Engineering Commons^™

Advances In Real-Time American Sign Language Recognition System Using Deep Learning Techniques For Enhanced Accessibility, Bader Alsharif

Electronic Theses and Dissertations

Advancements in technology have significantly contributed to the development of innovative tools aimed at improving communication and accessibility for individuals with hearing impairments. This dissertation explores various machine learning and deep learning techniques for recognizing American Sign Language (ASL) gestures, focusing on enhancing accessibility and bridging the communication gap between hearing-impaired and hearing individuals. Traditional machine learning models, such as Random Forest, Support Vector Machines (SVM), and K-Nearest Neighbors (KNN), alongside deep learning architectures like AlexNet, ResNet-50, EfficientNet, ConvNeXt, and VisionTransformer, were investigated for their effectiveness. Experiments conducted on an extensive dataset of 87,000 ASL gesture images revealed exceptional recognition …

A Capability Maturity Model For Artificial Intelligence Integration In Supply Chain Management, Lordt Becklines

Dissertations

Artificial Intelligence (AI) is transforming Supply Chain Management (SCM), yet many organizations struggle to assess their readiness for AI adoption and to understand how AI capabilities develop across maturity stages. This dissertation addresses this gap by developing a Capability Maturity Model (CMM) for AI integration in SCM, grounded in Organizational Information Processing Theory (OIPT), the Resource-Based View, and related capability frameworks. The model provides a structured approach for evaluating an organization's information-processing requirements, resource configurations, and alignment needed for effective AI-enabled supply chain operations.

Using a design science research approach, the AI-SCM CMM and its associated assessment instrument were derived …

Toward Transparent Bureaucracy: Nlp-Based Document Classification And Power Dynamics In The Srikandi System, Zulfatun Sofiyani, Suprayitno Suprayitno, Faisal Fahmi, Mega Putri Mahadewi

Proceedings from the Document Academy

As the Indonesian government advances digital document management through the SRIKANDI system, challenges persist regarding fragmented and subjective classification practices. This study proposes the integration of Natural Language Processing (NLP)-based classification within SRIKANDI to enhance consistency, transparency, and accountability in document management. Framed by an interdisciplinary theoretical foundation, the study synthesizes Michael Buckland’s document theory, viewing documents as dynamic social evidence, with Michel Foucault’s theory of power, highlighting classification as an exercise of institutional authority, and NLP methodologies that enable automated, content-driven categorization. The study positions documents as both technological artifacts and political constructs, whose classification practices simultaneously structure meaning …

Research On System And Application Framework Of Tactical Wargaming Simulation Driven By Ai4s, Dayong Liu, Qisheng Guo, Zhiming Dong, Xuehuan Qiu, Zhuoli Liu

Journal of System Simulation

Abstract: Tactical wargaming simulation, as a crucial tool for combat analysis, simulation training, and equipment demonstration and test, has become a significant means for generating combat effectiveness. Integrating AI into simulation not only enhances simulation efficiency but also diminishes reliance on humans. To assist professionals engaged in tactical wargaming simulation in mastering AI application methods, fostering a systematic mindset, and understanding evolving trends, this paper provided a concise overview of the principles behind AI for science (AI4S). Subsequently, it conducted an analysis of AI4S's application effectiveness in tactical wargaming simulation, established an AI4S-driven wargaming simulation system, and elucidated its composition, …

Social Cognition Simulation With Large Language Model-Driven Agents, Mingxin Zhang, Jinxuan Wu, Rui Zhu, Yunlong Wang, Wenjuan Meng, Zhe Liu, Xu Li, Xiaolei Chen, Yuxuan Liang, Yi Zheng, Xiangyang Xue

Journal of System Simulation

Abstract: With the continuous evolution of the capabilities of generative LLMs, their application in social cognition simulation is demonstrating paradigm-shifting potential. Traditional social simulation methods predominantly rely on static rules and simplified behavioral models, making it difficult to capture the dynamic evolution and cultural complexity of human social behavior. LLM-driven agents, equipped with contextual understanding and natural language generation capabilities, are emerging as novel tools for modeling social cognitive mechanisms, enabling the simulation of complex sociopsychological processes such as identity construction, value judgment, and intentional reasoning. This paper briefly introduced the technical foundations of LLMs and highlighted their suitability for …

