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Alzheimer Disease (AD) is the ordinary type of dementia which does not have any proper and efficient medication. Accurate classification and detection of AD helps to diagnose AD in an earlier stage, for that purpose machine learning and deep learning techniques are used in AD detection which observers both normal and abnormal brain and accurately detect AD in an early. For accurate detection of AD, we proposed a novel approach for detecting AD using MRI images. The proposed work includes three processes such as trilevel pre-processing, swin transfer based segmentation, and multi-scale feature pyramid fusion module-based AD detection. In pre-processing, noises are removed from the MRI images using Hybrid KuanFilter and Improved Frost Filter (HKIF) algorithm, the skull stripping is performed by Geodesic Active Contour (GAC) algorithm which removes the non-brain tissues that increases detection accuracy. Here, bias field correction is performed by Expectation-Maximization (EM) algorithm which removes the intensity non-uniformity. After completed pre-processing, we initiate segmentation process using Swin Transformer based Segmentation using Modified U-Net and Generative Adversarial Network (ST-MUNet) algorithm which segments the gray matter, white matter, and cerebrospinal fluid from the brain images by considering cortical thickness, color, texture, and boundary information which increases segmentation accuracy. The simulation of this research is conducted by Matlab R2020a simulation tool, and the performance of this research is evaluated by ADNI dataset in terms of accuracy, specificity, sensitivity, confusion matrix, and positive predictive value. [ABSTRACT FROM AUTHOR]

Retinopathy is an eye disease caused by diabetes, and early detection and treatment can potentially reduce the risk of blindness in diabetic retinopathy sufferers. Using retinal Fundus images, diabetic retinopathy can be diagnosed, recognized, and treated. In the current state of the art, sensitivity and specificity are lacking. However, there are still a number of problems to be solved in state-of-the-art techniques like performance, accuracy, and being able to identify DR disease effectively with greater accuracy. In this paper, we have developed a new approach based on a combination of image processing and artificial intelligence that will meet the performance criteria for the detection of disease-causing diabetes retinopathy in Fundus images. Automatic detection of diabetic retinopathy has been proposed and has been carried out in several stages. The analysis was carried out in MATLAB using software-based simulation, and the results were then compared with those of expert ophthalmologists to verify their accuracy. Different types of diabetic retinopathy are represented in the experimental evaluation, including exudates, micro-aneurysms, and retinal hemorrhages. The detection accuracies shown by the experiments are greater than 98.80 percent. [ABSTRACT FROM AUTHOR]

The secure electronic signature provides contracting parties, particularly the consumer, with safety and reassurance which has a favorable impact on business transactions due to the strong legal authority supplied by this signature, which is based on a method for its formation. Therefore, researchers are rushing to design safe and performance electronic signature schemes at the same time. We offer a novel signature technique based on two hard number theory issues in this work, Quadratic Residue (QR) and Chaotic Maps (CM). Several fields of study including mathematics, physics, and computer science have taken an interest in chaotic systems as a potential tool for cryptography. Analysis demonstrates that our strategy is more secure and efficient than other connected schemes, compared to other schemes. A proof of the proposed scheme's security against known key attacks is also provided in this article. [ABSTRACT FROM AUTHOR]

In recent years, much attention has been provided to developing the systems used for making medical diagnosis. That is because there are various developments made in the field of computing technology. The knowledge related to health and type of disease is essential for making a medical diagnosis by reliable and having an accurate detection. Regarding diabetic retinopathy (DR), it is a common cause for blindness. This disease can be detected in an early stage. It has various symptoms. The most distinctive symptoms are Micro-aneurysms (MAs), Hemorrhages (HAs) which are dark lesions and Hard Exudates (HEs), and Cotton Wool Spots (CWS) that are deemed as bright lesions. Through the present research, the researchers aimed at proposing an automated system for detecting exudates and cotton wool spots in an early stage and classifying them. Regarding the location of optic disc and the structure of blood vessels, they play a significant role in bright lesions for having the DR detected in an early stage. This paper presents algorithms and techniques for processing the retinal image, blood vessel segmentation and extraction, optic disc localization and removal, feature extraction and finally classification for different bright lesions using SVM and Naïve-Bayes classifiers. For testing retinal images, the researchers used image-Ret database which is a publicly available database. This database includes two sub-databases (i.e., DIARETDB0, and DIARETDB1). Finally, we compare the performance of recently published works and our proposed work. [ABSTRACT FROM AUTHOR]

