bluuebunny/crossref_metadata_2025_split huggingface dataset:
Quito’S Urban Imaginaries: Between Conserved and Intervened Green Spaces
Authors: Montero D , Estrella A , Oleas N , Cruz J , Salazar L , Santos F , Bonilla-Bedoya S 
Published: March 2021

Abstract
This manuscript presents the results of field research aimed to identify the perceptions that condition the urban imaginaries addressing the green spaces in Quito, Ecuador. Two focus groups were carried out with experts in designing green spaces. The results were evaluated through discourse analysis, applying the dialectical hermeneutical method, to finally establish open, axial, and selective categories. Six categories allow the understanding of the imaginaries associated with the city’s green spaces: conserved or intervened spaces, public and private spaces, and individual and collective subjectivity in relation to green areas; also relevant are the interventions of actors such as the state, the real estate market, and the community in the management of these spaces. In the city, conserved green spaces, such as urban and peri-urban forests, are for contemplative use and would be less attractive to the population than provoked green spaces, the latter of which are characterized by facilitating human interaction and by having the direct intervention of public institutions. The identification of public and private green spaces was related to potential forms of urban segregation. Participants stated that privileged social groups have direct access to customized architectural designs, whereas community organizations manage these spaces through social action.
Finite Element Analysis of Notch Sensitivity of PEEK under Monotonic Tension
Authors: Khan M A A , Asad M , Djavanroodi F , Nayfeh J F , Waqar T 
Published: March 2021

Abstract
Polyetheretherketone (PEEK) is of great interest to the biomedical industry especially for trauma, orthopedic and spinal implants. It is biocompatible and offers numerous advantages over other metallic biomedical implants. A good understanding of behavior of PEEK under different loading conditions is therefore necessary for its more efficient use. This paper investigates the notch sensitivity of PEEK in monotonic tension using the finite element modeling in ABAQUS. An unnotched specimen and three notched specimens (moderate, deep and razor) are modeled and the maximum true stress in each case is obtained. The stress distribution in front of the crack tip is also found by introducing a crack in the model for each specimen. It is observed that PEEK exhibits a ductile behavior in the absence of a stress concentration. The deformation mechanism is found to be dramatically changed from ductile to brittle in the presence of notches. Hence, the locations and severity of stress risers modeled as notches in PEEK should be taken into consideration prior to its use in biomedical implants.
A Generative Adversarial Network Based Segmentation and Classificationusing Deep CNN: Systematic Review
Authors: Sabeenian R S , Vijitha V 
Published: March 2021

Abstract
To become improved in the field of Deep Learning.it is a province system which is fully depended on learning methods and it upgraded one.Itpossesses to inspect closely to computer algorithms. In this detecting tumor system is one of the uppermost to confinement training tissue from the normal brain function tissue.it contributing numerous discipline such as medical image analysis, facial recognition system, virtualassistants,investment modelling like many applications its paly a various role to get accurate detection of the image. The motivation to this study is to layout an investigation based on deep learning network analysis. From this segmentation and classification is established to impose deep convolutional neural network and fully connected layer based Generative Adversarial Network (GAN).The model makes unfamiliar data that bear a resemblance training data in generator(generate images) and discriminator(it is classifies real and fake image).after a while GAN based segmentation and classification handout some predominance performance are confer. A good many multitude people were worked in this GAN based tumor segmentation and detection. From this most of the researches failed to get more accuracy in this technique.to summarize the state of art to help out experimentation in succeeding superintendence.
Automatic number plate recognition using deep learning
Authors: Gnanaprakash V , Kanthimathi N , Saranya N 
Published: March 2021

Abstract
In this era of fast growing technologies, there is a huge demand among the people for a secure lifestyle and travelling. In the past decade, the number of vehicles on road has been increased. Tracking of individual vehicle becomes a very challenging task with the massive growth in the vehicular sector every day. This paper suggests an automated vehicle tracking system for the fast moving vehicles with the help of the surveillance cameras on the roadside. The process of getting CCTV footage in the real time background is very tedious process. To cater to this problem, an efficient deep learning model such as You Only Look Once (YOLO) is used for object detection. The proposed work consists of four main steps. In the first step, video footage is converted into images and the car is detected from each of the frames. In the next step, license plate is detected from the detected cars. In the final step, the number plate characters reading are recognized from the detected number plates. The proposed deep learning model uses ImageAI library to make the training process easier. Tamil Nadu license plate images are used to analyse the performance of the model. The accuracy of 97% is achieved for car detection, accuracy of 98% is achieved for number plate localization and accuracy of 90% achieved for character recognition.
Analysis of Epilepsy Risk Levels from EEG Features through Support Vector Machine and Minimum Relative Entropy Model
Authors: Rajaguru Harikumar , Ramesh Monish , Manoranjith K 
Published: March 2021

