An Optimal Learning Model for Training Expert System to Detect Uterine Cancer

Mahmud, Tanjim; Sikder, Juel; Salma, Umme; Naher, Sultana Rokeya; Fardoush, Jannat; Sharmen, Nahed; Tripura, Sajib

doi:10.1016/j.procs.2021.03.045

Cited by 22 publications

(5 citation statements)

References 13 publications

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“…Integrating this system with the video camera of cricket grounds and incorporating a voice assistant system for taking immediate actions as soon as a no ball is detected could also be explored. Additionally, amalgamating evidential reasoning [48,49], adopting belief rule-based systems [50][51][52] with deep learning models, and employing explainable artifcial intelligence (XAI) [53] technologies could potentially yield better outcomes.…”

Section: Discussionmentioning

confidence: 99%

Deep Transfer Learning‐Based Foot No‐Ball Detection in Live Cricket Match

Das

Mahmud

Islam

et al. 2023

Computational Intelligence and Neuroscience

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Automation in every part of life has become a frequent situation because of the rapid advancement of technology, mostly driven by AI technology, and has helped facilitate improved decision-making. Machine learning and the deep learning subset of AI provide machines with the capacity to make judgments on their own through a continuous learning process from vast amounts of data. To decrease human mistakes while making critical choices and to improve knowledge of the game, AI-based technologies are now being implemented in numerous sports, including cricket, football, basketball, and others. Out of the most globally popular games in the world, cricket has a stronghold on the hearts of its fans. A broad range of technologies are being discovered and employed in cricket by the grace of AI to make fair choices as a method of helping on-field umpires because cricket is an unpredictable game, anything may happen in an instant, and a bad judgment can dramatically shift the game. Hence, a smart system can end the controversy caused just because of this error and create a healthy playing environment. Regarding this problem, our proposed framework successfully provides an automatic no-ball detection with 0.98 accuracy which incorporates data collection, processing, augmentation, enhancement, modeling, and evaluation. This study starts with collecting data and later keeps only the main portion of bowlers’ end by cropping it. Then, image enhancement technique are implied to make the image data more clear and noise free. After applying the image processing technique, we finally trained and tested the optimized CNN. Furthermore, we have increased the accuracy by using several modified pretrained model. Here, in this study, VGG16 and VGG19 achieved 0.98 accuracy and we considered VGG16 as the proposed model as it outperformed considering recall value.

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Section: Discussionmentioning

confidence: 99%

Deep Transfer Learning‐Based Foot No‐Ball Detection in Live Cricket Match

Das

Mahmud

Islam

et al. 2023

Computational Intelligence and Neuroscience

Self Cite

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“…Expert System is a sub-branch of artificial intelligence [16] [17]. Also, it is a computer system that utilises the ability and skills of one or more professionals to solve a specific concern [18] [19]. Moreover, these systems are consulting computer programs that seek to simulate the mind of experts in solving some specialised problems related to the field they are dealing with, which always leads to information-based operations [20][21][22].In short, they are computer programs that can solve any concern in a particular field in the same way that experts do.…”

Section: Expert Systemsmentioning

confidence: 99%

Comparison between Expert Systems, Machine Learning, and Big Data: An Overview

Mijwil

Mutar

Filali

et al. 2022

AJAS

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Today, the science of artificial intelligence has become one of the most important sciences in creating intelligent computer programs that simulate the human mind. The goal of artificial intelligence in the medical field is to assist doctors and health care workers in diagnosing diseases and clinical treatment, reducing the rate of medical error, and saving lives of citizens. The main and widely used technologies are expert systems, machine learning and big data. In the article, a brief overview of the three mentioned techniques will be provided to make it easier for readers to understand these techniques and their importance.

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“…In a future study an expert system [30,31] will be implemented into the suggested decision concept to reduce human involvement because stakeholders and experts must be involved throughout the process.…”

Section: Future Workmentioning

confidence: 99%

A Decision Concept to Support House Hunting

Mahmud¹,

Islam²,

Begum³

et al. 2022

IJACSA

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House hunting, or the act of seeking for a place to live, is one of the most significant responsibilities for many families around the world. There are numerous criteria/factors that must be evaluated and investigated. These traits can be both statistically and qualitatively quantified and expressed. There is also a hierarchical link between the elements. Furthermore, objectively/quantitatively assessing qualitative characteristics is difficult, resulting in data inconsistency and, as a result, uncertainty. As a result, ambiguity must be dealt with using the necessary processes; otherwise, the decision to live in a particular property would be incorrect. To compare criteria, the Analytic Hierarchy Process (AHP) is employed, evidential reasoning is used to evaluate houses based on each criterion, and TOPSIS is used to rank house sites for selection. It was necessary to analyze qualitative and quantitative elements, as well as economic and social features of these residences, in order to arrive at the final order of houses, which was not an easy process. As a result, the authors developed a decision support model to aid decision makers in the management of activities related to finding a suitable dwelling. This study describes the development of a decision support system (DSS) capable of providing an overall judgment on the location of a house to live in while taking into account both qualitative and quantitative factors.

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An Optimal Learning Model for Training Expert System to Detect Uterine Cancer

Cited by 22 publications

References 13 publications

Deep Transfer Learning‐Based Foot No‐Ball Detection in Live Cricket Match

Deep Transfer Learning‐Based Foot No‐Ball Detection in Live Cricket Match

Comparison between Expert Systems, Machine Learning, and Big Data: An Overview

A Decision Concept to Support House Hunting

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