A Novel Approach to Spoken Arabic Number Recognition Based on Developed Ant Lion Algorithm

2021 ◽  
pp. 270-275
Author(s):  
Fawziya Mahmood Ramo ◽  
Ansam Nazar Younis
Keyword(s):  
Arabic Language ◽  
Efficient Manner ◽  
Audio File ◽  
Recognition Process ◽  
Original Dataset ◽  
Novel Approach ◽  
Arabic Number ◽  
Visual Text ◽  
Audio Files ◽  
Initial Processing

Intelligent spoken system is constructed to recognize numbers spoken in Arabic language by different people. Series of operations are performed on audio sound file as pre-processing stages. A novel approach is applied to extract features of audio files called Max Mean Log to reduce audio file dimensions in an efficient manner. Several stages of initial processing are used to prepare the file for the next step of the recognition process. The recognition process begins with the use of Antlion’s advanced intelligence algorithm to determine the type of the spoken number in Arabic and later convert it to a visual text that represents the value of the spoken number. The current proposal method is relatively fast and very effective. The percentage of recognizing numbers spoken by the proposed algorithm is 99%. For 1,800 different audio files, the error rate was 1%. Additional 40 audio files were used that are different from people’s original dataset. Due to an additional examination of the system and its ability to recognize the audio file, the rate of discrimination for such files was 72.5%. 

scholarly journals Screening the ability of natural feed ingredients to interfere with the adherence of enterotoxigenicEscherichia coli(ETEC) K88 to the porcine intestinal mucus

2013 ◽  
Vol 111 (4) ◽  
pp. 633-642 ◽  
Author(s):  
Gemma González-Ortiz ◽  
José Francisco Pérez ◽  
Rafael Gustavo Hermes ◽  
Francesc Molist ◽  
Rufino Jiménez-Díaz ◽  
...  
Keyword(s):  
Adhesion Test ◽  
Weaned Piglets ◽  
Adhesive Properties ◽  
Feed Ingredients ◽  
Recognition Process ◽  
Intestinal Mucus ◽  
Novel Approach ◽  
Carbohydrate Components ◽  
Water Extractable

The inhibition of the attachment of bacteria to the intestine by receptor analogues could be a novel approach to prevent enterotoxigenicEscherichia coli(ETEC) K88-induced diarrhoea in piglets. The objective of the present study was to screen the ability of different feed ingredients (FI) to bind to ETEC K88 (adhesion test, AT) and to block its attachment to the porcine intestinal mucus (blocking test, BT) usingin vitromicrotitration-based models. In the AT, wheat bran (WB), casein glycomacropeptide (CGMP) and exopolysaccharides exhibited the highest adhesion to ETEC K88 (P< 0·001). In the BT, WB, CGMP and locust bean (LB) reduced the number of ETEC K88 attached to the intestinal mucus (P< 0·001). For WB and LB, fractionation based on their carbohydrate components was subsequently carried out, and each fraction was evaluated individually. None of the WB fractions reduced the adhesion of ETEC K88 to the mucus as did the original extract, suggesting that a protein or glycoprotein could be involved in the recognition process. With regard to the LB fractions, the water-extractable material reduced the adhesion of ETEC K88 (P< 0·001) to the mucus similar to the original extract (P< 0·001), indicating, in this case, that galactomannans or phenolic compounds could be responsible for the recognition process. In conclusion, among the FI screened, the soluble extracts obtained from WB, LB and CGMP exhibited the highest anti-adhesive properties against ETEC K88 in the BT. These results suggest that they may be good candidates to be included in diets of weaned piglets for the prevention of ETEC K88-induced diarrhoea.

