International Journal of Computer Techniques

1-4

Authors: - IJCTSunny Sharma, Karandeep Kaur, Amritpal Singh

The evolution to network and computational paradigm has gone through a amazing phase of expansion and development. The growth curve was indeed very steep in many major domains. The advent of Cloud computing & Machine learning has enhanced the implementation in application area like Bioinformatics. With huge application-domain scope Cloud computing has emerged as a special area of interest for many bioinformatics researchers. Research is being done on different aspects of Cloud computing with bioinformatics for identifying areas of improvement and their respective remedies for living beings. Specially the cloud computing are acting very helpful for identifying H1N1 virus in human. H1N1 is an infectious virus which, when spread affects a large volume of the population. It spreads very easily and has a high death rate. Similarly cloud computing doing good job for detection of Hypertension, Diabetics, Cancer and Heart patient with software as a service, so the development of healthcare support systems using cloud computing is emerging as an effective solution with the benefits of better quality of service, reduced costs. This paper, provide an effective review towards cloud computing important effort in a field of bioinformatics.

1-4

Authors: - IJCTL.Navaneeth M.E, V.Rukkumani

The need for power generation in India increases day by day due to various factors. Nearly 70% of the power production is from the thermal power plants in various locations of the country. Monitoring and control of these power plants at all times is a must, since these power plants are operated continuously. Boiler is the major part of any thermal power plant. Hence monitoring the boiler parameters such as temperature, pressure and humidity are of great importance in power plant. It is not always possible for continuous monitoring in the plant premises because of an unpleasant industrial environment. In this project it is proposed to develop remote monitoring and control of boiler parameters using wireless communication. The proposed method provides a complete solution for these constraints in remote monitoring by using various sensors for temperature, pressure and humidity measurement. This method uses Internet of Things (IoT) as the platform of communication. The proposed method also provides an option for monitoring and control even in remote location in addition to the control room. Internet of Things (IoT) will play a major role in the future concept of power plant integration. The proposed method will suit and provide a start-up initiation for this future concept.

1-7

Authors: - IJCTAlok Kumar Dwivedi, Gouri Sankar Prajapati

The congestion control within the TCP (Transmission Control Protocol) plays a critical role in amending data rate to evade congestion from happening possibilities. Based on TCP communication sender not only guarantees the successful packet delivery, but also maintains the correct sequence of packets by receiving the frequent acknowledgement from the receiver. In this research we proposed a congestion control scheme with modified TCP and queue length variation with OLSR routing protocol in MANET. The TCP protocol performance is modified by forwarding busy channel signals to predecessor nodes through intermediate nodes in network. The congestion is controlled by that novel method of detecting the node is busy or ready for communication. If the communication is start in network and the possibility if congestion is arise, then in that case the queue length is handle the possibility of congestion. The congestion is minimized due to awaring about the channel busy status and nodes buffer status or queue status. The TCP protocol is able to handle the congestion situation but i.e. completely based on acknowledgement of receiver and also not very effective to control it. The proposed TCP congestion control OLSR routing is improves the network performance by reducing packet loss. The performance of network is measure through performance metrics like throughput, PDF and Routing overhead in different node density scenarios. The performance of proposed scheme is provides the better results.

1-5

Authors: - IJCTP. Anjaneyulu, T. Venkata Naga Jayudu

It is very hard to debug networks. Every day network engineers struggle with router fiber cuts, misconfigurations, mislabeled cables, faulty interfaces, software bugs, intermittent links and additional reasons that are reason for networks to behave badly, or be unsuccessful completely. Network engineers chase down bugs by means of the most elementary tools like ping and trace route, and trail down origin causes by means of combination of perception and wisdom. Debugging networks is just becoming difficult as networks are getting larger and are getting more complex. This paper proposes an automatic testing and debugging procedure for verifying the various network conditions and to provide safe reaching of the packets to the desired destination. The paper uses the Automatic Test Packet Generation system for debugging and adds an enhancement to that system which restricts the number of test packets generated and removes the threshold violation over the file size limit. This reduces the time taken to debug the entire system.

1-5

Authors: - IJCTAnumolBabu, Rose V Pattani

Twitter has become popular social networking site where users share their up-to-date information. The error-prone and short nature of tweets makes the word-based representation less reliable. Tweet segmentation is the process of splitting tweets into meaning segments so that its semantic meaning is well conserved and is easy to be used by downstream applications. Segmentation is done based on stickiness score considering both global and local context. Clustering of tweets are done using DBSCAN method with Jaccard Coefficient as the similarity measure. The sentimental variations in tweets are measured based on segmentation. The experimental evaluation shows that the global terms using wikilinks are more efficient than the normal segmentation. Clustering is more effective using DBSCAN algorithm, which is best for uncertain data