International Journal of Innovations in Science & Technology
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Impact Assessment of Agro-Meteorological Drought Using Geo-Spatial Techniques. A Case Study of Southeastern Sindh-Pakistan
Get PDFThe frequency of droughts is increasing as global temperatures rise. To effectively monitor drought conditions, it is crucial to use the appropriate index. In this study, the Standardized Precipitation Index (SPI) and Reconnaissance Drought Index (RDI) were applied to evaluate droughts. The tool "DrinC" was used to calculate the RDI for 3-, 6-, and 12-month periods (Oct-Dec, Oct-March, and Oct-Sept) from 1981 to 2020. RDI values between -1.0 and -2.5 indicated moderate to extreme droughts across all districts. The RDI for 3, 6, and 12 months highlighted significant drought years, including 1984, 1992, 1994, 2010, 2011, 2015, and 2019, showing reduced productivity during these periods. Dry conditions were prevalent at most stations between 1981 and 2020. In South-Eastern Sindh, Pakistan, this study also assessed changes in Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), and Soil Moisture Index (SMI) over the last four decades (1981-2020). Satellite data analysis showed that NDVI peaked in 1988 (+0.53) and hit its lowest in 2021 (+0.48). Similarly, SMI ranged from +1.1 in 1988 to +0.98 in 2021, while LST increased from 35.1°C in 1988 to 53.4°C in 2021. A negative correlation between SPI and RDI was observed through linear regression, confirming the effectiveness of both indices in assessing drought severity. These findings can inform the development of drought preparedness plans, helping to mitigate the impact of drought on various economic sectors
Fabrication of Smart Syringe Infusion Device: A Solution for Healthcare Industry
Get PDFAccurate medication delivery is essential for patient outcomes in intensive care units, where precision in drug delivery is crucial. In order to address the need for increased accuracy and efficiency in workflow, this study proposes a semi-automated smart syringe infusion device with a unique refill mode integrated with electronic health records (EHR). The device was tested in both manual and virtual control modes, with a stepper motor-driven syringe used for precise fluid infusion. The refill mode was evaluated based on its ability to automate the refilling procedure. The results showed precise dosage control in a variety of scenarios, with minimal discrepancies between the desired and actual amounts. The refill mode effectively automated the withdrawal and refilling processes, lowering human error and increasing efficiency. Additionally, the device\u27s effortless interface with EHR systems streamlined the documentation process, enabling real-time data logging and enhancing workflow. This device offers a dependable, cost-effective solution for improving medication delivery, making it a valuable tool in healthcare, particularly in resource-limited environments
Assessment of ML Classifiers in Complex Human Activity Recognition Using Wearable Sensors Data
Get PDFHuman Activity Recognition (HAR) is essential for understanding daily behavior patterns, and wearable sensor data serves as a reliable source for monitoring complex activities. This study uniquely evaluates the performance of nine machine learning classifiers in the context of complex human activity recognition, relying solely on wearable sensors. It offers valuable insights into classifier effectiveness for real-world applications. Data from the PAMAP2 dataset, which was collected using three wearable IMUs placed on the hand, chest, and ankle, along with a heart rate sensor, was utilized to identify six daily complex activities. A 70/30 train-test split methodology was implemented to assess classifier performance. The Random Forest (RF) classifier achieved the highest performance, boasting 93% accuracy, precision, recall, and F1-score, followed closely by the K-Nearest Neighbors (KNN) classifier, which recorded 91% across all metrics. In contrast, the Logistic Regression (LR) classifier underperformed, achieving only 55% accuracy, likely due to its limitations in handling non-linear data. These results demonstrate that RF and KNN classifiers are effective for complex human activity recognition, while linear classifiers like LR are less suitable for such tasks. Overall, the Random Forest and KNN classifiers provide reliable performance for complex human activity recognition using wearable sensors, making them excellent choices for practical applications
AI-Powered Classification for Cheating Detection in Offline Examinations Using Deep Learning Techniques with CUI Dataset
