International Journal of Innovations in Science & Technology
Not a member yet
813 research outputs found
Sort by
Computation of Rotational Flow of the Sun Using Satellite Data and Doppler Shift Calculations
Full text linkSolar rotational flow governs the Sun’s magnetic activity, space weather variability, and long-term dynamo processes. Traditional tracer-based techniques offer limited precision in mapping these flows, creating the need for direct spectroscopic velocity measurements. This study presents a computational framework for deriving full-disk Doppler velocity maps of the Sun using high-resolution Hα spectra from the Chinese H-alpha Solar Explorer (CHASE) mission. The H-alpha Imaging Spectrograph (HIS) data cube (2304 × 2313 × 46 pixels) was processed through a workflow of preprocessing, continuum normalization, Voigt profile fitting, and pixel-wise Doppler conversion to retrieve line-of-sight velocities. The resulting field of ~5.3 million pixels shows clear differential rotation, with blue shifts up to −7.89 km s⁻¹ on the approaching limb and red shifts up to +2.19 km s⁻¹ on the receding limb, corresponding to equatorial and polar rotation periods of ~25 and ~31 days, respectively. Localized asymmetries in active regions further reveal small-scale velocity perturbations. These results validate CHASE–HIS spectroscopy as a reliable tool for global solar flow diagnostics and highlight the utility of Voigt-based Doppler modeling in resolving fine-scale plasma dynamics. The developed approach bridges spectroscopic and Helio seismic methods, offering a reproducible foundation for future studies on solar dynamo modeling and space weather prediction
Energy Harvesting Implementation in WBAN Routing Protocols with Multi-Relay Co-Operation
Full text linkMostly simulations are used to evaluate the performance of Wireless Body Area Networks (WBANs). The recent researches are focused on channel modelling and energy conservation at the Network/MAC layer. Normally, collaborative learning, path loss, and energy harvesting are ignored in these schemes of studies. In this research, we will try to use an Energy Harvesting (EH) mechanism to recharge the batteries instead of replacing them time and again. In contrast with the existing studies, the proposed scheme considers collaborative learning and energy harvesting. Cost functions are used to identify the most feasible wireless route from a given node to the sink while sharing each other’s distance and residual energy information. The human body temperature (thermal energy) and the pumping of the heart can be used for energy harvesting within the body, while solar energy can be used for energy harvesting of nodes on the human body
Decoding Cognitive States and Emotions Using the Electroencephalogram
Full text linkEmotions are essential in human communication, social interaction, and decision-making. However, accurately classifying emotions is difficult with many applications in various domains such as psychology, psychiatry, neuroscience, and human-computer interaction. Emotion detection is one of the key challenges in current research, especially when emotional words are used. It is already known that positive and negative words have an impact on human behaviour and emotions, but very rare study that focus on emotions based on the words. In this study, we propose a novel approach for emotion classification based on electroencephalogram (EEG) data elicited by text stimuli, which are various English words. Text stimuli can evoke rich and diverse emotions, but they have been less explored than other modalities for emotion elicitation. In this study, EEG data of 25 participants were used, which were collected using a 128-channel EGI system. The collected data was pre-processed, and features were extracted using four methods: Convolutional Neural Network (CNN), Wavelet Transform (WT), Power Spectral Density (PSD), and the raw data itself was used as features. The results showed that CNN features achieved an average accuracy of 80%, followed by WT with 75%, PSD with 72%, and raw data with 65%. Our study shows the feasibility and effectiveness of using CNN, PSD, and WT with SVM for emotion classification based on EEG data and text stimuli. Lastly, a hybrid model was proposed based on the combination of CNN for feature extraction and SVM for classification
Agentic AI for Autonomous Soil and Fertilization Management for Agriculture Sustainability
