Level Of Accuracy Research Articles

An Integrated Paradigm for Advanced Irrigation Systems Leveraging Internet of Things (IoT)

As the demand for precision in agriculture and effective sustainable resource management increases, there has been a lot of pressure to have more accurate and efficient soil moisture data-transmission systems. This paper includes the discussion on how machine learning (ML) and deep learning (DL) techniques can be improvise to further accuracy and performance in IoT-based smart irrigation systems. The system is based on the perception of soil moisture using IoT sensors collecting real-time environmental data in fields, such as soil moisture, temperature, humidity, and sunlight, to be transmitted to farmers or end-users using ThingSpeak and Thinger.io platforms for analysis, storage, and visualization. It enables real-time decisions and remote agricultural systems control using web page or mobile applications. The WSNs(Wireless sensor network) helps to automate irrigation and water management in agricultural sites. The developed SIS(Smart Irrigation Systems) is based on the sensor network that carries real-time information relating to moisture content in the soil, temperature, and humidity levels as very critical determinants of the proper irrigation schedule. These are analyzed every 15 minutes at the edge server. The system uses deep learning models to predict when the soil moisture falls below a threshold and automatically activates water pumps or sprinklers, which reduces human intervention and optimizes water usage in agriculture. An important part of research forms machine learning algorithms to improvise the performance. The recent advancements relies on several models, among them KNN (K-Nearest neighbors) and TimeGPT models [TimeGPT is a time-series forecasting model that utilizes the power of GPT (Generative Pre-trained Transformer) architecture to predict future values in a sequence.], in the prediction of soil moisture while achieving optimal irrigation schedules from previous available data and weather forecasting. The comparative analyses included an accuracy rate of 97 Parsant to 98 Parsant in KNN thus describing the high level of accuracy that the system can attain regarding the soil conditions and water requirements. This research provides the possibility of embedding IoT into machine learning that may form a new age smart agriculture system that, apart from irrigation automation, enhances decision-making in farmer practices. Adoption of such systems could contribute immensely to the sustainability of such agricultural practices mainly due to reduced water wastage, decrease operation costs, and improved crop yield. Integrating real-time environmental data and predictive analytics farmlands can be optimized and maintained even the land has water scarcity or difficult to receive rainfall or other climatic conditions.

Enhancing Security Against Adversarial Attacks Using Robust Machine Learning

Adversarial attacks pose a significant threat to machine learning models, particularly in applications involving critical domains such as autonomous systems, cybersecurity, and healthcare. These attacks exploit vulnerabilities in the models by introducing carefully crafted perturbations to input data, leading to incorrect predictions and system failures. This research focuses on strengthening machine learning systems by employing robust methodologies, including input normalization, randomization, outlier detection, manual dataset curation, and adversarial training. The study highlights how these strategies collectively enhance the resilience of models against adversarial manipulations, ensuring their reliability and security in real-world scenarios. Experimental evaluations demonstrate notable improvements in robustness, with attack success rates reduced significantly while maintaining high accuracy levels. The findings emphasize the importance of a comprehensive, multi-pronged approach to safeguard machine learning systems, paving the way for secure and trustworthy AI applications in dynamic environments.

Holomorphic embedding method for large-scale reverse osmosis desalination optimization

Large-scale optimal design problems involving nonlinear differential equations are widely applied in modeling such as craft manufacturing, chemical engineering and energy engineering. Herein we propose a fast and flexible holomorphic embedding-based method to solve nonlinear differential equations quickly, and further apply it to handle the industrial application of reverse osmosis desalination. Without solving nonlinear differential equations at each discrete point by a traditional small-step iteration approach, the proposed method determines the solution through an approximation function or approximant within segmented computational domain independently. The results of solving more than 11 million of nonlinear differential equations with various design parameters for the reverse osmosis desalination process indicate that the fast and flexible holomorphic embedding-based method is six-fold faster than several typical solvers in computational efficiency with the same level of accuracy. The proposed computational method in this work has great application potential in engineering design.

