bluuebunny/crossref_metadata_2025 huggingface dataset:
Estado moderno y monismo jurídico como excluyentes del Derecho Indígena en México
Authors: Ángeles-Hernández Elías
Published: October 2024

A partir del surgimiento del Estado moderno en el contexto de la Paz de Westfalia en 1648 en Europa, y con la consecuente centralización del poder y del derecho por parte de este ente soberano, se excluyó de manera sistemática a otros sistemas jurídicos hasta ese momento vigentes. Lo anterior, dio como resultado lo que se denomina en la doctrina como monismo jurídico, fundamentado teóricamente en el iuspositivismo. Con la premisa anterior como telón de fondo, el presente trabajo de investigación tiene como objetivo demostrar que, en un escenario como México cuya conformación como nación independiente se dio bajo la misma lógica europea a principios del siglo XIX, ha sido el propio Estado mexicano a través de la identificación Estado-derecho que se ha excluido desde entonces al Derecho Indígena. Lo anterior ha sido así tanto en la teoría como en la práctica, aun y cuando este sistema jurídico ha existido como tal, incluso desde antes de la creación de lo que ahora es México.
Evasión fiscal dentro del G-20. Una relación desleal
Authors: Battista Luis Carlos
Published: October 2024

El artículo analiza el problema global de la evasión fiscal, sus consecuencias, y posibles soluciones. Se discute la magnitud y complejidad de la elusión tributaria mediante el uso de entidades opacas, programas de ciudadanía y residencia por inversión, y el incentivo que ofrecen algunos países del G-20 con bajas tasas impositiva y la transferencia de riqueza hacía sus países con tal de captar activos para sus economías y entidades financieras.
De “pároco de aldeia” a macroinfluenciador digital
Authors: Sbardelotto Moisés
Published: October 2024

Neste texto, analisaremos o caso de Patrick Fernandes, um presbítero católico brasileiro que, em poucos meses ao longo de 2021, passou de poucos milhares para milhões de seguidores em sua conta no Instagram. Faremos isso a partir da noção de influência digital religiosa, entendida aqui como as interações, negociações, disputas e tensões de poder simbólico em redes digitais sobre a tomada de decisão em relação a atitudes e opiniões pessoais ou comunitárias relacionadas ao âmbito religioso; neste caso, o catolicismo. Primeiramente, apresentaremos brevemente algumas premissas teóricas sobre o conceito de influência digital religiosa. Em seguida, conforme método construído para uma macropesquisa sobre influenciadores digitais da fé (Medeiros et al., 2022), analisaremos aqui quatro ângulos principais de observação desse fenômeno – pessoa/persona, performance, conteúdo e interação – a partir das contas do presbítero no Instagram e no YouTube, a fim de compreender como emergiu e se manifesta a atuação digital do padre. Por fim, refletiremos sobre algumas tensões que emergem a partir do caso, acerca dos limites e das possibilidades do fenômeno da influência digital religiosa.
Psikolojik Sözleşme İhlalinin İşten Ayrılma Niyeti Üzerine Etkisinde Devam Bağlılığının Aracı Rolü
Authors: AKÇA Coşkun
Published: October 2024

Bu çalışmanın amacı psikolojik sözleşme ihlalinin işten ayrılma niyeti üzerine etkisinde devam bağlılığının aracı etkisini incelemektir. Bu ilişkiler sosyal değişim teorisi ve kaynakların koruması teorisi ilkelerine dayalı olarak oluşturulmuştur. Çalışma verileri, kesitsel araştırma deseninde TR82 bölgesindeki bir ilçe belediyesi çalışanlarından toplanmış ve verilerin analizinde istatistiksel yöntemlerden faydalanılmıştır. Çalışmanın bulguları, psikolojik sözleşme ihlalinin işten ayrılma niyeti üzerinde etkisi olduğunu ancak devam bağlılığının bu etkiye aracılık etmediği sonucunu göstermektedir. Bu çalışmanın sosyal değişim teorisi ve kaynakların korunması teorisi çerçevesinde literatüre katkıda bulunması ve psikolojik sözleşme ihlaliyle ilgili farkındalığı artırması beklenmektedir. Ayrıca devam bağlılığını artırmak ve yetenekleri elde tutmak için örgütlerin stratejiler geliştirmelerine katkı sağlamayı hedeflemektedir.
Data Leakage of the Indonesian Elections Commission in Legal Aspects of Personal Data Protection
Authors: utama Frendika suda , Purwoleksono Didik Endro , Rachman Taufik
Published: October 2024

Hackers illegally accessed the Indonesian General Elections Commission’s (KPU) voter data system to collect voter data to sell to third parties. The regulation requires accountability for voter data leakage to protect people’s privacy rights in Indonesia’s personal data protection concept. Legal analysis of the modus operandi of personal data sales cases results in patterns of information system vulnerabilities, which can then be used to prevent personal data leakage and improve voter data protection in Indonesian elections. One of the reasons for passing the personal data protection law is the rampant cases of confidential data leakage that occur in government and private institutions in Indonesia. Hackers of voter data systems aim to profit from personal data sold to third parties. The role of the cybersecurity task force team needs to be improved with more concrete arrangements in law enforcement, and mitigating voter data leakage can provide legitimacy for the implementation of credible, reliable, and professional elections in Indonesia. Establishing the task force will optimize the application of voter data systems in conducting general elections in Indonesia and improve personal data protection.
Optimization of Insurance Brokerage Institutions in Realizing the Trust of the Indonesian Community
Authors: Gultom Elisatris , Rohani Siti , Disyon Huta
Published: October 2024

