bluuebunny/crossref_metadata_2025 huggingface dataset:
Connected Safety: Revolutionizing Security with Smart Wearables and IoT Innovations
Authors: Krishna Pasupuleti Murali
Published: December 2024

Abstract:
This chapter explores the transformative role of smart wearables and Internet of Things (IoT) technologies in revolutionizing security and safety across personal, workplace, and urban environments. By integrating real-time data transmission, advanced sensors, and IoT ecosystems, these connected systems provide proactive monitoring, predictive analytics, and seamless responses to potential threats. The chapter delves into the evolution of safety solutions, showcasing the applications of IoT-enabled wearables in hazardous workplaces, personal safety for vulnerable populations, and public safety through smart city initiatives. It highlights advancements in wearable technology, such as energy-autonomous devices and enhanced sensor capabilities, as well as the integration of blockchain to ensure data security and authenticity. The challenges of scalability, data privacy, and accessibility are addressed, along with opportunities for global adoption to bridge the gap between developed and developing regions. With success stories and case studies demonstrating their impact, connected safety systems are positioned as pivotal innovations for creating safer and smarter environments worldwide.

Keywords
Connected safety, smart wearables, IoT innovations, real-time monitoring, predictive analytics, wearable technology, blockchain integration, personal safety, workplace safety, public safety, IoT ecosystems, energy-autonomous devices, data security, smart cities, global accessibility.
PENCEGAHAN STUNTING MELALUI PENYULUHAN PADA IBU HAMIL DI PUSKESMAS BANDAR AGUNG, LAMPUNG TENGAH
Authors: Sadiman Sadiman , Yuliawati Yuliawati , Widiyanti Septi
Published: December 2024

Stunting is a global problem, the incidence in the world in 2020 in toddlers is 149.2 million in the world with the highest incidence in Africa 31.7%. In Lampung Province in 2018 the stunting case was 27.28%. Stunting can inhibit the development of intelligence and motor skills and short-term physical growth failure. The method of implementing Community Service is in the form of counseling about stunting which includes: Pretest, counseling with lectures and questions and answers, discussion and posttest. The results showed an increase in the average value of pregnant women's knowledge about stunting pretest 10.867 to 14.067, and there was an influence of pocket book media on pregnant women's knowledge about stunting with a p-value of 0.001. Suggestions for the need for ongoing counseling and pocket books can be a medium for education about stunting in community services.
أسلوب تقديم خدمات آمنة دون انقطاع لمركز البيانات لمدينة (طرابلس)
Authors:   قسم الحاسب الآلي، كلية الهندسة العسكرية تاجوراء، ليبيا, شختور خالد محمد , ريحان احمد أسامة ,   قسم علوم الحاسوب، جامعة الرفاق للعلوم التطبيقية والإنسانية، طرابلس، ليبيا, المرابط تسنيم عبد الرحمن ,   قسم علوم الحاسوب، جامعة الرفاق للعلوم التطبيقية والإنسانية، طرابلس، ليبيا
Published: December 2024

This paper presents a comprehending technology to convince the secure and uninterruptible operation of data centers, addressing the dual challenges of flexibility against disruptions and national security against physical cyber threats. By integrating advanced encryption methods, dynamic access control, artificial intelligence-based anomaly detection, and sustainable energy solutions, the proposed approach provides a new framework for enhancing data center reliability and security. The study brings together interdisciplinary research, including cybersecurity, energy engineering and environmental sustainability, to build a flexible infrastructure capable of supporting vital functions that depend on data centers. The effectiveness of this technique is evaluated. Through a series of simulation scenarios, demonstrating its ability to significantly reduce downtime and mitigate security risks. This research contributes to this field by proposing a comprehensive strategy that not only secures data centers from emerging threats, but also ensures their continuous operation, thereby supporting the growth and reliability of the digital ecosystem.
Keywords: Data center, security, uninterrupted services, access control, sustainable energy solutions, resilience, reliability.
Investigating Noise Pollution Levels in Tripoli City
Authors:   Faculty of Engineering, University of Tripoli-Libya, Ahmida Khaled M. , Shnfier Nada H. ,   Faculty of Engineering, University of Tripoli-Libya
Published: December 2024

Noise pollution is known to have significant implications for public health and overall well-being. In this study, noise pollution levels were analyzed in selected areas of Tripoli, Libya. The investigation focused on seven distinct locations: Martyr’s square, Algeria square, University hospital, Al-Jalaa hospital, Al-Dehmani park, Al-Tholathaa park, and the Second Ring Road. Noise levels were assessed during working days and weekends across three different time periods: morning, afternoon, and evening. The primary objective was to conduct a thorough evaluation and analysis of environmental noise pollution in Tripoli. This was achieved by employing sound level measurements throughout the seven areas. Additionally, dB mapping has been utilized to visualize the spatial distribution and intensity of noise at the surveyed locations. The initial observations of the study revealed fluctuations in noise levels across the surveyed locations. These variations suggest potential correlations with temporal factors and human activities. This study provides valuable insights into the noise levels in Tripoli, thus creating a comprehensive database for further studies. The identification of elevated noise levels in specific areas and during particular periods underscores the necessity for targeted environmental control measures, such as the implementation of noise barriers. Therefore, a thorough investigation regarding noise barriers should be conducted. In general, the outcome of this study revealed that noise barriers appear to be necessary alongside the Second Ring Road, along with the planting of tall and dense trees, which play an important role in reducing noise in the surrounding areas.
Keywords: Noise pollution; environmental noise; noise mapping, Tripoli city.
COMPARISON OF SYNCHRONIZATION SPEED OF RING NETWORKS OF REACTION-DIFFUSION EQUATIONS OF FITZHUGH-NAGUMO TYPE WITH UNIDIRECTIONAL AND BIDIRECTIONAL COUPLING
Authors:   An Giang University, Vietnam National University, Ho Chi Minh City, Vietnam, Em Phan Van Long
Published: December 2024

