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421 results on '"Shamim Kaiser"'

1. An exploratory simulation study and prediction model on human brain behavior and activity using an integration of deep neural network and biosensor Rabi antenna

Author

Nhat Truong Pham, Montree Bunruangses, Phichai Youplao, Anita Garhwal, Kanad Ray, Arup Roy, Sarawoot Boonkirdram, Preecha Yupapin, Muhammad Arif Jalil, Jalil Ali, Shamim Kaiser, Mufti Mahmud, Saurav Mallik, and Zhongming Zhao

Subjects
Brain neural network, Deep brain sensors, Brain-Rabi antenna, Deep learning, Biosensors on human brain/action, Simulation, Science (General), Q1-390, Social sciences (General), H1-99
Abstract

The plasmonic antenna probe is constructed using a silver rod embedded in a modified Mach-Zehnder interferometer (MZI) ad-drop filter. Rabi antennas are formed when space-time control reaches two levels of system oscillation and can be used as human brain sensor probes. Photonic neural networks are designed using brain-Rabi antenna communication, and transmissions are connected via neurons. Communication signals are carried by electron spin (up and down) and adjustable Rabi frequency. Hidden variables and deep brain signals can be obtained by external detection. A Rabi antenna has been developed by simulation using computer simulation technology (CST) software. Additionally, a communication device has been developed that uses the Optiwave program with Finite-Difference Time-Domain (OptiFDTD). The output signal is plotted using the MATLAB program with the parameters of the OptiFDTD simulation results. The proposed antenna oscillates in the frequency range of 192 THz to 202 THz with a maximum gain of 22.4 dBi. The sensitivity of the sensor is calculated along with the result of electron spin and applied to form a human brain connection. Moreover, intelligent machine learning algorithms are proposed to identify high-quality transmissions and predict the behavior of transmissions in the near future. During the process, a root mean square error (RMSE) of 2.3332(±0.2338) was obtained. Finally, it can be said that our proposed model can efficiently predict human mind, thoughts, behavior as well as action/reaction, which can be greatly helpful in the diagnosis of various neuro-degenerative/psychological diseases (such as Alzheimer's, dementia, etc.) and for security purposes.

Published
2023
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2. Exploring Open Access Cybersecurity Datasets for Machine Learning-Based Cyberattack Detection

Author

Alam, Meherun Bintey, Chowdhury, Mariam, Easha, Umma Habiba, Shamim Kaiser, M., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Kaiser, M. Shamim, editor, Singh, Raghvendra, editor, Bandyopadhyay, Anirban, editor, Mahmud, Mufti, editor, and Ray, Kanad, editor

Published
2024
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3. Towards Federated-Deep Learning-Based Glaucoma Detection from Color Fundus Images

Author

Easha, Umma Habiba, Obaydullah, M., Arif, Jayed, Shamim Kaiser, M., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Kaiser, M. Shamim, editor, Singh, Raghvendra, editor, Bandyopadhyay, Anirban, editor, Mahmud, Mufti, editor, and Ray, Kanad, editor

Published
2024
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4. Turning Trash into Treasure: Developing an Intelligent Bin for Plastic Bottle Recycling

Author

Sirajam Munira, Niloy Paul, Md Ashraful Alam, Md. Mahmudur Rahman, and M. Shamim Kaiser

Subjects
smart environment, reward system, yolov5, deep learning, business model, Electronic computers. Computer science, QA75.5-76.95, Social sciences (General), H1-99
Abstract

Plastic pollution has emerged as a major global concern due to its enduring nature and limited recycling options. In response to this critical challenge, this paper presents a novel approach utilizing a Detection-Based Reward System (DBRS) alongside an innovative business model to promote effective plastic waste management, reduce plastic waste accumulation in the nature, and uphold environmental cleanliness. Leveraging the YOLOv5 algorithm for its exceptional accuracy, speed, and open-source availability, plastic bottle detection becomes a pivotal aspect of this system. Users seamlessly enroll in the system, triggering an automated detection process that computes reward points corresponding to their deposited plastic bottles. These reward points are meticulously stored within a centralized database. Beyond its operational facets, this comprehensive system encompasses a robust business model, strategically poised to capture widespread engagement with waste disposal practices, thereby contributing to the realization of Sustainable Development Goals (SDGs) geared towards fostering a healthier environment. Notably, the DBRS attains cutting-edge performance in plastic bottle detection, boasting an impressive mean Average Precision (mAP) of 0.973, underscoring its efficacy in tackling plastic pollution.