Public trust in the Indonesian insurance business is shaken by the crisis that rocked multiple insurance companies. This study investigates insurance brokerage institutions’ involvement in Indonesia’s insurance activities and how to improve public trust. This research uses normative legal research by examining library materials or secondary data. In contrast, the nature of the research is analytically descriptive, depicting the existence of insurance brokerage institutions in the Indonesian insurance industry for further analysis to complete the picture of brokerage companies’ role in improving the industry. The research found that insurance brokers help insureds choose an insurance carrier, handle the claims process, and resolve claim disputes, making them crucial to Indonesian insurance. Brokers can gain public trust by increasing insurance literacy, transparency of insurance product information, helping consumers resolve insurance claims and advising the Financial Services Authority on insurance regulations. To gain public trust in insurance, the insurance industry should prepare a more effective and comprehensive set of regulations to prevent legal uncertainty and provide adequate legal protection to customers. At a micro level, every regulation prepared by the company can provide certainty. The company’s ethics and corporate governance policy is explicit.
EU’s AI Regulation Approaches and Their Implication for Human Rights
Authors: Fitriyah Aidatul , Abdulovna Daryna Dzemish
Published: October 2024

The rapid development of Artificial Intelligence (AI) technologies presents significant opportunities, but also introduces critical challenges, particularly concerning the protection of human rights. In response, the European Union (EU) has developed comprehensive AI regulations, most notably the AI Act, positioning itself as a leader in global AI governance. However, questions remain regarding the effectiveness of these laws in safeguarding key human rights such as privacy, equality, autonomy, and freedom. This study aims to assess the sufficiency of the EU’s current AI regulations in protecting human rights, analyze the specific impacts of AI technologies on privacy and equality, and explore emerging legal trends in AI regulation within the EU and their broader global implications. Utilizing a mixed-method approach, this research integrates legislative analysis, comparative research, scientific forecasting, and interdisciplinary inquiry. The study critically examines the provisions of the AI Act and other relevant EU regulations, comparing them with international legal frameworks to identify gaps and future challenges. The findings reveal that, while the EU’s AI regulations offer foundational protections, there are significant deficiencies in addressing complex issues such as privacy, algorithmic bias, and transparency, particularly in high-risk areas like healthcare, law enforcement, and autonomous systems. The study also highlights that existing regulations are inadequate in managing the rapid integration of AI technologies across sectors and that marginalized groups are especially vulnerable to the risks posed by algorithmic decision-making systems. Furthermore, the EU’s AI regulatory framework is poised to become a global benchmark, but there is an urgent need for more adaptive and flexible legal mechanisms to keep pace with the dynamic nature of AI advancements. In conclusion, the study emphasizes the need for legal reforms to bridge the current gaps in privacy protection and address algorithmic bias, ensuring the robust protection of human rights in the age of AI.
Environmental management of microplastics and additives: a critical review of treatment technologies and their impact
Authors: Sawma Marie Joe , Zayyat Ramez M. , Ghaddar Rand , Ayoub George M.
Published: October 2024

Microplastics (MPs) and their associated chemical additives, such as bisphenol A (BPA), nonylphenol (NP), nonylphenol ethoxylate (NEPO), and tetrabromobisphenol (TBBPA), are widely recognized as significant environmental contaminants due to their widespread presence in ecosystems and their potential impact on human health. This review evaluates advanced treatment technologies, such as membrane filtration and oxidation processes, for mitigating these risks and highlights gaps in sustainability and efficiency. Actionable strategies for improving the removal of MPs and MP additives were presented in the assessment of innovative hybrid treatment tailored to different water matrices. The importance of anti-fouling technologies, comprehensive life cycle assessments (LCAs), and standardizing treatment methods to enhance the sustainability and applicability of these technologies were further highlighted. To further improve and optimize treatment processes and ensure sustainability, existing knowledge gaps must be addressed by framing a comprehensive understanding of the long-term ecological effects of MPs and their additives.
ATRIBUTOS QUÍMICOS E VARIAÇÃO NO ESTOQUE DE CARBONO EM SOLOS DE CERRADO SOB PASTAGEM
Authors: Oliveira Guida Thiago , Tonani Flavia Lucila , Bonfim Ribeiro dos Santos Domingos , Nogueira de Souza Rosinete
Published: October 2024

