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Factors Affecting Mental Health of High School Students due to COVID-19: An Exploratory Study

Factors Affecting Mental Health of High School Students due ...

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2023 international conference on next-generation computing, iot and machine learning, ncim 2023

作者： Reza, Md. Rokonuzzaman Islam, Muhammad Nazrul Islam, Mohian Mumu, Afnan Alauddin Faisal, Faiz Al The International University of Scholars Dept. of Computer Science and Engineering Dhaka Bangladesh Military Institute of Science and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh LTIMindtree Ltd. Washington United States Green University of Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350316001

Mental health is one of the most significant factors in the human life span. It also influences how we interact with people, manage stress, and make good decisions. From childhood and youth through maturity, mental health is crucial at every stage of life. In the era of the COVID-19 pandemic, it becomes more vulnerable and affects teenagers as others. In this study, high school students' (10-19 years old) mental health states and the most contributing factors that affect their mental health were assessed through a statistical and machine learning-based approach. The study data were collected through an online survey and received responses from 158 respondents. The data were analyzed statistically with a chi-square test and path analysis considering all the possible factors of anxiety and depression to determine the most contributing factors. Besides, a few machine learning (ML) algorithms have been developed such as Logistic Regression(LR), Support Vector machine(SVM), Naive Bayes(NB), AdaBoost(AB), and Random Forest(RF) for the prediction of anxiety and depression and found that AdaBoost achieved the highest score (86%). As outcomes, the overall analysis revealed that academic concerns such as poor understanding of online classes, daily activities like sleep pattern disruption, and extended uses of electronic devices are the major causes of the deterioration of high school students' mental health. © 2023 IEEE.

关键词： COVID-19

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Gas Stove Burner Shape with Inductive Tailed Rotational Symmetric Metamaterial Absorber for C and X Band Application

Gas Stove Burner Shape with Inductive Tailed Rotational Symm...

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next-generation computing, iot and machine learning (ncim), international conference on

作者： Apurba Ray Chowdhury Saif Hannan Md Kutub Uddin Md. Emdadul Hoque Bhuiyan Md Inzamul Hoque Mohammad Tariqul Islam Department of Electronic and Telecommunication Engineering International Islamic University Chittagong (IIUC) Chattogram Bangladesh Department of Electrical Electronic and Systems Engineering Universiti Kebangsaan Malaysia Bangi Malaysia

This paper presents a multiband metamaterial (MM) absorber that displays the metamaterial characteristics at each resonance frequency. Broadband absorption is achieved in the X band at the range of 10.373 GHz to 10.825 GHz. Four resonances gain in C and X band frequency with single negative (SNG) MM properties at the frequencies of 4. 12, 5.776, 10.472, and 10.696 GHz, where the maximum absorptions are 99.4%, 99.9%, 95%, and 90.4% respectively. At last, compared with recent work on MM concerning the highest angle insensitive in Transverse Electromagnetic (TEM) mode, broadband absorption, compact size identifies it as a more useful ideal absorber.

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A machine-learning Approach to Classify Depressive Posts from Reddit Social Media

A Machine-Learning Approach to Classify Depressive Posts fro...

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next-generation computing, iot and machine learning (ncim), international conference on

作者： Adib Wahid Quader Md Shafayet Jamil Mohammad Irfan Uddin Muhammed J. A. Patwary Department of Computer Science & Engineering International Islamic University Chittagong Bangladesh

Depression leading to suicide has become a major concern in our society. It's high time to investigate the reason behind it. As numerous people are expressing their concerns regarding depression on Reddit, such post can be taken into consideration in order to find the reason behind it. There has been a lot of research work in this field but most of it is done using traditional machine learning approach with NLP techniques. Moreover, recently there's has not been any significant research work regarding depression. In this paper, we found the indicators that could indicate how relevant Reddit users feel about depression using machine learning, ensemble learning, and deep learning techniques. Moreover, we pinpointed the terms that appear more frequently in narratives of depression. To strengthen the research work, we took the dataset over the period from 2017 to 2021. Furthermore, we demonstrated the effectiveness of LSTM & BERT on recent dataset.

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Prospects and Economic Feasibility Analysis of Solar PV/Hydrogen Fuel-based Power System for Green City

Prospects and Economic Feasibility Analysis of Solar PV/Hydr...

