KoomeshPub Date : 2018-08-01DOI: 10.1109/I-SMAC.2018.8653702
T. Suresh, A. Murugan
{"title":"Strategy for Data Center Optimization : Improve Data Center capability to meet business opportunities","authors":"T. Suresh, A. Murugan","doi":"10.1109/I-SMAC.2018.8653702","DOIUrl":"https://doi.org/10.1109/I-SMAC.2018.8653702","url":null,"abstract":"Considering current evolving technology and the way data are growing, IT consulting and outsourcing industry expected to be strategic partner for technology innovation in addition to support on-going business with reduced operational cost. Data Center is backbone for digital economy, big data, cloud, artificial intelligence, IoT or wearable technology. Data growth and on-demand data access changed the focus of data center as storage and disaster recovery to access data instantly from cloud without compromising security controls and data quality. These technology transformations create demand for latency. Every organization like Facebook, Equinix, Amazon, and Google are having their own data centers and expanding their business on cloud services. Data Center plays major critical on success of digital business. It is important to find possible options to optimize infrastructure and improve efficiency and productivity of Data Center. At the same time, we need to make sure that environment is up and running without compromising quality and security of data. This paper gives few solutions to get more from Data Center, reduce operational cost and optimize infrastructure utilization.","PeriodicalId":53631,"journal":{"name":"Koomesh","volume":"113 1","pages":"184-189"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80528481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
KoomeshPub Date : 2018-08-01DOI: 10.1109/I-SMAC.2018.8653647
Raghavi K. Bhujang, Suma V Dean
{"title":"Propagation of Risk across the Phases of Software Development","authors":"Raghavi K. Bhujang, Suma V Dean","doi":"10.1109/I-SMAC.2018.8653647","DOIUrl":"https://doi.org/10.1109/I-SMAC.2018.8653647","url":null,"abstract":"Software development is a process of well planned and defined steps that contains many series of systematic tasks to deliver the expected product or service to the client. While doing the same, it is likely that there can be many ups and downs in the tasks that are defined starting from the planning stage to completion of deliverable. Also, the series of planned tasks related to product/service delivery in the software development process is likely to fluctuate in terms of Cost, Time, People and Process due to various external factors. These fluctuations should be taken care at the right time with the right mitigation strategy as it spans up further ending with serious obstructions. This paper focuses on how the risk propagates further through the phases of software development with the increase in level of severity. A sample of empirical data taken from existing software development projects throws more light on propagation of severity from the lowest to the highest. This knowledge further aids software personnel and all potential stakeholders to accordingly formulate strategies to effectively manage risk.","PeriodicalId":53631,"journal":{"name":"Koomesh","volume":"13 1","pages":"508-512"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88738400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
KoomeshPub Date : 2018-08-01DOI: 10.1109/I-SMAC.2018.8653674
N. Radha, M. Maheswari
{"title":"An Efficient Implementation of BCD to Seven Segment Decoder using MGDI","authors":"N. Radha, M. Maheswari","doi":"10.1109/I-SMAC.2018.8653674","DOIUrl":"https://doi.org/10.1109/I-SMAC.2018.8653674","url":null,"abstract":"Now-a-days majority of practical applications such as valet car parking, larger temples necessitate, seven segment displays to give a visual token of the numbers. Digital counters are the one which are used for these applications. The four bit Binary Coded decimal form will normally be the output states of digital counters and thus they are not relevant for straightly activating 7 segment displays. The special BCD to 7 segment display decoder ICs are used in converting the incoming BCD signal to a form convenient for activating these displays. In this paper, an efficient BCD to seven segment converter is designed using Modified Gate Diffusion Input Technique (MGDI). The suggested MGDI based BCD to seven segment converter is contrasted with the conventional Complementary CMOS gates based BCD to seven segment converter. Both the implementations are done by means of Cadence 180 nm technology. Simulation result shows that the MGDI based BCD to seven segment display decoder consumes 51 % less area, 98.97 % power and 98.8 % delay compared with the conventional Complementary CMOS gates based BCD to seven segment display decoder.","PeriodicalId":53631,"journal":{"name":"Koomesh","volume":"57 1","pages":"475-479"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90171801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
KoomeshPub Date : 2018-08-01DOI: 10.1109/I-SMAC.2018.8653653
