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Article ; Online: NeuroNet19: an explainable deep neural network model for the classification of brain tumors using magnetic resonance imaging data.

Haque, Rezuana / Hassan, Md Mehedi / Bairagi, Anupam Kumar / Shariful Islam, Sheikh Mohammed

2024 Volume 14, Issue 1, Page(s) 1524

Abstract: Brain tumors (BTs) are one of the deadliest diseases that can significantly shorten a person's life. In recent years, deep learning has become increasingly popular for detecting and classifying BTs. In this paper, we propose a deep neural network ... ...

Abstract	Brain tumors (BTs) are one of the deadliest diseases that can significantly shorten a person's life. In recent years, deep learning has become increasingly popular for detecting and classifying BTs. In this paper, we propose a deep neural network architecture called NeuroNet19. It utilizes VGG19 as its backbone and incorporates a novel module named the Inverted Pyramid Pooling Module (iPPM). The iPPM captures multi-scale feature maps, ensuring the extraction of both local and global image contexts. This enhances the feature maps produced by the backbone, regardless of the spatial positioning or size of the tumors. To ensure the model's transparency and accountability, we employ Explainable AI. Specifically, we use Local Interpretable Model-Agnostic Explanations (LIME), which highlights the features or areas focused on while predicting individual images. NeuroNet19 is trained on four classes of BTs: glioma, meningioma, no tumor, and pituitary tumors. It is tested on a public dataset containing 7023 images. Our research demonstrates that NeuroNet19 achieves the highest accuracy at 99.3%, with precision, recall, and F1 scores at 99.2% and a Cohen Kappa coefficient (CKC) of 99%.
MeSH term(s)	Humans ; Brain Neoplasms/diagnostic imaging ; Glioma/diagnostic imaging ; Magnetic Resonance Imaging ; Neural Networks, Computer ; Meningeal Neoplasms
Language	English
Publishing date	2024-01-17
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-024-51867-1
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: When CVaR Meets With Bluetooth PAN: A Physical Distancing System for COVID-19 Proactive Safety.

Munir, Md Shirajum / Kim, Do Hyeon / Bairagi, Anupam Kumar / Hong, Choong Seon

IEEE sensors journal

2021 Volume 21, Issue 12, Page(s) 13858–13869

Abstract: In this work, we propose a risk-aware physical distancing system to assure a private safety distance from others for reducing the chance of being affected by the COVID-19 or such kind of pandemic. In particular, we have formulated a physical distancing ... ...

Abstract	In this work, we propose a risk-aware physical distancing system to assure a private safety distance from others for reducing the chance of being affected by the COVID-19 or such kind of pandemic. In particular, we have formulated a physical distancing problem by capturing Conditional Value-at-Risk (CVaR) of a Bluetooth-enabled personal area network (PAN). To solve the formulated risk-aware physical distancing problem, we propose two stages solution approach by imposing control flow, linear model, and curve-fitting schemes. Notably, in the first stage, we determine a PAN creator's safe movement distance by proposing a probabilistic linear model. This scheme can effectively cope with a tail-risk from the probability distribution by satisfying the CVaR constraint for estimating safe movement distance. In the second stage, we design a Levenberg-Marquardt (LM)-based curve fitting algorithm upon the recommended safety distance and current distances between the PAN creator and others to find an optimal high-risk trajectory plan for the PAN creator. Finally, we have performed an extensive performance analysis using state-of-the-art Bluetooth data to establish the proposed risk-aware physical distancing system's effectiveness. Our experimental results show that the proposed solution approach can effectively reduce the risk of recommending safety distance towards ensuring private safety. In particular, for a 95% CVaR confidence, we can successfully deal with 45.11% of the risk for measuring the PAN creator's safe movement distance.
Language	English
Publishing date	2021-03-24
Publishing country	United States
Document type	Journal Article
ISSN	1530-437X
ISSN	1530-437X
DOI	10.1109/JSEN.2021.3068782
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Article ; Online: A Smartphone-Based Detection System for Tomato Leaf Disease Using EfficientNetV2B2 and Its Explainability with Artificial Intelligence (AI).

Debnath, Anjan / Hasan, Md Mahedi / Raihan, M / Samrat, Nadim / Alsulami, Mashael M / Masud, Mehedi / Bairagi, Anupam Kumar

Sensors (Basel, Switzerland)

2023 Volume 23, Issue 21

Abstract: The occurrence of tomato diseases has substantially reduced agricultural output and financial losses. The timely detection of diseases is crucial to effectively manage and mitigate the impact of episodes. Early illness detection can improve output, ... ...

