RUTUT: Roman Urdu to Urdu Translator Based on Character Substitution Rules and Unicode Mapping

Shahroz, Mobeen; Mushtaq, Muhammad Faheem; Mehmood, Arif; Ullah, Saleem; Choi, Gyu Sang

doi:10.1109/access.2020.3031393

Cited by 9 publications

(7 citation statements)

References 45 publications

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“…The written script of this language does not follow any rigid set of grammatical rules or standards of spellings. A survey statistics in (Shahroz et al, 2020) affirms that about 300 million people are speaking Urdu language and approximately 11 million speakers are in Pakistan from which maximum users switched to Roman Urdu language for the textual communication, typically on social media (Shahroz et al, 2020). It is linguistically rich and morphologically complex language (Mehmood et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Development of computational linguistic resources for automated detection of textual cyberbullying threats in Roman Urdu language

Dewani¹,

Memon²,

Bhatti³

2021

3C TIC

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Automatic Cyberbullying detection has remained very challenging task since social media content and conversations are usually posted in unstructured free-text form leaving behind the language norms. The major concern and gap in formulating cyberbullying detection strategies is scarcity of available linguistic resources typically for newly evolved languages. Roman Urdu has recently emerged and hence is a resource poor language. Urdu has been widely known as the national language of Pakistan. However, because of socio-cultural and multilingual aspects, Roman Urdu is used widely on the Internet by Asians and more specifically Pakistanis.To fulfil the above stated gap, this research work presents guidelines for data annotation process and developed two linguistic resources: (i) Annotated corpus in Roman Urdu Language for cyberaggression and offensive language detection. The process of data annotation involved bilingual annotators instead of crowdsourcing. It has the benefit of correctly annotating instances that constitute clear cases of cyberbullying without compromising data quality. The developed corpus is highly balanced (with almost negligible skew) unlike most of the existing corpuses even in mature languages. (ii) Processing textual information for NLP tasks involves Stop-word elimination as a sub phase. Stop words carry least semantic information and increase feature space as compared to the other tokens and index terms in corpora. We have developed domain specific stop words for Roman Urdu Language considering all the lexical variants and typically in the context of aggression detection and collected data. The work has been carried out using python programming language and Pycharm IDE.

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Section: Introductionmentioning

confidence: 99%

Development of computational linguistic resources for automated detection of textual cyberbullying threats in Roman Urdu language

Dewani¹,

Memon²,

Bhatti³

2021

3C TIC

View full text Add to dashboard Cite

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“…In this study, an iAOI system with a camera and backlight unit (BU) hardware in an opaque box was used as a module. Nevertheless, for the iAOI procedure, the GUI was created by Python with application programming interfaces (APIs), such as DigiCam [ 9 ], Tkinter [ 10 ], OpenCV [ 11 ], and NumPy [ 12 ], to control the camera in the demonstrative module, perform image processing, and evaluate features with several operations in a workflow shown in Figure 2 . The complete script of Python was written through C (programming language); thus, the stability and efficiency were reliable.…”

Section: Workflowmentioning

confidence: 99%

Integrated Automatic Optical Inspection and Image Processing Procedure for Smart Sensing in Production Lines

Qiu,

Tsai,

Chen

et al. 2024

Sensors

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An integrated automatic optical inspection (iAOI) system with a procedure was proposed for a printed circuit board (PCB) production line, in which pattern distortions and performance deviations appear with process variations. The iAOI system was demonstrated in a module comprising a camera and lens, showing improved supportiveness for commercially available hardware. The iAOI procedure was realized in a serial workflow of image registration, threshold setting, image gradient, marker alignment, and geometric transformation; furthermore, five operations with numerous functions were prepared for image processing. In addition to the system and procedure, a graphical user interface (GUI) that displays sequential image operation results with analyzed characteristics was established for simplicity. To demonstrate its effectiveness, self-complementary Archimedean spiral antenna (SCASA) samples fabricated via standard PCB fabrication and intentional pattern distortions were demonstrated. The results indicated that, compared with other existing methods, the proposed iAOI system and procedure provide unified and standard operations with efficiency, which result in scientific and unambiguous judgments on pattern quality. Furthermore, we showed that when an appropriate artificial intelligence model is ready, the electromagnetic characteristic projection for SCASAs can be simply obtained through the GUI.

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“…Murtadha et.al [21] present two folds contribution. Firstly, the data set collects from CT scans of traumatic brain injury that manually outlined by two experienced radiologists [53], [54]. Second, apply the CNN on these image segmentation also known as U-Net and performance is evaluated by 5 fold cross-validation with a dice coefficient of 0.31.…”

Section: Related Workmentioning

confidence: 99%

BHCNet: Neural Network-Based Brain Hemorrhage Classification Using Head CT Scan

et al. 2021

Self Cite

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Brain Hemorrhage is the eruption of the brain arteries due to high blood pressure or blood clotting that could be a cause of traumatic injury or death. It is the medical emergency in which a doctor also need years of experience to immediately diagnose the region of the internal bleeding before starting the treatment. In this study, the deep learning models Convolutional Neural Network (CNN), hybrid models CNN+LSTM and CNN+GRU are proposed for the Brain Hemorrhage classification. The 200 head CT scan images dataset is used to boost the accuracy rate and computational power of the deep learning models. The major aim of this study is to use the abstraction power of deep learning on a set of fewer images because in most crucial cases extensive datasets are not available on the spot. The image augmentation and imbalancing the dataset methods are adopted with CNN model to design a unique architecture and named as Brain Hemorrhage Classification based on Neural Network (BHCNet). The performance of the proposed approach are analyzed in terms of accuracy, precision, sensitivity, specificity and F1-score. Further, the experimental results are evaluated by comparative analyses of the balanced and imbalanced dataset with CNN, CNN+LSTM and CNN+GRU models. The promising results are achieved with CNN by imbalancing the dataset and gain highest accuracy that outperforms the hybrid CNN+LSTM and CNN+GRU models. The results reveals the effectiveness of the proposed model for accurate prediction to save the life of the patient in the meantime and fast employment in the real life scenario.

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RUTUT: Roman Urdu to Urdu Translator Based on Character Substitution Rules and Unicode Mapping

Cited by 9 publications

References 45 publications

Development of computational linguistic resources for automated detection of textual cyberbullying threats in Roman Urdu language

Development of computational linguistic resources for automated detection of textual cyberbullying threats in Roman Urdu language

Integrated Automatic Optical Inspection and Image Processing Procedure for Smart Sensing in Production Lines

BHCNet: Neural Network-Based Brain Hemorrhage Classification Using Head CT Scan

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