VFAST - Virtual Foundation for Advancement of Science and Technology (Pakistan)
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Flood Prediction System Using IOT & Artificial Neural Network
No full textFloods pose significant challenges as one of nature\u27s most devastating disasters, making the development of accurate forecast model’s complex. This issue has led to severe consequences such as crop loss, population displacement, damage to infrastructure, and disruption of essential services. Advanced research on flood prediction models has played a crucial role in providing policy recommendations, mitigating risks, reducing human casualties, and minimizing property damage caused by floods. In this context, we propose an Internet of Things (IoT)-based flood prediction and forecasting model that prioritizes energy efficiency. Given the limited battery and memory capacity of IoT sensor nodes, we employ an energy-saving strategy within the fog layer, leveraging data diversity to minimize energy consumption. Additionally, cloud technology offers an effective storage solution. To accurately calibrate flood phases, we investigate climatic factors such as humidity, temperature, rainfall, as well as water body parameters including water flow and elevation. Neural networks are commonly used in constructing forecast systems, as they can replicate the complex calculations involved in flood physical processes, resulting in improved performance and cost-effectiveness. In our approach, the Artificial Neural Network (ANN) technique is employed for flood forecasting, and the effectiveness of different algorithms, such as Logistic Regression and Decision Tree, is assessed by comparing them to ANN. Accuracy values are computed using a classification report assessment, and graph parameters are carefully evaluated. Ultimately, our proposed system utilizes the ANN technique to train a predictive model by examining the dataset. This model generates real-time flood risk forecasts through a user-friendly graphical interface
AI-Powered Lung Cancer Detection From CT Imaging
Get PDFLung cancer is one of the deadliest forms of cancer, witnessing thousands of new diagnoses annually. Early detection remains paramount; without it, survival rates plummet drastically. This underscores the critical role of employing artificial intelligence (AI) for early diagnosis, a pivotal step in combating this devastating illness. This study introduces a sophisticated computer-aided system, aiming to revolutionize lung cancer detection through state-of-the-art convolutional neural network (CNN) technology. By harnessing the capabilities of AI and CNN\u27s, enabling precise categorization of patients into those exhibiting normal lung tissue, benign lung nodules, or malignant lung cancer.The primary objective is to streamline early diagnosis efforts, thereby facilitating prompt intervention and treatment initiation to enhance patient outcomes and bolster survival rates. Leveraging cutting-edge technology, this innovative approach aims to transform the landscape of lung cancer diagnosis, offering hope for more effective strategies in combating this deadly disease. Furthermore, by harnessing the capabilities of AI and CNN technology, this study aims to bridge existing gaps in lung cancer diagnosis, offering new insights and opportunities for advancements in medical research and clinical practice. Ultimately, the successful implementation of this innovative approach has the potential to significantly impact the field of lung cancer diagnosis and treatment, offering hope for improved patient outcomes and increased survival rates. Through continued research and development, further advancements in AI-based diagnostic tools can be achieved, paving the way for a brighter future in the fight against lung cancer
Formal Modelling and Model Checking of a Flood Monitoring and Rescue System: A Case Study of Safety-Critical System
Get PDFThe flood incidents are becoming more often and severe, thus extreme events require efficient and effective means of controlling and saving lives and property. The reason for this paper is to use Formal Modelling and Model Checking to analyse a new safety critical Flood Monitoring and Rescue System (FMRS) that shall form the basis for the efficient response to floods. Employing the TLA+ analysis, which outlines the FMRS’s dynamic behavior and operational specifications comprehensively. It is important to stress that in our work we address one of the most exciting directions of applying formal methods for the first time in collaboration with real-world safety-critical system designers and offer a powerful and transparent systematic approach to verifying safety-critical systems’ correctness, safety, and reliability. The TLA+ specifications are very carefully designed to represent multiple aspects of the FMRS, such as sensor systems, communication interfaces, as well as the rescue activity itself. To this end, we use model checking methodologies in order to assess the system’s compliance with the required safety properties, including timely detection of floods, correct delivery of data, and synchronization of rescue operations. The performed model checking demonstrates the presence of essential information about the system’s potential failure and weaknesses, which can be used for FMRS architecture improvement and development. Thus, this case shows that the best use of formal methods exist not as ad hoc methods for resolving some issues in the development of safety-critical systems, but a structured template that could be applied in other domains where high degree of assurance in the reliability of a system is needed. Besides the novel method for the better future of the field of formal verification, the proposal also sketches functional relevance of integrating the effective and efficient approaches for monitoring floods and emergency rescue operations in real-world contexts
An Improved Blended Numerical Root-Solver for Nonlinear Equations
Get PDFThis study presents a novel three-step iterative approach for solving nonlinear equations inthe domains of science and engineering. It represents a notable change from traditional methodslike Halley by eliminating the need for second derivatives. The suggested method exhibits asixth order of convergence and only requires five function evaluations, showcasing its efficiencywith an index of roughly 1.430969. The suggested method effectively solves nonlinear problemsinvolving equations with algebraic and transcendental terms. Comparative analysis againstexisting root-solving algorithms demonstrates their superior performance. The results not onlyconfirm the strength and effectiveness of the three-step iterative approach but also highlight itspotential for wide-ranging use in many scientific and technical situations
Homotopy Perturbation Method with Analytics for solving Bivariate type II Fuzzy Fredholm Integral Equations
