bluuebunny/crossref_metadata_2025 huggingface dataset:
The Modulatory Effect of an Ethanolic Extract of Anredera cordifolia (Ten.) on the Proliferation and Migration of Hyperglycemic Fibroblasts in an In Vitro Diabetic Wound Model
Authors: Heisler Elisa Vanessa , Osmarim Turra Bárbara , Cardoso de Afonso Bonotto Nathália , da Cruz Ivana Beatrice Mânica , Aurélio Echart Montano Marco , Farina Azzolin Verônica , Dal Magro Jacir , Zaniol Felipe , Perottoni Juliano , Chelotti Maria Eduarda , dos Santos Trombini Fernanda , Maia-Ribeiro Ednea A. , Barbisan Fernanda , Schimith Maria Denise
Published: October 2024

Diabetes mellitus is associated with chronic wound‐healing problems that significantly impact patients’ quality of life and substantially increase expenditure on healthcare. Therefore, the identification of compounds that can aid healing is justified. Anredera cordifolia (Ten.) has been used in folk medicine for curative purposes; however, the causal mechanisms underlying its healing effects remain to be elucidated. In this study, the effect of the ethanolic extract of A. cordifolia was evaluated in an in vitro healing model using fibroblasts cultivated under normoglycemic and hyperglycemic environments. The extract was predominantly composed of phytol and exhibited genoprotective activity. Fibroblast migration attenuated the adverse effects of hyperglycemia, favoring cell proliferation. Collagen levels were significantly increased in ruptured fibroblasts under both standard and hyperglycemic environments. The phytogenomic effect of the extract on three genes related to extracellular matrix formation, maintenance, and degradation showed that A. cordifolia increased the expression of genes related to matrix synthesis and maintenance in both normoglycemic and hyperglycemic individuals. Furthermore, it reduced the expression of genes related to matrix degradation. Overall, this is the first study to demonstrate the effectiveness of A. cordifolia in wound healing, elucidating possible causal mechanisms that appear to be based on the genoprotective effect of this plant on the migratory and proliferative phases of the wound healing process; these effects are probably related to phytol, its main constituent.
Mechanical Properties and Micromechanism of Calcium Sand–Cement Mortar Modified With Zirconium Dioxide and Basalt Fiber
Authors: Wang ZhiXin , Xu ChenMing , Xiong Hui , Zhang Shuai , Hu Jun , Ren JunHao , Zeng DongLing , Yang YongChang
Published: October 2024

In this paper, the modification effect of basalt fiber (BF) and nano‐zirconium dioxide on calcareous sand–cement mortar was studied according to the actual situation of the island construction project. Mechanical tests were carried out on specimens of calcareous sand–cement mortar doped with different dosages of BFs and nano‐zirconium dioxide, and the effects of BFs and nano‐zirconium dioxide on the compressive properties of calcareous sand–cement mortar were analyzed by using the unconfined compression test study and the modification mechanism was elaborated by using: (1) the stress–strain curves of calcium sand–cement mortar are all softened hump‐like convex curves, and the residual stresses tend to a stable constant value. (2) BF at 0.6 wt% was the optimum dosing and increased the compressive strength by 66%–94%, 55%–65%, and 44%–63%, respectively, compared to that of calcareous sand–cement mortar as the age of maintenance increased. The compressive strength of nano‐zirconium dioxide–cement was increased by 49.2%, 79.1%, 101.1%, and 95.4% at the dosage of 4%, 6%, 8%, and 10%, respectively, and the optimum dosage was 8% compared to that of calcareous sand–cement mortar. (3) BFs are interspersed in the cement mortar, forming a dense mesh structure, wrapping and binding the cement mortar, and playing the role of anchoring reinforcement under external load. Nano‐zirconium dioxide due to its filling and induced catalytic role in the formation of a certain plate‐like structure, so that it and the calcium sand–cement mortar between the overall more compact, improving the compactness of the specimen.
Health Expenditure Among the Outpatient of Type 2 Diabetes in Selected Hospital of Kathmandu District: A Cross‐Sectional Study
Authors: Shrestha Rasmita , Shakya Aditya , Sapkota Vishnu Prasad
Published: October 2024

Background: Out‐of‐pocket (OOP) expenditure is the dominant financing mechanism in low‐ and middle‐income countries. In these countries, the prevalence of diabetes has been rising more rapidly leading to various microvascular complications thus increasing the risk of dying prematurely.

Methods: A cross‐sectional hospital‐based study was carried out to compare the OOP expenditure of diabetic patients treating in public and private hospitals. A total of 154 diabetic patients, that is, 77 in each type of hospital were selected for face‐to‐face interviews using structured questionnaires. Lorentz curve and concentration curve were prepared using the income and expenditure of the patients.

Results: Among 154 patients, 97.4% of patients paid OOP for the treatment of diabetes. The mean direct cost per month was NPR 7312.17 (about 55 USD) in public and NPR 10,125.31 (around 76 USD) in private hospitals. Direct medical cost had a higher share in total direct cost, that is, 60.5% in public and 69.3% in private hospitals.

