Journal of Al-Farahidis Arts

This research aims to examine the impact of geomorphological phenomena on the expansion of Tuz Khurmatu city by employing spatial analysis tools and modern techniques to predict future changes in the city’s spatial structure. The study reflects a trend towards applied geomorphology, which goes beyond merely understanding landforms and their distribution, to analyzing their actual and direct influence on human processes, particularly urban expansion.
Tuz Khurmatu is characterized by diverse terrain, where units such as floodplains, hilly uplands, and valleys overlap. This makes geomorphological phenomena one of the main determinants in shaping the pattern of spatial expansion. Factors such as slope, elevation, and natural water drainage directly influence the selection of construction sites, guide expansion, and either restrict or accelerate urban development.
The study relies on Geographic Information Systems (GIS) and Remote Sensing (RS) techniques to establish an accurate spatial database, which is then utilized in spatial prediction models such as the CA–Markov model or Suitability Analysis tool. These models help in monitoring past changes, analyzing dominant geomorphological factors, and constructing future spatial scenarios of urban expansion

The study aims to develop cartographic models to analyze and interpret the spatiotemporal patterns of crime (homicide, theft) and to predict their occurrence in Salah al-Din Governorate during the period 2015–2024. Spatial analysis tools within a Geographic Information Systems (GIS) environment were employed, including Time Series Clustering (Machine Learning), Emerging Hot Spot Analysis, and Forecast Space-Time Cube. Artificial Intelligence (AI) algorithms were used to construct accurate predictive models for the future distribution of crime by transforming criminal data into spatiotemporal cubes and subsequently forecasting changes in crime hotspots. This study provides a comprehensive scientific perspective for understanding crime dynamics and contributes to supporting security agencies in the study area by providing analytical and predictive maps that can inform the development of effective security policies based on precise scientific methods for the spatiotemporal monitoring and prediction of criminal activities

This study examined the geographical and military dimensions of the Hamrin Hills in Iraq by analyzing their geomorphological and climatic characteristics and their impact on combat operations. The results showed that the steep slopes, deep valleys, and rocky outcrops present natural obstacles to the movement of regular forces. Simultaneously, they provide strategic refuge for armed groups, who exploit them for fortifications, tunnel construction, and camps. The climatic factors (temperature, rainfall, wind, and humidity) also emerged as a multiplier in hindering or facilitating movement, directly affecting the effectiveness of soldiers and equipment. The study relied on the analysis of statistical tables (slope, depth of land and valleys, water network density, and movement) and linked them to military field interpretations. Spatial maps were also produced, illustrating strategic corridors, obstacle zones, and dominant peaks. The results showed that approximately half of the area falls within the medium to high obstacle categories, necessitating flexible field plans based on rapid-response units supported by aerial and engineering reconnaissance.

This study aims to analyze and classify the street network in the city of Ramadi using two integrated approaches: morphological classification, which focuses on form and structure, and functional classification, which emphasizes street usage and its role within the urban fabric. The research is based on 2022 spatial data and utilizes Geographic Information Systems (GIS) to analyze the characteristics of the network and its spatial and functional distribution. The results showed that local streets make up the largest portion of the total network (68.43%), while main streets account for only 8.12%, indicating a functional distribution imbalance. The study also revealed weak coordination between urban design and actual street usage, negatively impacting urban transport efficiency and both mobility and accessibility. The study recommends the reclassification of streets based on scientific foundations that integrate both morphological and functional dimensions, and it highlights the importance of enhancing the use of GIS as a supportive tool for sustainable urban planning

Smart cartographic representation of agricultural activities in Al-Najaf Al-Ashraf Governorate is considered an advanced analytical tool that contributes to accurately understanding the agricultural reality and guiding sustainable development plans in a scientific manner. This study aims to highlight the role of geographic maps, particularly those enhanced by artificial intelligence techniques, in analyzing and distributing agricultural activities, especially plant production in its various types.
The findings of the study revealed spatial variation in the levels of productive (physiological) density among the administrative units within the governorate. The highest density was recorded in the center of Al-Kufa District, reaching (3272) persons/km², reflecting a high level of resource utilization in that area. In contrast, Al-Hurriya Subdistrict emerged as a significant rural agricultural region, with a density of (1819) persons/km², indicating the heavy reliance of its inhabitants on agriculture as a primary source of livelihood.

