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INDONESIA
Emerging Science Journal
Published by Ital Publication
ISSN : 26109182     EISSN : -     DOI : -
Core Subject : Social,
Environmental Science
Emerging Science Journal is not limited to a specific aspect of science and engineering but is instead devoted to a wide range of subfields in the engineering and sciences. While it encourages a broad spectrum of contribution in the engineering and sciences. Articles of interdisciplinary nature are particularly welcome.
Arjuna Subject : -
Articles 803 Documents
 76   77   78   79   80 
Performance Evaluation of Significant Feature for Interest Flooding Attack Detection on Named Data Networking Jupriyadi; Syambas, Nana R.; Mulyana, Eueung
Emerging Science Journal Vol. 9 No. 5 (2025): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-05-07

Abstract

One of the internet architectures of the future that has advantages over the current system is Named Data Networking (NDN). However, Denial of Service (DoS) attacks, such as interest flooding attacks (IFA), can still disrupt the network. Detecting IFA attacks is crucial for preventing further damage. Several approaches to detection systems have been proposed, including a classification approach to detecting attacks with multiple detection parameters or features. However, the many detection system features that can be extracted from the network result in longer computation times for the classification algorithms. This research focuses on enhancing the detection of IFA by evaluating the features of the detection system and identifying significant features to improve detection accuracy and reduce computation time. We employed various feature selection algorithms, including information gain, wrapper naive Bayes, gain ratio, and correlation-based feature selection (CFS). The selected features are tested to detect attacks using several classification algorithms, including naive Bayes, random forest, J48, and Bayesian network. Our proposed method found only three essential features for detecting IFA from 18 features available, resulting in better detection accuracy and increasing by 47.8% the time to build the model. This study enhances NDN security while reducing computational cost, making real-time attack detection more feasible.
Employment Components and Ecosystem on Working Poor in an Emerging Economy Ngah, Rohana; Abdurakhmanova, Gulnora K.; Fayzieva, Muyassarzoda K.; Kurbonov, Samandar P.; Goyipnazarov, Sanjar B.; Irmatova, Aziza; Sunnat kizi, Amirdjanova Sitora; Rakhmatullayeva, Shakhnoza
Emerging Science Journal Vol. 9 No. 5 (2025): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-05-021

Abstract

As an emerging economy, Uzbekistan is progressing rapidly in economic growth. The progress caused the rising number of working poor and poverty rate. Only a few studies focus on the impact on the working poor, especially in emerging economies. Employment components and ecosystems are critical to ensure a sustainable economy, stability, and growth. Information relating to the working poor is limited, and how the working poor navigate through challenges in employment is still unknown. The objective of the study is to explore the impact of employment and the ecosystem on the working poor in Uzbekistan. A quantitative approach was conducted through a face-to-face survey in fourteen states using simple random sampling. Data collected from 3298 respondents was then analyzed through descriptive analysis and multiple regression to investigate the relationship between variables. The findings revealed that employment components like opportunity, retraining, and income fairness are crucial to the working poor, as well as social infrastructure, taxes, and union support. This study contributes to the literature relating to poverty. The study also offers practical insights into how employment matters relating to the working poor, empowering policymakers and researchers to make informed decisions.
Unveiling the Power of Emotional Intelligence: A Dynamic Exploration of Its Impact on Proactive Decision-Making Demissie, Esayas Degago; Molnár, Edina
Emerging Science Journal Vol. 9 No. 5 (2025): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-05-015

