This paper explores the implementation of ternary (base-3) logic systems using global internet latency as a novel computational substrate. We present a theoretical framework, experimental methodology, and detailed results for a Ternary Internet Computer (TIC) that encodes computational states within the temporal behavior of networked IP addresses. Our findings demonstrate that this approach can reliably simulate basic logic operations and implement a simple perceptron capable of learning the XOR function. Through rigorous statistical analysis across multiple trials, we show that while network latency introduces variability, our redundancy mechanisms yield reproducible computational results. This work contributes to the growing field of unconventional computing by demonstrating how distributed network properties can be harnessed for non-binary computation.

This research attempts to solve the problem of traffic congestion at Bridge 3 Daet, Camarines Norte due to the constantly expanding percent of vehicles and poor traffic control. In proposing the development of a microscopic simulation model with VISSIM, the study seeks to simulate convergence and interaction between vehicles and infrastructure to detect traffic congestion, design efficient signalization periods, and design empirical algorithms for traffic flow. Possible impact of this study include; enhanced traffic flow knowledge, transport solutions and advisories on optimal management of traffic congestion. The combination of the qualitative surveys and the quantitative analysis of VISSIM simulation results offers a clear picture of the present day traffic problems and the remedial measures.

The integration of Artificial Intelligence (AI) into Human Resource Management (HRM) has transformed traditional practices, particularly in predicting employee retention and performance. This study explores the application of AI-driven analytics in HRM, emphasizing its potential to enhance decision-making processes related to workforce management. By leveraging machine learning algorithms and predictive modeling, organizations can analyze vast datasets encompassing employee demographics, engagement levels, performance metrics, and historical turnover patterns. This research highlights how AI tools can identify key factors influencing employee retention, thereby enabling HR professionals to implement targeted interventions to foster a more engaged and productive workforce. Furthermore, the study examines the implications of AI for performance assessment, demonstrating how data-driven insights can lead to more accurate evaluations and personalized development plans. The findings underscore the necessity for HR practitioners to embrace AI technologies to remain competitive in a rapidly evolving business landscape. Ultimately, this study contributes to the growing body of literature on AI in HRM, offering practical recommendations for integrating these tools into existing HR frameworks.

Background: Smartphone-based blood pressure (BP) monitoring using photoplethysmography (PPG) technology has emerged as a promising approach to empower users with self-monitoring for effective diagnosis and control of hypertension. Objective: This study aimed to develop a mobile personal health care system for noninvasive, pervasive, and continuous estimation of BP level and variability, which is user friendly for elderly people. Methods: The proposed approach was integrated by a self-designed cuffless, calibration-free, wireless, and wearable PPG-only sensor and a native purposely designed smartphone app using multilayer perceptron machine learning techniques from raw signals. We performed a development and usability study with three older adults (mean age 61.3 years, SD 1.5 years; 66% women) to test the usability and accuracy of the smartphone-based BP monitor. Results: The employed artificial neural network model had good average accuracy (>90%) and very strong correlation (>0.90) (P<.001) for predicting the reference BP values of our validation sample (n=150). Bland-Altman plots showed that most of the errors for BP prediction were less than 10 mmHg. However, according to the Association for the Advancement of Medical Instrumentation and British Hypertension Society standards, only diastolic blood pressure prediction met the clinically accepted accuracy thresholds. Conclusions: With further development and validation, the proposed system could provide a cost-effective strategy to improve the quality and coverage of health care, particularly in rural zones, areas lacking physicians, and areas with solitary elderly populations.

Artificial Intelligence (AI) shows the potential future of the health care industry. Understanding the factors that influence potential patients’ trust in incorporating AI into medicine will encourage not only health care providers, but also software development companies to invest in AI in medicine for better patient care as part of their business strategies. The goal of the current study is to gather feedback from individuals who reside in the United States or Thailand in order to gauge their level of confidence in AI in medicine. This descriptive study included 400 online survey responses. Half of the respondents live in the United States, while the other half live in Thailand. This study discovers that the quality (accuracy) of AI in medicine is the most important to the vast majority of participants (80.8%). Participants’ trust in AI in medicine grows as its quality (accuracy) improves. The majority of participants (67.1%) would likely trust AI in diagnosis and treatment if it were the only option available to them. The cost of AI is the least important factor in convincing the majority of participants (48%) to trust AI in medicine. The vast majority of participants (81%) thought AI doctor fees should be lower than traditional doctor fees. Nearly half of participants (47%) indicated that 90% to 100% accuracy is the accuracy level at which they would put their medical decisions in the hands of AI. The majority of participants (67.8%) would most likely use AI doctors for nonemergency situations for free in exchange for their health care data. There was a statistically significant difference between groups (the United States vs. Thailand) as determined by one-way ANOVA. Both groups show a positive attitude toward AI in medicine. The current study's findings may encourage AI technology providers to create affordable AI technology for health care providers in the near future.

