Background: The extensive study of clinical health systems is creating a paradigm for the newest computer-based systems that are emerging. Pancreatic cancer, which cannot be allowed to be treated efficiently once diagnosed and is frequently unanticipated due to its position in the belly below the stomach, is one of the most prevalent tumors that is believed to be irreversible. Biological therapies, sometimes referred to as immunotherapies or targeted therapies, are used to treat pancreatic cancer in order to control hormone pathways, target certain cancer cells, or strengthen the immune system. Method: Pancreatic cancer is the fourth leading cause of cancer deaths, and there currently is no reliable modality for the early detection of this disease. Here, identifies cancer-specific promoter DNA methylation of BNC1 and ADAMTS1 as a promising biomarker detection strategy meriting investigation in pancreatic cancer. Nanoparticles directly target tumor cells, allowing their detection and removal. It also can be engineered to carry specific payloads, such as drugs or contrast agents, and enhance the efficacy and precision of cancer treatment. This study develops a unique cascaded fully convolutional neural network (CFCNN) with Hybrid Krill Herd African Buffalo Optimization (HKH-ABO) mechanism for early pancreatic computed tomography (CT) image classification of pancreatic cancer. A new Wienmed filter is created for pre-processing the noisy CT image content after the system is successfully trained on pancreatic CT pictures. In addition, the proposed CFCN with the HKH-ABO pathway distinguishes between pancreatic cancerous and non-pancreatic cancerous forms of the disease. Results: The accuracy of the CFCNN for the analysis of pancreatic cancer was 98.87%, showing that the various volumes of the 3DIRCAD datasets analyzed had a combined accuracy rate of 99% for training and 99% for testing. Conclusion: The combination of advanced biomarker identification, BNC1 and ADAMTS1 methylation, and nanoparticle-based targeting further enhances the precision and efficacy of pancreatic cancer diagnosis and treatment. As a result, advancements in medical study are steadily going in the direction of the installation of automation machines that determine the phases of cancers and, if directly touched, provide better guidance and therapy.

Influence of long-term daily oral exposure of C57BL/6 male mice during up to 180 days to 50 μg/day/animal of silver citrate on their cognitive and behavioral functions was stud­ied in the present work. The behavioral and cognitive functions’ change can be expressed as the three-staged process, such as (1) anxiety increase, (2) susceptibility (sensitivity) increase, (3) tendency to improve long-term contextual memory at the background of decreased anxi­ety and locomotion increase. Thus, the influence of silver citrate on behavioral and cognitive functions of mice is complex, time-dependent alterations. The observed effect of stimulation of behavioral and cognitive functions at silver citrate exposure at the final stage can be applied in Medicine for treatment of neurodegenerative diseases as well symptoms of mental illness. Silver citrate may potentially replace silver nanoparticles, which are well-known from scien­tific literature to be toxic.

Cerebral Microbleeds (CMBs) are among the significant contributors to mortality worldwide and require accurate diagnosis for effective medical intervention. Owing to their wide variability in size, shape, and intensity, manual identification and classification of CMBs in brain imaging remain a complex and error-prone task. This study proposes an automated classification framework for brain MRI-filtered images, categorizing them as either normal or abnormal. The suggested methodology combines a tailored Convolutional Neural Network founded on the ResNet50 architecture, employing a blend of image processing and deep learning strategies. First, a number of preprocessing processes were implemented to increase the MRI pictures quality. One of these steps was the fusion of multi-focus images, which helped to make details more visible. These enhanced images were then processed through a 13-layer CNN architecture specifically designed for effective CMB classification. The strength of the proposed CNN-ResNet50 model was confirmed through validation with two independent datasets. Experiment one used a 10-fold cross-validation procedure, while experiment two split the dataset in half, with 80% used for training and 20% for testing. The model achieved a train-test split accuracy of 98.77% and a cross-validation accuracy of 98.33% while classifying Dataset 1. An accuracy of 92.22% and an accuracy of 93.33% were attained by the model in the two experimental setups for Dataset 2. All investigations used real-world MRI scans. This data set originated from Neyyoor, India's CSI Medical Mission Hospital's International Cancer Center (ICC). The efficacy of the suggested CNN-ResNet50 model was evaluated in comparison to established deep learning models, such as AlexNet and the original ResNet50. Experimental data indicate that our proposed method surpasses both comparative models regarding classification accuracy.

When faced with imbalanced data, classification techniques in the area of artificial intelligence have a tendency to Favor the majority class samples, which lowers the recognition rates of minority class samples. This problem is solved by undersampling, which reduces the quantity of majority class samples while trying to restore the original data distribution when the dataset is acquired. The initial imbalanced dataset and its classification accuracy as a whole are strongly impacted by the constraints of the clustering-based undersampling techniques utilized today. To solve these issues, in this research work, initially the highly imbalanced dataset is pre-processed using Non-Negative Matrix Factorization (NMF) Algorithm. Next, Hybrid Extremely Randomized Trees (HERT), an efficient ensemble learning-based method, is employed to quickly choose the features. Afterwards, to solve class imbalance issue, Generative Adversarial Network (GAN)-based oversampling is suggested. This method has shown exceptional capacity to solve class imbalance as it may detect the genuine data distribution of minority class samples and produce new samples. By selecting useful instances from each cluster and avoiding information loss, the Fuzzy C means (FCM) clustering system is suggested for the undersampling method. Here Combined form of Fuzzy C means clustering for majority class and Adasyn-GAN centred over sampling for minority class are together to produce better results. Finally, the sampled dataset has undergone classification using Adaptive Weight Bi-Directional Long Short-Term Memory (AWBi-LSTM) classifier. Three huge, unbalanced data sets are applied to assess the suggested algorithm. The suggested system’s efficiency was compared to those of cutting-edge machine learning (ML) techniques like XG boost and random forest. The suggested method’s effectiveness is demonstrated by the performance assessment with regard to accuracy, recall, precision, and F1-score. Furthermore, the suggested plan requires less training time than cutting-edge methods.

Due to rapid enhancement of digital communication in cloud paradigm, easier transmission & storage of the multimedia information in several platforms becomes challenging. The security of image information is vital since the images are considered as a major component of communication in cloud environment. The secret information is shared in the form of secured image which needs to be retrieved and send to user without losing the integrity and confidentiality of data. For this purpose, the proposed model is designed which employs feature extraction categorization process and transmitting extracted information securely via cryptographic process. Initially the input images are retrieved and parameter initialization is carried by bilinear matrix. An optimal feature extraction is carried using Rotational invariant Local Binary Pattern (RI-LBP) along with Enriched Shark smell optimization process for extracting features of secret information. E-IBE (Enhanced-Identity based encryption) is employed for private key generation followed by cryptographic process via Ensemble Improved Homomorphic Pailler and Quantized ElGammal Elliptic curve Cryptography (ECC) scheme. The decrypted outcome attained is then digitally verified by employing SHA3 verification model. Thus, retrieved data is provided to the user after validation in a secured manner. The simulation results are then observed by analyzing the proposed scheme performance on CIFAR-10 dataset &MNIST dataset attained outcomes are compared with traditional schemes to validate the enhancement of proposed model over other models. the performance is carried for various metrics like extraction accuracy, recall, precision F1-score, precision-recall curve, RoC curve, execution time, runtime & storage space of entire system.