Great Accuracy Rate Research Articles

PSII-9 Whole genome sequences of two nitrate-reducing strains of Selenomonas ruminantium isolated from the rumen

Abstract Selenomonas ruminantium is a functionally diverse, non-fibrolytic member of the rumen microbial community with two subspecies: lactilytica and ruminantium. Some strains of S. ruminantium can reduce nitrate and nitrite, which may be associated with ruminal fiber degradation and, thus, greater forage diets. Most work done with S. ruminantium has been culture-based, with less genomic and functional data. Here, we performed whole genome sequencing with an Oxford Nanopore GridION on two isolated, nitrate-reducing strains of S. ruminantium to provide complete genomic data and determine the nitrogen usage functional capacities of these novel strains. The two isolated strains, 223 and 231, were shipped on dry ice to the University of Wisconsin-Madison, where they were grown, pelleted, and observed under a microscope to determine purity. The samples were processed with a 3 min bead-beating step before extraction with the Zymo Quick-DNA High Molecular Weight MagBead Kit. Subsequently, the extracted samples underwent a quality assessment step before moving into library preparation for Oxford Nanopore sequencing. The two libraries were then loaded onto two R9.4.1 flow cells per library and were sequenced on an Oxford Nanopore GridION for 72 h. The data were extracted from the GridION, and the combined passed reads from each of the two flow cells per library were assembled with Canu and Flye, long-read de novo genome assemblers designed for noisy, single-molecule sequences. The assembled genomes were compared with a reference S. ruminantium genome with Quast, a quality assessment tool that evaluates assembly accuracy. Finally, the assembled genomes with the highest accuracy were analyzed with prodigal to generate protein-coding gene predictions and then with BLAST to assign functional predictions. For each strain, a different assembly was used. The Canu assembly of strain 223 had greater accuracy rates at 3205.23 mismatches per 100 kbp, while the Flye assembly of strain 231 had 2494.40 mismatches per 100 kbp. The two strains had similar genome sizes: 3,098,553 base pairs for strain 223 and 3,138,614 base pairs for strain 231. Strain 223 had less GC content than strain 231 of 51.02%, while strain 231 had a GC content of 52.80%, which were both less than the 50.68% GC content of the reference genome. These genomes provide a resource for understanding nitrate reduction in the rumen and work to build a better database for analyzing the rumen microbiome.

On the representation of /h/ by Quebec francophone learners of English

The current study investigates whether some of the variation in h-production observed among Quebec francophone (QF) learners of English could follow from their at times assimilating /h/ to /ʁ/. In earlier research, we attributed variation exclusively to QFs developing an approximate (“fuzzy” or “murky”) representation of /h/ that is not fully reliable as a base for h-perception and production. Nonetheless, two previous studies observed via event-related potentials differences in QF perceptual ability, which may follow from the quality of the vowel used in the stimuli: /ɑ/ vs. /ʌ/ (detection vs. no detection of /h/). Before the vowel /ɑ/, /h/ exhibits phonetic properties that may allow it to be assimilated to and thus underlyingly represented as /ʁ/. If /h/ is at times subject to approximate representation (e.g., before /ʌ/) and at others captured as /ʁ/ (before /ɑ/), we would expect production of /h/ to reflect this representational distinction, with greater accuracy rates in items containing /ɑ/. Two-way ANOVAs and paired Bayesian t-tests on the reading-aloud data of 27 QFs, however, reveal no difference in h-production according to vowel type. We address the consequences of our findings, discussing notably why QFs have such enduring difficulty acquiring /h/ despite the feature [spread glottis] being available in their representational repertoire. We propose the presence of a Laryngeal Input Constraint that renders representations containing only a laryngeal feature highly marked. We also consider the possibility that, rather than having overcome this constraint, some highly advanced learners are “phonological zombies”: these learners become so adept at employing approximate representations in perception and production that they are indistinguishable from speakers with bona fide phonemic representations.

