International Journal of Innovations in Science & Technology
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Command, Control, and Assuasive Measures: Policy-Based Information Dissemination in Environmental Governance of Pakistan
Full text linkSmog is a significant environmental and public health issue in Pakistan, particularly in Punjab, where the intensity of seasonal haze events has increased since 2016. The study analyzes government policies and initiatives from 2017 to 2025, focusing on three mitigation measures: command-and-control, economic, and assuasive, and identifies the weakest link in the current framework. A qualitative content analysis was conducted using national and provincial legislation, policy strategies, implementation reports, and media coverage, guided by the OECD (Organization for Economic Co-operation and Development) environmental policy classification. The findings reveal that Punjab\u27s smog control measures are primarily based on command-and-control measures, including industrial inspections, emission caps, and bans on high-pollution practices. Economic measures, including targeted subsidies for cleaner agricultural machinery, electric vehicle installment schemes, and initial proposals for an Emission Trading System, are emerging, but their scope is limited. Assuasive measures, which involve awareness campaigns and participatory tools, are underdeveloped, seasonal, and poorly integrated with enforcement and incentives. The absence of long-term environmental literacy programs and behavioral change initiatives hinders compliance with regulatory and market-based tools, thereby reducing the effectiveness of the policy. The study concludes that Punjab\u27s long-term smog reduction necessitates a balanced approach to policy, prioritizing continuous, well-funded assuasive measures alongside legal enforcement and economic instruments, to foster a lasting environmental responsibility culture and improve air quality outcomes
Analysis of Periodic Permeability on Free Convective Three-Dimensional Flow with Cattaneo-Christov heat transfer and Slip Effect
Full text linkThe present research paper contributes the slip effects on a three-dimensional viscous fluid flow for free convective boundary conditions with periodic permeability. Free convection fundamentally involves some heat transfer methods. In this work, the Cattaneo-Christov heat transfer method has been employed to develop the knowledge of heat transfer actions in complex flow porous system with periodic permeability. Moreover, the impact of the slip effect is investigated to more effectively deal with the boundary conditions. The mathematical model has designed for incompressible, viscous and laminar flow with free stream specifications. By using the regular perturbation approach, governing highly nonlinear partial differential equations are transferred into the ordinary differential equations in linear form together with certain linear partial differential equations. The separation variables approach is then used for transforming the linear PDEs to ODEs. Analytical solutions are obtained for the pressure, velocity field, components of skin friction, and temperature field. The influence of physical attributes existing in the mathematical representation of the physical occurrence is investigated and illustrated. Both the slip parameter and the Cattaneo-Christov heat flux have an impact of thickness on the thermal boundary layer of observed fluid flow
Automated Detection and Classification of Tomato Leaf Diseases Using EfficientNetB0 and Deep Learning Techniques
Full text linkTomato leaf diseases significantly impact agricultural productivity worldwide, necessitating accurate and timely detection methods. This research proposes a robust and efficient deep learning framework leveraging the “EfficientNetB0” architecture for the detection and classification of multiple tomato leaf diseases. Utilizing transfer learning alongside advanced data augmentation techniques, the model was trained on a comprehensive dataset comprising six disease categories and healthy samples, sourced from Kaggle. The proposed approach achieved an overall accuracy of 88.4%, outperforming traditional methods such as CNN, AlexNet, and S-V-M by a notable margin across all disease classes. Evaluation metrics, including precision, recall, and F1-score, further validate the model’s ability to accurately distinguish subtle disease symptoms despite class imbalance challenges. Additionally, the lightweight design of “EfficientNetB0” enables potential real-time applications in mobile and edge computing environments. These findings highlight the model’s promise as an effective tool for precision agriculture, facilitating early disease intervention and reducing crop loss. Future work will focus on expanding the dataset diversity and deploying the system in real-world agricultural settings through mobile and drone platforms
Digital Credibility and Social Gratification: Understanding How Generation Z in Pakistan Engages with Misinformation and Algorithmic Influence in the Contemporary Social Media Landscape
