Incorporating FCM and Back Propagation Neural Network for Image Segmentation

Hybrid image segmentation is proposed in this paper. The input image is firstly preprocessed in order to extract the features using Discrete Wavelet Transform (DWT) .The features are then fed to Fuzzy C-means algorithm which is unsupervised. The membership function created by Fuzzy C-means (FCM) is used as a target to be fed in neural network. Then the Back Propagation Neural network (BPN) has been trained based on targets which is obtained by (FCM) and features as input data. Combining the FCM information and neural network in unsupervised manner lead us to achieve better segmentation .The proposed algorithm is tested on various Berkeley database gray level images

Quantitative Evaluation by Protection Layer Analysis (LOPA) for Equipment in Imam Khomeini Petrochemical Aromatic Unit

Background: In the petrochemical industries, accidents are generally catastrophic which endanger human, environment and economic. In the industries, there is a wide range of flammable and toxic substances that affect health and safety of workers. They have also adverse effects on society. Numerical risk and impact assessment as well as design for protective layers against catastrophic events are necessary for designing process units.Methods: First, the occupational-process and environmental safety hazards were measured by hazard and operability (HAZOP) and environmental failure mode and effects analysis (EFMEA) techniques. Then, the risk was assessed using the layer and operability analysis (LOPA) method.Results: The results showed that a total of 50 safe and health items and 37 environmental risks were identified by HAZOP and EFMEA methods in Imam Khomeini Petrochemical Aromatic Unit. There were 17, 19 and 14 items with low, medium and high level risk, respectively.Conclusion: This study showed that the LOPA method is more comprehensive than hazard identification methods for the analysis of protective layers. The important actions were blockage of the excess gas to the flare and release the H2S gas. Also, evaluation of the environmental aspects of aromatic unit activities showed that air pollutant production in the power supply unit, waste disposal of reactor tank, waste disposal of condensate tank and reactor fire and explosion were at a high level risk

Machine Learning Styles for Diabetic Retinopathy Detection: A Review and Bibliometric Analysis

Diabetic retinopathy (DR) is a medical condition caused by diabetes. The development of retinopathy significantly depends on how long a person has had diabetes. Initially, there may be no symptoms or just a slight vision problem due to impairment of the retinal blood vessels. Later, it may lead to blindness. Recognizing the early clinical signs of DR is very important for intervening in and effectively treating DR. Thus, regular eye check-ups are necessary to direct the person to a doctor for a comprehensive ocular examination and treatment as soon as possible to avoid permanent vision loss. Nevertheless, due to limited resources, it is not feasible for screening. As a result, emerging technologies, such as artificial intelligence, for the automatic detection and classification of DR are alternative screening methodologies and thereby make the system cost-effective. People have been working on artificial-intelligence-based technologies to detect and analyze DR in recent years. This study aimed to investigate different machine learning styles that are chosen for diagnosing retinopathy. Thus, a bibliometric analysis was systematically done to discover different machine learning styles for detecting diabetic retinopathy. The data were exported from popular databases, namely, Web of Science (WoS) and Scopus. These data were analyzed using Biblioshiny and VOSviewer in terms of publications, top countries, sources, subject area, top authors, trend topics, co-occurrences, thematic evolution, factorial map, citation analysis, etc., which form the base for researchers to identify the research gaps in diabetic retinopathy detection and classification