VFAST - Virtual Foundation for Advancement of Science and Technology (Pakistan)
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Revolutionizing Network Intelligence: Innovative Data Mining and Learning Approaches for Knowledge Management in Next-Generation Networks
Full text linkThe Information and Communication (ICT) industry, a global giant among service sectors, is known for both its massive scale and its unforgiving demands. Here, downtime is unacceptable, requiring constant high availability – often at the stringent Sigma Six standard. Redundancy is a common solution, but it comes at a cost. To meet these demands proactively, the ability to predict load and growth becomes crucial. This project aims to develop a prototype, or proof of concept, that utilizes data mining to provide early warnings and growth forecasts for the ICT industry with good accuracy. Big data is key to making discoveries in any data analysis project. Normally, this data comes from real-time system logs. However, for this initial test, I used a dataset called MIT Reality Mining. This dataset is useful because real-world companies, especially in the tech industry (ICT), are often hesitant to share their current information. By using MIT Reality Mining, I could still find trends and potential reasons behind them in the ICT industry. It\u27s important to remember that this is a limited functionality prototype. While it can serve as a guideline for Telcos looking to implement data warehouses, the actual implementation details will need to adapt to the specific needs of each industry
A versatile optional randomized response technique for use with sensitive surveys
Full text linkIn data collection from human participants, researchers in almost every survey get refusals and/or false responses from the respondents. Such refusals and false reporting are particularly common in sample surveys where the participants are asked to answer questions on sensitive topics such as cheating in examination, illegal income, marks obtained in last examination, students’ satisfaction from the teaching method, and amount of money spent on luxury items, etc. A popular approach to deal with the problem of refusals and untruthful responses is the randomized response technique. This paper introduces a randomized response model which is more precise than the available models. The proposed randomized scrambling procedure guarantees the privacy protection of the respondents for motivating them to participate in the survey
Reasons for Revelation: A Scholarly Review of Insights Derived from Surah Bani Israel
Full text linkRecognized as the literal word of God revealed to Prophet Muhammadﷺ , the Quran serves as an essential guide for ethical conduct, moral principles, and spiritual enlightenment. It plays a pivotal role in guiding Muslims in matters of faith, law, and personal development, offering insights into life’s purpose, solace, wisdom, and a framework for a righteous way of living. The Quran\u27s verses are not only a cornerstone of religious belief but also a source of inspiration and a foundation for Islamic culture and civilization. Understanding the Quranic Sciences, particularly the cause of revelation, is indispensable for grasping the comprehensive messages conveyed within its verses. Surah Bani Israel (Al-Isrāʾ) is noted for its significant number of directives, and knowledge of its Sabab e Nuzul (Reasons of revelation) greatly facilitates the understanding of its commands. This paper aims to review the Sabab e Nuzul of Surah Bani Israel by examining various Tafaseer (commentaries on the Quran), highlighting the importance of Quranic Sciences among Islamic studies. The scholarly attention to Quranic Sciences, especially in the realm of Tafseer, underscores the profound engagement of Islamic scholars with these disciplines throughout Islamic literary history. This paper further explores the methodologies employed by two prominent Mufassereen (Quranic exegetes) of the fourteenth century Hijra in their Pashto language Tafaseer. It analyzes their approaches to utilizing the science of Asbab Ul Nuzool for interpreting the Quran, in accordance with the principles of Usul e Tafseer (fundamentals of Quranic interpretation) derived from the Quran and Sunnah. By examining these methodologies analytically and comparatively, the study seeks to contribute to the understanding of Quranic interpretation and the broader Islamic scholarly tradition
Racism and Colonialism in Foster’s A Passage to India: A Critical Discourse Analysis
Full text linkThe major concern of this research work is to analyze the concepts related to racism and the aftermaths of British colonial rule in India taking into consideration a very distinctive and remarkable work, A Passage to India (1924) by E.M. Forster in which these contemporary issues are discussed. Racism along with the colonialism has been a very integral part of the past and our society which this current study aims to explore. The qualitative method has been employed in this research and for this reason the present research paper is split into two major sections: The first portion investigates the meaning of racism along with colonialism and how people were treated discriminately during the colonial rule of mid of 19th century. The second section explores the concept of racism and colonialism according to E.M. Forster and the societal conditions in which he produced his work. The finding of present study shows that racism and colonialism creates the hatred between the oppressed and the oppressor, people belonging to the oppressors’ league enjoy more opportunities as compared to the oppressed ones and develops various limitations which lead to the creation of discrimination among the people belonging to different races.
