Distillation columns are considered the most essential units in chemical and petrochemical industries, particularly in oil refineries. This study aims to create and evaluate the empirical model for a multicomponent distillation column. An experimental model was constructed to represent the nonlinear behaviour of the column. The column is treated as a multi-input multi-output dynamic system. The industrial distillation columns used in this study has a diameter of 2.2 m and a height of 16.6 m. It is equipped with 20 valve trays. The process currently operates at Al-Doura Refinery as a part of the Midland Refinery Company in eastern Baghdad, Iraq. To find the model that describes the system, four manipulated variables (reflux flow rate, reboiler heat duty, the light component fraction in the feed and feed flow rate) and four controlled output variables (the light component fraction in the top product, the light component fraction in the bottom, the flow rate of the top product and the flow rate of the bottom product) were selected. The experiments were conducted by applying a pulse for 300 s to each of the manipulated variables while keeping the remaining variables constant. A 4×4 matrix consisting of 16 transfer functions (most of them are second order) was obtained. Each equation represents the relationship between the manipulated and controlled variables. These equations form the base for advanced control strategies such as fuzzy logic control, model predictive control or and other modern control approaches.
One of the most important testing tools for the assessment and diagnosis of cardiovascular diseases is the electrocardiogram (ECG) signal. However, the ECG signal is very sensitive to different types of noise, such as baseline drift, powerline interference and electromyogram (EMG) noise. Therefore, these noises need to be removed from the ECG signals due to their significance in detection and diagnosis of cardiovascular disease. In this paper, a novel method for simultaneously removing these different types of noise from the ECG signals by using a hybrid filtering framework is proposed. This hybrid approach assures efficient noise reduction, and the integrity of the ECG signal is preserved for accurate diagnosis. The proposed method consists of two stages. In the initial stage, the three filters that make up the hybrid filtering are applied: an elliptic high-pass filter to remove the baseline wander, a notch filter to reduce the 50/60 Hz power line interference and a Savitzky–Golay filter to reduce the high-frequency EMG noise. Second, the preprocessed ECG signal is divided into segments of different durations by segmentation to facilitate practical analysis and feature extraction in later stages. The effectiveness of the proposed method is shown by the tests and proven results using MATLAB R2023a. The method is evaluated using a large collection of real ECG signals from the MIT-BIH arrhythmia database. The proposed method outperforms other methods in signal-to-noise ratio (39.10 dB) and mean square error (0.06014*10⁻³); it can remove different types of noise without distorting the ECG signal characteristics. The study’s contribution to reliable noise removal of ECG signals for clinical applications is significant.
Communicating with the Deaf and non-speaking individuals and understanding sign language are complicated processes. With the continuous development in deep learning and automation of systems, however, solutions are available to overcome the difficulty of communicating with this segment of society and understanding sign language, especially in Arabic-speaking regions. This article proposes an automated sign language recognition system based on a hybrid convolutional neural network (CNN)–long short-term memory (LSTM) architecture, which represents two layers: CNN, which extracts spatial features from sign language motions, and LSTM, which records the temporal connections within gesture sequences. The model’s ability to provide smooth communication is demonstrated by evaluating its performance using a specially created Arabic Sign Language (ArSL) dataset, and then the model was tested and trained by using input videos recorded for three signers, each one of whom pronounced 50 different signs. We added three other signers to pronounce the days of the week in sign language, improving the accuracy of the pretraining. These videos served as added data to the ArSL dataset. The key point was extracted by using matplotlib, the key point values for training and testing were collected, and the data were preprocessed through feature extraction and batch normalisation. The signs were recognised in the Arabic language. The proposed model detects the sign language action and recognition according to the dataset training, which was achieved building and training by using the LSTM neural network and CNN. The accuracy rating, F-1 score, recall and precision of the proposed model for ArSL were 95%, 95%, 95% and 96%, respectively, with 126 epochs and batch normalisation equal to 30. Ultimately, the proposed model was able to recognise and detect ArSL accurately.
Conveyor belt systems facilitate the movement of goods and materials, making them an essential part of various fields. Deep learning control methods are therefore indispensable to assure their energy efficiency and optimal motor speed. Here, training is conducted via a convolutional neural network with transfer learning using VGG16 base net. Feature extraction layers are fixed, whilst fully connected layers are replaced. Thirty-three appropriate object classes are considered for classification, and images are acquired using an overhead camera. The obtained weight values are transmitted to Arduino that uses these data to control the stepper motor speed for optimal performance and minimum energy consumption, achieving approximate estimates of weight and efficient control of the motor. This work integrates the following hardware components: an Arduino microcontroller, a NEMA17 stepper motor, a GY-219 digital current sensor (INA219), an L298N motor driver and a high-resolution camera that processes images using Python with TensorFlow and OpenCV. These components enable the conveyor belt system to realise precise classification and instantaneous regulation of objects. Experimental results show the effectiveness of the proposed system in energising and managing the conveyor belt with an accuracy of 99.47% and a loss of 0.04874, highlighting how we can use the developed system to maximise energy economy and classification accuracy. For academics, engineers and business leaders, this study provides important new perspectives on the current scene and expected future developments in this expanding field.
Breast cancer as such, is one of the two fallaciously debilitating diseases known to woman (the other being cardiac disease) so in that context and with some qualifications it really shouldn't come as that much of a surprise. The concern, however, is: Yes, you could do it right — early detection is our nearest weapon to the Silent Killer (cancer). However, standard diagnostics are far from 100% accurate, can be mislead by virtually any kind of fake and often miss occasions that cry for assistance but which have not yet proceeded to the malignancy that will eventually confirm. More recently you would see the triggers of real momentum with machine learning(ML) one of the last great fortresses where the majority of frontier artificial intelligence (AI) resides. ML can also be leveraged to guide the accuracy and precision of a breast cancer diagnosis when considering a dataset carefully that includes patient data. The aim in this study was to compare three popular supervised ML algorithms on Logistic Regression (LR), Support Vector Machines (SVM) and K-Nearest Neighbors (KNN). In this domain, the main objective was to effectively categorize malignant breast depending on the benign breast tissue of widely used Wisconsin Breast Cancer Dataset (WBCD) features. An accurate extraction and integration of records belonging to 699 patients was performed for data formatting suitable for analysis. All the required cleaning of data was performed first. This is key to building reliable and credible outcomes, we standardized our functions in a thoroughgoing manner before feeding the model. For feature selection, Gain Ratio is used. Most data points can be covered in a fair manner and performance has to be squeezed up fully. It is an old-school style but it fits our case perfectly: we keep about 30% of records as test samples and the rest (70%) for training (so, we split by 70/30). With each algorithm you performed a full 10-fold cross validation. No stone unturned! We took a fairly wide range of metrics. You covered sensitivity and specificity but what got the most play was with benchmarked net accuracy rates, much less so about false positives/negatives. When we went to so much trouble with other parts of it, it seems an odd time to write off Precision or AUC-ROC as adjuncts to fill in this sector of the picture. They made use of ANOVA to provide statistical comparison among those sets of algorithms that were tested against the data — with a McNemar's test functioning as an objective measuring stick. Now the exciting part began, SVM model absolutely jumped to end up at amazing 97.1% accuracy and 0.994 on AUC score! Logistic Regression followed, with 96.4% accuracy(AUC=0.992), KNN takes the third place with a combined score of 95.6%(AUC=0.978).
