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Cloud-Based AI Robotics for Precision Pruning of Fruit Trees to Improve Sustainable Farming 3

Cloud-Based AI Robotics for Precision Pruning of Fruit Trees...

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3rd International conference on intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Ilambirai, R.C. Barkathulla, A. Mahalakshmi, G. Babu, J. Jagan Prabu, K. Srinivasan, C. SRM Institute of Science and Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology School of Computing Department of Computer Science and Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India R.M.D Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Gojan School of Business and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331523893

The crucial agricultural practice of precision pruning fruit trees greatly impacts fruit production yield and quality. Traditional pruning techniques take a lot of time and money because they need skilled workers and are labor-intensive. It suggests a novel cloud-based AI robotics system intended to precisely prune fruit trees to address these issues. This system uses cutting-edge robotics fitted with Convolutional Neural networks (CNNs) to carry out precise and effective tasks related to pruning. Cloud computing integration makes it possible to process and make real-time decisions, improving the system's flexibility in responding to changing tree conditions and pruning needs. The system's primary parts are robotic arms for accurate cuts, various sensors for analyzing tree structures, and CNN models for maximizing pruning techniques. Early field trials show noticeable gains in operational efficiency and pruning accuracy compared to conventional techniques. This study demonstrates how cloud-integrated AI robotics can transform agricultural practices and improve fruit farming's sustainability and productivity. © 2025 IEEE.

关键词： Agricultural Automation Fruit Trees Real-Time Data processing sensor Integration Sustainable Farming

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Framework Design of Fault Diagnosis System Based on Improved LSTM networks

Framework Design of Fault Diagnosis System Based on Improved...

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Power Electronics, Computer Applications (ICPECA), IEEE International conference on

作者： Yixin Wang University of Geosciences (Wuhan) Wuhan China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

In the current era of high industrialisation, fault diagnosis of complex industrial systems is facing unprecedented challenges. Traditional fault diagnosis methods based on physical models and signal processing gradually show their limitations, especially when facing nonlinear and nonsmooth signals as well as fusion of multi-source information. In this paper, we propose a framework design of a fault diagnosis system based on a long short-term memory network (LSTM) with improved attention mechanism. The system employs high-precision sensors to collect real-time operating signals from key components, and analyses and diagnoses these signals through a deep learning model to quickly and accurately determine whether there is a fault in the equipment and provide detailed fault type and location information. In particular, an attention mechanism is introduced to enhance the model's focus on key features in the fault signals, thereby improving the accuracy and reliability of fault diagnosis. Experimental results show that the proposed LSTM-Attention network performs well on the bearing fault dataset and achieves high classification accuracy.

关键词： Fault diagnosis Deep learning Attention mechanisms Accuracy Signal processing sensor systems Real-time systems Power electronics sensors Long short term memory

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Smart IoT-enabled Water Quality Management using Generative AI and Super-Resolution GAN for Enhanced Monitoring and Prediction

Smart IoT-enabled Water Quality Management using Generative ...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International conference on

作者： Shofia Priyadharshini D G.P. Ramesh Department of Electronics and Communication Engineering St. Peter's Institute of Higher Education and Research Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

An essential component of sustainable development is water quality management, which requires accurate monitoring and forecasting capabilities. Conventional methods for assessing water quality often face challenges such as sparse data, sensor inaccuracies, and real-time processing limitations. To address these challenges, this study proposes a Smart IoT-Enabled Water Quality Management System that integrates Super-Resolution Generative Adversarial networks (SR-GAN) with artificial intelligence (AI) for enhanced monitoring and prediction. The system utilizes IoT -based smart sensors to collect real-time data on temperature, turbidity, oxygen concentration, pH levels, and other water quality ***-GAN enhances both the temporal and spatial resolution of low-quality sensor data, enabling better feature extraction and more precise information. Generative AI models are then employed for automated decision-making, anomaly detection, and forecasting, effectively identifying potential water pollution threats. Compared to conventional AI and deep learning methods, the proposed framework achieves superior performance in terms of response time efficiency (30% improvement), data quality, and prediction accuracy (98.7%). The integration of edge computing ensures low latency and real-time data processing. This innovative approach significantly enhances water quality monitoring accuracy, facilitating proactive measures and sustainable water resource management.

关键词： Temperature sensors Accuracy Generative AI Superresolution Water quality Generative adversarial networks Real-time systems Reliability Monitoring Anomaly detection

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A Study on Smart Assistive Stick for Visually Impaired Individuals

A Study on Smart Assistive Stick for Visually Impaired Indiv...

