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Achieving dependability has always been a challenging task for the software developers. Ensuring the selection of suitable dependability attributes for the software development is crucial for the development of dependable software and to maintain trust among software developers. For multi criteria decision making a very helpful tool is identified called Fuzzy Analytic Hierarchy Process (FAHP). For the comparison of different factors and sub-factors (attributes) and to know the impact of one factor over the other the FAHP has been identified by researchers. The main objective of this paper is to implementation of Fuzzy Analytic Hierarchy Process for the estimation of software dependability, By which we can identify the most suitable dependability attributes and ranked them as per their weight-ages. The Fuzzy Analytic Hierarchy Process uses a defined range of values rather than a single crisp value to vanish the decision maker’s uncertainty. Using the definite range of values, the decision makers can choose the value that shows his confidence and also they can specify their stance like optimistic, pessimistic or moderate. This paper reveals the implementation of Fuzzy AHP technique to calculate the weight-ages of dependability attributes and ranked them. This study can be a guide of the methodology to be implemented by software developers for the selection of appropriate dependability factors and sub-factors for the development of dependable software

Software Dependability; Dependability Factors; Dependability Sub-Factors (Attributes); AHP; FAHP

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[2] S. Saif Ahmad Abidi, F. Farouqui, “Analytical Hierarchical Process for Software Dependability”, Proceedings of 6th International Conference on “Computing for Sustainable Global Development”, pp. 1456-1460, 13th – 15th March, 2019, New Delhi (INDIA)
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Syed Saif Ahmad Abidi, Mohd. Faizan Farooqui, A.A Zilli, "Software Dependability Estimation: Implementation through Fuzzy AHP," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.1-8, 2019.

Depression is considered as one of the main psychological well-being issues in this day and age. Affecting contrarily on physical wellbeing, social consideration and scholarly accomplishment, one`s emotional wellness issue remain a critical general medical issue. This examination accepts to prevent emotional wellness issues from creating and plans to spare people and families from misery and spare huge assets for the wellbeing framework. The initial step to begin with the treatment procedure is to recognize wretchedness. In this model tweets from twitter is examined with the assistance of Natural Language Processing and Python code. Tweepy and TextBlob are utilized for further usage.

Natural Language Processing, Depression, Twitter, Python, Tweepy, TextBlob

[1] Mandar Deshpande, VigneshRao- “Intelligent Sustainable Systems” (ICISS2017)IEEE Xplore Compliant - Part Number:CFP17M19-ART, ISBN:978-1-5386-1959-9
[2] Afzal Ahmad, Mohammad Asif, Shaikh Rohan Ali Review Paper on “Predicting Mood Disorder Risk Using Machine Learning” International Journal of Scientific Research in Computer Sciences and Engineering Vol.7 , Issue.1 , pp.16-22, Feb-2019
[3] Shrija Madhu, “An approach to analyze suicidal tendency in blogs and tweets using Sentiment Analysis” International Journal of Scientific Research in Computer Sciences and Engineering Vol.6 , Issue.4 , pp.34-36, Aug-2018
[4] Xinyu Wang, Chunhong Zhang, Yang Ji1, Li Sun1, and Leijia “A Depression Detection Model Based on Sentiment Analysis in Micro-blog Social Network”
[5] Shweta Oak “Depression Detection analysis”
[6] Stankevich, VadimIsakov, Dmitry Devyatkin and Ivan SmirnovInstitute for Systems Analysis, Federal Research Center ”Computer Science and Control” of RAS, a. Moscow, Russian Federation, Feature Engineering for Depression Detection in Social Media Maxim
[7] Shubham Dham, Anirudh Sharmay , Abhinav Dhallz “Depression Scale Recognition from Audio, Visual and Text Analysis” Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Ropar, India
[8] A.K.Jose, N.Bhatia, and S.Krishna, “Twitter
Sentiment Analysis,” National Institute of Technology,
Calicut,2010.
[9] Sharath Chandra Johannes CEichst “Detecting depression and mental illness on social media: an integrative review”

Unnati Chawda, Shanu K Rakesh, "Implementation and Analysis of Depression Detection Model using Emotion Artificial Intelligence," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.9-12, 2019.

The latest generation of Least Square Twin Support Vector Machine has achieved amazing outcomes in the field of image classification. This paper presents a new method Linear Norms Tree based Least Square Twin Support Vector Machine for developing a plant disease recognition system, which is based on high-resolution multispectral satellite imagery. This proposed model can identify and classify different kinds of plant diseases. Dataset is a composition of diseased trees and other land cover. That are used to identify whether the tree is diseased or not. Experiments with wilt disease data set carried out indicate that new classifier, Linear Norms Tree based Least Square Twin Support Vector Machine, yields a progressively balanced classification accuracy between classes compared to different classification schemes in resolving the imbalanced classification problem.

