Recall Time Research Articles

External Cervical Resorption: A Volumetric Analysis on Evolution of Defects over Time

IntroductionThe aim of this study was to assess the temporal evolution of external cervical resorption (ECR) defects using a volumetric quantification method. MethodsCone-beam computed tomographic (CBCT) images of patients diagnosed with ECR who chose not to receive treatment and attended recalls were collected. ECR defects were segmented in CBCT images at baseline and recall, and their volumes were quantified. The volumetric ratio of resorption defects/teeth was calculated. Three-dimensional classification of defects at baseline and recall and the prevalence of root surface perforations ≥1 mm were determined. The Wilcoxon matched pairs signed rank test, chi-square test, and linear regression models were used to analyze the data. ResultsFifteen patients with 20 teeth diagnosed with ECR and an average recall time of 21 months were included. Nine (45%) teeth showed a change in 3-dimensional classification at recall. The volume of resorption defects (P = .0001) and the volumetric ratio of resorption defects/teeth (P = .0001) increased over time. The prevalence of root surface perforations ≥1 mm was higher at recall (n = 17, 85%) compared with baseline (n = 9, 45%) (P = .008). Linear regression models showed significant associations between the resorption defect volume at recall compared with baseline (P < .0001; 95% confidence interval, 0.053–0.081) and the volumetric ratio of resorption defects/teeth at recall compared with baseline (P < .0001; 95% confidence interval, 0.205-0.356). There was no association between the volume of resorption defects at recall with the length of the recall period, sex, or age (P > .05). ConclusionsWhen left untreated, ECR defects can increase in size and develop more root surface perforations. ECR has a dynamic nature, and its volumetric increase over time does not result from uniform/linear expansion of the defects.

A Real-Time Detection Algorithm for Sweet Cherry Fruit Maturity Based on YOLOX in the Natural Environment

Fast, accurate, and non-destructive large-scale detection of sweet cherry ripeness is the key to determining the optimal harvesting period and accurate grading by ripeness. Due to the complexity and variability of the orchard environment and the multi-scale, obscured, and even overlapping fruit, there are still problems of low detection accuracy even using the mainstream algorithm YOLOX in the absence of a large amount of tagging data. In this paper, we proposed an improved YOLOX target detection algorithm to quickly and accurately detect sweet cherry ripeness categories in complex environments. Firstly, we took a total of 2400 high-resolution images of immature, semi-ripe, and ripe sweet cherries in an orchard in Hanyuan County, Sichuan Province, including complex environments such as sunny days, cloudy days, branch and leaf shading, fruit overlapping, distant views, and similar colors of green fruits and leaves, and formed a dataset dedicated to sweet cherry ripeness detection by manually labeling 36068 samples, named SweetCherry. On this basis, an improved YOLOX target detection algorithm YOLOX-EIoU-CBAM was proposed, which embedded the Convolutional Block Attention Module (CBAM) between the backbone and neck of the YOLOX model to improve the model’s attention to different channels, spaces capability, and replaced the original bounding box loss function of the YOLOX model with Efficient IoU (EIoU) loss to make the regression of the prediction box more accurate. Finally, we validated the feasibility and reliability of the YOLOX-EIoU-CBAM network on the SweetCherry dataset. The experimental results showed that the method in this paper significantly outperforms the traditional Faster R-CNN and SSD300 algorithms in terms of mean Average Precision (mAP), recall, model size, and single-image inference time. Compared with the YOLOX model, the mAP of this method is improved by 4.12%, recall is improved by 4.6%, F-score is improved by 2.34%, while model size and single-image inference time remain basically comparable. The method in this paper can cope well with complex backgrounds such as fruit overlap, branch and leaf occlusion, and can provide a data base and technical reference for other similar target detection problems.

