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Early Prediction of Plant Disease Using AI Enabled IOT
India is an industrialized country, and about 70% of the residents rely on agriculture. Leaves are damaged by chemicals, and climates issues. An unknown illness is found on plants leads to the lowering of quality of produced. Internet of Things is a practice of reinventing the wheel agriculture by enabling farmers to tackle the problems in the industry with practical farming techniques. IoT helps to inform knowledge about factors like weather, and moisture condition. We proposed IoT, ML, and image processing based method to identify the infection. IOT enabled camera to capture the image then required region of interest is extracted. After ROI extraction, image is enhanced to remove the unwanted details form the image and to improve image quality. We compute image features. At the end we do the classification which is a twostep process training and testing and done by SVM. Our proposed method gives 92% accuracy. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Systematic Review of Challenges and Techniques of Privacy-Preserving Machine Learning
Machine learning (ML) techniques are the backbone of Prediction and Recommendation systems, widely used across banking, medicine, and finance domains. ML techniques effectiveness depends mainly on the amount, distribution, and variety of training data that requires varied participants to contribute data. However, its challenging to combine data from multiple sources due to privacy and security concerns, competitive advantages, and data sovereignty. Therefore, ML techniques must preserve privacy when they aggregate, train, and eventually serve inferences. This survey establishes the meaning of privacy in ML, classifies current privacy threats, and describes state-of-the-art mitigation techniques named Privacy-Preserving Machine Learning (PPML) techniques. The paper compares existing PPML techniques based on relevant parameters, thereby presenting gaps in the existing literature and proposing probable future research drifts. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Comparison of Full Training and Transfer Learning in Deep Learning for Image Classification
The deep learning algorithms on a small dataset are often not efficient for image classification problems. Make use of the features learned by a model trained on large similar dataset and saved for future reference is a method to solve this problem. In this work, we present a comparison of full training and transfer learning for image classification using Deep Learning. Three different deep learning architectures namely MobileNetV2, InceptionV3 and VGG16 were used for this experiment. Transfer learning showed higher accuracy and less loss than full-training. According to transfer learning results, MobileNetV2 model achieved 98.96%, InceptionV3 model achieved 98.44% and VGG16 model achieved 97.405 as highest test accuracies. The full-trained models did not achieve as much accuracy as that of transfer learning models on the same dataset. The accuracies achieved by full-training for MobileNetV2, InceptionV3 and VGG16 are 79.08%, 73.44% and 75.62% respectively. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Automated Organic Web Harvesting on Web Data for Analytics
Automated Web search and web data extraction has become an inevitable part of research in the area of web mining. The web scraping has immense influence on ecommerce, market research, web indexing and much more. Most of the web information is presented in an unstructured or free format. Web scraping helps every user to retrieve, analyze and use the data suitably according to their requirement. There exist different methodologies for web scraping. Major web scraping tools are rule based systems. In the proposed work, an automated method for web information extraction using Computer Vision is proposed and developed. The proposed automated web scraping method comprises of automated URL extraction virtual extraction of required data and storing the data in a structured format which is useful in market research. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Reimagining the Digital Twin: Powerful Use Cases for Industry 4.0
Novel cohorts of information technologies are transformation and upgrading the global manufacturing sector. The analysis of product procedure in discrete globe might furnish significant perceptions resting on scheme routine which may change manufacturing product design. Digital twin predictive analysis on both historical and future performances of an organizations physical resources leading to proficient industry functioning. In digital twin, cloud-based virtual image of industrial asset is maintained throughout the lifecycle which can be accessed at any time. Digital twin enhances the degree and functions of manufacturing world by integrating with the physical world. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
The Preservative Technology in the Inventory Model for the Deteriorating Items with Weibull Deterioration Rate
An EOQ model for perishable items is presented in this study. The deterioration rate is controlled by preservative technology. This technology only enhances the life of perishable items. So, retailers invested in this technology to get extra revenue. The Weibull deterioration rate is considered for the ramp type demand. Shortages consider partially backlogged, and discount is provided to loyal customers. The concavity of the profit function is discussed analytically. Numerical examples support the solution procedure; then, Sensitivity analysis is applied to accomplish the most sensitive variable. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Prediction of Depression in Young Adults Using Supervised Learning Algorithm
