Human Behaviour Impact to Use of Smartphones with the Python Implementation Using Naive Bayesian

Talha, Iftakhar Mohammad; Salehin, Imrus; Debnath, Susanta Chandra; Saifuzzaman, Mohd.; Moon, Nazmun Nessa; Nur, Fernaz Narin

doi:10.1109/icccnt49239.2020.9225620

Cited by 17 publications

(9 citation statements)

References 12 publications

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“…Out of 4036 peoples 2300 peoples were age [20][21][22][23][24][25]. There were 825 peoples age [16][17][18][19][20], 512 peoples were age [25][26][27][28][29][30] and others 399 peoples were age 30 above. In this different age peoples, we got different accuracy for male and female both of them.…”

Section: Discussionmentioning

confidence: 99%

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Impact on Human Mental Behavior after Pass through a Long Time Home Quarantine Using Machine Learning

Salehin¹,

Dip²,

Talha³

et al. 2021

IJEME

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In the present situation, COVID-19 is a very common and dangerous issue in the whole world. Ensuring our healthy mental state is very essential at the period of COVID-19. But as a result of being in the home quarantine for a long time, people are going to notice a mental change such as stress, depression, mood swing. We proposed an RHMCD model which helps us to reach our required goal. This model contains machine learning algorithms. We examined our work with Naive Bayes classifiers, Support Vector Machine, and logistic regression. For gaining the report of mental conditions we used the sentiment analysis technique. For measuring the level of depression we also used a decision tree approach.

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Section: Discussionmentioning

confidence: 99%

“…Naive Bayes Algorithm serves based on the Bayes theorem. Most commonly used for Naive Bayes is Probability and Sentimental Analysis [18]. Algorithms have been used to diagnose the effects of mental health on quarantine.…”

Section: Naï Ve Bayes Classifiermentioning

confidence: 99%

Impact on Human Mental Behavior after Pass through a Long Time Home Quarantine Using Machine Learning

Salehin¹,

Dip²,

Talha³

et al. 2021

IJEME

Self Cite

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“…SVM classifier is applied in this study, which is widely used in sentiment recognition [13]. A simple and efficient recognition model is used in this study, which is similar to Chi-Jen [14].…”

Section: Svm For Sentimental Classificationmentioning

confidence: 99%

Analysis of student sentiment during video class with multi-layer deep learning approach

Salehin

Moon

Talha

et al. 2022

IJECE

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<p>The modern education system is an essential part of the rise of technology. The E-learning education system is not just an experimental system; it is a vital learning system for the whole world over the last few months. In our research, we have developed our learning method in a more effective and modern way for students and teachers. For significant implementation, we are implementing convolutions neural networks and advanced data classifiers. The expression and mood analysis of a student during the onlineclass is the main focus of our study. For output measure, we divide the final output result as attentive, inattentive, understand, and neutral. Showing the output in real-time online class and for sensory analysis, we have used support vector machine (SVM) and OpenCV. The level of 5*4 neural network is created for this work. An advanced learning medium is proposed through our study. Teachers can monitor the live class and different feelings of a student during the class period through this system.</p>

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“…Overall, we found sustainability, reliability and better accuracy using the Naive Bayes classification in our NLP based work for its simple design and efficient approach. A lyrics-based classification has performed based on textual data has performed using naive Bayes [14].…”

Section: Naive Bayes Classifiermentioning

confidence: 99%

Natural language processing based advanced method of unnecessary video detection

Moon

Salehin

Parvin

et al. 2021

IJECE

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<span>In this study we have described the process of identifying unnecessary video using an advanced combined method of natural language processing and machine learning. The system also includes a framework that contains analytics databases and which helps to find statistical accuracy and can detect, accept or reject unnecessary and unethical video content. In our video detection system, we extract text data from video content in two steps, first from video to MPEG-1 audio layer 3 (MP3) and then from MP3 to WAV format. We have used the text part of natural language processing to analyze and prepare the data set. We use both Naive Bayes and logistic regression classification algorithms in this detection system to determine the best accuracy for our system. In our research, our video MP4 data has converted to plain text data using the python advance library function. This brief study discusses the identification of unauthorized, unsocial, unnecessary, unfinished, and malicious videos when using oral video record data. By analyzing our data sets through this advanced model, we can decide which videos should be accepted or rejected for the further actions.</span>

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Human Behaviour Impact to Use of Smartphones with the Python Implementation Using Naive Bayesian

Cited by 17 publications

References 12 publications

Impact on Human Mental Behavior after Pass through a Long Time Home Quarantine Using Machine Learning

Impact on Human Mental Behavior after Pass through a Long Time Home Quarantine Using Machine Learning

Analysis of student sentiment during video class with multi-layer deep learning approach

Natural language processing based advanced method of unnecessary video detection

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