## 4 Evaluation and Discussion  
The goal of this analysis is to find a solution that will make it easier and more affordable for people with VI to go about their daily lives (obstacle detection). The experiments were conducted on an \(\times\)64-based processor and 8 GB of DDR3 memory on an Intel\(\otimes\)CoreTM i7-5500 CPU running at 2.50 GHz. We used the Python programming language to build the suggested obstacle detection model and handle the neutrosophic rules. The following are the characteristics of an ultrasonic sensor: working frequency: 40 Hz, maximum range: 5 m, minimum range 2 cm, trigger input signal: 10 \(\mu\)S TTL pulse, echo output signal: input TTL lever signal and the range in proportion, dimensions: 45 \(\times\) 20 \(\times\) 15 mm\({}^{3}\), a 60\({}^{\circ}\) beam angle. The user can adapt this wearable prototype according to their personal preferences by using a mobile device with Bluetooth communication. The suggested model was constructed based on the distance of the obstacle using two belt sensors and a single output (expected movement direction). After the activation of the device, the distances of the obstacle from two sensors will be sent to the neutrosophic controller. Then, a determination will be made using the nine neutrosophic rules. The user will get this input via their headphones. The whole procedure will be used iteratively. In the absence of a barrier, the user's route will remain unchanged (straight). A machine learning method based on neutrosophic logic has been developed to extract relationships, modeled as rules, from a dataset (the raw distances of an obstacle from two ultrasonic sensors). There are nine rules that are built according to the statistics of these dataset and their range classification summarize the strongest connections among the two variables \(SD_{front}\) and \(SD_{left}\). These could be employed to predict the expected movement that must be taken into account during the movement of the VIPs to avoid obstacles for an unseen piece of data collected under the same conditions, using the belt-like assistant device that interacts with the smart watch to provide an alarm message for obstacle avoidance, with a significant level of accuracy, while at the same time providing a linguistic snapshot of \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rule** & **Antecedent SD\({}_{front}\)** & **Antecedent SD\({}_{left}\)** & **Consequent EM** \\ \hline
1 & High & High & RT\({}_{sharp}\) \\
2 & High & Medium & \(SLT\) \\
3 & High & Low & \(SLT\) \\
4 & Medium & High & RT\({}_{sharp}\) \\
5 & Medium & Medium & RT\({}_{sharp}\) \\
6 & Medium & Low & RT\({}_{sharp}\) \\
7 & Low & High & RT\({}_{sharp}\) \\
8 & Low & Medium & \(MF\) \\
9 & Low & Low & \(SLT\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Neutrosophic logic-based indoor detection: a set of basic rules. the inherent pattern in the data set. This makes neutrosophic logic an important modelling tool in control systems where non-crisp inputs are required. To ensure the accuracy of our system, we used a dataset consisting of readings from two sensors (5456 samples) as input; 75% of the dataset was used to train the model, while the other 25% was used to evaluate its performance. The characteristics of the datasets used for the objective evaluation are mentioned in detail in the neutrosophication step. We separated the objects in each scene into two categories after realizing that certain objects don't count as obstacles unless they're directly in the way of the user or preventing him or her from moving forward. To evaluate the performance of the neutrosophic classifier in the process of detecting obstacles, two versions of the proposed model were built based on the core modules: one using a neutrosophic classifier and the other using a traditional fuzzy logic classifier inspired by the work in [39]. Table 5 shows the obstacle detection rate in the used dataset. The results reveal the superiority of the neutrosophic classifier, with an improvement of about 6%.