Research Work Research Articles

Optimization of PEMFC pressure control using fractional PI/D controller with non-integer order: design and experimental evaluation

The fuel-based proton exchange membrane (PEM) fuel cell is a promising technology for clean energy production owing to the several advantages including high efficiency (around 80% theoretical), quiet in operation, and almost zero emission as compared to conventional internal combustion engine. Only hydrogen and oxygen are supplied at the anode and cathode, respectively to generate power and water is produced as by product. However, it suffers to achieve its maximum theoretical efficiency due to lack of flow/pressure management of hydrogen and oxygen in the PEMFC stack which also causes flooding within the cell and reduce the performance of the catalyst and reduces the efficiency. The higher efficiency can be achieved with the proper control of the hydrogen and oxygen inlet flow rate and pressure at the PEMFC. Since it’s crucial to maintaining a consistent supply of exponential pressure, the main focus of this work is pressure regulation at the PEMFC cathode side. A fractional PI/D controller is designed to operate the PEMFC system more realistically. There are three primary objectives of this research work. In the first step, monitoring the PEMFC operating pressure to find out the suitable fractional PI-D controller for a given resilience level, which has the lowest Integration Absolute Error (IAE) to disturbances. The robustness level and/or threshold peak is considered as a tuning parameter for the evaluation. Second, compare the best IAE performance of the fractional PI-D controller with that of simple SIMC rules, where a certain level of resilience is achieved by varying the SIMC tuning variable. Through this comparison, the effectiveness of the recommended controller in achieving the optimal plant performance is evaluated. Thirdly, design a non-integer order PEMFC plant with a fractional controller using MATLAB software and compare the results with existing models. This comparison provides insight into the practical performance of the proposed controller. The results shows that the developed fractional PI/D controller is able to control the pressure very efficiently at the PEMFC cathode side. The findings further emphasise on the important to consider the resilience and robustness levels at the time of developing control systems for PEMFCs. The efficacy of the suggested unique technique is further confirmed by contrasting the suggested controller with the developed models.

Study of Structural Elucidation, Degradation Kinetics and Antimicrobial Analysis of Copper (II) Sesame-Soap Complexes with Urea and Thiourea Ligand in Non-aqueous Solvents

Abstract: Surfactants have great importance in biological and drug industries and the complexes of metallic soaps with various ligands are used in approximately every region of national economy. Therefore, our keen interest to study of degradation kinetics and biological importance of Cu (II) surfactants in non-aqueous and non-polar solvent benzene. Present research work has been initiated with synthesis, systematic study of structural elucidation, thermal degradation, kinetics and biocidal activities of copper (II) sesame-soap complexes with macrocyclic nitrogen and sulphur containing donar ligands like urea and thiourea. The thermal degradation of copper (II) sesame-soap complexes were carried out for analysis of degradation kinetics and estimation of kinetic and thermodynamic parameters using different methods at heating rate 10oC min-1. Copper (II) sesame-soap complexes of such ligands have also been analysed against Staphylococus aureus. This research work consolidates the synthesis of copper (II) sesame soap-urea and thiourea complexes by conventional methods and the structure of these complexes were assigned according to elemental analysis and molecular weight determinations. IR, NMR and ESR spectral studies have also been done to understand structural aspects. The anti-microbial activities of copper (II) sesame-soap urea and thiourea complexes have been evaluated by testing against Staphylococus aureus. The present research work includes information of thermal analysis using TGA technique to find out their kinetic and thermodynamic parameters by using diverse equations such as Coats-Redfern equation, Horowitz-Metzger equation, Broido equation, Piloyan–Novikova equation. Moreover, the results obtained from anti-microbial screening have been used to analyze the anti-microbial activities of copper (II) sesame-soap urea and thiourea complexes against gram-positive bacteria Staphylococus aureus. These results show that complexes of copper ion co-ordinated with different nitrogen, oxygen and sulphur containing ligands, are very important in the field of pharmaceutical chemistry due to its significant role in the inhibition activity. The study of these complexes concludes that the synthesized complexes were found to possess appreciable bactericidal properties at different concentrations because chelation increases the anti-microbial potency.

