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An Integrated Health Risk Assessment Approach to the Study of Carbonated Water Spring from Tuzla Area, Bosnia and Herzegovina 10th

An Integrated Health Risk Assessment Approach to the Study o...

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International Conference on New Technologies, Development and Application - Advanced Production Processes and Intelligent systems

作者： Husejnovic, Maida Sljivic Palavric, Almedina Suljic, Azra Mlivo, Emira Univ Tuzla Fac Pharm Tuzla 75000 Bosnia & Herceg Univ Travnik Fac Pharm & Hlth Travnik 72270 Bosnia & Herceg

ISBN: (纸本)9783031662706;9783031662713

This study investigates the quality and potential health risks associated with natural carbonated spring water taken in 2023 in Tuzla, Bosnia and Herzegovina. Employing a multidisciplinary approach, we conducted extensive physicochemical analyses to characterize the water, assessing mineral and ionic content, organoleptic properties, and potential contaminants of geological and anthropogenic origins. Subsequently, a deterministic human health risk assessment was undertaken for the ingestion of individual contaminants, while an integrated approach was used to assess the overall health risk from the consumption of this water by the local population. Surprisingly, despite its natural origin, the water exhibited elevated levels of fluoride (F-), cadmium (Cd), lead (Pb), and iron (Fe), exceeding recommended health standards. Although, the presence of F- in concentration of 1.65 +/- 0.29 mg/L, Cd 0.07 +/- 0.01 mg/L, Pb 0.176 +/- 0.02 mg/L, and Fe 0.69 +/- 0.24 mg/L resulted in an acceptable individual health risk, the integrated approach revealed an increased risk associated with the consumption of the water (HQ = 2.28). This underscores the importance of adopting the integrated approach in health risk assessment, considering the additive effect of various contaminants. The study emphasizes the necessity for ongoing monitoring, regulatory measures, and public awareness to mitigate the identified health risks, ensuring the safe consumption of natural carbonated spring water from Tuzla.

关键词： water pollution deterministic health risk assessment additive effect integrated approach

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YOLO V8: The AI-Enabled Cataract Detection System 8th

YOLO V8: The AI-Enabled Cataract Detection System

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8th Smart Trends in Computing and Communications (SmartCom)

作者： Angeline, R. Soorya, S. Sathyaa, M. G. Suriya, U. K. Tharun SRM Inst Sci & Technol Dept Comp Sci & Engn Chennai India

ISBN: (纸本)9789819713196;9789819713202

Cataract, a cause of vision loss and blindness worldwide, among older individuals affects almost half of the elderly population in India. Detecting and treating cataracts early is crucial in preventing vision impairment. YOLO (You Only Look Once) is a deep neural network algorithm renowned for its effectiveness in object detection tasks. Its speed and efficiency make it an excellent choice for real-time applications. In the mentioned research paper, the authors utilized YOLOv8 to detect cataracts. They achieved an accuracy of 95% using a dataset comprising 1015 images. We have also compared YOLOv8 with cataract detection algorithms like inception V3, VGGNet, AlexNet, and ResNet50. Notably YOLOv8 surpassed both algorithms in terms of accuracy and speed. Key advantages of utilizing YOLOv8 for cataract detection are as follows: 1. High accuracy, YOLOv8 achieved an accuracy rate of 96.1% on the cataract dataset surpassing algorithms, like VGG19 and ResNet50. 2. Fast processing, YOLOv8 stands out as an efficient algorithm making it well suited for real-time applications. 3. Cost effectiveness, being free and open source YOLOv8 offers affordability in its utilization. Possible applications of cataract detection using the YOLOv8 algorithm are as follows: 1. screening a number of individuals. 2. telemedicine services. 3. assisting decision-making. In conclusion, the YOLOv8 algorithm shows promise as a method for automated cataract detection.

