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Cloud computing mainly does not focus on local resource instead; it uses shared computing resources applications or resources. It has emerged as a new type of computing for accessing present network, managing computer resources and managing distributed computing across the network in order to achieve high degree of precision and reliability various challenges needs to be addressed. One of the challenges in cloud computing is load balancing. Load balancing is important due to the fact that it allows achieving balance in the load by distributing it across the system to all its nodes. Cloud environment allows various ways to achieve load balancing. This includes managing the load on CPU, network load and the load capacity of storage. The greatest impact of balancing the load in cloud computing environment is that it has higher satisfaction of the users as well as it utilizes the resources efficiently. Proper load balancing support substantial improvement of the system, building a fault tolerant system by creating backup and increase flexibility of the system so that it adapts the modification. In cloud computing, there are various algorithms to achieve load balancing and these algorithms behave differently with its some advantages and disadvantages. In this paper we present an Modified Ant Honey Bee based optimization algorithm (MAHO) to achieve load balancing. The results are analyzed with existing load balancing algorithm based on make span metrics

Cloud computing; Load balancing; Static Load balancing; Dynamic Load balancing; Algorithms; Load balancer; Load balancing metrics

[1] M. Armbrust, A. Fox, R. Griffith, A. D. Joseph, R. Katz, A.Konwinski, G. Lee, D. Patterson, A. Rabkin, I. Stoica, and M. Zaharia, ?Above the Clouds: A Berkeley View of Cloud Computing?, EECS Department, University of California, Berkeley, Technical Report No., UCB/EECS-2009-28, pages 1-23, February 2009.
[2] Wang, S.C., Yan, K.Q., Liao, W.P. and Wang, S.S., 2010, July. Towards a load balancing in a three-level cloud computing network. In 2010 3rd international conference on computer science and information technology (Vol. 1, pp. 108-113). IEEE.
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[8] Ishfaq Ahmad, Arif Ghafoor, ?Semi-Distributed Load Balancing for Massively Parallel Multicomputer Systems?, 1991.
[9] David Escalnte and Andrew J. Korty, ?Cloud Services: Policy and Assessment?, EDUCAUSE Review, Vol. 46, July/August 2011.
[10] Parin. V. Patel, Hitesh. D. Patel, Pinal. J. Patel, ?A Survey on Load Balancing in Cloud Computing? IJERT, Vol. 1, Issue 9, November 2012.
[11] Ali M Alakeel, ?A Guide to Dynamic Load Balancing in Distributed Computer Systems?, International Journal of Computer Science and Network Security, Vol. 10 No. 6, June 2010.
[12] Ram Prasad Padhey, P. Goutam Prasad Rao, ?Load Balancing in Cloud Computing Systems?, Department of Computer Science and Engineering, National Institute of Technology, May 2011.
[13] Abhijit A Rajguru, S.S. Apte, ?A Comparative Performance Analysis of Load Balancing Algorithms In Distributed Systems Using Qualitative Parameters?, International Journal of Recent Technology and Engineering, Vol. 1, Issue 3, August 2012
[14] Rajani Sajjan,? Load Balancing and its Algorithms in Cloud Computing: A Survey?, JCSE International Journal of Computer Sciences and Engineering, Volume-5, January 2017.
[15] Aarti Vig, Rajendra Singh Kushwah and Shivpratap Singh Kushwah, ?An Efficient distributed Approach for Load Balancing in Cloud Computing?, 2015 International Conference on Computational Intelligence and Communication Networks.
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[18] Shridhar G.Domanal and G.Ram Mohana Reddy,?Load Balancing in Cloud Computing Using Modified Throttled Algorithm ?, 2013 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM).
[19] SuchitaKhare, Asst. Prof. Shatendra Dubey, ?An Efficient Load Balancing in Public Cloud using Priority based SJF Scheduling?, International Journal of Scientific & Engineering Research, Volume 6, Issue 4, April-2015 1834
[20] Medhat A. Tawfeek, Ashraf E1-SiSi,Arnabi E.KeshK,Fawzy A.Torkey,?Cloud Task Scheduling Based on Ant Colony Optimization?.978-1-4799-0080-0/13@2013 IEEE.
[21] Nishant, K. P. Sharma, V. Krishna, C. Gupta, KP. Singh, N. Nitin and R. Rastogi, "Load Balancing of Nodes in Cloud Using Ant Colony Optimization." In proc. 14th International Conference on Computer Modelling and Simulation (UKSim), IEEE, pp: 3-8, March 2012.
[22] Dharmesh Kashyap, Jaydeep Viradiya, ?A Survey of Various Load Balancing Algorithms In Cloud Computing?, International Journal of Scientific & Technology Research, Vol. 3, Issue 11, November 2014
[23] Zhang Z. and Zhang X. ?A Load Balancing Mechanism Based on Ant Colony and Complex Network Theory in Open Cloud Computing Federation?, 2nd International Conference on Industrial Mechatronics and Automation, 240-243, 2010
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[26] Sushil Kumar, Deepak Singh Rana and Sushil Chandra Dimri, ?Fault Tolerance and Load Balancing algorithm in Cloud Computing: A survey?, International Journal of Advanced Research in Computer and Communication Engineering, July 2015.
[27] Pandey, Suraj, Linlin Wu, SiddeswaraMayura Guru, and Rajkumar Buyya. "A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments." In Advanced Information Networking and Applications (AINA), 2010 24th IEEE International Conference on, pp. 400-407. IEEE, 2010.
[28] Rodrigo N. Calheiros, Rajiv Ranjan, Anton Beloglazov, Cesar A. F. De Rose and Rajkumar Buyya, ?CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms?, Software practice and experience, August, 2010.
[29] Rathore, M., Rai, S. and Saluja, N.,. ?Load balancing of virtual machine using honey bee galvanizing algorithm in cloud.? IJCSIT, 6(4), pp.4128-4132. 2015