Conclusion: Direct medical cost was higher in private hospital compared to public hospital. All the income groups paid a similar amount of money for the treatment, placing a higher economic burden on poor patients, as there was no financial protection mechanism fully covering their health expenditure.
Implementing AI Solutions for Advanced Cyber‐Attack Detection in Smart Grid
Authors: Tightiz Lilia , Nasimov Rashid , Nasab Morteza Azimi
Published: October 2024

As the backbone of modern power systems, the smart grid is one of the most critical applications of the Internet of Things. The intelligent electricity system faces various types of unauthorized malicious access, that is, cyber‐attacks. With the development and application of information and communication technologies in traditional power systems, the improvement of physical‐cyber systems in the smart grid also increases. Nowadays, the deployment of defensive measures has surged in response to the growing number of attacks aimed at the smart grid. Therefore, in this paper, we investigate cyber‐attack identification using artificial intelligence‐based models and identify them by estimating the state vector of the electricity network. We accomplish simulations by extracting data from the five‐bus IEEE network to test the effectiveness of the proposed algorithm. False data attack vectors are then injected into the healthy measurements. In this way, to check the detection power of the proposed algorithms, 2,000 measurement samples have been taken from the five‐bus network, half of which are considered healthy data and the other half as manipulated data. After labeling healthy and false data, machine‐learning algorithms such as decision trees and k‐nearest neighbor (KNN) have been used to investigate and identify this type of attack. Comparative analysis of the two proposed algorithms against commonly used methods demonstrates significantly improved accuracy. Specifically, according to the best depth for the decision‐tree algorithm and k for the KNN algorithm, it is drawn with k = 3 and the decision‐tree algorithm with a depth of 9. According to the algorithms proposed in this article, the decision‐tree algorithm with a depth of 9 in p‐value of 0.45 and 0.64 and the nearest neighbor algorithm with k = 3 in p is equal to 0.72, 0.98, and 1 represent better accuracy. Also, the results indicate that the two proposed algorithms have performed much more favorably than other classification methods. Additionally, the detection accuracy increases with higher p‐values for these two algorithms. This problem shows that the detectors can detect false data injection attacks that cause more severe disturbances in the system.
Correlation Analysis between Meteorological Influencing Factors and Incidence Features of OIDDs in Guangzhou from 2006 to 2020
Authors: Pang Guqian , Ma Xiaowei , Li Jingbo , Lai Jianyu , He Jian , Wang Juanhuai , Guo Xiaokun , Zhang Zhoubin
Published: October 2024

Objectives. This study is to analyze the epidemiological characteristics of other infectious diarrhea diseases (OIDDs) reported in Guangzhou in the past 15 years and the meteorological data of the city in the same period, to explore the correlation between meteorological factors and the incidence. Methodology. This study starts with using quartiles to test the normal distribution of case data. This study has selected five meteorological factors with the highest correlation coefficients and case data from 2006 to 2018 were used to establish a multiple linear regression equation, and the regression equation was tested with case data from 2019 to 2020. Results. Regression modeling based on the statistics for OIDDs during 2006 to 2018 obtained a correlation coefficient R (multiple R) value of about 0.50. The R square value, also known as the coefficient of determination or goodness of fit, was about 0.25. The adjusted R square value was about 0.25. The overall significance test value (significance F) in the regression equation was much lower than the F statistic, thereby indicating that the dependent variable and independent variable were significant. Conclusion. The results indicated that factors other than meteorological or social factors should be considered to understand the outbreak of OIDDs in Guangzhou.
Figure 5 from Antiangiogenic Tyrosine Kinase Inhibitors have Differential Efficacy in Clear Cell Renal Cell Carcinoma in Bone
Authors: Maksimovic Stefan , Boscolo Nina C. , La Posta Ludovica , Barrios Sergio , Moussa Mohammad Jad , Gentile Emanuela , Pesquera Pedro I. , Li Wenjiao , Chen Jianfeng , Gomez Javier A. , Basi Akshay , Burks Jared K. , Alvarez-Breckenridge Christopher , Gao Jianjun , Campbell Matthew T. , Dondossola Eleonora
Published: October 2024

Ex vivo µCT analysis of TKIs’ effects on bone microarchitecture. A, Representative μCT micrographs of tibial metaphysis cross-sections extracted from mice treated with axitinib, cabozantinib, and lenvatinib or vehicle, Bar, 300 μm. B, Quantification of key bone histomorphometry parameters extrapolated from μCT analysis, mean ± SEM, n = 4/group. C, Impact of TKIs on TRAP+ cells in RENCA VHL− tumors in bone evaluated by IF analysis; representative images acquired at the confocal microscope (green, GFP; white, DAPI; red, TRAP) are shown; arrow, TRAP+ cells. Bar, 250 μm. P values by one-way ANOVA with Tukey honestly significant difference post hoc test; *, P < 0.05. A, axitinib; C, cabozantinib; L, lenvatinib; TRAP, tartrate-resistant acid phosphatase; V, vehicle.
Effect of Frail Phenotype on Cardiorenal Risk and Healthcare Utilization in Older Patients with Chronic Kidney Disease
Authors: Sun Chien-Yao , Chao Chia-Ter , Wu Shang-Han , Wu Jia-Ling , Ling Tsai-Chieh , Yang Deng-Chi , Lin Wei-Ren , Huang Chieh-Hsin , Chang Yu-Tzu
Published: October 2024

