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Interval graphs, their importance over the years can be seen in the increasing number of researchers trying to explore the field. The concept of the domination is a rapidly developing area in Graph Theory. In this paper, we tried to present some relations on the sum of degree of the vertices in dominating set and complementary dominating set using Euclidean division algorithm of divisor 5 for interval graph G.

Interval graph, Domination number, complementary dominating set, complementary domination number.

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A. Sudhakaraiah, A. Venkateswarrao, T. Venkateswarlu, K. Narayana, "Sum of The Degrees of Dominating Set And Complementary Dominating Set Using Eulidean Division Algorithm of Divisor 5 For Interval Graph G," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.189-201, 2019.

The Internet of things (IoT) is becoming apparent of the Internet which can connect nearly all environment devices, embedded with Internet Protocol (IP) and they are remotely monitored and controlled. Due to the huge growth of IoT devices, IoT networks are vulnerable to various security attacks. The implementation of efficient privacy and security protocols in IoT networks is extremely needed to ensure authentication, confidentiality, integrity and access control, among others. In this paper, I firstly present an in-depth meaning of the Internet of Things (IoT) and then propose a general framework for software-defined Internet of Things (SD-IoT) based on the SDx paradigm, software-defined everything (SDx) paradigm provides a way to strengthen the security of the IoT devices, as well as determines the impact of those new security and privacy issue and possible solutions. The proposed framework is implemented to strengthen the security of the IoT with heterogeneous and vulnerable devices. IoT, Software-defined Internet of Things (SDIoT), Security, Privacy.

IoT, Software-defined Internet of Things (SDIoT), Security, Privacy

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[3] Krishnan, Prabhakar & S. Najeem, Jisha & Achuthan, Krishnashree. (2018)."SDN Framework for Securing IoT Networks” 10.1007/978-3-319-73423-1_11.



[4] Fadele, Alaba & Othman, Mazliza & Abaker Targio Hashema, Ibrahim & Alotaibi, Faiz. (2017). “Internet of things Security: A Survey. Journal of Network and Computer Applications”. 88.
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[10] Yaser Jararweh1 ,Mahmoud Al-Ayyoub1, Ala’ Darabseh1 “SDIoT: a software defined based internet of things framework”, Springer-Verlag Berlin Heidelberg, pp.1-9, 2015.
[11] Krishnan, Prabhakar & S. Najeem, Jisha & Achuthan, Krishnashree. (2018)."SDN Framework for Securing IoT Networks” 10.1007/978-3-319-73423-1_11.
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Vishal S. Patil, Suraj S. Bhute, Gauri J. Chauhan, Aparna P. Morey, Tejaswini S. Borkar, "SD-IOT: Security and Privacy," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.202-207, 2019.

The Internet of Nano Things taking IoT to next level. Nanotechnology is science technology in which size of devices are much smaller which contributed greatly in computing and electronics, leading to faster ,smaller and more profitable system that can be use for communication and storing of large data. It is also use of creating devices, machines in nano size by integrating molecules or atoms. Nanotechnology is a building block of Internet of Nano Things(IoNT) which include basic unit called as Nano machines which perform simple task like sensing and actuating. The interconnection of nano devices with existing internet called as “Internet of Nano Things”. Nanotechnology has provide new solution to benefit society in different sector like information technology, medicines, transportation, military field , as well as industrial purpose. In this paper we are showing the architecture of Nano machines, network architecture ,application field, challenges in Internet of Nano Things (IoNT) and help to researchers to find solution for challenges and increase the use of Internet of nano Things (IoNT) in future in different field.

Internet of Things (IoT), Internet of Nano Things(IoNT), Nanotechnology, Nano Machines , Nano Sensor ,Nano communication.

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Vishal S.Patil, Gauri J. Chauhan, Aparna P. Morey, Suraj S. Bhute, Tejaswini S. Borkar, "A Review Paper on Internet of Nano Things," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.208-211, 2019.

The solar power monitoring system is used the Internet of Things for the purpose, to overcome the drawbacks of previous solar systems. An IoT is a joint network of the connected devices together and shares the data about how they are used in the environment in which they are operated. The solar power monitoring system is used for generating the electricity by using the energy of sunlight. This system is uses the Arduino Uno for enhancement of the solar systems. This solar power monitoring system uses the Arduino Uno. The Arduino Uno is microcontroller board, this microcontroller used the ATmega328p. ATmega328p is also a microcontroller chip which is developed by Atmel. By using Arduino Uno the solar panel is capable of moving in the direction where sunlight is moves , this is the additional feature of this solar system. This paper shows the working, architecture and connections of the solar power monitoring system using an IoT.

Internet of Things(IoT), Arduino Uno, ATmega328p, solar panel

[1] Manish Katyarmal1, Suyash Walkunde2, Arvind Sakhare3, Mrs.U.S.Rawandale4 “Solar power monitoring system using IoT”, International Journal of Engineering and Technology, Vol.05, Issue. 03, pp.1-2,2018.[Last accessed : 16-08-2019].
[2] R.L.R. Lokesh Babu1, D Rambabu2, A. Rajesh Naidu3, R. D. Prasad4, P. Gopi Krishna5“IoT Enabled Solar Power Monitoring System ”, International Journal of Engineering and Technology, Vol.07, Issue.3.12,pp.526-530,2018[Last accessed :15-08-2019].
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Vishal S. Patil, Aparna P. Morey, Gauri J. Chauhan, Suraj S. Bhute, Tejaswini S. Borkar, "A Review Paper on Solar Power Monitoring System using an IoT," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.212-215, 2019.

