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Protection of biometric data is gaining interest and digital watermarking techniques are used to protect the biometric data from either accidental or intentional attacks. Among all the biometric techniques, fingerprint-based identification is the oldest method which has been successfully used in numerous applications. The information used for identification or verification of a fingerprint, mainly lies in its minutiae. Advanced image watermarking is a valuable answer for the issue of data security, copyright and system security. The proposed algorithm is based on Singular Value Decomposition (SVD) and Discrete Wavelet Transform (DWT) to improve the recognition performance as well as the security of fingerprint based biometric system which will provide adequate security to fingerprint data without degrading visual quality. The algorithm converted the minutiae into binary watermark, increasing embedded information capacity. Different experiments are performed to test the effectiveness and robustness of proposed algorithm and the experimental results shows that the scheme is effective and robust against various image processing attacks. Results shows higher PSNR and NC values under general image processing.The algorithm can satisfy the transparency and robustness of the watermarking system very well and the useful information can be extracted accurately even if the fingerprint is severely degraded.

Arnold transform, Digital Watermarking, DWT, Fingerprint minutiae, Normalized coefficient, Peak signal to noise ratio, SVD

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Komal Ramteke, Akhil Anjikar, Sushil Chavhan, "Digital Image Watermarking Based on DWT and SVD for Fingerprint Security," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.1073-1078, 2019.

Current Era is the age of Information Technology. No single domain is untouched by digital systems. The backbone of this era is the programming languages which make such high quality software that can solve the most challenging problems to human mind and eliminate the chances of error. Java is such a language which spans across standalone software programming to web development to mobile application development to networking to cognitive programming to data mining and etc, which is the foundation of all modern programming. The remainder operation is a part of every programming language, so in Java, is a very important operation and has its use from mundane computing to the most technical implementations. This paper addresses the issue that remainder operation in mathematics is achieved by the definition given by the great mathematician Euclid, founder of geometry, whereas in Java the implementation of remainder division does not exactly follow the rules of mathematics, and due to this deviation and also for a programmer who does not know this fact can unknowingly program a software with wrong calculations which may result in catastrophe. Besides raising this issue this paper also provides suggestion(s) and solution(s) to overcome the matter and align properly with the underlying mathematics of remainder operation.

Remainder, Java, Euclidean theorem, Arithmetic Operators

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[3] Saurabh S. Patel and Sunil N. Kore, "Case Study: Digital Advertisement Board", ISROSET-Journal (IJSRCSE) , Vol.1 , Issue.3 , pp.48-50, May-2013
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Rishi Saxena, "Deviation of Remainder from Euclidean Definition, Java’s Perspective, Reason(s) and Suggestion(s).," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.1079-1083, 2019.

In this day and age correspondence has turned out to be so natural due to reconciliation of correspondence innovations with web. In any case the outwardly tested individuals think that its exceptionally hard to use this innovation on account of the way that utilizing them requires visual observation. Despite the fact that numerous new progressions have been executed to enable them to utilize the PCs productively no innocent client who is outwardly tested can utilize this innovation as effectively as an ordinary gullible client can do that is not normal for typical clients, they require some training for utilizing the accessible advancements. This paper goes for building up an email framework that will help even a credulous outwardly disabled individual to utilize the administrations for correspondence without past preparing. The framework won`t let the client make utilization of console rather will work just on mouse activity and discourse change to content. Additionally, this framework can be utilized by any typical individual additionally for instance the person who can`t peruse. The framework is totally founded on intuitive voice reaction which will make it easy to understand and productive to utilize. Web has turned out to be one of the essential civilities for everyday living. This design will decrease psychological burden taken by visually impaired individual to keep in mind and type characters utilizing console as all activities are going also empowered through mouse. This framework can be utilized successfully by incapacitated and uneducated people.

TSS(Text To Speech), ASR(Automatic Speech Recognition), IVR(Interactive Voice Response), STT(Speech To Text)

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T. Mujawar, P. Mule, P. Fartade, P. Bhoite, N. Ashtekar, P. Kurzekar, "Voice Based Gmail Service for Visually Challenged People," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.1084-1087, 2019.

