bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mechanical and Tribological behaviour of velocity oxygen fuel thermal spray coating: A Review
Authors: Jha Shivani , Mishra R.S. 
Published: August 2021

Abstract
For over decades thermal spraying has been present and optimized over the time and now it has become an effective method to thick coating deposition. Thermal spray coating especially high velocity oxygen fuel (HVOF) have served various industrial sector such as aerospace, defence and thermal electricity generation. To overcome disadvantages of traditional coating there was a need of smart coating process which could integrate new functionalities and coherent responses. The aim of this paper is to present a detailed review of current technological changes in HVOF thermal spray coatings. This paper gives insight view of different type of coatings on substrates, their mechanical and tribological properties. HVOF coating categorised according to various application sector has also been discussed in this paper.
Influenza prediction from social media texts using machine learning
Authors: Singh Sandeep , Kaur Harjot 
Published: August 2021

Abstract
The monitor and prediction of influenza through conventional modes seem obsolete in the Internet era. The flu prediction using machine learning methods is more resourceful for both economically as well as logistically, than traditional methods. The machine learning methods exploit freely available data on social media platforms. The main objective of this paper is to harness the power of publicly available data through machine learning methods. This work involves the scraping of textual data from six social media platforms and training of five machine learning predictors. The paper also proposes a three-layered prediction method based on support-vector-regression for predicting trends of influenza spread. The results obtained from proposed model are validated and analysed against other four machine learning methods. The results reveal that proposed model has better predictive accuracy in terms of least prediction errors among the five methods. Thus, it can provide effective means to control and to prevent the flu outbreak.
Analysis of the quasi-stability of kinematic parameters for manipulators system during the docking process using the Digital Twin approach
Authors: Jędrzejowski M , Błachowicz T , Krafczyk W , Pyka W , Tokarczyk O , Chudy M , Bzymek A. , Sokół Z , Ehrmann A 
Published: August 2021

AbstractThis study analyzes the acceleration of the arms of a dual system of manipulators that aim to achieve the minimum distance by mimicking the process of approaching spacecraft that are trying to connect. The research was performed simultaneously on a real model and its digital equivalent, in accordance with the Digital Twin (DT) approach. The obtained acceleration values show, for some set sampling times, read out positions and velocities, separate distributed regions of achievable values. The Digital Twin, Digital Shadow (DS) and Digital Model (DM) approaches have recently shown great progress in development, both in academic studies and at industrial level. Along a very wide range of applications, we propose some important issues related to satellite technologies.
A deep learning approach for Indian sign language gestures classification with different backgrounds
Authors: Dhiman R. , Joshi G. , Rama Krishna C. 
Published: August 2021

Abstract
The sign language recognition system recently has drawn the attention of various researchers as there is no universal sign language, moreover, it consists of many patterns and postures. Many methods for extracting features and classifying sign language have been proposed in the literature, most of them are based on machine learning techniques. In this article, a deep learning method has been adopted by designing a Convolution Neural Network (CNN) model to extract the sign language features where for classification softmax layer is used. All alphabets in simple as well as complex backgrounds have been considered, where data is collected from 100 subjects in different lighting conditions. The effect of various optimization techniques (Adam, Sgdm, RMSProp), activation functions (ReLU and Leaky ReLU) for generalization ability is also observed. The proposed approach has succeeded in attaining the testing accuracy of 99.10%, 92.69%, and 95.95% on the Indian sign language dataset with simple, complex backgrounds and on a mixed background scenarios, respectively. The model is also tested on NUS dataset-I, NUS dataset-II, their combination, and achieved the accuracy of 100%, 95.95%, and 97.22% respectively.
Design and Development of an Indian Harmonium with Automatic Bellow Mechanism
Authors: Guin B , Ghar K , Modak N 
Published: August 2021