Tony Bayan, Priyanka Sarma, "MAHO: Modified Ant Honey Bee Optimization Algorithm for Load Balancing in Cloud Environment," International Journal of Computer Sciences and Engineering, Vol.8, Issue.7, pp.122-131, 2020.

Satisfiability probability(SAT) solvers are algorithms that take well-formed formulas and return true if satisfiable and false otherwise. They are significantly useful in various tasks in-circuit verification and have prominent importance in various fields such as automated verification, Electronic Design Automation (EDA) which include formal checking of equivalence, artificial intelligence planning, and so on. The input to these SAT solvers is generally accepted in DIMAC CNF (conjunctive normal form), which is a textual representation of the equation in CNF form. Most verification tasks are commenced from a description of problem instances at the logic circuit level, deducing DIMAC CNF format from the given logic circuit could be tedious and time-consuming if done manually as the conversion includes complex formulas. This paper aims to present an effective code that could easily convert any given logic circuit to DIMAC CNF format just by selecting gate and proper variable names to operate upon according to the heuristic presented by a logic circuit. The output thus formed could be directly given as input to any SAT solver that uses DIMAC format readily, abating time and conversion efforts. The final part of the paper also demonstrates an example for the acyclic circuit and compares the result of generated output to manually derived formula for the same circuit on various parameters

DIMAC, CNF, SAT solvers, input generator

[1] C Gomes, H Kautz, A Sabharwal, B Selman. "Satisfiability Solvers"2008.https://www.cs.cornell.edu/gomes/pdf/2008_gomes_knowledge_satisfiability.pdf
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[3] F Zhaohui, Y Yinlei, M Sharad ." Considering Circuit Observability Don?t Care in CNF Satisfiability". DATE?05, Mar 2005, Munich, Germany. pp.1108-1113. ?hal-00181279?
[4] R Klimek, K Grobler-Dębska, E Kucharska . "System for automatic generation of logical formulas". MATEC Web of Conferences.,pp 252:03005,2019;.
[5] S Gopalakrishnan, V Durairaj, P Kalla. "Integrating CNF and BDD based SAT solvers". Eighth IEEE International High-Level Design Validation and Test Workshop; 2003.
[6] A Smith ." Diagnosis of combinational logic circuits using Boolean satisfiability". Ottawa: Library and Archives Canada = Bibliothèque et Archives Canada; 2005.
[7] N E?n, N S?rensson ." Translating Pseudo-Boolean Constraints into SAT". Journal on Satisfiability, Boolean Modeling, and Computation. pp 2(1-4):1-26.,2006.
[8] Ifat Jahangir, Anindya Das, Masud Hasan, ?Facile Algebric Representation of a Novel Quaternary Logic?.International Journal of Computer Sciences and Engineering Vol.4 Issue 5,pp 1-15, 2016.

