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Breast cancer is one amongst the leading cancers for ladies in developed countries including Asian nation .It is the second most typical explanation for cancer death in women. The high incidence of breast cancer in women has redoubled considerably within the last years. Naïve Bayes algorithm is used for carcinoma identification Prognosis and diagnosis. Carcinoma Diagnosis is identifying of benign from malignant breast lumps and carcinoma Prognosis predicts once Breast Cancer is to recur in patients that have had their cancers excised. In this paper Naïve Bayes Algorithm is used to classify the Datasets of Breast Cancer (Diagnosis). The classification results show that when two features of maximum radius and maximum texture is selected, the classification improved accuracy is 98.6%, which is improved compared with previous method.

Breast Cancer Dataset, NaïveBayes classification Algorithm

[1] Abdelghani Bellaachia, Erhan Guven, “Predicting Breast Cancer Survivability Using Data Mining Techniques”, The George Washington University, Washington DC 20052
[2] Shweta Kharya, “USING DATA MINING TECHNIQUES FOR DIAGNOSIS AND PROGNOSIS OF CANCER DISEASE”, International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.2, April 2012
[3] G. Ravi Kumar, Dr. G. A. Ramachandra, K.Nagamani, “An Efficient Prediction of Breast Cancer Data using Data Mining Techniques”, International Journal of Innovations in Engineering and Technology (IJIET)
[4] A.PRIYANGA, Dr.S.PRAKASAM, “The Role of Data Mining-Based Cancer Prediction system (DMBCPS) in Cancer Awareness”, International Journal of Computer Science and Engineering Communications- IJCSEC. Vol.1 Issue.1, December 2013
[5] Shelly Gupta, Dharminder Kumar, Anand Sharma “Data Mining Classification Techniques Applied For Breast Cancer Diagnosis And Prognosis”, Indian Journal Of Computer Science And Engineering (Ijcse)
[6] Sarvestan Soltani A. , Safavi A. A., Parandeh M. N. and Salehi M., “Predicting Breast Cancer Survivability using data mining techniques,” Software Technology and Engineering (ICSTE), 2nd International Conference, 2010, vol.2, pp.227-231.
[7] Anunciacao Orlando, Gomes C. Bruno, Vinga Susana, Gaspar Jorge, Oliveira L. Arlindo and Rueff Jose, “A Data Mining approach for detection of high-risk Breast Cancer groups,” Advances in Soft Computing, vol. 74, pp. 43-51, 2010.
[8] Abdelaal Ahmed Mohamed Medhat and Farouq Wael Muhamed, “Using data mining for assessing diagnosis of breast cnacer,” in Proc. International multiconfrence on computer science and information Technology, 2010, pp. 11-17.

Pushpraj Saket, Anshul khurana, "Research on Naive Bayes Algorithm of Breast Cancer Diagnose Data by Machine Learning", International Journal of Computer Sciences and Engineering, Vol.07, Issue.10, pp.149-151, 2019.

These Data mining is the procedure of analyzing unseen patterns of data according to different point of view for classification into useful information, which is collected and gathered in common areas, such as data warehouses, for proficient investigation, data mining algorithms, assisting business decision making and other information requirements to eventually cut costs and raise profits. Data mining is also recognized as data discovery and knowledge discovery. Frequent itemset mining is a significant task in data mining to discover the hidden, interesting associations between items in the database based on the user-specified support and confidence thresholds. Patterns detected by the Association Rule Mining technique are highly useful patterns as are playing vital role in decision making. In this paper we have focused on the other side of the ARM technique which should also get equal emphasis while decision making as the patterns which are not frequent can be more valuable as those are the Rare Itemsets. The fundamental technique of finding the frequent patterns can be used reversely for Rare Itemset Mining. In this paper the brief study of the technique available for Rare Itemset Mining is discussed and explored the utility of the Rare Itemsets in the decision making.

Data Mining, Association Rule Mining, Frequent Itemset, Rare Itemset, Minimal Rare Itemset

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S.Z. Ninoria, S.S. Thakur, "Review on Rare Itemset Mining", International Journal of Computer Sciences and Engineering, Vol.07, Issue.10, pp.152-157, 2019.

However, the growth in emails has also led to an unprecedented increase in the number of illegitimate mail, or spam 49.7% of emails sent is spam - because current spam detection methods lack an accurate spam classifier. We are excited by the decline in the volume of email spam but it also raises the question as to whether the email spam business is dying and will continue to decline. Besides the volume change, we also consider the quality of email spam and the impact, which may constitute a new trend of email spam business. For instance, spammers may post email spam in a more complicated way using spoofed email addresses and changing email relay servers. That kind of email spam may slip away under the inspection of spam filters. Thus, it motivated us to investigate the evolution of email spam using advanced techniques such as topic modelling and network analysis. We try to find out the real trend of email spam business through email content, meta information such as headers, and sender-to-receiver network over a long period of time.