Abstract	The occurrence of tomato diseases has substantially reduced agricultural output and financial losses. The timely detection of diseases is crucial to effectively manage and mitigate the impact of episodes. Early illness detection can improve output, reduce chemical use, and boost a nation's economy. A complete system for plant disease detection using EfficientNetV2B2 and deep learning (DL) is presented in this paper. This research aims to develop a precise and effective automated system for identifying several illnesses that impact tomato plants. This will be achieved by analyzing tomato leaf photos. A dataset of high-resolution photographs of healthy and diseased tomato leaves was created to achieve this goal. The EfficientNetV2B2 model is the foundation of the deep learning system and excels at picture categorization. Transfer learning (TF) trains the model on a tomato leaf disease dataset using EfficientNetV2B2's pre-existing weights and a 256-layer dense layer. Tomato leaf diseases can be identified using the EfficientNetV2B2 model and a dense layer of 256 nodes. An ideal loss function and algorithm train and tune the model. Next, the concept is deployed in smartphones and online apps. The user can accurately diagnose tomato leaf diseases with this application. Utilizing an automated system facilitates the rapid identification of diseases, assisting in making informed decisions on disease management and promoting sustainable tomato cultivation practices. The 5-fold cross-validation method achieved 99.02% average weighted training accuracy, 99.22% average weighted validation accuracy, and 98.96% average weighted test accuracy. The split method achieved 99.93% training accuracy and 100% validation accuracy. Using the DL approach, tomato leaf disease identification achieves nearly 100% accuracy on a test dataset.
MeSH term(s)	Artificial Intelligence ; Solanum lycopersicum ; Smartphone ; Algorithms ; Plant Leaves
Language	English
Publishing date	2023-10-24
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s23218685
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Article ; Online: AQIPred

Farhana Yasmin / Md. Mehedi Hassan / Mahade Hasan / Sadika Zaman / Jarif Huda Angon / Anupam Kumar Bairagi / Yang Changchun

Human-Centric Intelligent Systems, Vol 3, Iss 3, Pp 275-

A Hybrid Model for High Precision Time Specific Forecasting of Air Quality Index with Cluster Analysis

2023 Volume 295

Abstract: Abstract The discipline of forecasting and prediction is witnessing a surge in the application of these techniques as a direct result of the strong empirical performance that approaches based on machine learning (ML) have shown over the past few years. ... ...

Abstract	Abstract The discipline of forecasting and prediction is witnessing a surge in the application of these techniques as a direct result of the strong empirical performance that approaches based on machine learning (ML) have shown over the past few years. Especially to predict wind direction, air and water quality, and flooding. In the context of doing this research, an MLP-LSTM Hybrid Model was developed to be able to generate predictions of this nature. An investigation into the Beijing Multi-Site Air-Quality Data Set was carried out in the context of an experiment. In this particular scenario, the model generated MSE values that came in at 0.00016, MAE values that came in at 0.00746, RMSE values that came in at 13.45, MAPE values that came in at 0.42, and R 2 values that came in at 0.95. This is an indication that the model is functioning effectively. The conventional modeling techniques for forecasting, do not give the level of performance that is required. On the other hand, the results of this study will be useful for any type of time-specific forecasting prediction that requires a high level of accuracy.
Keywords	Modeling ; Prediction ; Time series forecasting ; Neural network ; Information technology ; T58.5-58.64 ; Electronic computers. Computer science ; QA75.5-76.95
Language	English
Publishing date	2023-08-01T00:00:00Z
Publisher	Springer Nature
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Performance evaluation of micro lens arrays: Improvement of light intensity and efficiency of white organic light emitting diodes.

Adhikary, Apurba / Bhuiya, Joy / Murad, Saydul Akbar / Hossain, Md Bipul / Uddin, K M Aslam / Faysal, Md Estihad / Rahaman, Abidur / Bairagi, Anupam Kumar

PloS one

2022 Volume 17, Issue 5, Page(s) e0269134

Abstract: This paper proposes a unique method to improve light intensity and efficiency of white organic light emitting diodes (OLEDs) by engraving micro lens arrays (MLAs) on the outer face of the substrate layer. The addition of MLAs on the substrate layer ... ...

Abstract	This paper proposes a unique method to improve light intensity and efficiency of white organic light emitting diodes (OLEDs) by engraving micro lens arrays (MLAs) on the outer face of the substrate layer. The addition of MLAs on the substrate layer improves the light intensity and external quantum efficiency (EQE) of the OLEDs. The basic OLED model achieved an EQE of 14.45% for the effective refractive index (ERI) of 1.86. The spherical and elliptical (planoconvex and planoconcave) MLAs were incorporated on the outer face of the substrate layer to increase the EQE of the OLEDs. The maximum EQE of 17.30% was obtained for Convex-1 (elliptical planoconvex) MLA engraved OLED where the ERI was 1.70. In addition, Convex-1 MLA engraved OLED showed an improvement of 3.8 times on the peak electroluminescence (EL) light intensity compared to basic OLED. Therefore, Convex-1 MLA incorporated OLED can be considered as a potential white OLED because of its excellent light distribution and intensity profile.
MeSH term(s)	Lenses ; Light ; Refractometry
Language	English
Publishing date	2022-05-27
Publishing country	United States
Document type	Journal Article
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0269134
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A Machine Learning Approach to Diagnosing Lung and Colon Cancer Using a Deep Learning-Based Classification Framework