Published
2024
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9. Bayesian Optimized Machine Learning Model for Automated Eye Disease Classification from Fundus Images

Author

Tasnim Bill Zannah, Md. Abdulla-Hil-Kafi, Md. Alif Sheakh, Md. Zahid Hasan, Taslima Ferdaus Shuva, Touhid Bhuiyan, Md. Tanvir Rahman, Risala Tasin Khan, M. Shamim Kaiser, and Md Whaiduzzaman

Subjects
eye disease, machine learning, principal component analysis, Bayesian optimization, Electronic computers. Computer science, QA75.5-76.95
Abstract

Eye diseases are defined as disorders or diseases that damage the tissue and related parts of the eyes. They appear in various types and can be either minor, meaning that they do not last long, or permanent blindness. Cataracts, glaucoma, and diabetic retinopathy are all eye illnesses that can cause vision loss if not discovered and treated early on. Automated classification of these diseases from fundus images can empower quicker diagnoses and interventions. Our research aims to create a robust model, BayeSVM500, for eye disease classification to enhance medical technology and improve patient outcomes. In this study, we develop models to classify images accurately. We start by preprocessing fundus images using contrast enhancement, normalization, and resizing. We then leverage several state-of-the-art deep convolutional neural network pre-trained models, including VGG16, VGG19, ResNet50, EfficientNet, and DenseNet, to extract deep features. To reduce feature dimensionality, we employ techniques such as principal component analysis, feature agglomeration, correlation analysis, variance thresholding, and feature importance rankings. Using these refined features, we train various traditional machine learning models as well as ensemble methods. Our best model, named BayeSVM500, is a Support Vector Machine classifier trained on EfficientNet features reduced to 500 dimensions via PCA, achieving 93.65 ± 1.05% accuracy. Bayesian hyperparameter optimization further improved performance to 95.33 ± 0.60%. Through comprehensive feature engineering and model optimization, we demonstrate highly accurate eye disease classification from fundus images, comparable to or superior to previous benchmarks.

Published
2024
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10. An Integrated Smart Pond Water Quality Monitoring and Fish Farming Recommendation Aquabot System

Author

Md. Moniruzzaman Hemal, Atiqur Rahman, Nurjahan, Farhana Islam, Samsuddin Ahmed, M. Shamim Kaiser, and Muhammad Raisuddin Ahmed

Subjects
smart fish farming, Internet of Things (IoT), smart robot, machine learning, solar power, real-time water quality, Chemical technology, TP1-1185
Abstract

The integration of cutting-edge technologies such as the Internet of Things (IoT), robotics, and machine learning (ML) has the potential to significantly enhance the productivity and profitability of traditional fish farming. Farmers using traditional fish farming methods incur enormous economic costs owing to labor-intensive schedule monitoring and care, illnesses, and sudden fish deaths. Another ongoing issue is automated fish species recommendation based on water quality. On the one hand, the effective monitoring of abrupt changes in water quality may minimize the daily operating costs and boost fish productivity, while an accurate automatic fish recommender may aid the farmer in selecting profitable fish species for farming. In this paper, we present AquaBot, an IoT-based system that can automatically collect, monitor, and evaluate the water quality and recommend appropriate fish to farm depending on the values of various water quality indicators. A mobile robot has been designed to collect parameter values such as the pH, temperature, and turbidity from all around the pond. To facilitate monitoring, we have developed web and mobile interfaces. For the analysis and recommendation of suitable fish based on water quality, we have trained and tested several ML algorithms, such as the proposed custom ensemble model, random forest (RF), support vector machine (SVM), decision tree (DT), K-nearest neighbor (KNN), logistic regression (LR), bagging, boosting, and stacking, on a real-time pond water dataset. The dataset has been preprocessed with feature scaling and dataset balancing. We have evaluated the algorithms based on several performance metrics. In our experiment, our proposed ensemble model has delivered the best result, with 94% accuracy, 94% precision, 94% recall, a 94% F1-score, 93% MCC, and the best AUC score for multi-class classification. Finally, we have deployed the best-performing model in a web interface to provide cultivators with recommendations for suitable fish farming. Our proposed system is projected to not only boost production and save money but also reduce the time and intensity of the producer’s manual labor.

