bluuebunny/crossref_metadata_2025_split huggingface dataset:
Experimental device design justification for radiation resistance tests of single-mode optical fibers and FBG-based sensors at the IVG.1M reactor
Authors: Gnyrya V , Gordienko Yu , Surayev A , Baklanova Yu , Kashaykin P , Britskiy V , Kenzhina I , Vityuk G , Tulubayev Ye , Bochkov V 
Published: January 2022

Abstract
One of the most important stages in the development of an experimental device is to carry out a series of computational studies to substantiate the compliance of device design with the objectives of the experiment, such as the choice of test modes and the study of standard and hypothetical emergency modes of its operation. Result of these studies is the neutron-physical, thermal, strength and hydrodynamic characteristics of the structural elements of the device and working bodies. During this work, a series of neutron calculations was conducted using the MCNP6 code and thermal-physical calculations using the ANSYS software package of two configurations of the experimental device. A feature of the calculated studies is the presence of specific requirements for the thermal state of the experimental device sleeve. Namely, ensuring a predetermined temperature gradient between its ribs, which should not exceed 4°K during the reactor tests.
Measurement of the spatial-energy distribution of neutrons in the irradiation channel of the critical facility
Authors: Aitkulov M T , Dyussambayev D S , Romanova N K , Gizatulin Sh H , Shaimerdenov A A , Bugybay Zh T , Kisselyov K S , Beisebayev A O 
Published: January 2022

Abstract
One of the basic installations of the Republican State Enterprise “Institute of Nuclear Physics” of the Ministry of Energy of the Republic of Kazakhstan is a critical assembly, which is a zero-power reactor. Desalinated water and beryllium serve as moderators and neutrons reflectors. The energy spectrum of neutrons in the core is thermal. The main purpose and area of application is the modeling and study of the neutronic characteristics of the cores of water-moderated research reactors of various types.
The paper presents the results of experimental measurements of the spatial-energy distribution of neutrons in the dry, central channel of the critical assembly. Measurements of the neutron flux were carried out using activation foils for three energy groups of neutrons: thermal, epithermal, and fast.
The measured thermal neutrons flux in the irradiation channel is ~ 3·108 cm‒2s‒1, and fast neutrons flux (with energies above 0.7 MeV) is ~ 8·108 cm‒2s‒1. The fraction of thermal neutrons in the integral flux was 0.23%, and the fraction of fast neutrons was 0.62%. In the axial distribution of thermal and fast neutrons, the maximum value of the neutron flux is 50 mm below the midplane of the core.
Calculation of radiation dose enhancement by gadolinium compounds for radiation therapy
Authors: Abdullaeva G.A. , Kulabdullaev G.A. , Kim A.A. , Nebesny A.F. , Yuldashev D.O. 
Published: January 2022

AbstractIn this study, we evaluate the features of dose enhancement with Gd contrast agent (Magnevist). Due to the increased relaxation time and high atomic number (z=64) Gd can be used in radiation therapy as a radiosensitizer. To perform a quantitative evaluation of the radiosensitization effect is determined a parameter called the dose enhancement factor - DEF. The DEF values were calculated based on the analysis of the mass absorption coefficients for gadolinium and biological tissue. An increase in DEF is observed when the radiation energy is higher than the K-shell ionization energy of Gd atoms. For the presence of 20315 ppm Gd contrast agent in biological tissue the dose enrichment factor is maximum DEF = 4.12 at photon irradiation energy 60 keV. Also, based on calculations for photon irradiation sources considered high degrees of dose enhancement occur for Am-241, Yb-196, and 100 kVp X-ray tube.
Prediction of Lung Cancer using Ensemble Classifiers
Authors: Ashwin Shanbhag G , Anurag Prabhu K , Subba Reddy N V , Ashwath Rao B 
Published: January 2022

