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The continuous advancement of computer networks has given rise to grave concerns regarding security and susceptibility. Network administrators utilize intrusion detection systems (IDS) to deliver essential network security. Current IDS devices produce false alarms in response to routine user activities rather than detecting novel assaults. Neural networks may be used to overcome this problem and increase detection accuracy. In this paper, we propose a hybrid approach based on neural networks and correlation-based feature selection to detect anomalies. Experimental research is done on the standard dataset NSL-KDD for intrusion detection using current attacks. We introduce a novel hybrid crowd search analysis optimization with an artificial neural network (HCSAOANN). The findings demonstrate that it outperforms in high accuracy, precision, F1-Score, and recall. In the proposed HCSAOANN algorithm, to explore the feature space, we merged the crow search optimization (CSO), which can converge into the overall best solution in the search function, with the upgraded version of the crow search analysis method. Some performance criteria were applied in the studies using an artificial neural network (ANN) as a classifier. The HCSAOANN methodology outperformed as compared to the previous fuzzy technique and achieved 98% accuracy and a 98% precision rate, which is 2.2% better than the previous CSO-ANFIS technique and 8.87% superior to the FC-ANN technique. [ABSTRACT FROM AUTHOR]

This research investigates the utilization of machine learning methodologies for the purpose of forecasting the fluctuations in stock values inside the financial market. The application of a Random Forest classifier is utilized on a dataset including historical stock prices (namely, the KSE-100 Index) to generate predictions regarding the future movement of stocks, specifically whether they would experience an increase or decrease. The model is trained via a sliding window methodology and is assessed through the utilization of precision, recall, and F1-score criteria. The study furthermore incorporates the utilization of back testing and hyper-parameter tweaking techniques in order to enhance the performance of the model. The findings indicate that the model demonstrates a precision score of 58%, representing an enhancement compared to the previous score of 48%. Nevertheless, the model's total accuracy stands at a mere 58%, underscoring the need for future enhancements. The report additionally proposes potential avenues for future research, such as exploring alternate data sources, employing sentiment analysis techniques, and developing more advanced algorithms. The findings of this study hold significant significance for investors and financial institutions, as they highlight the potential of machine learning in facilitating informed investment decisions and improving financial forecasts and analysis.

As we navigate our day-to-day lives, we regularly adapt our behaviour according to what we predict may happen next in a given context. When an unexpected event occurs, our predictions about the world are disrupted and must be updated. Unexpected, isolated events, particularly with high emotionality, are also better recalled. In the present work, we investigated how oddballs affect recall dynamics. Seventy young, healthy participants encoded word lists containing either emotional or perceptual oddballs at varying stimulus onset asynchronies followed by free recall. It is well established that after recalling an item, we have a higher probability of recalling items encoded nearby, particularly those that were encoded after the item was recalled, a phenomenon known as forward contiguity of recall. We tested how novelty (oddballs versus control words) modulated forward contiguity as a function of salience type (emotional versus perceptual). The present results provide empirical evidence of forward contiguity modulation selectively by emotional salience and suggest that recall patterns after presenting emotional and perceptual oddballs are mediated by different mechanisms.

We do not memorize items in our surroundings with equal priority. Previous literature has widely shown that emotional stimuli are better remembered than neutral stimuli. However, given emotional stimuli and neutral stimuli often differ in both valence and arousal dimensions, it remains unclear whether the enhancement effects can be attributed to valence, or just to arousal. Importantly, most prior studies relied on a relatively small number of stimuli and non-emotional factors such as word length, imageability and other confounds were hard to control. To address these challenges, we analyzed multiple large databases of recognition memory and free recall tasks from previous research by items with many lexical and semantic covariates included, examining the effects of valence or arousal when controlling for each other. Our results showed a U-shaped relationship between valence and memory performance for both recognition and free recall, and a linear relationship between arousal and memory performance for both tasks. These findings showed that the memory enhancement effects can be attributed to both valence and arousal. We demonstrated these effects with generalizability across many stimuli and controlled for non-emotional factors. Together, these findings disentangle the contribution of valence and arousal in emotional memory enhancement effects and provide insights for current major theories of emotional memory.

