bluuebunny/crossref_metadata_2025_split huggingface dataset:
Praktek Manajemen Risiko Terhadap Kinerja Keuangan
Authors: Octavera Sari , Rahadi Febri , Nasyrah Alam 
Published: February 2025

The more complex the problems faced in the banking industry, the more companies need to identify issues in bank operations to assess the risks that occur. The importance of risk management in the banking business lies in creating an early warning system to minimize the risks of the banking business, which is highly sensitive to economic conditions. Management of the bank's health level is necessary in the assessment of the bank's performance. The assessment of banking financial performance can be measured using the Return on Asset (ROA) approach. The assessment conducted is related to risk management practices. Risk management is assessed comprehensively from financial risk, business risk, and operational risk. Thus, this research can examine the impact of risk management practices on banking financial performance. This research is explanatory research with a quantitative approach that explains the relationship between financial risk, business risk, and operational risk on banking financial performance. The data for this research is secondary data in the form of annual financial statements of banking companies listed on the Indonesia Stock Exchange (IDX) for the period 2019-2023. The results of the simultaneous test explain that the independent variables collectively influence the dependent variable. Meanwhile, partially, only operational risk has a significant impact on ROA.

Abstrak
Semakin kompleksnya permasalahan yang dihadapi dalam industri perbankan, maka perusahaan perlu mengidentifikasi masalah dalam operasional bank untuk mengkaji risiko yang terjadi. Pentingnya manajemen risiko pada bisnis perbankan adalah dalam menciptakan early warning system dalam meminimumkan risiko bisnis perbankan yang sangat sensitive terhadap kondisi ekonomi. Manajamen pengelolaan tingkat kesehatan bank diperlukan dalam penilaian kinerja perbankan tersebut. Penilaian kinerja keuangan perbankan dapat diukur adalah dengan pendekatan Return on Asset (ROA). Penilaian yang dilakukan dihubungan dengan praktek manajemen risiko. Manajemen risiko dinilai secara komprehensif dari risiko keuangan, risiko bisnis dan risiko operasional. Sehingga penelitian ini dapat melihat pengaruh dari praktik manajemen risiko terhadap kinerja keuangan perbankan. Penelitian ini merupakan penelitian eksplanasi (explanatory research) dengan pendekatan kuantitatif yang menjelaskan bagaimana hubungan antara risiko keuangan, risiko bisnis dan risiko operasional terhadap kinerja keuangan perbankan. Data penelitian ini merupakan data sekunder berupa laporan keuangan tahunan perusahaan perbankan yang terdaftar di Bursa Efek Indonesia (BEI) dalam periode waktu 2019-2023. Hasil pengujian simultan menjelaskan bahwa variabel dependen secara bersama-sama berpengaruh terhadap variabel dependen. Sementara secara parsial hanya risiko operasional yang memberikan pengaruh secara signifikan terhadap ROA
Natural Language Processing (NLP) System for Cyberbullying Identification in Thai Language
Authors: Yakubu Bello Musa , Bumrungsri Worapong , Bhattarakosol Pattarasinee 
Published: February 2025

In modern life, the easy access to the internet and social media has become almost essential. Although online social media channels are good means of communication, their frequency of cyberbullying and harassment creates problems as well. On these sites, cyberbullying takes many forms that cause targeted victims to experience anxiety, guilt, and despair. With an eye on demographic factors and personal experiences of victimizing, this study explores the degree and impressions of cyberbullying within a Thai sample community. By means of a thorough questionnaire survey, important new perspectives on respondents’ awareness of cyberbullying, its underlying causes, and coping mechanisms for such events were obtained. Analyzing demographic data in tandem with cyberbullying experiences found a clear correlation between social media use length and the likelihood of running into cyberbullying. Moreover, the work suggests the creation of a customized natural language processing system specifically designed to identify Thai language cyberbullying events, therefore attaining an interesting accuracy rate of 84.23%. The study also points out the possible market chances for commercializing this method, implying it may be a profitable investment. These results provide a fresh understanding of language processing strategies for efficient content analysis on several internet platforms.