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Visual Analytics and Data Visualization (ICVADV), International conference on

作者： K.Y Megajai. P Nithish Kumar. K Lakshmi Prabha. Department of Advanced Communication Technology Chennai Institute Of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Vision impairment heavily affects mobility, information access, employment, and independence for millions of people across the globe. Assistive technology for blind and visually impaired persons has grown from conventional white canes to high-tech smart devices with ultrasonic sensors, GPS, GSM modules, and artificial intelligence (AI). The current review compares development in smart assistive devices, such as smart blind sticks, wearable navigation systems, and IoT-based solutions. New technology like AI-based obstacle detection, image processing, and sensor fusion provide higher accuracy and realtime feedback. Despite this, prohibitive costs, short detection ranges, and issues in dynamic environments prevent its broad application. This research highlights major advancements, their drawbacks, and directions for future enhancement of accessibility, safety, and autonomy for the visually impaired. The suggested smart assistive stick prototype combines multi-sensor technology with AI-based capabilities to improve mobility and independence, overcoming current limitations in assistive navigation devices.

关键词： GSM Accuracy Navigation Visual impairment Real-time systems Acoustics sensor systems Artificial intelligence intelligent sensors Global Positioning System

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23rd International Symposium on intelligent Data Analysis, IDA 2025

23rd International Symposium on Intelligent Data Analysis, I...

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23rd International Symposium on intelligent Data Analysis, IDA 2025

ISBN: (纸本)9783031913976

The proceedings contain 35 papers. The special focus in this conference is on intelligent Data Analysis. The topics include: Development of Models to Quantify Training Load in Outdoor Running Using Inertial sensors;estimating the Learning Capacity of Bacterial Metabolic networks;semi-supervised Learning with Pairwise Instance Comparisons for Medical Instance Classification;local-Global Data Augmentation for Contrastive Learning in Static Sign Language Recognition;siamCircle: Trajectory Representation Learning in Free Settings;synthetic Tabular Data Detection in the Wild;assessing the Graph Structure Learning in Graph Deviation networks;the When and How of Target Variable Transformations;Balancing Performance and Scalability of Demand Forecasting ML Models;balancing Global Importance and Source Proximity for Personalized Recommendations Using Random Walk Length;counterintuitive Behavior of Clustering Quality: Findings for K-Means on Synthetic and Real Data;BOWSA: A Contribution of Sensitivity Analysis to Improve Bayesian Optimization for Parameter Tuning;overfitting in Combined Algorithm Selection and Hyperparameter Optimization;local Subgroup Discovery on Attributed Network Graphs;imposing Constraints in Probabilistic Circuits via Gradient Optimization;improving Next Tokens via Second-to-Last Predictions with Generate and Refine;detection of Large Language Model Contamination with Tabular Data;Imbalanced Data Clustering via Targeted Data Augmentation Using GMM and LLM;make Literature-Based Discovery Great Again Through Reproducible Pipelines;extracting information in a Low-Resource Setting: Case Study on Bioinformatics Workflows;Vocabulary Quality in NLP Datasets: An Autoencoder-Based Framework Across Domains and Languages;expertise Prediction of Tetris Players Using Eye Tracking information;integrating Inverse and Forward Modeling for Sparse Temporal Data from sensor networks;bridging Spatial and Temporal Contexts: Sparse Transfer Learning;meta-learning and Data Au

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Research and Application of intelligent Driving Navigation Voice Based on Deep Learning Technology

Research and Application of Intelligent Driving Navigation V...

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sensors and information Technology (ICSI), International conference on

作者： Si Wu Sihui Cheng Baiqiang Gan Lishun Shen Nanfang College Guangzhou Country Guangzhou City Guangdong Province China Guangzhou Yangxin Technology Research Co. Ltd. Country Guangzhou City Guangdong Province China

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

With advancements in artificial intelligence, the Internet of Things, and sensor technology, intelligent driving-related technologies have emerged as a central focus for the global automotive industry's upgrade. Traditional in-vehicle voice systems primarily rely on rule-based engines or simple text-to-speech (TTS), which exhibit several limitations, including mechanical and monotonous prompt voices, inadequate multimodal coordination, and a lack of context awareness. These shortcomings hinder the ability of car navigation prompts to alleviate drivers' emotional anxiety during traffic jams. This paper proposes a design methodology for in-vehicle navigation voice systems based on deep learning technology, emphasizing the perception of pleasure. By integrating traffic environment data with driver emotion data and employing multimodal technology, we construct a CBGRU + graph neural network model. This model outputs navigation prompt voices that evoke pleasure and quantify the correlation between voice features (such as timbre, speech rate, emotional expression, and semantic content) and emotional perception (including pleasure and stress relief). Experimental results demonstrate that navigation prompt voices designed to evoke pleasure during traffic jams can effectively mitigate drivers' negative emotions and enhance the overall driving experience. This research on the design of car navigation prompt sounds, oriented towards the perception of pleasure, provides both theoretical and empirical support for voice interaction design in intelligent transportation systems.

