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Enhancing UAV Path Planning Efficiency through Adam-Optimized Deep Neural Networks for Area Coverage Missions

Procedia Computer science 2024年 235卷 2-11页

作者： Akshya J Neelamegam G C. Sureshkumar Nithya V Seifedine Kadry SRM Institute of Science and Technology Department of Computational Intelligence Chennai 603203 India Karpagam College of Engineering Department of Computer Science and Technology Coimbatore 641032 India KGiSL Institute of Technology Department of Information Technology Coimbatore 641032 India KGiSL Institute of Technology Department of Computer Science and Engineering Coimbatore 641032 India Noroff University College Department of Applied Data Science Kristiansand 4612 Norway

Efficient Unmanned Aerial Vehicle (UAV) trajectory generation is crucial for successful area coverage missions, aiming to maximize coverage while minimizing resource consumption. In this research, we present a comprehensive study on optimizing UAV trajectory generation using Deep Neural Networks (DNNs) with the Adam optimization algorithm. The DNNs are trained on historical data to produce smooth and continuous trajectories, thereby reducing abrupt changes in direction and enhancing overall efficiency during the mission. To evaluate the performance of the proposed approach, we conducted experiments comparing different activation functions, namely tanh, sigmoid, and ReLU, with the Adam-optimized DNN model. The trajectories generated by each activation function were analyzed using key metrics, including Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R 2 ) scores for both X and Y coordinates. The results of the comparative analysis revealed that the DNN model with the Adam optimizer exhibited superior performance over the other activation functions. It achieved lower MSE, MAE, and RMSE values, indicating better trajectory accuracy and smoother paths. Additionally, the R 2 scores demonstrated a higher correlation between the generated trajectories and the actual trajectories, highlighting the model's ability to capture underlying patterns effectively. The findings underscore the significance of leveraging the Adam-optimized DNN approach for UAV trajectory planning, offering promising opportunities for resource optimization, increased mission success, and further advancements in autonomous aerial systems. This research contributes to the ongoing efforts in UAV path planning, optimization, and intelligent control strategies, paving the way for enhanced autonomous systems in various real-world applications.

关键词： Unmanned Aerial Vehicles (UAVs) Trajectory Generation Deep Neural Networks (DNNs) Adam Optimization Area Coverage Missions

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Comparative Effectiveness of Classification Algorithms in Predicting Diabetes 16

Comparative Effectiveness of Classification Algorithms in Pr...

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16th IEEE International Conference on computational Intelligence and Communication Networks, CICN 2024

作者： Dael, Fares A. Mareyev, D. Shayea, Ibraheem Kulniyazova Korlan, S. Abitova, Gulnara İzmir Bakirçay University Management Information Systems Izmir Turkey Astana It University Department of Computational and Data Science Astana Kazakhstan Istanbul Technical University Electronic and Communication Engineering Istanbul Turkey L.N. Gumilyov Eurasian National University Department of Systems Analysis and Control Astana Kazakhstan Astana It University Department of Intelligent Systems and Cybersecurity Astana Kazakhstan

ISBN: (纸本)9798331505264

Diabetes mellitus poses a significant global health challenge, with increasing prevalence, particularly in low socioeconomic regions. Accurate and early diagnosis is crucial to prevent the severe long-term complications associated with diabetes. This study conducts a comprehensive comparison of six prominent machine learning algorithms-K-Nearest Neighbors (K-NN), Naive Bayes, Support Vector Machine (SVM), Decision Trees, Random Forest, and Logistic Regression-in predicting diabetes using a dataset of 768 individuals with diverse diabetic indicators from Kaggle. Each algorithm is rigorously evaluated based on precision, recall, and F1-score to determine the most effective method for diabetes diagnosis. The results indicate that Logistic Regression outperforms the other algorithms, achieving an accuracy of 81%. This superior performance is attributed to Logistic Regression's ability to effectively delineate linear separations, which is crucial for distinguishing between diabetic and non-diabetic individuals. The study underscores the importance of feature selection and model tuning in enhancing predictive performance. The findings suggest that integrating Logistic Regression into clinical settings can significantly improve the accuracy and timeliness of diabetes diagnosis, potentially leading to better patient outcomes and reduced healthcare costs. © 2024 IEEE.

关键词： Support vector regression

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Augmented Reality and Artificial Intelligence in Sign Language Expression

Augmented Reality and Artificial Intelligence in Sign Langua...

