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Web pages not only contains useful information, but also many features to improve readability and presentation which end up distracting the relevant content as well as occupying more precious memory space. It becomes even more problem while stored in limited mobile cache and prefetch area. While caching and prefetching or when a page is used repeatedly these unnecessary content, called noise such as banner, advertisements, copyright, background images and license information etc. occupy more space, bandwidth while it doesn’t add any value to the user of actual content. Eliminating such noises helps in overall performance improvement of mobile caching, and perfecting. If such noises are not removed, they will become nuisance in web content mining as well. There are many contents which can be identified as noise and there are many techniques to remove them. This paper identifies and removes irrelevant noises in web pages such as background images, search panel, copyright, license information, advertisement. Removing image heavy contents reduces cache memory utilisation, improves performance of content rendering considerably. Care is taken only to remove noises identified and leave the useful contents intact. A brief over view of noise removal and its benefits are discussed in this paper.

Noise reduction, web content extraction, caching, pre-fetching

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[19] Hitesh Kumar Azad, Rahul Raj, Rahul Kumar, Harshit Ranjan, Kumar Abhishek and M.P. Sing, “Removal of Noisy Information in Web Pages”, ICTCS, ACM, 2014.
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P. Amudha Bhomini, Jayasudha J.S, "Mobile Cache Memory Optimization using Noise Reduction," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.925-931, 2018.

Anomaly detection is a major requirement of the current Internet of Things (IoT) and inter-networked communication environment. This work analyzes recent and prominent contributions in the domain of Anomaly detection. The analysis is performed especially in domains related to real time operations and IoT environment. The review is performed and results from most prominent models in literature are considered for analysis. This paper discusses the working mechanisms and the major issues in Anomaly detection such as data imbalance and noise especially in IoT domain and the methods used to handle these issues. Experiments were performed using the NSL-KDD benchmark data set. Precision, False Positive Rate and Accuracy are used to analyze the effectiveness of the models.

Multi-Layered Clustering, Ensemble Models, Intrusion Detection, K-Means, SVM

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S.L. Sanjith, E George Dharma Prakash Raj, "A Comprehensive Analysis of Machine Learning Models for Real Time Anomaly Detection in Internet of Things," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.932-937, 2018.

Cloud computing is the emerging technology that delivers the services on demand using the underline internet based software’s. It is used to store a large amount of data and provide the accessibility to that data with minimum response time. Minimum bandwidth is required to assess the stored data from anywhere. In cloud there is a shared repository of resources that the cloud service providers try to utilize efficiently. Cloud vendors lease these services to the cloud customers as per their needs or demands. Customers also pay to the vendors as per their usage of cloud services. It is beneficial for both the service provider and customer as service provider earn money leasing their resources and customers also earn using the resources which otherwise they need to purchase. Cloud computing generally uses the concepts of virtual machines (VM) and their scheduling to feed maximum customers. This paper briefs about how to improve the quality of the services provided to the customer. Here we assign priorities to the customers on the basis of certain factors like number of VMs required, request type based on pricing and association with company etc. On the basis of these factors we assign priorities to the customers and the customers are feed on the basis of this priority. Here we also included the concepts of feeding the users request from the same geographical areas with minimum distance. This will improve the overall performance and throughput of the cloud with maximum customer satisfaction.

Cloud Computing, Load balancing, Priority , Laod balancer

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[12] Zhen Xiao, Weijia Song, Qi Chen, “Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Environment” , IEEE transactions on parallel and distributed systems, vol. 24, no. 6, pp 1107-1117, june 2013.
[13] Preeti Kushwah “A Survey on Load Balancing Techniques Using ACO Algorithm”, International Journal of Computer Science and Information Technologies (IJCSIT), Vol. 5 (5),pp 6310-6314 , 2014.
[14] M. Thenmozhi, N. Tamilarasi “Oppurtunistic Routing Through Delay Analytical Methods in Ad-Hoc Wireless Networks”International journal of Computer Science and Engineering, Vol.6 , Issue.11 , pp.246-253, Nov-2018.

Manmohan Sharma, Vinod Kumar Jain, "Load balancing in cloud using prioritization based on Quality of Services (QoS) demand," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.938-943, 2018.

