bluuebunny/crossref_metadata_2025_split huggingface dataset:
Density estimation using deep generative neural networks
Authors: Liu Qiao , Xu Jiaze , Jiang Rui , Wong Wing Hung 
Published: April 2021

SignificanceDensity estimation is among the most fundamental problems in statistics. It is notoriously difficult to estimate the density of high-dimensional data due to the “curse of dimensionality.” Here, we introduce a new general-purpose density estimator based on deep generative neural networks. By modeling data normally distributed around a manifold of reduced dimension, we show how the power of bidirectional generative neural networks (e.g., cycleGAN) can be exploited for explicit evaluation of the data density. Simulation and real data experiments suggest that our method is effective in a wide range of problems. This approach should be helpful in many applications where an accurate density estimator is needed.
Flexible graphene/GO electrode for gel-free EEG
Authors: Ko Li-Wei , Su Cheng-Hua , Liao Pei-Lun , Liang Jui-Ting , Tseng Yao-Hsuan , Chen Shih-Hsun 
Published: April 2021

Abstract

Objective. Developments in electroencephalography (EEG) technology have allowed the use of the brain–computer interface (BCI) outside dedicated labratories. In order to achieve long-term monitoring and detection of EEG signals for BCI application, dry electrodes with good signal quality and high bio compatibility are essential. In 2016, we proposed a flexible dry electrode made of silicone gel and Ag flakes, which showed good signal quality and mechanical robustness. However, the Ag components used in our previous design made the electrode too expensive for commercial adaptation. Approach. In this study, we developed an affordable dry electrode made of silicone gel, metal flakes and graphene/GO based on our previous design. Two types of electrodes with different graphene/GO proportions were produced to explore how the amount of graphene/GO affects the electrode. Main results. During our tests, the electrodes showed low impedance and had good signal correlation to conventional wet electrodes in both the time and frequency domains. The graphene/GO electrode also showed good signal quality in eyes-open EEG recording. We also found that the electrode with more graphene/GO had an uneven surface and worse signal quality. This suggests that adding too much graphene/GO may reduce the electrods’ performance. Furthermore, we tested the proposed dry electrodes’ capability in detecting steady state visually evoked potential. We found that the dry electrodes can reliably detect evoked potential changes even in the hairy occipital area. Significance. Our results showed that the proposed electrode has good signal quality and is ready for BCI applications.
Second Hankel determinant for tilted bi-starlike functions of order β
Authors: Soh Shaharuddin Cik , Kamal Zammariyah Mustafa , Dzubaidi Mohamad Huzaifah Mohd , Adam Noor Latiffah 
Published: April 2021

AbstractLetfbe analytic in the unit diskD={z∈C: |z| < 1}, andSbe the subclass of normalized univalent functions inD. We define the class of tilted bi-starlike functionsSσ*(β,δ), which satisfy the conditionRe{eiδ[zf'(z)f(z)]}>βwhere |δ| <πand cosδ>β. The second Hankel determinant|a2a4−a32|has been determined forSσ*(β,δ). In this paper, we also found the coefficients bound for |a2|, |a3| and |a4|.
Discrete Cosine Transformation Based Approach for Offline Handwritten Character and Numeral Recognition
Authors: Sethy Abhisek , Patra Prashanta Kumar 
Published: April 2021

Abstract
Handwritten Character Recognition (HCR) of Indic scripts is one of the most challenging as compared other scripts. Recognition of handwritten character is more difficult one as compares with the printed one due to their similar structure and orientation. To have a proper recognition system for handwritten characters various researchers have adapted various methods and still yet to have more feasibility and high recognition rate to achieve. Here in this proposed work we have completely focused on handwritten characters and numerals of various scripts of different regions of India such as Bengali, Odia scripts. To have a proper solution for the ambiguities that arises in handwritten one and which has been resolved using the Discrete Cosine Transformation (DCT) as feature extraction part. This transformed based featured is used to calculated GLCM values of each images of handwritten numerals and characters. To maintain the novelty of the work in simulation part we have listed up variance nature of the individual’s images. Subsequently we have also harnessed the SVM, Neural Network, and Quadratic Classifier over dataset to report the recognition rate. After validating the proposed work, we can conclude that adopted model is quite helpful in building the automatic recognition system for both handwritten character and numerals to have solution for real time problems.
Detecting Bone Fracture in Medical Images
Authors: Satya Sriveni N , Hema Latha K , Viji Amutha Mary A , Selvan Mercy Paul 
Published: April 2021

