The X-ray energy spectrum is crucial for image quality and dosage assessment in mammography, radiography, fluoroscopy, and CT which are frequently used for the diagnosis of many diseases including ...but not limited to patients with cardiovascular and cerebrovascular diseases. X-ray tubes have an electron filament (cathode), a tungsten/rubidium target (anode) oriented at an angle, and a metal filter (aluminum, beryllium, etc.) that may be placed in front of an exit window. When cathode electrons meet the anode, they generate X-rays with varied energies, creating a spectrum from zero to the electrons' greatest energy. In general, the energy spectrum of X-rays depends on the electron beam's energy (tube voltage), target angle, material, filter thickness, etc. Thus, each imaging system's X-ray energy spectrum is unique to its tubes. The primary goal of the current study is to develop a clever method for quickly estimating the X-ray energy spectrum for a variety of tube voltages, filter materials, and filter thickness using a small number of unique spectra. In this investigation, two distinct filters made of beryllium and aluminum with thicknesses of 0.4, 0.8, 1.2, 1.6, and 2 mm were employed to obtain certain limited X-ray spectra for tube voltages of 20, 30, 40, 50, 60, 80, 100, 130, and 150 kV. The three inputs of 150 Multilayer Perceptron (MLP) neural networks were tube voltage, filter type, and filter thickness to forecast the X-ray spectra point by point. After training, the MLP neural networks could predict the X-ray spectra for tubes with voltages between 20 and 150 kV and two distinct filters made of aluminum and beryllium with thicknesses between 0 and 2 mm. The presented methodology can be used as a suitable, fast, accurate and reliable alternative method for predicting X-ray spectrum in medical applications. Although a technique was put out in this work for a particular system that was the subject of Monte Carlo simulations, it may be applied to any genuine system.
The prediction of human diseases precisely is still an uphill battle task for better and timely treatment. A multidisciplinary diabetic disease is a life-threatening disease all over the world. It ...attacks different vital parts of the human body, like Neuropathy, Retinopathy, Nephropathy, and ultimately Heart. A smart healthcare recommendation system predicts and recommends the diabetic disease accurately using optimal machine learning models with the data fusion technique on healthcare datasets. Various machine learning models and methods have been proposed in the recent past to predict diabetes disease. Still, these systems cannot handle the massive number of multifeatures datasets on diabetes disease properly. A smart healthcare recommendation system is proposed for diabetes disease based on deep machine learning and data fusion perspectives. Using data fusion, we can eliminate the irrelevant burden of system computational capabilities and increase the proposed system’s performance to predict and recommend this life-threatening disease more accurately. Finally, the ensemble machine learning model is trained for diabetes prediction. This intelligent recommendation system is evaluated based on a well-known diabetes dataset, and its performance is compared with the most recent developments from the literature. The proposed system achieved 99.6% accuracy, which is higher compared to the existing deep machine learning methods. Therefore, our proposed system is better for multidisciplinary diabetes disease prediction and recommendation. Our proposed system’s improved disease diagnosis performance advocates for its employment in the automated diagnostic and recommendation systems for diabetic patients.
Driver behavior refers to the actions and attitudes of individuals behind the wheel of a vehicle. Poor driving behavior can have serious consequences, including accidents, injuries, and fatalities. ...One of the main disadvantages of poor driving behavior is the increased risk of road accidents, higher insurance premiums, fines, and even criminal charges. The primary aim of our study is to detect driver behavior early with high-performance scores. The publicly available smartphone motion sensor data is utilized to conduct our study experiments. A novel LR-RFC (Logistic Regression Random Forest Classifier) method is proposed for feature engineering. The proposed LR-RFC method combines the logistic regression and random forest classifier for feature engineering from the motion sensor data. The original smartphone motion sensor data is input into the LR-RFC method, generating new probabilistic features. The newly extracted probabilistic features are then input to the applied machine learning methods for predicting driver behavior. The study results show that the proposed LR-RFC approach achieves the highest performance score. Extensive study experiments demonstrate that the random forest achieved the highest performance score of 99% using the proposed LR-RFC method. The performance is validated using k-fold cross-validation and hyperparameter optimization. Our novel proposed study has the potential to revolutionize the early detection of driver behavior to avoid road accidents.
