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Hybrid Renewable Road Side Charging Station with I2V Communication Functionality
The faster adoption of Renewable-based Energy Sources for charging Electric Vehicles is highly required. The paper proposes a novel strategy of design and developing a hybrid Road Side Unit (RSU) that would be easy to install and provides easy access to Electric Vehicle charging. The system inculcates Infrastructure to Vehicle (I2V) communication framework enabling communication between the Infrastructure and the Vehicle to identify the nearest charging station based on the availability. The communication framework is based on Wi-Fi communication and enables bidirectional communication between the Vehicle and the Infrastructure as well. The modelling and development of the RSU, and the active power flow regulation from the RSU to the Charging Station is also developed, using a Fuzzy Controller. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Content Based Scientific Article Recommendation System Using Deep Learning Technique
The emergence of the era of big data has increased the ease with which scientific users can access academic articles with better efficiency and accuracy from a pool of papers available. With the exponential increase in the number of research papers that are getting published every year, it has made scholars face the problem of information overload where they find it difficult to conduct comprehensive literature surveys. An article recommendation system helps in overcoming this issue by providing users with personalized recommendations based on their interests and choices. The common approaches used for recommendation are Content-Based Filtering (CBF) and Collaborative Filtering (CF). Even though there is much advancement in the field of article recommendation systems, a content-based approach using a deep learning technology is still in its inception. In this work, a C-SAR model using Gated Recurrent Unit (GRU) and association rule mining Apriori algorithm to provide a recommendation of articles based on the similarity in the content were proposed. The combination of a deep learning technique along with a classical algorithm in data mining is expected to provide better results than the state-of-art model in suggesting similar papers. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Securing medical images using encryption and LSB steganography
Medical imaging plays a vital role in the healthcare industry. Due to the advancements in the healthcare industry medical images are being transferred between geographically spread regions. As the medical images are transmitted through public networks several security challenges may arise such as authentication, integrity, and confidentiality. In this paper, the research focus is on preserving the confidentiality and integrity of the medical images. In general, integrity and confidentiality can be achieved by encryption (a piece of cryptography). To incorporate one more layer of security, steganography is applied to the medical image. In this paper, a one-time pad encryption algorithm is used to encrypt the medical image, then the encrypted image is implanted into a cover image to give a stego image making the system more resilient to the attacker. Further in this paper, the above said method is being implemented using MATLAB and the experimental results are compared with hiding the medical image using LSB steganography without encryption (one layer of security). Various formats of medical images have been taken into consideration such as DICOM, TIFF, BMP, and JPEG. Results show that the combination of encryption and steganography performs better than applying only steganography, concerning MSE and PSNR values. 2021 IEEE -
Design and Development of Terahertz Medical Screening Devices
This paper highlights the prospect of design and development of a terahertz medical screening system, giving an overview of existing devices, systems, for THz spectroscopy and imaging of biological samples (e.g., cell, tissue imaging or screening). Considering the non-ionizing nature of THz waves along with its reasonable soft-tissue sensitivity, terahertz instrumentation has opened up possibilities for medical screening devices. Some THz imaging systems presently use raster scanning for calculation of image region of interest. Here, a particular system is proposed as a medical screening device and factors like signal-to-noise ratio, image resolution, image contrast, etc., have been described and correlated with relevant clinical results for exploring possible prospects in medical applications of terahertz waves. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Mechanical and tribological investigation on al lm4/tic composite fabricated through bottom pouring method
