Processing Model Research Articles

Cost-aware optimal resource provisioning Map-Reduce scheduler for hadoop framework

<p><span lang="EN-US">Distributed data processing model has been one of the primary components in the case of data-intensive applications; furthermore, due to advancements in technologies, there has been a huge volume of data generation of diverse nature. Hadoop map reduce framework is responsible for adopting the ease of deployment mechanism in an open-source framework. The existing Hadoop MapReduce framework possesses high makespan time and high Input/Output overhead and it mainly affects the cost of a model. Thus, this research work presents an optimized cost aware resource provisioning MapReduce model also known as the cost-effective resource provisioning MapReduce (CRP-MR) model. CRP-MR model introduces the two integrated approaches to minimize the cost; at first, this model presents the optimal resource optimization and optimal Input/Output optimization cleansing in the Hadoop MapReduce (HMR) scheduler. CRP-MR is evaluated considering the bioinformatics dataset and CRP-MR performs better than the existing model. </span></p>

Automatic grading of intervertebral disc degeneration in lumbar dog spines.

Intervertebral disc degeneration is frequent in dogs and can be associated with symptoms and functional impairments. The degree of disc degeneration can be assessed on T2-weighted MRI scans using the Pfirrmann classification scheme, which was developed for the human spine. However, it could also be used to quantify the effectiveness of disc regeneration therapies. We developed and tested a deep learning tool able to automatically score the degree of disc degeneration in dog spines, starting from an existing model designed to process images of human patients. MRI midsagittal scans of 5991 lumbar discs of dog patients were collected and manually evaluated with the Pfirrmann scheme and a modified scheme with transitional grades. A deep learning model was trained to classify the disc images based on the two schemes and tested by comparing its performance with the model processing human images. The determination of the Pfirrmann grade showed sensitivities higher than 83% for all degeneration grades, except for grade 5, which is rare in dog spines, and high specificities. In comparison, the correspondent human model had slightly higher sensitivities, on average 90% versus 85% for the canine model. The modified scheme with the fractional grades did not show significant advantages with respect to the original Pfirrmann grades. The novel tool was able to accurately and reliably score the severity of disc degeneration in dogs, although with a performance inferior than that of the human model. The tool has potential in the clinical management of disc degeneration in canine patients as well as in longitudinal studies evaluating regenerative therapies in dogs used as animal models of human disorders.

Linguistic Features of Secondary School Writing: Can Natural Language Processing Shine a Light on Differences by Sex, English Language Status, or Higher Scoring Essays?

This article provides three major contributions to the literature: we provide granular information on the development of student argumentative writing across secondary school; we replicate the MacArthur et al. model of Natural Language Processing (NLP) writing features that predict quality with a younger group of students; and we are able to examine the differences for students across language status. In our study, we sought to find the average levels of text length, cohesion, connectives, syntactic complexity, and word-level complexity in this sample across Grades 7-12 by sex, by English learner status, and for essays scoring above and below the median holistic score. Mean levels of variables by grade suggest a developmental progression with respect to text length, with the text length increasing with grade level, but the other variables in the model were fairly stable. Sex did not seem to affect the model in meaningful ways beyond the increased fluency of women writers. We saw text length and word level differences between initially designated and redesignated bilingual students compared to their English-only peers. Finally, we see that the model works better with our higher scoring essays and is less effective explaining the lower scoring essays.

Equivalent power densities model for multi-pulse ultrashort pulsed laser processing

Equivalent power densities model for multi-pulse ultrashort pulsed laser processing

FSN-YOLO: Nearshore Vessel Detection via Fusing Receptive-Field Attention and Lightweight Network