Investigation and analysis of four countries’ recalls of osteosynthesis implants and joint replacement implants from 2011 to 2021

BackgroundMedical devices are used in almost all orthopedic surgical subspecialties, and the frequency of adverse events is increasing, which should not be ignored. To provide suggestions on how to avoid implant recalls from the perspective of manufacturers, medical institutions and supervisions, as well as how to respond promptly to adverse events.MethodsThe research extracted recalls of osteosynthesis implants and joint replacement implants from January 1, 2011, to June 30, 2021, in the CNMPA, FDA, HC and ATGA websites and collected the information on device name, recall time, recall class, recall manufacturer, device classification and affected areas. Moreover, the McKinsey 7S model and fishbone diagram were used to analyze recall reasons.ResultsA total of 315 cases of osteosynthesis implants and 286 cases of joint replacement implants were reported in China, the USA, Canada and Australia. The recalls number from 2016 to 2021 was more than that from 2011 to 2015 for osteosynthesis implant (p = 0.012) and joint replacement implant (p = 0.002), and both mainly focused on class II (76.19% and 78.32%). There were statistical differences in the four countries for both implants (p = 0.000), especially osteosynthesis implant between China and the USA (p = 0.000), China and Canada (p = 0.001), the USA and Australia (p = 0.002), and joint replacement implant between China and Australia (p = 0.000).ConclusionsTo avoid the recalls of such implants, manufacturers should strictly select implant materials and components, develop detailed labels and instructions, severely control the packaging process and establish the integrity of medical device data. Medical institutions should standardize procurement procedures, use qualified equipment and train medical workers. It also requires supervisions to conduct premarket safety assessments. In addition, regulators should strengthen supervision and establish reporting systems to deal with the occurrence of adverse events promptly.

Consumption Behavior Prediction Based on Multiobjective Evolutionary Algorithm

Consumption behavior prediction reveals customer attributes, personal preferences, and intrinsic laws. Organizations would benefit from knowing further about customer needs and business desires by monitoring client behavior to provide more precise recommendations and boost acquisition rates. The economics of the customer, buyer groupings, and product quality are only a few of the numerous variables that influence customer behavior. The key issue that has to be resolved at this time is how to filter out useful information from these vast amounts of data to forecast customer behavior. For customer consumption behavior prediction and analysis with an advanced quantitative research process, we proposed the multiobjective evolutionary algorithm, which significantly boosts the accuracy of consumption behavior predictions. The dataset is initially gathered based on consumer preferences and behaviors as the essential information for the entire prediction model. Min-max normalization is used as a component of the preprocessing of the data to get the elimination of redundant and superfluous data. The Word2vec model is utilized for feature extraction, and boosted ant colony optimization (BACO) is employed to choose the best features. Utilizing the suggested multiobjective evolutionary algorithm (MOEA), the predictions are made. The suggested system’s performance is assessed, and the metrics are contrasted with more established methods. The findings demonstrate that the suggested MOEA technique performs well than the traditional ML, XGB, AI, and HNB algorithm methods in terms of accuracy (95 percent), quality of prediction (97 percent), precision (99 percent), recall (93 percent), F 1 -score (98 percent), and prediction time (50 seconds). Hence, the outcomes show that the regression model is sustainable. The suggested consumption behavior prediction system has demonstrated its efficiency in boosting profitability.

Efficient, Lightweight Cyber Intrusion Detection System for IoT Ecosystems Using MI2G Algorithm

The increase in internet connectivity has led to an increased usage of the Internet of Things (IoT) and devices on the internet. These IoT devices are becoming the backbone of Industry 4.0. The dependence on IoT devices has made them vulnerable to cyber-attacks. IoT devices are often deployed in harsh conditions, challenged with less computational costs, and starved with energy. All these limitations make it tough to deploy accurate intrusion detection systems (IDSs) in IoT devices and make the critical IoT ecosystem more susceptible to cyber-attacks. A new lightweight IDS and a novel feature selection algorithm are introduced in this paper to overcome the challenges of computational cost and accuracy. The proposed algorithm is based on the Information Theory models to select the feature with high statistical dependence and entropy reduction in the dataset. This feature selection algorithm also showed an increase in performance parameters and a reduction in training time of 27–63% with different classifiers. The proposed IDS with the algorithm showed accuracy, Precision, Recall, and F1-Score of more than 99% when tested with the CICIDS2018 dataset. The proposed IDS is competitive in accuracy, Precision, Recall, and training time compared to the latest published research. The proposed IDS showed consistent performance on the UNSWNB15 dataset.