In the present investigation LM4 reinforced 6 wt% Titanium Carbide particles composite was developed by stir casting bottom pouring method. The cast composite specimen was obtained in a cylindrical shape of dimensions 50 mm dia and 100 mm length. The composite specimens were prepared for mechanical and tribological test as per ASTM standards. The obtained results reveal that the mechanical properties are high as compared to the as cast LM4 alloy specimens. Microstructure analysis confirms that the uniform distribution of TiC particles. Tribological test was performed using pin-on-disc machine based on Taguchi's design of experiments. L27 orthogonal array was selected by changing test parameter like applied load (10, 20, 30 N), sliding distance (600, 800, 1000 m) and sliding velocity (1.5 m/s, 2.5 m/s and 3.5 m/s). The most influencing test parameters were identified by using S/N ratio and ANOVA. The wear results reveled that wear rate increases as applied load increases, and it decreases with decrease in velocity. Also wear rate decreases as sliding distance increases and at some point, it became linear. The applied load was found to be most dominating (77.61%), sliding velocity (10.44%) and sliding distance (4.47%) are less dominating factors. Worn surface morphology was studied to understand the type of wear. 2021 elsevier ltd. all rights reserved. -
A Brief Review of Intelligent Rule Extraction Techniques
Rule extraction is a process of extracting rules which helps in building domain knowledge. Rules plays an important role in reconciling financial transactions. This paper presents a brief study of intelligent methods for rule extraction. The paper touches upon heuristic, regression, fuzzy-based, evolutionary, and dynamic adaptive techniques for rule extraction. This paper also presents the state-of-the-art techniques used in dealing with numerical and linguistic data for rule extraction. The objective of the paper is to provide directional guidance to researchers working on rule extraction. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Defluoridation of Drinking WaterFluoride Wars
Fluorine is also known as two-edged sword. At lower doses, it influences tooth by inhibiting tooth caries, while in high doses, it causes dental and skeletal fluorosis. It is known that some quantity of fluoride is important for the formation of tooth enamel and mineralization in tissues. The present work aims at providing safe and potable water to rural areas where this element has created a menace. This work also suggests the use of few adsorbents such as paddy husk and coir pith which are affordable and removes fluorine to greater extent. The study concludes that materials which are used as adsorbents and can be safely inculcated as fluorine removal adsorbents which help people to have safe potable water. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Analysis of Unintelligible Speech for MLLR and MAP-Based Speaker Adaptation
Speech Recognition is the process of translating human voice into textual form, which in turn drives many applications including HCI (Human Computer Interaction). A recognizer uses the acoustic model to define rules for mapping sound signals to phonemes. This article brings out a combined method of applying Maximum Likelihood Linear Regression (MLLR) and Maximum A Posteriori (MAP) techniques to the acoustic model of a generic speech recognizer, so that it can accept data of people with speech impairments and transcribe the same. In the first phase, MLLR technique was applied to alter the acoustic model of a generic speech recognizer, with the feature vectors generated from the training data set. In the second phase, parameters of the updated model were used as informative priors to MAP adaptation. This combined algorithm produced better results than a Speaker Independent (SI) recognizer and was less effortful for training compared to a Speaker Dependent (SD) recognizer. Testing of the system was conducted with the UA-Speech Database and the combined algorithm produced improvements in recognition accuracy from 43% to 90% for medium to highly impaired speakers revealing its applicability for speakers with higher degrees of speech disorders. 2021, Springer Nature Singapore Pte Ltd. -
Predicting the Stock Markets Using Neural Network with Auxiliary Input
Predicting the stock market has always been a challenging task and has always had a certain appeal for researchers all around the world. Stock markets are supposed to be quite random and people with experience in the market strongly agree to the fact. Thus, predicting the stock market accurately paves the way for endless money. To date, no such algorithm has been devised that could even predict the stock market with a 90% accuracy rate. The difficulty lies in the randomness of the markets, and the various complexities involved in modeling market dynamics. Nevertheless, there have been algorithms with a decent success rate and researchers around the world have been in a constant attempt to improve over them. Thus, through this paper we attempt at predicting the return of a stock over a period of 10days after a particular news was out regarding the stock using the headlines of the news and certain other features important in determining the direction of a stock. The model was implemented with a sigma score of 0.81. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Classification of Vehicle Type on Indian Road Scene Based on Deep Learning
In Recent days an intelligent traffic system [ITS] is implemented on indian traffic sytem. Different applications are widely used to improvies the performance of the system. To improve the intelligence of the system deep learning can used to classify the vehicles into three different classes. The combination of Faster RCNN classifier and RPN can used to detect the objects and classify those objects into different classes. Analysis of the experimental results shows the improved accuracy and efficiency in classifying the vehicles on indian roads into different categories. 2021, Springer Nature Singapore Pte Ltd. -
