Objective In this report we present a rare case of a large cyst of Skene gland in a female patient with a palpable vaginal mass persisting for at least 2 years.Case Report A 67-year-old female admitted to the department of urology due to the presence of “a vaginal mass” for the past 2 years. A cyst of Skene's duct was suspected based on clinical manifestation and findings of magnetic resonance imaging showing an extensive cyst formation in the upper vaginal area and anterior to the urethra. Based on these findings, a decision for surgical removement of the cyst was made. The cyst was incised, drained, and marsupialized. The postoperative recovery was uneventful, and the patient was discharged on the second postoperative day.Conclusion High clinical suspicion is important to reach this rare diagnosis. Partial excision and marsupialization of the cyst is a simple procedure with low morbidity, without recurrence, and excellent results. Dutch histologist Reinier de Graaf was the first to describe the presence of the female prostate formed by the glands and ducts located around the female urethra.These glands are homologous to the male prostate that are developed from the same embryological tissues, being associated with various pathologies.Infections, cystic and solid, benign, or malignant tumors are typical disorders of these glands.In the present report, we aim to present an interesting case of a female patient with a large and symptomatic Skene's gland cyst, underlining the importance of high clinical suspicion to reach the rare diagnosis and present the current literature.A 67-year-old female admitted to the department of urology due to the presence of “a vaginal mass” for the past 2 years. Her symptoms included dyspareunia, frequency and urgency of urination, interruption, and distortion of urinary flow as well as episodes of vaginal bleeding. Her past medical history was insignificant.On vaginal examination, a cystic mass of 5 cm in diameter with ulceration covering the urethral meatus was observed . A cystBased on these findings, a decision for surgical removement of the cyst was made. The cyst was incised, drained, and marsupialized with interrupted 4–0 Vicryl sutures. The excised tissue was sent for histological examination while the posterior wall of the cyst remained in place avoiding the complete excision of the cyst to minimize the risk of urethral lesions. The postoperative recovery was uneventful, and the patient was discharged on the second postoperative day.The histopathology examination confirmed the diagnosis by reporting a cyst covered with stratified squamous epithelium. Patient remained asymptomatic, with no signs of recurrence during the next 6 months.Most common pathologies associated with Skene's glands are infections, such as gonorrhea, tuberculosis, and trichomoniasis, and can be present both in the glands and other parts of the reproductive system.Obstruction of the ducts of these glands leads to the formation of cysts that present as benign cystic masses inferolateral to the urethral meatus, typically affecting in women in the third or fourth decade of their life.According to Lucioni et al, Skene's gland is thought to be the female homologue of the male prostate.Since Skene's ducts arise from the urogenital sinus, these cysts are usually lined with stratified squamous epithelium.The history, physical examination, and urethrocystoscopy are often sufficient to set the diagnosis of the simple cyst or Skene's gland abscess. Transperineal ultrasound and MRI can be useful tools for a more detailed examination with the use of MRI as the gold standard examination of female urethra.21in a case series of 25 patients, found no recurrence of Skene's gland cysts after partial excision with marsupialization.When conservative treatments fail, surgical options include needle aspiration, partial excision, marsupialization, or complete excision of the cyst.Large Skene's cysts are very rare benign lesions. History, clinical examination, and endoscopy are often sufficient for a definitive diagnosis. High clinical suspicion is important to reach this rare diagnosis. Partial excision and marsupialization of the cyst is a simple procedure with low morbidity, no recurrence, and excellent results.


Oral cancer can occur in different parts of the mouth, including the lips, palate, gums, and inside the cheeks. If not treated in time, it can be life-threatening. Incidentally, using CAD-based diagnosis systems can be so helpful for early detection of this disease and curing it. In this study, a new deep learning-based methodology has been proposed for optimal oral cancer diagnosis from the images. In this method, after some preprocessing steps, a new deep belief network (DBN) has been proposed as the main part of the diagnosis system. The main contribution of the proposed DBN is its combination with a developed version of a metaheuristic technique, known as the Combined Group Teaching Optimization algorithm to provide an efficient system of diagnosis. The presented method is then implemented in the “Oral Cancer (Lips and Tongue) images dataset” and a comparison is done between the results and other methods, including ANN, Bayesian, CNN, GSO-NN, and End-to-End NN to show the efficacy of the techniques. The results showed that the DBN-CGTO method achieved a precision rate of 97.71%, sensitivity rate of 92.37%, the Matthews Correlation Coefficient of 94.65%, and 94.65% F1 score, which signifies its ability as the highest efficiency among the others to accurately classify positive samples while remaining the independent correct classification of negative samples. Cancer is the abnormal growth of cells that can occur in any part of the body2. Although most people with cancer are between the ages of 40 and 60, the prevalence of oral cancer has increased today due to the increasing use of drugs, chewing tobacco and smoking among young people. Therefore, it is very important to get acquainted with the signs and symptoms of oral cancer and see a specialist immediately if you notice any suspicious symptoms3.Cancer is defined as the uncontrolled cells growth that invades surrounding tissues and causes damage4. The human mouth is one of the most important parts of the digestive system and is actually the gateway6. The food and physical energy depend on keeping the mouth and teeth in perfect health. This part is one of the areas that contains the most bacteria, and if the number of oral bacteria is not controlled, the mouth and teeth will suffer from diseases. Due to the wrong lifestyle, many people in the world today get oral cancer7. Oral cancer results from abnormal growth or the presence of root tumors in different areas like tongue, lips, the mouth floor, cheeks, soft and hard stature, throat  which can be dangerous if diagnosis and treatment are delayed8.Getting timely treatment is the key to treating many cancers9. Generally, the danger of a lifetime of Infection of oral cancer is approximately 0.71% for women and 1.7% for men10. The underlying cause of oral cancer is still unknown, but research has shown that most of these cancers are caused by lifestyle-related factors11.The onset of oral cancer is often in the form of a lesion or a white or red spot that may appear anywhere on the mouth. Oral cancer is more common in men than women, and people over the age of 40 are more likely to get it12.It often occurs in people who use different forms of tobacco. Doctors examine the mouth for red and white lumps and a small piece of tissue is sampled to look for cancer cells. Radiotherapy and surgery are the most important methods for treating this cancer. Although oral cancer and surrounding tissues are not common cancers, they are of great importance because of their impact on the patient's quality of life. Knowledge of the epidemiological characteristics of these cancers leads to planning for timely and effective treatment of patients and improves the quality of life of the patientEarly detection of oral cancer leads to complete treatment and recovery. Therefore, it can be say that oral cancer diagnosis at an early stages that lesions are localized and small, is supposed to be the most operative means to decrease the disease and its death cases.13. Image processing and machine vision are a part of most important computer-aided diagnosis systems in the recent years. These techniques are continuously utilized in medical diagnosis systems with an increasing trend that can be utilized as an auxiliary tool for diagnosing the oral cancer14. Some examples of these cancers are as follows.By all of these explanations, accurate diagnosis of this cancer is now a challenging issue among the experts and the researchers. However, different techniques have been defined for diagnosing the cancer, there are still lots of gaps to provide more efficient techniques15 recognized mouth tumors utilizing machine learning and deep learning approaches. Mouth cancer can be called a deadly disease that one of the causes of this disease is smoking. Early detection of the disease causes rapid recovery of the disease. The innovation of this research is the usage of a deep neural network and machine learning model. In this research, CNN with 43 deep layers is used to analyze the data. The results of data analysis showed that the proposed strategy has a high accuracy in formatting and detecting CT scan images so it can simulate malignant oral lesions with the utmost accuracy.Nanditha et al.16 utilized intelligent computing to detect malignant lesions of the mouth. The third most common disease in the world is oral cancer. The cancer also affects other parts of the body, such as the mouth and neck. Therefore, rapid diagnosis of the disease leads to rapid treatment and also controls the disease's progress. Their innovation was to provide an intelligent computing strategy for detecting oral tumors. They used oral disease imaging data to evaluate the strategy presented. The results of the analysis represented that the suggested strategy can well detect mouth lesions in the early stages.Singh et al.17 analyzed machine learning algorithm employing histopathological images of malignant mouth lesions to identify mouth tumors. Artificial intelligence is one of the most powerful strategies in diagnosing some diseases, including cancer. In this study, six widely used machine learning algorithms were utilized to find the most accurate method of identifying mouth lesions. These are six strategies that includes random forest, support vector machine (SVM), simple bayes, neural network, K-nearest neighbor (KNN), and decision tree. The results of strategy analysis showed that the neural network has the highest accuracy compared to other strategies with a numerical value of 90.4%. According to the analysis, it can be said that this strategy has a satisfactory ability in diagnosing the disease.Panigrahi et al.18 analyzed the performance of artificial intelligence strategy in identifying mouth disease and predicting disease prevalence. One of the most common cancers is a mouth tumor that has complex and different causes. In this study, they used artificial intelligence strategies to diagnose and predict the occurrence of the disease. Factors used to diagnose