ObjectiveMemory deficits in individuals with schizophrenia are well-established, but less is known about how schizophrenia affects metacognitive processes such as metamemory. We investigated metamemory ability using the value-directed remembering task, which assesses the degree to which participants use value cues to guide their learning of a list of items (i.e., their memory selectivity).MethodParticipants were patients undergoing treatment following a recent first episode of schizophrenia (n = 20) and demographically comparable healthy controls (n = 18). Participants viewed six lists of 24 words where each word was paired with either a low value (1-3 points) or a high value (10-12 points), and they were instructed to maximize their score on free recall tests given after each list. After the final free recall test, participants completed a recognition test where they gave remember/know judgments.ResultsOn tests of free recall, patients showed reduced memory selectivity relative to healthy controls. On the recognition test, patients failed to show an effect of value on recognition of nonrecalled words, in contrast to healthy controls, who showed a significant value effect that was characterized by greater "remember" judgments. Patients initially overestimated their memory capacity but were able to adjust their estimates to be more accurate based on task experience. Patients' self-reports of memory selectivity were unrelated to their actual memory selectivity.ConclusionsPatients with first-episode schizophrenia had substantial impairments on the value-directed remembering task, but areas of preserved metamemory ability were also observed. These findings have potential implications for cognitive training interventions. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Abstract Objective To compare forgetting patterns between patients with temporal lobe (TLE) and generalized (GGE) epilepsies and to assess whether recall is associated with epileptic activity. Methods Thirty‐three patients with TLE (13 left, 17 right, and 3 nonlateralized TLE), 42 patients with GGE, and 57 healthy controls (HCs) were asked to recall words, verbal story material, and the Rey‐Osterrieth complex figure at two delays. Accelerated long‐term forgetting (ALF) was defined by group performance comparable to HCs at 30 min and worse recall than HCs after 4 weeks. ALF was assessed by comparing raw test scores in a two‐way repeated measures analysis of variance (ANOVA) adjusted for the learning capacity. Results Compared to HCs, patients with R‐TLE remembered fewer items of the word list after 30 min as well as after 4 weeks. Patients with L‐TLE and GGE had comparable learning‐adjusted performance to HCs at the 30 min delay but scored less after 4 weeks (group by delay interaction F(3, 124) = 3.2, P = 0.026, ηp2 = 0.07). The epilepsy group (patients with TLE and GGE combined) performed as well as HCs at 30 min but worse after 4 weeks irrespective of experienced seizures during the 4‐week delay or interictal bilateral (TLE) or generalized (GGE) activity before the study. We noted no statistically significant differences between patient and HC verbal story (group by delay interaction F(3, 124) = 0.7, P = 0.570, ηp2 = 0.02) or complex figure (F(3, 124) = 0.8, P = 0.488, ηp2 = 0.02) recall. Significance Our data support verbal and visual memory impairment in both TLE and GGE with different performances between these groups in the task of word recall. We suggest the presence of ALF in patients with GGE and left TLE after adjusting for learning capacity. We could not confirm the influence of epileptic activity on long‐term forgetting patterns. Future studies are required to better define domain‐specific differences in memory impairment in TLE and GGE.

The Legal Division of PT. ABC has implemented EDMS using the Integrated Legal Document Management Application, Centralized Legal Administration System (CLAS), to make the management of documents more accessible. The urgency is the system's accuracy in retrieving data or document input. The study employed a quantitative method with an experimental approach. Data collection techniques were observation, application search tests, documentation, and literature. Data analysis regarding the recall, precision, and F-measure calculation formulas will be compared with the standard information retrieval system effectiveness. The calculation results show that the average recall value for the search equals 1 or 100%. For the precision value that uses quotation marks, it is equal to 0.9713, while for searching without quotation marks, it is equal to 0.3920. From these results, the precision value is lower than the recall value. The precision value of 0.9713 is in the range of 0.68–1.00, which is placed as high precision, meaning that information retrieval is effective. The F-measure value is 0.98, close to 1, so the tracing system is sound. CLAS is quite effective in retrieving information according to user needs.

This paper describes a rich dataset from a registered report investigating sleep’s effect on false memory in the Deese-Roediger-McDermott (DRM) paradigm. 534 young adults completed free recall either shortly or 12 hours after studying lists of semantic associates (e.g., hospital, nurse). Collected online, our recall data showcase high data quality, replicating classic behavioural effects (e.g., serial position curve). The dataset contains raw recall data with original spelling and recall order, accompanied by demographic information (e.g., gender, time-of-day preference). Its versatility supports reuse in modelling memory decay and search processes, understanding lexical effects and individual differences, and benchmarking online memory studies.

In contrast to sighted students who acquire mathematical expressions (MEs) from their visual sources, blind students must keep MEs in their memory using the Tactile or Auditory Modality. In this work, we rigorously investigate the ability to temporarily retain MEs by blind individuals when they use different input modalities: Auditory, Tactile, and Auditory–Tactile. In the experiments with 16 blind participants, we meticulously measured the users’ capacity for memory retention utilizing ME recall. Based on a robust methodology, our results indicate that the distribution of the recall errors regarding their types (Deletions, Substitutions, Insertions) and math element categories (Structural, Numerical, Identifiers, Operators) are the same across the tested modalities. Deletions are the favored recall error, while operator elements are the hardest to forget. Our findings show a threshold to the cognitive overload of the short-term memory in terms of type and number of elements in an ME, where the recall rapidly decreases. The increase in the number of errors is affected by the increase in complexity; however, it is significantly higher in the Auditory modality than in the other two. Therefore, segmenting a math expression into smaller parts will benefit the ability of the blind reader to retain it in memory while studying.

