Experimental investigation on weak shock wave mitigation characteristics of flexible polyurethane foam and polyurea

In recent years,explosion shock wave has been considered as a signature injury of the current military conflicts.Although strong shock wave is lethal to the human body,weak shock wave can cause many more lasting consequences.To investigate the protection ability and characteristics of flexible materials and structures under weak shock wave loading,the blast wave produced by TNT explosive is loaded on the polyurethane foam with the density of 200.0 kg/m3(F-200)and 400.0 kg/m3(F-400),polyurea with the density of 1100.0 kg/m^(3)(P-1100)and structures composed of the two materials,which are intended for individual protection.Experimental results indicate that the shock wave is attenuated to weak pressure disturbance after interacting with the flexible materials which are not damaged.The shock wave protective capability of single-layer materials is dependent on their thickness,density and microscopic characteristics.The overpressure,maximum pressure rise rate and impulse of transmitted wave decrease exponentially with increase in sample thickness.For the same thickness,F-400 provides better protective capability than F-200 while P-1100 shows the best protective capability among the three materials.In this study,as the materials are not destroyed,F-200 with a thickness more than10.0 mm,F-400 with a thickness more than 4.0 mm,and P-1100 with a thickness more than 1.0 mm can attenuate the overpressure amplitude more than 90.0%.Further,multi-layer flexible composites are designed.Different layer layouts of designed structures and layer thickness of the single-layer materials can affect the protective performance.Within the research range,the structure in which polyurea is placed on the impact side shows the optimal shock wave protective performance,and the thicknesses of polyurea and polyurethane foam are 1.0 mm and 4.0 mm respectively.The overpressure attenuation rate reached maximum value of 93.3%and impulse attenuation capacity of this structure are better than those of single-layer polyurea and polyurethane foam with higher areal density.

Significant risk factors for intensive care unit-acquired weakness:A processing strategy based on repeated machine learning

BACKGROUND Intensive care unit-acquired weakness(ICU-AW)is a common complication that significantly impacts the patient's recovery process,even leading to adverse outcomes.Currently,there is a lack of effective preventive measures.AIM To identify significant risk factors for ICU-AW through iterative machine learning techniques and offer recommendations for its prevention and treatment.METHODS Patients were categorized into ICU-AW and non-ICU-AW groups on the 14th day post-ICU admission.Relevant data from the initial 14 d of ICU stay,such as age,comorbidities,sedative dosage,vasopressor dosage,duration of mechanical ventilation,length of ICU stay,and rehabilitation therapy,were gathered.The relationships between these variables and ICU-AW were examined.Utilizing iterative machine learning techniques,a multilayer perceptron neural network model was developed,and its predictive performance for ICU-AW was assessed using the receiver operating characteristic curve.RESULTS Within the ICU-AW group,age,duration of mechanical ventilation,lorazepam dosage,adrenaline dosage,and length of ICU stay were significantly higher than in the non-ICU-AW group.Additionally,sepsis,multiple organ dysfunction syndrome,hypoalbuminemia,acute heart failure,respiratory failure,acute kidney injury,anemia,stress-related gastrointestinal bleeding,shock,hypertension,coronary artery disease,malignant tumors,and rehabilitation therapy ratios were significantly higher in the ICU-AW group,demonstrating statistical significance.The most influential factors contributing to ICU-AW were identified as the length of ICU stay(100.0%)and the duration of mechanical ventilation(54.9%).The neural network model predicted ICU-AW with an area under the curve of 0.941,sensitivity of 92.2%,and specificity of 82.7%.CONCLUSION The main factors influencing ICU-AW are the length of ICU stay and the duration of mechanical ventilation.A primary preventive strategy,when feasible,involves minimizing both ICU stay and mechanical ventilation duration.

