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Natural Disaster Prediction by Using Image Based Deep Learning and Machine Learning
In recent years, diseases and disaster have become more unpredictable. The advent of technology has not only making our lives easier but also technology-dependent. Nevertheless, the natural disasters cause great adversity by disrupting considerable human lives. Also, the disasters obstruct and affect many industries and services either directly or indirectly. Hence, it is necessary to study and observe data patterns and warning signs that lead to a natural disaster, its potential risk and its ability to resolve management strategies, which can be implemented immediately to minimize the socio-economic loss. This article reviews the state-of-the-art research works and findings through a technological perspective on data analysis, natural disaster prediction, and the utilization of technology for deploying management strategy. Also, this paper focuses on investigating the today's Industry 4.0 that utilizes cognitive computing. The primary aim of this article is to review the research ideas that leverage big data and data mining to observe and track patterns, which can impelment predictive analysis to anticipate the forthcoming disasters. Furthermore, this research work analyzed the posed predictive models by specifically using ANN (Artificial Neural Networks), sentiment model, and smart disaster prediction application (SDPA) to predict the flash flood. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Lateral Load Behavior of Unreinforced Masonry Spandrels
Spandrels, are usually classified as secondary elements and even though their behaviour has not received adequate focus unlike piers, they significantly affect the seismic capacity of the structure. Masonry spandrels are often damaged and the first structural components that crack within Unreinforced Masonry structures. Despite this, existing analytical methods typically consider a limit case in which the strength of spandrels is either neglected, considered to be infinitely rigid and strong or treated as rotated piers. It is clearly evident that such an assumption is not plausible. Hence, reliable predictive strength models are required. This thesis attempts to re-examine the flexural behaviour of spandrels and proposes an analytical model. The model is based on the interlocking phenomena of the joints at the end-sections of the spandrel and the contiguous masonry. The proposed analytical model is incorporated within a simplified approach to account for the influence of spandrel response on global capacity estimate of URM buildings. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Enhanced Energy-Efficient Routing for Wireless Sensor Network Using Extended Power-Efficient Gathering in Sensor Information Systems (E-PEGASIS) Protocol
Recent technological advancements in wireless communication and sensors made Wireless Sensor Networks (WSNs) as one of the demanding platforms in the current scenario. In WSN, tiny sensor nodes are collecting and monitoring the biological data or physical data or environmental data and transmits to the Base Station (BS) through gateway routers. These data can be accessed anywhere and anytime. Usually, sensor nodes have restrained battery power which creates the rigorous lifetime duration issues in WSN. Sensor nodes can transmit the data with each other using various routing protocols. Data transmission devours more amounts of energy and power. So, energy preservation is an important factor in WSN. There are plenty of researches going on in designing less energy consuming protocols for data transmission which helps to increase the lifetime of WSN. In this manuscript, we have proposed Extended Power-Efficient Gathering in Sensor Information Systems (E-PEGASIS) protocol for enhanced energy-efficient data transmission based on PEGASIS protocol. In this proposed method, the average distance between the sensor nodes is considered as the criterion for chaining and fixing the outermost nodes radio range value to the base station. Later it chains the related nodes available in the radio range. Consequently, the chained node checks their distance with the next nearest end node to go on with the chaining procedure which will enhance the performance of data transmission amid the base station and sensor node. The simulation of the proposed work shows that lifetime of the network is increased when compared to the LEACH and PEGASIS protocol. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Analysis of Market Behavior Using Popular Digital Design Technical Indicators and Neural Network
Forecasting the future price movements and the market trend with combinations of technical indicators and machine learning techniques has been a broad area of study and it is important to identify those models which produce results with accuracy. Technical analysis of stock movements considers the price and volume of stocks for prediction. Technical indicators such as Relative Strength Index (RSI), Stochastic Oscillator, Bollinger bands, and Moving Averages are used to find out the buy and sell signals along with the chart patterns which determine the price movements and trend of the market. In this article, the various technical indicator signals are considered as inputs and they are trained and tested through machine learning techniques to develop a model that predicts the movements accurately. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Pragmatic Study on Movie Recommender Systems Using Hybrid Collaborative Filtering