Published
2024
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14. An Explainable Alzheimer’s Disease Prediction Using EfficientNet-B7 Convolutional Neural Network Architecture

Author

Jahan, Sobhana, Shamim Kaiser, M., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Hossain, Md. Sazzad, editor, Majumder, Satya Prasad, editor, Siddique, Nazmul, editor, and Hossain, Md. Shahadat, editor

Published
2023
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15. Use of Genetic Algorithm and Finite-Difference Time-Domain Calculations to Optimize 'Plasmonic' Thin-Film Solar Cell Performance

Author

Haque, Abrar Jawad, Shaky, Mustafa Mohammad, Hasan, Sabrina Nurhan, Khan, Tawseef Ahmed, Shamim Kaiser, M., Chowdhury, Mustafa Habib, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Hossain, Md. Sazzad, editor, Majumder, Satya Prasad, editor, Siddique, Nazmul, editor, and Hossain, Md. Shahadat, editor

Published
2023
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16. Deep Learning-Based Skin Disease Detection Using Convolutional Neural Networks (CNN)

Author

Sazzadul Islam Prottasha, Md., Mahjabin Farin, Sanjan, Bulbul Ahmed, Md., Zihadur Rahman, Md., Kabir Hossain, A. B. M., Shamim Kaiser, M., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Hossain, Md. Sazzad, editor, Majumder, Satya Prasad, editor, Siddique, Nazmul, editor, and Hossain, Md. Shahadat, editor

Published
2023
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17. Machine Learning and Deep Learning Methods for the Detection of Schizophrenia Using Magnetic Resonance Images and EEG Signals: An Overview of the Recent Advancements

Author

Shaffi, Noushath, Mahmud, Mufti, Hajamohideen, Faizal, Subramanian, Karthikeyan, Shamim Kaiser, M., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Joshi, Amit, editor, Mahmud, Mufti, editor, and Ragel, Roshan G., editor

Published
2023
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18. Designing and Implementing a Distributed Database for Microservices Cloud-Based Online Travel Portal

Author

Barua, Biman, Md Whaiduzzaman, Mesbahuddin Sarker, M., Shamim Kaiser, M., Barros, Alistair, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Shakya, Subarna, editor, Du, Ke-Lin, editor, and Ntalianis, Klimis, editor

Published
2023
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19. An enhanced round robin using dynamic time quantum for real-time asymmetric burst length processes in cloud computing environment.

Author

Most Fatematuz Zohora, Fahiba Farhin, and M Shamim Kaiser

Subjects
Medicine, Science
Abstract

Cloud computing is a popular, flexible, scalable, and cost-effective technology in the modern world that provides on-demand services dynamically. The dynamic execution of user requests and resource-sharing facilities require proper task scheduling among the available virtual machines, which is a significant issue and plays a crucial role in developing an optimal cloud computing environment. Round Robin is a prevalent scheduling algorithm for fair distribution of resources with a balanced contribution in minimized response time and turnaround time. This paper introduced a new enhanced round-robin approach for task scheduling in cloud computing systems. The proposed algorithm generates and keeps updating a dynamic quantum time for process execution, considering the available number of process in the system and their burst length. Since our method dynamically runs processes, it is appropriate for a real-time environment like cloud computing. The notable part of this approach is the capability of scheduling tasks with asymmetric distribution of burst time, avoiding the convoy effect. The experimental result indicates that the proposed algorithm has outperformed the existing improved round-robin task scheduling approaches in terms of minimized average waiting time, average turnaround time, and number of context switches. Comparing the method against five other enhanced round robin approaches, it reduced average waiting times by 15.77% and context switching by 20.68% on average. After executing the experiment and comparative study, it can be concluded that the proposed enhanced round-robin scheduling algorithm is optimal, acceptable, and relatively better suited for cloud computing environments.

Published
2024
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20. Generation of simulated data for Bengali text localization in natural images

Author

Sourav Saha, Md. Easin Arafat, Md Aminul Haque Palash, Dewan Md Farid, and M. Shamim Kaiser

Subjects
Text editing, Text style transfer, Simulated image, Bengali language, Generative adversarial network, Computer applications to medicine. Medical informatics, R858-859.7, Science (General), Q1-390
Abstract

