VFAST - Virtual Foundation for Advancement of Science and Technology (Pakistan)
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Optimized Sentiment Classification of Google Play Store App Ratings Using Advanced Machine Learning Models
Get PDFThe Google Play Store, the primary distribution platform for Android applications, hosts millions of apps and receives a large number of user reviews. However, extracting actionable insights from these reviews, particularly classifying rating sentiment (positive, negative, or neutral), remains a challenge. This paper addresses this issue by proposing a novel framework for app rating sentiment classification on Google Play Store data. We leverage a rich dataset of app reviews acquired from GitHub and employ a battery of advanced machine learning models. Specifically, we explore the efficacy of AdaBoost, XGBoost, and Artificial Neural Networks (ANNs) in conjunction with optimization techniques. Our approach significantly outperforms existing research, achieving superior accuracy ranging from 85% to 98% compared to the 78-95.9% accuracy reported in prior studies. This significant improvement translates to a deeper understanding of user sentiment across the app ecosystem. It enables developers to better gauge user satisfaction, prioritize improvements, and ultimately enhance user experience. Our work also paves the way for further research in sentiment analysis of app reviews, exploring fine-grained sentiment detection and analyzing sentiment dynamics over time
Several Topological Indices and Entropies for Certain Families of Commutative Graphs over Quaternion Groups
Get PDFA group graph is a type of graph formed by combining a group, usually a finite group, with a generating set for that group. Group graphs are employed in various mathematical situations, including algebraic and computational group theory. A graph G is known as a commutative graph if the vertex set of G is a group and two elements are adjacent to each other if they are commuting to each other. In this work, we consider the family of commutative graphs over Quaternion groups. The edge partition mappings related to the degree of each vertex of the graph G are computed. Further, we established many results on various kinds of topological indices and entropies by using M-polynomials. The numerical comparison among computed topological indices has been proposed
Islamophobia and Representation of the West in Pakistani Political Discourse: A Critical Discourse Analysis of Anti-Western Narratives
Get PDFThis study investigates the representation of Islamophobia and the West within Pakistani political discourse, analyzing how anti-Western narratives are constructed by political leaders and media. Through Critical Discourse Analysis (CDA), particularly Van Dijk\u27s Socio-Cognitive Approach, the study examines speeches, media statements, and social media posts to identify the linguistic and rhetorical strategies that frame Western nations as antagonistic toward Islam and Muslims. By leveraging the concept of \u27othering,\u27 these narratives depict Western nations as both cultural and ideological threats, reinforcing in-group identity among Pakistanis and promoting national solidarity. The research uncovers recurring themes, including the portrayal of the West as inherently Islamophobic and morally divergent, the invocation of religious and cultural symbols to appeal to collective Islamic identity, and the strategic use of emotional appeals and metaphors to solidify anti-Western sentiments. This study contributes to the understanding of how Islamophobia discourse is employed to influence public opinion, strengthen political legitimacy, and unify the nation against perceived external threats. Findings also suggest that anti-Western narratives serve as powerful rhetorical tools in shaping national identity and resisting perceived Western hegemony. Future research might further investigate similar discourses across other Muslim-majority nations to provide comparative insights into the role of political discourse in shaping perceptions of Islamophobia and foreign policy
Predicting Long-term Visual Outcomes for Robot Manipulation Using Vision-based Techniques
Get PDFPredicting long-term visual outcomes for robot manipulation tasks is crucial for enabling robots to anticipate future changes in their environment and plan optimal actions accordingly. This research is presents a novel approach to long-term visual prediction using vision-based techniques and deep learning models. We propose a hybrid convolutional neural network (CNN) and recurrent neural network (RNN) architecture that combines spatial feature extraction with temporal modeling to predict future visual states accurately. The predictive model is trained on annotated datasets of robot manipulation sequences, allowing it to learn complex spatial and temporal relationships in the data. Experimental results demonstrate the effectiveness of the proposed approach in accurately predicting long-term visual outcomes for a variety of manipulation tasks
Distance and energy aware DEAODV routing protocol in disastrous situation
Get PDFIn the disastrous or emergency situation a reliable and robust ad hoc mobile network communication is important. The natural disaster when occurs in the urban populated areas which damage Information technology infrastructure and other valuable assets within a movement. In such emergency situation when the disaster victims stuck in debris, lifesaving of people are important. The purpose of this study is to enhance lifetime and improve network performance when the lives of disaster victims are matters. Distance and Energy aware AODV (DEAODV) reactive routing protocol enhance the network performance and ensure the reliable data transmission during crisis situation. The DEAODV reactive routing protocol considered node Energy and shortest distance as a routing metrics during route decision process. DEAODV routing protocol is compared with traditional AODV routing protocol to evaluate the performance of network. The proposed routing protocol is more efficient in emergency situation than AODV in terms of Packet Delivery Ratio, End to End Delay and Consumed Energy
Empowering Sentiment Analysis with Deep Learning Model: Evaluating Social Media\u27s Benefits and Drawbacks