A Review on Utilization of Construction and Demolition Waste (CDW) Toward Green and Circular Economy
Globally, policy makers have realized the significance of infrastructure development with respect to safety and environment-friendly approach. This has resulted in reuse and recycling initiatives in various industries including construction and building sector. Further, it is imperative to understand new techniques and methods to improve the effectiveness of recycling, keeping environment and carbon emissions in check. Recently, utilization of construction and demolition waste (CDW) as precursors in synthesizing alkali-activated and geopolymer binders have caught attention of researchers as green building material. This review paper discusses the findings of the latest research and promotes the use of CDW as a potential starting or precursor material in alkali-activated or geopolymer concrete toward green and circular economy. If processed appropriately, CDW can be used to produce environment-friendly binders that can reduce our dependence on conventional binders like Portland cement, thus promoting recycling in sustainable and eco-friendly manner. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Stock price prediction based on technical indicators with soft computing models
Stock market prediction is a very tough task in the finance world. Since stock prices are dynamic, noisy, non-scalable, non-linear, non-parametric and complicated. In recent years, soft computing techniques are used for developing stock prediction model. The main focus of this study is to develop and compare the efficiency of the three different soft computing techniques for predicting the intraday price of individual stocks. The proposed models are based on Time Delay Neural Network (TDNN), Radial Basis Function Neural Network (RBFNN) and Back Propagation Neural Network (BPNN). The predictive models are developed using technical indicators. Sixteen technical indicators were calculated from the historical price and used as inputs of the developed models. Historical prices from 01/01/2018 to 28/02/2018, where the time interval between samples is one minute, are utilized for developing models. The performance of the proposed models is evaluated by measuring some metrics. Also, this study compares the results with other existing models. The experimental result revealed that the BPNN outperforms TDNN, RBFNN as well as other existing models considered for comparison. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021. -
Sign Language Recognizer Using HMMs
In our day to day lives, we come across especially abled people who perform their daily chores with the aid of motivation that they get from self-confidence. There are many with hearing impairment. Sign language is the most expressed and natural way for them to communicate. Some chains of restaurants have, in fact, recruited deaf servers providing them with employment opportunities. Therefore, automatic Sign language recognition has become the crux of vision research. This paper is based on a project that builds a system that can recognize words communicated using the American Sign Language (ASL). Having been provided with a preprocessed dataset of tracked hand and nose positions extracted from the video, the set of Hidden Markov Models are trained. Using a part of this dataset, identification of individual words from test sequences is done. It provides them with the ability to communicate better, opening up a lot of opportunities. 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Comparative Analysis of Prediction Algorithms for Heart Diseases
Cardiovascular diseases (CVDs) are the leading source of demises universally: More individuals perish yearly from heart disease than due to any other reason. An estimated 17.9 million humans died from CVDs in 2016, constituting 31% of all global deaths. [1] Such high rates of death due to heart diseases have to cease. This idea can be accelerated by the prediction of risk of CVDs. If a person can be medicated much earlier, before they have any symptoms that can be far more beneficial in averting sickness. The paper strives to communicate this issue of heart diseases employing various prediction models and optimizing them for better outcomes. The accuracy of each algorithm guides to a relative enquiry of these prediction models, forming a solid base for further research, finer prognosis and detection of diabetes. 2021, Springer Nature Singapore Pte Ltd. -
Dampers to Suppress Vibrations in Hydro Turbine-Generator Shaft Due to Subsynchronous Resonance
There are numerous applications to evaluate the damage caused by subsynchronous resonance (SSR) to a turbine-generator shaft. Despite multiple applications, there are relatively few studies on shaft misalignment in the literature. In this paper, stresses in the existing turbine-generator shaft due to subsynchronous resonance were studied using finite element analysis (FEA). The 3D finite element model reveals that the most stressed part of the shaft is near the generator terminal. A new nonlinear damping scheme is modeled to reflect the torsional interaction and to suppress the mechanical vibration caused by subsynchronous resonance (SSR). Stresses developed due to the addition of capacitors in the system at high rotational speeds and deformation of the shaft during various modes of oscillations were evaluated. Experimental investigations are carried out in reaction turbine connected to a 3kVA generator. Simulation is carried out for the experimental setup using ANSYS. According to the simulation results, the damper installed near the generator terminal provides satisfactory damping performance and the subsynchronous oscillations are suppressed. 2021, Springer Nature Singapore Pte Ltd. -
