bluuebunny/crossref_metadata_2025 huggingface dataset:
Sociological Analysis of the Determinants of Centripetal Migration Processes in the Volga Federal District
Authors:   Lobachevsky State University of Nizhny Novgorod, Voronin Gennady , Mamlev Dmitry ,   Nizhny Novgorod Institute of Management – Branch of the Russian Academy of National Economy and Public Administration under the President of the Russian Federation
Published: January 2023

The article presents the results of the analysis of socio-economic indicators of the functioning of the regions of the Volga Federal District, Moscow and the Moscow region. After the collapse of the USSR, the territorial distribution of migration processes in the western part of Russia contributes to the aggravation of the demographic situation in the Volga regions. The presented work is the first stage of the study of migration processes in large cities of the Volga Federal District, which provides for the study of factors that form trends in modern migration processes in the Volga region. Based on the materials of official statistics for the period 2000–2019, the coefficients of migration loss and population growth, as well as the migration attractiveness of the region, are derived. Additionally, the article provides a comparative and causal analysis of the following indicators characterizing the comfort level of residents of large cities in the region: the level of wages, the unemployment rate (as a percentage), the number of hospitals and polyclinic medical institutions per 10,000 population, the average area of residential premises per 1 inhabitant (sq.m. m), the number of operating enterprises and organizations per 10,000 population, the turnover of retail services per person (in actual prices, rubles), coverage of the preschool-age population by educational institutions (as a percentage), as well as the ratio of the monthly average salary of a resident of the region to the cost of 1 sq. m. in the primary and secondary housing market (typical premises). Based on the results of the analysis of these indicators, as well as the study of the dynamics of changes in the rates of migration loss/population growth in the regions under consideration, patterns have been identified that allow us to identify factors influencing migration processes in the Volga region. The projection of these factors on ongoing federal projects allows us to assess the possibility of managing these processes.
Russian Wanderers. Images of Orthodox Divine Service on the Roads
Authors:   Saint Petersburg Mining University, Dorofeev Daniil
Published: January 2023

The article is devoted to the study of the diverse manifestations of the phenomenon of religious wandering in Russian culture, especially the 19th century, with the study of their external aesthetic image, way of life, Orthodox foundations and principles, characteristic language, methods of organization and institutionalization. The author considers the concept of "wanderer" as one of the fundamental concepts in world and especially Russian culture, emphasizing its polysemantic and heterogeneous nature, and also pointing out its special value as a visible embodiment of the Orthodox understanding of God, man and the world, permeated with the spirit of hesychasm. At the same time, the author confines himself to considering in his article mainly folk religious forms of Russian wandering, which were widely developed and spread in the 19th century. It was at this time that the theme of wandering becomes one of the main ones in Russian and European literature. So, the author dwells in detail on the image of a wanderer in Dostoevsky's novel "The Teenager", on the work of N. Leskov "The Enchanted Wanderer", on the works of I. Shmelev. Particular attention is paid in the article to Russian painting of the mid-second half of the 19th century, in which the figure of the wanderer is one of the central ones, which all leading artists turned to in their paintings. The central place in the study (in the 2nd part of the article) is devoted to the consideration of the richest material of the book "Wandering Rus' for Christ's sake" by S.V. Maksimov, in which the features of life, image, language of the most diverse forms of Russian religious wandering are collected and preserved for us in detail. The author also refers to the composition "Frank stories of a wanderer to his spiritual father" to show the influence of hesychasm on the formation of the idea of wandering, in its highest and most perfect religious form, as a fundamental Orthodox value worthy of being presented and recognized as a separate form of holiness.
English First Additional Language: Teachers’ written feedback practices in multi-grade classrooms in rural South African primary schools
Authors: Taole Matshidiso Joyce
Published: January 2023

Multi-grade teaching is a widely accepted phenomenon in both developing and developed countries. In this article I report on a phenomenological inquiry into written feedback practices of teachers in multi-grade classrooms and how they described and interpreted their feedback practices pertaining to learners’ writing activities in English First Additional Language. Written feedback is widely believed to be central to learners’ learning and achievement. Feedback is assumed to change learners’ thinking or behaviour towards their work and is regarded as a tool to focus their attention on improving their learning. Despite the benefits of written feedback, however, there is a widespread belief that many learners are disengaged from the feedback they receive. The population in this study comprised 4 English First Additional Language teachers from 4 multi-grade primary schools. Content analysis was used to analyse the data collected from the participants through semi-structured interviews. The findings indicate that participants differed in their understanding of what was involved in feedback, and that there was a shared view that feedback was a product and not viewed as a process that could be used towards developing learners’ learning. It was also shown that feedback practices used by the teachers of multi-grade classes included providing correct answers and providing delayed feedback to learners. This study adds to the ongoing debates on multi-grade teaching and attempts to shed some light on the nature and the type of feedback that could motivate and enhance learners’ learning in multi-grade classrooms.
Model of population densification in consolidated collective housing in the urban fabric from Juliaca city
Authors: Mamani Grover Marin , Pérez Francisco Curro , Mamani Vitaliano Enríquez , Suaquita Jhon Richard Huanca , Ochoa Kely Lelia Cotacallapa , Condori Gladys Marleny Auquitias
Published: January 2023

