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Deoxyribonucleic acid (DNA) motif finding (discovery/mining) in biological chains is the most recent challenging and interesting trend in bioinformatics. It represents a crucial phase in most bioinformatics systems related to unravelling the secrets of gene functions. Despite the efforts made to date to produce robust algorithms, DNA motif finding remains a difficult task for researchers in this field. In general, biological pattern locating algorithms are categorized into two categories: probabilistic and numerical methods. In this paper, we provide a survey of exact DNA motif finding algorithms and their working principles with a suitable comparison among these algorithms to provide an essential step for researchers in this field.

The Porter stemmer algorithm is a broadly used, however, an essential tool for natural language processing in the area of information access. Stemming is used to remove words that add the final morphological and diacritical endings of words in English words to their root form to extract the word root, i.e. called stem/root in the primary text processing stage. In other words, it is a linguistic process that simply extracts the main part that may be close to the relative and related root. Text classification is a major task in extracting relevant information from a large volume of data. In this paper, we suggest ways to improve a version of the Porter algorithm with the aim of processing and overcome its limitations and to save time and memory by reducing the size of the words. The system uses the improved Porter derivation technique for word pruning. Whereas performs cognitive-inspired computing to discover morphologically related words from the corpus without any human intervention or language-specific knowledge. The improved Porter algorithm is compared to the original stemmer. The improved Porter algorithm has better performance and enables more accurate information retrieval (IR)., Алгоритм стеммер Портера является широко используемым и важным инструментом для обработки естественного языка в области доступа к информации. Стемминг используется для удаления слов, которые добавляют морфологические и диакритические окончания слов в английском языке к их корневой форме для извлечения корня слова, так называемого стема, на этапе первичной обработки текста. Другими словами, это лингвистический процесс, который просто извлекает основную часть, которая может быть близка к относительному и родственному корню. Классификация текстов является одной из основных задач при извлечении соответствующей информации из большого объема данных. В данной работе мы предлагаем способы улучшения версии алгоритма Портера с целью обработки и преодоления его ограничений, а также экономии времени и памяти за счет уменьшения размера слов. Система использует усовершенствованную технику вывода Портера для сокращения слов в то время как выполняет когнитивные вычисления для обнаружения морфологически связанных слов из корпуса без какого-либо вмешательства человека или специальных языковых знаний. Усовершенствованный алгоритм Портера сравнивается с исходным стеммером. Усовершенствованный алгоритм Портера имеет более высокую производительность и обеспечивает более точный поиск информации (ПИ)., Алгоритм стеммер Портера є широко використовуваним і важливим інструментом для обробки природної мови в області доступу до інформації. Стеммінг використовується для видалення слів, які додають морфологічні та діакритичні закінчення слів в англійській мові до їх кореневої форми для вилучення кореня слова, так званого стема, на етапі первинної обробки тексту. Іншими словами, це лінгвістичний процес, який просто витягує основну частину, яка може бути близькою до відносного та спорідненого кореня. Класифікація текстів є одним з основних завдань при добуванні відповідної інформації з великого обсягу даних. У даній роботі ми пропонуємо способи поліпшення версії алгоритму Портера з метою обробки і подолання його обмежень, а також економії часу і пам'яті за рахунок зменшення розміру слів. Система використовує вдосконалену техніку виведення Портера для скорочення слів в той час як виконує когнітивні обчислення для виявлення морфологічно пов'язаних слів з корпусу без будь-якого втручання людини або спеціальних мовних знань. Вдосконалений алгоритм Портера порівнюється з вихідним стеммером. Вдосконалений алгоритм Портера має більш високу продуктивність і забезпечує більш точний пошук інформації (ПІ).

Annually, lots of persons are carrying lifelong disabilities or losing their lives owing to fatal accidents on the road. In addition to mechanical failures and people errors, driver's drowsiness represents one of the fundamental reasons for fatal accidents on the road. When drivers feel drowsy, lots of physiological and behavioral symptoms appear such as changes in the waves of the human brain, changes in facial expressions, variations in eye activity, decreasing head movements, etc. Therefore, there is a significant necessity to provide developed models of driver's drowsiness detection that exploit these symptoms for reducing accidents by warning drivers of drowsiness and fatigue. This paper concentrates on proposing a driver's drowsiness assistance model to monitor and alarm drivers by utilizing a behavioral-based method (eye movements detection method). In the proposed method of detecting eye movements (closed/opened), the Advanced Local Binary Pattern (Advanced LBP) is used, in which the descriptors are utilized to represent eye images to extract the tissue features of different persons in the driving car to see if the driver is in a drowsy state or not and this occurs after recording the driver's video and detecting the eyes of the driver. To extract the features in this way, the image of the eyes is divided into small regions through the Advanced LBP and sequenced into a single feature vector, where this method is used to determine the similarity features in the training group and to classify the eye image. The Naive Bayes classifier (NB) and Support Vector Machine (SVM) are utilized for giving good accuracy. The results indicate that the system has a high accuracy rate compared with the other existing methods, where the accuracy rate of NB and SVM using an eye detection dataset with training 90% and testing 10% is 96% and 97%, respectively

Clustering text documents have become an increasingly important problem in recent years due to the availability of a huge amount of unstructured data in various forms, such as the web, social networks, and other information networks. It aims to organise and classify large document groups into smaller groups of meaning. This process is crucial because it is challenging to deal with a large amount and an increasing number of digital data. The documents are organised and classified to facilitate faster information retrieval (IR) and try to extract information with semantic knowledge. The process above enables the retrieve information, browsed and understood instead of clustering texts in the traditional way, i.e. compilation of data without descriptive concepts. As textual data has become a diverse set of vocabulary hence, there is an urgent need for text aggregation techniques based on semantic similarity is the primary solution to this problem as it is grouped into groups according to meaning rather than keywords. Several Papers that are using semantic similarity in various scopes. It has been reviewed in this research; some of them which are using similarity based on semantic using document cluster. For developing an effective and efficient clustering methodology to take care of the semantic structure of the text documents, and a compare of between them is presented according to (algorithms, tools, and assessment methods). Finally, extensive study and comparison of work are presented.