Twin SVM, SVM, Norm, wilt, diseases, least square, imbalance data

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Mayank Arya Chandra, S S Bedi, "Linear Norm Tree Based Least Square TSVM Scheme for Wilt Diseased Trees Classification," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.13-17, 2019.

In the modern field of Internet with the advance development of digital communication, image security is the important concern to store data for communication in various organizations. By the use of cryptography techniques, it provides best strength and reliability to encrypt the images more essentially in the differe¬¬¬¬¬¬nt type of organizations such as criminal law enforcement, Ministry of Defence. Reverse watermarking is used for authentication and to authorize the users of the respective panel where the original image and watermark image gets recover. In this paper, original secrete image is encrypted through S-DES (Simple-Data Encryption Standard) with the use of a key image. This encrypted image is called watermarked image and on this watermarked image, we applied RESIDUE NUMBER SYSTEM and get the DES watermarked RESIDUE NUMBER SYSTEM encoded image. For decoding, we go for reverse process and get the secrete image back

Image Security, Watermark Image, Data Encryption Standard, Residue Number System

[1] S. Samanta, S. Dutta, G. Sanyal, "An Enhancement of Security of Image using Permutation of RGB-Components", IEEE 3`d International Conference on Electronics Computer Technology (ICECT), Vol: 2, pp. 404-408, 2011.
[2] G. Huayong, H. Mingsheng, W. Qian, "Steganography and Steganalysis Based on Digital Image", IEEE 4th International Congress on Image and Signal Processing (CISP), Vol: 1, pp. 252-255, 2011.
[3] P. Bandyopadhyay, S.Das, S. Paul, A. Chaudhuri, M. Banerjee, "A Dynamic Watermarking Scheme for Color Image Authentication", IEEE Trans., International Conference on Advances in Recent Technologies in Communication and Computing, pp. 314-318, 2009.
[4] B. Madhu, G. Holi, S. Murthy, “An Overview of Image Security Techiques” International Journal of Computer Applications Vol. 154, No.6, pp. 37-46, 2016.
[5] B.D. Parameshachari, K. M. S. Soyjaudah, M.V. Chaaitanyakumar, "A Study on Different Techniques for Security of an Image", International Journal of Recent Technology and Engineering, ISSE: 2277-3878, Vol.- 1, Issue-6, pp. 14-19, 2013.
[6] P. Bhangale, A. Gawad, J. Maurya, R.S. Raje, “Image Security using AES and RNS with Reversible Watermarking”, IJISET - International Journal of Innovative Science, Engineering & Technology, Vol. 4, Issue 5, pp. 350-355, 2017.
[7] D. Younes, P. Steffan, "Efficient Image Processing Application using Residue Number System", 20th International Conference on Mixed Design of Integrated Circuits and Systems (MIXDES), Gdynia, Poland, pp. 468-472, 2013.
[8] H.K. Maitya, S.P. Maity, “Joint Robust and Reversible Watermarking for Medical Images”, 2nd International Conference on Communication, Computing & Security, Procedia Technology pp. 275 – 282, 2012.
[9] M. abdullatif, A. M. Zeki, J. Chebil, T.S. Gunawan, "Properties of Digital Image Watermarking", IEEE 9th International Colloquium on Signal Processing and its Application (CSPA), Kuala Lumpur, Malaysia, pp. 235-240, 2013.
[10] M. K. Ramaiya, N. Hemarajani, and A. K. Saxena, "Security Improvisation in Image Steganography using DES", 3`d IEEE International Advance Computing Conference, pp. 1094-1099, 2013.
[11] A. Rahman, M. T. Naseem, 1. M. Qureshi, M. Z. Muzaffar, "Reversible Watermarking using Residue Number System", IEEE Trans., 7th International Conference on Information Assurance and Security (lAS), pp. 162-166, 2011.
[12] S.S. Mamarde, S.A. Ladhake, “A Review on Secret Image Protection using Reversible Watermarking”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 6, Issue 1, pp. 188-190, 2017.
[13] S. Aguru , B.M. Rao, “Data Security In Cloud Computing Using RC6 Encryption and Steganography Algorithms”, International Journal of Scientific Research in Computer Science and Engineering, Vol.7, Issue.1, pp.6-9, 2019.
[14] S. Bansal, “Data Security by Steganography: A Review”, International Journal of Scintific Research in Network Security and Communication, Vol.7, Issue-1 pp. 10-12, 2019.
[15] M. Mundher, D. Muhamad, A. Rehman, T. Sab and F. Kausar, “Digital Watermarking for Images Security using Discrete Slantlet Transform”, Applied Mathematics & Information Sciences, An International Journal, Appl. Math. Inf. Sci. Vol. 8, No. 6, pp. 2823-2830, 2014.
[16] P. Parmar, N. Jindal, “Image Security with Integrated Watermarking and Encryption”, IOSR Journal of Electronics and Communication Engineering, Vol. 9, Issue 3, pp. 24-29, 2014.
[17] N. Chandra, J. Bagga, "Performance Comparison of Digital Image Watermarking Techniques: A Survey", International Journal of Computer Application Technology and Research, Vol. 2, lssue 2, pp. 126-130, 2013.
[18] M. Mangtani, N. Limbad, “A Survey on Various Watermarking and Cryptography Techniques for Data Hiding in Medical Images”, IJSRD - International Journal for Scientific Research & Development, Vol. 3, Issue 09 , pp. 633-637, 2015.
[19] L. Bin, L. Lichen, Z. Jan, "Image Encryption Algorithm based on Chaotic Map and S-DES”, IEEE 2nd International Conference on Advanced Computer Control (ICACC), pp. 41-44, 2010.
[20] A. Al-Haj, N. Hussein and G. Abandah, "Combining cryptography and digital watermarking for secured transmission of medical images," 2nd International Conference on Information Management (ICIM), London, pp. 40-46, 2016.