Introduction: Limited data have addressed frailty’s role in cardiorenal risk among older adult patients with chronic kidney disease (CKD). We investigated whether frailty could predict major renal and cardiovascular events, healthcare utilization, and mortality in these patients. Methods: We conducted a prospective cohort enrolling patients aged ≥75 years with a stable estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2. The frailty phenotype consists of shrinking, low activity, exhaustion, weakness, and slowness, scored 0 to 5. The primary composite renal outcome was a ≥25% decrease in eGFR concurrent with CKD stage progression or dialysis initiation. Secondary outcomes included major adverse cardiovascular events (MACE), emergency room (ER) visits, all-cause mortality, and hospitalization. Using multivariate Cox models with/without competing risk analyses, we explored frailty’s impact on these outcomes. Results: Among 203 older CKD patients (mean age: 81.6 ± 5.0 years, female: 40.9%, diabetes: 33.0%, body mass index: 24.9 ± 3.7 kg/m2), 67.9% were frail. Over 3.47 years, 38.9% faced composite renal outcomes; 13.3%, MACE; 15.3%, mortality; and more than half utilized healthcare. Every one-point frailty elevated renal outcome risk by 28.0% (HR: 1.28, 95% confidence interval [CI]: 1.03–1.59) and significantly increased secondary outcomes (MACE [HR: 1.43, 95% CI: 0.99–2.08], hospitalization [HR: 1.24, 95% CI: 1.06–1.46], unexpected ER visit [HR: 1.20, 95% CI: 1.03–1.39], and mortality [HR: 1.51, 95% CI: 1.06–2.16]). Results were consistent across subgroups and competing risk analysis. Conclusion: In CKD patients aged ≥75 years, frailty was associated with progressive kidney disease, increased mortality, and healthcare utilization.
Hypertension in Cardiovascular and Kidney Disease: Recent Trends – Treating Two Diseases as One
Authors: De Pascalis Antonio , Tomassetti Alessandro , Vetrano Daniele , Tringali Edoardo , Di Lullo Luca , Napoli Marcello , La Manna Gaetano , Cianciolo Giuseppe
Published: October 2024

Background: Hypertension and chronic kidney disease (CKD) are closely interlinked pathophysiologic states, such that high blood pressure (BP) is an independent risk factor for disease progression in both adult and pediatric patients with kidney disorders and progressive decline in kidney function can conversely lead to worsening BP control. Summary: Hypertension in CKD is not only associated with GFR loss, but increases cardiovascular risk, which is the leading source of mortality and morbidity in this population. Given this complex relationship between hypertension, CKD, and CVD, an optimal management of BP in CKD is mandatory to break an established vicious pathophysiological cycle that leads to adverse outcomes. Key Messages: New promising molecules for the treatment of CKD, with interesting mechanisms, particularly regarding their pathophysiological interactions with arterial hypertension, are available or under development and in the very next future they may change the way we treat high BP in CKD patients.
Three-Dimensional Total Body Photography, Digital Dermoscopy, and in vivo Reflectance Confocal Microscopy for Follow-Up Assessments of High-Risk Patients for Melanoma: A Prospective, Controlled Study
Authors: Hobelsberger Sarah , Steininger Julian , Meier Friedegund Elke , Beissert Stefan , Gellrich Frank Friedrich
Published: October 2024

Introduction: The combination of total body photography (TBP) and digital dermoscopy (DD) for monitoring patients with a high risk for melanoma can allow early detection of melanoma. This study aimed to examine if the use of three-dimensional (3D)-TBP, DD, and reflectance confocal microscopy (RCM) for regular monitoring of patients at high risk for melanoma was beneficial in comparison to monitoring using dermoscopy alone. Methods: The intervention group (IG) underwent 3D-TBP examinations at every visit, along with DD and/or RCM for diagnosis and/or monitoring of pigmented lesions if necessary. The control group (CG) underwent dermoscopy examinations alone. Results: A total of 600 patients (324 male and 276 female) were followed up over a median period of 23 months (mean, 2.85 visits) in the IG and 22 months (mean, 2.74 visits) in the CG (p = 0.009). DD and RCM monitoring were performed for 166 and 105 lesions, respectively. The number needed to treat (NNT) to diagnose melanoma with RCM was 2.83. The IG included more second primary melanomas (22 vs. 1, p = 0.022) and more excised nevi (186 vs. 10, p < 0.001), which consisted of more dysplastic nevi (137 vs. 2, p < 0.001). Among the melanomas diagnosed in the IG, three were diagnosed directly with RCM, nine with a combination of 3D-TBP and RCM, and 10 with dermoscopy alone. Conclusion: Follow-up assessments with a combination of 3D-TBP, DD, and RCM led to the detection of more melanomas in comparison to the CG. The use of RCM reduced the NNT for melanocytic lesions.
Methodology applied to computer audit with artificial intelligence: a systematic review
Authors: Reymundez Suarez Sheyla , Martínez Huamani Bryan , Acuña Meléndez María , Ovalle Christian
Published: October 2024

