VFAST - Virtual Foundation for Advancement of Science and Technology (Pakistan)
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Exploring Syed Sher Ali Bacha\u27s Impact on Politics and Progressive Literature as Marxist Leader, Writer, and Poet in Khyber Pakhtunkhwa
Get PDFSyed Sher Ali Bacha (1935-98) is one of the Pakhtun Marxist politicians who played a leading role in the leftist politics in the then NWFP (Khyber Pakhtunkhwa). He is known for his scholastic approach toward solution of Pakhtun problems because he drew insights from the Marxist philosophy of dialectical and historical materialism while approaching social, economic, and political issues of Pakhtun. He had resurrected the tradition of study circles in Pakhtun society. He was also the founding member and first General Secretary of two leftist political parties; Mazdoor Kisan Party (MKP) and Pakhtunkhwa Mili Awami Party (PMAP). Thus, his entire political career spanning over a four-decades, was devoted to left wing politics and peasant movements in Khyber Pakhtunkhwa, which is explicit in his long struggle in leftist political parties like, National Awami Party (NAP), MKP, and in later part in PMAP. This research paper focuses on his writings on class difference and social inequality, as well as on his vision of creating unity among Pakhtun belt. The Communist Party of India (CPI), which was the first leftist political party of undivided India and was established in Soviet Tashkent in 1920s by émigré revolutionaries, had also notable figure like Muhammad Shafiq (Secretary of CPI), and other émigré Muhajirin from the then NWFP
The Impact of COVID-19 on E-Learning: Context-Based Sentiment Analysis Discourse Using Text Mining
Get PDFFinding the most dominant and pertinent user opinions on a certain topic is crucial to the sentiment analysis success factor. During the pandemic lockdowns around the world, the suspension of academic institutions leads to an exceptional increase in distance education. Academic institutions closed their campuses immediately to mitigate the effects of COVID-19 and prevent its pervasive spread, and educational activities were shifted to online platforms. The effectiveness of online education is a significant topic of interest for both students and their parents, especially in terms of how students and teachers perceive it and how technologically viable it is in a range of social circumstances. Before such a wide adoption of e-learning is possible, these issues must be analyzed from multiple perspectives. The present research aims to evaluate the efficacy of e-learning by examining individuals\u27 perceptions of it. Opinions can be found on websites such as Instagram, Facebook, Twitter, etc. As social media has recently emerged as a significant means of communication. This study addresses factors connected to a significant change in the educational system. 200,000 tweets were gathered from Twitter to evaluate the opinions of Twitter users who were taking part in online learning. This study adopts VADER to analyze the subjectivity and polarity score of tweets, a topic model was also created using the LDA algorithm to determine the themes that were talked about on Twitter the most. The models have been constructed and evaluated using Word2Vec to capture the semantic relationships between words and LSTM and RNN sequential model for sentiment analysis. This study measured the efficiency of a sentiment analysis model using the accuracy metric, the conducted experiments reveal that the proposed hybrid model achieves an overall accuracy of 96.3%. The results also indicate a significant negative impact of the Covid-19 pandemic on individuals\u27 emotions, with 64.4% of the analyzed tweets displaying negative sentiments. These findings provide valuable insights into the relationship between global events and individual emotions on social media platforms
Impact of Child Neglect on Career Decision Making Difficulties and Disruptive Behaviour among Adolescents
Get PDFThis study was intended to investigate the impact of child neglect on career decision making difficulties and disruptive behavior among adolescents. The aim of this study was to determine whether child neglect scientifically impacts the career decision making and disruptive behavior or not. The study included both males and females from two major cities of Pakistan; Rawalpindi and Islamabad. 400 participants, 220 males and 180 females, age ranging from 15 to 19 were part of the survey. The study was quantitative in nature and purposive sampling technique was used for research purpose. The Multidimensional neglect scale Career Decision making difficulties questionnaire and Disruptive behavior scale for adolescents were administered on the participants of the study. For the purpose of testing hypothesis; descriptive statistics, Pearson’s Bivariate Correlation, T-Test analysis and linear regression analysis were used. The findings of the current study suggest that child neglect is strong predictor of career decision making difficulties and disruptive behavior. It also indicated that there is significant positive relation between Child neglect, career decision making difficulties and disruptive behavior. The results also demonstrate that males exhibit more disruptive behaviors compared to females whereas, female face more difficulties in career decision making as compared to males. Both actual as well as hypothetical results are closely related. This study will provide awareness regarding parental care and its positive impact child harmonious personality development
A Review of Cellphone Healthcare Applications: Content Privacy and Safety Issues, Challenges and Recommendations