Effect of Co on Solidification Characteristics and Microstructural Transformation of Non-equilibrium Solidified Cu-Ni Alloys

Non-equilibrium solidification structures of Cu55Ni45 and Cu55Ni43Co2 alloys were prepared by the molten glass purification cycle superheating method.The variation of the recalescence phenomenon with the degree of undercooling in the rapid solidification process was investigated using an infrared thermometer.The addition of the Co element affected the evolution of the recalescence phenomenon in Cu-Ni alloys.The images of the solid-liquid interface migration during the rapid solidification of supercooled melts were captured by using a high-speed camera.The solidification rate of Cu-Ni alloys,with the addition of Co elements,was explored.Finally,the grain refinement structure with low supercooling was characterised using electron backscatter diffraction(EBSD).The effect of Co on the microstructural evolution during nonequilibrium solidification of Cu-Ni alloys under conditions of small supercooling is investigated by comparing the microstructures of Cu55Ni45 and Cu55Ni43Co2 alloys.The experimental results show that the addition of a small amount of Co weakens the recalescence behaviour of the Cu55Ni45 alloy and significantly reduces the thermal strain in the rapid solidification phase.In the rapid solidification phase,the thermal strain is greatly reduced,and there is a significant increase in the characteristic undercooling degree.Furthermore,the addition of Co and the reduction of Cu not only result in a lower solidification rate of the alloy,but also contribute to the homogenisation of the grain size.

A Method for Detecting and Recognizing Yi Character Based on Deep Learning

Aiming at the challenges associated with the absence of a labeled dataset for Yi characters and the complexity of Yi character detection and recognition,we present a deep learning-based approach for Yi character detection and recognition.In the detection stage,an improved Differentiable Binarization Network(DBNet)framework is introduced to detect Yi characters,in which the Omni-dimensional Dynamic Convolution(ODConv)is combined with the ResNet-18 feature extraction module to obtain multi-dimensional complementary features,thereby improving the accuracy of Yi character detection.Then,the feature pyramid network fusion module is used to further extract Yi character image features,improving target recognition at different scales.Further,the previously generated feature map is passed through a head network to produce two maps:a probability map and an adaptive threshold map of the same size as the original map.These maps are then subjected to a differentiable binarization process,resulting in an approximate binarization map.This map helps to identify the boundaries of the text boxes.Finally,the text detection box is generated after the post-processing stage.In the recognition stage,an improved lightweight MobileNetV3 framework is used to recognize the detect character regions,where the original Squeeze-and-Excitation(SE)block is replaced by the efficient Shuffle Attention(SA)that integrates spatial and channel attention,improving the accuracy of Yi characters recognition.Meanwhile,the use of depth separable convolution and reversible residual structure can reduce the number of parameters and computation of the model,so that the model can better understand the contextual information and improve the accuracy of text recognition.The experimental results illustrate that the proposed method achieves good results in detecting and recognizing Yi characters,with detection and recognition accuracy rates of 97.5%and 96.8%,respectively.And also,we have compared the detection and recognition algorithms proposed in this paper with other typical algorithms.In these comparisons,the proposed model achieves better detection and recognition results with a certain reliability.

Audio2AB:Audio-driven collaborative generation of virtual character animation

Background Considerable research has been conducted in the areas of audio-driven virtual character gestures and facial animation with some degree of success.However,few methods exist for generating full-body animations,and the portability of virtual character gestures and facial animations has not received sufficient attention.Methods Therefore,we propose a deep-learning-based audio-to-animation-and-blendshape(Audio2AB)network that generates gesture animations and ARK it's 52 facial expression parameter blendshape weights based on audio,audio-corresponding text,emotion labels,and semantic relevance labels to generate parametric data for full-body animations.This parameterization method can be used to drive full-body animations of virtual characters and improve their portability.In the experiment,we first downsampled the gesture and facial data to achieve the same temporal resolution for the input,output,and facial data.The Audio2AB network then encoded the audio,audio-corresponding text,emotion labels,and semantic relevance labels,and then fused the text,emotion labels,and semantic relevance labels into the audio to obtain better audio features.Finally,we established links between the body,gestures,and facial decoders and generated the corresponding animation sequences through our proposed GAN-GF loss function.Results By using audio,audio-corresponding text,and emotional and semantic relevance labels as input,the trained Audio2AB network could generate gesture animation data containing blendshape weights.Therefore,different 3D virtual character animations could be created through parameterization.Conclusions The experimental results showed that the proposed method could generate significant gestures and facial animations.