Cloud computing is a concept of outsourcing the storage and processing capabilities for storing and managing data and computer programs. With the appearance of cloud computing, it has turned out to be progressively prominent for the owners of data to make available their information to public cloud servers at the time of permitting data users to retrieve this information. For some privacy reasons, a secure search on encrypted cloud information has boosted some of the research works in the single owner model. Normally with information measure capability and restricted battery life, looking on encrypted information imposes serious overhead to computing and communication also as a better power consumption for mobile device users that makes the encrypted search over mobile cloud terribly difficult. The aim of the paper is to conduct a survey on efficient searching techniques on data in cloud environments.

Cloud Computing, Cloud Storage, Mobile Cloud Storage (MCS),Cloud Searching, Keyword Search

[1] Z. Xia, X. Wang, X. Sun and Q. Wang, "A Secure and Dynamic Multi-Keyword Ranked Search Scheme over Encrypted Cloud Data," in IEEE Transactions on Parallel and Distributed Systems, vol. 27, no. 2,
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[3] C. Guo, Q. Song, R. Zhuang and B. Feng, "RSAE: Ranked Keyword Search over Asymmetric Encrypted Cloud Data," Big Data and Cloud Computing (BDCloud), 2015 IEEE Fifth International Conference on, Dalian, 2015, pp. 82-86.
[4] Deepali D. Rane and Dr.V.R.Ghorpade“ Multi-User Multi- Keyword Privacy Preserving Ranked Based Search Over Encrypted Cloud Data” International Conference on Pervasive Computing (ICPC), 2015.
[5] Zhihua Xia, Member, IEEE, Xinhui Wang, Xingming Sun, Senior Member, IEEE, and Qian Wang, Member, IEEE “A Secure and Dynamic Multi-keyword Ranked Search Scheme over Encrypted Cloud Data” IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, VOL., NO.1,2015.
[6] J.Li,,R.Ma and H.GuanTEES: An Efficient Search Scheme over
Encrypted Data on Mobile Cloud, IEEE.Transaction2015.
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[13] https://www.quora.com/What-is-a-multi-keyword-search-and-a-single-keyword-search-What-is-the-main-difference-between-these-two.

Priyanka Devi Sahu, Jasmine Minj, "A Survey on Searching Approaches on Issues in Cloud Based Environment," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.944-947, 2018.

Academic institutions use websites for several purposes like examination enrollment, form submission, result declaration and more. However, while accessing those websites applicants encounter several issues like server errors or response timeout failures. Several factors are responsible for such technical issues like heavy traffic, infrastructure limitations etc. The aim of this research paper is to evaluate the response time of different academic websites (both public and private) with the help of jmeter tool. The findings of this research indicate the critical factors behind delayed response time. The paper discusses these results and on their basis, provides certain insights for achieving better response time.

Response time, Jmeter tool

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[12] J., N., P., R., “Comparative Analysis of Web Applications using Jmeter”, International Journal of Advanced Research in Computer Science, Vol.8, Issue.3, pp.774-777, 2017.
[13] S. Kannan, T. Pushparaj, “A Review: Software Security Testing”, International Journal of Computer Science and Engineering, Vol.4, Issue.9, pp.1-8, 2017.
[14] M. Sharma, H.P. Singh, V. Pathak, “Critical Software Testing Using Cloud Computing Tools”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1141-1146, 2018.
[15] P. Singha, A., K. Dubey, J. Palli, "Toolkit for Web Development Based on Web Based Information System", International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.5, pp.1-5, 2018.
[16] A. Ahmad, M. Asif, S. R. Ali, "Review Paper on Shallow Learning and Deep Learning Methods for Network security", International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.5, pp.45-54, 2018.

