Traditional Classification Schemes Research Articles

Empowering Library Services through User‐Centric Analysis of Co‐loan Patterns

ABSTRACTBy examining loan data from public libraries in South Korea, we seek to understand patterns in user borrowing behaviors and explore thematic connections among borrowed books. The subject headings of 55.5 million book sets borrowed by individual users on the same day were analyzed using ITEM2VEC. We have identified 40 subject heading communities through cosine similarity of each subject vector, and we have labeled each community using a large language model. Two prominent communities were identified: Global Modern Literature and Novels and Children's Literature, Fairy Tales, and Folklore. The latter community was associated with a diverse array of subjects, indicating an expansion in children's reading preferences. The study results will be useful for improving collection development and the relevance of book recommendations, as well as for incorporating user information behavior into traditional library material classification schemes.

Machine Learning for Global Bioclimatic Classification: Enhancing Land Cover Prediction through Random Forests

Traditional bioclimatic classification schemes have several inherent shortcomings; they do not represent anthropogenic impact, they contain a bias for global north representation, and they lack flexibility regarding novel climates that may arise due to climate change. Here we present an alternative approach, using a machine learning approach. We combine European Space Agency Land Cover Classification data with traditional bioclimate classification climate variables, and additional variables; latitude, elevation, and topography. We utilise a random forest algorithm to create a classification system that overcomes the limitations and biases of the traditional schemes. The algorithm produced is able to predict land cover classification globally at 0.5-degree resolution with 93% accuracy. The resulting classifications account for human impact, particularly via agriculture, are informed by the topography of a region, and avoids the biases that traditional bioclimatic schemes contain. The algorithm can provide insights into the drivers of land cover change, the spatial distribution of land cover change, the potential impacts on ecosystem services and human well-being. Furthermore, the random forest model serves as a novel approach to the prediction of future land cover, and can be used to identify regions at risk of a land cover transition. Our data-based machine learning approach produces larger land-cover changes due to climate change than a traditional bioclimatic scheme, especially in sensitive regions such as Amazonia. Overall, our new approach projects approximately 17.4 million square kilometre of land-cover change per degree celsius of global warming.

Improving Estimates of Land–Atmosphere Coupling Through a Novel Framework of Land Aridity Classification

AbstractThe evapotranspiration (ET) regime is an illustration of the water‐energy interactions at the land surface and is important for regional climate. In this study, we propose a revised framework for land aridity classification based on the ET regime and the conditional mutual information method. The proposed framework effectively captures the influences of short‐term water and energy supply on ET and highlights their potential roles in extreme events. This framework can be a supplement to the traditional classification schemes that rely on long‐term mean climate. Furthermore, we examine the sensitivity of this land aridity framework to different model physics parameterization schemes. The cumulus schemes are found to have the most important impact, followed by the radiation and microphysics schemes, while the planetary boundary layer schemes have the weakest impact. These findings provide valuable insights for the identification of land aridity and its relationship with land–atmosphere interactions.

Epileptic Seizure Classification Based on Random Neural Networks Using Discrete Wavelet Transform for Electroencephalogram Signal Decomposition

An epileptic seizure is a brief episode of symptoms and signs caused by excessive electrical activity in the brain. One of the major chronic neurological diseases, epilepsy, affects millions of individuals worldwide. Effective detection of seizure events is critical in the diagnosis and treatment of patients with epilepsy. Neurologists monitor the electrical activity in the brains of patients to identify epileptic seizures by employing advanced sensing techniques, including electroencephalograms and electromyography. Machine learning-based classification of the EEG signal can help differentiate between normal signals and the patterns associated with epileptic seizures. This work presents a novel approach for the classification of epileptic seizures using random neural network (RNN). The proposed model has been trained and tested using two publicly available datasets: CHB-MIT and BONN, provided by Children’s Hospital Boston-Massachusetts Institute of Technology and the University of Bonn, respectively. The results obtained from multiple experiments highlight that the proposed scheme outperformed traditional classification schemes such as artificial neural network and support vector machine. The proposed RNN-based model achieved accuracies of 93.27% and 99.84% on the CHB-MIT and BONN datasets, respectively.

