Brahim El Bhiri

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It is with great pleasure that I present this preface to the proceedings of the 7th International Conference on Advanced Technologies for Humanity (ICATH 2025), held from 9 to 11 July 2025, at the National School of Applied Sciences (ENSA), Ibn Tofaïl University, Kenitra, Morocco

Cybersecurity has evolved significantly over the years, with a growing interest in biologically inspired models that emulate the immune system’s defense mechanisms. This paper provides a comparative analysis of various immunity-based approaches in cybersecurity, tracking their progression from their inception to the present. It explores the strengths and limitations of these methods across different cybersecurity areas, such as intrusion detection, malware analysis, and network protection. By reviewing foundational research, recent advancements, and existing challenges, this study aims to offer a well-rounded perspective on the effectiveness and constraints of immunity-driven strategies in protecting modern digital infrastructure. Additionally, it highlights emerging trends and future directions, stressing the importance of integrating these approaches with machine learning and other advanced technologies to strengthen cybersecurity resilience.

Traditional relational databases have been widely used for many years and have been the go-to choice for the industry. However, the emergence of NoSQL databases coincided with the increasing popularity of the Internet, social networks, and cloud computing. Data migration has become a crucial topic, due to the rapid growth of applications and the ever-expanding amount of data being collected. This has led to a shift from relational database management systems (RDBMS) to NoSQL databases, also known as not only SQL. This article compares the characteristics of both database architectures, SQL and NoSQL, focusing on aspects such as scalability and performance, flexibility, and security. Additionally, the role of AI in streamlining the migration process will be explored.

The widespread integration of low-power electronic devices in IoT, biomedical, and sensing applications has intensified the demand for energy-autonomous solutions. Radio Frequency Energy Harvesting (RFEH) offers a promising alternative by leveraging ambient RF signals available in both indoor and outdoor environments. Despite its conceptual appeal, practical deployment still faces major challenges. This systematic literature review (SLR) examines 25 recent studies, following the PRISMA methodology, to provide a comprehensive overview of current RFEH architectures. It focuses on three critical components: receiving antennas, impedance matching circuits (IMCs), and RF-to-DC rectifiers. Design strategies are reviewed and compared across antenna types, matching techniques, and rectifier configurations. The review also highlights persistent challenges and outlines directions for the development of compact, efficient, and robust energy-harvesting systems for next-generation wireless technologies.

With the rapid growth of LLM-based chatbots and their applications in fields such as health, education, and entertainment, there is a growing interest in developing systems capable of mimicking human behavior through conversation and natural language interaction. These chatbots are available in several languages, such as English, French, and Spanish. Unfortunately, Arabic chatbots—especially those that understand Arabic dialects—are still very limited. In this paper, we develop a chatbot for the Moroccan Arabic dialect, specifically designed for the public sector, such as the fiscal domain and government administration. These institutions require tools to reduce communication loads, limit human assistance, and minimize the time needed to find documents or complete payment procedures. Our optimized chatbot combines recent technologies like LLMs and semantic similarity. It supports Moroccan citizens by providing responses in the Moroccan dialect (Darija), both in text and speech, without requiring extensive resources. It also supports other citizens in French, Spanish, and English. Our chatbot was tested in a real use case in the tax domain, and the results were satisfactory, especially considering the general complexity of the Arabic language and the particular challenges of the Moroccan dialect.

The review “Symbolic Approaches for Explainable Artificial Intelligence” discusses the potential of symbolic AI to improve transparency, contrasting it with opaque deep learning systems. Though connectionist models perform well, their poor interpretability means that they are of concern for bias and trust in high-stakes fields such as healthcare and finance. The authors integrate symbolic AI methods—rule-based reasoning, ontologies, and expert systems—with neuro-symbolic integrations (e.g., DeepProbLog). This paper covers topics such as scalability and integrating knowledge, proposing solutions like dynamic ontologies. The survey concludes by advocating for hybrid AI approaches and interdisciplinary collaboration to reconcile technical innovation with ethical and regulatory demands.

Accumulation of dust on PV panels is a big challenge, especially in dry and semi-arid environments like Morocco, where the number of dust particles in the atmosphere diminishes the efficiency of solar panels severely. The review analyzes 30 recent studies, which provide insight into performance degradation by dust, as well as the search for solutions that mitigate this effect. Results show that dust reduced solar panel efficiency by between 10% and 40% based on environmental conditions, including dust density, composition, and length of exposure. Many technological approaches have been provided for the problem, including autonomous cleaning systems and advanced coatings, yet economic and scalability barriers are still in existence. Also, using AI in predictive maintenance provides good opportunities to optimize solar panel cleaning schedules to enhance energy production. This review concludes with the observation that, going forward, more research on long-term solutions and the development of sustainable and cost-effective cleaning technologies is urgently needed in order to better exploit solar energy in dusty environments.