Shaily Goyal, Manju Kaushik , "Response Time Analysis of Websites Using Jmeter Tool," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.948-952, 2018.

this research paper proposes an automated and intelligent classification technique of Magnetic Resonance Imaging (MRI) brain images which is extremely important for medical analysis and interpretation. ECG, CT-scan and MRI images are important ways to diagnose brain diseases efficiently. An abnormal growth of cells without any purposes is called a tumor. Sometimes doctors can tell if a tumor is cancer or isn’t using MRI. MRI also used to find the signs that cancer may have spread from its starting part to another part of the body. Radiologist or physician analyze tumor manually by visual inspection which is a conventional method. This may lead to error in classification while a large number of MRIs are to be analyzed. Brain cancer is the leading cause of death among people which is caused from malignant brain tumor. A benign tumor is one that does not invade nearby tissue but a malignant tumor does. The chances of survival can be increased if the tumor can be detected at its early stage. In this paper a novel method to classify brain tumors as benign (non-cancerous) or malignant (cancerous) is presented. MRI brain image database was used for training and testing. Images were filtered, skull-masked and segmented. The proposed method employed wavelet transform to extract features from several images. Principle component analysis (PCA) was applied to reduce dimensionality of features. Gray Level Co-occurrence Matrix (GLCM) based Features were selected and submitted to a kernel support vector machine (KSVM). To generalize KSVM, k-fold stratified cross validation was applied. Features were extracted from MRI images named gray scale, symmetrical and texture features. The main goal of this paper is to offer an excellent result of MRI brain tumor classification and cancer detection using SVM. Our proposed system achieved classification accuracy of 96% for RBF kernel.

Brain Tumor, Classification, MRI, K-means clustering, PCA, Wavelet, GLCM, SVM

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[6] Sheweta Jain, “Brain Cancer Classification Using GLCM Based Feature Extraction in Artificial Neural Network”, IJCSET, ISSN: 2229-3345, Vol. 4 No. 07 Jul 2013.
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[8] D. Sridhar and IV. Murali Krishna “Brain Tumor Classification using Discrete Cosine Transform and Probabilistic Neural Network” International Conference on Signal Processing Image Processing & Pattern Recognition (ICSIPR), pp. 1-5, 2013.
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[10] Walaa Hussein Ibrahim, Ahmed AbdelRhman, Ahmed Osman and Yusra Ibrahim Mohamed “MRI brain image classification using neural networks”, International Conference on Computing, Electrical and Electronics Engineering (ICCEEE), pp. 253-258, 2013.
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[14] Janki Naik and Prof Sagar Patel, “Tumor Detection and Classification using Decision Tree in Brain MRI”, IJEDR, ISSN: 2321-9939, 2013.
[15] Mr. Ajaj Khan and Ms. Nikhat Ali Syed, “Image Processing Techniques for Automatic Detection of Tumor in Human Brain Using SVM”, International Journal of Advanced Research in Computer and Communication Engineering (IJARCE), Vol. 4, Issue 4, pp. 541-544, 2015.
[16] Asha Patil, Kalyani Patil, Kalpesh Lad, "Leaf Disease detection using Image Processing Techniques", International Journal of Scientific Research in Computer Science and Engineering, Vol.06, Issue.01, pp.33-36, 2018.
[17] Amey Samant, Sushma Kadge, "Classification of a Retinal Disease based on Different Supervised Learning Techniques", International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.3, pp.9-13, 2017

M. A. Rahman, "Automated MRI Brain Tumor Classification and Cancer Detection Using Support Vector Machine," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.953-959, 2018.

Testing is an important stage of SDLC that determines the performance and accuracy of the software. Performance testing helps to understand the reliability, scalability, responsiveness, and throughput of software under a given workload owing to its popularity. Web services are increasingly used in web applications and testing them is comparatively difficult with regards to traditional applications in terms of unpredictable load, response time etc. This review paper presents a comparative study of web service testing tools by measuring response time and throughput. Moreover, after thorough examination changes are recommended for web services and testing tools.

Web services, LDAP, HTTP, generic TCP connections, JMS and native OS processes, GUI

