·         Table of Contents (Volume #7, Issue #3)

·         Cover Page (Volume #7, Issue #3)

·         Editorial (Volume #7, Issue #3)

Paper #1

A Leading but Simple Classification Method for Remote Sensing Images

Huaxiang Song

Abstract: Recently, researchers have proposed a lot of deep convolutional neural network (CNN) approaches with obvious flaws to tackle the difficult semantic classification (SC) task of remote sensing images (RSI). In this paper, the author proposes a simple method that aims to provide a leading but efficient solution by using a lightweight EfficientNet-B0. First, this paper concluded the drawbacks with an analysis of mathematical theory and then proposed a qualitative conclusion on the previous methods’ theoretical performance based on theoretical derivation and experiments. Following that, the paper designs a novel method named LS-EfficientNet, consisting only of a single CNN and a concise training algorithm called SC-CNN. Far different from previous complex and hardware-extensive ones, the proposed method mainly focuses on tackling the long-neglected problems, including overfitting, data distribution shift by DA, improper use of training tricks, and other incorrect operations on a pre-trained CNN. Compared to previous studies, the proposed method is easy to reproduce because all the models, training tricks, and hyperparameter settings are open-sourced. Extensive experiments on two benchmark datasets show that the proposed method can easily surpass all the previous state-of-the-art ones, with an outstanding accuracy lead of 0.5% to 1.2% and a remarkable parameter decrease of 78% if compared to the best prior one in 2022. In addition, ablation test results also prove that the proposed effective combination of training tricks, including OLS and CutMix, can clearly boost a CNN's performance for RSI-SC, with an increase in accuracy of 1.0%. All the results reveal that a single lightweight CNN can well tackle the routine task of classifying RSI.

Keywords: Deep learning; Image classification; LS-EfficientNet; Remote sensing; SC-CNN algorithm.

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Paper #2

Smart Transformation of EFL Teaching and Learning Approaches

Md. Russell Talukder

Abstract: The calibration of the EFL teaching and learning approaches with Artificial Intelligence can potentially facilitate a smart transformation, fostering a personalized and engaging experience in teaching and learning among the stakeholders. The paper focuses on developing an EFL Big Data Ecosystem that is based on Big Data, Analytics, Machine Learning and cluster domain of EFL teaching and learning contents. The framework has been developed on the basis of the theory that machine learning algorithms, when exposed to structured or semi-structure data stored in the cluster domains of EFL Big Data ecosystem, can cull out the patterns, similarities, and differences existing in the contents of the domains. Later these machine learning algorithms can apply these already identified patterns to perform new tasks on open Big Data platform and identify similar contents to be stored in the respective cluster domain of EFL Bigdata Ecosystem without being supervised. Accordingly, the paper uses two membranes to construe its framework, namely (i) Open Big Data Membrane that stores random data collected from various source domains and (ii) Machine Learning Membrane that stores specially prepared structured and semi-structured data. Theoretically, the structured and semi structured data are to be prepared skill-wise, attribute-wise, method-wise, and preference-wise to accommodate the personalized preferences and diverse teaching and learning needs of different individuals. Within the machine learning membrane, the paper includes a number of stages such as knowledge building, development of cluster domain of the EFL contents, integration of skill-wise cluster domain with the CEFR attribute-wise teaching and learning approaches, machine learning of the personalized preferences, resonating, machine learning of the cluster domain for proximity development and sustainable operation. The ultimate goal is to optimize the learning experience by leveraging machine learning to create tailored content that aligns with the diverse teaching and learning needs of the EFL communities. Developing a prototype following the framework exerts the potentials to provide an ‘alternative to methods’, transforming the process of learning into a process of acquisition.

Keywords: Acquisition; Analytics; Big Data; Cluster Domain; EFL Big Data Ecosystem; EFL Teaching and Learning Approach; Framework; Machine Learning; Personalized Experience; Proximity Development; Smart Transformation.

