Findings Of Study Research Articles

A Review Of Autism SpectrumDisorder Detection Using Machine Learning

A neurological condition known as an autism spectrum disorder (ASD) profoundly impacts an individual's lifelong ability to engage and interact with others. ASD is an illness that can be recognized at early stages in a person's life and is often classified as a "behavioral disease" due to the presence of numerous symptoms that frequently manifest within the first two years of life, as per what most people believe about autism theory, these challenges typically emerge during childhood and endure into adolescence and adulthood [1]. In response to the growing popularity of medical diagnosis, machine learning techniques [2] are used to help doctors make better decisions about a person's health, extensive efforts have been made to leverage various algorithms, including Naive Bayes, Support Vector Machine, Logistic Regression, K-Nearest Neighbors (KNN), Neural Networks, and Convolutional Neural Networks (CNNs) [3], to predict and analyze ASD-related issues across different age groups—children, teenagers, and adults. These endeavors have been detailed in a researchsurvey paper. These predictive models were assessed using the ABIDE dataset, which is openly accessible for research purposes. The study's findings underscore the effectiveness of CNN-based prediction models, which consistently outperformed other machine-learning techniques [4]. Notably, these models achieved impressive accuracy rates of 99.53%, 98.30%, and 96.88% for screening and diagnosing Autistic Spectrum Disorder in datasets representing adults, children, and adolescents, respectively.

Does the site, size, and number of necrotic collections affect the outcome of necrotizing pancreatitis? - a prospective analysis.

In patients with acute necrotizing pancreatitis (ANP), the site, size, and the number of acute necrotic collections (ANC) may determine the outcome of patients. The current study aimed to correlate the nature of ANC with the adverse outcomes in ANP patients. This was a single-center, prospective study (August 2019-August 2022) recruiting patients with ANP, correlating the site, size, and number of ANC with the length of hospital stay, intensive care unit (ICU) stays, development of organ failure and infection, need for intervention, and mortality. A total of 114 patients (mean age: 37.3 ± 13.4 years, 85.1% males) with ANP were included in the study. The number and maximum diameter of collections significantly correlated with the length of the hospital and ICU stay and the need for intervention. Taking a cut-off size of 8cm, the sensitivity and specificity for predicting the need for intervention were 82.7% and 74.2%, respectively. ANCs located in the perinephric, paracolic, subhepatic, and epigastric regions had a significant correlation with two or more adverse outcomes. Additional points were added to the modified CT severity index (mCTSI) based on the present study's findings. The new score had significantly higher AUROC than mCTSI for predicting infection, need for intervention, ICU stay > 1 week, and mortality. The site, size, and number of EPNs have a significant correlation with adverse clinical outcomes in patients with ANP. The inclusion of these parameters, along with present scoring systems, will help further improve the prognostication of patients.

Evaluation of the corrosion and oxidation resistance of NiCoCrAlY cladding on CMSX-4 by laser cladding

This study uses laser cladding to investigate the hot corrosion and oxidation behavior of CMSX-4 claddes with NiCoCrAlY powder. High-temperature oxidation tests were conducted at 1050°c for 50, 100, 150, and 200 h on 4 samples, while 6 samples underwent hot corrosion testing at 850°c for 6 h. The innovative application of laser cladding enables the creation of a γ′ and β interdendritic phase microstructure. High-temperature oxidation tests reveal a growth rate of 0.05 μm/h in oxide layer thickness between 50 and 200 h, accompanied by a surface weight increase rate of approximately 0.8 mg/cm2.h. The oxide layer's regeneration, continuity, and density are observed at 200 h, highlighting the innovative potential of this technique. Fracture oxidation is observed at 150 h in some TGO layer regions, but at 200 h of oxidation, the TGO layer's regeneration, continuity, and dense TGO was observed at the cladding interface. The TGO forms at 1050°c consist of Al2O3, Cr2O3, and CoO oxides, with Al2O3 as the main oxide phase. The study's findings demonstrate the ability of laser cladding to enhance the thermal protection of CMSX-4, showcasing a promising innovation in the field.