A systematic review of in-memory database over multi-tenancy

The significant cost and time are essential to obtain a comprehensive response, the response time to a query across a peer-to-peer database is one of the most challenging issues. This is particularly exact when dealing with large-scale data processing, where the traditional approach of processing data on a single machine may not be sufficient. The need for a scalable, reliable, and secure data processing system is becoming increasingly important. Managing a single in-memory database instance for multiple tenants is often easier than managing separate databases for each tenant. The research work is focused on scalability with multi-tenancy and more efficiency with a faster querying performance using in-memory database approach. We compare the performance of a row-oriented approach and column-oriented approach on our benchmark human resources (HR) schema using Oracle TimesTen in-memory database. Also, we captured some of the key advantages on optimization dimension(s) are the traditional approach, late-materialization, compression and invisible join on column-store (c-store) and row-base. When compression and late materialization are enabled in a query set; it improves the overall performance of query sets. In particular, the paper aims to elucidate the motivations behind multi-tenant application requirements concerning the database engine and highlight major designs over in-memory database for the tenancy approach on cloud.

NRMG: News Recommendation With Multiview Graph Convolutional Networks

The emergence of a news recommendation system can effectively improve the news reading experience of users. The most important task of the system is how to learn news and user representations accurately. In the process of learning news representations, most of the current research works do not fully utilize the news features, which makes it difficult to learn more comprehensive news representations. Most research work only learns user representations from a single perspective, which may not be sufficient to learn diverse and dynamic user representations. Therefore, we propose a news recommendation system with a multiview graph convolutional network (NRMG). It contains two parts: news representation and user representation. The knowledge–content collaboration network is adapted to learn news representations from news content and entities, while the multiview graph convolutional network (GCN) is utilized to learn user representations from the user’s click history. The advantage of the NRMG system is that we not only expand the available features by constructing a subclass knowledge graph (KG), but also effectively improve the ability of the news recommendation system to accurately learn news and user representations. Experimental results on the real dataset MIcrosoft News Dataset (MIND) show that the NRMG achieves a 2.25% improvement in area under the receiver operating characteristic (ROC) curve (AUC) value compared with state-of-the-art methods.

Biological Importance and Therapeutic Potential of Calycopterin From Dracocephalum kotschyi: An Overview of Current Scientific Research Work.

Dracocephalum kotschyi Boiss is an important medicinal plant of Asia, Europe and Iran. The therapeutic potential of Dracocephalum kotschyi has been mentioned in traditional as well as in modern medicine, mainly due to its therapeutic effectiveness against different types of human disorders and associated secondary complications. Calycopterin is a flavonoid class phytochemical commonly called 5,4-dihydroxy-3,6,7,8- tetramethoxyflavone, which has been isolated from Dracocephalum kotschyi. It is widely used for the treatment of human disorders and associated secondary complications. Medicinal importance and therapeutic potential of calycopterin was investigated in the present work through scientific data analysis of different research works. Scientific data on calycopterin published in different research articles were collected from PubMed, Google, Google Scholar, Science Direct, SpringerLink, and Scopus and analyzed in the present work. Further, scientific data was collected from books and book chapters. Analytical data on calycopterin was also collected and analyzed in the present work. Scientific data analysis of different research work revealed the biological importance and therapeutic potential of calycopterin. Calycopterin showed therapeutic effectiveness against different types of cancerous disorders, including human prostate cancer, breast cancer, cervical cancer, lymphocyte proliferation and hepatoblastoma cancer. Moreover, the biological effectiveness of calycopterin on neuroprotection and Pseudomonas aeruginosa has also been discussed in the present work with their pharmacokinetic parameters. Nevertheless, more scientific investigations must be conducted to explore the healthbeneficial aspects of calycopterin in medicine. Present work signified the biological importance and therapeutic potential of calycopterin for the treatment of human disorders and complications, including different types of cancerous disorders.

