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2024 International Conference on Smart Devices, ICSD 2024

作者： Malik, Pooja Malik, Praveen Kumar Ranjith Kumar, Gotte Bali, Jyoti S. Mahajan, Anshul Muhsen Hassan, M. Lovely Professional University School of Electronics and Electrical Engineering Punjab Phagwara India SR University School of Computer Science & Artificial Intelligence Warangal India Dept of Robotics and Automation Pune India The Islamic University Najaf Iraq

ISBN: (纸本)9798350394412

The extensive use of technology across several Modern civilization is clearly impacted by technology, with cutting-edge techniques and tools playing critical roles in sectors including healthcare, business, agriculture, and education. Processes have been transformed by these improvements, which have increased their dependability and efficiency. With the incorporation of technology, agriculture has made enormous strides, with techniques like aquaponics developing as extremely effective and sustainable systems. Aquaponics is a novel farming method that combines hydroponics (soilless plant cultivation) and aquaculture (fish farming) to grow vegetables and fish at the same time. But the performance of this incorporated system relies upon on careful observation of a number of vital elements, such as water high-quality, pH tiers, temperature, and dissolved oxygen levels. To keep the health and growth of both fish and flora in an aquaponics device, it's miles critical to hold perfect situations. This highlights the significance of technological intervention, especially via the integration of Internet of Things (IoT) era, and calls for precision in regulating the input time table for fish feed and plant vitamins. © 2024 IEEE.

关键词： Fish

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AI-Powered Precision: Revolutionizing Skin Cancer Detection with Advanced Machine Learning 6

AI-Powered Precision: Revolutionizing Skin Cancer Detection ...

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6th IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2024

作者： Abd-Elaziz, Mariam M. Elshafie, Basma M. Abdullah, Hagar Gerges, Karen Y. Ghanem, Ingy A. Fouad, Khaled M. El-Ghamry, Amir Moawad, Ibrahim F. El-Metwally, Sara New Mansoura University Faculty of Computer Science and Engineering Dakahlia Egypt Mansoura University New Mansoura University Faculty of Engineering Computer and Communication Program Faculty of Computer Science and Engineering Egypt Benha University Information Systems Faculty of Computers and Artificial Intelligence Egypt Mansoura University New Mansoura University Faculty of Computer and Information Sciences Faculty of Computer Science and Engineering Egypt School of Engineering and Computer Science University of Hertfordshire Gaf Egypt Ain Shams University Faculty of Computer and Information Sciences Cairo Egypt College of Computer Science and Engineering Taibah University Department of Artificial Intelligence Medina42353 Saudi Arabia

ISBN: (纸本)9798350378511

Skin cancer's increasing incidence rates necessitate advanced diagnostic tools. This research uses MobileNet architecture to develop an enhanced system for skin cancer detection. MobileNet's efficient CNN architecture, combined with Keras for model lifecycle management and TensorFlow for real-time inference, forms the foundation of this study. Using 10,015 dermatoscopic pictures from seven different classifications of skin cancer in the HAM10000 dataset, the methodology includes several key stages: image preprocessing to correct illumination and adjust resolution, data augmentation to balance the dataset, and model training using Transfer Learning. The MobileNet model was trained over 50 epochs with a comprehensive architecture incorporating multiple specialized layers. By combining large-scale data analysis and adaptive learning, this methodology demonstrates The revolutionary possibilities of AI and ML in improving skin cancer diagnostics and public health outcomes. Keywords Skin Cancer Detection, Dermatoscopic Images, Machine Learning, Deep Learning © 2024 IEEE.

关键词： Adversarial machine learning

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Transition in Focus of Prediction Tasks for Skeleton Graph Component Detection with Transformer 6

Transition in Focus of Prediction Tasks for Skeleton Graph C...