the disease included age, smoking, sex, and biomarkers. The results of the analyses showed that the AI had an acceptable analysis of the input data, which resulted in an accurate identification of oral lesions.Khanagar et al.19 predicted malignant lesions of multilayered squamous tissue of the skin using a deep learning strategy. In this study, data from 255 patients with oral tumor disease were used to analyze the proposed strategy. The results of data analysis by the model showed that the designed approach has an acceptable accuracy with a numerical value of 0.80. Therefore, diagnosis of the disease by deep learning strategy can provide the best results. This technique can be of great help to physicians in rapid detection and disease treatment.Kim et al.As can be concluded from the literature, although several types of artificial intelligence were proposed for computer aided detection of the mouth cancer, deep learning and metaheuristics can be considered two beneficial and high-performance methodologies.20. Moreover, the training and validation datasets often suffer from limited sample sizes and lack of diversity, potentially compromising the robustness and applicability of the developed models21. Consequently, it is imperative to conduct further research that addresses these limitations by implementing more rigorous evaluation methods and utilizing larger, more diverse datasets. This will ultimately enhance the clinical potential of these prediction models.Previous studies in the domain of tumor tissue-of-origin prediction exhibit certain common limitations through computational frameworks and machine learning techniques. Firstly, the methods employed for evaluating and validating the accuracy of predictions are frequently inadequately described, resulting in a lack of transparency that hinders the determination of model's reliability and generalizabilityAlthough several kinds of these techniques were presented before, an efficient combination of these two are not properly introduced and is still a challenging technique for oral cancer diagnosis. Therefore, in the current research, a new hybrid methodology founded on deep learning and metaheuristics has been proposed to represent the higher accuracy of the technique. Also, to prove its efficiency, the results were compared with some other previously published works.According to the literature, it can be said, there are many research works have been proposed for oral cancer detection based on image processing and deep training. However, using metaheuristics can be considered a good modification for improving the efficiency of the utilized methods. Therefore, in the present research, a new optimal methodology has been suggested for diagnosing the oral cancer with better accuracy.In this study, the research gap pertains to the inadequacy of an effective and precise methodology for diagnosing oral cancer that integrates deep learning and metaheuristics. Despite the numerous artificial intelligence techniques proposed for computer-aided detection of oral cancer in the literature, there remains a deficiency in efficient combinations of these two methodologies, particularly for oral cancer diagnosis. While deep learning has exhibited immense potential in analyzing medical images and extracting meaningful information for diagnostic purposes, metaheuristics offer a means to optimize performance and fine-tune the diagnostic process. Thus, the research gap in this study is the absence of an optimal methodology that can effectively merge deep learning and metaheuristics for oral cancer diagnosis.This study proposes a hybrid methodology combining deep learning and metaheuristics for more accurate and early diagnosis of oral cancer to bridge this gap by using the strengths of both techniques and enhancing the accuracy and efficiency of the diagnostic process. Identifying and addressing research gaps are crucial in advancing scientific knowledge, contributing to existing literature, and improving patient's outcomes.A new approach that merges deep learning and metaheuristics for oral cancer diagnosis.Efficient diagnostic techniques for oral cancer by integrating these two distinct methodologies.Utilizing a modified deep belief network (DBN) to analyze medical images with a high precision and accuracy.Using a combined version of Group Teaching algorithm to optimize the DBN and finally, the diagnostic process.Providing a comprehensive and effective diagnostic to improve early detection rates.A novel fusion of deep learning and metaheuristics, surpassing current approaches in terms of accuracy, efficiency, and overall performance.The present paper introduces a pioneering approach that combines deep learning and metaheuristics for the diagnosis of oral cancer. This integration of two distinct methodologies constitutes a unique contribution to the field, as it addresses the existing research gap in efficient diagnostic techniques for oral cancer. By harnessing the power of deep learning algorithms, the proposed method analyzes medical images with high precision and accuracy. Moreover, the incorporation of metaheuristics optimizes the diagnostic process, enhancing efficiency and fine-tuning the results. The interaction between these two approaches results in a comprehensive and effective diagnostic system that has the potential to improve early detection rates and ultimately save lives. The novelty of this approach lies in the innovative fusion of deep learning and metaheuristics, providing a valuable tool for oral cancer diagnosis that surpasses current approaches in terms of accuracy, efficiency, and overall performance. Therefore, here are the list the contributions of the proposed hybrid methodology combining deep learning and metaheuristics for the diagnosis of oral cancer:These contributions aim to fill the research gap and provide a valuable tool for oral cancer diagnosis, contributing to scientific knowledge and improving patient's outcomes.22. The images in the Oral Cancer (OCI) dataset are selected with a resolution of 256 × 256 pixels. The studied dataset contains a set of tongue and lips images that are classed into non-cancerous and cancerous groups. The dataset images include 44 set of oral non-cancer images along with 87 set of oral cancer images that are collected to be used in different medical imaging applications. The dataset has been captured and collected from dissimilar ENT hospitals of Ahmedabad that are categorized by the help of ENT clinicians. Figure 22.The oral cancer images utilized in the current study have been acquired by “Oral Cancer (Lips and Tongue) images (OCI) dataset”. This database with “*.jpg” format for all of its images can be accessible from the Kaggle websiteThe oral cancer (OCI) dataset faces challenges such as a small dataset size, class imbalance, annotation variability, variability of image quality, generalizability to other populations, and lack of clinical context. These issues can lead to overfitting, biases, and inconsistencies in the model's performance. To address these issues, techniques like data augmentation, handling class imbalance, consistent annotations, and evaluating performance are needed.23, the results show that using preprocessing operations can still have effect on the accuracy of these networks. In this research, this leads us to use a preprocessing stage before performing the main processing step. Based on different research works from the literature, it is too obvious that medical imaging is so sensitive to different noises. Although, some other factors like images' contrast can affect the quality of diagnosis9. Therefore, this research considers these two preprocessing steps to improve the raw data to feed into the main network.Though the main feature of deep neural networks is their higher accuracy that makes them almost independent from the preprocessing stepsThe noise reduction is a crucial aspect of medical image analysis for various reasons. Firstly, it serves to enhance diagnostic accuracy by improving the clarity and visibility of relevant structures and abnormalities. Additionally, it contributes to the improvement of image quality by mitigating the degradation of fine details, thereby facilitating the identification of critical features. Furthermore, noise reduction minimizes artifacts, which in turn enhances visualization and interpretation, ultimately leading to better patient care. Moreover, it increases the reliability of quantitative analysis by reducing noise in images, thereby ensuring more robust and trustworthy results. It also guarantees consistency and standardization across large-scale studies or multi-center collaborations, thereby reducing variability and inconsistencies across different images. Lastly, it preserves diagnostic information by minimizing noise reduction methods, ensuring the preservation of essential information while effectively reducing noise.24. Sometimes noise is caused by heat or radiation and low-energy ions. But noise can also be caused by unintentional changes in other committees. Noise is every place. Where a signal (video) is received, a kind of noise is created on it.In a general definition, any unintentional oscillation and change that occur on the signals being measured is called noise. Any quantity can accept noise. In electrical circuits, we mostly deal with voltage and current. This noise is caused by thermal changes and their effect on electronic carriers. In the radio and microwave area, we are faced with electromagnetic noise25. The importance of noise analysis becomes quite apparent if the measured signal quality isn't specified by the absolute amount of signal energy but is specified by the signal-to-noise ratio. This study shows that the most satisfactory way to enhance the signal-to-noise ratio is to decrease noise, not increase signal strength.Every precise and high-quality process that is performed in medical image processing science, requires great care to predict the environmental noise and decrease its influenceFind the appropriate length for the blockLoss of some edges of the image that causes the image to blur.Wavelet, that is an effective way to eliminate noise and find the right threshold value, has been one of the research topics in recent years. In some existing methods, it is usually assumed that the wavelet coefficients of the image have an independent distribution. Because it reduces the computational volume but in practice this distribution is not independent. The degradation quality of these methods is not suitable, so in the methods presented based on image blocking, we face two problems:Currently, fuzzy rule-based systems (FRBS) is the most significant applications of fuzzy set theory. FRBS are based on the development of classical rule-based systems; since FRBS deals with fuzzy rules and not with classical logic rules, it is used for various issues in various fields that have uncertainty. Modeling systems are one of the most significant applications of systems based on fuzzy rules. The two components of the fuzzy rule system are the inference system and the knowledge base. The knowledge database consists of two parts: the database and the rules database, and its function is to store information related to the problem