Vessel detection is critical for ensuring maritime transportation and navigational safety, creating a pressing need for detection methodologies that are more efficient, precise, and intelligent in the maritime domain. Nonetheless, accurately detecting vessels across multiple scales remains challenging due to the diversity in vessel types and locations, similarities in ship hull shapes, and disturbances from complex environmental conditions. To address these issues, we introduce an innovative FSN-YOLO framework that utilizes enhanced YOLOv8 with multi-layer attention feature fusion. Specifically, FSN-YOLO employs the backbone structure of FasterNet, enriching feature representations through super-resolution processing with a lightweight Convolutional Neural Network (CNN), thereby achieving a balance between processing speed and model size without compromising accuracy. Furthermore, FSN-YOLO uses the Receptive-Field Attention (RFA) mechanism to adaptively fine-tune the feature responses between channels, significantly boosting the network’s capacity to capture critical information and, in turn, improve the model’s overall performance and enrich the discriminative feature representation of ships. Experimental validation on the Seaship7000 dataset showed that, compared to the baseline YOLOv8l approach, FSN-YOLO considerably increased accuracy, recall rates, and mAP@0.5:0.95 by absolute margins of 0.82%, 1.54%, and 1.56%, respectively, positioning it at the forefront of current state-of-the-art models.

Real-time sports injury monitoring system based on the deep learning algorithm

In response to the low real-time performance and accuracy of traditional sports injury monitoring, this article conducts research on a real-time injury monitoring system using the SVM model as an example. Video detection is performed to capture human movements, followed by human joint detection. Polynomial fitting analysis is used to extract joint motion patterns, and the average of training data is calculated as a reference point. The raw data is then normalized to adjust position and direction, and dimensionality reduction is achieved through singular value decomposition to enhance processing efficiency and model training speed. A support vector machine classifier is used to classify and identify the processed data. The experimental section monitors sports injuries and investigates the accuracy of the system’s monitoring. Compared to mainstream models such as Random Forest and Naive Bayes, the SVM utilized demonstrates good performance in accuracy, sensitivity, and specificity, reaching 94.2%, 92.5%, and 96.0% respectively.

Linking child adjustment difficulties with mother's maladaptive parental behavior: The mediating roles of parental cognitions and parenting stress.

Child abuse is prevalent worldwide, with most of the burden in developing countries. To reduce and prevent child abuse occurrence, many efforts are directed toward reducing maladaptive parental behaviors (MPBs), a predictor of parents' risk of engaging in child abusive behaviors. MPBs have been associated with child (e.g., behavioral difficulties) and parent characteristics (e.g., parenting stress and parental cognitions), although little research tested for mediational pathways. This study aimed to test the pathways through which child and parent characteristics are linked to MPB. Consistent with the social information processing model of parenting, we hypothesized that child behavioral difficulties would exert an indirect influence on MPB through parenting stress and that parenting stress will exert a direct and indirect effect on MPB through parental cognitions (i.e., expectations, attitudes, and attributions). This study used data from 243 mothers of children aged between 2 and 9 years in Romania. Two-stage structural equation modeling was employed to test the hypothesized model. Results support the role of child behavior, parenting stress, and parental cognitions in predicting MPB (R2 = 0.69). Significant indirect effects were found from child behavior to MPB via parenting stress and parental cognitions. Direct effects from parenting stress and parental cognitions to MPB were significant. Findings show that parenting stress and parental cognitions are important mechanisms through which child behavioral difficulties influence maladaptive parental behavior, underscoring the need to focus on these mechanisms when assessing or intervening with families at risk for child abuse.

Hybrid Natural Language Processing Model for Sentiment Analysis during Natural Crisis

This paper introduces a novel natural language processing (NLP) model as an original approach to sentiment analysis, with a focus on understanding emotional responses during major disasters or conflicts. The model was created specifically for Croatian and is based on unigrams, but it can be used with any language that supports the n-gram model and expanded to multiple word sequences. The presented model generates a sentiment score aligned with discrete and dimensional emotion models, reliability metrics, and individual word scores using affective datasets Extended ANEW and NRC WordEmotion Association Lexicon. The sentiment analysis model incorporates different methodologies, including lexicon-based, machine learning, and hybrid approaches. The process of preprocessing includes translation, lemmatization, and data refinement, utilized automated translation services as well as the CLARIN Knowledge Centre for South Slavic languages (CLASSLA) library, with a particular emphasis on diacritical mark correction and tokenization. The presented model was experimentally evaluated on three simultaneous major natural crises that recently affected Croatia. The study’s findings reveal a significant shift in emotional dimensions during the COVID-19 pandemic, particularly a decrease in valence, arousal, and dominance, which corresponded with the two-month recovery period. Furthermore, the 2020 Croatian earthquakes elicited a wide range of negative discrete emotions, including anger, fear, and sadness, with the recuperation period much longer than in the case of COVID-19. This study represents an advancement in sentiment analysis, particularly in linguistically specific contexts, and provides insights into the emotional landscape shaped by major societal events.

