bluuebunny/crossref_metadata_2025_split huggingface dataset:
Endoscopic stenting of bile ducts for preventing mechanical jaundice recurrence in gallstone migration
Authors: Kotovskiy A. E. , Glebov K. G. , Magomedova B. M. , Onnicev I. E. , Hokonov M. A. , Prividentseva A. S. , Mahmudova A. K. , Milyushkova T. M. , Pershin V. V. , Hokonov A. M. 
Published: September 2023

Aim. To study the reasons for stone migration from the gallbladder into the main bile ducts after endoscopic lithoextraction and to develop methods for preventing recurrent mechanical jaundice before performing the cholecystectomy.Material and methods. Delayed cholecystectomy was recommended for 328 patients with calculous cholecystitis and mechanical jaundice after endoscopic papillosphincterotomy, lithoextraction and restoration of adequate bile outflow. 23 patients were readmitted to hospital with recurrent mechanical jaundice caused by recurrent choledocholithiasis. All patients underwent duodenoscopy, endoscopic retrograde cholangiography and lithoextraction within 12 hours after admission. The size of the gallstones, the diameter of the cystic duct, the level of its insertion into the common hepatic duct, and the length of the previously performed papillotomy were considered as possible causes of recurrent choledocholithiasis.Results. Multiple gallstones of 2–4 mm and low cystic duct insertion into the common hepatic duct were detected in all 23 patients. An increase in the diameter of the cystic duct >5 mm was revealed in 18 patients. In 10 patients, the initial papillotomy was <8 mm.Conclusion. The factors predisposing to recurrent mechanical jaundice due to migration of gallstones into the bile duct and indications for its stenting after lithoextraction before cholecystectomy are ultrasound signs of multiple small gallstones, low insertion the dilated cystic duct into the bile duct, and partial, non-extended papillotomy.
Spontaneous perforation in the bile duct system
Authors: Sigua B. V. , Rakita S. Y. , Rud V. Y. , Timofeeva E. G. , Zemlyanoy V. P. 
Published: September 2023

Aim. To evaluate different therapeutic and diagnostic approaches in spontaneous bile duct perforation.Materials and methods. The present study involved the reports on spontaneous perforations in the bile duct system, published from 01.01.2000 to 01.06.2022 and selected by structured search in PubMed/Medline database.Results. As a result, 58 articles describing 71 patients were selected for the study. The most frequent symptoms of the disease included abdominal pain (92.9%), fever (39.4%), and jaundice (33%). The provisional diagnosis was made correctly in 52.1% of cases. The development of the disease was most often associated with choledocholithiasis (38%). Bile ducts perforation of the left hepatic lobe was revealed in 40.6% of cases, perforation of the common bile duct – in 35.5%. Bile duct perforation resulted in peritonitis in 42.2% of patients and bile accumulation or abscess in 30.9%. Encapsulated and intrahepatic accumulation of bile was found in 18.3% of cases. Management of perforations with development of encapsulated and intrahepatic bile accumulation included combined treatment, embracing percutaneous transhepatic drainage and endoscopic methods. Perforation of bile ducts with development of extensive forms of the disease required two-stage treatment. The first stage involved lavage of the abdominal cavity and decompression of the biliary tract. The second stage involved resective and reconstructive surgery to eliminate the cause of the perforation. The mortality rate was 5.6%.Conclusion. Spontaneous perforation in the bile duct system is a rare pathological state appeared as a complication of hepatopancreaticoduodenal diseases. Its management requires greater awareness of abdominal surgeons. The surgical strategy is based on correctly diagnosed and staged treatment tactics, which allows the incidence of complications and mortality rate to be reduced.
Reviewing the Bio-Applications of SrAl2O4:Eu2+, Dy3+ Phosphor
Authors: Maryam Mollazadeh-Bajestani , AmirHossein Bahmanpour , Maryam Ghaffari , Fathollah Moztarzadeh , Azadeh Sepahvandi , Korebami Adebajo 
Published: September 2023