in the shape of “IF–THEN” language rules.The inference procedure is performed by the inference system based on the information stored in the knowledge database. Several things need to be done to design appropriate fuzzy rule-based systems for the problem. One problem is the description of information by an expert in the form of fuzzy rules that researchers have developed automated methods to do so. Some of these methods are simple and effective, and consequently easy to implement and comprehend; moreover, due to the high speed for usage in the first step of the process of simulation, they are very useful for producing the initial fuzzy model. Wang-Mendel method is a widely utilized and famous method that has been verified to be highly effective. In this method, the input and output datasets show the behavior of the solved state of the problem. Figure Improving the contrast of medical images is the most significant prerequisite employed in machine vision applications and medical image processing. Generally, direct and indirect methods are the two main methods for improving contrast. In the direct methods, a criterion for measuring image contrast is defined, and improvement of the contrast is a result of improving this criterion. Generating a proper image contrast measure is a significant step in direct improvement of the image. The direct contrast procedure takes both local and general information of the image into account, so it can perform better in numerous applications. For this purpose, there are several solutions that have been offered according to the principle of fuzzy entropy, which transfers the image to the fuzzy region, and fuzzy entropy is computed, and through this, local contrast is estimated.26.Improving contrast indirectly involves correcting the image histogram. In the indirect method, improved contrast is the result of increasing the dynamic range of the gray surface of the image. Indirect methods, which have received a lot of attention in recent years because of their understanding and direct presentation, contain four groups: (1) Processes in which the high and low frequency component of the image is changed, (2) Conversion-based methods, (3) Methods based on histogram corrections [10 and 9], and (4) Methods created on soft calculations. The proposed algorithm and techniques introduced in this paper are based on methods according to the histogram corrections. In this study, the Historical Recurrence of Discrete Mean Rate (RMSHE) method has been utilized27. This method can also maintain an adequate quantity of image brightness and improve the contrast. It splits the histogram into two sub-histograms according to the average measure of brightness and balances per part separately. If 27:Indeed, maintaining the brightness of two balanced histograms , we encounter the problem of unbalanced distribution in classes when the number of samples in two classes is very different. As explained in the data section, in this study, the number of cancerous and non-cancerous data are not equal. None of the machine learning algorithms can be properly trained with such datasets. Various techniques can be used to solve this problem.29. SMOTE produces new artificial specimens in the vicinity of the specimens because of the dominant relationships between the specimens30. The new artificial specimens are linearly attached to adjacent minority class specimens. The properties of the samples in adjacent classes do not change. For this reason, SMOTE can produce samples that belong to the same original distribution31. Unlike the sampling method, in this method, the new dataset will have a higher standard deviation, and a suitable classifier can easily find a better separator super plate.SMOTE is one of the suitable methods for data augmentation where, samples of new artificial data are produced in the neighborhood of the samples in the classes31:In SMOTE method, for the subset 32. The Boltzmann Machine is a binary version of Markov chain with several random hidden layers of symmetric binary random units33. Likewise, it includes some visible layers and some hidden layers. In Boltzmann finite machine, we can find no link between the same units.The idea is to use RBM in all layers that can be considered autonomously for encoding statistical reliance of each unit in the earlier layer. Deep belief networks (DBNs) are made by Boltzmann machineA multilayer superposition of the Boltzmann machine is specified by the DBN and it also excerpts the characteristics of the main data. Given that maximizing the probability of training data is the goal of DBN, the training procedure was begun with the low-level of RBM that gets the inputs of the deep belief network and gently rises in the hierarchy till the RBM is finally trained in the top layer, including the DBN production. This technique combines multiple and simpler models and presents an efficacious procedure to learn.34.Because the training of the RBM is done by the algorithm of layered contrast divergence, training prevents the great level of complication of training DBNs and facilitates the training approach of all RBMs. Research studies showed that the use of DBN in the training of multilayer neural network can solve optimal local problems and low convergence velocities in common reverse diffusion algorithmsIn a deep belief network (DBN), the initial layer is referred to as the visible layer, which directly interacts with the input data. Each node in this layer represents a feature or input variable. The subsequent layers are known as hidden layers, which serve as intermediate representations of the input data. Each hidden layer comprises multiple nodes that perform computations and transformations on the input. The connections between layers are bi-directional, allowing information to move both forward and backward through the network. Each node in one layer is connected to all the nodes in the adjacent layers, forming a densely interconnected structure. During the training process, DBNs employ a layer-wise pre-training approach, which involves training each layer in an unsupervised manner. This approach initializes the weights between layers to capture meaningful features from the data, thereby aiding in the effective initialization of the network's parameters. Following pre-training, the DBN is fine-tuned using supervised learning techniques, such as backpropagation, to further optimize its performance for a specific task.Figure 35:ith visible and jth hidden layers binary state, and During the training step, some undirected weights and biases exist between the visible and hidden layers. Energy function is used for defining the joint distribution function of each layer as follows35:35:Also, 40. The weaknesses of DBNs contain the restriction of the approximate inference method to a bottom-up transition. The greedy method only trained the properties of a unit at a time and is never configured with other units or network variables. While, based on the explanations above, the DBN delivers durable results for classification, optimal design of the DBN makes it more beneficial. This modification is done by different methodologies. In this research, we utilized an improved metaheuristic-based technique for this aim. The main effect of the benefits of the characteristics of DBN on oral cancer diagnosis are explained below:A quick learning capability of a collection of variables is the benefit of the DBN model, likewise, for models that include numerous variables and nonlinear layers through a greedy technique. An unsupervised pre-training technique is employed in DBNs also for databases of great unlabeled. Likewise, deep belief networks can calculate the output amounts of parameters at the bottom layer utilizing the approximate inference methodThe Significance of Quick Learning Capability in Oral Cancer Diagnosis: The ability to learn quickly is a crucial factor in the context of oral cancer diagnosis. The detection of oral cancer often involves processing a vast amount of complex and heterogeneous data, including clinical records, imaging data, and genetic information. By using their quick learning capability, deep belief networks (DBNs) can efficiently analyze and extract relevant features from this diverse data, enabling the identification of subtle patterns and abnormalities associated with early-stage oral cancer. This rapid learning ability of DBNs saves valuable time in the diagnostic process, allowing for timely detection and intervention.The Importance of unsupervised pre-training in early oral cancer diagnosis: Unsupervised pre-training is a critical aspect of early oral cancer diagnosis. It allows the DBN to initialize its parameters by learning meaningful representations of the input data without relying on labeled examples. This initialization stage helps the network capture intrinsic characteristics and variations present in the oral tissue data, which may not be evident in a supervised training setting. This unsupervised pre-training sets a strong foundation for subsequent supervised fine-tuning, enhancing the DBN's ability to discriminate between normal and abnormal oral tissue characteristics specific to early cancer stages.Feature extraction and representation learning in oral cancer diagnosis: DBNs excel in feature extraction and representation learning, which are essential for accurate oral cancer diagnosis. Through multiple layers of hidden units, DBNs can hierarchically learn and represent increasingly abstract and discriminative features from the input data. This hierarchical representation enables the DBN to capture complex relationships and variations in oral tissue characteristics, facilitating the identification of potential cancerous regions at an early stage.Model Interpretability in oral cancer diagnosis: DBNs also offer some degree of interpretability, as the learned features can be examined and analyzed to gain insights into the underlying factors driving oral cancer detection. This interpretability aspect can aid clinicians in understanding the reasoning behind the DBN's predictions, contributing to trust and acceptance of the model in clinical practice.41. Optimization methods and algorithms are divided into two categories: exact algorithms and approximate algorithms42. Accurate algorithms are able to find the optimal solution accurately, but in the case of difficult optimization problems, they are not efficient enough and their execution time increases exponentially according to the dimensions of the problems. Approximate algorithms are able to find good  solution in a short time for difficult optimization problems43. In fact, meta-heuristic algorithms are one of the types of approximation optimization algorithms that have solutions for exiting local optimal points and can be used in a wide range of problems46. Meta-heuristic algorithms are flexible and robust optimization techniques that can handle complex problems where traditional methods may struggle. They are widely used in various domains, such as engineering, operations research, data mining, and machine learning. Various classes of this type of algorithm have been developed in recent decades, all of which are subsets of meta-heuristic algorithms. The following sections