AbstractHarmonium is a reed instrument widely used in Indian classical music, utilizing a hand-driven bellow to pump air into the soundbox. The pumped air is released through the metallic reeds producing musical notes. The hand-driven bellow action can be challenging and tiresome for beginners as well as professionals during long practice sessions. This problem can be resolved by automating the bellow. The design process is twofold – 1stis the synthesis of a motor-driven linkage capable of generating the rocker motion of the bellow, and 2ndis to obtain the required specification of the motor that can drive the mechanism. A 4-bar crank-rocker linkage has been synthesized using a modified Freudenstein equation, which allows the bellow to compress and expand in one complete rotation of the motor shaft. The novelty of this work is that the nonlinear dynamics of the harmonium bellow has been modeled using a spring-mass equivalence technique. This helps in accurately predicting the operating force for the bellow, the force transmission through the linkage, and estimating the required motor torque. The final design is simple and ergonomic. Based on the design, a working prototype has been developed at the Machine Elements Laboratory of Jadavpur University.
Gar-Bot: Garbage Collecting and Segregating Robot
Authors: Gupta Shreya , Kruthik H M , Hegde Chaya , Agrawal Shreya , Bhanu Prashanth S B 
Published: August 2021

Abstract
This paper presents the design and development of the first prototype of an automated garbage collection robot (Gar-Bot). It operates efficiently in an indoor environment. Main functionality of the robot is to detect and classify the garbage into 3 types, and collect them in different bins. To efficiently use the space in the bin, plastics are crushed after collection. Gar-Bot uses convolutional neural networks implemented on NVIDIA’s Jetson Nano Developer Kit for garbage detection and classification. The IoT platform is used to notify the user with the status of the bin. The robot is equipped with a robotic manipulator, rotating bin for collecting garbage, Kinect V1, Processor and controller, and a base with wheels to facilitate navigation. The robot successfully detected and classified the garbage into various classes at an inference time of 5 seconds per frame, with an accuracy of 90 percent.
A study on influence of eco-friendly technologies in hospitality industry
Authors: Xess Anjali , Bhargave Harshita , Kumar Praveen 
Published: August 2021

AbstractThe Hospitality sector contributes initiatives taken towards the eco-friendly environment and faces lots of challenges towards customer satisfaction and repeat business. With a new modernization of market segments, technology awareness, customers are getting advancement in the field of technology. They are conscious about their health and environment, which is why the hospitality industry needs to pay attention to the customer’s and employee’s health and environment. Promoting eco-friendly technologies by the hospitality industry is useful in both the ways for the customers and sustainability for the environment by adopting the three Rs i.e. reduce, reuse, and recycle. The guests are also getting sensitive about environmental issues. The hospitality industry, therefore, strives to involve them in sustaining the mission of three Rs, which is good for the environment, customer, employee health and the hotel. This research paper aims to discover customer’s behavior, attitude, and level of satisfaction derived from the green technological approach of the hospitality industry and also to understand the level of satisfaction of the employee towards the Eco-Friendly technologies in the hospitality industry in terms of the employee engaging with eco-friendly hotels for the longer period.
Intellectual Tool to Compute Embodied Energy and Carbon Dioxide Emission for Building Construction Materials
Authors: Mukherjee Abhilash , Sumit  , Deepmala  , Dhiman Vishal Kumar , Srivastava Prateek , Kumar Ajay 
Published: August 2021