Spam detection, email, NLP, spam classification

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[6] F. Zhou, L. Zhuang, B.Y. Zhao, L. Huang, A.D. Joseph, and J.D. Kubiatowicz, “Approximate Object Location, and Spam Filtering on Peer-to-Peer Systems,” Proc. Middleware, pp. 1–20, 2003.
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[8] Haiying Shen, Senior Member, IEEE, and Ze Li, Student Member, IEEE, “Leveraging Social Networks for Effective Spam Filtering”, IEEE TRANSACTIONS ON COMPUTERS, VOL. 63, NO. 11, NOVEMBER 2014.
[9] Dr Devendra K. Tayal, Amita Jain, Kanak Meena,” Development of Anti-spam techniques using modified K-means & Naive Bayes Algorithms” IEEE-2016.
[10] Weimiao Feng, Jianguo Sun, Qing Yang, “A Support Vector Machine based Naive Bayes Algorithm for Spam Filtering”, IEEE-2016.
[11] Rohit Kumar Solanki, Karun Verma, Ravinder Kumar,” Spam Filtering Using Hybrid Local-Global Naive Bayes Classifier” IEEE-2015.

Neelam Choudhary, Nitesh Dubey, "Spam Detection Approach Using Modified Pre-processing With NLP", International Journal of Computer Sciences and Engineering, Vol.07, Issue.10, pp.158-161, 2019.

With the development of technology, secret communication with audio, image and video files has become important. In this case, encryption and steganography play a major role. While encryption deals with uncatchable of message content, steganography deals with failure to understand the existence of the messages used for secret communication. Because of these features steganography and encryption are the two main elements complementing each other. The steganography is the art of hidden; its main aim is to pass unnoticed data in another data. There are many types of data that used in steganography, such as message, image, and video. In this work, we are interested in hiding a message inside an image and also securing it. Our work focuses on the study of least significant bit (LSB) technique for embedding text into image. Moreover, we propose an improved approach for LSB based image steganography and encryption decryption using partitioning, zigzag and swapping.

Image steganography, LSB, Cryptography, Symmetric Encryption, Block Cipher, Security

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[15] Q. Kester, “A cryptographic Image Encryption technique based on the RGB PIXEL shuffling A cryptographic Image Encryption technique based on the RGB PIXEL shuffling”, International Journal of Advanced Research in Computer Engineering & Technology, vol. 2,no.2 pp.848-854, January 2013.
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Yogesh Gyarsia, Shiv Tiwari, "Steganography With Improved Cryptography", International Journal of Computer Sciences and Engineering, Vol.07, Issue.10, pp.162-166, 2019.

Interpretation of the dropout students and the reason behind is the most important for the universities. Due to many different reasons such as pressure, low performance, high expectations from family, faculties and individuals it is being tough to sustain for the students. Most important source of knowing the expressions of the students in these instances is their social media interactions with other students. They express their major problems on it. But this is a challenge to process such huge data and evaluating expressions from it. Data mining techniques have given a boost in such processing and application of machine learning has become boon for it. It is found that there are many such techniques available but newest techniques which are best fit in processing of expressional data is machine learning. Surveys of such techniques have become a great source of expression evaluation.

Cloud Computing, Fault Tolerance, Virtual Machines Migration, Resource Management

[1] V. Hegde and P. P. Prageeth, "Higher education student dropout prediction and analysis through educational data mining," 2018 2nd International Conference on Inventive Systems and Control (ICISC), Coimbatore, 2018, pp. 694-699. doi: 10.1109/ICISC.2018.8398887
[2] M. C. Nicoletti, M. Marques and M. P. Guimaraes, "A data mining approach for forecasting students` performance," 2018 13th Iberian Conference on Information Systems and Technologies (CISTI), Caceres, 2018, pp. 1-7. doi: 10.23919/CISTI.2018.8399389
[3] W. Zhang and S. Qin, "A brief analysis of the key technologies and applications of educational data mining on online learning platform," 2018 IEEE 3rd International Conference on Big Data Analysis (ICBDA), Shanghai, 2018, pp. 83-86. doi: 10.1109/ICBDA.2018.8367655
[4] S. R. Guruvayur and R. Suchithra, "A detailed study on machine learning techniques for data mining," 2017 International Conference on Trends in Electronics and Informatics (ICEI), Tirunelveli, 2017, pp. 1187-1192. doi: 10.1109/ICOEI.2017.8300900
[5] A. F. Meghji, N. Ahmed Mahoto, M. A. Unar and M. Akram Shaikh, "Analysis of Student Performance using EDM Methods," 2018 5th International Multi-Topic ICT Conference (IMTIC), Jamshoro, 2018, pp. 1-7. doi: 10.1109/IMTIC.2018.8467226
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[10] Saurabh Pal, “Data Mining: A Prediction For Performance Improvement Using Classification”, International Journal of Computer Science and Information Security, Vol. 9, Isuue 4, pp 136- 140, 2011. Proceedings of the International Conference on Inventive Computing and Informatics (ICICI 2017) IEEE Xplore Compliant - Part Number: CFP17L34-ART, ISBN: 978-1-5386-4031-9
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Sumit Gupta, Amit Ranjan, "Survey of Technologies for Evaluation of Student Dropout Using Educational Data", International Journal of Computer Sciences and Engineering, Vol.07, Issue.10, pp.167-171, 2019.

