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Cloud Computing is a service model for enabling convenient, on-demand network access to a shared pool of configurable computing resources which can be rapidly provisioned and released. In cloud data centers various computing resources like servers, network devices and cooling systems which constantly evolve in size and in complexity so it consumes large amount of energy which increase extensive power consumption in data centers. As cloud data center resources are not optimized for their maximum utilization, they consume more power so it needs to consolidate virtual machines (VMs) of servers of data center which helps to optimize the usage of cloud resources hence reduce the energy consumption. By considering the optimized power consumption of various data center resources, the researchers have proposed various methodologies and algorithms to reduce power consumption in servers and network devices. In this paper, we have done insightful study of the modern techniques on data center’s power model of servers, network components also on VM overload/under-load detection, VM selection and VM placement or consolidation of VMs which optimize the utilization of data center’s servers for power model which and reduce energy consumption in data center.

Server consolidation, VM Migration, Quality of Service, virtualized data center, Service Level Agreements, Highest Thermostat Setting, Energy efficient, virtual machine placement, migration, dynamic resource allocation, cloud computing, data centers

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Rajesh P. Patel, Ramji Makawana , "Energy-Aware Frameworks in Cloud Data Centers to Manage Workload and Diminish Power Consumption: A Survey," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.422-432, 2018.

With the invention of the cloud computing, the utilization of the physical resources has improved drastically. The main technology that enable the cloud computing is virtualization which allows to create several virtual machine (VM)onto the single physical machine (PM). It increased the utilization of the physical resources because single hardware resources are shared by the several users. Although virtualization technique optimize the server utilization but add new issue named load balancing that need to addressed for the effective utilization of the physical resource and maintain the quality of services (QoS). To deal with the load balancing VM migration approach is used which permit to travel the VM from physical machine (host) to another. Three stages are engaged with the relocation procedure i.e., source PM choice, VM selection and the last step is target PM selection. Plenty of work on the load balancing in cloud are presented in the last few decades and mostly they are differ in the VM selection and VM placement polices. After the study of previous work on the VM migration it can be says that choosing an appropriate VM is a non-trival task and the performance of the load balancing approach is mainly depends on the appropriate VM selection polices. In this paper we select the three different types of virtual machine for the migration and then placed it to the physical machine where the load on the physical machine is between 20 to 50. CloudSim simulator is used to evaluate the performance of the physical.

Migration, energy efficient, virtualization, VM selection, VM placement, SLA violation

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Damodar Tiwari, Shailendra Singh, Sanjeev Sharma, "A ProperFit Virtual Machine Migration Approach for the Load Balancing in Cloud," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.433-436, 2018.

In India, farmers are not getting expected crop yield from their productions. Crop production mostly depends on weather conditions and some statistical methodologies. To get higher crop production yield, farmers sometimes need advices for predicting and analyzing future crop production. This helps farmers to produce a crop with maximum yield. Such methods will be helpful for farmers and government to make a better decision to increase crop production. In this paper present a review on crop yield prediction (CYP) with different data mining (DM) techniques used to evaluate and predict the problem lead to increase CYP. The result analysis is performed on root mean square error (RMSE) and peak signal noise ratio (PSNR).

RMSE, ANFIS, Data mining, Crop Yield, PSNR

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Rabina Dayal, Arun Kumar Yadav, "A Review of Different Techniques Utilized for Crop Yield Prediction," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.437-442, 2018.

In the system and networks, abnormal behavior is detected by anomaly-based IDS (Intrusion Detection System). If the working of a computer system is different from normal working is considered as an attack. The difference of comparison relies on traffic rate, a variety of packets for every protocol etc. Malicious traffic or data on a system is detected by intrusion detection process. To detect illegal, suspicious and malicious information and data, IDS can be a part of the software or a device. First is Detection of an attack then using different method to stop, Prevent an attack and disaster is the user’s highest priority. Anomaly-based IDS satisfy their requirement and demand.In present scenario electricity theft isa major hurdle in front of government. This problem affects Indian economy. The loss on quantity of theft is mirrored in the electricity company.People are affording more charges because intruders steal electricity by many ways.

