Non-invasive, fast, and high-performance EGFR gene mutation prediction method based on deep transfer learning and model stacking for patients with Non-Small Cell Lung Cancer.

Purpose: To propose an intelligent, non-invasive, highly precise, and rapid method to predict the mutation status of the Epidermal Growth Factor Receptor (EGFR) to accelerate treatment with Tyrosine Kinase Inhibitor (TKI) for patients with untreated adenocarcinoma Non-Small Cell Lung Cancer.

Materials And Methods: Real-world data from 521 patients with adenocarcinoma NSCLC who performed a CT scan and underwent surgery or pathological biopsy to determine EGFR gene mutation between January 2021 and July 2022, is collected. Solutions to the problems that prevent the model from achieving very high precision, namely: human errors made during the annotation of the database and the low precision of the output decision of the model, are proposed.

An efficient watermarking algorithm for digital audio data in security applications.

Transform-domain audio watermarking systems are more robust than time-domain systems. However, the main weakness of these systems is their high computational cost, especially for long-duration audio signals. Therefore, they are not desirable for real-time security applications where speed is a critical factor.

Optimal reconstruction and compression of signals and images by Hahn moments and artificial bee Colony (ABC) algorithm.

In this paper, we present an efficient and optimal method for optimization of Hahn parameters and using the Artificial Bee Colony algorithm (ABC) in order to improve the quality of reconstruction and the compression of bio-signals and color images of large sizes. The proposed methods are essentially based on two concepts: the development of a recursive calculation of the initial terms of Hahn polynomials in order to avoid the problems of instability of polynomial values and the use of ABC algorithm to optimize the values of the parameters and of the discrete orthogonal Hahn polynomials () during the reconstruction and the compression of bio-signals and color images. The simulation results performed on bio-signals and on large size / color images clearly show the efficiency and superiority of the proposed methods over conventional methods in terms of reconstruction of signals and images.

Robust 2D and 3D images zero - watermarking using dual Hahn moment invariants and Sine Cosine Algorithm.

In this paper, we initially provide significant improvements on the computational aspects of dual Hahn moment invariants (DHMIs) in both 2D and 3D domains. These improvements ensure the numerical stability of DHMIs for large-size images. Then, we propose an efficient method for optimizing the local parameters of dual Hahn polynomials (DHPs) when computing DHMIs using the Sine-Cosine Algorithm (SCA).

Recognizing COVID-19 from chest X-ray images for people in rural and remote areas based on deep transfer learning model.

In this article, we propose Deep Transfer Learning (DTL) Model for recognizing covid-19 from chest x-ray images. The latter is less expensive, easily accessible to populations in rural and remote areas. In addition, the device for acquiring these images is easy to disinfect, clean and maintain.

Fast and stable computation of higher-order Hahn polynomials and Hahn moment invariants for signal and image analysis.

This article presents, on the one hand, new algorithms for the fast and stable computation of discrete orthogonal Hahn polynomials of high order (HPs) based on the elimination of all gamma and factorial functions that cause the numerical fluctuations of HPs, and based on the use of appropriate stability conditions. On the other hand, a new method for the fast and numerically stable computation of Hahn moment invariants (HMIs) is also proposed. This method is mainly based on the use of new recursive relations of HPs and of matrix multiplications when calculating HMIs.

A Design of a New Column-Parallel Analog-to-Digital Converter Flash for Monolithic Active Pixel Sensor.

The CMOS Monolithic Active Pixel Sensor (MAPS) for the International Linear Collider (ILC) vertex detector (VXD) expresses stringent requirements on their analog readout electronics, specifically on the analog-to-digital converter (ADC). This paper concerns designing and optimizing a new architecture of a low power, high speed, and small-area 4-bit column-parallel ADC Flash. Later in this study, we propose to interpose an S/H block in the converter.

Improving the performance of image classification by Hahn moment invariants.

The discrete orthogonal moments are powerful descriptors for image analysis and pattern recognition. However, the computation of these moments is a time consuming procedure. To solve this problem, a new approach that permits the fast computation of Hahn's discrete orthogonal moments is presented in this paper.