[1]. R. Bhatia, A. Ganpati, “In Depth Analysis of Web Performance Testing Tools”, Engineering Science and Technology: An International Journal, Vol.6, Issue.5, pp.15-19, 2016.
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[8]. N. M, K. Kumar, A. Saha, D. Chahar, “Comparative Study on Performance Testing with Jmeter”, International Journal of Advanced Research in Computer and Communication Engineering, Vol.5, Issue.2, pp.70-76, 2016.
[9]. Rina, S. Tyagi, “A Comparative Study of Performance Testing Tools”, International Journal of Advanced Research in Computer Science and Software Engineering Research, Vol.3, Issue.5, pp.1300-1307, 2013.
[10]. N. Sharma, P. Sharma, H. Singh, “A Comparative Study of Web Service Testing Tool”, International Journal of Engineering Development and Research, Vol.5, Issue.2, pp. 1457-1460, 2017.
[11]. T. Wala, A. K. Sharma, “A Comparative Study of Web Service Testing Tools”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol.2, Issue.2, pp.257-261, 2014.
[12] S. Kannan, T. Pushparaj, “A Review: Software Security Testing”, International Journal of Computer Science and Engineering, Vol.4, Issue.9, pp.1-8, 2017.
[13] M. Sharma, H.P. Singh, V. Pathak, “Critical Software Testing Using Cloud Computing Tools”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1141-1146, 2018.

Shaily Goyal, Manju Kaushik , "A Study on Performance Analysis of Web Services Using Various Tools," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.960-969, 2018.

The improper dumping of the municipal solid waste in the Kapulauppada area of Greater Visakhapatnam Municipal Corporation (GVMC) leads to serious pollution threat to the ground water of the study area. Thus, the present study deals with the physio-chemical parameters of the ground water in the dumping yard of Kapulauppada area which is occupying about 100 acres of the area. The investigation involves the detailed field survey and collection of data and analysis and displaying the results. In the study area, 21 different ground water samples have been collected in and around Kapulauppada. The analysis of the physio-chemical parameters of ground water like pH, total hardness, total alkalinity, chlorides, total dissolved solids, calcium, magnesium, turbidity and electrical conductivity are studied. The results are compared with Bureau of Indian Standards (BIS). Based on the standard calculation methods, the standard parameters such as Water Quality Index (WQI) is calculated for each sample location. The results show that 66.66 % of water is poor for drinking purpose, 23.8% of water is good water and 9.5% of water is excellent for drinking purpose. So improper dumping of municipal solid waste is leading to the pollution of the ground water in the Kapulauppada area.

Groundwater, Pollution, Water Quality Index, Remote Sensing, GIS

[1] T. B. Reddy, C. V. Ramana, C. Bhaskar, P. J. Chandrababu, “Assessment of heavy metal study on ground water in and around Kapulauppada MSW site, Visakhapatnam, AP.”, International Journal of Science and Nature, Vol.3, pp.468-471, 2012.
[2] T. J. R. Kumar, C. Dushiyanthan, B. Thiruneelakandan, R. Suresh, S. V. Raja, M. Senthilkumar, “Major and trace element characterization of shallow groundwater in coastal alluvium of Chidambaram Town, Cuddalore District, South India.”, Journal of Geoscience and Environment Protection, Vol.4, Issue.01, pp.64, 2015.
[3] S. Mor, K. Ravindra, , R. P. Dahiya, A. Chandra, ”Leachate characterization and assessment of groundwater pollution near municipal solid waste landfill site”, Environmental monitoring and assessment, Vol.118, Issue.1-3, pp. 435-456, 2006.
[4] A. Farooqi, H. Masuda and N. Firdous., “Toxic fluoride and arsenic contaminated groundwater in the Lahore and Kasur districts, Punjab, Pakistan and possible contaminant sources.” Environ. Pollution, Vol.145: pp.839-849, 2007.
[5] S. Devi, P R. Premkumar, “Physicochemical Analysis of Ground water samples near Industrial Area Cuddalore district, Tamil Nadu, India”, International Journal of Chem Tech Research, Vol.4, Issue.1,pp. 29-34, 2012.
[6] K. Hadjibiros , D. Dermatas, C.S. Laspidou,” Municipal solid waste management and landfill site selection in Greece: irrationality versus efficiency”, Journal of Global NEST,Vol. 13, Issue.2, pp. 150-161, 2011.
[7] Prasoon Kumar Singh, BinayPrakash Panigrahy, Ashwani Kumar Tiwari, Bijendra Kumar, PoornimaVerma, “A statistical evaluation for the groundwater quality of Jharia coalfield, India”, International Journal of ChemTech Research, Vol.7, Issue.4, pp 1880-1888. 2014-2015
[8] S. Gupta , G. Dutta , D. Mondal , “Mechanism of Fluoride Mobilization in an Alluvial Aquifer: a Kinetic Approach”, International Journal of ChemTech Research, Vol.9, Issue.4, pp 270-278, 2016.
[9] S. Kalaivani., K. Ramesh, “Groundwater Quality Assessment using WQI In South Coimbatore, Tamil Nadu, India”, International Journal of ChemTech Research, Vol.7, Issue.1, pp .316-322, 2014-2015
[10] N. Rajkumar, T. Subramani, L. Elango, “Groundwater contamination due to municipal solid waste disposal – A GIS based study in erode city”. International Journal of Environmental Sciences, Vol.1, Issue.4, pp. 39-55, 2010.
N. Kumar, D.K. Sinha , “Drinking water quality management through correlation studies among various physic- chemical parameters”. International Journal of Environmental Sciences, Vol.1, Issue.2, pp. 253-259,2010.
[11] M. Sunandana Reddy, L. Harish Kumar, “GIS based Land Information System for Medchal Mandal of R.R. District”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.43-49, 2018.
[12] Suraj Kumar Singh, Vikash Kumar, Shruti Kanga, “Land Use/Land Cover Change Dynamics and River Water Quality Assessment Using Geospatial Technique: a case study of Harmu River, Ranchi (India)”, International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.3, pp.17-24, 2017.

