The increasing number of diabetes individuals in the globe has alarmed the medical sector to seek alternatives to improve their medical technologies. Machine learning and deep learning approaches are active research in developing intelligent and efficient diabetes detection systems. This study profoundly investigates and discusses the impacts of the latest machine learning and deep learning approaches in diabetes identification/classifications. It is observed that diabetes data are limited in availability. Available databases comprise lab-based and invasive test measurements. Investigating anthropometric measurements and non-invasive tests must be performed to create a cost-effective yet high-performance solution. Several findings showed the possibility of reconstructing the detection models based on anthropometric measurements and non-invasive medical indicators. This study investigated the consequences of oversampling techniques and data dimensionality reduction through feature selection approaches. The future direction is highlighted in the research of feature selection approaches to improve the accuracy and reliability of diabetes identifications.

Early detection of Diabetic Retinopathy (DR) is crucial as it may cause blindness. Manual diagnosis of DR severity by ophthalmologists is challenging and time consuming. Therefore, there has been a significant focus on developing an automated system for identifying DR using retinal fundus images. Recent research has revealed that utilizing pre-trained deep learning networks for diverse image classification tasks provides notable benefits in this context. In this paper, a Transfer Learning (TL) approach with optimized feature weights and parameters is proposed for DR detection and grading tasks. To obtain better generalization during training and to optimize classification, features are extracted from the average pooling layers and fed to an Error Correction Output Code (ECOC) ensemble configuration. Two pre-trained networks (ShuffleNet and ResNet-18) are considered as each pre-trained network offers a different "point of view" of the fundus images, thereby providing more opportunities for accurate "grade-wise" discrimination. A simultaneous feature selection and parameter tuning of the ensemble is applied to further enhance the overall DR detection and grading. Adaptive Differential Evolution (ADE) is chosen for this purpose because it automatically configures the parameters, eliminating the need for manual parameter selection. In this paper, we evaluate two public domain datasets: 1) APTOS and 2) combination of EyePac + Messidor-2. Simulation results show that our proposed method performs better that the conventional deep learning models and are on a par with the existing research work. In particular, the optimal configuration for APTOS 5-class DR grading achieves an accuracy rate of 82%, while for APTOS 2-class grading, it achieves a higher accuracy rate of 96%. Finally, the best configuration for EyePac + Messidor-2 3-class grading results in 75% accuracy.

Malware detection in current times is increasingly important due to the presence of dangerous malicious software (malware) as well as ransomware in digital cyberspace. Conventional approaches such as using malware features (either static or dynamic or hybrid) have been applied for detection. Advances in Deep Learning (DL) has attracted a lot of interests in applications of malware detection. In particular, the file binaries are fed in to the DL neural network for training and testing. Despite the theoretical basis, we find that overfitting has occurred despite applying precaution, such as applying sufficient dropout layers. Limitations can also be attributed to the final classification layers (fully connected shallow network and softmax classification). In this paper, we apply transfer learning using ShuffleNet and DenseNet-201, two models trained on large dataset to recognize daily objects. This is done with the entire layers frozen to prevent overfitting and an optimal Error correction output code (ECOC) ensemble configuration of Support Vector Machines (SVM). Several ECOC coding matrices were applied, e.g., One vs. All (OVA), One vs. One (OVO), Dense Random (DR), and Sparse Random (SR). Each of these configurations represents varying complexity and ensemble size and, hence, a tradeoff between computation reduction and complex non-linear separation appears. Given that the continuous values of SVM parameters may take up high computation for acquiring the optimal parameter configuration, we apply discrete values combination using a grid search approach for parameter optimization. We test the proposed model on Malimg, MaleVis, virus-MNIST, and Dumpware10 datasets. The results show better/comparable accuracy compared with the existing work. The best/average accuracy values for each dataset over 10 trials are: Malimg (99.14%/98.87%), MaleVis (95.01%/93.91%), Virus-MNIST (86.36%/85.79%), Dumpware10 (96.62%/95.79%).

Social media has evolved into a platform for the dissemination of information, including fake news. There is a lot of false information about the current situation of the Coronavirus Disease 2019 (COVID-19) pandemic, such as false information regarding vaccination. In this paper, we focus on sentiment analysis for Malaysian COVID-19-related news on social media such as Twitter. Tweets in Malaysia are often a combination of Malay, English, and Chinese with plenty of short forms, symbols, emojis, and emoticons within the maximum length of a tweet. The contributions of this paper are twofold. Firstly, we built a multilingual COVID-19 Twitter dataset, comprising tweets written from 1 September 2021 to 12 December 2021. In particular, we collected 108,246 tweets, with over $67\%$ in Malay language, $27\%$ in English, $2\%$ in Chinese, and $4\%$ in other languages. We then manually annotated and assigned the sentiment of 11,568 tweets into three-class sentiments (positive, negative, and neutral) to develop a Malay-language sentiment analysis tool. For this purpose, we applied a data compression method using Byte-Pair Encoding (BPE) on the texts and used two deep learning approaches, i.e., the Multilingual Bidirectional Encoder Representation for Transformer (M-BERT) and convolutional neural network (CNN). BPE tokenization is used to encode rare and unknown words into smaller meaningful subwords. With the CNN, we converted the labeled tweets into image files. Our experiments explored different BPE vocabulary sizes with our BPE-Text-to-Image-CNN and BPE-M-BERT models. The results show that the optimal vocabulary size for BPE is 12,000; any values beyond that would not contribute much to the F1-score. Overall, our results show that BPE-M-BERT slightly outperforms the CNN model, thereby showing that the pre-trained M-BERT network has the advantage for our multilingual dataset.

While fake news is morally reprehensible, irresponsible parties intentionally use it to achieve their goals by disseminating it to vulnerable and targeted groups. Machine learning techniques have been researched extensively to detect fake news. On the other hand, evolutionary-based algorithms are now gaining popularity in the research community. In this study, a two-stage evolutionary approach is proposed to generate and optimize a mathematical equation for fake news detection. In the first stage, tree-based Genetic Programming (GP) algorithm is used to generate mathematical expressions to detect correlations between the language-independent (Lang-IND) features, extracted from Fake.my-COVID19 dataset, the newly curated fake news dataset in a mixed Malay - English language. The uniqueness of the proposed approach is that the mathematical expressions are formed by basic arithmetic operators or to include complex arithmetic operators such as addition, multiplication, subtraction, division, square, abs, log1p, sign, square root, and exponential together with Lang-IND features as the variables. Prior to second stage of the evolutionary approach, a sensitivity analysis is applied to shorten the best equation while maintaining the F1-score performance. In the second stage, an Adaptive Differential Evolution (ADE), is used to fine-tune the mathematical model. The experimental results conclude that the proposed two-stage evolutionary approach can be applied in fake news detection and the model can learn to predict using the Lang-IND features. Results from the first stage shows that the equation from GP scores F1-score of 83.23% on Fake.my-COVID19 dataset using complex arithmetic operators and at tree depth of 8. After the fine-tuning stage, the model performance increases the F1-score to 84.44%. The performance of the proposed two-stage evolutionary approach outperforms the baseline performance of six commonly-used machine learning algorithms, with Random Forest having the highest F1-score of 84.07%. The mathematical model is also tested separately on two other unseen datasets of different domain topic or language and achieves acceptable F1-scores.