Integrated Development Environment For Digital Test Applications Based On Cloud-Edge-End Architecture, Wenguang Yu, Qun Li, Hongjie Dang, Hao Chen, Ping Yang

Journal of System Simulation

Abstract: Digital test applications need to be constructed using the unified digital test development tool. After analyzing the features of digital test applications such as large-sample autonomous run, high computational efficiency requirement, and diverse task scenarios, this paper proposes the integrated development environment (IDE) for digital test applications based on cloud-edge-end architecture. The layered expandable architecture, the hybrid integration framework of multi-source heterogeneous models, and the cloud-edge-end collaborative deployment architecture are designed for the IDE of digital test applications. The IDE supports the rapid development, integration, and execution of digital test models and enables development of digital test applications on …

Research On Chain-Of-Thought Technology For Situational Awareness Based On Modular Reasoning, Hongyuan Ji, Duzheng Qing

Journal of System Simulation

Abstract: To address issues such as insufficient intelligence of situational understanding in traditional simulation systems, a situational visual question answering dataset was constructed, and a modular reasoning framework was proposed. The SACoT was built, which, under a zero-shot setting, employed expert prompts to guide the model in task decomposition and multimodal information fusion, generating reasoning chains to enhance semantic cognition and interpretability and offering a scalable solution with low computation cost. Experimental results indicate that SACoT improves task allocation, enables models to focus on query-relevant image details, mitigates the fragmentation of chain-of-thought induced by multi-step reasoning, and reduces long-form …

Simulation Of Robotic Arm Ball-Catching Strategy Based On Curriculum Rl Of Transformer, Ziyao Zhang, Yunfeng Ji

Journal of System Simulation

Abstract: Method integrating the PPO algorithm with Transformer network architecture is proposed, and curriculum learning strategy is introduced to solve the difficult training convergence and low efficiency of traditional RL methods in complex and dynamic high-degree-of-freedom tasks such as robotic arm ball catching. The Transformer is employed to effectively capture the complex high-dimensional dependency between the robotic arm's state space, ball trajectory, and environmental physical parameters. Curriculum learning progressively increases catching difficulty by designing training tasks from simple to complex objectives. The experimental results show this method increases the ball-catching success rate by over 60% compared to the traditional …

Research On Uav Target Tracking Algorithm For Simulation Scenarios, Xinyi Li, Zhenfei Wang, Han Wu

Journal of System Simulation

Abstract: To address the need for automatic UAV tracking of moving targets in simulated experiments, this paper proposed a long-term automatic tracking method based on an improved channel and spatial reliability-aware tracker (CSRT) algorithm. The target edge features were detected using the Laplacian of guided filter (LOGF) through guided filtering and then fused with the histogram of oriented gradient (HOG) and color names (CN) features to enhance the algorithm's discriminative ability for the target. To evaluate the target state, the paper used average peak correlation energy and perceptual hash Hamming distance. When the target was occluded, the paper employed YOLOv8 …

Distributed Optimization For Integrated Energy Based On Multi-Agent Reinforcement Learning, Caixia Tao, Naikun Chen, Fengyang Gao, Jiangang Zhang

Journal of System Simulation

Abstract: To address the energy management and privacy preservation problems faced by the coordinated optimization of distributed integrated energy systems, a distributed coordinated optimization strategy based on the multi-agent proximal policy optimization algorithm was proposed. An energy management model was established under the MDP framework; the electrical and thermal heterogeneous energy characteristics were considered; a multi-region two-layer interaction mechanism was constructed. Under the framework of centralized training and decentralized execution, homomorphic encryption was utilized to avoid privacy leakage during the coordination process, while accurately quantifying individual contributions to mitigate the problem of variance explosion in multi-agent policy evaluation. In the …

