VFAST - Virtual Foundation for Advancement of Science and Technology (Pakistan)
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HAFNet: A Hierarchical Attention-Based Deep Learning Model for Mango Leaf Disease Detection
Full text linkThis study introduces the Mango Branch - Multi Attention Fusion Network (MAFN) framework for the identification of mango leaf diseases, employing feature engineering techniques such as color histograms, texture analysis, and shape descriptors. The research implemented data preprocessing strategies and utilized a multi-branch architecture with an attention mechanism to extract hierarchical features from images. The model demonstrated robust performance, achieving 92% accuracy, 94% precision, 93% recall, and 94% F1 score. Additionally, a second model, the Hierarchical Attention Based Fusion Network (HAFNet), was developed to integrate multiple branches for multi-scale feature extraction while mitigating noise through attention-based features. The study advanced preprocessing methods for both MAFN and HAFNet, introducing novel attention-based features in the field of agricultural science. The findings indicate enhanced crop disease detection through feature engineering, thereby promoting sustainable farming management via early detection. The paper concludes by discussing the limitations of the models and potential avenues for future research
Research Study on the Methodology and Characteristics of Dr. Syed Sher Ali Shah’s (Introductory) Preface to Tafsir
Full text linkIn the subcontinent, the students are usually taught a useful academic case (Aloom Al Qura`an) before the course of interpretation and translation of the Holy Quran. This preliminary study serves as a comprehensive foundation that enables the learners to understand the basic sciences, structure, and context of the Quranic revelation in a more systematic manner. Discussions related to the interpretation (Tafseeri) of the Holy Quran, such as its Shari definition, main subjects, purpose, levels of tafsir, and other related branches of Quranic sciences have been taken place by the prominent scholars and researchers of this field since centuries.Late Dr. Sher Ali Shah of Akora Khatak, District Nowsera (Khyber Pakhtunkhwa), Pakistan, was among these learned jurists and distinguished scholars of the current century, whose contributions towards this sacred field shall always be remembered with deep respect and gratitude by students and teachers alike.In this paper, sincere efforts have been made to extract, describe, and highlight the fourteen significant characteristics from his famous published Tafseer, which has been compiled and preserved under the title of Muqaddema and Aloom Al Qura`an by his two great scholars, Maulana Faiz ur Rahman and Dr. Saeed ul Haq respectively, to serve as a lasting academic tribute to his intellectual legacy and Quranic scholarship
Russia And Iran Role In Post -Assad Syria: An Analysis
Full text linkThe Syrian war has garnered significant international attention, with Russia and Iran emerging as major foreign Forces backing the Assad administration. However, the fall of administration\u27s collapse in 2024 has put both nations at a strategic crossroads, and raised doubts about the sustainability of their involvement in post-Assad Syria The study uses a qualitative methodology that rely on secondary sources and adopting interpretivist ontological and subjectivist epistemological viewpoints, to analyze the evolving roles of Russia and Iran. It investigates whether their continued changing presence serves as a strategic asset or has growing become to be a burden in terms of liability amid shifting political, military, and local dynamics. This study examines how Russian and Iranian strategic interests in Syria are increasingly challenged by the emergence of non-state actors, growing grassroots resistance, and a changing regional order. While both countries were instrumental in securing Assad’s survival, their post-conflict roles are marked by contradictions. The military presence in Russia that is pegged on main facilities at Tartus and Hmeimim is getting shaky. Fractiousness within Syria, and an increased local animosity have undermined what was regarded as a stable basing point. All these weaknesses are made worse by the fact that the country has to deal with economic burden of international sanctions and enormous costs of reconstruction. The much older policy of the Iranian state called the Axis of Resistance also faces the pressure. Overland supply lines to Hezbollah (once considered the most valuable proxy group of Tehran) are destroyed by changed battlegrounds. At the same time, further escalation of sectarian forces and growth of both the Kurdish and Sunni militias have contributed to sinking the Iranian influence in the region. This study finds a marked reversal of fate in the creation of the same interventions that well assured Russian as well as Iranian power in Assad, becoming strategic liabilities in Syria post war palimpsest. The dominance of Iranian backed militias and Russian airpower have been replaced with the decentralization of power and local empowered forces openly fighting against foreign influence. This development is a warning example of the shortcomings of interventionist approaches. The analysis concludes that the active intervention of Moscow and Tehran in Syria will eventually have negative outcomes that do not save them, but rather reduce their greater geopolitical positioning in the Middle East, unless the much-needed realignment occurs. The results add to three main topical debates, the sustainability of proxy warfare in the long-term, the reality of reconstruction in occupation, and the ambiguity of great power presence in weak or transition states. The Syrian case presented in this analysis can serve, to some policymakers, as a cold reminder of how short-term military successes hide the longer term and impactful outcomes of foreign action
