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Intelligent Coaching Agent for Enhancing Proactive Behaviors in Human Teamwork Using Supervised Learning Algorithm
Chidi Ukamaka Betrand,
Sylvanus Okwudili Anigbogu,
Oluchukwu Uzoamaka Ekwealor,
Ifeoma MaryAnn Orji
Issue:
Volume 6, Issue 1, June 2022
Pages:
1-9
Received:
25 October 2021
Accepted:
11 November 2021
Published:
20 January 2022
DOI:
10.11648/j.ajai.20220601.11
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Abstract: Teamwork has been one of the effective responses to the changes over the years in the world of today. Teamwork is being relied upon as a preferred performance arrangement to fulfill visions, execute and achieve goals in all sectors. It is also one of the most important elements in continuous improvement systems, as it facilitates the sharing of information, problem solving and the development of employee responsibility. This study was on an intelligent coaching agent that modeled team performance using the Supervised Learning algorithm. The Object Oriented System Analysis and Design Methodology was used. For effective implementation of this study, some web application languages were used and these includes; Hypertext Markup Language (HTML), Hypertext Preprocessor (PHP), MySQL, Cascaded Style Sheet (CSS), Java Script, Dream weaver, and Fireworks. Dream weaver is an HTML-based application that is used to generate graphical user interfaces. This study was able to arrive at a system that will remove biases in performance evaluation since the performance appraisal is automatic; each task has been assigned a weighted score, so as soon as an employee performs the task the system automatically scores him/her. Making it easy to track individual performance as well as team performance. The system developed utilizes supervised learning to monitor the task executions and determine the weight score for the task before scoring the team. This system will help those that are worthy of keeping their jobs keep it and help improve employees that need to work on some specific areas to develop themselves as plainly revealed. The purpose of this study which is to demonstrate an event based performance approach via the design and implementation of intelligent coaching agents within a team training framework via supervised learning was achieved and the result shows positive impacts on team’s performance.
Abstract: Teamwork has been one of the effective responses to the changes over the years in the world of today. Teamwork is being relied upon as a preferred performance arrangement to fulfill visions, execute and achieve goals in all sectors. It is also one of the most important elements in continuous improvement systems, as it facilitates the sharing of inf...
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Artificial Corona Algorithm to Solve Multi-objective Programming Problems
Issue:
Volume 6, Issue 1, June 2022
Pages:
10-19
Received:
12 February 2022
Accepted:
18 March 2022
Published:
31 March 2022
DOI:
10.11648/j.ajai.20220601.12
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Abstract: Multi-objective optimization is a branch of mathematics used in a large range of applications. It deals with optimization problems involving two or more conflicting objective functions to be optimized. Consequently, there is not a single solution that simultaneously optimizes these objectives, but a set of compromise solutions. These compromise solutions are also called non-dominated, Pareto-optimal, efficient or non-inferior solutions. The best solution of this set is the one closest point to the utopia point. There are several approaches to perform multi-objective optimization. Undoubtedly the future of multi-objective optimization programming is in artificial intelligence applications. One of the artificial intelligence models is the Corona algorithm. It aims to simulate the epidemic behavior of the Corona virus that affects people's health and its treatment. In this paper, the artificial Corona algorithm is introduced and expanded for solving multi-objective programming problems in which other models are not effective. The algorithm operates by iteratively selecting the initial values for decision variables of a multi-objective programming problem. The values of objective functions and constraint(s) are calculated. This proposed approach depends on a linear formula to update the solution. An acceptable efficient solution that has a minimum distance value from the utopia point is selected as the best point. To demonstrate the effectiveness of the proposed approach, some illustrative examples are given. These examples include both linear and nonlinear problems. The results indicate that the proposed approach has a high speed and capability to obtain the best solution when compared with other similar works of literature.
Abstract: Multi-objective optimization is a branch of mathematics used in a large range of applications. It deals with optimization problems involving two or more conflicting objective functions to be optimized. Consequently, there is not a single solution that simultaneously optimizes these objectives, but a set of compromise solutions. These compromise sol...
