The Non-Photorealistic Rendering (NPR) demands are increased with the development of electronic devices. This paper presents a new model for a cartooning system, as an essential category of the NPR. It is used the concept of vector quantization and Logarithmic Image Processing (LIP). An enhancement of Kekre Median Codebook Generation (KMCG) algorithm has been proposed and used by the system. Several metrics utilized to evaluate the time and quality of the system. The results showed that the proposed system reduced the time of cartoon production. Additionally, it enhanced the quality of several aspects like smoothing, color reduction, and brightness.

Nowadays, internet security is a critical concern; the One of the most difficult study issues in network security is "intrusion detection". Fight against external threats. Intrusion detection is a novel method of securing computers and data networks that are already in use. To boost the efficacy of intrusion detection systems, machine learning and deep learning are widely deployed. While work on intrusion detection systems is already underway, based on data mining and machine learning is effective, it requires to detect intrusions by training static batch classifiers regardless considering the time-varying features of a regular data stream. Real-world problems, on the other hand, rarely fit into models that have such constraints. Furthermor

Stemming is a pre-processing step in Text mining applications as well as it is very important in most of the Information Retrieval systems. The goal of stemming is to reduce different grammatical forms of a word and sometimes derivationally related forms of a word to a common base (root or stem) form like reducing noun, adjective, verb, adverb etc. to its base form. The stem needs not to be identical to the morphological root of the word; it is usually sufficient that related words map to the same stem, even if this stem is not in itself a valid root. As in other languages; there is a need for an effective stemming algorithm for the indexing and retrieval of Arabic documents while the Arabic stemming algorithms are not widely available.

Establishing coverage of the target sensing field and extending the network’s lifetime, together known as Coverage-lifetime is the key issue in wireless sensor networks (WSNs). Recent studies realize the important role of nature-inspired algorithms in handling coverage-lifetime problem with different optimization aspects. One of the main formulations is to define coverage-lifetime problem as a disjoint set covers problem. In this paper, we propose an evolutionary algorithm for solving coverage-lifetime problem as a disjoint set covers function. The main interest in this paper is to reflect both models of sensing: Boolean and probabilistic. Moreover, a heuristic operator is proposed as a local refinement operator to improve the quality

     The increased food requirement puts intense pressure on the agriculture community to grow more from the same resources resulting in people leaving the farming business. This happened not exclusively due to the industrial pressure to produce more but to the lack of technology adoption among growers. The use of the sensor in agriculture is not new, but its adoption among agriculture producers is a challenge for industry and scientists. This study aimed to determine sensors used in agricultural fields with challenges and prospects. The study found that sensors have successfully been used at the industry level with highly skilled labor; however, their adoption is challenging in rural agriculture systems due to the lack of a support

A strong sign language recognition system can break down the barriers that separate hearing and speaking members of society from speechless members. A novel fast recognition system with low computational cost for digital American Sign Language (ASL) is introduced in this research. Different image processing techniques are used to optimize and extract the shape of the hand fingers in each sign. The feature extraction stage includes a determination of the optimal threshold based on statistical bases and then recognizing the gap area in the zero sign and calculating the heights of each finger in the other digits. The classification stage depends on the gap area in the zero signs and the number of opened fingers in the other signs as well as

     The extraction, study, and accurate interpretation of the morphology database of a basin are the basic blocks for building a valid geomorphological understanding of this basin. In this work, a new approach is presented which is to use three different GIS based methods to extract databases with specific geographical information and then use the concept of information intersection to make a realistic geomorphological perspective for the study area.

In the first method, data integration of remote sensing images from Google Map and SRTM DEM images were used to identify Horan basin borders.

In the second method, the principle of data integration was represented by extracting the quantitative values of the morphometric c

Article information: COVID-19 has roused the scientic community, prompting calls for immediate solutions to avoid the infection or at least reduce the virus's spread. Despite the availability of several licensed vaccinations to boost human immunity against the disease, various mutated strains of the virus continue to emerge, posing a danger to the vaccine's ecacy against new mutations. As a result, the importance of the early detection of COVID-19 infection becomes evident. Cough is a prevalent symptom in all COVID-19 mutations. Unfortunately, coughing can be a symptom of various of diseases, including pneumonia and inuenza. Thus, identifying the coughing behavior might help clinicians diagnose the COVID-19 infection earlier and distinguish

      Detecting protein complexes in protein-protein interaction (PPI) networks is a challenging problem in computational biology. To uncover a PPI network into a complex structure, different meta-heuristic algorithms have been proposed in the literature. Unfortunately, many of such methods, including evolutionary algorithms (EAs), are based solely on the topological information of the network rather than on biological information. Despite the effectiveness of EAs over heuristic methods, more inherent biological properties of proteins are rarely investigated and exploited in these approaches. In this paper, we proposed an EA with a new mutation operator for complex detection problems. The proposed mutation operator is formulated und