MLTB: Enhancing Transferability and Extensibility of Density Functional Tight-Binding Theory with Many-body Interaction Corrections.

We present a hybrid semiempirical density functional tight-binding (DFTB) model with a machine learning neural network potential as a correction to the repulsive term. This hybrid model, termed machine learning tight-binding (MLTB), employs the standard self-consistent charge (SCC) DFTB formalism as a baseline, enhanced by the HIP-NN potential as an effective many-body correction for short-range pairwise repulsive interactions. The MLTB model demonstrates significantly improved transferability and extensibility compared to the SCC-DFTB and HIP-NN models. This work provides a practical computational framework for developing reliable SCC-DFTB models with additional many-body corrections that more closely approach the DFT level of accuracy. We illustrate this method with the development of an accurate model for the thorium-oxygen system, applied to the study of its nanocluster structures (ThO2)n.

Advances in RNA editing in hematopoiesis and associated malignancies.

Adenosine-to-inosine (A-to-I) RNA editing is a prevalent RNA modification essential for cell survival. The process is catalyzed by the Adenosine Deaminase Acting on RNA (ADAR) enzyme family that converts adenosines in double-stranded RNAs (dsRNAs) into inosines, which are read as guanosines during translation. Deep sequencing has helped to reveal that A-to-I editing occurs across various types of RNAs to affect their functions. RNA editing detection is now so sophisticated that we can achieve a high level of accuracy and sensitivity to identify low abundance edited events. Consequently, A-to-I editing has been implicated in various biological processes, including immune and stress responses, cancer progression and stem cell fate determination. In particular, a crucial role for this process has been recently reported in hematopoietic cell development and hematologic malignancy progression. Results from genetic mouse models have demonstrated the impact of ADARs' catalytic activity on hematopoietic cells, complemented by insights from human cell studies. Meanwhile clinical studies have implicated ADAR enzymes and RNA editing events in hematologic malignancies and highlighted their potential as prognostic indicators. In this review, we outline the regulatory mechanisms of RNA editing in both normal hematopoiesis and hematologic malignancies. We then speculate on how targeting ADAR expression and site-specific RNA substrates might serve as a therapeutic avenue for affected patients.

Noise-Tolerant Force Calculations in Density Functional Theory: A Surface Integral Approach for Wavelet-Based Methods.

We introduce a method for computing quantum mechanical forces through surface integrals over the stress tensor within the framework of the density functional theory. This approach avoids the inaccuracies of traditional force calculations using the Hellmann-Feynman theorem when applied to multiresolution wavelet representations of orbitals. By integrating the quantum mechanical stress tensor over surfaces that enclose individual nuclei, we achieve highly accurate forces that exhibit superior consistency with the potential energy surface. Extensive benchmarks show that surface integrals over the stress tensor offer a robust and reliable alternative to the direct use of the Hellmann-Feynman theorem for force computations in DFT with discontinuous basis sets, particularly in cases where wavelet-based methods are employed. In addition, we integrate this approach with machine learning techniques, demonstrating that the forces obtained through surface integrals are sufficiently accurate to be used as training data for machine-learned potentials. This stands in contrast to forces calculated using the Hellmann-Feynman theorem, which do not offer this level of accuracy.

ОБЗОР МЕЖДУНАРОДНЫХ КАЛИБРОВОЧНЫХ СИСТЕМ И ИХ РОЛЬ В ЭКОЛОГИЧЕСКИХ ИССЛЕДОВАНИЯХ

The article provides examples of the use of gravimetric observations to monitor the environmental situation of mineral deposits. Such measurements are usually made by a group of devices. A high level of accuracy requires the consistency of all instruments used, which is achieved by calibration. An overview of gravimetric calibration systems, which play a key role in accurate and reliable measurement of gravity acceleration on the Earth’s surface, is given. Calibration bases are necessary to ensure metrological control and improve measurement accuracy in such fields of Earth sciences as geodesy, geophysics, geology and other related disciplines. This article discusses different gauge systems used in different countries, including Russia, Poland, Croatia, Switzerland, Canada, Austria, Germany, China, Estonia, Finland, Iran, Brazil, and also in the Republic of Kazakhstan.