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Paper #3

Handwritten Bengali Alphabets, Compound Characters and Numerals Recognition Using CNN-based Approach

Md Asraful, Md. Anwar Hossain and Ebrahim Hossen

Abstract: Accurately classifying user-independent handwritten Bengali characters and numerals presents a formidable challenge in their recognition. This task becomes more complicated due to the inclusion of numerous complex-shaped compound characters and the fact that different authors employ diverse writing styles. Researchers have recently conducted significant researches using individual approaches to recognize handwritten Bangla digits, alphabets, and slightly compound characters. To address this, we propose a straightforward and lightweight convolutional neural network (CNN) framework to accurately categorize handwritten Bangla simple characters, compound characters, and numerals. The suggested approach exhibits outperformance in terms of performance when compared too many previously developed procedures, with faster execution times and requiring fewer epochs. Furthermore, this model applies to more than three datasets. Our proposed CNN-based model has achieved impressive validation accuracies on three datasets. Specifically, for the BanglaLekha isolated dataset, which includes 84-character classes, the validation accuracy was 92.48%. On the Ekush dataset, which includes 60-character classes, the model achieved a validation accuracy of 97.24%, while on the customized dataset, which includes 50-character classes, the validation accuracy was 97.03%. Our model has demonstrated high accuracy and outperformed several prominent existing frameworks.

Keywords: Bangla Handwritten Recognition; Convolutional Neural Network; Deep Learning; Image Classification; Pattern Recognition.

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Paper #4

Enhancing the Efficiency of Diabetes Prediction through Training and Classification using PCA and LR Model

Mohammad Riyaz Belgaum, Telugu Harsha Charitha, Munurathi Harini, Bylla Anusha, Ala Jayasri Sai, Undralla Chandana Yadav and Zainab Alansari

Abstract: In this paper, we introduce a new approach for predicting the risk of diabetes using a combination of Principal Component Analysis (PCA) and Logistic Regression (LR). Our method offers a unique solution that could lead to more accurate and efficient predictions of diabetes risk. To develop an effective model for predicting diabetes, it is important to consider various clinical and demographic factors contributing to the disease's development. This approach typically involves training the model on a large dataset that includes these factors. By doing so, we can better understand how different characteristics can impact the development of diabetes and create more accurate predictions for individuals at risk. The PCA method is employed to reduce the dataset's dimensions and augment the model's computational efficacy. The LR model then classifies patients into diabetic or non-diabetic groups. Accuracy, precision, recall, the F1-score, and the area under the ROC curve (AUC) are only a few of the indicators used to evaluate the performance of the proposed model. Pima Indian Diabetes Data (PIDD) is used to evaluate the model, and the results demonstrate a significant improvement over the state-of-the-art methods. The proposed model presents an efficient and effective method for predicting diabetes risk that may have significant implications for improving healthcare outcomes and reducing healthcare costs. The proposed PCA-LR model outperforms other algorithms, such as SVM and RF, especially in terms of accuracy, while optimizing computational complexity. This approach can potentially provide a practical and efficient solution for large-scale diabetes screening programs.

Keywords: Diabetes Prediction; LR Model; Principal Component Analysis; Pima Indians' Diabetes Data.

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Paper #5

GreenPy: Evaluating Application-Level Energy Efficiency in Python for Green Computing

Nurzihan Fatema Reya, Abtahi Ahmed, Tashfia Zaman and Md. Motaharul Islam

Abstract: The increased use of software applications has resulted in a surge in energy demand, particularly in data centers and IT infrastructures. As global energy consumption is projected to surpass supply by 2030, the need to optimize energy consumption in programming has become imperative. Our study explores the energy efficiency of various coding patterns and techniques in Python, with the objective of guiding programmers to a more informed and energy-conscious coding practices. The research investigates the energy consumption of a comprehensive range of topics, including data initialization, access patterns, structures, string formatting, sorting algorithms, dynamic programming and performance comparisons between NumPy and Pandas, and personal computers versus cloud computing. The major findings of our research include the advantages of using efficient data structures, the benefits of dynamic programming in certain scenarios that saves up to 0.128J of energy, and the energy efficiency of NumPy over Pandas for numerical calculations. Additionally, the study also shows that assignment operator, sequential read, sequential write and string concatenation are 2.2 times, 1.05 times, 1.3 times and 1.01 times more energy-efficient choices, respectively, compared to their alternatives for data initialization, data access patterns, and string formatting. Our findings offer guidance for developers to optimize code for energy efficiency and inspire sustainable software development practices, contributing to a greener computing industry.

Keywords: Algorithmic Efficiency; Cloud; Comparison; Energy Consumption; Performance Analysis; Python.

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