Abstract
The goal of this research is to attain the epilepsy risk levels from Extracted features from EEG signals. This paper analyses a two-levelmethod of classification. At level one fuzzy method were endorsed to reach a multiple sub optimal solutions. Then the SVM and MRE classifiers were used to reach the singleton results. The Classifiers were compared by means of quality values. The SVM classifier accomplisher higher Quality Value of 23.02 when compared with other classifiers.
Performance Analysis of classifiers in Detection of Abnormalities from Ultrasonic Images-A Review
Authors: Harikumar R , Karthikamani R 
Published: March 2021

Abstract
In this paper the ultrasonic abdominal diseases are classified using various classifiers and their performances are analysed. Early diagnosis of abdominal disease is very important in deciding the proper treatment process. To carry out any research work without the comparison of the proposed one with the other already existing method is not effective to give better result. Objective of this paper is to review the classification methods based on the standard parameters like, Sensitivity, Specificity, Accuracy. In this review four classification algorithms Naive Bays Classifier, Support Vector Machine (SVM), k-Nearest Neighbor (kNN), Artificial Neural Networks(ANN) are compared.The SVM classifier attained higher accuracy of 98.33 % when compared with all the classifiers.
Investigation on enhancing the binary classification accuracy of supervised classifiers using various transforms
Authors: Issac K. , Bharadwaj K. , Bharanidharan N , Rajaguru Harikumar 
Published: March 2021

Abstract
The Classification is used for testing instances where the unknown class labels are assigned where the predictor features are known. This paper aims to investigate the classification performance improvement of popular supervised classification approaches using data transformation techniques. Hilbert Transform, Discrete Wavelet Transform, and Principal Component Analysis are investigated as data transformation techniques for improving the performance of four different supervised classification approaches namely K-Nearest Neighbor classifier, Random Forest Classifier, Naive Bayes Classifier, and Support Vector Machine. SONAR dataset is used in this research work and the highest Mathews Correlation Coefficient of 0.72 is attained for Random Forest Classifier.
Virtual Tollgate using Machine Learning
Authors: Manojkumar P , Raja Sekaran N , Dhanapal M , Srinivasan C 
Published: March 2021

Abstract
The traffic at toll gates is increasing day by day which leads to loss of time as well as fuel and pollution which is now a major concern. Even though there are smart payment gateways such as FASTAG the vehicle still needs to move in lower speeds to get detected by the readers. Our system is to implement a way smarter tollgate using machine learning algorithms. In these a tracking device is placed inside the corresponding vehicle and the path travelled by the vehicle for a day is retrieved as an image and pushed to the server through the internet connectivity provided in the device. In the server the received image is processed using the machine learning algorithms fed into it and allots the fare accordingly. The bill is generated on a monthly or weekly basis and sent to the user/owner of the vehicle. The utilization of mechanized cost assortment frameworks in numerous metropolitan urban areas would be an effective advance towards the congestion of the city roadways in substantial clog of traffic. As we as a whole know, transportation is the strength of our nation’s economy. There are different usage conventions in remote sensor organizations, for example, drain and segments, for example, RFID, NFC in this manner empowering decrease in activity expenses and rousing credit only exchanges. If there should arise an occurrence of manual cost assortment framework time utilization is much far more regrettable just as fuel consumption and most significant is the climate, the measure of air contamination that is made at the tollgate site is at elevated level, so our created framework will decrease time wastage and lessen air contamination as well as preserve fuel. The sole reason for this paper is to decrease the difficulties brought about by manual cost assortment frameworks and pass the subject’s vehicle through the cost obstruction in a matter of a couple of moments without stop.
Joint Sparsity and Total Variation Based Unmixing of Mixed Noise
Authors: Bachu Srinivas , Mounika Moturi , Nagabhushanam K 
Published: March 2021

Abstract
Hyperspectral unmixing is the procedure by which the end component elements are calculated and their fractional abundances are found in each pixel in hyperspectral images. Sometimes several types of sound harm a hyperspectral photo. In a general scenario that takes mixed noise into consideration, this study addresses the hyperspectral non-mixing problem. Gaussian and sparse noises are expressly taken into account in the unmixing model. The problem of unmixing was formulated to use the combined shortage of abundance maps. For the modelling of flatness diagrams, a complete variation-based regularisation has also been used. For the solution of an algorithm for the optimization problem, the split-Bregman technique was used. The advantages of the method proposed are revealed by detailed preliminary findings on both real and synthetic images.
Analysis of LSE and MMSE Pilot Based Channel EstimationTechniques for MIMO-OFDM System
Authors: Gayathri R , Perarasi T , Saranya S , Harikirubha S , Roobini K 
Published: March 2021

