Learning technology was used as standalone software to install in a particular system, which needs to buy learning software of a particular subject. It was costly and difficult to search CD/DVD of the particular program in the market. Nowadays the trend of learning is changed and people are learning via the internet and it is known as Electronic Learning (E-learning). Several e-learning web applications are available which are providing more stuff about students and it fulfills requirements. The aim of this paper is to present a wellstructured, user-friendly framework with the web application for e-learning, which does not need any subscription. The experiment was conducted with 691 students and teachers, the result shows 91.98% of participants were satisfied with the proposed E-learning system.
Abstract---The script of Sindhi Language is highly complex due to many complexities including abundance of homographic words. The interpretation of the text turns so tough due to the possibility of multitudinal meanings associated with a homographic word unless given specific pronunciation with the help of diacritics. Diacritics help the readers to comprehend the text easily. Due to the rapidly developing nature of this era, people don't bother writing diacritics in routine applications of life. Besides creating difficulties for human reading, the absence of diacritics does also make the text abstruse for machine reading. Relatively alike human, machines may also lead to semantic and syntactic complexities during computational processing of the language. Instant diacritics restoration is an approach emerged from the text prediction systems. This type of diacritics restoration is an unprecedented work in the realm of natural language processing, particularly in Indo-Aryan languages. A proposition for a framework using N-Grams and Memory-Based Learning approach is made in this work. The grab-point of this mechanism is its 99.03% accuracy on the corpus of Sindhi language during the experiments. The comparative edge of instant diacritics restoration is its being source of expedition in the performance of other natural language and speech processing applications. The future development of this approach seems vivid and clear for Sindhi orthography is highly similar to those of Arabic, Urdu, Persian and other languages based on this type of script.
Recent patterns of human sentiments are highly influenced by emoji based sentiments (EBS). Social media users are widely using emoji based sentiments (EBS) in between text messages, tweets and posts. Although tiny pictures of emoji contains sufficient information to be considered for construction of classification model; but due to the wide range of dissimilar, heterogynous and complex patterns of emoji with similar meanings (SM) have become one of the significant research areas of machine vision. This paper proposes an approach to provide meticulous assistance to social media application (SMA) users to classify the EBS sentiments. Proposed methodology consists upon three layers where first layer deals with data cleaning and feature selection techniques to detect dissimilar emoji patterns (DEP) with similar meanings (SM). In first sub step we input set of emoji, in second sub step every emoji has to qualify user defined threshold, in third sub step algorithm detects every emoji by considering as objects and in fourth step emoji images are cropped, after data cleaning these tiny images are saved as emoji images. In second step we build classification model by using convolutional neural networks (CNN) to explore hidden knowledge of emoji datasets. In third step we present results visualization by using confusion matrix and other estimations. This paper contributes (1) data cleaning method to detect EBS;(2) highest classification accuracy for emoji classification measured as 97.63%.
Speech signal analysis for the extraction of speech elements is viable in natural language applications. Rhythm, intonation, stress, and tone are the elements of prosody. These features are essential in emotional speech, speech to speech, speech recognition, and other applications. The current study attempts to extract the pitch and duration from historical Sindhi sound clips using the functional contours model's superposition. The sampled sound clips contained the speech of 273 undergraduates living in 5 districts of the Sindhi province. Several Python libraries are available for the application of this model. We used these libraries for the extraction of prosodic data from a variety of sound units. The spoken sentences were categorically segmented into words, syllables, and phonemes. A speech analyzer investigated the acoustics of sounds with the power spectral density method. Meanwhile, a speech database was divided into parts contains words of different sizes (ranging from 1-letter to 5-letter words). The results illustrated the production of both minimum and maximum μ sound durations and pitches from the inhabitants of Khairpur and Ghotki districts, respectively. Both districts lie in the upper part of the Sindh province. In addition, the second parameter approach, observed versus obtained, was used to compare outcomes. We observed 5250 and 4850 durations and pitches, respectively.
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