The analysis of the peripheries reveals the existence of important problems of residential precariousness and exclusion for a high-density collective housing project. The purpose is to model the population density in consolidated collective housing in the urban fabric of the city of Juliaca. Size and density are fundamental characteristics that define any city, but they have not always received the same attention. Although density figures prominently in any urban planning manual as a key variable governing the urban environment, city size is often treated as an immeasurable factor beyond the control of planning. However, it is unclear whether these two variables can be analyzed independently, as the correlation between the two is significant. To assess how scale and density interact, a new approach was adopted to new forms of modern urbanization. The results obtained from a sample of 96 blocks allow the classification of the two districts in terms of aggregation, which will be used to determine the location of the high-density residential project by analyzing one side of the site according to the calculated proportion. of housing. the combined urban development is located in the South, so the residential district will be located on the North side, as it is less consolidated and characterized by one, two, and three-story buildings according to the kernel density analysis.
Regulation of Cytotoxic Immune Effector Function by AJ3 Probiotic Bacteria in Amyotrophic Lateral Sclerosis (ALS)
Authors: Chen Po-Chun , Kaur Kawaljit , Ko Meng-Wei , Huerta-Yepez Sara , Jain Yash , Jewett Anahid
Published: January 2023

Our recent studies indicated that amyotrophic lateral sclerosis (ALS) patients suffer from significantly elevated levels of interferon-gamma (IFN-γ) secretion by natural killer (NK) and CD8+ T cells, which may be responsible for the immune-pathologies seen in central nervous system and in peripheral organs of the patients. In order to counter such elevated induction of IFN-γ in patients we designed a treatment strategy to increase anti-inflammatory cytokine interleukin-10 (IL-10) by the use of probiotic strains which significantly increase the levels of IL-10. Therefore, in this paper we demonstrate disease specific functions of Al-Pro (AJ3) formulated for the adjunct treatment of auto-immune diseases including ALS, and compared the function with CA/I-Pro (AJ4) for the treatment of cancer and viral diseases, and NK-CLK (AJ2) for maintenance of immune balance and promotion of disease prevention. The three different formulations of probiotic bacteria have distinct profiles of activation of peripheral blood mononuclear cells (PBMCs), NK, and CD8+ T cells, and their induced activation is different from those mediated by either IL-2 or IL-2 + anti-CD16 monoclonal antibodies (mAbs) or IL-2 + anti-CD3/CD28 mAbs. IL-2 + anti-CD16 mAb activation of PBMCs and NK cells had the highest IFN-γ/IL-10 ratio, whereas IL-2 combination with sAJ4 had the next highest followed by IL-2 + sAJ2 and the lowest was seen with IL-2 + sAJ3. Accordingly, the highest secretion of IFN-γ was seen when the PBMCs and NK cells were treated with IL-2 + sAJ4, intermediate for IL-2 + sAJ2 and the lowest with IL-2 + sAJ3. The levels of IFN-γ induction and the ratio of IFN-γ to IL-10 induced by different probiotic bacteria formulation in the absence of IL-2 treatment remained much lower when compared to those treated in the presence of IL-2. Of note is the difference between NK cells and CD8+ T cells in which synergistic induction of IFN-y by IL-2 + sAJ4 was significantly higher in NK cells than those seen by CD8+ T cells. Based on these results, sAJ3 should be effective in alleviating auto-immunity seen in ALS since it will greatly regulate the levels and function of IFN-γ negatively, decreasing overactivation of cytotoxic immune effectors and prevention of death in motor neurons.
A NEW METHOD FOR PREDICTION OF THE CONDENSATE BANKING SIZE AROUND A GAS CONDENSATE WELL
Authors: Sheydaeemehr Mohammad , Sedaee Behnam
Published: January 2023

The main problem in the production operation from gas condensate reservoirs is well deliverability loss due to condensate formation and banking around the well as pressure falls below the dew point pressure. It is a common practice to calculate gas condensate well production performance based on the three flow regions. The size of Region 1 (a near wellbore region with high condensate saturation) plays an essential role in the productivity loss of gas condensate wells. In this study, a radial reservoir model is constructed using a compositional model to perform a number of analyses on a single well model. Real data from a large gas condensate reservoir located in the Middle East is applied to the reservoir model. Then, the impacts of affecting parameters are studied using the validated reservoir model. The results of this study show that key parameters, which have a significant effect on the size of condensate banking around gas condensate wells, are rock permeability, rock porosity, reservoir thickness, cumulative gas production, and pressure-dependent fluid parameters such as interfacial tension, viscosity, and density. Finally, using dimensionless analysis, a new relation is proposed to predict the radius of condensate banking (Region 1). This procedure is applicable in similar cases.
Procedimientos en Cirugía General
Authors:   Interhospital S.A., Auria Aspiazu Danna Vannessa , Domínguez Freire Nervo David ,   Hospital Luis Vernaza, Salcedo Faytong Yolanda Ines ,   Hospital Básico Playas, Salcedo Faytong Yolanda Ines ,   Hospital Básico Playas, Calderón Reza Pablo Francisco ,   Hospital de especialidades Teodoro Maldonado Carbo
Published: January 2023

