A Decision Concept to Support House Hunting

Mahmud, Tanjim; Islam, Dilshad; Begum, Manoara; Das, Sudhakar; Dey, Lily; Barua, Koushick

doi:10.14569/ijacsa.2022.0131091

Cited by 33 publications

(2 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…Researchers and scientists have built and applied various methods in this growing research field. For uncertainty selection RIMER is treated as an appropriate method to solve the certain problems [13] [14]. In [7], ER deals with problems under various uncertainties such as incomplete information, vagueness, and ambiguity consisting of both quantitative and qualitative criteria.…”

Section: Introductionmentioning

confidence: 99%

An Expert System for Clinical Risk Assessment of Polycystic Ovary Syndrome under Uncertainty

Karim,

Khaliluzzaman,

Mahmud

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

This paper describes a prototype clinical expert system for risk stratification of patients with polycystic ovary syndrome (PCOS). Polycystic ovary syndrome (PCOS) is the most common hormonal disorder among women of reproductive age. It is a heterogeneous disorder of uncertain cause. Since the symptoms of PCOS are seemingly unrelated to one another the condition is often overlooked and undiagnosed. The determination of accurate degree or intensity of PCOS signs is difficult for the physician. Hence, the accuracy of the diagnostic process is difficult to achieve. The signs and symptoms of PCOS are usually expressed in qualitative and quantitative ways. Since the qualitative factors cannot measure in a quantitative way, various types of uncertainties may occur, such as incompleteness, vagueness and imprecision. For that, it is necessary to address the issue of uncertainty by using appropriate methodology. However, no existing system is able to address this issue of uncertainty. Therefore, this paper demonstrates the application of a novel method, named belief rule-based inference methodology-RIMER; this prototype can deal with uncertainties in both clinical domain knowledge and clinical data. This paper reports the development of a Belief Rule Based Expert System (BRBES) using RIMER approach, which is capable of detecting the PCOS by taking account of signs and symptoms.

show abstract