An overlapping conscious relief-based feature subset selection method

Feature selection is considered as a fundamental prepossessing step in various data mining and machine learning based works. The quality of features is essential to achieve good classification performance and to have better data analysis experience. Among several feature selection methods, distance-based methods are gaining popularity because of their eligibility in capturing feature interdependency and relevancy with the endpoints. However, most of the distance-based methods only rank the features and ignore the class overlapping issues. Features with class overlapping data work as an obstacle during classification. Therefore, the objective of this research work is to propose a method named overlapping conscious MultiSURF (OMsurf) to handle data overlapping and select a subset of informative features discarding the noisy ones. Experimental results over 20 benchmark dataset demonstrates the superiority of OMsurf over six existing state-of-the-art methods

Design and analysis of wideband four-port multiple input multiple output antenna using defective ground structure for 5G communication

This research work describes a compact four-port multiple input multiple output (MIMO) antenna using defective ground structure (DGS), and it perfectly supports the n77, n78, and n79 frequencies in 5G new radios (NR) bands. It can cover a wideband from 3.4 to 5.4 GHz with good impedance matching. The pair of antenna elements are orientated opposite to another pair with DGS. Due to this technique, it has minimal complexity, is less expensive, and improves isolation. It has also improved the frequency band's reflection coefficient and range using MIMO antenna with different stub lengths. Because it is less expensive, FR-4 substrate is used in the implementation of all antennas. Each antenna element has two identical stubs linked to the primary radiator. On the primary radiating element, a ''HI'' slot is created. The partial ground enhances impedance matching and radiation properties throughout the targeted band. <span>The total dimensions of the four-port MIMO antenna are 46×30×1.6 mm<sup>3</sup>. The array elements' mutual coupling in the simulation is -14 dB. The ECC value is below 0.01, and the diversity gain (DG) is less than 10 dB. The suggested designs' measured gain ranges from 10 to 11.0 dB, and the radiation efficiency is nearly 91%.</span>

Design Aspects of Lower Limb Exoskeleton

Aim: This research work aimed at the design, simulation, and validation of a lower limb exoskeleton for rehabilitation. The device can provide regressive gait training for patients suffering from lower limb mobility disorders. Background: People suffering from mobility disorders, such as spinal cord injuries, and other related diseases are in high proportion. Exoskeletons play a vital role in enhancing the lifestyle of people with disorders. Devices that provide locomotion assistance and help in reducing the burden of therapists through effective and repetitive gait training are in high demand. Exoskeletons have further extended to the fields of the military to enhance the performance of physically abled persons. Prototype development of lower limb exoskeletons is too expensive and many of them are patented. The requirement for this system to perform human trials is subjective to several medical and ethical norms. Thus, there exists a need to evaluate and validate the exoskeleton designs. Methods: In this work, the design has been made inclusive of different body shapes and sizes. The device has been modeled in SOLIDWORKS and its structural integrity has been analyzed using the ANSYS software. Later, the model has been subjected to environmental assessment and then motion analysis using the ADAMS software. Results: The structural integrity analysis has revealed the design to be adequate to carry the applied load as the stresses induced were less than the yield strength of the material. The sustainability analysis showed that LLE made of aluminium alloy had less impact on the environment relative to the other two materials. Conclusion: The kinematic simulation revealed that the angular amplitudes, the reaction force of the right hip and knee joint, and the contact force between the shoe and the ground of the exoskeleton agreed well with the experimental findings of the literature.