Idiosyncratic patterns of interhemispheric connectivity in the face and scene networks of the human brain

Abstract Neuroimaging studies have revealed a network of regions in both hemispheres of the human brain that respond selectively to faces. Neural models of face processing have typically focused on functional connectivity between regions in the same hemisphere (intrahemispheric), with a particular bias toward the right hemisphere. Here, we explored the role of interhemispheric connectivity using fMRI. We used three datasets to compare functional connectivity, as shown by correlations between the time-courses of neural activity of face regions during different natural viewing paradigms. We found higher correlations of neural activity between corresponding interhemispheric regions (e.g., rFFA–lFFA) than between intrahemispheric regions (e.g., rFFA–rOFA), indicating a bias towards higher interhemispheric than intrahemispheric connectivity. A similar interhemispheric bias was evident in scene-selective regions. In contrast, we did not find an interhemispheric bias in early visual regions (V1–V3), where intrahemispheric connectivity between corresponding regions was generally higher than interhemispheric connectivity. Next, we asked whether the higher interhemispheric connectivity in the face and scene networks between corresponding regions was consistent across participants. We found that the interhemispheric bias was significantly attenuated when we compared the time-course of response across participants. This shows that interhemispheric bias in connectivity between corresponding regions in the face and scene networks is specific to the individual. These findings raise the possibility that idiosyncratic variation in interhemispheric connectivity may explain individual differences in perception.

How Factually Accurate is GPT-3? A Focused Case Study on Helping Malaysia’s B40s Through e-Commerce

GPT-3 (Generative Pre-trained Transformer 3) is an advanced natural language processing model utilizing unsupervised learning to generate sophisticated human-like text. GPT-3 has been lauded for its potential to revolutionize the field of natural language processing, with its capacity to generate a variety of text with a high degree of fluency and accuracy. We examine the ability of GPT-3’s to produce text related to a focused subject matter: alleviating poverty in Malaysia through e-Commerce. We especially examine GPT-3’s ability to produce factual responses within this narrow context. It was discovered that GPT-3 could produce plausible statements, albeit some of them being factually debatable or incorrect due to how its training data was sourced. We also discuss how GPT-3 could be used unscrupulously to either produce academic-sounding responses that appear to be a product of research, but possibly untrue or inaccurate and discuss its potential ramifications (such as propaganda and disinformation). We end the paper with some suggestions to the brilliant team at OpenAI to further improve GPT-3 for the advancement of humankind.

Decolonisation: meaning, sentiments and implications for heritage

ABSTRACT Within the printed and online newspaper media in the UK, notions of ‘decolonisation’ referring to various contexts, such as historical, relating to museums and institutions, cultural decolonisation and in relation to modern independence discussions, can be traced. In this article, we have applied topic modelling and natural language processing methods to carry out a classification of, and sentiment analysis on, newspaper headlines and texts from leading British newspapers covering decolonisation over the past decade. The results show an abrupt change in the meaning of decolonisation starting in the middle of the 2010s with an increased focus on cultural and institutional matters, particularly in right-leaning media. Surprisingly, the editorial slant of broadsheets seemingly only had at best moderate effects on tone, while headlines in right-wing tabloids were significantly more negative. Articles covering cultural aspects of decolonisation were substantially more negative than those applying a more traditional, territorial definition of decolonisation. Given the influence of newspaper media on public and private opinions, we discuss the heritage implications of these findings and suggest avenues for further investigation.