Es importante conocer las características clínicas; no obstante, continuamente es elemental una intervención quirúrgica para tomar una biopsia, obteniendo así el diagnóstico definitivo, el cual nos guiará hacia la técnica más adecuada. Por otro lado, se procura así una intervención quirúrgica óptima dando buenos resultados y sobre todo genera gran satisfacción por los resultados estéticos
RADIONUKLIDA CESIUM SEBAGAI INDIKATOR PENENTUAN BURNUP BAHAN BAKAR U3Si2/Al SECARA MERUSAK
Authors: Ginting Aslina Br. , Yanlinastuti Yanlinastuti , Boybul Boybul , Supardjo Supardjo , Sungkono Sungkono
Published: January 2023

RADIONUKLIDA CESIUM SEBAGAI INDIKATOR PENENTUAN BURNUP BAHAN BAKAR U3Si2/Al SECARA MERUSAK. Penentuan burn-up secara merusak dilakukan dengan metode pemisahan 137Cs dalam PEB U3Si2/Al densitas 4,8 gU/cm3 pasca iradiasi.Penentuan burn-up secara merusak bertujuan untuk membuktikan kesesuaian besar burn-up yang diperoleh dengan metode tak merusak maupun menggunakan Origen Code. PEB U3Si2/Al dengan kode CBBJ 251 dipotong pada bagian top, middle dan bottom secara duplo dengan berat masing-masingTop-1= 0,049 gPEB dan Top-2=0,058 gPEB, Middle-1= 0,055 gPEB dan Middle-2= 0,024 gPEB, serta Bottom-1= 0,056 gPEB dan Bottom-2=0,075gPEB. PEB U3Si2/Al dengan berat tersebut dilarutkan menggunakan asam sehingga diperoleh larutan bahan bakar U3Si2/Al yang mengandung hasil fisi 134Cs, 137Cs, 235U dan isotop lainnya. Larutan bahan bakar U3Si2/Al dipipet 1mL kemudian ditransfer dari hotcell ke R.135 untuk dilakukan pemisahan 137Cs dari 235U dengan metode penukar kation menggunakan zeolit Lampung. Larutan bahan bakar U3Si2/Al dipipet 100 µL dan dimasukkan ke dalam vial secara duplo kemudian ditambahkan zeolit Lampung 1000 mg untuk dilakukan proses penukar kation.Hasil pemisahan diperoleh isotop 137Cs dalam fasa padat, sedangkan uranium dan isotop lainnya dalam fasa cair. Besarnya aktivitas (Bq) isotop 137Cs diukur dengan Spektrometer-g dan selanjutnya digunakan untuk perhitungan burn-up. Hasil perhitungan burn-up PEB U3Si2/Al densitas 4,8 gU/cm3 dengan metode merusak diperoleh sebesar 26,9714%; 55,1431%; dan 37,8855% masing-masing untuk potongan top, middle dan bottom dengan burn-up rerata 40%. Besaran ini tidak jauh berbeda dengan burn-up yang diperoleh dengan metode tak merusak menggunakan gamma scanning yaitu sebesar 24,4%; 52,7% ; 37,6 % masing-masing untuk potongan top, middle dan bottom dengan burn-up rerata 38,23% serta dengan burn up yang dihitung menggunakan Origen code yaitu sebesar 40%.Kata kunci :  Metode merusak, pemisahan, cesium, burn-up
PEMISAHAN DAN ANALISIS 137Cs DALAM LARUTAN ELEMEN BAKAR UJI U3Si2/Al DENSITAS 4,8 gU/cm3 DENGAN BURNUP 60% MENGGUNAKAN METODE PENUKAR KATION
Authors: Yanlinastuti Yanlinastuti , Nugroho Arif , Ginting Aslina Br. , Boybul Boybul , Noviarty Noviarty , Haryati Iis , Jamaludin Agus , Noerpitasari Erlina , Kriswarini Rosika
Published: January 2023

