In recent years, both machine learning and computer vision have seen growth in the use of multi-label categorization. SMOTE is now being utilized in existing research for data balance, and SMOTE does not consider that nearby examples may be from different classes when producing synthetic samples. As a result, there can be more class overlap and more noise. To avoid this problem, this work presented an innovative technique called Adaptive Synthetic Data-Based Multi-label Classification (ASDMLC). Adaptive Synthetic (ADASYN) sampling is a sampling strategy for learning from unbalanced data sets. ADASYN weights minority class instances by learning difficulty. For hard-to-learn minority class cases, synthetic data are created. Their numerical variables are normalized with the help of the Min-Max technique to standardize the magnitude of each variable’s impact on the outcomes. The values of the attribute in this work are changed to a new range, from 0 to 1, using the normalization approach. To raise the accuracy of multi-label classification, Velocity-Equalized Particle Swarm Optimization (VPSO) is utilized for feature selection. In the proposed approach, to overcome the premature convergence problem, standard PSO has been improved by equalizing the velocity with each dimension of the problem. To expose the inherent label dependencies, the multi-label classification ensemble of Adaptive Neuro-Fuzzy Inference System (ANFIS), Probabilistic Neural Network (PNN), and Clustering-Based Decision tree methods will be processed based on an averaging method. The following criteria, including precision, recall, accuracy, and error rate, are used to assess performance. The suggested model’s multi-label classification accuracy is 90.88%, better than previous techniques, which is PCT, HOMER, and ML-Forest is 65.57%, 70.66%, and 82.29%, respectively.

In this paper, the author introduces the Neural-ODE Hybrid Block Method, which serves as a direct solution for solving higher-order ODEs. Many single and multi-step methods employed in numerical approximations lose their stability when applied in the solution of higher-order ODEs with oscillatory and/or exponential features, as in this case. A new hybrid approach is formulated and implemented, which incorporates both the approximate power of neural networks and the stability and robustness of block numerical methods. In particular, it uses the ability of the neural networks to approximate the solution spaces, utilizes the block method for the direct solution of the higher-order ODEs and avoids the conversion of these equations into a system of the first-order ODEs. If used in the analysis, the method is capable of dealing with several dynamic behaviors, such as stiff equations and boundary conditions. This paper presents the mathematical formulation, the architecture of the employed neural network and the choice of its parameters for the proposed hybrid model. In addition, the results derived from the convergence and stability analysis agree that the suggested technique is more accurate compared to the existing solvers and can handle stiff ODEs effectively. Numerical experiments with ordinary differential equations indicate that the method is fast and has high accuracy with linear and nonlinear problems, including simple harmonic oscillators, damped oscillatory systems and stiff nonlinear equations like the Van der Pol equation. The advantages of this approach are thought to be generalized to all scientific and engineering disciplines, such as physics, biology, finance, and other areas in which higher-order ODEs demand more precise solutions. The following also suggests potential research avenues for future studies as well: prospects of the proposed hybrid model in the multi-dimensional systems, application of the technique to the partial differential equations (PDEs), and choice of appropriate neural networks for higher efficiency.

Chronic kidney disease (CKD) is a global public health concern, and the timely detection of the disease is priceless. Most of the classical machine learning models have the major drawbacks of being unsophisticated, non-robust, and non-accurate. This research work is therefore seeking to introduce OptiNet-CKD, a paradigm based on a DNN that has been integrated with a developed population optimization algorithm (POA) for CKD prediction optimization. POA is unlike gradient-based optimization methods in that it uses an initialized population of networks and perturbs their weight values to provide a broader exploration of the solution space. The model is more robust and less likely to overfit, and the predictions are likely to be more accurate since this approach helps to avoid the local minima problem suffered by gradient-based optimizers. To preprocess it for DNN learning, a CKD dataset with 400 records containing numerical and categorical features was imputed for missing data and scaled for its features. The model was evaluated using performance metrics such as accuracy, precision, recall, F1-score, and ROC AUC. OptiNet-CKD achieved 100% accuracy, 1.0 precision, 1.0 recall, 1.0 F1-score, and 1.0 ROC-AUC from traditional models (logistic regression, decision trees) and even fundamental deep neural networks. Results show that OptiNet-CKD is a reliable and robust prediction method for CKD, with more substantial generalization and performance than the existing methods. A combination of DNN and POA constitutes a promising approach for medical data analysis, especially for the diagnosis of CKD. POA expands the solution space, helping to expunge the model from falling into local minima and giving the model increased power in generalizing complicated medical data. Based on the simplicity of the algorithm, together with the structured formula and the extractions made in the preprocessing step, this framework can be extended to other medical conditions with similar data complexities, providing a potent tool for improving diagnostic accuracy in healthcare.