The purpose of this work is to research the numerical results of the comparison of the synchronization speed of ring neural networks with unidirectional and bidirectional coupling. Each neuron is linearly coupled with the others and represented by a system of reaction-diffusion equations of the FitzHugh-Nagumo type. The result shows that the necessary coupling strength for the synchronization in two cases increases when the number of neurons increases. In other words, the greater the number of neurons in the ring networks is, the more difficult the synchronization occurs. Moreover, synchronizing the ring networks with bidirectional coupling is easier than the one with unidirectional coupling over the same given number of neurons.
PROVING OPTIMAL MODEL SELECTION AND ZERO COEFFICIENT CASES IN TIME SERIES FORECASTING WITH THE GENERALIZED LEAST DEVIATION ALGORITHM
Authors:   Department of System Programming, South Ural State University, Chelyabinsk, Russia, Abotaleb Mostafa
Published: December 2024

This article provides a comprehensive analysis and proof of the Generalized Least Deviation Method (GLDM) in the context of time series forecasting, with a particular focus on optimal model order selection and the conditions that lead to zero coefficients. Central to this study is the GLDM Estimator, which determines the coefficients {aj} n(m) j=1 by minimizing the objective function F(a), defined as the sum of the arctangents of the absolute deviations from observed time series data {yt} T t=1 ⊂ R. The research not only proves GLDM’s ability to capture complex data interactions but also demonstrates its adaptability to varying model orders, showing that the selection of the optimal model order is influenced by the underlying characteristics of the data rather than just the data size. For example, temperature data with pronounced seasonal patterns and autocorrelations demands a fifth-order model, whereas wind speed and COVID-19 death cases in Russia are effectively modeled by a second-order structure. The study further examines the implications of higher-order models, advocating for a customized approach to model selection that enhances both predictive accuracy and interpretability in time series forecasting.
Integrating AI Chatbots in ESL and CFL Instruction: Revolutionizing Language Learning with Artificial Intelligence
Authors: Ying Soon Goh , Binti Abdullah Nurul Ain Chua , binti Abdul Rahman Nurul Ajleaaç , Suyan Zhang , Yiming Chen
Published: December 2024

The integration of artificial intelligence (AI) in language teaching has emerged as a transformative approach, particularly in the realms of English as a Second Language (ESL) and Chinese as a Foreign Language (CFL). This article explores the potential of AI chatbots as effective tools for enhancing language acquisition. By examining the current landscape of AI in language education, we identify the unique benefits that chatbots bring to the learning process, including personalized interaction, immediate feedback, and continuous engagement. The article delves into the design and implementation of AI chatbot systems tailored for ESL and CFL contexts, highlighting their role in vocabulary development, grammar practice, and conversational skills. Furthermore, it addresses the challenges and limitations of using chatbots in language teaching, proposing strategies for overcoming these obstacles. Through case studies and empirical data, the article demonstrates how AI chatbots can be harnessed to create a dynamic and interactive learning environment that caters to the diverse needs of language learners. Ultimately, this work advocates for the thoughtful integration of AI chatbots to complement traditional teaching methods, thereby paving the way for more effective and accessible language education
Surat Al-Baqarah Ayat 188: Prinsip Hukum Islam Untuk Mencegah Penyalagunaan Kekuasaan
Authors: Fairusah Qonita Najmah , Nasrulloh Nasrulloh
Published: December 2024