A atividade agropecuária contribui para o aumento de gases de efeito estufa, como o dióxido de carbono, devido a desmatamentos, especialmente no Brasil, onde o Cerrado perdeu 13% da cobertura vegetal entre 1990 e 2010. Este estudo visa quantificar o estoque de carbono em solos de Cerrado sob diferentes estados de degradação, incluindo áreas de mata nativa, pastagem recente, pastagem em bom estado e áreas de pastagem degradada. O estudo foi realizado na Fazenda Alta Mira, no Tocantins, e as variáveis estudadas incluem densidade do solo, teor de umidade, teor de matéria orgânica e carbono no solo, cálculo de calagem e adubação e estoque de carbono no solo. O delineamento experimental utilizado foi o inteiramente casualizado em parcelas subdivididas, sendo que nas parcelas o sistema e nas subparcelas a profundidade, com 4 repetições em cada profundidade, totalizando 32 parcelas. As variáveis estudadas foram: concentração de nutrientes, densidade do solo, teor de umidade, teor de matéria orgânica, carbono e estoque de carbono no solo. A análise mostrou que os sistemas de pastagens melhoraram a fertilidade natural do solo e mantiveram a condição natural do solo com relação ao estoque de carbono.
Islamic bank stability and efficiency: A cross-country analysis
Authors: Fakhrunnas Faaza , Boubechtoula Younes , Nahda Katiya , Rezoanul Hoque Mohammad
Published: October 2024

Purpose ― The study investigates the impact of the efficiency of Islamic banks on banking stability. Method ― A panel data analysis using the Least Square Dummy Variable Corrected (LSDVC) method is employed to examine the impact of efficiency on banking stability in Islamic banks. The study has a sample of 54 Islamic banks across eight countries from 2013 to 2021.Findings ― The findings reveal that the efficiency of Islamic banks has a positive and significant effect on banking stability. In addition, financial turmoil negatively and significantly affects the stability of Islamic banks but does not significantly affect institutional development. Additionally, financial turmoil can influence how effectively Islamic banks manage their businesses in response to banking stability. The outcomes are robust across various robustness methods. Implications ― The results imply that the efficiency of Islamic banks has a pivotal role in banking stability, considering the efficiency level. To ensure the stability of Islamic banks, practitioners and regulators of Islamic banks have to achieve and maintain the efficiency of Islamic banks by implementing the required policies and guidelines.Originality/Value ― Previous studies examining the impact of Islamic banks' efficiency on banking stability remain limited. The paper fills the research gap by examining how Islamic bank efficiency affects banking stability, considering the effects of financial turmoil and institutional development.
STEM-Project based Learning in Physics Concept of Measurement to Enhance High School Students' Scientific Literacy
Authors: Zaida Ilma Arina , Utami Estri , Kusumaningtyas Dian Artha
Published: October 2024

Students' scientific literacy in understanding physics concepts still needs to improve. This study aims to determine scientific literacy using STEM Project-based Learning (PjBL) in learning physics, especially measurement concepts. The study used the Classroom Action Research (CAR) method with the Kemmis and McTaggart model, including planning, acting, observing, and reflecting. The subjects were students in the tenth grade of SMA Negeri 8 Yogyakarta for the 2023/2024 academic year, consisting of 33 students. The data collection techniques and instruments used a written test with three questions describing scientific literacy, observation, and documentation. STEM-PjBL through project activities carried out in two learning cycles. In the first cycle, the teacher implemented a project for making posters regarding measuring instrument specifications, while in the second cycle, there was a project for making miniature measuring instruments. After the first cycle, students' scientific literacy increased to 0.48 with a moderate category. In the second cycle, it increased to 0.68. Using STEM-PjBL is thought to have just a modest improvement. These results underline the potential of STEM-PjBL as a fundamental teaching tool to improve students' scientific literacy as they show how well it fosters a strong awareness of physics ideas. Future studies could examine the effects of STEM-PjBL on more scientific ideas or within varied educational environments.
Motif Ziarah Makam Raja Brawijaya V dalam Perspektif Pilgrimage
Authors: Mujibuddin M.
Published: October 2024

Penelitian ini bertujuan untuk mengungkap motif pengunjung situs Brawijaya V dan Putri Campa di Trowulan Mojokerto, Jawa Timur. Situs Trowulan merupakan bukti kerajaan Majapahit menguasai Nusantara. Makam Brawijaya V dan Putri Campa tidak hanya dikenal sebagai situs sejarah tetapi juga tempat suci. Artikel ini merupakan studi lapangan etnografi dengan metode pencarian data menggunakan wawancara, observasi, dan teori haji untuk menganalisis hasil penelitian. Selain dikenal sebagai situs sejarah, Makam Brawijaya V dan Putri Campa juga dikenal sebagai surga dan pengasihan. Ada tiga motif yang melatarbelakangi para pengunjung makam tersebut. Yang pertama adalah motivasi haji. Bukti ekspedisi adalah mengikuti prosesi ritual peziarah, memilih hari suci, dan mengikuti perintah juru kunci. Kedua, peziarah sekaligus wisatawan. Peziarah dan wisatawan telah mengindikasikan wisatawan mereka mengunjungi kuburan pada hari libur untuk mengisi waktu luang mereka. Pengunjung juga berdoa secara mandiri tanpa dimediasi oleh juru kunci. Ketiga, wisatawan yang melakukan kunjungan tanpa motif religi. Dengan demikian, ziarah tidak hanya dilakukan di tempat-tempat suci tetapi juga di tempat-tempat bersejarah yang sakral.
PENGENALAN KETERAMPILAN MEMBACA ANAK USIA DINI DI TK IT QUANTUM MULIA KROYA
Authors: Wahyu  , Anjarwati Risna , Nurbaiti Amalia
Published: October 2024