Over the years, mental health has achieved an essential role in the pertinent development of a human being, and a large part of the population is affected by it. The most commonly affected community being college-going students, and the most common disorders being Anxiety and Depression. Depression is a leading cause of suicide in individuals, where suicide is the second most prevailing reason for death among 1529-year-olds. This study aims to identify the different reasons and other factors associated with depression to predict and determine whether an individual faces depressive disorders. For this research purpose, the most appropriate classifier is selected. The absolute accuracy of the proposed model is 91.17%, i.e., the model can correctly predict whether an individual has depression 91.17% of the time. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Rice Yield Forecasting in West Bengal Using Hybrid Model
Agriculture in India is the primary source of revenue, yet farmers still face challenges. The primary goal of agricultural development is to produce a high crop yield. The Datasets collected for the study of real-world time series include a blend of linear and nonlinear patterns. A mixture of linear and non - linear models, rather than a single linear or non - linear model, gives a more precise forecasting models for time series data. The ARIMA and ANN prediction models are combined in this paper to create a Hybrid model. This model is used to predict rice yield for all 18 West Bengal districts during the Kharif season, based on 20years of information(20002019) collected from various sources such as India Meteorological Department, Area, and production Statistics, DAV from NASA, etc. The hybrid model aims to enhance efficiency indicators such as MSE, MAE, and MAPE, demonstrating excellent performance for rice yield prediction in all the districts of West Bengal. In the future, it can be applied to other crops that can support farmers in their farming. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Design and Analysis of Vortex Bladeless Wind Turbine
Vortex bladeless turbine antiquates the conventional wind turbine and adopts a radically innovative and novel approach to captivate the moving wind energy. This device effectively captures the energy of vorticity, an aerodynamic instability condition. As the wind passes a structure, the flow steers and cyclical patterns of vortices are generated. Once the strength of wind force is suffice, the structure starts vibrating and reaches resonance. Vortex bladeless is a vortex induced vibration resonant power generator. It harnesses wind energy from a phenomenon of vorticity, called vortex shedding effect. Clearly bladeless technology consists of a cylinder fixed vertically on an elastic rod, instead of tower, nacelle and blades which are the crucial parts of a conventional wind turbine. The cylinder oscillates on a specifically mentioned wind range, which then generate electricity through an alternator and a tuning system. In this paper the vortex turbine is designed with certain existing parameters of dimensions in Solidworks and the same is analyzed for different materials and dimensions of mast, which is an important part in the vortex turbine. Also various performance parameters like displacement, frequency etc. are also compared among different models. 2021 Elsevier Ltd. All rights reserved. -
Document Classification for Recommender Systems Using Graph Convolutional Networks
Graph based recommender systems have time and time again proven their efficacy in the recommendation of scientific articles. But it is not without its challenges, one of the major ones being that these models consider the network for recommending while the class and domain of the article go unnoticed. The networks that embed the metadata and the network have highly scalable issues. Hence the identification of an architecture that is scalable and which operates directly on the graph structure is crucial to its amelioration. This study analyses the accuracy and efficiency of the Graph Convolutional Networks (GCN) on Cora Dataset in classifying the articles based on the citations and class of the article. It aims to show that GCN based networks provide a remarkable accuracy in classifying the articles. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
On the k-Forcing Number of Some DS-Graphs
Amos et al. introduced the notion of k-forcing number as a generalization of Zero forcing number and is denoted by Fk(G) where k> 0 is any positive integer, the k -forcing number of a graph is the minimum cardinality among all k -forcing sets of a graph G. In this paper, many bounds for k -forcing number of degree splitting graph DS(G) for different graph classes are found. We evaluate the value of k -forcing number of degree splitting graph of some of the Cartesian product graph for different values of k. Also we observed that for Tur graph Tn , t, upper and lower bound is given by, Fk(Tn , t) ? Fk(DS(Tn , t) ) ? Fk(Tn , t) + 1. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
User Authentication with Graphical Passwords using Hybrid Images and Hash Function