Masud, Mehedi / Sikder, Niloy / Nahid, Abdullah-Al / Bairagi, Anupam Kumar / AlZain, Mohammed A

Sensors (Basel, Switzerland)

2021 Volume 21, Issue 3

Abstract: The field of Medicine and Healthcare has attained revolutionary advancements in the last forty years. Within this period, the actual reasons behind numerous diseases were unveiled, novel diagnostic methods were designed, and new medicines were developed. ...

Abstract	The field of Medicine and Healthcare has attained revolutionary advancements in the last forty years. Within this period, the actual reasons behind numerous diseases were unveiled, novel diagnostic methods were designed, and new medicines were developed. Even after all these achievements, diseases like cancer continue to haunt us since we are still vulnerable to them. Cancer is the second leading cause of death globally; about one in every six people die suffering from it. Among many types of cancers, the lung and colon variants are the most common and deadliest ones. Together, they account for more than 25% of all cancer cases. However, identifying the disease at an early stage significantly improves the chances of survival. Cancer diagnosis can be automated by using the potential of Artificial Intelligence (AI), which allows us to assess more cases in less time and cost. With the help of modern Deep Learning (DL) and Digital Image Processing (DIP) techniques, this paper inscribes a classification framework to differentiate among five types of lung and colon tissues (two benign and three malignant) by analyzing their histopathological images. The acquired results show that the proposed framework can identify cancer tissues with a maximum of 96.33% accuracy. Implementation of this model will help medical professionals to develop an automatic and reliable system capable of identifying various types of lung and colon cancers.
MeSH term(s)	Artificial Intelligence ; Colonic Neoplasms/diagnosis ; Deep Learning ; Humans ; Lung ; Lung Neoplasms/diagnosis ; Machine Learning
Language	English
Publishing date	2021-01-22
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s21030748
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Article: IoT-Based Healthcare-Monitoring System towards Improving Quality of Life: A Review.

Abdulmalek, Suliman / Nasir, Abdul / Jabbar, Waheb A / Almuhaya, Mukarram A M / Bairagi, Anupam Kumar / Khan, Md Al-Masrur / Kee, Seong-Hoon

Healthcare (Basel, Switzerland)

2022 Volume 10, Issue 10

Abstract: The Internet of Things (IoT) is essential in innovative applications such as smart cities, smart homes, education, healthcare, transportation, and defense operations. IoT applications are particularly beneficial for providing healthcare because they ... ...

Abstract	The Internet of Things (IoT) is essential in innovative applications such as smart cities, smart homes, education, healthcare, transportation, and defense operations. IoT applications are particularly beneficial for providing healthcare because they enable secure and real-time remote patient monitoring to improve the quality of people's lives. This review paper explores the latest trends in healthcare-monitoring systems by implementing the role of the IoT. The work discusses the benefits of IoT-based healthcare systems with regard to their significance, and the benefits of IoT healthcare. We provide a systematic review on recent studies of IoT-based healthcare-monitoring systems through literature review. The literature review compares various systems' effectiveness, efficiency, data protection, privacy, security, and monitoring. The paper also explores wireless- and wearable-sensor-based IoT monitoring systems and provides a classification of healthcare-monitoring sensors. We also elaborate, in detail, on the challenges and open issues regarding healthcare security and privacy, and QoS. Finally, suggestions and recommendations for IoT healthcare applications are laid down at the end of the study along with future directions related to various recent technology trends.
Language	English
Publishing date	2022-10-11
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2721009-1
ISSN	2227-9032
ISSN	2227-9032
DOI	10.3390/healthcare10101993
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Article ; Online: Transfer Learning for Sentiment Analysis Using BERT Based Supervised Fine-Tuning.

Prottasha, Nusrat Jahan / Sami, Abdullah As / Kowsher, Md / Murad, Saydul Akbar / Bairagi, Anupam Kumar / Masud, Mehedi / Baz, Mohammed

Sensors (Basel, Switzerland)

2022 Volume 22, Issue 11

Abstract: The growth of the Internet has expanded the amount of data expressed by users across multiple platforms. The availability of these different worldviews and individuals' emotions empowers sentiment analysis. However, sentiment analysis becomes even more ... ...