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6th ACM International Conference on Multimedia in Asia, MMAsia 2024

作者： Wang, Zhiyuan Yang, Cong Zhang, Yulu Boukhers, Zeyd Sui, Wei Ji, Yi Liu, Chunping School of Computer Science and Techonology Soochow University Suzhou China School of Future Science and Engineering Soochow University Suzhou China Chery Hangzhou China Fraunhofer Institute for Applied Information Technology FIT Sankt Augustin Germany Horizon Robotics Beijing China

ISBN: (纸本)9798400712739

Recent advancements in skeleton extraction have significantly improved the process by simplifying the skeleton regression task into graph component detection. Despite the advancements in skeleton topology, accuracy in detailing skeletal parts remains challenging, with specific issues such as jagged edges in high-resolution images. This paper identifies the limitations of current detection models that can adapt during the decomposition and reconstruction phases, which impacts the overall precision of the extraction. In response, we propose an approach that revises the primary focus of the detection tasks. Inspired by the success of pixel-wise binary classification methods, we propose a gradual transition in focus from a coordinate localization regression task to a classification task of predicting points during the training process. This transition can be achieved by adjusting the number of object queries in the Transformer model. Theoretical and experimental evaluations validate the effectiveness of our approach. Our method yields significant improvements in performance over the baseline across various shape and image datasets (e.g., 0.836 vs. 0.826 for BlumNet on the SK1491 dataset). © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Musculoskeletal system

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CF-AMVRGO: Collaborative Filtering based Adaptive Moment Variance Reduction Gradient Optimizer for Movie Recommendations

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International Journal of computers and Applications 2022年第11期44卷 1015-1023页

作者： Lakshmi Chetana, V. Seetha, Hari School of Computer Science and Engineering VIT-AP University Andhra Pradesh Vijayawada India Center of Excellence AI and Robotics VIT-AP University Andhra Pradesh Vijayawada India

In recent decades, recommendation systems are used in a variety of applications like social connections, movies, music, and venues. The existing algorithms has certain limitations like data sparsity, cold start problem, and poor scalability to degrade the movie recommendation performance. To address the aforementioned concerns, a new matrix factorization-based collaborative filtering algorithm is developed to enhance movie recommendation performance. In a neural collaborative filtering algorithm, an adaptive moment variance reduction gradient optimizer is applied in low-rank matrix factorization for representing the users and items in a low dimensional latent space to obtain effective movie recommendation performance. In this paper, the proposed algorithm performance is evaluated on three benchmark datasets;movielens 100K, 1M, and 10M datasets. Simulation results showed that the proposed algorithm obtained the difference of 0.08 and 0.056 roots mean square error value in movielens 1M and 10M datasets, which are better compared to the existing collaborative filtering-based deep learning algorithm. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Collaborative filtering

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Intelligence Based Condition Monitoring Model 7th

Intelligence Based Condition Monitoring Model

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7th International Congress and Workshop on Industrial AI and eMaintenance, IAI 2023

作者： Jenab, Kouroush Ward, Tyler Isaza, Cesar Ortega-Moody, Jorge Anaya, Karina School of Engineering & Computer Science Morehead State University MoreheadKY United States Networking and Telecommunication Academic Program Polytechnique University of Queretaro Queretaro Mexico

ISBN: (纸本)9783031396182

Condition-Based Maintenance (CBM) is a maintenance strategy that reduces equipment downtime, production loss, and maintenance cost based on changes in equipment condition (e.g., changes in vibration, changes in power usage, changes in operating performance, changes in temperatures, changes in noise levels, changes in chemical composition, increase in debris content and changes in the volume of material). In this study, we present the newly developed Condition Monitoring Model (CMM) based on an ensemble machine-learning model that utilizes the random forest, support vector machine, and artificial neural network classifiers, to classify data points from the normal state of a rotating machine. The efficacy of the model in adequately detecting and diagnosing faults in the rotating machine for maintenance planning is discussed in this paper. The developed model can efficiently avoid unnecessary maintenance and make timely actions by analyzing the received vibration signals from the rotating machine. An illustrative example is demonstrated to present the application of the model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Decision making

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Machine learning and deep learning algorithms in detecting COVID-19 utilizing medical images: a comprehensive review

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Iran Journal of computer science 2024年第3期7卷 699-721页

作者： Nurjahan Mahbub-Or-Rashid, Md. Satu, Md. Shahriare Tammim, Sanjana Ruhani Sunny, Farhana Akter Moni, Mohammad Ali Department of IoT and Robotics Engineering Bangabandhu Sheikh Mujibur Rahman Digital University Kaliakair Bangladesh Department of Computer Science and Engineering Bangladesh University of Business and Technology Mirpur Bangladesh Department of Management Information Systems Noakhali Science and Technology University Noakhali Bangladesh Department of Computer Science and Software Engineering Auburn University Auburn AL United States Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Faculty of Health and Behavioural Sciences School of Health and Rehabilitation Sciences The University of Queensland St Lucia Australia

