surface, earth-movements, above-ground and underground facilities, etc., the RRS method provides an accuracy of 97.5% in identifying the risk and gives a precise classification of risk, whether the pipeline has a high, medium, or low risk without any delay on the prediction compared with Naive Bayes, decision tree, support vector machine, and graph convolutional network.

In this paper, we study the finite-time dissipativity analysis of Markovian jump-delayed neural networks (MJDNNs). The goal is to establish less conservative results for extended dissipativity conditions for delayed MJDNNs. To achieve this, an appropriate Lyapunov-Krasovskii functional (LKF) with novel inequality like composite slack-matrix-based integral inequality (CSMBII). Next, the CSMBII and other sufficient conditions are employed to estimate the derivative of the constructed LKF. Using these techniques, a delay-dependent finite-time dissipativity condition is derived in terms of linear matrix inequalities (LMIs). These LMIs are used to formulate the finite dissipativity condition for the delayed MJNNs. The utility of the suggested approach is then confirmed by a number of interesting numerical examples, one of which has been confirmed by a real-world application of the benchmark problem that is associated with the designed MJDNNs. The illustrative simulation results conclusively demonstrate the superior performance and success of the developed CSMBII technique in this proposal, surpassing the limitations of existing techniques.

Removing hazardous substances like azo dyes and nitrophenols from drinking water is essential for maintaining human health since these substances occur naturally in the environment. This research study used machine learning techniques to estimate the catalytic reduction performance of environmentally hazardous nitrophenols and azodyes pollutants. The catalyst PdO-NiO was used to eliminate contaminants in the water, including 4-nitrophenol (4-NP), 2,4-dinitrophenol (DNP), 2,4,6-trinitrophenol (TNP), methylene blue (MB), Rhodamine B (RHB), and Methyl Orange (MO). We conducted the experiments at different timings, and machine learning algorithms, including Linear Regression (LR), Support Vector Machines (SVM), Gradient boosted machines (GBM), Random forest (RF), and XGBTree (XGB), were used to predict the catalytic activity. The performance of these algorithms was measured using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The results showed that the XGB algorithm performs best with NP and DNP. RF algorithm performs best with TNP, MB, and RHB, and the SVM algorithm performs best with MO. PdO-NiO bimetallic catalyst showed 98% reduction efficiency of azo compounds mixture within 8 min. Hence, we found PdO-NiO to be an efficient catalyst for real-site applications.

PurposeThe objective of this study was to analyze proposed Korean nursing legislation as depicted in newspaper articles, to highlight issues related to the legislative process for this potential law, and to better understand social awareness regarding this matter.MethodsThe study focused on articles from 11 leading newspapers in Korea, published between February 2020 and August 2023, that pertained to nursing legislation. The articles were retrieved from the BigKinds database. Following text preprocessing, analytical methods including term frequency-inverse document frequency were employed, along with latent Dirichlet allocation (LDA), for word and topic modeling analysis. Additionally, LDA was applied across time periods to examine temporal changes in topics.ResultsFollowing preprocessing, a total of 7,967 words were extracted from the 991 articles selected for analysis. The primary themes identified in newspaper articles concerning the nursing legislation were organized into three main topics: 1) the necessity and impact of enactment of the nursing law, 2) the political context surrounding enactment of the law, and 3) the conflicts between and actions of healthcare organizations related to enactment of the law.ConclusionsThe findings confirmed that media coverage regarding the proposed nursing legislation primarily concentrated on the political and social conflicts associated with the law's passage, rather than its necessity and substance. More compelling evidence must be presented concerning the influence of the nursing workforce and the work environment of nurses on patient safety and health outcomes. Additionally, strategies should be devised to improve public comprehension of the nursing law's provisions.