Abstract

In this multifaceted, dynamic business world, leadership success and effective decision-making are highly dependent on understanding and controlling emotional intelligence (EI). In this multifaceted world of work, the integration and strategic use of emotional intelligence (EI) drives change and facilitates proactive and effective decision-making. The study aims to examine the direction and degree of relationship that exists between EI and proactive decision-making using one of the public service institutions in Ethiopia, the Ethiopian Customs Authority. The study uses a quantitative and correlational design utilizing a structured survey administered to a randomly selected sample of 156 employees. The study applied statistical analysis techniques, including correlation and stepwise regression analysis, to determine the strength and significance of the relationship between EI and proactive decision-making. Research has examined how and in what direction EI competencies (emotion perception, regulation, and utilization) influence features of proactive decision-making. The result accentuates that Emotional intelligence emerges as a critical driver, accounting for a substantial proportion of the observed differences in individuals' ability to make proactive decisions. Grounding on the findings, the study highlights the necessity of integrating EI training programs into organizational development strategies to enhance employees' decision-making capabilities, adaptability, and resilience in dynamic workplace environments.
Odor Profiling of Blood Shells Using TGS Gas Sensor and PCA-SVM Analysis Astuti, Suryani Dyah; Funabiki, Nobuo; Soelistiono, Soegianto; Winarno; Arifianto, Deny; Ramadhani, Nadia Nur; Permatasari, Perwira Annissa Dyah; Yaqubi, Ahmad Khalil; Susilo, Yunus; Syahrom, Ardiyansyah
Emerging Science Journal Vol. 9 No. 5 (2025): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-05-017

Abstract

Blood cockles (Andara granulosa) are among the most popular animal protein sources due to their rich nutritional content and high economic value. The storage period and temperature are two critical factors that significantly influence the freshness of blood cockles. One key indicator of blood cockle quality is the odor they emit. An unpleasant or inappropriate odor can indicate contamination or a decline in quality, posing potential food safety risks. However, conventional methods of odor quality testing are often subjective, require specialized skills, and may not always be reliable. To address the limitations of human olfaction, advancements in gas sensor technology, specifically gas array sensors (also known as the electronic nose), have been developed. This research aims to profile the freshness of blood cockles by identifying their odor under different storage conditions using electronic nose technology. The study used fresh blood cockle meat, which was stored under varying temperature conditions: at room temperature, in a cooler, and in a freezer. The storage periods for the samples were 1, 2, 3, 4, and 5 days. The samples were placed in sealed bottles and tested using a gas array sensor. The data collected from this process were in the form of voltage readings, which were analyzed using machine learning techniques, specifically Principal Component Analysis (PCA). The data were then classified using a Support Vector Machine (SVM) model. The study results showed that the gas array sensor successfully classified the odor profiles, with PCA explaining 93.83% of the variance in the data. The SVM model achieved an accuracy of 89.66% for PCA-reduced data and 91.44% for non-PCA data.
The Occupational Indemnity Insurance Modelling: Brighton Mahohoho XGBoost Probabilistic Automated Actuarial Reserving-Pricing-Underwriting Mahohoho, Brighton; Chimedza, Charles; Matarise, Florance; Munyira, Sheunesu
Emerging Science Journal Vol. 9 No. 5 (2025): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-05-030

Abstract

This paper introduces the IFRS 17-Compliant Brighton Mahohoho Probabilistic Framework for Inflation-Adjusted Frequency-Severity Modeling in Occupational Indemnity Insurance, integrating AI-driven actuarial methodologies for loss reserving, risk pricing, and underwriting. Objectives: The framework ensures IFRS 17 compliance while enhancing actuarial accuracy and operational efficiency. Methods/Analysis: A simulation-based dataset of policy, claims, premiums, inflation adjustments, and underwriting data is generated. Claim frequencies and severities are modeled using Poisson and Gamma distributions, with inflation adjustments incorporated into reserves. XGBoost is applied for Automated Actuarial Loss Reserving (ALR) and Automated Actuarial Risk Pricing (ARP), while a weighted average approach estimates Automated Actuarial Loss Reserve Risk Premiums (AALRRP). Findings: Model accuracy is validated through MAE, MSE, RMSE, residual analysis, and scatter plots. IFRS 17 metrics—Contractual Service Margin (CSM), Fulfillment Cash Flows (FCF), Risk Adjustments, and Liabilities—are simulated, with sensitivity analysis ensuring robustness. Policyholders are segmented into underwriting clusters, incorporating expenses, outgo, and revenue to derive the Automated Net Actuarial Underwriting Balance (ANAUB). Novelty/Improvement: This integrated AI-driven actuarial framework significantly advances IFRS 17-compliant pricing and reserving, offering enhanced predictive accuracy, regulatory alignment, and improved risk assessment in occupational indemnity insurance.
Agarose-Based Antibacterial Films from Gracilaria sp.: Isolation, Characterization, and Metal Nanoparticle Incorporation Ahmad, Ahyar; Zainuddin, Rahmaniah; Saksono, Budi; Anita, Sita Heris; Zulfiana, Deni; Ermawar, Riksfardini A.; Arfah, Rugaiyah; Natsir, Hasnah; Karim, Harningsih; Irmawati; Ramli, Siti R.; Karim, Abdul
Emerging Science Journal Vol. 9 No. 5 (2025): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-05-03

