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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Ramkumar, M. Siva Nivetha, R. Gopan, Gokul Kanan, Mohammad Baig, Rahmath Ulla Giri, Jayant Sns College of Technology Dept of Ece India Kumaraguru College of Technology Computer Science and Engineering India Saveetha University Department of Mechanical Engineering Chennai602105 India College of Engineering University of Business and Technology Department of Industrial Engineering Jeddah21448 Saudi Arabia College of Engineering Zarqa University Department of Mechanical Engineering Zarqa Jordan Cambridge Institute of Technology Department of Artificial Intelligence and Machine Learning Bangalore560086 India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India

ISBN: (纸本)9798331523923

Using deep learning algorithms to extract important agricultural traits has made crop production prediction based on environmental, soil, water, and crop parameters an important area of research. But conventional approaches have trouble creating a straight linear or non-linear mapping between yield values and raw data, and the quality of the characteristics that are extracted has a significant impact on how well they work. Deep reinforcement learning builds a strong prediction framework by combining the intelligence of deep learning with reinforcement learning to get beyond these drawbacks. In order to anticipate agricultural yields accurately and sustainably, this work aims to create a Dual-Branch Geometric Progressive Feedback Cosine Convolutional Network with Skill Optimization Algorithm (DB-GPFCNet-SOA). Robust Double-Weighted Guided Filtering (RDWGF) is used for data preprocessing in order to minimize noise and improve important components. To ensure a complete prediction model, the N-Branch retrieves local neighbor-based information while the C-Branch records global geometric properties. Moreover, the accuracy is enhanced by the SOA, which enhances loss parameters. The suggested model has RMSE of 0.25 and MAE of 0.15 with exceptional accuracy of 99.2%, precision of 99.1%, recall of 99%, and F1-score of 99%. These results validate the performance of the model, which renders it a suitable approach for real-time agricultural applications. © 2025 IEEE.

关键词： Deep reinforcement learning

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UAV-aided 1D Wireless Power Transfer with Non-Linear Energy Harvesting

UAV-aided 1D Wireless Power Transfer with Non-Linear Energy ...

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IEEE International Conference on Communications in China (ICCC)

作者： Liang Ma Yueling Che Sheng Luo C. M. Victor Leung College of Computer Science and Software Engineering Shenzhen University China

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

This paper considers the unmanned aerial vehicle (UAV) aided wireless power transfer (WPT) to one-dimensionally (1D) deployed ground nodes (GNs) with practical non-linear energy harvesting (EH) modules. We jointly optimize the UAV’s tr’ajectory and its WPT decisions over time to minimize the UAV’s total energy consumption, subject to each GN’s EH constraint for meeting its energy requirement. However, this problem is a mixed-integer and non-convex optimization problem, and is generally difficult to solve optimally. First, we focus on the single-GN scenario. We reveal that the optimal successive hover-then-fly (SHF) UAV trajectory proposed in the literature for 1D deployed GNs with linear EH is no longer optimal for our considered problem. We propose a novel two-case based UAV trajectory design. For the case when the UAV is far from the GN, the UAV does not transmit energy and adopts the one-way flight trajectory with the optimally determined speed; and for the case when the UAV is close to the GN such that the GN can harvest non-zero energy, the UAV keeps transmitting energy, and adopts a newly proposed two-way round flight trajectory, where the flight rounds, UAV speed and the returning points are all optimally designed. Next, by extending the solutions from the single-GN scenario to the general multi-GN scenario, the original UAV energy minimization problem is solved efficiently. Finally, numerical results are provided to show the superior performance of our proposed scheme than the SHF-based benchmark.

关键词： Energy consumption Wireless power transfer Benchmark testing Autonomous aerial vehicles Minimization Trajectory Topology

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Hand Gesture Recognition for Disabled Person Using IoT 1

Hand Gesture Recognition for Disabled Person Using IoT

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1st International Conference on computer science and Emerging Technologies, CSET 2023

作者： Anand, R. Yuvanchandru, M. Vishnu, B. Rahul, R.R. Arunkumar, Ranjan Joseph Aarupadai Veedu Institute of Technology Vinayaka Mission's Research Foundation Department of Computer Science and Engineering Chennai India Kcg College of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350341737

