Machine-Learning Techniques

TL;DR: In this paper, a local linear regression model is applied for the data fusion purpose in the Pontiac catchment, and four schemes according to temporal availabilities of the data sources are developed, which are pre-assessed and best selected by using the well-proven feature selection algorithm gamma test.

Abstract: Digital mental health interventions (DMHIs) have become increasingly prominent as scalable solutions to address global mental health needs. However, many existing tools lack the emotional sensitivity required to foster meaningful engagement and therapeutic effectiveness. Affective computing, a field focused on designing systems capable of detecting and responding to human emotions, offers promising advancements to the emotional responsiveness of these digital interventions. This narrative review examines how affective computing methods such as emotion recognition, sentiment analysis, emotion synthesis, and audiovisual and physiological signal processing, are being integrated into DMHIs to enhance user engagement and improve clinical outcomes. The findings suggest that emotionally adaptive systems can strengthen user engagement, simulate empathy, and support more personalized care. Early studies indicate potential benefits in terms of symptom reduction and user satisfaction, though clinical validation remains limited. Challenges such as algorithmic bias, privacy concerns, and the need for ethical design frameworks continue to shape the development of this emerging field. By synthesizing current trends, technological advancements, and ethical considerations, this review highlights the potential of affective computing in digital mental health and identifies key directions for future research and implementation.

TL;DR: This study evaluates CatBoost's performance in detecting DDoS attacks in SDN using Ryu controller, achieving high accuracy with KDD Cup 1999 and NSL-KDD datasets, and demonstrates the effectiveness of integrating machine learning into SDN controllers for improved network resilience.

TL;DR: This paper presents the results of a two-year study conducted at the Advanced Centre for Horticulture Research, Sher-e-Kashmir University of Agricultural Sciences & Technology of Jammu on how to identify the phytochemical properties of fruits and vegetables used in horticulture.

TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.

TL;DR: This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.

TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.