Vaishnavi Thorve, "Unsheathing input to SAT solver from logic circuit in DIMAC format," International Journal of Computer Sciences and Engineering, Vol.8, Issue.7, pp.132-135, 2020.

Now days we can observe that so many advancements are happening in the technologies and new Generations using various programs, software and applications are progressively complicated and needs lower latency requirements. So that clients would be able to get to these applications and information from anyplace at any time without any delay. Cloud computing is one of these technologies designed to deliver computer resources platforms and other various computing facilities. In this on ?demand services are provided to the users through the Internet. But network dependency, data security issues, lower bandwidth, variation of cost, location-awareness are some limitations that we are facing with cloud computing. To overcome inherent problems of this popular computing paradigm fog computing has come up as a promising framework to provide flexible and adaptable resources adjacent to network. Fog computing is generally an idea of a distributed network that brings the cloud services that are communication, computation and storage near upon edge devices and users. It is used to improve efficiency and Real-time processing of data. The major objective of fog computing is to shorten the particular data that should be delivered to the cloud for processing, study, storage and computational purpose. And at the present moment, fog computing appears to be the most alternate available that can handle all client requirements

fog computing, cloud services, Real-time processing, distributed network

[1] Jabril Abdelaziza, Mehdi Addab *, Hamid Mcheick, ?An architectural model of fog computing?.
[2] Ranesh Kumar Naha, Saurabh Garg , and Andrew Chan,? Fog Computing Architecture: Survey and Challenges?.
[3] Naha RK, Garg S, Georgakopoulos D, ?Fog Computing: A Survey of Trends, Its Architectures and Research Directions requirements?.
[4] Bishal Ranjan Swain1* , Jeevan Jyoti Sahoo2 , Ashutosh Prasad3 , D. Thamizh Selvam4 ? Rise of Fluid Computing: A Collective Effort Of Mist, Fog and Cloud?.
[5] Kalpit G. Soni1* , Hiren Bhatt2 , Dhaval Patel3 ?Fog Computing: A Look on Present Scenario and Hopes for Future Research? .

Mehnaz Bano , "A Survey of Fog Computing Architecture and Its Applications," International Journal of Computer Sciences and Engineering, Vol.8, Issue.7, pp.136-141, 2020.

There are very large number of vehicles in India as it is very densely populated country across world. So, there is a need of detecting vehicles accurately using traffic management system. This system detects the image of the number plate of a vehicle from video using video processing with raspberry pi and the number is extracted using different methods and algorithms. The system is applicable for entrances of gates in colleges and highly restricted areas. When any vehicle passes by the system the video is captured and then video is converted into images. In this review paper for number plate recognition of vehicles we had discussed various techniques that were used in to achieve recognition

Vehicle Number Plate Recognition

[1]. Salau, A.O.; Yesufu, T.K.; Ogundare, B.S.; ?Vehicle plate number localization using a modified GrabCut algorithm?, Journal of King Saud University ? Computer and Information Sciences, 2019
[2]. Kukreja, A.; Bhandari, S.; Bhatkar, S.; Chavda, J.; Lad, S.; ?International Research Journal of Engineering and Technology?, International Research Journal of Engineering and Technology, vol: 4, pp: 1825-1831, 2017.
[3]. Bailmare, S.H.; Gadicha, A.B.; ?A Review paper on Vehicle Number Plate Recognition (VNPR) Using Improved Character Segmentation Method?, International Journal of Scientific and Research Publications, vol: 3, pp:1-3, 2013
[4]. Kumar, K.K; Sailaja, S.; Khadheer, S.; Viswajith, K.; ?Automatic Number Plate Recognition System?, Indian Journal of Science and Technology, vol: 10, pp: 77967, 2017.
[5]. Gaikwad, D.Y.; Borole, P.B.; ?AReview Paper on Automatic Number Plate Recognition (ANPR) System?, International Journal of Innovative Research in Advanced Engineering, vol: 1, pp: 88-92, 2014.
[6]. Bagade, J.V.; Kamble, M.; Pardeshi, K.; Punjabi, B.; Singh, R.; ?Automatic Number Plate Recognition System: Machine Learning Approach?, IOSR Journal of Computer Engineering, pp: 34-39. 2016.
[7]. Goyal, A.; Bhatia, R.; ?Automated Car Number Plate Detection System to detect far number plates?, IOSR Journal of Computer Engineering, vol: 18, pp: 34-40, 2016.
[8]. Patel, C.; Shah, D.; Patel, A.; ?Automatic Number Plate Recognition System (ANPR): A Survey?, International Journal of Computer Applications, vol: 69, pp: 21-33, 2013.