first explain the group training algorithm and then the structure of the optimization model.Optimization is the process of solving some kinds of problems to get the maximum or minimum value of the considered functionThe group training (GT) method has improved and presented the group training optimization algorithm. The principle of group training is stated below.Response to the top student who is clever and energetic: perfect virtue is returning to competence and mastering oneself.Confucius told the subsequent student, who was hurried and loquacious, perfect virtue is caution and silence.The third student's answer, which was characterized by jealousy and ambition, is perfect virtue in promoting oneself and others.Confucius was one of the leading politicians and philosophers who first introduced the diversity of students' abilities in education. Accordingly, various methods should be used to teach each student. To better comprehend, we give an example of confucius: What does perfect virtue mean? An identical question was asked by three students, and confucius responded to them according to the attributes of each. The responses were as follows:In recent years, the group teaching method has been used to "teach students according to their capabilities". This method accentuates the psyche of each student. In other words, dissimilar procedures and courses are employed in school, regarding the dissimilarities of whole students. Each student has a unique level of intelligence, financial status, and educational practices. The offered method (GT) can improve the level of all students so that it does not use the identical technique.Dissimilarity between learners is regarded as the knack to obtain wisdom. When the learner has more capability to accept the learning, the teacher will be more challenged in designing the teaching approach.In education, a good teacher pays more consideration to weaker learners than to more potent students.Learning in learners' leisure time is both self-taught and in relation to others.Learners' progress in education can be achieved through the appropriate teacher allocation process.In the suggested algorithm, the group teaching process is simulated to improve and enhance the level of knowledge of each student. Given the contrasts that students have with each other, group implementation is absolutely complicated in practice. In group learning simulation as an optimization algorithm, students' learning, topics given to students, and students are regarded as objective value, decision variables, and population, respectively. Next, on the basis of the rules offered below, a simple group training technique is formed.The offered group teaching process consists of four phases, which are: learner step, teacher allotment step, teacher step, and capabilities of grouping step. These four steps are established on the four rules, explained below.47:For the learning of all learners, the normal distribution is supposed without the loss of publicity, and it is computed as the following equationIn the presented algorithm, two groups of learners are formed in accordance with their capabilities to obtain knowledge. Formed groups display the characteristics of group training. Presume that the significance of the two groups in the GTO algorithm is identical. The numeral of learners in each group is identical. The intermediate group is a group that has little ability to acquire knowledge and the elite group is a group that has a high ability to acquire knowledge. According to the first rule, the teaching process in traditional teaching is more challenging for the teacher than the ability grouping process. As a result, capability of grouping is an active approach in the proposed algorithm and is accomplished similarly after a teaching cycle.According to the second rule, all learners learn from their educator. In the optimization algorithm, the middle group and the elite group are trained according to various schedules.47:The educator focuses on growing the outstanding group’s knowledge at the persistent group teaching optimization algorithm, because they have a significant capability to acquire knowledge. The educator does his finest to improve the intermediate grade of whole learners’ knowledge. as well, the knowledge admission dissimilarities among learners should also be regarded. Acquisition of knowledge by the top group is according to the following equationtth time is indicated by In which, the learners' number is indicated by 47:47:According to the second law, the teacher spends more attention on the middle group than the elite group. Under this group, they have little capability to acquire knowledge. The equation for acquiring knowledge by intermediate group learners is conferring to the following formula48:48:As stated in the third rule, in leisure time, the learner learns in two modes: self-taught or through other learnersThe learner 48:According to Rule Four, the method of assigning a top educator is essential for the progress of learners. In grey wolf optimization, the 3 gained solutions so far have been stored that involved guiding the wolves hunting. The Gray Wolf optimization is motivated by hunting conduct, and the educator's tasks in the offered procedure are as followsThe first, second, and third learners are, in turn, indicated by However, the standard Group Teaching Optimization Algorithm can be considered one of the newly introduced efficient metaheuristics that is utilized for several optimization problems that in some cases, it has been stuck in the local optimum point and presents weak results. Also, in some cases, due to weak exploration, its convergence speed has been so complicated. In this study, we used the advantages of the particle swarm optimization (PSO) algorithm to enhance the algorithm efficiency. Which is one of the most effective and popular metaheuristic algorithms based on swarm intelligence. It should be noted that swarm intelligence refers to a collective behavior exhibited by decentralized and self-organized systems consisting of multiple interacting entities. Inspired by the behavior of social insects like ants, bees, and birds, swarm intelligence focuses on how a group of simple individuals can collectively solve complex problems or achieve coordinated actions.Based on the PSO algorithm, each candidate uses its experience and also the experience of the other candidates in the swarm to move to the best position. The PSO algorithm considers two terms of velocity and position for this purpose and forms the new position by the following equation:Therefore, according to Eq. , the newFigure 50. Here, the proposed method has been performed to 10 test functions collected from the CEC-BC-2017 test suite51. Then a comparison is made between the outcomes of the algorithm and some previously published algorithms, including two new metaheuristics, Pigeon-Inspired Optimization Algorithm52 and Supply–Demand-Based Optimization (SDO)53, and also two winner algorithms from the challenge, i.e., LSHADE-SPACMA54 and IPOP-CMA-ES55, and finally with the standard Group Teaching Optimization Algorithm (GTO)48 to show the ability of the proposed Combined Group Teaching Optimization Algorithm. Table This section investigates the performance ability of the suggested Combined Group Teaching Optimization Algorithm in solving different optimization problems57. Also, to achieve stable results for the algorithms, all of the algorithms were run for 25 times separately to all of the benchmark functions. Multiple independent runs are used in neural network optimization to reduce randomness and improve robustness. These runs help mitigate random factors that can affect network performance or convergence behavior. They also allow for evaluation of convergence and stability, with consistent convergence indicating reliability and stability. The best network training is determined by evaluating the performance of optimized networks on a separate validation dataset or through cross-validation. The network with the highest accuracy or the lowest error on the validation data is considered the best-performing model. Ultimately, multiple independent runs provide a comprehensive understanding of the algorithm's performance and help select the best network training configuration. The utilized functions have a solution range between − 100 and 100 and all of them have 10 dimensions. Figure Common parameter settings are considered for all algorithms to make a reasonable comparison. For instance, the maximum epoch and the number of population for all algorithms are specified 200 and 100To provide a proper validation to the Combined Group Teaching Optimization Algorithm against the other comparative algorithms, the standard deviation value and the average value of the functions during 25 performances are considered. Table By observing the results in Table Choosing the right optimization algorithm is very important for the deep learning model and has significant influence on the time to achieve the desired result. The presented Combined Group Teaching Optimization Algorithm is a kind of meta-heuristic algorithm. Meta-heuristic algorithms have newly been utilized broadly for applications of deep learning in various fields. A meta-heuristic algorithm is an optimization algorithm and can be utilized rather than the classical random reduction gradient method to update the network weights on the basis of the iteration in the data of training. The CGTO algorithm is various from the classical reduction gradient. The random decrement gradient sustains a rate of single learning . The process of supervised fine-tuning using CGTO is then executed to enhance the alignment between the retrieved characteristics and their corresponding diagnoses. The DBN-CGTO is utilized to extract pertinent and distinguishing features from the dataset, which are then employed as inputs for a classifier to classify each sample oral tissue as either normal or suggestive of early-stage oral cancer. This methodology aims to optimize the use of DBNs to enhance the acquisition of intricate representations from oral tissue data, thereby improving the accuracy of early oral cancer diagnosis and, ultimately, patient outcomes.18. Figure The ability of the suggested DBN-CGTO in diagnosis of the oral cancer cases has been investigated in this part. Like before, all simulations and also statistical investigations have been performed by MATLAB environment. As mentioned before, the proposed Deep Belief Networkhas been trained by the help of the Combined Group Teaching Optimization Algorithm. 