Optimal Artificial Intelligence Based Automated Skin Lesion Detection and Classification Model

Skin lesions have become a critical illness worldwide, and the earlier identification of skin lesions using dermoscopic images can raise the survival rate. Classification of the skin lesion from those dermoscopic images will be a tedious task. The accuracy of the classification of skin lesions is improved by the use of deep learning models. Recently, convolutional neural networks (CNN) have been established in this domain, and their techniques are extremely established for feature extraction, leading to enhanced classification. With this motivation, this study focuses on the design of artificial intelligence (AI) based solutions, particularly deep learning (DL) algorithms, to distinguish malignant skin lesions from benign lesions in dermoscopic images. This study presents an automated skin lesion detection and classification technique utilizing optimized stacked sparse autoencoder (OSSAE) based feature extractor with backpropagation neural network (BPNN), named the OSSAE-BPNN technique. The proposed technique contains a multi-level thresholding based segmentation technique for detecting the affected lesion region. In addition, the OSSAE based feature extractor and BPNN based classifier are employed for skin lesion diagnosis. Moreover, the parameter tuning of the SSAE model is carried out by the use of sea gull optimization (SGO) algorithm. To showcase the enhanced outcomes of the OSSAE-BPNN model, a comprehensive experimental analysis is performed on the benchmark dataset. The experimental findings demonstrated that the OSSAE-BPNN approach outperformed other current strategies in terms of several assessment metrics.     

Efficient deterministic secure quantum communication protocols using multipartite entangled states

We propose two deterministic secure quantum communication protocols employing three-qubit GHZ-like states and five-qubit Brown states as quantum channels for secure transmission of information in units of two bits and three bits using multipartite teleportation schemes developed here. In these schemes, the sender’s capability in selecting quantum channels and the measuring bases leads to improved qubit efficiency of the protocols.     

Thermal and aqueous stability improvement of graphene oxide enhanced diphenylalanine nanocomposites

Abstract

Nanocomposites of diphenylalanine (FF) and carbon based materials provide an opportunity to overcome drawbacks associated with using FF micro- and nanostructures in nanobiotechnology applications, in particular their poor structural stability in liquid solutions. In this study, FF/graphene oxide (GO) composites were found to self-assemble into layered micro- and nanostructures, which exhibited improved thermal and aqueous stability. Dependent on the FF/GO ratio, the solubility of these structures was reduced to 35.65% after 30 min as compared to 92.4% for pure FF samples. Such functional nanocomposites may extend the use of FF structures to e.g. biosensing, electrochemical, electromechanical or electronic applications.     

Sparse NMF based speech enhancement with bases update

In this paper, a combination of methods based on statistical modelling and Non-negative Matrix Factorization (NMF) for speech enhancement using speech and noise bases with on-line update is proposed. Template-based approaches are known to be more robust in the presence of non-stationary noises than methods based on statistical modeling. However, template-based approaches depend on a-priori information. The drawbacks of both the approaches can be avoided by combining them. In NMF approach, speech bases and noise bases are simultaneously adapted to further improve the performance. The proposed method outperforms other benchmark algorithms in terms of perceptual evaluation of speech quality (PESQ) and source-to-distortion ratio (SDR) in stationary and non-stationary noise environment conditions with matched and mismatched noise basis.     

Sustainable recovery of grape skins for use in an apple beverage with antiglycation properties

An apple puree formulated with red grape skins was developed on pilot scale as a new beverage with antiglycation properties. The addition level of grape skins was selected by a liking test with 70 consumers. The selected formulation was a fibre‐rich source and delivered grape anthocyanins, flavonols and flavanols resulting in ~ twofold higher antiglycation activity than the apple puree. Pasteurisation (3‐D in the target microorganism Alicyclobacillus acidoterrestris) did not affect the antiglycation activity, which decreased by 30% upon sterilisation. Storage for 1 month in the temperature range 15–35 °C affected the contents of anthocyanins, monomeric, dimeric and oligomeric flavanols, while chlorogenic acid, flavonols and dihydrochalcones were stable. About 90% antiglycation activity was retained after one‐month storage at 15 °C. The use of red grape skin as ingredient could represent an opportunity for the apple processing industry to develop a value‐added product.     

Intelligent System for Adapting to a User’s Characteristics

In this paper, we propose an intelligent system that adapts itself to a user’s characteristics or habits. The proposed intelligent system is composed of two Learning Vector Quantisation (LVQ) networks, commonly used in the fields of pattern recognition and signal processing. From the external condition of the plant, the first LVQ network learns to recognise the pattern of the sensed signal, and then aids the second LVQ to learn the user’s characteristics or habits so as to automatically produce the user’s favoured output. To verify the usefulness of the proposed method, we simulated and experimented with a variable illuminator. Both simulation and experimental results showed that the proposed intelligent system learns to automatically produce the illuminator output that the user most favours for the circumstances.     

Reports of the DAS02 working groups

This document is a collection of four working group reports in the areas of digital libraries, document image retrieval, layout analysis, and Web document analysis. These reports were the outcome of discussions by participants at the Fifth IAPR International Workshop on Document Analysis Systems held in Princeton, NJ on 19-21 August 2002.Published online: 16 March 2004