Syed Ibrar Hussain

In the current paper, an analysis of magnetohydrodynamic Williamson nanofluid boundary layer flow is presented, with multiple slips in a porous medium, using a newly designed human-brain-inspired Ricker wavelet neural network solver. The solver employs a hybrid approach that combines genetic algorithms, serving as a global search method, with sequential quadratic programming, which functions as a local optimization technique. The heat and mass transportation effects are examined through a stretchable surface with radiation, thermal, and velocity slip effects. The primary flow equations, originally expressed as partial differential equations (PDEs), are changed into a dimensionless nonlinear system of ordinary differential equations (ODEs) via similarity transformations. These ODEs are then numerically solved with the proposed computational approach. The current study has significant applications in a variety of practical engineering and industrial scenarios, including thermal energy systems, biomedical cooling devices, and enhanced oil recovery techniques, where the control and optimization of heat and mass transport in complex fluid environments are essential. The numerical outcomes gathered through the designed scheme are compared with reference results acquired through Adam’s numerical method in terms of graphs and tables of absolute errors. The rapid convergence, effectiveness, and stability of the suggested solver are analyzed using various statistical and performance operators.

The current research is a revolution in the field of neural computation as a quite new stochastic technique based on Ricker wavelet neural networks (RWNNs) is developed to analyze the Maxwell fluid (Max-F) boundary layer flow (BLF) with heat and mass transfer effects over an elongating surface. The global and local search solvers used with RWNNs are genetic algorithms (GAs) and sequential quadratic programming (SQP) respectively to design a new algorithm i.e. RWNNs-GASQP. The transformed nonlinear system of ODEs is acquired using the physical model represented by the flow and then solved using RWNNs-GASQP solver. The obtained numerical form results are successfully compared with reference results acquired through the Adams technique. The accuracy, convergence and effectiveness of the designed solver are identified using numerous statistical and performance analyses.

This study introduces deep convolutional neural network-based methods for the detection and classification of skin lesions, enhancing system accuracy through a combination of architectures, pre-processing techniques and data augmentation. Multiple networks, including XceptionNet, DenseNet, MobileNet, NASNet Mobile, and EfficientNet, were evaluated to test deep learning’s potential in complex, multi-class classification tasks. Training these models on pre-processed datasets with optimized hyper-parameters (e.g., batch size, learning rate, and dropout) improved classification precision for early-stage skin cancers. Evaluation measures such as accuracy and loss confirmed high classification efficiency with minimal overfitting, as the validation results aligned closely with training. DenseNet-201 and MobileNet-V3 Large demonstrated strong generalization abilities, whereas EfficientNetV2-B3 and NASNet Mobile achieved the best balance between accuracy and efficiency. The application of different augmentation rates per class also enhanced the handling of imbalanced data, resulting in more accurate large-scale detection. Comprehensive pre-processing ensured balanced class representation, and EfficientNetV2 models achieved exceptional classification accuracy, attributed to their optimized architecture balancing depth, width, and resolution. These models showed high convergence rates and generalization, supporting their suitability for medical imaging tasks using transfer learning.

This paper investigates the categorization of mammogram images into benign, malignant and normal categories, providing novel approaches based on Deep Convolutional Neural Networks to the early identification and classification of breast lesions. Multiple DCNN models were tested to see how well deep learning worked for difficult, multi-class categorization problems. These models were trained on pre-processed datasets with optimized hyperparameters (e.g., the batch size, learning rate, and dropout) which increased the precision of classification. Evaluation measures like confusion matrices, accuracy, and loss demonstrated their great classification efficiency with low overfitting and the validation results well aligned with the training. DenseNet-201 and MobileNet-V3 Large displayed significant generalization skills, whilst EfficientNetV2-B3 and NASNet Mobile struck the optimum mix of accuracy and efficiency, making them suitable for practical applications. The use of data augmentation also improved the management of data imbalances, resulting in more accurate large-scale detection. Unlike prior approaches, the combination of the architectures, pre-processing approaches, and data augmentation improved the system’s accuracy, indicating that these models are suitable for medical imaging tasks that require transfer learning. The results have shown precise and accurate classifications in terms of dealing with class imbalances and dataset poor quality. In particular, we have not defined a new framework for computer-aided diagnosis here, but we have reviewed a variety of promising solutions for future developments in this field.

