Volume 9 | Issue 6 |

 Abstract

The paper proposes a hand free human-machine interaction (HMI) system to give a better way for communication between humans and computers. It is mostly seen that long hours in front of the pc and using the mouse puts the user's hand for too long in a bad posture that increases inflammation in the wrist and hand, leading to some major issues. Additionally, the main focus is on the usage of the modern technology by the handicapped people who tend to get a lot of boost, since it is a hindrance to them to use the mouse. The following paper develops a modern way for differently abled people to use the mouse with just their eyes. The proposed system carries the human eyes to perform the movement of the mouse cursor. This paper also carries out the design of a machine learning and deep learning approach with some inbuilt dataset to classify the individual eyes of both human eyes with quite a high accuracy that ensures the control over the movement. The use of filters which is provided by the libraries which are used to remove the noise to obtain smooth and accurate operation the details along with the performance evaluation shows that the proposed HMI system has extensive control over its performance for differently abled people.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Webcam, Eye cursor, Deep learning, Machine learning .

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Ligament injury, Rehabilitation, Post operative management, Physiotherapy.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The main objective of this project is, any distribution network is likely to get faults, on and off the supplier as well as user. Majorly a supply line can be affected by conditions of over voltage and over current, and also under voltage condition. During the event of any fault, the event goes unreported for long length of time. Manual reporting can lead to long interruption. To overcome this problem, a system is developed that will detect the changes in voltage and current guidelines, and using a micro controller based circuit. The faults can be classified based on comparison between the values received from rated guidelines of the distribution side power lines. Whenever the preset dividing line is crossed, the micro controller instantly initiates a message to the area lineman and the control station stating the exact street location where fault is happening. The real purpose of detecting fault in real time is to protect the transformer at the earliest.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Microcontroller, Underground, Cable, LCD, Arduino.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In the present work, the aim is to successfully formulate solid dispersion and incorporated it into a fast disintegrating tablet of aprepitant. Firstly increases the solubility of poorly water-soluble aprepitant by solid dispersion and then it is formulated into FDTs with improved patient compliance and convenience. Solid dispersions are prepared by the fusion method and solvent evaporation method. The formulation F9 is selected for best formulation because it shows the % yield 95%, drug content, dissolution 94%. after then selected batch F9 is formulated into FDTs results are disintegration time 12 seconds, dissolution drug release 99 % of this formulation respectively.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Solubility, Solid dispersion, Fast disintegrating tablet, Aprepitant.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Optical character recognition, Number plate, Vehicle, Accident reporting system.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This project is aimed at developing an Online Help Desk (OHD) for the facilities available in the college campus and industries. This is an Intranet based application that can be accessed throughout the campus. This system can be used to automate the workflow of service requests for the various facilities in the campus. This is an integrated system that covers different kinds of facilities like class-rooms, labs, hostels, mess, canteen, gymnasium, computer centre, faculty club etc. Registered users (students, faculty, lab-assistants and others) will be able to log in a request for service for any of the supported facilities. These requests will be sent to the concerned people, who are also valid users of the system, to get them resolved. There are features like email notifications/reminders, addition of a new facility to the system, report generators etc.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Online Help Desk (OHD), Software Process Model

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Personality is a parameter that makes one individual different from the other individual. Predicting personality has many applications in real world. The main objective of this paper is to take textual data as input from the user and then run the trained machine learning model on this data to predict his 4 personality traits which are Introversion vs Extroversion, Sensing vs Intuition, Thinking vs Feeling, Judging vs Perceiving. The main objective is to build an application where users can answer the questions which are processed and analyzed to output his personality traits. The output is a string of 4 characters where each character determines a personality trait, total of 16 personality types are possible. The Machine learning model XGboost is used to classify the text and output four personality traits. Processing of large textual data is to be done using Natural Language Processing (NLP) techniques with the help of nltk libraries to process and categorize the data. In order to increase the performance of the model hyper parameter tuning along with cross fold validation is done

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Logistic regression, Naive Bayes classifiers, XGBoost, Decision Tree

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Emotional Intelligence, SMT, Wives and Dual Career Families

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Dropout rates for students are increasing. We need to make certain measurements to find out the factors which are leading to the dropout of the student by using machine learning techniques with which we can choose an alternate way to select the dropout rates of the student from the course. We can make an analysis of each and every student by identifying the most related features. There are many factors which are responsible for the dropouts of the students like financial, economic, and many other problems. If the dropout rate of the students is increasing then the employment opportunities of the students will decrease gradually. Here we are going to implement some of the Machine Learning techniques to identify the students who are likely to drop out. Our main task is to apply different machine learning techniques to the selected features. A comparison is made for machine learning algorithms with each metric, and the model that gives the more appropriate result is considered the best one.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Dropouts, Identification, Features, Machine Learning Techniques.

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Creative Commons Attribution 4.0 and The Open Definition