Identification, characterization, and up-scaling of pore structure facies in the low permeability reservoirs: Insight into reservoir quality evaluation and sweet-spots analysis

The traditional pore structure classification scheme separates the qualitative pore origin classification from the quantitative pore structure parameter classification. This separation prevents a comprehensive understanding and evaluation of the pore structure and characteristics of multi-scale reservoirs. To overcome this limitation, we propose a new pore classification scheme that combines the “geological origin and structure parameters” of pore systems, introducing the term “pore structure facies” (PSF). By considering the genetic types of pores, the pore structure can be qualitatively divided into several categories, each representing different pore genesis. Within each category, the pore structure is further subdivided into subcategories based on pore structure parameters. To validate the effectiveness of the PSF scheme, we focus on the low permeability reservoirs of the Weixinan Depression in the Beibuwan Basin of the South China Sea. We identify and characterize PSF types using a multi-scale approach, including thin section, porosity-permeability, digital image analysis, mercury intrusion test, micro-CT imaging, and simulation. The results reveal that the study area can be classified into three main categories and six subcategories: intergranular homogeneous type (macroporous, mesoporous, and microporous), heterogeneous dissolution-pore type (moderate and strong dissolution), and cementation-induced few-pore type. Significant differences are observed in the two-dimensional and three-dimensional pore structure, petrophysical properties, and pore-scale flow of PSF, leading to significant variations in logging responses. Moreover, we successfully predict and verify the PSF profile using artificial neural network algorithms and field test data. The predicted PSF profile exhibits substantial differences in productivity, with the combination of PSF types serving as an indicator of productivity. We compare the quality of multi-scale reservoirs in the study area using the “point (single well) line (inter-well profile) plane (reservoir)" method based on PSF. Finally, to quantitatively characterize reservoir quality, we propose the Reservoir Sweet-spot Index (RSI), considering the reservoir thickness. By using the boundary of sedimentary facies, we generate a map of sweet-spots reservoirs that provides guidance for future energy exploration and development. The analytical scale of PSF and RSI can be flexibly adjusted, from the heterogeneity within the sand body to the distribution characteristics of a reservoir plane, offering valuable insights into reservoir quality estimation at different scales.

Simplified Light's Criteria and Acute Phase Proteins Reflect Aetiology of Feline Body Cavity Effusions Better than the Traditional Classification Scheme.

The traditional veterinary classification (TVC) of effusions based on cell count and total protein (TP) does not adequately reflect the aetiology. Light's criteria (LC) (activity of lactate dehydrogenase [LDH] in the effusion [LDHef], effusion/serum LDH ratio [LDHr], effusion/serum TP ratio [TPr]), serum-effusion albumin gradient (ALBg), acute phase proteins (APPs) [serum amyloid A (SAA), α1-acid glycoprotein (AGP), haptoglobin] might aid classification. The aim was to evaluate the utility of these parameters except LDHr in differentiating exudates from transudates. Sixty-five cats with effusions (33 peritoneal, 31 pleural, 1 pericardial), with 18 transudates and 47 exudates based on aetiological classification (AC), were included. The sensitivity, specificity and accuracy of several parameters to identify exudates (based on AC) was assessed. APPs were compared between exudates and transudates based on AC and TVC, with receiver operating characteristics analysis identifying the best APP to recognise exudates. Simplified LC (LDHef, TPr) had an accuracy of 79% and TVC of 48%. ALBg had the highest sensitivity (98%) and LDHef the highest specificity (83%) in identifying exudates in cats. All APPs but effusion SAA could differentiate exudates from transudates based on AC (effusion AGP had the largest area under the curve 0.79) but not TVC. All parameters were better than TVC in identifying exudates. The conformity of APPs with AC but not TVC favours the use of AC to classify effusions.

Circuit analysis of the Drosophila brain using connectivity-based neuronal classification reveals organization of key communication pathways.

