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Author

L. SaiRamesh

Other affiliations: College of Engineering, Guindy
Bio: L. SaiRamesh is an academic researcher from Anna University. The author has contributed to research in topics: Routing protocol & Wireless sensor network. The author has an hindex of 5, co-authored 22 publications receiving 120 citations. Previous affiliations of L. SaiRamesh include College of Engineering, Guindy.

Papers
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Journal ArticleDOI
TL;DR: An adaptive IDS based on Fuzzy Rough sets for attribute selection and Allen's interval algebra is applied on network trace datasets in order to select a huge number of attack data for effective prediction of attacks in WSNs and a fuzzy and rough set based nearest neighborhood algorithm (FRNN) is proposed in this article for effective classification of network trace dataset.

67 citations

Journal ArticleDOI
TL;DR: A new and effective routing protocol named, “Intelligent Energy Aware Secured Algorithm for Routing (IEASAR)” is proposed which is secure by using a Trust based approach and is Energy efficient at the same time.
Abstract: Modern Wireless Sensor Networks (WSNs) requires special requirements in routing protocols because of nature of distribution and dynamic topology. The most important need for WSNs is energy efficient routing protocol that consumes optimal energy. It provides extension to the network’s life period. Nowadays a number of energy efficient routing protocols are proposed by various researchers in WSNs. However, security and energy efficiency in data collection and transmission in WSNs should be simultaneously considered for security challenges and to overcome limitation of WSNs. In this paper, we propose a new and effective routing protocol named, “Intelligent Energy Aware Secured Algorithm for Routing (IEASAR)” which is secure by using a Trust based approach and is Energy efficient at the same time. For this purpose, a new energy efficient protocol using Fuzzy C-means has been proposed in this paper. Moreover, a modified minimum spanning tree approach is applied here to identify the minimum distance path between the sender node and the destination node and hence an optimal and secured routing path is selected. Extensive simulations have been conducted in this work to verify the validity of our claims.

22 citations

Journal ArticleDOI
01 Sep 2020
TL;DR: The proposed intelligent learning model attains high classification accuracy for the imbalanced dataset above the traditional model, and could provide a better understanding regarding patient health.
Abstract: Medical decision support systems have been a core of intense research for years. The ongoing study shows that artificial intelligence has been accustomed to probe risk factors for hypertension. Factors, like health-damaging personal behaviors and changes in lifestyle and environment, are major contributors to chronic diseases. The goal of this research was to forecast the risk of developing hypertension by revealing hidden patterns in medical datasets. Quality of the data is the key to enhance the performance of learning model. But most healthcare data suffer from class imbalance problem, which induce the need for an intelligent model which can learn from such grimy data. This paper incorporates a novel approach by combing learning model and rule-based mining to offer decision support. Typically, the proposed work comprises two main implications. First suggests an intelligent learning model using boosting-based support vector machine to diagnose and expose multi-class categories in the imbalanced datasets. Finally, the enhanced predictive model is built upon the classification solution which will portray the innate data similarities. An intelligent fuzzy-based approach was employed to recognize frequent behavioral patterns. Based on these rules, valid decisions could be made to prevent hypertension. The suggested enhanced model is evaluated using a real-time hypertension dataset obtained through primary health centers. With the combination of ensemble strategies, the proposed intelligent learning model attains high classification accuracy for the imbalanced dataset above the traditional model. Thus, the efficient integration of personalized behavior with health data could provide a better understanding regarding patient health. In future this can serve as an eye toward personalized medicine.

