Pakistan Journal of Multidisciplinary Innovation

Comparative Analysis of State of the Art Deep Learning Models for Lung Cancer Detection

2026-05-15T07:58:16+00:00

This study is based on the reviews recent advancements in deep learning (DL) techniques applied to lung cancer detection from 2021 to 2025. Models such as lungs cancer prediction-Convolutional Neural Networks (LCP-CNN), Inception V3, EfficientNet-B3, and Convolutional Neural Networks Long Short-term Memory (CNN–LSTM) are achieved previous development model accuracies in between 86% and 99%, using deep learning techniques for diagnostic methods. Public datasets, including Kaggle and LIDC-IDRI, were most frequently used for train, test and validation, supporting model generalization and reliability. This Research trends show a peak in 2023 with increased use of hybrid and ensemble deep learning model integrating (CNN) and Vision Transformers (Vitis). Overall, the study concludes that deep learning based diagnostic systems significantly improve the accuracy and automation of lung cancer detection, reducing diagnostic errors and supporting early medical intervention. In this study the Convolutional Neural Network techniques has achieved the highest accuracy from other deep learning techniques.

Health Knowledge-Behavior Gap in Lifestyle Practices among University Students

2026-06-16T08:34:35+00:00

The gap between health-related information and real lifestyle practices of university students is the focal problem of modern research in the field of public health and health education. In spite of the availability of a complete health education curriculum through institutional courses, online media, and healthcare, university students around the world maintain inadequate diets, a sedentary lifestyle, irregular sleep, and high-risk health outcomes. This is known as the health knowledge -behavior gap and indicates the inherent weakness of models that consider information provision as the main determinant of behavioral change. A stringent quantitative cross-sectional survey design was used in the current research study in a sample of 200 enrolled Pakistani university students. The health knowledge, the intentions, and the actual practices of diet, physical activity, and sleep were assessed using a 5-point Likert scale in the form of a validated structured questionnaire. Descriptive statistics, Cronbach Alpha, Pearson, and full structural equation modeling (SEM) with bootstrapped mediation testing were analyzed using SmartPLS 3.0. Findings revealed a significant difference between mean health knowledge (M = 3.82, SD = 0.63) and total lifestyle practice scores (M = 2.67, SD = 0.69) with physical activity having the largest discrepancy ( 1.39). SEM found full mediation by behavioral intentions (indirect 0.201, 95% CI [0.142, 0.267]) but the direct knowledge-to-practice relationship was insignificant (0.09, p = 0.22). These results confirm that health interventions based on knowledge alone cannot be effective without simultaneous directing knowledge at intention formation, self-efficacy, and environmental support.

Impact of Poor Sleep Quality on Daily Functioning among University Students

2026-06-16T08:56:05+00:00

Sleep is a core biological need that forms the basis of nearly all aspects of daily life, and yet university students are a group at chronic risk of sleep disturbance because of the combination of educational demands, social pressures, use of digital technology, and the loss of patterns of daily life. A low quality of sleep, defined as the inability to fall asleep or sustain a sleep state, the lack of adequate sleep time and the inability to sleep restoratively, poor emotional control, and the lack of social interaction has been reported in the literature to be associated with deteriorated cognitive performance, low academic performance, high levels of fatigue, poor emotional regulation, and diminished social interaction. The current research was a quantitative descriptive and correlational cross-sectional study to examine the effects of poor sleep quality on various aspects of everyday functioning among university students in Pakistan. A total of 200 respondents were chosen through a convenience sample of the university campuses. The structured questionnaire with the use of Pittsburgh Sleep Quality Index (PSQI) to determine sleep quality and a tested Daily Functioning Scale (DFS) with items about concentration, academic productivity, physical fatigue, emotional regulation, and social functioning were used to collect data. The answers were noted on a 5-point likert scale with PSQI global scoring. The SPSS v.26 and SmartPLS 3.0 were used to perform the analysis and included descriptive statistics, Cronbach alpha reliability tests, Pearson correlation tests, independent samples t-tests comparing good and poor sleepers, and structural equation modeling (SEM) to evaluate the route data between sleep quality and functioning sub-domain to overall daily functioning. The global mean PSQI score was 7.84 (SD = 2.41) which showed that poor sleep quality was widespread; 76.5% of participants were considered poor sleepers (PSQI > 5). SEM established that the quality of sleep was a significant predictor of all five domains of functioning with physical fatigue having the greatest path coefficient (β = -0.61, p = 0.001) and concentration having the second best path coefficient (β = -0.58, p = 0.001). The overall daily functioning variance explained by functioning sub-domains was 48% (R2 = 0.48). These results highlight the importance of evidence-based sleep health interventions in Pakistani university contexts.

Impact of Job Training, Career Growth Opportunities and Job Stress on Financial Stability of Employees in Aviation

2026-07-01T09:25:42+00:00

The aviation industry is one of the most challenging and tension-filled industries in the world where the well-being of employees is already intertwined with the functioning of an organization and safety programs. The aviation industry has witnessed a tremendous growth over the last few years in Pakistan, but the financial security of its employees is a barely explored aspect of human resource management. This paper will discuss how job training, career development opportunities and job stress work together in influencing financial security among workers in Pakistani aviation companies. Basing on Human Capital Theory, Conservation of Resource Theory and the Job Demand Resource Model, the paper claims that strong training programs and well-visible promotion opportunities are the important work-related assets that strengthen the economic security of employees, and occupational stress is the negative force that undermines it. Quantitative and cross-sectional study was used, and data was gathered by administration of a structured survey to the staff working in the commercial airlines, airport authorities, ground handling companies, and civil aviation authorities in Pakistan. The data was explored using Structural Equation Modeling in SmartPLS, descriptive and reliability analysis in SPSS. Results show that job training and career advancement opportunities have significant positive effects on financial security, with training being the predominant factor; occupational stress being a significant negative factor. The model explains a significant proportion of variation in financial security of employees, which confirms the strength of the explanatory power of the integrated theoretical framework. These results have significant practical implications on HR professionals and aviation policy makers as they demonstrate that investing in employee development and proactively addressing workplace stress are not luxury practices but critical measures toward ensuring the development of economically secure and operationally viable aviation workforce in Pakistan.