Prediction Of Inflow Wind Field For Large-Scale Wind Turbines Based On Multimodal Hybrid Deep Learning, Jiheng Wang, Yang Hu, Ziqiu Song, Fang Fang, Jizhen Liu

Journal of System Simulation

Abstract: To address the demand for high-precision inflow wind field prediction in large-scale wind turbines, traditional CFD methods suffer from high computational costs and poor real-time applicability. This paper proposed a multimodal hybrid deep learning-based wind field prediction method. The proposed method took turbine operating parameters and far-range wind field images as inputs and generated short-range wind field images as outputs. By employing a U-Net-Transformer-GAN hybrid architecture, the model achieved multi-scale feature extraction, temporal dependency modeling, and highresolution wind field image generation. The vorticity transport equation and Kármán-Howarth turbulence statistics were incorporated as weak constraints to enhance physical consistency, while …

Llm-Driven Multi-Agent Social Network Simulation: Interdisciplinary Integration And Cutting-Edge Development, Jiting Li, Yi Sun, Yirong Wang, Yiqin Lin, Jun Jia, Gangsong Ding

Journal of System Simulation

Abstract: The breakthrough of LLMs has provided powerful tools for social network research, advancing multi-agent social network simulation into a new era. This review systematically examined recent progress in LLM-driven multi-agent social network simulation research through a integrated perspective of multi-disciplines such as artificial intelligence, psychology, communication studies, and sociology. A three-tiered research system, which has gradually formed in this field and encompassed micro-level individual behaviors, meso-level interactive relations, and macro-level system emergence, was summarized. At the micro-level, research focuses on individual human behavior simulation, and numerous studies are dedicated to developing human-like agents with complex cognitive and affective architectures …

An Adaptive Robot Path Planning Based On Improved Rea* Algorithm, Ling Zhu, Jing Li, Zhaohui Zhang

Journal of System Simulation

Abstract: In order to improve the computational efficiency and path smoothness in a robot's global path planning, an adaptive robot path planning strategy based on an improved unilateral rectangle expansion A*(REA*) algorithm was proposed. The robot's operational safety was ensured by setting a buffer around obstacles. A passable interval formed by unilateral rectangle expansion was used as the operation unit, and bidirectional alternating search was combined to enhance the path planning efficiency. Inspired by potential field theory, the evaluation function was optimized by introducing a vector form to achieve fast adaptive obstacle avoidance. A new path planning strategy was proposed …

Evolutionary Game-Based Analysis Of Responses To Hallucinations In Generative Artificial Intelligence, Qiang Yan, Qianyu Zhang, Na Wei

Journal of System Simulation

Abstract: The accelerated deployment of generative artificial intelligence, particularly large language models, has amplified the social risks of hallucinations, posing systemic threats to the credibility of the information ecosystem, the effectiveness of users’ cognitive decision-making, and the governance security in the public domain. Research primarily focuses on hallucination mitigation mechanisms at the technical level or the design of regulatory frameworks at the policy level, lacking a systematic theoretical analysis of the evolutionary logic of strategic interactions among the “large language models, users, and regulators” under conditions of bounded rationality. By introducing evolutionary game theory into the field of generative artificial …

Intelligent Air Combat Decision-Making Method Based On Bigru And Priority Dynamic Sampling, Zhengkun Ding, Jiaqi Liu, Junzheng Xu, Yuezhu Xu, Xingmei Wang

Journal of System Simulation

Abstract: Current multi-agent reinforcement learning algorithms suffer from low efficiency in utilizing experience data and difficulties in setting appropriate learning rates. To address these issues, this paper proposed a BiGRU multi-agent PPO with priority sampling and dynamic learning rate. The algorithm incorporated a BiGRU network to enhance the policy network's ability to model temporal information. A priority partial sampling mechanism was introduced to improve the utilization efficiency of high-value experience data. Additionally, an improved Adam optimizer with dynamic learning rate adjustment was employed to address the challenge of learning rate configuration. Simulation experiment results demonstrate that the algorithm significantly …

Knowledge-Enhanced Llm-Based Method For Regional Traffic Signal Control, Risheng Xu, Linyao Yang, Yuanqi Qin, Xiao Wang, Changyin Sun

