Any number that can be uniquely determined by a graph is called graph invariants. During the most recent twenty years' innumerable numerical graph invariants have been described and used for correlation analysis. In the fast and advanced environment of manufacturing of networks and other products which used different networks, no dependable assessment has been embraced to choose, how much these invariants are connected with a network graph or molecular graph. In this paper, it will talk about three distinct variations of bridge networks with great capability of expectation in the field of computer science, chemistry, physics, drug industry, informatics, and mathematics in setting with physical and synthetic constructions and networks, since K-Banhatti invariants are newly introduced and have various forecast characteristics for various variations of bridge graphs or networks. The review settled the topology of bridge graph/networks of three unique sorts with three types of K-Banhatti Indices. These concluded outcomes can be utilized for the modeling of interconnection networks of Personal computers (PC), networks like Local area network (LAN), Metropolitan area network (MAN) and Wide area network (WAN), the spine of internet and different networks/designs of PCs, power generation interconnection, bio-informatics and chemical structures.

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Software maintenance is the process of fixing, modifying, and improving software deliverables after they are delivered to the client. Clients can benefit from offshore software maintenance outsourcing (OSMO) in different ways, including time savings, cost savings, and improving the software quality and value. One of the hardest challenges for the OSMO vendor is to choose a suitable project among several clients' projects. The goal of the current study is to recommend a machine learning-based decision support system that OSMO vendors can utilize to forecast or assess the project of OSMO clients. The projects belong to OSMO vendors, having offices in developing countries while providing services to developed countries. In the current study, Extreme Learning Machine's (ELM's) variant called Deep Extreme Learning Machines (DELMs) is used. A novel dataset consisting of 195 projects data is proposed to train the model and to evaluate the overall efficiency of the proposed model. The proposed DELM's based model evaluations achieved 90.017% training accuracy having a value with 1.412 × 10 -3 Root Mean Square Error (RMSE) and 85.772% testing accuracy with 1.569 × 10 −3 RMSE with five DELMs hidden layers. The results express that the suggested model has gained a notable recognition rate in comparison to any previous studies. The current study also concludes DELMs as the most applicable and useful technique for OSMO client's project assessment.

Plethora of image encryption schemes exist in literature based on the construct of magic square for realizing the purpose of image obfuscation. This magic square carries out the scrambling project of the encryption. In these schemes, normally single and static magic square is implied. To render greater scrambling effects, this study proposes a novel image encryption scheme using all order-4 magic squares whose frequency reaches to the tune of 880. These magic squares have been dynamically selected to carry out the scrambling project. As the color image is input, it is broken into its gray scale red, green and blue components. These components are joined together to make a big gray scale image. Intertwining logistic map (ILM) has been used for the generation of random data. Besides, one more stream has been created through the arithmetic manipulation of the generated three streams. Streams generated by ILM has been used to realize the effects of confusion and diffusion. First and second streams out of the four streams randomly select the address from the big gray scale image to apply the randomly selected magic square by the third stream, in order to create the scrambling effects. The fourth and last stream of random numbers is used to create the diffusion effects in the scrambled image. Plaintext senstivity has been introduced by tempering the one initial value of the chaotic system through the usage of a characteristic of the given input color image. The experimentation and security analyses sections vividly demonstrate the strength, immunity from the diverse attacks and prospects for the real world application of the proposed image cipher. In particular, we got very promising stats of information entropy (7.9974) and computational time (0.9865 seconds). No doubt, they suggest the potential application of the proposed image cipher in some real world setting.