PEMISAHAN DAN ANALISIS 137Cs DALAM LARUTAN ELEMEN BAKAR UJI U3Si2/Al DENSITAS 4,8 gU/cm3 DENGAN BURNUP 60% MENGGUNAKAN METODE PENUKAR KATION. Telah dilakukan pemisahan dan analisis isotop cesium dalam PEB U3Si2/Al densitas4,8 gU/cm3 kode CBBJ 250 burnup 60% dengan metode penukar kation menggunakan zeolit Lampung dan resin Dowex. Tujuan penelitian adalah untuk mengetahui berat 137Cs di dalam PEB U3Si2/Al densitas 4,8 gU/cm3 burnup 60%. Potongan PEB U3Si2/Al pada bagian Bottom (B), Middle (M) dan Top (T) masing-masing secara duplo dilarutkan dengan HCl 6 M dan HNO3 6 M sehingga diperoleh larutan bahan bakar U3Si2/Al. Pemisahan 137Cs dengan metode penukar kation menggunakan zeolit Lampung, larutan bahan bakar sebanyak 50 µL dalam suasana 2 mL HNO3 0,1 M diukur dengan spektrometer gamma untuk mengetahui berat 137Cs awal. Larutan tersebut dimasukkan ke dalam kolom yang telah berisi zeolit Lampung kemudian dialirkan dengan kecepatan0,3 mL/menit. Hasil pemisahan Isotop 137Cs terikat pada zeolit dan efluen mengandung isotop hasil fisi lainnya. Pemisahan menggunakan resin Dowex, larutan bahan bakar ditambahkan Cs carrier dan HCl kemudian dimasukkan ke dalam kolom berisi resin R-Cl-. Efluen yang keluar dari kolom dimasukkan kembali ke dalam kolom berisi resin R-NH4+. Isotop 137Cs yang terikat resinR-NH4+di dielusi menggunakan HCI 1 M, kemudian diukur isotop 137Cs menggunakan spektrometer gamma. Hasil pengukuran isotop 137Cs pemisahan menggunakan zeolit Lampung diperoleh masing-masing berat isotop 137Cs dengan kode B1=0,1772 µg/g; B-2=0,1635 µg/g; M-1=0,1395 µg/g; T-1=0,1230 µg/g dan T-2=0,1036 µg/g dengan recovery sebesar B-1=99,54%; B-2=98,98%; M-1=98,99%; T-1=99,38% dan T-2=98,98%. Sementara itu, berat rerata isotop 137Cs pemisahan dengan resin Dowex diperoleh masing-masing sebesar B-1=0,1575 µg/g; B-2=0,1470 µg/g; M-1=0,1263 µg/g; T-1=0,1140 µg/g dan T-2=0,0952 µg/g dengan recovery sebesar B-1=94,80%; B-2=95,35%; M1=97,94%; T-1=98,15% dan T-2=98,89%. Dari berat 137Cs yang diperoleh disimpulkan bahwa metode pemisahan isotop 137Cs dalam larutan bahan bakar U3Si2/Al menggunakan zeolit Lampung maupun resin Dowex tidak mempunyai perbedaan yang signifikan, sehingga kedua metode tersebut dapat digunakan untuk pemisahan 137Cs dalam bahan bakar nuklir. Hasil analisis ini digunakan melengkapi data untuk mengitung nilai burnup. Kata kunci: pemisahan cesium, penukar kation, U3Si2/Al, zeolit Lampung, resin Dowex.
SIMULASI UJI TAK MERUSAK PELAT ELEMEN BAKAR PASCA IRADIASI MENGGUNAKAN METODE PENETRANT TEST
Authors: Wicaksono Yusuf Gigih , Rahmatullah Helmi Fauzi , Artika Refa , Ismarwanti Sri , Sigit Rohmad
Published: January 2023

SIMULASI UJI TAK MERUSAK PELAT ELEMEN BAKAR PASCA IRADIASI MENGGUNAKAN METODE PENETRANT TEST. Pengujian pasca iradiasi baik secara tidak merusak maupun merusak bertujuan untuk mengetahui unjuk kerja bahan bakar  nuklir selama iradiasi di reaktor. Saat ini, uji tak merusak di Instalasi Radiometalurgi dilakukan melalui pengamatan visual, radiografi sinar-X digital dan ultrasonic test dalam inspeksi potensi cacat atau anomali pada pelat elemen bakar (PEB). Keterbatasan radiografi sinar-X dan ultrasonic test dalam mendeteksi cacat permukaan menyebabkan perlunya pengembangan metode uji tak merusak lainnya, sehingga perlu dilakukan simulasi penggunaan metode penetrant test pada PEB dummy. Tujuan simulasi ini adalah mengetahui kemampuan metode penetrant test untuk melengkapi data uji tak merusak yang telah dimiliki sebelumnya sekaligus mengkaji kapabilitas hot cell dalam penerapan metode tersebut. Pada penelitian ini telah dilakukan simulasi menggunakan PEB dummy dengan cacat artifisial. Simulasi dilakukan dengan metode water washable visible penetrant dan solvent removable visible penetrant. Tahapan simulasi penerapan penetrant test antara lain precleaning, apply penetrant, dwell time, remove penetrant, apply developer, inspect dan post cleaning. Hasil simulasi dengan metode penetrant test mampu mendeteksi cacat permukaan berupa crack dan porosity, tetapi tidak dapat mendeteksi cacat blister. Berdasarkan hasil simulasi tersebut, dapat disimpulkan bahwa penetrant test dapat menjadi alternatif metode untuk melengkapi hasil pengujian tak merusak menggunakan metode radiografi sinar-X dan ultrasonik. Selain itu, untuk mengakomodasi penerapan metode penetrant test di hot cell IRM, diperlukan pengembangan fasilitas dukung dengan tetap memperhatikan kemudahan handling dan sistem keselamatan.Kata kunci: Uji tak merusak, penetrant test, PEB dummy, simulasi.
How to Write and Publish Quality Articles? Insights for Early-Stage Researchers
Authors: Kovid Raj K
Published: January 2023

How to write and publish quality papers and that too in a quality journal, is a puzzle that every scholar and researcher, particularly in their early careers, struggles to solve as early as possible. Here, I want to outline, in brief, how one can work toward writing and publishing a good paper. The writing style and structure of a paper depends on the type of manuscript, which can be broadly be categorised as empirical, and non-empirical. Empirical papers, which use empirical data to answer the research questions and/or testing hypotheses, are the most used form of published work in scientific journals. The types of non-empirical papers include conceptual, theoretical, review-based, case study, perspective, notes, commentary etc.
La representación mediática en los medios digitales de la violencia en contra de los líderes sociales en Colombia, 2018-2021
Authors: Cárdenas Ruiz Juan David
Published: January 2023