A Novel 6G Scalable Blockchain Clustering-Based Computer Vision Character Detection for Mobile Images

6G is envisioned as the next generation of wireless communication technology,promising unprecedented data speeds,ultra-low Latency,and ubiquitous Connectivity.In tandem with these advancements,blockchain technology is leveraged to enhance computer vision applications’security,trustworthiness,and transparency.With the widespread use of mobile devices equipped with cameras,the ability to capture and recognize Chinese characters in natural scenes has become increasingly important.Blockchain can facilitate privacy-preserving mechanisms in applications where privacy is paramount,such as facial recognition or personal healthcare monitoring.Users can control their visual data and grant or revoke access as needed.Recognizing Chinese characters from images can provide convenience in various aspects of people’s lives.However,traditional Chinese character text recognition methods often need higher accuracy,leading to recognition failures or incorrect character identification.In contrast,computer vision technologies have significantly improved image recognition accuracy.This paper proposed a Secure end-to-end recognition system(SE2ERS)for Chinese characters in natural scenes based on convolutional neural networks(CNN)using 6G technology.The proposed SE2ERS model uses the Weighted Hyperbolic Curve Cryptograph(WHCC)of the secure data transmission in the 6G network with the blockchain model.The data transmission within the computer vision system,with a 6G gradient directional histogram(GDH),is employed for character estimation.With the deployment of WHCC and GDH in the constructed SE2ERS model,secure communication is achieved for the data transmission with the 6G network.The proposed SE2ERS compares the performance of traditional Chinese text recognition methods and data transmission environment with 6G communication.Experimental results demonstrate that SE2ERS achieves an average recognition accuracy of 88%for simple Chinese characters,compared to 81.2%with traditional methods.For complex Chinese characters,the average recognition accuracy improves to 84.4%with our system,compared to 72.8%with traditional methods.Additionally,deploying the WHCC model improves data security with the increased data encryption rate complexity of∼12&higher than the traditional techniques.

Frontier Progress of Landscape Character Assessment in China

The role of Landscape Character Assessment(LCA)at the level of territorial landscape governance spans both natural and social sciences.By analyzing the development history,research distribution,methods and applications of cutting-edge cases of LCA in China,the following conclusions are drawn:①the LCA research in China originated earlier than that in Europe,but has not yet been systematically applied to the implementation of urban and rural planning at all levels;②the fundamental theory of LCA in China has been well constructed,with three main research directions:technologyled,assessment-led,and assessment combined with other theories;③the development of LCA in rural areas is more mature than in urban areas,but the progress of research is uneven across regions;④the current research presents significant“bottom-up”academic characteristics,and there is an urgent need for government decision-making authorities and academia to jointly promote a“top-down”standardized governance mechanism to comprehensively promote the modernization of territorial landscape governance.

Pioneering role of machine learning in unveiling intensive care unitacquired weakness

In the research published in the World Journal of Clinical Cases,Wang and Long conducted a quantitative analysis to delineate the risk factors for intensive care unit-acquired weakness(ICU-AW)utilizing advanced machine learning methodologies.The study employed a multilayer perceptron neural network to accurately predict the incidence of ICU-AW,focusing on critical variables such as ICU stay duration and mechanical ventilation.This research marks a significant advancement in applying machine learning to clinical diagnostics,offering a new paradigm for predictive medicine in critical care.It underscores the importance of integrating artificial intelligence technologies in clinical practice to enhance patient management strategies and calls for interdisciplinary collaboration to drive innovation in healthcare.