Improving prediction of plant disease using k-efficient clustering and classification algorithms

&lt;span lang="EN-US"&gt;Because plant disease is main cause of most plants’ damage, improving prediction plans for early detection of plant where it has disease or not is an essential interest of decision makers in the agricultural sector for providing proper plant care at appropriate time. Clustering and classification algorithms have proven effective in early detection of plant disease. Making clusters of plants with similar features is an excellent strategy for analyzing features and providing an overview of care quality provided to similar plants. Thus, in this article, we present an artificial intelligence (AI) model based on k-nearest neighbors (k-NN) classifier and k-efficient clustering that integrates k-means with k-medoids to take advantage of both k-means and k-medoids to improve plant disease prediction strategies. Objectives of this article are to determine performance of k-mean, k-medoids and k-efficient also we compare k-NN before clustering and with clustering in prediction of soybean disease for selecting best one for plant disease forecasting. These objectives enable us to analysis data of plant that help to understand nature of plant. Results indicate that k-NN with k-efficient is more efficient than other in terms of inter-class, intra-class, normal mutual information (NMI), accuracy, precision, recall, F-measure, and running time.&lt;/span&gt;

The Discovery of Data-Driven Temporal Dietary Patterns and a Validation of Their Description Using Energy and Time Cut-Offs.

Data-driven temporal dietary patterning (TDP) methods were previously developed. The objectives were to create data-driven temporal dietary patterns and assess concurrent validity of energy and time cut-offs describing the data-driven TDPs by determining their relationships to BMI and waist circumference (WC). The first day 24-h dietary recall timing and amounts of energy for 17,915 U.S. adults of the National Health and Nutrition Examination Survey 2007–2016 were used to create clusters representing four TDPs using dynamic time warping and the kernel k-means clustering algorithm. Energy and time cut-offs were extracted from visualization of the data-derived TDPs and then applied to the data to find cut-off-derived TDPs. The strength of TDP relationships with BMI and WC were assessed using adjusted multivariate regression and compared. Both methods showed a cluster, representing a TDP with proportionally equivalent average energy consumed during three eating events/day, associated with significantly lower BMI and WC compared to the other three clusters that had one energy intake peak/day at 13:00, 18:00, and 19:00 (all p < 0.0001). Participant clusters of the methods were highly overlapped (>83%) and showed similar relationships with obesity. Data-driven TDP was validated using descriptive cut-offs and hold promise for obesity interventions and translation to dietary guidance.

Effective Fault Detection and Diagnosis for Power Converters in Wind Turbine Systems Using KPCA-Based BiLSTM

The current work presents an effective fault detection and diagnosis (FDD) technique in wind energy converter (WEC) systems. The proposed FDD framework merges the benefits of kernel principal component analysis (KPCA) model and the bidirectional long short-term memory (BiLSTM) classifier. In the developed FDD approach, the KPCA model is applied to extract and select the most effective features, while the BiLSTM is utilized for classification purposes. The developed KPCA-based BiLSTM approach involves two main steps: feature extraction and selection, and fault classification. The KPCA model is developed in order to select and extract the most efficient features and the final features are fed to the BiLSTM to distinguish between different working modes. Different simulation scenarios are considered in this study in order to show the robustness and performance of the developed technique when compared to the conventional FDD methods. To evaluate the effectiveness of the proposed KPCA-based BiLSTM approach, we utilize data obtained from a healthy WTC, which are then injected with several fault scenarios: simple fault generator-side, simple fault grid-side, multiple fault generator-side, multiple fault grid-side, and mixed fault on both sides. The diagnosis performance is analyzed in terms of accuracy, recall, precision, and computation time. Furthermore, the efficiency of fault diagnosis is shown by the classification accuracy parameter. The experimental results show the efficiency of the developed KPCA-based BiLSTM technique compared to the classical FDD techniques (an accuracy of 97.30%).