Strontium aluminate (SrAl2O4) phosphor nanoparticles with Eu2+, and Dy3+ co-doping exhibit high brightness and long afterglow properties, storing light energy and glowing slowly under different conditions. It has been widely studied that SrAl2O4:Eu2+, Dy3+ (SAO) phosphor nanoparticles with a green visible emission can penetrate deep into the tissue, show low self-fluorescence, cause minimal light damage, and are biocompatible. SAO phosphor nanoparticle synthesis and modification mechanisms are outlined in this review. Biological therapies, in addition to the detection of substances in organisms, are provided by these excellent priorities. Despite the existing research, it has been demonstrated that nanostructures of SAO luminescence particles have great potential to be applied to tissue engineering and drug delivery systems. The current scientific achievements can provide a reference for research in the areas mentioned above, as well as stimulate biomedical disciplines to pay attention to SAO luminescence nanoparticles.
Türkiye’de kütük mobilya üretiminde kullanılan ahşabın görsel özelliklerinin incelenmesi
Authors: TIRAK HIZAL Kamile , ÖZTÜRK Emel 
Published: September 2023

Ekolojik sistemin korunması, çevre odaklılık, sürdürülebilirlik, geri dönüşüm, erişilebilir ve temiz enerji, sorumlu üretim ve tüketim kavramlarının öne çıktığı günümüz dünyasında doğal malzemelere dönüş olduğu görülmektedir. Ahşap malzeme, insanlığın var oluşundan beri insan hayatında ve yaşamın her alanında yer alan bir malzemedir. Ahşabın ham, işlenmiş ya da kompozit gibi farklı formları mobilyanın temel bileşenini oluşturmaktadır. Bireyselleşmenin ön planda olduğu, kişiye özel ürünlerin ön plana çıktığı bir dönemde mobilyaların da kişiye özel ve tek olması, her biri başka bir örneği olmayan görsellere sahip kütük mobilyaların tercih edilme nedenlerinden biridir.
Bu çalışmada, son yıllarda mobilyada doğal formların yükselişi ile üretimi ve kullanımı yaygınlaşan kütük mobilyalar ele alınmıştır. Ağacın doğal formları ile üretilen kütük mobilya üreticilerine web ortamından ve görsel sosyal medya platformu üzerinden ulaşılarak, üretim yapan işletmelerin nerelerde yoğunlaştıkları, kütük mobilya üretiminde kullanılan ağaç türleri ile kullanılan ağaç malzemenin görsel özellikleri üzerine içerik analizi yapılmıştır.
Yapılan çalışma sonucunda, 25 il ve 5 coğrafi bölgede yoğunlaşan işletmelerin mikro ve küçük ölçekte oldukları, en fazla işletmenin İstanbul, İzmir, Ankara ve Bursa’da bulunduğu, mobilya türü olarak en fazla yemek masası ve sehpa üretildiği, ürünlerin metal ve epoksi malzemeler ile birleştirildiği, en çok kullanılan ağaçların ceviz, meşe, kestane, zeytin olduğu, en fazla düzensiz yıllık halkalar, çürüklük, koyu renkli şeritler, gövde çıkıntıları kusurlarına rastlandığı tespit edilmiştir.
Twitter Sentiment Analysis on Meta: A Lexicon-Based Analysis using Rapidminer
Authors: Md Saad Nor Hasliza , Zainul Abidi Alya Syahirah , Yaacod Zulnaidi , Mohd Ali Muhamad Mu’az , Kun Zhu 
Published: September 2023

Meta is a new parent company of Facebook, Instagram, WhatsApp, and more as the tech giant tried to move on from its scandal-plagued social network to its metaverse vision. Meta-trended on Twitter as users worldwide tweeted their opinions about the rebrand. The rebranding of Meta has become a popular topic of debate. Social media users, companies, and investors desire to be a part of the next big thing. In this study, lexicon-based polarity detection automatically classifies data as positive, negative, and neutral. This study will investigate the main sentiments towards Meta and the topic social media users discussed Meta. In this study, 2997 tweets from the 9th to the 10th of November 2021 were scraped from Twitter using RapidMiner. The findings showed that 36% had a positive sentiment, 29% had a negative sentiment, and 35% were neutral about the Mete rebranding announcement. In addition, the topics often tweeted include NFT and stock, reaction, Metaverse, partnership, AR and VR, and policy. This study can help the Meta understand the people's multi-faceted views towards the Meta rebranding.
Acoustic Feature Identification to Recognize Rag Present in Borgit
Authors: Barthakur Spandan Kumar , Sarma Parismita , Baruah Gaurav Narayan 
Published: September 2023

