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.

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Steganography, the art and science of concealing messages within other data, has a rich history spanning centuries. In this review paper, we delve into various aspects of steganography, exploring its techniques, detection methods, evaluation criteria, and practical applications. We analyze steganalysis techniques, discuss time-sensitive steganography, and examine historical cases illustrating the ingenuity and effectiveness of covert communication methods. Through this comprehensive examination, we aim to provide insights into the evolving landscape of steganography and its significance in contemporary digital communication.

Along with growing AI development, it is important to consider both the benefits and risks that come with it. This research paper provides a deep analytical understanding of both aspects and their potential impact on humanity.

Design research topics attract exponentially more attention and consideration among researchers. This study is the first research article that endeavors to analyze selected design research publications using an advanced approach called “text mining”. This approach speculates its results depending on the existence of a research term (i.e., keywords), which can be more robust than other methods/approaches that rely on contextual data or authors’ perspectives. The main aim of this research paper is to expand knowledge and familiarity with design research and explore future research directions by addressing the gaps in the literature; relying on the literature review, it can be stated that the research area in the design domain still not built-up a theory, which can unify the field. In general, text mining with these features allows increased validity and generalization as compared to other approaches in the literature. We used a text mining technique to collect data and analyzed 3553 articles collected in 10 journals using 17,487 keywords. New topics were investigated in the domain of design concepts, which included attracting researchers, practitioners, and journal editorial boards. Such issues as co-innovation, ethical design, social practice design, conceptual thinking, collaborative design, creativity, and generative methods and tools were subject to additional research. On the other hand, researchers pursued topics such as collaborative design, human-centered design, interdisciplinary design, design education, participatory design, design practice, collaborative design, design development, collaboration, design theories, design administration, and service/product design areas. The key categories investigated and reported in this paper helped in determining what fields are flourishing and what fields are eroding

Improving the quality of digital health care through information and communication technology can mainly contribute to the clinical, social, fnancial, and economic systems’ success, especially during the COVID-19 pandemic period. The co-design approach, which unleashes the end-user power, can contribute actively in improving the healthcare systems. It deals with understanding the user behaviors, requirements, and motivations through observation, inspection, task analysis, and feedback techniques. Consequently, both the co-design and digital technologies might empower the management of patients’ health and that of their families. The research strategy is based on a systematic literature review and meta-analysis to summarize how the co-design methodologies handled the existing technology-based health systems for their improvement. Based on the findings, we establish the following hypotheses: (i) A user-centered methodology for service implementation might offer a promising tool to enhance the healthcare services quality before they be launched; (ii) Several limitations can affect the co-design approach in digital health, such as a bias for a patients’ group. Efforts have been made to reduce this risk by identifying bias at an early stage, or different groups should be included in the test phase for example; (iii) Use decision-making devices that handle technologies for patient and clinical healthcare solution