Journal of System Simulation

Abstract: Adaptive traffic signal control (ATSC) is crucial for alleviating regional traffic congestion, yet it faces severe challenges in real-time response to unexpected events and global coordination. The DRL method relies on pure data-driven approaches, suffering from core limitations such as poor generalization, weak interpretability, and a lack of guidance from emergency disposal knowledge, which makes them difficult to meet the demands of complex traffic scenarios. A control system that integrates knowledge-driven and data-optimized approaches was proposed. The GraphRAG was used to construct a dynamic traffic knowledge graph, providing LLMs with real-time updated historical emergency disposal experience and road …

Resource-Efficient Continuous Learning Framework For Edge Real-Time Video Analytics, Shuxia Wu, Junjie Zhang, Delong Chen, Zheyi Chen

Journal of System Simulation

Abstract: By deploying lightweight models at the network edge, edge systems can provide services of real-time video analytics. However, due to the data drift caused by the discrepancy between model training and actual deployment, it is challenging to construct lightweight models that match real-world environments. To address this challenge, a resource-efficient continuous learning framework for edge real-time video analytics (CL4VA) was proposed. A region of interest-granularity predictor for accuracy degradation was introduced to efficiently select key samples from real-time video streams. A two-layer mixed sample pool was constructed to adaptively trigger the model's continuous learning and avoid the issue of …

Reinforcement Learning Based Method For Uav Team Orienteering Optimization Under Multi-Constraint Condition, Can Yang, Kai Chen, Feng Zhu

Journal of System Simulation

Abstract: Traditional optimization methods struggle with efficiency, while reinforcement learning approaches often yield low solution quality and high training costs. In response, this paper proposes an attention mechanism-based reinforcement learning method. A dynamic attention strategy network with multi-information fusion is designed to improve solution quality. A visibility-graph approach is employed to simplify threat zone constraints and speed up convergence, and a decoding sequence reordering mechanism is introduced for further performance optimization of the solution. The simulation results show that the method generates high-quality solutions within milliseconds, achieving total rewards that approach or even surpass those obtained by traditional solvers …

Strike Strategy Planning Method Of Unmanned Ground Vehicles Based On Improved Ppo Algorithm, Bingkun Wang, Yue Wang, Mei Yang, Pengnian Zhang, Bohao Fan, Jie Tang

Journal of System Simulation

Abstract: An improved PPO algorithm based on the hybrid action space and gated recurrent unit (GRU) is proposed to address the limitations of predefined strike rules in maximizing the hitting accuracy of unmanned ground vehicles and the difficult coupling and optimization of continuous motion planning and discrete strike decision-making. The environmental model and target model are built for the process of unmanned ground vehicles' strike missions, coupled with a three-layer model for unmanned ground vehicles that fuses kinematic constraints, situational awareness, and dynamic decision-making. Two distinct policy networks are employed, including the continuous motion planning network for path planning, and …

Knowledge Closed-Loop Driving-Based Intelligent Game Confrontation Simulation, Quan Liu, Yu Wang, Linyue Liu, Hao Chen, Jian Huang

Journal of System Simulation

Abstract: For human-machine intelligence integration and collaborative intelligence enhancement, a “knowledge-model-data-knowledge” closed-loop paradigm for combat simulation is proposed to guide the design of a DRL-based game confrontation simulation architecture. By building a combat priori knowledge-guided DRL agent model, mining and analyzing the time series data of agent interactions generated during simulations, and extracting combat posterior knowledge that expands the cognition boundaries of commanders, the knowledge closed-loop driving mechanism for intelligent combat simulations is achieved. The experimental results indicate that the proposed mechanism can effectively endow the combat simulation system with intelligence growth capabilities, providing valuable reference for the deepening …

Intelligent Decision-Making Method In Imbalanced Air Combat Based On Asymmetric Self-Play, Wei Zheng, Jiahao Tang, Xiaoping Xiong, Xin Fan