Get PDFNowadays, cellphone healthcare applications are receiving a considerable amount of attentiveness from both researchers and developers. Advances in communication technologies have aided the evolution in the usage of cellphones and digital devices and present challenges in terms of the accuracy and reliability of cellphone healthcare applications. Nevertheless, these applications may compromise crucial threats related to the seclusion and safety of users’ health content. Awareness of these challenges can help cellphone and cellphone application producers to manufacture efficient tools to allow patients and users to access the services of these technologies very effectively. The main objective of this research paper is to recognize the potential limitations and strengths regarding content seclusion and safety for the development and widespread utilization of effective cellphone healthcare applications. We have employed a literature survey and a relative comparison of the top ten top-rated cellphone healthcare applications to recognize security threats and characteristics that can support developers in building healthcare applications with necessary seclusion and safety standards and allow the users to select the appropriate application for their personal use
Enhancing Card Swipe Machines using Mathematical Model with JFLAP Formal Methods and Automation: A Mathematical Model with JFLAP
No full textAutomation is a novel approach that can enhance production capacity, work quality, and working environment, while minimizing labor disputes by automating all handling parameters. Formal methods are scientific techniques used to design complex mathematical systems. They involve specifying requirements and verifying software systems. The card swipe machine is a widely used point-ofsale terminal in supermarkets, medical centers, and shopping malls. Customers can easily make payments through these machines, which also provide detailed receipts of all transactions, including reversed transactions. This resolves cash management issues, improves customer service, and supports marketing. While multiple conventional machines are available in the market, they lack visual representations, making it difficult to understand their working mechanism without graphical representations. Deterministic finite automata (DFA) is a mathematical model that has limited states and moves from one state to another based on input and transition functions. This study proposes the use of the JFLAP software to create a mathematical model of card swipe machine transactions. The proposed model allows for viewing each processing step in a card swipe machine, offering a new approach to understanding their working mechanism
Enhancing Breast Cancer Detection through Thermal Imaging and Customized 2D CNN Classifiers
Get PDFBreast cancer is one of the most prevalent and life-threatening forms of cancer due to its aggressive nature and high mortality rates. Early detection significantly improves a patient\u27s chances of survival. Currently, mammography is the preferred diagnostic method, but it has drawbacks such as radiation exposure and high costs. In response to these challenges, thermography has become a less invasive and cost-effective alternative, gaining popularity. We aim to develop a cutting-edge model for breast cancer detection based on thermal imaging. The initial phase involves creating a customized machine-learning (ML) model built on convolutional neural networks (CNN). Subsequently, this model undergoes training using a diverse dataset of thermal images depicting breast abnormalities, enabling it to identify breast cancer effectively. This innovative approach promises to revolutionize breast cancer diagnosis and offers a safer and more accessible alternative to traditional methods. In our recent study, we leveraged thermal image processing techniques to forecast breast cancer precisely based on its external manifestations, particularly in cases where multiple factors are interconnected. This research employed various image classification methods to categorize breast cancer effectively. Our comprehensive approach encompassed segmentation, texture-based feature extraction from thermal images, and subsequent image classification, leading to the successful detection of malignant images. Our study harnessed the power of machine learning to create a tailored classifier, merging key components from GoogleNet, including the utilization of 2D CNNs and activation functions, with the ResNet architecture. This hybrid approach incorporated batch normalization layers following each convolutional layer and employed max-pooling to enhance classification accuracy. Next, we used a sample dataset of carefully selected images from DMR-IR to train our proposed model. The outcomes of this training demonstrated significant improvement over existing methods, with our suggested 2D CNN classifiers achieving an impressive classification rate of 95%, surpassing both the SVM and current CNN models, which achieved rates of 91% and 71%, respectively
Challenges faced By Teachers and Learners During Covid-19: use of Online Learning in Higher Education