Abstract
Carcinoma detection from CT scan images is extremely necessary for numerous diagnostic and healing applications. Because of the excessive amount of information in CT scan images and blurred boundaries, tumor segmentation and class are extremely laborious. The intention is to categorize carcinoma into benign and malignant categories. In MR pictures, the number of facts is a lot for interpreting and evaluating manually. Over the previous few years, carcinoma detection in CT has grown to be a rising evaluation space in the area of the scientific imaging system. Correct detection of length and site of lung cancer performs a vital position in the designation of carcinoma. In this paper, we introduce a novel carcinoma detection methodology that helps in predicting the carcinoma from the CT scanned images. The methodology has 4 different stages, pre-processing the image data, segmentation, extracting features, and classification stage to categorize the benign and malignant. This work makes use of extraordinary models for detecting carcinoma in a CT test via way of means of constructing an ensemble classifier. Techniques proposed in the paper helped us achieve an accuracy of 85% using Ensemble-Classifier which showcases that model has the capability of predicting the malignant cases correctly. The ensemble classifier consists of 5 machine learning models like SVM, LR, MLP, decision tree, and KNN. The inevitable parameters like accuracy, recall, and precision is calculated to determine the accurate results of the classifier.
Image and pattern reconstruction using differentiable plasticity
Authors: Priyanka Jigalur , Prasad B G 
Published: January 2022

Abstract
The brain is a substantial boon to humankind that adapts nature accordingly. The brain can learn and unlearn based on the situation. This singularity of human learning led to the research creating models using Artificial Intelligence (AI) to incorporate the brain’s behavior. The investigation opened up many new approaches to study AI with neural networks by adding new techniques to imitate the human brain’s functionalities. Many models can learn from experience like Recurrent Neural Network(RNN’s), Long Short Term Memory (LSTM) with the fixed network size. This paper describes the simple method of creating the model which will behave similar to the biological brain and recreates its differentiable plasticity to adopt the features of neural network connection. It also shows that applying plasticity and the Hebbian plastic connection rule can result in optimization in RNN. This new approach of reconstruction of images based on plastic neural network experiments shows that the above novel approach gives more optimized results than the traditionally used RNN techniques. In this paper, a proposal is made where models can memorize and reconstruct unseen sets of images by solving recurrent networks using plasticity rules.
Prediction on Cardiovascular disease using Decision tree and Naïve Bayes classifiers
Authors: Sai Krishna Reddy V , Meghana P , Subba Reddy N V , Ashwath Rao B 
Published: January 2022

Abstract
Machine Learning is an application of Artificial Intelligence where the method begins with observations on data. In the medical field, it is very important to make a correct decision within less time while treating a patient. Here ML techniques play a major role in predicting the disease by considering the vast amount of data that is produced by the healthcare field. In India, heart disease is the major cause of death. According to WHO, it can predict and prevent stroke by timely actions. In this paper, the study is useful to predict cardiovascular disease with better accuracy by applying ML techniques like Decision Tree and Naïve Bayes and also with the help of risk factors. The dataset that we considered is the Heart Failure Dataset which consists of 13 attributes. In the process of analyzing the performance of techniques, the collected data should be pre-processed. Later, it should follow by feature selection and reduction.
Scale-Aware Cascading for Semantic Segmentation
Authors: Ahmed Ali Salim , Prasad B G 
Published: January 2022

Abstract
Semantic segmentation is an important technology commonly used in medical imaging, autonomous driving vehicles, and backgrounds for virtual meetings. Scale Aware approaches have become the standard when it comes to the semantic segmentation domain of Machine Learning. Multiple image scales are passed through the network allowing the result to use the regular CNN layers such as max-pooling as well as convolution layers. Also, a cascading hierarchy of attention has been shown to improve the accuracy of models for such segmentation tasks. The combination of both these approaches has been shown to greatly improve the accuracy of such models. A side effect of using the cascading approach is that the model turns out to use less memory in comparison to previous approaches. Auto-labelling engines are also helpful in generalizing the model further. The cityscapes dataset used here is a useful data bank as it consists of a myriad of situations where the model can be trained and tested on. This paper presents the tested results of such a segmentation model and incremental modifications to the model pipeline to understand and improve upon the existing architecture.
Data Clustering using Genomic Analysis in Graph model
Authors: Chithambarathanu M , Ganesh D R 
Published: January 2022

