bluuebunny/crossref_metadata_2025_split huggingface dataset:
Unilateral Percutaneous Kyphoplasty In Patients With Painful Osteoporotic Vertebral Compression Fractures; Single Center Experience
Authors: EGE Ferhat , AKBAS Mert 
Published: December 2023

ABSTRACT

Aim
The objective of this study is to evaluate the outcomes of unilateral percutaneous kyphoplasty (PKP) performed in treatment-resistant patients with osteoporotic vertebral fractures.

Material and Methods
The study sample consisted of eight patients with painful osteoporotic compression fractures who did not respond to conservative treatment methods. However, the respective clinical results were evaluated based on radiological findings pertaining to 18 operated vertebrae of the said eight patients. The Oswestry Disability Index (ODI) scores and Visual Analogue Scale (VAS) scores were recorded before the procedure and at the 1st-month and 6th-month follow-up visits. Vertebral angle (VA), and anterior height (AH), central height (CH) and posterior height (PH) of the superior and inferior endplates of the vertebrae were measured before the procedure and at the 6th-month follow-up using direct radiography.

Results
Mean VAS and ODI scores decreased 66.87% and 65.13%, respectively, over a 6-month period. The mean vertebral height increased after kyphoplasty, as reflected by a 4.70% increase in AH, 26.84% increase in CH and 1.63% increase in PH. Additionally, VA was found to have increased by 14.1%.

Conclusion
The results of this study featuring a small patient population indicated that unilateral balloon PKP can be used safely and effectively to achieve earlier mobilization with faster pain relief and fewer complications.
STUDENTS' METACOGNITIVE SKILLS AND CRITICAL READING SKILLS OF KOREAN TEXTS: HOW DO THEY CORRELATE?
Authors: Ansas Velayeti Nurfitriana , Azizah Asma , Oktavianto Dwi Bhakti , Nasihin Siti Balqis 
Published: December 2023

Metacognitive skills are considered a pivotal modal for students to develop critical literacy, especially in reading activities, as part of the main goals of 21st-century language education. These skills also help the students succeed in their learning process because they involve the ability to evaluate one's capacity and achievement in learning. Although much research has been initiated, Korean as a foreign language (KFL) student is little known. To fill the void, this ongoing study investigates the level of metacognitive skills in reading among Indonesian undergraduate students majoring in Korean language education. Employing a descriptive quantitative approach, the researchers distributed a questionnaire and interview on students’ metacognitive skills in critical reading. What about items about critical reading skills? It also turned out you have interview data. please include here in the abstract. The data were analyzed, focusing on the mean scores and percentages to get the main points of the student's responses and how students apply their metacognitive skills in critical reading. The results showed that as many as 75% of students had a moderate level of metacognitive skills, and only 5.5% had a high level of metacognitive skills. This shows that students' metacognitive abilities still need to be improved. Of the eight indicators measured, the ability in time management shows a low value. Meanwhile, the ability to implement strategies shows a high value. It can be concluded that students still have to improve their metacognitive skills in critical reading, especially in time management.
A GLANCE THROUGH THE LENS OF SENIOR TEACHERS’ IMPEDIMENTS AND SUCCESSES IN ONLINE TEACHING: A CASE IN A PROVINCE IN THE PHILIPPINES
Authors: De Jesus Jubille Melanie , Janga Janwin Miguel , Jimenez Joshua , Villanueva Jaimie , Lobo Joseph , Celis Michael Louie 
Published: December 2023

The education sector is a major industry badly affected by the spread of COVID-19. With the abrupt transition from traditional classrooms to online learning, several obstacles arose, particularly for more senior teachers who had less time to plan for and adjust to the new model of instruction. This study was conducted to explore the challenges and successes of senior teachers from Pampanga, Philippines, in the delivery of online learning. Banking on their wisdom and experiences, this qualitative-exploratory study is guided by Kolb’s Experiential Learning Cycle (1984) and Bandura’s Social Learning Theory (1968). In-depth interviews were conducted with eight senior teachers ranging in age from 55 to 78. Their accounts, experiences, and methods were transcribed, recorded, and classified after undergoing manual qualitative analysis. The major findings highlighted the difficulties, modifications, and countermeasures that experienced educators use when delivering online classes. These results can be used to strengthen educational institutions' ability to provide and support teachers from older ge-nerations, whose knowledge and comfort with technology are limited. Most importantly, it focused on filling the gap in the dearth of research publications that have examined the difficulties and triumphs of senior teachers in the context of the new normal in Philippine education
A TRANSFORMATIONAL LEADERSHIP OF PRIVATE HIGH SCHOOL PRINCIPALS IN MAKASSAR CITY
Authors: Arismunandar Arismunandar , Wahed Andi , Ervianti Ervianti , H Nurhikmah 
Published: December 2023