Bolstering the adaptive information processing model: a narrative review

In recent years, several theoretical models have been suggested as complementary to the adaptative information processing model of eye movement desensitization and reprocessing therapy. A narrative review of such models was conducted to assess the contributions of each, as well as their convergences, contradictions, and potential complementarity. Seven theoretical models were identified. All focus on the effects of EMDR therapy as a comprehensive psychotherapy approach with its principles, procedures, and protocols. Several refer to concepts related to propositional or predictive processing theories. Overall, the contribution of these proposals does appear to bolster Shapiro’s original AIP model, potentially offering additional depth and breadth to case conceptualization and treatment planning in clinical practice, as well as a more precise theoretical understanding. The current exploratory comparative analysis may serve as a preliminary baseline to guide research into the relative merit of suggested theoretical proposals to enhance current standards for the clinical practice and teaching of EMDR therapy.

Accurate wire rope defect MFL detection using improved Hilbert transform and LSTM neural network

ABSTRACT Wire rope is playing a significant role in various engineering fields. Although a great number of non-destructive testing methods are applied to wire rope defect detection, the results are greatly influenced by the signal noises and the quantitative defect detection of wire rope is consequently limited. Therefore, a new accurate and quantitative wire rope defect magnetic flux leakage (MFL) recognition method based on improved Hilbert transform and long short-term memory (LSTM) neural network is proposed. The theoretical background of the proposed signal processing models and preliminary results, as well as the influence of the main model parameters are analyzed first. Then, different wire rope defect classification principles through LSTM neural network and performance evaluation based on the improved signal processing and machine learning combined method are presented. Finally, the defect inspection and classification comparison results between the proposed method and other deep learning neural networks and pattern recognition strategies are given, which manifest that the proposed method has higher classification accuracy and shorter running time for five different wire rope defects under various servicing conditions, and is promising in accurate defect inspection for multi-scenario wire rope. Additionally, the main conclusions, disadvantages of the proposed method are summarized.

Understanding older people's voice interactions with smart voice assistants: a new modified rule-based natural language processing model with human input

The COVID-19 pandemic has expedited the integration of Smart Voice Assistants (SVA) among older people. The qualitative data derived from user commands on SVA is pivotal for elucidating the engagement patterns of older individuals with such systems. However, the sheer volume of user-generated voice interaction data presents a formidable challenge for manual coding. Compounding this issue, age-related cognitive decline and alterations in speech patterns further complicate the interpretation of older users’ SVA voice interactions. Conventional dictionary-based textual analysis tools, which count word frequencies, are inadequate in capturing the evolving and communicative essence of these interactions that unfold over a series of dialogues and modify with time. To address these challenges, our study introduces a novel, modified rule-based Natural Language Processing (MR-NLP) model augmented with human input. This reproducible approach capitalizes on human-derived insights to establish a lexicon of critical keywords and to formulate rules for the iterative refinement of the NLP model. English speakers, aged 50 or older and residing alone, were enlisted to engage with Amazon Alexa™ via predefined daily routines for a minimum of 30 min daily spanning three months (N = 35, mean age = 77). We amassed time-stamped, textual data comprising participants’ user commands and responses from Alexa™. Initially, a subset constituting 20% of the data (1,020 instances) underwent manual coding by human coder, predicated on keywords and commands. Separately, a rule-based Natural Language Processing (NLP) methodology was employed to code the identical subset. Discrepancies arising between human coder and the NLP model programmer were deliberated upon and reconciled to refine the rule-based NLP coding framework for the entire dataset. The modified rule-based NLP approach demonstrated notable enhancements in efficiency and scalability and reduced susceptibility to inadvertent errors in comparison to manual coding. Furthermore, human input was instrumental in augmenting the NLP model, yielding insights germane to the aging adult demographic, such as recurring speech patterns or ambiguities. By disseminating this innovative software solution to the scientific community, we endeavor to advance research and innovation in NLP model formulation, subsequently contributing to the understanding of older people's interactions with SVA and other AI-powered systems.