The analysis is intended to demonstrate two-phase flow of hybrid dusty Eyring–Powell nanofluid flow over a porous cylinder with Thompson and Troian slip boundary condition. The combination of (C2H6O2) ethylene glycol (40 %) and (H2O) water (60 %) -based TC4 (TC4 is an alloy of titanium consisting of 90 % titanium, 6 % aluminium, and 4 % vanadium) was utilized to preparing the hybrid nanofluids. Ti–6Al–4V is another name for this alloy) and nickel-chromium (NiCr) nanoparticles flow properties after the mechanism has been filled with dusty particles. A new approach to the execution of an intelligent numerical computing solution is shown in this paper, which uses the Levenberg-Marquard algorithm to drive MLP feed-forward back-propagation ANN. Data were collected for the testing, certification, and training of the ANN model. The bvp4c with MATLAB solver is used to resolve nonlinear versions of well-known mathematical problems, such as those involving the skin friction coefficient, heat transfer rates, velocity, temperature, and other variables. The ANN model is designed to perform the following tasks: selecting data, building networks, training networks, and assessing their effectiveness via the use of the mean square error measure. The influence of key factors on the fluid transport characteristics is demonstrated via tables and graphs. The velocity profile is higher values, resulting in an amplification of the curvature parameter. The temperature rises when the radiation parameter values decline, and the specific heat ratio parameter exhibits a similar pattern. Higher values of the velocity slip parameter and melting factor both are decays on the Nusselt number. This type of theoretical investigation is a necessary aspect of many aircraft applications, including power production, cooling of electrical and propulsion reactors.

It has been demonstrated that fuzzy systems are beneficial for classification and regression. However, they have been mainly utilized in controlled settings. An image clustering technique essential for content-based picture retrieval in big image datasets is developed using the contents of color, texture and shape. Currently, it is challenging to label a huge number of photos. The issue of unlabeled data has been addressed. Unsupervised learning is used. K-means is the most often used unsupervised learning algorithm. In comparison to fuzzy c-means clustering, K-means clustering has lower-dimensional space resilience and initialization resistance. The dominating triple HSV space was shown to be a perceptual color space made of three modules, S (saturation), H (hue) and V (value), referring to color qualities that are significantly connected to how human eyes perceive colors. A deep learning technique for segmentation (RBNN) is built on the Gaussian function, fuzzy adaptive learning control network (FALCN), clustering and the radial basis neural network. The segmented image and critical information are fed into a radial basis neural network classifier. The suggested fuzzy adaptive learning control network (FALCN) fuzzy system, also known as the unsupervised fuzzy neural network, is very good at clustering images and can extract image properties. When a conventional fuzzy network system receives a noisy input, the number of output neurons grows needlessly. Finally, random convolutional weights extract features from data without labels. Furthermore, the state-of-the-art uniting the proposed FALCN with the RBNN classifier, the proposed descriptor also achieves comparable performance, such as improved accuracy is 96.547 and reduced mean squared error of 36.028 values for the JAFE, ORL, and UMIT datasets.

Abstract COVID-19, a global pandemic, has killed thousands in the last three years. Pathogenic laboratory testing is the gold standard but has a high false-negative rate, making alternate diagnostic procedures necessary to fight against it. Computer Tomography (CT) scans help diagnose and monitor COVID-19, especially in severe cases. But, visual inspection of CT images takes time and effort. In this study, we employ Convolution Neural Network (CNN) to detect coronavirus infection from CT images. The proposed study utilized transfer learning on the three pre-trained deep CNN models, namely VGG-16, ResNet, and wide ResNet, to diagnose and detect COVID-19 infection from the CT images. However, when the pre-trained models are retrained, the model suffers the generalization capability to categorize the data in the original datasets. The novel aspect of this work is the integration of deep CNN architectures with Learning without Forgetting (LwF) to enhance the model’s generalization capabilities on both trained and new data samples. The LwF makes the network use its learning capabilities in training on the new dataset while preserving the original competencies. The deep CNN models with the LwF model are evaluated on original images and CT scans of individuals infected with Delta-variant of the SARS-CoV-2 virus. The experimental results show that of the three fine-tuned CNN models with the LwF method, the wide ResNet model’s performance is superior and effective in classifying original and delta-variant datasets with an accuracy of 93.08% and 92.32%, respectively.

Abstract The proposed framework consists of three modules as an outlier detection method for indoor air quality data. We first use a long short-term memory autoencoder (LSTM-AE) based reconstruction error detector, which designs the LSTM layer in the shape of an autoencoder, to build a reconstruction error-based outlier detection model and extract latent features. The latent feature class-assisted vector machine detector constructs an additional outlier detection model using previously extracted latent features. Finally, the ensemble detector combines the two independent classifiers to define a new ensemble-based decision rule. Furthermore, because real-time anomaly detection proceeds with unsupervised learning, more stable and consistent external detection rules are defined than when using a single ensemble model. Laboratory tests with five random cases were performed for objective evaluation. Thus, we propose a framework that can be applied to various industrial environments by detecting and defining stable outlier decision rules.

