Analyzing rock and underground layers is known as drill core lithology. The extracted core sample helps not only in exploring the core properties but also reveals the lithology of the entire surrounding area. Automating rock identification from drill cuttings is a key element for efficient reservoir characterization, replacing the current subjective and time-consuming manual process. The recent advancements in computer hardware and deep learning technology have enabled the automatic classification of various applications, and lithology is not an exception. This work aims to design an automated method for rock image classification using deep learning technologies. A novel CNN (Convolution Neural Network) is proposed for lithology classification in addition to thorough comparison with benchmark CNN models. The proposed CNN model has the advantageous of having very low complexity while maintaining high accuracy. Experimental results on rock mages taken from the “digitalrocksportal” database demonstrate the ability of the proposed method to classify three classes, carbonate, sandstone and shale rocks, with high accuracy, and comparisons with related work demonstrated the efficiency of the proposed model, with more than 98% saving in parameters.

Modern systems have been focusing on improving the quality of life for people. Hence, new technologies and systems are currently utilized extensively in different sectors of our societies, such as education and medicine. One of the medical applications is using computer vision technology to help blind people in their daily endeavors and reduce their frequent dependence on their close people and also create a state of independence for visually impaired people in conducting daily financial operations. Motivated by this fact, the work concentrates on assisting the visually impaired to distinguish among Iraqi banknotes. In essence, we employ computer vision in conjunction with Deep Learning algorithms to build a multiclass classification model for classifying the banknotes. This system will produce specific vocal commands that are equivalent to the categorized banknote image, and then inform the visually impaired people of the denomination of each banknote. To classify the Iraqi banknotes, it is important to know that they have two sides: the Arabic side and the English side, which is considered one of the important issues for human-computer interaction (HCI) in constructing the classification model. In this paper, we use a database, which comprises 3,961 image samples of the seven Iraqi paper currency categories. Furthermore, a nineteen layers Convolutional Neural Network (CNN) is trained using this database in order to distinguish among the denominations of the banknotes. Finally, the developed system has exhibited an accuracy of 98.6 %, which proves the feasibility of the proposed model.

Medical imaging is a crucial component of any modern healthcare system, and image classification is a key technique of Computer-Aided Diagnosis (CADx) system that provides accurate detection and treatment for various diseases. With the increasing use of digital medical images, there is a growing concern for their security and protection against unauthorized access, tampering, and copying. However, the impact of watermarking on the accuracy of image classification models, particularly in medical imaging, remains an undiscovered area of research. To the best of our knowledge, this work is one of the pioneering attempts in the field that examines the impact of watermarking on image classification accuracy with deep learning. Within this context, brain tumor and histopathologic cancer datasets are utilized. A Light-weight Conventional Neural Netwok (CNN) is developed, and several CNN models, such as ResNet-50, VGG16, VGG19, DarkNet-19, and AlexNet, are specially tailored to achieve accurate classification. Following the evaluation of the classification performance, a new watermarking scheme based on the Redundant Discrete Wavelet transform (RDWT), Multiresolution Singular Value Decomposition (MSVD), and Arnold Cat Map (ACM) is applied to the images, the classification accuracy of the watermarked testing-sets is subsequently re-evaluated. Results indicated that the proposed watermarking scheme is highly robust as the Normalized Correlation (NC) exceeded 0.9 and up to 1 for some attacks, and imperceptibility is well maintained at optimal levels as the Peak-Signal-to-Noise Ratio (PSNR) ranged from 66 dB and up to a maximum value of nearly 68 dB. Additionally, analyzing the effect of watermarking on classification accuracy indicated that the effect is found to be minimal, and ranged from 0% to 0.15% at most.