This systematic review focused on evaluating the impact of the machine learning operations (MLOps) methodology on anomaly detection and the integration of artificial intelligence (AI) projects in computer auditing. Data collection was carried out by searching for articles in databases, such as Scopus and PubMed, covering the period from 2018 to 2024. The rigorous application of the preferred reporting items for systematic reviews and metaanalyses (PRISMA) methodology allowed 88 significant records to be selected from an initial set of 1,389, highlighting the completeness of the selection phase. Both quantitative and qualitative analysis of the data obtained revealed emerging trends in the research and provided key insights into the implementation of MLOps in AI projects, especially in response to increasing complexity, whereby the adoption of the MLOps methodology stands out as a crucial component to optimize anomaly detection and improve integration in the context of information technology auditing. This systematic approach not only consolidates current knowledge but also stands as an essential guide for researchers and practitioners, and the information derived from this systematic review provides valuable guidance for future practices and decisions at the intersection of AI and information technology auditing.
A review of machine learning methods to build predictive models for male reproductive health
Authors: Adimoelja Ariawan , Firdaus Mahmudy Wayan , Kurnianingtyas Diva Kurnianingtyas
Published: October 2024

Developing of artificial intelligence (AI) technology in the medical sector, especially in the part of male reproduction and infertility, is growing rapidly. In both supervised learning and unsupervised learning, AI has been tested and applied to medical personnel to treat their patients. Calculations from simple to complex probability and a combination of some different methods have conducted results of accurate and precise. The results can help determine the condition of male infertility. Artificial neural network (ANN) and fuzzy inference system (FIS) are AI techniques applied to male health issues. ANN is adequate for processing large amounts of combined data in a short time. ANN also has a high level of accuracy and excellent adaptive capabilities. Afterwards, FIS can reflect problems using models with easy to understand, flexible, and also competent to model complex linear functions for decision-making. Based on the advantages of ANN and FIS, it is hoped acquiring prediction results of better and more accurate in male health issues.
Systematic review of artificial intelligence with near-infrared in blueberries
Authors: Cayhualla Amaro Liset , Rau Reyes Sebastian , Acuña Meléndez María , Ovalle Christian
Published: October 2024

The fruit quality has a direct impact on how the fruit looks and how tasty the fruit is. The correct use of tools to determine fruit quality is essential to offer the best product for the final consumer. This study has used the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology. The study objective was elaborate a systematic literature review (SLR) about research of the application of techniques based on artificial intelligence to analyze indicators obtained by near infrared spectroscopy (NIRS) and chemometrics to determine the quality of fruits, including blueberries. The most frequently addressed indicator is the soluble solids concentration (SSC) which was used in several studies with techniques such as support vector machines (SVM) and convolutional neural networks (CNN). According to the results obtained, it is possible to use these techniques to predict blueberry quality indicators. There was an acceptable performance and high accuracy of these models. However, future research could cover other techniques and help to provide better quality control of products in food industries.
Improving performance of air quality monitoring: a qualitative data analysis
Authors: Manongga Danny , Rahardja Untung , Sembiring Irwan , Aini Qurotul , Abas Sunarya Po
Published: October 2024

This research aims to improve performance of air quality monitoring and understand the latest relevant technological developments. Employing the Kitchenham systematic literature review (SLR) method, the study examines 436 journal articles and conference proceedings published from 2019 to 2023, sourced from the Web of Science (WoS) and Scopus databases. The analysis was carried out using Leximancer 5.0 and identified research five themes; i) air quality, ii) artificial intelligence (AI), iii) pollution, iv) middleware, and v) smart environment. The results showed that only 48 journals had strict inclusion and exclusion criteria include relevance to the research theme, methodological quality, and contribution to the research field. In addition, this research integrates AI and middleware, which has significantly contributed to improving air quality. These findings can become the basis for the development of air quality monitoring technology that is more sophisticated and responsive to environmental needs. This research contributes to further understanding air quality monitoring technology trends and designing solutions to improve overall air quality.
A novel energy efficient data gathering algorithm for wireless sensor networks using artificial intelligence
Authors: Chavva Swapna , R. Budyal Vijayashree
Published: October 2024

Energy efficiency is challenging task in wireless sensor network (WSN), it is the main barrier in extending network lifespan. In WSN, maximum energy is wasted during data gathering, hence energy efficient algorithms using artificial intelligence can be designed, that preserves energy while data gathering. Thus, our proposed methodology, A novel energy efficient data gathering algorithm using artificial intelligence for wireless sensor networks (NDGAI), uses novel artificial intelligence algorithms and addresses issue of energy consumption while gathering data. In our proposed work, mobile element is utilized to gather information from sensor nodes in the clusters, formed using amended-expectation-maximization. Each cluster should have a cluster leader and a virtual-point. These cluster leaders are formed utilizing fuzzy logic technique. Virtual-points are formed in the range of cluster leader, only when cluster leader has data. The mobile element reaches virtual point by taking the optimal path, that determined by the hybrid artificial intelligence algorithms, such as artificial-bee-colony (ABC) technique and particle swarm optimization (PSO) algorithms. Thus, by properly performing clustering, cluster leader selection, virtual-point selection and optimal path determination, lead to improved network lifetime and energy saving while gathering the data. Results are simulated and compared with scalable gridbased data gathering algorithm for environmental monitoring wireless sensor networks (SGBDN) and proposed algorithm performs better.
Design of an effective multiple objects tracking framework for dynamic video scenes
Authors: Kumar Karanam Sunil , Pokale Kavya Narasimha Murthy
Published: October 2024