Facial expressions reflect psychological states and are crucial for understanding human emotions. Traditional facial expression recognition methods face challenges in real-world healthcare applications due to variations in facial structure, lighting conditions and occlusion. We present a methodology based on transfer learning with the pre-trained models VGG-19 and ResNet-152, and we highlight dataset-specific preprocessing techniques that include resizing images to 124 × 124 pixels, augmenting the data and selectively freezing layers to enhance the robustness of the model. This study explores the application of deep learning-based facial expression recognition in healthcare, particularly for remote patient monitoring and telemedicine, where accurate facial expression recognition can enhance patient assessment and early diagnosis of psychological conditions such as depression and anxiety. The proposed method achieved an average accuracy of 0.98 on the CK+ dataset, demonstrating its effectiveness in controlled environments. However performance varied across datasets, with accuracy rates of 0.44 on FER2013 and 0.89 on JAFFE, reflecting the challenges posed by noisy and diverse data. Our findings emphasize the potential of deep learning-based facial expression recognition in healthcare applications while underscoring the importance of dataset-specific model optimization to improve generalization across different data distributions. This research contributes to the advancement of automated facial expression recognition in telemedicine, supporting enhanced doctor–patient communication and improving patient care.

Schizophrenia (SCZ) is a complex psychiatric disorder with unclear biological mechanisms. Spectrins, cytoskeletal proteins linked to neurodevelopmental disorders, are regulated by the AKT/GSK3 pathway, which is implicated in SCZ. However, the impact of SCZ-related dysregulation of this pathway on spectrin expression and distribution remains unexplored. Here, we show that βIV spectrin protein levels were reduced in neurons of the dorsolateral prefrontal cortex in SCZ postmortem samples compared to healthy control (HC) from the Human Brain Collection Core (HBCC). To investigate potential links between βIV spectrin and the AKT/GSK3 pathway, we analyzed the PsychEncode dataset, revealing elevated SPTBN4 and AKT2 mRNA levels with correlated gene transcription in both HCs and individuals with SCZ. Next, computational tools were employed to identify potential AKT and GSK3 phosphorylation sites on βIV spectrin, and two GSK3 sites were validated through in vitro assays. To assess whether βIV spectrin distribution and sensitivity to AKT/GSK3 are altered in SCZ, we used iPSC-derived neurons from two independent cohorts of patients with significantly increased familial genetic risk for the disorder. Alteration in βIV spectrin levels and sensitivity to AKT/GSK3 inhibitors were consistently observed across both cohorts. Importantly, a Random Forest classifier applied to βIV spectrin imaging achieved up to 98% accuracy in classifying cells by diagnosis in postmortem samples, and by diagnosis or diagnosis × perturbation in iPSC samples. These findings reveal altered βIV spectrin levels and AKT/GSK3 sensitivity in SCZ, identifying βIV spectrin image-based endophenotypes as robust, generalizable predictive biomarkers of SCZ, with the potential for scalable clinical applications.

In this research, a novel design stochastic numerical technique is presented to investigate the unsteady form magnetohydrodynamic (MHD) slip flow along the boundary layer to analyze the transportation and heat transfer in a solar collector through nano liquids which is a revolution in the field of neurocomputing. Thermal conductivity in variable form is dependent on temperature and wall slips are assumed over the boundary. For mathematical modeling, the solar collector is assumed in the form of a nonlinear stretching sheet and a quite new artificial neural networks (ANNs) based approach is used to solve the current problem in which inverse multiquadric radial basis (IMRB) kernel is sandwiched between a global search solver named genetic algorithms (GAs) and a highly effective local solver named sequential quadratic programming (SQP) i.e. IMRB-GASQP solver. The governing boundary value problem is altered in the form of a system of nonlinear ordinary differential equations (ODEs) through the utilization of similarity transformation and then the obtained system of ODEs is solved using IMRB-GASQP solver by altering the values of distinguished parameters involved in it to observe the fluctuation in the velocity and temperature profiles of nanofluid. The obtained results are effectively compared with the reference solutions using the Adams numerical technique in graphical and tabulated form. An exhaustive error analysis using performance operators is presented while the efficacy of the designed solver using various statistical operators is also part of this research.