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IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS)

作者： Kaviyaraj R P. Sathya P. Nandhini Vinoth Kumar P Sureshkumar Chelliah Ezhilmathi S Department of computational intelligence SRM institute of science and technology Kattankulathur India Department of Computer Science and Engineering Sri Shanmuga Collage of Engineering and Technology Sankari India Department of Artificial Intelligence and Data Science St. Joseph's Institute of Technology OMR Chennai

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361193

Sign language, the vibrant tapestry of hand gestures and facial expressions, is the lifeblood of Deaf and hardof-hearing communities. For millions of signers, American Sign Language (ASL) runs deeper than communication, fundamental to identity, expression, and belonging. And yet an unshakeable communication gap leaves users of ASL frequently marooned away from the hearing world, kept from education, healthcare, or employment, or from basic, everyday transactions. By posing this new unsolved challenge to the power and promise of Artificial Intelligence (AI), this work leads the way towards closing that chasm by real-time recognition and translation of full ASL. Our approach employs a novel variant of Random Forest and utilizes cutting-edge video processing techniques to identify and understand the nuanced, often exquisitely delicate detail of ASL signing, at unprecedented levels of accuracy, and at speed. Another layer of innovation that characterizes our work is our integration of augmented reality (AR). By embedding AR along with our translator of artificial intelligence tech, we intend to completely change the way American Sign Language (ASL) is conveyed by directly engraining our already robust Random Forest model and advanced video processing techniques to project the ASL translation directly into your visual field in real time. The goal to demystify this complex and vivid language and, in doing so, to remove the communication barriers that persist between the Deaf community and the rest of the world, thus, fostering inclusion.

关键词： Sign language computational modeling Signal processing algorithms Assistive technologies Predictive models Streaming media Feature extraction

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Analyzing Factors Influencing Driver Willingness to Accept Advanced Driver Assistance Systems

arXiv

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arXiv 2025年

作者： Musau, Hannah Gyimah, Nana Kankam Mwakalonge, Judith Comert, Gurcan Siuhi, Saidi Department of Engineering South Carolina State University OrangeburgSC29117 United States Department of Computational Engineering and Data Science North Carolina A&T State University GreensboroNC27411 United States

Advanced Driver Assistance Systems (ADAS) enhance highway safety by improving environmental perception and reducing human errors. However, misconceptions, trust issues, and knowledge gaps hinder widespread adoption. This study examines driver perceptions, knowledge sources, and usage patterns of ADAS in passenger vehicles. A nationwide survey collected data from a diverse sample of U.S. drivers. Machine learning models predicted ADAS adoption, with SHAP (SHapley Additive Explanations) identifying key influencing factors. Findings indicate that higher trust levels correlate with increased ADAS usage, while concerns about reliability remain a barrier. Specific features, such as Forward Collision Warning and Driver Monitoring Systems, significantly influence adoption likelihood. Demographic factors (age, gender) and driving habits (experience, frequency) also shape ADAS acceptance. Findings emphasize the influence of socioeconomic, demographic, and behavioral factors on ADAS adoption, offering guidance for automakers, policymakers, and safety advocates to improve awareness, trust, and usability. © 2025, CC BY-NC-SA.

关键词： Advanced driver assistance systems

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Unraveling Pedestrian Fatality Patterns: A Comparative Study with Explainable AI

arXiv

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arXiv 2025年

作者： Sulle, Methusela Mwakalonge, Judith Comert, Gurcan Siuhi, Saidi Gyimah, Nana Kankam Department of Engineering South Carolina State University OrangeburgSC29117 United States Department of Computational Data Science and Engineering North Carolina A&T State University GreensboroNC27411 United States

Road fatalities pose significant public safety and health challenges worldwide, with pedestrians being particularly vulnerable in vehicle-pedestrian crashes due to disparities in physical and performance characteristics. This study employs explainable artificial intelligence (XAI) to identify key factors contributing to pedestrian fatalities across the five U.S. states with the highest crash rates (2018–2022). It compares them to the five states with the lowest fatality rates. Using data from the Fatality Analysis Reporting System (FARS), the study applies machine learning techniques—including Decision Trees, Gradient Boosting Trees, Random Forests, and XGBoost—to predict contributing factors to pedestrian fatalities. To address data imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is utilized, while SHapley Additive Explanations (SHAP) values enhance model interpretability. The results indicate that age, alcohol and drug use, location, and environmental conditions are significant predictors of pedestrian fatalities. The XGBoost model outperformed others, achieving a balanced accuracy of 98%, accuracy of 90%, precision of 92%, recall of 90%, and an F1 score of 91%. Findings reveal that pedestrian fatalities are more common in mid-block locations and areas with poor visibility, with older adults and substance-impaired individuals at higher risk. These insights can inform policymakers and urban planners in implementing targeted safety measures, such as improved lighting, enhanced pedestrian infrastructure, and stricter traffic law enforcement, to reduce fatalities and improve public safety. © 2025, CC BY.