The Tunisian Central Bank’s Efficiency Benchmarks: A Dynamic Approach

The topic of banking performance and efficiency has received considerable attention, yet there is a noticeable gap in examining the performance and efficiency of central banks. Moreover, there is a scarcity of studies on central bank efficiency. This paper aims to investigate the factors that influenced the efficiency of the Central Bank of Tunisia from 2000 to 2020, using an Autoregressive Distributed Lag Model. The appropriate econometric methodology was first established to achieve this goal, after which the model's results were presented and interpreted. The study's findings indicate that the Tunisian Central Bank’s efficiency is influenced by several macroeconomic (inflation, public deficit, growth rate), international (exchange rate, foreign debt), and political variables (political and government instability, conflict of interest), each with a varying degree of impact. Interestingly, the transition of the Central Bank of Tunisia from a dependent to an independent institution in 2016 did not yield a notable change in efficiency.Copyright© 2024 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.

Adding barium sulfate (BaSO4) to fly ash geopolymer increases its compressive strength as X-ray shielding for medical imaging applications

BackgroundGeopolymers, a novel cementitious material, have the potential for reducing carbon dioxide wastes resulting from the manufacture of cement. PurposeThis study presents an experimental inquiry conducted to produce a fly ash geopolymer mortar with a good of compressive strength of the batch. MethodsActivation of the mortar is accomplished through the use of sodium hydroxide and sodium silicate. The mortar is created from a mixture of fly ash and sand. In order to determine the maximum load that a material is capable of bearing before experiencing failure, the compressive strength test was utilized. ResultsAccording on the findings of the compressive strength testing device, the fly ash geopolymer with 15 % barium sulfate (BaSO4) demonstrates the highest compressive strength, which is measured at 56 MPa. When compared to the results obtained from cement mortar, this one is twice as high, which indicates that the strength improvement factor is 2. This study's findings highlighted the necessity of employing fly ash geopolymer that contains 15 % BaSO4 for the purpose of providing shielding protection. Conclusion: Therefore, in terms of its performance, fly ash geopolymer is superior to cement mortar. This is due to the fact that it is manufactured with a high compressive strength.

Assessment of malaria transmission in Kenya using multilevel logistic regression

Background: Kenya has a lower malaria incidence in comparison to other African malaria-endemic nations. Malaria is a significant public health concern in the country. The malaria indicator survey (MIS) data were analyzed using the logistic regression model. Nonetheless, independent data may be the cause of most MIS's hierarchical structure. This approach does not consider any association between data points within a cluster, as it assumes that the individual malaria statuses are independent of their causes. The approach may lead to biased analysis conclusions. The primary goal of this research is to determine the impact of sample enumeration areas (SEAs) and SEA features on individual malaria rapid diagnostic test (RDT) results. We are interested in identifying key factors influencing household members' malaria RDT findings or Kenya's malaria prevalence and assessing variation. Methods: Our study utilized the robust 2020 Kenya National Malaria Indicator Surveys (KMIS) dataset, which is representative of the entire nation. This dataset, comprising 301 clusters (134 urban and 167 rural areas), was instrumental in applying several multilevel models, including random sample and sample Enumeration Area (SEA) effects. We also considered the weights used in the s survey design, which is used to adjust uneven probabilities of choice within clusters, further enhancing the reliability and relevance of our findings. The methods used in this study involved a rigorous analysis of the KMIS dataset, including applying multilevel models and considering survey design weights to ensure the robustness and strength of our results. ResultsThis study's findings are significant and crucial in understanding the prevalence of malaria in Kenya. The findings reveal that factors such as region, place of residence, mosquito bed net use, water source location, wealth index, age, household size, and altitude are significantly associated with malaria's prevalence.After accounting for these variables, systematic changes across SEAs accounted for approximately 47.1% of the remaining variability in malaria occurrence in the study locations. In contrast, the remaining 52.9% was projected to be unmeasured differences between individuals or family units. These findings provide a detailed explanation of the various processes that influence malaria prevalence in Kenya. Conclusions: The study's multilevel logistic regression model, which includes random effects, identified two SEA-level and eight individual/household risk factors for malaria infection. Thus, increasing the availability of insecticide-treated bed nets is one crucial element that public health policymakers should consider. Furthermore, health planners can organize spatially targeted initiatives to prevent malaria transmission with the help of spatial clustering data.

