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Software cost estimation considered to be the critical, is equally vital tasks in software project management. In a highly challenging environment, software project managers are always in a need of robust estimation models inorder to predict the cost of upcoming software development projects accurately. Software cost estimation is the prediction of development effort and calendar time required to develop a software project. It is considered to be the key task as accurate estimation of any software not only accurately estimates development effort, cost, time and growth of a software development project but also yields delivery exactness and correctness vis a viz return an organization in a better schedule of its futuristic software projects. In this paper, software cost estimation is done by proposing a cost driver selection model which is based on an optimization technique called as water cycle algorithm. The proposed cost driver selection model selects only relevant set of cost drivers as an input to estimation process and ignores the very irrelevant cost drivers. In step second, these relevant set of cost drivers originating from step first are assigned to an Artificial Neural Network as its input for the purpose of getting the accurate estimation of software development project cost that needs to be developed. For evaluation purposes, Magnitude of Relative Error, Mean of Magnitude of Relative Error and Median of Magnitude of Relative Error are used as three performance measures to simply weigh the obtained quality of estimation as accuracy. The obtained results were compared with the results of a benchmark study of COCOMO model and another artificial neural network based model. From the comparative result, it becomes evident that the proposed model outperforms the rest of the two existing models.

Artificial Neural Network, Cost Driver Reduction, Software Cost Estimation, Water Cycle Algorithm

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Zahid Hussain Wani, Kaiser Javeed Giri, Rumaan Bashir, "Improved Software Cost Estimation Model Using Cost Driver Reduction Based on Water Cycle Algorithm," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.64-72, 2019.

In current times, ubiquitous computing has given massive rise to research work in artificial intelligence, machine learning, software engineering and to research development in telecommunication, medicine, and image / audio / video processing. Due to the vastness of software being developed, software fault prediction is a very pertinent area for ensuring software quality and has so much scope to work. Machine learning now a days is one of the most promising way to deal with software fault prediction problems. The assumptions considered in a testing case need to be different from those in other testing cases because of the varying complexity of software testing. Although, there are software fault prediction models who can effectively assess software reliability in specific testing scenarios, no single model can accurately predict the fault numbers in a software in all testing conditions due to the fact that the modern software being developed are bigger and complex in both size and functions and thus, assessing the software reliability is a daunting task. Some popular approaches of Software fault prediction models use General Bayesian network and Augmented Naive Bayes classifiers, which do not impose any restriction on network architecture and are able to learn appropriate network architecture. An algorithm combining Fuzzy Attribute Clustering with Naive Bayes Classification has been worked out in this paper. The proposed Fuzzy Attribute Cluster Net Bayes (FACNB) algorithm is a machine learning-based prediction algorithm for software reliability prediction (using soft computing methods). It focusses on all data types in the area of software analytics. The prediction accuracy of the proposed algorithm shows improvement over other such algorithms.

FACNB, Fuzzy Attribute Clustering, Software reliability model, Software reliability prediction, Bayes classifier, Machine learning algorithm.

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Neeta Rastogi, Shishir Rastogi, Manuj Darbari, "A Novel Software Reliability Prediction Algorithm Using Fuzzy Attribute Clustering and Naïve Bayesian Classification," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.73-82, 2019.

Education lays foundation for the development of the country. Enhancements in the educational technologies has improved educational process. Educational institutes are now capable to store large volumes of data related to student admissions, course attendance, examination results and so forth which need to be analysed for the progress of institutes. Data mining provides techniques to explore educational data. Educational Data Mining (EDM) is such an emerging multidisciplinary research field which deals with developing methods to explore the educational data to gain knowledge. The knowledge gained can be used to improve teaching- learning process and decision making process of higher educational institutes. It also helps in detecting student behaviour and their learning outcomes which can be used for their future betterment. This paper puts forth an effort to study EDM, its environment and components including tools and techniques used. It also gives an insight into the Education Data Mining process of knowledge discovery. The present study also puts forth challenges involved in EDM which represents opportunities for future research work to be carried out.

Educational Data Mining (EDM), Data Mining (DM), Knowledge Discovery, EDM process, Student performance

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Balwinder Kaur, Anu Gupta, R.K.Singla, "An Insight into Educational Data Mining," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.83-90, 2019.