Full text linkSoil fertility loss and excessive chemical fertilization are major environmental and economic issues in developing regions such as Punjab, Pakistan. This paper proposes an Agentic AI framework for autonomous soil and fertilization management that combines (i) IoT soil sensing and drone-based crop monitoring for real-time perception, (ii) predictive modelling for short-horizon nutrient and moisture forecasting, and (iii) multi-agent reinforcement learning (MARL) for adaptive decision-making. The system operates with operational autonomy, executing daily management decisions without routine human-in-the-loop control. Agronomic expert knowledge is incorporated only offline as safety constraints and initialization priors (e.g., allowable nutrient ranges and stress-avoidance rules) to bound the action space and prevent unsafe behavior, rather than to prescribe actions. Experiments were conducted across two seasons at two sites (Sheikhupura and Multan) under four treatments: Farmer Practice (FP), Rule-Based Control (RBC), Machine Learning Predict (ML-Predict), and the proposed Agentic AI. Results show that Agentic AI reduces nitrogen fertilizer use while maintaining/improving yield proxy and improving soil indicators (including residual nitrate reduction and improved Soil Health Index). We also analyze irrigation outcomes as a sustainability objective and show how water usage must be treated as a constrained or multi-objective term in the reward function to avoid over-irrigation. Overall, the framework supports scalable, data-driven soil management with bounded autonomy, preserving expert-defined agronomic safety
Olive Leaf Disease Detection Using Transformer-Based Deep Learning Approach
Full text linkThe use of AI and DL in automated crop health monitoring and disease diagnosis, especially relevant to Pakistan\u27s burgeoning olive growing industry, has gained momentum. This paper proposes a transformer-based deep learning approach for the detection of olive leaf diseases due to significant shortcomings in the robustness and generalization of traditional convolutional neural networks. The proposed system makes use of a Vision Transformer (ViT) architecture to extract both local and global contextual features from the images of leaves using multi-head self-attention mechanisms. The developed Optimized ViT-Small model identifies olive leaves into three classes: Healthy, Aculus olearius, and Olive Peacock Spot. It is trained and tested on a pre-processed dataset of 3,400 high-resolution olive leaf images collected from olive-growing regions of Pakistan. Experimental results show strong performance with a test accuracy of 97% while demonstrating high precision, recall, and F1-scores throughout the classes. Moreover, performance assessment through confusion matrix analysis, ROC AUC, and precision-recall curves supports the developed model\u27s effectiveness. Although the dataset\u27s geographical coverage is limited, the results indicate that transformer-based architectures are an attractive alternative for the applications of precision agriculture in Pakistan
A Revolutionary Approach Using Artificial Intelligence and Quantum Cryptography – A Review
Full text linkData security is one of the most important aspects of the digital world as technology evolves and expands. Existing cryptographic systems are vulnerable due to quantum threats. The integration of Artificial Intelligence with Quantum Cryptography is an emerging field. AI-driven methods in QC to mitigate and be robust against the quantum threat. Quantum computing uses quantum mechanics to process data very quickly and accurately. Quantum Machine Learning can process big data as compare to classical methods with much more efficiency. The synergistic combination improves the threat detection and classification with accuracy. The integration also significantly enhances the speed and scalability of the large-scale deployment. AI enhances the efficiency and security of QC systems, and the challenges and opportunities of using AI-powered integration of quantum computing are reviewed
IoT-Driven Gas Safety: Combining Dual-Sensor Technology and Cloud Integration for Automated Risk Mitigation
Full text linkA gas leak in a home can be very dangerous and cause accidents or illness if it is not found soon enough. Many existing gas detection systems cannot avoid false alarms and delays, which means better, real-time systems are needed. A system that uses an ESP32 microcontroller, two sensors (MQ6 for high sensitivity and NDIR for confirmation), and detects gas leaks using the Internet of Things (IoT) is presented in this paper. The methodology of the system includes simulating sensor readings, code within the microcontroller, and MQTT cloud messages at gas concentrations running from 0 to 10,500 ppm. The simulation adds both sensor noise and delays from the network to reflect real life, as alarms are sounded only after both sensors agree. Tests showed the system stays true to zero false alarms and has detection rates above 95% up to 100% when gases reach over 5500ppm. Furthermore, MQTT provides consistently low communication latency of 26 to 32 milliseconds, which helps make responding to emergencies nearly real-time. The research introduces a new IoT approach that manages accuracy, dependability, and speed for residential gas safety, validated through detailed simulation experiments