关键词： Pedestrian safety

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Analysis of Distracted Pedestrians Crossing Behavior: An Immersive Virtual Reality Application

arXiv

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arXiv 2025年

作者： Sulle, Methusela Mwakalonge, Judith Comert, Gurcan Siuhi, Saidi Gyimah, Nana Kankam Roberts, Jaylen Ruganuza, Denis Department of Engineering South Carolina State University OrangeburgSC29117 United States Department of Computational Engineering and Data Science North Carolina A&T State University GreensboroNC27411 United States

Pedestrian safety is a critical public health priority, with pedestrian fatalities accounting for 18% of all U.S. traffic deaths in 2022. The rising prevalence of distracted walking, exacerbated by mobile device use, poses significant risks at signalized intersections. This study utilized an immersive virtual reality (VR) environment to simulate real-world traffic scenarios and assess pedestrian behavior under three conditions: undistracted crossing, crossing while using a mobile device, and crossing with Light-emitting diode (LED) safety interventions. Analysis using ANOVA models identified speed and mobile-focused eye-tracking as significant predictors of crossing duration, revealing how distractions impair situational awareness and response times. While LED measures reduced delays, their limited effectiveness highlights the need for integrated strategies addressing both behavioral and physical factors. This study showcases VR’s potential to analyze complex pedestrian behaviors, offering actionable insights for urban planners and policymakers aiming to enhance pedestrian safety. © 2025, CC BY-NC-SA.

关键词： Pedestrian safety

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HVAC Intelligence: Energy Conservation Based on Occupancy

HVAC Intelligence: Energy Conservation Based on Occupancy

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IEEE Southeastcon

作者： Hiba Darwish Raymond Tesiero Marwan Bikdash Balakrishna Gokaraju Computational Data Science and Engineering Department North Carolina A&T State University Greensboro USA Civil Architectural and Environmental Engineering North Carolina A&T State University Greensboro USA

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

This research investigates the effectiveness of implementing an occupancy-based control strategy for Heating, Ventilation, and Air Conditioning (HVAC) systems to minimize energy consumption and maintain thermal comfort. The proposed system utilizes an infrared camera to detect room occupancy. When occupancy is detected, the HVAC system adjusts the indoor temperature based on the threshold (changeover) temperature. Conversely, in unoccupied conditions, the system maintains default temperature settings designed to prevent extreme conditions (not excessively warm or overly cold). The HVAC system operation is further influenced by outdoor temperature conditions: cooling is activated when the outdoor temperature exceed the defined changeover, and heating is initiated when the outdoor temperatures fall below this threshold. The study's findings demonstrate that occupancy-based control can result in significant energy savings, quantified as 8,632 kWh/year for our small scale project, corresponding to a cost reduction of $627.06. These results highlight the potential for such systems to enhance energy efficiency while maintaining occupant thermal comfort.

关键词： Heating systems Energy consumption HVAC Costs Systems operation Energy conservation Cameras Ventilation Energy efficiency Software

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Reinforcement Learning in Cognitive Robots for Autonomous Path Planning

Reinforcement Learning in Cognitive Robots for Autonomous Pa...

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data Intelligence and Cognitive Informatics (ICDICI), International Conference on

作者： Archana Das R. Indu Poornima G. Gokila Deepa K. Selva Sheela Akshya J Mani Deepak Choudhry Department of Information Science and Engineering New Horizon College of Engineering Bengaluru Department of Information Technology KGiSL Institute of Technology Coimbatore Department of Artificial Intelligence and Data Science PPG Institute of Technology Coimbatore Department of Artificial Intelligence and Data Science KGiSL Institute of Technology Coimbatore Department of Computational Intelligence SRM Institute of Science and Technology Chennai Department of Computing Technologies SRM Institute of Science and Technology Chennai