Foundation models in bioinformatics

Abstract With the adoption of Foundation Models (FMs), Artificial Intelligence (AI) has become increasingly significant in bioinformatics and has successfully addressed many historical challenges, such as pre-training frameworks, model evaluation, and interpretability. FMs demonstrate notable proficiency in managing large-scale, unlabeled datasets, because experimental procedures are costly and labor-intensive. In various downstream tasks, FMs have consistently achieved noteworthy results, demonstrating high levels of accuracy in representing biological entities. A new era in computational biology has been ushered in by the application of FMs, focusing on both general and specific biological issues. In this review, we introduce recent advancements in bioinformatics FMs that employed in a variety of downstream tasks, including genomics, transcriptomics, proteomics, drug discovery, and single cell analysis. Our aim is to assist scientists in selecting appropriate FMs in bioinformatics, according to four model types: language FMs, vision FMs, graph FMs, and multimodal FMs. In addition to understanding molecular landscapes, AI technology can establish the theoretical and practical foundation for continued innovation in molecular biology.

Precision in Liver Diagnosis: Varied Accuracy Across Subgroups and the Need for Variable Thresholds in Diagnosis of MASLD.

The performance of non-invasive liver tests (NITs) is known to vary across settings and subgroups. We systematically evaluated whether the performance of three NITs in detecting advanced fibrosis in patients with metabolic dysfunction-associated steatotic liver disease (MASLD) varies with age, sex, body mass index (BMI), type 2 diabetes mellitus (T2DM) status or liver enzymes. Data from 586 adult LITMUS Metacohort participants with histologically characterised MASLD were included. The diagnostic performance of the Fibrosis-4 Index (FIB-4), enhanced liver fibrosis (ELF) and vibration-controlled transient elastography liver stiffness measurement (VCTE LSM) was evaluated. Performance was expressed as the area under the receiver operating characteristics curve (AUC). Thresholds for detecting advanced fibrosis (≥F3) were calculated for each NIT for fixed (high) sensitivity, specificity and predictive values. Differences in AUC between all subgroups were small and statistically not significant, indicating comparable performance in detecting ≥F3, irrespective of these clinical factors. However, different thresholds were needed to achieve the same level of accuracy with each test. For example, for a fixed sensitivity and specificity, the thresholds for all three NITs were higher in patients with T2DM. Effects for sex, age and liver enzymes were less pronounced. Performance of the selected NITs in detecting advanced liver fibrosis does not vary substantially with clinical characteristics. However, different thresholds have to be selected to achieve the same sensitivity, specificity and predictive values in the respective subgroups. Large prospective studies are called for to study NIT accuracy considering multiple patient characteristics.

ChatGPT in the Classroom: Evaluating its Role in Fostering Critical Evaluation Skills

Abstract The increasing prevalence of artificial intelligence in educational domains raises both opportunities and challenges in the context of academic integrity and pedagogical efficacy. This study outlines an innovative project that investigates the use of ChatGPT as a tool for enhancing the critical evaluation skills of master’s students in biomedical science. Using a dual approach combining AI-generated essay writing with subsequent student-led critical evaluation, this project sought to foster deeper critical evaluation skills in learners. By having participants critically assess AI-generated essays, supported critical evaluation based on peer-reviewed literature, the project aimed to deepen their evaluative skills. Outputs from the tasks were compared against academic benchmarks considering factors such as marks, writing, and overall quality. Participant perceptions were collected through a combination of a focus group session and an evaluation questionnaire. The key finding of this project was that while ChatGPT demonstrated proficiency in structural coherence and grammatical accuracy, it did not augment academic performance– participant marks for the AI-generated essays aligned closely with their overall module marks, showing no overall improvement. However, this study did see an increase in marks for participants’ critical evaluations. This suggests that ChatGPT was more effective as an assessment tool when used for critical evaluation tasks, aligning with pedagogical emphasis on nurturing critical evaluation skills. User interaction with AI emerged as a significant variable that influenced the tool’s efficacy, highlighting the need for a nuanced approach to its integration into educational settings. The study concludes that while ChatGPT offers promising avenues for both drafting and assessment, and demonstrated a high level of factual accuracy, it is not a substitute for human-led academic enquiry, and students preferred writing their own essays.

