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High dimensional data is a function of feature space inputs and noise. This makes it difficult to analyse and visualize the data, specially, in case of social network analysis where the aim is to analyse user’s behaviour. Heterogeneous contents (text, audio, video, images etc.) make it more difficult to model. However, it is known that the actual feature vectors lie on much lower dimensions which can be obtained through non-linear manifold learning techniques. In this paper, we propose iterative local tangent space alignment with adaptive neighbourhood to extract the true low dimensional representation of data by sequentially aligning the tangent spaces and thereby reducing the overall reconstruction error. As the sub-manifold regions become linear, its neighbourhood size increases which leads to more information fusion. Extensive experiments on both synthetic and real world dataset proves that proposed method outperforms existing non-linear dimensionality reduction technique in both low dimensional representation and classification.

Social Network Mining, Dimensionality reduction, Manifold learning, Local tangent space alignment, Adaptive neighbourhood

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S.K. Mishra, A. Agarwal, "Iterative Local Tangent Space Alignment with Adaptive Neighbourhood for Social Network Mining," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.259-265, 2019.

A novel color image steganography scheme based on discrete wavelet transforms (DWT) is proposed for better security. The proposed approach extracts R, G, and B planes from the color cover image. The DWT is applied on both secret image and RGB components of the cover image. The blocking process is applied to approximation coefficients of secret image and detail coefficients of RGB components of the cover image. The block of detail coefficients is replaced with approximation coefficient of secret image using root mean square error method. The key is used to store the position of best matching blocks. The proposed approach is compared with recently developed steganography technique. The experimental results reveal that the proposed approach improves the performance of steganography technique in terms of Peak Signal to Noise Ratio value. The stegano image has good visual quality also.

Color image steganography, Discrete wavelet transforms, PSNR

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A. Goel, V. Deswal, S. Chhabra, "A Novel Digital Color Image Steganography using Discrete Wavelet Transform (Digital Color Image Steganography using DWT)," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.266-270, 2019.

Machine learning is the most familiar division of Artificial Intelligence to perform exploratory data analysis tasks and to work out a variety of problems such as weather forecasting, drug discovery, encrypted image detection etc., This paper discusses about varieties of data mining classification algorithms that are commonly used to extract considerable knowledge from huge volumes of data. Identification of the healthiness of a baby with the observations during the gestation period of a mother requires various parameters to be taken into consideration during that period. Decision Tree (DT) algorithms could be very much helpful in predicting the healthiness of a baby. The numerical form of the data sets are taken and are fed to the DT algorithms to make calculations for the prediction of the healthiness of the baby. The data sets are taken and analyzed in the Waikato Environment for Knowledge Analysis (WEKA) platform.

Data Mining, Knowledge Discovery, Classification Algorithms, WEKA

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K. Menaka, B. KeerthanaKani, "Predicting the Birth of Healthy Babies with Gestation Period Observations using Machine Learning Algorithms," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.271-275, 2019.

Through the proposed method it can be clearly known that a human being (specially a human baby) is suffering from sickle cell anaemia or not by a fast and efficient way. This disease is caused by mutation of the gene controlling Beta-chain of haemoglobin (Hb). It replaces Glutamic acid (GAG) present at 6th position of the Beta-chain by Valine (GTG). The mutant haemoglobin molecule undergoes polymerization under low Oxygen tension causing the change in the shape of the RBC from biconcave disc to elongated Sickle-like structure. With the help of this test, it can be known that a human is a Mutant of this disorder or not, which can save the life of new generation.

Beta chain of Haemoglobin, Glutamic acid, Valine

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Anindya Sundar De, "Detection of the Sickle Cell Anaemia disease by simple and efficient way," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.276-279, 2019.