The introduction of early childhood reading skills at the Quantum Mulia Kroya Islamic Kindergarten is carried out through learning and playing activities which are based on three important activities: First, planning activities that organize the goals, time, and materials that have been determined for learning and playing activities, and teachers who already understand the important tasks in teaching. Second, learning and playing activities that organize bound books as a medium and learning resource; habituation as the basis for learning activities; and the preparation of a reading corner to support learning in one learning activity. Third, assessment activities are carried out by organizing the Children's Achievement Book and direct observation which functions to assess children's objective results in learning objectively and comprehensively. Through these three learning and playing activities, early childhood at TK IT Quantum Mulia Kroya can develop their reading skills recognition well.
An Eco-linguistic Analysis of Urban Landscape and Social Critique in James Joyce's Dubliners
Authors: Ajmal Muhammad , Siddiqui Safia , Hafeez Amber
Published: October 2024

The main objective of this research is to revisit Stibbe (2015) framework of Eco-linguistics where he believes that it analyses language to reveal the stories we live by, judges those stories from an ecological perspective, resists damaging stories and contributes to the search for new stories to live by. This study examines James Joyce's Dubliners through an eco-linguistic perspective, focusing on the interplay between language, environment, and social critique. The detailed descriptions of Dublin's urban landscape and ecological symbols reflect the characters' inner lives and critique early 20th-century social conditions. The analysis draws on theoretical contributions from eco -linguistics and environmental criticism, emphasizing the use of linguistic landscapes and ecological metaphors to convey themes of stagnation, aspiration, and interconnectedness. By immersing the reader in the physical and social environments of Dublin, Joyce's language deepens empathy for the characters and enhances understanding of broader socio-environmental issues.
Practices of critical challenges during requirements implementation in global software development:A systematic literature review
Authors: Bacha Mohim , Shah Ibrar Ali , Abrar Muhammad Faisal , Yaseen Muhammad
Published: October 2024

Context: Finding practices of critical challenges are essential in global software development (GSD).Practices of different challenges have discussed in literature in dispersed form that need a proper method to make them useful.Objective: To find out the practices of critical challenges in GSD. New practices helps team to work collaboratively with new products and features and to improve the overall quality of the development process. Some challenges have different background with unique practices which can be find out through a proper process. Method: Systematic literature review (SLR) is used to find out the practices of critical challenges in GSD.Result: Some common practices are ‘Use of synchronous and asynchronous communication technologies’, ‘Use of modern tools and technologies for GSD’, ‘Frequent/regular agile meetings’, ‘Role of effective management’, ‘Process maturity’, ‘Role of liaison’ and ‘Clear roles and responsibilities of duty’.
Traﬃc Road Congestion System Using by the Internet of Vehicles (IoV): A Systematic Literature Review
Authors: Latif Nawera , Kanwal Sawera , Farooq Muhammad Shoaib
Published: October 2024

Traﬃc problems have increased in modern life due to the signiﬁcant number of vehicles, urbanization, and non-compliance with traﬃc regulations. Vehicular ad hoc networks (VANETs) have made improvements to the traﬃc system in the past, playing a crucial role in establishing eﬃcient traﬃc control systems in large cities. However, VANETs alone cannot effectively address certain problems under speciﬁc conditions. Presently, the development of new Internet of Things (IoT) technologies has enabled collaborative and eﬃcient task execution. This technology has been implemented in the transportation system, transforming it into an intelligent transportation system (ITS), known as the Internet of Vehicles (IoV). This study investigates traﬃc issues within the traditional system and explores the beneﬁts, enhancements, and reasons for improving IoV through a comprehensive Systematic Literature Review (SLR). The SLR approach involved targeted searches using multiple search phrases, including 25 articles published between 2016 and 2023. Furthermore, discussion on the necessary IoV technologies and tools required to establish IoV and address speciﬁc traﬃc challenges. Simulation of Urban Mobility (SUMO) is employed for the design and simulation of road traﬃc and we aim to contribute to the development of an optimal traﬃc control system. This paper analyzes two vehicular congestion control models, selects the most optimized and eﬃcient model, and provides evidence for its effectiveness through a Systematic Literature Review (SLR)-based investigation based on its eﬃcient features, in the end, we propose IoV based on vehicular clouds as a superior model, surpassing the capabilities of the traditional model and enhancing the network system.
Adaptive Bug Localization Framework for Precision-Driven Bug Localization in Software Engineering
Authors: Ali Waqas , Soomro Saima siraj , Lakho Shamshad , Bhatti Nadeem Naeem , Memon Imran Ali
Published: October 2024

Software development always looks for automated methods to improve productivity and accuracy in issue detection. The paper conducts a comparative examination of several machine-learning techniques to tackle the bug localization difficulty. Our study compared the performance of Logistic Regression (LR), Random Forest Classifier (RFC), Support Vector Machine (SVM), Gradient Boosting Classifier (GBC), and Adaptive Bug Localization System (ABLS) on five dataset versions. The results demonstrate the superior performance of ensemble learning methods. The ABLS model regularly beats other models regarding F1 score, accuracy, and recall, indicating its strong potential for precise problem localization. The study highlights the necessity of continuously adapting models to tackle idea drift in dynamic datasets. Our research suggests a path for future endeavours involving improving feature engineering and integrating real-time online learning to sustain high performance in bug localization activities.
Generating synthetic data in biomedical imaging by designing GANs
Authors: Awan Tehreem , Mushtaq Muhammad Dawood , Shahzad Muhammad Amir , Ghani Usman , Muhammad Tariq  , Noon Serosh Karim
Published: October 2024

