Publications

1. M. A. K. Rifat, A. Kabir, and A. Huq, “An Explainable Machine Learning Approach to Traffic Accident Fatality Prediction,” Procedia Computer Science, vol. 246, pp. 1905–1914, 2024, doi: https://doi.org/10.1016/j.procs.2024.09.704. ‌[Presented at the 28th International Conference on Knowledge Based and Intelligent Information and Engineering Systems (KES 2024), as part of a special issue.]

Y. Sazid and K. Sakib

19th International Conference on Evaluation of Novel Approaches to Software Engineering | ENASE 2024

Y. Sazid, S. Kuri, K. S. Ahmed, and A. Satter

19th International Conference on Evaluation of Novel Approaches to Software Engineering | ENASE 2024

Y. Sazid, M. M. N. Fuad, and K. Sakib

30th Asia-Pacific Software Engineering Conference | APSEC 2023

Sanzana Karim Lora, M. Sohel Rahman, Rifat Shahriyar, “ConVerSum: A Contrastive Learning based Approach for Data-Scarce Solution of Cross-Lingual Summarization Beyond Direct Equivalents”, ACM Transactions on Asian and Low-Resource Language Information Processing, Volume 24, Issue 5, Article No.: 50, Pages 1 - 22, May 2025.

Sanzana Karim Lora, G. M. Shahariar, Tamanna Nazmin, Noor Nafeur Rahman, Rafsan Rahman, Miyad Bhuiyan, Faisal Muhammad Shah; “Ben-Sarc: A Self-Annotated Corpus for Sarcasm Detection from Bengali Social Media Comments and Its Baseline Evaluation”, Natural Language Processing.

Sanzana Karim Lora, Ishrat Jahan, Rahad Hussain, Rifat Shahriyar, A.B.M Alim Al Islam; “A transformer-based generative adversarial learning to detect sarcasm from Bengali text with correct classification of confusing text”, Heliyon, volume 9, issue 12.

Sanzana Karim Lora, Istiak Ahmed, Muhammad Abdullah Adnan, “Short Paper: A Cloud-based Distributed Approach for Social Media Sentiment Analysis using Machine Learning with Distributed Hyperparameter Tuning”, 11th International Conference on Networking, Systems, and Security. (11th NSysS 2024), Khulna, Bangladesh, ACM, New York, NY, USA.

Sanzana Karim Lora, Nusrat Jahan, Shahana Alam Antora, Nazmus Sakib, “Detecting Emotion of Users’ Analyzing Social Media Bengali Comments Using Deep Learning Techniques”, in Proceedings of the 2nd International Conference on Advanced Information and Communication Technology 2020 (ICAICT 2020), Dhaka, Bangladesh, 2020, pp. 88-93.

 MI Rakib, A Nobi, JW Lee (2021). Structure and dynamics of financial networks by feature ranking method. Published on: Scientific Reports - 11, pp: 17618. doi.org/10.1038/s41598-021-97100-1 [Q1, IF: 4.99]

MS Hossain, A.Q.M. SU Pathan, MN Islam, MIQ Tonmoy, MI Rakib, NM Bahadur, et al (2021). Genome-wide identification and prediction of SARS-CoV-2 mutations show an abundance of variants: Integrated study of bioinformatics and deep neural learning. Published on: Informatics in Medicine Unlocked – 27, pp: 100798.

MI Rakib, MJ Hossain, A Nobi (2022). Feature ranking and network analysis of global financial indices. Published on: Plos One – 17(6), pp: e0269483. doi.org/10.1371/journal.pone.0269483 [Q1, IF: 3.75]

 SBS Omit, S Akhter, HK Rana, ARM MH Rana, NK Podder, MI Rakib, A Nobi (2023). Identification of comorbidities, genomic associations, and molecular mechanisms for COVID-19 using bioinformatics approaches. Published on: BioMed Research International – 2023, pp: 6996307. doi.org/10.1155/2023/6996307 [Q2, IF: 3.41]