Object identification and classification has been topic of interest for researchers in computer vision due to its numerous applications in various domains since decades. But object detection and classification faces certain issues and challenges like scaling variations, rotational variations, occlusion, noise etc. Hence, there is need to design descriptors which are robust, compact and efficient. The extraction of features and the classification process should be done with minimal compromises in the performances. This paper proposes an orientation and rotation invariant feature descriptor named as ORB (Oriented FAST and Rotated BRIEF). This feature vector computes scale, rotation and translation invariant features for the test and trainee images. For matching the computed feature sets we used supervised classification method i.e. K-Nearest Neighbors Algorithm (K-NN) and Support Vector Machine (SVM) for the classification of various object categories in the dataset. Comparative experimental results based on analysis of the SVM and KNN classifiers on the basis of recognition accuracy and execution time is given. Results show that SVM gives better matching score whereas KNN is time efficient in comparison to SVM.

ORB, K-Nearest Neighbour Classifier, SVM, Object Recognition and Classification

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Ritu Rani, Ravinder Kumar, Amit Prakash Singh, "Implementation of ORB and Object Classification using KNN and SVM Classifiers," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.280-285, 2019.

The accurate prediction of student cognitive skill is important, for improving student academic performance.In this paper, a model is proposed to predict the students’ performance in an academic organization. A machine learning algorithm Naïve Bayes is used for prediction. Further, the importance of different cognitive factor is considered, in order to determine which of these are correlated with student performance. Result proves that Naïve Bayes algorithm provides more accuracy over other methods for comparison and prediction.

Cognitive skills, Students’ performance, Machine learning, Naïve Bayes

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J.Suganya, T. Chakravarthy, "Application of Machine Learning Algorithm for Predicting Students Skill," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.286-290, 2019.

Cloud computing has revolutionized the information technology industry by empowering versatile on-demand provisioning of computing resources. Rapid growth of demand for computational power by scientific, business and web applications has led to the creation of large scale data centers consuming enormous amounts of electrical power. How to exert energy and handle the issue concerned with energy efficiency has been the most important matter of Green Cloud Computing. This research presents the novel technique and algorithm for the Efficient Virtual Machine Management to achieve energy efficiency. Proper Virtual Machine Management is done by proper VM allocation. The overloaded host detection, VM selection, VM placement and at last under loaded host detection are four major steps carried out throughout the research. This is aimed for saving the energy and makes the virtual machine management efficient. According to the proposed work when any of the host will be shut down at the end of process the energy will not be used more and it will be saved.

Virtual Machine Management, Virtual machine, Cloud Computing, Virtualization, Power Consumption

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[3]. Abaslom Et-Shamir Ezugwu, Sahalu Junaidu, Seyed Buhari,”Virtual Machine Allocation in Cloud Computing” International Journal of Cloud Applications and Computing, 3(2), 47-60, April-June 2013
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Nital Patel, Nimisha Patel, Rajan Patel, "An Efficient Virtual Machine Management to Achieve Energy Efficiency in Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.291-296, 2019.

Mobile Ad-hoc network (MANET) is an ad-hoc wireless network with a routing network background typically located at the top of a link layer of the network. For the transmission of data routing protocols plays an essential role. Since the topology in MANET is not stable (nodes are moving) therefore routing as well as maintenance of the network is a challenging task. The difficulty that most of the researchers have analyzed is the energy consumed by the sensor nodes. The first problem of this research is to find a trust-based route so that the network can be protected against any additional cost used during the searching of an appropriate node. For this purpose, the Zone Routing Protocol (ZRP) routing mechanism with the concept of Artificial Bee Colony (ABC) algorithm has been used. Another problem that has been considered in this research is to protect the network from external attacks named as sinkhole attack. These attacks are also known as smart attack, as, when these attacks came into the network the sensor nodes do not know that whether the data is transmitted to the genuine node or to the malicious node. Therefore to resolve this problem, machine learning approach named as decision tree is used. The performance parameters are evaluated to measure the efficiency of the network. It has been determine that the Packet Delivery Ratio (PDR) of the proposed system has been increased by 1.19% compared to the existing work.

MANET, OLSR, ABC, Decision tree, sinkhole attack

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Rohit.Wandra, Parveen Kumar, Anita Suman, "Detection of Sink Hole Attack Using Decision Tree in Manet," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.297-302, 2019.