Received: 16 September 2024 |Revised: 12 November 2024 | Accepted: 20 December 2024

Conflicts of Interest
The authors declare that they have no conflicts of interest to this work.

Data Availability Statement
Data available on request from the corresponding author upon reasonable request.

Author Contribution Statement
Bello Musa Yakubu: Conceptualization, Validation, Formal analysis, Investigation, Writing – original draft, Writing – review & editing, Visualization, Funding acquisition. Worapong Bumrungsri: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Visualization. Pattarasinee Bhattarakosol: Conceptualization, Methodology, Formal analysis, Resources, Data curation, Writing – review & editing, Supervision, Project administration, Funding acquisition.
Teknik Prediksi Pembangunan Ekonomi Berbasis Ekonomi Hijau Pada Perencanaan Daerah
Authors: Santoso Dwi Budi , Pangestuty Farah Wulandari , Saraswati Erwin , Fuadi Hamdan 
Published: February 2025

Teknik prediksi pembangunan ekonomi berbasis ekonomi hijau pada perencanaan daerah di latar belakangi oleh keterbatasan kemampuan SDM Bappeda Kabupaten Malang melakukan analisis teknik prediksi secara optimal, seperti pengelompokan indikator pilar pembangunan ekonomi hijau, menetapkan target pilar utama kesejahteraan sosial. Hambatan ini menunjukkan perlunya dukungan tambahan, baik dari pemerintah untuk memastikan keberlanjutan dan efektivitas program pengabdian ini. Kegiatan pengabdian kepada masyarakat dalam rangka pelatihan teknik prediksi pembangunan ekonomi berbasis ekonomi hijau telah sukses dilaksanakan pada Rabu, 10 Oktober 2024. Kegiatan ini berlangsung di Aula Gedung Dekanat Lama Fakultas Ekonomi dan Bisnis Universitas Brawijaya dan diikuti oleh 30 anggota BAPPEDA Kota Malang. Pelatihan tersebut mencakup pendalaman konsep ekonomi hijau serta praktik teknis dalam memprediksi indikator pembangunan yang berbasis pada pilar ekonomi, sosial, dan lingkungan. Partisipasi aktif dari para anggota BAPPEDA menunjukkan antusiasme tinggi dalam memahami dan mengimplementasikan konsep ini dalam dokumen perencanaan daerah. Dengan suasana yang mendukung dan fasilitas yang memadai, kegiatan ini berhasil memberikan pemahaman yang mendalam serta meningkatkan kompetensi peserta, yang diharapkan dapat mendukung upaya penyusunan perencanaan pembangunan daerah yang berkelanjutan.
Algorithms for Computing Domination and Total Domination Numbers of RNPCG
Authors: Renuka Lakshmi Avvari  
Published: February 2025

Introduction: This paper presents an efficient algorithmic framework for computing the domination number (γ) and total domination number (γt) of graphs, with a focus on applications in collaboration networks. Using a greedy approach combined with minimality verification, the algorithm identifies dominating and total dominating sets that optimize connectivity within the graph structure.
Objectives: To design and implement algorithms for computing the domination number of RNPCGs, focusing on both time complexity and practical efficiency.
Methods: The methodology is demonstrated on the Rolf Nevanlinna Prize Collaboration Graph (RNPCG), showcasing its ability to handle complex, real-world datasets. Results reveal key insights into the connectivity and influence metrics of prominent academic networks, with implications for optimizing resource dissemination in such systems. The proposed algorithm balances computational efficiency with accuracy, offering a robust tool for graph-theoretic analysis in both theoretical and applied contexts
Results: A detailed computational complexity analysis was conducted for both the domination number and total domination number algorithms. It was found that the proposed solutions offer polynomial-time complexity for graphs of moderate size, making them practical for typical use cases. However, the complexity does increase with the size of the graph, especially as the randomness in RNPCGs increases.The algorithms were also compared with exact brute-force solutions for small graphs, showing that our approach provides a significant reduction in computation time while maintaining accuracy.
Conclusions: In this study, we developed an efficient algorithm for computing the domination number (γ) and total domination number (γt) in graphs, with a focus on its application to the Rolf Nevanlinna Prize Collaboration Graph (RNPCG). By leveraging a greedy approach and minimality verification, the algorithm effectively identified dominating and total dominating sets, providing insights into the connectivity and structural properties of the RNPCG. The analysis revealed the influence and connectivity of key vertices, illustrating the practical utility of domination metrics in understanding complex collaboration networks. The results emphasize the significance of domination parameters in optimizing resource dissemination and maintaining connectivity within academic or similar real-world networks.
Analyzing Air Traffic Control System Reliability with Markov Process Methods
Authors: Anmol Gupta  
Published: February 2025

