Two basic requirements must be met to encrypt data. These requirements are confusion and diffusion properties. In particular, block cipher algorithms are based on a cryptographic component known as substitution-box to provide the need for confusion. Therefore, attack scenarios generally focus on this cryptographic component. There are alternative design approaches for substitution-box design. It is

Ethernet is one of the widespread protocols residing in the second layer of the seven-layers Open Systems Interconnection (OSI) model. Ethernet offers various advantages which enable its widespread use in all types of network topology and becomes an essential part of computer and network architecture. Despite its features, Ethernet suffers from scalability issues where the increasing number of hosts

Autism Spectrum Disorder (ASD) is a deleterious neurodevelopmental disorder affecting 1 in 54 children. The complex interdisciplinary nature of ASD research, however, introduces challenges to the spread and accessibility of new discoveries among researchers in different disciplines; furthermore, this highly complex research environment makes it even harder for practicing physicians and primary care

New and advanced technologies have introduced amazing areas like the Internet of Vehicles (IoV) and Unmanned Aerial Vehicle (UAV). These two technologies are emerged to facilitate the ground users by providing more fast and convenient data communication services. Due to the open nature of these networks, security and user privacy is always a serious concern. Different types of security attacks have

Encryption schemes like AES require finite field modular multiplication. The encryption speed is highly dependent on the performance of the finite field multiplier. Several high-speed systolic bit parallel multipliers with low area complexity have been proposed in the literature. In this paper, a modular multiplication algorithm is used to propose a bit parallel polynomial basis systolic multiplier

Sustainable Farming requires up-to-date advice on crop diseases, patterns, and adequate prevention actions to face developing circumstances. Currently, in developing countries like Egypt, farmers’ access to such information is extremely limited due to the agriculture support being either not available, inconsistent, or unreliable. The presented Cloud-based Complaints Management and Decision Support

The need of developing systems that address traffic matters such as traffic control and accidents has led to the development of a number of techniques, methods and tools to address road usability issues. Many automated models have been proposed and applied in different scenarios such as road oddities. Despite of the advantages of these models the major problem of road accidents continue to pose problem

There has been an increased interest in the creation of AI social agents who possess complex behaviors that allow them to perform social interactions. Behavior trees provide a plan model execution that has been widely used to build complex behaviors for AI social agents. Behavior trees can be represented in the form of a memory structure known as cognitive scripts, which would allow them to evolve

Automated personality trait detection from text data has emerged and gained a great deal of attention in the subject area of affective computing and sentiment analysis. Most previous work has focused on features engineering such as linguistic styles and psycholinguistic databases which have correlations with personality. Recently, natural language processing has been affected significantly with transfer

The safety of cloud services within a Trust Model System (TMS) is certainly compromised by a lack of defense against security threats as well as by inaccuracy of the trust results. Our proposed model addresses well-known security threats to the reputation trust model system, and is shown to deal with all possible potential attack threats, such as Sybil, on–off, and collusion attacks, by specifying

E-learning sites are useful for improving the skills and awareness of the academic backbone, such as instructors, students, administrative staff, and those who are searching for current information about various educational institutes. Despite all the benefits of an online learning platform, users face some challenges and complexities, such as selecting appropriate learning material and courses based

In this contemporary era, recent developments have seen proliferation of methods on improving energy utilization for wireless sensor networks. Although the proposed methods can mitigate the problem of rapid battery depletion to some extent, the lifespan of sensor nodes is still a biggest constraint. Many studies conducted so far have shown continuous network functions with the help of external harvesting

Over the last few years, we have seen dramatic increase in the demand for radio spectrum in the world due to rapid growth in wireless services and applications. In order to meet the growing demand for radio spectrum, new schemes should be developed for utilizing free spectrum effectively. Cognitive radio (CR) is promising technology that enables solving spectrum scarcity problem effectively. Thus,

In view of most current studies on text sentiment classification focus on the deep learning model to obtain the sentimental characteristics of English text. Chinese text sentiment analysis is rarely involved, and only the context information of the statement is considered, but the syntax information of the statement is rarely considered. In this paper, a novel sentiment classification model is proposed

Security has become an important issue during communication among mobile nodes in an unfavorable condition. The mobile node’s property is dynamic, so it isn’t easy to manage security policies. These difficulties present a barrier to building multigene security arrangements that accomplish both assurance and attractive network execution. The proposed work suggests the mutual authentication-based protocol