Predicting intensive care unit-acquired weakness:A multilayer perceptron neural network approach

In this editorial,we comment on the article by Wang and Long,published in a recent issue of the World Journal of Clinical Cases.The article addresses the challenge of predicting intensive care unit-acquired weakness(ICUAW),a neuromuscular disorder affecting critically ill patients,by employing a novel processing strategy based on repeated machine learning.The editorial presents a dataset comprising clinical,demographic,and laboratory variables from intensive care unit(ICU)patients and employs a multilayer perceptron neural network model to predict ICUAW.The authors also performed a feature importance analysis to identify the most relevant risk factors for ICUAW.This editorial contributes to the growing body of literature on predictive modeling in critical care,offering insights into the potential of machine learning approaches to improve patient outcomes and guide clinical decision-making in the ICU setting.

Unveiling significant risk factors for intensive care unit-acquired weakness:Advancing preventive care

In this editorial,we discuss an article titled,“Significant risk factors for intensive care unit-acquired weakness:A processing strategy based on repeated machine learning,”published in a recent issue of the World Journal of Clinical Cases.Intensive care unit-acquired weakness(ICU-AW)is a debilitating condition that affects critically ill patients,with significant implications for patient outcomes and their quality of life.This study explored the use of artificial intelligence and machine learning techniques to predict ICU-AW occurrence and identify key risk factors.Data from a cohort of 1063 adult intensive care unit(ICU)patients were analyzed,with a particular emphasis on variables such as duration of ICU stay,duration of mechanical ventilation,doses of sedatives and vasopressors,and underlying comorbidities.A multilayer perceptron neural network model was developed,which exhibited a remarkable impressive prediction accuracy of 86.2%on the training set and 85.5%on the test set.The study highlights the importance of early prediction and intervention in mitigating ICU-AW risk and improving patient outcomes.

Machine learning insights on intensive care unit-acquired weakness

Intensive care unit-acquired weakness(ICU-AW)significantly hampers patient recovery and increases morbidity.With the absence of established preventive strategies,this study utilizes advanced machine learning methodologies to unearth key predictors of ICU-AW.Employing a sophisticated multilayer perceptron neural network,the research methodically assesses the predictive power for ICU-AW,pinpointing the length of ICU stay and duration of mechanical ventilation as pivotal risk factors.The findings advocate for minimizing these elements as a preventive approach,offering a novel perspective on combating ICU-AW.This research illuminates critical risk factors and lays the groundwork for future explorations into effective prevention and intervention strategies.

Clinical Effect of Modified Shengyang Yiwei Decoction in the Treatment of Diarrhea-Predominant Irritable Bowel Syndrome due to Spleen and Stomach Weakness

Objective:To explore the therapeutic effect of Shengyang Yiwei Decoction in patients with diarrhea-predominant irritable bowel syndrome(IBS)due to spleen and stomach weakness.Methods:40 patients with diarrhea-predominant IBS who were treated from April 2018 to April 2020 were taken as samples.TCM(traditional Chinese medicine)syndrome differentiation found that they were all due to spleen and stomach weakness.They were randomly divided into two groups.Group A was treated with modified prescriptions of Shengyang Yiwei Decoction,while Group B was treated with Western medicine.The therapeutic effects in the two groups were compared.Results:The treatment efficacy in Group A was higher than that in Group B(P<0.05);the symptom scores of Group A such as loose stools,chills,physical weakness,poor appetite,and abdominal distension after meals were all lower than those in Group B(P<0.05);the SF-36(36-Item Short Form Health Survey)scores of patients with diarrhea-predominant IBS in Group A were higher than those in Group B(P<0.05);the treatment satisfaction of Group A was higher than that of Group B(P<0.05).Conclusion:Treatment of diarrhea-predominant IBS patients with spleen and stomach weakness by Shengyang Yiwei Decoction can promote the disappearance of gastrointestinal discomfort symptoms,improve the quality of life,and enhance treatment efficacy.Hence,it is an efficient and feasible treatment for diarrhea-predominant IBS due to spleen and stomach weakness.

