Pioneering AI Solutions for Cancer Subtype Classification through Gene Expression

Reliability analysis of slope stability by neural network, principal component analysis, and transfer learning techniques

Transfer learning in environmental remote sensing

Background: To provide ease to diagnose that serious sickness multi-technique model is proposed. Data Analytics and Machine intelligence are involved in the detection of various diseases for human health care. The computer is used as a tool by experts in the medical field, and the computer-based mechanism is used to diagnose different diseases in patients with high Precision. Due to revolutionary measures employed in Artificial Neural Networks (ANNs) within the research domain in the medical area, which appear to be in the data-driven applications usually described in the domain of health care. Cardio sickness according to name is a type of an ailment that is directly connected to the human heart and blood circulation setup, so it should be diagnosed on time because the delay of diagnosing of that disease may lead the sufferer to death. The research is mainly aimed to design a system that will be able to detect cardiovascular sickness in the sufferer using machine learning approaches. Objective: The main objective of the research is to gather information of the six parameters that is age, chest pain, electrocardiogram, systolic blood pressure, fasting blood sugar and serum cholesterol are used by Mamdani fuzzy expert to detect cardiovascular sickness. To propose a type of device which will be successfully used in overcoming the cardiovascular diseases. This proposed model Diagnosis Cardiovascular Disease using Mamdani Fuzzy Inference System (DCD-MFIS) shows 87.05 percent Precision. To delineate an effective Neural Network Model to predict with greater precision, whether a person is suffering from cardiovascular disease or not. As the ANN is composed of various algorithms, some will be handed down for the training of the network. The main target of the research is to make the use of three techniques, which include fuzzy logic, neural network, and deep machine learning. The research will employ the three techniques along with the previous comparisons, and given that, the results will be compared respectively. Methods: Artificial neural network and deep machine learning techniques are applied to detect cardiovascular sickness. Both techniques are applied using 13 parameters age, gender, chest pain, systolic blood pressure, serum cholesterol, fasting blood sugar, electrocardiogram, exercise including angina, heart rate, old peak, number of vessels, affected person and slope. In this research, the ANN-based research is one of the algorithms collections, which is the detection of cardiovascular diseases, is proposed. ANN constitutes of many algorithms, some of the algorithms are employed in the paper for the training of the network used, to achieve the prediction ratio and in contrast of the comparison of the mutual results shown. Results: To make better analysis and consideration of the three frameworks, which include fuzzy logic, ANN, Deep Extreme Machine Learning. The proposed automated model Diagnosis Cardiovascular Disease includes Fuzzy logic using Mamdani Fuzzy Inference System (DCD-MFIS), Artificial Neural Network (DCD–ANN) and Deep Extreme Machine Learning (DCD–DEML) approach using back propagation system. These frameworks help in attaining greater precision and accuracy. Proposed DCD Deep Extreme Machine Learning attains more accuracy with previously proposed solutions that are 92.45%. Conclusion: From the previous comparisons, the propose automated Diagnosis of Cardiovascular Disease using Fuzzy logic, Artificial Neural Network, and deep extreme machine learning approaches. The automated systems DCDMFIS, DCD–ANN and DCD–DEML, the framework proposed as effective and efficient with 87.05%, 89.4% and 92.45 % success ratios respectively. To verify the performance which lies in the ANNs and computational analysis, many indicators determining the precise performance were calculated. The training of the neural networks is made true using the 10 to 20 neurons layers which denote the hidden layer. DEML reveals and indicates a hidden layer containing 10 neurons, which shows the best result. In the last, we can conclude that after making a consideration among the three techniques fuzzy logic, Artificial Neural Network and Proposed DCD Deep Extreme Machine, the Proposed DCD Deep Extreme Machine Learning based solution give more accuracy with previously proposed solutions that are 92.45%.