Recognizing text from the nature scene images and videos has been the challenging task of computer vision and machine learning research community in recent years. These texts are difficult to recognize because of their shapes, complex backgrounds, color, shape and size variations. However, text recognition is very much useful in indexing, keyword-based image and video search, and information retrieval. In this research paper, a model is proposed to detect the isolated text characters in the photographic images of natural scenes. The proposed model uses the combination of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) for recognizing the text in natural images. The model uses two networks, where the first network combines the low-level and middle-level features to increase the feature size and passes the enriched information to the second network. Here, features are again widened by combining with high-level features, resulting in powerful and robust features. To evaluate the proposed model, ICDAR2003 (IC03), ICDAR2013 (IC13), SVT (Street View Text) datasets have been used. And an extensive Tamil news tickers image dataset has been developed to evaluate the model. The experimental results show that the combined feature fusion technique outperforms the other methods on the ICDAR2003, ICDAR2013, SVT and Tamil news tickers datasets.

Cancer is the second biggest cause of death worldwide, accounting for one of every six deaths. On the other hand, early detection of the disease significantly improves the chances of survival. The use of Artificial Intelligence (AI) to automate cancer detection might allow us to evaluate more cases in less time. In this research, AI-based deep learning models are proposed to classify the images of eight kinds of cancer, such as lung, brain, breast, and cervical cancer. This work evaluates the deep learning models, namely Convolutional Neural Networks (CNN), against classifying images with cancer traits. Pre-trained CNN variants such as MobileNet, VGGNet, and DenseNet are employed to transfer the knowledge they learned with the ImageNet dataset to detect different kinds of cancer cells. We use Bayesian Optimization to find the suitable values for the hyperparameters. However, transfer learning could make it so that models can no longer classify the datasets they were initially trained. So, we use Learning without Forgetting (LwF), which trains the network using only new task data while keeping the network’s original abilities. The results of the experiments show that the proposed models based on transfer learning are more accurate than the current state-of-the-art techniques. We also show that LwF can better classify both new datasets and datasets that have been trained before.

Everyone is aware that air and environmental pollutants are harmful to health. Among them, indoor air quality directly affects physical health, such as respiratory rather than outdoor air. However, studies that have examined the correlation between environmental and health information have been conducted with public data targeting large cohorts, and studies with real-time data analysis are insufficient. Therefore, this research explores the research with an indoor air quality monitoring (AQM) system based on developing environmental detection sensors and the internet of things to collect, monitor, and analyze environmental and health data from various data sources in real-time. It explores the usage of wearable devices for health monitoring systems. In addition, the availability of big data and artificial intelligence analysis and prediction has increased, investigating algorithmic studies for accurate prediction of hazardous environments and health impacts. Regarding health effects, techniques to prevent respiratory and related diseases were reviewed.

This paper presents wire cut electrical discharge machining (WEDM) response characteristics of Aluminium 7075 (Al7075) reinforced with powdered activated carbon (PAC) composite. In recent days WEDM has become a significant machining process in targeting its benefits of contributing improved material removal rate (MRR) and low surface roughness (SR). This is due the rising need for intricate, accurate, and superior structural components, the WEDM process emerges as a formidable alternative to traditional machine tools. In this work Pulse-on time (Ton), pulse-off time (Toff), discharge current (IA) and servo speed rate (SS) are the variables to be given as input and machining responses such as MRR and SR are studied. From Analysis of Variance (ANOVA) study it is found that discharge current and servo speed is the significant parameters. The optimal desirability condition is obtained with input parameters Ip: 2000 mA; Ton: 8.9 μ s; Toff: 25 μ s and SS: 150 rpm for the precision machining. The optimum response parameters are found as MRR 10.46 mm ^3 /min and SR 3.32 μ m. Results shows that the model designed for the prediction of MRR produces an above 98.27% and the prediction of SR is above 97.17%. The error percentage among the experimental and predicted MRR and SR were estimated. Additionally confirmatory test is performed with optimal results achieved from response surface methodology (RSM) and desirability technique. Metallurgical tests like electron backscatter diffraction analysis (EBSD) and microstructure are conducted to confirm the surface properties and atomic force morphology (AFM) analysis is applied to clarify the structural features of machined composites. The results revealed that the variation of hard deflection is caused by depression of eroded materials on the top layers of machined surface.