Shill Bidding (SB) occurs when the fake bidders are introduced by the seller's side to increase the final price. SB is a crime committed during the e-Auction, and it is pretty difficult to detect ...because of its normal bidding behavior. The bidder gets a lot of loss because he pays extra money, and the sellers benefit from shill bidding, so this article proposed a fusion base model. This proposed model is split into two parts training and validation, into 70 and 30 percent. This model has been divided into three sub-modules; the first module, two machine learning algorithms named Support vector machine (SVM), and Artificial neural network (ANN) trained parallel on the same dataset and predicting the bidding fraud. The prediction of these models becomes the input of the fuzzy-based fussed module, and fuzzy decide the actual output based on SVM and ANN predictions. On every bid, it predicts whether the fraud is committed or not. If the bidding behavior is normal, continue the bidding; otherwise, cancel the bid and block the user. The prediction accuracy of the proposed fussed machine learning approach is 99.63%. Simulation results have shown that the proposed fussed machine learning approach gives more attractive results than state-of-the-art published methods.
Given the prevalence of handwritten documents in human interactions, optical character recognition (OCR) for documents holds immense practical value. OCR is a field that empowers the translation of ...various document types and images into data that can be analyzed, edited, and searched. In handwritten recognition techniques, symmetry can be crucial to improving accuracy. It can be used as a preprocessing step to normalize the input data, making it easier for the recognition algorithm to identify and classify characters accurately. This review paper aims to summarize the research conducted on character recognition for handwritten documents and offer insights into future research directions. Within this review, the research articles focused on handwritten OCR were gathered, synthesized, and examined, along with closely related topics, published between 2019 and the first quarter of 2024. Well-established electronic databases and a predefined review protocol were utilized for article selection. The articles were identified through keyword, forward, and backward reference searches to comprehensively cover all relevant literature. Following a rigorous selection process, 116 articles were included in this systematic literature review. This review article presents cutting-edge achievements and techniques in OCR and underscores areas where further research is needed.
Heart disease, which is also known as cardiovascular disease, includes various conditions that affect the heart and has been considered a major cause of death over the past decades. Accurate and ...timely detection of heart disease is the single key factor for appropriate investigation, treatment, and prescription of medication. Emerging technologies such as fog, cloud, and mobile computing provide substantial support for the diagnosis and prediction of fatal diseases such as diabetes, cancer, and cardiovascular disease. Cloud computing provides a cost-efficient infrastructure for data processing, storage, and retrieval, with much of the extant research recommending machine learning (ML) algorithms for generating models for sample data. ML is considered best suited to explore hidden patterns, which is ultimately helpful for analysis and prediction. Accordingly, this study combines cloud computing with ML, collecting datasets from different geographical areas and applying fusion techniques to maintain data accuracy and consistency for the ML algorithms. Our recommended model considered three ML techniques: Artificial Neural Network, Decision Tree, and Naïve Bayes. Real-time patient data were extracted using the fuzzy-based model stored in the cloud.
Pipeline installation is a time-consuming and expensive process in the oil sector. Because of this, a pipe is often utilized to carry diverse petroleum products; hence, it is crucial to use a precise ...and dependable control system to identify the kind and quantity of oil products being transported. This study attempts to identify four petroleum products by using an X-ray tube-based system, feature extraction in the frequency and temporal domains, and feature selection using Particle Swarm Optimization (PSO) in conjunction with a Group Method of Data Handling (GMDH) neural network. A sodium iodide detector, a test pipe that simulates petroleum compounds, and an X-ray source make up the implemented system. The detector's output signals were transmitted to the frequency domain, where the amplitudes of the top five dominant frequencies could be determined. Furthermore, the received signals were analyzed to extract five temporal characteristics-MSR, 4th order moment, skewness, WL, and kurtosis. The PSO system takes into account the extracted time and frequency features as input in order to introduce the optimal combination. Four different GMDH neural networks were constructed, and the chosen characteristics were used as inputs for those networks. Finding the volume ratio of each product was the responsibility of each neural network. The four designed neural networks were able to predict the amount of ethylene glycol, crude oil, gasoil, and gasoline with RMSE of 0.26, 0.17, 0.19, and 0.23, respectively. One compelling argument for using the proposed approach in the oil industry is that it can calculate the volume ratio of products with a root mean square error of no more than 0.26. The adoption of a feature selection method to choose the best ones is credited with this remarkable degree of precision. By providing appropriate inputs to neural networks, the control system has significantly outperformed its predecessors in terms of precision and efficiency.