Abstract
An Orthogonal Frequency Division Multiplexing(OFDM) has becoming a desirable modulation technique scheme for wireless systems. In recent years, with the advent of technology in Very Large-Scale Integration (VLSI), the number of higher data level is growing. Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems are used to meet these criteria due to their high performance, in terms of transmission range, data rate resistance to multi-path transmission. A MIMO OFDM framework is designed for systems such as IEEE 802.11n, 4 G and LTE in advanced communication systems. It provides secure transmission with a growing coverage area. The estimation of Channel State Information (CSI) is the biggest challenge in the implementation of MIMO-OFDM method. We evaluate and enforce the pilot-based channel analysis methods for MIMO-OFDM system such as Minimum Mean Square Errors (MMSE), Least Square (LS).
Analyzing The Performance Of On-Demand Routing Protocols Based On Rainfall Intensity In Wi-Fi Networks
Authors: Hariprasath N , Jayaram T , Prema R 
Published: March 2021

Abstract
During rainfall the information signal while transmission gets attenuated and the network performance is affected greatly in the regions where frequent rainfall occurs as they are prone to varying climatic conditions. Therefore in order to maintain a reliable communication the implementation of a MANET protocols during rainstorm or similar unfavorable environmental conditions should be paid great attention for the selection of routing protocols. The routing protocol must provide efficient and effective improvement in performance aspects such as total number of packets received, throughput, mean jitter and an end to end delay. This paper undergoes evaluation based study of MANET reactive/on-demand routing protocols namely LANMAR, OSPFV2, ANODR and DYMO in Wi-Fi networks (IEEE802.11n). The behavior of routing protocols is simulated and evaluated using Exata 5.1 network simulator by varying the intensity level of rainfall from light rain (2mm/hr) to cloud burst (100 mm/hr) to predict rainfall efficient routing protocol which can render maximum throughput and minimum delay.
Wireless Sensor Networks: A Review on Sensor Deployment and Routing Protocols for Different Application
Authors: Pavithra L , Abdullah M , Prakash S P , Karthick S , Ragavi B , Nandhini V D 
Published: March 2021

Abstract
In the past years, many research works has been performed on the basics of the wireless sensor networks for different application namely precision agriculture, environment, medical care, security and surveillance. Whereas, WSN faces the different kinds of challenges that includes energy consumption, routing protocol algorithm and selection of the sensor node from the particular application environment. Therefore, the implementation of the wireless sensor networks has overcome a numerous amount of challenges in the practical life. In this paper, we mainly focused on the two issues which mainly affect the transmission of the data to the base station they are sensor node deployment which is used to improve the coverage area of the environment and the routing protocol which is used to select the node to maintain a particular route for a network. Atlast, we have discussed about design challenges that are faced during the designation of the wireless sensor networks.
Wireless Home Automation System
Authors: Hamsagayatri P , Subash S , Susheel S , Venkatesh K M 
Published: March 2021

Abstract
In this paper, a novel design prototype for home automation is presented, where it uses WiFi technology as its communication network. The proposed prototype consists of two modules, where one is a web server the first one is the web server that serves as a system core which organizes, controls, and monitors users’ home. Users and system administrators are controlled either using LAN or internet. The second is a hardware module, which interface sensors and actuators in the home automation system. When compared to commercial home automation systems in the market, the proposed system is cost-effective. The proposed automation prototype controls different devices like security and power management components. The proposed system is highly flexible and scalable with good accuracy.
RETRACTED: Architecture of a HV Power Battery Protection Devices for Hybrid Electric Vehicles (HEV)
Authors: Thakre Mohan P. , Mahadik Yogesh V. , Sardar Nikhil 
Published: March 2021

Abstract
The Hybrid Electric Vehicles (HEV) has high voltage battery whose voltage is higher than the people can endure. The proper insulation should be provided to the HV battery pack but sometime due to abnormal condition the battery insulation get failed. Whereas the HV battery not grounded to chassis if an abnormal condition occurred then a human being get electrified. To resolve the insolation failure this paper aims on Designing HV battery Protection system for Electric vehicles. In this paper we design control strategy and algorithm for battery isolation in electric vehicle. This paper aims to Designing the hardware circuit and algorithm for the isolation. These systems detect resistance in between battery pack chassis and maintain this resistance with standard resistance value. If it is failed to maintain resistance it turned off the battery Precharge switch to avoid malfunctioning. It is convenient, simple and less in cost.
Design and Implementation of Intelligent Head Protective Gear for Accident Detection and Notification
Authors: Maheswari K T , Srimathi R , Jaanaa Rubavathy S , Karpagam R , Thanish Jackson V 
Published: March 2021