Penelitian ini membahas penyalahgunaan kekuasaan dalam konteks hukum Islam, dengan fokus pada ayat 188 Surat Al-Baqarah yang melarang manipulasi harta untuk kepentingan pribadi. Dalam sistem pemerintahan, kekuasaan harus dijalankan dengan mempertimbangkan prinsip keadilan, supremasi hukum, dan kesejahteraan masyarakat. Namun, praktik penyalahgunaan kekuasaan sering terjadi, merugikan masyarakat dan menciptakan ketidakpercayaan terhadap lembaga hukum. Penelitian ini bertujuan untuk mengeksplorasi nilai-nilai hukum Islam yang terkandung dalam ayat tersebut dan dampaknya dalam mencegah penyalahgunaan kekuasaan. Metode yang dipakai dalam jurnsl ini adalah pendekatan kualitatif menggunakan studi literatur, dengan analisis isi tematik untuk menggali prinsip-prinsip hukum Islam. Hasil penelitian menunjukkan bahwa keadilan sosial dan integritas merupakan pilar utama dalam ajaran Islam yang dapat mencegah korupsi dan penyalahgunaan kekuasaan. Ayat 188 berfungsi sebagai pedoman moral bagi individu dan masyarakat untuk menjaga keadilan dan transparansi, serta relevan dalam konteks kontemporer mengingat tingginya kasus korupsi di berbagai negara. Diharapkan penelitian ini dapat memberikan wawasan tentang etika hukum Islam dan peaplikasiannya dalam kehidupan sehari-hari guna mewujudkan masyarakat yang adil dan sejahtera.
ANALISIS KINERJA PENDAPATAN DALAM LAPORAN REALISASI ANGGARAN PROVINSI JAWA BARAT TAHUN 2022-2023
Authors: Clara Mutiara  , Hairun Nisa  , Nur Fahria Badraf  , Nurma Ranti Septiani
Published: December 2024

Penelitian ini berisi tentang Analisis Kinerja Pendaptan Dalam Laporan Realisasi Anggaran Provinsi Jawa Barat Tahun 2022-2023. Metode yang digunakan dalam penelitian ini adalah Deskriptif kuantitatif dengan menggunakan data sekunder. Hasil dari penelitian ini menunjukkan kinerja keuangan pendapatan provinsi Jawa Barat yang cukup baik dari beberapa aspek seperti pertumbuhan pendapatan pada PAD dengan rata-rata 7,22%, rasio kemandirian dengan rata-rata 69,35%, rasio efektifitas dengan rata-rata 99,99%, dan rasio efisiensi dengan rata-rata 1,41%. Namun sedikit ada penurunan pada varians dikarenakan lebih kecilnya realisasi pendapatan daripada anggaran yang direncanakan.
Exploring the Industrial Metaverse: Empowering Meta-Operators with Industry 5.0 Principles and XR Technologies
Authors: Kareem Ali Noori , Chyad M.A. , Salman Al-Gburib Zahraa Dawood , Ibrahim Husam Hamid , Sharaf Hussien Kadhim
Published: December 2024

The term "Metaverse" has recently gained significant attention. It refers to a concept aiming to immerse users in real-time 3D virtual worlds using XR devices like AR/MR glasses and VR headsets. When this idea is applied to industrial settings, it's termed the "Industrial Metaverse," where operators leverage cutting-edge technologies. These technologies align closely with those associated with Industry 4.0, evolving towards Industry 5.0 and prioritizing sustainable and human-centric industrial applications. The Industrial Metaverse stands to benefit from Industry 5.0 principles, emphasizing dynamic content and swift human-to-machine interactions. To facilitate these advancements, this article introduces the concept of the "Meta-Operator," essentially an industrial worker guided by Industry 5.0 principles, engaging with Industrial Metaverse applications and surroundings through advanced XR devices. It also delves into the key technologies supporting this concept: Industrial Metaverse components, the latest XR technologies, and Opportunistic Edge Computing (OEC) for interacting with surrounding IoT/IIoT devices. Furthermore, the paper explores strategies for developing the next generation of Industrial Metaverse applications based on Industry 5.0 principles, such as standardization efforts, integrating AR/MR devices with IoT/IIoT solutions, and advancing communication and software architectures. Emphasis is placed on fostering shared experiences and collaborative protocols. Lastly, the article presents a comprehensive list of potential Industry 5.0 applications for the Industrial Metaverse and an analysis of the main challenges and research directions. It offers a holistic perspective and practical guidance for developers and researchers venturing into Industrial Metaverse applications.
Exploring Current Methods and Trends in Text Summarization: A Systematic Mapping Study
Authors: Ahmad Raddi Muhammad Faris Faisal , Hassan Rohayanti , Zakaria Noor Hidayah , Sahid Mohd Zanes , Omar Nurul Aswa , Firosha Ardian
Published: December 2024

This paper presents a systematic mapping study of the current methods and trends in text summarization, a challenging task in natural language processing that aims to condense information from one or multiple documents into a concise and coherent summary. The paper focuses on applying text summarization for the Malay language, which has received less attention than other languages. The paper employs a three-phased quality assessment procedure to filter and analyze 27 peer-reviewed publications from seven prominent digital libraries, covering 2016 to 2024. The paper addresses two research questions: (1) What is the extent of research on text summarization, especially for the Malay language and the education domain? and (2) What are the current methods and approaches employed in text summarization, with a focus on addressing specific problems and language contexts? The paper synthesizes and discusses the findings from the literature review and provides insights and recommendations for future research directions in text summarization. The paper contributes to advancing knowledge and understanding of the state-of-the-art techniques and challenges in text summarization, particularly for the Malay language.
Attributes Classification for Elaborating the Information of Digital and Imaging Mapping
Authors: Mukhaiyar Riki , Mukhaiyar Utriweni
Published: December 2024