Intrusion detection system, Anomaly based system, Electricity theft, Intruders

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Uma Soni, Uma Kumari, "Prevention of Power Theft Using Concept of Multifunction Meter and PLC," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.443-447, 2018.

Bilateral filter is a nonlinear filter and the method image edge information mainly in filtering considers both gray level similarities and geometric closeness of the neighboring pixel without smoothing edges. Based on the study and research of bilateral filter found of the bilateral filter is well suited to image denoising. The bilateral filter is appropriate for color and grey picture filtering system with strong performance. It has appeared to be a successful picture denoising procedure. We can use it to the blocking artifacts reduce. A vital issue with the program with the bilateral filter is the choice of the channel parameters which influence the outcomes essentially. Other hand research interest of bilateral filter is increasing speed of the calculations rate. There are three main efforts of this dissertation. First I will discuss about empirical study of the optimal selection of parameter in image denoising. Here I proposed a development of multi resolution bilateral filter where bilateral filter is used to the low frequency sub-band of a signal decomposed through wavelet filter. Multi resolution bilateral filter combined with wavelet thresholding to develop a new image denoising development which finished up to be very efficient in noise eliminating in real noisy image. Second contribution is a flexible method to reduce compression artifacts for avoid over smoothing texture areas and to effectively eliminate blocking and performing artifacts. In this research first detected the block boundary discontinuities and texture regions these are then use to manage the spatial and strength parameters of bilateral filter. The analyze outcome confirm that the suggested method can improve the quality of renewed image far better than the most preferred bilateral filter. Third part is the development of the fast bilateral filter which is convenience for combination of multiple windows to estimate the Gaussian filter more accurately.

Bilateral Filter; Image Denoising; Multi Resolution

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Md Shaiful Islam Babu, Kh Shaikh Ahmed, Md Samrat Ali Abu Kawser, Ajkia Zaman Juthi, "An image Denoising Method Based On Multi Resulation Bilateral Filter," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.448-452, 2018.

With the capability to self-learn and succeed to achieve favourable results in various classification problem, deep learning techniques are increasingly used for Automatic Facial Expression Recognition (AFER). In this paper, we provide brief survey on deep learning technique particularly Convolutional Neural Networks (CNN) for Facial Expression Recognition (FER) and newly introduced Infrared based FER dataset. This review is focused on various CNN techniques applied in almost last half decade for FER on Infrared and Visible light images. There are certain unique advantages of using thermal and infrared images which can make FER techniques robust. Paper describes the standard flow of deep facial expression recognition and suggested methods based on research conducted specifically in this area. Later, review of existing novel deep neural networks and implementations for still images and video-based FER for Infrared Images is provided which subsequently follows glimpses of available well-known datasets. Since all types of cameras experience price reduction over the years, in near future integration and usage of such cameras would be common also because of its illumination invariant characteristic. It becomes evident at the end of the paper that there is a definite scope of developing promising and robust FER with use of Infrared and Thermal images.

Convolution Neural Networks, Deep Learning, Facial Expression Recognition, Infrared Images

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Reshma Nehlani, Devang Pandya, "A Bird View on Deep Learning Facial Expression Recognition Approaches for Thermal and Infrared Images," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.453-459, 2018.

cloud computing is said to be the next big boom technology in IT industry infrastructure. It is claimed that it provides new levels of efficiency, flexibility and cost savings of the resources that are used in industries. Mobile Cloud Computing (MCC) is applications, Internet-based data, and related services accessed via smartphones, laptop computers, tablets and other portable devices. By using MCC, the processing and the storage of intensive mobile device jobs will take place in the cloud system and the results will be back to the mobile device. But the mobile cloud computing have some issues like power consumption, bandwidth, mobility and security. Using the mobile devices for accessing the cloud it needs an efficient load balancing technique for offloading the data to the users. In this paper, there is a detailed review on different load balancing techniques which are existing model in cloud analyst tool and some policies by different authors.