Over the past few decades, many algorithms have been proposed to improve the performance of speech encryption over un-secure channel (i.e., Internet). In this paper, the security level was enhanced using a dynamic dual chaotic based on Hénon chaotic map. In the proposed algorithm, the speech elements are shuffled in a random fashion. Moreover, when both Hénon state variables are free to be used for shuffling the index is toggled randomly between them according to toggle bit. After index shuffling each speech element is modified with XOR operation between the original speech element value and the key that is selected randomly from the updated key table. The same chaotic map is used to initiate the empty or full table and provide new table entries from the values that are already shuffled. The experimental results show that the proposed crypto-system is simple, fast with extra random toggling behavior. The high order of substitution make it sensitive to initial condition, common cryptanalysis attacks such as linear and differential attacks are infeasible. [ABSTRACT FROM AUTHOR]

This paper introduces a dynamic speech encryption algorithm based on Lorenz chaotic map over internet protocol to enhance the services of the real-time applications such as increases the security level and reduces latency. The proposed algorithm was divided into two processes: dynamic key generation process using 128-bit hash value to dynamically alter the initial secret keys, and encryption and decryption process using Lorenz system. In the proposed algorithm, the performance evaluation is carried out through efficient simulations and implementations and statistical analysis. In addition, the average time delay in the proposed algorithm and some of the existing algorithms such as AES is compared. The obtained results concluded that, the proposed dynamic speech encryption algorithm is effectually secured against various cryptanalysis attacks and has useful cryptographic properties such as confusion and diffusion for better voice communication in the voice applications field in the Internet. [ABSTRACT FROM AUTHOR]

Public key cryptography has received great attention in the field of information exchange through insecure channels. In this paper, we combine the Dependent-RSA (DRSA) and chaotic maps (CM) to get a new secure cryptosystem, which depends on both integer factorization and chaotic maps discrete logarithm (CMDL). Using this new system, the scammer has to go through two levels of reverse engineering, concurrently, so as to perform the recovery of original text from the cipher-text has been received. Thus, this new system is supposed to be more sophisticated and more secure than other systems. We prove that our new cryptosystem does not increase the overhead in performing the encryption process or the decryption process considering that it requires minimum operations in both. We show that this new cryptosystem is more efficient in terms of performance compared with other encryption systems, which makes it more suitable for nodes with limited computational ability. [ABSTRACT FROM AUTHOR]

The profound characteristics of factoring problems and chaotic maps are utilized in this article to produce a new identity-based cryptographic scheme. These characteristics include random behavior and high performance (i.e., encryption and decryption) in terms of so many parameters such as computational delay, rate of security, and speed. To illustrate the performance of the proposed cryptographic scheme, some cryptographic attacks are proposed. The mathematical analysis have confirmed that the combined factoring problems and chaotic maps scheme has a high rate of security which satisfy the cryptographic requirements. To the best of our knowledge, this is the first time an ID-based cryptography based on chaotic maps and factoring problems has been proposed. [ABSTRACT FROM AUTHOR]

Over the past two decades, chaos-based encryption appeared as an original application for nonlinear dynamics and deterministic chaos to encrypt and decrypt data. In this paper, an implementation of digital image encryption scheme based on the Lorenz chaotic system is proposed. While in the process of generating chaotic key stream, the hash value of the plain image is embedded in the proposed cryptosystem to dynamically alter the initial secret keys to increase the security level. The proposed digital image encryption algorithm is described in detail along with its security analysis and implementation. The experimental results show that the proposed digital image encryption algorithm is efficient and has high security features and is suitable for practical uses across insecure networks. [ABSTRACT FROM AUTHOR]

In recent years, object detection and classification has been gaining more attention, thus, there are several human object detection algorithms being used to locate and recognize human objects in images. The research of image processing and analyzing based on human shape is one of the hot topic due to the wide applicability in real applications. In this paper, we present a new object classification approach. The new approach will use a simple and robust geometrical model to classify the detected object as human or non-human in the images. In the proposed approach, the object is detected. Then the detected object under different conditions can be accurately classified (i.e. human, non-human) by combining the features that are extracted from the upper portion of the contour and the proposed geometrical model parameters. A software-based simulation using Matlab was performed using INRIA dataset and the obtained results are validated by comparing with five state-of-art approaches in literature and some of the machine learning approaches such as artificial neural networks (ANN), support vector machine (SVM), and random forest (RF). The experimental results show that the proposed object classification approach is efficient and achieved a comparable accuracy to other machine learning approaches and other state-of-art approaches. [ABSTRACT FROM AUTHOR]