Similarity Identification Based on Word Trigrams Using Exact String Matching Algorithms

Several studies regarding excellent exact string matching algorithms can be used to identify similarity, including the Rabin-Karp, Winnowing, and Horspool Boyer-Moore algorithms. In determining similarities, the Rabin-Karp and Winnowing algorithms use fingerprints, while the Horspool Boyer-Moore algorithm uses a bad-character table. However, previous research focused on identifying similarities using these algorithms based on character n-gram. In contrast, identification based on the word n-gram to determine the similarity based on its linguistic meaning, especially for longer strings, had not been covered yet. Therefore, a word-level trigram was proposed to identify similarities based on the word trigrams using the three algorithms and compare each performance. Based on precision, recall, and running time comparison, the Rabin-Karp algorithm results were 100%, 100%, and 0.19 ms, respectively; the Winnowing algorithm results with the smallest window were 100%, 56%, and 0.18 ms, respectively; and the Horspool algorithm results were 100%, 100%, and 0.06 ms. From these results, it can be concluded that the performance of the Horspool Boyer-Moore algorithm is better in terms of precision, recall, and running time.

The effects of a utility value intervention combined with task relevance instructions

The current study examined the effect of combining a utility value intervention with task relevance instructions on utility value, recall, and reading time. A utility value intervention is an activity that assists students in making relevance connections between course content and their lives, while task relevance instructions are pre-reading cues designed to help readers determine the relevance of information to a reading goal. A total of 197 undergraduate students enrolled in introductory psychology courses participated in the study. Participants were randomly assigned to read a text as a prospective tourism coordinator of Honduras or Pitcairn or read a text about four remote countries. The intervention increased the utility value of relevant text information, meaning that students for whom Honduras information was relevant found Honduras text information more valuable than Pitcairn information. Similarly, students in the Pitcairn group found Pitcairn text information more useful than Honduras information. Participants also recalled more relevant content than irrelevant text information. This means that students in the Honduras group recalled more Honduras information than Pitcairn information. Likewise, participants in the Pitcairn condition recalled more Pitcairn information than Honduras information. Interestingly, the combined approach impacted reading times differently across experimental conditions. In particular, participants in the Honduras group spent more time reading Honduras text segments than Pitcairn information. However, students in the Pitcairn condition paid equal attention to Pitcairn and Honduras text information. The study has important theoretical implications and practical value.

Marijuana Content on Digital Media and Marijuana Use among Young People in the United States.

Health behavior theory establishes that exposure to media messages about a topic influences related knowledge, attitudes, and behavior. Marijuana-related messages proliferating on digital media likely affect attitudes and behavior about marijuana. Most research studying marijuana-related media effects on behavior relies on self-reported survey measures, which are subject to bias; people find it difficult to recall timing, frequency, and sources of messages. We calculated an exogenous measure of exposure to marijuana-related messages on digital media based on emerging public communication environment (PCE) theory. Aggregated online searches and social media posts related to marijuana for a given place reflect the marijuana-related PCE, where people are exposed to and engage with messages from multiple sources. Exogenous measures overcome bias in self-reported exposure and outcome data: simultaneity bias and endogeneity. The PCE reflects both potential exposure and relative importance of the topic in the local community, which may influence real-world marijuana use. Using 2017 Twitter and Google Search data, we measured the marijuana-related PCE to quantify where opportunities for exposure to marijuana-related posts were high and examined relationships between potential exposure and current marijuana use among youth and young adults in 2018. We found that marijuana-related online search and tweeting at the media market level are associated with offline marijuana use, controlling for demographics and state marijuana policy. The marijuana-related digital media environment may reflect and/or influence youth and young adult marijuana use. Social media and online search data offer platforms to monitor the marijuana-related PCE and supplement survey data to study media exposure and marijuana use behavior.