The Smart City (SC) framework has garnered global recognition for its transformative influence on society through innovative solutions. However, the extensive use of Internet of Things (IoT) devices in SCs raises concerns regarding electronic waste and resource consumption. Addressing this challenge necessitates integrating smart grid systems to safeguard SC residents’ environment and well-being. Accurate air quality prediction is essential for informed societal decisions, safe transportation, and disaster preparedness. This study introduces a novel approach: Towards Cleaner Industries: Smart Cities’ Impact on Predictive Air Quality Management (SPAM). The SPAM model utilizes a bidirectional stacking mechanism of long short-term memory neural networks, considering spatiotemporal correlations to forecast future air pollutant concentrations. Surpassing conventional methods, SPAM enhances accuracy while reducing computational complexity. Experimental findings demonstrate enhanced efficiency and accuracy, underscoring its practicality in industrial contexts. The SPAM model represents a significant advancement in promoting environmental sustainability within the SC framework.

Alzheimer’s disease (AD) is a progressive neurodegenerative disease and the current diagnosis tools, which are used, including clinical examination and neuroimaging, are costly, time consuming and complicated to generalize across demographics. Traditional machine learning (ML) methods such as Support Vector Machines (SVM), LightGBM, and Random Forest (RF) are known to be having weakness in terms of feature selection, class imbalance, and generalization. This paper presents a new Enhanced TabTransformer with Self-Supervised Learning (ETT-SSL) framework of AD classification. The structure combines the transformer-based architecture, self-supervised learning (SSL) for feature representation, SHAP-based feature selection, and the use of SMOTE to balance classes. Experimental results also indicate that ETT-SSL reaches a high accuracy of 95.8%, which is a much better result than baseline models (SVM: 72.1, RF: 78.3, LightGBM: 80.5) and even the standard TabTransformer (85.2%). Additionally, ETT-SSL offers greater accuracy and improved recall, overcoming the issue of false negatives in AD diagnosis. SHAP analysis can be used to improve transparency in clinical decision-making by making use of the model interpretability. The proposed framework recommends a salivary, interpretability and clinically practical methodology that can be used to expand to multimodal data source (e.g. MRI scans, genomic markers and electronic health records) to improve accuracy and generalisability.

In this paper, we introduce QProteoML, a new quantum machine learning (QML) framework for predicting drug sensitivity in Multiple Myeloma (MM) using high-dimensional proteomic data. MM, an extremely heterogeneous condition, displays often mixed responses to treatment, with a large number of patients showing drug resistance to proteasome inhibitors and immune modulatory agents. However, the methods previously used for genomic and proteomic data analysis techniques are plagued by issues of high dimensionality, imbalanced class distribution and feature redundancy, which work against the accurate predictability and generalizability of such methods. These are compounded by the so-called “curse of dimensionality”, with dimensions far outnumbering samples, hence classical model overfitting. In this work, we present QProteoML as an integration of quantum techniques purposefully developed to deal with high-dimensional, imbalanced and redundant data. The framework integrates a combination of Quantum Support Vector Machine (QSVM), Quantum Principal Component Analysis (qPCA), Quantum Annealing (QA) for feature selection and Quantum Generative Adversarial Networks (QGANs) for data augmentation. These quantum algorithms exploit certain quantum phenomena (superposition and entanglement) to perform modelling of nonlinear relationships, dimensionality reduction, and class-imbalance issues. QSVM employs quantum kernels to map data into a higher-dimensional Hilbert space, so that the model can detect complex patterns in MM drug resistance. qPCA reduces dimensionality without loss of important variance, and thus improves computation efficiency. In addition, Quantum Annealing successfully extracts the most informative biomarkers with low redundancy. QProteoML was experimentally tested by comparing accuracy, F1 score and AUC ROC between classical machine learning models such as Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), and K-Nearest Neighbors (KNN). Our results demonstrate that QProteoML performs better than classical models, particularly in identifying the drug resistant minority class of patients. Additionally, the model is interpretable and stresses important biomarkers of drug sensitivity in MM. This research opens the possibility of quantum machine learning in personalised medicine for Multiple Myeloma. It demonstrates that quantum algorithms can perform complex biological data suggesting more reliable and accurate drug sensitivity predictions. Future research will be directed toward clinical validation of the given system with larger and more diverse cohorts of MM patients; the integration of quantum hardware for practical applications.