Nowadays, the applications corresponding to video surveillance systems are getting popular due to their wide range of deployment in various places such as schools, roads, and airports. Despite the continuous evolution and increasing deployment of object-tracking features in video surveillance applications, the loopholes still need to be solved due to the limited functionalities of video-tracking systems. The existing video surveillance systems pose high processing overhead due to the larger size of video files. However, the traditional literature report quite sophisticated schemes which might successfully retain higher object detection accuracy from the video scenes but needs more effectiveness regarding computational complexity under limited computing resources. The study thereby identifies the scope of enhancement in traditional object-tracking functions. Further, it introduces a novel, cost-effective tracking model based on Gaussian mixture model (GMM) and Kalman filter (KF) that can accurately identify numerous mobile objects from a dynamic video scene and ensures computing efficiency. The study's outcome shows that the proposed strategic modelling offers better tracking performance for dynamic objects with cost-effective computation compared to the popular baseline approaches.
An artificial intelligence approach to smart exam supervision using YOLOv5 and siamese network
Authors: I. Zanoon Nabeel , A. Alhaj Abdullah , Alkharabsheh Khalid
Published: October 2024

Artificial intelligence has introduced revolutionary and innovative solutions to many complex problems by automating processes or tasks that used to require human power. The limited capabilities of human efforts in real-time monitoring have led to artificial intelligence becoming increasingly popular. Artificial intelligence helps develop the monitoring process by analysing data and extracting accurate results. Artificial intelligence is also capable of providing surveillance cameras with a digital brain that analyses images and live video clips without human intervention. Deep learning models can be applied to digital images to identify and classify objects accurately. Object detection algorithms are based on deep learning algorithms in artificial intelligence. Using the deep learning algorithm, object detection is achieved with high accuracy. In this paper, a combined model of the YOLOv5 model and network Siames technology is proposed, in which the YOLOv5 algorithm detects cheating tools in classrooms, such as a cell phone or a book, in such away that the algorithm detects the student as an object and cannot recognize his face. Using the Siames network, we compare the student’s face against the data base of students in order to identify the student with cheating tools.
Fog and rain augmentation for license plate recognition in tropical country environment
Authors: Wahyu Saputra Vriza , Suciati Nanik , Fatichah Chastine
Published: October 2024

Automatic license plate recognition (ALPR) is a critical component in modern traffic management systems. However, ALPR systems often face challenges in accurately recognizing license plates under adverse weather conditions, such as fog and rain, prevalent in tropical regions. Deep learning ALPR models necessitate huge and diverse datasets for robustness, but data availability remains a concern since unpredictable fog and rain patterns hinder data collection. In this study, we address the issue of enhancing ALPR's robustness by introducing a novel augmentation strategy that combines traditional and weather augmentation techniques. By augmenting the dataset with weather-induced variations, we aim to improve the generalization capability of ALPR models, enabling them to handle a wider range of weather-related challenges. We also investigate the synergy between these weather augmentations and established scene text recognition (STR) methods, such as convolutional recurrent neural network (CRNN), TPS-ResNet BiLSTM-attention (TRBA), autonomous bidirectional iterative scene text recognition (ABINet), vision transformer (ViTSTR), and permutated autoregressive sequence (PARSeq), to determine their impact on recognition accuracy. Experiments using different training data sets show that training data containing a combination of traditional and weather augmentation produces the best accuracy and 1-NED performance compared to training data without augmentation and traditional augmentation only. The average increase accuracy of all STR model is 1.13% with the best increase accuracy of 3.68% using TRBA.
Enhancing interpretability in random forest: Leveraging inTrees for association rule extraction insights
Authors: Hilali Moh’d Fatma , Anwar Notodiputro Khairil , Angraini Yenni
Published: October 2024

The random forest model is a powerful supervised learner, recognized for its ability to learn the pattern within data with superior predictive accuracy. However, it is a black box model because it lacks interpretability. This study addressed the interpretable challenge by employing the inTree framework. The rules were extracted from each decision tree in a random forest model, and the association rules were determined through measured matrix support and confidence to reveal the frequent variable interactions for predicting unemployment. This approach provided insight into the relationships between specific variables and unemployment outcomes. The developed method used data set from the integrated labor force survey (ILFS) 2020/2021 in Zanzibar. Zanzibar’s unemployment rate consistently increased across surveys conducted in 2006, 2014, and 2020/2021. Results have shown that the rules that most predict unemployment for individuals are female and lack of health insurance and secondary education level, female and youth age group and lack of health insurance and secondary education level with a high confidence level. This study provides practical insights for Zanzibar’s government to develop effective interventions, programs, and policies. Improving the interpretability of the random forest model enhances decision-making to address unemployment challenges.
Rice quality classification system using convolutional neural network and an adaptive neuro-fuzzy inference system
Authors: Kamelia Lia , Zaki Hamidi Eki Ahmad , Muhammad Fadilla Reno
Published: October 2024