Abstract	The growth of the Internet has expanded the amount of data expressed by users across multiple platforms. The availability of these different worldviews and individuals' emotions empowers sentiment analysis. However, sentiment analysis becomes even more challenging due to a scarcity of standardized labeled data in the Bangla NLP domain. The majority of the existing Bangla research has relied on models of deep learning that significantly focus on context-independent word embeddings, such as Word2Vec, GloVe, and fastText, in which each word has a fixed representation irrespective of its context. Meanwhile, context-based pre-trained language models such as BERT have recently revolutionized the state of natural language processing. In this work, we utilized BERT's transfer learning ability to a deep integrated model CNN-BiLSTM for enhanced performance of decision-making in sentiment analysis. In addition, we also introduced the ability of transfer learning to classical machine learning algorithms for the performance comparison of CNN-BiLSTM. Additionally, we explore various word embedding techniques, such as Word2Vec, GloVe, and fastText, and compare their performance to the BERT transfer learning strategy. As a result, we have shown a state-of-the-art binary classification performance for Bangla sentiment analysis that significantly outperforms all embedding and algorithms.
MeSH term(s)	Algorithms ; Humans ; Language ; Machine Learning ; Natural Language Processing ; Sentiment Analysis
Language	English
Publishing date	2022-05-30
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s22114157
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Book ; Online: Challenges in Blockchain as a Solution for IoT Ecosystem Threats and Access Control

Avik, Suranjeet Chowdhury / Biswas, Sujit / Ahad, Md Atiqur Rahaman / Latif, Zohaib / Alghamdi, Abdullah / Abosaq, Hamad / Bairagi, Anupam Kumar

A Survey

2023

Abstract: The Internet of Things (IoT) is increasingly influencing and transforming various aspects of our daily lives. Contrary to popular belief, it raises security and privacy issues as it is used to collect data from consumers or automated systems. Numerous ... ...

Abstract	The Internet of Things (IoT) is increasingly influencing and transforming various aspects of our daily lives. Contrary to popular belief, it raises security and privacy issues as it is used to collect data from consumers or automated systems. Numerous articles are published that discuss issues like centralised control systems and potential alternatives like integration with blockchain. Although a few recent surveys focused on the challenges and solutions facing the IoT ecosystem, most of them did not concentrate on the threats, difficulties, or blockchain-based solutions. Additionally, none of them focused on blockchain and IoT integration challenges and attacks. In the context of the IoT ecosystem, overall security measures are very important to understand the overall challenges. This article summarises difficulties that have been outlined in numerous recent articles and articulates various attacks and security challenges in a variety of approaches, including blockchain-based solutions and so on. More clearly, this contribution consolidates threats, access control issues, and remedies in brief. In addition, this research has listed some attacks on public blockchain protocols with some real-life examples that can guide researchers in taking preventive measures for IoT use cases. Finally, a future research direction concludes the research gaps by analysing contemporary research contributions.
Keywords	Computer Science - Cryptography and Security
Subject code	303
Publishing date	2023-11-26
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A Comparative Study, Prediction and Development of Chronic Kidney Disease Using Machine Learning on Patients Clinical Records

Md. Mehedi Hassan / Md. Mahedi Hassan / Swarnali Mollick / Md. Asif Rakib Khan / Farhana Yasmin / Anupam Kumar Bairagi / M. Raihan / Shibbir Ahmed Arif / Amrina Rahman

Human-Centric Intelligent Systems, Vol 3, Iss 2, Pp 92-

2023 Volume 104

Abstract: Abstract Chronic Kidney Disease (CKD) has become a major problem in modern times, and it is dubbed the silent assassin due to its delayed signs. To overcome these critical issues, early identification may minimize the prevalence of chronic diseases, ... ...

Abstract	Abstract Chronic Kidney Disease (CKD) has become a major problem in modern times, and it is dubbed the silent assassin due to its delayed signs. To overcome these critical issues, early identification may minimize the prevalence of chronic diseases, though it is quite difficult because of different kinds of limitations in the dataset. The novelty of our study is that we extracted the best features from the dataset in order to provide the best classification models for diagnosing patients with chronic kidney disease. In our study, we used CKD patients’ clinical datasets to predict CKD using some popular machine learning algorithms. After handling missing values, K-means clustering has been performed. Then feature selection was done by applying the XGBoost feature selection algorithm. After selecting features from our dataset, we have used a variety of machine learning models to determine the best classification models, including Neural Network (NN), Random Forest (RF), Support Vector Machine (SVM), Random Tree (RT), and Bagging Tree Model (BTM). Accuracy, Sensitivity, Specificity, and Kappa values were used to evaluate model performance.
Keywords	Chronic kidney disease ; CKD risk prediction ; XGboost ; Predictive analysis ; Information technology ; T58.5-58.64 ; Electronic computers. Computer science ; QA75.5-76.95
Subject code	006
Language	English
Publishing date	2023-02-01T00:00:00Z
Publisher	Springer Nature
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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