In recent years, the widespread adaptation of Unmanned Aerial Vehicles (UAVs), commonly known as drones, among the public has led to significant security concerns, prompting intense research into drones’ classification methodologies. The swift and accurate classification of drones poses a considerable challenge due to their diminutive size and rapid movements. To address this challenge, this paper introduces (i) a novel drone classification approach utilizing deep convolution and deep transfer learning techniques. The model incorporates bypass connections and Leaky ReLU activation functions to mitigate the ‘vanishing gradient problem’ and the ‘dying ReLU problem’, respectively, associated with deep networks and is trained on a diverse dataset. This study employs (ii) a custom dataset comprising both audio and visual data of drones as well as analogous objects like an airplane, birds, a helicopter, etc., to enhance classification accuracy. The integration of audio–visual information facilitates more precise drone classification. Furthermore, (iii) a new Finite Impulse Response (FIR) low-pass filter is proposed to convert audio signals into spectrogram images, reducing susceptibility to noise and interference. The proposed model signifies a transformative advancement in convolutional neural networks’ design, illustrating the compatibility of efficacy and efficiency without compromising on complexity and learnable properties. A notable performance was demonstrated by the proposed model, with an accuracy of 100% achieved on the test images using only four million learnable parameters. In contrast, the Resnet50 and Inception-V3 models exhibit 90% accuracy each on the same test set, despite the employment of 23.50 million and 21.80 million learnable parameters, respectively.

Fertility preferences have long played a key role in models of fertility differentials and change. We examine the stability of preferences over time using rich panel data on Kenyan women's fertility desires, expectations, actual fertility, and recall of desires in three waves over a nine-year period, when respondents were in their 20s. We find that although desired fertility is quite unstable, most women perceive their desires to be stable. Under hypothetical future scenarios, few expect their desired fertility to increase over time but, in fact, such increases in fertility desires are common. Moreover, when asked to recall past desires, most respondents report previously wanting exactly as many children as they desire today. These patterns of bias are consistent with the emerging view that fertility desires are contextual, emotionally laden, and structured by identity.

Many studies suggest that information about past experience, or episodic memory, is divided into discrete units called "events." Yet we can often remember experiences that span multiple events. Events that occur in close succession might simply be linked because of their proximity to one another, but we can also build links between events that occur farther apart in time. Intuitively, some kind of organizing principle should enable temporally distant events to become bridged in memory. We tested the hypothesis that episodic memory exhibits a narrative-level organization, enabling temporally distant events to be better remembered if they form a coherent narrative. Furthermore, we tested whether post-encoding memory consolidation is necessary to integrate temporally distant events. In three experiments, participants learned and subsequently recalled events from fictional stories, in which pairs of temporally distant events involving side characters ("sideplots") either formed one coherent narrative or two unrelated narratives. Across participants, we varied whether recall was assessed immediately after learning, or after a delay: 24 hours, 12 hours between morning and evening ("wake"), or 12 hours between evening and morning ("sleep"). Participants recalled more information about coherent than unrelated narrative events, in most delay conditions, including immediate recall and wake conditions, suggesting that post-encoding consolidation was not necessary to integrate temporally distant events into a larger narrative. Furthermore, post hoc modeling across experiments suggested that narrative coherence facilitated recall over and above any effects of sentence-level semantic similarity. This reliable memory benefit for coherent narrative events supports theoretical accounts which propose that narratives provide a high-level architecture for episodic memory.

Self-administered questionnaires often use examples or lists of examples to aid respondent recall. We report on the results of a field experiment examining how such examples in survey questions affect the episodic recall of events. Building on part-set cuing theory, the authors propose that examples increase recall when they cue low-accessibility subcategories of events, but may decrease recall when they cue high-accessibility subcategories. Further, cuing with examples rather than subcategory names may in some situations clarify questions and reduce non-useable open-ended responses. Findings from a survey of 2137 adult Medicaid recipients are generally consistent with these predictions. [ABSTRACT FROM AUTHOR]

Numerous studies show that exercise benefits memory and some show that acute exercise prior to encoding has larger benefits than exercise after encoding. This study was designed to investigate the effects of acute exercise on memory in middle-aged and older adults (Mage = 64.71 years) and to explore the influence of the timing of the exercise on these effects. Using a within-subjects design, moderate-intensity exercise (20 min) was either not performed (control), performed before the task (exercise prior), or performed after the task (exercise post). Memory was assessed using the Rey Auditory Learning Verbal Test. For short- and long-term memory and learning, significantly more words were remembered in the exercise-prior condition than the others. For 24-hr recall, participants remembered significantly more words in the exercise-prior condition than exercise post, which was better than control. Exercise benefits memory for healthy middle-aged and older adults, with the greatest benefits when performed prior to encoding. [ABSTRACT FROM AUTHOR]

In recent years, deep convolutional networks (DCNN) have gained popularity for different classification (or recognition) tasks. In this paper, three well known DCNN structures were used, i.e., AlexNet, SqueezeNet and GoogLeNet, and their classification performances in recognizing handwritten Bangla isolated characters were compared. These networks have simpler structures compared to the recent DCNNs. Experiments on a standard Bangla database revealed that the overall performance of GoogLeNet is slightly better than the other two networks. Further analysis using saliency maps of the test samples revealed the important features that are learned by the networks for classifying characters. This information led us to understand why classification of some samples fail and how to rectify these. [ABSTRACT FROM AUTHOR]