There is currently a great interest in semiconductor nanoparticles which are organically capped or embedded in polymeric matrices for their ready to use application in devices. These materials show excellent optical and mechanical behaviour at Nano scale. Nano crystalline powder and nanocomposites of II-VI compounds with different doping are being prepared by various routes. Poly-vinylcarbozole (PVK) is a hole transport organic semiconducting polymer. It has been widely used as an organic and optical material. Combination of polymer and semiconductor nanocrystals allows the fabrication of flexible and light weight EL devices. Electroluminescence of the films was studied at different voltage and frequency by placing the films between ITO coated conducting glass plate and aluminium electrodes. It is found that a turn on voltage is required for light emission and brightness increases exponentially with voltage. Turn on voltage is found to decrease as CdSe concentration is increased.

CdSe/PVK nanocomposites and Electroluminescence (EL)

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Sarita Kumari, Swati Dubey, Meera Ramrakhiani, "Synthesis and Electroluminescence Studies of CdSe Nanocrystals Embedded in PVK Matrix", International Journal of Computer Sciences and Engineering, Vol.07, Issue.10, pp.172-174, 2019.

In the current scenario, Cloud computing carved itself as an emerging technology which enables the organization to utilize hardware, software and applications without any upfront cost over the internet. A very efficient computing environment is provided by cloud computing where the customers or several tenants are in need of multiple resources to be provided as a service over the internet. The challenge before the cloud service provider is, how efficiently and effectively the underlying computing resources like virtual machines, network, storage units, and bandwidth etc. should be managed so that no computing device is in under-utilization or over-utilization state in a dynamic environment. A good task scheduling technique is always required for the dynamic allocation of the task to avoid such a situation. The utilization of resources is to be scheduled efficiently so that it helps in reducing the time for task completion. This is task scheduling which is most essential and important part in cloud computing environment. In task scheduling allocation of certain tasks to particular resources at a particular time instance is done. There are different techniques that are proposed to solve the problems of task scheduling. Through this we are going to present the new Algorithm based on task scheduling technique, which will distribute the load effectively among the virtual machine so that the overall response time (QoS) should be minimal. A comparison of this proposed Algorithm of task scheduling technique is performed on workflow simulator which shows that, this will outperform the existing techniques like FCFS, SJF and Genetic Model techniques.

Cloud Computing, Task Scheduling, FCFS, SJF, Genetic Algorithm, QoS

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Sushmita Barsainya, Anshul Khurana, "Task Schduling With Improved ACO In Cloud Computing", International Journal of Computer Sciences and Engineering, Vol.07, Issue.10, pp.175-180, 2019.

Robot path planning is a task to determine the most feasible path between origin and destination while avoiding collisions in the underlying environment. This task has always been characterized as a high dimensional optimization problem and is considered NP-Hard. Numerous algorithms have been proposed that provide solutions to the problem of path planning in a deterministic and non-deterministic way. However, the problem is open to new algorithms that have the potential to obtain better quality solutions with less time complexity. This paper presents a new approach to solve the problem of three-dimensional path planning of a flying vehicle while maintaining a safe distance from obstacles on the road. A new approach based on the modified grey wolf optimization algorithm is applied to the problem. The modified algorithm is compared to the standard GWO algorithm and have shown good results.

Grey Wolf Optimizer, GWO, Path Planning

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R. K. Dewangan, V. K. Bohat, "Three Dimensional multi-UAV path planning using Modified Grey Wolf Optimizer", International Journal of Computer Sciences and Engineering, Vol.07, Issue.10, pp.181-186, 2019.

Underwater imaging posts a challenge due to the degradation by the absorption, scattering and color distortion occurred during light propagation & in poor lighting conditions in water medium. Many different image filtering techniques are utilized to improve image quality effectively. Images which are captured under water are generally degraded due to the effects of absorption and scattering. For example, underwater images with low contrast decrease the accuracy rate of underwater object detection and marine biology recognition. To overcome those limitations, a systematic underwater image enhancement method, which includes a contrast enhancement algorithm, is proposed. In this paper, we proposed an image based pre-processing technique i.e. CAUF-MCAHE algorithm to enhance the quality of the underwater images. Experiments are performed with some underwater images and results are compared with different existing algorithm. For comparing the performance, MSE mean square error and PSNR peak signal to noise ratio is used as parameters.

MCAHE (Modified Contrast adaptive histogram equalization), UF (unsharp filter), AHE (Adaptive histogram equalization), PSNR (Peak signal to noise ratio) & MSE (Mean square error)

[1] Will be updated very soon

Ayushi Jaiswal, Jayprakash Upadhyay, "An Optimal Underwater Colour Image Enhancement Using CAUF-MCAHE Technique", International Journal of Computer Sciences and Engineering, Vol.07, Issue.10, pp.187-190, 2019.