In this modern world, finding the desired image from huge databases has been a vital problem. Content Based Image Retrieval is an efficient method to do this. Many textures based CBIR methods have been proposed so far, for better and efficient image retrieval. We aim to give a better image retrieval method by extending the Local Tetra Patterns (LTrP) for CBIR using texture classification by using extended version of LTrP. These features give additional information about the color and rotational invariance. So an improvement in the efficiency of image retrieval using CBIR is expected.

Local binary Patterns, Content Based Image Retrieval (CBIR), Local Ternary Patterns (LTP), Local Tetra Patterns (LTrP), Extended Local Tetra Pattern (ELTrP), Histogram Equalization

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Deepika Choudhary, Amit, Suraj malik, Pankaj Pratap Singh, "Content-Based Image Retrieval Using Extended Local Tetra Patterns," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.91-96, 2019.

Heart Disease is the one of the major cause of death especially in developed countries. Some of its types include Arrhythmia, Stroke, High Blood pressure, Cardiac Arrest etc. Thus to help clinicians for early diagnose disease related conditions, some medical decision support system are also designed. Data mining plays an essential role in analyzing huge amount of data. These quick predicting techniques helps medical practitioners to analyze the same. Classification is the most common Machine Learning algorithm used to classify the disease/non-disease patient. In this paper we will analyze and predict the occurrence of heart disease by applying some of the machine learning algorithms like K-Nearest Neighbor , Decision Trees , Random Forest , Adaptive boosting, SVM and Logistic Regression. It will help physicians to estimate the risk in different age groups. The dataset used is taken from Heart Disease database of UCI Machine Learning Datasets. Factors like blood pressure, heart rate, sugar level, cholesterol, age, gender etc. highly affects the result of the algorithm. The accuracy has been improved by working on high-contributing attributes found using feature importance technique.

Heart Disease, Predictive Analysis, Data Mining, SVM, Classification, Decision Tree

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Niraj Kalantri, Kumar R, "Predictive Analysis on Heart Disease Using Different Machine Learning Techniques," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.97-101, 2019.

Everywhere throughout the world part of waste materials is making issue as far as contamination and different issues. So is the human hair, hairdressers trim the hair of individuals and discard in sewage channels or some stream or in open, this straightforwardly influences human solace by blocking channel pipes or making contamination of land or water and even in some cases amid blowing air causes air contamination too. To acquire this waste material into utilization this examination the human hair is included in conventional concrete with no substitution. Five distinct rates of human hair by weight of cement are utilized. Human hair is effectively accessible wherever free of cost henceforth not any more additional expense is used. Human hair utilized in the concrete was right off the bat cleaned by evacuating additional rubbish and was then washed in a container utilizing cleanser at that point dried and utilized in concrete. We as a whole realize that the concrete is good in compression and weak in tension and the beneficial thing on utilizing hair in cement is that, on investigations it was discovered that the compressive quality of the solid increments on expanding the level of human hair and it was additionally discovered that the rigidity of the mortar increments by including human hair in cement. Furthermore regression examination was done on the outcomes got to fine the numerical condition which can be utilized in future to know the quality.

Waste material, human hair, compressive strength, tensile strength, regression analysis

[1]. Nila, V. M., Raijan, K. J., *Susmitha Antony, Riya Babu M. and Neena Rose Davis (2015) [5] stated that Human hair waste can be effectively managed to be utilized in fibre reinforced concrete constructions.
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[5]. Dr. R.C.Reddy and Shweta kaushik (2015), “An Experimental Study on Mechanical Properties of Human Hair Fibre Reinforced Concrete (M-40 Grade)”, IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE) e-ISSN: 2278-1684.
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[11]. C. Selin Ravikumar1*, Dr. V. Ramasamy2 , Dr. T.S. Thandavamoorthy3 “Effect of Fibers In Concrete Composites, ISSN 0973-4562 Volume 10, Number 1 (2015) pp. 419-430”
[12]. Ajna Manaf, Adarsh M V, Alphy Jomichan George, M Varghese, “Human Hair Fibre Reinforced Concrete” International Journal of Engineering Research & Technology (IJERT), ISSN: 2278-0181, Vol. 6 Issue 03, March-2017, pp. 460-465.