Background/introduction Concept-level sentiment analysis deals with the extraction and classification of concepts and features from user reviews expressed online about products and other entities like political leaders, government policies, and others. The prior studies on concept-level sentiment analysis have used a limited set of linguistic rules for extracting concepts and their associated features

As battery technology has not advanced as quickly as other technologies like semiconductor technology, energy efficiency is still one of the primary concerns in Wireless Sensor Networks (WSNs). One of the finest Wireless Energy Transfer (WET) technique is Magnetic Resonant Coupling (MRC) used to re-energize the nodes in WSNs. Recent advancements in MRC show that more than one node can be re-charged

Health related information of an individual is very sensitive and demands a high level of security and privacy. Healthcare providers have the responsibility to ensure that patient information is secure and accessible only to authorized users. Healthcare systems are using biometrics since long for authentication and/or access control purposes. Biometrics can also be used for healthcare data security

With the volume of data increasing exponentially, there is a growing interest in helping people to benefit from their data regardless of its poor quality. One of the major data quality problems is the imbalanced distribution of different categories existing in the data. Such problem would affect the performance of any possible of analysis and mining on the data. For instance, data with an imbalanced

Although wireless sensor networks (WSNs) have been utilized for over one decade, it is now heavily used by many modern applications such as medical observance, disaster management and environmental monitoring. This type of network suffers from limited energy and a short lifetime in addition to the low channel bandwidth. Bandwidth represents the major challenges of such systems due to the great impact

Cloud computing applications have grown rapidly in the last decade, where many organizations are migrating their applications to cloud data center as they expected high performance, reliability, and the best quality of service. Data centers deploy a variety of technologies, such as software-defined networks (SDN), to effectively manage their resources. The SDN approach is a highly flexible network

Among the current advanced driver assistance systems, Vehicle-to-Vehicle (V2V) technology has great potential to increase Vehicular Ad Hoc Network (VANET) performance in terms of security, energy efficiency, and comfortable driving. In reality, vehicle drivers regularly change lanes depending on their assumptions regarding visual distances. However, many systems are not quite well-designed, because

Layout analysis is essential step in information extraction from scanned document images. In this paper we propose an algorithm for aligning layouts generated with different OCR engines. The main requirement is to always generate the same layout for the given document image regardless of OCR engine used for image processing. In that way information extraction from scanned documents, that is heavily

To meet the performance and security challenges for users who have the same interests in the urban Peer-to-Peer VANET environment (UP2PVANET), the confidentiality & security scheme applied to the urban P2P VANET network (CSP2P) is introduced which helps maintain an effective certification framework. An intelligent cooperative detection system is also proposed, that uses homomorphic encryption to detect

Edge-based privacy preserving cryptosystem is identified as the upcoming amenities of cloud-based secure remote healthcare monitoring systems. Usually, the cloud-based healthcare system will directly collect the remote patient data through a sensor layer and provide the continuous monitoring and diagnosis through various prediction processes made by the decision support system. These sensing and processing

The automatic correction of grammar and spelling errors is important for students, second language learners, and some Natural Language Processing (NLP) tasks such as part of speech and text summarization. Recently, Neural Machine Translation (NMT) has been an out-performing and well-established model in the task of Grammar Error Correction (GEC). Arabic GEC is still growing because of some challenges

Named entity recognition (NER) is a subfield of information extraction, which aims to detect and classify predefined named entities (e.g., people, locations, organizations, etc.) in a body of text. In the literature, many researchers have studied the application of different machine learning models and features to NER. However, few research efforts have been devoted to studying annotation schemes used

The explosive growth of cloud usage encourages several challenges, especially high energy consumption of Cloud Data Centers (CDCs), new security risks to Virtual Machines (VMs) resulting from co-residency with other risky VMs on the same Physical Machine (PM), and the Quality of Service (QoS) degradation due to sharing resources. Many recent studies have proposed Dynamic VM Consolidation (DVMC) to

Personality Traits Detection is one of the important problems as a text analytics task in Natural Language Processing (NLP). Text analytics is the process of finding out insight knowledge over written text. Although most deep learning models give high performance, they often lack interpretability. Computer Vision (CV) has been affected significantly with inductive transfer learning, however training

It has been predicted that more devices will be connected to the internet network along with the development of IoT, and that will increase the complexity in the security system. The most important thing in security systems is the process of encryption and authentication. Various encryption techniques are developed to overcome security problems of the data. One of the methods used for authentication