Abstract
Bone fracture is one of the most ordinary problems in mortals because of accidents or other causes. Breaking bones can occur in our body, such as the wrist, heel, ankle, hip, rib, leg, chest, etc. Fractures cannot be seen with the naked eye, so X-ray/CT images are used to detect them. But sometimes these images lack sufficient detail for diagnosis. Today image processing plays an important role in detecting bone fractures. Image processing is important for the archiving and transmission of modern data, in particular for image transmission, video encoding, digital libraries, image databases, and remote sensing. This research paper presents detailed research on image processing methods for detecting bone fractures. The following research will help in studying various techniques that can be used to detect fractures in boned through the process of image processing and also includes updating of some new techniques as an improvement.
An Efficient Hybrid Machine Learning Classifier for Rainfall Prediction
Authors: Asha P. , Jesudoss A. , Mary S. Prince , Sandeep K. V. Sai , Vardhan K. Harsha 
Published: April 2021

Abstract
The most leading applications of Artificial Intelligence that seems to witness an immense Progression in the digital era are the Machine Learning (ML) Techniques. It learns itself from the past experiences and attempts at the best prediction of future instances or trends. Such progressive learning does not demand any explicit programming structures. Machine learning finds a wide range of application areas, and out of which accurate real time weather prediction gains importance. An interactive neural network based classification model for better prediction of rainfall has been put forth in this paper; we have proposed an interactive model for predicting rainfall using neural classification. The model is premeditated in a way, that it fetches feature extraction from a database including information about previous rainfalls in a specific area. The features were then pre-processed and further segmented by employing the random forest. The segmented outputs are then classified using neural networks. A comparison of spatial interpolation scheme is done with existing systems by deploying the hybrid classifier. The efficiency of the proposed model is evaluated and is compared with the traditional Deep Learning process and it is observed that the Random forest based interactive model provides better performance. Results of the model seem to be more accurate as the model uses an iterative approach for feature extraction.
Smart Car based on IOT
Authors: Praveena M. D. Anto , Christy A. , Helen L. Suji , Jancy S. , Nandini D. Usha 
Published: April 2021

Abstract
The good automotive, anyone will simply management the system along with the speech recognition and might conjointly supervision the mechanism to hide the checkout distance. The speech should recognize, to tend the square measure victimization Bluetooth module. It will grasp #4 completely various vibrant motion commands and therefore the main feature of this technique is that, if the user desires to track the system with an overt distance, it will acknowledge that conjointly. It will notice existence of universe and therefore the human intervention half is held by the supersonic sensing element. It’s a twin programming mechanism automotive within which it’s each manual and machine mode. In manual mode, we are ready to able provide the speech recognition and in machine mode, it goes on its means within which it can mechanically move once the machine mode is activated and it is often stationary by sensing the obstacle if something on its means and the obstacle detection method is held by the supersonic sensing element. The system is monitored from a laptop named Arduino Uno and the speech is recognized victimization BT speech-controlled APC reorganization engine. It’s not feasible to use custom management for the operational mechanism automotive and simple thanks to control a mechanism is certainly with speech. During the paper, we’ve propounded a speech recognition-based mostly system wherever the dominant method is going to be a great deal expedient to use.
An integrated and Dynamic Wireless Intrusion Exposure Solutions based on Neural Network
Authors: Shabu S.L. Jany , Refonaa J. , Maran Sardar , Dhamodaran S. , Vedanarayanan  
Published: April 2021

Abstract
Intrusion Exposure Solutions in wireless can be categorized into Outlier Intrusion Detection and Exploitation Intrusion Detection. Outlier Intrusion Detection consider the unusual identified wireless attacks. Misuse Intrusion Detection method is the identification of the known attacks. In this proposed solution, present a half breed wireless intrusion Exposure Model. To execute the wireless intrusion Exposure Model, we planned a straightforward lightweight operator. The proposed specialist identifies the most decimating and genuine assaults; Man The Middle and Denial of Provision; with the base chosen highlight set. To assess our proposed Exposure and its operator, we gather a total informational index utilizing open source assault generator virtual products. In the proposed system features are analyzed manually; it extracts the features from the raw dataset. The proposed system utilizes the LSTM systems to deal with the successive idea of the PC organize information. We dole out an edge esteem dependent on cross-approval to order whether the approaching system information succession is irregular or not. In addition, the proposed system can chip away at both stable and adjustable length information arrangement and works productively for unexpected and capricious system assaults. We at that point additionally utilize the unaided rendition of the Long Short Term Memory and Neural Networks.
To Enhance Security Mechanism in ATM Account Using Biometric System
Authors: Refonaa J. , Shabu S.L. Jany , Dhamodaran S. , Joshila Grace L.K. , Vedanarayanan  
Published: April 2021