The public’s health is seriously at risk from the coronavirus pandemic. Millions of people have already died as a result of this devastating illness, which affects countless people daily worldwide. Unfortunately, no specific therapeutic drugs or vaccines are available to cure patients completely. Therefore, early identification of infected individuals and their isolation can help reduce community transmission of COVID-19. Despite being commonly used, the reverse transcription polymerase chain reaction (RT-PCR) test is costly, time-consuming, and requires suitable kits, which are not always readily available. An alternative solution for the traditional RT-PCR test is machine learning and deep learning-based COVID-19 detection that utilizes clinical features of chest X-rays and computed tomography (CT) images. In this study, we conducted a detailed review of more than 100 recently published works to detect COVID-19-infected patients. Thus, different data preprocessing, data augmentation, image enhancement, feature extraction, machine learning, deep learning, Explainable Artificial Intelligence (AI) methods, etc. were explored to understand how these techniques were used to process images for identifying COVID-19. Additionally, we identified the current challenges in this field and suggested further research directions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Chest X-ray Computed tomography COVID-19 Deep learning Machine learning

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Gender-Based Bimodal Analysis of Speech and Text in Non-Contextual Emotion Recognition

Gender-Based Bimodal Analysis of Speech and Text in Non-Cont...

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2024 Beyond Technology Summit on Informatics International Conference, BTS-I2C 2024

作者： Angkasa, Abby Rasya Zahra, Amalia Fung, Wai-Keung Binus Graduate Program Computer Science Department Jakarta11480 Indonesia Cardiff Metropolitan University Cardiff School of Technologies Engineering Department Cardiff United Kingdom

ISBN: (纸本)9798331508579

Emotion recognition can help human-computer interactions by enabling systems to respond empathetically and adapt to users' emotional conditions. This capability improves user experience, supporting the development of a more intuitive and emotionally responsive communication system. This study analyzes a bimodal approach based on gender (male and female) to recognize emotions without contextual information in dialogue analysis. Utilizing the Multimodal EmoryNLP dataset extracted from the TV series Friends with acted speech, we focused on four primary emotions: Angry, Neutral, Joy, and Scared. The model used in this study for text classification is RoBERTa, and wav2vec 2.0 is used for audio feature extraction with the Bi-LSTM model for classification. The experiment results using weighted F1-score reveal that data augmentation enhanced the performance of analyzing the original dataset from 0.46% to 0.52% and the male dataset from 0.43% to 0.51 %. In comparison, the female dataset remained consistent at 0.46%. The weighted F1-score and Unweighted Averaged Recall (UAR) from the male dataset are higher, 51 % and 48%, respectively, than those from the female dataset, 46% and 47%, respectively. Gender-based analysis indicated that male and female datasets exhibited distinct performance patterns, highlighting variations in emotional expression and recognition between genders. These findings underscore the effectiveness of multimodal strategies in emotion recognition and suggest that gender-specific factors play a significant role in enhancing classification performance. While these results highlight performance trends, further validation through repeated trials and statistical analyses could provide stronger generalizations and insights into gender-based differences. © 2024 IEEE.

关键词： Emotion Recognition

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iDrone: IoT-Enabled Unmanned Aerial Vehicles for Detecting Wildfires Using Convolutional Neural Networks

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SN computer science 2022年第3期3卷 242页

作者： Sethuraman, Sibi Chakkaravarthy Tadkapally, Gaurav Reddy Mohanty, Saraju P. Subramanian, Anitha School of Computer Science and Engineering and Center of Excellence in Artificial Intelligence and Robotics (AIR) VIT-AP University Amaravati India Department of Computer Science and Engineering University of North Texas Denton 76207 TX United States School of Electronics Engineering and Center of Excellence in Artificial Intelligence and Robotics (AIR) VIT-AP University Amaravati India

