International Journal of Innovations in Science & Technology
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On The Crosstalk of Circadian Rhythm and Th17 cells: An Integrated Biological Regulatory Pathway
Full text linkTh17 cells play a pivotal role in cell-mediated immunity and also have implications for autoimmune disorders. The interplay between the circadian rhythm and the immune system has driven interest in developing novel therapies. Th17 cells have a robust relationship with the circadian rhythm through clock-controlled genes such as NFIL3 (E4BP4), RORA, RORB, NR3C1, and RORC. The purpose of this study is to construct a literature-curated updated biological regulatory network (BRN) of the molecular regulators of circadian rhythm and CD4+ Th17 cells. The integrated BRN will provide a holistic view of the differentiation process of Th17 cells from a circadian rhythm perspective, which will enhance our understanding of the interplay between the two systems. We aim to perform formal modelling and analysis of this BRN using our previously developed approaches to gain system-wide insights into various molecular expression dynamics and identify the significance of biological clocks in immunity in the future. In addition, biological pathway databases are an integral part of omics analytical workflows, and their continuous updates with the latest knowledge are crucial for gaining biological insights from such studies. Therefore, with this additional objective, we have also uploaded this pathway to WikiPathways (Database), to facilitate its use in future studies, which can be accessed via the following URL: https://classic.wikipathways.org/index.php/Pathway:WP5130. To our knowledge, this is the first study to report a literature-curated pathway of comprehensive regulatory interactions and crosstalk between Th17 cell differentiation and circadian genes
Spatio-Temporal Estimation of Glacier Dynamics under Climate Change Scenarios Using Machine Learning Techniques
Full text linkGlaciers of the Upper Indus Basin (UIB) play a vital role in providing water resources, hydropower generation, and livelihood, but they are very vulnerable and sensitive to continuous climate change impacts. This research presents a novel approach for accurate mapping of glacier extent, clean ice, debris cover, seasonal snow, and glacier melt across the Hunza Basin. We have used Grey Level Co-occurrence Matrix (GLCM), Machine Learning (ML) techniques of Random Forest (RF), Artificial Neural Networks (ANN), and Support Vector Machines (SVM) to conduct the purposeful research. ML models were trained on multispectral (Landsat, Sentinel-1 & 2, MODIS, and SPOT-5 from the last 35 years) and textural datasets. Overall, 6628 samples for training and 988 samples for testing were used to maintain a 70/ 30 ratio to evaluate overall accuracy (OA) and kappa coefficient (k̂). RF ensured the best results (OA = 95.4 %, k̂ = 0.965) in comparison of ANN (OA = 94%, k̂ = 0.92) and SVM (OA = 92 %, k̂ = 0.89). The accuracy of clean ice and seasonal snow remained consistent (producer accuracy and user accuracy >93%) compared to that of debris cover and glacier melt. Glacier retreat, increased ablation, formation of clean ice loss, and frequency of supraglacial melt due to expansion of debris cover up to 23.31% were witnessed spatially in the basin. Proposed approaches prove that ML techniques are very useful for the estimation of risk assessment in the climate-prone mountain basins and offer a robust way forward for hydrological modelling, glacier change monitoring, and water resource management
Artificial Intelligence Meets Endocrinology: A Machine Learning-Based Approach to Thyroid Disease Diagnosis Using Feature Selection Methods
Full text linkThyroid Disease (TD) arises when the thyroid gland either grows abnormally or does not generate enough thyroid hormones, and might cause serious health issues and consequences. Early and efficient identification of thyroid disease is important for improved clinical intervention and disease management. By combining sophisticated and advanced machine learning models with a range of advanced feature selection strategies, this research study aims to enhance the classification of thyroid disease based on a machine learning based diagnostic system. The preprocessed dataset used in this study and the trials were taken from the machine learning repository at the University of California, Irvine (UCI). We employ two popular feature selection techniques- Chi-Square, and Recursive Feature Elimination, and a dimensionality reduction technique Linear Discriminant Analysis (LDA), and to choose the best features from the dataset for experiments. After selecting the most suitable features, they were then used to train and test the machine learning models: Multi-Layer Perceptron (MLP), Gradient Boost (GB), and Recurrent Neural Network (RNN). Evaluation matrices, accuracy, precision, recall, and F1-score were used to assess models\u27 performance. The experimental results show that the machine learning model Gradient Boost (GB) outperformed the other models and yielded an accuracy of 99%, indicating its ability to classify the Thyroid Disease (TD) accurately. The proposed research work helps to create an intelligent decision-support system for medical diagnostics by offering an understandable and reliable framework for Thyroid Detection