J. Jain, A. Singh, "Robust & Secure Image Reverse Watermarking using Data Encryption Standard & RNS," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.18-23, 2019.

Stock Market Prediction is the demonstration of attempting to decide the future estimation of an organization stock or other money related instrument exchanged on a trade. Prediction on stock market is a great challenge as it is complex, dynamic and non-linear in nature. The main focus is on closing price of next day. High, Low, Volume is of importance but the closing price is of more value. There are numerous instances of Machine Learning algorithms possessed the capacity to achieve attractive outcomes while doing that kind of forecast. In this paper, the LSTM networks are used to predict future closing price of stock market based on the price history, alongside with technical analysis indicators. For that objective, a forecast model was built, and a series of experiments were performed and their outcomes were examined against various measurements to survey if this kind of calculation presents and enhancements when contrasted with other Machine Learning techniques.

Stock Market, Prediction, LSTM

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Rohit Kumar, Rohit Gajbhiye, Isha Nikhar, Dyotak Thengdi, Sofia Pillai, "Stock Market Prediction using Deep Neural Networks," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.24-28, 2019.

As we know wireless sensor network have many advantages such as its high speed and high quality which increase its use in modern time. As so the security in wireless sensor network became the most important factor. There are many security issues but the most important is “eavesdropping”. Eavesdropping is a type of attack in which the information being transmitted is attained or captured by some other devices. It is not possible to tell if a system had faced some kind of eavesdropping or not. In this paper various advantages and disadvantages will be discussed. There will be some application of WSNS and some introduction of eavesdropping issue with some techniques to detect this issue. We will also see various other attacks which leads to effect the security of wireless sensor networks. This paper will help in improving security in wireless sensor networks. This paper will further help in improving accuracy of wireless sensor networks. This paper will also light up various advantages and disadvantage of the technique which is being used for improving the security of wireless sensor networks.

Wireless sensor networks (WSNS), eavesdropping and security enhancement

[1]. .Xu, Q., Ren, P., Song, H., & Du, Q. (2016). Security Enhancement for IoT Communications Exposed to Eavesdroppers with Uncertain Locations. IEEE Access, 4, 2840-2853.
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[3]. 3) Chen, X., Zhang, Z., Chen, H., & Zhang, H. (2015). Enhancing wireless information and power transfer by exploiting multi-antenna techniques.IEEE Communications Magazine, 53(4), 133-141.
[4]. 4) Li, C., & Lee, C. (2012). A novel user authentication and privacy-preserving scheme with smart cards for wireless communications.Mathematical and Computer Modelling, 55(1-2), 35-44.
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[18]. K. Nagarathna, Kiran Y. B, J D. Mallapur, S. Hiremath (2012) worked on “Trust Based Secured Routing in Wireless Multimedia Sensor Networks”, 2012 Fourth International Conference on Computational Intelligence, Communication Systems and Networks.
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M. Dahiya, A. Sangwan, "A Review Paper on Various Attacks on Wireless Sensor Networks," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.29-35, 2019.