A Novel Virtual Planned-Orthodontic-Surgical Approach for Proportional Condylectomy in Condylar Hyperplasia

Background/Objectives: Condylectomy is a delicate and intricate procedure commonly employed in the management of temporomandibular joint (TMJ) disorders, osteochondromas, condylar hyperplasia, hemimandibular hyperplasia, and other pathologies affecting the condylar region. The advent of surgical cutting guides has introduced a new dimension to condylectomy procedures as they enable surgeons to plan and execute precise cuts with a heightened level of accuracy. In the literature already exists cases of cutting guide-based condylectomy, but they only depend on the mere mirroring procedure in virtual planning, which has accuracy limitations because it does not consider asymmetry of peri-condylar structures at the level of the ramus, body, and mandibular angle. Methods: CAD-CAM orthodontic preparation through the NEMOFAB Software was performed to correct the canting of the occlusal plane, following the “orthodontic first” technique. The same software was used for VSP of the surgical cutting guide to perform the condylectomy, basing not to the mere mirroring of the opposite side but considering the whole condylar-TMJ-glenoid fossa structure. Results: At 6 months follow-up, the patient showed good occlusion and an almost totally recovered lower third symmetry as median-upper and lower interincisive lines coincide with each other and with the chin median. A good occlusal and masticatory outcome was obtained. The joint structure was preserved with remodeling of the glenoid cavity caused by the presence of the joint disc, which was preserved during surgery. Conclusions: The goal of this study is to propose a method of therapeutic management of condylar hyperplasia that benefits from accurate pre-operative orthodontic treatment (orthodontics first) to maximize the results of proportional condylectomy, reducing post-operative orthodontic care as well as any need for any adjuvant orthognathic surgery. A new virtual surgical planning method is also proposed for creating a cutting guide that not only takes advantage of the mirroring technique to accurately calculate the amount of condyle to be cut but also considers the entire condyle–TMJ complex to perform a condylectomy that is more precise.

The influence of halogen-mediated interactions on halogen abstraction reactions by formyl radicals.

This article reports a theoretical study on the halogen exchange reactions YX + CHO → Y + XCHO (with Y = F, Cl, Br; X = Cl, Br, I) carried out at a high level of accuracy using coupled-cluster based methodologies including CCSD(T)-F12, CCSD(T)/CBS and CCSDT(Q)Λ. Most of the reactions are exothermic at room temperature, with the exception of the reactions FI + CHO → F + ICHO and ClI + CHO → Cl + ICHO. Exothermicity follows two concurrent trends established by the strength of the bonds being cleaved and formed: Y = F < Cl < Br (X-Y bond strength) and X = Cl > Br > I (C-X bond strength). Regarding the topology of the potential energy surfaces, we find that at the CCSD level of theory only some processes present the expected reaction profile: a pre-reactive complex (preRC) followed by a transition state (TS) and a post-reactive complex (postRC). However, when triple excitations are taken into account, all reactions become barrierless with no preRC/TS along the reaction profile. We propose that halogen-mediated interactions through the σ-hole, which represent the driving force in the early stages of the title reactions, are responsible for the absence of a tight transition state. We suggest that the strength of these interactions formed during these processes triggers the onset of the halogen atom exchange, before the preRC is formed. Therefore, this study aims to show the relevant role of halogen-mediated interactions in the mechanism of reactions in which a halogen atom is abstracted by the formyl radical (CHO).

