Design and Efficient Network Investigation of Passive Periodic Drop Attack

A Mobile Ad Hoc Network (MANET) is much more vulnerable to various security attacks due to its high mobility, multi-hop communication and the absence of centralized administration. In this paper, we investigate the impact of Jellyfish periodic dropping attack on MANETs under different routing protocols. This investigate is under the class of denial-of-service attack and targets closed loop flows which results in delay and data loss. In this paper, the simulation results are gathered using OPNET network simulator and its effect on network performance is studied by analysing re-transmission attempts, network load and throughput. The results have shown that the impact of Jellyfish periodic dropping attack which reduces the network performance. Performance shows OLSR performs better than AODV under periodic drop attack.

Data Preprocessing: The Techniques for Preparing Clean and Quality Data for Data Analytics Process

The model and pattern for real time data mining have an important role for decision making. The meaningful real time data mining is basically depends on the quality of data while row or rough data available at warehouse. The data available at warehouse can be in any format, it may huge or it may unstructured. These kinds of data require some process to enhance the efficiency of data analysis. The process to make it ready to use is called data preprocessing. There can be many activities for data preprocessing such as data transformation, data cleaning, data integration, data optimization and data conversion which are use to converting the rough data to quality data. The data preprocessing techniques are the vital step for the data mining. The analyzed result will be good as far as data quality is good. This paper is about the different data preprocessing techniques which can be use for preparing the quality data for the data analysis for the available rough data.

A Review of Efficient Dynamic Key Management Scheme for Heterogeneous Wireless Sensor Networks

Security has been one of the most critical concerns for wireless sensor network (WSN) systems in recent years. Application of WSN has faced criticism in several fields due to limited flexibility and security in the long term. This study aimed at reviewing the dynamic key management schemes for Heterogenous WSN systems to determine efficient management schemes. Applicability of notable schemes such as Basic scheme, and hybrid schemes under dynamic key management depends majorly on the central key controller presence

Technical Aspects of Wireless Sensor Networks (WSNs)

Wireless sensor networks (WSNs) are a new kind of wireless networks that are becoming very popular with a large number of civilian and military applications. A wireless sensor network (WSN) is a wireless network that contains distributed independent sensor devices that are meant to monitor physical or environmental conditions. AWireless Sensor Network consists of a set of connected tiny sensor nodes, which communicate with each other we can also interchange information and data. These nodes obtain information on the environment such as temperature, pressure, or humidity and this information is stored in a base station. The latter sends the info to a wired network or activates an alarm or an action, depending on the type of data being monitored.

Early Prediction of Potato Leaf Diseases Using ANN Classifier

Potatoes are cultivated in several states of India. Potatoes provides a low-cost energy in human diet. Potatoes are used in industry for making dried food products. Early blight and Late blight are major disease of potato leaf. It is estimated that the major loss occurred in potato yield due to these diseases. In this research, we have collected sample of potato leaf images from Plant Village dataset. This dataset contains 2152 images of potato leaf. It has 3 class of sample of Healthy Leaf, Early Blight and Late Blight. The 76 features are extracted from these images regarding color, texture and area. The extracted features are used to develop a classifier. The developed classifier is based on neural network for prediction and classification of potato image samples. The Feed Forward Neural Network (FFNN) Model is used for prediction and classification of unknown leaf. The accuracy of model is achieved 96.5%. Classifier is helpful in early and accurate prediction of the leaf diseases of potato crop.

Maintaining the Data Integrity and Data Replication in Cloud using Modified Genetic Algorithm (Mga) and Greedy Search Algorithm (Gsa)

Cloud computing is an emerging paradigm thatprovides computing, communication and storage resourcesas a service over a network. Data Integrity is an important aspect which ensures the quality of the data stored in the cloud. Data Replication is the key factor which restores the corrupted data stor

Human Activity Recognition System

Modern human activity recognAition systems are mainly trained and used upon video stream and images data that understand the features and actions variations in the data having similar or related movements. Human Activity Recognition plays a significant role in human-to-human and human-computer interaction. Manually driven system are highly time consuming and costlier. In this project, we aim at designing a cost-effective and faster Human Activity Recognition System which can process both video and image in order to recognize the activity being performed in it, thereby aiding the end user in various applications like surveillance, aiding purpose etc. This system will not only be cost effective but also as a utility-based system that can be incorporated in a large number of applications that will save time and aid in various activities that require recognition process, and save a lot of time with good accuracy Also, it will aid the blind people in availing the knowledge of their surroundings.

An Insight into Fuzzy Logic Computation Technology and Its Applications in Agriculture and Meteorology

Speaking of recent advances, many computing technologies have been applied to several domains and have proved to provide more approximate and acceptable results. Fuzzy logic being one of them has been very useful in solving many real-world problems that are inherent for their uncertainty, complexity, impreciseness and a high degree of randomness. Soft computing aims to mimic human thinking and thus solve problems as a human does. The systems embedded with one or more soft computing technologies tend to make decisions quicker (reducing the processing timeframe) and more accurate in the light of uncertain and indefinite data. This paper aims at an extensive review of fuzzy logic also unraveling some of the applications of the same in the field of agricultural science and meteorology.

An Overview of Multi Agent System for Sports and Healthcare Industry

Players more often engage in excessive physical activities during exercise session as well as in the game session because results of the games highly depend over the performance of participants that can be degraded due to various factors current health status, injury history, exercise types and duration, training and game experience. A Multi agent System can analyze all these factors and the overall performance of the participants can be improved using feedback. In this paper, the role of the Artificial Intelligence, Expert System, Machine/Deep Learning/Neural Networks in the sports and healthcare industry will be explored.

A Smart Structure for Environmental Sustainability in Rose Fields

Indian floriculture industry has grown by multiple folds in the past decade and has become one of the top ten leading producers of varieties of flower production. This tremendous growth of floriculture industry has proportionally also contributed to the depletion of environmental system. The current work narrows down to handle the environmental hazards of rose farming in the floriculture industry through technology-driven fertilization process. The composition and quantity of nutrients, and the period of its application is very crucial for the plant’s growth.The smart fertigation process helps in providing a balanced nutrient dose to the plants.The internet of things (IoT) is defined as a network of physical objects called as “things” that have embedded with sensors and software that allow these objects to communicate with each other. These objects areaccessed, monitored and controlled remotely. While the reduction of one of the nutrients might result in poor development of the plant, overfeeding of nutrients leads of stagnation of salts and soil wilting. The paper proposes a smart IoT-based fertilizer application infrastructure for an optimal growth of plants with under a controlled usage of fertilizers.The dependency of fertilizers and insecticides during each stage of the plant development is monitored and a smart fertigation infrastructure is built to automate the process of feeding the plants with appropriate chemicals at appropriate intervals.