This paper concludes the mathematical models of the Markov process to examine the reliability of Air Traffic Control (ATC) systems. The safety and effectiveness of air travel depend significantly on reliability of ATC systems, making it necessary to evaluate & enhance their performance using robust analytical techniques. We use continuous-time Markov chains to model the different operational, failure states and degrade state. We analyse Markov process alongside various component failures and repairs of the ATC that may occur during its operation. To assess the system's performance, we calculate reliability and mean time to failure (MTTF). Sensitivity analysis is used to identify the critical components of the ATC.
Influence of Masonry Strength and Column Reinforcement on the Lateral Performance of Infilled RC Frames
Authors: Jonathan T Mark  
Published: February 2025

Masonry infill strength and column flexural, shear reinforcement are the influencing factors in the lateral performance of infilled frame. Various numerical and experimental studies performed in the past decades bring out the beneficial and adverse effects of masonry infilled frame. In this numerically study masonry of different strengths i.e., Concrete Masonry Unit (CMU), Clay Brick (CB), Flyash brick (FA) and Aerated Autoclaved Concrete block (AAC) have been used as infill in a reinforced concrete frame and the behaviour is observed. Similarly, for strong masonry, CMU infilled frame, the flexural and shear reinforcement of column has been varied. From the numerical analysis performed it has been observed that irrespective of the masonry strength all the specimens showed predominant flexural failure of column whereas CMU infilled frame with a flexural reinforcement of 6.4% failed by shear. It is concluded that when the shear capacity of the frame is more than the flexural shear capacity of the frame the failure mode is observed to be flexure. From the conclusions a simple and effective analytical method for the identification of shear failure in column considering the influence of masonry strength has been proposed.
Isolating malicious nodes in the Internet of Things through a Trust-Based Mechanism
Authors: Zakiya Manzoor Khan  
Published: February 2025

Abstract: Introduction: IoT (Internet of Things) systems are open to attacks because of their resource-limited and ad hoc architecture. These systems support industrial and medical services and are used to manage massive amounts of data. IoT systems thus become vulnerable to a range of attackers, such as sporadic hackers, cybercriminals, hacktivists, and even governmental organizations. These hackers' main objective is to compromise IoT devices in order to obtain sensitive data, including credit card numbers, location information, bank account credentials, and health information. The version number attack is one serious danger; it is a malicious activity that negatively affects network performance. Malicious nodes launch this attack by overloading the network with hello packets, which causes a denial-of-service (DoS) condition. This study suggests a trust-based mechanism, in which each node is given a trust value determined by its activity, to solve this problem. The network isolates and labels as malicious the nodes with the lowest trust values. After the suggested scheme is put into practice in NS2, its effectiveness is assessed in terms of throughput, packet loss, delay, and energy usage.
A Secure Framework based on Sensor-Cloud architecture for Healthcare data using enhanced Elliptic Curve Encryption
Authors: Munish Saran,  
Published: February 2025