Detection of Automobile Insurance Fraud Using Feature Selection and Data Mining Techniques

2018 ◽  
Vol 5 (3) ◽  
pp. 1-20 ◽  
Author(s):  
Sharmila Subudhi ◽  
Suvasini Panigrahi
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Feature Selection Method ◽  
Automobile Insurance ◽  
Test Set ◽  
Data Mining Techniques ◽  
Weighted Extreme Learning Machine ◽  
Original Dataset ◽  
Novel Approach ◽  
Learning Machine

This article presents a novel approach for fraud detection in automobile insurance claims by applying various data mining techniques. Initially, the most relevant attributes are chosen from the original dataset by using an evolutionary algorithm based feature selection method. A test set is then extracted from the selected attribute set and the remaining dataset is subjected to the Possibilistic Fuzzy C-Means (PFCM) clustering technique for the undersampling approach. The 10-fold cross validation method is then used on the balanced dataset for training and validating a group of Weighted Extreme Learning Machine (WELM) classifiers generated from various combinations of WELM parameters. Finally, the test set is applied on the best performing model for classification purpose. The efficacy of the proposed system is illustrated by conducting several experiments on a real-world automobile insurance defraud dataset. Besides, a comparative analysis with another approach justifies the superiority of the proposed system.

Is P&T Ready to Add Rapid Cycle Analytics to Formulary?

2020 ◽  
pp. 001857872091834
Author(s):  
Diana Altshuler ◽  
Kenny Yu ◽  
John Papadopoulos ◽  
Arash Dabestani
Keyword(s):  
Real World ◽  
Average Length ◽  
Evaluation Process ◽  
Direct Patient Care ◽  
Pharmacy Staff ◽  
Efficient Manner ◽  
Data Set ◽  
Novel Approach ◽  
Minimum Criteria ◽  
Point Of Reference

Purpose: The intent of this article is to evaluate a novel approach, using rapid cycle analytics and real world evidence, to optimize and improve the medication evaluation process to help the formulary decision making process, while reducing time for clinicians. Summary: The Pharmacy and Therapeutics (P&T) Committee within each health system is responsible for evaluating medication requests for formulary addition. Members of the pharmacy staff prepare the drug monograph or a medication use evaluation (MUE) and allocate precious clinical resources to review patient charts to assess efficacy and value. We explored a novel approach to evaluate the value of our intravenous acetaminophen (IV APAP) formulary admittance. This new methodology, called rapid cycle analytics, can assist hospitals in meeting and/or exceeding the minimum criteria of formulary maintenance as defined by the Joint Commission Standards. In this particular study, we assessed the effectiveness of IV APAP in total hip arthroplasty (THA) and total knee arthroplasty (TKA) procedures. We assessed the correlation to same-stay opioid utilization, average length of inpatient stay and post anesthesia care unit (PACU) time. Conclusion: We were able to explore and improve our organization’s approach in evaluating medications by partnering with an external analytics expert to help organize and normalize our data in a more robust, yet time efficient manner. Additionally, we were able to use a significantly larger external data set as a point of reference. Being able to perform this detailed analytical exercise for thousands of encounters internally and using a data warehouse of over 130 million patients as a point of reference in a short time has improved the depth of our assessment, as well as reducing valuable clinical resources allocated to MUEs to allow for more direct patient care. This clinically real-world and data-rich analytics model is the necessary foundation for using Artificial or Augmented Intelligence (AI) to make real-time formulary and drug selection decisions

Accelerated Geometry Accuracy Optimization of Additive Manufacturing Parts

2017 ◽  
Author(s):  
Amir M. Aboutaleb ◽  
Linkan Bian ◽  
Prahalad K. Rao ◽  
Mark A. Tschopp
Keyword(s):  
Additive Manufacturing ◽  
Optimization Problem ◽  
Principal Component ◽  
Geometric Features ◽  
Simulation Studies ◽  
Fused Filament Fabrication ◽  
Efficient Manner ◽  
Geometric Dimensioning And Tolerancing ◽  
Novel Approach

Despite recent advances in improving mechanical properties of parts fabricated by Additive Manufacturing (AM) systems, optimizing geometry accuracy of AM parts is still a major challenge for pushing this cutting-edge technology into the mainstream. This work proposes a novel approach for improving geometry accuracy of AM parts in a systematic and efficient manner. Initial experimental data show that different part geometric features are not necessary positively correlated. Hence, it may not be possible to optimize them simultaneously. The proposed methodology formulates the geometry accuracy optimization problem as a multi-objective optimization problem. The developed method targeted minimizing deviations within part’s major Geometric Dimensioning and Tolerancing (GD&T) features (i.e., Flatness, Circularity, Cylindricity, Concentricity and Thickness). First, principal component analysis (PCA) is applied to extract key components within multi-geometric features of parts. Then, experiments are sequentially designed in an accelerated and integrated framework to achieve sets of process parameters resulting in acceptable level of deviations within principal components of multi-geometric features of parts. The efficiency of proposed method is validated using simulation studies coupled with a real world case study for geometry accuracy optimization of parts fabricated by fused filament fabrication (FFF) system. The results show that optimal designs are achieved by fewer numbers of experiments compared with existing methods.

