RUSAS: Roman Urdu Sentiment Analysis System

Sentiment analysis, the meta field of Natural Language Processing (NLP), attempts to analyze and identify thesentiments in the opinionated text data. People share their judgments, reactions, and feedback on the internetusing various languages. Urdu is one of them, and it is frequently used worldwide. Urdu-speaking people prefer tocommunicate on social media in Roman Urdu (RU), an English scripting style with the Urdu language dialect.Researchers have developed versatile lexical resources for features-rich comprehensive languages, but limitedlinguistic resources are available to facilitate the sentiment classification of Roman Urdu. This effort encompassesextracting subjective expressions in Roman Urdu and determining the implied opinionated text polarity. Theprimary sources of the dataset are Daraz (an e-commerce platform), Google Maps, and the manual effort. Thecontributions of this study include a Bilingual Roman Urdu Language Detector (BRULD) and a Roman UrduSpelling Checker (RUSC). These integrated modules accept the user input, detect the text language, correct thespellings, categorize the sentiments, and return the input sentence’s orientation with a sentiment intensity score.The developed system gains strength with each input experience gradually. The results show that the languagedetector gives an accuracy of 97.1% on a close domain dataset, with an overall sentiment classification accuracy of94.3%.

Trajectory of Walking Function in Late-Stage Older Individuals Managed with a Regular Exercise Program: A 5-Year Longitudinal Tracking with an IoT Gait Analysis System Using Accelerometers

Purpose: This study focused on maintaining and improving the walking function of late-stage older individuals while longitudinally tracking the effects of regular exercise programs in a day-care service specialized for preventive care over 5 years, using detailed gait function measurements with an accelerometer-based system. Methods: Seventy individuals (17 male and 53 female) of a daycare service in Tokyo participated in a weekly exercise program, meeting 1 - 2 times. The average age of the participants at the start of the program was 81.4 years. Gait function, including gait speed, stride length, root mean square (RMS) of acceleration, gait cycle time and its standard deviation, and left-right difference in stance time, was evaluated every 6 months. Results: Gait speed and stride length improved considerably within six months of starting the exercise program, confirming an initial improvement in gait function. This suggests that regular exercise programs can maintain or improve gait function even age groups that predictably have a gradual decline in gait ability due to enhanced age. In the long term, many indicators tended to approach baseline values. However, the exercise program seemingly counteracts age-related changes in gait function and maintains a certain level of function. Conclusions: While a decline in gait ability with aging is inevitable, establishing appropriate exercise habits in late-stage older individuals may contribute to long-term maintenance of gait function.

A Study on Gender Differences in Speech Interaction Behaviors of Preschool Teachers-Based on the Improved Flanders Interactive Analysis System(iFIAS)

The issue of the extremely imbalanced gender ratio in preschool teachers has received widespread attention,and there are few studies on teacher-child verbal interaction behavior based on gender differences in preschool teachers.This article takes the“Little Light Bulb Is On”,a scientific exploration activity done by 5-6 years old kindergarten students as an example,and uses the improved Flanders Interaction Analysis System(iFIAS)as a tool to analyze the speech interaction behavior of male and female preschool teachers.The research results indicate that there are gender differences in teacher child language interaction between male and female teachers in terms of the atmosphere,the teaching structure,the teaching tendency,the way of raising questions,and the overall trend of interaction.

Psychological Emotion Analysis System for Special Needs Children Based on Neural Network

With the development of modern society and the improvement of living standards,care for special needs children has been increasingly highlighted,and numerous corresponding measures such as welfare homes,special education schools,and youth care centers have emerged.Due to the lack of systematic emotional companionship,the mental health of special needs children are bound to be affected.Nowadays,emotional education,analysis,and evaluation are mostly done by psychologists and emotional analysts,and these measures are unpopular.Therefore,many researchers at home and abroad have focused on the solution of psychological issues and the psychological assessment and emotional analysis of such children in their daily lives.In this paper,a special children’s psychological emotional analysis based on neural network is proposed,where the system sends the voice information to a cloud platform through intelligent wearable devices.To ensure that the data collected are valid,a series of pretreatments such as Chinese word segmentation,de-emphasis,and so on are put into the neural network model.The model is based on the further research of transfer learning and Bi-GRU model,which can meet the needs of Chinese text sentiment analysis.The completion rate of the final model test has reached 97%,which means that it is ready for use.Finally,a web page is designed,which can evaluate and detect abnormal psychological state,and at the same time,a personal emotion database can also be established.