Lung Cancer Segmentation and Classification Using Integration of Convolutional Neural Network &amp; Unet Network Over CT Images: A Deep Learning Approach

Purpose: Cellular breakdown in the lungs screening is a cycle that is utilized to recognize the presence of cancer in the lungs in any case. Mostly it occurs among the elderly, especially smokers. Lung infections are lung-affecting illnesses that impede the respiratory mechanism. Cellulose breakdown in the lungs is one of the top causes of mortality in people all over the globe. Early recognition can improve endurance chances. As the world is revolutionizing with so many emerging technologies and one of the most popular technology is Deep Learning (DL) which has shown tremendous development in medical fields. So this paper brings an effective deep learning framework for lung cancer detection. Objective: To develop a CAD system for efficient lung cancer detection from CT images using a combination of CNN and UNET. For improving accuracy in the proposed system a better feature extraction and feature selection techniques is incorporate, i.e., by using autoencoder and selection based on Kernal function for effective boostings. Methodology: For this deep learning framework, the following are the stages. (a) Data Collection from the popular repository IQ-OTHNCCD lung cancer dataset which contains CT images of a total of 1198 from 110 CT slice cases, (b) Preprocessing CT images with an alpha-trimmed mean filter and CLAHE for improved enhancement, (c) Segmentation using CNN for segmenting the cancer regions) with the use of an autoencoder, extracting characteristics like area, perimeter, centroid, and mean intensity) feature selection using kernel function and finally f) classification using UNet network. Findings/Result: In terms of accuracy, sensitivity, specificity, recall, precision, F1-score, detection rate, TPR, FPR, and computation time, experiments are carried out on a range of cutting-edge models, and our suggested model surpasses them all (accuracy:0.95, sensitivity:0.97, specificity:0.98, detection rate:0.94). Originality: This paper is incorporating 2 neural networks over main stages such as segmentation and classification which eventually improves the quality of the model higher and also these are performed over real-time public medical records which shows the novelty of the model. Paper type: Methodology paper

A Deep Learning Modified Neural Network(DLMNN) based proficient sentiment analysis technique on Twitter data

ABSTRACT The rapid enhancement in social media over the internet generates massive information in real-time scenarios, which has a striking impact on big data analysis. It resulted in the elevated usage of emotions and sentiments in social media. This paper proffers a proficient sentiment analysis technique in Twitter data. The Twitter database is preprocessed includes, stemming, tokenisation, number removal and stop word removal, etc. The preprocessed words are then passed into the HDFS (Hadoop Distributed File System) to reduce the repeated words and are eliminated using the MapReduce technique. The emoticons and the non-emoticons are extorted as features. The resulted features are ranked with their intended meaning. Then, the classification is performed utilising the DLMNN (Deep Learning Modified Neural Network). The experimental results were examined by using the output parameter such as Accuracy, Recall, Precision, F-Score and Execution Time with other conventional techniques such as ANN, SVM, K-Means and DCNN to show the greatest outcome of the proposed model. Evaluation result shows that DLMNN achieved the greatest performance in terms of precision (95.78%), Recall (95.84%), F-Score (95.87%) and Accuracy (91.65%) when compared with the existing models.

LGMSU-Net: Local Features, Global Features, and Multi-Scale Features Fused the U-Shaped Network for Brain Tumor Segmentation

Brain tumors are one of the deadliest cancers in the world. Researchers have conducted a lot of research work on brain tumor segmentation with good performance due to the rapid development of deep learning for assisting doctors in diagnosis and treatment. However, most of these methods cannot fully combine multiple feature information and their performances need to be improved. This study developed a novel network fusing local features representing detailed information, global features representing global information, and multi-scale features enhancing the model’s robustness to fully extract the features of brain tumors and proposed a novel axial-deformable attention module for modeling global information to improve the performance of brain tumor segmentation to assist clinicians in the automatic segmentation of brain tumors. Moreover, positional embeddings were used to make the network training faster and improve the method’s performance. Six metrics were used to evaluate the proposed method on the BraTS2018 dataset. Outstanding performance was obtained with Dice score, mean Intersection over Union, precision, recall, params, and inference time of 0.8735, 0.7756, 0.9477, 0.8769, 69.02 M, and 15.66 millisecond, respectively, for the whole tumor. Extensive experiments demonstrated that the proposed network obtained excellent performance and was helpful in providing supplementary advice to the clinicians.