Maintaining data dependability within wireless sensor network (WSN) systems has significant importance. Nevertheless, the deployment of systems in unattended and hostile areas poses a major challenge in dealing with noise. Consequently, several investigations have been conducted to address the issue of noise-affected data recovery. Nevertheless, previous research has primarily focused on the internal noise of the system. Neglecting to include external factors that impact the WSN system in the study might lead to findings that are not true to reality. Hence, this research takes into account both internal and external noise factors, such as rain, fog, or snow conditions. Moreover, in order to maintain the temporal characteristics and intersensor relationships, the data from multiple sensor nodes are consolidated into a two-dimensional matrix format. The stacked convolutional autoencoder (SCAE) model is proposed, which has the capability to extract data features. The stack design of the SCAE enables it to effectively mitigate the issue of vanishing gradients. Moreover, the weight sharing approach used between the two subnetworks also enhances the efficiency of the weight initialization procedure. Thorough experiments are conducted using both simulated WSN systems and real-world sensing data. Experimental results demonstrate that the SCAE outperforms existing methods for reconstructing noisy data.

This current investigation aims to explore the significance of induced magnetic fields and double-diffusive convection in the radiative flow of Carreau nanofluid through three distinct geometries. To simplify the fluid transport equations, appropriate self-similarity variables were employed, converting them into ordinary differential equations. These equations were subsequently solved using the Runge–Kutta–Fehlberg (RKF) method. Through graphical representations like graphs and tables, the study demonstrates how various dynamic factors influence the fluid’s transport characteristics. Additionally, the artificial neural network (ANN) approach is considered an alternative method to handle fluid flow issues, significantly reducing processing time. In this study, a novel intelligent numerical computing approach was adopted, implementing a Levenberg–Marquardt algorithm-based MLP feed-forward back-propagation ANN. Data collection was conducted to evaluate, validate, and guide the artificial neural network model. Throughout all the investigated geometries, both velocity and induced magnetic profiles exhibit a declining trend for higher values of the magnetic parameter. An increase in the Dufour number corresponds to a rise in the nanofluid temperature. The concentration of nanofluid increases with higher values of the Soret number. Similarly, the nanofluid velocity increases with higher velocity slip parameter values, while the fluid temperature exhibits opposite behavior, decreasing with increasing velocity slip parameter values.

The Type-2 fuzzy set (T2 FS) is widely used for efficient control uncertainties, such as noise sensitivity in the fuzzy set. In addition, unsupervised machine learning requires a clustering parameter value in advance, and may affect clustering performance according to prior information such as the number and size of clusters. In this case, the fuzzifier value m to be applied is the most important factor in improving the accuracy of data. Therefore, in this paper, we intend to perform clustering to automatically acquire the determination of m1 and m2 values that depended on existing repeated experiments. To this end, in order to increase efficiency on deriving appropriate fuzzifier value, we used the Interval type-2 possibilistic fuzzy C-means (IT2PFCM), clustering method to classify a given pattern. In Efficient IT2PFCM method, used for clustering, we propose an algorithm that derives suitable fuzzifier values for each data. These values also extract information from each data point through the histogram approach and Gaussian Curve Fitting method. Using the extracted information, two adaptive fuzzifier value m1 and m2 are determined. Obtained values apply the new lowest and highest membership values. In addition, it is possible to form an appropriate fuzzy area on each cluster by only taking advantage of the characteristics of IT2PFCM, which reduces uncertainty. This doesn't only improve the accuracy of clustering of measured sensor data, but can also be used without additional procedures such as data labeling or the provision of prior information. It is also efficient at monitoring numerous sensors, managing and verifying sensor data collected in real time such as smart cities. Eventually, in this study, the proposed method is to improve IT2PFCM performance on accurate and quick clustering of large amount of complex data such as Internet of Things (IoT).

