Cocomo Model For Library Management System
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1.Organic: A development project can be treated of the organic type, if the project deals with developing a well-understood application program, the size of the development team is reasonably small, and the team members are experienced in developing similar methods of projects. Examples of this type of projects are simple business systems, simple inventory management systems, and data processing systems.
2. Semidetached: A development project can be treated with semidetached type if the development consists of a mixture of experienced and inexperienced staff. Team members may have finite experience in related systems but may be unfamiliar with some aspects of the order being developed. Example of Semidetached system includes developing a new operating system (OS), a Database Management System (DBMS), and complex inventory management system.
2. Intermediate Model: The basic Cocomo model considers that the effort is only a function of the number of lines of code and some constants calculated according to the various software systems. The intermediate COCOMO model recognizes these facts and refines the initial estimates obtained through the basic COCOMO model by using a set of 15 cost drivers based on various attributes of software engineering.
3. Detailed COCOMO Model:Detailed COCOMO incorporates all qualities of the standard version with an assessment of the cost driver?s effect on each method of the software engineering process. The detailed model uses various effort multipliers for each cost driver property. In detailed cocomo, the whole software is differentiated into multiple modules, and then we apply COCOMO in various modules to estimate effort and then sum the effort.
There are various types of models of cocomo that have been proposed to check the correctness of the software products and to calculate the cost estimations at the different levels. These levels also depend on the strategies to develop accurate software products. these strategies are as follows:
The COCOMO model does not support agile methodology because methods such as COCOMO and Function Point Analysis are based on construction characteristics of the system that has to be developed. Story points are a relative measurement, created by the team itself and not related to objective criteria.
Librarian cans add/delete/edit book and search books from the database. Members can Borrow reserveand return the book. They can search books also. Members would be fined 0.10$ on Overdue and theyhave to pay entire amount if they have lost the book. Members are of two kinds. VIP members canrequest book at home and they can also order Books which are not present in library, the library alsohas CD, DVDs and multimedia items which only VIP members can have access to them. Non VIPmembers can register a form and apply for VIP Membership by paying some extra amount, Searchfunction gives the information of books branch wise. User can search books by title and Several othercriteria, The feedback system allows users to give feedback and comments about the book which is returned.
As the size and capacity of the institute is increasing with the time, it has been proposed to develop a Library Information System (LIS) for the benefit of students and employees of the institute. LIS will enable the members to borrow a book (or return it) with ease while sitting at his desk/chamber. The system also enables a member to extend the date of his borrowing if no other booking for that particular book has been made. For the library staff, this system aids them to easily handle day-to-day book transactions. The librarian, who has administrative privileges and complete control over the system, can enter a new record into the system when a new book has been purchased, or remove a record in case any book is taken off the shelf. Any non-member is free to use this system to browse/search books online. However, issuing or returning books is restricted to valid users (members) of LIS only.
The SE VLabs Institute has a IT management team of it's own. This team has been given the task to execute the Library Information System project.The team consists of a few experts from industry, and a batch of highly qualified engineers experienced with design and implementation ofinformation systems. It is planned that the current project will be undertaken by a small team consisting of one expert and few engineers.Actual team composition would be determined in a later stage.
ML is a method that trains computing systems to improve itself by learning from previous data available. ML programs work by constructing a prediction model from a set of previously available training data, and this step is followed by the data-driven predictions [37].
In the proposed system, we used the deep learning model. Deep learning is a type of artificial neural network architecture (ANN). ANN represents a significant early breakthrough in the field of artificial intelligence. The ANN model is exceptionally dynamic in solving complex problems in various machine learning application areas [11] in the real world, such as health, agriculture, finance, and automobile industry. At the moment, ANN in single, hybrid, or ensemble form is still an active research area [12], and its role in autonomous vehicles is expected to receive more attention in the future. ANN, on the other hand, is trained using backpropagation algorithms and has some limitations, such as falling into local minima and overfitting training data. As a result, many researchers advocate using nature-inspired algorithms to train ANNs to avoid challenges. For example, GA [24], ABC [34], CSA [36], and particle swarm optimization (PSO) [41] were used to train ANN and were found to be superior to the back propagation algorithm in terms of avoiding the local minima problem.
As the results are shown in Table 4, HACO-BA performs better among the other meta-heuristics algorithms. So, we applied the hybrid algorithmic approach to optimize the deep learning training process. HACO-BA is used to assign the best values of initial weights to the deep neural network. The proposed deep neural model is compared with [42, 46, 47] in terms of accuracy and time required for training. We have to find out the mean RE (MRE), mean magnitude of RE (MMRE), mean balanced residual error (MBRE), and percentage of prediction (PRED) and then compared it to the NN model. The block diagram of the proposed systems is shown in Figure7.
The model is built using the TensorFlow. TensorFlow is an open-source software or a library for building and deploying machine learning methods. The Python programming language and other different libraries are also used to build the models.
In the future, we will improve the estimation models by experimenting with new methods and incorporating cloud computing for estimating purposes in order to obtain more comprehensive results in the future. Researchers and practitioners in [59] and [61] used the Strawberry Plant heuristics approach for software cost estimation and for energy management. In [60, 62], Grey Wolf and Bacterial Foraging approaches are used in smart grids for energy management and heterogeneous generalized signcryption to maintain the data integrity for estimation. 2b1af7f3a8