Get PDFOnline social networks (OSNs) have revolutionized communication by facilitating unprecedented information sharing and global connections. Despite these benefits, OSNs also present significant challenges, including the spread of misinformation, increased distraction, and adverse mental health effects. This study examines a dataset of 3,904 user reviews collected from online sources and personal networks, revealing a polarized sentiment distribution with 56% positive, 43.1% negative and 0.9% neutral views on the impact of social platforms. To capture the nuanced sentiments expressed, Long Short-Term Memory (LSTM) enhanced with preprocessing techniques such as tokenization, lemmatization, and word embeddings with Word2Vec was employed. The LSTM model achieved an accuracy of 86.43% in sentiment classification, significantly outperforming traditional baseline methods. These findings provide valuable information for platform developers, policymakers, and researchers aiming to understand and mitigate the social and psychological effects of digital platforms. Future research will focus on expanding the dataset and addressing class imbalance to further refine and enhance sentiment analysis models
Identification of Diseases caused by non-Synonymous Single Nucleotide Polymorphism using Machine Learning Algorithms
Get PDFThe production of vaccines for diseases depends entirely on its analysis. However, to test every disease extensively is costly as it would involve the investigation of every known gene related to a disease. This issue is further elevated when different variations of diseases are considered. As such the use of different computational methods are considered to tackle this issue. This research makes use of different machine learning algorithms in the identification and prediction of Single Nucleotide Polymorphism. This research presents that Gradient Boosting algorithm performs better in comparison to other algorithms in genic variation predictions with an accuracy of 70%
An NLP Approach to Predict and Suggest Next Word In Urdu Typing
Get PDFThe importance of fast speed typing is very important for computerization of contents in any language. Urdu which is a prominent language of south Asia also subjected to computerization and due to lack of resources available the process of computerizing the Urdu content has been hampered by the low speed in Urdu typing. Similarly high demand of Urdu content which needs to be digitized makes it more expensive. During this research we have worked on various aspects of Urdu language and discovered many limitations which exists which are creating hurdles in high-speed typing in Urdu language. As 35+ alphabets are in the Urdu language, the international ISO standard keyboards are only on English alphabets that are 25+ that make a quiet big difference of about 10 alphabets that means we have to press and hold SHIFT key while typing these 10+ alphabets that are wasting our time and slowing our speed of typing so we tried to solve this problem by keeping the standard along as they are. This paper is based on the word prediction and suggestion in Urdu Language (UL) based on a stochastic model, Hidden Markov Model is used to predict the next word, while Unigram Model was also used to suggest the current word and the next upcoming word, N-Gram Model was followed keeping N=2. Now, the biggest achievement in this Paper is POS tagging as each suggestion and prediction is also based upon Tagged words with a dataset of thousands of Tag combinations based upon frequency of occurrence is on test data. This tool is developed to implement this concept for Urdu Language (UL) and tested by regular and new URDU content writers to check their improvements in their typing speeds. We made some programs to let you type less and choose more
Learning English through Drama-Based Approach (DBA): A Pedagogical Stylistic Experiment with Pygmalion
Get PDFThis study explores the effectiveness of using a new approach called drama-based approach in helping to learn English language. This approach was pioneered by Dorothy Heathcote in 1950. Heathcote, a drama teacher, developed this technique to help young children communicate confidently in English. In this research, the same theatrical method is employed to enhance students\u27 spoken English skills. The chosen medium for the dramatic activity is George Bernard Shaw\u27s play Pygmalion (1916), focusing on selected passages relevant to English language learning. The research incorporates both qualitative and quantitative methods, demanding active participation from students in the play. They engaged by taking on various roles of characters, as opposed to being passive listeners under a teacher\u27s direction. This research took place in Dera Ismail Khan at Government College No. 1, involving 5th semester students who were divided into two groups: a control group and an experimental group. The experimental group performed the play through role-play and dialogue reading, while the control group read the play through traditional methods without any active engagement. The experimental study unfolded in three stages: a questionnaire, an intervention combined with improvisation, and an exam. Results were statistically measured and compared across both groups. The experimental group outperformed the control group in spoken English, demonstrating that a drama-based approach effectively facilitates the students in learning English language. This method allowed students to easily grasp themes, central message, linguistic elements, and character relationships, enhancing their understanding as well as their accents. The heightened engagement and enjoyment expressed by the students indicated a positive reception of this learning activity
Perceptions about Foreignization and Domestication: A Case Study of Undergraduate Students at NUML, Islamabad
Get PDFThe following study aims to find out the attitudes of the undergraduate students of 8th semester, studying at National University of Modern Languages, Islamabad, about the two techniques of translation, foreignization and domestication. The concept of foreignization and domestication was put forwarded by an American translation theorist, Lawrence Venuti, in 1995 in his work A Translator’s Invisibility. Two translated paragraphs of Ismat Chughtai’s short story, Do Hath, are given to the students. One is written using domestication, and the other is written leaving imprints of the source culture. The research is exploratory as interviews are conducted to corroborate the study. For analysis of interviews, Kathy Charmaz’s model of interview analysis is used. The model was given in 2012 in Charmaz’s book about the Grounded Theory. It is found that the students of the English Department of NUML, Islamabad, who have studied Translation Studies as a subject in their previous semester (7th semester), asserts that the technique of foreignization should be prioritized while translating texts from Urdu to English