Volumetric Classification: Unveiling the True Extent of Rotator Cuff Tears

Rotator cuff injury diagnosis involves comprehensive clinical, physical, and imaging assessments, with MRI being pivotal for detecting and classifying these injuries. However, the absence of a universally accepted classification system necessitates a more precise approach, advocating for the use of three-dimensional (3D) modeling to better understand and categorize rotator cuff tears. This research was conducted as a prospective, single-institution study on 62 patients exhibiting full-thickness rotator cuff tears. Utilizing preoperative 1.5T MRI, the study aimed to create a more detailed classification system based on volumetric and surface area measurements. Advanced 3D modeling software was employed to transform MRI data into precise 3D representations, facilitating a more accurate analysis of the lesions. The study unveiled a novel classification system rooted in volumetric and surface area assessments, revealing significant discrepancies in the existing two-dimensional classifications. Approximately 45% of the cases demonstrated inconsistencies between traditional classifications and 3D measurements. Notably, medium-sized lesions were often overestimated, while small and large lesions were consistently underestimated in their severity. The volumetric and surface area-based classifications provided a more accurate depiction, highlighting the limitations of relying solely on coronal plane assessments in MRI. Comparative analysis confirmed the improved accuracy of the 3D method. The integration of 3D imaging and volumetric analysis offers novel advancement in diagnosing and classifying rotator cuff injuries. This study's findings challenge the conventional reliance on 2D MRI, proposing a more detailed and accurate classification system that enhances the precision of surgical planning and potentially improves patient outcomes. The incorporation of comprehensive 3D assessments into the diagnostic process represents a significant step forward in the orthopedic imaging field.

Laryngotracheal stenosis: a single-center retrospective analysis of endoscopic treatment strategies and recurrence.

Laryngotracheal stenosis is a process of fibrosis that results in airway obstruction; it may be congenital or acquired. Acquired cases are due to iatrogenic, traumatic, infectious and autoimmune causes. Patients present with a spectrum of breathing difficulties that might be fatal. This article presents a unique retrospective cross-sectional study of patients with laryngotracheal stenosis who had endoscopic dilatation, and some had open surgical procedures to describe the evolution after the therapy, to compare the outcomes of the dilatation in comparison to surgical interventions and associated complications in other studies, to provide knowledge to help in dealing with these patients, and to enable educated, independent patient decision-making. In this observational and descriptive study, we aimed to highlight the clinical features, management, and treatment outcomes among twenty-nine patients with laryngotracheal stenosis who were managed by open and endoscopic surgical intervention over a period of 5 years. Data were collected retrospectively from the patients' medical records from February 2016 until July 2022 at a hospital in Jerusalem, which is a tertiary healthcare facility and the only referral center for similar cases from the West Bank, East Jerusalem, and Gaza, with a population of around 8 million. Tables and graphs are used to highlight the statistical study's findings. Data were analyzed using Microsoft Excel software. Twenty-nine patients were involved in the study, with an average age of 32.2 years; 51.7% of them were males. The patients had one or more chronic conditions, such as hypertension and coronary artery disease. The majority of patients (65.5%) had stenosis as a result of orotracheal intubation, and the most common site was the subglottic (68.9%). According to the Cotton-Myer classification, 44.8% of the patients were classified in grade III, 34.4% were included in grade I, 13.7% in grade II, and 6.8% in grade IV. Six patients (20.68%) received surgery for stenosis, and 23 patients had an endo-laryngeal dilatation. Restenosis happened in (58.6%). Mortality rates are almost negligible. Subglottic stenosis is still a challenging condition to manage. The authors describe a single-center experience approach dealing with these conditions. Long-term follow-up for these cases is mandatory as the recurrence rate is still high.

Entropically driven binding of Camptothecin in the minor groove of salmon testes DNA

The present study focuses on binding association of Camptothecin (CMT) towards natural deoxy-ribonucleic acid (salmon testes, ST) under physiological conditions of pH 7.4. Extensive spectroscopic and computational techniques have been employed to elucidate thermodynamics of the said interaction. UV and fluorescence analyses portrays significant intensity changes (hyper-chromic and hypsochromic) in the spectra, which confirms effective CMT binding to ST DNA. The McGhee-von Hipple method and Scatchard plot analyses estimated the binding affinities in 105 M−1 range. Associated thermodynamic data revealed spontaneous and exothermic nature of binding. Temperature-dependent fluorescence showed negative change in enthalpy and positive change in entropy, leading to the formation of a 1:1 adduct. Non-polyelectrolytic forces appeared to be the driving force of the ligand-DNA interaction, according to salt-dependent fluorescence. Dye displacement assay, viscosity study, DNA melting, iodide quenching, urea denaturation assay examined the minor groove nature of CMT. In silico docking study examined precise molecular representations of the minor groove binding mechanism that formed between the complex, and the study's findings were consistent with the experimental results. Simulation studies also validated the experimental analysis and docking data. These findings could expedite the process of creating new and improved CMT molecular derivatives and help in the creation of DNA-targeted medicines, which may be beneficial from a pharmaceutical point of view.