Get PDFThe spread of COVID-19 pandemic has compelled millions of students and teachers to shift from physical educational environment to online teaching and learning within the shortest time. Consequentially, it is inescapable to depend on technology. Although technology made the education process expedient, feasible and convenient for the students during the COVID-19 pandemic but there were many pitfalls which frustrated both teachers and students in this new digitalized system of education. The present research explored the most common online teaching and learning challenges faced by the teachers and learners during COVID-19. The present study is based on a qualitative and quantitative sample survey approach. The population of this study were teachers and students of Social Science departments (History, Economics, Education and Psychology) of BZU Multan who were selected through simple random sampling. The study was conducted in the month of June and July, 2021. Descriptive and financial statistics was used to analyze data. The findings of the study revealed that students and teachers in online classes faced a variety of problems such as low motivation of students due to open book exam, in competencies in technological skills, less interaction between teachers and students, low frequency of internet and health barriers etc. So, it is recommended to overcome the problems of online education, the government, policymakers and educational institutions must adopt certain remedial measures to better handle online classes by adopting the most up-to-date methodologies and regularly training teachers and students so that the teaching–learning process goes through more smoothly and effectively
Foundations of Peace in Islam: A Quranic Perspective
Get PDFThis research article explores the foundational principles of peace within the Islamic framework, focusing on domestic, social, economic, and political spheres. Drawing from Quranic teachings and Islamic jurisprudence, the study examines how Islam establishes and sustains peace in various aspects of human life. The analysis begins with an exploration of domestic peace, elucidating Islamic laws and practices concerning gender roles, family dynamics, marriage, and divorce. It then delves into social peace, highlighting the importance of mutual cooperation, justice, and ethical conduct within communities and societies. Furthermore, the article examines economic peace within Islam, emphasizing principles such as fair trade, wealth distribution, and prohibition of usury, all aimed at fostering economic stability and equitable prosperity. Lastly, the research investigates political peace, discussing Islamic principles of governance, consultation, and protection of rights, essential for ensuring stability, security, and cooperation among diverse populations. Through a comprehensive review of Quranic teachings and Islamic jurisprudence, this article provides valuable insights into how Islam addresses the multifaceted dimensions of peace, offering a holistic framework for fostering harmony and well-being within individuals and societies. It underscores the significance of adherence to these principles for achieving sustainable peace and prosperity in the contemporary world
Enhancing SCTP Performance through the Selection of Appropriate Retransmission Policies
Get PDFThe Stream Control Transmission Protocol (SCTP) is a reliable transport protocol that provides message oriented communication services between applications. One of the critical functions of SCTP is to ensure reliable delivery of data by detecting the lost or missing packets due to transmission errors. Once the errors are detected the SCTP uses retransmission policies for immediate retransmission of data along the same or alternate path. However, the performance of SCTP retransmission policies can significantly impact its efficiency and reliability in different network conditions. In this paper, we analyzed three retransmission policies of SCTP that are (1) CWND, (2) SSTHRESHOLD and (3) LOSSRATE, and evaluated their performance in terms of network bandwidth, propagation delay and packet loss. We conducted simulations using the NS-2 network simulator and evaluated the performance of each policy under different network conditions and in each simulation the impact on throughput is analyzed. From the simulation results, the retransmission policy that uses loss rate parameter (LOSSRATE) for the transmission of data outperforms the retransmission policy that uses parameters such as congestion window (CWND) and the slow start threshold (SSTHRESHOLD). The analysis on the obtained results provides valuable insights into the tradeoffs between different SCTP retransmission policies and can help network administrators and application developers optimize SCTP performance in different network environments
Using Machine Learning Models for The Prediction of Coronary Arteries Disease
Get PDFGlobally, the leading cause of mortality among both men and women is coronary heart disease. This disease is widely recognized as the primary killer worldwide, and its early detection poses a significant challenge. Given the current state of affairs, it is crucial to promptly identify heart disease in its initial stages to ensure successful patient treatment. Despite numerous attempts by various researchers to develop hybrid and ensemble models for early detection, the desired outcomes have not been achieved. Consequently, the machine learning and algorithmic research community has directed its focus towards improving these methodologies. In this particular study, six supervised machine learning classifiers, namely Random_Forest, extreme gradient boost, Logistic of Regression, Decision_Tree, KNN, and N-Bayes, were employed. The UCI repository dataset was utilized as the sample data, comprising attributes and corresponding values. Data preprocessing techniques were employed to eliminate any missing values. An ensemble model incorporating three algorithms, namely DT (decision-tree), RF (random-forest), and XGB, was constructed. Remarkably, the ensemble model achieved an impressive accuracy rate of 95.33% for predicting coronary heart disease