Full text linkIn the contemporary digital landscape, Generation Z increasingly relies on social media as a primary source of information, communication, and self-expression. While these platforms foster connectivity, learning, and creativity, they also amplify the circulation of misinformation due to limited regulation and inadequate fact-checking practices. This study investigates the motivations and behavioral patterns of Generation Z in Pakistan concerning online information engagement, focusing on the balance between social gratification and information credibility. Employing a qualitative exploratory design, data were collected through five focus group discussions (FGDs) comprising 25 participants across diverse academic disciplines, including Media Studies, Art & Design, Computer Science, Business Administration, and Allied Health Sciences. Thematic analysis revealed that social validation and entertainment are dominant motivators for content sharing, whereas critical evaluation and fact-checking remain secondary concerns. Instagram and WhatsApp emerged as the most frequently used platforms, followed by X (formerly Twitter), TikTok, and Facebook. Although participants acknowledged the prevalence of misinformation, only 52% consistently verified content prior to sharing. The study highlights how algorithmic reinforcement and emotional engagement contribute to selective exposure and echo chambers, intensifying the challenge of discerning credible information. Findings underscore the need for comprehensive digital literacy initiatives that integrate fact-checking, ethical sharing, and critical thinking into educational frameworks. The research contributes to the broader discourse on media ethics, algorithmic influence, and the sociocognitive dimensions of digital engagement among youth in developing contexts
Harnessing Unconventional Malware Detection Techniques to Equip Proactive and Resilient Cyber Defense Strategies Against the Constantly Changing Landscape of Sophisticated Cyber Threats
Full text linkThe rapidly changing intricacy of malware, specifically in the case of highly secured air-gapped networks, necessitates proactive and resilient detection mechanisms that are highly capable of detecting sophisticated, modern, and obfuscated malware. Instant work focuses on a model for malware detection that is vibrant and resilient and uses deep learning models to look at Windows API call patterns that come from executable files. The original dataset from Kaggle had a big class imbalance (malicious: 42,797; benign: 1,079), but the SMOTE approach helped balance the training data. In this regard, a comparison of seven deep learning models, including Simple ANN, MLP, DropConnect Improved ANN, Residual ANN, DenseNet ANN, RBF Network, and hybrid CNN-LSTM, has been conducted over both 50 and 150 training epochs on various metrics such as recall, accuracy, F1-score, precision, and ROC-AUC. As a result, the CNN-LSTM model, enhanced by an attention mechanism, exhibited superior efficacy in differentiating between benign and malicious samples. In this context, the accuracy improvement is minimal at +0.08%, but the most substantial increase in Class 0 recall is +4.1%, and the F1-score shows an enhancement of +2.7%. The most significant contribution of this study is the attention-augmented architecture that apparently diminishes interpretability and enhances focus on significant behavioral attributes
Exploring Contextual Similarity in Quranic Ayahs: A Case Study of Surah Al-Baqarah and Aal-e-Imran in Urdu Translations
Full text linkThe translation of sacred texts, particularly the Quran, requires a deep understanding of both linguistic and contextual nuances to preserve the original message. This research investigates the contextual similarity among Quranic Ayahs by analyzing the Urdu translations of Surah Al-Baqarah and Aal-e-Imran from Maulana Maududi\u27s Urdu Quranic translation. Given the importance of accurately conveying the essence of the original Arabic text, this study aims to quantify the contextual relationships between Ayahs within each Surah and assess the effectiveness of Maulana Maududi’s translation in maintaining these relationships. The novelty of this study lies in its application of deep learning, particularly Long Short-Term Memory (LSTM) networks, to evaluate the contextual similarity between Ayahs. The LSTM model is used to capture the deep linguistic and contextual relationships within the translation, offering a data-driven approach to Quranic translation evaluation. The dataset comprises the complete translations of Surah Al-Baqarah and Aal-e-Imran in Urdu, and each Ayah is compared with every other Ayah within the same Surah to compute similarity scores. The results show varying degrees of similarity among Ayahs, with some Ayahs exhibiting high contextual alignment while others display subtle divergences. These findings highlight the ability of LSTM models to uncover hidden patterns in translation, while also pointing out the challenges in preserving the full contextual integrity of the original Arabic text in translation. In conclusion, this study provides valuable insights into the complexities of Quranic translation and offers a novel approach to evaluating the quality of such translations. By combining advanced machine learning techniques with the study of sacred texts, it presents a new avenue for improving the accuracy and contextual coherence of Quranic translations, ultimately contributing to the field of computational linguistics and religious studies
Framework for Modeling Risk Factors in Green Agile Software Development for GSD Vendors