A Single Center Study Using CT Enterography to Evaluate Small Bowel Disorders

Background: A research was done on the efficiency of identifying small intestinal problems using computed tomography (CT) enterography. The findings showed that 93% of patients had appropriate diagnoses, while the remaining [07%] were still pending. These results indicate that CT enterography is a highly accurate, safe, and cost-effective imaging method for the diagnosis of small intestinal diseases. Aim: By using CT enterography as an imaging method, small intestinal problems may be precisely identified. Its cost-efficiency in comparison to other imaging modalities, as well as its safety and effectiveness in identifying small bowel illnesses, have been assessed. Additionally, various imaging modalities have been compared to CT enterography's accuracy, and its effect on patient outcomes has also been taken into account. Method: The Radiology Department of KTH Peshawar conducted a comprehensive research with hundred patients between Jan 2017 and jan 2018. Each patient performed a CT enterography, and based on precise data gathered from their medical records, their clinical findings were compared with the results, which were then assessed using SPSS Version 27. A descriptive statistical analysis was then performed on the data. Results: In our investigation, CT enterography had a good 93% diagnostic success rate in the diagnosis of small intestinal diseases. Unfortunately, for certain individuals, notably the remaining 7%, the findings were not conclusive. We found it interesting that CT enterography had a greater accuracy rate than other imaging methods like MRI and ultrasound. Conclusion: This research established CT enterography as a cost-efficient, secure, and accurate imaging method for the diagnosis of small intestinal illnesses. This non-invasive method also has high accuracy and safety. Keywords: imaging modalities, accuracy, cost-effectiveness, safety, and CT enterography.

Analisis Sentimen Masyarakat Indonesia Terhadap Dampak Penurunan Global Sebagai Akibat Resesi di Twitter

A recession is a significant reduction in economic activity and is spread across the economy at its greatest for more than a few months, but it can also be seen in Real GDP, Real Income, Employment, Industrial Production, and Wholesale-Retail Sales. Recently, there has been a lot of public opinion regarding the recession that will occur in 2023, especially in Indonesia, on various social media such as Twitter. Based on these problems, sentiment analysis was carried out on tweets to obtain information on the positive or negative polarity of these opinions using the Naive Bayes and Support Vector Machine (SVM) methods to choose a more effective way in case studies to determine sentiment predictions. The steps are taken consist of data collection, processing data, weighting data, classification process, evaluation, validation, and results and discussion. The web scraping technique was used, and after going through the data cleaning stages, a total of 780 tweet data was obtained. The results of the classification test show that the SVM method has a greater accuracy rate with a proportion of 79.5% compared to the Naive Bayes method with a proportion of 72.5%. The SVM method's prediction results also show several 144 positive and 636 negative sentiments. Judging from the Wordcloud that was formed, it can be assumed that people are worried about their economic conditions, one of which is the unstable oil price which can trigger a recession.

Counterfeit Currency Detection based on AI

Abstract: The use of technology has grown tremendously within the few years it has made it easier to have access to advanced printing equipment in the industry which resulted in color printing of currencies to produce counterfeit notes across the country. To eliminate such unethical activities of printing counterfeit currency it is mandatory to make a system that detects the fake currency, In systems such as a money exchanger for example ATMs and vending machines, counterfeit currency notes must be detected beforehand exchanging process takes place. In the past, there have been similar systems developed based on methods such as image processing techniques that are done on the Matlab platform and other such platforms these methods possess some limitations including being less efficient and time-consuming. Our system is designed to eliminate all of the above problems through the use of deep learning techniques by detecting the features of currencies and determining whether its fake with a great accuracy rate. our proposed system verifies the Indian currency notes using Deep learning, deep learning helps in extracting meaningful information from the dataset fed into the machine using a set of methods to perform the classification of images. Our project makes use of the deep learning framework TensorFlow and its high-level API Keras which simplifies the creation of the model making it easier to achieve a less time-consuming and accurate model. Index Terms: Currency Detection, Deep Learning, Deep Neural Network, Convolution Neural Network

