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The k-means algorithm and its variations are known to be fast clustering algorithms. However, they are sensitive to the choice of starting points and are inefficient for solving clustering problems in large datasets. Recently, incremental approaches have been developed to resolve difficulties with the choice of starting points. The global k-means and the fast global k-means algorithms are based on such an approach. They iteratively add one cluster center at a time. Numerical experiments show that these algorithms considerably improve the k-means algorithm. However, they require storing the whole affinity matrix or computing this matrix at each iteration. This makes both algorithms time consuming and memory demanding for clustering even moderately large datasets. Also the continuously arriving data stream has become common phenomenon for many fields recent years; for example, sensor networks, web click stream and internet traffic flow. Researchers proposes many innovative technologies to manage such streaming datasets. Finding efficient data stream mining algorithm has become an important research subject. In this paper we propose a fast global k-means algorithm for datasets having streaming behavior. Experiment shows that our proposed algorithm is more efficient than the fast global k-means algorithm in case of streaming datasets

k-Means, Global k-Means, Fast Global k-Means, Data Streaming

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Purnendu Das, Bishwa Ranjan Roy, Sanju Das, "A Fast Global k-means Algorithm for Datasets having Streaming Behavior," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.84-91, 2018.

Hexagonal network structures the base for mobile communication networks. To increase the efficiency of mobile communication, we implement the DFID algorithm for transmission. Let DFID denote the time taken by DFS and BFS preorder traversing to find the vertex x in graph DFID(x,G). In this section we discuss DFS and BFS for G beign a hexagonal mesh of dimension n i.e HXn..Thereby computing the time complexity of DFID algorithm for mobile networking.

DFID, BFS, DFS, Hexagonal mesh, Time complexity

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Justin Sophia. I, N. Rama, "DFID Time Complexity in Mobile Network," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.92-97, 2018.

Extracting and interpreting information from user generated content is a current topic in the scientific community and in the business world. Furthermore, data with a spatial component are even more important. This is proved by the numerous web applications that deal with processing and visualization of user generated content. The task of this extraction is to collect major life events in the form of retrievable entries that include structured data about major life event name, location and time which are often, categorized by complex, and nested structures involving ambiguous entities.

Events, tweets, LDA, Extraction

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Monika Gupta, Parul Gupta, "Event Extraction from Twitter using Scoring Function and LDA," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.98-103, 2018.

Objective of this research work is to build a driver monitoring system. Nowadays because of the road accidents death rate is keep on increasing and the vehicle production rate is also increasing which leads to pollution. Driver Alert application is developed to alert the drivers for dangerous driving events with respect to driver behavior and road conditions. This system focuses on the most commonly occurring dangerous driving events: drowsy driving, inattentive driving, lane weaving and drifting, and vehicle detection. Driver behavior is monitored with his head pose and eye state using front camera of the mobile. Similarly, in the rear camera the dangerous driving conditions at the road are detected with respect to lane conditions and vehicles (living being, non-living being) on the road. If the system detects any dangerous events on any of the camera, then it alerts the driver by displaying an alert icon on the mobile’s touch screen along with an audible alert message. To process the video frames from both front and rear camera a context-switching algorithm is used. So that the driver will be notices in both the way and alerted for them. An Emergency system is developed to tell about the driver problem to the outside people for dangerous events like accident, heart attack or any health issue which makes him distracted while driving

Head pose, lane detection, context switching, emergency system, driver alert

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J. Kanimozhi, R. Subramanian, "Driver Behavior Monitoring and Alerting System Using Samrt Phone," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.104-111, 2018.