Abstract
The main aim of the paper is to safeguard the rider with a smart helmet without which the rider cannot start the bike. So the bike can be started only by using both key and smart helmet. This smart helmet technology consists of alcohol detection, accident detection, alert system and women’s safety. If accident occurs, the information will be send to the emergency contacts by using a GPS and GSM technology. Also, the bike’s location is tracked by employing GPS (Global Positioning System) and provide information to contacts by using GSM (Global System of Mobile) technology. Similarly, when the rider has consumed alcohol, the controller will automatically shut downs the bike’s ignition system and will not allow the rider to ride the bike. An emergency alert system could be used in case of emergencies like provocation and molestation. The women have to hold the button of helmet and whenever she clicks the button of the helmet, its ends alert messages to family members or nearby police stations. Another important feature is that, when the rider’s helmet strikes the floor, vibration sensor senses the possibility of hitting and gives information to regulator. When the numbers surpasses smallest stress limit, GSM module alerts ambulance or family members along with location using GPS module. This smart and intelligent helmet is implemented and tested to enhance the protection of riders.
Pv Based Battery Less Cooling And Lighting Solution
Authors: Nıvesh Kumar K. , Mothınathan L. , Sathıyanarayanan T. , Arun Jayakar S. 
Published: March 2021

Abstract
Increase in usage of renewable energy has become mandatory as the world is moving towards green energy. Developing nations like India and many others aids the revolution as it has abundant solar energy to harness. A solar powered flyback converter with an inverter is presented in this paper. This system is of great use at street side vending shops and food trucks where variable loads like fan and incandescent lamp can be connected. Flyback converter provides galvanic isolation to protect the load from the source and minimal usage of circuit components which adds to the advantage. The proposed system has no battery and the power of the load changes as the solar radiation changes. A prototype with 35V DC input and 230V AC output was developed and tested to verify the performance of the proposed system. The efficiency of the system was found to be 81%.
Identification of Fruits and Vegetables using Embedded Sensor
Authors: Rajasekar L , Ganesh Babu C , Sharmila D 
Published: March 2021

Abstract
In precision agriculture, computational techniques play a critical role. It is a vital job to detect and classify fruits and vegetables from crops and gardens. In fruit detection and yield estimation, an intelligent detection system is used as a key technology where embedded sensor techniques are being implemented. Many researchers developed a different classification system for classifying the fruits or vegetables using image processing techniques and machine learning algorithm. Big computers with fast CPUs and GPUs, massive RAM sizes or cloud algorithms have often been associated with them even simple applications. In this article Tensorflow and Arduino BLE sense are used to identify the fruits and vegetable with less resource.
Modeling of an Adaptive Energy Based Route Broadcasting Algorithm (AERBA) Using Route Optimization in MANET
Authors: Manoojkumaar S B , Poongodi C 
Published: March 2021

Abstract
Owed to the uncertain and dynamic behaviour of nodes over Mobile Ad-Hoc Networks (MANETs), the reliable route discovery and data delivery is considered as a challenging factor. With absence of centralized infra-structure and global broadcasting nature, nodes over the distributed environment rely over the neighborhood nodes. For reliable communication, the network needs to broadcast the routing direction to chose the appropriate neighborhood partner to make the functionality effectual during packet relying. Therefore, the evaluation process is determined to be a crucial factor. Various methodologies use the trust-based concepts for choosing the route broadcasting region and neighborhood nodes. However, those methods are identified with certain limitations and the evaluation is performed over single node. The inadequate consideration of these factors leads to the wider range of network model that leads to inappropriate broadcasting. This leads to packet loss, higher energy consumption due to re-transmission of packets. In this work, an Adaptive Energy-based Route Broadcasting Algorithm (AERBA) using Route Optimization (RO) is applied along with the route optimization approach to derive the global solutions. This leads to the trust establishment among the network nodes and pretends to fulfill the constraints related to broadcasting and energy consumption. The anticipated model facilitates appropriate evaluation over the potential nodes with multiple hops for balancing the route broadcasting and energy consumption. Simulation is carried out in MATLAB environment and the results demonstrate lesser energy consumption, higher delivery ration when compared with prevailing approaches.
Certain Investigations and an experimental approach for the kerbside Rainwater collection
Authors: Mythili S , Kalamani M , Krishnaraj R , Soundarya B 
Published: March 2021

Abstract
Rainwater is a free and renewable source used for domestic purposes but due to storage constraints, 36% of rain water is used in India even though heavy rainfall occurs during monsoon months. Nowadays the roads and their development in city environments are incalculable. The outstanding backlog is that most of the time, the drainage is filled by rain that falls on roads lead to less possibility of rain water usage. This work describes of saving rain water via external sensor -equipped pipes attached to the roadside. Whenever rain water fall on sensors it will be sensed, so that the pipes will open and rain water will fall through the fitted pipes and further it is transferred to storage tank with the aid of servo motors and DC motors. Through this prototype construction the rain water quantity of 55% is getting saved and also supports for ground water recharge