We present a functionally relevant, quantitative characterization of the neural circuitry of Drosophila melanogaster at the mesoscopic level of neuron types as classified exclusively based on potential network connectivity. Starting from a large neuron-to-neuron brain-wide connectome of the fruit fly, we use stochastic block modeling and spectral graph clustering to group neurons together into a common "cell class" if they connect to neurons of other classes according to the same probability distributions. We then characterize the connectivity-based cell classes with standard neuronal biomarkers, including neurotransmitters, developmental birthtimes, morphological features, spatial embedding, and functional anatomy. Mutual information indicates that connectivity-based classification reveals aspects of neurons that are not adequately captured by traditional classification schemes. Next, using graph theoretic and random walk analyses to identify neuron classes as hubs, sources, or destinations, we detect pathways and patterns of directional connectivity that potentially underpin specific functional interactions in the Drosophila brain. We uncover a core of highly interconnected dopaminergic cell classes functioning as the backbone communication pathway for multisensory integration. Additional predicted pathways pertain to the facilitation of circadian rhythmic activity, spatial orientation, fight-or-flight response, and olfactory learning. Our analysis provides experimentally testable hypotheses critically deconstructing complex brain function from organized connectomic architecture.

Disk Dissipation, Giant Planet Formation, and Star Formation Rate Fluctuations in the 3 Myr History of Gould’s Belt

Although episodic star formation (SF) has been suggested for nearby SF regions, a panoramic view of the recent episodic SF history in the solar neighborhood is still missing. By uniformly constraining the slope α of infrared spectral energy distributions (SEDs) of young stellar objects (YSOs) in the 13 largest Gould’s Belt (GB) protoclusters surveyed by the Spitzer Space Telescope, we have constructed a cluster-averaged histogram of α representing the YSO evolution lifetime as a function of the α value. Complementary to the traditional SED classification scheme (0, i, f, ii, and iii) that is based on different α values, a staging scheme (A, B, C, D, and E) of SED evolution is proposed on the basis of the α statistical features that can be better matched to the physical stages of disk dissipation and giant planet formation. This has also allowed us to unravel the fluctuations of SF rate (SFR) in the 3 Myr history of these GB protoclusters. Diverse evolutionary patterns such as single peaks, double peaks, and ongoing acceleration of SFR are revealed. The SFR fluctuations are between 20% and 60% (∼40% on average) and no dependence on the average SFR or the number of SFR episodes is found. However, spatially close protoclusters tend to have similar SFR fluctuation trends, indicating that the driving force of the fluctuations should be at size scales beyond the typical cluster sizes of several parsecs.

The Effect of Regional Winds on Subtidal Along‐Inlet Currents at Oregon Inlet, NC

AbstractWind influence on tidal inlet hydrodynamics is examined using 40 days of wind, water level, and current observations collected in Spring 2019 at Oregon Inlet, NC, a large (1 km wide, 1–13 m deep) meso‐tidal inlet with complex delta systems. Wind velocities through the inlet (ranging 0–18 m/s) are modulated at subtidal timescales and are well correlated (R = 0.87) to a subtidal component of the water level slope through the inlet. The subtidal wind and water level slope are also well correlated to the subtidal current along the principal flow axis in the main inlet channel (R = 0.92 and 0.96, respectively). In combination with findings from previous studies, these findings suggest that regional winds induce the subtidal water level slope through the inlet by causing opposing setup/setdown to either side of the inlet. A force balance at the inlet demonstrates that the wind‐induced pressure gradient forces the subtidal currents, with wave forcing and local wind shear acting as lower‐order influences. The magnitude of the subtidal current is substantial, exceeding that of the tidal currents 45% of the time. Cumulatively, these findings indicate that regional winds exert a first‐order control on the currents at Oregon Inlet and cause irregular hydrodynamic patterns not well described by the traditional inlet classification scheme. Regional geographic characteristics may contribute to the high level of wind influence at Oregon Inlet, but similar processes are likely to be important to net flow dynamics at other inlets with large, shallow inland water bodies.