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next-generation computing, iot and machine learning (ncim), international conference on

作者： Shaharier Kabir Md Jawad-Al-Mursalin Hoque Md Asif Adnan Md Ashikul Islam Abu Shufian Dept. of EEE American International University-Bangladesh Dhaka Bangladesh Dept. of Electrical Engineering Lamar University Texas USA

Meeting the energy demands of self-sustaining off-grid systems, especially in regions with extreme solar intermittency and energy consumption patterns like northern climates, requires effective short-term and seasonal energy storage solutions. This research investigates the feasibility and economic viability of a solar PV/hydrogen fuel-based power system for a green city. Employing the Hybrid Optimization of Multiple Energy Resources (HOMER) software, an extensive analysis is conducted to optimize and simulate the proposed system. And a comprehensive assessment is performed to evaluate the technical and economic feasibility of implementing this system in a green city context. Key factors such as potential energy generation capacity, system efficiency, and economic viability are thoroughly analyzed. The findings reveal that the proposed system offers a reliable and sustainable energy source for a green city. By significantly reducing greenhouse gas emissions and providing a cost-effective solution to meet the city's energy requirements, this system showcases its potential in addressing environmental concerns.

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Prediction of Buying Intention: Factors Affecting Online Shopping

Prediction of Buying Intention: Factors Affecting Online Sho...

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next-generation computing, iot and machine learning (ncim), international conference on

作者： Md Shamiul Islam Julkar Naeem AL Shahriar Emon Abdul Baten Md.Abdullah Al Mamun G. M. Waliullah Md. Saifur Rahman M. F. Mridha Department of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh Department of Computer Science American International University-Bangladesh Dhaka Bangladesh

Recent times have witnessed a very significant increase in the use of electronic commerce all over the world. The fact that sales have been steadily climbing demonstrates that there is massive untapped market potential for online shopping. The process of analysing data to determine and categorise the intentions of online shoppers contributes to the accumulation of store earnings. The application of machine learning classification models to the data collected from e-commerce websites is the main goal of our endeavours. Specifically, We investigate whether machine learning is a reliable method for predicting the possibility that a consumer who explores a retailer's website will actually make a purchase. Exploratory data analysis was carried out so that we could visually examine our information and search for any patterns or trends that may have been there. In this article, we present a method that is capable of predicting the purpose of consumers who visit an e-commerce site to make a purchase based on data obtained while these users are browsing the websites in question. The Online Consumer Buying Sentiment Dataset that can be found in the UCI machine learning Repository is one that we use.

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Design and Analysis of an Antenna using Ring Slotted Rectangular Reflector with Double Substrate for 5G mm-Wave Application

Design and Analysis of an Antenna using Ring Slotted Rectang...

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next-generation computing, iot and machine learning (ncim), international conference on

作者： Shawon Hossen Md. Kutub Uddin Abdul Gafur Syed Zahidur Rashid Saif Hannan A. Z. M. Imran Department of Electronic and Telecommunication Engineering (ETE) International slamic University Chittagong (IIUC) Chittagong Kumira Banglaesh Dept. of ETE International Islamic University Chittagong (IIUC) Chittagong Kumira Bangladesh

This work aims to provide a high-gain antenna with good efficiency using ring-slotted rectangular metamaterial reflectors array with a double-layer substrate for 5G wireless application. The proposed antenna is designed on a compact Rogers Substrate Rt-5880 with a relative permittivity ε r of 2.2 and a loss tangent δ value of 0.0009. One of the unique characteristics of the suggested antenna is the very high gain of 13.21 dBi with 91.7% efficiency at the resonance frequency of 30 GHz. The proposed design's resonance frequency is 30 GHz, which is enough spectrum for error-free transmission. Ring-slotted rectangular metamaterial reflectors have been used on top to achieve the desired performance. The simulation findings meet the requirements for a reflection coefficient value of less than 10 dB. There is a directed radiation pattern. The suggested architecture is identified as a promising 5G wireless communication option by all these alluring performance criteria.

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A Novel Hybrid Evolutionary Mating Algorithm for Covid19 Confirmed Cases Prediction based on Vaccination

A Novel Hybrid Evolutionary Mating Algorithm for Covid19 Con...

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next-generation computing, iot and machine learning (ncim), international conference on

作者： Marzia Ahmed Ahmad Johari Mohamad Mostafijur Rahman Mohd Herwan Sulaiman Mohammod Abul Kashem Faculty of Electrical & Electronics Engineering Technology Universiti Malaysia Pahang (UMP) Pahang Malaysia Department of Software Engineering Daffodil International university Dhaka Bangladesh Department of Computer Science and Engineering Green University of Bangladesh Department of Computer Science and Engineering DUET Gazipur Bangladesh

Microorganisms may cause illness when they enter the body, multiply, and spread to other parts. The rapid spread of COVID-19 to neighboring countries is examined in this research. Anticipating a positive COVID-19 occurrence helps in determining risks and creating countermeasures. As a result, developing robust mathematical models with small error margins for predictions is crucial. Based on these findings, a combined method of evaluating confirmed cases of COVID-19 with universal immunization is recommended. First, the best hyperparameter values of the RBF kernel-based LSSVM (least square support vector machine) were determined using the most recent Evolutionary Mating Algorithm (EMA). After that, LSSVM will complete the task of prediction. This hybrid method has been utilized for time series forecasting in Malaysia since the country's immunization program against COVID-19 got underway. We evaluate our results next to those of well-known methodologies in nature-inspired metaheuristics.