16 citations

Proceedings ArticleDOI
R. Reshma, V. Sathiyavathi, T. Sindhu1, K. Selvakumar, L. SaiRamesh1 
07 Oct 2020
TL;DR: The proposed IoT system is composed of pH sensors, Humidity and temperature sensors, Soil moisture sensors, soil nutrient sensors (NPK) probes, microcontroller/microprocessor equipped with WiFi and Cloud storage, which helps to enhance the growth using an optimized farming process.
Abstract: Agriculture aided by IoT is called Smart Agriculture and it gives rise to precision farming. Soil Monitoring combined with Internet of Things (IoT) technology aids in the enhancement of agriculture by increasing yield through gauging the exact soil characteristics such as Moisture, Temperature, Humidity, PH, and Nutrition content/Fertility. This data is then gathered in cloud storage and with the appropriate data operations; it enabled us to optimize farming strategies and helped create a trend analysis. This, then, allows us to precisely utilize resources and steer the farming methods in prudent ways to optimize yield. The proposed IoT system is composed of pH sensors, Humidity and temperature sensors, Soil moisture sensors, soil nutrient sensors (NPK) probes, microcontroller/microprocessor equipped with WiFi and Cloud storage. When the sensors are implemented, they measure the corresponding characteristics and transmit time-stamped live data to the cloud server. These sensors work together and provide wholesome data to the analyst. For the recommending system, the SVM and Decision Tree algorithm is proposed to get the crop suitable for the given soil data and helps to enhance the growth using an optimized farming process.

16 citations

Proceedings ArticleDOI
01 Feb 2017
TL;DR: A novel approach based on the semi-supervised word alignment model (SWAM), which identifies the relations among the words in a sentence, is proposed, a graph-based algorithm where target opinion words are compared with the other opinion word and extract the long span relations between the words.
Abstract: In the fast moving world, peoples want to reduce the shopping time and purchase their needed products through online. In addition, online shopping provides the product reviews and helps the customer to get the better among the variety of brands. In this, mining the opinion words and the polarity of the reviews are the important task to detect the exact opinion of the customer reviews. In this paper, a novel approach is proposed based on the semi-supervised word alignment model (SWAM), which identifies the relations among the words in a sentence. It's a graph-based algorithm where target opinion words are compared with the other opinion word and extract the long span relations among the words. Unlike, syntax based method, this proposed model reduce the parsing errors by dealing with informal online texts. The mined reviews of this proposed system provides better precision when compared with standard unsupervised alignment review models. The experimental results show that this approach effectively mining the user reviews and provide the better recommendation.

16 citations


Cited by
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Journal Article
TL;DR: A survey on the techniques used for designing software to mine opinion features in reviews and how Natural Language Processing techniques such as NLTK for Python can be applied to raw customer reviews and keywords can be extracted.
Abstract: Now days, E-commerce systems have become extremely important. Large numbers of customers are choosing online shopping because of its convenience, reliability, and cost. Client generated information and especially item reviews are significant sources of data for consumers to make informed buy choices and for makers to keep track of customer’s opinions. It is difficult for customers to make purchasing decisions based on only pictures and short product descriptions. On the other hand, mining product reviews has become a hot research topic and prior researches are mostly based on pre-specified product features to analyse the opinions. Natural Language Processing (NLP) techniques such as NLTK for Python can be applied to raw customer reviews and keywords can be extracted. This paper presents a survey on the techniques used for designing software to mine opinion features in reviews. Elven IEEE papers are selected and a comparison is made between them. These papers are representative of the significant improvements in opinion mining in the past decade.

229 citations

Journal ArticleDOI
TL;DR: An improved version of Salp Swarm Algorithm (ISSA) is proposed in this study to solve feature selection problems and select the optimal subset of features in wrapper-mode and demonstrates that ISSA outperforms all baseline algorithms in terms of fitness values, accuracy, convergence curves, and feature reduction in most of the used datasets.
Abstract: Many fields such as data science, data mining suffered from the rapid growth of data volume and high data dimensionality. The main problems which are faced by these fields include the high computational cost, memory cost, and low accuracy performance. These problems will occur because these fields are mainly used machine learning classifiers. However, machine learning accuracy is affected by the noisy and irrelevant features. In addition, the computational and memory cost of the machine learning is mainly affected by the size of the used datasets. Thus, to solve these problems, feature selection can be used to select optimal subset of features and reduce the data dimensionality. Feature selection represents an important preprocessing step in many intelligent and expert systems such as intrusion detection, disease prediction, and sentiment analysis. An improved version of Salp Swarm Algorithm (ISSA) is proposed in this study to solve feature selection problems and select the optimal subset of features in wrapper-mode. Two main improvements were included into the original SSA algorithm to alleviate its drawbacks and adapt it for feature selection problems. The first improvement includes the use of Opposition Based Learning (OBL) at initialization phase of SSA to improve its population diversity in the search space. The second improvement includes the development and use of new Local Search Algorithm with SSA to improve its exploitation. To confirm and validate the performance of the proposed improved SSA (ISSA), ISSA was applied on 18 datasets from UCI repository. In addition, ISSA was compared with four well-known optimization algorithms such as Genetic Algorithm, Particle Swarm Optimization, Grasshopper Optimization Algorithm, and Ant Lion Optimizer. In these experiments four different assessment criteria were used. The rdemonstrate that ISSA outperforms all baseline algorithms in terms of fitness values, accuracy, convergence curves, and feature reduction in most of the used datasets. The wrapper feature selection mode can be used in different application areas of expert and intelligent systems and this is confirmed from the obtained results over different types of datasets.