Abstract
The services we use on our day-to-day life like ATM will raise a confidence that it is safe to use for daily transactions that have withdrawals, balance enquiry and take care of all other transactions ATM machine reads the card, which could be Savings or Current accounts. After verification process one can withdraw or transfer or deposit the amount. In such situations, some fraud may occur where some individuals notice others ATM account details and perform transaction process. To overcome such disturbances Biometrics can be applied. Every individual can perform his/her transactional process with Two step verification process i.e., with pin number and biometric verification process. In this way, for some extent the ATM account fraud can be controlled.
Application of Artificial Intelligence in the field of Medicine for Diagnosis
Authors: Selvan Mercy Paul , Gladence Mary , Viji Amutha Mary A. , Abitha Memala W. , Sravya M , Navya M 
Published: April 2021

Abstract
Because of data mining progress in biomedical and human services networks, precise investigation of clinical data benefits early illness acknowledgment, persistent consideration and network administrations. At the point when the nature of clinical data is inadequate the precision of study is diminished. In addition, various locales display one of a kind appearances of certain territorial maladies, which may brings about debilitating the forecast of illness flare-ups. In the proposed framework, it gives AI calculations to viable forecast of different illness events in sickness visit social orders. It try the adjusted gauge models over genuine medical clinic data gathered. To beat the trouble of inadequate data, it utilize an inert factor model to reconstruct the missing data. It probe a territorial interminable sickness of cerebral dead tissue. Utilizing organized and unstructured data from emergency clinic it use Machine Learning Decision Tree calculation. It predicts likely infections by mining informational indexes and gives recommended specialists and healing arrangements. It will likewise direct the clients by offering tips to carry on with a sound life, some eating routine tips and furthermore value of plants and nourishment things. As far as we could possibly know in the territory of clinical large data examination none of the current work concentrated on the two data types. Contrasted with a few run of the mill gauge calculations, the estimation precision of our proposed calculation arrives at 94.8% with a union speed which is quicker than that of the Decision tree ailment hazard forecast calculation.
Extracting image frames using image property matching technique
Authors: Saravanan D. , Immanuel Y. 
Published: April 2021

Abstract
Retrieve the needed content based on the users input query one of the challenging task today especially the input if it is image. This extraction procedure be different compare to the existing retrieve operation. In image data extraction user need to know the input field, based on his knowledge only the extraction take place because the complexity of the input data set. The given visual picture converted into meaningful static picture after eliminating the unwanted information from the data set then only user need to extract the needed information. This brings the huge demand in this field.
In the proposed article focus the drawback on the existing technique and provide the efficient model for extracting the needed information. The investigation outcome verifies this statement.
Modified Chorological Coded Data Compression Methodology for Wireless Sensor Networks
Authors: Jancy S. , Helen Suji , Selven Mercy Paul , Viji Amutha Mary A. , Antopraveena M.D , Roobini M.S. 
Published: April 2021

Abstract
Wireless Sensor Network (WSN) is a fast evolving current technology which is being employed in various applications. Despite its wide usage, WSNs have a few challenges to overcome to be called as an ideal technology. Some of the challenges are battery life time, memory storage and deployment issues. Batteries are the primary source of power supply to WSN and one of the major challenges is the energy constraint. This paper aims to propose a few techniques to better the energy efficiency of the sensor networks by saving sensor energy using data compression methodology. In this paper, a modified chorological coded data compression methodology is proposed (MCDC). This algorithm deals with assigning the sequence value to the given input information. If the assigned sequence value is a double digit number, it is converted in to single digit number. Double digit numbers and single digit numbers are combined. Separate location tables are generated for all double digit and single digit numbers. This procedure continues until all the sequence values are changed in to single digit number. Then the final single digit is assign with an equivalent Sequence Code (SC). MCDC algorithm is compared with DELTA compression and RUNLENGTH compression and a better compression ratio was achieved when compared with DELTA compression and RUNLENGTH compression algorithms.
Detection and Prevention of Spam Mail with Semantics-based text classification of Collaborative and Content Filtering
Authors: Prayla Shyry S. , Bevish Jinila Y. 
Published: April 2021