The accuracy of the recall of the severity of nausea and vomiting of pregnancy (NVP) with Pregnancy-Unique Quantification of Emesis (PUQE) questionnaire has been questioned. We aimed to compare PUQE scores of women recalling the worst episode of NVP of their current pregnancy in different gestational weeks (gwks). Total of 2343 pregnant women (gwks 7–40) were recruited. Four groups were formed according to the gwks at reply: ≤16 gwks (n = 554), ≤20 gwks (n = 1209), >20 gwks (n = 1134) and ≥24 gwks (n = 495). PUQE scores were similar between the groups. Consequently, consistency of PUQE scores across the groups endorses the useability of the PUQE questionnaire in retrospective assessment of the overall severity of NVP in different gwks, regardless of passing of the peak NVP symptoms. What is already known on this subject? Retrospective evaluation of the severity of nausea and vomiting of pregnancy (NVP) has been argued to be disposed to recall bias. Structured Pregnancy-Unique Quantification of Emesis (PUQE) questionnaire is a validated tool for assessing the severity of NVP. What do the results of this study add? When the women recalled the most severe NVP symptoms of their current pregnancy, no differences in the PUQE scores were found despite different gestational weeks at reply. Of distinct PUQE questions, women answering in early pregnancy reported longer duration of nausea than women answering in late pregnancy, but other questions were rated similarly. What the implications are of these findings for clinical practice and/or further research? Our aim was to compare the PUQE scores between the women who filled in the PUQE questionnaire in early or in late pregnancy, instructed to recall their worst symptoms in their current pregnancy. As there were no differences between the groups in total PUQE scores, our results support the application of PUQE questionnaire to assess the severity of NVP during pregnancy not only concurrent to the peak symptoms but also retrospectively. [ABSTRACT FROM AUTHOR]

The aim of this study is to compare substance use by forensic patients who have been formally recalled to hospital with those who remain conditionally discharged in the community. Data were collected from recalled patients (recalled group; RG) and those who remain conditionally discharged (discharged group; DG). A drug history was taken alongside wellbeing measures and screening of case notes. Cannabis use was significantly more prevalent in the RG post-discharge and subsequently in the community. Analysis comparing substance use in the community at different time points found that the DG was significantly less likely to be using cannabis post-discharge and subsequently in the community, compared to use at the time of their index offence. In contrast, the RG showed no change in cannabis use across time. Users of cannabis and alcohol post-discharge spent a significantly shorter amount of time in the community than non-users. Cannabis and alcohol appear associated with recall to hospital. [ABSTRACT FROM AUTHOR]

This study was designed to assess the knowledge of patients following their first seizure or blackout of unknown cause. We aimed to compare the advice our cohort of patients recalled against that suggested in the current literature. 5 % of the population will experience a non- febrile seizure in their lifetime. Education and advice for the patient and their family is an important aspect of their care. After reviewing the recommended guidelines from both the Scottish Intercollegiate Guidelines Network and the National Institute for Health and Care Excellence, a questionnaire was developed. From 1st May 2020 to 1st September 2022, a questionnaire was given to eligible patients attending St. James's Hospital for an electroencephalogram (EEG) following their first possible seizure or blackout of unknown cause. The patients were provided with a list of topics and were asked to select which, if any, were discussed with them. A total of 50 eligible adults participated in the study. Driving was the topic most frequently recalled as having been discussed at 66 % followed by family education. It has been recognised that patients remember as little as a fifth of information initially discussed, failing to recall 40–80 % of content within medical encounters and our study supports this. This highlights the need to review the information delivered and how this is achieved as well as using new methods to help increase the retention of this vital information. [ABSTRACT FROM AUTHOR]

Recall deals with the encoding, storage, and retrieval of information. Bilinguals have greater flexibility for recall as the person will have multiple language choices to come out with the target word. In other words, a bilingual will have more lexical choices to retrieve the target word. The current study investigates cross-language recall abilities in balanced bilinguals. A cohort group of participants were divided into three subgroups. The first sub-group of participants was asked to recall in second language while the stimuli/ target words were presented in the first language. The second group of participants was asked to recall in their first language while the stimuli/target words were presented in the second language. The third group of participants was allowed to carry out a free recall task. The descriptive analysis backed by statistical analysis revealed no significant difference between three groups suggesting that balanced bilinguals would have greater cognitive flexibility resulting in superior cross-language recall abilities in this population. [ABSTRACT FROM AUTHOR]