This study seeks to explore the current state of artificial intelligence (AI) applications in the field of applied geography in Libya, within the context of the developmental and spatial challenges facing the country. It emerges from the pressing need to adopt modern technological tools capable of supporting decision-makers and directing spatial development toward greater efficiency and sustainability. The significance of the research lies in its treatment of an advanced intersection between geography—as an analytical and spatial science—and artificial intelligence—as a revolutionary technology that offers unprecedented capabilities in processing spatial data, monitoring geographic phenomena, and providing accurate predictive models across various domains such as urban planning, resource management, and environmental change tracking.
The study adopts a descriptive-analytical methodology, based on a review of relevant literature and expert interviews with professionals in artificial intelligence and geographic information systems. This approach aims to understand the depth of current AI applications and assess the readiness of Libyan institutions to adopt such advanced technologies.
The findings reveal that the use of AI in applied geography in Libya remains limited, mostly confined to individual research initiatives or academic projects, and has yet to be integrated into sustainable institutional frameworks or national policy-making structures

Recent years have witnessed a radical shift in the nature of social interactions and population distribution as a result of the use of artificial intelligence technologies, particularly in culturally diverse urban environments such as Kirkuk. This research aims to analyze the impact of artificial intelligence on social interactions and population structure from a human geography perspective, based on a field study in the city of Kirkuk. Based on a social geography approach that combines quantitative and qualitative analysis, it is conducted in the field using a structured questionnaire and semi-directed interviews. A purposeful non-random sample of 120 participants from different areas of Kirkuk city was selected. Youth, employees, university students, and digital activists represent the groups most engaged with technology. Geographic analysis tools were also used to identify and determine the spatial distribution of digital activities related to social interaction within the city of Kirkuk. The study focused on understanding how smart algorithms such as content recommendations, spatial guidance, and smart services reshape social space, and demonstrating the extent of their impact on patterns of social convergence and distancing within The study also analyzes the relationship between the extensive use of smart technologies and shifts in demographic geography, whether in terms of mobility, settlement,

Iraq enjoys strategic importance distinct politically and economically as it contains the advantages of location and location in addition to the available resources of hydrocarbons available, which made this country a point of conflict for global forces aimed at dominating the oil wealth, as the importance of this study through the comprehensive geographical view of the situation of Iraqi youth and repercussions The political crisis that he is going through in the light of political and economic variables, which made him suffer from a weak point in political decision-making towards the youth group, which embodies a human force can be invested in many areas gives the state strength and vitality Despite the presence of a foreign agenda is working Hadfha to prevent political instability has. Is the role of the Iraqi government in containing the situation of Iraqi youth, and whether oil plays a role in the fabrication of the migration crisis of Iraqi youth, and whether the political events and regional and international changes in the Middle East is a real reflection on the deterioration of the situation of young people in Iraq, The scientific hypotheses as a geopolitical assessment of the problem of scientific research is that Iraq must recognize that it is a regional power, its strategic food and national will, which helps to reach and contribute to political decision-making, contain these human energies and create real opportunities for investment. A, Despite the continued aggravation of the Iraqi youth crisis under the policies that are based on unilateralism in the resolution and thus weakened the role of Iraq regionally and internationally geographical perspective, and continue to decline geopolitical work to break the social fabric and social exclusion in it and then strengthen the role of the external agenda in the country's march towards the abyss A thousand young people have been forced to emigrate from the perspective of contemporary geopolitics.

The study aims to model and analyze environmental degradation maps in Salah Al-Din Governorate using artificial intelligence techniques by examining the relationship between Land Surface Temperature (LST), the Normalized Difference Vegetation Index (NDVI), and the Standardized Precipitation Index (SPI) derived from satellite data. The research utilized Landsat-8 imagery for the period (2014–2024), with all processing and analysis conducted on the Google Earth Engine (GEE) platform. Artificial intelligence algorithms were applied to predict environmental degradation maps for the year 2025 and to classify environmentally affected areas based on the inverse relationship among the indices. High LST values coupled with low NDVI and SPI values were found to indicate zones of severe vegetation degradation. The results revealed a strong negative correlation between LST, SPI, and NDVI, highlighting the impact of thermal stress on vegetation cover degradation. Moreover, the intelligent models proved highly effective in accurately identifying degradation hotspots, demonstrating their potential as a powerful tool for detecting environmentally degraded areas in the study region

