Curse of Dimensionality for TSK Fuzzy Neural Networks: Explanation and Solutions

Takagi-Sugeno-Kang (TSK) fuzzy system with Gaussian membership functions (MFs) is one of the most widely used fuzzy systems in machine learning. However, it usually has difficulty handling high-dimensional datasets. This paper explores why TSK fuzzy systems with Gaussian MFs may fail on high-dimensional inputs. After transforming defuzzification to an equivalent form of softmax function, we find that the poor performance is due to the saturation of softmax. We show that two defuzzification operations, LogTSK and HTSK, the latter of which is first proposed in this paper, can avoid the saturation. Experimental results on datasets with various dimensionalities validated our analysis and demonstrated the effectiveness of LogTSK and HTSK

IoT-based Lava Flood Early Warning System with Rainfall Intensity Monitoring and Disaster Communication Technology

A lava flood disaster is a volcanic hazard that often occurs when heavy rains are happening at the top of a volcano. This flood carries volcanic material from upstream to downstream of the river, affecting populous areas located quite far from the volcano peak. Therefore, an advanced early warning system of cold lava floods is inarguably vital. This paper aims to present a reliable, remote, Early Warning System (EWS) specifically designed for lava flood detection, along with its disaster communication system. The proposed system consists of two main subsystems: lava flood detection and disaster communication systems. It utilizes a modified automatic rain gauge; a novel configured vibration sensor; Fuzzy Tree Decision algorithm; ESP microcontrollers that support IoT, and disaster communication tools (WhatsApp, SMS, radio communication). According to the experiment results, the prototype of rainfall detection using the tipping bucket rain gauge sensor can measure heavy and moderate rainfall intensities with 81.5% accuracy. Meanwhile, the prototype of earthquake vibration detection using a geophone sensor can remove noise from car vibrations with a Kalman filter and measure vibrations in high and medium intensity with an accuracy of 89.5%. Measurements from sensors are sent to the webserver. The disaster mitigation team uses data from the webserver to evacuate residents using the disaster communication method. The proposed system was successfully implemented in Mount Merapi, Indonesia, coordinated with the local Disaster Deduction Risk (DDR) forum. Doi: 10.28991/esj-2021-SP1-011 Full Text: PD

An Efficient Fuzzy Based Multi Level Clustering Model Using Artificial Bee Colony For Intrusion Detection

Network security is becoming increasingly important as computer technology advances. One of the most important components in maintaining a secure network is an Intrusion Detection System (IDS). An IDS is a collection of tools used to detect and report network anomalies. Threats to computer networks are increasing at an alarming rate. As a result, it is critical to create and maintain a safe computing environment. For network security, researchers employ a range of technologies, including anomaly-based intrusion detection systems (AIDS). These anomaly-based detections face a major challenge in the classification of data. Optimization algorithms that mimic the foraging behavior of bees in nature, such as the artificial bee colony algorithm, is a highly successful tool. A computer network's intrusion detection system (IDS) is an essential tool for keeping tabs on the activities taking place in the network. Artificial Bee Colony (ABC) algorithm is used in this research for effective intrusion detection. More and more intrusion detection systems are needed to keep up with the increasing number of attacks and the increase in Internet bandwidth. Detecting developing threats with high accuracy at line rates is the prerequisite for a good intrusion detection system. As traffic grows, current systems will be overwhelmed by the sheer volume of false positives and negatives they generate. In order to detect intrusions based on anomalies, this research employs an Efficient Fuzzy based Multi Level Clustering Model using Artificial Bee Colony (EFMLC-ABC). A semi-supervised intrusion detection method based on an artificial bee colony algorithm is proposed in this paper to optimize cluster centers and identify the best clustering options. In order to assess the effectiveness of the proposed method, various subsets of the KDD Cup 99 database were subjected to experimental testing. Analyses have shown that the proposed algorithm is suitable and efficient for intrusion detection system

Concise fuzzy system modeling integrating soft subspace clustering and sparse learning

202312 bckwAccepted ManuscriptRGCOthersNational Key Research Program of China; NSFC; Jiangsu Province Outstanding Youth Fund; National First-Class Discipline Program of Light Industry Technology and EngineeringPublishedGreen (AAM