Customer Experience and Satisfaction in Coffee Consumption: An Analytical Study of Customer Behaviour in Coffee Shops

This study explores how different aspects of customer experience such as Sensory, Affective, Behavioural, Intellectual, Digital and sustainability elements affect customer satisfaction & advocacy. The topic of customer experience has gained significant importance in recent years because it not only focuses on what businesses provide but also on how customers feel, think, and react during and after their interaction with a service. Although our study focuses on a specific group (the young generation in Pakistan), it still aligns with the original study by aiming to understand how experience-driven satisfaction can influence future behaviour like recommending the café or coming back again. The young generation is considered highly active, socially engaged, and responsive to experiences that combine both quality and emotional attachment, which makes them an important group for this kind of research.A structured questionnaire was used to collect data from 50 participants, focusing on aspects such as coffee quality, ambiance, service, and emotional connection. These variables were carefully selected because they reflect both the tangible and intangible elements of customer experience. The findings suggest that behavioural & affective, intellectual & digital experiences strongly influence customer satisfaction. This indicates that customers value not only the product but also how it is delivered, how it connects to their emotions, and how digital elements enhance convenience.These insights help better understand small businesses what drives customer behaviour, enabling them to create more meaningful experiences and develop effective marketing strategies. By focusing on these elements, cafés and similar businesses can strengthen customer loyalty, satisfaction, and advocacy

Vocal Sentiments: Transformer Based Speech Emotion Recognition

Speech Emotion Recognition (SER) plays a crucial role in Human–Computer Interaction (HCI) by enabling systems to interpret and respond to human emotions through speech analysis. This paper presents a Transformer-based SER framework that leverages the Wav2Vec2 model for self-supervised representation learning. Unlike conventional approaches relying on handcrafted acoustic features or shallow learning, our approach employs transfer learning to extract high-level contextual embeddings from raw audio. We integrate two benchmark datasets, RAVDESS and TESS, to improve generalization across diverse speakers and emotions, and further analyze system robustness by introducing varying levels of environmental noise. The proposed model achieves an accuracy of 79.01%, with balanced precision, recall, and F1-scores, demonstrating competitive performance compared with recent state-of-the-art SER models. The main contributions of this work are threefold: (i) a novel evaluation of Wav2Vec2 embeddings on combined RAVDESS–TESS data, (ii) a systematic assessment of noise robustness in Transformer-based SER, and (iii) a comprehensive benchmark that highlights the strengths and limitations of transfer learning in practical emotion recognition scenarios. These findings suggest broad applicability in voice assistants, call-center analytics, and mental health monitoring, while future extensions may incorporate multimodal data and advanced fine-tuning strategies to further enhance performance

Integrating Behavior Driven Testing Approach with Cypress and Cucumber

Software testing is integral to ensuring the functionality and quality of applications. This study highlights the implementation of Cypress and Node.js by the Nokia RON team to address the challenges of GUI testing. The adoption of Cypress enabled comprehensive and targeted testing, alongside efficient resolution of dependency and third-party package issues through selective installation. By integrating Cypress with Cucumber, an easy-to-use interface was developed to transform smoke test checklists into Gherkin syntax, enhancing readability and adaptability. Additionally, the use of Mochawesome Reporter provided detailed HTML reports, facilitating issue tracking and quick resolutions. This methodology, supported by a structured questionnaire, fostered stakeholder satisfaction and collaboration, resulting in an interactive and effective testing environment. The findings emphasize the role of Behavior Driven Development (BDD) in streamlining automated testing, improving communication among stakeholders, and ensuring higher software quality

The Impact of AI Tools (Like ChatGPT) on Student Learning Outcomes and Academic Integrity: A Mixed-Methods Study

The fast pace of the introduction of artificial intelligence (AI) tools (including ChatGPT) into the educational process has already raised a storm of controversy about the impact they have on student achievement and academic integrity. This combined research evaluates the impact of writing assistants powered by AI on academic achievements and critical thinking abilities and moral reflections of students as well as within the educational landscape. Surveys of 300 university students were used to collect quantitative data on their habits regarding the use of AI tools, perceived benefits and challenges of using them. It yielded qualitative data through a qualitative survey concerning 15 educators through in-depth interviews to discuss the responses of institutions, policy gaps and pedagogical issues. The preliminary findings suggest that although the AI instruments can increase the efficiency of research and the drafting process, they are also associated with the issue of excessive dependence, lack of original thinking, and the risk of plagiarism. There was a notable correlation between a high frequency of AI use and positive short-term educational achievements, yet long-lasting components of the cognitive effects are not certain in any way. Teachers had rather ambivalent opinions, with some supporting the introduction of AI literacy at education institutions, and others demanding enforcement and regulation. The paper underlines the importance of a balanced AI implementation- stimulating progress and protecting the campus integrity. Suggestions such as the creation of institutional rules, AI-detective devices, and critical thinking modules should be created in order to reduce the misuse. In terms of the continued debate about AI in education, this study adds to the empirical literature to be used by policy and practices in the age of digital learning