In the food sector, rice processing and classification are essential operations that help maintain strict quality and safety standards, satisfy various consumer preferences, and satisfy particular market demands. Artificial intelligence (AI) and machine learning techniques are used in automated systems to reliably and effectively classify rice quality. This research compares a rice quality classification system using a convolutional neural network (CNN) and an adaptive neuro-fuzzy inference system (ANFIS). Both methods are evaluated for their ability to classify rice based on quality, utilizing a dataset encompassing various physical characteristics. The comparative analysis results reveal the strengths and weaknesses of each approach in addressing this classification task. In this research, two classification systems for different varieties of rice-medium and premium—are compared. CNN and ANFIS are the techniques applied. The CNN accuracy on the rice picture is 62.5%. Thus, a contrast enhancement procedure was applied and had better accuracy at 75%. However, when contrasted with the classification made using the ANFIS approach, the ANFIS method continued to yield the best accuracy, 82.25%.
A new approach based on genetic algorithm for computation off-loading optimization in multi-access edge computing networks
Authors: Myyara Marouane , Lagnfdi Oussama , Darif Anouar , Farchane Abderrazak
Published: October 2024

The proliferation of smart devices and the increasing demand for resource- intensive applications present significant challenges in terms of computational efficiency, leading to surge in data traffic. While cloud computing offers partial solutions, its centralized architecture raises concerns about latency. Multi-access edge computing (MEC) emerges as promising alternative by deploying servers at the network edge to bring computations closer to user devices. However, op- timizing computation offloading in the dynamic MEC environment remains a complex challenge. This paper introduces novel genetic algorithm-based ap- proach for efficient computation offloading in MEC, considering processing and transmission delays, user preferences, and system constraints. The pro- posed approach integrates computation offloading and resource allocation al- gorithm based on evolutionary principles, combined with a greedy strategy to maximize overall system performance. By utilizing genetic algorithms, the pro- posed method enables dynamic adaptation to changing conditions, eliminating the need for intricate mathematical models and providing an appealing solution to the complexities inherent in MEC. The urgency of this research arises from the critical need to enhance mobile application performance. Simulation re- sults demonstrate the robustness and efficacy of our approach in achieving near- optimal solutions while efficiently balancing computation offloading, minimiz- ing latency, and maximizing resource utilization. Our approach offers flexibility and adaptability, contributing to advancement of MEC networks and addressing the requirements of latency-sensitive applications.
Elevating fraud detection: machine learning models with computational intelligence optimization
Authors: Angelica Cheryl , Charleen Charleen , Wibowo Antoni
Published: October 2024

The amount of crimes committed online has undoubtedly increased as more people use the internet for e-commerce and other financial transactions. Machine learning algorithms have been created to detect payment fraud in online purchasing in order to address the issue. This study performs a thorough comparative examination of different metaheuristic optimizations as hyperparameter tuning methods; these are Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). They are used to optimize the ROC AUC of the three machine learning algorithms, namely X-Gradient Boost, Random Forest Classifier, and Light Gradient Boost Machine. Since the study's data are unbalanced, the determined metrics were ROC AUC. PSO offers consistent conditions for finding the best solution, according to our experiment. Without the inclusion of population annihilation strategies, PSO can achieve the greatest results in various situations which are different from GA, a consistent condition for finding the best solution, according to our experiment. Without the inclusion of population annihilation strategies, PSO can achieve the greatest results in various situations. The findings indicate that Random Forest Classifier provided the highest ROC AUC value both before and after the hyperparameter tuning process, with a score of 88.69% attained while utilizing PSO.
Pothole Detection Model for Road Safety using Computer Vision and Machine Learning
Authors: Bidve Vijaykumar S , Kakakde Kiran S , Bhole Rahul H , Sarasu Pakiriswamy , Shaikh Ashfaq , Mehta Pradnya , Borde Santosh , Kediya Shailesh
Published: October 2024

Potholes pose significant threats to vehicular movement, causing damage to vehicles and risking the safety of drivers and pedestrians. The escalating issue of potholes has led to substantial financial losses for vehicle owners and drivers. Traditional methods of pothole detection are impractical, necessitating an innovative approach. The study focuses on implementing a detection system capable of accurately identifying potholes, empowering vehicles to adapt their speed or halt to prevent damage. The transformative solution presented in this research leverages cutting-edge technologies, specifically computer vision and machine learning, aiming to enhance road safety and streamline maintenance efforts. By addressing the interdependence of modern civilization on road networks, the Pothole Detection Model promises improved road safety, efficient maintenance practices, and the emergence of an era in intelligent transportation systems. The integration of technology into transportation infrastructure highlights the proactive measures needed to combat road imperfections, ensuring a safer and more efficient road network for the benefit of society.
Phishing detection using clustering and machine learning
Authors: Jazyah Yahia Hasan , Al Shalabi Luai
Published: October 2024