PREDIKSI FINANCIAL DISTRESS MENGGUNAKAN MODEL TAFFLER DAN GROVER PADA PERUSAHAAN YANG TERDAMPAK PANDEMI COVID-19

This study aims to predict and analyze financial distress using the Taffler and Grover prediction model in the companies affected by the Covid-19 pandemic. The data used is the company's quarterly listed in the Indonesia Stock Exchange for 2018-2020, which had positive profit in 2019 and suffered a decline in profits or losses in 2020. The technique used in the data sampling process is a purposive sampling technique of 46 companies. The study results depicts that the Taffler and Grover models can predict financial distress conditions in companies affected by the Covid-19 pandemic. The Taffler model has a greater accuracy rate than the Grover model, which is 73.19%, while the Grover model has an accuracy rate of 71,01%. Prediction results are less accurate due to the Covid-19 pandemic. Pearson's correlation test results showed a correlation between the Taffler model and Grover model that the relationship between variables is strong and unidirectional.Keywords: Covid-19, Financial Distress, Taffler, Grover, Pearson Correlation.

Zika Virus Prediction Using AI-Driven Technology and Hybrid Optimization Algorithm in Healthcare.

The Zika virus presents an extraordinary public health hazard after spreading from Brazil to the Americas. In the absence of credible forecasts of the outbreak's geographic scope and infection frequency, international public health agencies were unable to plan and allocate surveillance resources efficiently. An RNA test will be done on the subjects if they are found to be infected with Zika virus. By training the specified characteristics, the suggested Hybrid Optimization Algorithm such as multilayer perceptron with probabilistic optimization strategy gives forth a greater accuracy rate. The MATLAB program incorporates numerous machine learning algorithms and artificial intelligence methodologies. It reduces forecast time while retaining excellent accuracy. The projected classes are encrypted and sent to patients. The Advanced Encryption Standard (AES) and TRIPLE Data Encryption Standard (TEDS) are combined to make this possible (DES). The experimental outcomes improve the accuracy of patient results communication. Cryptosystem processing acquires minimal timing of 0.15 s with 91.25 percent accuracy.

Higher Education Environment Monitoring and Internationalization Strategy Model Using Big Data Analysis.

This paper creates a model based on big data, examines higher education's internationalization approach, and simultaneously conducts research. According to the research, the method used in this work has a greater accuracy rate than numerous other well-known methodologies for assessing the risk associated with higher education internationalization strategies, with the maximum accuracy rate reaching 68.9%. It can be seen that the methodology used in this report is more suited for researching the higher education sector's internationalization plan. Information integration management is advancing steadily in the age of big data. To ensure the standardization, informationalization, and scientificization of school education and teaching management, consideration should also be given to the organic integration and full utilization of education internationalization data in the field of higher education internationalization strategy.

A new ML-based approach to enhance student engagement in online environment.

The educational research is increasingly emphasizing the potential of student engagement and its impact on performance, retention and persistence. This construct has emerged as an important paradigm in the higher education field for many decades. However, evaluating and predicting the student’s engagement level in an online environment remains a challenge. The purpose of this study is to suggest an intelligent predictive system that predicts the student’s engagement level and then provides the students with feedback to enhance their motivation and dedication. Three categories of students are defined depending on their engagement level (Not Engaged, Passively Engaged, and Actively Engaged). We applied three different machine-learning algorithms, namely Decision Tree, Support Vector Machine and Artificial Neural Network, to students’ activities recorded in Learning Management System reports. The results demonstrate that machine learning algorithms could predict the student’s engagement level. In addition, according to the performance metrics of the different algorithms, the Artificial Neural Network has a greater accuracy rate (85%) compared to the Support Vector Machine (80%) and Decision Tree (75%) classification techniques. Based on these results, the intelligent predictive system sends feedback to the students and alerts the instructor once a student’s engagement level decreases. The instructor can identify the students’ difficulties during the course and motivate them through e-mail reminders, course messages, or scheduling an online meeting.