Modeling of historical and future changes in temperature and precipitation in the Panj River Basin in Central Asia under the CMIP5 RCP and CMIP6 SSP scenarios

This study examines the complexities of climate modeling, specifically in the Panj River Basin (PRB) in Central Asia, to evaluate the transition from CMIP5 to CMIP6 models. The research aimed to identify differences in historical simulations and future predictions of rainfall and temperature, examining the accuracy of eight General Circulation Models (GCMs) used in both CMIP5 (RCP4.5 and 8.5) and CMIP6 (SSP2–4.5 and 5–8.5). The evaluation metrics demonstrated that the GCMs have a high level of accuracy in reproducing maximum temperature (Tmax) with a correlation coefficient of 0.96. The models also performed well in replicating minimum temperature (Tmin) with a correlation coefficient of 0.94. This suggests that the models have improved modeling capabilities in both CMIPs. The performance of Max Plank Institute (MPI) across all variables in CMIP6 models was exceptional. Within the CMIP5 domain, Geophysical Fluid Dynamics (GFDL) demonstrated outstanding skill in reproducing maximum temperature (Tmax) and precipitation (KGE 0.58 and 0.34, respectively), while (Institute for Numerical Mathematics) INMCM excelled in replicating minimum temperature (Tmin) (KGE 0.28). The uncertainty analysis revealed a significant improvement in the CMIP6 precipitation bias bands, resulting in a more precise depiction of diverse climate zones compared to CMIP5. Both CMIPs consistently tended to underestimate Tmax in the Csa zone and overestimate it in the Bwk zone throughout all months. Nevertheless, the CMIP6 models demonstrated a significant decrease in uncertainty, especially in ensemble simulations, suggesting improvements in forecasting PRB climate dynamics. The projections revealed a complex story, as the CMIP6 models predict a relatively small increase in temperature and a simultaneous drop in precipitation. This indicates a trend towards more uniform temperature patterns across different areas. Nevertheless, the precipitation forecasts exhibited increased variability, highlighting the intricate interaction of climate dynamics in the PRB area under the impact of global warming scenarios. Hydrological components in global climate models can be further improved and developed with the theoretical reference provided by this study.

Comparison of Tensile Properties Data Analyzed Using ANOVA and MANOVA Methods: Case Study Data of PVA-Gambir Blend Film

In our previous research, the Analysis of Variance (ANOVA) method was applied to analyze data on the tensile properties of pure PVA films and blend films mixing PVA (10%), BA (0.5%), and UG (1%). The results using ANOVA analysis are not significant for treatments in samples. This study compares data from Multivariate Analysis of Variance (MANOVA) and ANOVA methods. The accuracy level of both methods is α = 5% (0.05). The result showed that the tensile properties of samples using MANOVA display a significant data effect. Therefore, we recommended MANOVA because the tests were performed simultaneously.

Implicit Central Difference Approximations of Averaged Dynamics of Highly Oscillatory Systems With Applications to Direct Optimal Control

ABSTRACTTrajectory optimization of highly oscillatory systems can require huge computational efforts if the horizon of the problem is much larger than the duration of a single cycle. To alleviate this effort, stroboscopic averaging methods can be used, which utilize local single‐cycle simulations of the oscillatory dynamics to then approximate the average dynamics of the system and integrate them with integration steps much larger than a single cycle. Targeting especially the field of direct optimal control, where, after discretization, the simulation of the dynamics is often included implicitly in the equality constraints of the nonlinear programming problem, we introduce an implicit central difference scheme for the approximation of the average dynamics. In the introduced implicit central difference methods, we solve a nonlinear system of equations that connects single‐cycle simulations of the oscillatory dynamics in order to approximate the average dynamics from a linear combination of points in state‐space. We derive the accuracy order for this generalized ‐point central difference approximation of the average dynamics and confirm the claims in numerical experiments. Compared to existing explicit approaches, the introduced methods are up to twice as efficient while maintaining the same level of accuracy.