As per human psychology, people remember visual objects more than texts. Although many user authentication mechanisms are based on text passwords, biometric characteristics, tokens, etc., image passwords have proven to be a substitute due to its ease of use and reliability. The technological advancements and evolutions in authentication mechanisms brought greater convenience but increased the probability of exposing passwords through various attacks like shoulder-surfing, dictionary, key-logger, and social engineering attacks. The proposed methodology addresses these vulnerabilities and ensures to keep up the usability of graphical passwords. The system displays hybrid images that users need to recognize and type the randomly generated alphanumeric or special character values associated with each of them. A mechanism to generate One Time Password (OTP) is included for additional security. As a result, it is difficult for an attacker to capture and misuse the password. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Ear Recognition Using Pretrained Convolutional Neural Networks
Ear biometrics, which involves the identification of a person from an ear image, is challenging under unconstrained image capturing scenarios. Studies in Ear biometrics reported that the Convolutional Neural Network is a better alternative to classical machine learning with handcrafted features. Two major concerns in CNN are the requirement of enormous computing resources and large datasets for training. The pretrained network concept helps to use CNN with smaller datasets and is less demanding on hardware. In this paper, three pre-trained CNN models, AlexNet, VGG16, and ResNet50 are used for ear recognition. The fully connected classification layers of the nets are trained with AWE, an unconstrained ear dataset. Alternatively, the CNN layers output (the CNN features) are extracted, and an SVM classification model is built. To improve the classification accuracy, the training dataset size is increased through data augmentation. Data augmentation improved the classification accuracy drastically. The results show that ResNet50, with the fully connected classification layer, results in higher accuracy. 2021, Springer Nature Switzerland AG. -
Variable parametric test to improve the machinability of Inconel-718 using Tungsten Carbide tool
The Inconel-718 is a nickel based super alloy containing an old age hardening alloy of nickel-chromium as addition which provides increased strength without its decrease in ductility. It is known as a difficult to cut material due to certain properties like high thermal resistance, high creep, corrosion resistance having the capability of retaining toughness and strength at high temperatures. Inconel-718 has a large number of applications in the world of manufacturing such as aircraft gas turbines, steam turbine power plants, reheaters and reciprocating engines. Due to such superior quality functions, its machining becomes more challenging for which Tungsten Carbide is one of the tools to improve the machinability to 2.64%. In this paper, parametric tests has been carried out in CNC machining to determine the tool performance and improve the machining conditions. 2021 Elsevier Ltd. All rights reserved. -
Physical Unclonable Function and OAuth 2.0 Based Secure Authentication Scheme for Internet of Medical Things
With ubiquitous computing and penetration of high-speed data networks, the Internet of Medical Things (IoMT) has found widespread application. Digital healthcare helps medical professionals monitor patients and provide services remotely. With the increased adoption of IoMT comes an increased risk profile. Private and confidential medical data is gathered across various IoMT devices and transmitted to medical servers. Privacy breach or unauthorized access to personal medical data has far-reaching consequences. However, heterogeneity, limited computational resources, and lack of standardization in authentication schemes prevent a robust IoMT security framework. This paper introduces a secure lightweight authentication and authorization scheme. The use of the Physical Unclonable Function (PUF) reduces pressure on computational resources and establishes the authenticity of the IoMT. The use of OAuth 2.0 open standard for authorization allows interoperability between different vendors. The resilience of the model to impersonation and replay attacks is analyzed. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Content-Restricted Boltzmann Machines for Diet Recommendation
Nowadays, society is leading towards an unhealthy and inactive and lifestyle. Recent studies show the rapid growth of people suffering from diseases caused due to unhealthy lifestyles and diet. Considering this, recognizing the right type and amount of food to eat with a suitable exercise set is essential to obtain good health. The proposed work develops a framework to recommend the proper diet plans for thyroid patients, and medical experts validate results. The experiments results illustrate that the proposed Content-Restricted Boltzmann Machines (Content-RBM) produces more relevant recommendations with content-based information. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Experimental study of response parameters during machining of Inconel 718 with cryogenically treated ceramic round tool using cutting fluid