 
Sight and Speech: Exploring Attention in Vision-Language Learning
Full text linkThis research investigates the attention influences on cognitive processing and task performance in vision-language learning. Using a within-subjects design, participants are exposed to both visual and linguistic stimuli under focused and divided attention conditions. Behavioral measures, such as accuracy rates and response times, are recorded, and neuroimaging techniques like fMRI are used to examine neural activation patterns. The results show that focused attention leads to higher accuracy rates and faster response times compared to divided attention. These findings highlight the critical role of attentional allocation in enhancing learning outcomes in multimodal contexts. The implications for theoretical models of attention and cognition, as well as practical applications in educational settings, are discussed. This study advances our understanding of the cognitive processes involved in vision-language integration and underscores the importance of attentional mechanisms in learning
IoT enabled implementation of a smart energy management system for real-time monitoring and controlling
Full text linkThe Pakistan power system faces a severe energy deficiency due to population growth. The Pakistan power system structure necessitates a smart load management system to address the mismanagement of load distribution that results in load shedding. A continuous power supply is essential for industrial and domestic sectors to enhance Pakistan\u27s economy. Imported fuel presents a significant economic challenge in Pakistan. This system is designed to control and monitor load management utilizing Internet of Things (IoT)-based technology. The implementation of an IoT-based approach enhances system performance by upgrading conventional load management. This study analyzed effective energy management during peak hours. This system employs a smart energy management system to improve load shedding patterns. The system components include a power monitoring module, current sensor, transformer, Wi-Fi module, and relay module integrated with an Arduino-based microcontroller for monitoring and controlling load management. To model a virtual analysis a simulation based circuit diagram is design using Proteus Software. The implementation of this study improves energy transactions between utilities and consumers. In this study, a prototype was designed and implemented using Arduino, and sensors were employed to control and observe a smart load management system. This system utilizes a hypertext preprocessor for login webpage arrangement for IoT control and monitoring. The results indicate that the cumulative analysis for the implementation of this system automatically deactivates unnecessary loads during peak hours. This system can conserve energy during peak-hour intervals. The load parameters for current, voltage, and power were displayed on the LCD screen. The system automatically deactivates the load during harmonics in the power supply until normal conditions are restored. As compared to existing studies this system detects and switch the system during on and off peak hours without any human interaction
Traffic Road Congestion System Using by the Internet of Vehicles (IoV): A Systematic Literature Review
No full textTraffic problems have increased in modern life due to the significant number of vehicles, urbanization, and non-compliance with traffic regulations. Vehicular ad hoc networks (VANETs) have made improvements to the traffic system in the past, playing a crucial role in establishing efficient traffic control systems in large cities. However, VANETs alone cannot effectively address certain problems under specific conditions. Presently, the development of new Internet of Things (IoT) technologies has enabled collaborative and efficient task execution. This technology has been implemented in the transportation system, transforming it into an intelligent transportation system (ITS), known as the Internet of Vehicles (IoV). This study investigates traffic issues within the traditional system and explores the benefits, enhancements, and reasons for improving IoV through a comprehensive Systematic Literature Review (SLR). The SLR approach involved targeted searches using multiple search phrases, including 25 articles published between 2016 and 2023. Furthermore, discussion on the necessary IoV technologies and tools required to establish IoV and address specific traffic challenges. Simulation of Urban Mobility (SUMO) is employed for the design and simulation of road traffic and we aim to contribute to the development of an optimal traffic control system. This paper analyzes two vehicular congestion control models, selects the most optimized and efficient model, and provides evidence for its effectiveness through a Systematic Literature Review (SLR)-based investigation based on its efficient features, in the end, we propose IoV based on vehicular clouds as a superior model, surpassing the capabilities of the traditional model and enhancing the network system
Applying Neural Networks to Predict Ventilator Demand: A Study of Pakistan\u27s Healthcare Sector
Full text linkThe distribution companies that deal with ventilators in Pakistan face challenges related to inventory control because of inadequate product shelf life, shortages, excess inventory, and unnecessary stock. This study, which focuses on Pakistani ventilator distribution companies, aims to offer a novel approach to sales estimation, avoid unnecessary stock expenditures, and stop clientele loss brought on by ventilator shortages. The results of this study will help determine key elements and standards that Pakistani distributors of ventilators might employ to boost sales. Most ventilator distribution businesses in Pakistan are independent wholesalers that purchase stock from their stores and distribute it to customers. To maximize ventilator distribution firms\u27 sales for various products, this study examined distribution and sales data from 2019 to 2024 for many locations and dates. To create an accurate sales forecasting model for a ventilator distribution company, this research also aims to apply artificial neural networks (ANN) for effective sales prediction. An Artificial Neural Network (ANN) model was trained using a dataset from ventilator distribution businesses and the proposed model produced an accuracy of 90%, which is good for early prediction
An Eco-linguistic Analysis of Urban Landscape and Social Critique in James Joyce\u27s Dubliners
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The main objective of this research is to revisit Stibbe (2015) framework of Eco-linguistics where he believes that it analyses language to reveal the stories we live by, judges those stories from an ecological perspective, resists damaging stories and contributes to the search for new stories to live by. This study examines James Joyce\u27s Dubliners through an eco-linguistic perspective, focusing on the interplay between language, environment, and social critique. The detailed descriptions of Dublin\u27s urban landscape and ecological symbols reflect the characters\u27 inner lives and critique early 20th-century social conditions. The analysis draws on theoretical contributions from eco -linguistics and environmental criticism, emphasizing the use of linguistic landscapes and ecological metaphors to convey themes of stagnation, aspiration, and interconnectedness. By immersing the reader in the physical and social environments of Dublin, Joyce\u27s language deepens empathy for the characters and enhances understanding of broader socio-environmental issues
Optimizing Brain Tumor Prediction: A Comparative Study Of Machine Learning Algorithms
Full text linkThe prediction of brain tumors using machine learning has become a pivotal area in medical diagnostics, offering the potential for enhanced early detection and treatment planning. This study evaluates the performance of various machine learning models in predicting brain tumors from medical clinical data. The eight models that we used for comparison are: ZeroR, K-Nearest Neighbour (KNN), KStar, J48, Multilayer Perceptron (MLP), Support Vector Machine (SVM), AdaBoost and Naïve Bayes. Features were also selected through feature selection, the data normalized and was split into training, validation and test sets with much attention paid to pre-processing the data. Based on the performance of each model on the training data set, we used other factors including precision, accuracy, recall, and F1 score to determine the performance of each model. The results prove that Naïve Bayes is the most accurate with 53.8%, with fairly even-recall, precision, and F1 measure, making it the best classifier that accurately predicts brain tumor. Overall, it was observed that SVM had the lowest values of accuracy, along with precision and recall. This comparative assessment indicates that Naïve Bayes is the most accurate of the models examined in this study and provides further understanding of the feasibility of using different algorithms to improve the identification and categorization of brain tumors. This analysis should be complemented by additional studies to develop these models and to examine other data characteristics