224 citations

Journal ArticleDOI
01 Mar 2021
TL;DR: This paper provides a holistic coverage of the Internet of Things in Smart Cities by discussing the fundamental components that make up the IoT based Smart City landscape followed by the technologies that enable these domains to exist in terms of architectures utilized, networking technologies used as well as the Artificial Algorithms deployed in IoTbased Smart City systems.
Abstract: Internet of Things (IoT) is a system that integrates different devices and technologies, removing the necessity of human intervention. This enables the capacity of having smart (or smarter) cities around the world. By hosting different technologies and allowing interactions between them, the internet of things has spearheaded the development of smart city systems for sustainable living, increased comfort and productivity for citizens. The IoT for Smart Cities has many different domains and draws upon various underlying systems for its operation. In this paper, we provide a holistic coverage of the Internet of Things in Smart Cities. We start by discussing the fundamental components that make up the IoT based Smart City landscape followed by the technologies that enable these domains to exist in terms of architectures utilized, networking technologies used as well as the Artificial Algorithms deployed in IoT based Smart City systems. This is then followed up by a review of the most prevalent practices and applications in various Smart City domains. Lastly, the challenges that deployment of IoT systems for smart cities encounter along with mitigation measures.

153 citations

Journal ArticleDOI
TL;DR: A critical literature survey of recent intrusion detection protocols for IoT and WSN environments along with their comparative analysis is provided and a taxonomy of security and privacy-preservation protocols in WSN and IoT is highlighted.
Abstract: As we all know that the technology is projected to be next to humans very soon because of its holistic growth. Now-a-days, we see a lot of applications that are making our lives comfortable such as smart cars, smart homes, smart traffic management, smart offices, smart medical consultation, smart cities, etc. All such facilities are in the reach of a common man because of the advancement in Information and Communications Technology (ICT). Because of this advancement, new computing and communication environment such as Internet of Things (IoT) came into picture. Lot of research work is in progress in IoT domain which helps for the overall development of the society and makes the lives easy and comfortable. But in the resource constrained environment of Wireless Sensor Network (WSN) and IoT, it is almost inconceivable to establish a fully secure system. As we are moving forward very fast, technology is becoming more and more vulnerable to the security threats. In future, the number of Internet connected people will be less than the smart objects so we need to prepare a robust system for keeping the above mentioned environments safe and standardized it for the smooth conduction of communication among IoT objects. In this survey paper, we provide the details of threat model applicable for the security of WSN and IoT based communications. We also discuss the security requirements and various attacks possible in WSN and IoT based communication environments. The emerging projects of WSNs integrated to IoT are also briefed. We then provide the details of different architectures of WSN and IoT based communication environments. Next, we discuss the current issues and challenges related to WSN and IoT. We also provide a critical literature survey of recent intrusion detection protocols for IoT and WSN environments along with their comparative analysis. A taxonomy of security and privacy-preservation protocols in WSN and IoT is also highlighted. Finally, we discuss some research challenges which need to be addressed in the coming future.

90 citations

Journal ArticleDOI
TL;DR: This paper presents a comprehensive investigation of the fuzzy misuse detection schemes designed using various machine learning and data mining techniques to deal with different kinds of intrusions.

75 citations