     Deep learning techniques allow us to achieve image segmentation with excellent accuracy and speed. However, challenges in several image classification areas, including medical imaging and materials science, are usually complicated as these complex models may have difficulty learning significant image features that would allow extension to newer datasets. In this study, an enhancing technique for object detection is proposed based on deep conventional neural networks by combining levelset and standard shape mask. First, a standard shape mask is created through the "probability" shape using the global transformation technique, then the image, the mask, and the probability map are used as the levelset input to apply the image segme

     Credit card fraud has become an increasing problem due to the growing reliance on electronic payment systems and technological advances that have improved fraud techniques. Numerous financial institutions are looking for the best ways to leverage technological advancements to provide better services to their end users, and researchers used various protection methods to provide security and privacy for credit cards. Therefore, it is necessary to identify the challenges and the proposed solutions to address them.  This review provides an overview of the most recent research on the detection of fraudulent credit card transactions to protect those transactions from tampering or improper use, which includes imbalance classes, concep

     Increasingly, the availability of personal genomic data in cloud servers hosted by hospitals and research centers has incentivized researchers to turn to research that deals with analyzing genomic data. This is due to its importance in detecting diseases caused by genetic mutations, detecting genes that carry genetic diseases, and attempting to treat them in future generations.  Secure query execution on encrypted data is considered an active research area in which encryption is used to ensure the confidentiality of genomic data while restricting the ability to process such data without first decrypting it. To provide a secure framework and future insight into the potential contributions of homomorphic encryption to the field o

Modern education incorporates strong elements of collaborative learning: activities that prompt students to collaborate on completing learning tasks. In this work we investigate the relationship between media type and student collaboration and attribution patterns during collaborative content creation. We run similarity analyses on text and video artifacts submitted by students as part of collaborative exercises in an undergraduate module. Our main finding is that the same cohort of students was significantly more likely to attribute non-original content to its sources when authoring text compared to video content and when this content is not produced by a peer student. Our preliminary results based on only two media suggest that media type

Coronavirus is an RNA (Ribonucleic acid) virus in the coronaviridian family that causes zoonotic and infectious diseases transmitted between animals and evolved between humans. This class of pathogens is responsible for respiratory diseases. Coronavirus refers to the crown-like protrusions on the outside surface of the virus. Corona is an infection that causes breathing difficulties in humans. In epidemics, symptomatic techniques based on graphic design are essential for examining the causes of influence, which leads to better results than primary radioscopy mechanisms for identifying and diagnosing COVID-19 cases. The urgent need to employ artificial intelligence in disease detection arose from this standpoint. In this paper, a system i

     By definition, the detection of protein complexes that form protein-protein interaction networks (PPINs) is an NP-hard problem. Evolutionary algorithms (EAs), as global search methods, are proven in the literature to be more successful than greedy methods in detecting protein complexes. However, the design of most of these EA-based approaches relies on the topological information of the proteins in the PPIN. Biological information, as a key resource for molecular profiles, on the other hand, acquired a little interest in the design of the components in these EA-based methods. The main aim of this paper is to redesign two operators in the EA based on the functional domain rather than the graph topological domain. The perturb

Data security is a fundamental parameter on communication system development. The capability of protecting and securing the information is a great essence for the growth of the data security and electronic commerce. The cryptography has a significant influence upon information security systems against the variety of the attacks, in which higher complexity in secret keys results in the increase of security and the cryptography algorithms’ complexity. The sufficient and newer cryptographic methods’ versions may helpful in the reduction of the security attacks. The main aim of this research is satisfying the purpose of the information security through the addition of a new security level to the Advanced Encryption Standard (AES) algorithm

In recent years, Elliptic Curve Cryptography (ECC) has attracted the attention of
researchers and product developers due to its robust mathematical structure and
highest security compared to other existing algorithms like RSA. It is found to give
an increased security compared to RSA for the same key-size or same security as
RSA with less key size. In this paper a new approach is proposed for encrypting
digital image using the arithmetic of elliptic curve algebra. The proposed approach
produced a new mask for encrypt the digital image by use a new convolution
processes based on ECC algebra operations and work as symmetric cryptographic
system instead of asymmetric system. A new approach combined both compression

     Developed countries are facing many challenges to convert large areas of existing services to electronic modes, reflecting the current nature of workflow and the equipment utilized for achieving such services. For instance, electricity bill collection still tend to be based on traditional approaches (paper-based and relying on human interaction) making them comparatively time-consuming and prone to human error.