The Movie Recommendation System (MRS) is part of a comprehensive class of recommendation systems, which categorizes information to predict user preferences. The sum of movies is increasing tremendously day by day, and a reliable recommender system should be developed to increase the user satisfaction. Most of the approaches are made to prevent cold-start, first-rater drawbacks, and gray sheep user problems, nevertheless, in order to recommend the related items, various methods are available in the literature. Firstly, content-based method has some drawbacks like data of similar user could not be achieved, and what category of these items the user likes or dislikes are also not known. Secondly, this paper discusses about collaborative filtering to find both user and item attributes that have been considered. Since there exist some issues pictured with collaborative filtering, so this paper further aims into hybrid collaborative filtering and deep learning with KNN algorithm of ratings of top K-nearest neighbors. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
CloudML: Privacy-Assured Healthcare Machine Learning Model for Cloud Network
Cloud computing is the need of the twenty-first century with an exponential increase in the volume of data. Compared to any other technologies, the cloud has seen fastest adoption in the industry. The popularity of cloud is closely linked to the benefits it offers which ranges from a group of stakeholders to huge number of entrepreneurs. This enables some prominent features such as elasticity, scalability, high availability, and accessibility. So, the increase in popularity of the cloud is linked to the influx of data that involves big data with some specialized techniques and tools. Many data analysis applications use clustering techniques incorporated with machine learning to derive useful information by grouping similar data, especially in healthcare and medical department for predicting symptoms of diseases. However, the security of healthcare data with a machine learning model for classifying patients information and genetic data is a major concern. So, to solve such problems, this paper proposes a Cloud-Machine Learning (CloudML) Model for encrypted heart disease datasets by employing a privacy preservation scheme in it. This model is designed in such a way that it does not vary in accuracy while clustering the datasets. The performance analysis of the model shows that the proposed approach yields significant results in terms of Communication Overhead, Storage Overhead, Runtime, Scalability, and Encryption Cost. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Curvature Ductility of Reinforced Masonry Walls and Reinforced Concrete Walls
Research conducted in this work proposes an equation to evaluate and compares the curvature ductility of reinforced masonry (RM) and reinforced concrete (RC) walls. The curvature ductilities are measured at varying levels of axial stresses for walls for aspect ratio (l/h) of 0.5, 1.0 and 1.5. The percentage of reinforcement is increased from 0.25% (minimum reinforcement for RC walls as per IS-13920) to 1.00%. The curvature ductilities are evaluated by plotting flexural strength (M) versus curvature (?) for the walls. The stressstrain curves of masonry, concrete and reinforcing steel are all adopted from existing literature. The compressive strength of masonry and concrete has been chosen as 10MPa and 25MPa, respectively. The yield strength of the steel is fixed as 415MPa. The height and thickness of the wall are 3000 and 230mm, respectively, and the length of the wall is varied to obtain different aspect ratios. Results obtained from this paper imply due to increase curvature ductility, RM walls provide a better alternative for the construction of structural walls compared to RC walls in regions of significant seismicity. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Investigations on Compression Behaviour of Short Reinforced SCC Columns
The objective of this work is to predict the values of deformation and load at cracking point, yielding point and ultimate point of short reinforced self-Compacting Concrete columns which was subjected to axially compression in loading frame. An ANN tool by giving proper inputs like fresh properties of materials, spacing of stirrups and percentage of longitudinal reinforcement and keeping target values obtained from experiments, it is compared with the experimental values accompanied by marginal errors. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A critical study on acetylene as an alternative fuel for transportation
With the traditional power sector hindered by fuel shortage and climate changes, the promotion of green energy becomes the most prioritized objective of the government. The ministry's move becomes significant because conversion to cleaner energy sources is the best way to minimize global warming and to reenergize the global economy. Among the available alternative gaseous fuels, acetylene caters to these needs because of its property similarities with hydrogen. In this research, the suitability of acetylene as an engine fuel is analyzed. Also, the production methods, combustion properties, abnormal combustion, and safety issues were discussed. This review paper describes about the various possible modes of fuel induction techniques to be adopted. The research establishes the utility of acetylene as a commercial fuel for internal combustion engines in the future years by the adoption of suitable methodologies. 2021 Author(s). -