Years lived with disabilities of the working-age population owing to injuries

Introduction: Cases of mortality and illness caused by injury in Mongolian population have increased drastically in recent years, and in 2022, 5th cause of illness and the 3rd cause of mortality. On average, more than 146,000 new cases of accidents and injuries are registered in Mongolia every year. More than 69% of it is a working-age population. Purpose and objectives: The purpose of the research work is to calculate the number of years lived with disability due to accidents among the working-age population of Mongolia. Research materials and methods: The study design is a Retrospective Cohort Study. The research work was carried out according to the WHO's burden of disease determination method. We collected quantitative data on disease numbers in Mongolia from the year 2016 to 2020 from the database of the Health Development Centre and National Center for Traumatology. The number of years lived with a disability was calculated using 306,823 quantitative information of the working-age (15-61) population of Mongolia in 2016-2020. Results: In Mongolia, 61.0% (n=187047) of the causes of accidents and injuries among the working-age population over 5 years were men and 39.0% (n=119776) were women. Considering the causes of accidents and injuries among the working-age population, 49.3% of all illnesses are falls, 25.3% are homicides, 22.9% are road accidents, and the rest are suicides, intoxication/exposure to toxic substances.120469 years of living with disability due to accidents caused by external causes of the working-age population of Mongolia over 5 years. The number of years lived with a disability was 77,135 years (64.0%) for men and 43,334 (36.0%) years for women. Conclusion: The working-age population of Mongolia has 120,469 years of living with disabilities caused by accidents caused by external causes. 1.8 times more years are lost than men and women.

Assessment of density and specific gravity tests of some selected Ayurvedic medicinal plants

The Guna(Static properties) in common terms known as properties or qualities. There are 41 Gunas in Ayurvedc classics. Among them the Gurvadi Gunas and Paradi Gunas are used in the various treatment measures. The Guru is known as heaviness. Gurutwa is correlated with Gravity in modern science. Parthiva and Apya Mahabhuta are responsible for this Guru Guna. The Guru drugs act as Brumhana(Nourishing as well as help to increase the mass of the tissues), Anuloman(Mild laxative). The exact opposite action of Guru drug is seen in Laghu Dravya. The Laghu drugs give Lightness to the body, as it is light in nature. The Akashiya, Vayavya, Agneya, Laghu Gunas are predominant in this drugs. These drugs act as Agnideepan(Appetizer), Strotoshodhan(Cleanses the channels). The more Parthiva Dravya, more the Guru can be considered. Less Parthiva Dravya more the Laghu can be considered. To assess the Parthivatwa by its correlation with Density and Specific gravity. Same can be considered for Jaliyatwa and Vayaviyatwa. Assessment of objective Parametric measures of Guru and Laghu Guna can be done by Density(Bulk), Specific Gravity(Liquid and solid). The Specific gravity, more formally known as relative density, is a measure of the density of a substance in comparison to the density of water. In this present research work there are 16 drugs for specific gravity for solids, 12 drugs for specific gravity for liquids, 20 drugs for Bulk density and the same 20 drugs for True density drugs have been selected.

Analysis of preferences of households for choosing heating and cooling systems: A discrete choice analysis in Iran

The residential sector uses a large fraction of energy for heating and cooling to provide comfort conditions for households. Technical features of appliances are determining factors of the demand for energy. The present paper thus aims to study the determinants of household adoption decision by applying the discrete choice model. Households face with unordered choice, and multinomial and mixed logit models are employed to study the heterogeneity of choices of households related to heating and cooling technologies. While most of the research works have considered preferences based on cross-sectional data collected through discrete choice experiments, the present study employs micro-level household survey data including observed preferences in order to contribute to better understanding of the actual choice behavior of households. In addition, the dynamic effect of variation of variables on the household preferences have been investigated over time. The results show that heterogenous preferences of families in choosing appliances are affected by socio-demographic characteristics. Analysis of marginal effects indicate that urbanization, education, building type and households expenditure are the most effective parameters. The level of education has the most positive impact on the adoption of wall-mounted package and split air conditioner where the probability of using them has increased by 1.65% and 4.87%, respectively. On the other hand, high education level reduces the probability of gas heater and fan adoption by 3.49% and 3.43%. The estimated results show that urbanization increases the probability of choosing central heating by 3.73% and split air conditioner by 6.1% while it decreases the adoption probability of oil heater by 5.02% and fan as by 8.9%.