The rapid development of information has made it possible for everyone to obtain the latest information, complete and accurate, in real time, anytime, anywhere, all over the world. Any information is fine catching up by updated with the latest news and even detailed information on local conditions. With the same analogy, detailed information regarding land utilization, land containing, landscape provision, earth surface contour, etc., are required to inform and elaborate any appropriate decision needed. The Geographic information systems (GIS) is a recent technology commonly used by research in earth science to facilitate many layered detail information by one way to get up-to-date, detailed information. In this research, the GIS utilizes several types of imaging data such remote sensing images and digitize images. As the name suggests, this system captures detailed geographic information about a location or region. By inputting classified images of remote sensing results into a GIS database at regular intervals (adjusted as necessary, such as every year, every two years, every three years, etc.), the number of information sources that can be obtained increases. There are several reasons for that. First, remote sensing images are images that cover the entire surface of the Earth. Next, remote sensing images are images that contain information about the state of the earth's surface. Third, a variety of information can be obtained by performing appropriate image processing. Furthermore, this research could be elaborated by implementing an artificial intelligent algorithm to create a robust outcome.
An Innovative Approach for Improving Navigation Performance of Robust Land-Based Vehicles
Authors: Ghouneem Mostafa Mahmoud , Wassal Amr
Published: December 2024

The Extended Kalman Filter (EKF) stands as a prominent choice within navigation systems, particularly in scenarios involving the integration of a Reduced Inertial Sensor System (RISS) with the Global Positioning System (GPS). However, despite its widespread adoption, the EKF grapples with many challenges, including the propensity to underestimate filter uncertainties, contend with unreliable GPS signals, and confront errors stemming from linearization processes. These issues invariably contribute to a decline in overall system performance. Considering these challenges, this paper endeavors to introduce a groundbreaking integration algorithm to mitigate the inherent shortcomings of the EKF. The proposed algorithm employs innovative strategies to address these challenges comprehensively. Firstly, it incorporates a dynamic self-tuning mechanism meticulously designed to improve filter configuration in real-time, ensuring adaptability to varying operating conditions. The algorithm also integrates a meticulously engineered GPS Integrity algorithm to filter out mistaken readings and bolster the reliability of the navigation solution. Furthermore, the algorithm adopts the Unscented Kalman Filter (UKF), renowned for handling non-linearities directly, thereby cutting the need for the cumbersome linearization procedures inherent in the EKF. Comparative evaluations against the traditional EKF method prove the effectiveness of the proposed approach. Significant performance enhancements are evident using two datasets from a VTI SCC1300-D04 IMU unit compared to high-precision Novatel SPAN ground truth data. These improvements are quantified through RMSE analysis, showing substantial strides in navigation accuracy. Overall, the results underscore the transformative potential of the proposed integration algorithm in advancing navigation system capabilities.
Predicting and Explaining Customer Response to Upselling in Telecommunications: A Malaysian Case Study
Authors: Abdullah Railey Shahril , Shastera Nulizairos Nur Shaheera , Mohd Ariffin Nor Hapiza , Witarsyah Deden , Maskat Ruhaila
Published: December 2024

This research explores the predictive capabilities of XGBoost (XGB) and Random Forest (RF) models for customer upsell responses, emphasizing the use of Explainable Artificial Intelligence (XAI) techniques to gain insights. Initially trained without hyperparameter tuning, both models were later optimized using 5-fold cross-validation. While RF consistently achieved high accuracy (0.99), XGB exhibited lower accuracy (0.85) yet demonstrated superior precision and recall. Post-tuning, XGB maintained its competitive edge despite a slight decrease in ROC-AUC scores (0.76 and 0.75 versus RF's 0.67 and 0.72), indicating proficiency in classifying positive cases. XAI techniques complemented XGB’s prediction, revealing significant predictors such as inactive duration in days, race (Chinese), total communication count, age, and active period in days. Lesser predictive value was attributed to factors such as race (Indian), gender (female), and region (northern). While the feature importance plot provided a broad overview, it did not detail specific attribute relationships to predictions. To address this, a summary violin plot was employed to illustrate how feature importance varies with actual values, enhancing the understanding of each feature's impact. Results indicated that longer inactivity periods negatively influenced predictions, while non-Chinese ethnicity, higher communication frequency, and younger age were associated with positive outcomes. Dependence plots further elucidated these relationships, highlighting how older non-Chinese customers and those with shorter inactive periods and frequent communication were more likely to accept offers. Local explanations using Shapley's force plot and LIME offered deeper insights into specific instances. Overall, the study underscores the complementary use of XAI techniques to understand a model’s predictions.
The Attitudes of Student-teachers toward Moodle as a Supplementary Learning Platform
Authors: Suparjan - , Purwanta Edi , Septiarti Serafin Wisni , Purnomo Yoppy Wahyu
Published: December 2024