La representación mediática de la violencia en contra de líderes sociales tiene una alta incidencia en su comprensión por parte de la opinión pública y la acción gubernamental para enfrentar dicha problemática. Gran parte de ese proceso se adelanta hoy en día en los medios digitales y las redes sociales. El estudio busca explorar las narrativas de los medios digitales, intentando encontrar patrones característicos de construcción de la realidad desde una perspectiva teórica de la comunicación política digital. En este escenario, el Observatorio de medios de la Universidad de La Sabana realizó un análisis de contenido a 905 publicaciones de medios digitales buscando la manera en cómo estos construyen la representación mediática de la violencia en contra de lideresas y líderes sociales en Colombia. Se concluye que la representación mediática de estos actos violentos por parte de los medios digitales puede contribuir a la construcción de una opinión pública con una visión parcial del problema, despojándolo de su causalidad política y proveyéndolo de atributos de orden público y drama social.
Voluntourism: draft tourist offer
Authors:   Taras Shevchenko national University of Kyiv, Boiko Y. , Kupach T. ,   Taras Shevchenko national University of Kyiv
Published: January 2023

The restoration of tourism in Ukraine after the war will be one of the key problems for the state and business. It is predicted that the flow of tourists will increase in the post-war period. Voluntary tourism can become an interesting direction of tourism during the restoration of Ukraine. There were many interesting places to visit in Ukraine, but the Russian soldiers destroyed many things, such as, for example, the "XII Months" zoo, which suffered a lot as a result of the occupation of Kyiv region. A zoo "XII Months" was a modern park where the residents of the surrounding areas liked to spend their weekends, here all the conditions were provided for visitors to immerse themselves in the interesting world of animals on any of the 365 days of the year. All safety conditions for animals and visitors were observed, but since February 24, the zoo was under occupation, and was cut off from all the benefits of civilization for about a month. That's when life in the zoo seemed to stop. Animals began to die, lack of food, cold, etc. led to negative consequences for the natural environment. To restore the functioning of the park, a project of solidarity work was proposed, which aims to attract tourists from different countries of the world to help with restoration, care for animals, as well as to get acquainted with Ukrainian culture, customs and cuisine.
A Machine Learning Technique for Abstraction of Modules in Legacy System and Assigning them on Multicore Machines Using and Controlling p-threads
Authors: T R Vinay , Chikkamannur Ajeet A
Published: January 2023

Hardware and Software technology has undergone a sea-of-change in recent past. Hardware technology has moved from single-core to multi-core machine, thus capable of executing multi-task at the same time. But traditional software’s (Legacy system) are still in use today in business world. It is not easy to replace them with new software system as they carry loads of knowledge, business value with them. Also, to build new software system by taking the requirements afresh involves lot of resources in terms of skilled human resources, time and financial resources. At last the customer may not have confidence in this new software. Instead of building a new software, an attempt is made to develop a semi-automated methodology by learning about the program itself (machine learning about the program) to abstract the independent modules present in the same abstraction level (implementation level) and recode the legacy program (single threaded program) into multi-threaded parallel program. A case study program is considered and execution time is noted and analyzed for both the original program and reengineered program on a multi-core machine.
New Biomedical Applications Approach using Shape Memory Polymers for Muscles Rehabilitation and the Accompanying Wounds after Severe Bone Fractures
Authors: Hashim Warqaa , Al-Sultan Tahani G. , Alhialy Azza , Al-Sawaff Zaid H. , Kandimerli Fatma
Published: January 2023

This article introduces a new approach for modern applications of medical devices using shape memory polymers to aid in the rehabilitation of muscles and injuries attached to severe fractures.  The group of heaters connected with the splint controls the temperature and humidity inside the affected area. Microcontrollers were attached to the polymer plate in order to directly control the required parameters and changes in addition to giving direct commands to the connected sensors. Because of the property of changing the outer shape of the polymer after being exposed to a certain temperature and returning to the normal shape after the removal of the external influence, the designed polymer plate applies light intermittent pressure on the wound area, muscles, and surrounding tissues, which helps to speed up the rehabilitation of these muscles, especially after She suffered from stiffness due to lack of movement during the period of treatment.
Deciphering Voice of the Customer using Text Analytics and Sentiment Analysis: An Interpretable Review Rating Prediction using RoBERTa
Authors: U.C. Harish , N.M. Dhanya
Published: January 2023

In this era of cut-throat competition among traditional and newer digital organizations, capturing, listening, and understanding customer voices are critical for success in the marketplace. The challenge to decipher the voice of the customer (VOC) has multiplied many times today, as now the number of customer reviews are present in multiple  platforms and the data to be analyzed is huge. Sentiment analysis, and text analytics using machine learning, deep  learning tools and transformer-based tools can be applied to gather meaningful insights from these data. This paper applies the traditional machine learning tools of the Naive Bayes classifier, Random Forest and AdaBoost to predict  the customer review ratings. These results are compared with deep learning methods of CNN, RNN and Bi-LSTM and  transformer-based approaches of BERT, DistilBERT and RoBERTa. The results show that RoBERTa has the highest accuracy among these methods. Paper also uses the explainable AI tool of LIME to understand the customer sentiments deeper in terms of why customers are giving a particular rating to the product. Business organizations will continue to use more and more AI tools to understand the customer feedback and the attempt in this paper is to learn how we can make predictions faster and more accurately.
Development of Deep Learning based Intelligent Approach for Credit Card Fraud Detection
Authors: Krishnan V. Gokula , Saradhi M. V. Vijaya , Prakash T. A. Mohana , Kannan K. Gokul , Julaiha AG. Noorul
Published: January 2023