The objective of Image steganalysis is the detection of presence of hidden content in any given image. Steganalysis is a binary classification problem for classifying a given image into one of two classes either Stego or Cover. Conventional Steganalysis consisted of a two step method, feature extraction followed by classification using machine learning. This feature extraction process required an in-depth knowledge of image statistics which are affected by hiding the secret data. With the advent of Deep Learning, Convolution neural networks(CNN) are being widely used for image classification, with an advantage of automatic feature learning. CNN based Steganalysis methods have made the feature extraction step simple as the steganalyzer does not need to specify the features which are affected by data hiding. Added to this feature extraction step and classification step are integrated into a single step. In this paper we have reviewed the existing CNN based steganalysis methods and proposed a novel CNN architecture customized for the task of steganalysis named StegNet. StegNet is built based on deep residual learning. And each feature map is assigned a weight to determine the priority by using global average pooling.

Steganalysis, feature leaning, CNN, Steganography, residual learning

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[37] J.-F. Couchot, R. Couturier, C. Guyeux, and M. Salomon, “Steganalysis via a convolutional neural network using large convolution filters for em- bedding process with same stego key,” arXiv preprint arXiv:1605.07946,
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John Babu G, Sridevi Rangu, "StegNet: An Efficient CNN based Steganlyzer," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.1088-1093, 2019.

Machine Learning, a subset of Artificial Intelligence is very popular and emerging field of science which basically focus on designing the approach enabling the computer or machines to learn from the input provided or past experience. There are lots of application of Machine Learning in day-to-day life such as face detection and recognition, decision making in business forecasting, etc. It is also becoming the business subject to various giant enterprises like Amazon, Google, FaceBook, etc. In this paper, we have focused our discussion to some popular Supervised Machine Learning algorithms that are SVM, logistic regression, Multinomial Naive Bayes, KNN apart from some other supervised Machine Learning algorithms like Linear Regression, Linear Discriminant Analysis, Decision Tree, Random Forest, Naïve Bayes, etc. and we determine the most efficient classification algorithm based on the data set which is multiclass dataset. This research paper gives some clarity to the selection of algorithm specific to some application. And we have shown the comparative results.

Machine Learning (ML), K-nearest neighbours (KNN), Logistic Regression (Log), Multinomial Naïve Bayes (MulNB), Support Vector Machine (SVM), Classifier

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Sandeep Kumar Verma, Turendar Sahu, Manjit Jaiswal, "A Deep Analysis and Efficient Implementation of Supervised Machine Learning Algorithms for Enhancing The Classification Ability of System," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.1094-1101, 2019.

The people of various domains, mechanical engineering for machine drawing, electronics engineering for circuit drawing, computer engineering for architectural design etc. generally use sketches. Sketch recognition is defined as the process of identifying symbols that user draw using single or multiple stroke. We have developed MAHI a sketching language based on geometrical rules for multi-domain Sketch Recognition MAHI offers the user more liberty for free-style sketching as well as for grouping the strokes, reducing the complexity for recognizing the sketches. And at the same time it edits the sketches as well as recognizes the sketches efficiently natural environment to the user. The recognition engine interprets the sketches based on the information obtained from the description of domains associated with the input from the user. We have created and tested a Multi-domain recognition engine for Sketch Recognition based on the system use multi-layer architecture for recognition engine.

Sketch recognition, Bayesian Network, Heuristic

[1] Alvarado, C. (2000) ‘A natural sketching environment: bringing the computer into early stages of mechanical design’ Master’s thesis, MIT.
[2] Alvarado, C.(2004) ‘Multi-domain sketch understanding’ PhD thesis, Department of Electrical Engineering and computer science, Massachusetts Institute of Technology.
[3] Alvarado, C.(2004) ‘Sketch Recognition And User Interfaces: Guidelines for Design and Development’, AAAI Fall Symposium on Pen-Based Interaction.
[4] Alvarado, C. Davis, R. ‘Sketchread a multi-domain sketch recognition engine’ In Proceedings of UIST ’04, pp.23–32.
[5] Alvarado. C. and Oltmans. M. (2002) ‘A Framework for multi-domain sketch recognition’ Proceedings of AAAI Spring Symposium on sketch Understandings, Stanford University, pp.1-8.
[6] Alvarado, C. Oltmans, M. Davis, R (2002) ‘A Framework for multi-domain sketch recognition’ Proceedings of AAAI Spring Symposium on sketch Understandings, Stanford University, pp.1-8.
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[24] Sahoo, G. and Singh, B.K.(2008) ‘MAHII: Machine And Human Interactive Interface’ International Journal of Image Processing(IJIP), Vol. 2, Issue 3, pp.1-11.
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Bhupesh Kumar Singh, Meghna Luthra, "MAHI As a Sketching Language," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.1102-1109, 2019.

