Wasit Journal of Computer and Mathematics Science

In recent years large number of emerging automated applications faces the need to have recognition abilities of persons using their own self biometrics, before they can access the applications services. Nowadays, Biometric recognition is used, it can be used as automatic identification or automatic verification of persons based on their physiological or behavioral characteristics. There are no perfect biometric measurements; each biometry has its advantages and limitations. Each biometry requires specific vital identity to answer the identification or verification question. The suitability of a particular biometry for a particular application depends on many factors. Hand geometry/shape is a very simple biometric technology that uses the measurements of human hand to verify the identity of the individuals. The measurements include the distance between certain mark points, shape and width of fingers and size of palm. The biometric systems that employing hand geometry become widely used since they have high public acceptance. This article aims to survey several articles found in literature about hand based biometric system, and to compare different methods of biometric recognition that based on hand geometry.

Predictive analytics and medical diagnostics are two of the most important fields of study that have
a lot of room for growth. Today, the COVID-19 virus has a huge impact, but it changes a lot. This virus has spread
across the world, and there is currently no vaccine for it. The number of cases in India now stands at more than 10,000,
and more than 300 people have died from it. Twenty people in the world have COVID. Neuronal network technology
has made big changes. The Generative Adversarial Network (GAN) is used to analyse pictures and multimedia data
in huge areas with great speed. Medical images from COVID-19 data sets will be looked at to see if they can predict
what will happen to patients. Medical images, such as X-rays and CT scans, are used to train the GANs, which build,
change, and analyse data sets and key points with advanced deep learning models. If GANs are used in the general
prediction study, they can help traditional neural networks outperform them in a lot of places. This study is meant
to help people better plan for mining and information exploration by combining work done on Benchmark data sets
with more advanced text.

The growing Internet access and easy access to it have resulted in a significant increase in e-content,
which, along with many benefits, has caused problems for users. Internet users simply cannot find the content they
need from this massive amount of data. Users are faced with a lot of suggestions for choosing goods, buying items,
selecting music and videos, and more. Advantage systems can be used to overcome these problems. Today, with the
spread of people’s use of cyberspace, such as web sites and social networks, and increasing the need for conscious and
clever selection of people, recommender systems has been extensively investigated. Although the neural network can
identify the connections between the inputs and outputs of a dataset, but in order to achieve the proper performance
of the neural network, a proper structure should be considered. We will use the mantle algorithm to determine this
structure. The mantle algorithm is a form of traditional genetic algorithm that uses local search to reduce the time to
achieve optimal response. Genetic algorithms are created to search across the search space, while the local search,
the neighborhood of the neighborhood, finds every response found by the genetic algorithm to find better answers.
This algorithm seeks to find the optimal values for the parameters of the neural network method, so optimal solutions
of the memetic algorithm is considered to be used to set parameters for the neural network method. The results of
this study show the desirable performance of the proposed approach in this study.

Computational grades have emerged as a new approach to solving large-scale problems in the fields of science, engineering, and commerce. The computing grid is a hardware and software infrastructure that provides affordable, reliable, comprehensive, and affordable access to the computational abilities of others. A computational grid is associated with a set of resources on a large scale. Computational grades have emerged as a new approach to solving large-scale problems in the fields of science, engineering, and commerce. The computing grid is a hardware and software infrastructure that provides affordable, reliable, comprehensive, and affordable access to the computational abilities of others. A computational grid is associated with a set of resources on a large scale. The purpose of this thesis is to provide a method that optimizes resource management and scheduling in at least one direction. The main focus of the research is on the time criterion, the deadline for doing things and receiving the response from the grid are parameters that can be examined. From a more general perspective, the aim of the research is to get the answer as quickly as possible from the calculation grid. The proposed algorithm improves the scheduling and resource management of the grid in the direction of improvement, and the structure and form of this problem have not yet been resolved. The proposed solution has been considered hypothesis and removed some of the definitions of grid scheduling, such as cost, quality of service, architectures, and others, but ultimately heed it and timed the grid in some ways.

As social communications become widespread, social networks are expanding day by day, and the number of members is increasing. In this regard, one of the most important issues on social networks is the prediction of the link or the friend's suggestion, which is usually done using similarities among users. In the meantime, clustering methods are very popular, but because of the high convergence velocity dimensions, clustering methods are usually low. In this research, using spectral clustering and diminishing dimensions, reducing the amount of information, reduces clustering time and reduces computational complexity and memory. In this regard, the spectroscopic clustering method, using a balanced index, determines the number of optimal clusters, and then performs clustering on the normal values ​​of the normalized Laplace matrix. First, the clusters are divided into two parts and computed for each cluster of the harmonic distribution index. Each cluster whose index value for it is greater than 1 will be redistributed to two other clusters, and this will continue until the cluster has an index of less than 1. Finally, the similarity between the users within the cluster and between the clusters is calculated and the most similar people are introduced together. The best results for the Opinions, Google+ and Twitter data sets are 95.95, 86.44 and 95.45, respectively. The computational results of the proposed method and comparison with previous valid methods showed the superiority of the proposed approach.

With the development of information systems, data has become one of the most important sources of organizations. Therefore, methods and techniques are needed to efficiently access data, share data, extract data from data, and use this information. By creating and expanding the Web and a significant increase in the volume of information and web development, the need for methods and techniques that can provide data efficiently and extract information from them is felt more than ever. Web mining is one of the areas of research that uses data mining techniques to automatically discover information from web services and documents. In fact, Web mining is a process of discovery of unknown and useful information from web data. Web mining methods are categorized into three types of web content exploration, exploration of Web structures, and exploration of the use of the Web, based on what type of data they are exploring. This research investigates the relationship between the idea of mining and other research fields and examines some of the previous methods used. Finally, a method is proposed based on two decision tree and machine model algorithms that will improve the results of the idea of mining. The results of the simulation of the proposed method were evaluated and compared with the previous methods. The results show that the proposed method has higher accuracy and speed