Credit card fraud (CCF) has long been a major concern of institutions of financial groups and business partners, and it is also a global interest to researchers due to its growing popularity. In order to predict and detect the CCF, machine learning (ML) has proven to be one of the most promising techniques. But, class inequality is one of the main and recurring challenges when dealing with CCF tasks that hinder model performance. To overcome this challenges, a Deep Learning (DL) techniques are used by the researchers. In this research work, an efficient CCF detection (CCFD) system is developed by proposing a hybrid model called Convolutional Neural Network with Recurrent Neural Network (CNN-RNN). In this model, CNN acts as feature extraction for extracting the valuable information of CCF data and long-term dependency features are studied by RNN model. An imbalance problem is solved by Synthetic Minority Over Sampling Technique (SMOTE) technique. An experiment is conducted on European Dataset to validate the performance of CNN-RNN model with existing CNN and RNN model in terms of major parameters. The results proved that CNN-RNN model achieved 95.83% of precision, where CNN achieved 93.63% of precision and RNN achieved 88.50% of precision.
Analysis for Symptoms of Human Fall using Pre-Processing and Segmentation based on Deep Learning Architectures
Authors: Kumar G Raj , Jinila Y Bevish
Published: January 2023

By building sensor-based alert systems, physical therapists can not only decrease the after-fall repercussions but even save lives.Older people are prone to several diseases, and falling is a regular occurrence for them during this period.Various fall detection systems have recently been developed, with computer vision-based approaches being one of the most promising and effective. Here, the sensor-based data has been analysed for a patient's human fall symptoms. This data has been pre-processed using Gaussian filtering with kernel neural network in which the data has been normalized and trained based on neural network. The trained normalized data has been segmented using encoded Stacked Deconvolutional Network (EnSt-DeConvNet). We found that the suggested method predicts such fall symptoms with the highest accuracy from sensor data. Other algorithms' accuracy results, on the other hand, are also fairly close. Experiments reveal that the suggested technique, when compared to other generally utilized techniques based on multiple cameras fall dataset, produced reliable findings and that our dataset, which consists of more training samples, produced even better results. Experimental results showaccuracy of 96%, Precision of 94%, Recall of 88% and F-1 score of 82%, computational time of 69%.
Relevant Theoretical Characteristics and Trends of the “Field” Era of Live E-commerce: Knowledge Graph Analysis based on Cite Space
Authors: Huang Wenjun , Leong Yee Choy , Ismail Noor Azlin
Published: January 2023

Existing research primarily focuses on the two core elements of “people” and “goods”, but research on the “field” element is relatively rare and scattered. Therefore, this paper will analyse the relevant research literature in the core collection database based on the Cite Space software from the two dimensions of field theory and place attachment theory. To systematically review and sort out the theoretical basis of the influence of consumers’ online behavior in live streaming and service scenarios. The current research’s characteristics and status quo are reflected through literature citation data and keyword co-occurrence. The research conclusions: the model application and interpretation situation of the theoretical basis in the live e-commerce “field” era keeps pace with the times; the research on consumer behavior in the live streaming field needs to be further expanded; Few scholars pay attention to the consumer attachment emotion formed by the live streaming scene construction.
Modeling of the Payload Signal Generator of the UAV Radio Transmitter based on the Phase Distortion Autocompensator
Authors: Dmitry I. Surzhik , Oleg R. Kuzichkin , Gleb S. Vasilyev
Published: January 2023

A block diagram of a payload signal generator of a UAV radio transmitter with an autocompensator for phase distortion of a digital computational synthesizer with improved spectral characteristics compared to known solutions is considered. Schematic diagrams and circuit design models of the main paths of the shaper in the Micro-Cap environment with the analysis of the characteristics of the necessary radio elements and microcircuits have been developed. The simulation of the device operation in the frequency domain is carried out. Based on the simulation results, the conditions for selecting the parameters of the shaper to achieve the greatest compensation of phase distortions are formulated.
A Framework for Computerized Adaptive Assessment based on Trajectory Driven Pedagogy Implemented in an Engineering Course
Authors: Asthana Pallavi , Kumar Anil , Tanwar Sudeep , Mishra Sumita
Published: January 2023

Engineering education needs to be flexible with the changing technology, and it must blend traditional and new teaching pedagogy for the overall knowledge creation in the students. A survey of prevalent experiential learning methods has shown tremendous potential to improve engineering students' learning. However, existing experiential learning methods are hard to integrate with current teaching-learning process at Amity University, Uttar Pradesh, Lucknow Campus, India. A pilot study conducted during Power plant Instrumentation taught in the seventh semester of the Electrical and Electronics undergraduate program balances the current teaching method with the proposed Trajectory -driven pedagogy as an alternative teaching pedagogy. A trajectory driven computerized adaptive assessment procedure for teaching has been proposed in this paper. The system follows a trajectory of courses to generate the subsequent questions from the vast database of questions. A sequence of questions is guided by Concept Map which represents the questions from three courses in a hierarchical manner. Analysis of students' assessments shows that the proposed methodology could is accurate for quantitative measurement of the course learning outcomes in a summative assessment.
An Evaluation of Deep Learning-Based Object Identification
Authors: Kolluri Johnson , Das Ranjita
Published: January 2023