During the COVID-19 pandemic, Moodle (Modular Object-Oriented Dynamic Learning Environment) emerged as a leading online learning platform, providing essential support for educational institutions transitioning to remote learning. However, its role as a supplementary tool alongside traditional teaching methods in non-pandemic contexts remains underexplored in academic research. This study aims to investigate the attitudes of higher education student-teachers toward Moodle as an asynchronous online learning platform that enhances traditional classroom learning. The research employed a descriptive-quantitative method, focusing on students from the Teacher Training and Education Faculty, specifically those in the Elementary School Teacher Education Department at Universitas Tanjungpura, Indonesia. Participants were selected through purposive sampling, targeting individuals with experience using Moodle in online and offline learning environments. Data were collected using a structured questionnaire to assess student-teacher attitudes toward Moodle. It underwent a rigorous validation process to ensure the instrument's validity, including expert reviews. The reliability of the questionnaire was assessed through a pilot test conducted with 25 elementary school student-teachers from a state university. The results indicate that most student-teachers have positive attitudes toward Moodle, particularly valuing its effectiveness in enhancing their traditional learning experiences. Despite these favorable views, further research is necessary to develop a more comprehensive understanding of Moodle as a learning management system. Future studies should explore a broader range of educational contexts and levels to better understand the diverse challenges and benefits of applying Moodle in different teaching environments.
Analysis of the Alignment of Bauran System Features Based on Outcome-Based Education Rules Using Feature-Oriented Domain Analysis
Authors: Wicaksono Galih Wasis , Saleh Abd , Wahyuni Evi Dwi , Setiawan Erwin Budi
Published: December 2024

Implementing information systems in higher education curriculum design is a crucial tool for academics, enabling them to design, develop, and evaluate the curriculum more dynamically, responsively, and structurally. However, it is not just about having a tool. It is about ensuring that the tool aligns with curriculum design standards. This study, therefore, measures and analyses the conformity of the Bauran system as a curriculum management information system with the established stages and standards of curriculum design. The analysis is based on the Indonesia National Standards for Higher Education (Standar Nasional Pendidikan Tinggi (SN DIKTI)) by referring to the Guidebook for Higher Education Curriculum Development in Indonesia and best practices in the implementation of Outcome-Based Education (OBE) curriculum design. The method used in this research is feature-oriented domain analysis (FODA), which includes context analysis, domain modeling, and architecture modeling. Experts in the field of OBE curriculum then validate the results of feature measurement and mapping. The study compares 27 Bauran features to 10 stages in the curriculum design guidebook and nine stages in the OBE curriculum design flow. The analysis results show that the Bauran system has implemented 10 out of 10 stages (100%) of curriculum design according to the curriculum design guidebook. However, Bauran has only implemented 8 out of 9 stages (89%) in the OBE curriculum flow. These findings not only provide feature recommendations for future Bauran development and other higher education curriculum management systems but also highlight the potential of the Bauran system for future development.
Integrating Spatial Computing with Clinical Pathology for Enhanced Diagnosis and Treatment Informatics in Healthcare
Authors: Chituru Chinwe Miracle , Ho Sin-Ban , Chai Ian
Published: December 2024

This paper investigates spatial computing, which is a pathological transformational modern technology that integrates the physical and digital realms and has the potential to revolutionize pathology healthcare. Pathology as a medical specialist plays a crucial role in patient care by providing essential information for diagnosis, treatment planning, and disease monitoring. It studies and diagnoses diseases by examining tissues, organs, bodily fluids, and cells. Pathology is a broad field with three main branches: Anatomic pathology, Clinical pathology, and Molecular pathology. This study investigates the possibilities of spatial computing in radiography and clinical pathology with emphasis on diagnosis accuracy, medical education, workflow efficiency, and the outcomes in the patients. Augmented Reality (AR) medical devices guide pathologists in real-time during diagnostics procedures. The digital reproduction of tissue samples to allow pathologists to examine specimens in three dimensions is a significant utilization of spatial computing in virtual microscopy. This process allows remote collaboration between pathologists and laboratories, provides health informatics as seen in electronic health records (EHRs), improves diagnosis, and presents a platform with learning experiences in the medical field. Patients can interact with three-dimensional simulations of their anatomy, which helps them make more educated treatment decisions provided via the pathology findings and treatment alternatives in an immersive format. As this technology advances, its potential to transform pathology practice and improve patient care remains high. This review describes technological perspectives and discusses the statistical methods, clinical applications, potential obstacles, and directions of spatial computing in clinical pathology.
Deep Learning Approach EEG Signal Classification
Authors: Lew Kai Liang , Sim Kok Swee , Ting Zehong
Published: December 2024

The introduction of deep learning technology has greatly benefited the neuroscience field by improving the electroencephalogram (EEG) signal analysis. These technologies have greatly improved the understanding of complex brain activity by interpreting the signal as normal or abnormal. The EEG signal requires expertise to interpret the pattern, and only then can the EEG signal be differentiated as normal or abnormal. However, some variations always complicate the analysis of the EEG signal by creating noise in the signal. This paper introduces a deep learning model, NeuroNetFlex (NFF), to classify the EEG signal as normal or abnormal. The NNF is designed to classify the EEG signal by using multiple combinations of modules such as one-dimension convolutional neural networks (1D-CNN), Squeeze-and-Excitation (SE) blocks, and the parallel processing fusion of recurrent neural networks (RNN), Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) layers are used to analyze the temporal features of the EEG data and learn the signal pattern. The performance of the NNF was evaluated using evaluation metrics such as accuracy, precision, recall, and f1 score. The model achieved an accuracy of 75.33%, a precision of 76.39%, a recall of 75.33%, and an F1 score of 75.08% with a training time of 16.88 minutes, outperforming the existing models. These results demonstrate the promising potential of the NNF to significantly improve the analysis of brain activities
ChatGPT in Science Education: A Visualization Analysis of Trends and Future Directions
Authors: Festiyed - , Tanjung Yul Ifda , Fadillah Muhammad Aizri
Published: December 2024

