Computer Engineering Commons^™

Comment La Digitalisation Affecte-T-Elle Les Interactions Sociales, Culturelles Et Économiques Au Maroc ?, Redouane El Majdouli

Dirassat

This study explores the multidimensional impact of digitalization on social, cultural, and economic interactions in Morocco. By leveraging modernization and social network theories, the analysis reveals how digital technologies transform social dynamics, facilitate innovation, and pose challenges in terms of access inequalities and privacy protection. Using a mixed- method approach combining quantitative surveys and qualitative analyses, the results show a growing adoption of digital technologies and their varied effects on Moroccan society. The proposed recommendations aim to maximize the benefits of digitalization while minimizing its risks, contributing to sustainable and inclusive development.

Impact Des Plateformes De Streaming Sur Les Métiers De La Scène, Ismail Madani Alaoui, Mourad Oumzi, Abdessamad Boulkhalil

Dirassat

This article examines the impact of streaming platforms, particularly Netflix, on performing arts professions. It explores how Netflix has transformed production practices, distribution methods, and modes of reception for artistic performances. By redefining accessibility, integrating advanced technologies, and offering alternative financing models, Netflix has created a dynamic and inclusive environment for artists and audiences. The challenges and opportunities related to these transformations are also analyzed, including the emergence of new economic models and their long- term viability for the entertainment industry. The article highlights the profound implications of these changes for artists, cultural institutions, and global audiences

L'Utilisation Des Technologies Numériques Dans Les Musées Contemporains Au Maroc : Les Défis De La Préservation Culturelle, De L'Accès Et De La Justice Dans L'Utilisation De La Réalité Virtuelle Et Augmentée, Fadma Fangach

Dirassat

This study investigates the impact of immersive technologies, particularly Virtual Reality (VR) and Augmented Reality (AR), on visitor experiences at two distinct museums: the Museum of Modern and Contemporary Art Mohamed the Sixth and the Exhibition and Museum of the Prophet’s Biography and Islamic Civilization. Employing a qualitative approach and a comparative case study methodology, the research examines the employment of VR and AR within each museum, focusing on their respective modes of projection and exhibition. While the Museum of Modern and Contemporary Art Mohamed the Sixth uses VR to establish a virtual space online, the Exhibition and Museum of …

L'Appel Du Rupestre Land : Revisiter L’Art Rupestre En Le Numérisant, Ghizlane Assif, Abdelkrim M’Hammed Oubella

Dirassat

This article attempts to explore the use of digital technology in preserving Moroccan rock art, emphasizing the significance of this art form in understanding the history and culture of our ancestors through digital tools such as 3D modeling, virtual reality, and others. This digitization effort aims to document and safeguard these cultural treasures while making them accessible to the public. The objective of the article is to examine the potential of digitizing rock art in our documentary film, "The call of the rupestre- land" We aimed to contribute to the digital conservation of heritage through audiovisual means. Indeed, this modest …

Numérisation Des Œuvres D’Art : L’Exemple Du Musée Numérique De La Micro-Folie, Meriem Benmhamed

Dirassat

Digitization refers to the creation of a digital copy of a text, image or other object. And if we make a copy of something, it's often to prevent its waste. It's a process which look like photocopying. We can digitize or scan silver photos, for example, to save them digitally or publish them online. In the art world, digitization makes it possible to save certain works of art from oblivion and deterioration. Paradoxically, it saves them, while at the same time falsifying them.

