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Research Paper

Automated road tolling system using RFID

In this endeavor we address the issues saw toll court and furthermore present ID structure for vehicles against which stolen and fiasco cases are chosen utilizing RFID. Right when vehicle encounters Toll Collection Unit it is named pilgrim or things passing on vehicle subject to its Unique Identification Number. A stock vehicle is weighed at TCU and in the event that it is over-load, by then accused for additional expense. UIN is passed to Central Server Unit where the correspondence gets deducted from record. Right when the night out is deducted at CSU it will show TCS to open the blockade and vehicle is permitted to pass. On the off chance that vehicle is perceived to be stolen at CSU it will display TSC not to open the blockade. Besides to vanquish the issue of endeavor at homicide cases crash territory part is acknowledged utilizing piezoelectric sensor in vehicle to perceive RFID of influenced vehicles. These subtleties can be utilized for further development.

Published by: Sai Sharan, Jagadeesh Babu

Author: Sai Sharan

Paper ID: V5I2-2038

Paper Status: published

Published: April 24, 2019

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Review Paper

Bone graft in a Crux

While the conventional periodontal therapies achieve management of periodontal diseases by repair, they are insufficient in reconstructing the lost structures. One major goal of periodontal surgical therapy includes regeneration of lost periodontal tissues including periodontal ligament, cementum and alveolar bone. The prerequisites for bone regeneration are cells, molecules, scaffold, and blood supply. Bone grafts provide the cells or may act as a scaffold for new bone formation. The search for an ideal bone graft perpetually continues. The following article presents an overview of the commonly used bone grafts in the management of periodontal affliction touching upon the topics of objectives and indications of bone grafts and their classification.

Published by: Dr. Manisha Mallik, Dr. Aaysha Tabinda Nabi, Dr. Rajat Sehgal, Dr. Toshi

Author: Dr. Manisha Mallik

Paper ID: V5I2-2027

Paper Status: published

Published: April 24, 2019

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Research Paper

The frequency of Vaginal Birth After Cesarian-section in pregnant women at Malalai and Shahr-Araa teaching hospitals

Vaginal Birth After Cesarean Section is one of the reasonable and practical methods to control and decrease the rate of unnecessary and elective Cesarean sections in women, which in turn decreases the potential risks resulting from C-section affecting mother and baby. Therefore, it is imperative to have a clear understanding of the context, factors, and goals of such interventions. Goal: The major goal of this research paper is to find the incidence of Vaginal Birth After Cesarean-section (VBAC) in pregnant women at Malalai and Shah-Araa Teaching Hospital. Method: This research uses the Descriptive Cross-Sectional method. The data is taken from patients’ documents who were admitted to the Malalai Maternity Hospital and Shahr-Araa Teaching Hospitals for VBAC during April and May of 2018. Results: The current study which took place for two months of April and May of 2018 in Malalai Maternity Hospital and Shah-Araa Teaching Hospital reveals that that 50 out of 120 women who had previous scars of Cesarean section (41.6%) received VBAC, who averagely aged 28±6 (±SD) years within the age interval of 20-40 years. The median of pregnancy counts was 2 and its range was 1-11, the median of birth spacing was 3 and its range was 1-12 and the median weight of baby was 3 Kilograms and it’s range was 1.5-4 Kgs and the average gestational age was 38±2 (±SD), It is seen with the highest incidence at 20-30 years of age, new-born with 2.5-3.5 kgs., gestational age of 39 weeks or higher, first parity, and a pregnancy gap of 2-4 years. Additionally, 4 of them had experienced premature rupture of the amniotic membrane. Final Result: The current study shows that by carefully selecting individuals, we can decrease unnecessary Cesarean sections through using VBAC, and performing this operation is directly dependent on the decision of obstetrics/gynecology professionals. Keywords: previous scar of Cesarean section, VBAC (Vaginal Birth After C-section), pregnancy.

Published by: Fahima Aram

Author: Fahima Aram

Paper ID: V5I2-1844

Paper Status: published

Published: April 24, 2019

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Research Paper

Fluidized bed with heat exchanger: An advance equipment for moisture removal from coal

In the rainy season, the coal from coal mines are totally wet and it takes lots of time (10 to 12 days) to get dry. Such type of coal cannot be used for the boiler. The Additional new equipment fluidized bed with heat exchanger for removal of coal moisture can be used. If CHP will work on such advanced equipment which is reliable and have less maintenance cost then the efficiency of the plant get an increase. In the present paper, the advance coal handling unit consists of a fluidized bed with heat exchanger. An integrated solution of coal handling plant is given to meet the increasing production needs.

Published by: Nilesh V. Pise, V. V. Dongaonkar, Shubham R. Lonkar, Akash A. Kolhe

Author: Nilesh V. Pise

Paper ID: V5I2-2052

Paper Status: published

Published: April 23, 2019

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Research Paper

A novel approach for indoor-outdoor scene classification using transfer learning

Scene understanding and analysis has gained significant importance and widely used in computer vision and robotics field. Classification of complex scenes in a real-time environment is a difficult task to solve. Convolution Neural Networks (CNNs) is a widely used deep learning technique for the image classification. But the training of CNNs is not an easy task since it requires large scale datasets for training. Also, the construction of CNN architecture from the scratch is a complex work. The best solution for this problem is employing transfer learning which gives desired result with small scale datasets. A novel approach of Alexnet based transfer learning method for classifying images into their classes has been proposed in this paper. We selected 12 classes from publicly available SUN397 dataset out of which 6 are indoor classes and the remaining 6 are outdoor classes. The model is trained with indoor and outdoor classes separately and the results are compared. From the experimental results we found that the model exhibited the accuracy of 92% for indoor classes and 98% for outdoor classes.

Published by: A. Yashwanth, Shaik Shammer, R. Sairam, G. Chamundeeswari

Author: A. Yashwanth

Paper ID: V5I2-2051

Paper Status: published

Published: April 23, 2019

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Research Paper

An implementation framework for real-time spam detection in Twitter

With the expanded prominence of online informal community, spammers discover these stages effectively available to trap clients in noxious exercises by posting spam messages. In this work, we have taken the Twitter stage and performed spam tweets identification. To stop spammers, Google Safe Perusing and Twitter's BotMaker instruments identify and square spam tweets. These instruments can square noxious connections, anyway, they can't ensure the client continuously as ahead of schedule as could be expected under the circumstances. Along these lines, businesses and specialists have connected diverse ways to deal with make spam free informal community stage. Some of them are just founded on client-based highlights while others depend on tweet based highlights as it were. Nonetheless, there is no extensive arrangement that can solidify tweet's content data alongside the client based highlights. To illuminate this issue, we proposed a system which takes the client and tweet based highlights alongside the tweet content component to order the tweets. The advantage of utilizing tweet content element is that we can recognize the spam tweets regardless of whether the spammer makes another record which was unrealistic just with client and tweet based highlights. We have evaluated our solution with two different machine learning algorithms namely – Support Vector Machine and Random Forest. We are able to achieve an accuracy of 86.75% and surpassed the existing solution by approximately 17%.

Published by: Poornima N. C.

Author: Poornima N. C.

Paper ID: V5I2-2050

Paper Status: published

Published: April 23, 2019

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