Description:Title of The Invention
AN EDGE AND VISION BASED SYSTEM FOR PREDICTION OF EMPLOYEE STATE WITH DEEP LEARNING
Field of the Invention
This invention relates to an edge and vision based system for prediction of employee state with deep learning.
Background of the Invention
US0160224913A1 says that a method for estimating an employee engagement indicator is described, which includes receiving an employee's engagement and criticality data as input via a user interface. This employee's engagement data and criticality data are analyzed to categorize the employee based on engagement level and criticality level, which are determined based on the analyzed engagement data and criticality data, respectively. Furthermore, mapping the categorized engagement levels onto the categorized criticality levels aids in determining the employee's risk level, generating a notification over a computer network to another employee based on the mapping.
Research Gap: The method is only limited to measuring and tracking employee engagement levels of the employee, while the proposed method will be using deep learning methods to track turnover intention of the employee. Hence the invention will be focusing more on turnover intention (TO) to curb problem of employee turnover.
US2017030881A1 says that the invention relates to a method for analysing a liquid when inside a container in order to detect counterfeiting or adulteration of the liquid, the container being at least partially transparent to visible light. The method comprises the steps of: (a) measuring a first transmission spectrum through the container and the liquid at a first orientation of the container which defines a first optical path length through the liquid, (b) measuring a second transmission spectrum through the container and the liquid at a second orientation of the container which defines a second optical path length through the liquid, the second optical path length being different from the first optical path length, and the second spectrum at least partially overlapping with the first spectrum, (c) calculating the ratio (R(?)) of the first and second spectral intensities at each wavelength in the area of overlap, and(d) comparing this ratio (R(?)) to a reference measurement of the ratio for a non-counterfeit and unadulterated sample of the liquid being tested.
Research Gap: The model per research is focused on prediction of turnover intention, but the proposed invention can be used for tracking the turnover intention and taking corrective measures before the employee leaves the organization.
None of the prior art indicate above either alone or in combination with one another disclose what the present invention has disclosed.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
AI and machine learning based framework to predict the employee engagement in the industry. The claimed patent will enable to predict the retention a of an employee. It will help the organization to know about the employee retention period and also We are living in the business world where delivery of goods and services in time is highly required. An organization cannot sustain if it is unable to deliver the products and services in time. To deliver the services in time, it becomes imperative for an organization to be well equipped with trained and competent workforce so that organization can gain competitive advantage over its competitors. The to find to any suitable framework which can assist in retention of employees and above the predict key factors.
Disclosed herein an Edge And Vision Based System For Prediction Of Employee State With Deep Learning comprises Computing unit (40), Camera Module (41), Finger Print Module (42), ESP 32 Module (43), Display Unit (44), Battery Power Supply (45), Vision based attendance node ‘1’ (10), Vision based attendance node ‘2’ (11), Vision based attendance node ‘n’ (12), Vision based attendance node ‘1’ (20), Vision based attendance node ‘2’ (21), Vision based attendance node ‘n’ (22), Computing Unit (50), Co-processor (51), ESP 32 Module (52), Pre-trained DL Module (53), Battery Power supply (54), Edge based gateway with deep learning (DL) (30) and Cloud analytics dashboard (31).
In another embodiment one widely used method of tracking attendance and monitoring of employees is through the Camera module and fingerprint module.
In another embodiment these modules relate to uninterrupted power supply; these chips can be scanned at various checkpoints such as the entrance to the building, individual cabin, and extracurricular activity locations.
In another embodiment daily basis data collected through computing unit and employees can be assessed through computed data.
In another embodiment cloud dashboard helps to store all type of data in system; in addition to RFID tracking, there are also several software-based systems that can be used to monitor employees.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
Figure 1: Proposed architecture
Figure 2: Vision based attendance node
Figure 3: Edge based gateway with DL
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Disclosed herein An Edge And Vision Based System For Prediction Of Employee State With Deep Learning comprises Computing unit (40), Camera Module (41), Finger Print Module (42), ESP 32 Module (43), Display Unit (44), Battery Power Supply (45), Vision based attendance node ‘1’ (10), Vision based attendance node ‘2’ (11), Vision based attendance node ‘n’ (12), Vision based attendance node ‘1’ (20), Vision based attendance node ‘2’ (21), Vision based attendance node ‘n’ (22), Computing Unit (50), Co-processor (51), ESP 32 Module (52), Pre-trained DL Module (53), Battery Power supply (54), Edge based gateway with deep learning (DL) (30) and Cloud analytics dashboard (31); wherein said modules relate to uninterrupted power supply; these chips can be scanned at various checkpoints such as the entrance to the building, individual cabin, and extracurricular activity locations.
METHOD OF WORKING
One widely used method of tracking attendance and monitoring of employees is through the Camera module and fingerprint module. These modules relate to uninterrupted power supply. These chips can be scanned at various checkpoints such as the entrance to the building, individual cabin, and extracurricular activity locations. Daily basis data collected through computing unit and employees can be assessed through computed data. Cloud dashboard helps to store all type of data in system. In addition to RFID tracking, there are also several software-based systems that can be used to monitor employees.
ADVANTAGES OF THE INVENTION
It will help the organization to know the following ways:
1. To increase the employee retention.
2. To predict the factors responsible for leaving the organization.
3. a novel Artificial Intelligence and deep Learning Web-based framework to enhance the employee retention in organization. We claim that the proposed framework will help the organization to retain the employees and to predict the factors which are responsible due to which employee leaves the organization so frequently.
, Claims:1. An Edge And Vision Based System For Prediction Of Employee State With Deep Learning comprises Computing unit (40), Camera Module (41), Finger Print Module (42), ESP 32 Module (43), Display Unit (44), Battery Power Supply (45), Vision based attendance node ‘1’ (10), Vision based attendance node ‘2’ (11), Vision based attendance node ‘n’ (12), Vision based attendance node ‘1’ (20), Vision based attendance node ‘2’ (21), Vision based attendance node ‘n’ (22), Computing Unit (50), Co-processor (51), ESP 32 Module (52), Pre-trained DL Module (53), Battery Power supply (54), Edge based gateway with deep learning (DL) (30) and Cloud analytics dashboard (31); wherein said modules relate to uninterrupted power supply; these chips can be scanned at various checkpoints such as the entrance to the building, individual cabin, and extracurricular activity locations.
2. The system as claimed in claim 1, daily basis data collected through computing unit and employees can be assessed through computed data.
3. The system as claimed in claim 1, cloud dashboard helps to store all type of data in system; in addition to RFID tracking, there are also several software-based systems that is used to monitor employees.