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

Cognitive robots are intelligent systems that learn from their environment, adapt to dynamic changes, and make decisions without a human's direct intervention. This research studies the integration of Reinforcement Learning in cognitive robots with Q-Learning for autonomous path planning. The study addresses the most critical issues in traditional A* and Dynamic Programming algorithms by devising path-planning techniques, mainly the lack of adaptability and computational efficiency in real-time and unpredictable environments. Our approach is based on Q-Learning, which is a model-free RL algorithm allowing the robots to find paths autonomously by optimizing their paths and avoiding obstacles. The Q-Learning algorithm provides the possibility of learning optimal policies for the robot through iterative interaction with its environment balanced between exploration and exploitation of the decision process. A reward system is utilized by the proposed model, encouraging the robot to explore shorter, collision-free paths and adjust based on feedback in real time from its surroundings. The model was found to be significantly better than its counterparts. In this context, as indicated, the Q-Learning model runs at 11.5 seconds, faster than A* at 18.3 seconds and Dynamic Programming at 16.7 seconds, yet with an accuracy of 94% and was found to have the highest collision avoidance rate at 98%. Additionally, the adaptability of the model about the environment presents a marked difference in terms of path length optimization, having done so with a mean path length of 12.3, compared to approaches or models. The robustness and scalability of the Q-Learning model make it highly applicable in real-world applications.

关键词： Adaptation models Q-learning Accuracy Navigation Heuristic algorithms Real-time systems Dynamic programming Cognitive robotics Collision avoidance Robots

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NC-ALG: Graph-Based Active Learning under Noisy Crowd 40

NC-ALG: Graph-Based Active Learning under Noisy Crowd

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40th IEEE International Conference on data engineering, ICDE 2024

作者： Zhang, Wentao Wang, Yexin You, Zhenbang Li, Yang Cao, Gang Yang, Zhi Cui, Bin Center for Machine Learning Research Peking University China Key Lab of High Confidence Software Technologies Peking University China Institute of Advanced Algorithms Research Shanghai China Institute of Computational Social Science Peking University Qingdao China National Engineering Labratory for Big Data Analytics and Applications China TEG Tencent Inc. Department of Data Platform China Beijing Academy of Artificial Intelligence China

ISBN: (纸本)9798350317152

Graph Neural Networks (GNNs) have achieved great success in various data mining tasks but they heavily rely on a large number of annotated nodes, requiring considerable human efforts. Despite the effectiveness of existing GNN-based Active Learning (AL) methods, they assume that the annotated labels are always correct, which is contradictory to the error-prone labeling process in a practical crowdsourcing environment. Besides, due to this impractical assumption, existing works only focus on optimizing the node selection in AL but neglect optimizing the labeling process. Therefore, we present NC-ALG, the first GNN-based AL framework that optimizes both the node selection and node labeling process under a noisy crowd. For node selection, NC-ALG introduces a new measurement to model influence reliability and an effective influence maximization objective to select nodes. For node labeling, NC-ALG significantly reduces the labeling cost by considering the model-predicted labels and the labels of mirror nodes. To the best of our knowledge, this is the first attempt to consider GNN-based AL under the practical noisy crowd. Empirical studies on public datasets demonstrate that NC-ALG significantly outperforms existing methods in terms labeling efficiency. Notably, it only takes NC-ALG one-third of the labeling budget that the competitive baseline GRAIN needs to achieve an accuracy of 70.7 % on PubMed. © 2024 IEEE.

关键词： Crowdsourcing

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Mental Workload Classification from fNIRS Signals by Leveraging Machine Learning

Mental Workload Classification from fNIRS Signals by Leverag...

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2023 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2023

作者： Hasan, M. Mahmud, M. Poudel, S. Donthula, K. Poudel, K. University of Memphis Department of Electrical and Computer Engineering TN United States Senior Software Engineer Optum Inc MurfreesboroTN United States Middle Tennessee State University Computational and Data Science MurfreesboroTN United States Middle Tennessee State University Computer Science MurfreesboroTN United States

ISBN: (纸本)9798350341256

Mental workload (MWL) identification is vital to know human cognitive functioning, performance, and well-being. In this work, we develop models for identifying low vs. high MWL using different genres of machine learning classifiers. We used non-invasive functional near-infrared spectroscopy (fNIRS) signals while participants classified the low vs. high levels of MWL tasks. Our analysis shows the low vs. high MWL can be identified best from the whole brain data. The k-nearest neighbors classifier showed the best performance with an accuracy of 98.8%, an area under the curve (AUC) of 98.8%, F1 score, precision, and recall of 98.0% from the whole brain data without overlapping signals. A separate hemisphere analysis using left hemisphere (LH) and right hemisphere (RH) activity showed that the LH activity has better classification ability than the RH activity. We also examined the classification with the top six features that could identify the low vs. high MWL with an accuracy of 97.4%, (AUC) 97.4%, F1 score, precision, and recall of 97.0%. These findings would be useful for developing more intuitive and user-friendly interfaces in the human-computer interface. © 2023 IEEE.

关键词： Infrared devices

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