44 research outputs found
Atmospheric Influence on the Path Loss at High Frequencies for Deployment of 5G Cellular Communication Networks
Over the past few decades, the development of cellular communication
technology has spanned several generations in order to add sophisticated
features in the updated versions. Moreover, different high-frequency bands are
considered for advanced cellular generations. The presence of updated
generations like 4G and 5G is driven by the rising demand for a greater data
rate and a better experience for end users. However, because 5G-NR operates at
a high frequency and has significant propagation, atmospheric fluctuations like
temperature, humidity, and rain rate might result in poorer signal reception,
and higher path loss effects unlike the prior generation, which employed
frequencies below 6 GHz. This paper makes an attempt to provide a comparative
analysis about the influence of different relative atmospheric conditions on 5G
cellular communication for various operating frequencies in any urban microcell
(UMi) environment maintaining the real outdoor propagation conditions. In
addition, the simulation dataset based on environmental factors has been
validated by the prediction of path loss using multiple regression techniques.
Consequently, this study also aims to address the performance analysis of
regression techniques for stable estimations of path loss at high frequencies
for different atmospheric conditions for 5G mobile generations due to various
possible radio link quality issues and fluctuations in different seasons in
South Asia. Furthermore, in comparison to contemporary studies, the Machine
Learning models have outperformed in predicting the path loss for the four
seasons in South Asian regions.Comment: Accepted for presentation at THE 14th INTERNATIONAL CONFERENCE ON
COMPUTING, COMMUNICATION AND NETWORKING TECHNOLOGIES (ICCCNT
Survival Prediction of Children Undergoing Hematopoietic Stem Cell Transplantation Using Different Machine Learning Classifiers by Performing Chi-squared Test and Hyper-parameter Optimization: A Retrospective Analysis
Bone Marrow Transplant, a gradational rescue for a wide range of disorders
emanating from the bone marrow, is an efficacious surgical treatment. Several
risk factors, such as post-transplant illnesses, new malignancies, and even
organ damage, can impair long-term survival. Therefore, technologies like
Machine Learning are deployed for investigating the survival prediction of BMT
receivers along with the influences that limit their resilience. In this study,
an efficient survival classification model is presented in a comprehensive
manner, incorporating the Chi-squared feature selection method to address the
dimensionality problem and Hyper Parameter Optimization (HPO) to increase
accuracy. A synthetic dataset is generated by imputing the missing values,
transforming the data using dummy variable encoding, and compressing the
dataset from 59 features to the 11 most correlated features using Chi-squared
feature selection. The dataset was split into train and test sets at a ratio of
80:20, and the hyperparameters were optimized using Grid Search
Cross-Validation. Several supervised ML methods were trained in this regard,
like Decision Tree, Random Forest, Logistic Regression, K-Nearest Neighbors,
Gradient Boosting Classifier, Ada Boost, and XG Boost. The simulations have
been performed for both the default and optimized hyperparameters by using the
original and reduced synthetic dataset. After ranking the features using the
Chi-squared test, it was observed that the top 11 features with HPO, resulted
in the same accuracy of prediction (94.73%) as the entire dataset with default
parameters. Moreover, this approach requires less time and resources for
predicting the survivability of children undergoing BMT. Hence, the proposed
approach may aid in the development of a computer-aided diagnostic system with
satisfactory accuracy and minimal computation time by utilizing medical data
records.Comment: 25 pages, 14 figures, 38 table
Wave-Shaped Microstructure Cancer Detection Sensor in Terahertz Band: Design and Analysis
For the quick identification of diverse types of cancer/malignant cells in the human body, a new hollow-core optical waveguide based on Photonic Crystal Fiber (PCF) is proposed and numerically studied. The refractive index (RI) differs between normal and cancerous cells, and it is through this distinction that the other crucial optical parameters are assessed. The proposed cancer cell biosensor’s guiding characteristics are examined in the COMSOL Multiphysics v5.5 environment. The Finite Element Method (FEM) framework is used to quantify the display of the suggested fiber biosensor. Extremely fine mesh elements are additionally added to guarantee the highest simulation accuracy. The simulation results on the suggested sensor model achieve a very high relative sensitivity of 99.9277%, 99.9243%, 99.9302%, 99.9314%, 99.9257% and 99.9169%, a low effective material loss of 8.55×10−5 cm−1, 8.96×10−5 cm−1, 8.24×10−5 cm−1, 8.09×10−5 cm−1, 8.79×10−5 cm−1, and 9.88×10−5 cm−1 for adrenal gland cancer, blood cancer, breast cancer type-1, breast cancer type-2, cervical cancer, and skin cancer, respectively, at a 3.0 THz frequency regime. A very low confinement loss of 6.1×10−10 dB/cm is also indicated by the simulation findings for all of the cancer cases that were mentioned. The straightforward PCF structure of the proposed biosensor offers a high likelihood of implementation when used in conjunction with these conventional performance indexes. So, it appears that this biosensor will create new opportunities for the identification and diagnosis of various cancer cells
Impact of mobile connectivity on students' wellbeing: Detecting learners' depression using machine learning algorithms.