Mandeep Kumar, S. Mini, T. Panigrahi
{"title":"A scalable approach to monitoring air pollution using IoT","authors":"Mandeep Kumar, S. Mini, T. Panigrahi","doi":"10.1109/I-SMAC.2018.8653653","DOIUrl":"https://doi.org/10.1109/I-SMAC.2018.8653653","url":null,"abstract":"Rapid industrialization has caused an increase in the pollution levels. The release of harmful gases, particulate matter, dust and detritus into the atmosphere leads to air pollution. One can reduce air-borne diseases by controlling the air pollution. In this paper, we design an Internet of Things (IoT) system to monitor the air quality at desired location(s). The IoT system monitors five different gases with the help of air quality monitoring sensors. The system detects the concentration of gases and sends the data to the ThingSpeak cloud for storage. The results of such a system may be useful for alerting the people and the authorities, in case of high air pollution.","PeriodicalId":53631,"journal":{"name":"Koomesh","volume":"88 1","pages":"42-47"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85849133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
KoomeshPub Date : 2018-08-01DOI: 10.1109/I-SMAC.2018.8653780
S. Mhamane, Mr. Pranav Shriram
{"title":"Digirail- The Digital Railway System and Dynamic Seat Allocation","authors":"S. Mhamane, Mr. Pranav Shriram","doi":"10.1109/I-SMAC.2018.8653780","DOIUrl":"https://doi.org/10.1109/I-SMAC.2018.8653780","url":null,"abstract":"one of the major challenges in present ticketing provision is QUEUE in buying and railway ticket checking. In this fast world people want all work is to be done within minutes with help of digitalization and usage of smartphone it is all possible. Users ticket information is stored in a database for security, which is absent in present system. Ticket checker is having admin login in application to look for user ticket with the ticket number in the database which scans in the form of QR code. Dynamic seat allocation also gives 100% utilization of seats as well proper allocation for waiting list passenger during Journey.","PeriodicalId":53631,"journal":{"name":"Koomesh","volume":"20 1","pages":"384-387"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90963180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
KoomeshPub Date : 2018-08-01DOI: 10.1109/I-SMAC.2018.8653694
R. Shalini, S. Sasikala
{"title":"A Survey on Detection of Diabetic Retinopathy","authors":"R. Shalini, S. Sasikala","doi":"10.1109/I-SMAC.2018.8653694","DOIUrl":"https://doi.org/10.1109/I-SMAC.2018.8653694","url":null,"abstract":"Visual perception is very important for human life. Although several medical conditions can cause retinal disease, the most common cause is diabetes. Diabetic Retinopathy (DR) can be identified using retinal fundus images. Detection and classification of deformation in Diabetic retinopathy is a challenging task since it is symptomless. Several algorithms were analyzed for the identification of abnormality. The analysis of different models in detecting the abnormalities from the image is done which includes various preprocessing techniques to standardize the image and post-processing techniques are applied for morphological adjustments, segmentation algorithms for segmenting the Lesion of Interest(LOI ) namely white lesions and red lesions, further feature extraction methods extracts the features like Micro Aneurysms, Hemorrhages, Exudates and Cotton Wool Spots and so on finally, classification methods were utilized which concludes the presence or absence of DR symptoms along with the severity based on the count of the features extracted in the given retinal image. This survey study aims to develop a novel algorithm to identify and detect types of above mentioned diseases and find out the severity of those diseases also examine with 100% accuracy.","PeriodicalId":53631,"journal":{"name":"Koomesh","volume":"75 1","pages":"626-630"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85771428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
KoomeshPub Date : 2018-08-01DOI: 10.1109/I-SMAC.2018.8653752
Aniket Kodre, Komal Tikone, Mansi Sonawane, Pratik Jare, P. Shinde
{"title":"Smart and Efficient Personal Car Assistant System","authors":"Aniket Kodre, Komal Tikone, Mansi Sonawane, Pratik Jare, P. Shinde","doi":"10.1109/I-SMAC.2018.8653752","DOIUrl":"https://doi.org/10.1109/I-SMAC.2018.8653752","url":null,"abstract":"India was the fourth largest motor vehicle/car manufacturer in the world in 2016. The growth rate of car ownership is raising big time in India. Presently, the average level of ownership stands at 13 per 1,000 populations and this is expected to increase exponentially. Car owners and Car users sometimes face problems related to their vehicles like remembering the renewal date of PUC, Routine check-ups, maintenance and accordingly periodical expenditure of the vehicle related things. Also trapping in a car or overheating of car causes suffocation kind of problems, where immediate communication is very much needed. So there is a need of a system that will support