In the domain of vision-based applications, the importance of text cannot be underestimated due to its natural capacity to provide accurate and comprehensive information. The application of scene text editing systems enables the modification and enhancement of textual material included in natural images while maintaining the integrity of the overall visual layout. The complexity of keeping the original background context and font styles when altering, however, is an extremely difficult challenge considering the changed image must perfectly blend with the original without being altered. This article contains significant simulated data on the dynamic features of digital image editing, advertising, content development, and related fields. The system comprises key components such as 2D simulated text on the styled image (is), text image (it), masking of text (maskt), real background image (tb), real sample image (tf), text skeleton (tsk), and text styled image (tt). The source dataset contains diverse components such as background images, color variations, fonts, and text content, while the synthetic dataset consists of 49,000 randomly generated images. The dataset provides both researchers and practitioners with a rich resource for identifying and evaluating these dynamic features. The dataset is publicly accessible via the link: https://data.mendeley.com/datasets/h9kry9y46s/3

Published
2023
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42. COVID-19 Chest X-Ray Classification with Augmented GAN

Author

Mahmud Pranto, Md. Appel, Al Asad, Nafiz, Adnan Palash, Md. Istakiak, Islam, A. K. M. Mohaiminul, Shamim Kaiser, M., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Hossain, Sazzad, editor, Hossain, Md. Shahadat, editor, Kaiser, M. Shamim, editor, Majumder, Satya Prasad, editor, and Ray, Kanad, editor

Published
2022
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43. Toward Deep Learning-Based Automated Speed and Line Change Detection System in Perspective of Bangladesh

Author

Abdullah-Al-Mahmod, Usmani, Sabbir Ahmed, Salam, Mohammad Abdus, Foyjul Haque Somrat, Md., Shamim Kaiser, M., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Kaiser, M. Shamim, editor, Bandyopadhyay, Anirban, editor, Ray, Kanad, editor, Singh, Raghvendra, editor, and Nagar, Vishal, editor

Published
2022
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44. GSR Signals Features Extraction for Emotion Recognition

Author

Kipli, Kuryati, Latip, Aisya Amelia Abdul, Lias, Kasumawati, Bateni, Norazlina, Yusoff, Salmah Mohamad, Tajudin, Nurul Mirza Afiqah, Jalil, M. A., Ray, Kanad, Shamim Kaiser, M., Mahmud, Mufti, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Kaiser, M. Shamim, editor, Bandyopadhyay, Anirban, editor, Ray, Kanad, editor, Singh, Raghvendra, editor, and Nagar, Vishal, editor

Published
2022
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45. Development of Mobile Application for Detection and Grading of Diabetic Retinopathy

Author

Kipli, Kuryati, Hui, Lee Yee, Tajudin, Nurul Mirza Afiqah, Sapawi, Rohana, Sahari, Siti Kudnie, Mat, Dayang Azra Awang, Jalil, M. A., Ray, Kanad, Shamim Kaiser, M., Mahmud, Mufti, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Kaiser, M. Shamim, editor, Bandyopadhyay, Anirban, editor, Ray, Kanad, editor, Singh, Raghvendra, editor, and Nagar, Vishal, editor

Published
2022
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46. Toward Machine Learning-Based Psychological Assessment of Autism Spectrum Disorders in School and Community

Author

Ahmed, Sabbir, Hossain, Md. Farhad, Nur, Silvia Binte, Shamim Kaiser, M., Mahmud, Mufti, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Kaiser, M. Shamim, editor, Bandyopadhyay, Anirban, editor, Ray, Kanad, editor, Singh, Raghvendra, editor, and Nagar, Vishal, editor

Published
2022
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47. A Brief Review of Computation Techniques for ECG Signal Analysis

Author

Hussain, Salleh Sh, Noman, Fuad, Hussain, Hadri, Ting, Chee-Ming, bin Hamid, Syed Rasul G. Syed, Sh-Hussain, Hadrina, Jalil, M. A., A.L., Ahmad Zubaidi, Rizvi, Syed Zuhaib Haider, Kipli, Kuryati, Jacob, Kavikumar, Ray, Kanad, Shamim Kaiser, M., Mahmud, Mufti, Ali, Jalil, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Kaiser, M. Shamim, editor, Ray, Kanad, editor, Bandyopadhyay, Anirban, editor, Jacob, Kavikumar, editor, and Long, Kek Sie, editor

Published
2022
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49. Developing a Framework for Credit Card Fraud Detection

Author

Arafath, Yeasin, Roy, Animesh Chandra, Shamim Kaiser, M., Arefin, Mohammad Shamsul, Xhafa, Fatos, Series Editor, Arefin, Mohammad Shamsul, editor, Kaiser, M. Shamim, editor, Bandyopadhyay, Anirban, editor, Ahad, Md. Atiqur Rahman, editor, and Ray, Kanad, editor

Published
2022
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