<p class="VARAbstract">The growing availability of three-dimensional (3D) data, such as point clouds, coming from Light Detection and Ranging (LiDAR), Mobile Mapping Systems (MMSs) or Unmanned Aerial Vehicles (UAVs), provides the opportunity to rapidly generate 3D models to support the restoration, conservation, and safeguarding activities of cultural heritage (CH). The so-called scan-to-BIM process can, in fact, benefit from such data, and they can themselves be a source for further analyses or activities on the archaeological and built heritage. There are several ways to exploit this type of data, such as Historic Building Information Modelling (HBIM), mesh creation, rasterisation, classification, and semantic segmentation. The latter, referring to point clouds, is a trending topic not only in the CH domain but also in other fields like autonomous navigation, medicine or retail. Precisely in these sectors, the task of semantic segmentation has been mainly exploited and developed with artificial intelligence techniques. In particular, machine learning (ML) algorithms, and their deep learning (DL) subset, are increasingly applied and have established a solid state-of-the-art in the last half-decade. However, applications of DL techniques on heritage point clouds are still scarce; therefore, we propose to tackle this framework within the built heritage field. Starting from some previous tests with the Dynamic Graph Convolutional Neural Network (DGCNN), in this contribution close attention is paid to: i) the investigation of fine-tuned models, used as a transfer learning technique, ii) the combination of external classifiers, such as Random Forest (RF), with the artificial neural network, and iii) the evaluation of the data augmentation results for the domain-specific ArCH dataset. Finally, after taking into account the main advantages and criticalities, considerations are made on the possibility to profit by this methodology also for non-programming or domain experts.</p><p>Highlights:</p><ul><li><p>Semantic segmentation of built heritage point clouds through deep neural networks can provide performances comparable to those of more consolidated state-of-the-art ML classifiers.</p></li><li><p>Transfer learning approaches, as fine-tuning, can considerably reduce computational time also for CH domain-specific datasets, as well as improve metrics for some challenging categories (i.e. windows or mouldings).</p></li><li><p>Data augmentation techniques do not significantly improve overall performances.</p></li></ul>

Breast cancer (BC) continues to be a major global health concern, with rising incidence rates each year. Timely identification is essential for enhancing patient outcomes, but conventional diagnostic techniques often fall short in terms of precision and effectiveness. This review explores the role of artificial intelligence (AI) and machine learning in transforming BC detection, with a focus on advancements up to 2024. A thorough review of recent studies was conducted, emphasizing the application of machine learning in BC detection across diverse data sources, including microarray data, medical imaging such as mammography, ultrasound, (Magnetic Resonance Imaging) (MRI), and histopathology, and clinical records. The analysis traces the progression from traditional machine learning methods to sophisticated deep learning frameworks, especially convolutional neural networks (CNNs), and assesses their effectiveness in real-world clinical environments. Advances in AI have led to notable gains in diagnostic accuracy, with deep learning models delivering exceptional performance in experimental studies. Hybrid imaging strategies that integrate multiple imaging modalities with AI algorithms have proven particularly effective, especially in detecting abnormalities in dense breast tissue. Innovations like transfer learning and explainable AI have enhanced the adaptability and transparency of these models. Nevertheless, issues related to data quality, computational demands, and the lack of standardized protocols remain unresolved. Although AI-driven detection systems exhibit considerable potential in research contexts, their broader adoption in clinical practice faces several hurdles. Future progress will depend on overcoming challenges such as data standardization, improving model interpretability, and optimizing computational efficiency. Combining AI technologies with established diagnostic practices offers a promising approach to advancing the accuracy and accessibility of BC detection.

Holistic Approaches to Music Genre Classification using Efficient Transfer and Deep Learning Techniques

Artificial Intelligence is radically transforming various fields including the field of medical diagnosis and imaging especially for Computer-Aided Diagnosis (CAD). Automated disease detection from the retina has become increasingly important, especially in ophthalmology, where the eye offers a non-invasive way of visualizing and monitoring the progression of diseases. Early detection of these diseases is essential for preventing irreversible blindness. Although, various research have been carried out in Ghana in the area of artificial intelligence using convolutional neural network and machine learning, there is gap in literature on artificial intelligence focusing on local retinal fundus images using deep transfer learning techniques in Ghana. This study address the gap by using 184 retinal fundus images for patients between the ages of 10-70 years from Ghana using Artificial Intelligence Deep Transfer Learning (AIDL) techniques with the VGG-19 architecture augmentation to prepare them for training, testing, and validation, employing a deep transfer learning algorithm known as Convolutional Neural Network (CNN) due to the image size. After a two-stage classification approach enabled the distinction between healthy and unhealthy retinal images, and subsequently, classifying diverse retinal conditions from the unhealthy images including glaucoma, hypertensive and diabetic retinopathy, as well as chorio retinal and macular changes. The performance of the proposed solution was evaluated using various metrics such as accuracy, precision, recall, and AUC for the binary classification and the deep learning task. The results showed that, the proposed solution achieved high accuracy of 97.31%, precision of 96.85%, recall of 98.06%, and AUC of 0.993. This demonstrates the effectiveness in detecting various retina diseases. This solution enhance significant potential automated retinal disease screening, early diagnosis and tele optometry support services, contributing to the eradication of irreversible blindness especially for low resource communities in Ghana and Africa at large.