Malicious Code Classification Method Based on Deep Residual Network and Hybrid Attention Mechanism for Edge Security

Edge computing is a feasible solution for effectively collecting and processing data in industrial Internet of Things (IIoT) systems, and edge security is an important guarantee for edge computing. Fast and accurate classification of malicious code in the whole lift cycle of edge computing is of great significance, which can effectively prevent malicious code from attacking wireless sensor networks and ensure the stable and secure transmission of data in smart devices. Considering that there is a large amount of code reuse in the same malicious code family, making their visual feature similar, many studies use visualization technology to assist malicious code classification. However, traditional malicious code visual classification schemes have the problems such as single image source, weak ability of deep-level feature extraction, and lack of attention to key image details. Therefore, an innovative malicious code visual classification method based on a deep residual network and hybrid attention mechanism for edge security is proposed in this study. Firstly, the malicious code visualization scheme integrates the bytecode file and assembly file of the malware and converts them into a four-channel RGBA image to fully represent malicious code feature information without increasing the computational complexity. Secondly, a hybrid attention mechanism is introduced into the deep residual network to construct an effective classification model, which extracts image texture features of malicious code from two dimensions of the channel and spatial to improve the classification performance. Finally, the experimental results on the BIG2015 and Malimg datasets show that the proposed scheme is feasible and effective and can be widely applied used in various malicious code classification issues, and the classification accuracy rate is relatively higher than the existing better-performing malicious code classification methods.

Using Big Data to Model Brain and Behavioral Heterogeneity in Neurodevelopmental Disorders

Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are characterized by considerable phenotypic heterogeneity and comorbidity. Traditional diagnostic classification schemes are imperfect predictors of etiology and treatment response. Alternative systems such as the Research Domain Criteria (RDoC) focus on the full range of variation in behavior across clinical and non-clinical populations. As neurodevelopmental disorders are often accompanied by executive function (EF) deficits (Dajani and Uddin, 2015), we focus on this set of abilities.

Fad Foods & FPIES – Is Avocado Really a Low Risk FPIES Food?

Food protein-induced enterocolitis syndrome (FPIES) is a non-IgE mediated food allergy with vomiting 1-4 hours after trigger ingestion and diarrhea within 24 hours. Our center appreciated an increase in avocado triggered FPIES and desired to stratify frequency compared to traditional classification schemes.

Research on Chest Disease Recognition Based on Deep Hierarchical Learning Algorithm.

Chest X-ray has become one of the most common ways in diagnostic radiology exams, and this technology assists expert radiologists with finding the patients at potential risk of cardiopathy and lung diseases. However, it is still a challenge for expert radiologists to assess thousands of cases in a short period so that deep learning methods are introduced to tackle this problem. Since the diseases have correlations with each other and have hierarchical features, the traditional classification scheme could not achieve a good performance. In order to extract the correlation features among the diseases, some GCN-based models are introduced to combine the features extracted from the images to make prediction. This scheme can work well with the high quality of image features, so backbone with high computation cost plays a vital role in this scheme. However, a fast prediction in diagnostic radiology is also needed especially in case of emergency or region with low computation facilities, so we proposed an efficient convolutional neural network with GCN, which is named SGGCN, to meet the need of efficient computation and considerable accuracy. SGGCN used SGNet-101 as backbone, which is built by ShuffleGhost Block (Huang et al., 2021) to extract features with a low computation cost. In order to make sufficient usage of the information in GCN, a new GCN architecture is designed to combine information from different layers together in GCNM module so that we can utilize various hierarchical features and meanwhile make the GCN scheme faster. The experiment on CheXPert datasets illustrated that SGGCN achieves a considerable performance. Compared with GCN and ResNet-101 (He et al., 2015) backbone (test AUC 0.8080, parameters 4.7M and FLOPs 16.0B), the SGGCN achieves 0.7831 (−3.08%) test AUC with parameters 1.2M (−73.73%) and FLOPs 3.1B (−80.82%), where GCN with MobileNet (Sandler and Howard, 2018) backbone achieves 0.7531 (−6.79%) test AUC with parameters 0.5M (−88.46%) and FLOPs 0.66B (−95.88%).