Implications of Monitoring and Evaluation Systems for SMEs in some Selected Metropolis in Ghana

The purpose of this research was to examine how monitoring and evaluation systems affect small and medium-sized businesses in selected Ghanaian metropolises. The research design used was explanatory. The population for this study comprised owner-managers of SMEs in Ghana captured in the GEA and the AGI databases. From these two sources, a sampling frame was created for those in the selected metropolis. This gave a total population size of 1,189. For this frame, a sample of 423 SMEs was selected comprising SMEs from the Sunyani, Accra, Kumasi, Cape Coast, and Tamale metropolitan areas. The sample size for the study was 423 SMEs. The primary data collection tool was a self-administered questionnaire. Data was collected, cleaned, and coded before being entered into statistical software programs like SPSS and Smart-PLS. For the study's key findings, it was noted that the SMEs that took part in the study had monitoring and evaluation practices in place. The third and fourth objectives showed that monitoring and evaluation systems had a significant effect on both business resilience and business sustainability, respectively. It was discovered that monitoring and evaluation systems had a large and favourable effect on business growth and the digitalisation business respectively, for objectives 5 and 6. The study recommended that managers of SMEs within the selected area improve upon their monitoring and evaluation systems. This will increase the effect it will have on their resilience, growth, sustainability and digitalisation.

Capital Structure And Earnings Per Share: An Empirical Analysis Of Selected Companies Listed On The Nigerian Exchange Group

Focussing on the moderating influence of company size, this study analyses the implication of capital structure on earnings per share (EPS) for a subset of businesses listed on the Nigeria Exchange Group. The research examines data from fifteen publicly listed food and drink firms over a certain time frame using a random effects model. According to the results, some parts of the capital structure, like LDTA and EQTA, significantly boost earnings per share (EPS), while others, like TPTA, have the opposite effect. Including company size as a moderating variable significantly improves the model's explanatory power. The modified R-squared value goes up from 7.5% to 55.8%. Firm-specific features heavily influence capital structure decisions and their influence on financial viability. Firm size should be a major aspect in financing decisions, according to the report, since larger organisations are better able to optimise their capital structure to boost profitability. Corporate managers, lawmakers, and investors can benefit from the study's new findings, which add to the current literature by demonstrating how business size affects the correlation between capital structure and profitability

Prognostic factors of extracorporeal shockwave therapy in the treatment of nonunion in long bones: a retrospective study.

Nonunion of long bone fractures is a significant complication following surgical fixation, with an incidence ranging from 5% to 10%. Surgical intervention is the standard treatment for nonunions, but it may come with potential complications. Nonoperative approaches, such as Extracorporeal Shockwave Therapy (ESWT), have been advocated as alternatives. The retrospective study, conducted between January 2004 and January 2018, 91 patients who underwent ESWT for tibia or femur nonunions were included. Nonunion was defined based on radiographic criteria and clinical symptoms. The nonunion morphology was categorized as hypertrophic, oligotrophic, or atrophic. ESWT was administered using the OssaTron device in a single treatment session. Bony union was defined as the presence of bridging callus over fracture site with more than three-fourths of the circumference in both planes within the 12-month postoperative period. The study included 91 patients, with an overall union rate of 62.6%. Higher healing rate was observed in trophic nonunion(69.9%) than atrophic nonunion(33.3%). Multivariate analysis identified the number of surgeries, maximum fracture gap, and atrophic nonunion as independent factors influencing the risk of fracture nonunion after ESWT. ROC curves were generated for these factors, providing more than one surgical interventions, and fracture gap greater than 3.94mm as negative predictors of ESWT for long bone nonunions. The study's primary findings suggest that ESWT is effective in achieving bony union for nonunions in long bones(62.6%). Despite the overall positive results, the study highlights that atrophic nonunions, larger fracture gaps more than 3.94mm, and multiple surgeries are associated with poorer outcomes.

