Volume 5 | Issue 4 |

 Abstract

Availability of organic input and output is critical for improve of organic forming in the country. Development of efficient marketing system is the need of the hour for strengthening the organic production in India. This paper made a humble attempt to understanding the buyer behavior and brand loyalty about organic product and marketing in Coimbatore city. The results concluded that most of the consumer especially in urban people prefer organic food product. Marketing of organic product is so poor in study area so the demand for organic product is increases but supply is very low. The major reasons are organic producer are low, adequate market facility is not there, few number of shops, lack of awareness, and so on. Therefore if farmer as well as government give interest to organic farming easily enhancing good marketing system in Coimbatore.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Organic products, price, attitude, Health consciousness, consumer satisfaction.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Differentiation is a key factor to position a product in the mind of the customer. The concept when applied by a marketer can either be a relevant differentiation or an irrelevant differentiation. Studies reveal that there are instances where customers view irrelevant differentiation as a cue for purchase. But other studies propone that only relevant differences can provide substantial benefit for the product. This study has intended to find the mindset of a customer with regard to differentiation. The conceptual explanations to product differentiation as seen through relevant studies in the domain are brought out in the first phase and later information relevant to those were collected from consumers to gain clarity about the concept. Differentiation is seen as a strategic advancement that a firm brings forth against competition. A marketing strategy is effective only when it is able to create a long term impact on the consumer for purchase and repurchase. So it has to be understood whether any type of gimmicks in the name of differentiation is accepted by the consumer or is it that the consumer looks for substantial improvements that benefit him/her. The study is done on the consumers of Kerala with regard to selected personal care products. The study indicates that consumers are conscious of the benefits they receive from products they choose and act accordingly. Differentiation is used as a key to choose from umpteen numbers of variants but only when it could provide benefits that are important to the consumer. A repeated purchase is apparently considered only when such a differentiation is plausible to the consumer.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Consumer behavior, differentiation, personal care products, Kerala

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Ad hoc network is a self organizing neteork connection and it has no infrastructure to form any specific network that can change locations on the fly. Packet losses in network due to link failure in ad hoc network. In this paper, we maintain log at each router to find out where the loss occurred and a special scheme used is Optimum Link State Routing Protocol (OLSR) that uses the effective combination of proactive and reactive routing protocols. This protocol uses intra-zone and inter zone-protocol to route the packet to its destination without any loss.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Log record, proactive, reactive, OLSR.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

A wireless sensor network (WSN) is a wireless network consisting of spatially distributed autonomous devices using sensors to monitor physical or environmental conditions. WSNs measure environmental conditions like temperature, sound, pollution levels, humidity, wind speed and direction, pressure, etc. The development of wireless sensor networks was motivated by military applications such as battlefield surveillance, today such networks are used in many industrial and consumer applications, such as industrial process monitoring and control, machine health monitoring, and so on. This paper describes about the application of wireless sensor networks in water pipeline monitoring. Water distribution systems present a significant challenge for structural monitoring. They comprise a complex network of pipelines buried underground that are relatively inaccessible. Maintaining the integrity of these networks is vital for providing clean drinking water to the general public. This paper presents the design development and testing of a smart wireless sensor network for leak detection in water pipelines, based on the measurement of relative indirect pressure changes in pipes. The sensors are capable of measuring pressure changes due to leaks. These pressure profiles can also be used to locate the leaks. These capabilities are vital for reducing the time taken to identify and repair failures and hence, mitigating impacts on water supply. The developed monitoring system has significant advantages such as low cost, great flexibility, low power at high duty cycles, scalability etc over currently used telemetry systems.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Autonomous Devices, Structural Monitoring, Sensors, Scalability.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

With the growth of Internet, there has been a tremendous increase in the number of attacks and therefore Intrusion Detection Systems (IDS’s) has become a main stream of information security. The purpose of IDS is to help the computer systems to deal with attacks. This anomaly detection system creates a database of normal behaviour and deviations from the normal behaviour to trigger during the occurrence of intrusions. Based on the source of data, IDS is classified into Host based IDS and Network based IDS. In network based IDS, the individual packets flowing through the network are analyzed where as in host based IDS the activities on the single computer or host are analyzed. The feature selection used in IDS helps to reduce the classification time. In this paper, the IDS for detecting the attacks effectively has been proposed and implemented. For this purpose, a new feature selection algorithm called Optimal Feature Selection algorithm based on Information Gain Ratio has been proposed and implemented. This feature selection algorithm selects optimal number of features from KDD Cup dataset. In addition, two classification techniques namely Support Vector Machine and Rule Based Classification have been used for effective classification of the data set. This system is very efficient in detecting DoS attacks and effectively reduces the false alarm rate. The proposed feature selection and classification algorithms enhance the performance of the IDS in detecting the attacks.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Intrusion Detection; Information Gain; Support Vector Machine; Feature Selection Technique and Classification