An Efficient Internet Traffic Classification System Using Deep Learning for IoT

Internet of Things (IoT) defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location. These IoT devices are connected to a network therefore prone to attacks. Various management tasks and network operations such as security, intrusion detection, Quality-of-Service provisioning, performance monitoring, resource provisioning, and traffic engineering require traffic classification. Due to the ineffectiveness of traditional classification schemes, such as port-based and payload-based methods, researchers proposed machine learning-based traffic classification systems based on shallow neural networks. Furthermore, machine learning-based models incline to misclassify internet traffic due to improper feature selection. In this research, an efficient multilayer deep learning based classification system is presented to overcome these challenges that can classify internet traffic. To examine the performance of the proposed technique, Moore-dataset is used for training the classifier. The proposed scheme takes the pre-processed data and extracts the flow features using a deep neural network (DNN). In particular, the maximum entropy classifier is used to classify the internet traffic. The experimental results show that the proposed hybrid deep learning algorithm is effective and achieved high accuracy for internet traffic classification, i.e., 99.23%. Furthermore, the proposed algorithm achieved the highest accuracy compared to the support vector machine (SVM) based classification technique and k-nearest neighbours (KNNs) based classification technique.

New Insight regarding Legionella Non-Pneumophila Species Identification: Comparison between the Traditional mip Gene Classification Scheme and a Newly Proposed Scheme Targeting the rpoB Gene.

ABSTRACTThe identification of Legionella non-pneumophila species (non-Lp) in clinical and environmental samples is based on the mip gene, although several studies suggest its limitations and the need to expand the classification scheme to include other genes. In this study, the development of a new classification scheme targeting the rpoB gene is proposed to obtain a more reliable identification of 135 Legionella environmental isolates. All isolates were sequenced for the mip and rpoB genes, and the results were compared to study the discriminatory power of the proposed rpoB scheme. Complete concordance between the mip and rpoB results based on genomic percent identity was found for 121/135 (89.6%) isolates; in contrast, discordance was found for 14/135 (10.4%) isolates. Additionally, due to the lack of reference values for the rpoB gene, inter- and intraspecies variation intervals were calculated based on a pairwise identity matrix that was built using the entire rpoB gene (∼4,107 bp) and a partial region (329 bp) to better evaluate the genomic identity obtained. The interspecies variation interval found here (4.9% to 26.7%) was then proposed as a useful sequence-based classification scheme for the identification of unknown non-Lp isolates. The results suggest that using both the mip and rpoB genes makes it possible to correctly discriminate between several species, allowing possible new species to be identified, as confirmed by preliminary whole-genome sequencing analyses performed on our isolates. Therefore, starting from a valid and reliable identification approach, the simultaneous use of mip and rpoB associated with other genes, as it occurs with the sequence-based typing (SBT) scheme developed for Legionella pneumophila, could support the development of multilocus sequence typing to improve the knowledge and discovery of Legionella species subtypes.IMPORTANCE Legionella spp. are a widely spread bacteria that cause a fatal form of pneumonia. While traditional laboratory techniques have provided valuable systems for Legionella pneumophila identification, the amplification of the mip gene has been recognized as the only useful tool for Legionella non-pneumophila species identification both in clinical and environmental samples. Several studies focused on the mip gene classification scheme showed its limitations and the need to improve the classification scheme, including other genes. Our study provides significant advantages on Legionella identification, providing a reproducible new rpoB gene classification scheme that seems to be more accurate than mip gene sequencing, bringing out greater genetic variation on Legionella species. In addition, the combined use of both the mip and rpoB genes allowed us to identify presumed new Legionella species, improving epidemiological investigations and acquiring new understanding on Legionella fields.

