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Detection and Prediction of Polycystic Ovary Syndrome Using Attention-Based CNN-RNN Classification Model

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2025年第2期16卷 687-700页

作者： Pulluparambil, Siji Jose Bhat, B. Subrahmanya Srinivas Univ Inst Comp Sci & Informat Sci Mangalore 574146 India Adi Shankara Inst Engn & Technol Dept Artificial Intelligence & Data Sci Ernakulam 683574 Kerala India Srinivas Univ Inst Comp Sci & Informat Sci Mangalore 574146 India

Polycystic Ovary Syndrome (PCOS) has many challenges when it comes to its diagnosis and treatment due to the diversity of presentation and potential long-term consequences for health. For this reason, sophisticated data pre-processing and classification methods are implemented to enhance the accuracy of PCOS diagnosis. A number of innovative techniques are employed in the process to enhance the accuracy and reliability of PCOS diagnosis. To identify ovarian cysts, real-time ultrasound images are pre-processed initially with the Contrast-Limited Adaptive Histogram Equalization (CLAHE) model to improve image contrast and sharpness. The ultrasound images are segmented with the k-means clustering algorithm, Particle Swarm Optimization (PSO), and a fuzzy filter, enabling precise analysis of regions of interest. An attention-based Convolutional Neural Network-Recurrent Neural Network (CNN-RNN) model is employed for classification and does so effectively to capture the temporal and spatial characteristics of the segmented data. The proposed model has a very good accuracy rate of 96% and works very well on a variety of evaluation metrics such as accuracy, precision, sensitivity, F1-score, and specificity. The results are evidence of the robustness of the model in minimizing false positives and enhancing PCOS diagnostic accuracy. Nevertheless, it is noted that bigger data sets are required to maximize the precision and generalizability of the model. The aim of subsequent research is to use Explainable AI (XAI) methods to enhance clinical decision-making and establish trust by making the model's predictions clearer and understandable for patients and clinicians. Along with enhancing PCOS detection, this comprehensive approach sets a precedent for integrating explainability into AI- based medical diagnostic devices.

关键词： Polycystic ovary syndrome contrast limited adaptive histogram equalization particle swarm optimization k- means clustering algorithm convolutional neural network recurrent neural network

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Common lecturers timetabling among departments based on funnel-shape clustering algorithm

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APPLIED INTELLIGENCE 2017年第2期46卷 386-408页

作者： Babaei, Hamed karimpour, Jaber Hadidi, Amin Islamic Azad Univ Ahar Branch Dept Comp Engn Ahar Iran Univ Tabriz Dept Comp Sci Tabriz Iran Islamic Azad Univ Ahar Branch Dept Mech Engn Ahar Iran

The university course timetabling problem is considered one of the NP-problems which should be performed for each semester repeatedly, and it is an exhausting and time consuming task. The main technique of the proposed approach is focused on extension and scalability of common lecturers timetabling process across different departments of a university. In this paper the clustering algorithms, including k-means and Fuzzy c-means, are used to schedule common lecturers between departments considering the constraints and their priorities offered, for the first time. For this purpose, a new clustering algorithm named funnel-shape clustering is proposed to schedule common lecturers between departments based on their constraints proposed. The main goals of this paper are to improve the satisfaction of common lecturers across departments and minimize the loss of resources within departments. In this method, all the departments perform their scheduling process locally;After that the clustering agent is applied to cluster common lecturers across departments by using the proposed funnel clustering algorithm and then the traversing agent is used to find the useless resources across departments. Following the clustering and traversing processes, mapping is performed based on the common lecturers' constraints in the excess resources in order to reach the problem goals. The applied dataset based on the real world requirements is across various departments of Islamic Azad University, Ahar Branch and the results' success would be based on uniform distribution and allocation of common lecturers on useless resources across different departments.

关键词： Common lecturer timetabling problem (CLTTP) Fuzzy c-means clustering algorithm University course timetabling problem (UCTTP) k- means clustering algorithm

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