Enhanced Tunicate Swarm Optimization with Transfer Learning Enabled Medical Image Analysis System

Medical image analysis is an active research topic,with thousands of studies published in the past few years.Transfer learning(TL)including convolutional neural networks(CNNs)focused to enhance efficiency on an innovative task using the knowledge of the same tasks learnt in advance.It has played a major role in medical image analysis since it solves the data scarcity issue along with that it saves hardware resources and time.This study develops an EnhancedTunicate SwarmOptimization withTransfer Learning EnabledMedical Image Analysis System(ETSOTL-MIAS).The goal of the ETSOTL-MIAS technique lies in the identification and classification of diseases through medical imaging.The ETSOTL-MIAS technique involves the Chan Vese segmentation technique to identify the affected regions in the medical image.For feature extraction purposes,the ETSOTL-MIAS technique designs a modified DarkNet-53 model.To avoid the manual hyperparameter adjustment process,the ETSOTLMIAS technique exploits the ETSO algorithm,showing the novelty of the work.Finally,the classification of medical images takes place by random forest(RF)classifier.The performance validation of the ETSOTL-MIAS technique is tested on a benchmark medical image database.The extensive experimental analysis showed the promising performance of the ETSOTL-MIAS technique under different measures.

ILIDViz:An incremental learning-based visual analysis system for network anomaly detection

Background With the development of information technology,there is a significant increase in the number of network traffic logs mixed with various types of cyberattacks.Traditional intrusion detection systems(IDSs)are limited in detecting new inconstant patterns and identifying malicious traffic traces in real time.Therefore,there is an urgent need to implement more effective intrusion detection technologies to protect computer security.Methods In this study,we designed a hybrid IDS by combining our incremental learning model(KANSOINN)and active learning to learn new log patterns and detect various network anomalies in real time.Conclusions Experimental results on the NSLKDD dataset showed that KAN-SOINN can be continuously improved and effectively detect malicious logs.Meanwhile,comparative experiments proved that using a hybrid query strategy in active learning can improve the model learning efficiency.

Evaluation of visual quality of spherical and aspherical intraocular lenses by Optical Quality Analysis System

AIM： To evaluate the impact of spherical and aspherical intraocular lenses on the postoperative visual quality of age-related cataract patients using Optical Quality Analysis System （OQAS）. METHODS： Seventy-four eyes with age-related cataracts were randomly divided into spherical and aspherical lens implantation groups. Best-corrected visual acuity （BCVA） was measured preoperatively, one day, one week, two weeks, one month and two months after surgery. A biometric systems analysis using the OQAS objective scattering index （OSI） was performed. RESULTS： There were no significant differences in visual acuity （P〉0.05） before and after spherical and aspheric lens implantation. There was a negative linear correction between the OSI value and BCVA （t-=-0.634, P=-0.000）, and positive corrections between the OSI value and the lens LOCUS III value of nucleus color （NC）, nucleus opacity （NO）, cortex （C） and posterior lens capsular （P） （r=0.704, P=0.000; r=0.514, P=0.000; r=0.276, P=0.020; r=0.417, P=-0.000, respectively）. OSI values of spherical vs aspherical lenses were 11.5±3.6 vs 11.8±3.4, 4.1±0.9 vs 3.3±0.8, 3.5±0.9 vs 2.7±0.7, 3.3±0.8 vs 2.6±0.7, 3.2±0.7 vs 2.5±0.8, and 3.2±0.8 vs 2.50.8 before and ld, 1, 2wk, 1 and 2mo after surgery, respectively. All time points varied significantly （P〈0.01） between the two groups. CONCLUSION： Aspherical IOLs does not significantly affect visual acuity compared with spherical IOLs. The OSI value, was significantly lower in the aspherical lens group compared with the spherical lens. This study shows that objective visual quality of aspheric IOLs is better than that of the spherical lens by means of OQAS biological measurement method.