Full text linkIn the last decades, agile methodologies are commonly employed to develop and deliver valuable software, with high user satisfaction at a comparatively low cost. However in recent years, the emergence of Green Software Engineering has necessitated that software developers prioritize the development of Green And Sustainable Software (GSS). Green software development is about developing and utilizing software with restricted energy and computing resources. In recent years, as the application of Global Software Development (GSD), software engineers have applied agile methods for fast, interactive, and green software development. However, such adoption of agile methods poses certain risks. The contribution of this study is two-fold. First, it identifies 8 Risk Factors (RFs), through a Systematic Literature Review (SLR), in which 42 relevant papers are identified and reviewed. The identified RFs need to be avoided by the GSD vendors while using agile methods to deliver GSS. Second, the findings of the SLR study are empirically validated through a questionnaire survey from 106 GSD experts belonging to 25 disparate countries. The results of the SLR and survey were compared and analyzed through a two-proportion Z test using R, which shows some significant variation for some RFs. Lastly, a framework for modeling structural association among RFs was established using an interpretive structural modeling approach. Research results illustrate that the outcomes of our industrial survey are mostly coherent with the SLR findings. Future, research should focus on developing predictive models using Artificial Intelligence (AI) and Machine Learning (ML) to analyze project data in real-time, promoting proactive decision-making for GSS development
Machine Learning-Based Asthma Diagnosis Prediction Using Lung Function and Demographic Features
Full text linkAsthma is a prevalent chronic respiratory disease, which poses significant diagnostic challenges because of its multifactorial nature. This study aims to develop a machine-learning approach for predicting asthma diagnosis using key features such as body mass index (BMI), age, lung function parameters (FEV1 and FVC), and demographic information. A dataset containing clinical and demographic records was utilized to train and evaluate models, including Random Forest, Neural Networks, and XGBoost classifiers. The performance of the following models was assessed using metrics such as precision, recall, accuracy, and F1-score, with Random Forest showing/exhibiting the highest predictive performance. In addition to traditional performance metrics, advanced visualization techniques like SHAP (Shapley Additive ex Planation’s) values were employed to interpret model predictions and assess feature importance. Results demonstrate that age, BMI, and lung function are key predictors of asthma diagnosis, with lung function parameters showing/exhibiting the strongest correlation with diagnosis outcomes. The study also explores various 3D and interactive visualizations to enhance the interpretability of the models. The proposed approach demonstrates that machine learning models when combined with clinical data, can accurately predict asthma diagnosis and potentially aid healthcare professionals in early detection and personalized treatment plans. This research highlights the potential of data-driven models in improving asthma diagnosis and contributing to better clinical decision-making
Generative AI’s Impact on Industry: Unveiling Transformative Applications, Opportunities and Challenges
Full text linkWith the advancement of Artificial Intelligence, a new branch of AI has emerged which is known as Generative AI. It has gained a lot of popularity in a very short time because of its human-like computational capabilities. It has the potential to automate hectic work with its efficient processing capabilities. Overall, it can help in solving complex problems and optimizing mundane and redundant tasks. The main objective of this paper is to conduct a thorough analysis of the impacts of Generative AI on the industry and its benefits, advantages, and application. This will help future generations in opting for Generative AI to automate mundane tasks. The methodology includes in-depth secondary research on research papers related to Generative AI and its applications. The findings show that the recent generative AI applications have emerged as the fastest-growing user base. However, there are lots of limitations to incorporating AI models in the industry. Therefore, it will be beneficial to use Generative AI applications but relying on them can be threatening to employment opportunities and may lead to misleading and falsifying information. It is necessary to have human evaluation, considering specific constraints to accomplish desired results
Extraction of Bio-Oil from The Pyrolysis of Banana Tree Waste Using A Fixed-Bed Reactor
Full text linkThe rapid and ongoing depletion of fossil fuel reserves is driving up energy costs and harming the environment due to greenhouse gas emissions, leading to a global energy crisis. This situation highlights the urgent need to produce renewable fuel from biomass. This research focuses on extracting bio-oil from banana tree waste under different operating conditions. In this study, the pyrolysis process of banana tree waste was carried out in a fixed-bed reactor to maintain controlled conditions and prevent unwanted cracking. To optimize the process, the effects of temperature, particle size, and nitrogen flow rate on bio-oil yield were investigated. Experiments were conducted at temperatures ranging from 400 to 600 ℃, with feedstock particle sizes of 0.5 – 2.0 mm and nitrogen flow rates between 0.5 and 2 liters per minute. The optimal conditions for maximizing bio-oil yield were determined. Under these conditions, the maximum bio-oil yield of 32.13% was obtained at a temperature of 500 ℃, with a particle size of 1.2 – 2.0 mm and a nitrogen flow rate of 1 liter per minute. The results also demonstrate how temperature, particle size, and nitrogen flow affect the bio-oil yield during pyrolysis. The study concludes that banana tree waste can be efficiently converted into bio-oil through proper processing, contributing to sustainable energy production while minimizing environmental impact. The chemical composition of the bio-oil was analyzed using the GC-MS technique, which identified various compounds, including phenols, acids, and other chemical components