ZM Koli, A Nobi, MI Rakib, MJ Alam, JW Lee (2023). Modular structures of trade flow networks in international commodities. Published on: Sustainability – 15(22), pp: 15786. doi.org/10.3390/su152215786 [Q1, IF: 3.9]

MI Rakib, MJ Alam, N Akter, KH Tuhin, A Nobi (2024). Change in hierarchy of the financial networks: A study on firms of an emerging market in Bangladesh. Published on: Plos One – 19(5), pp: e0301725. doi.org/10.1371/journal.pone.0301725 [Q1, IF: 3.75]

KH Tuhin, A Nobi, MI Rakib, JW Lee (2025). Long Short-Term Memory Autoencoder Based Network of Financial Indices. Published on: Humanit Soc Sci Commun–12(100). https://doi.org/10.1057/s41599-025-04412-y [Q1, IF: 3.7]

SS Hassan, MI Rakib, KH Tuhin, A Nobi (2023). Structure of global financial networks before and during COVID-19 based on mutual information. Published in: The Proceedings of International Conference on Machine Intelligence and Emerging Technologies (MIET 2022), LNICST – 491, Springer. doi.org/10.1007/978-3-031-34622-4_50

MI Rakib, M Akter, SA Milu, A Nobi (2024). Entropy and relative entropy in the composition of commodities. Published in: The Proceedings of 26th International Conference on Computer and Information Technology (ICCIT), Cox’s Bazar, IEEE Bangladesh. PP. 1-6. doi.org/10.1109/ICCIT60459.2023.10441532

 KH Tuhin, A Nobi, MJ Sadique, MI Rakib, JW Lee (2024). Effect of network size on comparing different stock networks. Published on: Plos One – 18(12), pp: e0288733. doi.org/10.1371/journal.pone.0288733 [Q1, IF: 3.75]

Cryptocurrency mining data centers consume 100-200 times more energy than conventional office areas annually. Regulating power consumption, cooling mechanisms, and thermal control performance is crucial to creating a greener and more energy-efficient crypto-mining data center. This paper presents a new cryptocurrency mining data center design that is both environmentally friendly and energy-efficient. The design considers popular green and energy-saving data center cooling and temperature management approaches, as well as cost-effective operations. The total monthly cost of the proposed data center is 358025 USD, with renewable energy generating 68520 kW of electricity. The monthly profit from Bitcoin mining is 3200806.969 USD, while Ethereum mining is 2317353.503 USD. The PUE number is 1.04, and the DCiE is 96.15 percent. These statistics help determine the model’s conclusion. 

Power consumption has a significant influence on resource allocation, which has a negative effect on the environment. To lessen the negative effects, an effective resource allocation algorithm is needed. In this paper, we suggested a unique hybrid approach for energy-efficient scheduling of virtual machines (VMs) in cloud data centers called Energy Efficient Particle Swarm Optimization (EE-PSO), which combines Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). This integration is special since it makes use of both algorithms’ advantages to improve scheduling effectiveness and energy consumption. The novelty lies in how GA and PSO are combined, which differs from previous attempts where these algorithms were either used separately or in a less integrated manner. EE-PSO represents significant achievement toward achieving the goals of decreasing power usage and promoting green …

One of the most prevalent common cancers among men is prostate cancer. Therefore, early detection is crucial for effective treatment. This study aims to detect prostate cancer using four Convolutional Neural Network (CNN) architectures. We evaluated our trained models and found a lower Root Mean Square Error (RMSE) of 2.9960 on the validation set indicating that our model can accurately detect prostate cancer in medical images. Our study suggests a promising prostate cancer detection model that could help improve patients' early diagnosis and treatment outcomes.