This paper aims to address the communication barrier between individuals who have audio-vocal impairments and those who do not. Communication is typically reliant on verbal and auditory cues, making it difficult for individuals with audio-vocal impairments to engage in conversations effectively. The primary challenge stems from the limited understanding of sign language, which is commonly used by the audio-vocally impaired community. To overcome this challenge, the paper introduces a smart glove as a solution. The smart glove is designed to enable communication between disabled individuals and nondisabled individuals by utilizing finger movements. Each finger of the glove can be bent or manipulated in different ways to convey specific messages or meanings. The smart glove acts as a bridge, allowing the disabled person to express themselves through intuitive finger movements, while the non-disabled person can interpret and understand the intended message. By converting finger movements into understandable information, the smart glove facilitates real-time communication and enhances the inclusivity of conversations between individuals with audio-vocal impairments and those without. This innovative solution opens opportunities for improved social interactions, educational experiences, and professional engagements for individuals with audio-vocal impairments. It helps to break down the barriers that prevent effective communication, promoting a more inclusive society where people of all abilities can interact and understand each other more easily. © 2023 IEEE.

关键词： Cryptography

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Application of Deep Neural Network for Extraction of Roads from High-Resolution Satellite Images

Application of Deep Neural Network for Extraction of Roads f...

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2024 International Conference on Smart Systems for Electrical, Electronics, Communication and computer engineering, ICSSEEC 2024

作者： Lafta, Alaa M. Taha, Ahmed Abed, Ansam Mohammed Ali, Hanan Mahmood Shukur Majed, Safa Abdulaziz, Ismail Hussein, Laith National University of Science and Technology Department of Medical Devices Engineering Technologies Nasiriyah Dhi Qar Iraq Al-farahidi University Medical Instruments Engineering Techniques Baghdad Iraq Al-Turath University College Computer Technologies Engineering Baghdad Iraq Al-Kitab University Department of Computer Engineering Technologies Kirkuk Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq Al-Maamoun University College Computer Technology Engineering Baghdad Iraq College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq

ISBN: (纸本)9798350378177

Recent breakthroughs in deep-learning approaches have revolutionized the extraction of roads from high-resolution satellite images. However, most currently available deep learning techniques and network approaches produce inconsistent and partial results due to shadows and occlusions. The effectiveness of plan creation is substantially improved by road segmentation from satellite photos, which may be of considerable use in automobile navigation systems and every rescue technology that requires immediate mapping. In light of this, more and more effort is being put into discovering effective strategies for extracting geographically relevant elements (such as roads) from digitized distant sensing photos. Remote sensing may now be widely used to evaluate road conditions without causing any damage. With these instruments, transport infrastructure gets thoroughly and regularly surveyed. Road extraction from remote sensing is always present in all neural networks, notwithstanding their wide variability. Retrieval methodologies that investigate various road features and models include classification-based approaches, knowledge-based approaches, conceptual frameworks, dynamic shaping models, and quadratic programming methods. Road features, specimens for testing, and flaws were compared across studies using the REHR-DNN technique described here. The paper suggests using the REHR-DNN method for automatic road extraction, which involves segmenting the picture to locate the road network regions and employing a continuity mechanism to accurately detect the roads and isolate the straight or curved sections that make up the roads. Further, the suggested network can investigate and include the concealed relationships between roadway characteristics and their global interdependence in geographical and temporal aspects by developing a novel focus module. Finally, the results of a very recent study were concisely described. It is impossible to produce a good extraction effect by relyi

关键词： Satellite imagery

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Trajectory Planning of Underwater Snake-Like Robot Base on Gaussian Pseudospectral Method 42

Trajectory Planning of Underwater Snake-Like Robot Base on G...

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42nd Chinese Control Conference, CCC 2023

作者： Guo, Pengcheng Sun, Junqing Sun, Hao College of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China College of Artificial Intelligence Nankai University Tianjin300350 China

ISBN: (纸本)9789887581543

Compared with the traditional underwater unmanned system, the underwater snake-like robot shows higher performance and terrain avoidance ability in complicated external environment. However, it also puts higher requirement on its control methodology. To achieve accurate obstacle avoidance in underwater terrain constraints, a new trajectory planning scheme is designed in this paper. First, the model of underwater snake-like robot is built. The shape of snake is considered in the model. Then, the hp adaptive Radau pseudospectral is applied to plan the trajectory of three degree of freedom of snake-like robot model. Finally, the simulation results show that the method is feasible and effective. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Trajectories

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Deep Learning-based Predictive Maintenance for Industrial IoT Applications 7

Deep Learning-based Predictive Maintenance for Industrial Io...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Jeba Emilyn, J. Vinod Kumar, D. David Samuel Azariya, S. Prakash, M. Sam Thamburaj, A. Sona College of Technology Information Technology Tamil Nadu Salem India Biomedical Engineering Vinayaka Mission's Kirupananda Variyar Engineering College Tamil Nadu Salem India Computer Science Vinayaka Mission's Kirupananda Variyar Arts And Science College Tamil Nadu Salem India Vinayaka Mission's College of Physiotherapy Tamil Nadu Salem India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359299