Healthcare data security is of utmost importance in today's era due to the several reasons such as Privacy Protection, Legal and Regulatory Compliance, Protection against Data Breaches and Cyber Attacks, Patient’s Safety and Continuity of Care, Research and Innovation, Trust and Patient’s Confidence. There must be a security authority deployed that can provide efficient as well as transparent defence mechanism. Traditional technique makes use of access control lists which allows the provision for coarse-grained access to data. Whereas the proposed approach makes use of Attribute-Based Encryption (ABE) which provides fine-grained access control of data. Fine-grained access mechanism provides detailed level of authorization as compared to coarse-grained access mechanism. Ciphertext Policy Attribute Based Encryption (CP-ABE) provides fine-grained access mechanism but involves some complex computations due to which there is computational delay involved. Our proposed approach makes use of enhanced ECC encryption algorithm based on user defined attributes on the sensor-cloud architecture, in order to provide an efficient as well as transparent security mechanism through which data owner can have complete control over their data by specifying the role based access control rules. The proposed fine grained access control approach guarantees the confidentiality and integrity and at the same time reducing the overall computational overhead and in turns makes the system more efficient as well as robust. The results clearly depicts that the proposed approach achieves faster encryption, decryption and key generation along with the more complex ECC keys as compared with traditional CP-ABE.
Results on Fixed Points in Vector Valued G-Metric Spaces
Authors: Pooja Yadav  
Published: February 2025

Introduction: Fixed point theory furnishes a robust framework in mathematics for examining the function and system behavior by centering on fixed points. Kirk et al. brought forth the concept of cyclic transformation in 2003. Cyclic contraction broadens the standard contraction transformations in metric space. By relaxing the requirement of the triangle inequality, -metric space extends the scope of metric spaces. Vector -metric space is GMS in which the metric is lattice valued.
Objectives: We give the concept of a vector metric space and define some related concepts. We establish some fixed point results on vector -metric space by using cyclic contraction. To underscore the practical implications and importance of our findings, we offer a range of examples and corollaries.
Methods: This research paper outlines fixed point theorems for self-transformations in vector -metric space, with the help of cyclic contraction.
Conclusions: Within this manuscript, we illustrate fixed point theorems for self-transformations in V-complete vector -metric space by utilizing cyclic contraction. These outcomes are expected to inspire researchers to explore problem-solving opportunities in diverse areas such as differential equations and functional analysis.
Introduction: Fixed point theory furnishes a robust framework in mathematics for examining the function and system behavior by centering on fixed points. Kirk et al. brought forth the concept of cyclic transformation in 2003. Cyclic contraction broadens the standard contraction transformations in metric space. By relaxing the requirement of the triangle inequality, -metric space extends the scope of metric spaces. Vector -metric space is GMS in which the metric is lattice valued.
Objectives: We give the concept of a vector metric space and define some related concepts. We establish some fixed point results on vector -metric space by using cyclic contraction. To underscore the practical implications and importance of our findings, we offer a range of examples and corollaries.
Methods: This research paper outlines fixed point theorems for self-transformations in vector -metric space, with the help of cyclic contraction.
Conclusions: Within this manuscript, we illustrate fixed point theorems for self-transformations in V-complete vector -metric space by utilizing cyclic contraction. These outcomes are expected to inspire researchers to explore problem-solving opportunities in diverse areas such as differential equations and functional analysis.
Proposed Improving Protection of Cloud Computing Environments Based on Machine Learning Techniques
Authors: Osamah M. Abduljabbar  
Published: February 2025

Cloud computing contains a huge amount of data, which makes it a common target for cyberattacks to access confidential data using various illegal methods. One of these attacks is the Structure Query Language injection attack SQLIA, It is categorised as one of the most prevalent threats to obtaining, modifying, or destroying data by the Open Web Application Security Project (OWASP). Therefore, it has become necessary to create a model to detect attacks on data on the cloud to protect it, in order to increase trust between individuals and institutions and not make this data available to people who are not authorized to access it. To solve these problems, this study presents a proposal to improve the protection of the cloud computing environment through two contributions.The first contribution is developing a machine learning model, known as a logistic regression framework, that serves as a mediator between the client and the server. Its goal is to ascertain the kind of requests that are received from the customer layer and whether or not they include hazardous or typical payloads. The second contribution is illustrating how dangerous it is for the cloud computing infrastructure and for consumers and organisations to rely on false forecasts regarding the confidentiality, integrity, and real-time availability of data.. The results obtained from applying the proposed model showed a very high accuracy of 99.82, and showed low rates of false negatives and positives. In addition, the time it takes to determine the type of request sent is 0.1514 seconds.
Estimating Pain Intensity from Facial Expressions
Authors: Yogita Chavan  
Published: February 2025