Nadeem Gulzar Shahmir, "Experimental and Regression Analysis on Human Hair Fibered Concrete," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.102-105, 2019.

Classification of large amount data is one of the major difficult tasks in data science. This problem can be solved by using deep learning techniques like CNN and RNN. In computational bio informatics, protein sequence classification plays a crucial role to determine the accuracy. The proposed approach uses the RNN based architecture with GRU, LSTM, and basic LSTM and find the accuracy of training data and testing data by considering mean value of three methods. In this method the top fifteen proteins which are obtained by using preprocessing and sequence analyzer methods as one set of input and TCGA breast cancer dataset as second input to this proposed method. Every sequence in test dataset will compare with sequences in train dataset to get accurate classification results. Supervised learning requires complete labeled data where as unsupervised learning requires unlabelled data. In this approach semi supervised learning is used to get high throughput.

Deep Learning, Accuracy, Protein Sequence, Classification

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[14] Xingyou Wang , Weijie Jiang , Zhiyong Luo,” Combination Of Convolutional And Recurrent Neural Network For Sentiment Analysis Of Short Texts”, Proceedings Of COLING 2016, The 26th International Conference On Computational Linguistics: Technical Papers, Pages 2428–2437, Osaka, Japan, December 11-17 2016.
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B Madhav Rao, V Srinivasa Rao, K Srinivasa Rao, "Classification of Breast Cancer Proteins Using DRNN Method," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.106-109, 2019.

Mobile Ad hoc Networks (MANET) possess dynamic topology and have no fixed infrastructure. Numerous challenges in routing in MANETs exist because of its frequent and unpredictable topology. One of the major constraints in designing of these protocols is the battery power. Various routing protocols have been implemented for finding optimal path from source to destination considering the cost and efficient use of energy. This paper explores different types of routing protocols, their merits and demerits, approach of routing protocols and implementation of one such energy efficient routing protocol technique using Genetic Algorithm to determine the shortest path between the source and the destination. Routing protocols based on Genetic Algorithm gives us the insight that how the concepts of genetics are applied to MANETs and is used to determine an optimal route taking into account the optimization of battery power. Genetic Algorithm takes less computational time, provides multiple optimal paths in case of failure of one path as well as increases the throughput of the network. In addition it covers the significance of Genetic Algorithm in MANETs.

Mobile Ad hoc Network (MANET), Routing Protocol, Efficient Routing Protocols, Genetic Algorithm(GA)

[1] Arun Biradar, Ravindra C. Thool, Vijaya R. Thool, “Genetic Algorithm Based Unipath and Multipath Intelligent Routing for Mobile Ad-hoc Networks”, International Journal of Advances in Computer Science and Technology, Vol.3, Issue.4, pp.276-282, 2014.
[2] Shipra Gautam, Rakesh Kumar, “A Review of Energy-Aware Routing Protocols in MANETs”, International Journal of Modern Engineering Research, Vol.2, Issue.3, pp.1129-1133, 2012.
[3] Upasna, Jyoti Chauhan, Manisha, “Minimized Routing Protocol in Ad-Hoc Network with Quality Maintenance Based on Genetic Algorithm: A Survey”, International Journal of Scientific and Research Publications, Vol.3, Issue.1,pp.1-5, 2013.
[4] Anjum Asma, Gihan Nagib, “Energy Efficient Routing Algorithms for Mobile Ad Hoc Networks –A Survey”, International Journal of Emerging Trends & Technology in Computer Science, Vol.1, Issue.3, pp.218-222, 2012.
[5] N. Kumar, Dr.C.Suresh, Gnana Dhass, “Power Aware Routing Protocols in Mobile Adhoc Networks-Survey”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol.2, Issue.9, pp.121-128,2012.
[6] Ravi G, Reemlus Jacob D, “Energy Aware Routing For Ad-hoc Networks Using Dynamic Path Switching”, International Journal of Ad hoc, Sensor & Ubiquitous Computing, Vol.5, Issue.3, pp.1-11, 2014.
[7] Kewal Vora, Jugal Shah, Shreyas Parmar, Shivani Bhattacharjee “MANETs: Overview of Vulnerabilities, Security Threats and Prevention and Detection Techniques”, International Journal of Computer Sciences and Engineering, Vol.3, Issue.10, pp.26-31 2015.
[8] N. Kohila, R. Gowthami, “Routing Protocols in Mobile Ad-Hoc Network”, International Journal of Computer Science and Mobile Computing, Vol.4, Issue.1, pp.159-167, 2015.
[9] Sonam Jain, Sandeep Sahu, “The Application of Genetic Algorithm in the design of Routing Protocols in MANETs: A Survey”, International Journal of Computer Science and Information Technologies, Vol.3, Issue.3, pp.4318-4321, 2012.
[10] Sumathy S, Sri Harsha E, Yuvaraj Beegala, “Survey of Genetic Based Approach for Multicast Routing in MANET”, International Journal of Engineering and Technology, Vol.4, Issue.6, pp.474-485, 2013.
[11] Lubdha M. Bendale, Roshani. L. Jain, Gayatri D. Patil, “Study of Various Routing Protocols in Mobile Ad-Hoc Networks”, International Journal of Scientific Research in Network Security and Communication, Vol.6, Issue.1, pp.1-5, 2018.
[12] Rajeev Ranjan, P.J. Pawar, “Assembly Line Balancing Using Real Coded Genetic Algorithm”, International Journal of Scientific Research in Computer Science and Engineering, Vol.2, Issue.4, pp.1-5, 2014.