Abstract
Every material has its capacity to consume energy called embodied energy (EE) and to emit CO2 during the manufacturing process. The estimation procedure of EE and CO2 emission is predominantly based on the existing case examples from various nations across the globe. There is an impressive aperture in instances from developing nations concerning an appraisal of embodied energy and emission of CO2 for used building construction materials. The available tools are also not much user friendly and hence not everyone is capable to use it easily. From this point of view, this research aims to develop an intellectual tool to find out embodied energy and CO2 emission for building materials. This research presents a model for estimation of EE and emission of CO2 (E4-Model) for various building materials used in construction. This research focuses on construction of building in the Indian Construction Section. For the assessment of EE and CO2 emission, the concept of Convolutional Neural Network (CNN) is used and all the values are by weight of material. From the experimental analysis, it is observed that the estimation accuracy of the system is more than 99.62% for small as well as large buildings. The research concluded that the use of CNN is far better than the artificial neural networks in terms of system response time and complexity to estimate the EE and CO2 emission rates. In the research proposed E4-Model is applied and developed using the concept of deep learning and curve-fitting toolboxes within MATLAB Software. The actual emission for all the tested projects were found to be lesser than the estimated emission. The tool developed using CNN proved to work with high accuracy.
Experimental Analysis of Various Materials on Custom-Fit Ankle Foot Orthosis
Authors: Pandey Richa , Singh Rahul 
Published: August 2021

Abstract
Traditional Ankle Foot Orthoses (AFOs) is designed to work for different range of patients. They could not perform comfortable orthotic function arising due to individual differences. This problem can be solved by custom fit AFO’s but the current method involves a lot of manual labor. Rapid Prototyping and reverse engineering can be used on 3-D parts or models designed in CAD software to solve the problem. Through the help of 3-D scanning, customized AFO’s is designed to an individual foot’s anatomy. The scanned data was used to design an AFO structure. The designed structure was virtually tested for different materials and an analysis of the same was done.
Cardiovascular Disease Detection using Artificial Immune System and other Machine Learning Models
Authors: Gupta Ishan , Shangle Ruchir , Latiyan Vishwas , Soni Umang 
Published: August 2021

Abstract
Researchers and medical institutions face problems in detecting and diagnosing cardiovascular diseases in the early stages. Therefore, having a tool for detecting cardiovascular diseases in early stages will be helpful for the medical institutions to combat the disease. In this paper, we have presented a solution for detection of cardiovascular diseases by using clonal selection algorithm. Clonal selection is an Artificial Immune system (AIS) based algorithm which is often used for pattern recognition problems. Here, we propose modified clonal selection algorithm (CLONALG) which effectively detects cardiovascular diseases. With the proposed algorithm, we have achieved an average accuracy of 78%. Further, we compared the accuracy of CLONALG algorithm with different models of Machine Learning, viz. Random Forest Classifier (RFC), Decision Tree Classifier (DTR), Support Vector Machines (SVM), Logistic Regression (LR) and Artificial Neural Networks (MLP-ANN) for cardiovascular disease detection.
Factors influencing environmental perception: A Systematic Review
Authors: Shynu R.V , Santhosh Kumar K.G , Sambath R.D 
Published: August 2021

Abstract
This paper is a systematic meta-analysis based on selected empirical studies to evaluate human-environmental perception. The research concentrates on Eye-tracking technology, electroencephalograph (EEG), electrocardiograph (ECG), and electrodermal activity (EDA). This study investigates the realm of human cognition and its engagement towards the environment. Nevertheless, the studies focus on the factors that enhance human perception towards the surrounding environment by exploring virtual reality. Methods of the review follow systematic meta-analysis guidelines. Meta-analysis is conducted from selected empirical articles, aligned with research questions, published in journals between 2005 and 2020 in the English language. ProQuest, MDPI, PubMed, and Google scholar are the Electronic databases used to extract research works till September 2020 for data analysis. The study analyses the scope for further research using multivariable investigation in the domain of human visual perception. Future explorations could focus on factors such as Semi Immersive technology, the Elderly category, and Color-based influence. Result also suggest the necessity of exploring EEG and Eye-tracking technology in the domain of environmental perception. This work would be a reference for the psycho-physiological researchers on human-environment studies.
Parametric Study of Laser Welding on Polyamide using Design of Experiments
Authors: Girish Kumar R , Narasimha Murthy H N , Anand B , Jabiulla Syed 
Published: August 2021

