Deep Joint Source-Channel Coding for DNA Image Storage: A Novel Approach with Enhanced Error Resilience and Biological Constraint Optimization

In the current era, DeoxyriboNucleic Acid (DNA) based data storage emerges as an intriguing approach, garnering substantial academic interest and investigation. This paper introduces a novel deep joint source-channel coding (DJSCC) scheme for DNA image storage, designated as DJSCC-DNA. This paradigm distinguishes itself from conventional DNA storage techniques through three key modifications: 1) it employs advanced deep learning methodologies, employing convolutional neural networks for DNA encoding and decoding processes; 2) it seamlessly integrates DNA polymerase chain reaction (PCR) amplification into the network architecture, thereby augmenting data recovery precision; and 3) it restructures the loss function by targeting biological constraints for optimization. The performance of the proposed model is demonstrated via numerical results from specific channel testing, suggesting that it surpasses conventional deep learning methodologies in terms of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Additionally, the model effectively ensures positive constraints on both homopolymer run-length and GC content

APIC: A method for automated pattern identification and classification

Machine Learning (ML) is a transformative technology at the forefront of many modern research endeavours. The technology is generating a tremendous amount of attention from researchers and practitioners, providing new approaches to solving complex classification and regression tasks. While concepts such as Deep Learning have existed for many years, the computational power for realising the utility of these algorithms in real-world applications has only recently become available. This dissertation investigated the efficacy of a novel, general method for deploying ML in a variety of complex tasks, where best feature selection, data-set labelling, model definition and training processes were determined automatically. Models were developed in an iterative fashion, evaluated using both training and validation data sets. The proposed method was evaluated using three distinct case studies, describing complex classification tasks often requiring significant input from human experts. The results achieved demonstrate that the proposed method compares with, and often outperforms, less general, comparable methods designed specifically for each task. Feature selection, data-set annotation, model design and training processes were optimised by the method, where less complex, comparatively accurate classifiers with lower dependency on computational power and human expert intervention were produced. In chapter 4, the proposed method demonstrated improved efficacy over comparable systems, automatically identifying and classifying complex application protocols traversing IP networks. In chapter 5, the proposed method was able to discriminate between normal and anomalous traffic, maintaining accuracy in excess of 99%, while reducing false alarms to a mere 0.08%. Finally, in chapter 6, the proposed method discovered more optimal classifiers than those implemented by comparable methods, with classification scores rivalling those achieved by state-of-the-art systems. The findings of this research concluded that developing a fully automated, general method, exhibiting efficacy in a wide variety of complex classification tasks with minimal expert intervention, was possible. The method and various artefacts produced in each case study of this dissertation are thus significant contributions to the field of ML

Sustainable Agriculture and Advances of Remote Sensing (Volume 1)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publishing the results, among others

Deep multiple-instance learning for detecting multiple myeloma in CT scans of large bones

S nástupem moderních algoritmů strojového učení vzrostla popularita tématu automatické interpretace výstupů zobrazovacích metod v medicíně pomocí počítačů. Konvoluční neuronové sítě v současné době excelují v mnoha oblastech strojového vidění včetně rozpoznávání obrazu. V této diplomové práci zkoumáme možnosti využití konvolučních sítí jako diagnostického nástroje pro detekci abnormalit v CT snímcích stehenních kostí. Zaměřujeme se na diagnózu mnohočetného myelomu pro nějž jsou charakteristické viditelné léze v kostní dřeni, které lze pozorovat při vyšetření pomocí počítačové tomografie. Bylo otestováno několik různých přístupů včetně učení z více instancí. Náš klasifikátor podává spolehlivý výkon v experimentech s plně supervizovaným učením, vykazuje ovšem zásadní neschopnost konvergence při učení z více instancí. Předpokládáme, že náš navrhovaný neuronový model potřebuje ke konvergenci silnější chybovou odezvu a na toto téma navrhujeme budoucí možná vylepšení.The employment of computer aided diagnosis (CAD) systems for interpretation of medical images has become an increasingly popular topic with the arrival of modern machine learning algorithms. Convolutional neural networks perform exceptionally well nowadays in various pattern recognition tasks including image classification. In this thesis we examine the capabilities of a convolutional neural network binary classifier as a CAD system for detection of abnormalities in CT images of femurs. We focus on the diagnosis of multiple myeloma characterized by symptomatic bone marrow lesions commonly observable through computer tomography screening. Different approaches to the problem including multiple instance learning (MIL) were tested. The classifier showed a solid performance in our fully supervised experimental setting, it however exhibits a serious inability to learn from multiple instances. We conclude that the proposed neural model needs a stronger error signal in order to converge in the standard MIL setting and suggest potential improvements for further work in this area

On Body Mass Index Analysis from Human Visual Appearance

In the past few decades, overweight and obesity are spreading widely like an epidemic. Generally, a person is considered overweight by body mass index (BMI). In addition to a body fat measurement, BMI is also a risk factor for many diseases, such as cardiovascular diseases, cancers and diabetes, etc. Therefore, BMI is important for personal health monitoring and medical research. Currently, BMI is measured in person with special devices. It is an urgent demand to explore conveniently preventive tools. This work investigates the feasibility of analyzing BMI from human visual appearances, including 2-dimensional (2D)/3-dimensional (3D) body and face data. Motivated by health science studies which have shown that anthropometric measures, such as waist-hip ratio, waist circumference, etc., are indicators for obesity, we analyze body weight from frontal view human body images. A framework is developed for body weight analysis from body images, along with the computation methods of five anthropometric features for body weight characterization. Then, we study BMI estimation from the 3D data by measuring the correlation between the estimated body volume and BMIs, and develop an efficient BMI computation method which consists of body weight and height estimation from normally dressed people in 3D space. We also intensively study BMI estimation from frontal view face images via two key aspects: facial representation extracting and BMI estimator learning. First, we investigate the visual BMI estimation problem from the aspect of the characteristics and performance of different facial representation extracting methods by three designed experiments. Then we study visual BMI estimation from facial images by a two-stage learning framework. BMI related facial features are learned in the first stage. To address the ambiguity of BMI labels, a label distribution based BMI estimator is proposed for the second stage. The experimental results show that this framework improves the performance step by step. Finally, to address the challenges caused by BMI data and labels, we integrate feature learning and estimator learning in one convolutional neural network (CNN). A label assignment matching scheme is proposed which successfully achieves an improvement in BMI estimation from face images

A simplified predictive framework for cost evaluation to fault assessment using machine learning

Software engineering is an integral part of any software development scheme which frequently encounters bugs, errors, and faults. Predictive evaluation of software fault contributes towards mitigating this challenge to a large extent; however, there is no benchmarked framework being reported in this case yet. Therefore, this paper introduces a computational framework of the cost evaluation method to facilitate a better form of predictive assessment of software faults. Based on lines of code, the proposed scheme deploys adopts a machine-learning approach to address the perform predictive analysis of faults. The proposed scheme presents an analytical framework of the correlation-based cost model integrated with multiple standards machine learning (ML) models, e.g., linear regression, support vector regression, and artificial neural networks (ANN). These learning models are executed and trained to predict software faults with higher accuracy. The study considers assessing the outcomes based on error-based performance metrics in detail to determine how well each learning model performs and how accurate it is at learning. It also looked at the factors contributing to the training loss of neural networks. The validation result demonstrates that, compared to logistic regression and support vector regression, neural network achieves a significantly lower error score for software fault prediction