Mobile Device, Mobile Computing, Cloud Computing, Power Consumption, Bandwidth, Mobility, Security

[1]. M. Satyanarayanan, P. Bahl, R. Caceres, and N. Davies, The case for VM-based cloudlets in mobile computing, IEEE Pervasive Computing, 8(4), 14–23, 2009.
[2]. KalaiSelvi B. Mary L. (2014, August). A Survey of Load Balancing Algorithms using VM. , International Journal of Advancements in Research & Technology: IJOART 2014, pp 68-76, ISSN: 2278-7763
[3]. Kaur R. And Luthra P. (2012) .Load Balancing in Cloud Computing. Association of Computer Electronics and Electrical Engineers: ACEE 2014, pp. 375-381, ISSN: 1899-0142, DOI:02.ITC.2014.5.92
[4]. Panwar R., Mallick B. (2015, May) .A Comparative Study of Load Balancing Algorithms in Cloud Computing. , International Journal of Computer Applications: IJCA 2015, pp.33-37, ISSN: 0975 – 8887, DOI: 24, May 2015
[5]. L. Shakkeera and Latha Tamilselvan ,” QoS and load balancing aware task scheduling framework for mobile cloud computing environment”, Int. J. Wireless and Mobile Computing, Vol. 10, No. 4, 2016 309.
[6]. A.Singh, D.Juneja and M.Malhotra,” Autonomous Agent Based Load Balancing Algorithm in Cloud Computing”, International Conference on Advanced Computing Technologies and Applications (ICACTA-2015), Procedia Computer Science 45 ( 2015 ) 832 – 841
[7]. K.Singh,” Energy Efficient Load Balancing Strategy for Mobile Cloud Computing “,International Journal of Computer Applications (0975 – 8887) Volume 132 – No.15, December2015 6
[8]. X.Wei, J.Fan, Z.Lu and K.Ding,” Application Scheduling in Mobile Cloud Computing with Load Balancing”, Journal of Applied Mathematics,Volume 2013.
[9]. I. Cushman, M.Sadi, L.Chen and R. Haddad,” A Framework and the Design of Secure Mobile Cloud with Smart Load Balancing”, DOI: 10.1109/MobileCloud.2017.41
[10]. B.Kim, H.Byun, Y.Heo and Y.Jeong ,” Adaptive Job Load Balancing Scheme on Mobile Cloud Computing with Collaborative Architecture”, DOI: 10.3390/sym9050065
[11]. D.Yao, C.Yu, H.Jin, and J.Zhou,” Energy Efficient Task Scheduling in Mobile Cloud Computing”, DOI: 10.1007/978-3-642-40820-5_29.
[12]. C. Chen, R.Stoleru and G.ie,” Energy-efficient Load-balanced Heterogeneous Mobile Cloud”, DOI: 10.1109/ICCCN.2017.8038422.
[13]. J.Grover, S.Katiyar ,”Agent Based Dynamic Load Balancing in Cloud Computing”, DOI: 10.1109/ICHCI-IEEE.2013.6887799
[14]. R.Hasan and M.Mohammed ,”A Krill Herd Behaviour Inspired Load Balancing of Tasks in Cloud Computing “Studies in Informatics and Control, ISSN 1220-1766, vol. 26(4), pp. 413-424, 2017.
[15]. B.Zhou, A.V.Dastjerdi, R.N. Calheiros, S.N.Srirama, and Rajkumar Buyya,”mCloud: A Context-aware Offloading Framework for Heterogeneous Mobile Cloud,” IEEE Transactions on Services Computing vol: 10, issue: 5, 2017, pp: 797 - 810.
[16]. Changboka, H.Chang, H.Ahn and Euiin Choi,” Efficient Context Modeling Using OWL in Mobile Cloud Computing”, Energy Procedia, vol:16, Part B, 2012, pp:1312-1317.
[17]. M. Rahimi, N.Venkatasubramanian, and A. Vasilakos,”MuSIC: Mobility-Aware Optimal Service Allocation in Mobile Cloud Computing”, International Conference on Cloud Computing (CLOUD), DOI: 10.1109/CLOUD.2013.100
[18]. D.Parmar,A. Kumar, A.Nivangune ,P.Joshi and U.Rao,”Discovery and Selection Mechanism of Cloudlets in a Decentralized MCC environment”, International Conference on Mobile Software Engineering and Systems (MOBILESoft), 2016, DOI: 10.1109/MobileSoft.2016.017
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C. Arun, K. Prabu, "Load Balancing In Mobile Cloud Computing: A Review," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.460-465, 2018.