Recent advances in deep learning techniques have made medical analysis available with enhanced accuracy and efficiency, where brain tumor classification is automatically identified in an influential role. Hence, one of the synthesized approaches of an innovative idea to use GANs in this paper development is the synthesis of T1-weighted and post-contrast ischemic stroke brain MRIs to increase performance in the classification of the mentioned diseases according to deep learning. This paper, therefore, has the following objective: to evaluate the efficiency of GAN-generated images in learning deep in the transfer learning models and the performance in both tumor and non-tumor brain images. We use the two main architectures of GAN in our process: Vanilla and Deep Convolutional GAN (DCGAN). Details of the three major deep transfer learning models below portray the Convolutional Neural Network (CNN), MobileNetV2, and ResNet152v2. This learned weight would become a pre-trained representation of the models combined with the augmented dataset for feature extraction and classification purposes. I.e., where transfer learning is applied in the models, it is way more accessible for those architectures of the neural network to tap into the knowledge learned by the former from large-scale datasets and adapt it for tasks at hand on classifying brain tumors. Concerning training and validation, Python programming language integrated with the Keras deep learning framework was employed to implement the indicated operations. In terms of training, GPU processing power was available to allow the model to learn faster. In this regard, this was incorporated with the GPU processing by using the NVIDIA GeForce RTX 2060 GPU. Both Vanilla GAN and DCGAN have counterparts when generating images.
IoT-Driven Approaches for Early Detection and Monitoring of Heart Disease: Current Trends and Future Directions
Authors: Areebah  , Butt Muhammad Rabbi , Aslam Faheem
Published: October 2024

Cardiovascular disease (CVD) is a serious disease having a widespread effect on individuals across the world. Early and accurate detection of cardiac disease is crucial in healthcare, especially in the domain of cardiology. Currently, a non-invasive ultrasound imaging method is used that evaluates the structure, performance, and blood, allowing for the precise identification of a number of cardiac ailments, such as valve problems, heart failure, and congenital anomalies. These traditional techniques have some limitations, including high cost, the need for medical expertise and equipment, and the fact that they often create incorrect results due to human involvement. Furthermore, the traditional method takes more time to predict heart disease. Electrocardiogram (ECG) signals play a critical role in reducing death rates caused by CVDs, and they provide details regarding the heart patient’s health to a medical expert by employing an automated heart failure detection system. Recent developments in deep learning-based health care systems, such as ECG signal analysis, include CNN, LSTM, and other neural networks. In this research, we provide a hybrid deep learning based approach for the timely and accurate diagnosis of cardiovascular disease. The proposed system uses a hybrid of convolutional neural networks (CNN) and long short-term memory (LSTM) and utilizes the MIT-BIH ECG signal dataset for heart disease diagnosis. This study uses two different approaches with MIT-BIH arrhythmia imbalanced and balanced datasets. The first approach uses CNN and CNN-LSTM with an imbalanced dataset, and the second approach uses CNN and CNN-LSTM with a balanced dataset. The performance of both approaches was analyzed. The experimental outcomes show that the overall performance of both CNN, CNN-LSTM was excellent on a balanced dataset compared to imbalanced dataset. The proposed system achieved a better result than the previous suggested methods. Additionally, it is easy to adopt the suggested technique in the field of healthcare in order to identify heart disease.
Enhancing MBTI Personality Prediction from Text Data with Advance Word Embedding Technique.
Authors: Ashraf Nimra , Ahmad Rao Sohail , Bano Shehar , Azeem Hafiz Muhammad , Naz Shagufta
Published: October 2024

Understanding human personality traits is crucial for various domains, including psychology, education, and human resources. The Myers-Briggs Type Indicator (MBTI) is a widely recognized psychological assessment tool, categorizing individuals into one of sixteen distinct personality types. The existing methodologies, which primarily relied on Word2Vec embeddings and traditional machine learning models, showed promise but left room for improvement. To address this problem, this research focused on enhancing Myers-Briggs Type Indicator (MBTI) personality prediction from text data through advanced word-embedding techniques, specifically GloVe and BERT. The research investigates the effectiveness of various Machine Learning Classifiers, including Random Forest, XGBoost, LinearSVC, SGD, Logistic Regression, and CatBoost, in predicting MBTI personality types. Additionally, the impact of preprocessing techniques such as text cleaning, tokenization, TF-IDF vectorization, GloVe, and BERT embeddings on classification performance is examined. Furthermore, the research explores strategies for addressing class imbalance through upsampling techniques. Results indicate high accuracy and performance across multiple classifiers, with XGBoost achieving the highest accuracy of 97.33%. The analysis of MBTI dimensions reveals nuanced insights into the classifiers' ability to capture specific personality traits.
Machine Learning Techniques for Cyber Security in Internet of Robotic Things
Authors: Raza Asad , Memon Shahzad , Nizamani Muhammad Ali , Dhomeja Lachhman Das , Memon Nisar , Charan Khalid
Published: October 2024