Until recently, the concept of an archive was something tangible and material, but now we're talking more …

La Mise En Visibilité Pratiquée Par/Sur Les Réseaux Socionumériques (Rsn) : Identité Numérique Et Motivations D’Usage Cas Des Jeunes Artistes En Développement D’Agadir En Art Visuel, Abderrahmane Ait Rahou, Nadia Bouaafia, Zohra Zemmit

Dirassat

The objective of this article is to present the results of a qualitative study conducted with around ten artists. The issue addressed is as follows: How are the practices of young artists affected by the visibility carried out by/on social networking sites (SNS)? We start from the premise that an artist's relationship with SNS should be viewed not as a given, but as a construct subject to the influence of factors both intrinsic and extrinsic to the artist. Theoretically, we consider that the sociology of uses provides a suitable analytical framework to frame the normativity of this digital environment alongside …

Design Et Intelligence Artificielle : Esquisse D’Une Logique De Créativité Générative Artificielle, Nada Ouerghi

Dirassat

This article questions the creative process in design compared with that of generative artificial intelligence (GAI), and attempts to define a relationship between creative and generative logic.

As a result, a resonance between the design mindset and AGI is identified, thanks to the poetics and use of the design toolbox borrowed to GAI .

Creative cogeneration reconciles human and machine. But the logic of artificial imagination led to question the future of the designer's profession and calls for a mandatory updating of the designer's profile!

Quantum Machine And Deep Learning For Medical Image Classification: A Systematic Review Of Trends, Methodologies, And Future Directions, Eman A. Radhi, Mohammed Y. Kamil, Mazin Abed Mohammed

Iraqi Journal for Computer Science and Mathematics

Quantum Learning (QL) has emerged as a promising approach to medical image classification, leveraging the principles of quantum mechanics to improve the performance and efficiency of machine learning algorithms. This systematic review provides a comprehensive critical assessment of the current status of QL techniques developed for medical image classification, with a specific focus on trends, methodologies, and future directions in this rapidly evolving field. A thorough literature search was conducted across five major databases, resulting in a total of 28 relevant studies published between 2018 and 2024. The studies were analyzed and classified based on the type of quantum algorithm, …

Different Methods To Estimate Stress-Strength Reliability Function For Modified Exponentiated Lomax Distribution, Raya Salim Al-Rassam, Khalida Ahmed Mohammed, Safwan Nathem Rashed

Iraqi Journal for Computer Science and Mathematics

When ensuring the reliability of device or the suitability of a material, it is necessary to take into consideration the stress cases in the operating environment. This means that the uncertainty about the reality environmental stress must be taken into as random. The stress-strength (S-S) model treated the stress and strength variables as random. In the simplest form of stress-strength model, y represents the stress put on the unit by the operating environment, and the strength of the unit represented by x. A unit is able to perform its required function if its stress imposed on it is less than …

A New Ridge-Type Estimator In The Zero-Inflated Bell Regression Model, Nawal Mahmood Hammood, Nadwa Khazaal Rashad, Zakariya Yahya Algamal

Iraqi Journal for Computer Science and Mathematics

Count data modeling requires usage of the Poisson regression model as a primary analytic method. Excess dispersion in variables makes the model unfit to use when the Poisson distribution mean value differs from its variance value. Data fits well with the results obtained by using the Bell regression model. Excess zeros occur frequently in the observed count data records. The Zero-Inflated Bell regression model is a substitute for the Bell regression model in this situation. The approach of maximum likelihood is mostly used to estimate the Zero-Inflated Bell regression model's parameters. When modeling the link between the response variable and …

An Improvement Finger Vein Authentication System Based On Pcanet Deep Learning And Hyper Parameter Machine Learning, Raniah Ali Mustafa, Tarek Abbes

Iraqi Journal for Computer Science and Mathematics

Finger vein authentication is a biometric technique that uses unique vein patterns within the finger to verify identity, ensuring security by scanning veins with near-infrared light to prevent counterfeiting. The reliability of this technology and its resistance to skin disorders make it highly appreciated. The motivation for using PCANet deep learning in finger vein authentication arises from the need for a more precise, secure, and effective biometric system. Conventional methods have trouble with image quality, noise, and illumination, whereas PCANet improves classification accuracy by employing principal component analysis (PCA) to extract deep features. Its lightweight structure guarantees computational efficiency while …

