Friction stir welding (FSW) is increasingly utilized in aerospace for welding dissimilar Al–Mg alloys without melting, overcoming fusion welding challenges. This summary highlights FSW’s key aspects for dissimilar Al–Mg alloys and its aerospace relevance. These alloys are widely used in aerospace due to their beneficial properties, but fusion welding faces issues like brittle intermetallic compounds (IMC) and decreased mechanical properties. FSW addresses these challenges by using a rotating tool to generate frictional heat, plasticizing the material for solid-state joining without melting. This reduces IMC formation, enhancing joint strength and mechanical properties. Critical parameters like rotational speed, traverse speed, tool design, and process variables are emphasized for optimal FSW of dissimilar Al–Mg alloys. Joining these alloys is crucial in aerospace for applications such as aircraft structures, engine components, and fuel tanks. FSW offers advantages like weight reduction, improved fuel efficiency, and structural integrity enhancement. It allows welding dissimilar Al–Mg alloys with varying compositions for tailored material combinations meeting specific needs. In conclusion, FSW of dissimilar aluminum alloys is promising for aerospace, creating defect-free joints with improved mechanical properties. However, further research is needed to optimize parameters, explore tool designs, and validate long-term performance in aerospace environments.

Alzheimer’s disease (AD), a prevalent neurodegenerative disorder, leads to progressive dementia, which impairs decision-making, problem-solving, and communication. While there is no cure, early detection can facilitate treatments to slow its progression. Deep learning (DL) significantly enhances AD detection by analyzing brain imaging data to identify early biomarkers, improving diagnostic accuracy and predicting disease progression more precisely than traditional methods. In this article, we propose an ensemble methodology for DL models to detect AD from brain MRIs. We trained an enhanced Xception architecture once to produce multiple snapshots, providing diverse insights into MRI features. A decision-level fusion strategy was employed, combining decision scores with a RF meta-learner using a blending algorithm. The efficacy of our ensemble technique is confirmed by the experimental findings, which categorize Alzheimer’s into four groups with 99.14% accuracy. This methodology may help medical practitioners provide patients with Alzheimer’s with individualized care. Subsequent efforts will concentrate on enhancing the model’s efficacy via its generalization to a variety of datasets.

Driver fatigue is one of the most common causes of road accidents, which means that there is a great need for robust and adaptive monitoring systems. Current models of fatigue detection suffer from domain-specific limitations in generalizing across diverse environments, sensor variability, and individual differences. Moreover, they are not resilient to real-time sensor quality issues or missing data, which limits their practical applicability. To overcome the aforementioned challenges, we propose a holistic Dynamic Cross-Domain Transfer Learning framework for fatigue monitoring application using multi-modal sensor data fusion. There are four innovations involved with this framework. Firstly, the domain adversarial neural network in EEG, ECG, and video inputs ensures cross-domain invariance of features. The gap of adaptation at the domain goes below 5%, while there is an improvement of the cross-domain accuracy to as high as 15% from 10%. The ASF-Transformer uses adaptive cross-modal attention for fusing heterogeneous sensor data effectively. Accuracy improves by 5–8% and remains robust under modality dropout conditions. Third, the GMSN dynamically evaluates sensor quality and selectively enables modalities to mitigate performance drops to < 5% even with noisy or missing inputs in process. Fourth, Online Personalized Fine-Tuning (OPFT) allows for real-time adaptation of the model to individual drivers, achieving an improvement in accuracy by 5–7% within 2 h with a latency of < 50ms. Thorough evaluations show that the framework can achieve 85–90% accuracy on target domains while maintaining robustness under 20% sensor dropout. Addressing the issue of domain variability, sensor quality, and personalization, this work has improved the reliability, adaptability, and real-time feasibility of fatigue monitoring systems to provide significant advancements for driver safety in dynamic real-world environments.

