After listening to a wake word or order, voice assistants come in quite compact packages and can accomplish a range of acts. They are able to switch on the lights, answering basic questions, playing music, place online orders, etc. As voice assistants become more robust, their usefulness can also be extended in both the personal and business areas. Various descriptive variables are fused in speech signals, leading to considerable difficulties interpreting any of the variables. There are various effective algorithms for speech and face recognition for the real - world applications. When a human try to communicate with a bot or vice-versa, there occurs so many difficulties in information sharing. This paper deals with one such application of bot assistance in hospitals, where a patient communicates with the bot assistant. Though the task seems to be quite easy, it involves a main threat: surrounding noise, word error rate, accuracy of the speech. An obvious impression is to factorize the frame of audio into various enlightening variables, but it turns out to be extremely difficult. The face of the patient who enters the healthcare premise is captured and analysed through the Deep Neural Network (DNN) based face recognition algorithm. Once identifying the user, the information regarding the patient is gathered from the database and given as a voice over output. Now, it is necessary to measure the accuracy of the word recognition and the word error rate. For this Cascade Deep Factorization (CDF) and Iterative Signal Enhancement (ISE) algorithm are used and discussed in this paper. For several speech processing functions like speaker recognition, this factorization and reconstruction method offers possible values.

Voice assistants usually respond to voice commands and provide the user with specific details about his question. Currently, voice assistants can process product orders, answer questions, perform tasks such as playing music, or initiate a quick phone call with a friend. For voice assistants, the long-term goal is to serve as a smart bridge between humans and the immense information and capabilities delivered by the Internet. Taking away the need to use some gadget or screen to communicate in various locations with the internet, technology or other humans. This paper deals with such a voice assistance for schedule maintenance for individual which starts with the face recognition. Once a person enters the classroom / office, their face is captured and the face identification is done. If the identified person is the authorised one, then the system responds with a greeting message. When the person starts to ask about the schedule to the system, it responds with the schedule of the day of that identified person. In case of emergency schedules like meetings, the intimation is sent to the user as a message to alert him/her.

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The technological advancements applied in the area of healthcare systems helps to meet the requirement of increasing global population. Due to the infections by the various microorganisms, people around the world are affected with different types of life-threatening diseases. Among the different types of commonly existing diseases, diabetes remains the deadliest disease. Diabetes is a major cause for the change in all physical metabolism, heart attacks, kidney failure, blindness, etc. Computational advancements help to create health care monitoring systems for identifying different deadliest diseases and its symptoms. Advancements in the machine learning algorithms are applied in various applications of the health care systems which automates the working model of health care equipment’s and enhances the accuracy of disease prediction. This work proposes the ensemble machine learning based boosting approaches for developing an intelligent system for diabetes prediction. The data collected from Pima Indians Diabetes (PID) database by national institute of diabetes from 75664 patients is used for model building. The results show that the histogram gradient boosting algorithms manages to produce better performance with minimum root mean square error of 4.35 and maximum r squared error of 89%. Proposed model can be integrated with the handheld biomedical equipment’s for earlier prediction of diabetes.

Home security system plays a predominant role in the modern era. The purpose of the security systems is to protect the members of the family from intruders. The main idea behind this system is to provide security for residential areas. In today’s world securing our home takes a major role in the society. Surveillance from home to huge industries, plays a significant role in the fulfilment of our security. There are many machine learning algorithms for home security system but Haar-cascade classifier algorithm gives a better result when compared with other machine learning algorithm This system implements a face recognition and face detection using Haar-cascade classifier algorithm, OpenCV libraries are used for training and testing of the face detection process. In future, face recognition will be everywhere in the world. Face recognition is creating a magic in every field with its advanced technology. Visitor/Intruder monitoring system using Machine Learning is used to monitor the person and find whether the person is a known or unknown person from the captured picture. Here LBPH (Local Binary Pattern Histogram) Face Recognizer is used. After capturing the image, it is compared with the available dataset then their respective name and picture is sent to the specified email to alert the owner.