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Classification problems in high dimensional data with small number of observations are becoming more common especially in microarray data. The increasing amount of text information on the Internet web pages affects the clustering analysis [1]. The text clustering is a favorable analysis technique used for partitioning a massive amount of information into clusters. Hence, the major problem that affects the text clustering technique is the presence uninformative and sparse features in text documents. A broad class of boosting algorithms can be interpreted as performing coordinate-wise gradient descent to minimize some potential function of the margins of a data set [1]. This paper proposes a new evaluation measure Q-statistic that incorporates the stability of the selected feature subset in addition to the prediction accuracy. Then we propose the Booster of an FS algorithm that boosts the value of the Q statistic of the algorithm applied.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

High dimensional data classification; feature selection; stability; Q-statistic; Booster

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Consider objects that are tagged with keywords and are embedded in a vector space. The presence of keywords in feature space allows for the development of new tools to query and explore these multi-dimensional datasets. We propose a method called Projection and Multi Scale Hashing that uses random projection and hash-based index structures, and achieves high scalability and speedup. In multi-dimensional spaces, it is difficult for users to provide meaningful coordinates, and our work deals with another type of queries where users can only provide keywords as input. Images are represented using color feature vectors, and usually have descriptive text information (e.g., tags or keywords) associated with them. Our system is based on real datasets shows that we can show the efficient searching of keywords in multidimensional datasets.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Querying, High dimensional Data, Indexing, Hashing, Dataset.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Relevance Feedback, indexing, information retrieving, classifying, collecting data.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In recent years, shopping online is becoming more popular. When it need to decide whether to purchase a product or not on line, the opinions of others become important. It presents a great opportunity to share our viewpoints for various products purchase. However, people face the information overloading problem. How to mine valuable information from reviews to understand a user’s preferences and make an accurate recommendation is crucial. Traditional recommender systems consider some factors, such as user’s purchase records, product category, and geographic location. In this work, it proposes a sentiment-based rating prediction method to improve prediction accuracy in recommender systems. Firstly, it proposes a social user sentimental measurement approach and calculates each user’s sentiment on items. Secondly, it not only considers a user’s own sentimental attributes but also take interpersonal sentimental influence into consideration. Then, consider item reputation, which can be inferred by the sentimental distributions of a user set that reflect customers’ comprehensive evaluation. At last, by fusing three factors-user sentiment similarity, interpersonal sentimental influence, and item’s reputation similarity into recommender system to make an accurate rating prediction. It conducts a performance evaluation of the three sentimental factors on a real-world dataset. Experimental results show the sentiment can well characterize user preferences, which help to improve the recommendation performance.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Item reputation, Reviews, Rating prediction, Recommender system, Sentiment influence, User sentiment.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Climate change is a multifaceted phenomenon with a wide range of impacts on the environment. Currently, with the competing uses of land and the growing world population, we are challenged to produce more in less area with diminishing resources, confronted with climate change and the unpredictable local microclimate adversely affecting crop productivity. Abiotic stresses will remain a challenge to the natural environment and agriculture. The challenges before us in plant biology and crop improvement are to integrate the systems level information on abiotic stress response pathways, identify stress protective networks, and engineer environmentally stable crops that yield more. In the present study, two different antioxidant enzymes namely copper-zinc superoxide dismutase derived from Potentilla astrisanguinea (SOD) and ascorbate peroxidase (APX) from Rheum austral both of which are high altitude cold niche area plants of Western Himalaya were cloned and simultaneously over-expressed in Arabidopsis thaliana to alleviate PEG stress. It was found that the transgenic plants over-expressing both the genes were more tolerant to PEG-induced stress during growth and development. In both transgenic plants higher levels of total antioxidant enzyme activities, chlorophyll content, total soluble sugars, proline content and lower levels of ROS, ion leakage were recorded when compared to the wild type (WT) during PEG stress. The transgenics in the present study showed 2-3 fold increase in SOD and APX activity resulting in the enhanced stress tolerance to PEG stress than WT. In terms of growth and development in number of leaves, rosette area and root length, which was observed to be significantly higher in transgenic lines compared to WT. Also, the transgenic lines showed higher germination percentage at various levels of PEG. Overall, SOD and APX transgenic lines were able to express greater drought tolerance and thus the present work would pave way for the judicious use of these genes effectively into the relevant crop plants leading to optimum growth and enhanced yield under environmentally stressed conditions.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Climate Change, Abiotic stress, Antioxidant genes, Arabidopsis thaliana

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Creative Commons Attribution 4.0 and The Open Definition