Weather is the most important factor in terms of farming and agriculture. It continuous, data-intensive, multidimensional, and chaotic process. These properties of weather make its forecasting a formidable challenge. The most technologically challenged problems of the last century are weather forecasting. The harvest of crops is dependent on this factor. To make an accurate weather prediction is one of the major challenges that is being faced all over the world. Scientists have tried their best to forecast environmental characteristics using a number of methods and some of these methods are more accurate than others. Weather forecasts provide critical information about future weather. Every year notorious weather harms the life, property and many government activities which is usually heavily funded is destroyed, as a result weather forecasting would help government to plan out things in advance to prepare its citizens for the worst of the weather. There are many different methodologies that have come into observation regarding weather prediction. This paper describes one of the many techniques used for prediction of weather which will be beneficial for the farmers, agricultural and scientists. It will help them to better understand the weather for yielding crops and for studying environment too.

Regression, Pandas, Scikit Learn, Numpy

[1]. Pushpa Mohan, Dr. Kiran Kumari Patil,” Survey on Crop and Weather Forecasting based on Agriculture related Statistical Data”, International Journal of Innovative Research in Computer and Communication Engineering, Bangalore, India, Vol. 5, Issue 2, February 2017, pp no. 2320-9801. [1]
[2]. Sneha S. Gumaste, Anilkumar J. Kadam, “Future weather prediction using genetic algorithm and FFT for smart farming”, India.[2]
[3]. M. Manikandan , R. Mala,” Optimal Prediction of Weather Condition Based on C4.5 Classification Technique”, International Journal of Computer Sciences and Engineering, Vol.-6, Issue-10, Oct 2018, pp no. E-ISSN: 2347-2693 [3]
[4]. K.P. Mangani, R. Kousalya, “Big Data Approach for Weather Based Crop Insurance”, IJSRNSC Volume-5, Issue-3, June 2017, pp no. E-ISSN: 2347-2693 [4]
[5]. Amit Palve, Ajit Patil, Amol Potgantwar, “Big Data Analysis Using Distributed Approach on Weather Forecasting Data”, Volume-5, Issue-3, June 2017, India, pp no. ISSN: 2321-3256[5]
[6]. L. Shaikh, K. Sawlani,“A Rainfall Prediction Model Using
[7]. Articial Neural Network”, IJSRNSC Volume-5, Issue-1,
[8]. April 2017, pp no. ISSN: 2321 3256.[5]

Sudhnya Kashikar, Sumedha Patil, Ameya Vedantwar, Shivani Katpatal, Sofia Pillai, "Weather Prediction using Scikit-Learn," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.36-40, 2019.

Wireless sensor network (WSN) consists of a number of dedicated sensors which are spatially dispersed for different purposes such as monitoring and recording the various physical conditions of the environment and organising the data collected at a central location for further operations. In wireless sensor networks, the sensor nodes have limited energy which affects the lifetime of the nodes. Hence the system performance can be enhanced by minimising energy dissipation and maximising the lifetime of the nodes in the wireless sensor networks. Low Energy Adaptive Clustering Hierarchy(LEACH) protocol is a very important energy efficient protocol .This protocol improves the method of selection of cluster heads among the various sensor nodes by examining the residual energy of the different nodes and then the node which is having more residual energy is made the cluster head for that group of nodes .This protocol is highly efficient as it reduces energy consumption and decreases system delay which results in improvement of lifetime of the sensor nodes. So, we propose to improve the protocol to enhance the wireless sensor nodes performance through the energy optimisation.