Copyright of Bingol University Journal of Economics & Administrative Science is the property of Bingol University Journal of Economics & Administrative Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective: To compare forgetting patterns between patients with temporal lobe (TLE) and generalized (GGE) epilepsies and to assess whether recall is associated with epileptic activity. Methods: Thirty‐three patients with TLE (13 left, 17 right, and 3 nonlateralized TLE), 42 patients with GGE, and 57 healthy controls (HCs) were asked to recall words, verbal story material, and the Rey‐Osterrieth complex figure at two delays. Accelerated long‐term forgetting (ALF) was defined by group performance comparable to HCs at 30 min and worse recall than HCs after 4 weeks. ALF was assessed by comparing raw test scores in a two‐way repeated measures analysis of variance (ANOVA) adjusted for the learning capacity. Results: Compared to HCs, patients with R‐TLE remembered fewer items of the word list after 30 min as well as after 4 weeks. Patients with L‐TLE and GGE had comparable learning‐adjusted performance to HCs at the 30 min delay but scored less after 4 weeks (group by delay interaction F(3, 124) = 3.2, P = 0.026, ηp2 = 0.07). The epilepsy group (patients with TLE and GGE combined) performed as well as HCs at 30 min but worse after 4 weeks irrespective of experienced seizures during the 4‐week delay or interictal bilateral (TLE) or generalized (GGE) activity before the study. We noted no statistically significant differences between patient and HC verbal story (group by delay interaction F(3, 124) = 0.7, P = 0.570, ηp2 = 0.02) or complex figure (F(3, 124) = 0.8, P = 0.488, ηp2 = 0.02) recall. Significance: Our data support verbal and visual memory impairment in both TLE and GGE with different performances between these groups in the task of word recall. We suggest the presence of ALF in patients with GGE and left TLE after adjusting for learning capacity. We could not confirm the influence of epileptic activity on long‐term forgetting patterns. Future studies are required to better define domain‐specific differences in memory impairment in TLE and GGE. [ABSTRACT FROM AUTHOR]

Prompt diagnostics and appropriate cancer therapy necessitate the use of gene expression databases. The integration of analytical methods can enhance detection precision by capturing intricate patterns and subtle connections in the data. This study proposes a diagnostic-integrated approach combining Empirical Bayes Harmonization (EBS), Jensen–Shannon Divergence (JSD), deep learning, and contour mathematics for cancer detection using gene expression data. EBS preprocesses the gene expression data, while JSD measures the distributional differences between cancerous and non-cancerous samples, providing invaluable insights into gene expression patterns. Deep learning (DL) models are employed for automatic deep feature extraction and to discern complex patterns from the data. Contour mathematics is applied to visualize decision boundaries and regions in the high-dimensional feature space. JSD imparts significant information to the deep learning model, directing it to concentrate on pertinent features associated with cancerous samples. Contour visualization elucidates the model's decision-making process, bolstering interpretability. The amalgamation of JSD, deep learning, and contour mathematics in gene expression dataset analysis diagnostics presents a promising pathway for precise cancer detection. This method taps into the prowess of deep learning for feature extraction while employing JSD to pinpoint distributional differences and contour mathematics for visual elucidation. The outcomes underscore its potential as a formidable instrument for cancer detection, furnishing crucial insights for timely diagnostics and tailor-made treatment strategies. [ABSTRACT FROM AUTHOR]

In June 2021, HVAD System distribution ceased due to observational data demonstrating increased mortality and neurological events compared to another commercial device, and a device malfunction with delay or failure to restart, especially in certain subpopulations. To assess ongoing risk for patients on support following subsequent device recalls, the manufacturer's Intermacs HVAD System 2022 Quarterly Reports were queried to identify mortality and adverse events trends in a contemporary cohort of 3110 primary HVAD implantations since October 2017, stratified by year-of-implant. Mean duration of support was 21 ± 16 months, with 33% alive on original device, 25% transplanted, 6% undergoing device exchange, 4% recovered, and 32% expired. Kaplan–Meier and event-per-patient-year estimates for survival, freedom from device explant, stroke, and pump thrombus were similar across year-of-implant. Following market withdrawal and recent device recalls, there appears to be no increase in mortality, stroke, pump thrombus, or explant for HVAD-supported patients. Quarterly report monitoring is ongoing. [ABSTRACT FROM AUTHOR]

> Context • Reliably differentiating between memories of events that happened and those that did not (commonly referred to as "false memories" in the literature) is essential for both personal and societal well-being, yet studies increasingly show that this is nearly impossible to achieve. > Problem • Significant support for this conclusion comes from the research using the Deese-Roediger-McDermott (DRM) paradigm, which has been limited in terms of behavioral and phenomenological measures employed, particularly concerning recall memory (as opposed to recognition memory). > Method • We used a combination of disciplined first-person methodology and behavioral measures to explore the process of recall in the DRM paradigm. Participants were interrupted during word recall and underwent phenomenological interviews about their recall processes. Reaction times and performance accuracy were measured for each recalled word. > Results • We identified a diverse selection of categories pertaining to the phenomenology of recall, describing the mechanisms of attention, structural and processual characteristics, mood and motivation, and other experiences indirectly related to recall. These categories were used to construct the phenomenological indicators of accurate and inaccurate recall. The most coherent accurate recall indicators were related to the experience of strong certainty or the emergence of a word matching the critical lure during encoding as a word that should be avoided during recall. The most coherent inaccurate recall indicators were linked to a deliberate, inferential type of recall or an automatic and fluid recall. Preliminary joint analysis of phenomenological and behavioral data indicated support for the most coherent inaccurate recall indicators, showing varying reaction times associated with different indicators and noticeable differences in the serial position of recalled words corresponding to the indicators. > Implications • This study sheds light on the complexity of recall in the DRM paradigm, highlighting the need for a phenomenologically driven mixed-methods approach in a quest to better understand the nature of both accurate and inaccurate memories. > Constructivist content • The research design for this study draws heavily on Varela's neurophenomenological framework. [ABSTRACT FROM AUTHOR]