Phishing is a prevalent and evolving cyber threat that continues to exploit human vulnerability to deceive individuals and organizations into revealing sensitive information. Phishing attacks encompass a range of tactics, from deceptive emails and fraudulent websites to social engineering techniques. Traditional methods of detection, such as signature-based approaches and rule-based filtering, have proven to be limited in their effectiveness, as attackers frequently adapt and create new, previously unseen phishing campaigns. Consequently, there is a growing need for more sophisticated and adaptable detection methods. In recent years, Machine Learning (ML) and Artificial Intelligence (AI) have played a significant role in enhancing phishing detection. These technologies leverage large datasets to train models capable of recognizing subtle patterns and anomalies in both email content and website behavior. This research proposes a hybrid algorithm to detect phishing attack based on clustering and classification ML methods (CMLM): Deep Learning (DL) and Decision Tree (DT). Simulation results show that the proposed technique achieves high percentage of accuracy in detecting phishing
Multi-Label Feature Aware XGBoost Model For Student Performance Assessment Using Behavior Data in Online Learning Environment
Authors: H Shashirekha
Published: October 2024

In light of recent outbreaks like COVID19, the use of online-based learning streams (i.e., e-Learning systems) has increased significantly. Institutional efforts to boost student achievement have made precise predictions of academic success a priority. To analyze student sessions-streams and anticipate academic success, e-Learning platforms are starting to combine Datamining (DM) with Machine-Learning (ML) techniques. Recent research highlights the difficulties that ML-based methods have while dealing with unbalanced data. In tackling ensemble-learning, we combine several ML algorithms to select the most appropriate approach for the given data. Current ensemble-based approaches for predicting student achievement, nevertheless, don't do exceptionally well, particularly when it comes to multi-label classification, because they don't factor the relevance of features into their approaches. This study presents multi-label feature aware XGBoost (MLFA-XGB) method that improves upon the previously used ensemble-learning technique. The MLFA-XGB makes use of a robust cross-validation approach for gaining a deeper understanding of feature relationships. The experimental results demonstrate that in comparison with the state-of-the-art ensemble-based student achievement predictive approach, this suggested MLFA-XGB based approach provides much higher accuracy for prediction.
Efficient cross-lingual plagiarism detection using bidirectional and auto-regressive transformers
Authors: Bouaine Chaimaa , Benabbou Faouzia
Published: October 2024

The pervasive availability of vast online information has fundamentally altered our approach to acquiring knowledge. Nevertheless, this wealth of data has also presented significant challenges to academic integrity, notably in the realm of cross-lingual plagiarism. This type of plagiarism involves the unauthorized copying, translation, ideas, or works from one language into others without proper citation. This research introduces a methodology for identifying multilingual plagiarism, utilizing a pre-trained multilingual bidirectional and auto-regressive transformers (mBART) model for document feature extraction. Additionally, a siamese long short-term memory (SLSTM) model is employed for classifying pairs of documents as either "plagiarized" or "non-plagiarized". Our approach exhibits notable performance across various languages, including English (En), Spanish (Es), German (De), and French (Fr). Notably, experiments focusing on the En-Fr language pair yielded exceptional results, with an accuracy of 98.83%, precision of 98.42%, recall of 99.32%, and F-score of 98.87%. For En-Es, the model achieved an accuracy of 97.94%, precision of 98.57%, recall of 97.47%, and an F-score of 98.01%. In the case of En-De, the model demonstrated an accuracy of 95.59%, precision of 95.21%, recall of 96.85%, and F-score of 96.02%. These outcomes underscore the effectiveness of combining the MBART transformer and SLSTM models for cross-lingual plagiarism detection.
Sarcasm detection on social data: heuristic search and deep learning
Authors: Palaniammal Arumugham , Anandababu Purushothaman
Published: October 2024

Due to the significant surge in online activity, sarcasm detection (SD) has attracted major attention in social media networks. Sarcasm is a lexical item of negative sentiments or dislikes by utilizing exaggerated language constructs. SD has created a natural language processing (NLP) procedure focused on the intricate and unclear aspects of sarcasm, primarily used in sentiment analysis (SA), human-computer interaction, and various NLP applications. Concurrently, advancements in machine learning (ML) approaches facilitate the creation of effective SD systems. This manuscript presents the future search algorithm with deep learning assisted sarcasm detection and classification on social networking data (FSADL-SDCSND) approach. The major intent of the FSADL-SDCSND approach is in the effective and automated recognition of sarcastic text. In the presented FSADL-SDCSND technique, several data pre-processing stages are achieved to transform the data into a compatible format. Besides, the FSADL-SDCSND approach applies a bidirectional serial-parallel long short-term memory (BS-PLSTM) approach for SD and classification. The hyperparameter tuning process is accomplished by employing the future search algorithm for improving the recognition of the BS-PLSTM model. For superior output of the FSADL-SDCSND model, a sequence of simulations can be applied. The investigational outputs highlighted the improved solutions of the FSADL-SDCSND model with other approaches under diverse performance measures.
Enhanced detection of tomato leaf diseases using ensemble deep learning: INCVX-NET model
Authors: Kikkeri Subramanya Shruthi , Bettahalli Naveen
Published: October 2024