car users in maintaining vehicle related issues in easy way.This project work, proposed a system that helps car user to manage car related things. An android application is developed to provide the features like reminders for PUC renewal, Routine check-ups and maintenance, which will reduce the efforts of the car users. It provides necessary help to the car user by giving information whenever required. User can explore new cities around him/her very easily. Misplaced objects in the car are detected through the system. Based on traveling pattern future destinations are predicted.Thus, the project work resulted into development of a system which assists the car user by providing the necessary support.","PeriodicalId":53631,"journal":{"name":"Koomesh","volume":"32 1","pages":"12-17"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88642481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
KoomeshPub Date : 2018-08-01DOI: 10.1109/I-SMAC.2018.8653756
Vaishali Malpe, Prathamesh S. Tugaonkar
{"title":"Machine LearningTrends in Medical Sciences","authors":"Vaishali Malpe, Prathamesh S. Tugaonkar","doi":"10.1109/I-SMAC.2018.8653756","DOIUrl":"https://doi.org/10.1109/I-SMAC.2018.8653756","url":null,"abstract":"Machine Learning is ruling the world due to its accuracy and timely predictions for the given set of problems. Machine Learning is highly used for health monitoring to reduce the mortality rate and enhance the life expectancy. Organs such as kidneys, pancreas are highly affected in the run of life. Cancers like breast cancer has shown increase in the count since last decade. This leads to invent new techniques in the field of medical sciences which can give accurate and timely predictions to reduce the mortality rate. This paper presents comparative study of the current research using various machine learning algorithms and big data techniques to handle huge volume of data.","PeriodicalId":53631,"journal":{"name":"Koomesh","volume":"39 1","pages":"495-499"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75320123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
KoomeshPub Date : 2018-08-01DOI: 10.1109/I-SMAC.2018.8653603
R. Preethi, M. Sughasiny
{"title":"PBGTR: PRICE BASED GAME THEORY ROUTING FOR MINIMUM COST ROUTING PATH IN MANET","authors":"R. Preethi, M. Sughasiny","doi":"10.1109/I-SMAC.2018.8653603","DOIUrl":"https://doi.org/10.1109/I-SMAC.2018.8653603","url":null,"abstract":"In MANET, when a mobile node needs to communicate with a remote destination, it relies on the other nodes to relay the packets. This multi-hop packet transmission can extend the network coverage area using limited power and improve area spectral efficiency. Since the mobile nodes are battery driven and one of the major sources of energy consumption is radio transmission, selfish nodes are unwilling to lose their battery energy in relaying other users’ packets. To tackle this problem, this paper proposed a novel price based game theory routing (PBGTR) algorithm in MANET. Using this routing algorithm, a source node finds Minimum Cost Routing Path (MCRP) within destination node, then forward packet to destination via this MCRP. After the successful transmission, a source node pays the payment to each participated nodes. The simulation results shows that the proposed PBGTR routing algorithm is more efficient than other existing routing protocols. Furthermore, it consumes minimum cost for routing.","PeriodicalId":53631,"journal":{"name":"Koomesh","volume":"4 1","pages":"469-474"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78440576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
KoomeshPub Date : 2018-08-01DOI: 10.1109/I-SMAC.2018.8653709
N. Sivaramakrishnan, V. Subramaniyaswamy
{"title":"GPU-based Collaborative Filtering Recommendation System using Task parallelism approach","authors":"N. Sivaramakrishnan, V. Subramaniyaswamy","doi":"10.1109/I-SMAC.2018.8653709","DOIUrl":"https://doi.org/10.1109/I-SMAC.2018.8653709","url":null,"abstract":"Collaborative filtering is one among the top most preferred techniques when implementing recommendation systems. In recent times, more interest has turned towards parallel GPU-based implementation of collaborative filtering algorithms. Concurrent way of solving any problem is more preferable by everyone nowadays. The objective of GPU-based collaborative filtering recommender system is to produce recommendations in parallel and choosing the best among all. We have proposed three different methods namely Parallel Item Average Computation (PIAC), Parallel User Based Collaborative Filtering (PUBCF) and Parallel Item Based Collaborative Filtering (PIBCF).We have evaluated all these methods with standard evaluation metrics. As a result of task parallelism, the PIBCF method produces optimum choice for providing better recommendation results.","PeriodicalId":53631,"journal":{"name":"Koomesh","volume":"17 1","pages":"111-116"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72858480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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