Journal of System Simulation

Abstract: To solve the problem of strategy convergence caused by role homogenization in traditional self-play for imbalanced air combat, an intelligent decision-making method based on asymmetric selfplay was proposed. This method decoupled tactics from control by employing a hierarchical reinforcement learning framework and designed differentiated reward functions for advantaged and disadvantaged sides. Bidirectional independent policy pools were constructed to promote the co-evolution of strategies. The proximal policy optimization algorithm was utilized to train the model. Experiments in 1v1 weapon-imbalanced and 2v1 numerically-imbalanced scenarios demonstrate that compared to symmetric self-play, the proposed method increases the kill rate of the advantaged …

Agent-Based Pathfinding Method For Indoor Fire Emergency Evacuation, Ao Tian, Jianqin Zhang, Zheng Wen, Chaonan Hu, Hong Zhao, Bo Shen

Journal of System Simulation

Abstract: To improve emergency evacuation efficiency and reduce casualties in dynamic fire scenarios, a real-time path re-planning method based on agents and dynamic A* algorithm framework is proposed. The behavior and actions of the agent is modeled. A reward function is designed, and an agent-based evacuation framework is constructed. Based on fire simulation data, a dynamic cost network involving parameters such as thermal radiation, smoke visibility, and CO concentration is constructed to achieve spatiotemporally continuous modeling of fire environments. By optimizing the composite cost function through dynamic weight allocation, combined with an improved heuristic function and dynamic search mechanism, local …

A Semantic Knowledge-Enhanced Assessment Method For Spectrum Effectiveness Of Low Earth Orbit Constellations, Yiqing Liu, Qiuyang Zhang, Chunyu Liu, Yao Xue, Zhiwei Wei, Yan Feng

Journal of System Simulation

Abstract: In order to scientifically assess the spectrum effectiveness of low earth orbit constellations, the problems of insufficient adaptability of the traditional assessment framework and inaccurate estimation of KPIs under sparse data conditions were solved, a semantic knowledge-enhanced assessment method for spectrum effectiveness of low earth orbit constellations was proposed. A comprehensive multi-level, all band, and multi-dimensional spectrum effectiveness assessment framework covering link level, system level, geographical level, and service application level was constructed. A KPI intelligent prediction agent model integrating semantic knowledge and machine learning was proposed to quantify text-like design parameters using SentenceTransformer, so as to rapidly …

Simulation Method For Multi-Crew Construction Processes Based On Large Language Model-Powered Agent, Yifan Wang, Bin Yang, Congjun Wang

Journal of System Simulation

Abstract: Traditional construction simulation methods typically rely on predefined rules or static scheduling mechanisms, making it difficult to simulate interactive decision-making under complex constraints by dynamically adapting multi-crew construction scenarios. As a result, workforce idleness caused by process dependencies and spatial occupation fails to be solved. A simulation method for multicrew construction processes based on a LLM-powered agent was proposed. The distributed crew agents endowed with construction scenario understanding and reasoning capabilities were constructed, as well as a centralized project manager agent. A “single-manager and multiple-crew” decision-making mechanism for multi-agent interaction in construction was designed. Autonomous decision-making and coordination mechanism …

Ai Applications In Assessing Risk For Periodontal Disease: A Systematic Review, Grant O. Korte, Claudia M. Tellez Freitas

Annual Research Symposium

Objectives:

The goal of this systematic review is to examine the impact of using artificial intelligence (AI) to predict a patient’s risk level for periodontal disease by analyzing proven systemic health diseases linked to periodontitis. This is being done by primarily focusing on prevention and early diagnosis using machine learning programs that have been proven effective in other fields of periodontitis research.