Quadrotor unmanned aerial vehicles (UAVs) have been developed and applied into several types of workplaces, such as warehouses, which usually involve human workers. The co-existence of human and UAVs brings new challenges to UAVs: potential failure of UAVs may cause risk and danger to surrounding human. Effective and efficient detection of such failure may provide early warning to the surrounding human workers and reduce such risk to human beings as much as possible. One of the most common reasons that cause the failure of the UAV's flight is the physical damage to the propellers. This paper presents a method to detect the propellers damage only based on the audio noise caused by the UAV's flight. The diagnostic model is developed based on convolutional neural network (CNN) and transfer learning techniques. The audio data is collected from the UAVs in real time, transformed into the time-frequency spectrogram, and used to train the CNN-based diagnostic model. The developed model is able to detect the abnormal features of the spectrogram and thus the physical damage of the propellers. To reduce the data dependence on the UAV's dynamic models and enable the utilization of the training data from UAVs with different dynamic models, the CNN-based diagnostic model is further augmented by transfer learning. As such, the refinement of the well-trained diagnostic model ground on other UAVs only requires a small amount of UAV's training data. Experimental tests are conducted to validate the diagnostic model with an accuracy of higher than 90%.

Brain tumor detection and classification using medical imaging has become a critical area of research in healthcare informatics. This comprehensive review examines recent advances in machine learning and deep learning methodologies applied to automated brain tumor detection and segmentation from magnetic resonance imaging (MRI) scans. Thirty state-of-the-art approaches are examined and organized into four categories: traditional machine learning, deep learning, transfer learning, and hybrid approaches. Traditional methods leveraging Support Vector Machines (SVM), Random Forests, and feature extraction techniques such as Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA) demonstrate effectiveness in smaller datasets. Deep learning approaches, particularly Convolutional Neural Networks (CNNs), U-Net architectures, and Capsule Networks, have shown superior performance in complex tumor segmentation tasks. Transfer learning using pre-trained models like ResNet, VGG, and Inception has proven particularly effective for limited medical datasets. This review identifies key challenges including dataset limitations, computational complexity, and generalization across different imaging protocols, while highlighting promising directions for future research in multimodal learning, explainable AI, and clinical deployment strategies.

In this paper, an approach leveraging a shared embedding layer-based quantum long short-term memory (QLSTM) neural network and transfer learning technique is proposed to enhance the accuracy of lithium-ion battery (LIB) pack degradation predictions. The model is especially applied for lithium-ion battery (LIB) aging prognosis when there is limited real-time training data. The framework integrates various methods, including a shared linear embedding layer, QLSTM algorithm, and sliding window techniques. It employs a QLSTM with embedding layers to compress feature dimensions and make predictions. The model is trained offline using historical pack capacity degradation data from a source domain (LIB1) and then transferred and fine-tuned to a target domain (LIB2). Comparative experiments demonstrate the robustness of the proposed model, with the root mean square errors (RMSEs) of pack LIB capacity predictions consistently below 0.002 as the training data increases. The proposed approach exhibits promising results in improving the accuracy of pack capacity degradation predictions for LIB aging prognosis.

Transfer learning for deep neural networks is the process of first training a\nbase network on a source dataset, and then transferring the learned features\n(the network's weights) to a second network to be trained on a target dataset.\nThis idea has been shown to improve deep neural network's generalization\ncapabilities in many computer vision tasks such as image recognition and object\nlocalization. Apart from these applications, deep Convolutional Neural Networks\n(CNNs) have also recently gained popularity in the Time Series Classification\n(TSC) community. However, unlike for image recognition problems, transfer\nlearning techniques have not yet been investigated thoroughly for the TSC task.\nThis is surprising as the accuracy of deep learning models for TSC could\npotentially be improved if the model is fine-tuned from a pre-trained neural\nnetwork instead of training it from scratch. In this paper, we fill this gap by\ninvestigating how to transfer deep CNNs for the TSC task. To evaluate the\npotential of transfer learning, we performed extensive experiments using the\nUCR archive which is the largest publicly available TSC benchmark containing 85\ndatasets. For each dataset in the archive, we pre-trained a model and then\nfine-tuned it on the other datasets resulting in 7140 different deep neural\nnetworks. These experiments revealed that transfer learning can improve or\ndegrade the model's predictions depending on the dataset used for transfer.\nTherefore, in an effort to predict the best source dataset for a given target\ndataset, we propose a new method relying on Dynamic Time Warping to measure\ninter-datasets similarities. We describe how our method can guide the transfer\nto choose the best source dataset leading to an improvement in accuracy on 71\nout of 85 datasets.\n

