In this article, a new path planning algorithm is proposed. The algorithm is developed on the basis of the algorithm for finding the best value using multi-objective evolutionary particle swarm optimization, known as the MOEPSO. The proposed algorithm is used for the path planning of autonomous mobile robots in both static and dynamic environments. The paths must follow the determined criteria, namely, the shortest path, the smoothest path, and the safest path. In addition, the algorithm considers the degree of mutation, crossover, and selection to improve the efficiency of each particle. Furthermore, a weight adjustment method is proposed for the movement of particles in each iteration to increase the chance of finding the best fit solution. In addition, a method to manage feasible waypoints within the radius of obstacles or blocked by obstacles is proposed using a simple random method. The main contribution of this article is the development of a new path planning algorithm for autonomous mobile robots. This algorithm can build the shortest, smoothest, and safest paths for robots. It also offers an evolutionary operator to prevent falling into a local optimum. The proposed algorithm uses path finding simulation in a static environment and dynamic environment in conjunction with comparing performance to path planning algorithms in previous studies. In the static environment (4 obstacles), the shortest path obtained from the proposed algorithm is 14.3222 m. In the static environment (5 obstacles), the shortest path obtained from the proposed algorithm is 14.5989 m. In the static environment (6 obstacles), the shortest path obtained from the proposed algorithm is 14.4743 m. In the dynamic environment the shortest path is 12.2381 m. The results show that the proposed algorithm can determine the paths from the starting point to the destination with the shortest distances that require the shortest processing time.

License Plate Tracking used to verify the legal. Created to reduce the work of traffic cop and increase the accuracy of the traffic from the camera. The Capturing of car registration and send readable information compared to the department of transportation's database. It is a government database and the result is returned to the examiner that' what type of car? A real or fake registration to reduce the inspection process of the staff. It also protects against theft as well.

Regular screening and timely treatment play a crucial role in addressing the progression and visual impairment caused by cataracts, the leading cause of blindness in Thailand and many other countries. Despite the potential for prevention and successful treatment, patients often delay seeking medical attention due to the gradual and relatively asymptomatic nature of cataracts. To address this challenge, this research focuses on the identification of cataract abnormalities using image processing techniques and machine learning for preliminary assessment. The LeNet-convolutional neural network (LeNet-CNN) model is employed to train a dataset of digital camera images, and its performance is compared to the support vector machine (SVM) model in categorizing cataract abnormalities. The evaluation demonstrates that the LeNet-CNN model achieves impressive results in the testing phase. It attains an accuracy rate of 96%, exhibiting a sensitivity of 95% for detecting positive cases and a specificity of 96% for accurately identifying negative cases. These outcomes surpass the performance of previous studies in this field. This highlights the accuracy and effectiveness of the proposed approach, particularly the superior performance of LeNet-CNN. By utilizing image processing technology and convolutional neural networks, this research provides an effective tool for initial cataract screening. Patients can independently assess their eye health by capturing self-images, facilitating early intervention and medical consultations. The proposed method holds promise in enhancing the preliminary assessment of cataracts, enabling early detection and timely access to appropriate care.

This work aims to develop an elderly assistance system using voice commands by applying speech recognition technology. The system is designed to enhance convenience for elderly individuals and also reduce labor costs associated with hiring caregivers. It leverages the principles of the Internet of Things (IoT) to control various household electrical devices through voice commands. Through Natural Language Processing (NLP), the system processes natural language to interpret voice commands, allowing users to control lighting, open applications, make phone calls, and more. The researchers utilized a Confusion Matrix model to measure the accuracy of the voice command recognition system. It assessed whether the system correctly interpreted the voice commands issued by users. The testing, conducted with a group of individuals aged 55-60, revealed an accuracy rate of 85%. This demonstrates the system’s effectiveness and potential to augment the independence of elderly individuals. In essence, this work presents a voice-command assistance tool that combines speech recognition and IoT, promising enhanced convenience for the elderly and potential savings in caregiving expenses.

Researchers propose new algorithm to monitor fatigue of driver by tracking the movements of the eyes. This can classify the opening and closing eyes by using Haar-Like Features and Region of Interest techniques. The fatique can be classified by decision tree classification. The system works in real time and sends message to the mobile phone to alert the driver via Line Application. The results showed that the program can detect face and classify opening and closing eyes accurately at 99.93 percent in which it yielded higher accuracy than other algorithms.

In this research, we study the problem of biometric data classification with face recognition. By used the process of RTSP (Real Time Streaming Protocol) is an application-level protocol for control over the delivery of data with real-time properties. RTSP provides an extensible framework to enable controlled, on-demand delivery of real time data, such as audio and video surveillance system which used IP Camera for target person detection on Google Map.