关键词： Convolution YOLO Convolutional matrix Epoch Testing Training Validation

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Investigating Augmentation Techniques for Camera-Radar Object Detection 1

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18th International Conference on Intelligent Autonomous systems (IAS)

作者： Park, Jinah Lee, Christina Dao Wen Ang, Marcelo H., Jr. Natl Univ Singapore Singapore Singapore

ISBN: (数字)9783031448515

ISBN: (纸本)9783031448508;9783031448515

In autonomous driving, a variety of sensors are used for the perception of the surroundings. Commonly, camera and LiDAR sensors are used due to their availability and details in the sensor data. Radar sensors have been gaining popularity due to the radar's sensor robustness in harsh weather conditions. Datasets with radar data are essential for training accurate models for radar-based sensor fusion algorithms in autonomous driving. Perception neural network algorithms rely on data for training and evaluation and often suffer from a class imbalance in the data as well. This paper uses augmentation on existing data, nuScenes dataset to address the class imbalance problem. The annotations of minority classes of train split were duplicated to a specific multiple. The performance was evaluated on nuScenes validation dataset, which showed improvements in the performance on most of the minority classes.

关键词： Radar Autonomous driving dataset Class imbalance

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Fuzzy Direct Torque Control for an Electric Vehicle Equipped with a Doubly Fed Induction Motor 4th

Fuzzy Direct Torque Control for an Electric Vehicle Equipped...

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4th International Conference on Digital Technologies and Applications (ICDTA)

作者： Chantoufi, Ahmed Derouich, Aziz Ouanjli, Najib E. L. Mahfoud, Said Idrissi, Abderrahman E. L. Sidi Mohamed Ben Abdellah Univ Higher Sch Technol Ind Technol & Serv Lab Fes Morocco Hassan First Univ Fac Sci & Technol Lab Mech Comp Elect & Telecommun Settat Morocco Sultan Moulay Slimane Univ Higher Sch Technol Polydisciplinary Lab Sci Technol & Soc Khenifra 54000 Morocco

ISBN: (纸本)9783031686740;9783031686757

The control of the electric machines in an electric vehicle (EV) plays a significant role in the performance of the vehicle's traction system. Indeed, the Direct Torque Control (DTC) approach applied to the doubly fed induction machine (DFIM) used for electric propulsion in the EV's traction system represents an appealing solution to maximize the EV's performance. However, despite the advantages of DTC, it presents drawbacks such as oscillations in the electromagnetic torque. To overcome these limitations, this study proposes an enhancement of DTC by integrating fuzzy logic. The entire study is conducted in the Matlab/Simulink simulation environment. The analysis of the results shows that the adopted approach provides a more precise and adaptive response, significantly reducing electromagnetic torque ripples.

关键词： fuzzy logic DTC electric vehicle

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A Survey of Machine Learning Assistance in Seismic Interpretation 1

引用

2nd International Conference on Forthcoming networks and Sustainability in the AIoT Era (FoNeS-AIoT)

作者： Al Anbagi, Mohammed Kamoona, Zaid Kufa Univ Najaf Iraq Minist Oil Baghdad Iraq

ISBN: (数字)9783031628719

ISBN: (纸本)9783031628702;9783031628719

The exploration and drilling of oil and gas are so expensive and takes a high responsibility for the geoscientists and interpreters to make their decision about the accurate place for drilling and this decision depends on a lot of underground factors. As researchers, we decided to conduct a survey about how to assist the geoscientists and seismic interpreters in making the right decision, with some of the underground characteristics that oil detection depends on. Our survey explains the use of machine learning algorithms to find geological characteristics such as Horizon, Fault, and Stratigraphy. The results show that interpretation by using machine learning algorithms has high accuracy and so close to the real seismic interpretations. Finally, it is noticed that there are a few experiments in this field due to the lack of available data set, although the result is acceptable, it needs time and a lot of experiments to apply on this important part.

关键词： Seismic Horizon Facies Fault Deep Learning Semantic Algorithms

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Classification of Stunting Events: Case Study in West Java, Indonesia 6th

Classification of Stunting Events: Case Study in West Java, ...