Spectral computed tomography (CT) material decomposition algorithms require accurate physics-based models or empirically derived models. This study investigates a machine learning algorithm and transfer learning techniques for Spectral CTimaging of K-edge contrast agents using simulated and experimental measurements. A feed forward multilayer perceptron was implemented and trained on data acquired using a step wedge phantom containing acrylic, aluminum, and gadolinium materials. The neural network estimator was evaluated by scanning a rod phantom with varying dilutions of gadolinium oxide nanoparticles and by scanning a rat leg specimen with injected nanoparticles on a bench-top photon-counting computed tomography system. The algorithm decomposed each spectral projection measurement into path lengths of acrylic and aluminum and mass lengths of gadolinium. Each basis material sinogram was reconstructed into basis material images using filtered backprojection. Machine learning techniques of data standardization, transfer learning from aggregated pixel data, and transfer learning from simulations were investigated to improve image quality. The algorithm was compared to a previously published empirical method based on a linear approximation and calibration error look-up tables. The combined transfer learning techniques did not improve quantification in the rod phantom and provided only a small qualitative improvement in ring artifacts. Transfer learning from aggregated pixel data and from simulations improved the qualitative image quality of the rat specimen, for which the calibration data were limited. Transfer learning from aggregated pixel data andsimulations estimated 3.26, 6.26, and 12.45mg/mL Gd concentrations compared to true 2.72, 5.44, and 10.88mg/mL concentrations in the rod phantom. Additionally, the neural networks were able to separate the soft tissue, bone, and gadolinium nanoparticles of the ex vivo rat leg specimen into the different basis images. The results demonstrate that empirical K-edge imaging from calibration measurements using machine learning and transfer learning is possible without explicit models of material attenuations, incident spectra, or the detector response.

The fast-evolving nature of urbanization and its complex patterns require precise and interpretable machine learning models to effectively predict urban growth. To address this challenge, this study introduces a novel framework combining Hybrid Intelligence and Explainable AI (XAI), specifically Shapley Additive Explanations (SHAP) to improve model performance, robustness, and transparency. Using a weighted ensemble technique, the proposed method systemically integrates linear, tree-based, and neural network models to propose a hybrid of Elastic Net, XGBoost, and Wide & Deep Neural Network (EN-XGB-WDN) frameworks for urban growth prediction. The methodology follows a multistep approach and includes the development of the hybrid model, its evaluation for binary classification, integration of SHAP-based feature analysis to identify key drivers of urban growth and improve model interpretability, retraining of the hybrid model to increase accuracy and reduce overfitting, and validation of the proposed framework using standard evaluation metrics including accuracy, precision, recall, F1 score, and AUC. The hybrid model achieves an overall accuracy of 87.34%, a weighted F1-score of 87.18%, and an AUC of 0.9442. The SHAP analysis revealed that Drive Time (DT), Distance from Roads (DfR), and Elevation are the most impactful features to understand the dynamics of urban growth. The findings revealed how variations in specific features, such as higher DT and lower DfR, significantly affect urban growth probabilities. The hybrid model also categorized urban growth probabilities into five classes: very low (40.62%), low (23.27%), moderate (15.38%), high (12.10%), and very high (8.63%), revealing spatial patterns of urban expansion. The framework combines hybrid ensemble methods with SHAP-based explanations to significantly enhance the predictive and explanatory power of urban growth models compared to the limitations of traditional approaches. This study highlights the efficiency of integrating hybrid machine learning and Explainable AI to understand and predict complex urbanization dynamics. The outcomes offer actionable insights for policymakers and urban planners, facilitating data-driven strategies for sustainable urban development. This research demonstrates the effectiveness of hybrid intelligence coupled with Explainable AI, offering a scalable and interpretable framework to better understand and predict urbanization patterns.

Image identification is a useful tool for classifying and organizing fruits in agribusiness. This study aims to use deep learning to construct a design for Sapodilla identification and classification. Sapodilla comes in a various of varieties from throughout the world. Sapodilla can come in different sizes, form, and taste depending on species and kind. The goal is to create a system which uses convolutional neural networks and transfer learning to extract the feature and determine the type of Sapodilla. The system can sort the type of Sapodilla. This research uses a dataset including over 1000 pictures to demonstrate four different types of Sapodilla classification approaches. This assignment was completed using Convolutional Neural Network (CNN) algorithms, a deep learning technology widely utilised in image classification. Deep learning-based classifiers have recently allowed to distinguish Sapodilla from various images. Furthermore, we utilized different versions of hidden layer and epochs for various outcomes to improve predictive performance. We investigated transfer learning approaches in the classification of Sapodilla in the suggested study. The suggested CNN model improves transfer learning techniques and state-of-the-art approaches in terms of results.

Electroencephalography-Based Machine and Deep Learning Approaches for the Diagnosis of Dissociative Disorders: A Comprehensive Review