International Journal of Innovations in Science & Technology
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Realistic Face Super-Resolution via Generative Adversarial Networks: Enhancing Facial Recognition in Real-world Scenarios
Get PDFhe accuracy of real-world facial recognition operations faces challenges because of the difficulties connected to Low-Resolution image quality. This indicates that super-resolution methods play a vital role in improving recognition outcomes. Currently, available SR techniques do not achieve generalization due to their dependence on synthetic LR data that uses basic down sampling processes. The proposed GAN-based approach establishes a solution to this challenge through its simulation of actual degradation algorithms which combine Gaussian blur with noise addition and color modification and JPEG compression. Random application of augmentation parameters allows the GAN model to acquire knowledge about diverse and realistic low-resolution data distribution patterns during training. A unique unaligned face image pair dataset was made specifically for research using Zoom-In and Zoom-Out methods to capture high-resolution and low-resolution images from the same individuals. The dataset presents authentic real-life scenarios better than conventional paired collection methods. Based on experimental results our method produces substantial gains in performance compared to other super-resolution methods across both self-created face data as well as established surveillance data. The proposed model achieves higher visual quality standards while improving facial recognition accuracy under different operational situations. In conclusion, this study implements an effective SR solution for facial recognition which tackles problems with standard training datasets while creating authentic face image data. The proposed method shows promise for enhancing SR applications which need high-quality facial recognition capability in surveillance systems and other security-based operations
FeelSafe: A Women\u27s Safety and Security System
No full textWomen’s safety is regarded as one of the most critical issues faced by women today. Many women feel unsafe when leaving their homes due to the increasing number of crimes in society, such as abuse, harassment, and violence. The business and IT industries are currently thriving, and many women are working in these sectors, often staying late into the night. However, despite these advancements, there remains a sense of insecurity among working women, even during daylight hours. To address this concern, the Women Safety System, called FeelSafe, has been developed to provide women with a sense of security when they are away from home. The system consists of a device equipped with an emergency button. In case of an emergency, a woman simply needs to press this button. Once activated, the system sends an alert message, along with the user’s location, to pre-defined emergency contact numbers. Additionally, the person registered as the emergency controller can manage the system using predefined commands. Timely intervention in such situations can help protect women from abuse or harassment. The system has been tested in various environmental settings and has demonstrated adequate accuracy. A testing scenario is presented in Section IV, which illustrates the complete sequence of actions taken by FeelSafe in response to an emergency
NeuroWise: AI-Based NLP Model for Early Alzheimer’s Detection Using Clinical Text
Get PDFAlzheimer\u27s disease (AD) is a background neurodegenerative illness that affects millions of people worldwide. Early diagnosis and management are important for successful intervention and better patient outcomes. This study introduces a method of AD diagnosis using NLP from clinical notes and medical records. Machine learning algorithms are used for symptom classification and prediction from text data, yielding high accuracy and scalability. The suggested technique provides an affordable solution for early diagnosis, allowing increased access to cognitive healthcare
Cost-Effective Energy Management of a Microgrid Using a Hybrid Yellow Saddle Goatfish Optimization Algorithm
Get PDFThe increasing integration of renewable energy sources into hybrid Microgrid presents challenges such as power fluctuations, system complexity, and high operational costs. This paper proposes an optimized energy management framework that combines the Hybrid Yellow Saddle Goatfish Optimization Algorithm (HYSGA) with Sequential Quadratic Programming (SQP) to improve system efficiency, stability, and cost-effectiveness. The HYSGA approach efficiently manages energy distribution among solar photovoltaic (PV) systems, Battery Energy Storage Systems (BESS), and the power grid, ensuring reliable and cost-effective operation. HYSGA quickly identifies near-optimal solutions for complex energy management issues, while SQP fine-tunes these solutions to improve precision and convergence speed. Extensive simulations and cost comparisons confirm the framework\u27s performance. In the baseline scenario, the hybrid Microgrid incurs an annual operational cost of 13,800, achieving 49% savings. Further optimization with HYSGA reduces the cost to $13,430.08, resulting in a 50.118% savings. Additionally, comparative evaluations show that HYSGA outperforms traditional techniques like Mixed-Integer Nonlinear Programming (MINLP) in terms of cost savings, computational efficiency, and solution accuracy. This study provides a detailed analysis of the research methodology, solution approach, and performance evaluation, ensuring clarity. The results demonstrate that the HYSGA framework is a scalable, computationally efficient, and economically viable solution for hybrid Microgrid energy management. The proposed method offers a promising approach for enhancing energy efficiency and reducing costs in modern smart grid applications
A Computer Vision Based Child Safety Solution Using YOLOv8 Architecture
Get PDFChild safety continues to be a major concern in homes, public spaces, and schools. Physical barriers and supervision by parents or guardians are often not enough to prevent accidents in restricted or high-risk areas such as swimming pools, staircases near sharp objects, electrical sockets or places where drugs are stored. This project proposes a real-time computer vision-based solution to enhance child safety by detecting the presence of children in restricted zones and alerting guardians, caregivers or authorities immediately. The system is built using YOLOv8 (You Only LOOK Once version 8) for object detection, combined with distance estimation and an alarm-triggering mechanism. A custom dataset containing over 30,000 labeled images across eight categories was used for model training and validation. The euclidean distance formula was applied to measure the spatial relationship between the detected children and nearby hazards, enabling accurate risk assessment in real-time. The proposed model achieved a mean Average Precision (mAP) of 90% and showed high accuracy in detecting critical proximity scenarios instantly. The solution is scalable and deployed in various environments, offering a proactive approach to preventing accidents. This project aims to deliver and effective system using readily available hardware, making it easy to install in both private and public spaces. Early testing demonstrated high levels of accuracy, speed, and real-time performance, positioning this system as a potential breakthrough in child safety technology
Experimental Design-Based Optimization of Football Manufacturing: A Case Study of Anwar Khawaja Industries
Get PDFThis study aims to investigate the critical factors influencing the weight and quality of football bladders during the manufacturing process, with a focus on optimizing production at Anwar Khwaja Industries (Pvt) Limited, Sialkot. This research employs the Definitive Screening Design (DSD) to identify and quantify the impact of key variables, including material composition and process parameters, on the final product’s performance. Among the factors analyzed, Calcium Carbonate (CaCO3) emerged as the most significant factor, demonstrating a strong effect on the response variable. Additionally, interactions between Sulphur–CaCO3, Zinc Oxide–BHT, and CaCO3–BHT were found to be critical in determining football quality, durability, and cost–efficiency. Statistical analysis, including regression modeling and ANOVA, underscores these relationships but also reveals model limitations. This study also addresses model accuracy concerns, reporting an R–R-squared value of 52.2%, while the low adjusted R2 (19.4%) and predicative R2 (0.0%) indicate limited generalizability. To address multicollinearity concerns, the factor reduction technique was applied, improving the reliability of experimental findings. The study emphasizes the role of advanced statistical techniques in optimizing manufacturing processes to maintain Pakistan’s global leadership in football production
Extractive Text Summarization-Based Framework for Sindhi Language
Get PDFThis paper presents an extractive text summarization method specially designed for Sindhi, a culturally rich but low-resource Indo-Aryan language spoken widely in Pakistan. The study focuses on selecting the most relevant sentences from Sindhi texts, employing Natural Language Processing (NLP) techniques to generate concise summaries.
