The main target of the research is to gather dog data and general data from dog owners. With that strategy in mind, data gathered by calling through phone, physically visiting and by Google Surveys, and then used for the analysis process. After gathering of data phase, organized and stored data in an excel sheet as in Comma-Separated Values (CSVs) [27] format which was more suited when feeding to the machine learning model in the model training process. Dataset contained same dogs’ adult dog behaviors and puppy behaviors. Dog behaviour attributes such as energy level, shyness/outgoingness, reaction to owner and the family, reaction to new people, behavior around other animals, responsiveness to common commands, specific behavioural issues, and general data attributes such as having other animals at home, having children in home, perform trainings for the dog, each day hours spending with the dog, etc. contributed to predict the adult dog temperament in the solution of the study. Most of these attributes are highly valued key variables to affect dog temperament [4] [28] - [33] . Then, the data preprocessing part done through feature engineering by focusing on categorizing behaviour data and handling missing values to attain the best results. Overall, all the findings provided the training data for the ML predictive model. In the Discussion and Findings section, some of the graphs and charts are displaying processed data related to dogs and their owners. Then, the dataset absorbed into the ML model which uses the Random Forest ML approach that can handle complicated relationships with a medium to large dataset [34] [35] . While Decision Trees approach is useful but may not capture the complexity of the relationships in the data as effectively in this research because it is good for small, simple and well-structured datasets [35] . While focusing on Neural Networks approach, it typically requires much larger datasets. Also, the training of Neural Networks approach is very time-consuming and can be computationally intensive [36] . The difficulty of interpreting trained models is one of the primary issues with neural networks approach [36] . When comparing with other ML approaches, Random forest approach is fast to train [37] . Random Forest approach can outperform Neural Networks approach over specific cases and also can have advantages when comparing with Neural Networks approach [36] . The Random Forest approach was chosen as the better option for this research project. The model was then trained. After the model training process, it was evaluated. Then the predicting model integrated with a web application. Users can be able to input puppy data into the integrated web application to get adult dog temperament predictions of those puppies. Also additionally, based on the predicted adult dog temperament results, the software provides valuable insights such as recommendations, insights and training plan generators to users. By making better decisions such as perform trainings for the dog, etc. can improve the well-being of their dogs and their relationships [38] . Overall, the solution tool contributes to enhance the well-being of dogs by these strategies.

Below conceptual framework is showcasing the structure for organizing the research process ( Figure 1 ).

The analysis had a broad area of dog data and general data. In this section, some of the charts and graphs are showcasing processed data related to owners and their dogs.

Data were collected on the dog breeds of American Bully, American Pit Bull Terrier, German Shepherd, Rottweiler, Labrador Retriever and Boxer. The study’s aim is on the guard dog category and also because of the low number of responses to several dog breeds, had to drop Labrador Retriever, Rottweiler and Boxer dog breed data. The research then targeted American Bully, American Pit Bull Terrier and German Shepherd guard dog breeds. Forty nine percent of German Shepherd, 28.1 percent of American Pit Bull Terrier and 22.9 percent of American Bully data were analysed in the total of 96 data in the dataset ( Figure 2 ). Collected dog data count displayed these guard dog breeds are crucial for the temperament prediction by displaying different patterns regarding the temperament throughout the breeds, had to consider that different breeds have different behaviours. Within the breeds that identified, 60.4 percent of male animals and 39.6 percent of female animals were found. In the real world, both male and female dogs have different temperaments and having data on both genders provide a significant impact to the solution. Because male and female dogs are having different temperaments, the user can understand and determine which dog gender temperament traits are positive or negative at the final result.

Dog’s energy level was one of the most essential choices because it is a key indicator to the activity level of dogs and by being aware of this trait aids to predict dog temperament [7] . Ninety-nine percent of energy levels were at high, while moderate energy levels were at one percent in the collected dataset ( Figure 3 ). Dog’s shyness/outgoingness is a main factor in sociability level of dogs. Canines can have confident behaviours, fearful behaviours, etc. [8] . Regarding the research, outgoingness simply spells out that adaptation can be done easily, and shyness requires more socialization. Outgoingness was at 94.8 percent, while shyness was at 5.2 percent in the gathered dataset. By being aware of this attribute aids to predict dog temperament ( Figure 4 ). In the collected dataset, most of the dogs were friendly to their owner and their family at a percentage of 97.9. Dogs who aggressive towards the owner and family members were very lower at a percentage of 2.1 ( Figure 5 ). When concentrating on the dog’s reactions to new people in the gathered dataset, majority of dogs were friendly at a percentage of 59.4, some of the dogs were reserved at 31.3 percent and lesser number of dogs were aggressive at a percentage of 9.4 ( Figure 6 ). And when concentrating on the dog behaviour around other animals in the dataset, majority of dogs were friendly at a percentage of 60.4, some of the dogs were reserved at a percentage of 31.3 and lesser number of dogs were aggressive at a percentage of 8.3 ( Figure 7 ). Friendly reactions indicate positivity of trust and sociability. Reserved reactions indicates neutral action between friendly and aggressiveness but still needs light training. Aggressive reactions indicate negativity and attention required for training [3] . These traits are heavily required for predicting temperament of dogs. Also, the research identified that some dogs had some specific behavioural issues such as aggressiveness that led them to attack new people and other animals sometimes, and very lower number of dogs were aggressive towards their owner and their family. If specific behavioural issues like aggression are discovered, then required to have the attention for training methods. This trait is strongly recommended for predicting temperament of adult dogs in the research solution. When concentrating on responsiveness to common commands trait, most of the dogs were responsive at a percentage of 77.1 and some of the dogs were not responsive to their owners at a percentage of 22.9 which refers to dog obedience and the trainability ( Figure 8 ). This trait is also, strongly aids to predict the adult temperament of dogs. Dog’s energy level, shyness/outgoingness, reaction to owner and the family, reaction to new people, behavior around other animals, specific behavioral issues, responsiveness to common commands are key and main ingredients in the study because these traits provide a thorough overview of a dog’s early stage which is definitely affecting that dog’s adult temperament [8] .

