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All Journal BULLET : Jurnal Multidisiplin Ilmu
Shameem Ahsan, Md
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Humanities Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing

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Unraveling The Complexity: Root Cause Analysis Of Electrical Faults In Industry & Other Facility Shameem Ahsan, Md; Khaledur Rahman, Md; Ahmed, Manam; Amin Tanvir, Faysal; Saiful Islam, Md
BULLET : Jurnal Multidisiplin Ilmu Vol. 3 No. 3 (2024): BULLET : Jurnal Multidisiplin Ilmu
Publisher : CV. Multi Kreasi Media

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The sole purpose of this analysis was to discuss the broader picture of fire incidents that occurred in industry due to electrical faults. Nowadays, fire incidents in industries as well as in residential and commercial buildings are a common case. Local and international print & electronic media report fire incidents every day. The fatalities are remarkable and also caused extensive property damage, and undoubtedly, it can be mitigated upon consideration of a few factors. This article identifies the exact causes of electrical faults, investigating previous fire incident reports in various industries and other possible reasons. During the past decades, there has been a noticeable change in infrastructure and industrial development worldwide. Because of land shortage and improper planning, the number of high-rise buildings in the industry sector increases without maintaining electrical standards, increasing fire incidents, fatality rates, and substantial property damages. Though strong laws, national and international standards, and regulatory measures existed, accidents occurred regularly. Also, there is no significant guideline for electrical product quality, run time, and materials selection, which plays a major role in fire incidents. This report anticipates understanding the lack in the existing system, considerable findings for installations, and extending to further studies to find out proper remediation.
Machine Learning Methodologies for Electric Vehicle Energy Management Strategies Khaledur Rahman, Md; Saiful Islam, Md; Jakaria Talukder, Md; Nazmul Islam, Md; Shameem Ahsan, Md
BULLET : Jurnal Multidisiplin Ilmu Vol. 4 No. 1 (2025): BULLET : Jurnal Multidisiplin Ilmu
Publisher : CV. Multi Kreasi Media

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This research study explores the usage of machine learning techniques in the improvement of energy executives’ strategies for electric vehicles (EVs), with a particular accentuation on estimating EV-related variables and classifying price ranges. The study uses machine learning such as linear regression, random forest regression, decision tree, random forest classifier, and artificial neural network (ANN). The dataset involves fundamental electric vehicle (EV) attributes, including acceleration time, maximum speed, range, efficiency, and fast charging capacity. Information readiness includes the chores of handling missing values and changing category labels into a numerical column. The evaluation measures incorporate mean squared error, R-squared, and accuracy. The outcomes exhibit the efficacy of machine learning models in estimating EV-related variables and classifying price levels. The key discoveries highlight the unique performance of regression and classification models. This examination upgrades the cognizance of machine learning applications in EV energy the executives and gives important bits of knowledge to further develop determining accuracy and decision-making processes.
Co-Authors Ahmed, Manam Amin Tanvir, Faysal Jakaria Talukder, Md Khaledur Rahman, Md Nazmul Islam, Md Saiful Islam, Md