This research aims to recognize numbers in mechanical electricity meters and convert them to digital figures utilizing Optical Character Recognition (OCR) in Matlab. The research utilized the location of red region in color electricity meters image to determine the crop region that contain the meters numbers, then

In this paper, the combined source coding with Multi Carrier Code Division Multiple Access (MC-CDMA) system is proposed, where the transmission of the compressed image produced from source coding through Additive White Gaussian Noise (AWGN) channel for a single user and multi users. In which the (MC-CDMA) system removes Inter Symbol Interference (ISI) and Inter Carrier Interference (ICI). The hybrid compression system of Discrete Cosine Transform (DCT) and predictive coding (PC) technique are integrated as a source coding. The simulation results indicates that the transmission system of a single user was much better than the transmission system of multi users. When the number of users increased, the Bit Error Rate (BER) increased. For a

In this paper, a hybrid image compression technique is introduced that integrates discrete wavelet transform (DWT) and linear polynomial coding. In addition, the proposed technique improved the midtread quantizer scheme once by utilizing the block based and the selected factor value. The compression system performance showed the superiority in quality and compression ratio compared to traditional polynomial coding techniques.

Rivest Cipher 4 (RC4) is an efficient stream cipher that is commonly used in internet protocols. However, there are several flaws in the key scheduling algorithm (KSA) of RC4. The contribution of this paper is to overcome some of these weaknesses by proposing a new version of KSA coined as modified KSA . In the initial state of the array is suggested to contain random values instead of the identity permutation. Moreover, the permutation of the array is modified to depend on the key value itself. The proposed performance is assessed in terms of cipher secrecy, randomness test and time under a set of experiments with variable key size and different plaintext size. The results show that the RC4 with improves the randomness and secrecy with

Automatic document summarization technology is evolving and may offer a solution to the problem of information overload. Multi-document summarization is an optimization problem demanding optimizing more than one objective function concurrently. The proposed work considers a balance of two significant objectives: content coverage and diversity while generating a summary from a collection of text documents. Despite the large efforts introduced from several researchers for designing and evaluating performance of many text summarization techniques, their formulations lack the introduction of any model that can give an explicit representation of – coverage and diversity – the two contradictory semantics of any summary. The design of gener

The aim of this paper is to compare between classical and fuzzy filters for removing different types of noise in gray scale images. The processing used consists of three steps. First, different types of noise are added to the original image to produce a noisy image (with different noise ratios). Second, classical and fuzzy filters are used to filter the noisy image. Finally, comparing between resulting images depending on a quantitative measure called Peak Signal-to-Noise Ratio (PSNR) to determine the best filter in each case.
The image used in this paper is a 512 * 512 pixel and the size of all filters is a square window of size 3*3. Results indicate that fuzzy filters achieve varying successes in noise reduction in image compared to

In this paper, an adaptive polynomial compression technique is introduced of hard and soft thresholding of transformed residual image that efficiently exploited both the spatial and frequency domains, where the technique starts by applying the polynomial coding in the spatial domain and then followed by the frequency domain of discrete wavelet transform (DWT) that utilized to decompose the residual image of hard and soft thresholding base. The results showed the improvement of adaptive techniques compared to the traditional polynomial coding technique.

The presence of White Blood Cells (WBCs) in the body of human has a great role in the protection of the body against many pathogens. The recognition of the WBC is the first important step to diagnose some particular diseases. The pathologists usually use an optical microscope to recognize WBCs, but, this process is a quite tedious, time-consuming, error prone, very slow, and expensive. In addition, it needs experts with long practice in this field. For these reasons, a computer assisted diagnostic system that helps pathologists in the process of diagnosis can be effective, easy and safe. This research is devoted to develop a system based on digital image processing methods to localize WBCs nuclei. The proposed system involved a collectio

Extractive multi-document text summarization – a summarization with the aim of removing redundant information in a document collection while preserving its salient sentences – has recently enjoyed a large interest in proposing automatic models. This paper proposes an extractive multi-document text summarization model based on genetic algorithm (GA). First, the problem is modeled as a discrete optimization problem and a specific fitness function is designed to effectively cope with the proposed model. Then, a binary-encoded representation together with a heuristic mutation and a local repair operators are proposed to characterize the adopted GA. Experiments are applied to ten topics from Document Understanding Conference DUC2002 datas

     Due to the significant role in understanding cellular processes, the decomposition of Protein-Protein Interaction (PPI) networks into essential building blocks, or complexes, has received much attention for functional bioinformatics research in recent years. One of the well-known bi-clustering descriptors for identifying communities and complexes in complex networks, such as PPI networks, is modularity function.   The contribution of this paper is to introduce heuristic optimization models that can collaborate with the modularity function to improve its detection ability. The definitions of the formulated heuristics are based on nodes and different levels of their neighbor properties.  The modulari

Images are important medium for conveying information; this makes improvement of image processing techniques also important. Interpretation of image content is one of the objectives of image processing techniques. Image interpretation that segments the image to number of objects called image segmentation. Image segmentation is an important field to deal with the contents of images and get non overlapping regions coherent in texture and color, it is important to deal only with objects with significant information. This paper presents survey of the most commonly used approaches of image segmentation and the results of those approaches have been compared and according to the measurement of quality presented in this paper the Otsu's threshol