Abstract
In the event that the data is addressed as a diagram, wherein the hubs are devices and the hyperlinks establish associations among devices then a bunch might be defined as an associated perspective; i.e., a gathering of devices that are identified with each other, yet that don’t have any association with objects outside the gathering. Bunching is an essential test in the quality examination. This ponders monster impact genetic field. Thusly in the current system, the various genomic assessments are scattered in various dispersed structures. In our proposed work, we endeavour to develop a normal data base for genomic and proteomic assessment using diagram grouping.
A comparative study using supervised learning for anomaly detection in network traffic
Authors: Garg R , Mukherjee S 
Published: January 2022

Abstract
A user connects to hundreds of remote networks daily, some of which can be corrupted by malicious sources. To overcome this problem, a variety of Network Intrusion Detection systems are built, which aim to detect harmful networks before they establish a connection with the user’s local system. This paper focuses on proposing a model for Anomaly based Network Intrusion Detection systems (NIDS), by performing comparisons of various Supervised Learning Algorithms on metric of their accuracy. Two datasets were used and analysed, each having different properties in terms of the volume of data they contain and their use cases. Feature engineering was done to retrieve the most optimum features of both the datasets and only the top 25% best features were used to build the models – a smaller subset of features not only aids in decreasing the capital required to collect the data but also gets rid of redundant and noisy information. Two different splicing methods were used to train the data and each method showed different trends on the ML models.
A foundational model to spot indications of generalized anxiety disorder and assist mental well being
Authors: Revanthsai Puttaparthi , Sai Maadhurya N , Dhruthi Varna Konijeti Sai , Priya B. K. 
Published: January 2022

Abstract
Mental health, a subject that carries equal importance as physical health, has always remained in the limelight with least medical assistance sought, often leading not only to difficulty in the carrying out day-to-day activities but also to death by suicide. The number has begun to peak since the onset of the pandemic and thus needs to be addressed. Early detection and treatment play an extremely important role in treatment of mental illness. Though the period of recovery is gradual, constant screening and a follow up is sufficient enough in aiding the path back to change in lifestyle. Generalized Anxiety Disorder is one of the most common of mental illnesses among a never-ending list in the 21st century’s fast paced world and is often neglected, but curable if detected in early stages and treated. With mental well-being gaining importance and people moving forward to seek help, a technology to help them in their betterment would be benefiting. A design that is not only user-friendly but also cost effective can come handy to enable simultaneous monitoring.
Depression and Suicide Analysis Using Machine Learning and NLP
Authors: Jain Pratyaksh , Ram Srinivas Karthik , Vichare Abhishek 
Published: January 2022

Abstract
Depression is a common type of mental illness that can impair performance and lead to suicide ideation or attempts. Traditional techniques used by mental health experts can assist in determining an individual’s type of depression. Machine learning and NLP were used to understand how to predict posts that indicate depression in people and their accuracy. For this work, we have used a dataset from reddit. Reddit is an ideal destination to use as a supplement to the traditional public health system because of its punctuality in exchanging ideas, versatility in presenting emotions, as well as compatibility to use medical terms. We examined the comments and posts about suicidal ideation. We used NLP to gain a better understanding of interdisciplinary fields which are related to suicide. We discovered two help groups for depression and suicidal thoughts: r/depression and r/SuicideWatch. The famous “SuicideWatch” subreddit is commonly used by people who have thoughts of suicide and gives significant signals for suicidal behavior. A brief scan through the articles discloses that the subreddits are legitimate online spots to seek assistance and provide honest text data about people’s mental state. We have used multiple ML algorithms such as Naïve Bayes, SVM. To address the research problem, we have considered two subreddits that provided us with appropriate information to track people at risk. We achieved results of 77.29 % accuracy and 0.77 f1-score of Logistic Regression, 74.35 % accuracy and 0.74 f1-score of Naïve Bayes, 77.120% accuracy and 0.77 f1-score of Support Vector Machine, 77.298% accuracy, and 0.77 f1-score of Random Forest.
On the solar dynamics from radio signal communication by recent solar probes
Authors: Banerjee Abhijit , Bhattacharya Rina 
Published: January 2022

