SEARCH ENGINE OPTIMIZATION: A REVIEW

The Search Engine has a critical role in presenting the correct pages to the user because of the availability of a huge number of websites, Search Engines such as Google use the Page Ranking Algorithm to rate web pages according to the nature of their content and their existence on the world wide web. SEO can be characterized as methodology used to elevate site keeping in mind the end goal to have a high rank i.e., top outcome. In this paper the authors present the most search engine optimization like (Google, Bing, MSN, Yahoo, etc.), and compare by the performance of the search engine optimization. The authors also present the benefits, limitation, challenges, and the search engine optimization application in business

Falcon Optimization Algorithm for Bayesian Networks Structure Learning

In machine-learning, one of the useful scientific models for producing the structure of knowledge is Bayesian network, which can draw probabilistic dependency relationships between variables. The score and search is a method used for learning the structure of a Bayesian network. The authors apply the Falcon Optimization Algorithm (FOA) as a new approach to learning the structure of Bayesian networks. This paper uses the Reversing, Deleting, Moving and Inserting operations to adopt the FOA for approaching the optimal solution of Bayesian network structure. Essentially, the falcon prey search strategy is used in the FOA algorithm. The result of the proposed technique is compared with Pigeon Inspired optimization, Greedy Search, and Simulated Annealing using the BDeu score function. The authors have also examined the performances of the confusion matrix of these techniques utilizing several benchmark data sets. As shown by the evaluations, the proposed method has more reliable performance than the other algorithms including producing better scores and accuracy values

An evaluation of CNN and ANN in prediction weather forecasting: A review

Artificial intelligence through deep neural networks is now widely used in a variety of applications that have profoundly altered human livelihoods in a variety of ways.  People's daily lives have become much more convenient. Image recognition, smart recommendations, self-driving vehicles, voice translation, and a slew of other neural network innovations have had a lot of success in their respective fields. The authors present the ANN applied in weather forecasting. The prediction technique relies solely upon learning previous input values from intervals in order to forecast future values. And also, Convolutional Neural Networks (CNNs) are a form of deep learning technique that can help classify, recognize, and predict trends in climate change and environmental data. However, due to the inherent difficulties of such results, which are often independently identified, non-stationary, and unstable CNN algorithms should be built and tested with each dataset and system separately. On the other hand, to eradicate error and provides us with data that is virtually identical to the real value we need Artificial Neural Networks (ANN) algorithms or benefit from it. The presented CNN model's forecasting efficiency was compared to some state-of-the-art ANN algorithms. The analysis shows that weather prediction applications become more efficient when using ANN algorithms because it is really easy to put into practice

Analysis of Expert System for Early Diagnosis of Disorders During Pregnancy Using the Forward Chaining Method

oai:ojs.ijair.id:article/203Nowadays technological developments are increasingly having a positive influence on the development of human life, including in the health sector. One of them is an expert system that can transfer an expert's knowledge into a computer application to simplify and speed up the diagnosis of a disorder or disease in humans. The purpose of this final project is to design an application to diagnose diseases that occur during pregnancy which is caused by the existence of these pregnancies to simplify and speed up the diagnosis of diseases experienced by pregnant women. This study uses the forward chaining method. By involving experts in this expert system analysis according to current needs. Users are given easy access to information on several types of pregnancy disorders and their symptoms, as well as consultation through several questions that the user must answer to find out the results of the diagnosis. While experts are facilitated in system management, both the process of adding, updating and, deleting data

Failure to thrive and it's Risk factors among children under 5 years old in Al-Batool Teaching Hospital in Baquba city