This study aims to obtain an overview of the transformational leadership of the head of a private high school in Makassar City. The approach in this study is Quantitative with a descriptive type of research that seeks to describe the four dimensions of transformational leadership of the head of a private high school in Makassar City. In this study, the data collection technique used was a questionnaire that was distributed to 92 samples from 1203 teacher populations who gave assessments to the heads of private high schools in Makassar City. The data analysis technique used in this study is descriptive statistics that only describe the state of the data as it is through parameters such as mean, median, mode, frequency distribution and other statistical measures. The results showed that the transformational leadership of the head of a private high school in Makassar City has (1) a tendency to ideal influence or charisma with a presentation of 77 or 83.16% is in a very high category, (2) inspirational motivational tendencies with presentations of 68.5% are in a very high category, (3) intellectual stimulation tendencies with a percentage of 66.3% are in a very high category, and (4) the tendency of individual consideration with presentations of 50% is in the high category. Research findings: Principals as transformational leaders can improve response, and commitment, pay attention to the concerns and needs of teachers and employees under them, change the awareness of teachers and employees under them about problems by helping them see problems in new ways, and excite and inspire teachers and employees under them to make extra efforts to achieve school goals. can help teachers and employees to see the positive impact of their work through direct interaction by responding, building commitment and making extra efforts to achieve school goals.
YIELD AND QUALITY OF SOME MAIZE HYBRIDS (ZEA MAYS L.) UNDER DIFFERENT PLANTING DATES
Authors: MOUSAVİ NASAB Maryam Sadat , TOHİDİ –NEJAD Enayatollah , AFSHAR MANESH Gholamreza , HEİDARİ SHARİFABAD Hosein , AIEN Ahmad 
Published: December 2023

For optimal maize yield, selecting the appropriate planting date based on a region's climatic conditions is crucial.
This is especially true when considering the varying needs of different maize hybrids. To better understand the
relationship between planting dates and yield for various maize hybrids, a study was conducted in Jiroft during
2018 and 2019. This research examined ten maize hybrids across three distinct planting dates.The findings
revealed that delaying the planting date reduced yield components, SPAD, GDD, oil percentage, and overall oil
yield.Interestingly, the amino acid content increased with delayed planting. Among the hybrids, the 703 variant
achieved its peak grain yield on July 18. In contrast, the lowest yield was recorded on August 1, marking a
substantial 30.07% reduction in grain yield. These results underscore the significance of timely planting in
maximizing both the yield and quality attributes of maize hybrids.
A new approach to spatial localization of EEG-based electrical activity
Authors: Vartanov А. V. 
Published: December 2023

Background. Accuracy and reliability of methods for spatial localization of brain activity based on electroencephalography (EEG) data do not lose relevance. Existing localization methods are coupled to specific difficulties due to ambiguity of the resulting solution.Ojective: verification of a new method for localizing brain activity using EEG data «Virtually Implanted Electrode» and demonstratoin of its capabilities.Material and methods. The new method was verified using deep brain stimulation (DBS) data. There were used the data from scalp electrical activity induced solely by potentials from two electrodes implanted into deep areas of human brain. It was achieved by filtering EEG signals at the stimulation frequency and excluding signals from other brain areas. A clinical case of applying the new method for EEG analysis of patient paroxysmal activity is presented as well.Results. «Virtually Implanted Electrode» method allowed to quite accurately determine the localization of DBS current sources. The effectiveness of using the method for analyzing the mechanisms of paroxysmal activity was demonstrated: based on change in potentials calculated in 33 sites of the brain (brain areas), three groups of areas were identified specifically contributing to development of paroxysmal activity. For comparison, the same artificial DBS sources were localized using other verified methods: the BrainLoc (Russia) and sLORETA (Switzerland) software providing substantially worse data.Conclusion. Identifying spatial localization of electrical potentials recorded on the scalp surface by using the new approach with the «Virtually Implanted Electrode» method shows high efficiency and reliability, demonstrating a clear advantage over other known localization methods.
Cyber security Reinforcement through Firewall Log Analysis and Machine Learning
Authors: Afrah Fathima Et al. 
Published: December 2023