Copyright of Farmacia is the property of Societatea de Stiinte Farmaceutice Romania and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We propose a quality control method based on machine learning neural networks to enhance the quality of high-frequency (HF) radar data. Unlike traditional quality control methods that rely on radar signals as indicators and involve extensive data manipulation in specialized software, our approach employs a Bi-LSTM neural network model. This method aims to improve data quality and streamline the quality control process. Through a series of analyses, we demonstrate the feasibility of using machine learning techniques to enhance radar data quality. [ABSTRACT FROM AUTHOR]

Driven by Internet technology, the explosion of video content is growing exponentially. Hence, the need for video analysis and interpretation by human cognition is taken into account. Memorability is one of the cognitive measures that define the recall ability of watched visual content. Prior research works are done on image memorability, and video memorability modeling is least focused. Moreover, the computational features need to be robust to build a strong video memorability predictor model that is also not focused earlier. This paper proposes a robust, scalable and novel Stacked Bin- Convolutional Neural Network (SB_CNN) based Sparse Low-Rank Regressor (SLRR) model that classifies and predicts the interesting events of videos by performing a robust feature scaling process and intelligent memorability tasks. Inspired by the Low-Rank Representation (LRR) property in noise data handling, the proposed feature extraction process covered the low-rank issue on attributes such as objects, scenes, and annotated to the original feature space. The estimated sparse coefficient vector is fed into the Stacked Bin- Convolutional Neural Networks (SB-CNN) model that establishes the connections between video frames and their memorability scores by means of electing the best frames by following the objectives of Multi-Attribute Decision Making (MADM) technique. The selected frames are fed into the CNN classifier. With the estimated decision score, the proposed classifier has effectively predicted the events from relevant video with the better recall time 49. 9247. Experiments conducted on public datasets, SumMe and SUN database demonstrates the efficacy of the proposed technique. [ABSTRACT FROM AUTHOR]

Background: Antidepressants have been proposed to act via their influence on emotional processing. We investigated the effect of discontinuing maintenance antidepressant treatment on positive and negative self-referential recall and the association between self-referential recall and risk of relapse. Methods: The ANTLER trial was a large (N = 478) pragmatic double-blind trial investigating the clinical effectiveness of long-term antidepressant treatment for preventing relapse in primary care patients. Participants were randomised to continue their maintenance antidepressants or discontinue via a taper to placebo. We analysed memory for positive and negative personality descriptors, assessed at baseline, 12- and 52-week follow-up. Results: The recall task was completed by 437 participants. There was no evidence of an effect of discontinuation on self-referential recall at 12 [positive recall ratio 1.00, 95% CI (0.90–1.11), p = 0.93; negative recall ratio 1.00 (0.87–1.14), p = 0.87] or 52 weeks [positive recall ratio 1.03 (0.91–1.17), p = 0.62; negative recall ratio 1.00 (0.86–1.15), p = 0.96; ratios larger than one indicate higher recall in the discontinuation group], and no evidence of an association between recall at baseline or 12 weeks and later relapse [baseline, positive hazard ratio (HR) 1.02 (0.93–1.12), p = 0.74; negative HR 1.01 (0.90–1.13), p = 0.87; 12 weeks, positive HR 0.99 (0.89–1.09), p = 0.81; negative HR 0.98 (0.84–1.14), p = 0.78; ratios larger than one indicate a higher frequency of relapse in those with higher recall]. Conclusions: We found no evidence that discontinuing long-term antidepressants altered self-referential recall or that self-referential recall was associated with risk of relapse. These findings suggest that self-referential recall is not a neuropsychological marker of antidepressant action. [ABSTRACT FROM AUTHOR]

In modern medicine, medical tests are used for various purposes including diagnosis, disease screening, prognosis, and risk prediction. To quantify the performance of the binary medical test, we often use sensitivity, specificity, and negative and positive predictive values as measures. Additionally, the F1$$ {F}_1 $$‐score, which is defined as the harmonic mean of precision (positive predictive value) and recall (sensitivity), has come to be used in the medical field due to its favorable characteristics. The F1$$ {F}_1 $$‐score has been extended for multi‐class classification, and two types of F1$$ {F}_1 $$‐scores have been proposed for multi‐class classification: a micro‐averaged F1$$ {F}_1 $$‐score and a macro‐averaged F1$$ {F}_1 $$‐score. The micro‐averaged F1$$ {F}_1 $$‐score pools per‐sample classifications across classes and then calculates the overall F1$$ {F}_1 $$‐score, whereas the macro‐averaged F1$$ {F}_1 $$‐score computes an arithmetic mean of the F1$$ {F}_1 $$‐scores for each class. Additionally, Sokolova and Lapalme1$$ {}^1 $$ gave an alternative definition of the macro‐averaged F1$$ {F}_1 $$‐score as the harmonic mean of the arithmetic means of the precision and recall over classes. Although some statistical methods of inference for binary and multi‐class F1$$ {F}_1 $$‐scores have been proposed, the methodology development of hypothesis testing procedure for them has not been fully progressing yet. Therefore, we aim to develop hypothesis testing procedure for comparing two F1$$ {F}_1 $$‐scores in paired study design based on the large sample multivariate central limit theorem. [ABSTRACT FROM AUTHOR]