Cellular automata-based MapReduce design: Migrating a big data processing model from Industry 4.0 to Industry 5.0

A successful deployment of Industry 5.0 is significantly dependent on the synergetic integration of several advanced technologies such as big data processing, Artificial Intelligence (AI) integration, and several effective digitization techniques that emphasize the uses of Robotics, Internet of Things (IoTs), Cloud Computing, etc. with active participations from human workers. Several researchers have explored the importance of big data processing in view of Industry 4.0 as it facilitated enhanced production at any smart manufacturing line by ensuring efficient process management, typically involving big data processing. Researchers presented several MapReduce-based data processing models at smart manufacturing lines to facilitate big data processing. Among several others, the Elementary Cellular Automata (ECAs)-based MapReduce model was introduced as an energy-efficient low-cost model for big data processing in view of the Industry 4.0 scenario. In the present research, an investigation is further proposed to explore the true potential (if any) for the ECAs-based MapReduce model with reference to available several MapReduce models, to migrate an existing big data processing model from Industry 4.0 into the future i.e., Industry 5.0. Investigation results as achieved from the comparison among several MapReduce models and further examinations about the inherent quality of shuffle in those MapReduce blocks, explore the inherent advantages of ECAs-based MapReduce model towards its choice for big data processing in Industry 5.0 scenario.

Exploring the journey of influencers in shaping social media engagement success

This study unfolds nuanced insights into the diverse dimensions dictating the success of social media influencers. Analyzing more than 210,000 social media posts and utilizing the Heuristic-Systematic Model of Information Processing (HSM), this study explores diverse factors, including individual appearance characteristics, depth of persuasive power, and various influencer types. The findings of this study shed light on the distinct impacts of varying influencer archetypes, such as celebrities and micro-celebrities, on user engagement and reveal the nuanced moderating effects of these archetypes on the relationships intertwined with personal attributes, persuasive potency, and influencer success. The proposed model advocates that influencers who leverage more profound, systematic processing strategies, marked by detailed information analysis and conveyance, are poised to experience elevated user engagement compared to counterparts employing heuristic modalities, distinguished by practical mental shortcuts and superficial examinations. This elucidation accentuates the imperative of harmonizing heuristic and systematic methodologies for emerging influencers and brands aspiring to optimize user engagement and efficaciously mold consumer behavior. This paper encapsulates a comprehensive exploration of the dynamic landscapes of influencer marketing via the HSM prism, delivering profound insights and practical ramifications for scholars, marketers, and influencers aiming to navigate and exploit the intricate networks of influential determinants in the ever-evolving digital marketing domain.

Application and Challenges of the Technology Acceptance Model in Elderly Healthcare: Insights from ChatGPT

The Technology Acceptance Model (TAM) plays a pivotal role in elderly healthcare, serving as a theoretical framework. This study aimed to identify TAM’s core components, practical applications, challenges arising from its applications, and propose countermeasures in elderly healthcare. This descriptive study was conducted by utilizing OpenAI’s ChatGPT, with an access date of 10 January 2024. The three open-ended questions administered to ChatGPT and its responses were collected and qualitatively evaluated for reliability through previous studies. The core components of TAMs were identified as perceived usefulness, perceived ease of use, attitude toward use, behavioral intention to use, subjective norms, image, and facilitating conditions. TAM’s application areas span various technologies in elderly healthcare, such as telehealth, wearable devices, mobile health apps, and more. Challenges arising from TAM applications include technological literacy barriers, digital divide concerns, privacy and security apprehensions, resistance to change, limited awareness and information, health conditions and cognitive impairment, trust and reliability concerns, a lack of tailored interventions, overcoming age stereotypes, and integration with traditional healthcare. In conclusion, customized interventions are crucial for successful tech acceptance among the elderly population. The findings of this study are expected to enhance understanding of elderly healthcare and technology adoption, with insights gained through natural language processing models like ChatGPT anticipated to provide a fresh perspective.