Effectiveness of Problem-Based Learning Textbooks: ICARE's Pedagogical Approach to the Teaching Profession

The objectives of the study are to design a problem-based learning-based teaching profession textbook using the ICARE (Introduction, Connection, Application, Reflection, Extension) pedagogical approach; to assess the viability and efficacy of a PBL-based teaching profession textbook. Using the Borg and Gall paradigm, this research methodology called research and development (R&D). The study was carried out at the FIP Unimed, PGSD Department, Teaching Profession course. Students and instructors with expert validation—that is, specialists in learning design, visual design, and materials—were the study's subjects. Additionally, by carrying out field, small-group, and individual trials. Expert validation from subject matter experts, learning design experts, and graphic design experts is required for the entire product's results. In order to identify a teaching profession textbook based on PBL and assess its viability and efficacy, the following step involves conducting individual, small group, and field trials as well as carrying out revisions and analysis. ANOVA and/or the t-test will be used to examine the quasi-experimental approach (pretest-posttest with control group design) and the study's findings. The study's findings show that the PBL-Based Teaching Profession Textbook is deemed useful and appropriate for use in the classroom, with the potential to enhance student learning outcomes.

Faktor Yang Mempengaruhi Kinerja Pegawai Dilihat Dari Disiplin dan Motivasi Kerja (Studi Pada Kantor Dinas Sosial Kabupaten Solok)

Well-performing personnel are a major source of motivation for social care agencies. Employee motivation and discipline are good indicators of this. Finding out how work motivation and discipline affected employees' performance at the Solok Regency Social Service Office was the aim of this study. This research falls under the category of quantitative research. The primary data that was used was gathered by having respondents fill out questionnaires. The study's population comprised all 39 employees who worked at the Solok Regency Social Service Office. Total sampling was the method employed for sampling in this investigation. 39 persons were the total number of samples that were to be used. Multiple linear regression analysis, determination coefficient testing, hypothesis testing, and research instrument testing were all used in the data analysis technique. The study's findings showed that work motivation affected employee performance, work discipline had no effect on employee performance, and work motivation and discipline had an impact on employee performance at the same time.

An Expert Model Using Deep Learning for Image-based Pest Identification with the TSLM Approach for Enhancing Precision Farming

The rise of digital agriculture has sparked considerable interest in its potential to revolutionize farming practices by integrating of advanced technologies. Unmanned aerial vehicles (UAVs), commonly known as drones, have emerged as a crucial tool in precision agriculture, offering diverse applications for real-world scenarios. This research focuses on harnessing the power of drones in the context of digital agriculture support, particularly in addressing the challenges of crop protection and pest management. The objective is to develop an innovative approach for crop-disease diagnosis using an upgraded Transfer-Driven Self-Adaptive Learning Model (TSLM) that leverages multispectral remote sensing data of drone-captured images to improve pest classification and streamline pesticide application. The study explores nine potent deep neural network models' capabilities for identifying plant diseases using various approaches within the digital agriculture framework. Transfer learning and advanced feature extraction techniques are employed to tailor these deep neural networks to the specific crop protection context. Using of pre-trained deep learning models for feature extraction and fine-tuning enhances the effectiveness of the proposed model. Evaluating the model's performance, precision, sensitivity, specificity, and F1-score metrics are assessed, leading to the discovery that deep feature extraction and Self-Adaptive Learning Model (SLM) classification outperform traditional transfer learning methods. The novelty of this work lies in its application of communication protocols to coordinate a fleet of drones for crop protection within the digital agriculture framework. By combining deep learning techniques with transfer-driven self-adaptive learning, the proposed approach significantly enhances the accuracy and efficiency of pest classification in precision agriculture. The study's findings offer valuable insights into optimizing drone-based technologies to combat plant disease epidemics, thereby contributing to the advancement of digital agriculture and its role in supporting sustainable farming practices.

The Influence of Governance on Competence and Partnership in Higher Education

The study assesses the influence of governance on competence and partnership in university education in The Gambia using the situation as a mediating factor in a trilateral relationship. The study applied a questionnaire survey design using three universities as the study population. The PLS-SEM approach assesses the measurements and structural models. The study's findings established that governance significantly influences competence and partnership in university education in The Gambia. However, the situation as an environmental factor lacks the mediation capacity in the trilateral relationship. The study outcome shows differences, but only two were significantly contextual in relation to governance. The study recommends further research that adopts a qualitative approach to help provide a proper understanding of university governance in The Gambia.

