bluuebunny/crossref_metadata_2025_split huggingface dataset:
An Empirical Research and Comprehensive Analysis of Stock Market Prediction using Machine Learning and Deep Learning techniques
Authors: Singh Aryendra , Gupta Priyanshi , Thakur Narina 
Published: January 2021

Abstract
Financial markets are inherently unpredictable. They continue to change based on the performance of the company, past records, market value and are also dependent on news & timings. By carrying out trend analysis, one has the ability to prejudge stock prices. Machine Learning Techniques that are available, have the potential to forecast future stock prices. Each stock represents a different trend, so a singular machine learning model can’t be applicable to other stocks. Thus, one model giving a high degree of precision can’t guarantee working on another. Too many variables are involved while predicting stocks-physical factors vs. psychological, irrational and rational behaviour, etc. All of these factors combined indicate stock prices as capricious and difficult to foresee. In order to resolve this challenge, a comprehensive study with historical data on stock prices of listed firms was performed. The main premise behind this research was to illustrate how to apply machine learning algorithms such as Averaging, Linear Regression including advanced deep learning techniques such as Long-Term Short Memory and applying technical tools like the Modern Portfolio Theory and Bollinger bands.
Integration of Internet of Things, Cloud Computing: Review
Authors: Patil Charushila , Chaware Anita 
Published: January 2021

Abstract
As the Network of Things has grown exponentially, the Internet of Things devices generate tremendous data. IoT provides virtualization with or without the presence of being with the services. Managing this vast volume of data becomes a very tedious task. So in diverse sectors, including such as healthcare, education, agriculture, smart cities, there are various iot applications. Using different technologies, sensor data is acquired and data collection, data cleaning, data processing, and analysis are done with the help of cloud computing. We present a survey of the integration of the Internet of Things, cloud computing, and healthcare in this paper. The paper focuses on the different IoT Cloud computing platforms used with the Internet of Things and is an advantage of the healthcare domain’s usefulness.
Impact of Similarity Measures in K-means Clustering Method used in Movie Recommender Systems
Authors: Rahul Mayur , Pal Parashuram , Yadav Vikash , Dellwar Devendra Kumar , Singh Swarnima 
Published: January 2021

Abstract
One of the most challenging tasks in today’s era is to deliver personalized information to the user or group of users according to their preferences. The decision making process is used as a tool by the recommender systems to suggests various products and items. The goal of recommender system is to deliver germane information to the user based on their likings. In this research work, we study and compare the effect of similarity measures used in k-means clustering in movie recommender systems. Our proposed method used the sampling, PCA and k-means clustering to recommend movie from the MovieLens dataset. In the whole process, some similarity measures are used in k-means clustering such as Euclidean, Minkowski, Mahalanobis, Cosine similarity and Pearson correlation. The aim of our work is to study the effect of similar measures in movie recommender systems in terms of standard deviation (SD), mean absolute error (MAE), root mean square error (RMSE), t-value, Dunn Matrix, average similarity and computational time using publicly available MovieLens dataset. The results achieved from the experiments indicates that Cosine similarity is best technique in movie recommender system in terms of accuracy, efficiency and processing speed and also able to get MAE of 0.65, which is best between all similarity measures.
Integration of Blockchain and IoT (B-IoT): Architecture, Solutions, & Future Research Direction
Authors: Aggarwal Vikash Kumar , Sharma Nikhil , Kaushik Ila , Bhushan Bharat , Himanshu  
Published: January 2021

Abstract
Internet of Things (IoT) plays a very important role in every field. With increase in its features it is almost used in every sector. As it is used everywhere so key elements of security are very much critical components which are to be preserved. For formulating these concepts, a new technology called blockchain can be used. In order to have secure transaction between various items, this technology is tremendously used without involving any third party. In this paper, we present introductory part of IoT enabled with blockchain, their key features, architecture layout, characteristic features of both the technologies, their futuristic solutions for different real-world problems, different communicational models etc. Both the technologies possess a number of characteristic features in every field but certain limitations too exist which gives futuristic direction for research.
Method for dataset preparation for soil data analysis in decision support applications
Authors: Motia Sanjay , Reddy SRN 
Published: January 2021