In this paper, the impact of different input variables on the performance and emission features of a pongamia pinnata and rapeseed oil biodiesel with n-Butanol additive were investigated, statistically analyzed, and optimized by employing the powerful response surface methodology (RSM) based design of experiment (DOE) techniques. The vegetable oils (pongamia pinnata and rapeseed oils) were transesterified and their corresponding methyl esters were blended with diesel and n-Butanol at blend ratios 10:84:6, 10:78:12, 20:74:6 and 20:68:12. The samples were tested on a direct injection CI engine at a rated speed of 1500 rpm and standard CR of 17.5:1 at different loads. In each test, performance and emission parameters were measured. Expert machine learning (ML) methods were used to forecast these features. In addition, polynomial equations were developed for each blend using regression techniques and compared with an artificial intelligence technique. It was observed that the engine performance increased as biodiesel and additive weight percentage increased. Regardless of the loads placed on the engine and the blend ratios, the use of PPME and RSME combined with n-Butanol blends demonstrated a clear decrease in NOx compared to diesel (7.07% for P20B12 and 6.58% for R20B12). As per the trend, it is seen that the percentage reduction in CO₂ emissions is greater with high percentage increase of n-Butanol in the tested sample irrespective of loads applied on the engine (2.95% more P20B12 for as compared to P20B6). For the emission characteristics, ANN demonstrated a range of 87.92% to 98.83% prediction accuracy while that of regression varies from 81.4% to 98.8% for all the samples of PPME blended biodiesel.

Early detection of colorectal carcinoma (CRC), one of the most prevalent forms of cancer worldwide, significantly enhances the prognosis of patients. This research presents a new method for improving CRC detection using a deep learning ensemble with the Computer Aided Diagnosis (CADx). The method involves combining pre-trained convolutional neural network (CNN) models, such as ADaRDEV2I-22, DaRD-22, and ADaDR-22, using Vision Transformers (ViT) and XGBoost. The study addresses the challenges associated with imbalanced datasets and the necessity of sophisticated feature extraction in medical image analysis. Initially, the CKHK-22 dataset comprised 24 classes. However, we refined it to 14 classes, which led to an improvement in data balance and quality. This improvement enabled more precise feature extraction and improved classification results. We created two ensemble models: the first model used Vision Transformers to capture long-range spatial relationships in the images, while the second model combined CNNs with XGBoost to facilitate structured data classification. We implemented DCGAN-based augmentation to enhance the dataset’s diversity. The tests showed big improvements in performance, with the ADaDR-22 + Vision Transformer group getting the best results, with a testing accuracy of 93.4% and an AUC of 98.8%. In contrast, the ADaDR-22 + XGBoost model had an AUC of 97.8% and an accuracy of 92.2%. These findings highlight the efficacy of the proposed ensemble models in detecting CRC and highlight the importance of using well-balanced, high-quality datasets. The proposed method significantly enhances the clinical diagnostic accuracy and the capabilities of medical image analysis or early CRC detection.

In this study, we examine the assessment of surface roughness on turned surfaces of Ti 6Al 4V using a computer vision system. We utilize the Dual-Tree Complex Wavelet Transform (DTCWT) to break down the images of the turned surface into sub-images oriented in directions. Three different methods of feature generation have been compared, i.e., the use of Gray-Level Co-Occurrence Matrix (GLCM) and DTCWT-based extraction of second-order statistical features, DTCWT Image fusion, and the use of GLCM for feature extraction, and DTCWT image fusion using Particle Swarm Optimization (PSO) based GLCM features. Principal Component Analysis (PCA) was utilized to identify and select features. The model was developed using a Radial Basis Function Neural Network (RBFNN). Accordingly, six models were designed based on the three feature generation methods, considering all features and features selected using PCA. The RBFNN model, which incorporates DTCWT Image fusion and utilizes PSO with PCA features, achieved a training data prediction accuracy of 100% and a test data prediction accuracy of 99.13%.