Anurag Sharma

Bio: Anurag Sharma is an academic researcher from Acropolis Institute of Technology & Research. The author has contributed to research in topics: Wireless sensor network & Fuzzy logic. The author has an hindex of 7, co-authored 36 publications receiving 258 citations.

ANFIS: Adaptive Neuro-Fuzzy Inference System- A Survey

Abstract: paper, we presented the architecture and basic learning process underlying ANFIS (adaptive-network-based fuzzy inference system) which is a fuzzy inference system implemented in the framework of adaptive networks. Soft computing approaches including artificial neural networks and fuzzy inference have been used widely to model expert behavior. Using given input/output data values, the proposed ANFIS can construct mapping based on both human knowledge (in the form of fuzzy if-then rules) and hybrid learning algorithm. In modeling and simulation, the ANFIS strategy is employed to model nonlinear functions, to control one of the most important parameters of the induction machine and predict a chaotic time series, all yielding more effective, faster response or settling times.

Geographic Routing Protocol: A Review

Abstract: Mobile Adhoc Networks (MANETs) are the self-organized networks in which connections are not established for exchange the information. In MANET, there are major problems like scalability, dynamic topology, high mobility and routing. The network can be damaged due to the high mobility. Topology based routing can fail when there is a dynamic change in the network topology. To avoid these problems, Geographic routing is used. The geographic routing protocols are more efficient and scalable when there is a dynamic change in the network topology and when the mobility is high. In this paper, we have surveyed on the Hybrid Routing and Geographic routing protocol. The hybrid routing can be done into two ways i.e. greedy routing and face-2 algorithm or perimeter routing.

Fuzzy expert system for diagnosis of Breast Cancer

Abstract: In a way to deal with real heath care problems, we proposed a fuzzy based decision- making system for diagnosis of breast cancer. Breast cancer is the most far- reaching disease today, so primary detection of Breast Cancer is very significant. The proposed paper was provided with artificial intelligent techniques such as fuzzy logic to give correct decision making. The fuzzy rule- based makes use of expert knowledge to deal with patient's symptom and give an accurate decision according to rules constructed.

Performance Optimization of DCF-MAC Standard Using Enhanced RTS Threshold under Impact of IEEE 802.11n WLAN

Abstract: Wireless Local Area Networks (WLANs) communication means gained widespread reception in recent years and widely used all over world due to its wireless fidelity feature with high data rates assets. With emerged multimedia application on wireless networks and the integration with other cellular networks, requires support for quality of service. In this paper, performance optimization of DCF based IEEE 802.11n WLAN is carried out in terms of QoS, by varying random RTS threshold using OPNET Modeler 17.5 academic. It is observed that WLAN behavior widely diverge in terms of QoS with variations in RTS threshold values.

Performance Investigation of Reactive AODV and Hybrid GRP Routing Protocols under Influence of IEEE 802.11n MANET

Abstract: In modern technology era, the endeavor of MANET is to provide proficient wireless communication by adopting adhoc routing functionality in mobile nodes. The MANET nodes result in frequent network topology changes, making routing a challenging task. In past, Reactive routing approaches are used as a popular technique that provides scalable solution to relatively large MANET networks, where Hybrid MANET routing algorithms are introduced comprises of both reactive and proactive routing properties. This work is an attempt towards a comprehensive performance evaluation of commonly used reactive AODV protocol with hybrid GRP routing algorithm under varying node density conditions in terms of QoS using OPNET Modeler 17.5 Academic.

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

Abstract: 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.

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

Abstract: 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.

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

Abstract: 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.