Facial expressions are intricate, change over time, and are difficult to decipher, hence automated emotion recognition using facial expressions has been a popular research area in computer vision, which focuses on image processing and pattern recognition. The research community has access to many facial expression databases, which are crucial tools for analyzing a variety of face expression detection methods, but still the existing models are affected by over-fitting issues. In this research work, a framework using Hybrid optimization based deep CNN (HOA based deep CNN) has been developed that can recognize pain by facial expression and evaluate the pain's intensity. The proposed model uses UNBC-McMaster Shoulder Pain Expression Archive Database (UNBC) as the database. From the identified region of interest (ROI), feature extraction is done using the RESNET 101, facial activity descriptors and hybrid weighted facial activity descriptors. The collected features are processed using ensemble deep CNN classifiers. The optimization methods Adam, Stochastic Gradient Descent (SGD), Cat Swarm, and Grey Wolf (GWO) are used together to optimize the ensemble deep CNN. The hybrid optimization offers the best tuning, which substantially reduces the computing time and speeds up the convergence. The accuracy, sensitivity, and specificity achieved by the HOA-based deep CNN model based on the TP are 90.06%, 98.38%, and 99.35%, respectively. For the K-fold, the accuracy, sensitivity, and specificity achieved are 94.95%, 97.33%, and 99.04%, respectively.
Programa de Pós-Graduação em Geografia da Unicentro, Guarapuava, Paraná
Authors: Silva Márcia da , Gomes Marquiana de Freitas Vilas Boas , Brumes Karla Rosário 
Published: February 2025

O Programa de Pós-graduação em Geografia (PPGG) da Unicentro (Mestrado e Doutorado), em Guarapuava, no Paraná, está vinculando ao Departamento de Graduação em Geografia. Foi fundado em 2009 e teve sua primeira turma de mestrado neste mesmo ano. Em março de 2017 teve início a primeira turma de doutorado, aprovado e criado no limiar do processo de expansão da pós-graduação no Brasil. O PPGG tem como Área de Concentração o tema Dinâmica da Paisagem e dos Espaços Rurais e Urbanos (Mestrado) e Geografia (Doutorado) e está dividido em duas Linhas de Pesquisa articuladas com seus objetivos e missão: Linha 1: Dinâmica da Paisagem, Geomorfologia e Análise Ambiental; e Linha 2: Dinâmica dos Espaços Rurais e Urbanos, que têm como objetivo a formação de profissionais para atuarem na área de pesquisa e docência, estimulando, assim, a produção do conhecimento geográfico a partir de suas diversas abordagens. Nesse sentido, este texto conta, para a comunidade científica geral, um pouco do Programa nos seus 13 anos de existência. Além disso, também é um convite a discentes e egressos dos cursos de Geografia e de áreas afins para que conheçam o Programa e possam sentir-se estimulados a dele participarem em suas diversas possibilidades.
A produção do espaço e comunicação popular em Curitiba-PR: o caso da rádio comunitária do Bairro Novo
Authors: Cigolini Adilar Antonio , Fedel André Souza 
Published: February 2025

Só recentemente a geografia têm se voltado para o estudo das comunicações e suas relações com a produção do espaço geográfico, em múltiplas escalas. O trabalho ora apresentado parte de uma escala muito particular: o olhar geográfico sobre o fenômeno da comunicação popular, com base num estudo de caso: a rádio comunitária do bairro Novo, em Curitiba-PR. A questão central é compreender a relação entre a produção do espaço e a comunicação popular e livre. A problemática e a forma de abordagem exigiu que fossem adotadas alguns procedimentos metodológicos específicos, como a observação participante e a pesquisa ação. Os resultados apontam que a produção do espaço, via rádio comunitária, é inerente a outras pautas locais, como a questão da moradia e das ocupações, ou seja, esse tipo de comunicação tem um histórico vinculado ao movimento popular, sua organização e pautas que giram em torno da vida cotidiana do bairro.