A PRELIMINARY STUDY ON THE 3DVAR ASSIMILATION OF THE AMSU-A DATA IN SPACE-TIME MULTISCALE ANALYSIS SYSTEM

Assimilating satellite radiances into Numerical Weather Prediction(NWP) models has become an important approach to increase the accuracy of numerical weather forecasting. In this study, the assimilation technique scheme was employed in NOAA's STMAS(Space-Time Multiscale Analysis System) to assimilate AMSU-A radiances data.Channel selection sensitivity experiments were conducted on assimilated satellite data in the first place. Then, real case analysis of AMSU-A data assimilation was performed. The analysis results showed that, following assimilating of AMSU-A channels 5-11 in STMAS, the objective function quickly converged, and the channel vertical response was consistent with the AMSU-A weighting function distribution, which suggests that the channels can be used in the assimilation of satellite data in STMAS. With the case of the Typhoon Morakot in Taiwan Island in August 2009 as an example, experiments on assimilated and unassimilated AMSU-A radiances data were designed to analyze the impact of the assimilation of satellite data on STMAS. The results demonstrated that assimilation of AMSU-A data provided more accurate prediction of the precipitation region and intensity, and especially, it improved the 0-6h precipitation forecast significantly.

Influence of lens position as detected by an anterior segment analysis system on postoperative refraction in cataract surgery

AIM:To predict postoperative intraocular lens(IOL)position using the Sirius anterior segment analysis system and investigate the effect of lens position and IOL type on postoperative refraction.METHODS:A total of 97 patients(102 eyes)were enrolled in the final analysis.An anterior segment biometry measurement was performed preoperatively with Sirius and Lenstar.The results of predicted lens position(PLP)and IOL power were automatically calculated by the software used by the instruments.Effective lens position(ELP)was measured manually using Sirius 3 mo postoperatively.Pearson's correlation analysis and linear regression analysis were used to determine the correlation of lens position to other parameters.RESULTS:PLP and ELP were positively correlated to axial length(AL;r=0.42,P<0.0001 and r=0.49,P<0.0001,respectively).There was a weak correlation between the peLP(ELP-PLP)and the prediction error of spherical refraction(peSR;r=0.34,P<0.0001).The peLP of Softec HD IOL differed statistically from those of both the TECNIS ZCB00 and Sensor AR40E IOLs.Multiple linear regression was used to obtain the prediction formula:ELP=0.66+0.63×[aqueous depth(AQD)+0.6 LT](r=0.61,P<0.0001),and a new variable(AQD+0.6 LT)was found to have the strongest correlation with ELP.CONCLUSION:The Sirius anterior segment analysis system is helpful to predict ELP,which reduces postoperative refraction error.

Methodology for Developing a Nursing Administration Analysis System

Nursing administration requires a large volume of wide-ranging information, and nurse administrators are limited in their ability to compile and analyze information for nursing administration. The purpose of this study is to create methodology for developing a nursing administration analysis system to aid nurse administrators in performing outcome analysis. In this methodology, information required for nursing administration in the PSYCHOMS? (Psychiatric Outcome Management System, registered trademark) database is analyzed according to the individual needs of nurse administrators. It features a combination of a classification method and an extraction method for obtaining quantitative and qualitative data as information required for nursing administration, and enables nurse administrators to easily obtain analysis results that they directly need. This methodology converts the time required nurse administrators to collect and organize information into time for making considerations in order to devise strategies for improving the quality of nursing care services, and can improve the quality and efficiency of nursing administration. This may lead to an increase of the quality of nursing care services at psychiatric hospitals. This methodology is highly versatile as it can be applied in information management, not only for nursing, but for the entire psychiatric hospital.