60% of the images from the Oral Cancer (Lips and Tongue) images (OCI) dataset are used for training the proposed DBN-CGTO and the remained 40% has been utilized for testing the network. The method is then validated by the following metrics of performance58.The confusion matrix is a table with rows representing predicted and actual class labels. It helps understand a model's performance in cancer diagnosis by categorizing predictions. Each measurement index in the confusion matrix is defined as true negative (TN), true positive (TP), false negative (FN), and false positive (FP). Analyzing these values allows for computation of performance metrics like accuracy, precision, specificity, sensitivity, F1-score, and Matthew's correlation coefficient (MCC). The confusion matrix serves as a foundation for calculating these metrics, allowing for a comprehensive evaluation of the model's effectiveness in cancer diagnosis. Based on the explained fractions above, the defined accuracy, specificity, precision, F1 score, sensitivity, and Matthew’s correlation coefficient (MCC) of the automated oral cancer diagnosis system have been evaluated by the following equationPresented below is a brief explanation of each performance metric commonly employed for evaluation.Accuracy: Accuracy is a measure of the overall correctness of the classification or prediction results. It is computed as the ratio of the number of correct predictions to the total number of predictions made, thereby indicating the model's ability to correctly classify instances.Precision: Precision focuses on the proportion of correctly identified positive instances out of all instances predicted as positive. It aids to evaluate the model's ability to avoid false positives, which is a measure of the reliability of positive predictions.Specificity: Specificity determines the proportion of correctly identified negative instances out of all instances that are actually negative. It provides insight into the model's ability to avoid false negatives, indicating how well it identifies the true negative instances.F1-score: The F1-score combines precision and recall (sensitivity) into a single metric that balances both measures. It is the harmonic mean of precision and recall and provides a comprehensive evaluation of the model's performance by considering both false positives and false negatives.Sensitivity : Sensitivity evaluates how well the model identifies the positive instances out of all actual positive instances. It measures the proportion of correctly identified positive instances with respect to the actual positive instances and is particularly useful in scenarios where detecting positive cases is crucial.MCC (Matthews Correlation Coefficient): MCC takes into account the balance between true positives, true negatives, false positives, and false negatives. It provides a measure of the quality of binary classifications, taking values between − 1 and + 1, where + 1 indicates a perfect prediction, 0 indicates random prediction, and − 1 indicates an inverse prediction.59, Bayesian60, CNN61, GSO-NN62, and End-to-End NN63. Table Based on the mentioned indicators, the effectiveness of the proposed DBN-CGTO has been validated and compared with some previously published methods, containing ANNFig. According to Table At the end stage, the accuracy/loss graphs of the methods are analyzed and discussed. The results are shown in Fig. As can be observed, the DBN-CGTO achieved an accuracy of 97.71% and a loss of 2.29%, which were significantly superior to the other methods. Specifically, the GSO-NN achieved the second-best result among the compared methods, with an accuracy of 91.6%. The exceptional accuracy and low loss values of the proposed DBN-CGTO suggest its efficacy as a highly effective tool for diagnosing oral cancer cases. Furthermore, the results indicate that the Combined Group Teaching Optimization Algorithm holds promise as an approach for training deep learning models in medical image analysis.The timely detection of oral cancer is of utmost importance in achieving optimal patient outcomes. The implementation of computer-aided diagnosis (CAD) systems can aid clinicians and healthcare practitioners in identifying oral cancer in its nascent stages. This early detection facilitates timely intervention and treatment, which can result in improved outcomes and potentially avert fatalities. Computer-aided diagnostic systems are widely used in the various detections of oral cancer. Therefore, improving the accuracy of a CAD system has become an important area of research. Among different types of cancers, oral is one of the dangerous cancers in the world. Therefore, using CAD systems can be also so helpful for early detection and healing this cancer. The present study proposed a new CAD system in accordance with deep learning for the optimal diagnosis of the oral cancer from the images. The method began with some preprocessing operations, including noise reduction, contrast enhancement, and data augmentation to prepare the raw image for the main process. Then, an optimized deep belief network was introduced based on an enhanced version of a metaheuristic technique, named combined group teaching optimization algorithm. The proposed method was then performed to a standard dataset, Oral Cancer (Lips and Tongue) images (OCI) dataset and comparison was made between its results and some state-of-the-art methodologies, comprising ANN, Bayesian, CNN, GSO-NN, and End-to-End NN to show the method's efficiency. The proposed methodology combines the DBN and CGTO algorithms to enhance the accuracy and efficiency of oral cancer diagnosis. With a precision rate of 97.71% and a sensitivity rate of 92.37%, the method demonstrates its ability to accurately classify positive samples. The high Matthews Correlation Coefficient of 94.65% and F1 score of 94.65% emphasize the robustness of the proposed technique. Final results indicated that the suggested method with the highest indicators provides the best outcomes. The proposed method provides an optimal system for oral cancer diagnosis from images, offering a valuable tool for clinicians and medical practitioners. Its high precision and sensitivity rates enable the identification of potential cancerous lesions, even in subtle or early-stage cases. Timely detection empowers healthcare providers to initiate appropriate treatment plans promptly, increasing the chances of successful outcomes and improved patient well-being. Overall, the proposed method's potential impact on oral cancer detection lies in its ability to facilitate early diagnosis, leading to enhanced treatment efficacy and better patient outcomes. By using deep learning techniques and optimization algorithms, this methodology contributes to the advancement of oral cancer diagnosis, ultimately helping to save lives and improve the quality of care for individuals affected by this disease. Through the method's contribution to the field of cancer diagnosis, this research has opened up avenues for further advancements. Future studies could explore the scalability and applicability of the proposed method in larger datasets or diverse patient populations. Additionally, the integration of other advanced deep learning models or optimization algorithms may further enhance the accuracy and efficiency of oral cancer diagnosis. Finlly, the research's findings have the potential to benefit both patients and healthcare providers. Early detection can lead to increased chances of successful treatment and improved quality of life for patients. Healthcare providers can influence the proposed method to enhance their diagnostic capabilities, enabling more accurate and efficient decision-making.


The earlier in life that a child can begin mastering fundamental movement skills (FMS), the more positive their physical activity (PA) trajectories and health outcomes are. To achieve sufficient development in FMS, children must be guided with tuition and practice opportunities. Schools and educators provide an opportunity for interventions that improve health behaviours and outcomes for children. The aim of this study was to use intervention mapping (IM) to design a programme of school-based intervention to improve FMS for children aged 4–5 years old. Following the six steps of IM, with each step comprising three to five tasks that require the input of a planning group formed by key stakeholders, a programme of intervention was planned. Prior knowledge and primary and secondary evidence was used to support the development of the programme. A logic model of the problem as well as logic models of change, programme design, production, implementation, and evaluation were proposed or completed within the study. The results can be used to begin to implement an FMS-focussed intervention within school settings within England and propose a sustainable and realistic approach for helping children to develop FMS with the support of well-informed educators who are confident to deliver better FMS practice and PA opportunities. There is an abundance of studies in the literature supporting the relationship between FMS competency and PA throughout childhood ,2. The eInterventions to increase FMS competency in children are common ,7,8,9, sIt is recognised that behaviour change, public health, and PA intervention is complex, multifaced, and interactive ,18. AlthAs mentioned, health promotion interventions must consider the target population, their physical and social environment determinants, and as many influential determinants of behaviour as possible. For children, these factors range from family influence ,22, deprIntervention mapping was initially designed to be used in health promotion interventions and programmes . It is uIndividual determinants are examined, including a child’s beliefs and attitude towards PA in addition to their enjoyment of activity. These determinants can be affected by a child’s immediate interpersonal environmental determinants, including members of a child’s family, their friends, and their peers, with knowledge and belief being shared between these groups. Organisational and institutional determinants are highly influential for children, especially in the early years. All children in England can experience the school setting, and therefore, it remains a key determinant influencing children’s choices and behaviours. A sense of community can help to foster better health behaviours. Stronger relationships between parents and schools, in addition to efforts by local authorities, can determine the health of a community. The highest-level determinant is public policy. When this determinant is considered, examining the policy for early PA and PE, in addition to dissemination of policy and statutory training of practitioners are key determinants. It is therefore important that programmes of intervention focus beyond the end recipient and includes key stakeholders.Reflecting an understanding of the wider influences on health, intervention mapping is heavily influenced and informed by Bronfenbrenner’s  socioecoCombining what the authors of this study know about FMS and PA, interventions during early childhood, and the school environment, these three elements should be considered to be important for early childhood health and PA. As aforementioned, there have been multiple interventions at the school level, and although IM has been used to plan interventions to combat obesity in early childhood populations ,39,40, tConsidering the evidence introduced here, the authors believe IM proposes a suitable and robust method to plan interventions, having been recommended as a valuable tool for early childhood intervention development . The aimThis study followed a non-traditional IM format, detailing the initial stages of IM development with scope for further study consideration. This means that the researchers were pragmatic and practical when developing this