With the advancement of technology and growing business needs it is vital that the services offered to customers across various applications need to be proactive and not reactive. This work extends the existing cloud framework available for smart homes that offers extended potential business opportunities to service providers and optimistic smart services to end users. Based on the defined framework cluster analysis is done to derive the customer segmentation that facilitates smart home service providers to offer efficient services and to improve the product upselling. The predicted energy consumption is derived using linear regression, for the end users to be energy efficient. Here a deeper analysis based on the data generated from devices and smart home applications is carried out to offer proactive consumer services and to have economical energy consumption.

Smart Serivces, Machine Learning, Smart Homes, Internet of Things

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[16] Eastern Power Distribution Company of AP Ltd.
https://www.apeasternpower.com/EPDCL_Home.portal;jsessionid=k23ybnwM1pFsk4GZBQcQTLZmJnypTJ5x1pnp8PhZz20ySkr4LfYn!161743796?_nfpb=true&_pageLabel=EPDCL_Home_portal_page_97

K. Suresh, PVGD. Prasad Reddy, P. Pushkal, "Extending Business Opportunities and Smart Services using Machine Learning," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.216-220, 2019.

With rapid advancement in Technology and its demand in industry rapidly forcing Universities to take help of course content writers or open education resources to make changes in the existing curriculum, that must fulfill requirements of industries which they’re looking from fresh graduates. Even though development of new courses or enhancing the quality of existing courses by adding topics of more demanded technologies is a time consuming job yet as a result these time variant practices makes candidates job ready. In reality there are huge innovations in our lives based on engineering technology, but university curriculum not keeping pace. In this particular research article thorough study about bachelor level Software Development program discussed and suitable comparison with respect to existing program provided.

Course Content Writers (CCW), Communication Skills (CS), Distance Education (DE), Faculty Development Program (FDP), Open Education Resources (OER), Open Sources, Software Development Program (SD), Self-Learning-Material (SLM), Video Conferencing (VC)

[1]. Emory W., ‘New Course Development in a Distance Collaborative Environment’, 33rd ASEE/IEEE Frontiers in Education Conference, Nov 5-8, 2003, Boulder, Colorado.
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Arvind K. Sharma, Gunjan Sethi, Martin Radley, "Challenges in Acquisition of New Courses in Education, Bachelor of Technology in SD VS Traditional CSE Program: A Case Study of M.M. Deemed to Be University," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.221-226, 2019.

Due to the rapidly increasing use of mobile phones, it is essential to provide a secure solution for mobile phones; with the evolution of mobile commerce, it is obvious to provide a secure and efficient solution for the mobile environment. Day by day usages of mobile devices is huge and consumers are getting familiar with the various purposes of devices such as mobile banking. This paper focuses and describes various types of mobile banking methods and their possible security solutions. In the first part mobile banking solutions using contactless technology – Near Field Communication (NFC) is discussed with its limitations in terms of security. In addition, we also address the security weaknesses of Wireless Application Protocol (WAP) and Wireless Transport Layer Security (WTLS). To overcome the above mention weaknesses, in the second part different solutions of mobile banking with Public Key Infrastructure such as MPKI, WPKI, ECC, LPKI are discussed with its limitations and possible solutions to overcome the limitations.

NFC, TSM, WAP, PKI, GSM, GPRS, SIM, SMS, Mobile PKI, Wireless PKI, Lightweight PKI

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Mubina Malik, Shreya Banker, "Comparative Study and evolution of Mobile Banking Security Solutions and Comparision between Various Solutions," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.227-232, 2019.

This paper surveys the state of the art techniques which have been reviewed to develop the overall classification meth- odology of this research work. The feature selection methods, traditional classification algorithms followed by a brief description of theoretical works on data mining are summarized. The major classification approaches and the techniques which is used for improving classification performance are analyzed. In addition, some important issues affecting classification performance are discussed. In this paper we have gone through the existing work in the area of classification which will allow us to have a fair evaluation of the progress made in the field of Classification.

Machine Learning, Feature Selection methods, Classification

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M. Balasaraswathi, A. Uthiramoorthy, "A Survey of Classification Methods and Techniques for Improving Classification Performance," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.233-240, 2019.

This paper explores software development through early prediction of planning phase . It summarizes a variety of techniques for software planning prediction in the domain of software engineering. The objective of this research is to apply the various machine learning approaches, such as Case-Based Reasoning and Fuzzy logic, to predict software planning. The system predicts the planning phase activity after accepting the values of certain parameters of the software. This paper advocates the use of case-based reasoning (i.e., CBR) to build a software development prediction system with the help of human experts. The prediction is based on analogy. We have used different similarity measures to find the best method that increases reliability. It can be readily deployed on any configuration without affecting its performance.

Software Engineeering,SDLC Model,Machine Learning, CBR

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Madhup Kumar, Anuradha Sharma, "Improvisation of SDLC Model Using Machine Learning Technique (CBR) For Development of Software," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.241-246, 2019.

Credit fraud is a broad term associated with theft or fraudulent transactions that involve the usage of a credit card. The fraud detection systems today are only capable of preventing one-twelfth of one percent of all transactions processed, which still results in huge losses. To the human eye, fraudulent transactions are indistinguishable from real ones. However, there are underlying patterns common to these transactions that can be recognized by machine learning algorithms. In this paper, we have trained supervised learning models on a dataset containing more than 280,000 transactions. We go on to evaluate the performance of each of these models on the dataset in terms of accuracy and precision and compare them with each other. With this, we show that the Random Forest model shows promising results for identifying credit fraud when trained on a labelled dataset.

Machine Learning, Supervised Learning, Fraud Detection, Random Forest, Regression, Classifier

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Advait Maduskar, Aniket Ladukar, Shubhankar Gore, "Supervised Learning Techniques for Identifying Credit Fraud," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.247-250, 2019.