Classification of Soil Images using Convolution Neural Networks
Classification of soil is crucial for the agricultural domain as it is an essential task in geology and engineering domains. Various procedures are proposed to classify soil types in the literature, but many of them consumed much time or required specially designed equipments/applications. Classification of soil involves the accounting of various factors due to its diversified nature. It can be observed that several critical domain-oriented decisions often depend on the type of soil like farmers might be benefitted from knowing the kind of soil to choose crops accordingly for cultivation. We have employed different Convolution Neural Network (CNN) architectures to identify the soil type accurately in real-time. This paper describes the comparative evaluation in terms of performances of various CNN architectures, namely, ResNet50, VGG19, MobileNetV2, VGG16, NASNetMobile, and InceptionV3. These CNN models are used to classify four types of soils: Clay, Black, Alluvial, and Red. The performance of the ResNet50 model is the best with a training accuracy and training loss of 99.47% and 0.0252, respectively compared to other competing models considered in this paper. 2021 IEEE. -
Consumer Characteristics and Consumption Patterns of Soft Drinks
A soft drink is generally treated as very common product aimed at a very casual consumption. Normally, not much of attention is paid to this product, which has almost become 'commoditized'. But, a deeper and more careful observation would reveal that soft drinks are strong demographic descriptors of their consumers. Key insights into the characteristics and consumption patterns of consumers can be obtained through an incisive study of the soft drinks market. This research paper makes a concerted effort at unearthing the demographic details and consumption contours of the soft drink users in Kanpur, Agra, Varanasi, Allahabad, and Lucknow - the five representative cities of Uttar Pradesh, the most populous state of India. It has been conclusively established through this research that the residents of these five cities - which are demographically similar in nature - exhibit varying consumption patterns when it comes to soft drinks. It was also found that demographic variables like age, gender, educational qualification, income, and marital status do not significantly impact the consumption of soft drinks, whereas employment status is a key influencer of the same. 2021 IEEE. -
Machine Learning Approach for the Prediction of Consumer Food Price Index
The price of food and food related items are dynamic. A measure change in the price affects the buying behaviour of the consumer and monetary policies by the Government. The Consumer Food Price Index (CFPI) reflects the variations in food prices during a certain period. In India, the CFPI is released monthly by the Central Statistical Organization. It also reflects the inflation and helps the Government to take corrective measures in time. In this paper we have applied the machine learning approach in forecasting the consumer food price index in India. In specific, this work has focused on the applicability of Artificial Neural Network (ANN) models with back propagation learning in predicting the future values of CFPI. The monthly data for rural, urban and combined from the period 2013 to 2021 have been used to train and validate the models. The Mean Absolute Percentage Error (MAPE) values have been used to validate the accuracy of the models. The experimental results show that a simple ANN model with back propagation algorithm is highly capable in forecasting the future values of CFPI. 2021 IEEE. -
Energy saving, waste management, and pollution free steps for university campuses
Global warming is a worldwide concern and the documents related to the need for sustainable measures seen in academic and non-academic literature. In a highly populated country like India, these are more severe worries. Multiple established educational institutions across India have taken significant steps in educating their students on sustainable development goals (SDG). Currently there is a need to assess the extent of effect such training has on student populations of such institutes. Present study attempted to assess the efficacy of SDG-implementation training programmes in a reputed private university, through student assessment of student behaviors outside the institute and in their personal life. Using semi-structured in-depth interview methods, interviewed eight students of Undergraduate and Postgraduate programmes. These students were active participants of community service programmes arranged by the university within a sustainable development model. Data were analyzed using reflexive thematic analysis methods. Emerged themes from data analysis indicate a positive change in their worldview and significant modifications in their personal behavior towards sustainability because of being part of such programmes. Educating others through practice and increased socio-environmental awareness were also major themes. Current study contributes in assessing efficacy of sustainability programmes in educational institutions. This study also suggests few recommendations for increasing competence of the same. 2021 Author(s). -