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6th International Conference on Soft Computing and Data Mining (SCDM)

作者： Rachmawati, Ummi Azizah Pratiwi, Puspa Setia Yusnita Abirami, K. Rama Praditya, Farrel Yuda Univ YARSI Fac Informat Technol Dept Informat Jakarta Indonesia Indonesia Int Inst Life Sci I3L Jakarta Indonesia Univ YARSI Dept Publ Hlth Fac Med Jakarta Indonesia Curtin Univ Fac Sci & Engn Dept Elect & Comp Engn Sarawak Malaysia

ISBN: (纸本)9783031669644;9783031669651

This study created a categorization model based on stunting episodes, separating stunting and non-stunting groups. Stunting is a persistent nutritional condition in toddlers who are shorter in height than other children their age. Data mining is one of the strategies used to extract knowledge from a large amount of big data and convert it into fresh data that can be understood as technology advances. The categorization model results provide a foundation for mapping the possibility of stunting events using the following methods: K-Nearest Neighbour, Logistic Regression, and Support Vector Machine (SVM). The proposed categorization model identifies stunting in toddlers from Pandeglang Regency, West Java, Indonesia, and Malaysia. The sample taken in this study consists of 798 children data. This study concludes that the three tests were successfully carried out, with data sharing ratios of 80%: 20%, 75%: 25%, and 70%: 30%, respectively. The model was repeated 100 times using a Support Vector Machine (SVM) and Logistic Regression (LR) and evaluated for precision, recall, f-1 score, and accuracy. The results of three experiments revealed that the best method is LR utilizing the chi-squared test feature selection, which achieved an accuracy of 85%.

关键词： Stunting Data Mining Classification SVM Logistic Regression

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Pursuing the Perfect Projection: A Projection Pursuit Framework for Deep Learning 15th

Pursuing the Perfect Projection: A Projection Pursuit Framew...

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15th International Workshop on Self-Organizing Maps, Learning Vector Quantization and Beyond (WSOM)

作者： Perschewski, Jan-Ole Schmidt, Johann Stober, Sebastian Otto von Guericke Univ AILab Magdeburg Germany

ISBN: (纸本)9783031671586;9783031671593

The curse of dimensionality refers to phenomena occurring with increasing dimensionality such as marginal differences in distances. Projection pursuit solves this issue by projecting high-dimensional data into a low-dimensional space where meaningful distances allow unbiased function estimation. However, projection pursuit only considers projections onto lines and the unbiased function depends on the sample size. We introduce deep projection pursuit (DPP) to remedy these limitations by using an ensemble of projections on parameterized surfaces combined with neural networks to solve the learning task. Furthermore, we demonstrate the capabilities of the DPP framework by training principal component curves and solving supervised tasks with interpretable models. Finally, we show the ability to maintain group properties in the projection space. Due to these applications, deep projection pursuit is a flexible design paradigm with various use cases.

关键词： Projection Pursuit Deep Learning Interpretability

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Green Deep Learning: Comparative Study of Road Object Detectors Between Jetson Boards and PC 6th

Green Deep Learning: Comparative Study of Road Object Detect...

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6th Iberian Robotics Conference (Robot)

作者： Sanchez-Garcia, Fabio Arango, Felipe Gomez-Huelamo, Carlos Ocana, Manuel Revenga, Pedro Bergasa, Luis M. Univ Alcala Dept Elect RobeSafe Res Grp Madrid Spain