Bridging Global Frameworks and Local Realities: Towards Localizing the City Essentials Approach in Pakistan’s Urban Planning Systems
Full text linkCities across the Global South are increasingly exposed to compound and cascading risks—ranging from climate-induced disasters to governance, infrastructure, and institutional failures. Global frameworks such as the UNDRR Making Cities Resilient 2030 (MCR2030) Campaign, the City Resilience Index (CRI), UN-Habitat’s City Resilience Profiling Tool (CRPT), and the ISO 37123 Indicators for Resilient Cities have collectively redefined resilience as a governance-driven, system-wide process. However, their translation into the planning and institutional realities of developing countries remains partial and fragmented. This paper bridges these global frameworks with local contexts through a comparative synthesis that identifies areas of convergence—such as governance, preparedness, and coordination—and divergence in adaptability, innovation, and modularity. Focusing on Pakistan as a representative case, the study examines how the City Essentials Approach under MCR2030 can be embedded within national and local urban planning systems to operationalize resilience. Findings from the comparative review reveal that frameworks like MCR2030 and LGSAT align with Pakistan’s disaster management architecture (NDMA–PDMA), while data-intensive tools such as CRI and ISO 37123 remain constrained by limited institutional capacity. The paper proposes the City Essentials Localization Pathway (CELP) as a conceptual bridge to integrate global principles into local governance, enabling performance-based resilience assessment, policy coherence, and data-driven decision-making within Pakistan’s urban systems
Machine Translation of Quranic Verses: A Transformer-Based Approach to Urdu Rendering
Full text linkTranslate Quranic Arabic into Urdu is a Challenge due to linguistics and theological differences. While machine translation has advanced significantly, transformer-based Neural Machine Translation (NMT) models have not yet been utilized for Quranic Arabic to Urdu translation. This study addresses this gap by developing a transformer-based model that ensures accurate and context-sensitive translation of Quranic verses. A dataset has been initialized that contains Quranic Arabic text and Urdu translation of respected. I performed preprocessing on the dataset by applying it towards tokenization, stemming, and lemmatization, without compromising the theological nature of the theme. To enrich the model to mine the linguistic and stylistic cues, transformer architectures such as Helsinki NLP/MiarinMT were used with the transfer learning. Finally, the model was evaluated for theological correctness by Islamic scholars, and, secondly, by some automated metrics (BLEU, Rouge, and Cosine Similarity). Results show that the transformer model is a better model by far that provides better translation quality in the sense that meanings are preserved, that is, contextual meaning as well as religious meaning, implying better accessibility to Urdu-speaking Muslims. This research proposes a new approach to the problem of translating sacred texts and solves, albeit theologically correct, otherwise unsolvable problems in Quranic translation, computational linguistics, and AI development. This research introduces a novel approach to Quranic translation, and Future work will explore multimodal learning for deeper contextual understanding
Building Robust Context Aware IoT Applications: Methods and Strategies for Detecting and Resolving Context Inconsistencies
Full text linkThe Internet of Things (IoT) has revolutionized connectivity, creating a vast network of interconnected devices that seamlessly exchange and analyze data. Within this dynamic IoT ecosystem, context-aware applications have emerged, enabling autonomous responses to events triggered by contextual information, thereby enhancing user experiences and facilitating intelligent decision-making. However, the utilization of contextual data in IoT applications has introduced a key challenge: context inconsistencies. Context inconsistency is defined as the condition in which contextual data collected from multiple sources is inaccurate, incomplete, or conflicting, leading to incorrect processing that may disrupt the behavior of context-aware applications. Context inconsistencies arise from various factors, including sensor noise, communication errors, and contradictory data sources (e.g., two motion detection sensors located in the same area may report different readings, where one sensor detects one person, and the other sensor detects three people). These inconsistencies can significantly impact the reliability and precision of IoT applications, potentially resulting in erroneous decisions and degraded user experiences. To address this critical concern, this research paper undertakes a comprehensive review of contemporary methodologies developed for detecting and resolving context inconsistencies in IoT environments. This study explores various strategies, discusses their features in detail, and contributes by classifying them into different categories for better understanding. Through a detailed examination of the effectiveness, strengths, and limitations of each classified method, the paper aims to offer valuable insights into managing context inconsistencies in IoT applications. More precisely, this paper serves as a valuable resource for researchers, practitioners, and industry professionals in the IoT domain, providing them with a comprehensive understanding of context inconsistency detection and resolution methods