Background: Failure to thrive (FTT) indicates insufficient weight gain or absence of an appropriate physical growth,it is a sign not a disease ,commonly seen by the primary health care physicians. It might be due to organic or non-organic causes and it is usually of a multifactorial etiology. Objective: To detect the prevalence of failure to thrive and risk factors in pediatric age group - under 5 year old in Al-Batool teaching hospital in Baquba city through 2020-2021. Patients and Methods: This study was across sectional study that took place In Al-Batool Teaching Hospital for maternity and children,in Baquba Distract-Diyala province, Iraq.during the period from 1st of February 2020-31st of July 2021. Throughout this study Two hundred and fifty (250) child were randomly  selected under -5years old . Informations were collected from the patients files including age,current  weight,birth weight,type of feeding,weaning,history of prematurity, history of  chronic diseases and socioeconomic conditions. Results: Showed that children below 12 months were (45.2%), males (51.6%)and females (48.4%). male:female ratio was 1:1 ,majority of children were from Bohris . medium socioeconomic level was the largest with (61.6%). regarding mother education,mothers who have secondary education  represent the largest level with (45.6%),children with bottle feeding were the largest with (45.0%),premature birth represent (2.0%),children with chronic  diseases (14.0%),children with UTI (32.0%). Children with (FTT)were(23.2%),while (76.8%) were of normal growth. Regarding age groups most of the affected children were below 24 months  of age with (36.5%) (P value was 0.0001) which is considered to be a significant.  Males with (FTT) (24.8%) ,while Females (21.5%). (Pvaluewas0.534) which is  considered to be of no significance. Regarding children with (FTT) and low socioeconomic level it was (68.8%)  (p value was 0.0001)which is considered to be a significant. Children with  (FTT) and illutrate mother was(60.0%) (p value was 0.0001)which is considered  to be a significant. children with breastfeeding showed no (FTT) ,while those with bottle feeding was (37.0%) (P value was 0.0001 ) which is considered to be a significant. children with (FTT) and UTI (58.8%)(P value was 0.0001)which is considered to be a significant. Conclusion: This study concluded that failure to thrive is strongly correlated with poverty, mothers education ,chronic diseases, premature birth and type of feeding.failure to thrive showed no correlation with gender

Search engine optimization: a review

The Search Engine has a critical role in presenting the correct pages to the user because of the availability of a huge number of websites, Search Engines such as Google use the Page Ranking Algorithm to rate web pages according to the nature of their content and their existence on the world wide web. SEO can be characterized as methodology used to elevate site keeping in mind the end goal to have a high rank i.e., top outcome. In this paper the authors present the most search engine optimization like (Google, Bing, MSN, Yahoo, etc.), and compare by the performance of the search engine optimization. The authors also present the benefits, limitation, challenges, and the search engine optimization application in business

Hybrid Data Mining with the Combination of K-Means Algorithm and C4.5 to Predict Student Achievement

Getting academic achievement is the dream of every student who studies at higher education, especially undergraduate level. Undergraduate students aspire to the highest achievement (champion) at the last achievement of their studies. However, students cannot predict whether these students with the habits that have been done and the current conditions will make them excel or not. Apart from that, of course, students also want to know what factors and conditions influence the achievement the most. The objective to be achieved in this research is how to predict which number of students among them are predicted to excel (champion) at the end of the semester with a combination of the K-Means and C4.5 methods. Besides, the purpose of this study reveals how the K-Means algorithm performs data clustering of student data who will excel or not and how the C4.5 algorithm predicts students who have been grouped. Data processing in this study uses the Rapid Miner software version 9.7.002. The result of this research is that it is easier to group data in numerical form than data in polynomial form. Other results in this study were that out of 100 students, 27 students (27%) were predicted to excel (champions) and 73 (73%) did not achieve (not champions)

A Comparative Evaluation of Bayesian Networks Structure Learning Using Falcon Optimization Algorithm

Bayesian networks are analytical models that may represent probabilistic dependent connections among variables and are useful in machine learning for generating knowledge structure. Due to the vastness of the solution space, learning Bayesian network (BN) structures from data is an NP-hard problem. The score and search technique is one Bayesian Network structure learning strategy. In Bayesian network structure learning the Falcon Optimization Algorithm (FOA) is presented and evaluated by the authors. Inserting, Reversing, Moving, and Deleting, are used in the method to create the FOA for finding the best structural solution. The FOA algorithm is based on the falcon's searching technique during drought conditions. The suggested technique is compared to the score metric function of Pigeon Inspired search algorithm, Greedy Search, and Antlion optimization search algorithm. The performance of these techniques in terms of confusion matrices was further evaluated by the authors using a variety of benchmark data sets. The Falcon optimization algorithm outperforms the previous algorithms and generates higher scores and accuracy values, as evidenced by the results of our experiments

A comparative evaluation for Detection Brain Tumor in MRI Image using Machine learning algorithms

In medical imaging, automated defect identification of defects has taken on a prominent position. Unaided prediction of tumor (brain) recognition in magnetic resonance imaging process (MRI) is vital for patient preparation. With traditional methods of identifying z is designed to reduce the burden on radiologists. One of the problems with MRI brain tumor diagnosis is the size and variation of their molecular structures. This article uses deep learning techniques (Artificial neural network ANN, Naive Bayes NB, Multi-layer Perceptron MLP ) to discover brain tumors in the MRI scans. First, the brain MRI images are run through the preprocessing steps to remove texture features. Next, these features are used to train a machine learning algorithm