- Home
- A-Z Publications
- Recent Patents on Electrical & Electronic Engineering (Formerly Recent Patents on Electrical Engineering)
- Fast Track Listing
Recent Patents on Electrical & Electronic Engineering (Formerly Recent Patents on Electrical Engineering) - Online First
Description text for Online First listing goes here...
-
-
Enhanced Computer-Aided Digital Imaging Technique for Predictions in Breast Cancer
Authors: Sushma Nagdeote, Sapna Prabhu and Jayashri ChaudhariAvailable online: 04 November 2024More LessBackgroundBreast cancer (BRCA) is the most frequently diagnosed cancer in women, with a rise in occurrences and fatalities. The field of BRCA prediction and diagnosis has witnessed significant advancements in recent years, particularly emphasizing enhanced computer-aided digital imaging techniques, and has emerged as a powerful ally in the prediction of BRCA through histopathology image analysis. A number of approaches have been suggested in recent years for the categorization of histopathology BRCA images into benign and malignant as it examines the images at cellular level. The histopathology slides must be manually analysed which is time consuming and tiresome and is prone to human error. Additionally, different laboratories occasionally have different interpretation of these images.
MethodsThis paper focuses on implementing a framework for Computer-Aided digital imaging technique that can serve as a decision support. With recent advancements in computing power the analysis of BRCA histopathology image samples has become easier. Stain normalization (SN), segmentation, feature extraction and classification are the steps to categorize the cancer into benign and malignant. Nuclei segmentation is a crucial step that needs to be taken into account in order to establish malignancy. These are considered essential for early diagnosis of BRCA. A unique method proposed for BRCA prediction is put forward. To maximize the prediction accuracy, the suggested method is integrated with machine learning (ML) techniques and clinical data is used to evaluate the suggested approach.
ResultsThis strategy is adaptable to many cancer types and imaging techniques. The suggested technique is applied to clinical data and is integrated with logistic regression and K-Nearest Neighbor resulting in accuracy of 92.10% and 86.89% respectively for BRCA histopathology images.
ConclusionThe objective of this work is to validate the proposed model which takes input as feature pattern for a given label. For the collected clinical samples, the model is able to classify the input as benign or malignant. The proposed model worked efficiently for different BC datasets and performed classification task successfully. Integrating mathematical model (MM) with ML model for interpreting histopathology BRCA is a potential area of research in the field of digital pathology.
-
-
-
Optimizing Federated Reinforcement Learning Algorithm for Data Management of Distributed Energy Storage Network
Authors: Yuan Li and Yuancheng LiAvailable online: 03 October 2024More LessBackgroundThe development of energy storage networks has facilitated the rapid expansion of new energy-based power systems. However, the emergence of large-scale energy storage devices has also led to a significant increase in energy data volumes. Federated learning provides a solution by allowing energy data owners to train AI models without sharing local energy data, which is particularly advantageous for handling heterogeneous data.
ObjectiveThis paper explores the application of federated learning in managing energy data within distributed energy storage networks. Specifically, we leverage deep reinforcement learning algorithms to optimize the selection of device subsets, aiming to mitigate data bias caused by non-identically and independently distributed (non-IID) data while enhancing convergence rates.
MethodTo achieve our objectives, we employ deep reinforcement learning to dynamically select the optimal subset of devices in the federated learning process. Additionally, we introduce a reputation replay array mechanism to address the issue of free-rider users and ensure fair modeling without payment penalties. We analyze energy data characteristics within distributed energy storage networks and simulate unstructured short data fragments using datasets such as 20 Newsgroups and AG News.
ResultsOur experiments show that our proposed model outperforms FedAvg and TiFL on the 20 Newsgroups and AG News datasets, especially under non-iid conditions. Our model significantly reduces communication rounds by up to 47% and 39%, respectively. It also maintains high accuracy and resilience against dishonest nodes, ensuring the quality of the training model.
ConclusionOur research concludes that combining federated learning with deep reinforcement learning not only solves the problems of data management and privacy protection in distributed energy storage networks, but also promotes the sustainable development of new energy systems.
-