Liberated Arabic Handwritten Text Recognition Using Convolutional Recurrent Neural Networks, Ahmad Abdulqadir Alrababah, Mohammed Khalid Aljahdali, Abdulrahim Abdulhamid Al Jahdali, Mohammed Saleh Alghanmi, Israa Ibraheem Al_Barazanchi

Iraqi Journal for Computer Science and Mathematics

Arabic script is exhibited in a cursive style, which is a departure from the norm in many common languages, and the shapes of letters are contingent on their positions within words. The form of the first letter is influenced by the subsequent letter, middle letters are shaped by both preceding and succeeding letters, and the shape of the final letter is determined by the preceding letter. Additionally, certain letters are found to have strikingly similar shapes, making Arabic text recognition a formidable challenge in computer vision. The challenge of detecting and recognizing Arabic handwritten text is addressed in this paper …

Asymmetric Effects Of The Ebbinghaus Illusion On Relative Depth Judgments In A Perceptual Matching Task, Caden J. Thompson

Honors Theses

The Ebbinghaus illusion, also know as Titchner Circles, is a perceptual illusion that is typically presented as a two-dimensional set of disks. These disks are configured as a central disk surrounded by an annulus of smaller or larger disks. Numerous studies have found the illusion to have an effect on size perception, but fewer studies have analyzed its effects on depth judgments. This study utilized a head-worn virtual reality environment as well as a three-dimensional display to examine the effects of the Ebbinghaus illusion on relative depth judgments in a perceptual matching task. Findings indicated that Ebbinghaus configurations featuring an …

Mobile Robots And Autonomous Vehicle Control: A Comprehensive Review Of The Advancements And Challenges, Elvis Tamakloe, Benjamin Kommey, Ernest Ofosu Addo, Daniel Opoku

Makara Journal of Technology

Since their inception, mobile robots have enormously changed the landscape of robotics engineering in recent years. Imperatively, the impact of mobile robots has positively transformed many sectors of human endeavors, i.e., complemented and substituted humans in areas where human interactions were difficult, hazardous, and impossible to thrive and operate. In this regard, the contributions of mobile robots to scientific, social, and economic growth, development, and advancement cannot be overlooked, especially through its decades of transition from Industry 3.0 to 4.0 over the years. To achieve maximum benefits from the use of mobile robots across all important facets, their advancements and …

Usb-Powered Virtual Cyber Labs: A Plug-And-Play Solution For Cybersecurity Education, Danny Galvis, Keith Fernandez, David Abiandu

Center for Cybersecurity

No abstract provided.

Rfid Tag For Continuous User Verification, Christian Zambrano

Center for Cybersecurity

RFID Tag for Continuous User Verification — Kean University Research Days

Machine Learning Techniques For Detecting Distributed Denial Of Service (Ddos) Attacks, Caesar Marte

Center for Cybersecurity

Machine Learning Techniques for Detecting Distributed Denial of Service (DDoS) Attacks — Kean University Research Days

Comparative Analysis Of Operating System Security: Evaluating The Most Secure Os, Rodrigo Gonzalez

Center for Cybersecurity

https://www.keanresearchdays.com/student-poster-presentation-2025-feed/comparative-analysis-of-operating-system-security-evaluating-the-most-secure-os

Uta Datathon 2025 Challenges (Archive), Rubab Shahzad, Srinivasa Sai Abhijit Challapalli, Ghanbarian Behzad, Mei Yang, Clivin Geju, Zecil Yogeshkumar Jain, Jay Mahavirkumar Shah, Sai Nikhitha Chandana

2025 Datathon Challenges-Archive

Timed and Model Challenges from the UTA Datathon 2025

Digital Thread: Bridging Macro–Micro Services In System-Of-Systems, Joshit Mohanty

Graduate Student Government Association Research Conference

Organizations and industries increasingly rely on distributed services in decentralized environments—ranging from large-scale, system-of-system architectures to fine-grained, agent-based microservices. While this distributed paradigm offers flexibility and innovation, it presents critical challenges such as interoperability gaps, inconsistent data formats, and a lack of holistic oversight. Traditional integration approaches, including ad-hoc middleware or enterprise service buses, tend to solve these issues reactively. As a result, technical debt accumulates, stakeholder misalignments persist, and scaling to new demands becomes complex.