This study aims to study and assess the feasibility of modeling climatic factors' kinetic maps for Iraq's southern states based on Geographic Information Systems (GIS) as a starting point for preparing dynamic maps capable of depicting temporal and spatial changes. This study focuses on major climatic variables, solar radiation, relative humidity, and evaporation rates, because they have direct implications on the environment and spatial planning. It employs climatic information from 1989 to 2024 and develops dynamic map models in color gradients and isopleths to delineate climate change dynamics. The output reflects a direct increase in solar radiation and evaporation rates with a gradual decrease in relative humidity, reflecting the impact of climate change in the region under study. Kinetic maps were also proven capable of revealing temporal patterns in climatic changes and detecting impacted zones with precision at high spatial resolution. In terms of design, the study identified a variety of cartographic and perceptual issues faced by the map maker, most notably the difficulty of selecting line weights, colour overlap, and scale and visual contrast issues. The study concludes that kinetic modeling is an advanced approach to examining phenomena and an effective method of helping decision-makers plan the environment, especially during drought hazards and thermal transformation.

This study aims to analyze the spatial distribution of environmental pollution in the Erbil Plain using remote sensing data and Geospatial Artificial Intelligence (GeoAI) techniques. Satellite imagery from Landsat and Sentinel was utilized to calculate key environmental indicators representing different ecosystem components, including the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), and Normalized Difference Built-up Index (NDBI), as well as Sentinel-5P data for air pollutants such as sulfur dioxide (SO₂). Several GeoAI and spatial analysis methods were applied, including the Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN) algorithms, to classify pollution-affected areas and predict future patterns. The results revealed that integrating remote sensing with GeoAI significantly enhanced the accuracy of spatial pollution mapping, identifying high-pollution zones concentrated within urban and industrial centers of Erbil, whereas rural and vegetated areas showed lower pollution levels. The study recommends adopting smart environmental monitoring systems based on GeoAI technologies to support sustainable resource management and evidence-based environmental planning.

The contemporary world is witnessing an unprecedented evolution in artificial intelligence (AI) and its diverse applications, which have extended to the field of historical studies and the analysis of historical sources. This paper explores the potential of AI technologies in reading and analyzing historical texts, particularly primary sources and ancient manuscripts. It highlights the capabilities of machine learning and natural language processing in extracting meanings, identifying historical patterns, and revealing deep contextual links. The study also addresses the main challenges of this integration, such as reliability, interpretation, and the cultural limitations of AI understanding. Ultimately, the paper offers a balanced critical perspective on the effectiveness of AI in supporting historical research, while preserving the unique value of human academic work

With the acceleration of technological progress in the twenty-first century,artificial intelligence has become one of the most prominent drivers reshaping various fields of knowledge, including history, Thanks to data analysis and machine learning tools
methods for accessing sources, analyzing documents, and even writing historical narratives have changed, Historians are no longer dependent solely on traditional methods, but are now able to leverage intelligent algorithms that allow them to gain deeper insights into historical patterns and process vast amounts of information in record time, The profession of historian is one of
the most challenging in the field of scientific research, especially if it encounters a scarcity of historical knowledge sources or obstacles to accessing them. In light of the transformations humanity has witnessed in the modern and contemporary era, characterized by abundant intellectual production and the digital scientific revolution, this topic aims to examine the impact of technology on the profession of historian. It also explores the advantages that digitization offers to historical researchers, including the electronic availability of historical sources in various forms,and demonstrates its role in facilitating access to information sources. It also discusses the possibility of employing modern technologies to combat the phenomenon of plagiarism and to plagiarism in the field of historical studies. It also opens the door examining the potential positive and negative impacts of artificial intelligence on the profession of historian in the foreseeable future, This study seeks to analyze how artificial intelligence has impacted historical research methodology by reviewing the tools used demonstrating the transformations that have occurred in the mechanisms of information collection and analysis

significantly reshaped the landscape of human and social sciences, with historical studies being no exception. AI tools have introduced novel opportunities for historians and researchers to analyze, interpret, and preserve historical sources in unprecedented ways. Techniques such as Natural Language Processing (NLP) enable the systematic examination of vast textual corpora, allowing the extraction of entities, events, and temporal patterns from historical narratives. Similarly, Optical Character Recognition (OCR) technologies have revolutionized the digitization of ancient manuscripts, transforming fragile documents into searchable and analyzable datasets