IM iteration. This article uses multiple perspectives based upon the SEM, focussing on the child at the centre , their parents as key interpersonal influences, teachers at the organisational level, and researchers who could be considered to be on both the community and national levels, depending on their work and impact. Data was taken from multiple sources, including the authors’ own work, to identify the most important determinants to these children. The authors also used these multiple perspectives and sets of data to shape the solutions that are proposed throughout this article, showing a strong alignment with IM principles, including participation, a bottom-up approach, and intervention. Sharing the information from IM development at formative and summative phases on process and impact are important in order for others to gain from this work, and as recommended by the IM workbook, publishing work before the end of the process is a valuable process . FurtherUsing the six-step process of IM  proposedThe six-step model and its associated tasks are further explained in the following sections; because IM is an iterative process, the authors discuss the steps/tasks combined with the results from this study that emerged from their deployment, and this helps preserve the context in a way that would not be possible if the method and the results were separated. Each section states how each task within each step of the mapping process was completed and how the planning group was involved. Although further involvement of the planning group may have been advantageous at various stages of this study’s process, this was not always realistic for the lead researcher and the members of the planning group. The authors used a more pragmatic approach by using several initial focus group sessions within Step 1, with the planning group split into respective groups  where necessary. Proposed questions for each step of the planning process were discussed at this first step  was gained from the University of Derby Science and Engineering Ethics Committee, and all participants completed written and verbal assent before involvement in any stage of the IM process.As shown in The planning group included representatives of the target population—i.e., children—and environmental agents and programme implementers—i.e., teachers, members of school senior leadership teams, parents, and PA and motor competence researchers. Other stakeholders to consider in further IM iterations include local authority representatives and community coaches.Participants of the planning group were recruited through word of mouth, social media, contacts from previous research studies, and outreach via email by the lead researcher, and where necessary, gatekeeper approval to work with children in school settings was gained. The planning group was considered to be trustworthy and knowledgeable in the subject matter. Children who participated in this study were recruited from two schools in the central area of England. As a preliminary and small-scale mapping process, it was concluded that samples should reflect critical mass and representativeness, and therefore, for every SLT member, there should be 3–4 early years foundation stage (EYFS) teachers , and foPrior to their involvement, participants were provided with a participant information sheet and the opportunity to contact the lead researcher to ask any questions they had about the research project before consenting to take part. For child participants, consent was first gained from the school headteacher, and subsequently, parents were asked to consent to their child’s participation within the study. Verbal consent was gained from the children at each visit.Planning group focus groups were used to explore and to execute several of the tasks in Steps 1–6; however, the focus group protocols are explained in Step 1. The lead researcher felt it was important to engage with the stakeholders in multiple ways to ensure all views could be expressed by both the group and as individuals and to reduce participant burden by splitting participation into smaller activities. The lead researcher held regular discussions with all members of the research team at each stage of the process, ensuring reliable and valid research outcomes. Three focus groups took place following a semi-structured approach using a proposed schedule of questions covering areas for Step 1–6 see ; the maiTwo adult focus groups were formed. Group one consisted of four parents of 4–5-year-olds (FG1P). Group two consisted of three PA and MC researchers (FG1R). The final sub-group of the planning group comprised two groups of children aged 4–5 years of age (FG1C). Although key to the planning group, specific focus groups were not held with teachers, coaches, or school senior leadership members. This was deemed appropriate, as previous work by the authors collected a wealth of information related to IM from these key stakeholders (see ). Where Traditional, formal focus groups were deemed unsuitable for young children; therefore, during their focus group, children completed a ‘write, draw, show and tell’ task  to facilProblem: children are not sufficiently competent at FMS in the early years. This negatively affects their levels of PA, quality of PA, and opportunities for PA social interactions; this leads to unhealthy PA habits developing, causing poor health outcomes such as obesity.Within the second task of Step 1, a needs assessment of the literature and existing knowledge was conducted to establish the problem, whose problem it is, who the problem was affecting, what behaviours and environmental conditions are causing or are related to the problem, and the determinants of these behaviours and the environment . First, The planning group was consulted during the focus group discussions about their thoughts and ideas for areas that had been missed, overlooked, or that should be added into the needs assessment. A summary of themes and comments made in the focus groups relating to Step 1 can be found in Following the focus group discussions and answering of questions appropriate to Step 1, a final logic model of the problem  was creaThe planning group was key in describing the context and setting of the interventions as well as key population characteristics and needs. The information provided during the focus groups, which was expressed in a study by Dobell et al. , researcThe final task of Step 1 was to state the broad programme goals, i.e., what the programme hopes to achieve overall. Using the logic model of the problem, broad outcomes to be changed through the implementation and maintenance of the intervention programme were stated: (1) increase the FMS competency of children in EYFS; (2) increase the quality of PA of children in EYFS; (3) improve the quality of FMS tuition/guidance.Step 2 comprised five tasks . The expFollowing the production of the logic model of the problem, expected behavioural and environmental outcomes for the programme of change were stated. Programme outcomes are the desired changes to be made by implementing the programme, leading to the overall broader programme goals. By ‘doing the flip’ and moving from focussing on the problem to focussing on the solution, a logic model of change can be formed. Behaviours causing poor health and health problems from the logic model of the problem were ‘flipped’ to reveal several desirable and health-promoting behaviours, and a selection of the most important behaviours and environmental conditions was made based on the literature and previous knowledge. A list of the potential desired outcomes for the target population’s behaviours and environmental outcomes were stated . NICE 44 recommeBehavioural: 1.0 Increase the number of EYFS children engaging in FMS practice at school.Environmental: 2.0 Increase the provision of FMS delivery by 20% at the EYFS in schools.Engaging children in more FMS practice was identified as key. When a cohort of 4–5-year-old children (unpublished data) were examined, most were meeting the PA guidelines, but many children had low FMS competency. This made a clear case to focus on improving FMS over simply improving the children’s PA levels. Second, improving the provisions of FMS delivery in school was also revealed as a key outcome. Although teachers feel that there are enough PA opportunities in schools , there wTo reach the desired programme outcomes, performance objectives for the population behaviours and their environment were established. These performance objectives are specific sub-behaviours for the children’s health-promoting behaviours; if the children are to perform more FMS practice, they must first engage in these sub-behaviours.Performance Objectives for Behavioural Outcomes:1.1. Increase the percentage of children spending 10 % more time doing PA in school each week1.2. Increase the percentage of the structured and unstructured FMS-related activities a child partakes in each week by 25%1.3. Increase the number of goals set for children’s FMS and PA performance by two each termThe following performance objectives are specific sub-environmental actions for teachers/senior leadership teams (SLT) to engage with to change the environmental conditions so that schools can be better prepared to deliver further FMS-related activities.Performance Objectives for Environmental Outcomes2.1. Increase the number of teachers facilitating FMS practice in EYFS settings2.2. Increase the number of teachers planning for PE, PA, and FMS in schools2.3. Increase the number of teachers aware of the benefits of providing EYFS children with PA opportunitiesBehaviours are formed by determinants of the individual, whereas environmental conditions are formed by the determinants within the environment which impact people. These determine if a child or teacher  will complete a performance objective (behaviour). Determinants need to be targeted to increase the likelihood of change for the individual and their environment. The determinants for the individual and health-promoting behaviours included: mastery level of FMS (skills), self-efficacy and physical self-concept, perceived norms, knowledge, fitness level, and sedentary behaviour. The determinants for environment and agents within them included: knowledge, self-efficacy, social norms, parental beliefs, outcome expectations of the setting, and the attitude of the school to PA, physical skills, and health.The most influential determinants were selected from the previous lists according to how important they were and how changeable they could be with intervention, according to (i) research conducted by the authors  to create change objectives. Using this information, matrices of change objectives were constructed, bringing all the tasks in Step 2 into a consolidated place  and to be delivered by an individual external to the school (researchers/coaches). Pedological literature continues to argue that delivery by teachers that work with children on a continued and regular basis is important not only for development of skills, but also for the relationships between children and teachers . This meThe sequence of the intervention relates to the order of how the components should be delivered. This intervention is made