Killana Dileep, G.Jaishankar, P. Jagadeeswara Rao, M. Durgadevi, M.D.R Srinivas, "Impact Assesment of Municipal Solid Waste on Ground Water Quality in and around Kapulauppada Dumpyard in GVMC using Remote Sensing and GIS Techniques," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.970-975, 2018.

The main aim of the research is to analyze the content of various genres of films released in South India. Also, it aimed to study the elements of commercial cinema and other genres of cinema. In this research, a case study was conducted to identify the impacts of various factors such as star value, characterisation and other elements like story, script, cinematography, music, editing, and visual effects. The South Indian film industry is very well developed in terms of film making methods, genres and visual effects. In this research, this development in films related to genres and different making methods is gathered and compared to study the current trends. The films which are being analyzed in this case study are, and CHANDRA MUKI, THEERAN ADHIGARAM ONDRU and MERKKU THODARCHI MALAI. These film genres are categorised into various formats such as a hybrid of horror and comedy genres, semi non-fictional and realistic fictional film. These films and formats reflect the current cinematic trends, changing mindsets of the south Indian filmgoers and the current society. This case study research tries to compare the current cinematic trends, current filmmaking genres and the latest developments in South Indian cinema industry. From this research, it is observed that many south films have been released during the past ten years with different genres and with different storytelling methods. The star value of the film plays an important role in the success of the film, particularly in the south Indian film industry. At the same time, the filmgoers of south India also accepted the newcomers and directors and new genres.

Commercial Films, Genre, Hero value, Hybrid Formats, Fictional, Non-Fictional Formats

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B. Senthil Kumar, Ayem Perumal, Col. Manoj Kumar, D. Nivedhitha, "A Comparative Study On Genre, Format And Changing Trends," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.976-978, 2018.

The demand for Graphical Processing Units or GPUs, gained a tremendous hike during the past few years as a result of its migration from processing and representation of mere high dimensional graphical patterns to a heterogeneous high performance computing capability. The future generation data science requirements like Big Data Analysis and Deep Learning increased the popularity of GPUs to a wide extend. Graphical Processing Units or GPUs are well suited for parallel processing which enables visualization of vast amount of real time processed data in a more significant manner than CPU. From processing mere graphical algorithms, GPU has gone through numerous advancements in the past few decades. They can be used to improve the performance and efficiency of any algorithm nowadays. The expenditure of installation and use of GPUs have come down to a great extent from the initial huge amount. Data classification tasks like kNN classification can be done more efficiently and cost effectively by applying parallelism using GPU. kNN algorithms are the most popular data classification algorithm, because of its simplicity, high accuracy and versatility. This paper studies four major kNN algorithms developed for GPU processing and compares the techniques and methodologies used in them.

GPU, BF CUDA, CUBLAS, CUKNN

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Sneha Jacob, Anuj Mohamed, "A Review on Various Nearest Neighbor Searching Algorithms Using Graphical Processing Units," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.979-982, 2018.