In the world of Indian classical music, raga recognition is a crucial undertaking. Due to its particular sound qualities, the traditional wind instrument known as the borgit presents special difficulties for automatic raga recognition. In this research, we investigate the use of auditory feature identification methods to create a reliable raga recognition system for Borgit performances. Each of the Borgits, the devotional song of Assam is enriched with rag and each rag has unique melodious tune. This paper has carried out few experiments on the audio samples of rags and a few Borgits sung with those rugs. In this manuscript three mostly used rags and a few Borgits with these rags are considered for the experiment. Acoustic features considred here are FFT (Fast Fourier Transform), ZCR (Zero Crossing Rates), Mean and Standard deviation of pitch contour and RMS(Root Mean Square). After evaluation and analysis it is seen that FFT and ZCR are two noteworthy acoustic features that helps to identify the rag present in Borgits. At last K-means clustering was applied on the FFT and ZCR values of the Borgits and were able to find correct grouping according to rags present there. This research validates FFT and ZCR as most precise acoustic parameters for rag identification in Borgit. Here researchers had observed roles of Standard deviation of pitch contour and RMS values of the audio samples in rag identification.
Oxygen Saturation and Heart Rate Monitoring System by Mobile
Authors: Perez-Siguas Rosa , Matta-Solis Hernan , Matta-Solis Eduardo , Matta-Perez Hernan , Remuzgo-Artezano Anika , Perez-Siguas Luis , Meneses-Claudio Brian 
Published: September 2023

COVID-19 changed the way people live, causing a high mortality rate and making the entire health system in the world difficult due to the lack of equipment and infrastructure, causing many people to be treated from home, constantly measuring their saturation level and heart rate with the help of a pulse oximeter, since the virus directly affects the respiratory system, it has been added that people with lung or cardiovascular problems also have to perform the constant measurement of both vital signs to visualize that they are in a normal range, otherwise, they would have to attend a medical center, so it is important to monitor both vital signs. In view of this problem, in this article a monitoring system of oxygen saturation and heart rate was carried out by mobile application, in such a way that both vital signs can be constantly monitored and an alert can be given to the health center with the coordinates of the person when the system detects a measurement outside the normal range, with this, it would help people who may need medical attention immediately, being able to save their lives. Through the development of the monitoring system, it was possible to observe the operation of the mobile application with an efficiency of 99.15% in monitoring the level of oxygen saturation and 98.78% in monitoring the heart rate, taking 4 seconds to send the alert to the health center to assist the patient.
Resource Allocation Challenges and Strategies for RF-Energy Harvesting Networks Supporting QoS
Authors: Magar Harshada B. , Sharma Pramod , Ubale Archana S. , Ahire Sandhya O. , Philip Vineeta 
Published: September 2023

This paper specifically addresses the resource allocation challenges encountered in wireless sensor networks that incorporate RF energy harvesting capabilities, commonly referred to as RF-energy harvesting networks (RF-EHNs). RF energy harvesting and transmission techniques bring substantial advantages for applications requiring Quality of Service (QoS) support, as they enable proactive replenishment of wireless devices. We commence by providing an overview of RF-EHNs, followed by an in-depth examination of the resource allocation challenges associated with this technology. In addition, we present a case study that focuses on the design of an efficient operating strategy for RF-EHN receivers. Our investigation highlights the critical aspects of service differentiation and QoS support, which have received limited attention in previous research. Besides, we explore previously unexplored areas within these domains.
Enhancing Security in Internet of Healthcare Application using Secure Convolutional Neural Network
Authors: Singh Sanjeev , Singh Amrik , Limkar Suresh 
Published: September 2023