Addressing unfamiliar ship type recognition in real-scenario vessel monitoring: a multi-angle metric networks framework

Intelligent ship monitoring technology, driven by its exceptional data fitting ability, has emerged as a crucial component within the field of intelligent maritime perception. However, existing deep learning-based ship monitoring studies primarily focus on minimizing the discrepancy between predicted and true labels during model training. This approach, unfortunately, restricts the model to learning only from labeled ship samples within the training set, limiting its capacity to recognize new and unseen ship categories. To address this challenge and enhance the model’s generalization ability and adaptability, a novel framework is presented, termed MultiAngle Metric Networks. The proposed framework incorporates ResNet as its foundation. By employing a novel multi-scale loss function and a new similarity measure, the framework effectively learns ship patterns by minimizing sample distances within the same category and maximizing distances between samples of different categories. The experimental results indicate that the proposed framework achieves the highest level of ship monitoring accuracy when evaluated on three distinct ship monitoring datasets. Even in the case of unfamiliar ships, where the detection performance of conventional models significantly deteriorates, the framework maintains stable and efficient detection capabilities. These experimental results highlight the framework’s ability to effectively generalize its understanding beyond the training samples and adapt to real-world scenarios.

Improved YOLOv5: Efficient Object Detection for Fire Images

Camera-based fire detection systems have been found to yield significantly superior results compared to their sensor-based counterparts, and are thus widely employed for this purpose across the globe. This study presents an effective and lightweight fire detection technique based on deep learning. A novel convolutional neural network has been created specifically for the purpose of identifying fire areas. This network utilizes the YOLOv5 algorithm. The default versions of the You Only Look Once (YOLO) technique demonstrate limited accuracy when applied to fire detection instances, even after undergoing training and testing. After training and testing in fire detection instances, the default versions of the YOLO technique exhibit a significantly low level of accuracy. We chose the YOLOv5 network to augment its capabilities and maximize its effectiveness in identifying fire disasters. The self-designed convolution module, also known as the improved convolution module, is implemented to substitute the regular convolution module in YOLOv5. By dividing the input features into two main portions, it helps to realize the lightweight of the network. Additionally, the network incorporates the attention mechanism. The testing findings clearly show that the proposed method is more effective than previous ways of identifying fire, with a precision of 91.22% and a recall of 93.78%.

P0376 Advancing intestinal ultrasound for IBD in Turkey: results of a national survey

Abstract Background Intestinal ultrasound (IUS) is increasingly valued as a noninvasive tool for inflammatory bowel disease (IBD) management, enabling real-time, radiation-free assessment of bowel wall thickness, vascularity, and complications like abscesses.1 Studies show that IUS achieves accuracy levels comparable to traditional imaging methods.2,3 ECCO guidelines recommend the application of IUS for monitoring disease activity and assessing response to treatment.4 IUS is increasingly adopted in Europe and other regions. To date, there has been scarce data on the application of IUS in Turkey, a gap this study seeks to fill. Methods We conducted a nationwide, cross-sectional survey targeting both adult and pediatric gastroenterologists across Turkey. The survey was distributed to 817 adult and 150 pediatric gastroenterologists through the Turkish Society of Gastroenterology. The survey included 26 structured questions divided into sections on demographic data, familiarity with and usage of IUS, and perceived barriers to the implementation of IUS. Participants also provided information about their training in ultrasound and their institution type, experience level, and specialty focus. Descriptive statistics were calculated to summarize the findings, and the Pearson Chi-square test was used to compare categorical data between groups. Results Of the 817 adult and 150 pediatric gastroenterologists contacted, a total of 191 responses were received, comprising 84 pediatric gastroenterologists (response rate: 56.0%) and 107 adult gastroenterologists (response rate: 13%). Regarding training in IUS, 73% (n=140) of respondents noted they had not received formal training. Significant differences in IUS utilization were observed between pediatric and adult gastroenterologists (Figure 1). Adult gastroenterologists were more likely to perform IUS independently compared to pediatric gastroenterologists (29.0%, n=31 versus 2.4%, n=2; p&amp;lt;0.001). Of note, 62.6% (n=67) of adult gastroenterologists and 46.4% (n=39) of pediatric gastroenterologists did not use IUS at all. Altogether, 94% (n=179) of participants emphasized the necessity of expanding educational opportunities for gastroenterologists and expressed their willingness to attend a dedicated course (Figure 2). Additionally, 86.4% (n=165) favored the establishment of national guidelines to increase IUS adoption. Conclusion Our findings reveal a significant gap between the potential value of IUS in managing IBD and its current level of utilization in Turkey. Key barriers identified in our study include insufficient training opportunities, suggesting a clear need for structured educational programs and national IUS guidelines. Expanding IUS utilization has the potential to optimize IBD management in Turkey.