Abstract
Nowadays, publically available soil health cards (SHC) are easily understandable and useful means of knowledge transfer about soil conditions to farmers for sustainable soil management in farms. The SHCs are the true representative of precise and useful information about the physicochemical properties and nutrient status of agricultural soil. Before SHCs, the soil test records generally lie with soil test laboratories (STLs) and their availability in form of soil datasets is very limited. Moreover, the datasets are not big enough to be utilized as test datasets in soil investigation and development of futuristic and intelligent decision support and advisory systems. To address the issue, this paper presents a simple method for soil dataset preparation from SHCs for machine learning (ML) and artificial intelligence (AI) based soil research and analysis applications. The method collects wide number of SHCs for particular location and applies simple parsing and data cleaning concepts, implemented by utilizing open-source python libraries, to set of SHCs to prepare soil dataset. The dataset prepared by the proposed method can be used in a variety of intelligent data-driven decision support applications such as soil classification, nutrient prediction, and fertilizer recommendation. The dataset thus generated by the proposed method would help both researchers and soil scientists in their future research on soil health monitoring and decision support for sustainable farm management.
Chaos Based Secure Medical Image Transmission Model for IoT- Powered Healthcare Systems
Authors: Rajendran Sujarani , Doraipandian Manivannan 
Published: January 2021

Abstract
Due to the extensive development of Internet of Things (IoT) in e-healthcare environment, security and integrity of the medical data especially medical images became a big issue. This paper proposes a chaotic security architecture for ensuring the security of the medical images during transmission and storage. The proposed model is built by comprising of three main stages. At first, message digest 5 algorithm is applied to the plain image for generating the seed key of Lorenz chaotic map. Subsequently Lorenz map is iterated to generate the chaotic key series utilized for further process. In second stage, dual confusion such as row-by-row and column-by-column confusion is executed on the plain image. At last, dual diffusion process is performed by applying binary reverse and compliment operation, in addition to that XOR operation is executed between diffused image and Lorenz chaotic key image. Simulation results and analysis of security level by applying different attacks indicates that the developed cryptosystem has the potential of satisfying the security requirements of IoT healthcare applications.
Handwritten Digits Identification Using Mnist Database Via Machine Learning Models
Authors: Gope Birjit , Pande Sagar , Karale Nikhil , Dharmale Shivani , Umekar Pooja 
Published: January 2021

Abstract
The identification of hand-written digits is among the most significant issue in the applications for pattern detection. In many application such as postal code, check online routing bank accounts, data form entry, etc., the applications of digits recognition include the center of the issue is the need to construct an appropriate algorithm that can identify hand-written digits and that users upload through a smartphone and scanner and other digital devices. In this paper, we took a repository of MNIST, which is a sub-set of the database of NIST results. The MNIST dataset accommodates the collection of hand-written scanned images from a broader variety of NIST repository produced by hand. The method proposed in this paper is centered on numerous machine learning methods to perform hand-written digit detection that is off-line in the python language platform. The primary objective of this paper is to render hand-written digits recognition reliable and precise. For the identification of digits using MNIST many machine learning algorithms have been used including Support Vector Machine, Multilayer Perceptron, Decision Tree, Naïve Bayes, K-Nearest Neighbor, and Random Forest.
Analytical Study of the Robustness of the Different Variants of Fractional-Order Self-Tuned Fuzzy Logic Controllers
Authors: Agrawal Alka 
Published: January 2021

Abstract
Due to the evolution in technologies, it has become necessary for the systems to work in real time and for efficient output they have to be adaptive. Since the conventional Proportional-Integral-Derivative (PID) controller lacks to control the less informative non-linear systems and has fixed parameters, therefore, artificial techniques with the inclusion of adaptability has been studied in this paper. It has been observed that by implementing an extra layer of fuzzy controller in parallel, the gains of the controller could be change in real time and can highly reduce the external effects on the system as compared to the non-adaptive controller. In addition to this, different variants of fuzzy controllers like self-tuned fractional-order fuzzy PD (STFOFPD), self-tuned fractional-order fuzzy PI (STFOFPI) and self-tuned fractional-order fuzzy PID (STFOFPID) are compared and it was found that the STFOFPID outperforms all the controllers in the servo-regulatory mechanism.
Empirical analysis of regression techniques by house price and salary prediction
Authors: Bansal U , Narang A , Sachdeva A , Kashyap I , Panda S P 
Published: January 2021

AbstractRegression analysis is extensively used for prediction and prognostication, and its use has substantial overlap with the domain of machine learning. The main objective of this paper is to compare the performance of two regression techniques namely Simple Linear Regression (SLR) and Multiple Linear Regression (MLR) algorithms by two cases: predicting the salary of employees after certain years and predicting the prices of real estates. An employee’s salary depends on numerous factors, such as total employee experience, certifications, and overall experience as a lead and manager. The factors in predicting house prices are the area of land (sqft_living), condition, waterfront, number of bedrooms, and so on. The dataset used in this experiment is an open-source dataset from KaggleInc. The algorithms were compared using parameters like R-squared value, Mean absolute error (MAE), Mean Squared Error (MSE), Median Absolute Error (MDAE), Variance Score, and Root Mean Square Error (RMSE). Results have shown that MLR provides the better efficiency in comparison to SLR.
Classification and recognition of online hand-written alphabets using Machine Learning Methods
Authors: Popli R , Kansal I , Garg A , Goyal N , Garg K 
Published: January 2021