The proposed system incorporates essential preprocessing steps, including text cleaning, tokenization, and stemming/lemmatization. For future extraction, it utilizes TF-IDF and sentence embeddings. After scoring the sentences, the most significant ones are selected to form the final summary.
To evaluate the system\u27s performance in five test paragraphs, several metrics are used, including F1 score, precision, recall, cosine similarity, normalization level distance, and accuracy. The system demonstrates reliable and accurate summarization, and consistency achieving high precision (1.0), strong F1 score (0.89-0.92), a low a low normalized error (0.04), and an overall accuracy of 0.86. Graphic analysis further confirms the model\u27s coherence, semantic retention, and low error rates.
By leveraging NLP for information summarization, this study contributes to preserving and promoting the Sindhi language—potential applications including digital accessibility, education, and content curation. Future research aims to enhance contextual understanding by exploring transformer-based models like BERT and extending the approach to abstraction summarization
Micro Hydro Power in Pakistan: A Comprehensive Review of Development, Applications, Challenges, and Future Prospects
Get PDFAmid Pakistan’s evolving energy landscape—marked by a 62.1% fossil fuel dependency and persistent rural-urban access disparities—micro hydro power (MHP) systems offer a cost-effective and environmentally resilient solution. This review integrates technical, economic, and policy perspectives to evaluate the current and potential role of MHP in the country. It presents a structured analysis of turbine technologies (Pelton, Cross-flow, Kaplan, Turgo), their performance characteristics, and appropriate deployment contexts across various head and flow conditions. Drawing on case studies from Khyber Pakhtunkhwa, Gilgit-Baltistan, and Punjab, the study highlights site-specific generation capacities, operational challenges (e.g., sedimentation, seasonal variability), and socio-economic impacts. Furthermore, it explores the institutional governance structure WAPDA, AEDB, PEPCO, and IPPs—and national policy initiatives under CPEC and the Alternative Energy Policy. The findings reveal that Pakistan’s mini-hydro capacity remains underutilized despite recent advancements in turbine efficiency and feed-in tariffs. Strategic expansion of MHP through modular designs, smart grid integration, and rural electrification incentives could significantly bridge the country’s energy access gap while aligning with its 2030 renewable energy targets.
 
ASAN MANDI: Digital Transformation of Pakistan’s Fruit and Vegetable Market
Get PDFPakistan’s agricultural sector, particularly its traditional Mandi markets, suffers from inefficiencies due to manual processes that result in time delays & data inaccuracies. ASAN MANDI is a mobile application to automate & centrally manage data for improving productivity, transparent transactions, and profits for farmers and traders. The app is developed using cross-platform technology (Flutter) and integrated primary modules, including electronic billing (e-billing), digital ledger management, real-time inventory tracking system, etc. All testing was conducted on devices with varying specifications to ensure app usability, interface consistency, and the effectiveness of urban and rural study devices. Results highlighted the reductions of manual errors made, time and effort in transaction processing and inventory management, with 88% of users asserting satisfaction towards the intuitiveness of app design as well as bilingual support (Urdu and English). Nonetheless, network dependence in remote regions and user adjustment were some challenges to be addressed in the future. To summarize, ASAN MANDI is a useful platform to address the issues being faced in conventional agricultural markets of Pakistan and could be a role model for other developing economies striving to improve their agricultural productivity
IoT-Enabled Assistive Glove for Real-Time Sign Language Translation Using Machine Learning
Get PDFThis paper presents a real-time system for translating gestures from American Sign Language (ASL) using an IoT-enabled smart glove. The glove is equipped with five flex sensors and an MPU-6050 gyroscope to capture finger movements and wrist orientation, processed by an Arduino Nano. Sensor data is transmitted via a Bluetooth module to a mobile application, where a Random Forest machine learning model with 97% accuracy classifies the gestures. The recognized gestures are displayed as text and vocalized through a speaker. Moreover, the app has a feature that allows users to see ASL signs with its corresponding vocabulary, thus enabling accessibility and making language more accessible to learn. It enhances the communication between the deaf and the hearing community since it offers an accurate, portable, and interactive sign recognition application