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Now a day’s sentiment analysis performs a very vital role in text mining. In essence web mining is a very broad area in a data mining field for extracts the sentiment of the text. To identify the sentiment of the textual data is a very challenging task. The present work focuses on sentence level negation identification and calculation from the News articles and Blogs. Two step approaches generally used for analysis namely preprocessing and post processing. Preprocessing consists of the tasks like stop word removing, punctuation mark removal, number removal, white space removal etc. Post processing comprises identification of sentiments from the text and calculation of score. The work analyses the performance of support vector machine, Naïve Bayes for the dataset collected online.

Sentiment Analysis, Support Vector Machine, Naïve Bayes, Machine Learning Algorithm

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[17] Rinker, T. W. (2018). sentimentr: Calculate Text Polarity Sentiment version 2.6.1. http://github.com/trinker/sentimentr
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Vishal Shirsat, Rajkumar Jagdale, Kanchan Shende, Sachin N. Deshmukh, Sunil Kawale, "Sentence Level Sentiment Analysis from News Articles and Blogs using Machine Learning Techniques," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1-6, 2019.

In recent times, there exist several approaches for differential-style attacks like truncated differential attack, high-level differential attack, boomerang attack etc. This paper involves the study of boomerang-style attack on S-boxes and a new SAC analysis approach to test the strength of S-boxes against such attacks. The proposed analysis is tested on each input elements of 8 S-boxes of DES and 8 input elements on the S-box of AES. The vulnerability factor n⁄2 has been measured by calculating all 1`s of every column from the generated SAC matrix. Finally a comparison of standard deviation, coefficient of variance and other factors show the way towards the conclusion.

Block Cipher, S-box, Differential Cryptanalysis, Boomerang attack, Truncated Differential

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[8] Avijit Datta, Dipanjan Bhowmik, Sharad Sinha, "A New Approach towards Confusion Analysis of S-boxes using Truncated Differential Cryptanalysis", International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.249-256, 2019.
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[17] P. Sharma, D. Mishra, V.K. Sarthi, P. Bhatpahri, R. Shrivastava, "Visual Encryption Using Bit Shift Technique", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.3, pp.57-61, 2017

Avijit Datta, Dipanjan Bhowmik, Sharad Sinha, "A Novel Technique for SAC Analysis of S-Boxes for Boomerang-Style Attacks," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.7-13, 2019.

The new arrivals and trends in the technology have attracted many different areas to make the world modernized and smart. The autonomous driverless car is unique example in the category. The aim of this work is to presents a computer vision-based system for real-time traffic sign identification, recognition and retrieval system for Indian traffic signage’s. The system consists of two phases. Firstly the signage’s are detected and recognized for a given video using state-of-art detector method known as aggregated channel features. Second, retrieval of videos is performed using two distance measures known as Euclidean and Jaccard matrices. Compared to the previous approaches our method offers the detection recognition and retrieval of signage’s of different shape and colors in heterogeneous climatic conditions. The results demonstrate the proposed method performs good detection, recognition, and retrieval accuracy with 60 frames per second in less time complexity.

Traffic Signage’s, identification, recognition, retrieval, Traffic Sign recognition system

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Shivanand S Gornale, Ashvini K Babaleshwar, Pravin L Yannawar, "Content Based Video Retrieval for Indian Traffic Signage’s," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.14-20, 2019.

This paper presents a usability testing of a Moodle mobile application which is Learning Management System (LMS) in the context of HE students and teachers perspectives. This paper begins with discussing how mobile learning could help in teaching learning process. Digitization has changed the complete teaching-learning process in higher education in India. During last decade or so usage of mobile devices has been increased tremendously as mobile technology allows the learners to perform various tasks as far as education is concerned. In M-learning technology, usability of mobile applications plays a vital role in the context teaching-learning process. ISO defines usability as “The extent to which a product can be used by specified users to achieve specified goals with effectiveness, efficiency and satisfaction in a specified context of use”. Aim of this research study is to test Moodle application in the context of HE (Higher Education) in India with special reference to MBA and MCA courses. With this research, we tried to acquire important finding and information for administrator, teachers and students on how Moodle is effective in teaching-learning process. In this paper researchers have used convenience sampling method to collect qualitative and quantitative data from students and teacher of MBA and MCA courses. This research study conducted using One hundred forty three (143) students and seventy two (72) teachers from the professional courses like MCA and MBA. The paper concludes with a discussion of how various usability dimensions makes impact on M-Learning application in the context of Higher Education is concerned.

