Internet of Things (IoT) consists of sensors embed with physical objects that are connected to the Internet and able to establish the communication between them without human intervene applications are industry, transportation, healthcare, robotics, smart agriculture, etc. The communication technology plays a crucial role in IoT to transfer the data from one place to another place through Internet. This paper presents various communication protocols, namely Zigbee, Bluetooth, Near Field Communication (NFC), LoRA, etc. Later, it provides the difference between different communication protocols. Finally, the overall discussion about the communication protocols in IoT.

A Recent Survey on Internet of Things (IoT) Communication Protocols

The world is surrounded by technology. There are lots of devices everywhere around us. It is impossible to imagine our lives without technology, as we have got dependent on it for most of our work. One of the primary functions for which we use technology or computers especially is to store and transfer data from a host system or network to another one having similar credentials. The restriction in the capacity of computers means that there’s restriction on amount of data which can be stored or has to transport. So, in order to tackle this problem, computer scientists came up with data compression algorithms. A file compression system’s objective is to build an efficient software which can help to reduce the size of user files to smaller bytes so that it can easily be transferred over a slower Internet connection and it takes less space on the disk. Data compression or the diminishing of rate of bit includes encoding data utilizing less number of bits as compared to the first portrayal. Compression can be of two writes lossless and lossy. The first one decreases bits by recognizing and disposing of measurable excesses, and due to this reason, no data is lost or every info is retained. The latter type lessens record estimate by expelling pointless or less vital data. This paper proposed a file compression system for big data as system utility software, and the users would also be able to use it on the desktop and lossless compression takes place in this work.

Storage Optimization Using File Compression Techniques for Big Data

Event Recommendation System Using Machine Learning Techniques

AbstractLoans are a very fundamental source of any bank’s revenue, so they work tirelessly to make sure that they only give loans to customers who will not default on the monthly payments. They pay a lot of attention to this issue and use various ways to detect and predict the default behaviors of their customers. However, a lot of the time, because of human error, they may fail to see some key information. This paper proposes a better approach using machine learning approaches like KNN, decision tree, SVM and logistic regression to predict defaulters. The accuracy of these methods will also be tested using metrics like log loss, Jaccard similarity coefficient and F1 Score. These metrics are compared to determine the accuracy of prediction. This can help banks conserve their manpower and fiscal resources by reducing the number of steps they have to take in order to check if somebody is eligible for a loan.KeywordsMachine learningLoan predictionBankingCredit risk managementPredictorClassifiersPython 

Loan Default Prediction Using Machine Learning Techniques

AbstractPresently, healthcare is the most important domain across the globe. Amidst this pandemic, all the doctors are much occupied and extremely busy. Getting proper treatment from doctor is getting difficult due to unavailability of doctors. Moreover, if a person feels uneasy and does not know what exactly the problem is, they really do not know which doctor they should take an appointment with. So, in this chapter intend to provide a web application as an all-in-one solution with an integrated voice assistant, which can help you with the process of disease diagnosis. For those doctors and patients who may need a second brain to make sure the diagnosis is correct, this is a platform with an AI-based medical voice assistant. It can work verbally with the doctor/patient and can assist him/her with the diagnostic process. This voice assistant should be able to select a patient’s illness on a confidence score to support diagnosis operations. Such a software can help both physicians and patients. Depending on the predicted disease/illness, the doctor may give the patient an e-prescription using the help of a voice assistant. The patient can also order medication using a voice assistant. The archives are stored in an orderly fashion, so users do not have to worry about losing them. This application is exclusively made to assist doctors and patients with intention of giving medical care in an interactive, lifesaving and resource saving manner. This application is a minor step in achieving a bigger target of completely digitalizing our medical care..KeywordsAICovid-19PredictionNLPEMR’s 

AI-Based Medical Voice Assistant During Covid-19

In our present age, there are numerous applications and devices for converting a standard picture into a picture, which is built from its predominant shading palettes. ‘predominant colors’ are the hues in the picture, in which the pixels of the picture contain the particular shading. These hues are then removed and another picture is then framed from this picture. However, this is just a speculation of the issue. In this undertaking, we dive into much more proficient techniques with a specific end goal to remove the prevailing shading palettes from the picture. In this manner, utilizing a grouping calculation and a significantly more ideal instatement technique, effective shading palette is extracted. The grouping calculation being utilized here is K-implies bunching calculations which is much more effective calculation than the other bunching calculations out there. The bunching calculation groups the pixels in bunches in view of their shading. The palettes separated from the picture are then utilized and another picture is reproduced.

V. P. Hara Gopal

Papers

Dominant Color Palette Extraction by K-Means Clustering Algorithm and Reconstruction of Image