Abstract
The very inquisition of the humanity always remains about its parent star of this planetary system. Scientists across the world are always egger to investigate the details of the phenomenon of the solar flares and coronal mass ejections (CMEs). There are some fundamental mysteries related to the solar coronal heating along with the acceleration of the solar wind and energetic particles. In this context we have discussed on the solar radio signal data obtained from the Parker Solar Probe (PSP) mission of National Aeronautics and Space Administration (NASA), USA in course of its journey towards the Sun and the very recent data of Solar and Heliospheric Observatory (SOHO) space probe of European Space Agency (ESA) and NASA. In this work the simultaneous and periodical analysis of the data from the SOHO and PSP will light into the delicate features of the near and far Earth observations on the solar coronal mass ejections related dynamics and that reveals some interesting facts in relation to the solar magnetic field.
Mental Health Assist and Diagnosis Conversational Interface using Logistic Regression Model for Emotion and Sentiment Analysis
Authors: Moulya S , Pragathi T R 
Published: January 2022

Abstract
The aim of this work was to create a fully functional AI-ML based conversational agent that behaves like a real time therapist which analyses the user’s emotion at every step and provides appropriate responses and feedback. AI chatbots, although fairly new to the domain of mental health, can help in destigmatizing seeking help, and are more easily accessible to everyone, at any time. Chatbots provide an effective way to communicate with a user and offer helpful emotional support in a more economical way. While making regular psychiatric visits often require a fixed duration/appointment which can be time consuming and is restricted to a fraction of the day, the proposed chatbot can keep track of your health on the go at any time. The application will have a self-healing kit suggesting various exercises, both mental and physical that the user may implement in his day-to-day life. The study below goes into further detail on the major insinuations for future chatbot agent design and assessment
Performance Improvement in E-Gun Deposited SiOx- Based RRAM Device by Switching Material Thickness Reduction
Authors: Roy Sourav , Maikap Siddheswar 
Published: January 2022

Abstract
A performance improvement by reduction in switching material thickness in a e-gun deposited SiOx based resistive switching memory device was investigated. Reduction in thickness cause thinner filamentary path formation during ON-state by controlling the vacancy defects. Thinner filament cause lowering of operation current from 500 μA to 100 μA and also improves the reset current (from >400 μA to <100 μA). Switching material thickness reduction also cause the forming free ability in the device. All these electrical parametric improvements enhance the device reliability performances. The device show >200 dc endurance, >3-hour data retention and >1000 P/E endurance with 100 ns pulses.
Protall (An Intelligent, multi-sensor, comprehensive obstacle avoidance system for automobiles and UAVs)
Authors: Dhelia Manav , Chaughule Saurabh , Choraria Amit , Hariharan Arjun , Manohara Pai M M 
Published: January 2022

Abstract
In this era of Artificial Intelligence and Automation, manufacturing and testing of self-driving cars and autonomous delivery of parcels to the desired location with the help of UAVs have a considerable amount of growth in the industry. This advancement in technology also raises safety issues due to the failure of sensors to detect the object or sometimes because of the dynamic environment. Protall (Protect-all) is an integrated solution for UAV and automobile vehicles to provide ultimate safety to both itself and its surroundings. With the strategic sensor integration and its intelligent processing, it aims to produce controlled output and hence, ensures to prevent any possible failures from occurring. The system constantly reacts to the environment and maintains comprehensive interaction with the user thus, enabling it to handle any dynamic situation and hence, it emerges as an optimal solution for Vehicles and UAVs.
Analysis of Total Harmonic Distortion and implementation of Inverter Fault Diagnosis using Artificial Neural Network
Authors: Manjunath T G , Vikramathithan A C , Girish H 
Published: January 2022