Novel Stacking Classification and Prediction Algorithm Based Ambient Assisted Living for Elderly

Recognition of human activity is a significant area of research with numerous uses. In developed countries, the rising age of citizens requires the improvement of the medical service structure, which raises the price of resources, both financial and human. In that sense, ambient assisted living (AAL) is a relatively novel information and communication technology (ICT) that presents services and recognizes various products that enable older people and the disabled to live autonomously and improve their quality of life. It further assists in reducing the cost of hospital services. In the AAL environment, various sensors and devices are fixed to gather a broad range of data. Moreover, AAL will be the motivating technology for the latest care models by acting as an adjunct. This will become thought-provoking research in a fast-growing world, but exploring different ADL and self-classification will become a major challenge. This paper proposed a Novel Stacking Classification and Prediction (NSCP) algorithm based AAL for the elderly with Multi-strategy Combination based Feature Selection (MCFS) and Novel Clustering Aggregation (NCA) algorithms. This paper’s main aim is to recognize the activity of older people, such as standing, walking, sitting, falling, cramps, and running. The dataset is derived from the Kaggle repository, which refers to data collection from wearable IoT devices. The experimental outcomes demonstrate that the MCFS, NCA, and NSCP algorithms work more efficiently than existing feature selection, clustering, and classification algorithms, respectively, regarding the accuracy, sensitivity, specificity, precision, recall, F-measure, and execution time dataset size and the number of features. Furthermore, the NSCP algorithm provided high accuracy, precision, recall, and F-measure are 98%, 0.96, 0.95, and 0.98, respectively.

The magnitude and timing of recalled immunity after breakthrough infection is shaped by SARS-CoV-2 variants

Vaccination against SARS-CoV-2 protects from infection and improves clinical outcomes in breakthrough infections, likely reflecting residual vaccine-elicited immunity and recall of immunological memory. Here, we define the early kinetics of spike-specific humoral and cellular immunity after vaccination of seropositive individuals and after Delta or Omicron breakthrough infection in vaccinated individuals. Early longitudinal sampling revealed the timing and magnitude of recall, with the phenotypic activation of B cells preceding an increase in neutralizing antibody titers. While vaccination of seropositive individuals resulted in robust recall of humoral and T cell immunity, recall of vaccine-elicited responses was delayed and variable in magnitude during breakthrough infections and depended on the infecting variant of concern. While the delayed kinetics of immune recall provides a potential mechanism for the lack of early control of viral replication, the recall of antibodies coincided with viral clearance and likely underpins the protective effects of vaccination against severe COVID-19.

Contesting Mintzberg's five Ps for strategy: it is time for a product recall

PurposeThe purpose of this paper is to contest Mintzberg's influential “five Ps for strategy”. It exposes the negative side effect of these “five Ps” and urges a rethinking of the concept of strategy. It also points to an alternative direction for further research building on a more robust definition of strategy that does not aim to combine the five Ps but to focus instead on, and to draw boundaries around, the substance of strategy.Design/methodology/approachThe key arguments of Mintzberg's article are critically evaluated and alternative arguments are advanced.FindingsNone of the “five Ps for strategy” satisfies the criteria of a good definition. However, their impact is still evident, especially the definition of “strategy as pattern” and the idea that any decision can be “more or less” “strategic”. The “five Ps” have served their intent at the time, and their impact now is more negative than positive.Research limitations/implicationsThe “five Ps” are no longer useful in advancing the descriptive or prescriptive purposes of the strategy field. Researchers need to rethink the concept of strategy.Practical implicationsLeaders should not be confused by the “five Ps”. Instead, they should look for more rigorous and relevant definitions that help them think through their dynamic and uncertain environment.Originality/valueThis paper is probably the first to specifically contest the five definitions of strategy offered by Mintzberg and the argument behind them.

AI bot to detect fake COVID-19 vaccine certificate.

