Elliptic Curve Cryptography (ECC) is one of the public key cryptosystems that works based on the algebraic models in the form of elliptic curves. Usually, in ECC to implement the encryption, the encoding of data must be carried out on the elliptic curve, which seems to be a preprocessing step. Similarly, after the decryption a post processing step must be conducted for mapping or decoding the corresponding data to the exact point on the elliptic curves. The Memory Mapping (MM) and Koblitz Encoding (KE) are the commonly used encoding models. But both encoding models have drawbacks as the MM needs more memory for processing and the KE needs more computational resources. To overcome these issues the proposed enhanced Koblitz encodi

The investigation of signature validation is crucial to the field of personal authenticity. The biometrics-based system has been developed to support some information security features.Aperson’s signature, an essential biometric trait of a human being, can be used to verify their identification. In this study, a mechanism for automatically verifying signatures has been suggested. The offline properties of handwritten signatures are highlighted in this study which aims to verify the authenticity of handwritten signatures whether they are real or forged using computer-based machine learning techniques. The main goal of developing such systems is to verify people through the validity of their signatures. In this research, images of a group o

This research introduces a proposed hybrid Spam Filtering System (SFS) which consists of Ant Colony System (ACS), information gain (IG) and Naïve Bayesian (NB). The aim of the proposed hybrid spam filtering is to classify the e-mails with high accuracy. The hybrid spam filtering consists of three consequence stages. In the first stage, the information gain (IG) for each attributes (i.e. weight for each feature) is computed. Then, the Ant Colony System algorithm selects the best features that the most intrinsic correlated attributes in classification. Finally, the third stage is dedicated to classify the e-mail using Naïve Bayesian (NB) algorithm. The experiment is conducted on spambase dataset. The result shows that the accuracy of NB

Ad hoc networks are characterized by ease of setup, low costs, and frequent use in the corporate world. They ensure safety to the user and maintain the confidentiality of the information circulated. They also allow the user to address the cases of communication failure in areas subject to destruction of communication infrastructure. The proposed protocols in the ad hoc networks often build only one path to achieve communication between the nodes, due to the restrictions of battery run out and the movement of the nodes. This connection is often subject to a failure within a certain range. Thus, multiple alternate paths in ad hoc networks use a solution to failing node communications. In addition, when looking at the situation where interf

The aim of this scientific study is to diagnose the nature of the relationship between academic education in the field of media, communication and media practice. The university offers theoretical and practical lessons to students on the concepts of communication sciences, theories, models, legislations, ethics, and techniques over three years or more. And the diploma in the field of communication and Media Sciences gives students the opportunity to work in the field of journalism and media in the future, this is what many academics and media thinkers in Algeria believe, while another trend sees the exact opposite: The study of information and communication sciences within the university in the form of theoretical lessons cannot contribu

Engineering geological study of rock slope stability in two stations lying in the SW of Haibat Sultan mountain, along the Kalksmaq - Koisanjaq road was carried out. At each station, rock slopes and discontinuities were comprehensively surveyed and the relationships with failures were determined. The limestone rock was described in engineering terms, the types of failures recorded during field study were rock roll and toppling whiles the probable failures were sliding, toppling, and rock roll. The study also revealed that the factors affecting slope stability in the study area were slope angle, height, dip of strata, and discontinuities (which are almost perpendicular to the bedding plane). The laboratory test, of the rock samples (point-

Lung cancer is one of the most serious and prevalent diseases, causing many deaths each year. Though CT scan images are mostly used in the diagnosis of cancer, the assessment of scans is an error-prone and time-consuming task. Machine learning and AI-based models can identify and classify types of lung cancer quite accurately, which helps in the early-stage detection of lung cancer that can increase the survival rate. In this paper, Convolutional Neural Network is used to classify Adenocarcinoma, squamous cell carcinoma and normal case CT scan images from the Chest CT Scan Images Dataset using different combinations of hidden layers and parameters in CNN models. The proposed model was trained on 1000 CT Scan Images of cancerous and non-c

Neural cryptography deals with the problem of “key exchange” between two neural networks by using the mutual learning concept. The two networks exchange their outputs (in bits) and the key between two communicating parties ar eventually represented in the final learned weights, when the two networks are said to be synchronized. Security of neural synchronization is put at risk if an attacker is capable of synchronizing with any of the two parties during the training process.

Classification of network traffic is an important topic for network management, traffic routing, safe traffic discrimination, and better service delivery. Traffic examination is the entire process of examining traffic data, from intercepting traffic data to discovering patterns, relationships, misconfigurations, and anomalies in a network. Between them, traffic classification is a sub-domain of this field, the purpose of which is to classify network traffic into predefined classes such as usual or abnormal traffic and application type. Most Internet applications encrypt data during traffic, and classifying encrypted data during traffic is not possible with traditional methods. Statistical and intelligence methods can find and model traff