Premananda Sahu

Bio: Premananda Sahu is an academic researcher from University Institute of Engineering and Technology, Panjab University. The author has contributed to research in topics: The Internet & Image segmentation. The author has co-authored 3 publications.

Internet of Things: Security, Challenges, Open Problems & Solutions

Abstract: The recent development of IOT emphasizes on the connection of most of the devices by means of smarter and intelligence way. These devices communicate with each other via internet without or/very less communication link of human being. The network of IoT is highly heterogeneous, uncontrolled, broad and perspicacious. All the data centric activities are done by 3 vital steps i.e sensing, analyzing and processing. The Internet of Things (IoT) is a cooperative system which connects a large number of computing devices which includes sensors, actuators, processors to a single IP address and the computing devices of IoT are provided with Unique Identifiers (UIDs). The major goal of IoT is to create a real world environment where frequently used agile objects coherently linked with internet. The growth rate of IoT devices has been increasing at a very high speed, so IOT can find its application in every field. All most all areas are using IoT to improve efficiency, customer service and value of the business, but in all respect IOT devices are still requires security and privacy optimization for future sustainable development.

Forecasting of Precipitation in India by Different Data Types using Investigation of Radial Basis Function Neural Network Model

Abstract: There is a rainy season occurs during the period from June to august in almost all geographical parts of India. Moreover, some of the states like Uttarakhand, Cherapunji, Mumbai, Tamil Nadu etc. may suffer from some natural disaster. If we early predict such misfortunes through the variety of big data collected for such distinct positions at a particular amount of time then certainly can save the life and goods from such big natural calamities. Such normalized data can be updated at a regular interval of time. In view of this, the time series data analysis provides a method to early aware and protects the life of people from such natural disasters. The proposed method exploited the use the Radial Basis Function Neural Network Model with back propagation algorithm to make compatible with time series data analysis to forecast the predication of rainfall for the state of Punjab, India. In this technique, two types of predictions are used which are based on fifteen and twenty days. The comparison results reveal those fifteen days prediction provides more effective classification accuracy than twenty days prediction.

Forecasting of Statistical Crisp Weather in India by Exploration of Different Machine Learning Techniques

Abstract: The winter season starts suddenly in the mid of the Dew and ends in Spring. If we will analyze in a better way, the cold starts in the mid of October and continue up to mid of February month. Generally, some the states like Jammu, Kashmir, Uttar Pradesh, Uttarakhand, Punjab etcetera the people are suffering from a heavy cold. At that time the temperature is as much as decreased so that the old generation people cannot tolerate it. Sometimes also the meteorological department is very busy predicting temperature because in India other natural disasters like floods, droughts, cyclones etcetera are also coming suddenly. So in this investigation work, we have designed an automatic model for crisp weather forecasting which may be considerate to the people, so that they can take precautions earlier. For this work, we have worked for Odisha state for the investigation purpose which uses barometric parameters as well as the machine learning techniques like Support Vector Machine along with Random Forest for our investigation work. The dataset that we have used is IMD which is used for both training and testing. In this model, we have used two prognoses which are based on five and seven days respectively. The contrast result babbles 5 days produce better classification accuracy than 7 days' prediction.

Automatic Crop Securing System Using IoT

Abstract: In terms of the development of a country's economy, agriculture is crucial as it is the main source for the survival of human life. But due to unexpected or unseasonal rains, there are lots of issues where farmers suffer a lot as the crops are destroyed and washed away due to heavy rains. By considering this issue we build our project which helps the farmers to be free from worries when there are heavy unexpected rains. The model which we built helps the farmers from unseasonal rains and saves water. This saved water can be used for various purposes and the main use among it is can reuse the saved water for farming, which decreases the regular water usage for the farmers. In this model, an automated sheet is inculcated which works by taking the inputs from the rain sensor and moisture sensor and protecting the whole field from unexpected or unseasonal rains. When it rains, the rain sensor turns on, and the soil sensor embedded in the ground begins responding to how much water is in the soil. If there is more water in the soil than is necessary, the controller receives the inputs which indicate the DC motor to run which opens the sheet automatically to close the crops using a polystyrene sheet. If there is any issue opening the sheet, information is passed to the farmers and then the operation is performed manually.

Medical Insurance Fraud Detection Based on Block Chain and Machine Learning Approach

Abstract: With the significant rise in medical costs, the Health Insurance Department's duty of controlling medical expenses has become increasingly vital. Traditional medical insurance settlements are paid per-service, which results in a lot of unnecessary costs. Now a day, the single-disease payment mechanism has been frequently employed to address this issue. However, there is a possibility of fraud with single-disease payments. In this work, the authors have presented a methodology for detecting the health insurance fraud entrenched block chain and Machine learning techniques like Support Vector Machine (SVM) and logistic Regression, that can automatically recognize apprehensive medical records to assure sustainable execution of single-disease payment and reduce medical insurance worker's workload. The authors have also proposed a medical record storage and management procedure based on consortium block chain to assure data security, immutability, traceability, and audit ability. The suggested system may effectively identify fraud and considerably increase the efficiency of medical insurance evaluations, as demonstrated by experiments on two real datasets from two 3A hospitals.

Medical Insurance Fraud Detection Based on Block Chain and Deep Learning Approach

Abstract: To control the medical expenses people are decided to do some insurance plans and the Health Insurance Department's duty of controlling medical expenses has become increasingly vital. Traditional medical insurance settlements are paid per-service, which results in a lot of unnecessary costs. Now a day, the single-disease payment mechanism has been frequently employed to address this issue. However, there is a possibility of fraud with single-disease payments. In this work, we have presented a methodology for detecting the health insurance fraud entrenched block chain and deep learning techniques, that can automatically recognize apprehensive medical records to assure sustainable execution of single-disease payment and reduce medical insurance worker's workload. We also proposed a medical record storage and management procedure based on consortium block chain to assure data security, immutability, traceability, and audit ability. The suggested system may effectively identify fraud and considerably increase the efficiency of medical insurance evaluations, as demonstrated by experiments on two real datasets from two hospitals.