Abstract
As power electronics devices dependability is very significant to guarantee Multi Level Inverter (MLI) systems stable functioning, it is imperative to identify and position faults as promptly as possible. Due to the fault occurrences, the Total Harmonic Distortion (THD) on the system gets a hit. In this perspective, to improve fault diagnosis accuracy and efficient working of a Cascaded Multi level Inverter System (CHMLIS), a quick and accurate fault diagnosis strategy with an optimized training algorithm using Artificial Neural Network (ANN) is presented. Also, Total Harmonic Distortion (THD) is analyzed for each switch Fault simulated using MATLAB/Simulink and the results are presented. Results shows the efficacy of Algorithm in identifying the fault. The auxiliary cell is replaced while the fault occurs in the main cell thus making the uninterrupted working of the Multi-Level Inverter (MLI) in the Induction Motor Drive (IMD).
Real time Face Detection and Optimal Face Mapping for Online Classes
Authors: Archana MCP , Nitish CK , Harikumar Sandhya 
Published: January 2022

Abstract
The main objective of this paper is to provide a web-based tool for identifying faces in a real-time environment, such as Online Classes. Face recognition in real-time is now a fascinating field with an ever-increasing challenge such as light variations, occlusion, variation in facial expressions, etc. During the current pandemic scenario of COVID-19, the demand for online classrooms has rapidly increased. This has escalated the need for a real-time, economic, simple, and convenient way to track the attendance of the students in a live classroom. This paper addresses the aforementioned issue by proposing a real-time online attendance system. Two alternative face recognition algorithms are perceived in order to develop the tool for realtime face detection and recognition with improved accuracy. The algorithms adopted are Local Binary Pattern Histogram(LBPH) and Convolutional Neural Network (CNN) for face recognition as well as Haar cascade classifier with boosting for face detection. Experimental results show that CNN with an accuracy of 95% is better in this context than LBPH that yields an accuracy of 78%.
Automatic Remote Farm Irrigation System with WSN and Weather Forecasting
Authors: Sai Kilaru Aditya , Madishetty Prem , Vardhan Naidu Yamala Harsha , Giriraja C V 
Published: January 2022

Abstract
The paper showcases the system used for automating agriculture using wireless sensor network (WSN) and weather prediction. WSN, is more efficient than IoT as it avoids connecting all the sensor nodes directly to Internet, thus reducing the traffic over Internet and energy consumption of the sensor network. The system consists of a clustered tree topology to increase the range of operation, connectivity and easily connect new nodes dynamically. The sensor nodes being the leaves, local gateways being the branches and the global gateway being the root node. The system is implemented using cost effective micro-controllers, robust communication modules and reliable data showcasing platforms. Our implementation uses weather prediction to minimize the water needed for irrigation. Thereby minimizing cost and increasing efficient usage of resources.
A Review on Classifications of Tracking Systems in Augmented Reality
Authors: Bhakar Suman , Pallavi Bhatt Devershi , Singh Dhaka Vijaypal , Kumar Sarma Yogender 
Published: January 2022

AbstractAugmented Reality (AR) is now becoming an exceptional technology that offers a new world. Users to enable their senses to feel, listen and see the surroundings in different and better ways witness a unique experience. AR technology is used to superimpose the real view of the user with the virtual scenes. This research work illustrates the core details of AR, its definition, history, and development process. In addition, discusses ideas having various approaches during utilization of AR frameworks along with glyph tracking system. Current applications of AR technology completely depend on the proper utilization of AR frameworks.