Video surveillance has been one of the ubiquitous aspects of life since few decades. However, there are certain places that demand privacy of an individual like washrooms, changing rooms, baby feeding rooms at airports, etc where cameras cannot be installed / are restricted. Thus, it has raised concerns about safety and security of the public. The objective of our research is to design and analyze the processes and various conceptual models to automate the Entry-Exit surveillance of the people entering into or exiting from the Camera restricted areas. As part of the objective, in this paper, work is carried out to detect or determine the Entry-Exit events using the video frames captured at the entrances of the camera restricted areas by analyzing the variations in histograms of colors-RGB in the video frames using Histogram distance measures. Few grids in the Camera View Scene are selected by continuous learning and are extracted to determine the events happening in the scene thus contributing to improvement in computing time. Confirmation of event happening and classifying it as Entry or Exit or Miscellaneous is presented by temporal analysis of these grids. Experiments are conducted on few standard data sets like SBM datasets transforming them to our scenario, as well as our manual data sets captured in real time with few assumptions to test the techniques proposed.

Computer vision, video surveillance, camera prohibited areas ,color histograms, regression lines

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Vinay Kumar V, P Nagabhushan, "Entry-Exit event detection from video frames," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.112-118, 2018.

The concept of home automation for realizing the remote access cum control of electrical appliances has been migrating to the concept of smart environment by the rapid incorporation of the information and communications technologies. In this regard, several research works are currently going on all around the globe by different researchers of different technical fields at different levels. This work is to design an efficient software framework for smart home environment through the use of RFID communication technology for identifying the presence of inhabitants individually and also for providing the required services accordingly from the environment based on previous behaviors of the individuals. One algorithm/ method has been introduced in this work to identify the user on his/ her presence in the environment and also to access the required information from the system database about the services, as per his/ her previous settings of the appliances. Again another one data mining based algorithm is also introduced in this work in order to learn the association of the appliances available in the environment so that proactive services from the environment can be initiated. The work is mainly to focus over the sensing and processing part of the proposed smart home environment, rather than giving stress over the deployment of real actuators and hence the status of the appliances has been shown in GUI based platform. This work concludes with challenges related to the smart home environment in order to make the entire technology more efficient and robust.

RFID, Smart Home, Data Mining, RS 232 Serial Communication, Qt, MySQL databse

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Vaskar Deka, Shikhar Kumar Sharma, "Design and Development of RFID based Software Framework Prototype for Smart Home," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.119-124, 2018.

Routing is a mechanism to find an optimal route from a source node to destination which is the responsibility of routing protocols. Two prominent routing protocols are reactive or on-demand routing protocols and proactive or table-driven routing protocols. Both types of protocols are efficient in themselves. Performance evaluation of protocols by using performance metrics such as PDR, Average End to End delay, Routing Overhead and throughput etc. is an important aspect in estimating the efficiency of protocols. It helps in making choice of protocols according to the network environment and conditions. This paper describes routing behavior of on-demand routing protocols (DSR, AODV) and table-driven routing protocol (DSDV). Simulations of these three protocols have been performed on NS-2 by varying pause time and speed and then results have been analyzed.

AODV, DSDV, DSR, Metrics, PDR, Routing, Simulation

[1] Doina Bein, "Self-Configuring, Self-Organizing, and Self-Healing Schemes in Mobile Ad Hoc Networks," in Guide to Wireless Ad Hoc Networks, Sudip Misra, Isaac Woungang, and Subhas Chandra Misra, Eds.: Springer London, 2009, ch. 2, pp. 27-41.
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[6] A.H. Abd Rahman and Z.A. Zukarnain, "Performance Comparison of AODV, DSDV and IDSDV Routing Protocols in Mobile Ad-hoc Networks," E..J of Scientific Research, vol. 31, no. 4, 566-576.
[7] Muhammad Awais Nawaz, Dr Mudassar Iqbal, Saleem Khan, Zia ul Haq Zafar Mahmood, "Varying Pause Time Effect on AODV, DSR and DSDV Performance," International Journal of Wireless and Microwave Technologies, vol. 5, no. 1, pp. 21-33, March 2015.
[8] Nasru Minallah, Sadiq Shah,Shahzad Rizwan,Hameed Hussain Muhammad Shoaib, "Investigating the Impact of Group mobility models over the On-Demand Routing Protocol in MANETs," in Eighth International Conference on Digital Information Management (ICDIM), 2013, pp. 29-34.

Swati Atri, Sanjay Tyagi, "Simulating and Analyzing the Behavior of Table-Driven and On-Demand Routing Protocol," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.125-129, 2018.