Robots are becoming common in domestic, medical, industrial, entertainment, and educational routine activities. The use of robots automates the work processes thus minimizes human labor. The Robots perform complex and repetitive tasks with efficiency and agility, therefore, the conventional industrial manufacturing process are being replaced by smart manufacturing. Robotics encompasses the design and development of robot-based automated systems. It integrates various emerging technologies i.e. operational technology (OT), cloud computing, and artificial intelligence (AI). The Internet of Robotic Things (IoRT) seamlessly combines robots and Internet of Things (IoT) devices, enabling connectivity through the Internet. IoRT enables simple robots to coordinate with each other to achieve well-defined goals by creating a multi-robot system. Cyber security is an inherent challenge for IoRTs because of the interconnected infrastructure and reliance on critical industrial operations on the internet. Any cyber-attack can affect the ongoing operations and compromise the safety of robots. The growing interest among governments, researchers, and industries in robotics and automation demands a dependable cyber-security solution. This paper explores machine learning (ML) based cyber security solutions to mitigate cyber vulnerabilities and threats to IoRT and its dependent systems.
Early Detection and Prediction of Pests in Field Crops Using Transfer Learning
Authors: Haider Zeeshan Ali , Khan Fida Muhammad , Khan Inam Ullah , Khan Muhammad Ali , Khan Rahim
Published: October 2024

This research study addresses the problem of early detection and prediction of pests in field crops. The primary objective of this research is to identify and distinguish pest species from an open-source dataset that includes 5,494 images across 12 classes. We developed an efficient model with a high probability of detecting pests in field crops using pre-trained models such as EfficientNetV2 and deep learning techniques. We applied hyperparameter tuning to the model to enhance its accuracy. Our proposed model is designed to detect and predict pests at an early stage, thereby preventing crop damage. Experimental results demonstrate that the performance of the proposed model is more accurate and precise compared to state-of-the-art existing studies. The F1 scores of the model for different classes of pest images are as follows: Ants 0.96, Bees 0.98, Beetles 0.97, Caterpillars 0.98, Earthworms 0.95, Earwigs 0.97, Grasshoppers 0.96, Moths 0.96, Slugs 0.97, Snails 0.99, Wasps 0.99, and Weevils 0.98. The overall accuracy of the model across all classes is 97.17. These results demonstrate the improved performance of the proposed model for early pest detection. In the agricultural sector, this model can be immensely beneficial, aiding in quick, accurate, and reliable pest detection to support decision-making processes. Identification of pest occurrence at their early stages leads to actions on interventions, which helps in reducing crop losses avoids unnecessary spraying for chemicals, and ensures sustainable eco-friendly agricultural practices. An approach like this would help in maintaining food security and economic sustainability of farmer communities.
Revolutionizing Network Intelligence: Innovative Data Mining and Learning Approaches for Knowledge Management in Next-Generation Networks
Authors: Khan Daud , Khan Haseeb , Khan Muhammad Abrar , Ahmad Waqas , Shaukat Zain , Islam Shahab Ul
Published: October 2024

The Information and Communication (ICT) industry, a global giant among service sectors, is known for both its massive scale and its unforgiving demands. Here, downtime is unacceptable, requiring constant high availability – often at the stringent Sigma Six standard. Redundancy is a common solution, but it comes at a cost. To meet these demands proactively, the ability to predict load and growth becomes crucial. This project aims to develop a prototype, or proof of concept, that utilizes data mining to provide early warnings and growth forecasts for the ICT industry with good accuracy. Big data is key to making discoveries in any data analysis project. Normally, this data comes from real-time system logs. However, for this initial test, I used a dataset called MIT Reality Mining. This dataset is useful because real-world companies, especially in the tech industry (ICT), are often hesitant to share their current information. By using MIT Reality Mining, I could still find trends and potential reasons behind them in the ICT industry. It's important to remember that this is a limited functionality prototype. While it can serve as a guideline for Telcos looking to implement data warehouses, the actual implementation details will need to adapt to the specific needs of each industry.
Security Based SCURM Process (SUBSCRUM) For Secure Agile Development
Authors: Maher Zulfikar Ahmed , Ghulam Ali , Koondhar Muhammad Yaqoob , Mahar Iftekhar Ahmed , Hyder Mansoor
Published: October 2024

This study presents an integrated approach for the consideration of security during agile development. A security-based SCRUM process (SBSCRUM) is introduced by taking advantage of the security patterns presented by security experts which contains the solution for a security threat. In the proposed security based SCRUM process the SCRUM team will be responsible for identifying the expected security threats to the system, in which all the expected threats to the system are modelled using mis-use case diagrams and then these modelled threats are mitigated using the security patterns which provide detailed solution to mitigate the threats and at final stage the security testing is performed to ensure that all the security threats identified are mitigated using security patterns. SBSCRUM is evaluated through the case studies in this paper. SBSCRUM will enable the software developers to develop secure software using agile development methods which lacks in the past.
Optimisation of Sentiment Analysis for E-Commerce
Authors: Bakhsh Pir , Ismail Muhammad , Khan Muhammad Asif , Ali Muhammad , Memon Raheel Ahmed
Published: October 2024

