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.     

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.     

One-step synthesis of composite electrolytes of Eu-doped ceria and alkali metal carbonates

Europium-doped ceria (EDC, Ce_0.9Eu_0.1O_2−δ)/alkaline carbonate (LNC, (Li,Na)₂CO₃) composite ceramics prepared through a one-step citrate-based route were analyzed by powder X-ray diffraction, infrared and laser Raman spectroscopies as well as scanning and transmission electron microscopy. The electrochemical behavior of the electrolyte material was studied by impedance spectroscopy in air, CO₂ and N₂ + H₂ (90/10 vol%, respectively) gas mixtures, in the temperature range 300–600 °C. The sub micrometric and even nanosized ceramic particles appeared as merged inside the mixed carbonates, with modest grain to grain necking. The EDC/LNC composite electrolytes showed a conductivity of 0.27 S cm⁻¹ at 600 °C in air, amongst the best ever reported, exceeding the usual requirements for fuel cell applications.     

Generalised rate N/(N+1) codes

A generalised construction of rate M(N+1) run-length limited codes is presented. The (d, k, l, r) constraints are (0, N/4+1, N/8: N/8). Encoding and decoding based on the use of multiple pivot bits are described. The encoding and decoding are systematic enough to allow the writing of software to generate efficient logic equations for different N. The logic equations can be further minimised using an automatic synthesis tool     

The bioclimatic design approach to low-energy buildings in the Klang Valley, Malaysia

Formulating passive energy design strategies require an understanding of the climatic influence on buildings and the thermal comfort of their occupants. This paper presents the bioclimatic approach in building design as well as the techniques which are applied to formulate various strategies in order to achieve indoor comfort conditions. The paper deals with the Bioclimatic Chart, the Building Psychrometric Chart, Mahoney Tables and the Control Potential Zones which utilise the thermal neutrality concept. Regional climatic data from the Klang Valley area in Malaysia were utilised in formulating the design strategies. The most appropriate design strategies for hot, humid regions were then deduced. The most preferred strategies found were the use of ventilation, dehumidification and shading. Consequently, a full recommendation for the integral use of these three passive methods are suggested to be used in all buildings in Malaysia.