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Next Generation of Process Monitoring and Diagnostics: Applications of AI and Machine Learning to Enable Early Equipment Fault Prediction and Diagnostics
Kaushik Ghosh,
Gokula Krishnan Sivaprakasam,
Aparnadevi Minisankar
Issue:
Volume 6, Issue 1, June 2022
Pages:
20-26
Received:
20 January 2022
Accepted:
10 March 2022
Published:
9 April 2022
DOI:
10.11648/j.ajai.20220601.13
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Abstract: Several rotating equipment such as – centrifugal pumps and positive displacement pumps are extensively used in Water treatment plant for producing potable water from raw water. Centrifugal pumps are required for delivering water from one unit of the plant to the others, while the positive displacement pumps are used for dosing different chemicals at the various stages of water treatment process. Smooth normal operation of these pumps is essential for ensuring both the production quality and quantity. It is extremely important to detect any anomaly or malfunction in this rotating equipment at an early stage. This helps to take the appropriate corrective maintenance actions and prevent any catastrophic failure, equipment down time, quality deviation and/or production loss. However, there are very few methods available in the literature for detecting faults or anomalies in the pumps, particularly for the positive displacement pumps in real industrial application using only routinely available process data -such as: flow, speed, stroke, discharge pressure etc. In this paper, a machine-learning based Early Fault Detection & Diagnostic system is developed to monitor the rotating equipment in operation, detect a fault at initiation, pinpoint the root cause, and to send out alerts for corrective maintenance with suggested remedial actions. The detection works by building a baseline machine learning model of the equipment performance under normal operating conditions which is then used to monitor the health deviation of the equipment in real time and predict a fault at a very early stage, much before it is observed by operations personnel. The proposed fault detection method relies only on routine process data – flow, speed, stroke etc. and does not require any additional measurements like vibration, motor current, acoustic emission data. The diagnostics tool identifies the most probable root causes of the failures and provides the possible failure resolution methods based on the historical maintenance records of similar equipment. The proposed algorithm combines data-driven and knowledge-based approaches. The efficacy of the proposed method was demonstrated to detect and identify incipient faults in positive displacement chemical dosing pumps in a water treatment plant. The detected and identified faults were validated using the maintenance records of the pumps.
Abstract: Several rotating equipment such as – centrifugal pumps and positive displacement pumps are extensively used in Water treatment plant for producing potable water from raw water. Centrifugal pumps are required for delivering water from one unit of the plant to the others, while the positive displacement pumps are used for dosing different chemicals a...
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AGD-Autoencoder: Attention Gated Deep Convolutional Autoencoder for Brain Tumor Segmentation
Issue:
Volume 6, Issue 1, June 2022
Pages:
27-30
Received:
22 November 2021
Accepted:
14 December 2021
Published:
22 April 2022
DOI:
10.11648/j.ajai.20220601.14
Downloads:
Views:
Abstract: Brain tumor segmentation is a challenging problem in medical image analysis. The endpoint is to generate the salient masks that accurately identify brain tumor regions in an fMRI screening. In this paper, we propose a novel attention gate (AG model) for brain tumor segmentation that utilizes both the edge detecting unit and the attention gated network to highlight and segment the salient regions from fMRI images. This feature enables us to eliminate the necessity of having to explicitly point to-wards the damaged area (external tissue localization) and classify (classification) as per classical computer vision techniques. In order to provide the useful constraints to guide feature extraction, we incoorporate the edge attention-gated unit. The explicit edge-attention unit is devoted to model the image boundaries as well as enhancing the representation. AGs can easily be integrated within the deep convolutional neural networks (CNNs). Minimal computional overhead is required while the AGs increase the sensitivity scores significantly. We show that the edge detector along with an attention gated mechanism provide a suffcient enough method for brain segmentation reaching an IOU of 0.78. With this methodology, we attempt to bring deep learning closer to the hands of human level performance providing useful information to the process of diagnosis.
Abstract: Brain tumor segmentation is a challenging problem in medical image analysis. The endpoint is to generate the salient masks that accurately identify brain tumor regions in an fMRI screening. In this paper, we propose a novel attention gate (AG model) for brain tumor segmentation that utilizes both the edge detecting unit and the attention gated netw...
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