A new perspective for support design of shallow tunnels in mudstone formation: basic philosophy and engineering practice

The traditional support design of rock tunnels mainly relies on the comparison of the geological condition and tunnel characteristics with those prescribed in rock mass classification schemes. The supporting patterns and supporting parameters can then be empirically determined in accordance with the classified grades of rock masses. However, a variety of rock mass classification schemes are used worldwide, which require different input indices. Different classification schemes may lead to varied design suggestions for the same project. Thus, determining the most reliable suggestion presents a challenge. In addition, most of the traditional rock mass classification schemes neglect the intrinsic and distinct properties of rock masses and often either underestimate or overestimate the necessary support requirements. In this study, a new design philosophy is proposed by emphasizing the importance of considering the intrinsic properties of rock mass. Based on a tunneling project in Pakistan, we first determined the basic mechanical properties of mudstone, which is the major formation in the tunneling region. Then, a series of model tests was conducted to analyze the tunnel stability under different scenarios, from which a reduced supporting system that was weaker and more flexible than those determined from the classic rock mass classification schemes was derived. Practical application showed that the proposed supporting system was adequate and reliable. Apart from referring to the rock mass classification scheme, the current study highlights the importance of considering the mechanical properties of rock masses for tunnel support design.

Polyphase Taxonomy of Antarctic Bacteria

The phylogenetic structure of bacteria is not always consistent with the traditional classification scheme based on the phenotypic properties of bacteria. That is one of the problems of modern bacterial taxonomy. In addition, traditional methods to identify bacteria using phenotypic properties have a number of disadvantages. In recent decades, significant progress was achieved in the investigation of microbial world using molecular methods for fast identification. The aim of the study was to clarify the species status of four strains of bacteria isolated from black lichens of the cliffs of Galindez Island in the Antarctic on the basis of phenotypic and genetic analysis. Methods. Morphological and cultural properties of bacteria were studied according to generally accepted microbiological methods. Physiological and biochemical ones were investigated using test systems API Coryne and API 20E (bioMerieux SA, France), according to the manufacturer’s instructions. Phylogenetic analysis was performed based on the nucleotide sequences of the 16S rRNA gene. To identify closely related species, a comparative analysis of the nucleotide sequences of 16S rRNA genes was performed using the BLAST software package. The phylogenetic position was determined by constructing trees (dendrograms) to show the position of the studied strains among closely related and typical species (programs ClustalX 2.1, Mega 6.06). The tree was constructed applying ClustalX 2.1 by comparing the nearest neighbors with bootstrap analysis (bootstrap NJ tree) using 1000 bootstrap tests (1000 alternative trees). Then the phylogenetic tree was edited by the program Mega v. 6.00. Results. Based on the results of comparative, phylogenetic and phenotypic analysis, the studied Antarctic strains 180n1, 181n2, 188n2, 190n2 were identified as Pseudomonas fluorescens, Microbacterium foliorum, Sporosarcina aquimarina and Rothia sp., respectively. The coefficient of similarity of 16S rRNA genes of strain 180n1 with such a closely related species from the database P. fluorescens NBRC 14160 was 99.5%; 181n2 with M. foliorum P 333/02 – 99.4%; 188n2 with S. aquimarina SW28 – 99.7%. These strains form common clusters with closely related species on phylogenetic dendrograms. The strain 190n2 can be considered as Rothia sp., since has the remote position from closely related strains in the cluster Rothia and a low percentage of similarity (97.3%) with the species Rothia endophytica YIM 67072. These strains belong to the phyla: Firmicutes, Actinobacteria, Proteobacteria. Conclusions. Phylogenetic and phenotypic analyzes allowed determining the taxonomic position of isolated aerobic chemoorganotrophic microbial strains of the Antarctic. Nucleotide sequences of 16S rRNA genes are deposited in the International GenBank database under numbers HG518622, HG518623, HG518625, HG518626.