The process of classifying brain tumors using MRI data is difficult to do accurately. The challenges and limitations of manual analysis in treatments can be overcome by implementing Computer-Aided Diagnosis (CAD) systems for categorizing brain tumors. This study assesses the performance of five well-known Transfer Learning CNN architectures - VGG19, VGG16, GoogleNet, ResNet-50, and DenseNet-121 - using magnetic resonance imaging (MRI) scans. The main goal is to classify brain malignancies into four groups: no tumor, pituitary, meningioma, and glioma. The focus of this research for the classification task was on data preparation, training on preprocessed data, and comparing each model using evaluation metrics such as f1-score, accuracy, recall, and precision. Following preprocessing and training, the evaluation of the five CNN models revealed promising results for the VGG models, with both …

The most advanced methods available today are convolutional neural networks (CNNs), which are frequently used for image categorization tasks. This article uses sophisticated neural network models to explore the categorization of peripheral blood smear pictures for B-ALL diagnosis and its subtypes. We introduce a method for classifying images using a modified VGG19 model. Images are pre-processed at first before being input into the multi-class classification algorithms. We discovered throughout this study that the suggested methods improve model performance. Our study focused on two types of images: benign and malignant, as well as three subtypes of malignant lymphoblasts: Early Pre-B, Pre-B, and Pro-B ALL. The model has a validation loss of 0.1499 and an accuracy of 94.63%, whereas its training loss is 0.1127 and 96.97%, respectively. These findings demonstrate how well the VGG19-based model …

White Blood Cell (WBC) count is a significant task in identifying leukemia, a widely known malignancy that can be devastating gradually. Infantile WBCs existing in the sponge tissues of bone marrow affect the superfluous expansion, which in turn produces leukemia cancer. Deep learning and image processing techniques models can be applied in the field to detect leukemic blood and generate outstanding outcomes. Leukemia occurs from the leukocyte blood type, which is one kind of white blood cell. This proposed system introduces a method of classifying leukemic blood images and counting the number of white blood cells in a Leukemic blood image. This is a hybrid procedure combining deep learning and image processing techniques. A collection of 221 blood cell images available on a website known as ‘RaabinData’ is used, which were collected from patients at the Takht-e Tavoos Medical School …

Anemia, a condition affecting human red blood cells (RBCs), presents in various forms. Different blood cell types and anemia variants exist. Addressing such a significant challenge requires integrating pathophysiology, advanced technology, and a comprehensive understanding of RBC classifications. In this pursuit, we utilize Deep Learning (DL) models to establish connections and propose innovative solutions to pathophysiological issues related to anaemia diagnosis through RBC classification. The customized Convolutional Neural Network (CNN) demonstrates exceptional performance, boasting a Training Accuracy of 99.42% and a Test Accuracy of 98.88%. Furthermore, the Training Loss is impressively low at 0.0232, while the Validation Loss remains minimal at 0.0964. The associated confusion matrix attests to the model's robust performance, affirming its accuracy in classification tasks.

To address the challenge of flattening the recall line in Forest Cover Type data classification, this study focuses on the application of a Voting Classifier. Forest cover is crucial for biodiversity preservation and climate regulation, and accurate classification of forest cover types is essential for effective forest management. The paper utilizes a dataset containing attributes related to forest cover, and models such as K-Nearest Neighbors (KNN), Extra Tree, Random Forest (RF), and Extreme Gradient Boosting (XGBoost) are employed. However, the individual performance of the models varies for recall. To overcome this, a Voting Classifier is introduced, which combines the predictions from multiple models using a majority or weighted vote. The experiments demonstrate the effectiveness of the Voting Classifier in flattening the recall line and enhancing the accuracy of forest cover type classification.

Cloud-dedicated servers can better meet green computing standards by being ecologically friendly. “Green cloud computing” refers to utilizing information technology and other technological achievements to help the environment. Task scheduling is one of the biggest challenges in cloud-based systems that must be addressed to improve system efficacy and user experience. The primary goal of this study is to develop an algorithm that focuses on minimizing green cloud computing execution times while remaining environmentally friendly. We compared task scheduling algorithms based on execution time, such as FCFS, SJF, and Round Robin, to the approach we recommended, the generalized priority (GP) algorithm. We experimented with evaluating our technique using the CloudSim 3.0.3 simulator. The algorithm we suggested has the shortest runtime out of all the algorithms which is 97.91.