Computing eye gaze metrics for the automatic assessment of radiographer performance during X-ray image interpretation

AimTo investigate image interpretation performance by diagnostic radiography students, diagnostic radiographers and reporting radiographers by computing eye gaze metrics using eye tracking technology. MethodsThree groups of participants were studied during their interpretation of 8 digital radiographic images including the axial and appendicular skeleton, and chest (prevalence of normal images was 12.5%). A total of 464 image interpretations were collected. Participants consisted of 21 radiography students, 19 qualified radiographers and 18 qualified reporting radiographers who were further qualified to report on the musculoskeletal (MSK) system. Outcome measuresEye tracking data was collected using the Tobii X60 eye tracker and subsequently eye gaze metrics were computed. Voice recordings, confidence levels and diagnoses provided a clear demonstration of the image interpretation and the cognitive processes undertaken by each participant. A questionnaire afforded the participants an opportunity to offer information on their experience in image interpretation and their opinion on the eye tracking technology. ResultsReporting radiographers demonstrated a 15% greater accuracy rate (p≤0.001), were more confident (p≤0.001) and took a mean of 2.4s longer to clinically decide on all features compared to students. Reporting radiographers also had a 15% greater accuracy rate (p≤0.001), were more confident (p≤0.001) and took longer to clinically decide on an image diagnosis (p=0.02) than radiographers. Reporting radiographers had a greater mean fixation duration (p=0.01), mean fixation count (p=0.04) and mean visit count (p=0.04) within the areas of pathology compared to students. Eye tracking patterns, presented within heat maps, were a good reflection of group expertise and search strategies. Eye gaze metrics such as time to first fixate, fixation count, fixation duration and visit count within the areas of pathology were indicative of the radiographer's competency. ConclusionThe accuracy and confidence of each group could be reflected in the variability of their eye tracking heat maps. Participants' thoughts and decisions were quantified using the eye tracking data. Eye tracking metrics also reflected the different search strategies that each group of participants adopted during their image interpretations. This is the first study to use eye tracking technology to assess image interpretation skills between various groups of different levels of experience in radiography, especially on a combination of the MSK system, chest cavity and a variety of pathologies.

Improving the Accuracy of Early Diagnosis of Thyroid Nodule Type Based on the SCAD Method.

Although early diagnosis of thyroid nodule type is very important, the diagnostic accuracy of standard tests is a challenging issue. We here aimed to find an optimal combination of factors to improve diagnostic accuracy for distinguishing malignant from benign thyroid nodules before surgery. In a prospective study from 2008 to 2012, 345 patients referred for thyroidectomy were enrolled. The sample size was split into a training set and testing set as a ratio of 7:3. The former was used for estimation and variable selection and obtaining a linear combination of factors. We utilized smoothly clipped absolute deviation (SCAD) logistic regression to achieve the sparse optimal combination of factors. To evaluate the performance of the estimated model in the testing set, a receiver operating characteristic (ROC) curve was utilized. The mean age of the examined patients (66 male and 279 female) was 40.9 ± 13.4 years (range 15- 90 years). Some 54.8% of the patients (24.3% male and 75.7% female) had benign and 45.2% (14% male and 86% female) malignant thyroid nodules. In addition to maximum diameters of nodules and lobes, their volumes were considered as related factors for malignancy prediction (a total of 16 factors). However, the SCAD method estimated the coefficients of 8 factors to be zero and eliminated them from the model. Hence a sparse model which combined the effects of 8 factors to distinguish malignant from benign thyroid nodules was generated. An optimal cut off point of the ROC curve for our estimated model was obtained (p=0.44) and the area under the curve (AUC) was equal to 77% (95% CI: 68%-85%). Sensitivity, specificity, positive predictive value and negative predictive values for this model were 70%, 72%, 71% and 76%, respectively. An increase of 10 percent and a greater accuracy rate in early diagnosis of thyroid nodule type by statistical methods (SCAD and ANN methods) compared with the results of FNA testing revealed that the statistical modeling methods are helpful in disease diagnosis. In addition, the factor ranking offered by these methods is valuable in the clinical context.