ISBN: (纸本)9783031586750;9783031586767

Recent advancements in deep learning have provided powerful tools for intelligent vehicle tasks, particularly in the field of perception. However, achieving real-time performance with low power consumption remains a challenge included in the hot research topic known as green deep learning. In this paper, we present a comparative analysis of various YOLOv5 weights trained on the KITTI and SHIFT datasets using two platforms with different power consumption profiles: the NVIDIA Jetson AGX Xavier and a desktop computer equipped with a NVIDIA GTX 1080 Ti. Our analysis focuses on the average inference time and precision metrics for road objects detection, a key task for intelligent vehicles. Additionally, we apply TensorRT to optimize and accelerate the architecture on both platforms, resulting in significant speed improvements, particularly on the low-power Jetson AGX Xavier (30W). Our ultimate goal is to implement our whole autonomous driving architecture on several Jetson AGX Xaviers connected to a PC where the hyper-realistic CARLA simulator, is replicating the real-world autonomous vehicle environment. We obtain compelling validation results on KITTI and CARLA, achieving real-time performance on a lightweight Jetson AGX Xavier with a powerful object detector such as YOLOv5m.

关键词： green deep learning algorithm acceleration embedded systems TensorRT

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Strategic Intelligence: Navigating Hallucinations with Knowledge Graphs and Multi-Agent systems in Chatbots

Strategic Intelligence: Navigating Hallucinations with Knowl...

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International Conference on Intelligent and Fuzzy systems (INFUS)

作者： Rafalia, Najat Moumen, Idriss Ziyad, Mouhssine Abouchabak, Jaafar Ibn Tofail Univ Fac Sci Lab Res Informat Kenitra Morocco

ISBN: (纸本)9783031671944;9783031671951

The pervasive influence of ChatGPT and similar chatbots on information retrieval and creative tasks is undeniable. However, their susceptibility to hallucinations and inaccuracies, inherent in large language models (LLMs), presents a significant challenge for critical applications. To address this, our paper proposes a novel approach that leverages a structured knowledge graph or knowledge vector as a repository for chatbot responses. This method enables explicit control over the information conveyed, thereby reducing instances of errors and hallucinations. Our primary objective is to engineer a chatbot adept at navigating and comprehending vast amounts of textual data, while providing responses exclusively from a predefined knowledge base for enhanced reliability. Beyond knowledge management, our approach introduces a Multi-Agent System (MAS) to optimize process execution time, significantly improving operational efficiency and overall performance. The combination of knowledge control and a Multi-Agent System not only tackles LLM limitations but also amplifies chatbot responsiveness and optimizes processes. Our proposed framework not only safeguards against inaccuracies but also significantly elevates the practical utility of chatbots across diverse applications.

关键词： Chatbots Knowledge Graphs Multi-Agent systems Information Retrieval Large Language Models Hallucination Mitigation Operational Efficiency

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Adaptive Formation Control of Underactuated Autonomous Underwater Vehicles with Multiple Constraints 7th

Adaptive Formation Control of Underactuated Autonomous Under...

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7th International Conference on Green Technology and Sustainable Development

作者： Pham Nguyen Nhut Thanh Ngo An Thuyen Ho Pham Huy Anh Ho Chi Minh City Univ Technol HCMUT 268 Ly Thuong Kiet StDist 10 Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City VNU HCM Linh Trung Ward Ho Chi Minh City Vietnam

ISBN: (纸本)9783031761966;9783031761973

This study investigates an adaptive formation control scheme for multiple underactuated autonomous underwater vehicles (AUVs) considering model constraints and ocean currents. Firstly, extended disturbance observers (EDOs) are constructed to estimate ocean velocity components accurately. Then, a novel observed-based guidance law, which uses the estimated values and constant bearing guidance method, is introduced to eliminate the drift effect of ocean currents. Next, to guarantee the model constraints, dynamic surface control and barrier Lyapunov function are adopted to design a dynamic controller. The adaptive fuzzy technique is also incorporated to enhance the robustness of the designed dynamic controller against model uncertainties and disturbances. The overall system's stability is proved through the Lyapunov theorem, and the feasibility of the proposed control scheme is verified by numerical simulation results.

关键词： Adaptive formation control Autonomous underwater vehicle Constant bearing guidance Dynamic surface control Barrier Lyapunov function

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