Recommending sustainable products to the target users in a timely manner is the key drive for consumer purchases in online stores and served as the most effective means of user engagement in online services. In recent times, recommender systems are incorporated with different mechanisms, such as sliding windows or fading factors to make them adaptive to dynamic change of user preferences. Those techniques

In the virtual world, the authentication process is used to prove the identity for both of the server and the clients. Therefore, the authentication is an urgent issue for sharing data between users in the data warehouse systems. This paper presents the enhancement of Data Warehouse Signature (DWS) framework through proposing a novel authentication and group-key distribution model, based on the modular

Due to increasing complexity of mobile applications, and limited computation resources of smart mobile devices, the quality of service requirements of mobile application can be enhanced by offloading the computation tasks of the mobile applications to edge servers, such as cloudlets, which exist at the edge of wireless networks. However, improper placement of mobile tasks on the edge servers may increase

Multi-label classification assigns multiple labels to each document concurrently. Many real-world classification problems tend to employ high-dimensional label spaces, which can be naturally structured in a hierarchy. In this type of problem, each instance may belong to multiple labels and labels are organized in a hierarchical structure. It presents a more complex problem than flat classification

The concept of picture fuzzy sets (PFS) is a generalization of ordinary fuzzy sets and intuitionistic fuzzy sets, which is characterized by positive membership, neutral membership, and negative membership functions. Keeping in mind the importance of similarity measures and applications in data mining, medical diagnosis, decision making, and pattern recognition, several studies have been proposed in

A novel evolutionary algorithm called learner performance based behavior algorithm (LPB) is proposed in this article. The basic inspiration of LPB originates from the process of accepting graduated learners from high school in different departments at university. In addition, the changes those learners should do in their studying behaviors to improve their study level at university. The most important

Each organization faces threats and risks in daily operations. One of the main risks is how to assess the security level to protect from the increasing risks associated with technology evolution. So, organizations can specify the required approaches and skills. In this paper, we propose a security maturity model that classifies the organizations into five levels. Each level determines the technologies

Machine-based classification usually involves some computer programs, known as algorithms, developed using several mathematical formulations to accelerate the automated classification process. Along with the exponential increase in the size and computational complexity of the data today, such optimized, robust, agile and reliable computational algorithms are required which can efficiently carry out

Nowadays, human emotion recognition is a mandatory task for many human machine interaction fields. This paper proposes a novel multi-modal human emotion recognition framework. The proposed scheme utilizes first the 3D-Convolutional Neural Network (3D-CNN) deep learning architecture for extracting the spatio-temporal features from the electroencephalogram (EEG) signals, and the video data of human faces

Electronic Medical Records (EMRs) can provide many benefits to physicians, patients and healthcare services if they are adopted by healthcare organizations. But concerns about privacy and security that relate to patient information can cause there to be relatively low EMR adoption by a number of health institutions. Safeguarding a huge quantity of health data that is sensitive at separate locations

This paper presents a generalization of chaotic systems using two-dimensional affine transformations with six introduced parameters to achieve scaling, reflection, rotation, translation and/or shearing. Hence, the location of the strange attractor in space can be controlled without changing its chaotic dynamics. In addition, the embedded parameters enhance the randomness and sensitivity of the system

This study presents a new approach for calculations within the Common Vulnerability Scoring System that scoring the effects of vulnerabilities in software on the security status. These calculations is the method that is most commonly used in scoring software vulnerabilities. The present model demonstrates how software security vulnerabilities can be calculated using linguistic terms. Therefore, the

Hyperspectral data are a curse with huge dimensionality, high redundancy of spectral information, and are noisy in nature. Hundreds of narrow adjacent bands are present in HS (Hyperspectral) data with high spectral information and it always leads to a computational complexity in space and time. The information theoretic methods are used for hyperspectral band selection to avoid computational complexity

Local outlier detection is a hot area and great challenge in data mining, especially for large-scale datasets. On the one hand, traditional algorithms often achieve low-quality detection results and are sensitive to neighborhood size. On the other hand, they are infeasible for large-scale datasets due to at least O(N2) time and space complexity. In light of these, we propose a new local outlier detection