A color image Chaotic systems are commonly used in cryptosystems because chaotic system is very sensitive to initial conditions and also have the property of unpredictability as well as ergodicity. We have modified the two dimensional logistic sine map. This modified two dimensional sine logistic map enhanced the unpredictability and ergodicity. It also enhances the range of chaotic map. With the help of this modified two dimensional sine logistic map image encryption is performed. In the proposed technique confusion and diffusion both operations are performed, further to enhance the security level random values are added to the original image. With the help of simulation results and analysis of security, it can be proved that modified two dimensional sine logistic map can encrypt several types of images. The proposed algorithm also has the ability to resist from different types of attack.

Chaotic Logistic map, XOR operation, Modified Sine Logistic Map System

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Subodh Kumar, Rajendra Kumar, "A Modified Image Encryption Technique Using Two Dimensional Sine Logistic Map," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.1110-1115, 2019.

This paper clarifies the expectation of a stock utilizing Machine Learning. The specialized and crucial or the time arrangement investigation is utilized by the vast majority of the stockbrokers while making the stock expectations. In this setting this investigation utilizes an AI system called Support Vector Machine to foresee stock costs for the vast and little capitalizations and in the three distinct markets, utilizing costs with both every day and regularly updated frequencies. In the money world stock exchanging is a standout amongst the most imperative exercises. Securities exchange expectation is a demonstration of attempting to decide the future estimation of a stock other money related instrument exchanged on a budgetary trade. In this paper, propose a Machine Learning and novel stock prediction approach that will be prepared from the accessible stocks information and increase insight and after that utilizes the procured learning for an exact forecast. The programming language is utilized to foresee the financial exchange utilizing AI.

Support vector machine, Machine Learning, Artificial Intelligence

[1] Sykes A. O., "An Introduction to Regression Analysis", The Inaugural Coase Lecture, 1993 [2] Yue Xu S., "Stock Price Forecasting Using Information from Yahoo Finance and Google Trend", UC Berkeley, 2012
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S.Srividhya, R.Kayalvizhi, "A Financial Exchange Using Novel Stock Prediction," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.1116-1120, 2019.

The thyroid is one in every of the foremost necessary organ in our body. It secretes thyroid hormones that area unit to blame for dominant metabolism. The less secretion endocrine causes adenosis and far secretion of thyroid causes glandular disease. For deciding, data processing technique is principally employed in tending sectors, sickness identification and giving higher treatment to the patients. during this paper we`ve got bestowed an summary and comparison of assorted existing data processing techniques used for thyroid diseases identification. most ordinarily used techniques area unit call Tree, Support Vector Machine and Neural Networks that has been resulted as a high accuracy. the most objective of this study are to hold out the survey of knowledge mining techniques accustomed identification of assorted thyroid ailments, to gift the techniques used and its accuracy.

Thyroid diseases, Neural network, Support Vector Machine, Decision tree, KNN, Learning Vector Quantization, etc.

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Using Extreme Learning Machine”J Med Syst (2012) 3327–3337.
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[17]Li-Na Li,Ji-Hong Ouyang ,Hui-Ling Chen &Da-You Liu”A Computer Aided Diagnosis System for Thyroid Disease
Using Extreme Learning Machine”J Med Syst (2012) 3327– 3337.
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[21] Suman Pandey, Deepak Kumar Gour, Vivek Sharma” Comparative Study on Classification of Thyroid Diseases” International Journal of Engineering Trends and Technology (IJETT) – Volume 28 Number 9 - October 2015.
[22]G. Rasitha Banu “ A Role of decision Tree classification data Mining Technique in Diagnosing Thyroid disease” International Journal of Computer Sciences and Engineering Volume-4, Issue.

Anuradha Shyam, Mohanrao Mamdikar, Pooja Patre, "Thyroid Disease Detection and Classification using Machine Learning Techniques: A Review," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.1121-1125, 2019.