The glm R package is commonly used for generalized linear modeling. In this paper, we evaluate the ability of the glm package to predict binomial outcomes using logistic regression. We use single-cell RNA-sequencing datasets, after a series of normalization, to fit data into glm models repeatedly using 10-fold cross-validation over 100 iterations. Our evaluation criteria are glm's Precision, Recall, F1-Score, Area Under the Curve (AUC), and Runtime. Scores for each evaluation category are collected, and their medians are calculated. Our findings show that glm has fluctuating Precision and F1-Scores. In terms of Recall, glm has shown more stable performance, while in the AUC category, glm shows remarkable performance. Also, the Runtime of glm is consistent. Our findings also show that there are no correlations between the size of fitted data and glm's Precision, Recall, F1-Score, and AUC, except for Runtime. [ABSTRACT FROM AUTHOR]

Guided by the fostering relationships function of patient-centered communication (PCC), the present study utilized a 2 (high/low nonverbal immediacy) x 2 (high/low verbal immediacy) between-subjects experimental design to determine whether physician verbal and nonverbal immediacy influenced participant liking for physician, motivation to process a health message, and recall of the health message. An actor physician delivered a 3–4 minute video-recorded message, diagnosing U.S. adult participants, serving as analogue patients, with a health issue. The results indicated main effects for physician verbal immediacy and nonverbal immediacy on participant liking for physician and motivation such that participants had greater liking for the physician and motivation to do what the physician requested when the physician demonstrated greater verbal and nonverbal immediacy. However, physician verbal and nonverbal immediacy did not influence participantsʻ recall. Physicians should consider displaying verbal and nonverbal immediacy to create a positive impression among patients, and to motivate patients to take steps to improve their health. [ABSTRACT FROM AUTHOR]

Purpose: Free recall tasks target cognitive and linguistic processes such as episodic memory, lexical access, and retrieval. Consequently, the free recall paradigm is suitable for assessing memory deterioration caused by aging, which can be influenced by stimulus presentation modality. Hence, the present study aimed to determine if aging has an effect on visual and auditory recall abilities. Materials and Methods: Twenty young adults (mean age: 25.4 ± 0.99) and older adults (mean age: 63.3 ± 3.51) participated in the study. Participants performed a free recall task under two conditions - related and unrelated and two modalities - visual and auditory where they were instructed to recall as many items as possible with no specific order and time limit. Results: Free recall performance was calculated as the mean number of correctly recalled items. Although younger participants recalled a higher number of items, the performance across conditions and modality was variable. Conclusions: The findings of the present study revealed an age-related decline in the efficiency of episodic memory, which is crucial to remember recent events. [ABSTRACT FROM AUTHOR]

Experiments comparing intuitive and reflective decisions provide insights into the cognitive foundations of human behavior. However, the relative strengths and weaknesses of the frequently used experimental techniques for activating intuition and reflection remain unknown. In a large-scale preregistered online experiment (N = 3667), we compared the effects of eight reflection, six intuition, and two within-subjects manipulations on actual and self-reported measures of cognitive performance. Compared to the overall control, the long debiasing training was the most effective technique for increasing actual reflection scores, and the emotion induction was the most effective technique for increasing actual intuition scores. In contrast, the reason and the intuition recall, the reason induction, and the brief time delay conditions failed to achieve the intended effects. We recommend using the debiasing training, the decision justification, or the monetary incentives technique to activate reflection, and the emotion induction, the cognitive load, or the time pressure technique to activate intuition. [ABSTRACT FROM AUTHOR]

Credit card Fraud detection is a critical task in various industries, including finance and e-commerce, where identifying fraudulent activities can help prevent financial losses and protect users. It begins by combining two datasets containing fraudulent and non-fraudulent transactions to create a comprehensive dataset for analysis. Data is preprocessed by removing unnecessary features, calculating distance metrics, and generating new variables to capture temporal patterns and transaction history. Multicollinearity issues are addressed through feature selection. Improved Random Forest (RF) algorithm is used to improve fraud detection. The experimental results indicate that the improved Random Forest algorithm achieves commendable accuracy in fraud detection. The proposed model achieves 99.87% training accuracy and 99.41% testing accuracy. The Model's performance is evaluated by measuring precision, recall, F1-score and support. Our research emphasizes the importance of considering improved algorithms to achieve better results. The findings provide valuable insights for organizations aiming to enhance their fraud detection capabilities and make informed decisions to protect their systems and users. [ABSTRACT FROM AUTHOR]

Copyright of Journal of the College Of Basic Education is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper examines the use of deep recurrent neural networks to classify traffic patterns in smart cities. We propose a novel approach to traffic pattern classification based on deep recurrent neural networks, which can effectively capture traffic patterns' dynamic and sequential features. The proposed model combines convolutional and recurrent layers to extract features from traffic pattern data and a SoftMax layer to classify traffic patterns. Experimental results show that the proposed model outperforms existing methods regarding accuracy, precision, recall, and F1 score. Furthermore, we provide an in-depth analysis of the results and discuss the implications of the proposed model for smart cities. The results show that the proposed model can accurately classify traffic patterns in smart cities with a precision of as high as 95%. The proposed model is evaluated on a real-world traffic pattern dataset and compared with existing classification methods. [ABSTRACT FROM AUTHOR]