Depression is a psychological state of mind that often influences a person in an unfavorable manner. While it can occur in people of all ages, students are especially vulnerable to it throughout their academic careers. Beginning in 2020, the COVID-19 epidemic caused major problems in people's lives by driving them into quarantine and forcing them to be connected continually with mobile devices, such that mobile connectivity became the new norm during the pandemic and beyond. This situation is further accelerated for students as universities move towards a blended learning mode. In these circumstances, monitoring student mental health in terms of mobile and Internet connectivity is crucial for their wellbeing. This study focuses on students attending an International University of Bangladesh to investigate their mental health due to their continual use of mobile devices (e.g., smartphones, tablets, laptops etc.). A cross-sectional survey method was employed to collect data from 444 participants. Following the exploratory data analysis, eight machine learning (ML) algorithms were used to develop an automated normal-to-extreme severe depression identification and classification system. When the automated detection was incorporated with feature selection such as Chi-square test and Recursive Feature Elimination (RFE), about 3 to 5% increase in accuracy was observed by the method. Similarly, a 5 to 15% increase in accuracy has been observed when a feature extraction method such as Principal Component Analysis (PCA) was performed. Also, the SparsePCA feature extraction technique in combination with the CatBoost classifier showed the best results in terms of accuracy, F1-score, and ROC-AUC. The data analysis revealed no sign of depression in about 44% of the total participants. About 25% of students showed mild-to-moderate and 31% of students showed severe-to-extreme signs of depression. The results suggest that ML models, incorporating a proper feature engineering method can serve adequately in multi-stage depression detection among the students. This model might be utilized in other disciplines for detecting early signs of depression among people
Gender-based depression severity distribution.
Depression is a psychological state of mind that often influences a person in an unfavorable manner. While it can occur in people of all ages, students are especially vulnerable to it throughout their academic careers. Beginning in 2020, the COVID-19 epidemic caused major problems in people’s lives by driving them into quarantine and forcing them to be connected continually with mobile devices, such that mobile connectivity became the new norm during the pandemic and beyond. This situation is further accelerated for students as universities move towards a blended learning mode. In these circumstances, monitoring student mental health in terms of mobile and Internet connectivity is crucial for their wellbeing. This study focuses on students attending an International University of Bangladesh to investigate their mental health due to their continual use of mobile devices (e.g., smartphones, tablets, laptops etc.). A cross-sectional survey method was employed to collect data from 444 participants. Following the exploratory data analysis, eight machine learning (ML) algorithms were used to develop an automated normal-to-extreme severe depression identification and classification system. When the automated detection was incorporated with feature selection such as Chi-square test and Recursive Feature Elimination (RFE), about 3 to 5% increase in accuracy was observed by the method. Similarly, a 5 to 15% increase in accuracy has been observed when a feature extraction method such as Principal Component Analysis (PCA) was performed. Also, the SparsePCA feature extraction technique in combination with the CatBoost classifier showed the best results in terms of accuracy, F1-score, and ROC-AUC. The data analysis revealed no sign of depression in about 44% of the total participants. About 25% of students showed mild-to-moderate and 31% of students showed severe-to-extreme signs of depression. The results suggest that ML models, incorporating a proper feature engineering method can serve adequately in multi-stage depression detection among the students. This model might be utilized in other disciplines for detecting early signs of depression among people.</div
Comparison of ROC-AUC between PCA and SparsePCA.