Integrating Deep Learning-based Predictive Maintenance (DL-PM) in Industrial Internet of Things (IoT) applications optimizes operational efficiency and reduces downtime. This reaserch work explores the synergy between deep learning and predictive maintenance within the Industrial IoT context. The study traces the evolution from traditional maintenance to modern deep learning methods facilitated by IoT's real-time data acquisition. Methodologically, it details data preprocessing, model selection, and design for DL-PM. Convolutional Neural Networks (CNNs) analyze sensor data, Recurrent Neural Networks (RNNs) predict time-series patterns and hybrid models incorporate transfer *** reaserch work demonstrates DL-PM's application across industries through diverse case studies, evaluating its performance and comparing it to conventional approaches. It highlights challenges like data quality, model interpretability, scalability, ethical concerns, and biases. Future directions encompass advanced deep learning techniques, edge-cloud integration, collaborative learning, and strategies to overcome challenges. DL-PM's potential to revolutionize industrial processes in the IoT era is *** reaserch work underscores the transformative impact of Deep Learning-based Predictive Maintenance in Industrial IoT. It provides insights into implementation, challenges, and prospects, guiding industries towards efficient, downtime-minimized operations through DL-PM integration. © 2024 IEEE.

关键词： Internet of things

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Biologically Interpretable Model for Precise Recurrence Prediction of Non-Small Cell Lung Cancer 46

Biologically Interpretable Model for Precise Recurrence Pred...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Ai, Yang Du, Xiuju Liu, Jing Li, Yinhao Lin, Lanfen Li, Jingsong Chen, Yen-Wei Ritsumeikan University Graduate School of Information Science and Engineering Shiga Japan Research Center for Healthcare Data Science Zhejiang Lab Hangzhou China Ritsumeikan University College of Information Science and Engineering Shiga Japan Zhejiang University College of Computer Science and Technology Hangzhou China

ISBN: (纸本)9798350371499

The precise prediction of preoperative recurrence in non-small cell lung cancer (NSCLC) that is suitable for clinical application is still an open question. Recent advancements integrating genomic data with deep learning have shown promise in enhancing recurrence analysis in NSCLC patients. However, the lack of interpretability in the decision-making process of DNN models has hindered their clinical trustworthiness. In this paper, we propose a novel Biologically Informed Pathway-Aware Neural Network (BioPAN). By automatically extracting biological prior knowledge to guide the architecture of DNN models, we design a unified architecture of gene-pathway-biological process-disease. This approach endows each neuron with entity meaning and learns a multi-level view of biological pathways and processes related to recurrence for fully interpretable NSCLC recurrence prediction. We demonstrated that the proposed model well explains the molecular mechanisms linking genes to NSCLC recurrence, identifies several genes that significantly promote and inhibit recurrence, and elucidates the pivotal roles of various gene pathways at the biological process level. Moreover, it outperforms classical machine learning methods, provides fully interpretable biological information, and requires fewer parameters. Broadly, BioPAN enables clinical discovery and prediction, and may have general applicability across cancer types. © 2024 IEEE.

关键词： Lung cancer

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Application of 3D Magnetic Resonance Imaging in the Characterization of Brain Tumors 2

Application of 3D Magnetic Resonance Imaging in the Characte...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Singh, Samender Kishore, Lalit Badhoutiya, Arti Sagar, Pramod Kumar Saraswat, Laxmi Ajay Kumar Garg Engineering College Department of Computer Science & Engineering UP Ghaziabad India SRMIST Delhi Ncr Department of Computer Application Modinagar UP Ghaziabad India Gla University Department of Electrical Engineering UP Mathura India Raj Kumar Goel Institute of Technology Srmist Department of Computer Science & Engineering UP Ghaziabad India Abes Engineering College Department of Computer Science UP Ghaziabad India