As the world is now fighting against rampant virus COVID‐19, the development of vaccines on a large scale and making it reach millions of people to be immunised has become quintessential. So far 40.9% of the world got vaccinated. Still, there are more to get vaccinated. Those who got vaccinated have the chance of getting the vaccine certificate as proof to move, work, etc., based on their daily requirements. But others create their own forged vaccine certificate using advanced software and digital tools which will create complex problems where we cannot distinguish between real and fake vaccine certificates. Also, it will create immense pressure on the government and as well as healthcare workers as they have been trying to save people from day 1, but parallelly people who have fake vaccine certificates roam around even if they are COVID/Non‐COVID patients. So, to avoid this huge problem, this paper focuses on detecting fake vaccine certificates using a bot powered by Artificial Intelligence and neurologically powered by Deep Learning in which the following are the stages: a) Data Collection, b) Preprocessing to remove noise from the data, and convert to grayscale and normalised, c) Error level analysis, d) Texture‐based feature extraction for extracting logo, symbol and for the signature we extract Crest‐Trough parameter, and e) Classification using DenseNet201 and thereby giving the results as fake/real certificate. The evaluation of the model is taken over performance measures like accuracy, specificity, sensitivity, detection rate, recall, f1‐score, and computation time over state‐of‐art models such as SVM, RNN, VGG16, Alexnet, and CNN in which the proposed model (D201‐LBP) outperforms with an accuracy of 0.94.

The CannTeen study: verbal episodic memory, spatial working memory, and response inhibition in adolescent and adult cannabis users and age-matched controls

BackgroundPreclinical and human studies suggest that adolescent cannabis use may be associated with worse cognitive outcomes than adult cannabis use. We investigated the associations between chronic cannabis use and cognitive function in adolescent and adult cannabis users and controls. We hypothesised user-status would be negatively associated with cognitive function and this relationship would be stronger in adolescents than adults.MethodsAs part of the ‘CannTeen’ project, this cross-sectional study assessed cognitive performance in adolescent cannabis users (n = 76; 16–17-year-olds), adolescent controls (n = 63), adult cannabis users (n = 71; 26–29-year-olds) and adult controls (n = 64). Users used cannabis 1–7 days/week. Adolescent and adult cannabis users were matched on cannabis use frequency (4 days/week) and time since last use (2.5 days). Verbal episodic memory (VEM) was assessed using the prose recall task, spatial working memory (SWM) was assessed using the spatial n-back task, and response inhibition was assessed with the stop-signal task. Primary outcome variables were: delayed recall, 3-back discriminability, and stop signal reaction time, respectively.ResultsUsers had worse VEM than controls (F(1,268) = 7.423, p = 0.007). There were no significant differences between user-groups on SWM or response inhibition. Null differences were supported by Bayesian analyses. No significant interactions between age-group and user-group were found for VEM, SWM, or response inhibition.ConclusionsConsistent with previous research, there was an association between chronic cannabis use and poorer VEM, but chronic cannabis use was not associated with SWM or response inhibition. We did not find evidence for heightened adolescent vulnerability to cannabis-related cognitive impairment.

Tucker's congruence regressive feature projected Tversky discriminant multiple instance learning boost data classification for school student dropout prediction

AbstractPrediction of student dropout in high school is a significant concern in education that affects both a state's education system and its financial system. Early prediction of school student dropout is not an easy issue to resolve since many factors that can influence student retention. The traditional classification techniques were used to solve this problem normally but the higher accuracy was not obtained. In order to improve the accuracy, a novel technique called Tucker's Congruence Regressive Target Feature Matching‐based Tversky Discriminant MIL Boost Data Classification (TCRTFM‐TDMBDC) is introduced. The proposed TCRTFM‐TDMBDC technique consists of four different processes namely data preprocessing, feature extraction, feature selection, and classification. At first, the data preprocessing is carried out for cleaning and altering the raw input data into a valuable and understandable format to minimize the complexity of the classification. After the preprocessing, the feature extraction is carried out by applying Modified Tucker's congruence correlative regression. Thirdly, the feature selection process is performed using Gaussian kernelized target projection feature matching to select the feature subset for accurate classification with minimum time consumption. Finally, the ensemble technique called Tversky Indxive generalized discriminant MIL boost is applied for classifying the given input student data with help of the weak learners. Based on the classification results, the student dropout prediction is accurately performed with minimum time. Experimental results reveal that the proposed technique noticeably predicts student dropout by means of prediction accuracy, precision, recall, F‐measure, and prediction time with respect to the number of student data. The discussed results illustrate that the proposed TCRTFM‐TDMBDC technique achieves higher accuracy with minimum prediction time than the state‐of‐the‐art methods.