Designing deep learning based methods with medical images has always been an attractive area of research to assist clinicians in rapid examination and accurate diagnosis. Those methods need a large number of datasets including all variations in their training stages. On the other hand, medical images are always scarce due to several reasons, such as not enough patients for some diseases, patients do not want to allow their images to be used, lack of medical equipment or equipment, inability to obtain images that meet the desired criteria. This issue leads to bias in datasets, overfitting, and inaccurate results. Data augmentation is a common solution to overcome this issue and various augmentation techniques have been applied to different types of images in the literature. However, it is not clear which data augmentation technique provides more efficient results for which image type since different diseases are handled, different network architectures are used, and these architectures are trained and tested with different numbers of data sets in the literature. Therefore, in this work, the augmentation techniques used to improve performances of deep learning based diagnosis of the diseases in different organs (brain, lung, breast, and eye) from different imaging modalities (MR, CT, mammography, and fundoscopy) have been examined. Also, the most commonly used augmentation methods have been implemented, and their effectiveness in classifications with a deep network has been discussed based on quantitative performance evaluations. Experiments indicated that augmentation techniques should be chosen carefully according to image types. 

/pdf/medical-image-data-augmentation-techniques-comparisons-and-1z5iqsm1.pdf

Medical image data augmentation: techniques, comparisons and interpretations

Diabetes Mellitus, or Diabetes, is a disease in which a person's body fails to respond to insulin released by their pancreas, or it does not produce sufficient insulin. People suffering from diabetes are at high risk of developing various eye diseases over time. As a result of advances in machine learning techniques, early detection of diabetic eye disease using an automated system brings substantial benefits over manual detection. A variety of advanced studies relating to the detection of diabetic eye disease have recently been published. This article presents a systematic survey of automated approaches to diabetic eye disease detection from several aspects, namely: i) available datasets, ii) image preprocessing techniques, iii) deep learning models and iv) performance evaluation metrics. The survey provides a comprehensive synopsis of diabetic eye disease detection approaches, including state of the art field approaches, which aim to provide valuable insight into research communities, healthcare professionals and patients with diabetes.

/pdf/automatic-detection-of-diabetic-eye-disease-through-deep-3huixujqw0.pdf

Automatic Detection of Diabetic Eye Disease Through Deep Learning Using Fundus Images: A Survey

A large number of researchers, academia, government sectors, and business enterprises are adopting the cloud environment due to the least upfront capital investment, maximum scalability, and several other features of it. Despite the multiple features supported by the cloud environment, it also suffers several challenges. Data protection is the primary concern in the area of information security and cloud computing. Numerous solutions have been developed to address this challenge. However, there is a lack of comprehensive analysis among the existing solutions and a necessity emerges to explore, classify, and analyze the significant existing work for investigating the applicability of these solutions to meet the requirements. This article presents a comparative and systematic study, and in-depth analysis of leading techniques for secure sharing and protecting the data in the cloud environment. The discussion about each dedicated technique includes: functioning for protecting the data, potential and revolutionary solutions in the domain, the core and adequate information including workflow, achievements, scope, gaps, future directions, etc. about each solution. Furthermore, a comprehensive and comparative analysis of the discussed techniques is presented. Afterward, the applicability of the techniques is discussed as per the requirements and the research gaps along with future directions are reported in the field. The authors believe that this article’s contribution will operate as a catalyst for the potential researchers to carry out the research work in the area.

/pdf/secure-data-storage-and-sharing-techniques-for-data-3np3fd5n.pdf

Secure Data Storage and Sharing Techniques for Data Protection in Cloud Environments: A Systematic Review, Analysis, and Future Directions