Traffic on public roads all over the world is a crucial problem and is becoming a major pretend to conclusion makers. Urban region have a great stack of traffic jams. Cloud computing is turning a good engineering to provide a potent and scalable computing at low cost. This paper proposes a cloud-controlled smart traffic management system based on Internet-of-Things (IoT). The proposed system collects vehicle count at various roads with the help of RFID receivers and uses this data to manage traffic efficiently. It will be capable of overcoming all the pain points observed with minimum cost and best-in-class quality of services.

Traffic Management, Cloud Computing, Internet of Things (IoT), Radio-frequency identification (RFID), Service Oriented Architecture (SOA)

[1] Geoffrey Raines, “Cloud Computing and SOA”. Systems Engineering at MITRE Service-Oriented Architecture Series.
[2] Geoffrey Raines, “Cloud Computing and SOA”. Systems Engineering at MITRE Service-Oriented Architecture Series.
[3] Arun.V, Dr.M.Poongothai, ” Implementation of IoT Based Intelligent Transportation System”, Asian Journal of Applied Science and Technology, Vol. 2, Issue. 2, pp.933 – 946, 2018.
[4] Hon Fong Chong, Danny Wee Kiat Ng, “Development of IoT Device for Traffic Management System”, IEEE Student Conference on Research and Development (SCOReD), Malaysia, 2016, ISBN 9781-5090-2948-8
[5] Jan Vascak, Jakub Hvizdos† and Michal Puheim, “Agent-Based Cloud Computing Systems for Traffic Management”, International Conference on Intelligent Networking and Collaborative Systems (INCoS), Czech Republic, 2016, ISBN 978-1-5090-4124-4
[6] Maram Bani Younes, Azzedine Boukerche, “An Intelligent Traffic Light Scheduling Algorithm Through VANETs”, 39th Annual IEEE Conference on Local Computer Networks Workshops, Canada, 2014, ISBN 978-1-4799-3784-4
[7] Aiswaria. Mohan, Janet Lourds. Rani, “An Intelligent Algorithm for Traffic Signal Scheduling”, International Journal of Chemical, Environmental & Biological Sciences (IJCEBS), Vol. 1, Issue. 1, pp.119 – 123, 2013
[8] Wu He, Gongjun Yan, and Li Da Xu, “Developing Vehicular Data Cloud Services in the IoT Environment”, IEEE Transactions on Industrial Informatics, Vol. 10, Issue. 2, pp.1587 – 1595, 2014

Sravya E U, Jisha George A, "A Cloud-Based Smart Traffic Management in the Internet-of-Things (IoT) Environment," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.303-309, 2019.

The concept of Basic control structure (BCS) of software and their cognitive weights have been proposed in theory. However not much work has been done to validate weights assigned to various programming constructs. One of the primary reasons for same is that it is difficult to design the experiment to measure the mental effort involved in understanding the effect of various programming constructs and their interplay. The paper discusses some of the challenge involved in setting up such psychological experiment. In such experiments we cannot select and compare any random code snippets of various programming constructs- the variations are endless. We identified different approaches to conduct such experiments. We explained with example various factors and issues involved in selecting the code snippets which resulted in minimum variations in code snippets of various programming constructs, other than that is inherent in syntax. The code snippets design approach proposed here can be used to conduct series of psychological experiments in software studies. We need series of such experiments not only to validate the cognitive weights of different programming constructs, but also it will go long way in having robust metrics for software complexities. These types of experiments can be extremely useful in the field of computer science education in understanding the cognitive load required for learning the concepts of programming languages.

Software Complexity;Code complexity;Code Comprehension; Cognitive Weights;Basic control structure;cognitive metrics;Cognitive load; Software Experimentation; computer science education; Code snippets;human brain working

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Leena Jain, Satinderjit Singh, "Designing The Code Snippets for Experiments on Code Comprehension of Different Software Constructs," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.310-318, 2019.