In the structure of ANFIS, there are two different parameter groups: premise and consequence. Training ANFIS means determination of these parameters using an optimization algorithm. In the first ANFIS model developed by Jang, a hybrid learning approach was proposed for training. In this approach, while premise parameters are determined by using gradient descent (GD), consequence parameters are found out with least squares estimation (LSE) method. Since ANFIS has been developed, it is used in modelling and identification of numerous systems and successful results have been achieved. The selection of optimization method utilized in training is very important to get effective results with ANFIS. It is seen that derivate based (GD, LSE etc.) and non-derivative based (heuristic algorithms such us GA, PSO, ABC etc.) algorithms are used in ANFIS training. Nevertheless, it has been observed that there is a trend toward heuristic based ANFIS training algorithms for better performance recently. At the same time, it seems to be proposed in derivative and heuristic based hybrid algorithms. Within the scope of this study, the heuristic and hybrid approaches utilized in ANFIS training are examined in order to guide researchers in their study. In addition, the final status in ANFIS training is evaluated and it is aimed to shed light on further studies related to ANFIS training.

Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey

Despite the extensive use of mortars materials in constructions over the last decades, there is not yet a reliable and robust method, available in the literature, which can estimate its strength based on its mix parameters. This limitation is due to the highly nonlinear relation between the mortar’s compressive strength and the mixed components. In this paper, the application of artificial intelligence techniques toward the prediction of the compressive strength of cement-based mortar materials with or without metakaolin has been investigated. Specifically, surrogate models (such as artificial neural network, ANN and adaptive neuro-fuzzy inference system, ANFIS models) have been developed to the prediction of the compressive strength of mortars trained using experimental data available in the literature. The comparison of the derived results with the experimental findings demonstrates the ability of both ANN and ANFIS models to approximate the compressive strength of mortars in a reliable and robust manner. Although ANFIS was able to obtain higher performance prediction to estimate the compressive strength of mortars compared to ANN model, it was found through the verification process of some other additional data, the ANFIS model has overfitted the data. Therefore, the developed ANN model has been introduced as the best predictive technique for solving problem of the compressive strength of mortars. Furthermore, using the optimum developed model an ambitious attempt to reveal the nature of mortar materials has been made.

A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength

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How the mobile communication

Neural network and multi-fractal dimension features for breast cancer classification from ultrasound images

Since the inception of IEEE 802.11 wireless local area networks (WLANs) in 1997, wireless networking technologies have tremendously grown in the last few decades. The fundamental IEEE 802.11 physical (PHY) and medium access control (MAC) protocols have continuously been enriched with new technologies to provide the last mile wireless broadband connectivity to end users. Consequently, several new amendments of the basic IEEE 802.11 gradually came up in the forms of IEEE 802.11a, IEEE 802.11b, and IEEE 802.11g. More recently, IEEE 802.11n, IEEE 802.11ac, and IEEE 802.11ad are introduced with enhanced PHY and MAC layers that boost up physical data rates to the order of Gigabit per second. So, these amendments are generally known as high throughput WLANs (HT-WLANs). In HT-WLANs, PHY layer is enhanced with multiple-input multiple-output antenna technologies, channel bonding, short guard intervals, enhanced modulation and coding schemes. The MAC sublayer overhead is reduced by introducing frame aggregation and block acknowledgement technologies. However, several existing studies reveal that, many a time, the aforesaid PHY and MAC enhancements yield negative impact on various upper layer protocols, that is end-to-end transport and application layer protocols. As a consequence, a large number of researchers have focused on improving the coordination among PHY/MAC and upper layer protocols. In this survey, we discuss impact of HT-WLAN PHY and MAC layer enhancements on various transport and application layer protocols. This paper also summarizes several research works that use aforesaid enhancements effectively to boost up data rate of end-to-end protocols. We also point out limitations of the existing researches and list down different open challenges that can be meaningfully explored for the development of the next generation HT-WLAN technologies.

Impact of IEEE 802.11n/ac PHY/MAC High Throughput Enhancements on Transport and Application Protocols—A Survey

Nowadays, wireless sensor network are generally deployed a sensor nodes to detection an event and transmit the data to the base station (BS). When clustering is an approach to use to minimized the energy consumption of such network. Therefore, in this paper low energy adaptive clustering hierarchy (LEACH) protocol and stable election protocol (SEP) is proposed. The performance analysis show that the comparison of LEACH and SEP protocol on the basis of different factors like as delay, packet loss and throughput.

A comparative analysis of LEACH and SEP using NS2

Epilepsy is a perilous neurological disease covering about 4-5% of total population of the world. Its main characteristics are seizures which occur due to certain disturbance in brain function. During epileptic seizures the patient is unaware of their physical as well as mental condition and hence physical injury may occur. Proper health care must be provided to the patients and this can be achieved only if the seizures are detected correctly in time. In this dissertation work, a system is designed using wavelet decomposition method and different training algorithms to train the neural network for classification of the EEG signals. The system was tested and compared with Support Vector Machine (SVM) classifier. The system accuracy comes out to be 99.97%.

http://www.sersc.org/journals/IJBSBT/vol8_no2/2.pdf

Epilepsy Seizure Detection Using Wavelet Support Vector Machine Classifier

To achieve the high speed connectivity for access networks, the combination of the large bandwidth of the fiber medium with the flexibility of the Code Division Multiple Access (CDMA) technique is used and referred as Optical Code Division Multiple Access(OCDMA). This paper presents the simulation results for 24 user OCDMA environment system with the help of various system parameters like Bit Error Rate(BER), Q- factor and eye pattern with Raised Cosine filter, Gaussian filter, Fabry Perot filter, Trapezoidal filter and Lorentzian filter. The proffered network concede high number of users to communicate with high data rate over the conventional OCDMA system. The analysis of simulation assures that the system frame with Fabry Perot filter method is of minimum distortion, while maintaining BER 6.81×e -20 for the correctly decoded signal.

To analyze the performance of various digital filters in ocdma multi-user environment with 3d codes

An accurate artificial intelligence system for the detection of pulmonary and extra pulmonary Tuberculosis.

The efficiency of Wireless Sensor Networks (WSNs) depends on the routing protocols used, since routing protocol provide the best possible data transmission route from sensor nodes to sink to save energy of nodes in the network. The clustering schemes enhance the network lifetime, raise the scalability and reduce the energy consumption of the sensor network. The work in this paper presents the comparative analysis of the energy efficient routing protocols for WSN such as SEP, TSEP and DSEP. The optimized routing protocol has been proposed on the basis of the network life time, stability and cluster head selection for efficient working of the sensor networks.

Anurag Sharma

Papers

A comparative analysis of LEACH and SEP using NS2

Epilepsy Seizure Detection Using Wavelet Support Vector Machine Classifier

To analyze the performance of various digital filters in ocdma multi-user environment with 3d codes

An accurate artificial intelligence system for the detection of pulmonary and extra pulmonary Tuberculosis.

Comparative Analysis of Energy Efficient Protocols for Prolonged Life of Wireless Sensor Network