Get PDFA numerical scheme known as homotopy perturbation method (HPM) is a powerful tool for solving a wide range of problems arising in several scientific applications. In this manuscript, we focus on bivariate type II fuzzy fredholm integral equations (BTII-FF-IEqs) to obtain fuzzy approximate solutions using HPM. The efficiency and effectiveness of the approach is tested upon numerical example and the obtained numerical results are compared with the existing exact solutions. The results reveal that the proposed method is straightforward, accurate and convenient
A Study of Mathematical Models Used in Anaerobic Digestion of Organic Refuse
Get PDFThe mathematical computation and process evaluation of anaerobic digestion (AD) treatment of organic refuse (OR) has become quite common in process designing. Furthermore, the modelling of AD process is considered as well established and mature, and largely known as a mechanistic model structure. There are various mathematical models being used to comprehend the biochemical variations, rate of decomposition, and the methane or biogas production potentials. Moreover, these mathematical analyses of the models determine the computation of equilibrium points, helping to understand their internal stability with respect to feeding parameters, and compatibility to the output static characteristics of the entire AD process. Whereas numerical simulations are also carried out for specific biodegradation on web-based software. These simulations are useful to demonstrate the dynamic responses of the mathematical models to present the most mathematical viable solutions. However, the major issues lie in the application of AD in lab-based modelling needs improvement in characterization and the adaptation of new approaches to optimize bioenergy recoveries. Hence, this review paper discusses the selection and utilization of mathematical models for different conditions in AD treatment of OR for the best mathematical representations
Efficient Class of Variance Estimators for Population using Supplementary Information in Stratified Random Sampling
Get PDFThis paper addresses an efficient class of variance estimators for population using stratified random sampling. The suggested class of estimators using supplementary information has been studied in different circumstances. The expressions of bias and mean square error (MSE) of the proposed estimators are derived up to the first degree of approximation. The theoretical comparison of the proposed and considered estimators is also discussed, which shows that the proposed estimators are more efficient than the existing estimators. Theoretical findings are validated by three different types of real data sets and simulation studies. The numerical results of the proposed and existing estimators are compared in terms of mean square error, percentage relative efficiency and diagrams. It is observed that all the proposed estimators outperform the existing estimators. For instance, the traditional unbiased estimator Ozel et.al [6] and other existing estimators. Lastly, appropriate recommendations have been provided for researchers to use these suggested estimators to solve real-world issues
Time Series Modeling and Forecasting of the Patients’Inflow and Admission in the Hospitals: A cases study of LUMHS Hospital Jamshoro Pakistan
Get PDFThe patients’ crowding in the hospitals is an international phenomenon that demands much attention to avoid harm to the lives of patients. The quantitative based models have been successfully investigated to predict the crowding of patients. Thus, the main objective of this study is to probe a statistically feasible forecasting model capable of estimating the crowding of patients (patients’ inflow and patients’ admission specifically). As a case study, the Liaquat University of Medical and Health Sciences (LUMHS) Hospital Jamshoro was chosen. The patients’ secondary data was collected form hospital and commercial computational software MATLAB was used to carry out all the calculations and manipulations by writing a concise user defined program (code). The Autoregressive Integrated Moving Average (ARIMA) modeling approach is adopted to investigate the best forecasting model. It is found that among the various six combinations of ARIMA (p,d,q) the ARIMA (1,0,1) are the best fit models for the patients’ inflow and the patients’ admission respectively; having the lowest AIC, BIC and p-values. Since the forecast accuracy contains minimal contains minimal errors thus forecast trends show very good results. The presented procedure can be helpful to manage the patients’ volume in the hospitals and can also predict the future trend of patients’ inflow and patients’ admission with good accuracy
Beyond Time and Space: A Comparative Exploration of Metaphysical and Ascetic Journeys in Islam, Spiritualism, and Mysticism
Get PDFThis study explores the notion of spatiotemporal phenomena, particularly bilocation within the religious and mystical traditions of Islam and Sufism to understand deeper spiritual truths and their implications for the interconnectedness of material and metaphysical realms. Hermeneutical and phenomenological analyses have been employed to discern the complexities of bilocation (Time and Space) with a comprehensive examination of scriptural texts, and historical accounts of spirituality, asceticism, and mysticism to uncover the realities. Findings reveal that time and space; a bilocation with collective consciousness disrupts and transcends ordinary life experiences. Moreover, integrating insights from comparative religions and transpersonal psychology this research strengthens the scientific study of religion under mystical interpretations of physical bodies
Critical Discourse Analysis of Islamic Ideological Expressions in Acknowledgement Sections of Theses Written by Scholars in English Language Teaching (ELT)
Get PDFThe objective of current research is to analyze the expressions of Islamic ideology in the acknowledgement sections of theses written by researchers in the field of English Language Teaching (ELT) from Pakistan. These acknowledgements, a specific concept in academic writing, offers a particular frame to capture the religious and cultural aspects on scholarly discourse. 30 theses were examined and data were gathered from electronic libraries of universities, online thesis databases, and institutional archives sections of the required theses. It provided enough material and reach data in this regard. Fairclough’s three-dimensional model of Critical Doscourse Anaysis (CDA) was applied, centering to Textual Analysis and analyzes concrete words, phrases and sentences that contain the ideology of Islam. The results reveal clear Islamic orientations in the features of the Arabian language used, with recurrent appeals to Allah, prayer and blessing calls, as well as direct references to both Quran and Hadiths. These acknowledgements are not only the writers own faith but it also reveal the inteewined cultural and religious value