Methods:

To conduct this study, a comprehensive literature review of journals published after 2020 was performed through four databases: PubMed, Scopus, Web of Science and Dentistry and Oral Science Source. The search was completed in adherence …

Flexible Fault-Tolerant Multi-Die Fpga-Based Architectures For Varying Space Environments, Yosof Ali Seif El Din Ali Maklad

Theses and Dissertations

It is well-known fact that spacecraft’s electronic components operate in an extreme harsh and varying space environments, beside changing orbit or passing through Van Allan Belts during orbital course results of radiation levels change. This thesis focuses on SRAM-based FPGA systems on-board of such spacecrafts, that are commonly utilized in space applications’ critical applications due to their capabilities and flexibility to reconfigure, since these systems are vulnerable to frequent negative impacts of ionizing radiation, thus inducing soft and hard errors leading to disastrous failures that could jeopardize the entire spacecraft. The soft errors’ effects are frequent yet can be mitigated, …

Expression Of Concern: The Innovative Technique For Obtaining The Solutions Of Jeffery Hamel Nano-Fluids Flow Problem, Haedir Abd Alrazak Namoos, Abeer Majeed Jasim

Iraqi Journal for Computer Science and Mathematics

EXPRESSION OF CONCERN FOR: H.A.A. Namoos and A.M. Jasim, ``The Innovative Technique for Obtaining the Solutions of Jeffery Hamel Nano-fluids Flow Problem,'' Iraqi Journal for Computer Science and Mathematics, vol. 6, no. 3, 2025, Article 31. DOI:https://doi.org/10.52866/2788-7421.1303.
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss3/31.

Reason for Expression of Concern:The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “The Innovative Technique for Obtaining the Solutions of Je ery Hamel Nano-fluids Flow Problem (Manuscript 1303)”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to …

Expression Of Concern: Software Engineering Approach To Enhancing Privacy Protection: Automated Face Blurring Using Deep Learning In Arab Social Media, Yasmin Makki Mohialden, Nadia Mahmood Hussien, Mostafa Abdulghafoor Mohammed

Iraqi Journal for Computer Science and Mathematics

EXPRESSION OF CONCERN FOR: Y. M. Mohialden, N. M. Hussien, and M. A. Mohammed, ``Software Engineering Approach to Enhancing Privacy Protection: Automated Face Blurring Using Deep Learning in Arab Social Media,'' Iraqi Journal for Computer Science and Mathematics, vol. 6, no. 3, 2025, Article 43. DOI: https://doi.org/10.52866/2788-7421.1317.
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss3/43.

Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “Software Engineering Approach to Enhancing Privacy Protection: Automated Face Blurring Using Deep Learning in Arab Social Media (Manuscript 1317)”. The journal has initiated an additional …

Expression Of Concern: 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

EXPRESSION OF CONCERN FOR: E. A. Radhi, M. Y. Kamil, and M. A. Mohammed, ``Quantum Machine and Deep Learning for Medical Image Classification: A Systematic Review of Trends, Methodologies, and Future Directions,'' Iraqi Journal for Computer Science and Mathematics, vol. 6, no. 2, 2025, Article 9. DOI: https://doi.org/10.52866/2788-7421.1252.
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss2/9.

Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “Quantum Machine and Deep Learning for Medical Image Classification: A Systematic Review of Trends, Methodologies, and Future Directions (Manuscript 1252)”. The journal has initiated …

Expression Of Concern: Ontology Features-Based Arabic Text Augmentation Using Word2vec, Enas Tariq Khudair, Onsa Lazzez, Mourad Zaied, Tarek M. Hamdani, Ahmed T. Sadiq, Habib Chabchoub, Adel M. Alimi

Iraqi Journal for Computer Science and Mathematics

EXPRESSION OF CONCERN FOR: E. T. Khudair, O. Lazzez, M. Zaied, T. M. Hamdani, A. T. Sadiq, H. Chabchoub, and A. M. Alimi, “Ontology Features-Based Arabic Text Augmentation Using Word2Vec,” Iraqi Journal for Computer Science and Mathematics, vol. 6, no. 3, 2025, Article 40. DOI: https://doi.org/10.52866/2788-7421. 1302 Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss3/40

Editor's Note: The Expression of Concern (DOI: https://doi.org/10.52866/2788-7421.1386) regarding the Original Article (DOI: https://doi.org/10.52866/2788-7421.1302) has been resolved. The publication timeline is as follows: Original Article → Expression of Concern → Resolution.