Abstract
Laser welding was primarily employed for metals but recent developments in laser technology have enabled its applications in the polymers field. Laser transmission welding or joining of two polymers and metal-polymer has become feasible. The study was mainly aimed to investigate the influence of laser power, speed of welding, and number of passes based on Taguchi orthogonal array on responses by using continuous mode high power diode fiber laser on polyamide. The weld quality was assessed by measuring the strength of weld and width of the weld seam. Initial study on diode fiber laser welding machine and the parameters associated with it was essential to design the experimental layout. Polyamide, which is widely used in the automotive industry was selected as the material and the influence of various parameters on the laser welds were studied using design of experiments. The experimental studies were performed based on orthogonal array method. The responses of the weld were measured using an optical metallurgical microscope. The ideal parameters for maximum joint strength and minimum weld width are investigated using Taguchi method. The multi-objective optimization has been done by applying grey relational analysis to identify the levels of factors that result in the maximum strength and minimum weld seam width.
Experience with advanced welding techniques (RMD & P-GMAW) with seamless metal cored wire for Oil & Gas pipeline industries
Authors: Das Subhash , Vora Jaykumar , Patel Vivek , Bogum Surender 
Published: August 2021

Abstract
Regulated Metal Deposition (RMD™) process is a variant of Gas metal arc welding process (GMAW), which was developed with the aim to effectively control the metal transfer in the short-circuiting mode. The process is fundamentally a modified short-circuit GMAW process wherein a uniform droplet deposition, making it easier for the welder to control the puddle and hence achieve an enhanced quality of welded joints. In the present study, RMD technique has been established for API grade pipe (API 5L PSL2 X70 grade) particularly for depositing the root pass on 24” OD and 10.3 mm thick pipe. In addition to this, the RMD technique is tried for the first time with seamless metal cored wires (Megafill 240 M) in order to enhance the deposition rates in Indian Oil & Gas pipeline industry. The joint fill up is further attempted with pulsed GMAW technique using metal cored wires and further analysed. The weldments were tested by mechanical characterization. Mechanical characterization such as tensile properties, impact properties, bend test as well as all weld tensile properties of the weld joint were evaluated and found to be acceptable. A techno-commercial evaluation of the technique was carried out vis-à-vis conventional welding techniques such as SMAW process for root passes and Fill & Cap. It was found the proposed technique was cost effective as well as reliable. One of the inherent benefits of the RMD and Pulse GMAW technique is the reduction in the overall heat input of the welded joint imparting superior mechanical properties. The use of metal cored wires also established the proposed technique as a potential technique for heavy thick weldments by enhancing the overall productivity.
Out Domain Data Augmentation on Punjabi Children Speech Recognition using Tacotron
Authors: Hasija Taniya , Kadyan Virender , Guleria Kalpna 
Published: August 2021

Abstract
The performance of Automatic Speech Recognition (ASR) is directly proportional to the quality of the corpus used and the training data quantity. Data scarcity and more children’s speech variability degrades the performance of ASR systems. As Punjabi is a tonal language and low resource language, less data is available for Punjabi children’s speech. It leads to poor ASR performance for Punjabi children speech recognition. To overcome limited data conditions, in this paper, two corpora of different domains are evaluated for testing the feasibility of ASR performance. We have implemented Tacotron as an artificial speech synthesis system for Punjabi Language. The speech audios synthesized by Tacotron are merged with available speech corpus and tested on Punjabi children ASR using Mel Frequency Cepstral Coefficients (MFCC) + pitch feature extraction, and Deep Neural Network (DNN) acoustic modeling. It is noticed that the merged data corpus has shown reduced Word Error Rate (WER) of the ASR system with a Relative Improvement (RI) of 9-12%.
Emotion Recognition in Hindi Language using Gender Information, GMFCC, DMFCC and Deep LSTM
Authors: Kumar Sandeep , Yadav Jainath 
Published: August 2021