Abstract

The incorporated metal nanoparticles in a polysaccharide-based film exhibit efficient antibacterial activity against harmful germs. However, previous studies have used a commercial polysaccharide for their film production. Therefore, this study aimed to develop a natural polysaccharide-based film extracted from the local algae Gracilaria sp. originating from Sinjai Regency, South Sulawesi, Indonesia. Firstly, the polysaccharide agarose was isolated and its properties compared with those of commercial agarose. A present low-cost isolation process produces agarose with 1.8% (w/w) of yield. Results also showed physicochemical properties similar to those of the commercial agarose. Secondly, the agarose-based antibacterial film was synthesized at 0, 0.5, and 1% glycerol concentrations. The synthesized film was incorporated with silver (Ag) and copper (Cu) nanoparticles (NPs). Morphological, mechanical, and physicochemical properties of the incorporated Ag-agarose and Cu-agarose films were characterized using Field Emission Scanning Electron Microscope (FESEM), Universal Testing Machine (UTM), and Fourier Transform Infrared Spectroscopy (FTIR), respectively. Results showed the film stiffness and tensile strength increased by incorporating either AgNPs or CuNPS. The interaction of AgNPs-agarose most likely involves physical bonds, while the interaction of CuNPs-agarose forms coordination bonds. An antibacterial test showed that the Ag-agarose nanocomposite inhibited the growth of Escherichia coli, Salmonella typhimurium, Staphylococcus aureus, Staphylococcus epidermidis, and Bacillus subtilis. In the meantime, Cu-agarose prevented the growth of Staphylococcus aureus. Overall, antibacterial activity was influenced by the interaction between metal nanoparticles and agarose, the concentration of metal nanoparticles, and the film's solubility. An agarose-based antibacterial film from Gracilaria sp. has the potential for use in various applications, including food packaging, pharmaceuticals, and other industries.
LRX: A Hybrid-based Real-Time Air Quality Index Prediction and Visualization Model Jayapradha, J.; Haw, Su-Cheng; Palanichamy, Naveen; Arunesh, V.; Pranav, Surajith; Senthil Kumar, T.
Emerging Science Journal Vol. 9 No. 5 (2025): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-05-010

Abstract

Accurately predicting the air quality index significantly reduces health risks and supports urban environmental planning. This paper presents LRX, a hybrid predictive model, for Air Quality Index (AQI) prediction. The model employs Long short-term memory to capture temporal dependencies, Random Forest to fine-tune the features, and Extreme Gradient Boosting to enhance the final predictions. The objective of the study is to build a model that can accurately predict air quality index numbers in real time for many cities in India. The proposed model LRX design influences the depth of each algorithm to enhance accuracy and generalization. The experimental results show the model's ability to predict the AQI forecast of various cities in India with a root mean square error of 0.014 and R2 of 0.948, performing better compared to the models individually. To enhance this, a Stream lit-based user interface has been developed to enable real-time AQI predictions and visualization. The interface incorporates tabs for interactive inputs, model selection, graphical representation of predicted trends, ensuring accessibility and usability, and enhancing the practical applicability of the proposed model. This easy-to-navigate tool not only makes the prediction process more accessible but also helps bridge the gap between complex model results and practical environmental decision-making, enhancing the overall impact of the research. This research contributes to air quality prediction by presenting a robust modelling approach that can be applied in the real world.
Deep Learning-Based Behavior Recognition for Group-Housed Pigs: Advancing Livestock Management with Segmentation Techniques Akkajit, Pensiri; Sukkuea, Arsanchai
Emerging Science Journal Vol. 9 No. 5 (2025): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-05-013