Deep tracking in the wild: End-to-end tracking using recurrent neural networks

2017 ◽  
Vol 37 (4-5) ◽  
pp. 492-512 ◽  
Author(s):  
Julie Dequaire ◽  
Peter Ondrúška ◽  
Dushyant Rao ◽  
Dominic Wang ◽  
Ingmar Posner
Keyword(s):  
Object Tracking ◽  
Autonomous Vehicle ◽  
Urban Environments ◽  
Positive Contribution ◽  
Efficient Manner ◽  
Model Free ◽  
Novel Approach ◽  
In The Wild ◽  
End To End ◽  
Traditional Approaches

This paper presents a novel approach for tracking static and dynamic objects for an autonomous vehicle operating in complex urban environments. Whereas traditional approaches for tracking often feature numerous hand-engineered stages, this method is learned end-to-end and can directly predict a fully unoccluded occupancy grid from raw laser input. We employ a recurrent neural network to capture the state and evolution of the environment, and train the model in an entirely unsupervised manner. In doing so, our use case compares to model-free, multi-object tracking although we do not explicitly perform the underlying data-association process. Further, we demonstrate that the underlying representation learned for the tracking task can be leveraged via inductive transfer to train an object detector in a data efficient manner. We motivate a number of architectural features and show the positive contribution of dilated convolutions, dynamic and static memory units to the task of tracking and classifying complex dynamic scenes through full occlusion. Our experimental results illustrate the ability of the model to track cars, buses, pedestrians, and cyclists from both moving and stationary platforms. Further, we compare and contrast the approach with a more traditional model-free multi-object tracking pipeline, demonstrating that it can more accurately predict future states of objects from current inputs.

scholarly journals Cointegration: a novel approach for the removal of environmental trends in structural health monitoring data

2011 ◽  
Vol 467 (2133) ◽  
pp. 2712-2732 ◽  
Author(s):  
Elizabeth J. Cross ◽  
Keith Worden ◽  
Qian Chen
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Environmental Variability ◽  
Stationary Time Series ◽  
New Approach ◽  
Operational Conditions ◽  
Structural Health ◽  
Original Dataset ◽  
Novel Approach ◽  
Static Responses

Before structural health monitoring (SHM) technologies can be reliably implemented on structures outside laboratory conditions, the problem of environmental variability in monitored features must be first addressed. Structures that are subjected to changing environmental or operational conditions will often exhibit inherently non-stationary dynamic and quasi-static responses, which can mask any changes caused by the occurrence of damage. The current work introduces the concept of cointegration , a tool for the analysis of non-stationary time series, as a promising new approach for dealing with the problem of environmental variation in monitored features. If two or more monitored variables from an SHM system are cointegrated, then some linear combination of them will be a stationary residual purged of the common trends in the original dataset. The stationary residual created from the cointegration procedure can be used as a damage-sensitive feature that is independent of the normal environmental and operational conditions.

scholarly journals RANCANG BANGUN APLIKASI DETEKSI KEASLIAN FILE AUDIO MENERAPKAN METODE CRC 32

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Dian Paramita Br Perangin-angin
Keyword(s):  
Matlab Software ◽  
Audio File ◽  
Digital File ◽  
Safety Technique ◽  
Audio Files ◽  
Previous Paragraph ◽  
Hash Codes

The authenticity of a digital file is something that must be able to be guaranteed its existence, considering that there are so many devices that can be used to carry out manipulation of the digital file. One of the digital files discussed in this study is an audio file with MP3 file extension. Please note that MP3 audio files are now very easy to obtain, even very easy to manipulate and insert objects in, so we need a safety technique to maintain the authenticity of the audio file. Overcoming the problem that has been explained in the previous paragraph, the appropriate security technique used is the hash cryptographic technique, by applying the CRC 32 algorithm. The application of CRC 32 aims to generate hash codes from MP3 audio files that can be used as key codes for authentication (key authenticity) of MP3 audio files. The results of this study are a representation of the technical explanation of the application of the CRC 32 algorithm in generating MP3 audio file hash codes, which the CRC 32 algorithm is applied to applications that have been designed and built using the help of MATLAB software version 6.1.Keywords: File Authenticity, Audio File, CRC 32, MATLAB 6.1