Copyright of Direito da Cidade is the property of Editora da Universidade do Estado do Rio de Janeiro (EdUERJ) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Assez méconnues dans leur diversité, les procédures d’initiative populaire sont de plus en plus présentes dans les constitutions des États. Permettant aux citoyens d’être plus directement impliqués dans le processus normatif et dans le contrôle de l’activité de leurs représentants, de telles procédures tentent de répondre à la défiance citoyenne et de donner plus de légitimité à la manière dont fonctionnent les démocraties. Dans les travaux de recherche, elles sont généralement appréhendées à travers l’étude d’un système juridique en particulier ou d’un type de mécanisme. Ainsi, les procédures de référendum d’initiative populaire, appelées « initiatives populaires directes », ont tendance à mobiliser les commentaires de la doctrine et des médias en raison de leur visibilité et de la radicalité de certains sujets adoptés. Les initiatives populaires indirectes, ou d’agenda, sont beaucoup moins étudiées et pourtant sont souvent présentes à l’échelle nationale et même supranationale, à l’instar de l’Initiative citoyenne européenne. Les initiatives suspensives ou abrogatives sont également prévues dans certains États mais, à l’exception de l’Italie et de la Suisse, font l’objet de peu de commentaires. Les procédures révocatoires, permettant notamment d’éprouver les choix effectués par les élus ou leur probité au cours d’un mandat, font l’objet d’études qui restent souvent cantonnées aux États-Unis. L’intérêt de cette contribution est de proposer une typologie des différentes formes d’initiatives populaires pour témoigner de la richesse des mécanismes consacrés en droit constitutionnel. Même si beaucoup de ces initiatives échouent à atteindre leur objectif premier, elles peuvent contribuer à sensibiliser les élus aux effets de leurs choix et comportements et attirer l’attention de l’ensemble de la communauté politique sur certains sujets.

The advent of the big data era brings opportunities and challenges to the innovation of mental health education for college students in higher education institutions. In this paper, we monitor the mental health of college students in higher education institutions by collecting students’ consumption data, access control data, network data and historical achievement data, and use a convolutional neural network to extract features from behavioral data and then input the feature set into random forest model for training. The recognition effect of the RF algorithm is analyzed by comparing it with other algorithms, and the problems of college students’ mental health education are analyzed based on the correlation of features. The accuracy of identifying mental health problems of college students based on RF analysis of big data reached 0.72, the recall rate reached 0.58, and the F1-Measure was 0.67. For the correlation of different features, the most significant correlation effect was the time spent in the dormitory on rest days and the number of swiping access cards, with the correlation coefficients reaching 0.2719 and -0.2191, respectively. The analysis based on big data can accurately grasp the current situation and problems of mental health education of college students in higher education institutions and promote the comprehensive development of mental health education of college students.

Abstract Objective: To compare the agreement and cost of two recall methods for estimating children’s minimum dietary diversity (MDD). Design: We assessed child’s dietary intake on two consecutive days: an observation on day one, followed by two recall methods (list-based recall and multiple-pass recall) administered in random order by different enumerators at two different times on day two. We compared the estimated MDD prevalence using survey-weighted linear probability models following a two one-sided test equivalence testing approach. We also estimated the cost-effectiveness of the two methods. Setting: Cambodia (Kampong Thom, Siem Reap, Battambang, and Pursat provinces) and Zambia (Chipata, Katete, Lundazi, Nyimba, and Petauke districts). Participants: Children aged 6–23 months: 636 in Cambodia and 608 in Zambia. Results: MDD estimations from both recall methods were equivalent to the observation in Cambodia but not in Zambia. Both methods were equivalent to the observation in capturing most food groups. Both methods were highly sensitive although the multiple-pass method accurately classified a higher proportion of children meeting MDD than the list-based method in both countries. Both methods were highly specific in Cambodia but moderately so in Zambia. Cost-effectiveness was better for the list-based recall method in both countries. Conclusion: The two recall methods estimated MDD and most other infant and young child feeding indicators equivalently in Cambodia but not in Zambia, compared to the observation. The list-based method produced slightly more accurate estimates of MDD at the population level, took less time to administer and was less costly to implement.

In this paper, we first use CNNS to collect rich samples of folk dance music, establish a model framework and a functional system framework, and conduct a comprehensive process analysis of the music. Then, the instrumental features, frequency features, and timbre features are extracted to obtain the spectral information. In the stage of chord analysis and encoding, a multivariate chord encoding model is established based on the acquired spectral information, including two parts: chord representation preprocessing and chord encoding. By utilizing this model, the chord structure of music was successfully and accurately encoded, allowing for analysis with up to 98% accuracy. Furthermore, significant recall results were achieved, reaching over 0.9, which suggests that the extracted chord features are highly reliable and accurate in recognizing musical chord information.