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Research Paper

Traffic prediction for intelligent transportation system using machine learning

Traffic is the most major issue in an urban area. The most problem comes because of traffic in all developed cities, So people suffer a lot of time in the road traffic. This time is totally a waste for me personally and for society. In most urbanized areas space is a scarce commodity. Therefore, better management of the existing roads to increase or maintain their capacity level is the only solution. So this project is to develop a tool for predicting accurate and also predict time to help the community. Because of accidents, traffic signals, even repairing the roads these problems are causing traffic. If we get information which is near about all above and many more daily life situations which can affect traffic jam then deriver take the decision on the basis of the situation to move or take other decision. In the current situation, traffic data have been generating exponentially. The already some available prediction methods for traffic flow but those are unsatisfactory to handle real-world application. In this project, we proposed to use a machine-learning algorithm to analyze the traffic with better performance

Published by: Dhanashree Dnyaneshwar Raut, Susmita Salvi, Ankita Gupta, Pranali Patil

Author: Dhanashree Dnyaneshwar Raut

Paper ID: V7I3-1190

Paper Status: published

Published: May 8, 2021

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Research Paper

Measures of lexical diversity

The “natural” and the “formal” acquisition of a language by a child are best seen as two ends of a continuum. The acquisition of languages involves a combination of formal instruction and natural acquisition. This paper captures an overview of select prominent tools used for measuring vocabulary diversity across first and second language contexts. The tools include type-token ratios (TTR) and the more recent automated measure of VOCD.

Published by: Vijaya

Author: Vijaya

Paper ID: V7I3-1222

Paper Status: published

Published: May 7, 2021

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Research Paper

Optimization of bio-composite helmet

The primary aim of our project is to increase the safety of helmet users and also to reduce the usage of plastic. This led us to the idea of BIO-COMPOSITE HELMET. Generally, bio-composite materials are synthesized using natural fibers as reinforcements together with matrix due to their low density with high specific mechanical strength, availability, renewability, degradability. The present work attempts to make an improvement in the existing helmet manufacturing process. The materials that are used have better mechanical properties in order to enhance the compatibility between fibers and the matrix. The biocomposites are prepared using unsaturated polyester matrix and fibers such as coconut coir and areca using hand lay-up method with appropriate proportions to result in helmet shell structure. The fabricated helmets will be evaluated for their mechanical properties such as hardness, impact strength, and compression strength. Thus, we constructed bio-composite helmets that have highly strong and environmentally friendly.

Published by: Sabari Srinivas S., Gokulkrishna R., Hari Prasanth R.

Author: Sabari Srinivas S.

Paper ID: V7I3-1209

Paper Status: published

Published: May 7, 2021

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Research Paper

Task scheduling in the cloud computing using an improved cuckoo search algorithm

Cloud computing is an advanced internet resources network that is used by many users remotely. The resources include software, hardware, and various applications. The main challenge in cloud computing is task scheduling due to numerous requests are generated simultaneously from remote locations. To overcome this challenge, task scheduling algorithms are designed that appropriately arrange the tasks. In the literature, metaheuristic algorithms have been deployed for optimal task scheduling. The most popular algorithms are genetic algorithm, particle swarm, and cuckoo search algorithm. However, if the initial population of these algorithms is properly not defined then it is easily trapped into the local optimal solution and causes low precision. In this paper, we have overcome this issue and designed an improved cuckoo search algorithm. In the proposed method, the initial population is defined using the chaotic map algorithm and after cuckoo search algorithm is applied to determine optimal task scheduling. The experimental results show that the proposed method is superior in terms of convergence rate, makespan, average waiting time, and average turnaround time as compared to the existing algorithm.

Published by: Parminder Kaur, Sarabjeet Kaur

Author: Parminder Kaur

Paper ID: V7I3-1208

Paper Status: published

Published: May 7, 2021

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Review Paper

Review on wireless sensor network routing and topology approaches

The distinct advancement done in the field of wireless technology possibly developed the use of wireless sensor networks (WSN) consisting of small devices which are used for collecting the information with proper cooperation planning with each of its parts. These small types of devices are generally known as the nodes and it consists of memory used for storing the data, CPU for data processing, transceiver enabling the communication signals between the sender and the receiver, and the battery for energy fulfilling requirements a Wireless sensor network is a group of nodes that are connected to each other by wireless connection. These types of networks work on the dynamic topology of the network because the positions of nodes in the wireless network are changing continuously. The nodes in WSN are basically made up of small electronic device which is used for sensing, computing and transmitting the data. The nodes are run on battery power during the communication process.

Published by: Nighat Yaqoob, Madhvi Arya

Author: Nighat Yaqoob

Paper ID: V7I3-1200

Paper Status: published

Published: May 7, 2021

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Research Paper

Driver drowsiness detection system

Making insightful systems to prevent minor accidents can be particularly incredible in limiting the difficulty and loss of life. Street crashes and related types of mishaps are a typical reason for injury and death among the human population. One of the elements which assume a significant part in mishaps is the human blunders including driving exhaustion depending on new brilliant methods; this application recognizes the indications of weariness and lethargy despite the individual at the hour of driving. The proposed framework depends on three separate calculations. In this model, the individual’s face is monitored by a camera in the initial step by getting 14-16fps video grouping. At that point, the pictures are changed from RGB space into YCbCr and HSV spaces. The face territory is isolated from different parts and exceptionally precise HDP is accomplished. That the eyes are open or shut in a particular time span is controlled by zeroing in on thresholding and conditions concerning the balance of human appearances. The proposed framework has been carried out in excess of thirty distinctive video successions with normal precision of93.18 and a discovery rate (DR) of 92.71 out of approximately 2500 picture outlines. High precision in the division, low blunder rate, and fast preparation of information recognize this framework from comparable ones. This framework can limit the number of mishaps brought about by drivers’ exhaustion.

Published by: Paneri Sahare

Author: Paneri Sahare

Paper ID: V7I3-1191

Paper Status: published

Published: May 6, 2021

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