Sentiment analysis is widely used today to make data-driven decisions in different industries, starting from marketing and including brand management, reputation monitoring, and customer satisfaction analysis. Its growing importance is closely linked with so-called ‘word-of-mouth’ communication, from reading online reviews to writing comments on social networks. Effective separation of sentiments ensures that companies' responses are timely and critical patterns are seen in big data sets. Statistical measures, information gain, correlation-based approaches, etc, have been employed for the feature selection. Still, the problem associated with text data mining is that they don’t convey the text's relative difficulty and additional features. To fill this gap, our research proposes a new feature selection technique through Ant Colony Optimization (ACO) and K Nearest Neighbour (KNN) performed on 28,000 customer reviews in different product categories. The results, therefore, showed an overall accuracy of 80.1%, with the Support Vector Machine (SVM) set at 80.5% on each selected feature, which was slightly higher than the Convolutional Neural Network (CNN), which scored a 78.41% accuracy. SVM remains on the mark of 83%, and for CNN, the rate achieved on the same was 80.8% when both were applied to the entire dataset. These facts rejected the infallibility of the simple and complex algorithms used singly in the sentiment classification, indicating that more sophisticated algorithms like ACO and KNN can provide business solutions to improve their service delivery based on customers’ feedback.
BERT Model Adoption for Sarcasm Detection on Twitter Data
Authors: Javed Tayyaba , Nouman Muhammad Asif , Zahid Rushna
Published: October 2024

Sarcasm is a term used to criticize someone's feelings. Sometimes, humans are not able to identify sarcastic comments, and they typically express the reverse of what they mean when they make snarky remarks. Therefore, the detection of sarcasm within a text automatically is a difficult task. Its significance in enhancing sentiment analysis has also made it an important study field. In previous studies, different approaches to deep learning (DL) and machine learning (ML) have been explored. However, previous approaches mainly depend on the lexical and linguistic aspects. Therefore, these techniques could not perform well in the context of sentiment accuracy. In this research, an efficient approach for detecting sarcasm is proposed. A Bidirectional Encoder Representation from a Transformer (BERT) is proposed to improve the sentiment accuracy in this research. This research also aims to compare the two models of deep learning, the BERT and LSTM (Long Short-Term Memory) models. This comparative analysis aims to provide a detailed overview of the pros and cons of each approach for the detection of sarcasm. The primary aim of this study is to examine the different existing ML and DL approaches for the identification of sarcasm. Apart from this, the comparison of BERT and LSTM contributes to the ongoing debate about whether models work best for sarcasm detection in social media. In this study, sentiment analysis's accuracy is improved by making better decisions, especially when it concerns Twitter interactions.
Machine Learning-based Prediction of African Swine Fever (ASF) in Pigs
Authors: Nadeem Rana Muhammad , zia ullah Sibgha , Tahir Bajwa Muhammad Talha , Mahmood Marwah , Saleem Dr. Rana Muhammad , Maqbool Muhammad Nazam
Published: October 2024

African Swine Fever (ASF) is a contiguous viral disease of the pig with serious economic threats to the pork industry. Early identification of ASF infection is important to support sustainable developments in the ASF industry. There is also a need for a solution to identify the ASF infection as early as possible based on apparent symptoms of ASF to screen the infected animals, that are not targeted in the existing literature. Many machine learning (ML) solutions have been proposed in recent years for the prediction and identification of human, animal, and plant diseases. To deal with ASF in pigs ML-assisted model is proposed for the early identification of ASF infection without medical diagnosis and expert opinion. The data regarding apparent symptoms are collected from Chinese small pig farms. The loss of appetite, weakness, diarrhea, vomiting, coughing, skin redness, and breathing difficulty levels are taken as major apparent symptoms of ASF infection. Moreover, different ML models are also evaluated for their performance in the prediction of ASF infection based on selected apparent symptoms of ASF infection. In this regard, Support Vector Machine (SVM), K-Nearest Neighbor (k-NN), Decision Tree (DT), Random Forests (RF), and Gaussian Naïve Bayes ML models are evaluated for ASF infection prediction. The implementation of the proposed solution reveals that the GNB model is more accurate as compared to the other evaluated models for the identification of ASF infection from the apparent ASF symptoms in infected pig animals, with 94.31\% accuracy. The proposed solution would be very effective in the early screening of ASF-infected pig animals without medical diagnosis and expert judgment.
X-ray Image Analysis for Dental Disease: A Deep Learning Approach Using EfficientNets
Authors: Hasnain Muhammad Adnan , Ali Zeeshan , Maqbool Muhammad Sajid , Aziz Musfira
Published: October 2024