Most dogs are living with their owners in suburban areas comparing to rural areas, at 82.3% and 17.7% respectively, according to the dataset. When focusing on home type, 84.4 percent of dogs are living in houses with big yards and 14.6 percent of dogs are living in houses with small yards ( Figure 9 ). There was just only one dog counted as living in an apartment with the owner. It was clear that having no children at home was at 62.5% and having children at home was at 37.5% ( Figure 10 ). Furthermore, having no other animals at home was at 81.3% and having other animals was at 18.8% ( Figure 11 ). These environmental factors such as availability of space, having children and other animals at home are impacting dog’s stress levels and make them crucial attributes to predict adult dog temperament [13] . Other environmental factors such as home type, having children and other animals at home attributes were affecting the adult dog temperament development because housing conditions can affect mental fitness of dogs and coexisting with children and other animals foster relationships and socialization to the temperament of dogs [13] .

It was evident that most dog owners are spending five hours of time with their dogs on each day, as a percentage of 44.8, while 10 hours, 9 hours, 4 hours, 8 hours, 3 hours, 7 hours, 2 hours, 6 hours were minor contributors at 21.9%, 13.5%, 6.3%, 5.2%, 3.1%, 3.1%, 1%, 1% respectively ( Figure 12 ). Most dog owners were not giving training sessions for their dogs. It was indicated at 71.9%, while owners who give training sessions for their dogs were indicated as 28.1% ( Figure 13 ). Spending time with canines and giving training for them can be a good choice to manage temperament issues or else may arise issues in the future [3] . These attributes directly contribute to strengthen the bond between the owner and the dog [3] . Also, it enhances dog’s behaviors and obedience. These attributes are also influencing for the predicting temperament of dogs.

In conclusion, the temperament prediction section is chosen as the main feature of the solution. Furthermore, we decided to add more extra features like providing recommendations, insights and training plan generators based on the predicted adult dog temperament results. Each and every dog owner who provided the data to the research was very concerned regarding behaviours of dogs as dogs mature into adult dogs. That makes the project solution is required to build and will be successful among end users. By understanding dog behaviours properly, dog owners can have positive impact on their dog’s well-being.

Following is the diagram explaining the development ( Figure 14 ).

Frontend application, backend application, machine learning model and database acted as the main units in the software architecture that the study followed. In the frontend application, interactive interfaces were designed using React library to handle client-side interactions. Integrated with the Python ML model, backend is implemented using Flask (Python) framework and Node.js to handle server-side interactions. The machine learning model is the unit that is responsible for the adult dog prediction based on early puppy behavior data and the model was trained using dog data and general data in the dataset that was collected from dog owners. The model is implemented primarily using Python programming language. Data preprocessing part is done through feature engineering by focusing on categorizing behaviour data and handling missing values. In the process, split data into training (80%) and testing (20%) sets. Trained the model using Random Forest ML approach because Random Forest approach is more suitable to handle complex relationships with a medium-large dataset, as encountered in this research. Also, performed hyperparameter tuning and evaluated the model to enhance the accuracy to fulfill the expectations of the study. After saving and loading the model, predicted the target values. After implementing the ML model, integration of the model to the backend application process happened. MongoDB Atlas cloud database is used to perform CRUD operations for software data and model parameters. After all processes were done, to validate the finalized software solution, tests were conducted and found issues were instantly addressed and fixed.

Below use case diagram is showcasing the features and scope of the software ( Figure 15 ).

Below is the solution logo ( Figure 16 ).

Below screenshots are showcasing user interfaces in the software.

User should be able to register to the app using the sign-up screen ( Figure 17 ).

User should be able to log in to the app using the login screen ( Figure 18 ).

User should be able to see dogs that are added to dog profile section in the dashboard ( Figure 19 ).

User should be able to view and update their user profile ( Figure 20 ).

User should be able to add dog profiles to their account ( Figure 21 ).

User should be able to answer the questions regarding puppies asked from the app to predict the adult dog temperament ( Figure 22 ).

User should be able to see the predicted adult dog temperament after answering to the questions ( Figure 23 ).

User should be able to view visualization section regarding their dog information ( Figure 24 ).

User should be able to receive recommendations based on their adult dog temperament ( Figure 25 ).

User should be able to receive training plans based on their adult dog temperament ( Figure 26 ).

User should be able to give feedback regarding the app solution ( Figure 27 ).

User should be able to submit support tickets regarding the app solution ( Figure 28 ).

Admin should be able to manage user accounts ( Figure 29 ).

Admin should be able to manage user feedback ( Figure 30 ).

Admin should be able to manage user support tickets ( Figure 31 ).

The finalized software solution provides adult dog temperament based on puppy behaviours to the users so they can understand dog behaviours and make decisions related to the well-being of dogs. Finally, the software can be able to give valuable insights to users to improve public safety, increase adoption rates and reduce costs regarding shelters and animal control units.