Identification of instances of semantic objects of a particular class, which has been heavily incorporated in people's lives through applications like autonomous driving and security monitoring, is one of the most crucial and challenging areas of computer vision. Recent developments in deep learning networks for detection have improved object detector accuracy. To provide a detailed review of the current state of object detection pipelines, we begin by analyzing the methodologies employed by classical detection models and providing the benchmark datasets used in this study. After that, we'll have a look at the one- and two-stage detectors in detail, before concluding with a summary of several object detection approaches. In addition, we provide a list of both old and new apps. It's not just a single branch of object detection that is examined. Finally, we look at how to utilize various object detection algorithms to create a system that is both efficient and effective. and identify a number of emerging patterns in order to better understand the using the most recent algorithms and doing more study.
Tuberculosis Prediction by Machine Learning Techniques
Authors: Godiyal Kuldeep , Pokhriyal Surabhi
Published: January 2023

Tuberculosis is one of the top reasons of death all over the planet. Mycobacterium tuberculosis, bacteria that infects the lungs, is what causes it. For professionals working in the medical field, accurately identifying and timely predicting tuberculosis are major challenges. The course of treatment also varies from patient to patient since occasionally a patient develops drug resistance. Doctors will be given algorithmic support while using machine learning to help them diagnose, treat patients appropriately, and make quicker and better judgments. This paper discusses the many tuberculosis causes and symptoms as well as how accurate and fast prediction and diagnostic investigations have been carried out in recent years with the aid of machine learning (ML) techniques
Approximation Knowledge-Based Recurrent Neural Network for Estimating N-Terminal Reliability
Authors: Anuradha Anuradha , Solanki A. K. , Kumar Harish
Published: January 2023

The main problem dispersed with in this paper is to find a novel method for the improvement in the reliability analysis of Computer Network. Reliability prediction are estimated during the life cycle of a computer network with the aim of estimating failure. In designing a variable size network, the serviceability, availability and reliability of the any network is a primary consideration. The reliability calculation in varying size network is a problem of NP-hard; it requires more calculation and effort with the amplifying no of nodes and links. Many different approaches have been taken for reliability and probability calculation for triumphant communication between any pair of computers. The paper presents a method for identifying n-terminal network reliability based on RNN technique. The method derived in this paper preceding inputs which increases the speed of computation. The approach works efficiently and overcome the difficulties of the previous approaches defined with neural network model and other reliability estimation techniques. It is proposed that the RNN model be used to replace the most time-consuming component of the system reliability evaluation approach. A variable-length sequence input can be handled by RNN. The main goal of this paper is to predict asperity of reliability which is highly correlated with performance of network in any unfavorable conditions.
Statistical Analysis with Machine and Neural Learning-Based Model on Cardiovascular Diseases and Stroke Prediction
Authors: Jayasree Lakkala , Usha D.
Published: January 2023

Several risk factors, such as hypertension, hyperlipidemia, and an irregular heart rhythm, make an early diagnosis of cardiovascular disease challenging. Reducing cardiac risk calls for precise diagnosis and therapy. Clinical practice in the healthcare business is likely to evolve in tandem as a result of advancements in machine learning. Therefore, scientists and doctors need to acknowledge machine learning's significance. The fundamental purpose of this research is to a reliable analyzing Risk Factors for Cardiovascular Disease method that makes use of machine learning. Classifying well-known cardiovascular datasets But, on the other hand, is a job for state-of-the-art machine learning techniques and neural network algorithms. Several statistical and visualization indicators were used to assess the efficacy of the suggested approaches and to determine the optimal machine-learning and neural-network approach. Using these modeling methods acquired high and accurate accuracy on stroke and heart disease prediction.
Applications of Machine Learning for Fake News Detection in Social Networks
Authors: Kumar Ch. N. Santhosh , Sailaja M. , Hussain Md. Ali , Rahman Syed Ziaur
Published: January 2023

The value of online media for getting news is questionable. People seek out and devour news from online media because it is convenient, inexpensive, and widely disseminated. In contrast, it facilitates the widespread distribution of "counterfeit news," or news of lower quality that includes fabricated data. Many people and institutions are negatively impacted by the widespread circulation of false information. As a result, detecting fake news via social media has emerged as a topic of interest for academics. Searching for and reading the news is becoming increasingly convenient as a result of the widespread availability, quick expansion, and widespread dissemination of traditional news outlets and social media. Nowadays, there is a plethora of information that can be found on social media, and it can be difficult to tell what is real and what is not. The distribution costs of releasing news via social media are inexpensive, and anyone can do it. The widespread circulation of false information could have devastating effects on both individuals and communities. Developing a reliable machine learning method for spotting fake news is the focus of this work.
Analysis of the Efficacy of Real-Time Hand Gesture Detection with Hog and Haar-Like Features Using SVM Classification
Authors: Theresa W. Gracy , Prabha S. Santhana , Thilagavathy D. , Pournima S.
Published: January 2023