up of two key components, and one must be delivered before the other can occur: teacher training by delivery partners, followed by a school-based framework implementation delivery by teachers at the EYFS. These would both require evaluation following their delivery as carried out by the implementer (see Step 5). Following Task 3.1, initial theory and evidence-based change methods that were well-suited to the ideas generated through the previous steps were chosen to deliver the programme of interventions. Change methods are rooted in behavioural theories and psychological principles and are defined as general processes for influencing change at individual and environmental levels and the determinants within these. Kok et al.  specificThe final task of Step 3 was to start to design practical applications, practical techniques that operationalise the change methods chosen, to be used within the individual interventions of the programme, fitting within the intervention group(s) and the context. Therefore, this task collated information provided by the planning group, the Dobell et al.  practitiIn Step 4, there were four tasks . Task 4.Within Step 4, the aim was to produce an effective programme by refining the structure and organisation of the theoretical change methods and applications that were proposed in Step 3. This step organised how these systems of change would be delivered within the programme itself and the chosen implementation environments. The application created at this stage described the theme, scope, sequence of delivery, and delivery channels/vehicles of this programme. At the highest level, this programme has two main interventions: teacher training and the delivery of an adaptable framework for children. The interventions were made up of multiple constituent parts (applications) to achieve the programme’s goals, which include the behavioural and environmental outcomes, performance objectives, and change objectives that were established in the previous IM steps. Within Using the information gathered in the prior IM steps and from the planning group’s input , plans fIn the next task, the plans to create the components were put into practice . This is further developed by acknowledging how FMS should be part of PE for EYFS children while linking back to health and academic outcomes for children. The second section of the booklet focusses on how to plan for FMS tuition in school and how teachers can improve and promote it within the EYFS as well as on key delivery methods. This information within these two sections compliments the first session of training, which explores why FMS and PA are important for children’s health and how the school environment can help to improve these outcomes for children. In the practical workshop setting, this is achieved by first surveying the teacher’s current school environment and what they already do well before exploring where improvements could be made.The third section of the booklet provides an array of practical activity suggestions, with many being informed by the children’s preferences presented in FG1C . The praThe fifth section provides information to help teachers prepare to help children set goals and to subsequently assess their improvements in FMS and PA as a whole group of children and individually. These sections were strongly informed by the voice of the parents (FG1P) and researchers (FG1R), who commented on the need for assessment to be implemented into the existing school reporting framework. Finally, the sixth section lays out ideas for setting homework tasks for children with the consideration of the home environment. This section also details how to engage parents in aiding the success of the programme. Previous qualitative work highlighted the differences in home environments, especially those of deprivation and the need for practice activities to be on a small scale with none or limited equipment use . This seThe materials drafted in Task 4.3 were produced as prototypes  for this programme of study. This aided gathering data to validate ideas proposed before from researchers, the literature, and the planning group. Future research should engage in the development of all programme materials with the required experts to produce them, including media promotions and parent packs. These were not developed in this study due to the constraints of this being a PhD project. Within the process of IM, once the materials have been produced, they should be pre-tested with the target population of the interventions, and the results of these pre-tests should be evaluated. Pre-testing should include the involvement of the planning group, particularly the intervention end users/participants (children), and those delivering it (teachers). Testing the structure and organisation of the programme and individual intervention applications and components reveals any issues or ineffective elements but also strengths, which can help establish the most effective protocol and materials when identified. Using planning group feedback and questions such as ‘does this method interest you?’ and ‘would this protocol/delivery suit your setting?’ embraces stakeholder involvement and ownership. Within these testing periods, it is key to observe the thoughts around sustainability of the interventions, such as the regularity of delivery and the potential for other factors/determinants within the environment to disrupt this. The evaluation methods should be well-designed to fit within the pre-test delivery rather than as a standalone element. In this study, there was scope to pre-test one element of the intervention materials with the planning group. By using Step five comprised four tasks. First, we identified the potential adopters of implementation for the programme, which was followed by stating the outcomes and performance objectives for the programme’s use by the adoptees. The creation of matrices of change objectives for use with the programme and designing the implementation interventions to be used were the final two tasks. Step 5 requires the key stakeholders for intervention success (effectiveness) to be identified. Using the planning group’s previous inputs within the initial focus groups  and previous interviews , the impImplementers are considered to be those who will put the interventions within the programme into practice; in this case, they are those who will implement teacher training and framework intervention delivery. For interventions to be successfully adopted, they should be specific to the setting of adopters of the programme, such as schools, clubs, and local authorities. Finally, the implementers and adopters will work together to become maintainers of the programme of change, especially where positive changes are observed, leading to the specified programme objectives and outcomes. Implementers: Local authorities, delivery partnersAdopters: Teachers, School senior leadership teams (SLT)Maintainers: Local authorities, delivery partners, teachers, School SLTWithin this programme, four key stakeholders were identified:Stakeholders may not exclusively have a singular role in intervention delivery, as mentioned in the list. Local authorities must engage with schools and leadership teams to get them on board with the intervention and to train educators to use the intervention/programme, making them implementers. Consequently, the local authorities must have a method of disseminating the training to educators, and this is via delivery partners. Delivery partners may have different roles in different local authorities but are likely to be public health staff, health visitors, or PA and sports-based professionals. Importantly, it is not specified who this would be, giving authorities the freedom to choose, which could ultimately result in better sustainability. These delivery partners must ensure the maintenance of a relationship with a school and of the training intervention. The adopters of the programme are those based within the school setting, hosting and delivering the programme. Teachers are the ‘frontline’ staff to this intervention by delivering it to children, aided with the support of their school SLT. These teachers must be trained by the implementers. Finally, all roles come together to be the maintainers of the programme. A local authority must commit to keeping delivery partners available for the training of teachers. School SLT and teachers must commit to continuing to deliver the programme to future classes to help change social norms and beliefs while increasing FMS mastery in EYFS children through the programme. Considerations to the maintenance of the programme, such as funding and higher-level policy, to ensure time and sufficient structure is in place to allow for the interventions are pivotal. Therefore, members of the school SLT, local authority, and national government should be considered to be key maintainers of the programme and influence later iterations of the IM process. For the implementers, adopters, and maintainers, the main outcomes were stated for the dissemination, implementation, adoption, and maintenance of the interventions. These outcomes were then split down into performance objectives in a similar way to the tasks in Step 2. These were informed by questions used within the focus groups, with a focus on the sustainability of the programmes and their implementation. The outcome and performance objectives are stated in Using these outcomes and performance objectives for the programme, further matrices of change (as in Step 2) were produced  to proviIn Finally, within Step 6 , a plan The first task of the evaluation plan was to write effect and process evaluation questions for the programme. Has the programme improved children’s physical self-efficacy and academic performance?How much does the PA level and FMS mastery of the children completing the programme change from pre- to post-intervention?What was the impact of the programme on teachers’ knowledge and self-efficacy to teach and plan for FMS at the EYFS?What was the impact on the children’s knowledge and enjoyment of FMS?What was the structure of FMS delivery like in the participating schools before the intervention?What parts of the intervention worked well, and why? What did not work as well regarding the implementation, and why?What elements of the interventions have been sustained post-intervention?What aided dissemination and adoption of the programme?How often are teachers planning and using the framework in schools?If schools continue to use the programme, why?Have the participants (children) enjoyed the delivery of the intervention in schools?The final three tasks of Step 6 were collapsed together to achieve a more concise nature of work. With effect and process questions established, the identification of the indicators and measurements for these variables were chosen , informeCard sort activities SurveysInterviewsObservationsDevice-based measures (accelerometery)Based on planning group consultation, the literature, and the researcher’s own knowledge, the chosen evaluation methods for this programme of interventions consist of:Each of these methods may capture more than one element of evaluation, which can be observed in What is the