Chaman Verma

Neuro-imaging data is necessary for timely and correct diagnosis of the disease Alzheimer’s (AD). Early clinical intervention is challenging due to the high-dimensional nature of the images, making it computationally difficult to work on representations, sparse annotated datasets and inter-subject variance. Deep convolutional VGG, ResNet, DenseNet, MobileNet and other neural networks (CNNs) have shown good performance. Diagnostic performance, but they are based on a high number of parameters and require highly trained personnel. Data can often times restrict resilience to data-skimy clinical settings. To address these challenges, the current paper presents a hybrid Quantum Machine Learning (QML) architecture for Alzheimer’s disease Detection and classification based on a combination of classical CNN-based feature extraction and Variational quantum classifier. In the suggested method, diseased, relevant, and compact features are extracted using pre-trained CNN backbones. Data from structural MRI scans is later translated into quantum states and computed. With parameterised variational quantum circuits optimised with a hybrid quantum-classical learning loop. The proposed method has been experimentally evaluated on benchmark datasets of MRI scans of Alzheimer’s patients. The hybrid CNNQML model has a mean classification accuracy of about 93% which is similar to deep CNN models, including ResNet and DenseNet (94%), and much more. Beating lightweight models like MobileNet and shallow CNNs by six percentage points. It is worth noting that the hybrid QML model performs better in conditions with limited training data. Performance degradation is observed in accuracy and AUC for all CNN variants, with up to 5% lower performance to deep CNNs. Cross-validation, confidence interval, paired significance test, and statistical validation. Effect size analysis proves the fact that the hybrid model produces statistically significant performance, as well as cases of clinically meaningful improvements over lightweight CNN baselines. Similar to the state-of-the-art deep networks with significantly reduced trainable parameters in the classification stage. These findings show that hybrid QML is a data-efficient and powerful substitute in the diagnosis of Alzheimer’s. On the whole, this is a work that sets a practical way to integrate quantum machine learning into clinical neuro-imaging pipelines and hybrid settings. QML has supplementary deep-learning enhancement to classics in next-generation Alzheimer’s Decision-support systems.

The fast growth of Industry 4.0 and Industry 5.0 is causing an increasing need for high-quality electronics and, consequently, defect-free Printed Circuit Boards (PCBs). Traditional computer vision-based inspection systems cannot meet modern manufacturing environment demands since these systems fail to meet the requirements of real-time processing. The present paper provides a review and analysis of existing PCB defect detection technologies, paying attention to four areas: deep learning-based defect detection algorithms, specialised computing platforms, hardware acceleration approaches, and their hybrids. Among the available deep learning approaches, the You Only Look Once (YOLO) framework stands out as a high-performing method, achieving high detection accuracies while enabling real-time defect detection. The specialized computing platforms, particularly Field-Programmable Gate Arrays (FPGAs), have demonstrated considerable potential due to their low-latency processing capabilities and high energy efficiency. However, several challenges remain unresolved, including limited generalization across different defect categories, the scarcity of adequately annotated datasets, and insufficient validation under real-world operational conditions. To overcome these challenges, this paper suggests adopting a comprehensive framework that combines image acquisition, image pre-processing, deep learning-based defect detection, and an adaptive system updating process. Further directions for future studies may include utilizing physics-based modeling for better defect representation, improving model explainability via data-centric methods, and exploring the concept of model-less or self-adapting neural networks. This will enable the development of highly efficient and real-time PCB quality inspection frameworks for use in industry.

Optimization search algorithms plays a crucial role in resolving complex constrained engineering and Multi Layer Perceptron (MLP) classification problems, often requiring efficacious and robust search strategies. This study introduces the Chaotic Zebra Optimization Algorithm (CZOA), an enriched version of the classical Zebra Optimization Algorithm (ZOA), incorporating chaotic maps to enhance search efficiency and convergence speed. The CZOA variants (CZOA1, CZOA2, CZOA3, and CZOA4) are evaluated using 23 standard benchmark functions across multiple dimensions (10D, 30D, 50D, and 100D), the CEC 2022 benchmark test suite, and three constrained engineering optimal design problems (three-bar truss, pressure vessel, and multi-disk clutch optimization). Furthermore, CZOA’s effectiveness is evaluated on four MLP classification datasets (Balloon, Iris, Breast Cancer, and XOR dataset). Simulation results reveal that CZOA1 consistently outperforms other variants and classical ZOA, achieving the lowest fitness values, lowered standard deviations, and enriched convergence rates. Specifically, in F1 (10D), CZOA1 reduces computation time from 4559.9 s (ZOA) to just 0.1563 s, achieving faster speed up. In CEC 2022 functions test suit, CZOA1 delivers the best performance with an average fitness of 333.86 and a standard deviation of 0.69, surpassing state-of-the-art metaheuristics such as GWO, WOA, and TLBO. Engineering optimal design results confirm CZOA1’s superior fitness values, with 263.8965 in the three-bar truss problem and 6849.485 in pressure vessel optimization, outperforming classical methods like GWO, DE, and PSO. For MLP classification tasks, CZOA1 achieves a 99.00% classification rate in breast cancer detection, highlighting its superior ability in handling high-dimensional search spaces. Comparative evaluations demonstrate that chaotic maps significantly enhance exploration–exploitation balance, reduce computational search complexity, and improve solution accuracy. The findings establish CZOA as a promising optimization technique for real-world engineering and classification challenges.

Skin diseases frequently cause mental and physical distress and are major global health concern. Because early detection is crucial to successful treatment, accurate diagnosis is challenge for dermatologists as well. Diagnostic accuracy could be significantly enhanced using methods like machine learning (ML) and deep learning (DL). However, substantial datasets are required for these models to make accurate predictions. The healthcare providers frequently encounter data shortages, and privacy regulations restrict data sharing. A privacy-preserving federated transfer learning for diagnosing skin diseases which incorporate four key strategies to enhance effectiveness. The transfer learning is used to train a model with dense neural network (DNN) for skin diseases detection. The feature extraction is performed using pre-trained architectures and DNN is used for classification. The federated learning (FL) replaces the transfer learning to train the model across distributed nodes with the DNN used to disease detection. The FL is combined with transfer learning to build a cohesive ecosystem where data privacy is maintained. The model performance was validated on both IID and non-IID database, with the proposed feature extraction with federated learning model achieving cross validation accuracy of 99.528% and 99.689% for IID and non-IID database, respectively. Results indicate that feature extraction with FL model can produce efficient, lightweight models—well-suited for resource-constrained devices—while ensemble learning enhances edge device performance, offering a powerful and privacy-preserving solution for skin disease diagnosis in modern healthcare.

The evolution of lane-keeping assistance systems (LKAS) has significantly contributed to enhancing road safety, driving comfort, and reducing driver workload. The automotive sector is moving towards a safer driving experience with autonomous agricultural vehicles in the field. The article intends to design and develop a system capable of detecting agricultural lanes and giving appropriate steering control outputs. The research aim is to build a system of Level-2 autonomous agricultural vehicle facilities for advanced driver assistance systems, and allow the driver to assist the vehicle in the left and right lane turns. The experimental model uses image processing to detect lanes on roads. The experimental results show that the LKAS model output is derived from an entry speed of 30 km/h to 60 km/h, a controller with a prediction horizon of 1.75 s to 2.5 s, and a control horizon of 0.5 to 0.75 s on a 3 km straight test track. The proposed approach has a 14% better performance in terms of execution time for steering inputs applied by the driver.