AI-powered Body Type Analysis for Fashion Recommendation System
Full text linkThis paper presents an AI-powered fashion recommendation system that analyzes body types to offer personalized clothing suggestions. The system uses Convolutional Neural Networks (CNN) to classify male body shapes into three categories (ectomorph, mesomorph, endomorph) and matches them with suitable fashion items. We developed a web-based platform using the React-Django framework, allowing users to upload photos, receive a body type analysis, and get customized fashion advice. Testing shows our approach achieves a 94% success rate in body type classification, significantly outperforming existing methods. This study addresses a key gap in current fashion recommendation systems, which often overlook body type considerations for men. Our solution provides an effective and user-friendly way to enhance online shopping and build greater trust in fashion choices
Assessing the IoT Acceptance at Public Sector Universities of Sindh, Pakistan
Full text linkThe Internet of Things (IoT) technology blends the real world and digital life, ensuring seamless integration for accomplishing tasks to make life easier. Analysts often get lost in the deep technical details of IoT, but there is a lack of focus on student acceptance and willingness to adopt these technologies. Concentrating on the factors that drive the adoption of IoT technologies. This study employs a quantitative approach to investigate the deep interrelations and interactions in the process with the Unified Theory of Technology Acceptance (UTAUT2) and other factors like IoT Skills, Trust, and Personal Innovativeness. Through an explanatory survey method, data was collected from 389 students across 5 public universities in Sindh, Pakistan, to assess the level of acceptance of IoT technologies in universities among the students. This study produces existing literature by expanding the UTAUT2 model to incorporate novel elements relevant to the acceptability and application of IoT in developing nations. It provides important recommendations for policymakers and university stakeholders. The results highlight the need for improving IoT infrastructure, incorporating central IoT courses in academic offerings, and developing an enabling environment for successful technology adoption. The evidence presents inadequate proper IoT infrastructure and supporting environment in institutions. In addition, the adoption of IoT among students is evidenced by the study field instead of by the professional need for IoT
Automated HMI Generation via Component-Based Virtual Engineering
Full text linkAs system complexity rises and the demand for shorter time-to-market grows, there is a need to change our traditional methods of building automation systems. Developing code for Programmable Logic Controllers (PLCs) and HMIs is often a challenging and time-consuming part of designing automation systems. Typically, PLC and HMI codes are developed using vendor-specific tools and IEC-based languages. Secondly, code reuse usually involves a lot of manual copy-pasting, which is prone to errors. This method lacks proper version control and direct integration between PLC and HMI, making updates and maintenance not only challenging but also costly. This research provides a novel method to create an Auto HMI for component-based production machines by utilizing their associated virtual models. The production machine is initially modelled in Computer-Aided Design (CAD) tools and commissioned inside an emulated engineering environment to test and optimize the control behaviour. A methodology is presented to reuse the control data in the virtual models to build an Auto HMI. At last, the suggested solution is executed and verified on a conveyor-built system rig to demonstrate its feasibility
AI-Powered Chatbot for Conversational Understanding in Roman Urdu
Full text linkMany people, especially in Pakistan and India, speak Urdu. However, when they write it online, they often use Roman Urdu (Urdu written with English letters). The problem is that most chatbots struggle to understand Roman Urdu because there is no standard way to write it—people spell the same words differently. This research aims to develop an intelligent AI chatbot that can understand and respond accurately in Roman Urdu. To achieve this, we will use advanced AI techniques such as Retrieval-Augmented Generation (RAG) and GPT-based models. The goal is to improve the chatbot’s accuracy and relevance, making it better at handling conversations in Roman Urdu. This study will explain how the chatbot is designed, trained, tested, and improved, helping AI work more effectively with languages that lack fixed writing rules