Transliterating the text of a language to a foreign script is called forward transliteration and transliterating the text back to the original script is called backward transliteration. In this work, we perform both forward as well as backward transliteration on Punjabi. We transliterate Punjabi person names from Gurmukhi script to English Roman script and from English Roman script back to Gurmukhi script using n-gram language model. We used more than one million parallel entities of person names in Gurmukhi and Roman script as the training corpus. We generated English to Punjabi and Punjabi to English n-grams databases from the corpus. To get better results, we tried to create as long n-grams as possible ranging from bi-gram to 30-gram. Our n-grams database contains more than 10 million n-grams and each n-gram having multiple mappings of the other script. The most challenging part is to find the mapping for the given n-gram from the parallel name entity while creating n-grams databases. As per the orthography rules, the same combination of letters may have different pronunciation depending upon its location in the word. Therefore, we categorized n-grams into starting, middle and ending n-grams and used them accordingly in the transliteration process. The transliteration process works like the merge sort. We start searching the longest possible n-gram in the database and split the string recursively until the match is found. The transliterated strings are merged back to form the final output. In English to Punjabi transliteration, we achieved 96% accuracy using gold standard and 99.14% accuracy using minimum edit distance. In Punjabi to English transliteration, the result showed 96.85% and 99.35% accuracy for the gold standard and minimum edit distance respectively.
<p>Today, Mental health problems are getting grave and need technological solutions. Irrational anticipated fear is Anxiety Disorder. Specific Phobia disorders are a type of Anxiety disorder; these phobias are rarely detected in clinical settings and are presence indicators of other serious mental problems. VR is considered a potent tool for treatment and diagnosis. In this study, we investigated the parameters for predicting participants' severity level of Cynophobia and Astraphobia by using the following measures: "APA Specific Phobia Severity Measure - Adult", "Distance and Time", "Heart Rate and Oxygen levels for each level" in VR-specific-phobia diagnostic environment, "symptoms" observed during experimentation, and "causes" described by DSM-5. The "APA Specific Phobia Severity Measure - Adult" is attributed as the standard used by psychiatrists for clinical evaluation. We used the score of this measure to classify instances for each participant. The other parameters serve as attributes for predicting class, implementing the process of Data Mining. The literature supports the prior mentioned parameters for assessing severity levels for specific phobia. The participant walks or runs along a road in a Virtual Reality Environment to achieve the objective. The first scenario is a neutral environment with no phobic stimulus; the afterward situations pose for a dog cue, thunder lightning stimulus, and a combination of both stimulation consecutively. The 'Distance' traveled and 'Time' taken in units for each VR scenario generated using a Bluetooth controller is saved in a file with time stamps. The participant subsequently fills Google Form to record the parameters. The dataset is converted to ARFF format, and the process of Knowledge Discovery is applied using the WEKA tool. The results suggest that the presence of Cynophobia and Astraphobia are highly interrelated. The study advised that Dog-Phobia severity level confidently predicts with the parameters "Age", "Time" in Neutral scenario, "Distance" covered in Cynophobic scenario", "Difference in Oxygen levels" of Cynophobic VRE and scenario with both (Dog and Thunder Lightning) stimuli and "DSMAstraphobia". The research analysis concludes that thunder-lightning phobia severity level effectively forecasts with these attributes: "Velocity", "Distance" and "Time" in Neutral VRE scenario"; "Velocity", "Time" VRE scenario for both pre-mentioned phobic stimuli; "Time" in Cynophobic scenario, "Velocity" calculated in Astraphobic VRE, "Age" of the participant and DSMCynophobia. This study will help in suggesting standards for diagnosing mental health problems with the advantages of VR.<br></p>
<p>Today, Mental health problems are getting grave and need technological solutions. Irrational anticipated fear is Anxiety Disorder. Specific Phobia disorders are a type of Anxiety disorder; these phobias are rarely detected in clinical settings and are presence indicators of other serious mental problems. VR is considered a potent tool for treatment and diagnosis. In this study, we investigated the parameters for predicting participants' severity level of Cynophobia and Astraphobia by using the following measures: "APA Specific Phobia Severity Measure - Adult", "Distance and Time", "Heart Rate and Oxygen levels for each level" in VR-specific-phobia diagnostic environment, "symptoms" observed during experimentation, and "causes" described by DSM-5. The "APA Specific Phobia Severity Measure - Adult" is attributed as the standard used by psychiatrists for clinical evaluation. We used the score of this measure to classify instances for each participant. The other parameters serve as attributes for predicting class, implementing the process of Data Mining. The literature supports the prior mentioned parameters for assessing severity levels for specific phobia. The participant walks or runs along a road in a Virtual Reality Environment to achieve the objective. The first scenario is a neutral environment with no phobic stimulus; the afterward situations pose for a dog cue, thunder lightning stimulus, and a combination of both stimulation consecutively. The 'Distance' traveled and 'Time' taken in units for each VR scenario generated using a Bluetooth controller is saved in a file with time stamps. The participant subsequently fills Google Form to record the parameters. The dataset is converted to ARFF format, and the process of Knowledge Discovery is applied using the WEKA tool. The results suggest that the presence of Cynophobia and Astraphobia are highly interrelated. The study advised that Dog-Phobia severity level confidently predicts with the parameters "Age", "Time" in Neutral scenario, "Distance" covered in Cynophobic scenario", "Difference in Oxygen levels" of Cynophobic VRE and scenario with both (Dog and Thunder Lightning) stimuli and "DSMAstraphobia". The research analysis concludes that thunder-lightning phobia severity level effectively forecasts with these attributes: "Velocity", "Distance" and "Time" in Neutral VRE scenario"; "Velocity", "Time" VRE scenario for both pre-mentioned phobic stimuli; "Time" in Cynophobic scenario, "Velocity" calculated in Astraphobic VRE, "Age" of the participant and DSMCynophobia. This study will help in suggesting standards for diagnosing mental health problems with the advantages of VR.<br></p>
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