Analysis of Shoreline Changes in Ikoli River in Niger Delta Region Yenagoa,Bayelsa State Using Digital Shoreline Analysis System(DSAS)

The use of Digital Shoreline Analysis System was used to determine shoreline changes in Ikoli River,Yenagoa,Bayelsa State.Shoreline data were extracted from satellite imagery over thirty years(1991-2021).The basis of this study is to use Digital Shoreline Analysis System to determine erosion and accretion areas.The result reveals that the average erosion rate in the study area is 1.16 m/year and the accretion rate is 1.62 m/year along the Ikoli River in Ogbogoro Community in Yenagoa,Bayelsa State.The mean shoreline length is 5.24 km with a baseline length of 5.2 km and the area is classified into four zones to delineate properly area of erosion and accretion based on the five class of Linear regression rate,endpoint rate and weighted linear rate of which zone Ⅰ contain very high erosion and high erosion with an area of landmass 255449.93 m^(2) of 38%,zone Ⅱ contain moderate accretion,very high accretion and high accretion with a land area of 1666816.46 m^(2) with 24%,zone Ⅲ has very high erosion and high erosion with an area of landmass 241610.85 m^(2) of 34% and zone Ⅳ contain moderate accretion and high accretion with land area 30888.08 m^(2) with 4%.Out of the four zones,zone Ⅰ and Ⅱ were found to be eroding with 72% and zone Ⅱ and Ⅳ contain accretion with 28%.The result shows that 44% of the area have been eroded.Therefore,coastal engineers,planners,and shoreline zone management authorities can use DSAS to create more appropriate management plans and regulations for coastal zones and other coastal parts of the state with similar geographic features.

A Quantitative Analysis System for Continental Extensional Basin

Continental extensional basin is one of the most important oil and ga s bearing basin types in the world and is main basin type in east China. The qu antitative analysis for this kind of basins has important significance for oil a nd gas exploration and development in east China. Sedimentary basin is a geodyna mic system including sedimentary basin itself, the crust and the mantle under it . Basin evolution is affected by regional structure stress field geophysical sta tus in the deep of the earth and outer condition, such as climate changes, water supplying etc. Based on the concept of basin dynamic system, the authors develo ped a geological process modeling and analyzing system-Continental Extensional B asin Quantitative Analysis System (CEBQAS). The system consists of basin geodyna mic modeling, structural modeling, sedimentary modeling, geologic analysis, data base and display subsystem. The system can reappear structural and sedimentary e volution history to an extent and provide basin geodynamic information as well a s retrieving parameter for modeling from original data such as logging, core an d seismic data .

Discussion on the Application of Enterprise Business Intelligence Data Analysis System

Enterprise Business Intelligence(BI)system refers to data mining through the existing database of the enterprise,and data analysis according to customer requirements through comprehensive processing.The data analysis efficiency is high and the operation is convenient.This paper mainly analyzes the application of enterprise BI data analysis system in enterprises.

Risk analysis system of geo-hazard based on GIS technique

The socio-economic attribute of geo-hazard made us distinguish it from the traditional engineering geology study. It will get more social benefit from the analysis of the geo-hazard in the socio-economic attribute. The hazard and the vulnerability of the element controls the risk level of the regional geo-hazard. The risk analysis supported by GIS in geo-hazard study is one of the most important directions. Based on the author’s studies in recent years, a risk analysis system of regional geo-hazard (RiskAnly) has been developed on the basis of software MAPGIS. The paper introduces the train of system design, the structure and the workflow of RiskAnly. As a case study, the paper also deals with the risk zonation of the regional landslide hazard of China.

Impact of the Assimilation Frequency of Radar Data with the ARPS 3DVar and Cloud Analysis System on Forecasts of a Squall Line in Southern China

Assimilation configurations have significant impacts on analysis results and subsequent forecasts. A squall line system that occurred on 23 April 2007 over southern China was used to investigate the impacts of the data assimilation frequency of radar data on analyses and forecasts. A three-dimensional variational system was used to assimilate radial velocity data,and a cloud analysis system was used for reflectivity assimilation with a 2-h assimilation window covering the initial stage of the squall line. Two operators of radar reflectivity for cloud analyses corresponding to single-and double-moment schemes were used. In this study, we examined the sensitivity of assimilation frequency using 10-, 20-, 30-, and 60-min assimilation intervals. The results showed that analysis fields were not consistent with model dynamics and microphysics in general;thus, model states, including dynamic and microphysical variables, required approximately 20 min to reach a new balance after data assimilation in all experiments. Moreover, a 20-min data assimilation interval generally produced better forecasts for both single-and double-moment schemes in terms of equitable threat and bias scores. We conclude that a higher data assimilation frequency can produce a more intense cold pool and rear inflow jets but does not necessarily lead to a better forecast.