Impacts of Climate Variability and Urban Expansion on Groundwater Systems in Mansehra
Full text linkGroundwater serves as the principal source of domestic water supply in Mansehra District, Khyber Pakhtunkhwa, Pakistan. However, rapid urbanization and climate change have increasingly impacted groundwater quality and recharge dynamics in the region. This study investigates groundwater quality and identifies potential recharge sources under shifting environmental conditions. A total of eleven water samples were collected from wells, rivers, and surface channels across five distance-based zones (0–200 m, 201–400 m, 401–600 m, 601–800 m, and 801–1000 m) relative to the nearest river. The samples were taken in sterile, dry containers and were transported within 24 hours to the Pakistan Council of Research in Water Resources (PCRWR), Islamabad, for detailed physicochemical analysis. Parameters measured included pH, electrical conductivity (EC), total hardness, alkalinity, turbidity, calcium (Ca), and potassium (K). The findings show extensive groundwater quality deterioration in areas subject to high urban activity and climatic fluctuation. More specifically, samples from Baffa showed higher hardness, Ca, and K levels above WHO allowable limits, indicating contamination potential. On the other hand, samples from Nokot and Ichria mostly met WHO standards, with turbidity being the only parameter of concern. Comparison of analyses of well, river, and pond samples revealed rivers and surface water bodies as the preeminent sources of groundwater recharge. The findings highlight the imperative need for sustainable groundwater management practices to mitigate the adverse impacts of anthropogenic stresses and climate change in the Mansehra Basin
Impact of Peri-Urban Agriculture on Food Self-Sufficiency of Faisalabad: Exploring the Contribution of Per-Urban Agriculture to Sustainable Food Systems in Faisalabad
No full textIntroduction/ Importance of Study: Peri-urban agriculture plays a vital role in enhancing food self-sufficiency and improving nutritional outcomes, particularly in growing cities like Faisalabad, Punjab. This study assesses how it affects local production, household food supply, and stakeholder integration along the urban–rural interface.Materials and Methods: Data were collected from 100 peri-urban farming households in Faisalabad using structured questionnaires. The survey included variables such as land ownership, crop types, agricultural income, and vegetable consumption. Additionally, land use changes from 2018 to 2023 were analyzed using GIS tools to observe the impact of urban expansion. Descriptive statistics and Chi-Square tests were applied to assess relationships between food access, nutritional perceptions, and consumption patterns.Results and Discussion: Findings revealed that households allocated an average of 12.9 Kanals for agriculture, growing seasonal vegetables like turnip, carrot, spinach, and peas. Nearly half of the produce was consumed domestically, while the remainder was sold locally. A significant association (p < 0.05) was found between positive nutritional perceptions and regular access to fresh produce. However, limited government support, weak stakeholder coordination, and inadequate market access emerged as key barriers.Conclusion: Peri-urban agriculture significantly contributes to household nutrition and food access in Faisalabad. Yet, its broader impact is limited by institutional gaps. Strengthening collaboration among farmers, policymakers, extension workers, and markets is essential for making peri-urban agriculture more resilient and sustainable in urban Pakistan
Enhancing Pakistani Jaggery Exports: An AHP Driven Analysis
Full text linkThe demand for natural raw products is increasing worldwide, especially in areas that prioritize health and wellness. Jaggery, a non-refined natural sweetener, has emerged as an economic and ecological alternative to processed white sugar. Despite Pakistan’s notable production capacity, the inconsistent quality, inefficient processing technologies, and government policies are hindering its export potential. This study employs the analytical hierarchy process (AHP) to identify and prioritize key factors that influence jaggery export potential. The data for AHP were extracted from a structured questionnaire, which was completed by 100 respondents, including producers, exporters, and farmers. The insights from the analysis revealed that skilled labor, mechanized crushers, and quality of raw material are the most critical factors. However, government policies, water consumption, water wastage, carbon emissions, carbon credits, fair trade, and sustainable fuel are undervalued, which pose a long-term threat to this industry. By prioritizing challenges, decision makers can amicably enhance the sector’s viability. This paper contributes to agro-industrial development by offering recommendations for sustainable jaggery production and export