Firewalls play a crucial role as a primary protective measure in safeguarding network security, effectively mitigating risks posed by external vulnerabilities and internal security breaches. This study presents a new framework that utilizes firewall log data to classify incoming data packets as either permitted or forbidden. The dataset utilized in this research is obtained from Department of CS&IT, MANU University and is subjected to a thorough data pre-processing procedure. This procedure includes several tasks such as managing missing values, encoding categorical variables, standardizing numerical attributes, and guaranteeing data coherence. In order to mitigate the issue of class imbalance within the target variable, we utilize a range of machine learning models and assess their efficacy through the examination of fundamental metrics such as accuracy, precision, recall, and F1-score. The results of our study demonstrate that the AdaBoost model has superior performance compared to other models, achieving a remarkable accuracy rate of 99.00%. This study demonstrates the application of machine learning methods to automatically identify the activities indicated in firewall logs, thereby improving the security of corporate networks. Through the implementation of automation, we facilitate a more dependable and efficient method of detecting and addressing possible risks, thereby strengthening network security measures and protecting valuable corporate information.
A Conceptual Cloud Forensic Investigation Process Model for Software as a Service(SaaS) Applications
Authors: Varshapriya N Jyotinagar Et al. 
Published: December 2023

This paper explore a structured and systematic approach cloud forensic investigation process model for SaaS applications, to investigate the digital crimes in the cloud environment and contributing to enhanced security and privacy of acquired data during forensic investigation .The proposed model offers the distinctive characteristics of cloud environments and the varying levels of access and control within them. In this proposed model, the systematic forensic investigation process is detailed with microscopic details with four phases namely the initial phase, the acquisition phase, the analysis phase, and the reporting phase in Cloud environment. Ultimately, this research aims to enhance the overall trustworthiness and reliability of SaaS applications forensic for fostering a safer and more secure cloud computing forensic investigation landscape by using the chain of custody.
Audio Transcription and Summarization System using Cloud Computing and Artificial Intelligence
Authors: Kaushal Rajendra Khonde Et al. 
Published: December 2023

In the modern era, organizations increasingly rely on virtual meetings to address customer issues promptly and effectively. However, dealing with recorded customer calls can be arduous. This review abstract introduces an innovative methodology to summarize audio data from customer interactions, which can streamline virtual meetings. Leveraging a speech recognizer, like AssemblyAI's API, the methodology converts audio data into text, and then employs a Graph-theoretic approach to generate concise summaries.
This review abstract delves into the growing prominence of cloud-based AI and ML services in the tech industry. It underscores the unique competitive strategies and focuses of major players, namely Amazon, Microsoft, and Google, in the realm of AI and ML platform development. The analysis explores these companies' internal applications and external ecosystem, dissecting their respective AI and ML development strategies. Finally, it predicts future directions for AI and ML platforms, including potential business models and emerging trends, while considering how Amazon, Microsoft, and Google align their platform development strategies with these future prospects.
Mental Depression Deduction Using Modified Regression Model to Prevent Suicidal Attempt
Authors: S. Poongothai Et al. 
Published: December 2023

This study explores a novel approach for predicting depression using association-based multilevel linear regression. The suggested approach, known as association-based multilevel linear regression, uses data on mental depression to predict the prevalence of depression. Several statistical techniques can be used to forecast depression. Several statistical methods, including Linear Regression (LR), Multilevel Linear Regression (MLR), Naïve Bayes algorithm and Decision Tree (DT) were used in this investigation. Because these algorithms are able to predict mental depression based on certain characteristics such as precision and efficiency, their performance reduces. The results of these algorithms' predictions vary significantly, especially in terms of accuracy. The mental health data is fed into a developed model that has been trained to make predictions in order to address the aforementioned problem. Depression is the subject of conversation. A great degree of accuracy is shown by the association-based multilevel linear regression technique and the evaluation of prediction of accuracy in relation to other statistical methods. This study used association-based multilevel linear regression technique. When compared to traditional methods, the method exhibits a substantially greater level of accuracy, almost 99%.
The Idea of Digital Currency for India based on Blockchain Technology: A Conceptual Study
Authors: August Keshav Et al. 
Published: December 2023

This paper aims to explain the idea of digital currency, its prospects in the Indian context, its role in digital transformation, and how it would be beneficial to our country, businesses, and different user segments. As in the phase of digital transformation, a constant improvement in technology is happening day by day globally. In today's virtual world, where everything is on its way forward, why should we not develop our economy accordingly so that we will be able to strengthen ourselves globally? And we had to begin the work of transformation by converting our currency to digital mode, i.e., digital currency, which is accessible in virtual or electronic form rather than physical.
The paper opted for an exploratory study using existing literature, case studies, books, electronic resources, speeches of various officials of the central bank, and experts on national as well as international platforms, which gave clarity on some of the core elements involved in digital currency, like the concept of blockchain with its kinds and segments like wholesale & retail and also with use case data in different counties across the globe, strengths, and weaknesses; initial coin offerings (ICO); and central bank digital currency (CBDC).
In this paper, data were complemented by documentary analysis, in which literature like the concept note of the Reserve Bank of India, the discussion paper of the Bank of England, and existing literature regarding centrally backed currency were studied for the clarity of the above work through figures like the money flower issued by the BIS (Bank for International Settlements). The paper concludes with discussion, suggestions, and future directions, with the conclusion that the user segments for the virtual currency backed by a central bank are classified.
Survey on Therapy Prediction using Deep Learning for Pores and Skin Diseases
Authors: C Mohammed Gulzar Et al. 
Published: December 2023