Artificial intelligence based real-time microcirculation analysis system for laparoscopic colorectal surgery

BACKGROUND Colonic perfusion status can be assessed easily by indocyanine green(ICG)angiography to predict ischemia related anastomotic complications during laparoscopic colorectal surgery.Recently,various parameter-based perfusion analysis have been studied for quantitative evaluation,but the analysis results differ depending on the use of quantitative parameters due to differences in vascular anatomical structure.Therefore,it can help improve the accuracy and consistency by artificial intelligence(AI)based real-time analysis microperfusion(AIRAM).AIM To evaluate the feasibility of AIRAM to predict the risk of anastomotic complication in the patient with laparoscopic colorectal cancer surgery.METHODS The ICG curve was extracted from the region of interest(ROI)set in the ICG fluorescence video of the laparoscopic colorectal surgery.Pre-processing was performed to reduce AI performance degradation caused by external environment such as background,light source reflection,and camera shaking using MATLAB 2019 on an I7-8700k Intel central processing unit(CPU)PC.AI learning and evaluation were performed by dividing into a training patient group(n=50)and a test patient group(n=15).Training ICG curve data sets were classified and machine learned into 25 ICG curve patterns using a self-organizing map(SOM)network.The predictive reliability of anastomotic complications in a trained SOM network is verified using test set.RESULTS AI-based risk and the conventional quantitative parameters including T1/2max,time ratio(TR),and rising slope(RS)were consistent when colonic perfusion was favorable as steep increasing ICG curve pattern.When the ICG graph pattern showed stepped rise,the accuracy of conventional quantitative parameters decreased,but the AI-based classification maintained accuracy consistently.The receiver operating characteristic curves for conventional parameters and AI-based classification were comparable for predicting the anastomotic complication risks.Statistical performance verifications were improved in the AI-based analysis.AI analysis was evaluated as the most accurate parameter to predict the risk of anastomotic complications.The F1 score of the AI-based method increased by 31% for T1/2max,8% for TR,and 8% for RS.The processing time of AIRAM was measured as 48.03 s,which was suitable for real-time processing.CONCLUSION In conclusion,AI-based real-time microcirculation analysis had more accurate and consistent performance than the conventional parameter-based method.

Study on element detection and its correction in iron ore concentrate based on a prompt gamma-neutron activation analysis system

A prompt gamma-neutron activation analysis(PGNAA) system was developed to detect the iron content of iron ore concentrate. Because of the self-absorption effect of gamma-rays and neutrons, and the interference of chlorine in the neutron field, the linear relationship between the iron analytical coefficient and total iron content was poor, increasing the error in the quantitative analysis. To solve this problem, gamma-ray self-absorption compensation and a neutron field correction algorithm were proposed, and the experimental results have been corrected using this algorithm. The results show that the linear relationship between the iron analytical coefficient and total iron content was considerably improved after the correction. The linear correlation coefficients reached 0.99 or more.

Fuzzy-Based Sentiment Analysis System for Analyzing Student Feedback and Satisfaction

The feedback collection and analysis has remained an important subject matter for long.The traditional techniques for student feedback analysis are based on questionnaire-based data collection and analysis.However,the student expresses their feedback opinions on online social media sites,which need to be analyzed.This study aims at the development of fuzzy-based sentiment analysis system for analyzing student feedback and satisfaction by assigning proper sentiment score to opinion words and polarity shifters present in the input reviews.Our technique computes the sentiment score of student feedback reviews and then applies a fuzzy-logic module to analyze and quantify student’s satisfaction at the fine-grained level.The experimental results reveal that the proposed work has outperformed the baseline studies as well as state-of-the-art machine learning classifiers.