Abstract
Eventhough, conventional technologies are quiet good in separating spam messages, still soo many measures have to be considered to make more accuracy in spam filtering. In this work, we worked towards detecting spam mails and filtering it during its transmission. We proposed Collaborative filtering approach hybrid with text classification (semantics based). The related feature are retrieved from the text content. Also, another filtering method known as Content-based filtering is proposed which filters the same spam mail with more precision and better accuracy. Along with the semantic texts the Content-based filtering filters the special symbols such as HTML tags, @,/etc. Results are compared and the accuracy of detecting spam e-mails of Content-based filters is more than that of Collaborative filters. Both Collaborative and Content-based filters perform keyword check available in the spam keyword database and detects whether the mail sent by the sender is genuine or spam. Genuine emails are sent successfully and the spam emails are blocked at the server side. Content-based email classification requires an understanding of both structural and semantic attributes of email. Conventional research is focussed on semantic properties through structural components of email. After analysing the emails as events (as a major subset of the class of email), a rich contextual test-bed representation for an understanding of the semantic attributes of emails has been devised. The event-based emails have traditionally been studied based on simple structural properties.
Regulation of Speed for Driver Assistanceand Removal of Haze using Imageprocessing Algorithm
Authors: Delsy T. Thaj Mary , Krishnamoorthy N R , Rani D. Jamuna , Rajalakshmi G , Rani V. Amala , Thoomas Ebin.G. 
Published: April 2021

AbstractThe clarity of the outdoor scene images very bad due to the environmental condition result in hazy image. The process of removing the fog from that image is called dehazing. The dehazing process is very difficult in the moving image or video image. The proposed method should improve this problem for the smooth driving of the vehicles by controlling the speed by comparing the image and the scene of video taken during driving. The proposed method should remove the haze so that it will increase the visibility of the image and multi scene. The proposed method can improve the colour changes in the image which is caused by airlight. The single frame is used for the enhancement of the foggy image remove the fog using the transmission map which contains multilevel. We make use of boundary constraints and global airlight estimation is taken into account. The method is fast and noise-free that generally arise in such enhancement techniques.
Industrial Parameter Monitoring and Alarming System using IOT
Authors: Rajalakshmi G , Delsy T.Thaj Mary , Jamunarani D. , Amalarani V. , Chakravarthy P Kalyan Sai 
Published: April 2021

Abstract
Today, smart grids, smart homes, smart water systems, intelligent transportation, are infrastructure systems that connect our world more than we thought possible. The common vision of such systems is usually associated with a single concept, the Internet of Things (IOT), where through the use of sensors, the entire physical infrastructure is closely related to the information e-communication technologies where intelligent monitoring and management can be achieved through the use of integrated networks devices. These devices will connect to the Internet to share different types of data. The proposed system uses the ubidots server and detection applications for the internet of things for an industrial monitoring system. In this document we use detection devices to check different parameters such as the number of production, the intensity of lighting, the detection of toxic gases and the ambient temperature.
Secure K-NN Query on Encrypted Cloud Data with Multiple Keys
Authors: Vignesh R. , Deepa D. , Mana Suja Cherukullath , Keerthi Samhitha B. 
Published: April 2021

AbstractCloud computing has become an undeniably famous assistance for its adaptability and versatility, which rouses numerous associations, foundations and organizations to want to re-appropriate data administrations to cloud stage. Simultaneously, much consideration has been paid to adapt to the extraordinary security and protection issues in recloud cloud. The general methodology is to scramble data by the data owner (DO) before re-appropriating; the approved query user play out an unpredictable arrangement of encryption and decoding tasks during question execution. In any case, the above plans have accepted that the question clients are completely trusted and have the entrance to the key for scrambling and unscrambling recloud data. It will achieve a few issues in reality. So we propose a novel plan for secure KNN inquiry on encoded cloud data with various keys, in which the data owner and each question client all hold their own various keys, and don’t impart them to one another; in the interim, the data owner scrambles and decodes re-appropriated data utilizing the key of his own. Our plan is built by set of conventions to jelly the data classification and question over scrambled cloud data without key-sharing.
Experimental Study on Concrete With Partial Replacement of Coarse Aggregate with Cashew Shells
Authors: Priyadharshini B , Nirmala R , Eshanthini  , Manoj Kumaar C 
Published: April 2021

Abstract
Concrete is a mix of cement, fine aggregate and coarse aggregate with water in correct proportion. Many attempts had been to replace the fine aggregate, coarse aggregate and cement in concrete. In this row replacement of coarse aggregate by cashew shells was tried. Over 66% of the overall country export cashew kernels. The cashew seeds is well acclaimed for its good quality and appearance. Concrete cubes and beams had casted using cashew shell as a partial replacement of the coarse aggregate in the proportion of 10%,20% and 30% replacement. the specific gravity of the shell was analyzed and 16mm shell were selected for the experiment. The characteristic of concrete was determined and the result showed that 10% replacement of shell with coarse aggregate gives the optimum strength.