Ramneek Kaur, Supreet Kaur, "Survey for Vehicle Number Detection Technique," International Journal of Computer Sciences and Engineering, Vol.8, Issue.7, pp.142-145, 2020.

A wireless sensor network ( WSN) contains a series of detector clients for local measurements in a region focuses on ecological information. The crux of the issue is: "How can you prolong your network life for such a long time?" "A major challenge in WSN is therefore to maximize the grid experience by minimizing energy. The sensors can not be easily recharged or replaced as a result of their ad-hoc preparation in hazardous environments. As energy efficiency is one of the hottest subjects in wireless sensor networks, we will investigate important techniques for energy maintenance in the sensor network. This article focuses primarily on the most efficient energy-saving technologies for tariff cycling systems and also on Information-Aimed strategies for enhancing energy quality. Finally, we should review some of the latest sensor network communication protocols

Wireless sensor networks, energy maintenance, task cycling, information-aime

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Bhuvan Chandra Joshi, Narendra Pal Singh, "Reducing Energy Wastage Implementation in Wireless Sensor Network," International Journal of Computer Sciences and Engineering, Vol.8, Issue.7, pp.146-154, 2020.

Abusive language refers to an insult or vulgarity which harass or deceive the target. Social media is a famous platform for the people to express their opinions publicly and to interact with other people in the world. Some of them may misuse their freedom of speech to bully others through abusive language. This will leads to the need for detecting abusive speech. Otherwise, it may severely impact the user?s online experience. It may be a time-consuming task if the detection and removal of such offensive material are done manually. Also, human supervision is unable to deal with large quantities of data. Therefore automatic abusive speech detection has become essential to be addressed effectively. For detecting abusive speech, context accompanying abusive speech is very useful. In this work, abusive language detection in online content is performed using Bidirectional Recurrent Neural Network (BiRNN) method. Here the main objective is to focus on various forms of abusive behaviors on Twitter and to detect whether a speech is abusive or not. The results are compared for various abusive behaviors in social media, with Convolutional Neural Netwrok (CNN) and Recurrent Neural Network (RNN) methods and proved that the proposed BiRNN is a better deep learning model for automatic abusive speech detection

text classification, abusive language, BiRNN, deep learning, natural language processing

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Dincy Davis, Reena Murali, Remesh Babu K.R., "Abusive Language Detection and Characterization of Twitter Behavior," International Journal of Computer Sciences and Engineering, Vol.8, Issue.7, pp.155-161, 2020.

Computer Vision and Machine Learning are two domains that are upcoming and helpful in the modern era. Computer Vision is a science that is designed to try to make a computer as good as a human. Machine Learning helps improve computer vision by training it to improve every time it is used. This paper presents a model of Support Vector Machine (SVM) with the AdaBoost classifier, which has proven results in recognizing different types of patterns. In this model, SVM is used as a recognizer. This model automatically extracts features from the raw images and generates predictions. The results are subject to experiments conducted on the well-known MNIST digit database

Computer Vision, Machine Learning, Classifier, SVM, Digit Recognition

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Aditya Naik, Vijay Gaikwad, "Handwritten Digit Recognition Using Support Vector Machine," International Journal of Computer Sciences and Engineering, Vol.8, Issue.7, pp.162-165, 2020.

Image Enhancement is one of the important and tough techniques in digital image processing. The main objective of image enhancement is to find out the hidden details in an image. Image Enhancement improves the quality of image for human presentation. Contrast increment, elimination of noise and blurring and enlightenment of details are examples of enhancement operation. Image enhancement is basically divided into two main categories such as spatial domain and Frequency domain. In this paper we discuss and compare these two techniques with their related techniques. Thus, the contribution of this paper is to various image enhancement techniques

Image Enhancement

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Abinasi Singh, Rachhpal Singh, "A Survey for Various Techniques of Image Enhancement," International Journal of Computer Sciences and Engineering, Vol.8, Issue.7, pp.166-169, 2020.