Moodle, Usability Testing , M-Learning, Higher Education, Learning Management System

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Kamlesh A. Meshram, Manimala Puri, "Usability testing of Moodle application in the Context of M-learning in HE in India with Special Reference to MBA and MCA courses," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.21-27, 2019.

Now a day, all over the world 70-80% of people with poor reading skills are likely dyslexic. One in five a student, or 15-20% of the population, has a language based learning disability. Dyslexia is the most common of the language based learning disabilities. Nearly the same percentage of males and females has dyslexia. Children suffering from a learning disability might face difficulties with reading, writing or mathematics but they excel in other areas of interests. It is in the interest of the society and especially the parents to identify the problem early in the development of the child and steer him/her towards a preferred field. They might lose their sense of self-worth and blame themselves for their situation. The model being proposed is a Web-based tool incorporating machine learning techniques (Decision trees) for predicting whether children (8-10 years) are at a risk of having Specific Learning Disability by showing the areas of learning disability on the basis of the clinical information and research.

Dyslexia, Weka, SVM, Naïve Bayes, J48 Decision Tree, Neural Network

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[19] Folorunsho, Olaiya. "Comparative Study of Different Data Mining Techniques Performance in knowledge Discovery from Medical Database." International Journal 3, no. 3 (2013). International Journal of Engineering & Technology 3411
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H. Selvi, M.S. Saravanan, "Diagnosis of Dyslexia Students Using Classification Mining Techniques," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.28-33, 2019.

In a adhoc network, the performance of the network will be measured by the PDR (Packet Delivery Ratio), PLR (Packet Lost Ratio), Delay and throughput. There are various parameters that affects these network performance measuring characteristics, among others one parameter packet size is important. Node survival and the draining speed of the node’s energy is another very important factor for consideration of the nodes presence in the network. In this research paper, we have presented two set of results recorded. In the first set of results PDR, PLR, Delay, and throughput are recorded by varying the packet size from 48 bytes to 80 KB in an adhoc network of 25 nodes. In the second set, the remaining energy and node’s draining energy speed is recorded at different time stamps during the communication in the adhoc network with the same setup of 25 nodes.

adhoc network, QoS, packet size, PDR, PLR, Delay, throughput

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[3] G. Santhi, and A. Nachiappan, “A SURVEY OF QOS ROUTING PROTOCOLS FOR MOBILE AD HOC NETWORKS”, International journal of computer science & information Technology (IJCSIT) Vol.2, No.4, August 2010
[4] Orchu Aruna, A.K. Prathipati, “QoS Signing and Routing in MANET”, International Journal of Engineering Science Invention, ISSN (Online): 2319 – 6734, ISSN (Print): 2319 – 6726 www.ijesi.org ||Volume 6 Issue 9|| September 2017 || PP. 14-19
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[29] J. Kumar, R. K. Varun, R. Yadav, “Security Issues and Quality of Services Challenges in Mobile Ad-hoc Networks”, International Journal of Engineering Research and Development, Volume 2, Issue 9 (August 2012), PP. 33-36
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Amit Garg, Ashish Kumar, Amit Chaturvedi, "Analyzing Adhoc Network’s performance on QoS requirements by varying Packet size and measuring the node’s remaining energy," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.34-40, 2019.

Code smells identified by Fowler [1] is as symptoms of possible code or design problems. Code smells have adverse affecting the quality of the software system by making software challenging to understand and consequently increasing the efforts to maintenance and evolution. The detection of code smells is the way to improve software quality by recovering code smells and perform the refactoring processes. In this paper, we propose a code- smells detection approach based on a decision tree algorithm and software metrics. The datasets we used to train the models are built by reforming the datasets used by Arcelli Fontana et al. work [2]. We use two feature selection methods based on a genetic algorithm to select the most essential features in each dataset. Moreover, we use the grid search algorithm to tuning the decision tree hyperparameters. We extract a set of detection conditions using decision tree models, that are considered as prediction rules to detect each code smell in our binary-class datasets.

code smells, code smells detection, Feature selection, decision tree, prediction rules

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Mohammad Y. Mhawish, Manjari Gupta, "Generating Code-Smell Prediction Rules Using Decision Tree Algorithm and Software Metrics," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.41-48, 2019.