This research analyzes the risks of artificial intelligence (AI) to society, focusing on the social, economic, cultural, and security threats posed by the rapid spread of AI technologies. Despite the significant benefits these technologies offer, their dark side poses major challenges to societal stability, human jobs, cultural identity, and cybersecurity. This research aims to provide a balanced, critical perspective that identifies the sources of risk, while proposing future visions for responsible engagement with AI

This research aims to examine the impact of artificial intelligence (AI) on the writing and study of modern history in Egypt, by analyzing the tools and algorithms used and identifying the benefits and challenges facing historians in this context. The research focuses on how AI techniques, such as historical text analysis, document classification, and pattern extraction, can be employed to reread and analyze events in 19th and 20th century Egyptian history. The research also discusses the methodological and cognitive impact of this technology on the profession of historian and academic research in Egypt

This study explores the evolving relationship between artificial intelligence and the humanities through the lens of Applied Behavior Analysis (ABA). It examines how AI is transforming humanistic inquiry, research methods, and knowledge production, particularly in literature, philosophy, sociology, and psychology. The paper highlights AI's capacity to analyze texts, interpret human behavior, and offer modern solutions to traditional challenges. However, it also emphasizes the need to preserve humanistic and ethical values at the core of these disciplines.
Keywords: Humanities - Artificial Intelligence - Ethical Challenges - Future Visions

This work investigates the role of the Facebook page "The Checker" in detecting fake content produced using artificial intelligence techniques. This type of content has become increasingly prevalent in recent years due to the ease of sharing artificially generated or manipulated images and videos. The study focused on monitoring the content published by the page between January 1, 2023, and December 29, 2024, to identify the nature of the misleading news that was verified and the methods the Checker team used to detect it.
The researcher employed a descriptive-analytical approach, analyzing a comprehensive sample of 20 pieces of fake content. These were categorized according to their type, format, reach, and degree of manipulation. The results showed that political content was the most prevalent, followed by social and religious content. The text-to-image format was the most widely shared among users. Furthermore, the majority of the verified news items were international in nature, and most fell under the category of total fabrication, while deepfakes constituted the largest proportion of manipulation cases.
The results showed that the investigation team relied primarily on digital verification tools and direct communication with sources to refute claims, and that social media was the primary source of the spread of fake content. The study also demonstrated that the platform effectively contributes to reducing the spread of rumors by providing comparative evidence and clarifying the true contexts of posts

Artificial intelligence (AI) has provided vast capabilities for news platforms through tools such as automated news verification, audience behavior analysis, content personalization tailored to audience interests, and enhanced transparency in data and source presentation. These applications have contributed to increased credibility and strengthened the relationship between journalists and the public. However, the growing reliance on algorithms also raises questions about the limits of professionalism and impartiality, and the potential for data bias or misuse for commercial or political purposes.
This underscores the importance of studying the impact of AI on building digital trust between news platforms and the public, particularly within the Arab media landscape, and specifically in Iraq, where technological challenges intersect with social, political, and economic conditions. Research in this area not only clarifies the dimensions of the relationship between technology and trust but also helps formulate future visions for the application of AI.

The current research aims to determine the role of Iraqi digital media in raising public awareness about cybercrime techniques. A field survey methodology was employed, with the research population comprised of Iraqi citizens exposed to digital media, representing diverse demographics and educational backgrounds, and who are interested in the topic of cybercrime. The researcher used a questionnaire, and the study concluded the following:
1. Iraqis exhibit a high level of caution regarding cybercrime. They believe that immediately reporting to security authorities upon becoming a victim is the best course of action. They also support the development of a highly secure and reliable national internet browser to prevent cybercrime.
2. Telephone communications and social media platforms provide fertile ground for cybercrime in Iraq due to Iraqis' primary reliance on smartphones and their high level of social media use.