Performance of K-Nearest Neighbor Algorithm and C4.5 Algorithm in Classifying Citizens Eligible to Receive Direct Cash Assistance in Bandar Mahligai Village

Direct Cash Assistance, commonly called BLT, is one of the many programs the Indonesian government held to reduce the poverty rate of the Indonesian population. This study compares the KNN and C4.5 methods to determine the eligibility of residents eligible to receive Direct Cash Assistance in Bandar Mahligai Village. This study began with collecting resident data from the Bandar Mahligai village office. Then, the data obtained was taken into several attributes to be used in the classification process, namely the name of the head of the family, KK number, NIK, number of dependents, occupation, income, and monthly expenses. After the data is collected, the data will be classified using the KNN and C4.5 algorithms. There is a significant difference between the two algorithms in the classification process; the KNN algorithm by looking for the nearest neighbor data value, in this study, the K value = 9, while the C4.5 algorithm by building a decision tree from the attribute values taken based on resident data used as training data. The classification results of the two methods will be compared using a confusion matrix to obtain a higher accuracy technique. The results of testing using a confusion matrix for both algorithms are the accuracy produced by the KNN and C4.5 algorithms in classifying residents eligible for Direct Cash Assistance (BLT) of 90% in the system that has been built. The results of comparing the KNN and C4.5 algorithms for this study show that the KNN algorithm is better because the accuracy level reaches 90% in manual and system calculations. While the C4.5 method only gets 85% for the accuracy of its manual calculations, it receives an accuracy level of 90% in the system that has been built.

Analysis of Factors Influencing the Decision to Select Thai Nguyen University of Information Technology and Communication

This article presents a study that aims to examine and analyze the factors that influence students' decisions in choosing the Thai Nguyen University of Information and Communication Technology. The research intends to assist universities in gaining a deeper understanding of the crucial factors that students consider when selecting a university. Consequently, this will lead to the development of more effective admission policies and strategies. The study involved collecting data from a random sample of first-year students at Thai Nguyen University of Information and Communication Technology during 2022. Data was gathered using questionnaires and both qualitative and quantitative research methods, utilizing SPSS 20 software. The findings from factor analysis indicated that a multiple regression model was suitable for assessing the factors that influence decisions to enroll in the Thai Nguyen University of Information and Communication Technology. Variables such as CSVC1 (Adequate classrooms meeting to learning needs), CSCV4 (Modern and well-equipped library with ample study materials), HP3 (Generous scholarships for various groups), VL3 (Supportive policies for employment during studies), DT2 (Curriculum aligned with social needs), DT4 (High-earning graduates), and DNGV1 (Highly qualified and standardized faculty) showed significant correlations with the dependent variable - students' career choices. The results demonstrated that the proposed model was well-fitted, with favorable levels of compatibility indices (CFI, TLI) and accuracy (GFI, RMSEA). Additionally, the outcomes of the Structural Equation Modeling (SEM) supported the appropriateness of the proposed model and its capability to explain the relationships between variables in the study. Based on these research findings, the article provides recommendations, including enhancing faculty quality, improving infrastructure and facilities, diversifying programs, and enhancing university information dissemination to elevate the quality and quantity of future admissions.