This research proposes digital thread (DT) as the unifying framework to create an authoritative source of truth: a continuous flow of information across the …

Meta-Clustering For Specialized Language Models: Enhancing Contextual Adaptation And Mitigating Hallucinations In Diverse Healthcare Environments, Joshit Mohanty, Vaishali Vaishali, Sandeep Kumar Nayak, Sumit Lahiri

Graduate Student Government Association Research Conference

Large Language Models (LLMs) have significantly advanced conversational AI by enabling dialogic information-seeking and task execution across diverse domains. However, their extensive parameters and broad domain scope lead to “data hallucinations.” These shortcomings are particularly evident in dynamic and diverse environments like India’s healthcare sector, where myriad languages, regional practices, and cultural nuances demand specialized, localized expertise rather than one-size-fits-all generalist models. This paper introduces a meta-clustering framework that integrates Distilled Language Models (DLMs) and Small/Specialized Language Models (SLMs) with meta-learning principles to address these limitations. By drawing on evidence from works such as MedHalu and Med-HALT, the framework seeks …

Leadership In The Age Of Ai: Review Of Quantitative Models And Visualization For Managerial Decision-Making, Satyadhar Joshi

Harrisburg University Other Works

This paper offers a comprehensive review of existing literature on the intersection of Artificial Intelligence (AI) and leadership, drawing on both theoretical insights and practical implementations. By analyzing scholarly publications from the past two years (2023-2025), the review traces emerging patterns in how AI technologies are being integrated into leadership practices. Key themes include the growing relevance of learning-based systems for adaptive decision-making and the application of attention-based models to improve responsiveness in dynamic environments. The review also addresses ethical dimensions of AI-enabled leadership, emphasizing the need to balance algorithmic efficiency with human judgment and oversight. Concerns around transparency, psychological …

Balancergnn: Balancer Graph Neural Network For Imbalanced Datasets, Mallika Boyapati

Dissertations

Addressing imbalanced datasets is challenging due to machine learning models' inclination to learn the majority class. Graph construction plays a major role in determining how Graph Neural Networks (GNNs) perform on imbalanced datasets. In this research, we introduce the BalancerGNN framework to tackle highly imbalanced datasets, demonstrating its effectiveness in fraud detection as one of the case studies. This framework is designed to work for any binary node classification dataset with significant class imbalances. This research addresses the following questions: i) How effective are feature engineering techniques in the case of imbalanced datasets? ii) How do graph representation learning and …

Survey On Intelligent Planning Methods From Large Language Models Perspective, Yanzhong Zhou, Junren Luo, Xueqiang Gu, Wanpeng Zhang

Journal of System Simulation

Abstract: Starting from the perspective of large language models, this paper gives an overview of the definition and development of intelligent planning, and briefly introduces the traditional methods of intelligent planning; based on the close relationship between large language model intelligent agents and intelligent planning, introduces the architecture of large language models and typical large model intelligent agents; focusing on the intelligent planning for large language models, combs through the learning of planning languages, chain of thought, feedback optimization, and process automation; combining with the current challenges and difficulties, introduces the outlook of cutting-edge research on intelligent planning with large …

Simulation Environment Construction Of Track Segment Association And Algorithm Performance Evaluation, Dian Ding, Guangfen Wei, Zheng Cao, Shaohui Wen

Journal of System Simulation

Abstract: In order to study the applicability of Track Segment Association (TSA) algorithms in actual radar working environment , a TSA simulation environment which can simulate the real movement of the target is constructed. By constructing a rich set of target motion sets, the state switching process of target motion is described based on Markov state transition matrix, and the density is flexibly controlled through track translation. The simulation results show that this environment can evaluate the performance of the current classical TSA algorithms. The evaluation results provide a good reference for the practical engineering application of interrupted track association.