ChatGPT, as one of the products of artificial intelligence (AI)-based technology, has shown significant potential in science education. This study aims to analyze the development trends and focal points of ChatGPT research, especially in science education from the Scopus database in 2022-2024. This study used Bibliometric analysis which is a quantitative and qualitative evaluation technique of documents in a database. The search method was carried out with the Dimensions and Publish or Perish (PoP) databases using Scopus and data visualization using VOSviewer. Searches were conducted on article titles, abstracts, and keywords at once (TITLE-ABS-KEY) with the keywords "ChatGPT" and "science education". The results of the bibliometric analysis showed a significant increase in the number of ChatGPT-related publications in the field of science education, with several key topics taking center stage, such as pedagogical adaptation, AI-based learning, and evaluation of technology effectiveness in the teaching and learning process. Visual analysis using VOSviewer identified a clustering of research covering the integration of ChatGPT in the science education curriculum, the role of AI in facilitating collaborative learning, and the impact of using ChatGPT on student motivation and learning outcomes. This suggests that the use of AI, particularly ChatGPT, in science education is a growing area of research with significant potential impact. This research provides a comprehensive overview of recent developments in the use of ChatGPT in the field of science education and provides insights for future research.
Segmentation of Plain CT Image of Ischemic Lesion based on Trans-Swin-UNet
Authors: Luo Zhiqiang , Lim Tek Yong , Hua Xia
Published: December 2024

The present study aims to build a hybrid convolutional neural network and transformer UNet-based model, Trans-Swin-UNet, to segment ischemic lesions of the plain computed tomography (CT) image. The model architecture is built based on TransUnet and has four main improvements. First, replace the decoder of TransUNet with a Swin transformer; second, add a Max Attention module into the skip connection; third, design a comprehensive loss function; and last, speed up the segmentation performance. The present study designs two experiments to evaluate the performance of the built model using both the self-collected and public plain CT image datasets. The model optimization experiment evaluates the improvements of Trans-Swin-UNet over TransUnet. The experimental results show that each improvement of the built model can achieve a better performance than TransUNet in terms of dice similarity coefficient (DSC), Jaccard coefficient (JAC), and accuracy (ACC). The comparison experiment compares the built model with four existing UNet-based models. The experimental results show that the built model had a DSC of 0.72±0.01, a JAC of 0.78±0.04, an ACC of 0.75±0.03 using the self-collected plain CT image dataset and a DSC of 0.73±0.02, a JAC of 0.79±0.03, an ACC of 0.76±0.02 using the public plain CT image dataset, achieving the best segmentation performance among five UNet-based neural network models. The two experimental results conclude that the built model could accurately segment ischemic lesions of the plain CT image. The limitations and future work of this study are also discussed.
Handwritten Hiragana Letter Detection Using CNN
Authors: Fernandi Arya , Sa'idah Sofia , Magdalena Rita
Published: December 2024

Hiragana is one of the primary alphabets used in Japanese. Hiragana is a phonetic symbol; each letter represents one syllable. Hiragana letters are formed from curved lines and strokes. However, detecting Hiragana letters causes many errors because people still rely on their vision to detect the letters, especially people familiar with them for the first time. It will be difficult and not very clear to read the letters. Therefore, a Convolutional Neural Network (CNN) method is used to detect handwritten Hiragana letters and help people who first get to know Hiragana letters when the letters are too complicated for human eyes to detect. This research uses the YOLOv8 model as a handwritten Hiragana letter detection algorithm. The Hiragana letters to be detected are basic letters with 46 characters. This research uses the YOLOv8 model run on Google Collaboratory with the Ultralytics library version 8.0.20 using the Python programming language. The dataset is collected from the internet and annotated using the Roboflow framework and dataset 4600 Hiragana letters. From the test results, the best model is YOLOv8l using SGD optimizer and learning rate 0.01 with a precision value of 98.5%, recall value of 95.7%, f1-score value of 97.1%, and mAP value of 95.5%. In the future, we aim to expand the number of datasets and employ a broader range of hyperparameter values to optimize the classification precision and accuracy of the Hiragana Letter Detection system.
A Comprehensive Review on Cancer Detection and Classification using Medical Images by Machine Learning and Deep Learning Models
Authors: J Jayapradha , Su-Cheng Haw , Naveen Palanichamy , Anaam Elham Abdulwahab
Published: December 2024