ISBN: (纸本)9798350371055

MRI has revolutionized the analysis and remedy of disorder by using imparting precise and accurate images of soft tissue. This technology has been similarly superior by means of the improvement of 3-D MRI, which permits improved detection and characterization of tumors and improves accuracy of diagnosis and remedy. 3D MRI has been found to be in particular beneficial within the characterization of brain tumors as it offers a greater detailed assessment of tumor size, shape and composition, in comparison to traditional imaging techniques including X-ray or CT. This information can help in identifying the nature of the tumor that may impact its remedy and analysis. Furthermore, 3D MRI is useful for figuring out the chance of malignant invasion, which can provide important information for affected person control. Additionally, the advanced decision of 3-D MRI permits better delineation of areas of healthy and diseased tissue that can assist clinicians in making plans complex surgeries. (3-D) MRI has emerged as an increasingly more critical tool inside the characterization of brain tumors. The three-D imaging affords distinct anatomical visualization of the tumor and it's courting to surrounding systems, allowing for extra effective remedy making plans. Furthermore, the potential to visualize tumor tissue parameters together with edema, necrosis, and perfusion may be used to decide whether a tumor is malignant or no longer. 3D MRI has additionally enabled the evaluation of tumor vascularization, which serves as a vital prognostic indicator. The statistics received from 3-D imaging can then be used to be expecting the recurrence of a tumor following treatment, supporting to manual decisions on comply with-up remedies. The reduction in test time, decrease value, and better resolution of 3-D MRI in contrast to standard x-ray imaging also makes the technology more and more attractive for brain tumor management. © 2024 IEEE.

关键词： Attractive Characterization Superior Traditional

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Real Time Object Detection & Recognition: A ComparativeStudy of YOLOv3 and YOLOv7 in OpenCV 15

Real Time Object Detection & Recognition: A ComparativeStudy...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Geethanjali, T.M. Prithviraj, B. Prajwal, K.M. Gowda, Prajwal C.M. Priyanka Sujithra, M. Chaba, Yogesh PES College of Engineering Department of ISE Mandya India Department of Computing-Data Science Coimbatore Institute of Technology Coimbatore India Department of Computer Science and Engineering Guru Jambeshwar University of Science and Technology Haryana Hisar India

ISBN: (纸本)9798331300579

Real-time object detection is a fundamental task in computer vision, finding applications in various domains such as autonomous vehicles, surveillance systems, robotics, and *** proposed work presents the design and implementation of a real-time object detection system using OpenCV (Open-Source computer Vision Library).The system aims to accurately and efficiently detect and localize objects in video streams or captured *** proposed work begins with dataset collection and annotation, acquiring a diverse dataset of images with annotated bounding boxes representing objects of *** annotated dataset is used for model training and *** deep learning algorithms are considered for object detection, including Single Shot MultiBox Detector (SSD), You Only Look Once (YOLO), and Faster R-CNN, and their performance is compared to identify the most suitable *** techniques like resizing, normalization, and noise reduction are applied to enhance the quality of the input *** extraction is performed using deep learning models VGG16, which is fine-tuned on the annotated *** selected deep learning model is integrated into the real-time system using OpenCV's *** system is evaluated using standard metrics like precision, f1 score, recall, and mean average precision (mAP) to assess its detection *** evaluation is carried out on benchmark datasets and real-world scenarios to gauge the system's robustness and generalization capabilities using two different YOLO models i.e., YOLOv3 and YOLO v7. © Grenze Scientific Society, 2024.

关键词： Deep learning

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Adaptive dynamic surface control for quadrotor-slung load transportation system with uncertainties

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science China(Technological sciences) 2023年第10期66卷 2917-2929页

作者： DING Feng HUANG Jian SUN Chong AI Yong XU WenXia YANG ChenGuang School of Computer Science South-Central Minzu UniversityWuhan 430074China Key Laboratory of Ministryof EducationforImage Processing and Intelligent ControlHubei Key Laboratoryof Brain-inspired Intelligent Systems School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Engineering Wuhan Institute of TechnologyWuhan 430205China Key Laboratory of Ministry of Education for Autonomous Systems and Networked Control Guangdong Engineering Technology Research Center of Control of Intelligent SystemsCollege of Automation Science and EngineeringSouth China University of TechnologyGuangzhou 510640China

Quadrotors play a significant role in our lives and are transforming our *** cable-suspended loads is an unavoidable quadrotor application trend and a hot research topic in the control ***,the load swing and unpredictability pose significant challenges to the quadrotor's *** this paper,an anti-swing controller with an inner-outer control strategy for the quadrotor-slung load transportation system is *** facilitate the controller design,the outer position dynamics are restructured in the form of ***,a virtual controller is created to force the underactuated states to the dynamic surface to ensure the position subsystem's *** improve robustness,an adaptive law is used to eliminate the effects of uncertain cable ***,a dynamic surface controller for the inner attitude subsystem is presented to drive the actual force to the virtual *** is demonstrated that the control strategy can stabilize the quadrotor despite mass and cable length *** results are provided to demonstrate the efficacy and durability of the proposed method.

关键词： quadrotor-slung load transportation system dynamic surface control adaptive law cascade system model uncertainties

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