Introduction: Prediction and detection of skin ailments have generally been a hard and important task for health care specialists. In the cutting-edge situation majority of the pores and skin care practitioners are the uses of traditional techniques to diagnose the ailment which may also take a large amount of time. Skin Diseases are excessive troubles in recent times as it is a consider form of environmental factors, socioeconomic elements, loss of entire weight loss program, and so on. Identifying the particular skin disease by computer vision is introduced as a novel task. Based on skin or pore disease, certain therapy can be suggested. In proposed study there are different applications based on deep learning are studied with computer vision task for better performance of proposed application. Famous deep learning algorithms may include CNN (convolutional neural network) , RNN (Recurrent Neural network), etc.
Objective: To diagnose skin disease with dermoscopic images automatically. Developing automated strategies to improve the accuracy of analysis for multiple psoriasis and skin diseases
Methods: In existing techniques many machine learning models are used which is having high complexity and require more time for analysis. So, in this study different deep learning models are studied for understanding performance difference between different models. This paper is a comparative check about skin illnesses related to ordinary skin issues in addition to cosmetology. Image selection, segmentation of skin disease detection and classification are the important steps can be used for oily, dry, and ordinary pores.
Result: The field of dermatology has seen promising results from studies on various Convolutional Neural Network (CNN) algorithms for classifying skin diseases based on clinical images. These studies have concentrated on utilizing the strength of deep learning and computer vision techniques to classify and diagnose different skin conditions using facial images precisely.
Conclusion: A survey of numerous papers is achieved on basis of technologies used, outcomes with accuracy, moral behavior, and number of illnesses diagnosed, datasets. Different existing research methodologies are compared with present deep learning architectures for understanding superior performance of deep learning models. Using deep learning, we can predict pore and skin diseases. In proposed study, introduction to different algorithms of deep learning which are combined with computer vision tasks to find the skin disease and pore disease are studied. Therapy can be predicted based on type of skin or pore disease.
Stochastic Gradient Deep Multilayer Neural Network based Linear Congruential Generative Cryptosystem for Secured Data Communication in Cloud
Authors: Krishnaveni R Et al. 
Published: December 2023

Cloud computing is a kind of distributed computing that use a vast network of interconnected resources accessible over the internet. Security is a crucial concern in cloud computing due to the fact that users save their data on the cloud for convenient access from any location and at any time. Consequently, many users are worried about safeguarding their sensitive data in an unsafe location. Therefore, cloud computing architecture requires an innovative cryptographic method that ensures the secrecy, authenticity, integrity, and non-repudiation of data transfer in the cloud. A new technique called SEMcrypt, which stands for Stochastic Gradient DEep Multilayer Neural Network based Linear Congruential Generative Cryptography, has been developed to enhance secure data transmission. SEMcrypt ensures higher data confidentiality and reduces the time required for communication between the cloud user (i.e., patient) and the server. The SEMcrypt approach has two distinct processes: categorization and secure data transport. Initially, the data is gathered from the patients and is used as input for the stochastic gradient regularised deep multilayer neural network. The deep neural network consists of one input layer, two hidden layers, and one output layer. At first, the information is gathered from the patients and sent to the input layer. Next, the patient data that has been gathered is examined in hidden layer 1 using the generalised Tikhonov regularisation function. The patient data that has been analysed is sent to hidden layer 2. The hyperbolic tangent activation function is used at that layer to classify the patient data. Subsequently, the categorised data undergoes encryption via the use of Linear Congruential Generative Goldwasser-Micali encryption, ensuring safe transfer of the data. Subsequently, the encrypted data is sent to the cloud server. The patient data is encrypted on the server side using the Linear Congruential Generative Goldwasser-Micali decryption technique to prevent unauthorised access or assaults. Consequently, the authorised recipient receives the unaltered information, which is then kept in the database for further analysis. Secured data transmission is achieved by ensuring better levels of data confidentiality and reducing the time required for the process. The experimental assessment focuses on criteria such as the time it takes to generate keys, the level of data confidentiality and integrity, the computing time, and the accuracy of categorization. The empirical findings demonstrate that our suggested SEMcrypt approach delivers efficient performance outcomes by attaining superior levels of data confidentiality and integrity within a minimal timeframe.