Comparative Study on Gasoline and Methanol in a Twin Spark IC Engine
In search of a viable alternative to petrol and diesel, methanol, ethanol and biodiesel play an important role. Methanol and ethanol are traditional alternatives to petrol(gasoline) because of better engine performance and reduced emission of carbon monoxide, oxides of nitrogen (NOx), unburnt hydrocarbon (UBHC) and other harmful gases. This work represents the result of four sets of spark timings on engine performance and engine emissions when run on methanol and petrol. Exhaustive investigations are carried out on a variable compression ratio DTSi engine for both methanol and gasoline. Engine was run at full throttle and at a constant speed of 1600RPM. Theefficiency of the engine found to be enhanced with methanol fuel which has higher octane number and high laminar flame speed. Maximum efficiency was found to be ~25.45% and ~28.7% at compression ratio 10 for gasoline and methanol fuel, respectively. This is observed at 2624 BTDC (before top dead center) spark advance combination. Optimum compression ratio for gasoline and methanol is found to be 6.8 and 7.4, respectively, at this spark advance angle combination. Moreover, methanol fuel eventually emits lesser amount of CO, UBHC and NOx than gasoline under all experimental combinations. 2021, Springer Nature Singapore Pte Ltd. -
Online Voting System Using Blockchain
One of the major areas in technical development is blockchain and bitcoin. These technologies have enabled many simulations in in-hostile applications that have major issues with security and integrity of data. To provide more relevance to the available cyberphysical systems in the dimension of security, the blockchain technology offers a major help. If the present scenario is considered, we have multiple day-to-day applications that have been simulated and require more security enhancement. For example, the E-voting systems are a trend and their security features have to be upgraded to authenticate both systems and processes. The present research paper focuses on the same application and aims to provide security upgradation by proposing a working model of e-voting systems. 2020, Springer Nature Singapore Pte Ltd. -
Context Driven Software Development
The Context-Driven Software Development (CDSD) is a novel software development approach with an ability to thrive upon challenges of 21st century digital and disruptive technologies by using its innovative practices and implementation prowess. CDSD is a coherent set of multidisciplinary innovative and best practices like context-aware and self-adaptive system modelling, human-computer interaction, quality engineering, software development-testing-and continuous deployment frameworks, open-source tools-technology-and end-to-end automation, software governance, engaging stakeholders, adaptive solutioning, design thinking, and group creativity. Implementation prowess of CDSD approach stems from its three unique characteristics, namely, its principles, Contextualize-Build-Validate-Evolve (CBVE) product development element, and iterative and lean CDSD life cycle with Profiling, Contextualizing, Modelling, Transforming, and Deploying phases with in-process and phase-end Governance and Compliances. CDSD approach helps to address issues like complexity, software ageing, risks related to internal and external ecosystem, user diversity, and process-related issues including cost, documentation, and delay. 2021, Springer Nature Switzerland AG. -
COVID-19 outbreak prediction using quantum neural networks
Artificial intelligence has become an important tool in fight against COVID-19. Machine learning models for COVID-19 global pandemic predictions have shown a higher accuracy than the previously used statistical models used by epidemiologists. With the advent of quantum machine learning, we present a comparative analysis of continuous variable quantum neural networks (variational circuits) and quantum backpropagation multilayer perceptron (QBMLP). We analyze the convoluted and sporadic data of two affected countries, and hope that our study will help in effective modeling of outbreak while throwing a light on bright future of quantum machine learning. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021. -
E-Commerce in Indian Retail Industry: Its Proliferation and Performance
The growth of the e-commerce industry in India has seen a multitude of growth since the growth of netizens in India has reached its peak post the demonetization in Indian economy. Research in e-commerce acts as a catalyst for studies in the field of digital innovation. The developments made by India in the field of e-commerce are notable by the world. India has made extensive use of the advancement in the field of technology. Recent years have seen a transformation in the way Indian shops and exchanges grew from cash mode payments to digital mode of service delivery and payments. This research is focused on studying the parameters that have acted as impetus in the expansion of e-commerce in the Indian retail sector. 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.