Get PDFSupervising students during examinations is a very demanding process, and real-time supervision by human proctors proves to be challenging. This method is time-consuming and involves the extra work of monitoring several students concurrently. Automating exam activity recognition is perceived as one of the ways to address these challenges. In this work, we designed, implemented, and tested an accurate deep learning-based system that classifies student activities during examinations using a pre-trained ResNet50 model. This new concept helps expedite the rate at which exams are monitored and supervised, minimizing the role of human proctors. An amalgamated dataset was used, and the model works with six types of student behaviors, incorporating dropout layers for better performance. The Adam optimizer was employed for fine-tuning the learning process, and k-fold cross-validation was utilized to ensure the model\u27s robustness. The system achieved a high training accuracy of 96%, with 57% of all the documents producing output close to 1 for all categories, demonstrating high precision rates. The results indicate that the proposed method is reliable for future automated proctoring systems. Supervisors can focus on more critical tasks, such as addressing student concerns, rather than constantly monitoring every student\u27s movement. Moreover, the automation system provides consistent and unbiased supervision, eliminating human errors and fatigue that could otherwise affect the monitoring process. This ensures a fairer examination environment where all students are treated equally under constant vigilance. The system can also be scaled to handle larger examination rooms or remote testing, providing flexibility in its deployment
Exploring the Efficacy of CNN Architectures for Esophageal Cancer Classification Using Cell Vizio Images
Get PDFEsophageal cancer, as with the global burden of disease, is usually due to Barrret\u27s esophagus and gastroesophageal reflux disease. Fortunately, the disease is amenable to early detection; however, early diagnosis has been complicated by the limitations of the existing diagnostic technologies. To address this problem, a new Convolutional Neural Network and ResNet50 architecture are presented in this study to aid esophageal cancer diagnosis through the classification of Cell Vizio images. This diagnosis is made by the deep learning architecture which does tissue classification into four categories thus improving the diagnostic sensitivity. For model training and testing preoperative perforations in sixty-one patients, 11,161 images were used. Data augmentation and normalization techniques were also performed on the images to help improve the outcome. Our training accuracy reached an impressive 99% 12, while our final f1 score was 93.05%. Our Res Net 50 model obtained an F1 score of 93.26%, precision of 94.05%, recall of 93.52 %, and validation accuracy of 93.32 %. These results indicate how well our deep learning-based technique can be used as a quick, non-embolic, accurate method for early detection of esophageal carcinoma
ML-Driven Lightweight Botnet Detection System for IoT-Networks
Get PDFThe integration of cloud computing with the Internet of Things (IoT) seeks to create seamless connections between humans and devices, enhancing applications in areas like smart healthcare and home automation. However, this also brings significant security challenges. Our study addresses the critical need for an efficient anomaly detection system specifically designed for IoT-enabled cloud computing environments, a gap not previously explored at this scale. Utilizing the IoT-23 dataset, we evaluated various feature selection techniques in conjunction with classification algorithms to develop a lightweight anomaly detection model. Our results demonstrate that the decision tree classifier, paired with the correlation coefficient method for feature selection, achieved an impressive 99.98% accuracy rate, with an average processing time of just 5.2 seconds. This combination proved to be the most effective for real-time anomaly detection, presenting a promising approach for ensuring robust security in IoT networks as connectivity continues to grow
Comparison of IoT Messaging Protocols: A novel Crop-Specific Protocol for Wheat, Banana, and Chili
Get PDFIoT systems mostly depend on messaging protocols to facilitate the exchange of IoT data, with various protocols or frameworks available to support different types of messaging patterns. Choosing a suitable IoT messaging protocol for a specific application is a significant task. It is crucial to select a protocol that meets criteria such as reliability, lightweight, scalability, extensibility, interoperability, and security. It is crucial to opt for a protocol that meets criteria such as being reliable, lightweight, scalable, extensible, interoperable, and secure. With the increasing prevalence of machine-to-machine communication, numerous standardized communication protocols have emerged for IoT applications. However, performance characteristics of IoT protocols can vary expressively, even when operating under the same conditions. This research paper presents a quantitative comparison of three well-known IoT messaging protocols: MQTT (Message Queuing Telemetry Transport), AMQP (Advance Message Queuing Protocol), and HTTP (Hypertext Transfer Protocol). This research focuses on comparison of existing protocols to latency and throughput. A novel crop-specific protocol is also designed for wheat, Banana, and chili crops