Enhancing fall prediction in the elderly people using LBP features and transfer learning model

In an era where the detection and prevention of falls are crucial for the well-being of elderly and vulnerable individuals, achieving high accuracy in identifying such incidents is of utmost importance. This study introduces a modified NASNet, a novel transformer learning model designed for the classification of fall and not fall people based on local binary patterns (LBP) features. The modified NASNet model demonstrates high performance, achieving an accuracy, precision, recall, and F1 score of 99% with LBP features in differentiating fall from not fall. To assess the performance of NASNet, this research work conducted a comparative analysis with other prominent deep learning, and transfer learning models, as well as state-of-the-art machine learning frameworks. The findings indicate NASNet's enhanced performance in fall detection, highlighting its potential for application in real-time fall detection systems, contributing to a safer environment for individuals at risk. This research sets the stage for enhanced healthcare and assistance for vulnerable populations, addressing a critical concern in the healthcare sector.

Integrated Intelligent Computing Models for Cognitive-Based Neurological Disease Interpretation in Children: A Survey

INTRODUCTION: This piece of work provides the description of integrated intelligent computing models for the interpretation of cognitive-based neurological diseases in children. These diseases can have a significant impact on children's cognitive and developmental functioning. 
 OBJECTIVES: The research work review the current diagnosis and treatment methods for cognitive based neurological diseases and discusses the potential of machine learning, deep learning, Natural language processing, speech recognition, brain imaging, and signal processing techniques in interpreting the diseases.
 METHODS: A survey of recent research on integrated intelligent computing models for cognitive-based neurological disease interpretation in children is presented, highlighting the benefits and limitations of these models.
 RESULTS: The significant of this work provide important implications for healthcare practice and policy, with strengthen diagnosis and treatment of cognitive-based neurological diseases in children.
 CONCLUSION: This research paper concludes with a discussion of the ethical and legal considerations surrounding the use of intelligent computing models in healthcare, as well as future research directions in this area.

Research on the Influence of Humidity on the Manufacture of GFRP Vessels in the Equatorial

The objective of this research work is to analyze the behavior between fiberglass laminate under tensile tests, assembled under different humidity conditions. For which specimens were designed under the regime of the international standard ASTM D3039; which took an assembly process within a controlled environment; the design variables used were relative humidity and curing time. Subsequently, the traction-displacement behavior was checked under a uniaxial force, obtaining the maximum take-off force. In addition, Simpson numerical integration was applied to calculate elastic energy. Obtaining that the relative humidity and the days of curing influence the chemical and mechanical properties of the material. Se shows that the percentage of humidity recommended for assembling laminates in GRP is 66% since it has greater elastic energy and take-off force. Finally, it is concluded that to have a high resistance in the material at least 7 days of curing of the epoxy resin must be applied.

Data Information integrated Neural Network (DINN) algorithm for modelling and interpretation performance analysis for energy systems

Developing a well-predictive machine learning model that also offers improved interpretability is a key challenge to widen the application of artificial intelligence in various application domains. In this work, we present a Data Information integrated Neural Network (DINN) algorithm that incorporates the correlation information present in the dataset for the model development. The predictive performance of DINN is also compared with a standard artificial neural network (ANN) model. The DINN algorithm is applied on two case studies of energy systems namely energy efficiency cooling (ENC) & energy efficiency heating (ENH) of the buildings, and power generation from a 365 MW capacity industrial gas turbine. For ENC, DINN presents lower mean RMSE for testing datasets (RMSE_test = 1.23 %) in comparison with the ANN model (RMSE_test = 1.41 %). Similarly, DINN models have presented better predictive performance to model the output variables of the two case studies. The input perturbation analysis following the Gaussian distribution for noise generation reveals the order of significance of the variables, as made by DINN, can be better explained by the domain knowledge of the power generation operation of the gas turbine. This research work demonstrates the potential advantage to integrate the information present in the data for the well-predictive model development complemented with improved interpretation performance thereby opening avenues for industry-wide inclusion and other potential applications of machine learning.