Implementasi Penyembunyian Pesan Pada Audio Dengan Metode Bit-Plane Complexity Segmentation (BPCS)

2020 ◽  
Vol 7 (2) ◽  
pp. 285
Author(s):  
Ira Sarifah Rangkuti ◽  
Edward Robinson Siagian
Keyword(s):  
Audio File ◽  
Plane Method ◽  
Bit Plane ◽  
Audio Files ◽  
Application Design

Cryptography is the science used to maintain the confidentiality of messages, by scrambling messages that are illegible. However, the results of randomization can raise suspicions that confidential communications are being carried out. Steganography can be used to overcome these problems. The trick is the message is inserted in the audio file by the Bit-Plane method. then add a message behind the file. To prevent messages from being read, the message is encrypted first with the Bit-Plane method before inserting. Application design results can be used to hide secret messages that have been encrypted with the Bit-Plane method to audio files, so as to avoid suspicion of confidential communications

scholarly journals Design and Application of a Multi-Variant Expert System Using Apache Hadoop Framework

2018 ◽  
Vol 10 (11) ◽  
pp. 4280 ◽  
Author(s):  
Muhammad Ibrahim ◽  
Imran Bajwa
Keyword(s):  
Recommender Systems ◽  
Qualitative Data ◽  
Similarity Measures ◽  
Efficient Manner ◽  
Apache Hadoop ◽  
Novel Approach ◽  
Polarity Score ◽  
Movie Recommendation ◽  
Sentiment Score ◽  
Big Data Technology

Movie recommender expert systems are valuable tools to provide recommendation services to users. However, the existing movie recommenders are technically lacking in two areas: first, the available movie recommender systems give general recommendations; secondly, existing recommender systems use either quantitative (likes, ratings, etc.) or qualitative data (polarity score, sentiment score, etc.) for achieving the movie recommendations. A novel approach is presented in this paper that not only provides topic-based (fiction, comedy, horror, etc.) movie recommendation but also uses both quantitative and qualitative data to achieve a true and relevant recommendation of a movie relevant to a topic. The used approach relies on SentiwordNet and tf-idf similarity measures to calculate the polarity score from user reviews, which represent the qualitative aspect of likeness of a movie. Similarly, three quantitative variables (such as likes, ratings, and votes) are used to get final a recommendation score. A fuzzy logic module decides the recommendation category based on this final recommendation score. The proposed approach uses a big data technology, “Hadoop” to handle data diversity and heterogeneity in an efficient manner. An Android application collaborates with a web-bot to use recommendation services and show topic-based recommendation to users.

scholarly journals Semantic Partitioning and Machine Learning in Sentiment Analysis

Data ◽  
2021 ◽  
Vol 6 (6) ◽  
pp. 67
Author(s):  
Ebaa Fayyoumi ◽  
Sahar Idwan
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Arabic Language ◽  
Writing Style ◽  
Semantic Meaning ◽  
Original Dataset ◽  
Overall Performance ◽  
Better Than

This paper investigates sentiment analysis in Arabic tweets that have the presence of Jordanian dialect. A new dataset was collected during the coronavirus disease (COVID-19) pandemic. We demonstrate two models: the Traditional Arabic Language (TAL) model and the Semantic Partitioning Arabic Language (SPAL) model to envisage the polarity of the collected tweets by invoking several, well-known classifiers. The extraction and allocation of numerous Arabic features, such as lexical features, writing style features, grammatical features, and emotional features, have been used to analyze and classify the collected tweets semantically. The partitioning concept was performed on the original dataset by utilizing the hidden semantic meaning between tweets in the SPAL model before invoking various classifiers. The experimentation reveals that the overall performance of the SPAL model competes over and better than the performance of the TAL model due to imposing the genuine idea of semantic partitioning on the collected dataset.

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