MANET is aggregation of wireless mobile nodes that can transmit in absence of main controller and do not have fixed configuration. MANET has dynamic nature due to which every node travel effortlessly in network. The multipaths has to be constructed in order to avoid and control congestion. In MANET, link collapse or node failure could be a reason for packet loss. There are many problems and outcomes in mobile ad-hoc network. Presence of many nodes transmitting packets simultaneously over network, the probability of mislaying packet over the network advances to a larger scope. Various congestion control techniques and algorithms has been discussed in this paper. The motive of the paper is to examine and contrast among many proposed techniques of congestion control in MANETs.

MANET, CONGESTION, AODV ROUTING PROTOCOLS

[1]. Sandeep Kumar , Suresh Kumar, “ Study of MANET: Characteristics, Challenges, Applications, Routing Protocol and Security Attacks ”, In: International Journal of R & D in Engineering Science and Management (ISSN 2393-865X), Vol-2, Issue 5, p.p.266-274. July 2015.
[2]. Sapna Khurana, Suresh Kumar, “A Study of Congestion Control in MANET: Review”, In: International Journal of Engineering Technology Science and Research IJETSR ( ISSN 2394 – 3386) Volume 4, Issue 5 pp 94-98, May 2017.
[3]. Sandeep Kumar , Suresh Kumar, “Analysis of SEAHN Demand Distance Vector in Wireless Mobile Ad-hoc Network”, In: International Journal of Enhanced Research In Science, Technology & Engineering (ISSN : 2319-7463), Vol-04 Issue 7, p.p.273-281.July 2015.
[4]. Deepak Sharma, Suresh Kumar, “A Comprehensive Review of Routing Protocols in Heterogeneous Wireless Networks”, In: International Journal of Enhanced Research in Management and Computer Applications, ISSN: 2319-7471Vol. 4, Issue 8, pp: 101-121, August-2015.
[5]. Bhupinder Kaur, Vaibhav Pandey, “A Survey on Congestion Control Techniques in MANETs”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 5, Issue 5, pp(1441-1446), May 2015.
[6]. N. Sharma, A. Gupta, S. S. Rajput and V. K. Yadav, “Congestion Control Techniques in MANET: A Survey”, In 2016 Second International Conference on Computational Intelligence & Communication Technology (CICT), Ghaziabad, 2016, pp. 280-282.doi: 10.1109/CICT.2016.62May 2017 .
[7]. Geetika Maheshwari, Mahesh Gour, Umesh Kumar Chourasia, “A Survey on Congestion Control in MANET”, In (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (2) ,pp(998-1001) ,(2014)
[8]. D. A. Tran and H. Raghavendra, “Congestion Adaptive Routing in Mobile Ad Hoc Networks”, In IEEE Transactions on Parallel and Distributed Systems, vol. 17, no. 11, pp. 1294-1305, Nov. 2006. doi: 10.1109/TPDS.2006.151
[9]. Sandeep Dalal, Renu Ruhil, “Routing based Congestion Control Metric:RFR”, In International Journal of Computer Applications (0975 – 8887) Volume 99, Issue 11, pp(20-24), (2014).
[10]. V. Thilagavathe, Dr. K. Duraiswamy, “ Cross Layer based Congestion Control Technique for Reliable and Energy Aware Routing in MANET”, In International Journal of Computer Applications (0975 – 8887) Volume 36, Issue 12, pp (1-6), (2011).