Dental cavities are a highly common persistent dental problem that impacts populations across different age groups on a global scale. It is crucial to get a dental issue diagnosed as early as possible and with as much accuracy as possible to treat it efficiently and prevent any related issues. If a dental infection is not treated, it will eventually grow and cause tooth loss. Dental X-ray images are crucial and beneficial in the diagnostic process of dental diseases for dentists. By applying Deep Learning (DL) techniques to dental X-ray images, dental experts can efficiently and precisely detect dental conditions, including dental cavities, fillings and implants. The objective of this research is to assess the performance of DL-based methods for dental disease detection via panoramic radiographs. In this study, we evaluated the performance of all of the EfficientNet variants (e.g., EfficientNets B0-B7) to determine which one is the most effective model for detecting dental disease. Moreover, we utilized the Borderline Synthetic Minority Oversampling Technique (SMOTE) to cope with the issue related to the minority classes contained in the dataset. To assess the efficacy of the model, various metrics are employed, including recall, accuracy, precision, loss, and F1-score. As a result, the performance of the EfficientNet-B5 model was superior to that of the other EfficientNet models. The EfficientNet-B5 model achieved the following values for its metrics: F1-score, accuracy, recall, AUC, and precision: 98.37%, 98.32%, 98.32%, 99.21%, and 98.32%, respectively. The accuracy rates for the EfficientNet-B0, EfficientNet-B1, EfficientNet-B2, EfficientNet-B3, EfficientNet-B4, EfficientNet-B6, and EfficientNet-B7, are 91.59%, 94.12%, 93.28%, 85.71%, 94.96%, 96.64% and 90.76%, respectively. The results indicated that the EfficientNet-B5 model performs better than other EfficientNet classifiers, which supports dental professionals significantly in the recognition of dental diseases.
Applying Neural Networks to Predict Ventilator Demand: A Study of Pakistan's Healthcare Sector
Authors: Abbasi Mohsin Mubeen , Daniyal Syed Muhammad , Abro Abdul Ahad , Hussain Dilbar , Amjad Usama , Zahid Noman Bin
Published: October 2024

The distribution companies that deal with ventilators in Pakistan face challenges related to inventory control because of inadequate product shelf life, shortages, excess inventory, and unnecessary stock. This study, which focuses on Pakistani ventilator distribution companies, aims to offer a novel approach to sales estimation, avoid unnecessary stock expenditures, and stop clientele loss brought on by ventilator shortages. The results of this study will help determine key elements and standards that Pakistani distributors of ventilators might employ to boost sales. Most ventilator distribution businesses in Pakistan are independent wholesalers that purchase stock from their stores and distribute it to customers. To maximize ventilator distribution firms' sales for various products, this study examined distribution and sales data from 2019 to 2024 for many locations and dates. To create an accurate sales forecasting model for a ventilator distribution company, this research also aims to apply artificial neural networks (ANN) for effective sales prediction. An Artificial Neural Network (ANN) model was trained using a dataset from ventilator distribution businesses and the proposed model produced an accuracy of 90%, which is good for early prediction.
The Study on Installation Standards for Solar Road Studs in CPTED Project
Authors:   Korean Association of Public Safety and Criminal Justice, Lee Hyung Bok
Published: October 2024

This study aims to establish installation standards for solar road studs, which are frequently utilized by law enforcement and municipal authorities as part of Crime Prevention Through Environmental Design initiatives. In the wake of the Gangnam Station incident, there has been a significant increase in societal concern regarding women's safety, leading to greater attention on solar road studs as a means to enhance pedestrian safety in poorly lit alleyways. Solar road studs are devices that store solar energy during the day and emit light at night, offering benefits such as low maintenance costs and environmental sustainability.
This research, using the case of Daejeon City, assesses the psychological impact of the color and spacing of solar road studs on the perceived safety of participants. The findings reveal that green solar road studs installed at 4-meter intervals provide the highest sense of security among participants. Although white solar road studs may also be effective in low-light conditions, their combination with street lighting may reduce visibility, necessitating careful consideration.
Based on these findings, this study proposes optimal installation standards for solar road studs to maximize their effectiveness in crime prevention. It specifically recommends the use of green solar road studs at 4-meter intervals as the most effective configuration. The study aims to clarify installation guidelines to enhance the crime prevention potential of solar road studs, thereby offering practical insights for practitioners in the field.
Conditional learning increases the inhibition of layer 4 excitatory neurons by somatostatin- and parvalbumin-expressing interneurons in the barrel cortex
Authors: Kanigowski Dominik , Urban-Ciecko Joanna
Published: October 2024

Synaptic strength underlies information processing, learning, and memory storage, yet little is known about how learning impacts synaptic inputs and outputs of specific GABAergic interneurons, particularly in the somatosensory cortex. Using a simple conditional model of learning in mice, where whisker stimulation was paired with a tail shock, we investigated plastic changes of inhibition mediated by somatostatin- (SST-), parvalbumin- (PV-), and vasoactive intestinal polypeptide-expressing interneurons (VIP-INs) in the barrel cortex. In vitro patch-clamp recordings and optogenetics revealed that conditional learning increases SST-IN and PV-IN synaptic outputs onto layer 4 (L4) excitatory neurons. A small fraction of L4 excitatory neurons in the barrel cortex receives inhibition from local VIP-INs, but learning does not affect this inhibition. Additionally, learning does not alter excitatory inputs to all three interneuron types. These findings suggest that enhanced inhibition by SST-INs and PV-INs may improve information processing and memory coding in L4 of the barrel cortex.