Advancing Agriculture with IoT and a Smart Fertilizer Recommendation System

Agriculture is a key contributor to Pakistan’s GDP, and optimizing fertilization is crucial for enhancing crop yield and ensuring food security. This research presents a real-time, IoT-based soil analysis model that replaces traditional off-site testing, providing instant and site-specific fertilizer recommendations. The system integrates an IoT-enabled device to assess soil nutrient levels and employs a regression algorithm to predict the required NPK quantities. A realistic soil dataset is used to train and validate the model, ensuring accurate predictions. With an 88-92% accuracy rate, the system effectively recommends fertilizers, enabling precision farming and optimizing resource utilization. This reduces reliance on conventional soil testing methods, minimizing fertilizer wastage and improving soil sustainability. The real-time analysis supports data-driven farming decisions, ensuring balanced nutrient application and promoting sustainable agricultural practices. Additionally, this innovation aligns with the Sustainable Development Goals (SDGs) by modernizing agricultural techniques, enhancing food security, and supporting economic growth in farming communities.The IoT-based smart fertilizer recommendation system offers a cost-effective, accurate, and sustainable solution to improve agricultural productivity and promote precision farming.

Impact of Online Learning on Students’ Academic Performance: A Comparative Study of Online and Face-to-Face Learning

This study investigates the impact of online learning on students' academic performance in comparison to traditional face-to-face learning, focusing on higher education institutions in Mardan, Khyber Pakhtunkhwa, Pakistan. The COVID-19 pandemic accelerated the shift toward virtual education, yet disparities in technological infrastructure, digital literacy, and instructional methods created challenges for students and educators alike. Employing a mixed-methods approach, the research collected data from 200 undergraduate students through structured questionnaires and interviews to evaluate key indicators such as academic achievement, engagement, motivation, and technological accessibility.Results revealed that face-to-face learners consistently outperformed their online counterparts in terms of GPA, participation, and comprehension. Online learning was significantly hindered by unreliable internet access, limited availability of devices, and insufficient teacher training in digital pedagogy. Furthermore, students reported difficulties in maintaining attention, managing self-directed learning, and engaging with instructors in online environments.The study concluded that while online learning can supplement education during emergencies, it lacks the structural and interpersonal benefits of conventional classroom settings, especially in under-resourced areas like Mardan. It was suggested that using a mix of learning methods, training for teachers and building better digital tools will boost the results of online education.This thesis supports educational policy talks by suggesting ways to close the gap in digital access and enhance student results using online approaches

Artificial Intelligence in Sustainable Smart Agriculture: Concepts, Applications, and Challenges

Artificial 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. Consisting of several layers that enable data collecting, analysis, and decision-making in farming operations, we suggest in this paper an AI-enabled Internet of Things (IoT) architecture for smart agriculture. This paper also investigates several AI-driven technologies like Machine Learning (ML), computer vision, and IoT integration, which enable farmers with real-time data insights, predictive analytics, and autonomous decision-making capability. We also look at how AI might solve important agricultural problems, including resource optimization, climate resilience, insect control, and crop monitoring. This paper clarifies the bright future of smart agriculture driven by AI in guaranteeing sustainable farming and world food security

Research Study on the Methodology and Characteristics of Dr. Syed Sher Ali Shah’s (Introductory) Preface to Tafsir

In 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. Discussions related to the interpretation (Tafseeri) of the holy Quran, such as its Shari sdefinition, main subjectst, purpose, levels of tafsir etc have been taken place by the prominent scholars of this field. Late Dr. Sher Ali Shah of Akora Khatak Distt:Nowsera (KP) pakistan was among these learned jurisrt of the current century whom contributions towards this field shall always be remembered. In this paper efforts have been made to extract and highlight the 14 characteristics from his famous published Tafseer which has been compiled under the title of Muqaddema and Aloom Al Qura`an by his two great Scholars Maulana Faiz ur Rahman and Dr.Saeedul Haq respectivel

A Smart Cybersecurity Scheme for MIoT Systems: Simulation and Evaluation

Cybersecurity 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

Investigating the Role of LASSO in Feature Selection for Educational Data Mining (EDM) Applications

With the advent of digitalization, education-related activities have started generating massive amounts of data from various facets, such as student interaction, assessment, and learning management systems. Such vast amounts of data become suitable areas for Educational Data Mining (EDM) to reveal insights for actionable improvement in academic outcomes and personalized learning experiences. However, high dimensionality and the redundancy of the educational data also pose considerable threats to the accuracy, interpretability, and computational efficiency of modeling. Least Absolute Shrinkage and Selection Operator (LASSO) is one powerful technique for simultaneous regression and feature selection. By introducing sparsity, LASSO minimizes the absolute sum of regression coefficients, thereby forcing insignificant features to be reduced to zero automatically. This feature is handy in EDM, where relevant indicators such as attendance, quiz scores, or study patterns must be distinguished from noisy or redundant variables. This paper systematically investigates the application of LASSO in EDM by giving the mathematical background and geometric interpretation, along with practical usage recommendations. Also, LASSO performance has been checked on synthetic and real datasets, including the famous dataset UCI Student Performance. The findings prove that LASSO significantly enhances model interpretability, predictive accuracy, and a decline in complexity. In conclusion, limitations are discussed, as well as practical considerations and future directions for LASSO applications to next-generation educational analytics