[11]. Sreenivas B.C, G.C. Bhanu Prakash, K.V. Ramakrishnan, “M-ADTCP: An Approach for Congestion Control in MANET”, In Advances in Computing & Inf. Technology, AISC 178, pp. (531–540),2013.
[12]. H. Gupta and P. Pandey, “Survey of routing base congestion control techniques under MANET”, 2013 IEEE International Conference ON Emerging Trends in Computing, Communication and Nanotechnology (ICECCN), Tirunelveli, pp. 241-244, 2013. doi: 10.1109/ICE-CCN.2013.6528501
[13]. Y. J. Lee and G. F. Riley, “A workload-based adaptive load-balancing technique for mobile ad hoc networks”, IEEE Wireless Communications and Networking Conference, 2005, New Orleans, LA, 2005, pp. 2002-2007 Vol. 4.doi: 10.1109/WCNC.2005.1424826
[14]. Anju, Sugandha Singh, “Modified AODV for Congestion Control in MANET”, In International Journal of Computer Science and Mobile Computing, Vol.4 Issue.6, pp. 984-1001, (June- 2015)
[15]. Y. Mai, F. M. Rodriguez and N. Wang, “CC-ADOV: An effective multiple paths congestion control AODV”, 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, pp. 1000-1004, 2018.doi: 10.1109/CCWC.2018.8301758
[16]. N. Sharma, A. Gupta, S. S. Rajput and V. K. Yadav, “Congestion Control Techniques in MANET: A Survey”, 2016 Second International Conference on Computational Intelligence & Communication Technology (CICT), Ghaziabad, 2016, pp. 280-282. doi: 10.1109/CICT.2016.62
[17]. M. Sedrati,A. Benyahia,” Multipath Routing to Improve Quality of Service for Video Streaming Over Mobile Ad Hoc Networks”, In Wireless Pers Commun, Volume 99, Issue 2, pp 999–1013 (2018)
[18]. Sukhwinder Singh, Vijay Laxmi , “ A Review on Techniques for Controlling the Congestion in MANET”, IJCSN - International Journal of Computer Science and Network, Volume 6, Issue 3, June 2017
[19]. Sapna Khurana, Suresh Kumar , Deepak Sharma, “Performance Evaluation of Congestion Control in MANETs using AODV, DSR and ZRP Protocols” International Journals of Advanced Research in Computer Science and Software Engineering, ISSN: 2277-128X Volume-7, Issue-6,pp 398-403, 2018. DOI 10.23956/ijarcsse /V716/0218

Bhawna Ahlawat, Vikas Nandal, "A Review Paper On - Analysis And Performance Evaluation For Congestion Control Routing Protocols In Manet," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.466-470, 2018.

Intrusion detection system is a system combining both software and hardware that monitors and analysis huge volume of network traffic and detects malicious activities. The role of IDS in system security is significant but not sufficient. Data analysis is a part of the IDS process. Data mining is a data analytic tool. If it is integrated with IDS, performance of IDS will be elevated. One of the data mining classification algorithms is SVM. It is widely applied in IDS. In this paper a methodical study on SVM in IDS was done. This paper reports the effect of SVM in IDS. It is observed that SVM increases the performance of IDS and also it has some limitations. This review provides new ways for further research to overcome these limitations.