This paper presents a new method for generating all prime numbers up to a particular number m∈N,m⩾9, by using the set theory. The proposed method is explicit and works oriented in finding the prime numbers in order. Also, we give an efficiently computable explicit formula which exactly determines the number of primes up to a particular number m∈N,m⩾9. For the best of our knowledge, this is the first

The extensive growth of the Internet of Things (IoT) devices leads to the evolution of the broad range of smart applications in numerous fields such as smart home, wearable, education, agriculture, health care, transportation and many more. But security for IoT devices is still a challenging issue as many attacks are possible in the environment. Therefore strong security requirements are an important

Non-negative matrix factorization (NMF) is an effective model in converting data into non-negative coefficient representation whose discriminative ability is usually enhanced to be used for diverse pattern recognition tasks. In NMF-based clustering, we often need to perform K-means on the learned coefficient as postprocessing step to get the final cluster assignments. This breaks the connection between

Internet of things architecture is the integration of real-world objects and places with the internet. This booming in technology is bringing ease in our lifestyle and making formerly impossible things possible. Internet of things playing a vital role in bridging this gap easily and rapidly. IoT is changing our lifestyle and the way of working the technologies, by bringing them together at the one

Although the number of Arabic language writers in social media is increasing, the research work targeting Author Profiling (AP) is at the initial development phase. This paper investigates Gender Identification (GI) (male or female) of authors posting Egyptian dialect tweets using Neural Networks (NN) models. Various architectures of NN are explored with extensive parameters’ selection such as simple

Searching relevant articles from medical resources is an important search task in clinical decision support system. The technical contents and long length of biomedical articles make this search task more complicated than many other search tasks. Previous research on biomedical information retrieval (IR) is typically based on keyword search. In this paper we propose a new approach. Using our approach

Background/introduction The dawn of the internet opened the doors to the easy and widespread sharing of information on subject matters such as products, services, events and political opinions. While the volume of studies conducted on sentiment analysis is rapidly expanding, these studies mostly address English language concerns. The primary goal of this study is to present state-of-art survey for

Modern medical diagnosis heavily rely on bio-medical and clinical data. Machine learning algorithms have proven effectiveness in mining this data to provide an aid to the physicians in supporting their decisions. In response, machine learning based approaches were developed to address this problem. These approaches vary in terms of effectiveness and computational cost. Attention has been paid towards

Software defined networks are an emerging category of networks in which the data plane and control plane are separated. This separation of planes opens the door for designing sophisticated routing algorithms that would overwhelm the computing power of traditional networking nodes. In this paper, we consider the possibility of introducing node trust into the routing problem. There are many ways for

This paper describes how a simple novel Galois Power-of-Two (GPOW2) real-time embedding scheme is used to improve the performance and accuracy of downstream NLP tasks. GPOW2 computes embeddings live on the fly (real time) in the context of target NLP tasks without the need for tabulated pre-embeddings. One excellent feature of the method is the ability to capture multilevel embeddings in the same pass

The analysis of human emotions plays a significant role in providing sufficient information about patients in monitoring their feelings for better management of their diseases. Audio-based emotions recognition has become a fascinating research interest for such domains during the last decade. Mostly, audio-based emotions systems depend on the recognition stage. The existing model has a common issue

Pain is defined as “a distressing experience associated with actual or potential tissue damage with sensory, emotional, cognitive and social components”, knowing the exact level of pain experienced to have a critical impact for caregivers to make diagnosis and make he suitable treatment plan, but the available methods depend entirely on the patient self-report, which increase the difficulties of knowing

This work is inspired by Kaggle competition which was part of the Fine-Grained Visual Categorization workshop at CVPR 2019 (Conference on Computer Vision and Pattern Recognition) we participated in. It aimed at detecting cassava diseases using 5 fine-grained cassava leaf disease categories with 10,000, labeled images collected during a regular survey in Uganda. Traditionally, this detection is done

The data capturing and access process is an important stage in the big data applications. Most of these applications are exploited the wireless sensor networks (WSNs) to accomplish this process through the sensor nodes that are deployed in unsecure and unattended environments. Therefore, these applications are suffered from numerous security weaknesses, an adversary may exploit such weaknesses to break

Many IoT applications have been developed recently, this leads to increasing the network load. To avoid the lack of spectrum, IoT can use cognitive radio network concept. This paper presents most of the researches addressing channel allocation and packets scheduling, when merging the cognitive radio networks with the IoT technology. Surveying the researches done in this area shows the work done is