Abstract
The hand-written alphabet recognition and classification plays an important role in pattern recognition, computer vision as well as image processing. In last few decades, a plethora of applications based on this area are developed such as sign identification, multi lingual learning systems etc. This paper classifies samples of hand-written alphabets into different classes using various machine learning methods. The challenging factor in hand written alphabets recognition lie in variations of style, shape and size of the letters. In this paper a simplified and accurate methodology is proposed based upon engineered features which are evaluated and tested using MatLab tool in comparison to other existing methods. The proposed system achieves a substantial amount of accuracy of 98% as compared to the state of the art approaches.
Comparative Analysis of Detection of Email Spam With the Aid of Machine Learning Approaches
Authors: Madhavan Mangena Venu , Pande Sagar , Umekar Pooja , Mahore Tushar , Kalyankar Dhiraj 
Published: January 2021

Abstract
Over the past few decades, Technology has gained a rapid pace in its development making communication easier. Considering several modes of communication, E-mails(Electronic mails) are the best means for both informal and formal conversations. Some also use e-mails to store and share important information in the form of text, images, documents, etc. between people using electronic devices. Besides, some people improperly use this means of communication by sending useless or unwanted e-mails in bulk i.e., spammed emails which could result in disproportionate usage of memory in the mailbox. There are many suggested approaches in practice that could identify spam emails from the mailbox using machine learning methods. This paper mainly deals with the comparative analysis of detecting Spam Emails by various machine learning methodologies along with the proposed methodology. Considering various evaluation metrics such as Accuracy, Error, Evaluation time, Efficiency, and so on for the evaluation of models. This document draws the contrast on strengths, drawbacks, and limitations of some of the existing techniques that use the approaches of machine learning to detect spam emails. The machine learning method is further resourceful than the acquaintance approach of engineering which does not involve the specifications of any instructions. Considering various evaluation metrics such as Accuracy, Error, Evaluation time, Efficiency, and so on for the evaluation of models. The various accuracies obtained in this framework are KNN – 96.20%, Naïve Bayes – 99.46%, SVM – 96.90, Rough Sets Classifiers – 97.42%.
Twitter Sentiment Analysis Using Deep Learning
Authors: Neha  , Gupta Hritik , Pande Sagar , Khamparia Aditya , Bhagat Vaishali , Karale Nikhil 
Published: January 2021

Abstract
It is well established that the tweet sentiment analysis is still focused on conventional messages, such as film reviews and product reviews, while significant improvement has been made as deep learning becomes widespread, and comprehensive data sets are accessible for training (far from just emoticons and hashtags). Nevertheless, prior opinion analysis experiments typically performed on tweets, i.e. only two forms of global polarities (i.e. optimistic and negative) occur with their work/validation/test data sets. What is more, systems’ judgments are not actively aligned with the specified appraisal objects. In this paper, we have discussed some deep learning approaches for twitter sentiment analysis. We also trained our model using CNN and RNN to get some good accuracy results.
Dental cavity Classification of using Convolutional Neural Network
Authors: Sonavane Apurva , Yadav Rohit , Khamparia Aditya 
Published: January 2021

Abstract
Dental and Oral diseases are very common diseases and half of the world population suffers from it. Due to poverty or unhygienic practices, these diseases are common, and it is estimated that 5% of total medical expenditure in the world is on oral diseases. In this paper, we have focused on detecting cavities. Recent developments Machine Learning and Artificial Intelligence have helped a lot in medical science. Due to these algorithms, diagnosis and treatment of diseases can be done efficiently. To detect dental cavities different imaging modalities are used by doctors, however, in this paper we have used visual images of teeth’s and applied deep convolution neural network(CNN) to classify the teeth into caries or non-caries. We have used the images from the Kaggle dataset, and after tuning our model we were able to achieve 71.43% accuracy.
Extraction and Summarization of Reviews using Lexicon based Approach
Authors: Sheikh A A , Arif T , Malik M B , Bhat S I 
Published: January 2021

AbstractOnline service providers have enabled users to freely comment in the form of review on an online items. These large scale of reviews contains valuable information and poses the need of automatic summarization. In this paper the extraction and summarization of reviews expressed by customers on Samsung mobiles has been carried out. The proposed approach extracts the Samsung reviews using custom build scraper from Flipkart and classify them as positive, negative and neutral using Lexicon based approach (SentiWordNet).
Optimized Sequential model for Plant Recognition in Keras
Authors: Aggarwal Shilpi , Bhatia Madhulika , Madaan Rosy , Pandey Hari Mohan 
Published: January 2021