A Transfer Learning-Based Hybrid Model For Pm2.5 Concentration Prediction, Xinbiao Lu, Chunlin Ye, Yisen Chen, Wen Wu, Yudan Chen

Journal of System Simulation

Abstract: In order to solve the problems of increased computational cost due to irrelevant features and decreased prediction accuracy due to the difference in probability distribution caused by the change of data distribution over time in PM2.5 concentration prediction, this paper constructs a hybrid deep learning model TraTCN-LSTM-BiGRU based on migration learning. The meteorological factors related to PM2.5 concentration are selected as the model input using the mean-value heat map algorithm features; the source domain data and target domain data are divided by KL scatter and an adaptive layer is introduced into the model to achieve inter-domain distribution adaptation; the …

Trajectory Planning Of Quadruped Robot Over Obstacle With Single Leg Based On Deep Reinforcement Learning, Min Li, Sen Zhang, Xiangguang Zeng, Gang Wang, Tongwei Zhang, Dijie Xie, Wenzhe Ren, Tao Zhang

Journal of System Simulation

Abstract: Aiming at the problems of joint vibration and high energy consumption of quadruped robot in the process of walking over obstacles, a foot trajectory planning method of quadruped robot based on deep reinforcement learning SAC algorithm is proposed. Based on robot kinematics and Monte Carlo method, the motion space of the single-legged foot of quadruped robot is analyzed. A compound seventhdegree polynomial trajectory of the quadruped robot is planned. The SAC algorithm is used to train and obtain the low energy consumption obstacle crossing strategy of four-legged robot under different obstacle environment. The simulation results show that the compound …

Optimization Of Vehicle Routing For Cross-Infection Risk In The Epidemic, Xiaodong Shi, Yongcheng Guo, Mingqi Ma, Jiarui Pan

Journal of System Simulation

Abstract: In view of the safety risks associated with logistics distribution route optimization during public health emergencies, this paper investigates the vehicle routing problem by incorporating the risk of cross-infection, integrates the cross-infection risk caused by logistics activities in the epidemic area into the logistics distribution model, and establishes a logistics vehicle distribution model with the goal of cross-infection risk and cost. An improved genetic algorithm is designed for model optimization and solution. Based on the integration of chaos initialization population and adaptive crossover and mutation operations, a neighbor exclusion operator is further proposed to enhance the global search ability …

A Method For Road Extraction Using Masked Image Modeling And Contrastive Learning, Jiangjiang Wu, Zhenghong Li, Zhichao Sha, Hao Chen, Shuang Peng, Chun Du, Jun Li

Journal of System Simulation

Abstract: Aiming at the occlusion problem of road extraction from remote sensing images, a road extraction method combining MIM and CL is proposed, the model training process includes a masked pretraining stage and a contrast training stage. The masked pre-training stage mainly carries out mask image reconstruction, and trains the model to recover the whole image from some areas that are randomly occluded. The comparison training stage is mainly for the prediction error and low confidence regions to learn the comparison, to narrow the distance between the features of the same category and increase the distance between the features of …

Research On The Resilience Of Integrated Urban Passenger Transport Network In Urban Agglomerations Considering The Intra-Urban Service Network, Shida Nie, Chengbing Li, Bowei He, Xintao Li

Journal of System Simulation

Abstract: In order to solve the problem of insufficient comprehensiveness and refinement of the urban agglomeration passenger transport network model, Space L modelling method in complex network theory is adopted to construct a comprehensive urban passenger transport network model considering the urban internal service network. A comprehensive urban passenger transport network composed of intercity networks and intra-city service networks is built, and time-dependent edge weights in the network is considered. A time-weighted network efficiency model is proposed to evaluate the network resilience. The results show that under a random attack strategy, the relative time efficiency of the network fluctuates less, …