Insights into Artificial Neural Network techniques, and its Application in Steganography
Deep Steganography is a data concealment technology that uses artificial intelligence (AI) to automate the process of hiding and extracting information through layers of training. It enables for the automated generation of a cover depending on the concealed message. Previously, the technique depended on the existing cover to hide data, which limited the number of Steganographic characteristics available. Artificial intelligence and deep learning techniques have been used to steganography recently and the results are satisfactory. Although neural networks have demonstrated their ability to imitate human talents, it is still too early to draw comparisons between people and them. To improve their capabilities, neural networks are being employed in a number of disciplines, including steganography. Recurrent Neural Networks (RNN) is a widely used technology that automatically creates Stego-text regardless of payload volume. The features are extracted using a convolution neural network (CNN) based on the image. Perceptron, Multi-Layer Perceptron (MLP), Feed Forward Neural Network, Long Short Term Memory (LSTM) networks, and others are examples of this. In this research, we looked at all of the neural network approaches for Steganographic purposes in depth. This article also discusses the problems that each technology faces, as well as potential solutions. 2021 Institute of Physics Publishing. All rights reserved. -
A comparative study of the impact of thermal indices on Indian coral ecosystem
Coral reefs have been the diversified ecosystem in the planet. Advantages are opportunities in tourism, coastal protection and fisheries production. Corals, as key ingredient is sourced got drug manufacturing. Its distribution is evident in locations of where sea water temperature ranges between 16C to 30C. Their presence is >0.2% of ocean area and supports >25% of marine species. India has five reef formations. Globally, last two decades have seen an increase in reporting reef deterioration. The reason significantly attributed to be climate change, apart other challenges such as pollution, sedimentation, oil spillage, etc. Such events lead to widespread mortality of corals. Mortality during bleaching events are inevitable and varied; depends on intensity of such events. The primary reason is due to significant rise in average sea surface temperature (SST). Recovery takes time after such events, and it becomes worse with recurring events. The reefs of Indian seas have reported events of severe bleaching during 1998, 2010 and 2016. IPCC reviews show mass bleaching will be prominent in future due to elevated SST. This work tries to compare the HS values of a few regions. The data collected is from 2001 to 2017. A few significant observations are drawn which could further help us to extend the work to take help from Artificial Intelligence to make predictions for the future. This study uses the indices derived out of SST to look at relative risk faced by Indian reefs. The need for comprehensive and localized actions will be discussed. 2021 Author(s). -
Ban or boon: Consumer attitude towards plastic bags ban
In Tamil Nadu, the state government has imposed a ban on plastic bags two years ago. This has created a major impact of the day to day life of common people. Though it has positive effect on the environment, the common public had different perception as a consumer. This paper aimed at studying the consumer attitude towards the ban on plastic bags. A descriptive research design adopted to address the various dimension of consumer perception towards the ban on plastic ban. A sample size of 400 respondents was selected on the basis of systematic random sampling technique to collect data through structured questionnaire. For conducting the survey, consumers of retail shops in urban and rural places were chosen as target respondents. The collected data were analyzed with the help of statistical tools such as ANOVA, t-Test, Correlation, Linear Regression and Structural equation modelling and the interpretation reported. The result revealed that only 34 percentage of respondent were aware the environmental impact of plastic bags. About 71 percentage of consumers reported that they have faced difficulties in their day to day life due to plastic ban. 2021 American Institute of Physics Inc.. All rights reserved. -
Enhancing the confidentiality of text embedding using image steganography in spatial domain
Rapid growth in technological development, the use of the internet has grown many folds. Along with it, the sharing of privacy information in networks creates ownership issues. In order to create a high level of security for sharing private information, the concept of steganography is introduced along with encryption based invisible watermarking techniques. The proposed system hides the encrypted private messages by using onetime pad which follows the concept LSB algorithm in spatial domain. The system combines steganography and encryption for enhancing the confidentiality of the intended messages. At first, the private information of the user is encrypted by using the onetime pad algorithm. Then the encrypted text is hidden the Least Significant Bit (LSB) of the different components of the color image in such a way that as to minimize the perceived loss of quality of the cover image. The beneficiary of the message is able to retrieve the hidden back and from the stego-image and extract the cipher text and find the plaintext from using the onetime pad algorithm. The proposed algorithm will be tested and analysed against three different hiding positions of color image components. 2021 American Institute of Physics Inc.. All rights reserved. -