In Agriculture, leaf diseases have grownup to be a dilemma because it will cause vital diminution in each quality and amount of agricultural yields. Thus, automatic recognition of diseases on leaves plays a vital role in agriculture sector. This paper reviews all major techniques used for plant disease identification and also focuses on role of image processing techniques and machine learning in identification and classification of these disease. In this paper we are focusing on major fungal and bacterial disease found on leaves of plants, through this paper we also tried to focus on various studies have been done for the detection of such diseases. Finally, we conclude at the end gaps found in the previous studies and suggest some possible improvements for researchers.

plant disease, Machine Learning Techniques, bacterial disease, fungal disease

[1] Devi, D.A. and Muthukannan, K., 2014, May. Analysis of segmentation scheme for diseased rice leaves. In Advanced Communication Control and Computing Technologies (ICACCCT), 2014 International Conference on (pp. 1374-1378). IEEE.
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[4] Vijayakumar, J. and Arumugam, S., 2013, October. Certain investigations on foot rot disease for betelvine plants using digital imaging technique.InEmerging Trends in Communication, Control, Signal Processing & Computing Applications (C2SPCA), 2013 International Conference on (pp. 1-4). IEEE.
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[7] Paproki, A., Fripp, J., Salvado, O., Sirault, X., Berry, S. and Furbank, R., 2011, December. Automated 3D segmentation and analysis of cotton plants.In Digital Image Computing Techniques and Applications (DICTA), 2011 International Conference on (pp. 555-560). IEEE.s
[8] Choong, M.Y., Kow, W.Y., Chin, Y.K., Angeline, L. and Teo, K.T.K., 2012, November. Image segmentation via normalised cuts and clustering algorithm.In Control System, Computing and Engineering (ICCSCE), 2012 IEEE International Conference on (pp. 430-435).IEEE.
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[10] Stephen Gang Wu, Forrest Sheng Bao, Eric You Xu, Yu – Xuan Wang and Yi – Fan Chang, “A leaf recognition algorithm for plant classification using probabilistic neural network”, IEEE 7th International Symposium on Signal Processing and Information Technology,2007.
[11] Vijay Satti, AnshulSatya and Shanu Sharma, "An Automatic Leaf Recognition System for Plant Identification Using Machine Vision Technology", International Journal of Engineering Science and Technology (IJEST) ISSN:0975-5462, Vol 5, Issue 4, pp. 874-879, 2013.

[12]. TasneemTazeen Rashid Thuza Md. SazzadHossain – “Mobile Application for Determining Input Level Of Fertilizer And Detecting Diseases In Crops” – Thesis.
[13]. B. Klatt , B. Kleinhenz, C. Kuhn, C. Bauckhage, M. Neumann, K. Kersting, E.-C. Oerke, L. Hallau, A.-K.Mahlein, U. Steiner-Stenzel, M. Röhrig-"SmartDDS-Plant Disease Detection via Smartphone", EFITA-WCCA-CIGR Conference “Sustainable Agriculture through ICT Innovation”, Turin, Italy, 24-27 June 2013.
[14]. ShovonPaulinusRozario- “ Krishokbondhu - An automated system for diagnosis of paddy disease, Thesis, SCHOOL OF ENGINEERING AND COMPUTER SCIENCE, Department of Computer Science and Engineering, BRAC University, Submitted on September 1, 2014.
[15]. Shitala Prasad, Sateesh K. Peddoju and DebashisGhosh – “ AgroMobile: A Cloud-Based Framework for Agriculturists on Mobile Platform”, International Journal of Advanced Science and Technology Vol.59, (2013), pp.41-52 http://dx.doi.org /10.14257/ijast.2013.59.04 ISSN: 2005-4238 IJAST Copyright ⓒ 2013 SERSC.
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[17]. Shitala Prasad • Sateesh K. Peddoju • DebashisGhosh – “Multi-resolution mobile vision system for plant leaf disease diagnosis”, Received: 16 December 2013 / Revised: 17 September 2014 / Accepted: 31 January 2015 © Springer-Verlag London 2015.
[18]. RahatYasir and Nova Ahmed- “Beetles: A Mobile Application to Detect Crop Disease for Farmers in Rural Area”, Workshop on Human and Technology, 8 – 10 March 2014, Khulna, Bangladesh.
[19]. Alham F. Aji, QoribMunajat, Ardhi P. Pratama, HafizhKalamullah, Aprinaldi, Jodi Setiyawan, and Aniati M. Arymurthy- “ Detection of Palm Oil Leaf Disease with Image Processing and Neural Network Classification on Mobile Device “, International Journal of Computer Theory and Engineering, Vol. 5, No. 3, June 2013.
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Ramesh Kumar Singh, Jasmine Minj, "Detection of Bacterial and Fungal Leaf Diseases using Image Processing and Machine Learning Techniques," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.1126-1129, 2019.