Summary of Point Transformer with Federated Learning for Predicting Breast Cancer HER2 Status from Hematoxylin and Eosin-Stained Whole Slide Images

This study introduces a federated learning-based approach to predict HER2 status from hematoxylin and eosin (HE)-stained whole slide images (WSIs), reducing costs and speeding up treatment decisions. To address label imbalance and feature representation challenges in multisite datasets, a point transformer is proposed, incorporating dynamic label distribution, an auxiliary classifier, and farthest cosine sampling. Extensive experiments demonstrate state-of-the-art performance across four sites (2687 WSIs) and strong generalization to two unseen sites (229 WSIs).

Voice Information Retrieval In Collaborative Information Seeking

Voice information retrieval is a technique that provides Information Retrieval System with the capacity to transcribe spoken queries and use the text output for information search. CIS is a field of research that involves studying the situation, motivations, and methods for people working in a collaborative group for information seeking projects, as well as building a system for supporting such activities. Humans find it easier to communicate and express ideas via speech. Existing voice search like Google and other mainstream voice search does not support collaborative search. The spoken speeches passed through the ASR for feature extraction using MFCC and HMM, Viterbi algorithm precisely for pattern matching. The result of the ASR is then passed as input into CIS System, results is then filtered to have an aggregate result. The result from the simulation shows that our model was able to achieve 81.25% transcription accuracy.

Customers Churn Prediction in Financial Institution Using Artificial Neural Network

In this study, a predictive model using Multi-layer Perceptron of Artificial Neural Network architecture was developed to predict customer churn in a financial institution. Previous researches have used supervised machine learning classifiers such as Logistic Regression, Decision Tree, Support Vector Machine, K-Nearest Neighbors, and Random Forest. These classifiers require human effort to perform feature engineering which leads to over-specified and incomplete feature selection. Therefore, this research developed a model to eliminate manual feature engineering in data preprocessing stage. Fifty thousand customers? data were extracted from the database of one of the leading financial institution in Nigeria for the study. The multi-layer perceptron model was built with python programming language and used two overfitting techniques (Dropout and L2 regularization). The implementation done in python was compared with another model in Neuro solution infinity software. The results showed that the Artificial Neural Network software development (Python) had comparable performance with that obtained from the Neuro Solution Infinity software. The accuracy rates are 97.53% and 97.4% while ROC (Receiver Operating Characteristic) curve graphs are 0.89 and 0.85 respectively.