A new dynamic speech encryption algorithm based on lorenz chaotic map over internet protocol

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

A new speech encryption algorithm based on dual shuffling Hénon chaotic map

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

A new RSA public key encryption scheme with chaotic maps

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

A novel framework for intelligent surveillance system based on abnormal human activity detection in academic environments

Abnormal activity detection plays a crucial role in surveillance applications, and a surveillance system thatcan perform robustly in an academic environment has become an urgent need. In this paper, we propose a novel framework for an automatic real-time video-based surveillance system which can simultaneously perform the tracking, semantic scene learning, and abnormality detection in an academic environment. To develop our system, we have divided the work into three phases: preprocessing phase, abnormal human activity detection phase, and content-based image retrieval phase. For motion object detection, we used the temporal-differencing algorithm and then located the motions region using the Gaussian function.Furthermore, the shape model based on OMEGA equation was used as a filter for the detected objects (i.e.,human and non-human). For object activities analysis, we evaluated and analyzed the human activities of the detected objects. We classified the human activities into two groups:normal activities and abnormal activities based on the support vector machine. The machine then provides an automatic warning in case of abnormal human activities. It also embeds a method to retrieve the detected object from the database for object recognition and identification using content-based image retrieval.Finally,a software-based simulation using MATLAB was performed and the results of the conducted experiments showed an excellent surveillance system that can simultaneously perform the tracking, semantic scene learning, and abnormality detection in an academic environment with no human intervention

Combining Artificial Intelligence and Image Processing for Diagnosing Diabetic Retinopathy in Retinal Fundus Images

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

Analytical Approach for Data Encryption Standard Algorithm

Although it was first developed and studied in the late 1970s and early 1977s, the Data Encryption Standard (DES) algorithm has grown in popularity. There are two causes for this occurrence. First, the DES algorithm's complex mathematical structure allows it to serve as the theoretical foundation for a wide variety of applications. Second, the encryption technique works quite well in practice for a variety of applications when implemented correctly. In this paper, we undertake a thorough and practical review of the theoretical aspects of this sort of encryption algorithm and demonstrate how they have been implemented by executing multiple encryption configurations. &nbsp

Swin Transformer-Based Segmentation and Multi-Scale Feature Pyramid Fusion Module for Alzheimer’s Disease with Machine Learning

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 tri-level 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 Kuan Filter and Improved Frost Filter (HKIF) algorithm, 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. After that, multi-scale feature extraction is performed by Multi-Scale Feature Pyramid Fusion Module using VGG16 (MSFP-VGG16) which extract the features in multi-scale which increases the detection and classification accuracy, based on the extracted features the brain image is classified into three classes such as Alzheimer Disease (AD), Mild Cognitive Impairment, and Normal. 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.