The rise of global temperatures, over the past few decades, has disrupted the usual balance of nature. As a result of increasing temperatures, wildfires have destroyed millions of acres of land, thousands of structures, and homes. The pollution and toxic gases produced by the wildfires are carried out to thousands of miles, thus threatening the lives all around the world. Most wildfires occur due to anthropogenic factors, which cannot be predicted solely based on climate conditions. Henceforth, to detect wildfires before escalating, we propose iDrone, which is a wildfire detection system equipped with an end-to-end CNN image classification model: XtinguishNet, trained on a wildfire imagery dataset to detect the possible flames or smokes in an image. In addition, our approach also acquires the weather data and the intensity of the fire. Contrasting with existing wildfire detection systems, our proposed solution is a fusion of the Internet of Things (IoT) and Deep Learning, aiming to provide a one-stop solution for all the needs required to minimize the damage caused by wildfires. When validated and tested using various benchmark datasets, video surveillance, iDrone acquired a high accuracy of 98.36% with the least computational power. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： CNN computer vision Deep learning Forest fires iDrone Wildfires

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A 4-channel acoustic sensor system for sound source detection of the service robot

A 4-channel acoustic sensor system for sound source detectio...

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2024 IEEE International Conference on Real-Time Computing and robotics, RCAR 2024

作者： Hu, Jiaxin Lu, Bo Liu, Jinlin Zhou, Tiancheng Fan, Xinjian Liu, Xiaoang Dong, Dianbiao Chen, Long Northwestern Polytechnical University School of Mechanical Engineering Xi'an China Soochow University Robotics and Microsystems Center School of Mechanical and Electric Engineering Suzhou China Huazhong University of Science and Technology School of Computer Science & Technology Wuhan China Hebei University of Technology Tianjin Key Laboratory of Power Transmission and Safety Technology for New Energy Vehicles School of Mechanical Engineering Tianjin China

ISBN: (纸本)9798350372601

Constrained by the physical geometry of microphone arrays on the service robot, there exist limitations on both the minimum and maximum frequency bounds along with the coverage area. While the mobility of a microphone array carried by a robot offers the potential for a broader operational frequency range and a more comprehensive perspective, achieving this necessitates a practically more extensive and densely packed array configuration. This is often accomplished by employing non-synchronous measurements in beamforming. Here, we propose a novel approach for locating broadband sound sources using an asynchronous measurement technique that employs Laplacian-enhanced low-rank tensor completion. Our technique analyzes the tensor data structure within broadband signals and incorporates an alternating-direction approach based on multiplier optimization. Through the simulation study using only four physical channels, our Laplacian-enhanced algorithm has demonstrated the ability to generate a highly accurate sound map capturing distinct speech signal sources, thus providing a superior global view compared to the alternative convex surrogate. This advancement holds substantial promise for applications in which precise localization of broadband sound sources is paramount. © 2024 IEEE.

关键词： Tensors

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AgileVision Academy: Indoor Drone Navigation and Obstacle Avoidance 14

AgileVision Academy: Indoor Drone Navigation and Obstacle Av...

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14th International Conference on Information Technology in Medicine and Education, ITME 2024

作者： Jiang, Haolong Gu, Lingdan Wang, Jiayu Lan, Xiao Liu, Qinyao Wang, Fanxin Xi'an Jiaotong Liverpool University University of Liverpool School of Advanced Technology Department of Electrical and Electronic Engineering Suzhou China Xi'an Jiaotong Liverpool University School of Robotics Department of Intelligence Robotics Engineering With Contemporary Entrepreneurialism Suzhou China Xi'an Jiaotong Liverpool University School of Advanced Technology Department of Information and Computer Science Suzhou China Xi'an Jiaotong Liverpool University School of Advanced Technology Department of Electrical and Electronic Engineering Suzhou China Xi'an Jiaotong Liverpool University School of Advanced Technology Department of Mechatronics and Robotics Suzhou China

ISBN: (纸本)9798350356670

Navigating cluttered indoor environments presents a significant challenge for aerial robots, requiring agility, speed, and a high level of reliability to avoid collisions. This project aims to address this challenge by developing advanced algorithms for lightweight drones, enabling them to reach target location and carrying out delivery efficiently and safely. Utilizing camera data to map and avoid obstacles, we create a system that not only maps and perceives the environment but also navigates its path to the destination. Included in the educational content, this project demonstrates the effectiveness of the technology through practical applications and real-world testing, helping the next generation of engineers accumulate technical experience and broaden their understanding of drone navigation algorithms and applications in system integration. It has a milestone impact on education and development of the drone field. © 2024 IEEE.

关键词： Drones

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