Owing to their wide application, Internet of Things systems have been the target of malicious attacks. These attacks included DoS, flood, SlowITe, malformed, and brute-force attacks. A dataset that includes these attacks was recently released. However, the attack detection accuracy reported in previous studies has not been satisfactory because the studies used too many features that are not important in detecting malicious message queue telemetry transport (MQTT) traffic. Therefore, this study aims to analyze these attacks. Herein, a novel feature extraction method is proposed that includes the source port index, TCP length, MQTT message type, keep alive, and connection acknowledgment. The attacks were classified using the Seq2Seq model. During the experiment, the accuracy of the proposed method was 99.97%, which is 7.33% higher than that of previously reported methods.

With rapid urbanization, awareness of environmental pollution is growing rapidly and, accordingly, interest in environmental sensors that measure atmospheric and indoor air quality is increasing. Since these IoT-based environmental sensors are sensitive and value reliability, it is essential to deal with missing values, which are one of the causes of reliability problems. Characteristics that can be used to impute missing values in environmental sensors are the time dependency of single variables and the correlation between multivariate variables. However, in the existing method of imputing missing values, only one characteristic has been used and there has been no case where both characteristics were used. In this work, we introduced a new ensemble imputation method reflecting this. First, the cases in which missing values occur frequently were divided into four cases and were generated into the experimental data: communication error (aperiodic, periodic), sensor error (rapid change, measurement range). To compare the existing method with the proposed method, five methods of univariate imputation and five methods of multivariate imputation—both of which are widely used—were used as a single model to predict missing values for the four cases. The values predicted by a single model were applied to the ensemble method. Among the ensemble methods, the weighted average and stacking methods were used to derive the final predicted values and replace the missing values. Finally, the predicted values, substituted with the original data, were evaluated by a comparison between the mean absolute error (MAE) and the root mean square error (RMSE). The proposed ensemble method generally performed better than the single method. In addition, this method simultaneously considers the correlation between variables and time dependence, which are characteristics that must be considered in the environmental sensor. As a result, our proposed ensemble technique can contribute to the replacement of the missing values generated by environmental sensors, which can help to increase the reliability of environmental sensor data.

The Conventional Cloud Common Data Model (CDM) uses a centralized method of user identification and credentials. This needs to be solved in a decentralized way because there are limitations in interoperability such as closed identity management and identity leakage. In this paper, we propose a DID (Decentralized Identifier)-based cloud CDM that allows researchers to securely store medical research information by authenticating their identity and to access the CDM reliably. The proposed service model is used to provide the credential of the researcher in the process of creating and accessing CDM data in the designed secure cloud. This model is designed on a DID-based user-centric identification system to support the research of enrolled researchers in a cloud CDM environment involving multiple hospitals and laboratories. The prototype of the designed model is an extension of the encrypted CDM delivery method using DID and provides an identification system by limiting the use cases of CDM data by researchers registered in cloud CDM. Prototypes built for agent-based proof of concept (PoC) are leveraged to enhance security for researcher use of ophthalmic CDM data. For this, the CDM ID schema and ID definition are described by issuing IDs of CDM providers and CDM agents, limiting the IDs of researchers who are CDM users. The proposed method is to provide a framework for integrated and efficient data access control policy management. It provides strong security and ensures both the integrity and availability of CDM data.

Since 2009, the Korean Society of Plant Pathology has established the Committee on Common Names of Plant Disease to systematically review and determine plant disease names and related terminologies. The committee published the 6th edition of the List of Plant Diseases in Korea (LPDK) in 2022, and the list has been made publicly accessible online. The online database has significantly enhanced user accessibility, expedited update processes, and improved interoperability with other databases. As a result, the 6.1 edition of the list was released by online LPDK in 2023, detailing new disease names added over the preceding year and revisions to existing names. Subsequently, in 2024, the 6.2 edition was published, encompassing 6,765 diseases caused by 2,503 pathogen taxa across 1,432 host species. The public release of the online database has, however, introduced several challenges and tasks. Addressing these issues necessitates the development of modern, standardized nomenclature guidelines and a robust system for the registration of new disease names. Open communication and collaboration among the diverse members of the Korean Society of Plant Pathology are required to ensure the reliability of the LPDK.