The ubiquity of Internet of Things (IoT) devices has completely changed the healthcare industry by presenting previously unheard-of potential for remote patient monitoring and individualized care. In this regard, we suggest a unique method that makes use of Secure Convolutional Neural Networks (SCNNs) to improve security in Internet-of-Healthcare (IoH) applications. IoT-enabled healthcare has advanced as a result of the integration of IoT technologies, giving it impressive data processing powers and large data storage capacity. This synergy has led to the development of an intelligent healthcare system that is intended to remotely monitor a patient's medical well-being via a wearable device as a result of the ongoing advancement of the Industrial Internet of Things (IIoT). This paper focuses on safeguarding user privacy and easing data analysis. Sensitive data is carefully separated from user-generated data before being gathered. Convolutional neural network (CNN) technology is used to analyse health-related data thoroughly in the cloud while scrupulously protecting the privacy of the consumers.The paper provide a secure access control module that functions using user attributes within the IoT-Healthcare system to strengthen security. This module strengthens the system's overall security and privacy by ensuring that only authorised personnel may access and interact with the sensitive health data. The IoT-enabled healthcare system gets the capacity to offer seamless remote monitoring while ensuring the confidentiality and integrity of user information thanks to this integrated architecture.
Breast Cancer Analytics Classification using MEFBUD DCNN Techniques
Authors: Priyadharsini .M Suriya , Sathiaseelan J.G.R. 
Published: September 2023

Breast cancer is the most dangerous and deadly form of cancer. Initial detection of breast cancer can significantly improve treatment effectiveness. The second most common cancer among Indian women in rural areas. Early detection of symptoms and signs is the most important technique to effectively treat breast cancer, as it enhances the odds of receiving an earlier, more specialist care. As a result, it has the possible to significantly improve survival odds by delaying or entirely eliminating cancer. Mammography is a high-resolution radiography technique that is an important factor in avoiding and diagnosing cancer at an early stage. There are numerous procedures and approaches for detecting cancer in the tissues of the breast. This work presents the image processing, segmentation, and deep learning methodologies and approaches for the diagnosis of breast cancer. This research will help people make better decisions and use trustworthy techniques to find breast cancer early enough to save a woman's life. Pre-processing, segmentation, and classification are some of this system's steps. We've included a thorough study of several techniques or processes, along with information on how they're used and how performance is measured. The stated results lead to the conclusion that, in order to increase the chances of surviving breast cancer, it is crucial to develop new procedures or techniques for early diagnosis. For researchers to effectively diagnose breast cancer, segmentation and classification phases are also difficult. Therefore, the precise diagnosis and categorization of breast cancer still require the use of more advanced equipment and techniques.
Image Tagging using Modified Association Rule based on Semantic Neighbors
Authors: Phakatkar Anupama Ganesh , Sonkamble Balwant A. 
Published: September 2023

With the rapid development of the internet, mobiles, and social image-sharing websites, a large number of images are generated daily. The huge repository of the images poses challenges for an image retrieval system. On image-sharing social websites such as Flickr, the users can assign keywords/tags to the images which can describe the content of the images. These tags play important role in an image retrieval system. However, the user-assigned tags are highly personalized which brings many challenges for retrieval of the images. Thus, it is necessary to suggest appropriate tags to the images.
Existing methods for tag recommendation based on nearest neighbors ignore the relationship between tags. In this paper, the method is proposed for tag recommendations for the images based on semantic neighbors using modified association rule. Given an image, the method identifies the semantic neighbors using random forest based on the weight assigned to each category. The tags associated with the semantic neighbors are used as candidate tags. The candidate tags are expanded by mining tags using modified association rules where each semantic neighbor is considered a transaction. In modified association rules, the probability of each tag is calculated using TF-IDF and confidence value.
The experimentation is done on Flickr, NUS-WIDE, and Corel-5k datasets. The result obtained using the proposed method gives better performance as compared to the existing tag recommendation methods.
Machine Learning based Stress Detection using Keyboard Typing Behavior
Authors: Chunawale Abhishek , Bedekar Mangesh 
Published: September 2023