Increasing Minority Recall Support Vector Machine Model for Imbalanced Data Classification

Imbalanced data classification is gaining importance in data mining and machine learning. The minority class recall rate requires special treatment in fields such as medical diagnosis, information security, industry, and computer vision. This paper proposes a new strategy and algorithm based on a cost-sensitive support vector machine to improve the minority class recall rate to 1 because the misclassification of even a few samples can cause serious losses in some physical problems. In the proposed method, the modification employs a margin compensation to make the margin lopsided, enabling decision boundary drift. When the boundary reaches a certain position, the minority class samples will be more generalized to achieve the requirement of a recall rate of 1. In the experiments, the effects of different parameters on the performance of the algorithm were analyzed, and the optimal parameters for a recall rate of 1 were determined. The experimental results reveal that, for the imbalanced data classification problem, the traditional definite cost classification scheme and the models classified using the area under the receiver operating characteristic curve criterion rarely produce results such as a recall rate of 1. The new strategy can yield a minority recall of 1 for imbalanced data as the loss of the majority class is acceptable; moreover, it improves the g -means index. The proposed algorithm provides superior performance in minority recall compared to the conventional methods. The proposed method has important practical significance in credit card fraud, medical diagnosis, and other areas.

An argumentation enabled decision making approach for Fall Activity Recognition in Social IoT based Ambient Assisted Living systems

With the advancement in Information and Communication Technologies (ICTs), smart devices are becoming even more smart and intelligent with every passing day. Further, the evolution of speaking and hearing enabled devices in an IoT network is transforming the face of research in the Social IoT domain. However, the integration of argumentation enabled devices in Social IoT network has not been fully explored by researchers in the past. Therefore, this research work focuses on development of argument enabled Social IoT networks. In this paper, a fuzzy argument based classification scheme termed as Classification Enhanced with Fuzzy Argumentation (CleFAR) is proposed. The proposed scheme is deployed for classification of fall activities in fall prevention applications. A novel framework for fall prevention system using Fall Activity Recognition (FAR) is presented. The proposed system is designed for the purpose of fall activity recognition in smart home Ambient Assisted Living (AAL) systems. To experimentally evaluate the system’s performance, a smart home AAL environment is simulated and the inhabitant’s routine activity dataset is generated. The fall activities are simulated using wearable fall detection systems. The proposed scheme is trained and tested on generated datasets and its performance is compared with traditional classification algorithms such as Random Forest (RF), Support Vector Machines (SVM), Naive Bayes (NB), Decision Tree (DT) and Artificial Neural Networks (ANN) as well as existing argumentation based game theoretic Weighted Voting Scheme (WVS). Experimental results indicate that the proposed scheme outperforms the traditional classification schemes and WVS approach with prediction accuracy up to 91%. It turns out that the proposed approach achieves significant improvement over the existing schemes.

Human papillomavirus vaccination and all-cause morbidity in adolescent girls: a cohort study of absence from school due to illness.

BackgroundA growing body of evidence supports the safety of the human papillomavirus (HPV) vaccines. However, concerns about autonomic dysfunction syndromes and non-specific symptoms continue to linger. These conditions are not easily captured by traditional diagnostic classification schemes and call for innovative approaches to the study of vaccine safety which take more general measures of all-cause morbidity into account.MethodsTaking advantage of the unique Danish registers, including regional registration of absence from school, we conducted a cohort study of 14 068 adolescent Danish girls attending 5th through 9th grade in public schools in the municipality of Copenhagen during 1 August 2013–23 January 2018. We obtained time-varying HPV vaccination status and demographic information from nationwide registers. Using Poisson regression with random effects, we estimated rate ratios (RRs) of absence due to illness, comparing HPV-vaccinated girls with unvaccinated girls with adjustment for grade, season, calendar period, demographic factors and random effects at the individual, class and school levels.ResultsOur study included 6 206 188 school days with 213 221 days of absence from school due to illness (absence rate, 3.4%). Comparing absence rates due to illness in HPV-vaccinated and unvaccinated girls yielded an adjusted RR of 1.00 (95% confidence interval [CI], 0.98–1.03).ConclusionsOur study provides an important and novel contribution to HPV vaccine safety. Using absence from school records, we were able to address important safety concerns without relying on medical diagnoses. We conclude that HPV vaccination does not increase the risk of morbidity in any manner that manifests as absence from school due to illness.