As cyber-attacks rapidly evolve across communication, infrastructure and data layers, traditional security solutions such as rule-based intrusion detection systems (IDS) or signature-based antivirus programs are effective at detecting known threats, but they often lack the contextual understanding and semantic interpretation necessary to detect complex or evolving attacks. For example, spear-phishing campaigns, advanced persistent threats (APTs), and multi-stage attacks often escape detection due to their subtle and context-dependent nature. This limitation creates a critical gap in detecting coordinated or subtle attack patterns that span multiple systems and domains. The need for semantic understanding, cross-domain visibility, and adaptive detection is increasingly urgent, particularly as threat actors employ polymorphic and AI-driven strategies that traditional systems cannot interpret or correlate effectively. This paper presents a modular multi-agent architecture that integrates established cybersecurity analysis tools with large language models (LLMs) to achieve intelligent, explicable and highly accurate detection of threats across diverse data types. Three specialized agents: (1) email verification, (2) log analysis, and (3) IP address scanning each operate independently with tailored detection pipelines that combine domain-specific tools and LLM-powered semantic analysis components to identify, characterize, and report threats specific to their domain. At the core of the system lies a contextual recommendation system that processes and cross-analyzes the outputs of all specialized agents to detect complex threat patterns such as multi-vector, time-based, or stealth attacks that would otherwise evade isolated detection mechanisms. The evaluation on benchmark datasets, including CIC-IDS 2017, SpamAssassin, and custom simulated network environments, demonstrates threat detection accuracy of 93.6%, multi-agent correlation accuracy of 87%, and false positive reduction of 41.3% compared to traditional approaches. The use of LLMs for both structured explanations and chain-of-thought reporting further enhances analyst confidence and reduces triage time.

Attention Deficit Hyperactivity Disorder (ADHD) is a disorder that starts in childhood, sometimes persisting into adulthood. It puts a strain on their social, professional, family, and environmental lives, which can exacerbate disorders such as anxiety, depression, and bipolar disorder. Background/Objectives: This paper aims to predict ADHD in children and adults and explain the main factors impacting this disorder. Methods: We start by introducing the main symptoms and challenges ADHD poses for children and adults such as epilepsy and depression. Then, we present the results of existing research on three ADHD comorbidities: anxiety, depression, and bipolar disorder, and their possible continuity in adulthood with therapeutic implications. After that, we explain the impact of this disorder and its relationship with these comorbidities on the affected patient’s health and environment and list proposed treatments. We propose a methodology for predicting this impairment in children and adults by using Machine Learning algorithms (ML), Explainable Artificial Intelligence (XAI), and two datasets, the National Survey for Children’s Health (NSCH) (2022) for the children and the ADHD|Mental Health for the adults. Results: Logistic Regression (LR) was the most suitable algorithm for children, with an accuracy of 99%. As for adults, the XGBoost (XGB) was the most performant ML method, with an accuracy of 100%. Conclusions: Lack of sleep and excessive smiling/laughing are among the factors having an impact on ADHD for children, whereas anxiety and depression affect ADHD adults.

Background: A child with Attention-Deficit Hyperactivity Disorder (ADHD) faces two issues: inattention and hyperactivity/impulsivity. These two symptoms make the child’s life more challenging compared to non-ADHD individuals. Therefore, one of the steps toward better quality of life involves cooperation with and contact with the environment to better address this condition. Thanks to Artificial Intelligence (AI), doctors, caregivers, and parents are increasingly better able to understand the hardships these children face. One AI technique is Reinforcement Learning (RL). Methods: We propose an RL model simulation with 44 child agents with or without ADHD, using the Independent Deep Q Network (IDQN), Value Decomposition Network (VDN), and QMIX algorithms. Results: Comparing the results obtained with these three algorithms, children with ADHD find it more challenging to choose the maximum rewards than neurotypical children (395 at episode 300 for non-ADHD compared to 340 at episode 120 for ADHD using IDQN, 69 from episode 90 for ADHD compared to 82 for non-ADHD via VDN, and 31 at episode 110 for ADHD versus 28 at episode 110 for non-ADHD with QMIX). Conclusions: The simulated ADHD agents struggle to aim for the maximum rewards as much as neurotypical children.

Access control systems rely increasingly on multimodal biometric and behavioral signals to enhance security and robustness against sophisticated attacks. However, when heterogeneous modalities provide conflicting evidence, such as valid biometric credentials accompanied by abnormal behavioral or acoustic patterns, traditional fusion strategies based on static thresholds or majority voting often fail, leading to false alarms or insecure authorization decisions. This paper addresses this critical limitation by proposing a contextual decision-making fusion framework designed to resolve conflicting multimodal evidence at the decision-making level. The proposed approach models access control as a decision-making problem in a context of uncertainty, where independent agents generate modality-specific evidence from authentication channels based on face, voice, and fingerprints. A centralized fusion mechanism integrates heterogeneous results using adaptive reliability weighting and contextual reasoning to resolve conflicts before operational decisions are made. Rather than treating each modality independently, the framework explicitly considers inconsistencies, uncertainties, and situational context when aggregating evidence. The framework is evaluated using public benchmarks, including VGGFace2, VoxCeleb2, and FVC2004, combined with controlled multimodal scenarios that induce conflicting evidence. Experimental results obtained under controlled contradiction scenarios show that the proposed fusion strategy reduces false alarms and improves decision consistency by approximately 18%. These results are interpreted within the scope of controlled multimodal simulations.