درخت حوزه‌های تخصصی

Background: Understanding how humans perceive and estimate distances in built environments is critical not only for advancing perceptual psychology but also for informing the design of computational models in computer vision, robotics, and architectural design. Aims: This study investigates the mechanisms and limitations of human distance estimation within a controlled architectural environment. Methodology: While some previous experiments focused on estimating distances in virtual settings, the current study examines real-world estimation accuracy across a series of predefined points within an unobstructed corridor. Participants were asked to visually estimate the distance between their position and seven distinct target locations, ranging from near to far without the aid of physical reference cues. The core objective was not simply to measure accuracy, but to identify the perceptual threshold beyond which estimation errors significantly increase. A one-way ANOVA model was employed to assess the influence of variables such as actual distance and participant age on perceptual accuracy. Results: Results revealed a consistent estimation performance up to approximately 2 m, beyond which the margin of error grew increasingly pronounced. Notably, a critical threshold was identified at 7.476 m, where estimation errors sharply escalated. The maximum observed discrepancy occurred at a distance of 10.186 m, suggesting a cognitive boundary in spatial awareness. Conclusion: These findings contribute to the understanding of visual-spatial perception mechanisms and offer theoretical insights relevant to applications in robotics, image processing, virtual reality, and navigation system design.

Background: Network security and penetration pose a significant challenge in the extensive IoT research of recent years. System security and user privacy demand security solutions that are carefully planned and diligently maintained. Aims: This paper introduces a novel three-stage hybrid IDS, IoT-APOSCA, leveraging machine learning and meta-heuristics for attack detection; stages include pre-processing, feature selection, and attack detection. The pre-processing steps are: cleaning, visualization, feature engineering, and vectorization. Methodology: Networks use Intrusion Detection Systems (IDSs) to monitor and detect malicious activities as a key security feature. The Arctic Puffin Optimization (APO) and Sine-Cosine Algorithm (SCA) are used in the feature selection stage, while a changed Random Neural Network (RNN) is employed in the attack detection stage. Results: The proposed technique is assessed using the DS2OS dataset, and the outcomes show that the approach, integrating multiple learning models, led to an accuracy enhancement to 99.66%. Also, the values Recall and False Alarm Rate obtained are equal to 0.9926 and 0.003, respectively. Conclusion: Intrusion detection system efficacy is directly tied to the quality of its classification method. Enhanced neural network performance is achievable through adjustments to parameters, such as network weights.

Background: Cyberspace is understood in this paper as a place for possible moral and immoral conducts, just as it is the case with physical societies across the world. However, the Nigerian cyberspace has, in recent times, experienced increase in moral decadence, with young people engaging in acts of vices, including disrespect, fraud, and phishing among other cybercrimes. This set of behaviours negates the values expected of a well-ordered and well-meaning cyberspace, all of which threaten physical societal cohesion and ethical standards. Aims: This paper examines Omolúàbí ethos, a traditional embodiment of moral principle of the Yoruba people, as a remedial framework. The Omolúàbí ethos places virtues such as good character, respectful conduct, gentleness, honesty, trust, humanness, among others, as a necessity for building a responsible society, even in the cyberspace. Methodology: Being philosophical research, the paper adopts qualitative method to unpack the relevance of ancient Yoruba proverbs and ethical practices to contemporary online engagement. Findings: The paper argues that if the Omolúàbí ethos is imbued into cyberspace culture, through the medium of philosophical counseling in collaboration with the National Orientation Agency, it could help ensure a more suitable online environment where respect, dignity, and virtue are paramount. Conclusion: This paper contributes to scholarship by positioning an indigenous ethical framework for addressing contemporary cyberspace’s ethical challenges.

Background: Economic misinformation may result in people making poor financial choices. Multi-level marketing organizations often target women as potential salespeople. The majority of multi-level marketing organizations are predatory. Oftentimes, their online promotion effectively reaches people. Aims: In order to better identify the differences in color used in online advertisements for multi-level marketing organizations, I chose to explore whether health multi-level marketing organization advertisements had colors that corresponded with traditional gender norms or traditional health advertisements. Methodology: I obtained and analyzed exclusively online advertisements for multi-level marketing organizations. I found that there was indeed a difference in both color and brightness between organizations that targeted men and organizations that targeted women. Results: Health multi-level marketing organizations pose a unique danger. Specifically, they promote both economic and health misinformation. Not only do these organizations recruit people to sell products using spurious techniques, but in many cases, these products may be directly harmful to consumers’ health. As a result, more insight into detecting a health multi-level marketing advertisement is necessary. Conclusion: Color is a major element in advertisements. Specifically, distinct colors may be used in advertisements that target different genders. Understanding the differences in multi-level marketing advertisement colors is important: if consumers can identify which organizations are multi-level marketing companies, they will distrust the advertisements.

Background: This study explores the paradoxical role of artificial intelligence as both a tool of exploitation within surveillance capitalism and a force for resilience in disaster risk management, highlighting the ethical and governance challenges that arise at the intersection of these domains. Aims: This article aims to enable the ethical use of AI in DRM while insulating public systems from the structural harms of commercial data exploitation. Methodology: Drawing from a comparative qualitative analysis of 35 academic sources, the study investigates how BigTech corporations and data brokers leverage AI to commodify personal data, consolidate informational power, and erode democratic agency. Results: The present study critically examines the dual role of artificial intelligence in contemporary digital society, contrasting its exploitative deployment within surveillance capitalism with its constructive use in disaster risk management (DRM). Simultaneously, it highlights a parallel body of research showcasing AI’s capacity to enhance early warning systems, situational awareness, and post-disaster recovery, especially in resource-constrained and climate-vulnerable regions. Conclusion: To reconcile these conflicting trajectories, the article proposes the Public AI for Resilience (PAIR) framework—a governance model grounded in data sovereignty, public infrastructure, algorithmic explainability, and cross-sectoral collaboration. Ultimately, the article argues for a normative recalibration of AI governance that prioritizes equity, transparency, and collective resilience over market imperatives, demonstrating that AI’s public good potential can be realized without surrendering to surveillance-based capitalism.

Background: Since the advent of modern policing, technological innovations in communication and information management have significantly shaped investigative practices and crime detection strategies. Aims: The current research study explores the transformative role of Artificial Intelligence in modern crime detection and prevention across diverse domains including cybercrime, environmental crime, financial fraud, and urban surveillance. Methodology: Employing a qualitative meta-synthesis methodology, the research critically examines peer-reviewed literature published between the years 2015 and 2025 to identify emerging trends, technological innovations, and socio-legal implications associated with AI-driven policing. Findings: The key findings highlight the integration of machine learning, computer vision, and natural language processing techniques into the predictive and real-time law enforcement systems. These technologies have demonstrably enhanced the accuracy, efficiency, and responsiveness of crime prevention strategies. However, the current study also reveals significant challenges, including algorithmic bias, lack of transparency, and inadequate regulatory oversight, particularly in socially stratified or underregulated contexts. Conclusion: The current article underscores the necessity of embedding explainability, accountability, and human oversight into Artificial Intelligence systems to ensure the ethical and equitable implementation of AI-driven policing systems.