Highly advanced superalloys are being rapidly spreading throughout the globe. It's in need of the hour to produce similar materials which are being used in several industries similar as petrochemical, biomechanical, aerospace and marine industries. Inconel 718 is one similar superalloy which is being used due to its better characteristic features like high attrition resistance, high temperature burden conditions, thermal fatigue resistance, and cryogenic temperatures. Owing to the hardness conditions, tools indicate the low tool life and high wear characteristics. Ceramic insert is one such tool that is being used to machine Inconel 718 which is cryogenically treated to improve tool life. The use of emulsified cutting fluid reduces tool wear and improve durability of the tool, thereby improving the efficiency of the machining of Inconel 718. In this paper, experimental investigation has been carried to find the use of emulsified cutting fluid that improves the machinability of Inconel 718 based on parameters such as surface roughness and tool wear under the effect of cutting parameters which are cutting speed, feed rate and depth of cut. 2021 Elsevier Ltd. All rights reserved. -
An overview of nanomaterial technologies in the management of wastewater treatment
Nanomaterials are the foundation stone of nanotechnology. It is a broad and trans disciplinary section of exploration. Its developmental commotion has been rising intensively across the world in the last few years. Applications of nanotechnology are abundant in various fields like medicines, electronics, machines, and so on. This paper describes one such application that is nanotechnology in wastewater treatment viz nanosorption, nanophotocatalysis and Nano membrane technology. The Nano compounds engaged in such treatments have been discussed here. As of now the world is in distress from the non-Availability of drinking water and even the very existence of drinking water in nature are becoming toxic due to the addition of chemicals and heavy metals by man-made and by natural happenings as well. The distinctive properties of nanomaterial such as surface area, competent to toil at required and even at low concentration and their potential have prodigious prospects to reform wastewater treatment. Carbon based nanomaterial, metal oxide nanomaterials and nanomebranes have been discussed at this juncture. There are several disputes in treating wastewater with nanomaterials such as insufficient information about the nanomaterial. Still researchers have a lack of knowledge about how these materials are travelling and the effect of nanomaterial on human health. Though the nanostructured catalytic membranes, Nano sorbents and nanophotocatalyst are the established methods to eliminate water pollutants from wastewater, they need more energy and additional investment. 2021 Elsevier Ltd. All rights reserved. -
Convolutional Autoencoder Based Feature Extraction and KNN Classifier for Handwritten MODI Script Character Recognition
Character recognition is the process of identifying and classifying the images of printed or handwritten text and the conversion of that into machine-coded text. Deep learning techniques are efficiently used in the character recognition process. A Convolutional Autoencoder based technique for the character recognition of handwritten MODI script is proposed in this paper. MODI script was used for writing Marathi until the twentieth century. Though at present, Devnagari is taken over as the official script of Marathi, the historical importance of MODI script cannot be overlooked. MODI character recognition will not be an easy feat because of the various complexities of the script. Character recognition-related research of MODI script is in its initial stages. The proposed method is aimed to explore the use of a deep learning-based method for feature extraction and thereby building an efficient character recognition system for isolated handwritten MODI script. At the classification stage, the features extracted from the autoencoder are categorized using KNN classifier. Performance comparison of two different classifiers, such as KNN and SVM, is also carried out in this work. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Broad-spectrum of sustainable living management using green building materials-an insights
Owing to the recurrent modifications in the lifestyle and demands of humans the regular life of buildings is decreasing whereas the demolition or renovation of the buildings increases. Building materials and their components ingest just about 40 percent of world-wide vigour per annum in their life segments such as fabrication and procurement of building materials, construction and demolition. The development of the construction industry completely relies on the deployable resources. To abate the consumption of construction materials in current years, the construction industry has established an environmental track, which wishes to use naturally available materials. Reviving such technology, further developing this technology green building materials are paramount for constructing green buildings. Such a green-building constructional model does not require energy contributions frequently for production. The advantage of reducing the energy used in manufacturing, increases strength. Green Building material is one which utilizes less water, optimizes energy efficiency, conserves natural resources, generates less waste, produces less carbon dioxide emissions and provides improved space for inhabitants as compared to conventional buildings. It includes environmental, economic, and social benefits as well. This paper aims to provide knowledge about some of the green building materials that help for sustainable living. These elucidations can obligate a significant influence in contemporary construction owed to the escalation in the charges of traditional construction materials. 2021 by the Authors.