Pedagogical Suitability: A Software Metrics-Based Analysis of Java and Python
Get PDFProgramming is one of the foundational skills essential for computer science professionals, yet attaining proficiency in this skill is widely acknowledged as a formidable challenge. The intrinsic complexity of programming is often cited as the primary factor contributing to its difficulty. The choice of programming language for IP courses typically relies on past experiences and empirical evidence, rather than on a quantitative basis, which can affect its effectiveness and suitability for novice learners. The study presented in this article conducted a quantitative analysis of Java and Python to assess their suitability for use in IP courses. The analysis involved evaluating programs based on a total of 210 elementary programming algorithms using HCM. The results of the study indicated that Python programs, compared to Java programs, have a reduced reliance on lexical elements, are less complex, and have a smaller code size. Additionally, Python was found to produce less complex programs and required less effort and time for development and maintenance. Moreover, Python programs tend to have fewer bugs. Overall, the study concluded that Python is better suited for IP courses than Java. The novelty of this study lies in its quantitative comparison of Java and Python using HCM, revealing that Python is more appropriate for IP courses due to its lower complexity, reduced development effort, and fewer bugs
A Comprehensive Review and Analysis on Voltage Stability Enhancement Using Distributed Generation
Get PDFThe present-day scenario of electrical power system engineering mainly comprises issues like power paucity, blackout, load shedding, and ineptness in meeting the necessary demand for power. Therefore, new power plants are built and old ones are expanded and upgraded. Although these developments play a key role in today’s scenario, there remains a field where the scope of development persists. This review article focuses on incorporating load models in traditional (OPF) studies and comparing the results of the above with those obtained from OPF analysis with the incorporation of (DG). The study of stability analysis and its behavior is a very important aspect of power system planning and its design for reliable operation. The determination of the transient stability of an electric power system is a crucial step in power system analysis. It has become imperative for power engineers to look out for improvement in the voltage stability of a power system. For this purpose, the IEEE 9 bus system is considered. The main objective is the analysis of the transient stability of an IEEE 9 bus system consisting of three generators and nine buses. Further, transient analysis of the power system network will facilitate the design of a better DG network. Here, MATLAB software is used to analyze the stability of the power system
Real Time Detection of Diabetic Retinopathy using Deep Learning Techniques
Get PDFDiabetic Retinopathy is a prevalence disease which is a medical condition frequently caused due to high sugar levels of blood. It deteriorates the optic nerve as it compresses and blurs the vision, which is used to detect white light and transmits signals to your cerebrum using a nerve. There has been a massive increase in the statistics having diabetic retinopathy which causes the loss of sight in any age group with no treatment Every diabetic patient is required to visit their ophthalmologist after every two weeks or mandatorily in a month. Moreover, bi-annual inspection is required to notice the amount of vision to see the objects. For this reason, Pakistan lacks to have ophthalmologist which are expert in their domain. Mostly, they are not available round the clock especially in less privileged areas. Therefore, we have developed a smartphone-based handheld AI-integrated product which is cost-effective and portable which detects the visual Impairment and produces reports of the concern patient with a minor intervention on the same day by an eye specialist. This research project focuses on diabetic retinopathy detection by utilizing 20D (20 Diopter) Lens and camera of any random smart phone which captures fundus images which are further spitted and compared against various models of deep learning . In this research, VGG-15, ResNet50 and Custom CNN was undertaken. As a result, VGG16 outperformed other models by obtaining highest validation accuracy that is 74.53% as well as lowest validation loss of 55.94%. Moreover, ResNet50 yielded 74.08% validation accuracy and computing validation loss of 58.72%. Consequently, Custom CNN Model achieves 57.26% validation accuracy and 57.26% validation loss. Thus, VGG16 performed best on the dataset provided and is deployed in the smartphone application which is a portable and cost-effective method for Diabetic Retinopathy screening in less privileged areas. This project aims to target three Sustainable Development Goals including Affordable and clean energy ,Good health and well-being, and Industry Innovation and Infrastructure respectively.