Characterizing large-scale weak interlayer shear zones using conditional random field theory

The shear behavior of large-scale weak intercalation shear zones(WISZs)often governs the stability of foundations,rock slopes,and underground structures.However,due to their wide distribution,undulating morphology,complex fabrics,and varying degrees of contact states,characterizing the shear behavior of natural and complex large-scale WISZs precisely is challenging.This study proposes an analytical method to address this issue,based on geological fieldwork and relevant experimental results.The analytical method utilizes the random field theory and Kriging interpolation technique to simplify the spatial uncertainties of the structural and fabric features for WISZs into the spatial correlation and variability of their mechanical parameters.The Kriging conditional random field of the friction angle of WISZs is embedded in the discrete element software 3DEC,enabling activation analysis of WISZ C2 in the underground caverns of the Baihetan hydropower station.The results indicate that the activation scope of WISZ C2 induced by the excavation of underground caverns is approximately 0.5e1 times the main powerhouse span,showing local activation.Furthermore,the overall safety factor of WISZ C2 follows a normal distribution with an average value of 3.697.

血清sTWEAK、Netrin-1联合APACHEⅡ评分对重型颅脑损伤患者术后预后不良的预测价值

目的探讨血清可溶性肿瘤坏死因子样凋亡弱诱导因子(sTWEAK)、神经轴突导向因子-1(Netrin-1)联合急性生理学与慢性健康状况评分系统Ⅱ(APACHEⅡ)评分对重型颅脑损伤患者术后预后不良的预测价值。方法选取2020年6月至2022年6月该院收治的120例重型颅脑损伤患者,根据术后30 d预后情况分为预后良好组和预后不良组。对比两组血清sTWEAK、Netrin-1水平及APACHEⅡ评分。采用单因素和多因素Logistic回归分析重型颅脑损伤患者术后预后不良的影响因素,并据以构建血清sTWEAK、Netrin-1及APACHEⅡ评分联合应用的预测模型,受试者工作特征(ROC)曲线分析血清sTWEAK、Netrin-1水平及APACHEⅡ评分对重型颅脑损伤患者术后预后不良的预测价值。结果预后不良组的重症监护室居住时间长于预后良好组,白蛋白水平、入院时格拉斯哥昏迷评分法评分和血清Netrin-1水平低于预后良好组,多发脑挫裂伤占比、机械通气占比、入院时APACHEⅡ评分和血清sTWEAK、血清肌酐、血尿素氮水平均高于预后良好组,差异有统计学意义(P<0.05)。多因素Logistic回归分析结果显示,多发脑挫裂伤、Netrin-1水平降低、入院时APACHEⅡ评分升高、sTWEAK水平升高为重型颅脑损伤患者术后预后不良的危险因素(P<0.05)。ROC曲线分析结果显示,血清sTWEAK、Netrin-1及APACHEⅡ评分3个指标单独及联合应用时曲线下面积及其95%CI分别为0.742(0.552~0.925)、0.731(0.488~0.963)、0.714(0.502~0.911)、0.882(0.795~0.947)。结论血清sTWEAK、Netrin-1联合APACHEⅡ评分对重型颅脑损伤患者术后预后不良具有较好的预测价值,可为临床治疗方案的制订提供参考。