problem?What can be changed?How can it be changed?What is the design of change?Who needs to be involved in change and how?How can change be evaluated?This study was the first to begin to plan an FMS intervention for early childhood populations using IM by iteratively planning and developing interventions in collaboration with key stakeholders. The authors appreciate that not all steps of the IM process were completed, but they were given consideration and recommendation for further work to be completed. This work is important for advancing knowledge of what really matters within FMS and PA interventions for children, and this work goes beyond the traditional approach of many interventions where only Step 4 of the principles of IM (programme production) are adopted for their organisation within reporting. The many tools and tasks involved in the IM process were operationalised within this article to ensure new learning emerges; this knowledge is essential for the future planning of programmes of intervention. The key findings show the thorough and rigorous process of the IM-enabled development of a practical and feasible plan for future intervention for the early years and their specific health behaviours. This section will summarise the emerging outcomes from the current IM process with a socioecological focus, highlighting areas for future development through the following structure:This study collated evidence from two previous studies by the authors ,24, unpuThe use of a systems science approach, including creating a causal systems map of how this intervention is to be implemented beyond the school level, should be established . This apWithin the current programme of intervention, the individual, their environment, and the environmental agents within it  need to be changed to provide a multi-level approach to intervention. Addressing this problem by creating supportive environments for children to practice FMS within is essential. The majority of children in England attend school at ages 4–5 years old , meaningAlthough this IM process focussed on teacher-level intervention, the researchers felt there would also be a need for an intervention at the senior leadership team  level , which reflects the IM principle of multi-level influences and stakeholder involvement. These individuals may not implement the intervention but are influential in decisions that facilitate engagement with the intervention. The intervention at the SLT level would be similar in terms of information to influence teacher training programmes; however, this would be based around the key benefits of improving children’s FMS and how to promote this at the early years and as a whole school. The primary PE and sport premium funding  providesParental attitudes and beliefs around school provision should also be considered an important determinant of the environment to target. Parental attitudes are particularly influential on their child’s behaviours, including their motivation to perform PA ,58, and When identifying the determinants of behaviours and the environment, there is a clear vision to what needs to be changed for the individual and their environment. By establishing broad programme goals, behavioural and environmental outcomes, and performance objectives and change objectives by giving a broad-to-narrow and specific approach, intervention methods and applications can be planned .It is easy to say ‘the self-efficacy of teachers to provide FMS specific teaching needs to increase’, but how can this process be achieved? This is where small and manageable change objectives should be established. As seen in the Matrices of Change , it can Likewise, for children, it is known that their mastery level of FMS needs to be improved, but what are the potentially effective methods for achieving this within the proposed intervention? By increasing the time children have for PA at school, there may be greater improvements in their FMS ,62 when What has emerged from this IM process is the need to identify the most influential determinants of the behaviours and environments from multiple sources of knowledge, the literature, and the planning group. As the first IM study to do so for this age group, it should be considered that the most influential determinants are actually identified in future IM iterations, and that some determinants may be considered to be less important in future iterations.Intervention design can take many forms, and IM processes target multiple behaviour change theories  to addreIntervention design should be pragmatic and realistic to the users and adopters, ensuring the intervention can be effectively implemented in the desired setting with the target population . The intParents—although parents have no direct role to play within the current intervention and its delivery, they can enhance the success and long-term outcomes achieved during the intervention period by engaging with the resources provided to them during the intervention. Engaging directly with parents is at the discretion of each individual school, and as with all interventions, it will have varying degrees of success. Despite this, communication with parents and the role they can play should be considered to be critical, especially in future work.School SLT—these key stakeholders must agree that using the intervention, training their staff, and use of the framework intervention is sustainable and worthwhile within their school.Local authorities—they are key partners in the implementing the programme in schools. Without local authorities, there is no initial platform to deliver and communicate the intervention from. Their influence on schools within the local authority should be key in ensuring sufficient and successful intervention uptake. In the long-term, which is dependent on intervention success and stakeholder opinion, local authorities could stipulate a mandatory need for the intervention in schools at the EYFS level.Delivery partners—they are key players within the local authority set-up, as they provide the training sessions for the intervention to teachers. They should support schools and teachers beyond the training, ensuring successful implementation within school environments.Public—policy makers at the government level represent the highest and possibly most influential level this intervention could reach: affecting public policy. The requirement for a statutory FMS intervention or improved framework at the EYFS could be pivotal to ensuring healthier and more active lifestyles for children from an early age. This programme could be delivered locally but be evaluated at a national level, much in the way children are assessed in literacy and maths skills.Intervention requires the collaborative efforts of many stakeholders to improve the possibility of success, as highlighted by the systems science approach . This prWhen evaluating the changes achieved by the intervention, the process and its effects should be considered. As shown in Step 6, curating questions, followed by planning the use of practical techniques, is important for evaluation implementation. Using the RE-AIM framework  and evalEnvironmental outcome: teachers planning to teach FMSBehavioural outcome: children practicing FMSDeterminants: improving teachers, parents, and children’s knowledge of FMS, increasing the self-efficacy of the teachers to deliver FMS content/activity in school settings, providing children with an enjoyable intervention/FMS practiceThis IM plan developed the effect evaluation around the most important variables to be changed during the intervention, according to the environment, individual behaviour, and their determinants:Future intervention should consider if the intervention approach being used targets the elements listed previously while considering how these may be evaluated. The completeness of delivery: was the programme delivered as intended with all its elements, and if not, why?Continuation of the intervention: once implemented in the school setting, was it continued successfully and appreciated by the adopters, maintainers, and users of the programme?Participant exposure: did the participants of the intervention receive the appropriate dose of the intervention?During the process evaluation, attention should be paid to how the intervention is delivered during implementation and in practice. This evaluation process should provide important information about:These elements of process evaluation may help provide important answers as to why the intervention did or did not work . The metWhen examining the strengths of the current study, to the authors’ knowledge it is the first to use IM to plan an FMS intervention for EYFS children within school settings, thereby providing a high level of novelty. This means that the processes throughout were strongly rooted in the use of the socioecological model and engagement with a planning group throughout that included stakeholders from varying levels of the model. The study brought together researcher knowledge, existing literature, and stakeholder engagement to address their wants and needs.Reflecting on the limitations of the current study, many of the materials proposed in this study have not been drafted, produced, or pre-tested with the planning group or stakeholders. This also represents a need to engage with the appropriate producers to establish and create these materials. Despite the current planning group in this study, there is certainly still scope to engage with other key members, including local authorities, third sector organisations and charities, and the national government. Due to local authorities being identified as implementers in the current IM process, their involvement in future development and research should be considered pivotal. A further limitation of this study is the small sample size used with the current planning group and the use of convenience sampling. This limits the cultural, socioeconomic, and geographical transferability of the results within this study. However, the authors recommend the drafting, production, and pre-testing of materials with larger planning groups across a varied socioeconomical, cultural, and geographical sample to improve these outcomes. Engaging with planning groups representative of communities across England would be important in future work.This study aimed to provide an important and well-informed basis for the development of future interventions. By considering the socioecological model when designing the current programme of intervention, the researcher’s attention was focussed on the individual and the influence of their interpersonal environment, organisational environment, community environment, and policy environment on their behaviours and health outcomes. The identification of these influences and knowledge built within this study as well as in previous research has played a key role in the aims of the intervention and the outcomes to be achieved. This IM study shows that initial intervention may aim to aid children and their interpersonal and organisational environment at the school and teacher levels to provide better structure and opportunity to develop FMS, which may lead to improved levels of PA and positive changes in health outcomes.