0% Complete
صفحه اصلی
/
سی و یکمین کنفرانس بین المللی مهندسی برق
A modified Dempster Shafer approach to classification in surgical skill assessment
نویسندگان :
Arash Iranfar
1
Mohammad Soleymannejad
2
Behzad Moshiri
3
Hamid D. Taghirad
4
1- دانشگاه تهران
2- دانشگاه تهران
3- دانشگاه تهران
4- دانشگاه صنعتی خواجه نصیرالدین طوسی
کلمات کلیدی :
Skill Assessment،Classification،Evidence combination،Dempster-Shafer theory
چکیده :
Artificial intelligence systems are usually implemented either using machine learning or expert systems. Machine learning methods are usually more accurate and applicable to a broader range of applications. Expert systems, on the other hand, require much less data for training and generate more comprehensible results. These characteristics are typically desired in the fields of surgery and medicine because there isn’t much data available. In order to give a machine’s decisions a deeper level of semantics, it is also advantageous to incorporate a doctor’s expertise into it. Furthermore, it is safer to understand the reasoning behind a machine’s choices. In this paper, a Dempster-Shafer Theory (DST) based expert system is suggested for the task of surgical training skill assessment. An interval-based probabilistic feature analysis was applied to the data to assign values to the mass functions. Zhang’s rule of combination was applied to handle the conflicting evidence in the prediction phase. The performance of the proposed method was compared to another DST classifier, SVM, and XGBoost. Our method outperforms SVM and other DST classifiers, but it is not as precise as XGBoost. By reducing the size of the dataset, the added benefit of using an expert system as opposed to a machine learning method was explored further. The performance of the suggested method is not adversely affected by the size of the dataset, whereas the XGBoost classifier is.
لیست مقالات
لیست مقالات بایگانی شده
Fractional-Order Model Prediction Attitude Control For Unmanned Aerial Vehicles
Hossein Hassanzadeh Yaghini
Model Reference Adaptive Control for Nonlinear Systems in the Presence of Unknown External Disturbances
Ehsan Nazemorroaya - Mohsen Shafieirad - Majid Hajatipour
A New Approach to Solve MDVRP in Lower Computation Time
Reza Rahimi Baghbadorani - Mohammad Amin Zajkani - Mohammad Haeri
طراحی کنترل کننده امن سیستمهای غیرخطی با استفاده از یادگیری تقویتی و بهینه سازی مجموع مربعات
حسین قلی زاده - احسان رضوی - سجاد پاک خصال - سعید شمقدری
Community Energy Management Using MARL: Synergy of Price-Based and Incentive-Based Demand Response
Mohammad Hashemnezhad - Hamed Delkhosh - Ahmad Shahabi - Mohsen Parsa Moghaddam
طراحی و مدلسازی امولاتور دریچه گاز الکترونیکی برای کاربرد در خودرو
محمدرضا درزی - مجید شالچیان
A New High gain Transformerless DC-DC Converter with Low Voltage Stress on Power Switches
Amirreza Bahadori - Ali Nadermohammadi - Mohammad Maalandish - Seyed Hossein Hosseini - Mehran Sabahi
Decentralized Optimization in Scheduling Virtual Power Plant (VPP)
Amir Hossein Gholami - Amir Abolfazl Suratgar - Mohammad Bagher Menhaj - Mohammad Reza Hesamzadeh
تشخیص حالت عادی و غیرعادی شبکه برق هوشمند با استفاده از شبکه عصبی مصنوعی
محمد گنج خانی - علی عباسپورطهرانی فرد - سجاد فتاحیان دهکردی - محمد غلامی
A Novel Low Torque Ripple Hexagon Biased Flux Doubly Salient Permanent Magnet Motor
Mohammad Amirkhani - Behnam Mohammadian Mosammam - Mojtaba Mirsalim
بیشتر
ثمین همایش، سامانه مدیریت کنفرانس ها و جشنواره ها - نگارش 41.7.4