International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials 

About: International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials is an academic conference. The conference publishes majorly in the area(s): Systems thinking & Soft systems methodology. Over the lifetime, 308 publications have been published by the conference receiving 1994 citations.

Crop Selection Method to maximize crop yield rate using machine learning technique

Abstract: Agriculture planning plays a significant role in economic growth and food security of agro-based country Se- lection of crop(s) is an important issue for agriculture planning It depends on various parameters such as production rate, market price and government policies Many researchers studied prediction of yield rate of crop, prediction of weather, soil classification and crop classification for agriculture planning using statistics methods or machine learning techniques If there is more than one option to plant a crop at a time using limited land resource, then selection of crop is a puzzle This paper proposed a method named Crop Selection Method (CSM) to solve crop selection problem, and maximize net yield rate of crop over season and subsequently achieves maximum economic growth of the country The proposed method may improve net yield rate of crops

Comparative analysis of machine learning algorithms along with classifiers for network intrusion detection

Abstract: Intrusion detection is one of the challenging problems encountered by the modern network security industry. A network has to be continuously monitored for detecting policy violation or suspicious traffic. So an intrusion detection system needs to be developed which can monitor network for any harmful activities and generate results to the management authority. Data mining can play a massive role in the development of a system which can detect network intrusion. Data mining is a technique through which important information can be extracted from huge data repositories. In order to spot intrusion, the traffic created in the network can be broadly categorized into following two categories- normal and anomalous. In our proposed paper, several classification techniques and machine learning algorithms have been considered to categorize the network traffic. Out of the classification techniques, we have found nine suitable classifiers like BayesNet, Logistic, IBK, J48, PART, JRip, Random Tree, Random Forest and REPTree. Out of the several machine learning algorithms, we have worked on Boosting, Bagging and Blending (Stacking) and compared their accuracies as well. The comparison of these algorithms has been performed using WEKA tool and listed below according to certain performance metrics. Simulation of these classification models has been performed using 10-fold cross validation. NSL-KDD based data set has been used for this simulation in WEKA.

Smart garbage monitoring and clearance system using internet of things

Abstract: The increase in population, has led to tremendous degradation in the state of affairs of hygiene with respect to waste management system. The spillover of waste in civic areas generates the polluted condition in the neighboring areas. It may aggravate numerous severe diseases for the nearby people. This will humiliate the appraisal of the affected area. For eliminating or mitigating the garbage's and maintains the cleanness, it requires 'smartness based waste management system. This paper is proposed IOT based smart waste clean management system which checks the waste level over the dustbins by using Sensor systems. Once it detected immediately this system altered to concern authorized through GSM/GPRS. For this system used Microcontroller as an interface between the sensor system and GSM/GPRS system. To monitor and integrate an android application is developed for the desired information which is related to the various level of waste in different locations. This is ensued the greenish in the environment and support for swachh bharat for cleanness.

Prediction of breast cancer using voting classifier technique

Abstract: Breast cancer became one of the deadliest cancer in women. It occurs when the growth of the cells in breast tissue become out of control. Cells are the building blocks for the organs and tissues in the body. When the growth of new cells are uncontrolled then they build-up mass of tissue called tumor. The tumors are categorized in to benign and malignant tumors. Early diagnosis needs an accurate diagnosis procedure that can be used by physicians to classify whether the tumor is benign or malignant. Classification algorithms are used to classify whether it is benign or malignant tumor. The main objective of this paper is to compare the results of supervised learning classification algorithms and combination of these algorithms using voting classifier technique. Voting is one of the ensemble approach where we can combine multiple models for the better classification. The dataset is taken from Wisconsin University database.