Adult T-cell leukemia-lymphoma (ATLL) is an aggressive Human T-cell Leukemia Virus Type 1 (HTLV-1)-driven malignancy. Although Western hemisphere (Afro-Caribbean and South American) patients face worse prognoses, our understanding of ATLL molecular drivers derives mostly from Japanese studies. We performed multi-omic analyses to elucidate the genomic landscape of ATLL in Western cohorts. Recurrent deletion and/or damaging mutations involving FOXO3, ANKRD11, DGKZ, and PTPN6 implicate these genes as potential tumor suppressors. RNA-seq, published functional data and in vitro assays support the roles of ANKRD11 and FOXO3 as regulators of T-cell proliferation and apoptosis in ATLL, respectively. Survival data suggest ANKRD11 mutation may confer a worse prognosis. Japanese and Western cohorts, in addition to acute and lymphomatous subtypes, demonstrated distinct molecular patterns. GATA3 deletion was associated with unfavorable chronic cases. IRF4 and CARD11 mutations frequently emerged in relapses after interferon therapy. Our findings reveal novel putative ATLL driver genes and clinically relevant differences between Japanese and Western ATLL patients.

Recent advances in deep learning (DL) have led many contemporary automatic modulation classification (AMC) techniques to use deep networks in classifying the modulation type of incoming signals at the receiver. However, current DL-based methods face scalability challenges, particularly when encountering unseen modulations or input signals from environments not present during model training, making them less suitable for real-world applications like software-defined radio devices. In this paper, we introduce a scalable AMC scheme that provides flexibility for new modulations and adaptability to input signals with diverse configurations. We propose the Meta-Transformer, a meta-learning framework based on few-shot learning (FSL) to acquire general knowledge and a learning method for AMC tasks. This approach empowers the model to identify new unseen modulations using only a very small number of samples, eliminating the need for complete model retraining. Furthermore, we enhance the scalability of the classifier by leveraging main-sub transformer-based encoders, enabling efficient processing of input signals with diverse setups. Extensive evaluations demonstrate that the proposed AMC method outperforms existing techniques across all signal-to-noise ratios (SNRs) on RadioML2018.01A. The source code and pre-trained models are released at https://github.com/cheeseBG/meta-transformer-amc.

More than a year has passed after the 6th edition of ‘List of Plant Diseases in Korea (LPDK)’ was published in April 2022. The 6.1st edition (2023) of List of Plant Diseases in Korea was made by correcting errors found in the 6th edition of list and adding new diseases reported after the 6th edition. There were 397 corrections from the 6th edition, most of which were simple spelling errors or minor issues. However, 12 diseases were deleted due to duplication or unclear literature proof, and 2 diseases had their diseases’ common names changed. We added 158 diseases that were reported before 2021 but not included in the 6th edition, or reported after the 6th edition. After all, 146 diseases were added to the 6,534 diseases in the 6th edition, resulting in a total of 6,680 diseases in the 6.1st edition. Thirty host taxa were also added, increasing the number from 1,390 in the 6th edition to 1,420 in the 6.1st edition. Pathogens were also added to 62 taxa, from 2,400 in the 6th edition, bringing the total to 2,462 taxa in the 6.1st edition. Ultimately, the 6.1st edition (2023) of ‘The List of Plant Diseases in Korea’ contains 6,680 diseases caused by pathogens of 2,462 taxa on 1,420 hosts. The 6.1st edition is not printed as a book, but is provided through the online ‘List of Plant Diseases in Korea’ (https://genebank. rda.go.kr/kplantdisease.do).

AbstractSpecies in the genus Trametes (Basidiomycota, Polyporales) have been used in natural medicine for a long time. Many studies reported that mycelia or fruiting bodies of Trametes spp. exhibited effects of antioxidant, anti-inflammatory, anticancer, and antimicrobial activities. However, comparative analysis in this genus is scarce due to limitation of morphological identification and the sample number. In this study, the 19 strains of seven Trametes species were chosen to generate a five-gene-based phylogeny with the 31 global references. In addition, 39 culture extracts were prepared for 13 strains to test for anticancer and antibacterial activities. Strong anticancer activities were found in several extracts from T. hirsuta and T. suaveolens. Anticancer activities of T. suaveolens, T. cf. junipericola and T. trogii were first described here. The antibacterial ability of T. versicolor and T. hirsuta extracts has been confirmed. The antibacterial activities of T. suaveolens have been reported at the first time in this study. These results suggest an efficient application of the genus Trametes as the drug resources especially for anticancer agents.