WSN, LEACH

[1]. Sunkara Vinodh Kumar and Ajit Pal “Assisted-Leach (A-Leach) Energy Efficient Routing Protocol for Wireless Sensor Networks”, International journal of computer and communication engineering, Vol. 2, No. 4, pp. 420-424, 2013.
[2]. Shio Kumar Singh, M P Singh and D K Singh, "A Survey of Energy-Efficient Hierarchical Cluster-Based Routing in Wireless Sensor Networks”, International Journal of Advanced Networking and Application Volume 02, Issue 02, Pages: 570-580, 2010.
[3]. BS Mathapati, SR Patil, VD Mytri, “Energy efficient reliable data aggregation technique for wireless sensor networks”, International Conference on Emerging Technology Trends in Electronics, Communication and Networking, 1-6, 2012
[4]. Hanady M. Abdulsalam and Layla K. Kamel, "W-LEACH Weighted Low Energy Adaptive Clustering Hierarchy Aggregation Algorithm for Data Streams in Wireless Sensor Network", IEEE International Conference on Data Mining Works, pp. 1 -8, 2010.
[5]. Bilal Abu Bakr and Leszek Lilien, “Extending Wireless Sensor Network Lifetime in the LEACH-SM Protocol by Spare Selection”, Fifth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, pp. 277-282, 2011.
[6]. Hiren Thakkar, Sushruta Mishra and Alok Chakrabarty, “A Power Efficient Cluster-based Data Aggregation Protocol for WSN”, International Journal of Engineering and Innovative Technology (IJEIT) Volume 1, Issue 4, April 2012.
[7]. Parul Khurana and Inderdeep Aulakh, “Wireless Sensor Network Routing Protocols: A Survey”, International journal of computer applications, vol. 75, No. 15, 2013.
[8]. Md. Faruqul Islam, Yogesh Kumar , Saurabh Maheshwari, Neeti Jain ,” Recent trends in Energy Efficient Clustering in WSN”, International Journal of Computer Applications, vol. 95, No.20, pp. 44-48,2014.
[9]. Pankaj Chauhan and Tarun Kumar," Power Optimization in Wireless Sensor Network: A Perspective", International Journal of Engineering and Technical Research (IJETR), vol. 3, issue 5, May 2015
[10]. Amit Bhattacharjee, Balagopal Bhallamudi and Zahid Maqbool, “Energy- Efficient Hierarchical Cluster Based Routing Algorithm in WSN: A Survey “, International Journal of Engineering Research & Technology (IJERT), Vol.2, Issue 5, may, 2013, pp.302-311
[11]. Sandeep Verma, Richa Mehta, Divya Sharma, Kanika Sharma, “Wireless Sensor Network and Hierarchical Routing Protocols: A Review”, International Journal of Computer Trends and Technology(IJCTT), Vol.4, Issue 8, August 2013,pp.2411 -2416
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[13]. Sudhanshu Tyagi, Neeraj Kumar,”A systematic review on clustering and routing techniques based upon LEACH protocol for wireless sensor networks”, Journal of Network and Computer Applications 36(2), 623-645, 2013
[14]. Meena Malik, Yudhvir Singh, Anshu Arora,”Analysis of LEACH protocol in wireless sensor networks”, International Journal of Advanced Research in Computer Science and Software Engineering 3(2), 2013
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Deepika, Anil Sangwan, "Review on LEACH (Low Energy Adaptive Clustering Hierarchy) Protocol for Enhancing Energy Efficiency of WSN," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.41-43, 2019.

Personalization of services can be proclaimed as the fulcrum of today’s industries. Recommender engines are vastly employed to lend personal relevance to the consumers of services. Recommenders are a class of systems built upon the semantics derived off of consumer profiling, quantitative representations of preferences and so on. They draw a lot of their underlying mechanisms from the field of computational methods, that is, the use of mathematical models and methods to effect relevant suggestions. This paper proposes a framework for the creation of a recommender engine that is capable of predicting a rating matrix as well as providing suggestions. The key advantage introduced by the framework is that in addition to collaborative methods it also employs the techniques of topic modelling and fuzzy logic to find latent topics of interest that are not evidently visible as well as latent groupings of particulars modelled. This framework is a three-component engine consisting of an exploratory module augmented with statistical analysis, visualizations and algorithmic analysis, a latent factor analysis module built on the principles of topic modelling, fuzzy c means algorithm and a prediction-suggestion module built on the concept of singular value decomposition. Thus, this engine can be used in emerging problem domains of relevance to today’s society.

Natural Language Processing, Topics Modelling, Recommender Systems, Latent Factor Analysis, Framework

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Aishwarya Rajamani, Alpha Vijayan, "A Framework of Computational Methods for Recommender Engine," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.44-48, 2019.

Big data refers to voluminous amount of structured or unstructured data . This voluminous data is a blend of substantial and informational collections that has extensive volume of information, online networking examination, information administration proficiency, continuous information and so forth. Enormous information examination is the methodology of dissecting immense measures of information. Enormous Data has a few properties ie. volume, assortment, speed and veracity. For preparing such huge informational indexes there is an approach which is called Hadoop which handles the huge information.

Big Data

[1] A Research Paper on Big Data and methodology by Shilpa and Manjit Kaur
[2] Review Paper on Use of Big Data in E-Governance of India by Shubham Kalbande, Sumant Deshpande
[3] Review paper on big data and Hadoop by Harshawardhan
S. Bhosale, Prof. Devendra P. Gadekar
[4] Big Data in Big Companies by Thomas H. Davenport Jill Dyché
[5] Big Data And Hadoop: A Review Paper by Rahul Beakta CSE Deptt., Baddi University of Emerging Sciences & Technology, Baddi, India

Harshit Gupta, "Review Paper on Handling Big Data," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.49-51, 2019.