Efficient pothole detection using smartphone sensors

Road safety remains a casualty in India, with potholes wrecking asphalt pavements by the dozens. A study in 2017 recorded that potholes caused the budget for road safety to increase by a whopping 100.4 per cent, and even doubled the death toll from that of the year prior. To address this situation, an effective solution is required that ensures the drivers’ safety and can prove beneficial for long term measures. This can be established by employing an apt pothole detection system which is simple yet functional. In this paper, the method for such a system is described which uses accelerometer and gyroscope, both built in the modern day smartphones, to sense potholes. Pothole induced vibrations can be measured on the axis reading, making them distinguishable. Our proposed Neural Network model is trained and evaluated on the data acquired from the sensors and classifies the potholes from the non-potholes. The neural network gives a classification accuracy of 94.78 per cent. It also presents a solid precision-recall trade-off with 0.71 precision and 0.81 recall, considerably high for a problem with class imbalance. The results indicate that the method is suitable for creating an accurate and sensitive supervised model for pothole detection

Image Processing Based Intelligent Traffic Controller

The frequent traffic jams at major junctions call for an efficient traffic management system in place. The resulting wastage of time and increase in pollution levels can be eliminated on a city-wide scale by these systems. The paper proposes to implement an intelligent traffic controller using real time image processing. The image sequences from a camera are analyzed using various edge detection and object counting methods to obtain the most efficient technique. Subsequently, the number of vehicles at the intersection is evaluated and traffic is efficiently managed. The paper also proposes to implement a real-time emergency vehicle detection system. In case an emergency vehicle is detected, the lane is given priority over all the others

Analysis of taxi dataset to categorize city rankings

The city administration values the district attraction rating since it can aid in extracting the desirability of the location and hence support the officials in making smart city development decisions. Traditional urban planning tactics mostly rely on Gross Domestic Product, rate of employment, number of people per unit area, and district statistics gleaned through surveys and questionnaires, among other factors. As a point of reference, such knowledge becomes less and less helpful over time. The volume of urban data is growing at an exponential rate. Furthermore, these tactics suffer from a fatal flaw: they are unsuccessful. Independent representations of a district's appeal, as well as inter-district interactions, are not taken into account. It is now feasible to use urban data efficiently for urban planning thanks to advances in urban computing. To that end, this paper proposes PageRank, a district attractiveness rating algorithm based on taxi big data, which is the first to do so. A working software is constructed for visualization motives. To begin, the total area is split into numerous parts using the k-means algorithm

Product Evaluation Using Entropy and Multi Criteria Decision Making Methods

Abstract: There is variety of products of different brands available in the market for the customer of different levels which can satisfy their specific demands. The customer has been offered by means of variety of products of the same species and category with different features and attribute. This enhance the competition between the brands, resultantly make efforts to stimulate the customers towards their products by means of different policies, which sometimes can make customer confuse between the brands and their products to – what to pick and what not to. In this research paper we have taken nine laptops of different brands of nearly same range of specifications and Multi Criteria Decision Making (MCDM) Methods are applied to choose the best option among the different alternatives. Entropy method is used to evaluate the weight of the feature attributes

Intradural tumor and concomitant disc herniation of cervical spine

We report a rare patient of a simultaneous extradural and intradural compression of the cervical spinal cord due to co-existent intervertebral disc herniation and an intradural schwannoma at the same level. The intradural lesion was missed resulting in recurrence of myelopathy after a surprisingly complete functional recovery following anterior cervical discectomy. Retrospectively, it was noted that the initial cord swelling noticed was tumor being masked by the compression produced by the herniated disc. A contrast magnetic resonance imaging scan is important in differentiating intradural tumors of the spinal cord. A high index of suspicion is often successful in unmasking both the pathologies

A child with debilitating pruritus

We describe a case of two-year-old boy presenting with debilitating pruritus, patchy alopecia and jaundice since the age of 6 months. On evaluation he had intrahepatic cholestasis with persistently raised serum alkaline phosphatase, normal Gamma glutamyl transferase and raised serum bile acid levels. His liver biopsy showed bland cholestasis and electron microscopy showed granular bile suggestive of progressive familial intrahepatic cholestasis type I. Medical therapy with ursodeoxycholic acid, cholestyramine, rifampicin with nutritional modification was successful in alleviating the symptoms and correcting the nutritional status. To our knowledge this is only the sixth case of progressive familial intrahepatic cholestasis type I reported from India. Herein we discuss the diagnostic and therapeutic hurdles that one encounters in managing progressive familial intrahepatic cholestasis and also review the literature regarding this rare disorder

TEQIP - III Sponsored First International Conference on Innovations and Challenges in Computing, Analytics and Security

This book contains abstracts of the various research papers of the academic & research community presented at the International Conference on Innovations and Challenges in Computing, Analytics and Security (ICICCAS-2020). ICICCAS-2020 has served as a platform for researchers, professionals to meet and exchange ideas on computing, data analytics, and security. The conference has invited papers in seven main tracks of Data Science, Networking Technologies, Sequential, Parallel, Distributed and Cloud Computing, Advances in Software Engineering, Multimedia, Image Processing, and Embedded Systems, Security and Privacy, Special Track (IoT, Smart Technologies and Green Engineering). The Technical and Advisory Committee Members were from various countries that have rich Research and Academic experience. Conference Title: TEQIP - III Sponsored First International Conference on Innovations and Challenges in Computing, Analytics and SecurityConference Acronym: ICICCAS-2020Conference Date: 29-30 July 2020Conference Location: Pondicherry Engineering College, Puducherry – 605014, India (Virtual Mode)Conference Organizer: Department of Computer Science and Engineering, Pondicherry Engineering College, Puducherry, India.Conference Sponsor: TEQIP-III NPIU (A Unit of the Ministry of Human Resource Development, India)