Automated leaf disease detection quickly identifies early symptoms, and saves time on large farms. Traditional methods like visual inspection and laboratory detection are prevalent despite being labor-intensive, time-consuming, and susceptible to human error. Recently, deep learning (DL) has emerged as a promising alternative for crop disease recognition. However, these models usually demand extensive training data and face problems in generalization due to the diverse features among different crop diseases. This complexity makes it difficult to achieve optimal recognition performance across all scenarios. To solve this issue, a novel ensemble approach INCVX-Net is proposed to integrate the three DL models, ‘Inception, visual geometry group (VGG)-16, and Xception’ using weighted averaging ensemble for tomato crop leaf disease detection. This approach utilizes the strengths of three DL models to recognize a wide range of disease patterns and captures even slight changes in leaf characteristics. INCVX-Net achieves an impressive 99.5% accuracy in disease detection, outperforming base models such as InceptionV2 (93.4%), VGG-16 Net (92.7%), and Xception (95.2%). This significant leap in accuracy demonstrates the growing power of ensemble DL models in disease detection compared to standalone DL models. The research paves the groundwork for future advancements in disease detection, enhancing precision agriculture through ensemble models.
Detection and identification of un-uniformed shape text from blurred video frames
Authors: Channegowda Ravikumar Hodikehosahally , Srinivasaiah Raghavendra , Jankatti Santosh Kumar , B Meenakshi , Jinachandra Niranjana Shravanabelagola , Tavarekere Hombegowda Raveendra Kumar
Published: October 2024

The identification and recognition of text from video frames have received a lot of attention recently, that makes many computer vision-based applications conceivable. In this study, we modify the picture mask and the original identification of the mask region convolution neural network and permit detection in three levels, including holistic, sequence, and at the level of pixels. To identify the texts and determine the text forms, semantics at the pixel and holistic levels can be used. With masking and detection, existences of the character and the word are separated and recognised. In addition, text detection using the results of 2-D feature space instance segmentation is done. Moreover, we explore text recognition using an attention-based optical character recognition (OCR) method with mask region convolution neural networks (R-CNN) to address and detect the problem of smaller and blurrier texts at the sequential level. Using attribute maps of the word occurrences in sequence to seq, the OCR method calculates the character sequence. At last, a fine-grained learning strategy is proposed to constructs models at word level using the annotated datasets, resulting in the training of a more precise and reliable model. The well-known benchmark datasets ICDAR 2013 and ICDAR 2015 are used to test our suggested methodology.
Deep learning model for detection acute cardiogenic pulmonary edema in cases of preeclampsia
Authors: Hayat Cynthia , Soenandi Iwan Aang
Published: October 2024

The physiological changes during the pregnancy period increase the risk of developing pulmonary edema and acute respiratory failure. This condition falls under critical medical emergencies associated with maternal mortality. This study utilized a convolutional neural networks (CNN) architectural model employing chest Xray dataset images. CNN utilizes the convolution process by moving a convolutional kernel of a certain size across an image, allowing the computer to derive new representative information from the multiplication of portions of the image with the utilized filter.To simplify, the vanishing gradient issue occurs when information dissipates before reaching its destination due to the lengthy path between input and output layers. This study was developed model for detection acute cardiogenic pulmonary Edema in pre-eclampsia cases using chest Xray images, implemented using PyTorch, Keras, and MxNet. The validated model achieved its optimum with accuracy 90.65% and binary cross-entropy loss (BCELoss) value of 0.4538. It exhibited an improved sensitivity value of 83.514% using a 5% dataset and a specificity value of 57.273%. This indicates an increase in sensitivity value by 83.514% using a 5% data set and a specificity value of 57.273%. The research results demonstrate an improvement in accuracy compared to several similar studies that also utilized CNN models.
Enhanced scene text recognition using deep learning based hybrid attention recognition network
Authors: Patil Ratnamala S , Hanji Geeta , Huded Rakesh
Published: October 2024

The technique of automatically recognizing and transforming text that is present in pictures or scenes into machine-readable text is known as scene text recognition. It facilitates applications like content extraction, translation, and text analysis in real-world visual data by enabling computers to comprehend and extract textual information from images, videos, or documents. Scene text recognition is essential for many applications, such as language translation and content extraction from photographs. The hybrid attention recognition network (HARN), unique technology presented in this research, is intended to greatly improve efficiency and accuracy of text recognition in complicated scene situations. HARN makes use of cutting-edge elements including alignment-free sequence-to-sequence (AFS) module, creative attention mechanisms, and hybrid architecture that blends attention models with convolutional neural networks (CNNs). Thanks to its novel attention processes, HARN is capable of comprehending wide range of scene text components by capturing both local and global context information. Through faster network convergence, shorter training times, and better utilization of computing resources, the suggested technique raises bar for state-of-the-art. HARN’s versatility makes it a good choice for range of scene text recognition applications, including multilingual text analysis and data extraction. Extensive tests are conducted to assess the effectiveness of HARN approach and demonstrate it is ability to greatly influence real-world applications where accurate and efficient text recognition is essential.