A New Pattern Recognition Technique in Non Destructive Testing by the Use of Linear Discriminate Analysis

One of the most challenging scientific industrial courses in recent years is intelligent defect detection. Non Destructive Testing (NDT) techniques are the most useful methods due to their efficiency and low cost. Models were developed to determine surface-breaking defects along the applied field when using the magnetic flux leakage (MFL) non-destructive technique. The theoretical model fits the experimental MFL results from simulated defects. For MFL sensors, the normal magnetic leakage field is subsequently used for evaluation of defects. Permeability variations were neglected by employing a flux density close to sample saturation. Three different defect geometries were experimentally investigated and the validity of the analytical model was verified. Different Feature extractor functions are applied in this paper to yield fast decision and more accurate. Indeed more accuracy is because of decision on different features that yields by employing two kinds of feature extractors, PCA and DCT. By hiring a BELBIC (Brain Emotional Learning Based Intelligent Controller) controller on the extracted features, the results are more accurate in some cases. Linear Discriminate Analysis (LDA) is another helpful instrument that is employed precise decision. All feature extractions LDAs and Multilayer perceptron (MLP), are methods for identifying erosion defects are described and employed in this paper. Great accuracy rate in compare between results of related approaches suggests that this Method can be used as an algorithm of MFL data interpretation technique.

Increased pitch increases accuracy of voice identification.

Previous work suggested that greater accuracy rates in identifying voices that have been increased in frequency over those that have been decreased in frequency may be due to complex vocal characteristics and specific memory for familiar voices. Here we asked 17 men and 21 women between the ages of 18 and 21 to learn a simple vowel exemplar produced by an unfamiliar target speaker and measured the proportion of times the frequency-shifted exemplar was identified as the originally encoded target speaker. Analysis showed that exemplars when increased in frequency were perceived as belonging to the target speaker significantly more often than exemplars which were decreased in frequency. These findings suggest that the greater accuracy in identifying speakers with increased frequency voice samples does not require previous familiarity with the vocalizations of a particular speaker or complex memory schemata for familiar voices.

Measurement of Carotid Stenosis: Comparisons Between Duplex and Different Angiographic Grading Methods

Endarterectomy has been proved to be an effective stroke prevention procedure. However, there are still inconsistencies between the results of different preoperative evaluation methods, which may sometimes complicate treatment plans. This study measured the discrepancies between different angiographic grading methods and attempted to further assess the accuracy of the carotid duplex examination according to these different angiographic grading methods. One hundred seventy-one preendarterectomy carotid duplex examinations and angiograms were reviewed. All angiograms were measured blindly by one of the authors using the North American Symptomatic Carotid Endarterectomy Trial (N), the European Carotid Surgery Trial (E), and the common carotid (C) methods. The measurement results were further converted into the area of stenosis indices (N2, E2, and C2, respectively). By using regression testing, all results could be compared. The duplex examination data were then compared with the results of different angiographic measurement methods to evaluate their accuracy. The measurement results of all angiographic grading methods were well correlated. Using different angiographic grading systems as the gold standard, duplex examination for screening endarterectomy candidates produced the following results: According to the N method, accuracy was 74%; according to the E method, accuracy was 90%; and according to the C method, the accuracy rate was 92%. According to N2, accuracy was 88%; according to E2, accuracy was 94%; and according to C2, the accuracy rate was 93%. The measurement results of all 3 commonly used angiographic grading methods were linearly correlated with one another. (It is important to note that none of those standards should be treated as the only gold standard.) In this study, the duplex criteria have a greater accuracy rate according to E or C rather than N. However, this study also demonstrated that the cut point of the Doppler criteria is the determinant factor for accuracy rather than which gold standard was compared. Through careful adjustment of the cutoff criteria, carotid duplex can be highly accurate, despite the use of different reference standards.