In this modern era where energy is the major concern worldwide, it is our prior responsibility & liability to save energy effectively. With the development of technology, where automation system plays a vital role in daily life experience and also it is being preferred over the traditional manual system today. The main purpose of this project is to invent an intelligent system which can make decisions for luminous control (ON/OFF/DIM) considering the light intensity. Here the day and night mode can be identified by fixing a particular intensity value on LDR sensor and street light can be controlled by IR sensor. The interesting part of this paper is the installation of solar cell for the power supply but in course of circumstances, if the solar cell is unable to do so, a secondary backup DC current will maintain the situation immediately. Another remarkable part of this project is to maintain the traffic signal automatically without any help of traffic police and monitor the entire system through internet by installing surveillance camera. All the components of this project are very simple and cost effective but efficient to make a reliable intelligence system.

The modern era is Robotic and IoT era. Robots are reducing human works. Robots are broadly using in Restaurants, Farming, Hospitals and various fields now a days. This work results a low cost IoT Robot that can work like a waiter in a restaurant. The developed robot can follow line and avoid obstacles. The robot can take and serve orders to the customers. It reads RFID from tables to recognize particular orders from customers. The collected data will be saved in a cloud database. Taken orders by robots will automatically go to the chefs. Each order bill will also be generated on the robot. The robot will be able to take the bills also. The full system is made in very low cost than previously made systems. It will not increase unemployment problem too.

Botnets is one of the most critical cyber security threats that confronted by organizations day by day. Botnet has used distinctive strategies, topologies and communication protocol in specific levels in their life cycle. Identifying of botnets has emerged as a very challenging topic, particularly for the reason that they can improve their method at any time. Nowadays, most of the recent botnet detection techniques cannot locate modern botnets in an early stage. Mainly, botnets follow command and control infrastructure. Nowadays, botnet is an interesting and very vital research topic for researchers in our cyber security. This Paper represents a completely comprehensive evaluation that extensively discusses the botnet problem; this survey classifies all of the possible botnet detection methods, also summarizes previously published studies and recent works.

One of the most critical issues of the mortality rate in the medical field in current times is breast cancer. Nowadays, a large number of men and women is facing cancer-related deaths due to the lack of early diagnosis systems and proper treatment per year. To tackle the issue, various data mining approaches have been analyzed to build an effective model that helps to identify the different stages of deadly cancers. The study successfully proposes an early cancer disease model based on five different supervised algorithms such as logistic regression (henceforth LR), decision tree (henceforth DT), random forest (henceforth RF), Support vector machine (henceforth SVM), and K-nearest neighbor (henceforth KNN). After an appropriate preprocessing of the dataset, least absolute shrinkage and selection operator (LASSO) was used for feature selection (FS) using a 10-fold cross-validation (CV) approach. Employing LASSO with 10-fold cross-validation has been a novel steps introduced in this research. Afterwards, different performance evaluation metrics were measured to show accurate predictions based on the proposed algorithms. The result indicated top accuracy was received from RF classifier, approximately 99.41% with the integration of LASSO. Finally, a comprehensive comparison was carried out on Wisconsin breast cancer (diagnostic) dataset (WBCD) together with some current works containing all features.