In day-to-day life, machine learning and deep learning plays a vital role in healthcare applications to predict various diseases such as cancer, heart attack, mental problem, Parkinson, etc. Among these diseases, cancer is the life-threatening disease that leads a human being to death. The primary aim of this study is to provide a quick overview of various cancers and provides a comprehensive overview of machine learning and deep learning techniques in the detection and classification of several types of cancers. The significance of machine learning and deep learning in detecting various cancers using medical images were concentrated in this study. It also discusses various machine learning and deep learning algorithms that lead to accurate classification of medical images, early diagnosis, and immediate treatment for the patients and explores the methodologies which has been used to predict the cancer with the help of low dose computer tomography to reduce cancer related deaths. As the study narrows down the research into lung cancer, it combats the findings limitations in lung cancer detection models and highlights the need for a deep study of novel cancer detection algorithms. In addition, the review also finds the role of setting up data in lung cancer and the potential of genetic markers in stabilizing the accuracy of machine learning models. Overall, this study gives valuable suggestions to achieve more accuracy in cancer detection and classification using machine learning and deep learning techniques.
Enhancing Early Detection of Melanoma: A Deep Learning Approach for Skin Cancer Prediction
Authors: Al Huda Md Sadi , Ali Md. Asraf , Hossain Ajran , Tuz Johora Fatama , Liew Tze Hui , Sadib Ridwan Jamal , Hossen Md. Jakir , Ahmed Nasim
Published: December 2024

Melanoma, a form of skin cancer, is a substantial global public health threat due to its rising prevalence and the potential for severe outcomes if not promptly identified and managed. Detecting skin cancer lesions in their first stages enhances patient outcomes and decreases mortality rates. The core issue investigated in this research paper is the enduring problem of early skin cancer prediction. In the past, individuals often lacked awareness of their skin cancer condition until it had reached late stages. Consequently, this resulted in delayed diagnoses, which restricted the available treatment options and perhaps led to worse outcomes.  This research focuses on finding key attributes and methods in a specialized dataset to effectively differentiate between benign and potentially malignant skin lesions, particularly the implementation of an early-stage skin cancer prediction model. It aims to accurately categorize skin mole pictures as benign or malignant using a Convolutional Neural Network (CNN) model built within the PyTorch framework. The primary aim of this study was to enhance the accuracy and effectiveness of diagnosing skin problems by implementing deep learning algorithms to automate the process of showing such conditions. The model underwent training using 3600 skin mole images sourced from the ISIC-Archive on a GPU RTX 3080. Its outstanding performance is shown by an F1 score of 0.8496 and an accuracy rate of 85%. This research aims to create a predictive model and offer a practical solution that healthcare professionals can readily use for early skin cancer prediction.
Development of extraction features for Detecting Adolescent Personality with Machine Learning Algorithms
Authors: Wisky Irzal Arief , Defit Sarjon , Nurcahyo Gunadi Widi
Published: December 2024

This study aims to develop a Natural Language Processing (NLP)-based feature extraction algorithm optimized for personality type classification in adolescents. The algorithm used is TF-IDF + N-Gram Z, which combines Term Frequency-Inverse Document Frequency (TF-IDF) with the N-Gram Z technique to improve the feature representation of the analyzed text. TF-IDF functions to measure the importance of words in a document, while N-Gram Z enriches the context by considering the order of words that appear sequentially. The dataset in this study consists of 3,200 sentences generated by adolescent respondents through a survey designed to explore aspects of their personality. After the feature extraction process is complete, three variants of the Naïve Bayes method are applied for classification, namely Multinomial Naïve Bayes, Bernoulli Naïve Bayes, and Complement Naïve Bayes. Each variant has distinctive characteristics in handling certain data types, such as binomial and multinomial data. The results of the study show that the combined TF-IDF + N-Gram Z algorithm can produce highly representative features, as evidenced by high classification performance. The Multinomial Naïve Bayes and Complement Naïve Bayes variants each achieved 98% accuracy. These findings provide significant contributions to the development of NLP-based personality classification methods for Detecting Adolescent Personality. The combination of the TF-IDF + N-Gram Z algorithm with various Naïve Bayes variants produces an exceedingly high level of accuracy and can be applied in practice in the fields of psychology and adolescent education.
Cybersecurity Behavior in the West Sumatra Universities
Authors: Gushelmi - , Latih Rodziah , Mohd. Zin Abdullah
Published: December 2024

User cybersecurity behavior refers to the actions, habits, and decisions made by individuals when using technology and information that affect the level of security of the data and systems they access. Previous research has shown that user cybersecurity behavior is one of the leading causes of computer and information security issues in many organizations, particularly education. To address this issue, researchers must find solutions to improve user cybersecurity behavior within an organization. Therefore, this study aims to find the factors influencing user cybersecurity behavior in higher education institutions in West Sumatra in 2020. This study was conducted using a survey research method. A questionnaire was distributed to 155 respondents. The questionnaire consisted of 28 questions covering seven factors influencing user cybersecurity behavior. The survey data will be analyzed using the Structural Equation Model based on Partial Least Square. The research findings indicate that all variables, such as Misuse Prevention and Compliance, Body of Knowledge, Skill, Behavioral Intervention, Attitude, Security Compliance Behavior, and Technology, have significant relationships. The relationships between these factors will be shown in the framework to be developed. This indicates that the education sector in Indonesia is aware of cyber threats and the importance of security procedures in the workplace. For further research, a deeper exploration of specific security issues is needed to propose potential solutions or actions that can be implemented to improve user cybersecurity behavior in the education sector, particularly in Indonesia.
Prediction of ROI Achievements and Potential Maximum Profit on Spot Bitcoin Rupiah Trading Using K-means Clustering and Patterned Dataset Model
Authors: Parlika Rizky , Isnanto R. Rizal , Rahmat Basuki
Published: December 2024