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A Study on Future Lockdown Predictions using ANN

A Study on Future Lockdown Predictions using ANN

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next-generation computing, iot and machine learning (ncim), international conference on

作者： Shuvra Smaran Das Anirban Saha Anik Md. Muzakker Hossain Md. Kishor Morol Fariha Jahan Md. Abdullah Al-Jubair Department of Computer Science American International University-Bangladesh (AIUB) Dhaka Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh

More than three years have passed since the first detection of COVID-19, and our generation had no clue as to what was coming or how dangerous the disease was. Unfortunately, this is not the end of such diseases, and in order to minimize losses, we may have to deal with many more lockdowns over new diseases. With the data from the recent pandemic, we investigated how to anticipate the potential lockdown date in the future. We took into account the first detection, the first death by COVID-19, the WHO emergency declaration, the total GDP of the various nations, the GDP growth, the population density, etc. This paperwork predicts the day passed after the first COVID-19 positive detection inside the country when the country's government declared a lockdown (FDL) and the day when the WHO announced COVID-19 as an emergency of international concern (WEL). This research uses a variety of machine learning models, including a modified Artificial Neural Network (ANN), for the prediction. This study presents the successful tuning and training of an ANN model using the lockdown dataset, ultimately achieving an impressive accuracy rate of 96% for FDL (First Detection Lockdown) and 98% for WEL (World Emergency Lockdown), which is more accurate than any other applied traditional and/or tree-based machine-learning models.

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Performance Enhancement of UWB Antenna Using FSS Layer for millimeter-wave 5G

Performance Enhancement of UWB Antenna Using FSS Layer for m...

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next-generation computing, iot and machine learning (ncim), international conference on

作者： Tanvir Hossain Sourav Chakraborty Mohammed Jashim Uddin Abdul Gafur Syed Zahidur Rashid Muhammad Asad Rahman Md. Ibrahim Department of Electronic and Telecommunication Engineering International Islamic University Chittagong Chittagong Bangladesh Department of Electrical & Electronic Engineering Chittagong University of Engineering & Technology Chittagong Department of Electronic & Telecommunication Engineering International Islamic University Chittagong

This research encapsulates the design of a compact Ultra-Wide Band (UWB) antenna resonating in 5G mm-wave, covering the respective n257, n258, and n261 bands. The circular patch with slots aids in enhancing the gain and radiation efficiency of our proposed antenna. The antenna's bottom ground plane is Defected Ground Structure (DGS), which aids in extending its bandwidth. The antenna is compact, having a dimension of 12×12 mm 2 . This antenna has a wide bandwidth of 19.73 GHz and a maximum efficiency of 91%. A Frequency Selective Surface (FSS) with L shaped unit cell is used at the antenna's back end (5 mm) to increase gain even further, achieving a maximum gain of 8.24 dBi. The antenna element for our proposed antenna is mounted over a Rogers RT 5880 substrate. Two circular parasitic elements are designed on the bilateral side of the feedline to facilitate a better radiation pattern.

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Grid-tied Smart Microgrid with Heuristic Optimized Energy Management System (EMS)

Grid-tied Smart Microgrid with Heuristic Optimized Energy Ma...

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next-generation computing, iot and machine learning (ncim), international conference on

作者： Abu Shufian Shaharier Kabir Md Ashikul Islam Md Jawad-Al-Mursalin Hoque Md Asif Adnan Nur Mohammad Dept. of EEE American International University-Bangladesh Dhaka Bangladesh Dept. of EEE Chittagong University of Engineering and Technology Chattogram Bangladesh Dept. of Electrical Engineering Lamar University Texas USA

There is a global shift towards integrating renewable energy sources into existing power systems to address the increasing energy demand while minimizing environmental impact. One popular approach is the utilization of solar/battery-based grid-connected microgrid systems, which have gained significant popularity. However, the intermittent nature of solar power poses a challenge in ensuring a steady energy supply. To overcome this challenge, this research proposes an optimized microgrid EMS that efficiently manages energy consumption, generation, and distribution, aiming to minimize operational costs. A cost-effective solution for microgrid operation, monitoring, and administration has been developed by incorporating demand side management (DSM) techniques and employing a heuristic optimization approach. The results demonstrate a remarkable 33.6% reduction in operational costs compared to systems without an EMS. These findings highlight the effectiveness of the proposed optimized EMS in generating and delivering electricity to consumers at affordable prices. This research contributes to the advancement of microgrid systems and provides valuable insights for policymakers, industry stakeholders, and researchers, supporting the adoption of renewable energy and enhancing microgrid performance.

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