Data mining, Intrusion Detection System, SVM

[1]. Jamal Hussain, Aishwarya Mishra “An Effective Intrusion Detection Framework Based On Support Vector Machine Using Nsl - KDD Dataset”, in Indian Journal of Computer Science and Engineering (IJCSE), e-ISSN : 0976-5166, Vol. 8 No. 6 PP: 703 -713, Dec 2017-Jan 2018
[2]. Jiapu Zhang, “A Complete List of Kernels Used in Support Vector Machines” in Biochemistry & Pharmacology: Open Access, DOI: 10.4172/2167-0501.1000195, PP: 4-5. 2015
[3]. Jayshree Jha, Leena Ragha, “ Intrusion Detection System using Support Vector Machine”, in International Journal of Applied Information Systems (IJAIS) – ISSN : 2249-0868,PP:25-30,2013.
[4]. Minakshi Bisen1, Amit Dubey2, “An Intrusion Detection System Based On Support Vector Machine Using Hierarchical Clustering And Genetic Algorithm” in International Journal Of Engineering And Computer Science ISSN:2319-7242, Volume 4 Issue 1, PP: 10062-10064, January 2015.
[5]. Zhenlong Li, Qingzhou Zhang and Xiaohua Zhao, “Performance analysis of K-nearest neighbor, support vector machine, and artificial neural network classifiers for driver drowsiness detection with different road geometries” in International Journal of Distributed Sensor Networks 2017, Vol. 13(9) PP:1-9, 2017
[6]. Sandeep Ranode, “Intrusion Detection System Using SVM Classification” in International Journal of Innovative Research in Computer and Communication Engineering, ISSN(Online): 2320-9801, Vol. 4, Issue 6, PP: 12180- 12184, June 2016.
[7]. Liliya Demidova ,Evgeny Nikulchev ,Yulia Sokolova , “ Big Data Classification Using the SVM Classifiers with the Modified Particle Swarm Optimization and the SVM Ensembles”, in International Journal of Advanced Computer Science and Applications (IJACSA), Vol. 7, No. 5, PP: 294-31, 2016
[8]. Lei Shi1, Qiguo Duan2, Xinming Ma1, and Mei Weng1, “The Research of Support Vector Machine in Agricultural Data Classification” in IFIP International Federation for Information Processing 2012, CCTA 2011, Part III, IFIP AICT 370, PP: 265–269, 2012.
[9]. Vitthal Manekar1, Kalyani Waghmare2, “Intrusion Detection System using Support Vector Machine and Particle Swarm Optimization (PSO)” in International Journal of Advanced Computer Research (ISSN (print): 2249-7277 ISSN (online): 2277-7970) Volume-4 Number-3 Issue-PP: 808-812, 16 September-2014.

C. Amali Pushpam, J. Gnana Jayanthi, "A Review on effect of SVM in Intrusion Detection System," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.471-474, 2018.

This research has been undertaken to examine the impact of occupational hazards on health of Computer Professionals, The essential data have been collected using Community-Survey method. Additionally books, journals, and websites have been referred for getting certain conclusions from the collected survey samples. In this survey based descriptive research, Opinions of diverse Computer professionals in pune city, Maharashtra are taken to find out the average health of the eyes. This research also tries to find certain patterns on the usage of electronic gadget in a particular age group and their tendency towards getting a particular eye disease.

Carpal Tunnel, Hazards, Health, Syndrome, Repetitive Strain

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[2]. Dr. D. Rajan,"Occupational Hazards And Health: A Comparative Study Among Medical Laboratory Technician"s in International Journal for Research in Applied Science and Engineering Technology (IJRASET),Vol. 5, No.7, pp. 305-315
[3]. Ahmed, H. O., and Mark, S., Newson Smith, "Knowledge and Practices of Cement Workers Related to Occupational Hazard" in United Arab Emirates. J Egypt Public Health Assoc, Vol. 85, No. 3, pp. 149-167.
[4]. Javed Sadaf and Tehmina Yaqoob "Gender Based Occupational Health Hazards among Paramedical Staff In Public Hospitals of Jhelum", International Journal of Humanities and Social Science, Vol. 1, No. 3, pp. 175-180.
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[7]. Rajan, D."Occupational Hazards among Medical Laboratory Technicians", SCMS Journal of Indian Management, Vol. 11, No. 1, pp. 134 – 148.
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Neeta A. Deshpande, Pramod B. Deshmukh, Prateek Thakare, "Occupational Hazards and its Impact on Health of Eyes: A Survey," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.475-480, 2018.