Development of artistic and imaginative thinking of the future choreography teacher in the process of professional training

ABSTRACT The relevance of the subject matter is conditioned by the fact that an important task of professional training of future specialists in the field of choreography is the development of artistic and imaginative thinking. The purpose of the study is to consider the structural components of artistic and imaginative thinking and analyse the features of the development of artistic and imaginative thinking of future choreography teachers in the process of professional training. The basis of the methodological approach in this research work is a qualitative combination of theoretical research methods, in particular analysis, synthesis, interpretation, generalisation and abstraction used to determine theoretical approaches to explaining the concept of artistic and imaginative thinking and its structural components. The results that were obtained in the framework of this study are the substantiation of various approaches to the definition of the concept of artistic and imaginative thinking, the structural components of imaginative thinking, identification of features of its development during the professional training of future choreography teachers, and the development of a special methodology aimed at the development of artistic and imaginative thinking.

Solar drying of oregano leaves (Plectranthus amboinicus, Lour): Analysis of experimental performance under a tropical climate

Despite the multiple applications of the oregano ( Plectranthus amboinicus (Lour)), an important herbaceous plant because of its medicinal and culinary properties, limited research has been conducted on this species’ solar drying. Thus, the experimental drying of oregano leaves in a tropical climate was performed using direct and indirect solar drying and an electric oven in this research work. Also, mathematical modeling of the drying process, moisture diffusivity, activation energy, and colorimetric analysis (as a quality parameter) were accomplished to complement the experimental evaluation and understand the drying process behavior. The results show a shorter drying time obtained with the electric oven with 210 min reached at 65°C; however, the minimum solar drying time (250 min) was achieved with a maximum drying rate of 0.025 g water/g dry matter per minute and minimum final moisture between 0.28 and 0.13 g water/g dry matter was obtained with indirect solar dryer. Moreover, the models that better represented the experimental data were Modified Page, Page, and Logarithmic, with a maximum value of R2 obtained of 0.9980 corresponding to the Logarithmic model. Furthermore, a maximum effective diffusivity value of 1.81E-9 was obtained with direct solar dryer natural convection, while for the electric oven, were 1.14E-10, 2.280E-10, and 1.026E-09 for 45, 55, and 65°C, respectively. The activation energy for water diffusion in oregano leaves was 55.66 and 97.78 kJ/g mol for solar dryers and electric ovens, respectively, which were values very close to those reported in the literature. Finally, the colorimetric analysis exhibited a minimum color change value of 17.1 obtained for the indirect solar dryer, allowing better oregano preservation and maintaining its quality for the market, which makes it ideal for drying oregano in tropical climates and promotes solar drying use for sustainable growth of the region.

Hybridization in meta-heuristic techniques for green layout design in the tyre manufacturing industry: An optimal analysis