Remote monitoring of a patient’s vital activities has become increasingly important in dealing with various medical applications. In particular, machine learning (ML) techniques have been extensively utilized to analyze electrocardiogram (ECG) signals in cardiac patients to classify heart health status. This trend is largely driven by the growing interest in computer-aided diagnosis based on ML algorithms. However, there has been inadequate investigation into the impact of risk factors on heart health, which hinders the ability to identify heart-related issues and predict the conditions of cardiac patients. In this context, developing a GUI-based classification approach can significantly facilitate online monitoring and provide real-time warnings by predicting potential complications. In this paper, a general framework structure for medical real-time monitoring systems is proposed for modeling the vital signs of cardiac patients in order to predict the patient’s status. The proposed approach analyzes AI-driven interventions to provide a more accurate cardiac diagnosis and real-time monitoring system. To further demonstrate the validity of the presented approach, we employ it in a LabVIEW-based remote tracking system to predict three healthcare statuses (stable, unstable non-critical, and unstable critical). The developed monitoring system receives various information about patients’ vital signs, and then it leverages a novel encoding-based machine learning algorithm to pre-process, analyze, and classify patient status. The developed ANN classifier and proposed encoding-based ML model are compared to other conventional ML-based models, such as Naive Bayes, SVM, and KNN for model accuracy evaluation. The obtained outcomes demonstrate the efficacy of the presented ANN and encoding-based ML approaches by achieving an accuracy of $98.4\%$ and $98.8\%$ for the developed ANN classifier and the proposed encoding-based technique, respectively, whereas Naive Bayes and quadratic SVM algorithms realize $94.8\%$ and $96\%$, respectively. In short, this study aims to explore how ML algorithms can enhance diagnostic accuracy, improve real-time monitoring, and optimize treatment outcomes. Meanwhile, the proposed tracking system outperforms most existing monitoring systems by offering high classification accuracy of the heart health status and a user-friendly interactive interface. Therefore, it can potentially be utilized to improve the performance of remote healthcare monitoring for cardiac patients.

Medical diagnostics require brain tumor classification to operate in real-time so the task demands accurate results with efficientprocessing abilities. A new hybrid deep learning solution merges convolutional neural networks (CNNs) with support vector machines(SVMs) to improve classification results as this paper describes. A total of four tumor categories including glioma, meningioma, andpituitary tumors together with no tumor appearance contribute to the magnetic resonance imaging (MRI) dataset are used for analysis. We applied and organized three pre-trained deep learning models: Alex-Net, DarkNet-19, and ResNet-50 for comparison. Anewly engineered compact CNN model linked with an SVM classifier brought decreased model dimensions while keeping excellent accuracy rates. Aproposed compact CNN model delivers 97.50% accuracy through its smaller 2.38 MB size and an additional SVM integration results in 97.45% accuracy using 1.43 MB. A Graphical User Interface (GUI) system comprising automated tumor classification capabilities is created to improve real-time systems that visualize MRI scans and illustrate predicted labels in addition to displaying confidence scores. A GUI enables smooth access to the trained model while being suitable for medical practice mobile healthcare environments and edge computing needs. The proposed system shows that lightweight architectures work excellently in real-time system applications especially when used for edge computing and mobile healthcare frameworks. The proposed solution demonstrates superiority over established models through its ability to scale efficiently.

Inverted pendulum systems, such as double or single, rotational or translational inverted pendulums are non-linear and unstable, which have been the most dominant approaches for control systems. The double inverted pendulum is one kind of a non-linear, unstable system, multivariable, and strong coupling with a wide range of control methods. To model these types of systems, many techniques have been proposed so that motivating researchers to come up with new innovative solutions. The Linear Quadratic Regulator (LQR) controller has been a common controller used in this field. Meanwhile, the Artificial Bee Colony (ABC) technique has become an alternative solution for employing Bee Swarm Intelligence algorithms. The research solutions of the artificial bee colony algorithm in the literature can be beneficial, however, the utilization of discovered sources of food is ineffective. Thus, in this paper, we aim to provide a double inverted pendulum system for stabilization by selecting linear quadratic regulator parameters using a bio-inspired optimization methodology of artificial bee colony and weight matrices Q and R. The results show that when the artificial bee colony algorithm is applied to a linear quadratic regulator controller, it gains the capacity to autonomously tune itself in an online process. To further demonstrate the efficiency and viability of the suggested methodology, simulations have been performed and compared to conventional linear quadratic regulator controllers. The obtained results demonstrate that employing artificial intelligence (AI) together with the proposed controller outperforms the conventional linear quadratic regulator controllers by more than 50 % in transient response and improved time response and stability performance