Browsing by Author "Barros, Alistair"

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A Review on VANET Research
(Daffodil International University, 2022-06-24) Mahi, MD. Julkar Nayeen; Chaki, Sudipto; Ahmed, Shamim; Biswas, Milon; Kaiser, M. Shamim; Islam, Mohammad Shahidul; Sookhak, Mehdi; Barros, Alistair; Whaiduzzaman, MD
Recent technology has modeled VANET (vehicular adhoc network) communication well in terms of privileges to derive vehicular communication technologically to save time, energy, and money. Due to the increase in powerful technology in modern times, VANETs play a vital role in uplifting daily concerns across vehicles and vehicular identities. Hence, to tune VANETs to become compatible with traditional technologies and increase demand, VANETs require upgrading. The severity and frequency of unwanted occurrences have become a considerable concern for our day-to-day lives relating to vehicular position. Thus, verily updated methodologies or working procedures are needed for the future VANET interplay to eradicate such problems occurring through vehicular identities. This article outlines in technology related to VANETS, future developments, and coping issues by deriving comprehensive frameworks, workflow patterns, upgrading procedures including big data, fog computing, SDN (software defined networking), and SIoT (social Internet of Things). This article provides a high-level overview of a complete VANET upgrade solution to address future problem management issues under a range of acceptable scientific themes, indicators, and combinations.
A Review on VANET Security: Future Challenges and Open Issues
(Institute of Electrical and Electronics Engineers Inc., 2023-05-02) Mahi, Md. Julkar Nayeen; Chaki, Sudipto; Humayun, Esraq; Imran, Hafizul; Barros, Alistair; Whaiduzzaman, Md.
Vehicular Adhoc Network (VANET) is an established technology that is well-suited for emerging technologies such as the Internet of Vehicles (IoV) and Unmanned Aerial Vehicles (UAVs). However, while VANET offers improved methods for addressing contemporary technology, it also presents significant challenges in providing adequate security measures for intended access. VANET operates on multiple execution platforms, such as roadside units, vehicle-tovehicle, vehicle-to-device, and vehicle-to-everything (V2X) communication. As a result, VANET must establish robust security measures for future purposes and strengthen protocol authentications to ensure secure data delivery and network-wide execution. In this work, we provide an overview of some of the recent security problems faced by VANET to raise awareness among developers and engineers about the specific security needs of VANET and how to avoid errors or intrusions when deploying VANETs in cities and urban areas. We cover topics such as the classification of security attacks, standard or security protocol problems and solutions, and the best feasible security criteria for extended VANETs. Finally, we discuss open issues and future VANET security developments or concerns.
Impact Prediction of Online Education During COVID-19 Using Machine Learning: A Case Study
(Springer Nature, 2023-01-25) Hossain, Sheikh Mufrad; Rahman, Md. Mahfujur; Barros, Alistair; Whaiduzzaman, Md.
The transition from traditional to online education is challenging and has many obstacles in various situations. Due to the Covid-19 situation, we use digital blended education from the traditional system. However, in some cases, it can harm our student’s academic performance. In this research, we aim to identify the factors that impact the student’s academic performance in online education. On the other hand, this study also finds the student Cumulative Grade Point Average (CGPA) fluctuation using machine learning classifiers. To achieve this, we survey to gather data perspective of Bangladesh private university, and this data allows us to analyze and classify using machine learning techniques such as Logistic Regression (LR), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), Decision Tree (DT), and Random Forest (RF). This study finds Random Forest (RF) outperforms the other state-of-art classifiers.
IoT Based Low-Cost Posture and Bluetooth Controlled Robot for Disabled and Virus Affected People
(Daffodil International University, 2022-06-15) Akhund, Tajim Md. Niamat Ullah; Hossain, Mosharof; Kubra, Khadizatul; Nurjahan; Barros, Alistair; Whaiduzzaman, Md.
IoT-based robots can help people to a great extent. This work results in a low-cost posture recognizer robot that can detect posture signs from a disabled or virus-affected person and move accordingly. The robot can take images with the Raspberry Pi camera and process the image to identify the posture with our designed algorithm. In addition, it can also take instructions via Bluetooth from smartphone apps. The robot can move 360 degrees depending on the input posture or Bluetooth. This system can assist disabled people who can move a few organs only. Moreover, this system can assist virus-affected persons as they can instruct the robot without touching it. Finally, the robot can collect data from a distant place and send it to a cloud server without spreading the virus.
Unmasking Banking Fraud: Unleashing the Power of Machine Learning and Explainable AI (XAI) on Imbalanced Data
(MDPI, 2024-05-23) Nobel, S. M. Nuruzzaman; Sultana, Shirin; Singha, Sondip Poul; Chaki, Sudipto; Mahi, Md. Julkar Nayeen; Jan, Tony; Barros, Alistair; Whaiduzzaman, Md
Recognizing fraudulent activity in the banking system is essential due to the significant risks involved. When fraudulent transactions are vastly outnumbered by non-fraudulent ones, dealing with imbalanced datasets can be difficult. This study aims to determine the best model for detecting fraud by comparing four commonly used machine learning algorithms: Support Vector Machine (SVM), XGBoost, Decision Tree, and Logistic Regression. Additionally, we utilized the Synthetic Minority Over-sampling Technique (SMOTE) to address the issue of class imbalance. The XGBoost Classifier proved to be the most successful model for fraud detection, with an accuracy of 99.88%. We utilized SHAP and LIME analyses to provide greater clarity into the decision-making process of the XGBoost model and improve overall comprehension. This research shows that the XGBoost Classifier is highly effective in detecting banking fraud on imbalanced datasets, with an impressive accuracy score. The interpretability of the XGBoost Classifier model was further enhanced by applying SHAP and LIME analysis, which shed light on the significant features that contribute to fraud detection. The insights and findings presented here are valuable contributions to the ongoing efforts aimed at developing effective fraud detection systems for the banking industry.
Unmasking Banking Fraud: Unleashing the Power of Machine Learning and Explainable AI (XAI) on Imbalanced Data
(MDPI Publications, 2024-06-23) Nobel, S. M. Nuruzzaman; Sultana, Shirin; Singha, Sondip Poul; Chaki, Sudipto; Mahi, Md. Julkar Nayeen; Jan, Tony; Barros, Alistair; Whaiduzzaman, Md.
Recognizing fraudulent activity in the banking system is essential due to the significant risks involved. When fraudulent transactions are vastly outnumbered by non-fraudulent ones, dealing with imbalanced datasets can be difficult. This study aims to determine the best model for detecting fraud by comparing four commonly used machine learning algorithms: Support Vector Machine (SVM), XGBoost, Decision Tree, and Logistic Regression. Additionally, we utilized the Synthetic Minority Over-sampling Technique (SMOTE) to address the issue of class imbalance. The XGBoost Classifier proved to be the most successful model for fraud detection, with an accuracy of 99.88%. We utilized SHAP and LIME analyses to provide greater clarity into the decision-making process of the XGBoost model and improve overall comprehension. This research shows that the XGBoost Classifier is highly effective in detecting banking fraud on imbalanced datasets, with an impressive accuracy score. The interpretability of the XGBoost Classifier model was further enhanced by applying SHAP and LIME analysis, which shed light on the significant features that contribute to fraud detection. The insights and findings presented here are valuable contributions to the ongoing efforts aimed at developing effective fraud detection systems for the banking industry.