Depression is a psychological state of mind that often influences a person in an unfavorable manner. While it can occur in people of all ages, students are especially vulnerable to it throughout their academic careers. Beginning in 2020, the COVID-19 epidemic caused major problems in people’s lives by driving them into quarantine and forcing them to be connected continually with mobile devices, such that mobile connectivity became the new norm during the pandemic and beyond. This situation is further accelerated for students as universities move towards a blended learning mode. In these circumstances, monitoring student mental health in terms of mobile and Internet connectivity is crucial for their wellbeing. This study focuses on students attending an International University of Bangladesh to investigate their mental health due to their continual use of mobile devices (e.g., smartphones, tablets, laptops etc.). A cross-sectional survey method was employed to collect data from 444 participants. Following the exploratory data analysis, eight machine learning (ML) algorithms were used to develop an automated normal-to-extreme severe depression identification and classification system. When the automated detection was incorporated with feature selection such as Chi-square test and Recursive Feature Elimination (RFE), about 3 to 5% increase in accuracy was observed by the method. Similarly, a 5 to 15% increase in accuracy has been observed when a feature extraction method such as Principal Component Analysis (PCA) was performed. Also, the SparsePCA feature extraction technique in combination with the CatBoost classifier showed the best results in terms of accuracy, F1-score, and ROC-AUC. The data analysis revealed no sign of depression in about 44% of the total participants. About 25% of students showed mild-to-moderate and 31% of students showed severe-to-extreme signs of depression. The results suggest that ML models, incorporating a proper feature engineering method can serve adequately in multi-stage depression detection among the students. This model might be utilized in other disciplines for detecting early signs of depression among people.</div
Academic level-based depression severity distribution.
Academic level-based depression severity distribution.</p
The mean value of confusion matrix for Catboost, KNN (SparsePCA).
The mean value of confusion matrix for Catboost, KNN (SparsePCA).</p
Result of the data analysis among age groups.
Depression is a psychological state of mind that often influences a person in an unfavorable manner. While it can occur in people of all ages, students are especially vulnerable to it throughout their academic careers. Beginning in 2020, the COVID-19 epidemic caused major problems in people’s lives by driving them into quarantine and forcing them to be connected continually with mobile devices, such that mobile connectivity became the new norm during the pandemic and beyond. This situation is further accelerated for students as universities move towards a blended learning mode. In these circumstances, monitoring student mental health in terms of mobile and Internet connectivity is crucial for their wellbeing. This study focuses on students attending an International University of Bangladesh to investigate their mental health due to their continual use of mobile devices (e.g., smartphones, tablets, laptops etc.). A cross-sectional survey method was employed to collect data from 444 participants. Following the exploratory data analysis, eight machine learning (ML) algorithms were used to develop an automated normal-to-extreme severe depression identification and classification system. When the automated detection was incorporated with feature selection such as Chi-square test and Recursive Feature Elimination (RFE), about 3 to 5% increase in accuracy was observed by the method. Similarly, a 5 to 15% increase in accuracy has been observed when a feature extraction method such as Principal Component Analysis (PCA) was performed. Also, the SparsePCA feature extraction technique in combination with the CatBoost classifier showed the best results in terms of accuracy, F1-score, and ROC-AUC. The data analysis revealed no sign of depression in about 44% of the total participants. About 25% of students showed mild-to-moderate and 31% of students showed severe-to-extreme signs of depression. The results suggest that ML models, incorporating a proper feature engineering method can serve adequately in multi-stage depression detection among the students. This model might be utilized in other disciplines for detecting early signs of depression among people.</div
Comparison with other research on automated depression detection in Bangladesh.
Comparison with other research on automated depression detection in Bangladesh.</p