To stop or lessen factory pollution, many academics in the field of study have produced a vast array of strategies to achieve optimization goals. But, it is unable to follow a uniform solution for all production facilities. In this research work, green layout design (GLD) is proposed to minimize total material handling cost (TMHC) and CO2 emissions. The green facility layout refers to the facility layout with minimum CO2 emissions and to eliminate unnecessary energy dissipation from material handling equipments & production machineries, as well as plant re-layout in-term on material handling cost. In this study context, a Lagrangian relaxation model has been used at the allocation level. It computes a suboptimal solution to the dual problem in order to obtain a feasible solution. This enhanced algorithm method iteratively tries to minimize the gap between the dual and feasible solutions on minimal of TMHC and CO2 emission rate. It has been experimentally simulated using the MATLAB (Matrix Laboratory) application and upgraded adaptive salp swarm optimization (ASSO), dragon fly optimization (DFO) and Hybrid (combination) of ASSO-DFO evolutionary optimization techniques. The primary focus of this implementation is to control material handling between departments or cells, production flow between departments, energy consumption (electrical and gas consumption for production), and manpower utilization within and across departments. This research has chosen an existing and acceptable designs from targeted tyre manufacturing company in South India. The hybrid ASSO-DFO implementation TMHC differs from ABC by 60%, DFO by 65%, 58% with simulated annealing (SA), and ASSO by 48%. Emission rate decreased by 26% ABC, 21% DFO, and 24% SA & 18% with ASSO. An average of 50% decrease in TMHC and 25% reduction of CO2 emission rate in GLD have been achieved.

The Structure and the Distribution of Phraseological Units in Intermediate and Upper- Intermediate EFL Textbooks

The knowledge of native phrases and idiomatic expressions is considered to be an essential aspect of one’s fluency in a foreign language and can advance learners toward more excellent proficiency in foreign language acquisition. However, not much research has been dedicated to how they are implemented in the school textbooks. This research work aims to examine the occurrence of phraseological units in the four selected English textbooks for B1 and B2 levels, which are recommended for use at secondary grammar schools and secondary vocational schools in Slovakia by the Ministry of Education. They aim to prepare the secondary school learners for their final school-leaving exams – which are based on and in line with the Common European Framework of Reference for Languages requirements for the specific proficiency levels. To fill the gap in present studies, a detailed analysis was implemented, which included not only the exercises explicitly aimed at phraseological units but also the parts of the textbook, which dealt with the formulaic language implicitly. The main question posed dealt with the categories of phraseological units and their distribution in the analyzed textbooks. Phraseological units were identified, classified, analyzed, and counted by quantitative content analysis. The results showed that although there were phraseological units found in all analyzed textbooks, their distribution and classification differed significantly. Once they were included in the very limited number and variety of exercises, they were never used or mentioned again. Moreover, their explanation was absent or insufficient. The listed findings indicate that learners might encounter significant issues when trying to advance toward higher EFL proficiency levels.

A Cloud-Based Ambulance Detection System Using YOLOv8 for Minimizing Ambulance Response Time

Ambulance vehicles face a challenging issue in minimizing the response time for an emergency call due to the high volume of traffic and traffic signal delays. Several research works have proposed ambulance vehicle detection approaches and techniques to prioritize ambulance vehicles by turning the traffic light to green for saving patients’ lives. However, the detection of ambulance vehicles is a challenging issue due to the similarities between ambulance vehicles and other commercial trucks. In this paper, we chose a machine learning (ML) technique, namely, YOLOv8 (You Only Look Once), for ambulance vehicle detection by synchronizing it with the traffic camera and sending an open signal to the traffic system for clearing the way on the road. This will reduce the amount of time it takes the ambulance to arrive at the traffic light. In particular, we managed to gather our own dataset from 10 different countries. Each country has 300 images of its own ambulance vehicles (i.e., 3000 images in total). Then, we trained our YOLOv8 model on these datasets with various techniques, including pre-trained vs. non-pre-trained, and compared them. Moreover, we introduced a layered system consisting of a data acquisition layer, an ambulance detection layer, a monitoring layer, and a cloud layer to support our cloud-based ambulance detection system. Last but not least, we conducted several experiments to validate our proposed system. Furthermore, we compared the performance of our YOLOv8 model with other models presented in the literature including YOLOv5 and YOLOv7. The results of the experiments are quite promising where the universal model of YOLOv8 scored an average of 0.982, 0.976, 0.958, and 0.967 for the accuracy, precision, recall, and F1-score, respectively.