Distributed Denial of Service (DDoS) attacks pose a severe threat to network infrastructures, causing downtime and significant financial losses. Machine learning (ML) algorithms have emerged as a promising approach for predicting and mitigating these attacks. This abstract explores the application of ML in tackling DDoS attacks, focusing on predictive modeling and mitigation strategies. Predictive modeling involves using historical attack data to train supervised learning algorithms such as Support Vector Machines (SVM), Random Forests, and Neural Networks. These models analyze network traffic patterns to detect anomalies indicative of potential DDoS attacks. Feature selection techniques enhance model accuracy by identifying critical indicators of attack behavior. Mitigation strategies leverage ML algorithms in real-time to distinguish between legitimate and malicious traffic during an attack. Anomaly detection algorithms like k-means clustering and Isolation Forests flag abnormal traffic patterns, triggering adaptive responses such as traffic rerouting or filtering through Intrusion Prevention Systems (IPS). Challenges include the dynamic nature of network traffic and the need for robust, scalable algorithms capable of processing vast datasets in real-time. In conclusion, ML algorithms offer effective tools for predicting and mitigating DDoS attacks by enhancing detection accuracy and response capabilities. Future advancements will focus on improving algorithm efficiency and resilience against evolving attack strategies.

The Protection of Human Beings in the Use of Artificial Intelligence from the Perspective of International Laws and the Convention on Human Rights

En el presente artículo se presentará un estudio sobre el reconocimiento de frutas nativas del Perú (Camu Camu, maracuyá, aguaje, aguaymanto, etc.). Utilizando técnicas de modelos de aprendizaje profundo y procesamiento de imágenes de manera conjunta para su correcto funcionamiento, el modelo de reconocimiento será puesto en práctica de manera supervisada. Además, se agregará información sobre el valor nutricional de cada fruta de manera cuidadosa con información verificada. Las propiedades nutricionales de cada fruta que se mostrarán al realizar el reconocimiento serán su fibra, la vitamina de cada fruta, sus minerales, y su porcentaje de proteínas. Toda esta información podrá ser utilizada por distintas personas en casos diferentes ya que está diseñada para ayudar a formar dietas equilibradas de acuerdo a la necesidad de cada uno.

This paper presents about “Weather Assistant” a web application utilizing speech recognition using a web browser (SRWB) which permits browsing or surfing the internet with the use of a standard voice-only and vocal user interface (VUL) development and using speech synthesis to act as a voice assistant in a web application. This web application is a software program that provides up-to-date weather information and forecasts for a particular location or region. This web application is designed for mobile devices, desktop computers. From an abstract view, our web application will have the features like displaying current weather conditions for the user's location, including temperature, humidity and wind speed with voice commands so we can say that this application can be regarded as a voice assistant in web application domain. It could also determine the weather conditions for up to a week from the current date in the particular location. Upon developing the web application we will implement displaying the satellite maps of the user's location, providing information on current weather patterns and potential weather events. We will make this web application to weather alerts. So this web application could send alerts and notifications to the user when severe weather is expected in their area, allowing them to take appropriate safety measures. And this application could provide access to historical weather data for the user's location, allowing them to see how weather patterns have changed over time. The user can use built in voice assistant to know the weather of certain location. This is all done by using open source Application Programming 2 Interface (API’s) that includes Open meteo API which provides weather information of a location and acts as a voice assistant using Web Speech API.s. The SRWB system operates by accepting user input in the form of vocal commands, which are then converted into HTTP requests. This process involves the use of an algorithm within the system. The primary objective of this algorithm is to accomplish various tasks related to web content. These tasks include classification, analysis, and extraction of significant information from web pages. Once these operations are completed, the system sends the identified important parts of the web pages back to the end-user. In summary, the SRWB system combines vocal command input, HTTP request conversion, and algorithmic processing to effectively handle web content for the user. This web application is deployed in Microsoft Azure so that this web application can be be accessed globally.

Accurate prediction of automobile prices is important for good decision-making by automobile market participants and business competition. This study presents a comparison of three popular regression methods (linear regression, random forest regression, and decision tree regression) used for traffic cost estimation. The model is trained and analyzed using a comprehensive database containing various features such as car make, model, year, mileage, engine size and other irrelevant factors. Thanks to detailed data analysis, model design and preliminary procedures, the data becomes ready for modeling. Our regression models are then implemented and improved using cross-validation techniques to improve their performance. Statistical measures such as mean error (MAE), mean square error (MSE), and R-squared were used to evaluate the prediction accuracy of each model. The results show that random forest regression outperforms linear regression and decision tree regression in terms of prediction accuracy. Random forest regression shows excellent performance in handling non-linearities, interactions between features, and outliers present in the dataset. Its conditions allow the decision tree to reduce its bounds, resulting in good predictions. Linear regression, although simple and interpretable, often performs poorly when faced with relationships between features and target variables. Although decision tree regression is capable of capturing interactions, it can suffer from overfitting and poor generalization. This study provides useful information on the advantages and limitations of different methods for estimating traffic costs. It provides practical advice to automotive industry participants on selecting appropriate regression models for accurate and reliable vehicle price prediction. In summary, this study enables the estimation of vehicle costs by comparing regression methods. Using these insights, stakeholders can make informed decisions that will ultimately improve pricing strategies and market competitiveness in the automotive industry.