Accurately determining phase fractions in two-phase flows is among the most significant issues in Industries related to the production and processing of petroleum and petrochemicals. There are ...numerous sensor types and configurations for measuring the void fraction. In this respect, the capacitance-based sensor is commonly recognized as one of the most precise and widely utilized sensors. In this essay, COMSOL Multiphysics software, which has been benchmarked, was used for simulations with various electrode architectures for measuring oil-air two-phase flow in an annular pattern. The initial electrode configurations were helix, double ring, concave and parallel plates. Finite element analysis utilizing COMSOL Multiphysics was executed to compare the electrode configurations. Results exposed disparate sensitivities for different electrode geometries. To get better results, a new electrode geometry called arrow-shaped which was optimized with Artificial intelligence (AI) was proposed and compared with the others. The sensor responses presented demonstrated that the proposed arrow-shaped capacitance-based sensor had 21% higher sensitivity than the best-performing sensor among four other existing sensor designs, including concave, helix, double ring, and parallel plates. These results indicate the superior performance of the arrow-shaped sensor and its potential for use in high-sensitivity applications.
The main objectives of neuromorphic engineering are the research, modeling, and implementation of neural functioning in the human brain. We provide a hardware solution that can replicate such a ...nature-inspired system by merging multiple scientific domains and is based on neural cell processes. This work provides a modified version of the original Fitz-Hugh Nagumo (FHN) neuron using a simple <inline-formula><tex-math notation="LaTeX">\rm 2^{V}</tex-math></inline-formula> term called Hybrid Piece-Wised Base-2 Model (HPWBM), which accurately reproduces numerous patterns of the original neuron model. With reduced terms, we suggest modifying the original nonlinear term to achieve high matching accuracy and little computing error. Time domain and phase portraits are used to validate the proposed model, which shows that it can reproduce all of the FHN model's properties with high accuracy and little mistake. We provide an effective digital hardware approach for large-scale neuron implementations based on resource-sharing and pipelining strategies. The Hardware Description Language (HDL) is used to construct the hardware on an FPGA as a proof of concept. The recommended model hardly uses 0.48 percent of the resources on a Virtex 4 FPGA board, according to the results of the hardware implementation. The circuit can run at a maximum frequency of 448.236 MHz, according to the static timing study.
Assessing the void fraction in diverse multiphase flows across industries, including petrochemical, oil, and chemical sectors, is crucial. There are multiple techniques available for this objective. ...The capacitive sensor has gained significant popularity among these methods and has been extensively utilized. Fluid properties have a substantial impact on the performance of capacitance sensors. Factors such as density, pressure, and temperature can introduce significant errors in void fraction measurements. One approach to address this issue is a meticulous and laborious routine calibration process. In the current study, an artificial neural network (ANN) was developed to accurately Assess the proportion of gas in a biphasic fluid motion, irrespective of variations in the fluid phase form or variations, eliminating the need for frequent recalibration. To achieve this objective, novel combined capacitance-based sensors were specifically designed. The sensors were simulated by employing the COMSOL Multiphysics application. The simulation encompassed five distinct liquids: oil, diesel fuel, gasoline, crude oil, and water. The input for training a multilayer perceptron network (MLP) came from data gathered through COMSOL Multiphysics, simulations for estimating the Percentage of gas content of an annular two-phase fluid with a specific liquid form. The MATLAB software was utilized to construct and model the proposed neural network. The utilization of the novel and precise apparatus for measuring the intended MLP model demonstrated the ability to prognosticate the volume percentage with a mean absolute error (MAE) of 0.004.
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