Ovarian cancer histotype classification is challenging because of substantial morphological heterogeneity and subtle subtype-specific features. This work presents a controlled evaluation of high-resolution pathology representations and slide-level aggregation methods for automated classification of five ovarian cancer subtypes from whole-slide histopathology images. Precomputed CONCH patch embeddings were aggregated using mean pooling, max pooling, gated attention-based multiple-instance learning, and a max-pooling cascade. The models were evaluated on 513 non-TMA whole-slide images from UBC-OCEAN using leakage-controlled five-fold cross-validation, with each slide receiving exactly one out-of-fold prediction. Gated ABMIL achieved a balanced accuracy of 81.39% and a macro-F1 score of 81.96%. Max pooling produced the highest numerical performance, with a balanced accuracy of 81.44%, macro-F1 of 82.59% (95% CI: 78.84-86.26%), macro-AUROC of 96.99%, and macro-AUPRC of 90.79%. However, paired slide-level bootstrap comparisons found no statistically significant differences among the CONCH aggregation strategies after Holm correction. Compared with the EfficientNet-B0 thumbnail baseline, CONCH max pooling improved macro-F1 by 30.30 percentage points and balanced accuracy by 25.70 percentage points, with both paired bootstrap confidence intervals excluding zero. Attention weights enabled visualization of influential patches, although these regions were not independently validated by pathologists. The findings show that high-resolution pathology foundation-model representations support ovarian cancer subtyping, while greater aggregation complexity does not necessarily improve performance. External multi-institutional validation is required before clinical generalizability can be established.

Educational assessment has shifted from a “one size fits all” model to learner-centered, responsive, and adaptable dynamic rubrics and feedback procedures. Dynamic rubrics boost engagement and assessment confidence by improving instructor-student communication and transparency.  Dynamic rubrics could improve feedback and assessment. Pre-processed standard dataset texts are used. Pre-processed texts are delivered to Word2Vec to extract key features and vectorize them. A fuzzy clustering model with dynamically weighted rubrics evaluates the assignment. The dynamic rubric clearly outlines the assignment's evaluation criteria, and weights assist in deciding how much each criterion contributes to the overall grade using the Foraging Phase Updated Addax Optimization (FPUAO) algorithm. Subsequently, the assessment score is obtained, and based on this score, feedback is generated using a Generative Attention Long Short-Term Memory (GenA-LSTM) model. Finally, the developed model provides the optimal responses to the students by using the dynamic rubric with a deep learning model and an enhanced optimization algorithm. Experimental results on two datasets show an average accuracy of 99.36%, representing a 2.7% improvement over K-Means Clustering.  The average Mean Absolute Error (MAE) is 31.85%, an 81% reduction compared to K-Means. Average efficiency increases by 21.30% compared to other models. Thus, the developed model is more effective and robust than the existing approaches.

The automated speaker endorsement technique used for recognition of a person by his voice data. The speaker identification is one of the biometric recognition and they were also used in government services, banking services, building security and intelligence services like this applications. The exactness of this system is based on the pre-processing techniques used to select features produced by the voice and to identify the speaker, the speech modeling methods, as well as classifiers, are used. Here, the edges and continuous quality point are eliminated in the normalization process. The Mel-Scale Frequency Cepstral Coefficient is one of the methods to grab features from a wave file of spoken sentences. The Gaussian Mixture Model technique is used and done experiments on MARF (Modular Audio Recognition Framework) framework to increase outcome estimation. We have presented an end pointing elimination in Gaussian selection medium for MFCC.