Digital Education on Hospital Nutrition Diets: What Do Patients Want to Know?

Therapeutic nutrition plays an imperative role during a patient's hospital course. There is a tremendous body of literature that emphasizes the systematic delivery of information regarding hospital nutrition diets. A major component of delivering healthcare information is the principle of providing quality healthcare information, but this has not yet been investigated on hospital nutrition diets. This study aimed to evaluate the comprehension and readability of patient education materials regarding therapeutic hospital diets. The methodology employed the use of publicly available questions regarding hospital nutrition diets and categorized them per Rothwell's Classification of Questions. Additionally, the questions were extracted online and have an associated digital article linked to the question. These articles underwent analysis for readability scores. This study's findings reveal that most hospital diets do not meet the recommended grade-reading levels. This underscores the need for healthcare providers to enhance patient education regarding hospital diets. The prevalence of "Fact" questions showcases the importance of clearly explaining diets and dietary restrictions to patients.

Effect of Cold Application on Pain Relief during Intravenous Cannulation for Cancer Patients

Background: The cannulation of intravenous Analgesia is rarely experienced after this unpleasant procedure, which can also induce stress and worry. Objective: The purpose of the study was to ascertain how cold affected the degree of pain during intravenous cannulation. Methodology: A randomized controlled trial was carried out at the oncology department of AL-Al-Haboubi Teaching Hospital in Dhi-Qar from January 4, 2024, until February 3, 2024. After the sample was selected, 102 patients were divided into two groups in order to achieve the study's objective: 50 patients were assigned to the control group and 52 patients to the experimental group. The experimental group was given cold gel, and self-report data collection was used. A visual analogue scale of 10 cm was used to measure pain. Results: The experimental group's post-test results showed a low mean pain score (28.76+20.491), while the control group's data analysis results showed a high mean score (M+SD 76.54+20.427) and severe discomfort during cannulation insertion. The mean pain score during cannulation insertion varied significantly (p=.001) between the experimental and control groups following the use of cold treatment. Conclusions: According to the study's findings, the cool approach helped to lessen intravenous cannulation pain. Recommendations: The study advised patients to be chilled when receiving intravenous cannulation.

Numerical and Symbolic Analysis for Mathematical Problem-Solving with Maple

This study explores the versatile capabilities of Maple, a widely used mathematical software, in addressing a wide range of numerical and symbolic computations essential for scientific and engineering applications. The researchers investigated Maple's diverse suite of tools, including numerical integration, nonlinear equation solving, polynomial interpolation, symbolic integration, and various numerical methods. Through an in-depth literature review, illustrated case studies, and detailed performance evaluations, the paper demonstrates the effectiveness and accuracy of Maple's computational approaches in dealing with complex problems in various areas of applied mathematics. This study's findings underscored Maple's tremendous value as a reliable and comprehensive software package for researchers, scientists, and professionals involved in advanced mathematical analysis and scientific computing. Furthermore, the paper highlighted Maple's versatility in creating high-quality three-dimensional plots, crucial for visualizing and analyzing complex mathematical and scientific data. Using either sets or lists, the ability to display multiple surfaces in a single three-dimensional plot showcases Maple's power in data visualization and communicating complex ideas. By positioning Maple as a powerful platform for solving versatile mathematical problems, this study highlights the software's indispensable role in advancing scientific discoveries and engineering innovations.

AI Tutors vs. Human Instructors: Perceptions of Higher Education Students in Hungary and Spain

Integrating AI-powered tutoring systems in higher education represents a significant advancement in educational technology, offering personalized and adaptive learning experiences. This study investigates the perceptions and expectations of higher education students in Hungary and Spain regarding AI tutors. Despite extensive research on the technological efficacy of AI systems, there is limited understanding of student attitudes in these specific cultural contexts. This research aims to fill this gap by exploring student expectations, satisfaction levels, and perceived benefits of AI tutors compared to human instructors. To achieve this, a validated questionnaire was administered to 184 higher education students from Hungary and Spain, capturing data on various dimensions of their expectations. The study's findings indicate that students appreciate the adaptability and continuous guidance provided by AI tutors, with Hungarian students showing higher overall expectations compared to their Spanish counterparts. These insights suggest that AI tutoring systems can enhance the learning experience by addressing individual student needs more effectively. The implications of this study are significant for higher education institutions seeking to integrate AI technologies.