A comparative analysis of opinions and sentiments on clean India campaign and sustainability goals of 2030
Human are blessed with natural intelligence. Artificial Intelligence can help human minds to make a best usage of machines to handle huge amount of data with accuracy and precision. AI has a widespread application in 21st century. Opinion mining is an application of artificial intelligence. The opinions expressed in social media can be extracted using python which can be used as an input for various machine learning algorithms to identify many patterns which can help policy makers to make effective policies. Clean India Campaign started in India with a set of goals to be achieved. Sustainability goals of 2030 given by United Nations puts light on many important aspects which need immediate attention in the next 9 years. Current pandemic Covid-19 has also triggered the necessity behind putting immediate attention for a better tomorrow. Without proper awareness programs, brainstorming knowledge cultivation, orienting minds towards the "what-why-where"aspects of sustainable growth in each sphere of life, aligning industrial development and digital era towards sustainable industrial development in digital era, sustainable economy, sustainable care of each natural resource; it is not easy to accomplish the sustainability goals of 2030 given by United Nations.This work emphasizes on the case study conducted as an initiative to motivate future policy makers to be aware of the different dimension of 2030 United Nations Agenda and the clean India campaign to take initiatives as a professional through the skills learned focusing on India. Realizing Individual social Responsibility can make a big difference in the planning and implementation of the goals and missions. Swachch Bharat Abhiyan (Clean India Campaign) started Swachch Bharat Mission-Urban (SBM-U) with a few objectives to make India Clean.This work has proposed two phases for analyzing opinions. This research have provided a methodology to apply AI to improve the opinion mining. The conventional opinion analysis is limited by reachability but the automated opinion analysis can be scaled up using artificial intelligence based applications. The uniqueness of the work lies in its focus on 'one-three verticals' in phase 1 of the methodology. Many prominent regions of India are considered as a part of the study. It helps us to provide a clearer picture across different regions of India. It also provide an avenue to list tasks to be done for each region and a set of ways which could be adopted by the future professionals and current stakeholders of higher education institute. Phase 2 focusses on more number of opinions collected from across the globe through digital platforms. 2021 Author(s). -
Stock Market Trend Analysis on Indian Financial News Headlines with Natural Language Processing
Predicting the stock movement in the real-time scenario has been the most challenging and sophisticated in business. This business is affected by several factors from physical to psychological as well as rational to irrational. So far only few aspects have been taken into account while breaking down the conclusion. Implementing sentiment analysis, a subfield of Natural Language Processing (NLP), from the news, social media or financial document, investors decide whether they should invest for the company. The results have shown a significant and a feasible method for predicting the stock market trend with higher accuracy. The current research has mainly focus on finding the sentiment score from the news headlines and finding the hidden trend from it. Further the trading signals are generated based on the prevailing trend and trends are executed by the automated trading system. Using this algorithm, traders can reduce the manual intervention in the buy and sell decisions related to the stock market. 2021 IEEE. -
Design Cognition while using digital tools: A Distributed Cognition Approach
The use of digital tools in the conventional architecture design thinking process which derives its basis from sketching is followed in many colleges in India. Various shortcomings due to the integration of digital tools to the manual design process have been enumerated during the past 30 years. Digital tools provide affordances different from the manual sketching design process, the effects of which can be understood by adopting a distributed cognition approach. The paper builds on design cognition research while using externalization tools in the design process. It does so by developing a theoretical framework derived from distributed cognition and an understanding of visual thinking processes from design literature. The paper utilizes the distributed cognition framework by Zhang and Norman, to arrive at resultant affordances of externalization tools in design. The same is then utilized for a protocol study which was coded for its visual thinking components and other relevant codes. The same protocol study was also coded for ideation flow analysis. The findings pointed towards compromised visual thinking and reduced ideation while utilizing digital tools in quick conceptualization. 2021 ACM.