Abstract
There are huge varieties of floras in the world. Lots of varieties of species are beneficial to the human’s life. Plant recognition is a very important task to segregate the huge amount of floras which belong to various categories. Various researchers have applied different approaches to recognize the plant family. Deep learning is a subset of machine learning. This is one of the accepted technologies that automatically extracts features, processes them and yields the best results. Keras is a widely used deep learning framework which is employed in this work. Five different plant species are chosen as samples among the Indian species, namely OcimumTenuiflorum, Sansevieriatrifasciata, Chlorophytumcomosum, Azadirachtaindica, Aloe Vera. These samples are rich in oxygen. From these samples the features like shape, color, texture, corners are extracted. One hot encoding is also applied onto the target values to optimize the results of recognition. The extracted features are fed into the sequential keras model which recognizes the plant species. The accuracy of the training set is 100 percent and the testing set is 96.7percent. Confusion matrix is drawn to show the correctly classified and misclassified samples.
Potato Plant Leaves Disease Detection and Classification using Machine Learning Methodologies
Authors: Singh Aditi , Kaur Harjeet 
Published: January 2021

Abstract
Agriculture is one of the essential sectors for the survival of humankind. At the same time, digitalization touching across all the fields that became easier to handle various difficult tasks. Adapting technology as well as digitalization is very crucial for the field of agriculture to benefit the farmer as well as the consumer. Due to adopting technology and regular monitoring, one can able to identify the diseases at the very initial stages and those can be eradicated to obtain a better yield of the crop. In this document, a methodology was proposed for the detection as well as the classification of diseases that occur for the potato plants. For this scenario, the openly accessible, standard, and reliable data set was considered which was popularly known as Plant Village Dataset. For the process of image segmentation, the K-means methodology was considered, for the feature extraction purpose, the gray level co-occurrence matrix concept was utilized, and for the classification purpose, the multi-class support vector machine methodology was utilized. The proposed methodology able to attain an accuracy of 95.99%.
Improving the lubricating properties of transmission oils by activating the processes of boundary films formation
Authors: Ponomarenko A G , Boiko T G , Bicherov A A , Bicherov A V , Shiryaeva T A , Kulemzin D V 
Published: January 2021

Abstract
The properties of the boundary lubricating films determine the performance of friction units, which is most important in the event of a loss of lubricant. It has been found that when sunflower oil is added to mineral transmission oil, strong boundary layers are formed during friction. These layers reduce wear and friction. Vegetable oils with the lowest iodine value have the best antiwear properties. The additional introduction of the product of the interaction of rosin with copper (II) hydroxide into the transmission oil makes it possible to form lubricating structured layers that improve the tribotechnical properties.
Design technique of highly efficient technological processes for preparation of polymeric composite parts for adhesive bonding
Authors: Tamarkin M A , Tishchenko E E , Murugova E V , Kozulko N V 
Published: January 2021

Abstract
This paper presents the results of technological machining of polymer composite parts with the flexible flap wheels for further adhesive bonding. The peculiarities of polymer composite materials and their application field are revealed. Constructions that use polymer composite material parts are described.
The article identifies the process of gluing parts made of polymer composites, as well as features of ensuring the strength of the adhesive joint after mechanical processing of bonded parts.
Methodology of modes processing selection and structural-geometric tool parameters for the processing of surface parts under the adhesive bonding is elaborated. Theoretical studies of processing the polymer composite parts with the help of flap wheels are conducted. The surface roughness model is developed. A considerable amount of experimental research has been carried out. The tools for machining and cutting modes are selected. The strength of the glue connection of the details processed by the flap wheels is examined. To compare the durability of the received glue connections and the combinations executed according to the factory technology, the reference surfaces of the samples used at comparative control of quality sanding are investigated. The theoretical and experimental results are compared. Their high convergence is established. The adequacy of the theoretical dependence in compliance with the Fisher criterion is checked. Effective methodology for designing highly efficient technological processes tested in industrial conditions is presented. It allowed to reduce the processing time in more than 2 times, while providing the high strength adhesive bonds.
Improvement of friction units limiting the durability of mechanical systems
Authors: Demyanov Aleksey , Demyanov Alexander , Pavlova Irina , Pavlitskiy Boris 
Published: January 2021

Abstract
The efficiency and competitiveness of transport, other things being equal, directly depends on improving the safety of the transportation process, which in turn depends on the actual technical condition and durability of the rolling stock. To solve problems of this type, it is proposed a set of solutions is proposed that increases the durability of especially critical friction units that affect the safety of operation of technical systems used in loading and unloading and transport processes. The proposed solutions are characterized by manufacturability, low resource consumption and versatility.