Since Satoshi Nakamoto first proposed the idea of bitcoin in 2009, the cryptocurrency and prediction methods for it have grown and changed exceptionally quickly. The Patterned Dataset Model was a valuable tool in earlier studies to explain how changes in the price of Bitcoin affect the movements of other cryptocurrencies in a digital trading market. Three different kinds of datasets are generated by this model: patterned datasets under full conditions, patterned datasets under dropping prices (Crash), and patterned datasets under rising prices (Moon). The K-means approach was then used to cluster these three datasets. Specifically, each dataset was split into two clusters, and the clustering score was determined by utilizing eight unique clustering metrics. Consequently, the best clustering score was found in the patterned dataset in the crash situation. Additionally, from 2022 to 2024, the raw data from this crash-condition-patterned dataset is used to determine the possibility of reaching maximum profit and return on investment (ROI) daily and monthly. According to the calculation results, the range computed over the course of a whole month (30 to 31 days) is significantly larger than the daily range (24 hours multiplied by one month), which represents the most significant profit and ROI attained before the emergence of the first diamond crash level. This research also covers the application of a deep learning model to forecast patterned datasets for crash scenarios that may occur many days in advance. The ConvLSTM2D Model performs better in predicting pattern dataset values for the subsequent crash scenario, according to the hyperparameter comparison between the Gated Recurrent Unit (GRU) Model and the 2D Convolutional Long Short-Term Memory Model.
Implication of ICWFPSO as Optimization Neural Network Algorithm on Sales Forecasting System
Authors: Swari Made Hanindia Prami , Rizki Agung Mustika , Satwika I Kadek Susila , Handika I Putu Susila
Published: December 2024

Predictive systems play a crucial role in a company's operations and strategy by aiding in more informed and data-driven decision-making and more effective planning and budgeting. It is possible to develop an intelligent system to perform forecasting. Neural networks offer significant advantages in forecasting systems due to their flexible modeling capabilities. However, this algorithm's fundamental weakness is the slow convergence rate and being trapped in a local minimum. To overcome it, this research is conducted to optimize the NN algorithm using the ICWFPSO to produce a forecasting algorithm with high accuracy and faster execution time using real e-commerce sales data for the past 7 years.  Algorithm performance testing tests the Mean Absolute Error (MAE) value of the forecasting system using three scenarios: the NN forecasting algorithm, the NN optimized with ICWFPSO on the weight value, and the same scheme. Still, the optimized value is the hyperparameter value.  ICWPSO has been shown to enhance the performance of PSO by tuning the inertia weight dynamically, which helps balance exploration and exploitation during the optimization process. The best prediction result is obtained when optimizing the hyperparameters using the ICWFPSO optimization technique compared to using traditional NN or optimizing weight value with ICWFPSO with the MAE value of 245.32958984375, and the best performance is obtained at iterations below 100. Further, gradient-based optimization methods might be generally preferred for their efficiency and effectiveness in handling large-scale neural network training.
Energy-Efficient Rainfall Prediction Using Support Vector Machine on Edge Ai Platforms
Authors: Putra Willy Permana , Robiyanto Robi , Puspaningrum Alifia , Rifai Ahmad , Burjulius Renol , Sumarudin A
Published: December 2024

The integration of AI into various sectors, including agriculture, has been advancing significantly. Implementing AI in the context of IoT and edge AI presents challenges due to resource limitations. Current climate changes affect planting strategies, pest management, and harvest timing. This study explores an SVM-based machine-learning model with multiple kernels to classify weather conditions as rainy or clear. The research includes two phases: model training on a PC-based system and model deployment on an edge AI device. The training phase includes preprocessing with PCA and fine-tuning of parameters, such as kernel types (linear, polynomial, sigmoid, and RBF), C and gamma. The development phase involves deploying the model on an ESP32, where execution time and power consumption are evaluated. The results show that the SVM model with an RBF kernel, C of 0.1, and gamma of 1 achieves a precision of 79.37%. Inference on the ESP32 yields an average execution time of 35.5 ms and a power consumption of 66 mA, showing a 202-fold reduction in power usage compared to the PC-based system and a 59-fold increase in execution time. This reduced power consumption supports the feasibility of edge AI for climate-based agricultural applications, enabling effective rainfall prediction. The findings contribute to the development of precision agriculture by providing insights into climate prediction, which can inform planting decisions, pest management, and harvest timing, thereby advancing the application of edge AI in response to global climate change.