ОДНОВИМІРНА ЗГОРТКОВА МОДЕЛЬ НЕЙРОННОЇ МЕРЕЖІ ДЛЯ ОБРОБЛЕННЯ СИГНАЛІВ З АМПЛІТУДНОЮ МОДУЛЯЦІЄЮ БАГАТЬОХ СКЛАДОВИХ

The processing of radio signals using artificial neural networks (ANNs) has great potential for research, which can be explained by the adaptability of ANNs to various transmission conditions and the ability to detect abstract patterns of changes in signal parameters. The article reviews the works of other authors devoted to different ways of using ANNs for processing radio signals. Taking into account the information in the reviewed works, the research task was formed, which consists in developing an optimized ANN model for radio signal processing. Signals with amplitude modulation of many components (AMMC) were chosen to form training samples for ANN. The choice of modulation type is justified by greater energy efficiency compared to other widely used digital modulation types, such as quadrature amplitude modulation. Mathematic basis of AMMC signal generation is described. The process of finding the coordinates of three component 8-AMMC signal constellation is explained, the formation of signals in the time plane based on the found coordinates is explained as well as their discretization and the addition of white noise. An iterative algorithm for generating initial data for ANN based on the described ratios is proposed. The general structure of one-dimensional convolutional neural network is considered. Functions of individual neurons, connections between them, the formation of layers and the convolution operation are described mathematically. On the basis of the previously given ratios, a final display of the network was formed. Specific dimensions and activation functions for layers are selected. The use of convolutional layers is justified by time invariance. Based on the reviewed mathematical models, selected activation functions and dimensions, a neural model was formed. The process of validating the effectiveness of the formed neural model is described, which is based on comparing the symbolic error probabilities of the proposed and reference models at different signal-to-noise ratios. The validation results are presented. The advantages of the obtained model over the previously proposed purely recurrent model and the AMMC reference receiver are explained.

INAPPROPRIATE CONTENT DETECTION IN IMAGE

The proliferation of social media platforms has led to a significant increase in the sharing of memes and other image-based content. However, some of these images may contain inappropriate or offensive text, which can be challenging to detect using traditional methods. Because the open-source existing solutions does not classify the text content in images. This project aims to develop a robust solution for detecting Not Safe for Work (NSFW) text content in images, with a particular focus on social media memes. The objectives of this project are to accurately extract text from images using optical character recognition (OCR) techniques, and to classify the extracted text as either NSFW or Safe for Work (SFW) using a fine-tuned natural language processing (NLP) model. The methodology employed in this project involves several key steps. First, the Keras OCR library is utilized to extract text from input images, as it has demonstrated superior performance compared to other OCR tools for social media images. Next, a preprocessing step is performed to align the extracted text in the correct sequence using coordinates, distance from the image origin, and the Pythagorean theorem. The pre-processed text is then passed to a fine-tuned BERT-BASE-UNCASED language model, which has been trained on a dataset of Reddit posts (NSFW and SFW) to classify the text as either NSFW or SFW. Key Words: OCR, Inappropriate, BERT, NSFW, SFW, Image, Text

Zero-Shot Strike: Testing the generalisation capabilities of out-of-the-box LLM models for depression detection

Depression is a significant global health challenge. Still, many people suffering from depression remain undiagnosed. Furthermore, the assessment of depression can be subject to human bias. Natural Language Processing (NLP) models offer a promising solution. We investigated the potential of four NLP models (BERT, Llama2-13B, GPT-3.5, and GPT-4) for depression detection in clinical interviews. Participants (N = 82) underwent clinical interviews and completed a self-report depression questionnaire. NLP models inferred depression scores from interview transcripts. Questionnaire cut-off values for depression were used as a classifier for depression. GPT-4 showed the highest accuracy for depression classification (F1 score 0.73), while zero-shot GPT-3.5 initially performed with low accuracy (0.34), improved to 0.82 after fine-tuning, and achieved 0.68 with clustered data. GPT-4 estimates of symptom severity PHQ-8 score correlated strongly (r = 0.71) with true symptom severity. These findings demonstrate the potential of AI models for depression detection. However, further research is necessary before widespread deployment can be considered.