Data mining techniques can be used by Health organizations to identify the diseases like heart, tumor, diabetic, liver and thyroid disease using symptoms as parameters. Diabetic disorder is one of the growing diseases worldwide currently faced by people because of modified life style. Valuable data can be observed from application of knowledge mining techniques in the fitness care system particularly in Diabetic Disease. In this direction, this research paper studies the performance of three classifier algorithms available, namely JRip, PART and Random Tree using WEKA tool and proposed a new algorithm Weighted Classifier to classify the data a diabetic data set. The objective of this research is to classify data, assist the people by extracting useful knowledge from classified data and identify the efficient algorithm to best prediction of disease. From the experimental analysis, it is concluded that weighted Classifier is the effective algorithm for classification accuracy. The result will help doctors in a diagnosis process.

Data mining, Diabetes disease, JRip, PART, Random Tree, Weighted Classifier, classification, WEKA tool

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[12] Dr. V. Karthikeyini, I. Parvin Begum,” Comparison a Performance of Data Mining Algorithms (CPDMA) in Prediction Of Diabetes Disease”, International Journal on Computer Science and Engineering (IJCSE), Vol.5 Issue.3, 2013.
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[14] I.Parvin begum, V. Karthikeyini, K. Tajuddin, I. Shahina Begum, “Comparative of data mining classification algorithm (CDMCA) in Diabetes Disease Prediction”, International journal of Computer Applications, Vol.60, Issue.12, pp. 26-31, 2012.
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P. Hema, K. Palanivel, "Performance Study on Diabetic Disease Prediction Using Classification Techniques," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.130-135, 2018.

The ‘Modi’ script originated as a cursive variant of the script during the 17th century CE and used to write the Marathi language spoken in the Indian state of Maharashtra. Modi script evolved over time and found in many styles of writing. There is no standardization for writing characters and numerals of ‘Modi’ script but largely written without lifting the pen. The cursive nature of the script and lack of standardization in writing style pose challenges in digital recognition of documents written using Modi script including historical ones. Being largely medieval era script, modern document recognition systems lack support for recognizing handwritten texts using ‘Modi’ script. In this paper, we have described the framework of digital recognition of characters of handwritten ‘Modi’ script using empirically determined heuristics for determining the contribution of features from hybrid feature space for recognition of the ‘Modi’ character. The hybrid feature space uses normalized chain code together with feature vector encompassing a number of holes, endpoints, and zones associated with the character. The proposed framework for digital recognition of ‘Modi’ character using empirically determined heuristics provides a naïve model for recognizing a class of Indic scripts especially based on the cursive style of writing. The average and best recognition performance for the proposed method was measured to be 91.20% and 99.10% respectively.

Chain Code, OCR, Medieval Script, ‘Modi’ Script Recognition, Empirical Heuristic-based OCR

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R. K. Maurya, S. R. Maurya, "Recognition of a Medieval Indic-‘Modi’ Script using Empirically Determined Heuristics in Hybrid Feature Space," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.136-142, 2018.

Association rule mining is a data mining technique in which pattern of occurrences of one set of items with another set of items in databases of transactions are discovered as rules of implications with certain measures of interestingness. Support or the frequency of occurrences of sets of items and confidence are the most widely used measures of interestingness of association rules of the form X→Y where X and Y are disjoint sets of items. Though the problem of association rule mining emerged from analysis of market basket data in supermarket there are numerous areas of applications of association rule mining technique. In this paper, an association rule based model for discovery of eligibility criteria for jobs is proposed. For this the eligibility requirements of jobs are converted to a set of transactions and then a data base of such transactions is prepared for discovering the association rules in such a way that the antecedent of a rule represents the eligibility requirements and the consequent represents the concerned job. Such rules once discovered can be used for various purposes by the employers, job seekers and the policy makers for proper planning related to recruitment, employment and creation of need based vacancies respectively.

Data mining, big data, association rule, support, confidence, classification

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P. K. Deva Sarma, "An Association Rule Based Model for Discovery of Eligibility Criteria for Jobs," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.143-149, 2018.