Arpit Kumar Jain, Manish Kumar, "Multi - Objective Genetic Algorithm based Study for Energy Efficient Routing in MANET," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.110-114, 2019.

Mining Frequent pattern is a common technique of data mining and used as a preliminary step to mine association rules. Some frequent patterns are sensitive as they may disclose confidential information to adversaries and needs to be hidden in the data before sharing. Many of the existing techniques hide sensitive itemsets at a single sensitive support threshold. Also, these techniques generate various side effects and suffer from unexpected information loss. In this paper, a novel approach to hide sensitive itemsets at multiple sensitive support thresholds is proposed. The database is modeled as a set of closed itemsets which are selectively sanitized to hide sensitive itemsets. The proposed Recursive Pattern Sanitization algorithm for Personalized Itemsets Hiding (RPS-PIH) sanitizes the closed itemsets to hide sensitive itemsets at multiple sensitive support thresholds without generating any side effects. The sanitized model represents privacy preserved patterns of the database which may be shared to the third party for further data analysis without disclosing private information. Experimental results indicate that the proposed approach is efficient in hiding sensitive itemsets at multiple sensitive support thresholds. The effectiveness of the proposed approach is measured using popular metrics for side effects and information loss. The proposed approach is effective in reducing information loss and eliminating the generation of side effects compared with existing state-of-the-art techniques.

Itemset Hiding, Multiple Support Threshold, Privacy Preserved Data Publishing (PPDP), Personalized Privacy Preservation, Pattern Sharing, Pattern Sanitization, Sensitive Knowledge

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Surendra H, Mohan H S, "Hiding Sensitive Itemsets at Multiple Support Thresholds without Side Effects," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.115-120, 2019.

Pulse Width Modulation (PWM) is an essential technique for many electrical applications. It is mainly used for controlling DC power to an electrical device. It can be used in applications where its duty cycle is used to convey information over a communication channel. Though many software designs and hardware models are available for PWM, FPGA modules are reliable in terms of speed and complexity. Hardware optimization is possible in FPGAs. Hence work is focused to realize the PWM on FPGA. In this paper hardware model of PWM using HDL is presented. This model is designed using VHDL and implemented on VIRTEX FPGA Device. Simulation is carried using Xilinx ISE. The RTL Design is synthesized using Xilinx XST and the generated bit stream file is implemented on Virtex FPGA board.

PWM, RTL, VHDL, FPGA

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[13] Kumar R., "Candidate Job Recommendation System", International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.6, pp.12-15, 2018.
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V.V.S.Vijaya Krishna, "FPGA Realization of PWM using HDL," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.121-125, 2019.