This paper aims to provide a comparative study of the different artificial intelligence (AI) algorithms applied to solve the traveling salesman problem (TSP). Four (4) AI algorithms such as genetic algorithm, nearest neighbor, ant colony optimization, and neuro-fuzzy are executed in MatLab software to determine which among these techniques will provide the least execution time to solve a TSP. The objective of comparing and analyzing each AI algorithm as applied to a single problem with the different program execution - is to identify if significant difference in execution time could lead to significant saving in energy consumption. The simulations using MatLab resulted to strong correlation at an R² of 0.95 in the average execution time with the number of code lines, but do not give a significant execution time variance as when ANOVA and t-test measures were performed. The result of this paper could be used as a basis in the design phase of software development life cycle to arrive into an energy efficient software application with respect to time needed to execute a program.

Portable electrocardiogram (ECG) capturing device can be interfaced to a smart phone installed with an android-based application (app). This app processes and analyses the data sent by the device to provide an interpretation of the patient/user's heart current condition (e.g.: beats per minute, heart signal waveform, R-R interval). The ECG recorded by the app is stored in the smart phone's Secure Digital (SD) card and cloud storage which can be accessed remotely by a physician to aid in providing medical diagnosis. The project aims to help patients living at a far distance from hospitals and experience difficulty in consulting their physician for regular check-ups, and assist doctors in regularly monitoring their patient's heart condition. The hardware data acquisition device and software application were subjected to trials in a clinic with volunteer-patients to measure the ECG and heart rate, data saving speed on the SD card, success rate of the saved data and uploaded file. Different ECG tests using the project prototype were done for 12 patients/users and yielded a reading difference of 7.61% in an R-R interval reading and 5.35% in heart rate reading as compared with the cardiologist's conventional 12-electrode ECG machine. Using the developed ECG device, it took less than 5 seconds to save ECG reading using SD card and approximately 2 minutes to upload via cloud.

Split air conditioning units are crucial for ensuring the thermal comfort of buildings. Conventional scheduling or pre-timed system activities result in high consumption and wasted energy. This study proposes an intelligent control system for automatic setpoint adjustment in an educational building based on real-time indoor and outdoor environmental and room occupancy data. Principal component analysis was used to identify energy consumption components in ramp-up and steady-state power usage scenarios. K-means clustering was then used to categorize environmental scenarios and occupancy patterns to identify operational states, predict power consumption and environmental variables, and generate fuzzy inference system rules. The application of rough set theory eliminated rule redundancy by at least 99.27% and enhanced computational speed by 96.40%. After testing using real historical data from an uncontrolled environment and occupant thermal comfort satisfaction surveys reflecting a range of ACU setpoints, the enhanced inference system achieved daily average power savings of 25.56% and a steady-state power period at 63.24% of the ACU operating time, as compared to conventional and variable setpoint operations. The proposed technique provides a basis for dynamic and data-driven decision-making, enabling sustainable energy management in smart building applications.

The present study analyzes the microstrip antenna design for wireless power transfer in smart buildings, harnessing the ambient electromagnetic radiation due to common electronic gadgets that energize wireless sensor networks, computing devices, and connected appliances. With the increased number of these devices, so does the potential for health problems caused by electromagnetic radiation. However, these devices also provide a renewable energy source through their emissions. This study suggests the creation of a 5G Microstrip antenna that enhances the absorption of this radiation for the purpose of recharging batteries in smart buildings. The design capitalizes on the inherent low-profile and cost-effective features of microstrip antennas, making them well-suited for incorporation into building infrastructure and 5G wireless technologies. Although each individual device emits a little amount of energy, the combined effect achieved by advanced antenna design and power converters is anticipated to result in a substantial energy production. The antenna designer tool from MATLAB was used to carry out a conceptual simulation of the microstrip antenna. This has set up the framework of a feasible way of predicting the performance with high efficiency and sustainability for a wireless power transfer (WPT) system.

The COVID-19 pandemic has introduced the new normal of introducing contactless transactions and interactions. This made voice assistive systems useful to business process requiring company to client communications. A generative-based interactive speech artificial intelligence for Asia Pacific College's (APC) Admissions Office is discussed in this paper. It collects data from user's voice input and generates a response that answers questions and inquiries concerning the services of the office. Software tests revealed an average accuracy in getting the right answers to questions of 88%. The system was able to provide the correct response in the presence of background noise at 91.5%. The average response time of the system was 2.36 seconds.