自发性急性脑出血患者血浆sCD163/sTWEAK比值与预后的关系

目的探究自发性急性脑出血(ACH)患者血浆可溶性CD163(sCD163)/可溶性肿瘤坏死因子样凋亡弱诱导因子(sTWEAK)比值与预后的关系。方法纳入ACH患者90例作为病例组,根据格拉斯哥预后评分将病例组分为预后不良组(38例)和预后良好组(52例);另选取同期体检健康者45例为对照组。酶联免疫吸附试验检测血浆sCD163、sTWEAK水平并计算sCD163/sTWEAK比值。分析血浆sCD163、sTWEAK水平及sCD163/sTWEAK比值与临床资料的相关性;Logistic回归分析ACH患者预后不良的影响因素;受试者工作特征(ROC)曲线分析sCD163/sTWEAK比值对ACH患者预后不良的预测价值。结果病例组血浆sCD163、sTWEAK水平及sCD163/sTWEAK比值均显著高于对照组;预后良好组上述指标均低于预后不良组(P<0.05)。预后良好组血肿体积、美国国立卫生研究院卒中量表(NIHSS)评分、高血压及幕下出血比例均低于预后不良组,低密度脂蛋白胆固醇(LDL-C)高于预后不良组(P<0.05)。相关性分析表明,血浆sCD163、sTWEAK水平及sCD163/sTWEAK比值与出血部位、血肿体积、NIHSS评分、白细胞计数、血小板计数、中性粒细胞/淋巴细胞比值(NLR)呈正相关(P<0.05)。Logistic回归分析显示,sCD163/sTWEAK比值、出血部位、血肿体积、NIHSS评分为ACH患者预后不良的影响因素(P<0.05)。ROC曲线结果表明,sCD163/sTWEAK比值评估ACH患者预后不良的AUC为0.850,敏感度和特异度分别为86.84%和69.23%。结论sCD163/sTWEAK比值在ACH患者血浆中水平较高,并与预后不良有关,该值对此类患者的预后有一定预测价值。

Analysis of Young Girls’Eagerness to Spend for Disney Virtual Characters Based on Marketing Mix Theory

A virtual character is a design of a fictitious creature with distinctive characteristics created by people,and Disney virtual characters are those Intellectual Property(IP)images that appeared in Disneyland and Disney movies.This investigation aimed to explore why many younger females are keen to spend money on Disney virtual characters.This paper adopted the Marketing Mix Theory strategy(product,price,placement,and promotion(4P)),and the SWOT analysis method has been utilized.This paper investigated the relationship between the 4Ps and consumers’purchasing intentions,and it turned out that unique design and effective promotion in this Disney case would promote consumers’purchase intention,while the higher price and less accessible placements affected their purchase intentions.Thus,the high price and limited places somewhat inhibit customers’desire to buy;due to the attractiveness of the product itself and the promotion on the internet,the target consumers are still willing to consume.

Discussion of Online Teaching Strategies for Teaching Elementary Chinese Characters to Foreigners Based on Error Analyses

The expanding role of the Chinese language in international communications has become increasingly prominent as China’s comprehensive national power continues to grow,leading to a significant rise in the number of Chinese language learners.Since online teaching is not limited by time and space,its application is widespread.For beginners in the Chinese language,the Chinese characters are both a priority and a challenge.The“Chinese Character Classification,”also known as the“Six Writings,”is the earliest systematic theory of Chinese character structures,and teaching Chinese characters in categories based on the“Chinese Character Classification”is a method that fits the cognition of beginners.In order to teach Chinese characters in a targeted approach,based on the collection and analysis of the common errors of Chinese characters among beginners,(1)this paper proposes that(a)the intuitive method can be applied to teach pictographic characters,indicative characters,and associative compound characters in online teaching;(b)the inductive-deductive method of“basic characters to new characters”can be applied for the teaching of pictophonetic characters and associative compound characters;(c)the learning of character patterns should be approached in a whole-part-whole process,while importance should be attached to the suggestion of the frequency effect with a view to facilitating the online learning of Chinese characters for beginners.The aim of this paper is to provide some practical implications for the online teaching of Chinese characters to foreigners.