For sustainable maize production, reliable and prompt disease identification is crucial. Maize diseases represent a danger to world food security. In this work, utilizing decentralized and privacy-sensitive data, to constructed and evaluated a Federated Learning CNN model to identify and diagnose maize illnesses. The 9878 maize photos in to dataset, divided into training, validation, and test sets, depict a variety of diseases and conditions. To prepare the dataset for training the model, to did data pre-processing, such as picture scaling, normalization, augmentation, and label encoding. The Federated Learning CNN model outperformed conventional CNNs and other frequently used machine learning algorithms in agriculture, achieving an overall accuracy of 89.4% on the test set. The Federated Learning CNN model can precisely identify and categorize maize illnesses, as shown by the model's superior precision, recall, and F1-score values compared to other algorithms for each disease class. The CNN model from Federated Learning also showed resilience and consistency across the various disease classes, demonstrating the model's ability to adjust to local circumstances and variances in maize illnesses across multiple locations and farms. Additionally, to visualized the feature maps and activation patterns to understand the model, revealing how it generates predictions and which aspects of the maize photos are most crucial for disease diagnosis. To work underscores the significance of creating decentralized, privacy-preserving machine learning models in agriculture and shows the promise of federated learning CNNs for crop disease diagnosis and management.

Revolutionizing Maize Disease Management with Federated Learning CNNs: A Decentralized and Privacy-Sensitive Approach

Exploring the revolution in healthcare systems through the applications of digital twin technology

The paper describes the usage of self-learning Hierarchical LSTM technique for classifying hatred and trolling contents in social media code-mixed data. The Hierarchical LSTM-based learning is a novel learning architecture inspired from the neural learning models. The proposed HLSTM model is trained to identify the hatred and trolling words available in social media contents. The proposed HLSTM systems model is equipped with self-learning and predicting mechanism for annotating hatred words in transliteration domain. The Hindi–English data are ordered into Hindi, English, and hatred labels for classification. The mechanism of word embedding and character-embedding features are used here for word representation in the sentence to detect hatred words. The method developed based on HLSTM model helps in recognizing the hatred word context by mining the intention of the user for using that word in the sentence. Wide experiments suggests that the HLSTM-based classification model gives the accuracy of 97.49% when evaluated against the standard parameters like BLSTM, CRF, LR, SVM, Random Forest and Decision Tree models especially when there are some hatred and trolling words in the social media data.

/pdf/hatred-and-trolling-detection-transliteration-framework-1v29w8b1k6.pdf

Hatred and trolling detection transliteration framework using hierarchical LSTM in code-mixed social media text

Spoofing detection system for e-health digital twin using EfficientNet Convolution Neural Network

DNA (Deoxyribonucleic Acid) Cryptography has revolutionized information security by combining rigorous biological and mathematical concepts to encode original information in terms of a DNA sequence. Such schemes are crucially dependent on corresponding DNA-based cryptographic keys. However, owing to the redundancy or observable patterns, some of the keys are rendered weak as they are prone to intrusions. This paper proposes a Genetic Algorithm inspired method to strengthen weak keys obtained from Random DNA-based Key Generators instead of completely discarding them. Fitness functions and the application of genetic operators have been chosen and modified to suit DNA cryptography fundamentals in contrast to fitness functions for traditional cryptographic schemes. The crossover and mutation rates are reducing with each new population as more keys are passing fitness tests and need not be strengthened. Moreover, with the increasing size of the initial key population, the key space is getting highly exhaustive and less prone to Brute Force attacks. The paper demonstrates that out of an initial 25 × 25 population of DNA Keys, 14 keys are rendered weak. Complete results and calculations of how each weak key can be strengthened by generating 4 new populations are illustrated. The analysis of the proposed scheme for different initial populations shows that a maximum of 8 new populations has to be generated to strengthen all 500 weak keys of a 500 × 500 initial population.

/pdf/best-fit-dna-based-cryptographic-keys-the-genetic-algorithm-1fq5wnzk.pdf

Best Fit DNA-Based Cryptographic Keys: The Genetic Algorithm Approach

Deep learning system applicability for rapid glaucoma prediction from fundus images across various data sets

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Hatred and trolling detection transliteration framework using hierarchical LSTM in code-mixed social media text

Spoofing detection system for e-health digital twin using EfficientNet Convolution Neural Network

Spoofing detection system for e-health digital twin using EfficientNet Convolution Neural Network

Best Fit DNA-Based Cryptographic Keys: The Genetic Algorithm Approach

Deep learning system applicability for rapid glaucoma prediction from fundus images across various data sets