Human beings are capable of identifying and recognizing patterns in an image but if these tasks are done in repetitive manner they are subjected to errors. The same happens in vehicle license plate recognition, especially because the number of vehicles is very large. That is why automated systems are required for this job. The objective is to extract and recognize vehicle registration numbers from vehicle images, process the image data finally utilize for access record and then use it for further necessity. Monitoring the vehicle traffic and the management of parking areas are most labor-intensive job. In this paper we propose a new technique for automatic payment method in tollgate

Numberplate, Vehicle, Detection

[1] Muhammad Tahir Qadri and Muhammad Asif, “Automatic Number Plate Recognition System for Vehicle Identification Using Optical Character Recognition”, 2009 International Conference on Education Technology and Computer, pp. 335–338. 2009.
[2] Neng-Sheng Pai, Sheng-Fu Huang, Ying-Piao Kuo1, Chao-Lin Kuo, “License Plate Recognition Based on Extension Theory,” 2010 International Symposium on Computer, Communication, Control and Automation, pp. 164–167, IEEE 2010.

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G. Sudhakar Babu, K. Chandra sekhar Reddy, "Automatic Payment System in Tollgate Using Number Plate Recognition," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.49-51, 2019.

In the recent era of technology, recognition of text and non-text images is a major challenge in the field of computer vision so as to efficiently extract the text from that image. There are many algorithms available for the extraction of the text from the image, however, the algorithm used for the extraction of the text from the images would have a higher efficiency if it is known beforehand that the image is a text image or a non-text image. However, in old manuscripts, the extraction of the text is very difficult. In that case, the algorithm for the distinction between the text and non-text becomes very easy for detection of any such text in the manuscript and extract the text from it. In our approach, we have built a system that takes any sort of image as an input. After the input of the image, it is then processed and converted into a binary image. Distance transform method is then applied and the measure of the distance between the various points in the image are then calculated. From the calculated points, duplicate points are merged into one point and are sorted in ascending order. The total area of the binary image is then calculated and also the image corresponding to each of the distance transform points are then calculated. The total area of the binary image is then divided by each of the area value of the corresponding distance transform points are the value extracted is known as the feature values. After getting all the feature values the whole value is then divided into small intervals and is then processed through the classifier. For our experimental purpose, we have chosen the ensemble classifier for our study and experimental analysis. The correctness of the classifier is then calculated and evaluated for the distinction between text and non-text images. This method is a very simple and accurate method for the distinction between the text and the non-text images and also helps in the extraction of the text from the image. Experiment have been done with simple text and non-text image dataset and the efficiency of the proposed method is then demonstrated.

distinction between text and non-text, bar chart, classifier, ensemble classifier

[1]. Najwa Maria Chidiac, Pascal Damein and Charles Yacoub, “A robust algorithm for text extraction from images”, 39th International conference on Telecommunication and Signal Processing, 2016.
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[4]. Sezer Karaoglu, Ran Tao, Theo Gevers and Arnold W. M. Smeulders, “Words matter: Scene Text for Image Clssification and Retrieval”, IEEE transactions on multimedia, vol. 19, no. 5, may 2017.
[5]. Chengquan Zhang, Cong Yao, Baoguang Shi and Xiang Bai, “Automatic discrimination of text and non-text natural images”, 13th International Conference on Document Analysis and Recognition, 2015.
[6]. Vishal Chowrasia, Sanjay Shilakari and Rajeev Pandey, “Implementation of Optical Character Recognition Using Machine Learning”,International Journal of Computer Science and Engineering, Vol.-6, Issue-6, jun 2018.

Pradipta Karmakar, Chowdhury Md. Mizan, Sayak Dasgupta, Saptaparna Das, "Distinction between Text and Non-Text Using Ensemble Classifier," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.52-56, 2019.

Reliability of software is an important factor of today’s software industry. The techniques involved in the designing, testing & evaluation of a software system is called as software reliability engineering. The increasing demand for the reliability of software products need to be estimated and the estimation of such software systems in reliability engineering aspects become more critical for large scale projects. The ability of a system to perform system required operations or functions under the given condition for a specified period of time is called the reliability of the system. Software reliability is described as the probability of failure-free software operation for a given period of time in assign environment. This paper proposes an algorithmic design of reliability analysis model for predicting quantitative & qualitative analysis of software reliability by using probability theory & statistical analysis with a set of techniques and models. Reliability of software is a squeeze with the prevention of errors, faults finding or detection of faults & removal. Reliability of hardware is not predicted with probabilistic functions where the reliability of software is measured of probabilistic function with the notion of time.

Software Reliability, Reliability analysis, reliability growth model performance testing

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