Abstract
Long Short-Term Memory (LSTM) captures long-term dependencies accurately than other types of neural networks, and it is frequently used in deep learning. In this work, we have explored Deep LSTM with a dropout layer that minimizes the training overfitting. We have considered IITKGP-SEHSC emotional dataset for emotion recognition. We only deal with five types of emotions, namely angry, fear, happy, neutral, and sad emotions recorded from male and female speech. Since the IITKGP-SEHSC dataset is monolingual that means only spectral features are sufficient for emotion recognition. Traditional MFCC deals with low-frequency information. Here, we have explored two features, namely Gammatone Mel Frequency Cepstral Coefficient (GMFCC) and Discrete wavelet Mel Frequency Cepstral Coefficient (DMFCC). GMFCC deals with basilar membrane displacement obtained from the gammatone filter, and it is useful for recognizing gender from emotional speech. DMFCC deals with MFCC analysis on the high-frequency components of speech rather than the low-frequency components. In the proposed work, DMFCC has been explored for recognizing emotions from speech. The average accuracy of gender classification with Deep LSTM and GMFCC is 98.3%. The average emotion recognition rate with Deep LSTM and DMFCC is 92% and 88.7% individually for male speech and female speech, respectively. Our proposed model is built by combining the above sub-models, and it gives emotion recognition accuracy of 91.2% for male speech and 87.6% for female speech, respectively.
Ergonomic support system for construction worker’s hand arm for lifting tasks
Authors: Jain Saksham , Verma Himanshu , Khan Abid Ali 
Published: August 2021

Abstract
In this paper, a novel passive arm exoskeleton is introduced to assist the construction workers in load lifting/carrying tasks. The exoskeleton is designed based on torsion springs to generate assistive torque at the elbow and wrist joint and can fully support the weight of 8 kg task load. Static simulation of the springs and arm bracing was performed to check the correctness of the design parameters in ANSYS. The exoskeleton is extremely lightweight, economical, and easy to use as it provides a novel locking arrangement, which may allow workers to instantly disconnect the springs as per their need, making it highly suitable for the construction workers.
Detecting Pneumonia Lung Infection From X-Ray Images with Deep Learning
Authors: Raheja K , Goel A , Mahajan M 
Published: August 2021

Abstract
Pneumonia is one of the most prominent basis of premature death globally, according to some of the major health institutions like WHO more than 1 billion people around the world get infected and more than 4 million people die prematurely due to pneumonia lung infection. Detection of pneumonia infection requires a lot of medical tests, which can be expensive and time consuming. Deep Learning, the technology which gives computer systems the ability to learn and adapt through unstructured data in order to complex solve real world problems has the potential to make the detection of pneumonia easier, cost effective and less time consuming. The motivation behind this paper is to grasp the criticalness and utilizations of deep computational learning and Convolutional Neural Network (CNN) by executing it so as to recognize Pneumonia by examining a patient’s chest x-beam pictures.
IoT and Machine Learning Based Health Monitoring and Heart Attack Prediction System
Authors: Sahoo Sudarsan , Borthakur Priyanuj , Baruah Niharika , Chutia Bhaskar Pratim 
Published: August 2021

Abstract
The increased use of mobile technologies and smart devices in the area of health has caused great impact on the world. Health experts are increasingly taking advantage of the benefits of these technologies and hence generating a significant improvement in health care in clinical settings. Internet of things (IoT) and machine learning techniques can be used efficiently for this purpose. The objective of this work is to design and develop a real-time IoT based health monitoring and heart attack prediction system that integrates vital signs sensors, location sensors, ad-hoc networking and web portal technology to allow remote monitoring of patient’s health status and to predict the heart disease through various machine learning techniques. In this work it has ensured the correct and efficient transmission of the vital signs data to the ThingSpeak server through the internet via a given access point(AP) and notified the user of the same through the GSM module. A heart attack prediction system is also developed to predict the probability of heart attack from the available parameters. The novelty of the proposed work is that it takes the advantage of both the IoT and machine learning technology to monitor and predict the diseases. The proposed system is inexpensive too.