Abstract

The increasing demand for sustainable, welfare-oriented livestock management necessitates innovative solutions for behavior monitoring, particularly in group-housed settings, where challenges such as animal density and overlapping bodies hinder traditional observation methods. This study introduces a Convolutional Neural Network (CNN)-based model enhanced with segmentation techniques to accurately classify behaviors among group-housed pigs, a context in which individual monitoring is crucial for welfare assessment, disease prevention, and production efficiency. By leveraging segmentation, the model isolates individual pigs in video footage, overcoming occlusion issues and significantly improving classification accuracy. This approach not only advances the analysis of animal behavior in dense environments but also aligns with the principles of innovation, promoting the adoption of AI-driven monitoring solutions in livestock management. In comparison with various models, YOLOv11m-augmentation achieved the highest mAP@0.5 score of 0.969 and a notable precision of 0.925. This CNN and segmentation-based method effectively identifies key behaviors, including eating, drinking, sleeping, and standing, with particularly high precision for behaviors most indicative of animal welfare. This research contributes to sustainable livestock practices by offering a scalable, cost-effective technology for real-time welfare assessment, potentially reducing labor requirements, enhancing farm management decisions, and promoting animal health. The study’s findings underscore the potential of integrating innovation principles with AI in agriculture, presenting a viable pathway toward sustainable livestock management practices that balance productivity with animal welfare.
Integrated AI, IoT, and Blockchain for Enhancing Security and Traceability in Perishable Logistics Villegas-Ch, William; Gutierrez, Rommel; Govea, Jaime; Garcí­a-Ortiz, Joselin
Emerging Science Journal Vol. 9 No. 5 (2025): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-05-011

Abstract

The perishability of food products in the supply chain poses a significant challenge in ensuring quality and safety. Inefficient monitoring of temperature, humidity, and storage time results in substantial economic losses and increased health risks. Traditional traceability systems rely on manual audits or essential IoT platforms that lack predictive capabilities, leading to delayed anomaly detection and inefficient intervention. Blockchain-based solutions improve transparency but primarily focus on record verification rather than active anomaly detection and automated decision-making. This study proposes an integrated system combining Artificial Intelligence (AI), the Internet of Things (IoT), and blockchain to optimize food traceability through real-time monitoring, predictive analytics, and secure decentralized record management. The system deploys smart sensors across storage and transportation units to continuously collect environmental data, which is processed by a deep learning model trained to detect deviations with 92.4 % accuracy. Detected anomalies trigger automated responses via smart contracts in a blockchain network, ensuring immediate corrective actions while maintaining immutable audit records. Results demonstrate a 64.3 % reduction in response time, improving reaction efficiency to critical storage failures. Additionally, false positive alerts decreased by 73.1 %, optimizing operational efficiency and minimizing unnecessary interventions. The blockchain implementation reduced storage overhead by 76.9%, ensuring scalability and long-term feasibility. This research establishes a foundation for intelligent, automated food supply chain management, demonstrating that integrating AI, IoT, and blockchain enhances safety, reduces waste, and optimizes logistics. Future work will focus on improvements in large-scale deployment and computational efficiency to refine this innovative approach.
Real-Time FPGA-Based ADAS Solution for Driver Drowsiness Detection and Autonomous Stopping Almomany, Abedalmuhdi; Marouf, Zaid; Jarrah, Amin; Sutcu, Muhammad
Emerging Science Journal Vol. 9 No. 5 (2025): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-05-023

Abstract

This study addresses driver drowsiness, a leading cause of traffic accidents, by developing a real-time Advanced Driver Assistance System that integrates biometric detection and autonomous vehicle control. The objective of this study is to enhance road safety through the early detection of drowsiness and automated intervention. The proposed system detects signs of drowsiness by monitoring facial and ocular features using a real-time video stream. Once a predefined threshold is exceeded, an audible alert is triggered. If the driver remains unresponsive, the system gradually reduces the vehicle’s speed and initiates an automated stop procedure. Methodologically, the system employs OpenCV for image processing and a convolutional neural network for lane detection and vehicle control. It is implemented on a high-performance hardware platform using field-programmable gate arrays programmed via Vivado High-Level Synthesis to ensure low-latency operation. The results confirm the system’s real-time capability, accuracy in drowsiness detection, and effective vehicle control under drowsy driving conditions. The system’s novelty lies in its combination of biometric monitoring, deep learning, and hardware acceleration to provide faster and more reliable intervention than existing Advanced Driver Assistance System technologies. This integration sets a new benchmark for proactive road safety measures.

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