As a Semitic language, Arabic is well known for its long-standing derivational system, where one root can make a lot of words in many different contexts, but with the same basic meaning. This work tackles how words from verbs of Quranic chapter Al-Baqara, have added to the richness of the Quran language. It shows how these words are formed and how they carry a wider theological, moral and social meaning. The work also compares these Arabic words with their English equivalents based on English translations available on quran.com. The research adopts the translation of Muhsin Khan and Hilali of Quran in (https://quranenc.com/en/browse/english_hilali_khan/2) as a model to study the translated texts.
The unique feature of this work is the inclusion of AI images made through ChatGPT which visually portray the meaning of the selected derived nouns. These pictures help a new way to teach students, especially people who are not native Arabic speakers, to understand the difficult language of the Quran. The study shows that most of the derived words here are from the al-ishtiqāq al-ṣaghīr (الاشزمبق اصٌغيس ) , which means small derivation. In this type, the root letters stay the same, but they go into different word forms to give new meanings. This research uses the old methods of language study together with new digital tools. It makes a mixed way to read and explain the Quran. The goal is to help students, translators,

This article analyses how Simon McBurney’s The Encounter rethinks the experience of theatre by deploying artificial intelligence, binaural sound technology and 3D audible narrative to produce  a post-human form of subjectivity. The analysis borrows from post-humanist theory and media philosophy to explore how  the production deconstructs conventional binaries of human/machine, real/virtual and presence/mediation. The  main focus is how the play's sensory displacement and AI-facilitated narrative disrupt conventional anthropocentric models of consciousness and theatrical embodiment. Key research questions include:  How does the play use AI and spatial sound to disrupt audience expectations of space and time? What subjectivities are engendered when mediated technology is at the heart of  live performance? And how can the play itself perform a post-human  subjectivity that goes beyond the stable, unified self? Methodologically, the article uses a close performance analysis of The Encounter with theoretical readings of post-humanism (Hayles, Braidotti), affect theory (Massumi) and media ecology (Hansen). The study's results imply that the play turns the observer from a passive spectator into an embodied  system interface which mediates data blurring the sensory boundary between memory, fiction, and algorithmic presence. In the process, the production not only rewrites  the ontological parameters of theatrical reality but also places the audience in a distributed, post-human consciousness

With the fast-growing of neural machine translation (NMT), there is still a lack of insight into the performance of these models on semantically and culturally rich texts, especially between linguistically distant languages like Arabic and English. In this paper, we investigate the performance of two state-of-the-art AI translation systems (ChatGPT, DeepSeek) when translating Arabic texts to English in three different genres: journalistic, literary, and technical. The study utilizes a mixed-method evaluation methodology based on a balanced corpus of 60 Arabic source texts from the three genres. Objective measures, including BLEU and TER, and subjective evaluations from human translators were employed to determine the semantic, contextual and cultural quality. Our results show that our model, ChatGPT, consistently achieves performance gains over DeepSeek, especially when applied to technical and journalistic text and with higher BLEU scores and lower TER values. But neither these models nor any of the state-of-the-art models perform well for the literary texts, the ones that can hint to the difficulties these models face to deal with idiomatic expressions, metaphor, narrative tone. The results illustrate genre sensitivity in AI translation quality and emphasize the ongoing importance of human supervision, particularly in cultural and stylistic contexts. This work aims to contribute to the growing corpus of AI translation literature by providing a genre-specific, empirically grounded comparison of two of the most high-profile models, and to draw attention to the necessity of greater context-sensitive and culturally sensitive translation algorithms

The rapid Advances in digital and technological revolution has led to the emergence of Artificial Intelligence (hereafter AI) as an effective tool to disseminate knowledge at all levels, more specifically the religious. However, there are concerns about this tool as it can be challenging or promising in religious translation. Based on Reed’s (2021) proposition that there is an interconnectedness between AI and religion, this paper contributes to the existing literature on these two domains. It aims to examine the differences between AI and human translation, focusing on the prospects and pitfalls of the former. To achieve this goal, five Hadiths from the Arabic version of Riyad-us-Saliheen have been selected as the data of this paper. They have been already translated by Muhammad Amin and Abu Usamah Al-Arabi bin Razduq. The findings revealed that despite the promising performance of AI, there are still concerns related to translation ineligibility and infidelity