This paper describes an implementation of a web-based system using classification techniques for prediction of water pollution type and appropriate treatments depending on the water quality Index. The benefits of data mining lie in the extraction of new knowledge automatically from the raw data to progress decision making. (C4.5) The decision tree was used for classifying water quality into five classes using fourteen parameters according to the World Health Organization's requirements. These parameters are taken for each sample of water in each ten water stations that selected for the investigations. First two classes were suitable for drinking water while other classes were not, therefore two types of classification techniques ((c4.5) decision trees and artificial neural network, millstone machine learning technique) were suggested to produce a decision concerning the type of pollution and devise proposition for the treatment of pollution. The experiment was carried on a real database validated by (Iraqi Ministry of Environment) gathered from ten authenticated treatment stations. The results show that using C4.5 decision tree classifier found to be the better in terms of the execution time while using NNT algorithm gave slightly better results in terms of the accuracy and error percentages. Also, the work shows that the techniques of data mining have the prospect to fast predict of the water quality class, as long as the given data are a true representation of the scope of knowledge.

Due to the rising of human populations more high-quality water were requested for local purposes and economic activities. The Safe Drinking Water need founding and enforcing standards that systems of public drinking water must follow. In this paper, a survey of water quality of Tigris River through the ten water treatment plants of Baghdad during (2018) was done using Weight Arithmetic Index method for fourteen water-quality parameters through the spatial distribution of water quality. It became clear that water quality is good for the most station but poor in (T20, T21, and T24) stations because of the increased concentrations of some parameters especially (PH, Mg and Do2). The seasonal distribution of water quality is (excellent - good) in Autumn, (good) in Spring, its (good) in winter but (poor) in January for south Baghdad's stations, while in Summer is (good) over all stations but (poor) in June for central Baghdad's station.

Many of the women in worlds die because of breast cancer and early detection will save the lives of many women. A mammogram is a special medical image in the breast. The mammogram image contains artifacts and wedge. In this paper the mammogram image enhances by applied the Wiener filter to remove noise and apply Contrast Limited Adaptive Histogram Equitation (CLAHE) to improve a quality of mammogram image. Before applying them made number step to remove artifacts and wedge, background and pectoral muscle these know. The result indicted obtain mammogram image only breast profile, with a nice smooth, safe edge, and high quality. These results prove that effective and convenient assistance for medical diagnosis. Hence, the proposed method definitely can be considered for automated detection of abnormality like benign, malignant and micro calcifications.

Digital images have become an important tool of providing evidences for personal imageries; several Forensic techniques have been developed to preserve digital images from fraud. An attention should bring the researchers eyes on new techniques used currently in Anti-Forensics science progression and, to what extent the level of manipulation could sit beyond our expectation. On the other hand, Forensics Techniques have an extremely powerful advantage to stand against Anti-Forensics Techniques (AFTs), by determining the source and the authenticity of digital image's content, trace its processing history. Furthermore, by finding the location of forging and the coding fingerprints left by compressing process as an objective of this study. Recently, AFTs have gained a significant popularity to conceal the artifacts that left via compressed image. This paper provides blueprints of various AFTs based compressed image used to misguide forensics detectors.

Nature-inspired algorithms (NIAs) have gained a significant popularity in recent years to tackle hard real world problems and solve complex optimization functions whose actual solution does not exist. Many new algorithms have been developed which show their capabilities almost in every aspect, where rapid solutions are needed. A survey of the NIAs that are used to find the optimal digital image watermarking has been presented in this paper. Different paradigms have been considered, Genetic algorithm (GA), particle swarm optimization (PSO), differential evolution (DE), ant colony optimization (ACO), bee algorithm (BA), cat swarm optimization (CSO), firefly algorithm (FA) and cuckoo search algorithm (CS) that help to find the optimal digital image watermarking.

There have been many hidden communication techniques proposed in the last few years. The focus of this development was given to steganography to build such techniques. This paper presents a novel technique for Image steganography based on a frequency domain, where DCT is used to transform original image (cover image) blocks from spatial domain to frequency domain, and then Huffman encoding is performed on the secret messages/images before it is embedded in the frequency domain. The number of payload bits L is embedded into the DCT coefficient based on the DCT coefficient value of cover image in order to maximize the hiding capacity. It is observed that the proposed algorithm has better PSNR, Security and capacity compared to the existing algorithm.

With the quick arrival of high grades of acceptance, the QR Code technology is becoming more adaptable and usable every day by more and more people. Lately, the utilization of two dimensional QR codes for the powerful encoding of information with a big capacity becomes significantly growing; especially for encoding the identity. Because the identity is private information, it requires being further authenticated. This paper firstly works on analyzing the content of the QR Code and finding the unimportant locations inside it; Then, a combination of the digital watermarking concept and QR Code technology is used to substitute the founded unimportant locations by the watermark for the purpose of increasing the authenticity for the owner of the identity. The experimental results show that the inclusion of watermark bits inside the detected unimportant locations did not affect the process of decoding and retrieving information from the QR Code.