Emotion detection is one of those areas where technological advances have brought about significant changesin the human lifestyle. During COVID-19 pandemic, due to the work from home culture, use of computers and laptop was suddenly increased. Introduction of digital environments gave it a whole new dimension. Emotion detection is a virtual or computerized way to detect stress. People suffer from various kinds of stress in day to day activities and it is directly connected to their performance. The stress factor can be expressed through a number of ways and human behavior. The way in which humans interact with the computer can reveal the emotional state of the user, mainly the stress. Keyboard typing behavior or characteristics can be used for stress detection. This paper focuses on understanding typing behaviour of human and indicate their stress level. Relevant features are extracted from typing behavior of a user and used for training machine learning models for detection of stress. K-Nearest Neighbor algorithm gave highest accuracy of 84.21% with dimensionality reduction approach.
Implementation of Super Resolution Techniques in Geospatial Satellite Imagery
Authors: Baraskar Trupti , Raman Vignesh Charan , Panchal Poojan 
Published: September 2023

The potential for more precise land cover classifications and pattern analysis is provided by technological advancements and the growing accessibility of high-resolution satellite images, which might significantly improve the detection and quantification of land cover change for conservation. A group of methods known as "super-resolution imaging" use generative modelling to increase the resolution of an imaging system. Super-Resolution Imaging, which falls under the category of sophisticated computer vision and image processing, has a variety of practical uses, including astronomical imaging, surveillance and security, medical imaging, and satellite imaging. As computer vision is where deep learning algorithms for super-resolution first appeared, they were mostly created on RGB images in 8-bit colour depth, where the sensor and camera are separated by a few meters. But no evaluation of these methods has been done.
A Framework for Credit Risk Prediction Using the Optimized-FKSVR Machine Learning Classifier
Authors: Devi Usha , Batra Neera 
Published: September 2023

Transparency is influenced by several crucial factors, such as credit risk (CR) predictions, model reliability, efficient loan processing, etc. The emergence of machine learning (ML) techniques provides a promising solution to address these challenges. However, it is the responsibility of banking or nonbanking organizations to control their approach to incorporate this innovative methodology to mitigate human preferences in loan decision-making. The research article presents the Optimized-Feature based Kernel Support Vector Regression (O-FKSVR) model which is an ML-based CR analysis model in the digital banking. This proposal aims to compare several ML methods to identify a precise model for CR assessment using real credit database information. The goal is to introduce a classification model that uses a hybrid of Stochastic Gradient Descent (SGD) and firefly optimization (FFO) methods with Support Vector Regression (SVR) to predict credit risks in the form of probability, loss given, and exposure at defaults. The proposed O-FKSVR model extracts features and predicts outcomes based on data gathered from online credit analysis. The proposed O-FKSVR model has increased the accuracy rate and resolved the existing problems. The experimental study is conducted in Python, and the results demonstrate improvements in accuracy, precision, and reduced error rates compared to previous ML methods. The proposed O-FKSVR model has achieved a maximum accuracy rate value of 0.955%, precision value of 0.96%, and recall value of 0.952%, error rate value of 4.4 when compared with the existing models such as SVR, DT, RF, and AdaBoost.
A Systematic Study of Stock Markets Using Analytical and AI Techniques
Authors: Patidar Neha , Shah Harshal 
Published: September 2023

Predicting stock market patterns is seen as a crucial and highly productive activity. Therefore, if investors make wise choices, stock prices will result in significant gains. Investors face a lot of difficulty making predictions about the stock market because of the noisy and stagnating data. As a result, making accurate stock market predictions is difficult for investors who want to put their money to work for them. Predictions of the stock market are made using mathematical techniques and study aids. Out of 30 research papers advocating approaches, this study offers a thorough analysis of each, including computational methodologies, AI algorithms( machine learning and deep learning), performance evaluation parameters, and chosen publications. Research questions are used to choose studies.
As a result, these chosen studies contribute to the discovery of ML methods and their corresponding data set for predicting security markets. The majority of Artificial Neural Network and Neural Network techniques are employed for producing precise stock market forecasts. The most recent stock market-related prediction system has significant limitations despite the substantial amount of work that has gone into it. In this survey, one may infer that the stock price forecasting procedure is a comprehensive affair and it is very necessary to look more closely at the typical parameters for the stock market prediction.