The field of hand gesture recognition has recently reached new heights thanks to its widespread use in domains like remote sensing, robotic control, and smart home appliances, among others. Despite this, identifying gestures is difficult because of the intransigent features of the human hand, which make the codes used to decode them illegible and impossible to compare. Differentiating regional patterns is the job of pattern recognition. Pattern recognition is at the heart of sign language. People who are deaf or mute may understand the spoken language of the rest of the world by learning sign language. Any part of the body may be used to create signs in sign language. The suggested system employs a gesture recognition system trained on Indian sign language. The methods of preprocessing, hand segmentation, feature extraction, gesture identification, and classification of hand gestures are discussed in this work as they pertain to hand gesture sign language. A hybrid approach is used to extract the features, which combines the usage of Haar-like features with the application of Histogram of Oriented Gradients (HOG).The SVM classifier is then fed the characteristics it has extracted from the pictures in order to make an accurate classification. A false rejection error rate of 8% is achieved while the accuracy of hand gesture detection is improved by 93.5%.
Tukey Regressive Hoover Indexed Deep Shift-Invariant Neural Network for Student Behavior Prediction
Authors: Pushpavalli R. , Mary C. Immaculate
Published: January 2023

Prediction of student performance in the academic field creates significant challenges in developing reliable and accurate diagnosis models. Through the use of online learning behavior data, this paper may assist teachers in identifying students with learning challenges in advance and providing timely assistance. A novel technique called Tukey Regressive Hoover indexed Deep Shift Invariant Structure Neural Network (TRHIDSISNN) Model is introduced for student behaviour analysis with lesser time consumption. Initially, the student data and features are collected and transmitted to the input layer. After that, the features of collected student data are analyzed in hidden layer 1 with help of the Tukey Regression. The correlation between one or more independent features is identified to find the dependent feature. The relevant features are sent to the hidden layer 2. In that layer, the Hoover index is applied for analyzing the training and testing features. Finally, the hidden layer result is sent to the output layer where the hyperbolic tangent activation function is used to classify the data that belongs to that particular class. Based on the classification, the student grade level is predicted as high, medium and low based on their behavior gets displayed. Experimental assessment is carried out using different parameters such as prediction accuracy, false-positive rate, prediction time, and space complexity with respect to the number of student data.  The discussed results show that when compared to state-of-the-art approaches, the suggested TRHIDSISNN model achieves higher accuracy with shorter prediction times.
Spatio-Temporal Human Action Recognition Model using Deep Learning Techniques
Authors: Sarabu Ashok , Santra Ajit Kumar
Published: January 2023

Two-stream human recognition achieved great success in the development of video action recognition using deep learning. Recently many studies have shown that two-stream action recognition is a powerful feature extractor. The main contribution in this work is to develop a two-stream model based on spatial and temporal networks using convolutional neural networks with a convolution long-short term memory. The two-stream model with ImageNet pre-trained weights is used to retrieve spatial and temporal features. Output feature maps of the two-stream model are fused using sum fusion and fed as input to convolutional long-short-term memory. SoftMax function is used to get the final classification score. To avoid overfitting, we have adopted the data augmentation techniques. Finally, we demonstrated that the proposed model performs well in comparison to state-of-the-art two-stream models with an accuracy of 96.1% on UCF 101 dataset and 70.9% accuracy on the HMDB dataset.
Performance Comparison of TR and FSRUWB System Using Particle Filter: Effects of Frequency, Data Rate, Multi-Path and Multi-Channel Communication
Authors: Raskar Vishal B. , Lahudkar Swapnil L.
Published: January 2023

In this study, we introduced a novel scheme based on Transmitted References (TR) and Frequency Shifted Reference (FSR) for ultra-wideband (UWB) system. By taking into account tracking loop-based particle filtering together with a data collecting approach for single and multi-path channel situations, the suggested method is an enhanced model. Each particle's location is determined using this filtering technique, which is then utilised to calculate the timing inaccuracy and regulate the UWB system's timing pulse. Also, it can tackle the multimodal distribution of errors then effectively approximate the optimal solution. The data distribution is discretised via a number of particles that are weighted samples evolving concerning time duration. The simulation results show that, in terms of error rate, number of particles, and delay response, the recommended model of FSR-UWB with particle filter performs better than the TR-UWB with and without considering particle filter.
Deep Learning for User Behaviour Prediction Using Streaming Analytics
Authors: Gayatri Mantri , Satheesh P. , Rao R. Rajeswara
Published: January 2023

Streams of web user interactions reflect behaviour of customers or users of a web application through which a company is being operated online. The interactions may be in the form of visits to web components and even purchases made by users in case of e-Commerce applications. Modelling user behaviour can help the organizations to ascertain patterns of user behaviours and improve their products and services to meet their needs besides making promotional schemes. There are many existing methods for modelling user behaviour. However, of late, deep learning models are found to be more accurate and useful. In this paper a deep learning based framework is proposed for predicting web user behaviour from streams of user interactions. The framework is based on the mechanisms that exploit Recurrent Neural Network (RNN), one of the deep learning approaches, to learn from low-level features of sequential and streaming data. The mechanisms are used to model user interactions and predict the user behaviour with respect to purchasing items in future. In presence of plenty of items, item embeddings is explored for better results. In addition to this, attention mechanisms are employed to achieve RNN model interoperability. The empirical study revealed that the proposed framework is useful besides helping to evaluate different variants of attention mechanisms and item embeddings.