Customer Experience and Satisfaction in Coffee Consumption: An Analytical Study of Customer Behaviour in Coffee Shops
Full text linkThis paper discusses the impacts of various elements of customer experience like Sensory, Affective, Behavioural, Intellectual, Digital and sustainability as far as customer satisfaction and advocacy are concerned. Customer experience has become an important subject matter over the last few years since it does not just deal with what is offered by businesses but with how customers experience, think, and respond upon their encounter with a service. Even though our own research is based on a particular group (the young generation in Pakistan), this does not differ with the original research since we also aim to know how satisfaction due to experience can affect future behaviour such as recommending the cafe or returning to the cafe. The data were gathered with the help of the well-structured questionnaire and targeted 50 participants to provide information on the issues of coffee quality, atmosphere, service, and emotional bonding. These variables have been chosen with a lot of care since they represent both tangible and intangible customer experience aspects. The results indicate that behavioural experiences and affective experiences, intellectual experiences and digital experiences have a strong impact on customer satisfaction. It means that customers do not just appreciate the product but how it is presented, how it ties to customer feelings, and how the digital aspects contribute to convenience.This set of insights can help them improve the meaning created in digital projects and develop appropriate marketing plans. With the emphasis on these factors, cafes and other similar companies can enhance customer retention, retention, and word of mouth
Leveraging Social Media Streams for Disaster Prediction: A Machine and Deep Learning Approach
Full text linkNatural disasters have an impact on the lives of people and animals worldwide. Twitter, which is a social networking tool that enables individuals to share news, opinions, and even their experiences, has emerged as a valuable source of real-time information. Since real-time data is easily accessed, many service providers make an effort to analyze it on a daily basis for purposes of detecting emergencies and lowering risk factors. As for people, a person cannot sit and analyze thousands of records and, at the same time, recognize threats in real time. To address this challenge, machine learning (ML) and deep learning (DL) techniques have become essential tools for automating disaster detection and classification. The article examines the disaster prediction capabilities of Twitter text data and evaluates the power of diverse supervised machine learning models (logistic regression, naive Bayes, support vector machines (SVM), k-nearest neighbors (KNN) and XGBoost). Also, a deep learning model based on Long Short-Term Memory (LSTM) is tested to see how it performs on sequential data in tweets. The main goal of the work is investigating the impact of the usage of different data modes, including text, visual data (including pictures and satellite data), audio data (including sounds of emergency calls or acoustic patterns), and blog or news posts, on effective disaster prediction and classification. Although the nature of Twitter text data (real-time, short, and publicly available) has been prioritized in this research, the property of multimodal data sources to improve situational awareness is also recognized as their increasing role. Through the analysis of these varied streams of information with both a traditional machine learning approach and an advanced deep learning approach, the comprehensive study will attempt to create a more holistic and responsive disaster detection framework that could serve as an aid to decision-makers and emergency responders in their attempt to mitigate losses and improve the response strategy
A Smart Cybersecurity Scheme for MIoT Systems: Simulation and Evaluation
Full text linkCybersecurity is essential to safeguarding intellectual property, patient information, and other sensitive data from unauthorised access by cybercriminals. As healthcare technology advances, integrating the Medical Internet of Things (MIoT) into smart diagnostic laboratories has become instrumental in enhancing diagnostic accuracy and efficiency in patient care. However, this integration also introduces significant cybersecurity and privacy risks, given the high confidentiality of patient information stored and processed by MIoT systems. Ensuring the security of these systems is critical to maintaining trust and safety in digital health platforms. To address these cybersecurity challenges, this study proposes a smart cybersecurity scheme for MIoT systems. Using the Emulated Virtual Environment for Network Graphing (EVE-NG), we simulate potential cyberattacks targeting diagnostic laboratory software to evaluate the system’s resilience and identify risk levels. This simulation-based approach enables cybersecurity professionals to develop, test, and improve defensive mechanisms in a controlled virtual environment. The proposed cybersecurity scheme is assessed for its effectiveness in mitigating cyber threats within MIoT systems, providing insights into safeguarding sensitive health data, and ensuring reliable diagnostic processes