Eccentric exercise-induced muscle weakness abolishes sex differences in fatigability during sustained submaximal isometric contractions

Background:Females are typically less fatigable than males during sustained isometric contractions at lower isometric contraction intensities.This sex difference in fatigability becomes more variable during higher intensity isometric and dynamic contractions.While less fatiguing than isometric or concentric contractions,eccentric contractions induce greater and longer lasting impairments in force production.However,it is not clear how muscle weakness influences fatigability in males and females during sustained isometric contractions.Methods:We investigated the effects of eccentric exercise-induced muscle weakness on time to task failure(TTF)during a sustained submaximal isometric contraction in young(18-30 years)healthy males(n=9)and females(n=10).Participants performed a sustained isometric contraction of the dorsiflexors at 35°plantar flexion by matching a 30%maximal voluntary contraction(MVC)torque target until task failure(i.e.,falling below 5%of their target torque for>2 s).The same sustained isometric contraction was repeated 30 min after 150 maximal eccentric contractions.Agonist and antagonist activation were assessed using surface electromyography over the tibialis anterior and soleus muscles,respectively.Results:Males were~41%stronger than females.Following eccentric exercise both males and females experienced an~20%decline in maximal voliuntary contraction torque.TTF was-34%longer in females than males prior to eccentic exercise-induced muscle weakness.However,following eccentric exercise-induced muscle weakness,this sex-related difference was abolished,with both groups having an"45%shorter TTF.Notably,there was~100%greater antagonist activation in the female group during the sustained isometric contraction following exercise-induced weakness as compared to the males.Conclusion:This increase in antagonist activation disadvantaged females by decreasing their TTF,resulting in a blunting of their typical fatigability advantage over males.

Research on the Path of Integrating Ideological and Political Courses into College Physical Education Courses Under the Background of Cultivating Moral Character and Cultivating People

The national education department clearly pointed out in the“Guidelines for the Construction of Ideological and Political Curriculum in Colleges and Universities”that colleges and universities need to take on the fundamental task of cultivating moral character and cultivating people in the process of education,and reasonably integrate ideological and political courses into the curriculum to promote the overall improvement of students’ideological and political quality.This article outlines the connotation of ideological and political courses in the context of cultivating moral character and cultivating people,analyzes and summarizes the significance of integrating ideological and political courses into college physical education courses,summarizes existing problems in the construction of ideological and political courses in college physical education,and explores the path of integrating ideological and political courses into college physical education courses under the background of cultivating moral character and cultivating people,with a view to providing guidelines for teachers.

Sailfish Optimizer with Deep Transfer Learning-Enabled Arabic Handwriting Character Recognition

The recognition of the Arabic characters is a crucial task incomputer vision and Natural Language Processing fields. Some major complicationsin recognizing handwritten texts include distortion and patternvariabilities. So, the feature extraction process is a significant task in NLPmodels. If the features are automatically selected, it might result in theunavailability of adequate data for accurately forecasting the character classes.But, many features usually create difficulties due to high dimensionality issues.Against this background, the current study develops a Sailfish Optimizer withDeep Transfer Learning-Enabled Arabic Handwriting Character Recognition(SFODTL-AHCR) model. The projected SFODTL-AHCR model primarilyfocuses on identifying the handwritten Arabic characters in the inputimage. The proposed SFODTL-AHCR model pre-processes the input imageby following the Histogram Equalization approach to attain this objective.The Inception with ResNet-v2 model examines the pre-processed image toproduce the feature vectors. The Deep Wavelet Neural Network (DWNN)model is utilized to recognize the handwritten Arabic characters. At last,the SFO algorithm is utilized for fine-tuning the parameters involved in theDWNNmodel to attain better performance. The performance of the proposedSFODTL-AHCR model was validated using a series of images. Extensivecomparative analyses were conducted. The proposed method achieved a maximum accuracy of 99.73%. The outcomes inferred the supremacy of theproposed SFODTL-AHCR model over other approaches.