Emojis Segmentation from WhatsApp Chat Messages Using K-Means Clustering Technique for Students Sentiment Analysis
Full text linkThe ideas, attitudes, and thoughts expressed through social media networks are a major factor in sentiment analysis. Through WhatsApp chat messages; there has been a noticeable increase in the use of Emojis by students. Although the semantics and grammatical structure of sentiments based on simple Emojis communicate an extensive amount of information and are thought of as comprehensive communications. A single WhatsApp group message may include a variety of Emojis in addition to text. When used in a single message, numerous Emojis can express multiple emotions or sensations simultaneously, such as happiness, sadness, confusion, etc. In this situation, important areas or objects from message screenshots might be extracted and then subjected to sentiment analysis using image segmentation technique. This study employs a K-means clustering algorithm for segmentation of Emojis from the students chat messages. To assess the effectiveness of the system, ten frequently used Emojis are selected and experimented and obtained a total Emojis segmentation accuracy of 95.61%. The outcome of Emoji segmentation will be used for features extraction and classification of the students’ sentiments
Islamic Reformative and Spiritual Dimensions in the Novels of Saleha Abid Hussain: An Analytical Study
Full text linkThis paper undertakes a research-based analytical study of the reforma- tive and spiritual dimensions in the novels of Saleha Abid Hussain, one of the leading female voices in twentieth-century Indian Muslim literature. Born in Panipat in 1913 and influenced by the Islamic reformist legacy of Altaf Hussain Hali, Saleha Abid Hussain used her fiction as a medium of social and moral awakening. It investigates how her fiction reflects Islamic values, particularly themes of Tasawwuf, moral reform, women’s education, family life, and social justice. Using a textual and thematic anal- ysis of selected novels such as Azra, Atish Khamosh, and Rahi Amal, the study explores how Saleha Abid Hussain employed literature as a medium to promote Islamic ideals of justice, equity, and spiritual awareness. The paper further highlights her role in presenting women not only as social beings but also as active participants in moral and religious reform. The findings suggest that her novels go beyond literary expression to serve as vehicles of Islamic guidance and reformative thought, thereby establishing her as a significant contributor to both Urdu literature and Islamic intel- lectual tradition
Artificial Intelligence in Sustainable Smart Agriculture: Concepts, Applications, and Challenges
Full text linkArtificial Intelligence (AI) has emerged as a transformative force in modern agriculture, revolutionizing traditional farming practices into smart agriculture ecosystems. This paper presents the ideas and uses of AI in smart agriculture, therefore highlighting its great influence on improving farming efficiency, sustainability, and production.Based on a number of layers that facilitate data collection, data analysis, and decision-making, in the farming processes, we propose in this paper an AI-based Internet of Things (IoT) platform of smart agriculture. There are also other AI-based technologies such as Machine Learning (ML), computer vision, and IoT integration, explored in this paper, that can give farmers the ability to access real-time data, predictive analytics, and autonomous decision-making power. We also discuss the ways AI would address some significant agricultural challenges, such as optimisation of resources, climate resilience, insect control and monitoring of crops. The paper explains the promising future of smart farming based on AI in ensuring sustainable farming and food security in the world
Introduction to Syed Amir Ali Malih Abadi and a Review of His Religious and Scholarly Contributions
Full text linkThis research paper presents a comprehensive introduction to Syed Amir Ali Malih Abadi, a distinguished Islamic scholar and exegete. It highlights his life, intellectual background, and contributions to religious scholarship, particularly in the field of Tafsir (Quranic exegesis). The study examines his major works, analyzes his methodology in interpreting Islamic teachings, and evaluates the impact of his scholarly efforts on contemporary Islamic thought. Through an in-depth review, the paper aims to shed light on the significance of his contributions to religious education and Islamic literature. This research not only acknowledges Syed Amir Ali Malih Abadi’s services but also situates his legacy within the broader tradition of Islamic scholarship