关键词： Deep learning Correlation Navigation Anxiety disorders Semantics Graph neural networks Acoustics Timbre Vehicle dynamics Heart rate variability

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Research on intelligent Monitoring System for Special Vehicles Based on Cloud Computing and Internet of Things 25

Research on Intelligent Monitoring System for Special Vehicl...

引用

Proceedings of the 2025 3rd International conference on Communication networks and Machine Learning

作者： Kuang Lu Xia Song Wuhan Vocational College of Software and Engineering Wuhan Hubei China Wuhan Polytechnic Wuhan Hubei China

ISBN: (纸本)9798400713231

As cloud computing and Internet of Things (IoT) technologies continue to advance rapidly, there is a growing focus on intelligent monitoring systems for special vehicles. This paper investigates the design and implementation of an intelligent monitoring system that leverages cloud computing and IoT to enhance the management efficiency and safety of these vehicles. By combining the robust storage and processing capabilities of cloud computing with the extensive sensing and real-time communication features of IoT, a comprehensive monitoring platform is developed. The system utilizes a variety of sensors and network devices to gather real-time data on operational status, location, and environmental conditions of special vehicles, which is then transmitted to the cloud for analysis and processing. The system not only supports real-time monitoring, data storage and historical query functions, but also uses intelligent algorithms to realize anomaly detection, early warning and decision support, which effectively improves the safety management level of special vehicles. The research shows that the intelligent monitoring system for special vehicles based on cloud computing and Internet of things can effectively solve the problems of information island and data processing lag in traditional monitoring methods, and improve the timeliness and accuracy of monitoring. Through the actual case analysis, the system has achieved good application results in special vehicle scheduling, maintenance, fault warning and other aspects, and has important promotion value. This paper offers a theoretical foundation and practical insights for the optimized development of intelligent monitoring systems for special vehicles.

关键词： Cloud Computing

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Optimization of Enterprise Management Accounting based on Temporal Convolution Network with Gated Recurrent Unit

Optimization of Enterprise Management Accounting based on Te...

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intelligent Systems and Computational networks (ICISCN), International conference on

作者： Tingting Zhou School of Accounting and Finance Wuxi Vocational Institute of Commerce China

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

In recent years, Enterprise Management Accounting (EMA) has developed rapidly by transforming from traditional cost accounting to extensive range of activities which includes risk management and performance measurement. However, EMA consists several challenges such as slow manual peed and analysis which provides difficulty in processing huge quantity of data. To resolve the above issues, Temporal Convolutional Network (TCN) with Gated Recurrent Unit (GRU) namely (TCN-GRU) is proposed for optimizing the EMA. Initially, an enterprise financial management model is designed for incorporating multiple modules. Then, the proposed TCN-GRU is employed for predicting the financial risk effectively by capturing the high-order features and filters unwanted data from financial information. After that, the Multi-Objective Dragonfly Algorithm (MODA) is applied for optimizing the EMA model by generating various solutions that represents exchanges among minimizing risk and maximizing returns. From the results, the proposed TCN-GRU attained better results in terms of True Positive Rate (TPR) of 80.90%, True Negative Rate (TNR) of 83.67%, Total Rate (TR) of 82.40% when compared to the existing TCN-Long Short-Term Memory (LSTM).

关键词： Adaptation models Computational modeling Soft sensors Refining Predictive models Prediction algorithms Convolutional neural networks Risk management Forecasting Optimization

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Design and Implementation of a Flexible Wireless sensor Network for Environmental Monitoring with in Digital Cities Framework

Design and Implementation of a Flexible Wireless Sensor Netw...

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intelligent Systems and Computational networks (ICISCN), International conference on

作者： B. Sai Manogna R Gowri Shankar Rao K Ravi Chythanya Navamani C K. S. Raghuram J. Samson Isaac Department of Computer Science and Engineering Marri Laxman Reddy Institute of Technology and Management Hyderabad India Department of Physics Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India School of Computer Science and Artificial Intelligence SR University Warangal India Department of Computer Science and Engineering Nandha Engineering College (Autonomous) Erode India Department of Mechanical Engineering Vignan's Institute of Information Technology (A) Visakhapatnam India Department of Biomedical Engineering School of Engineering and Technology Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

The paper highlights a Wireless sensor Network (WSN) implementation for environmental monitoring, emphasizing cost-effectiveness and adaptability. APIs designed to handle values instead of voltage levels increase system resilience, while open standards promote security, cost efficiency, and auditability. The data shows a diurnal temperature range from 20°C (night) to 38°C (day), stable humidity at 38%, and UV radiation peaking at 6 (day) and dropping to 1.5 (night). Smoke levels are consistently high at 490 μg/m 3 , while toxic gas concentrations slightly rise to 0.5 ppm during the day from 0.45 ppm at night. Fire metrics remain low and stable. These findings highlight the need for UV protection, air quality controls, and consistent fire safety measures. The system achieves effective environmental monitoring while remaining adaptable for future needs, underscoring its value in scalable, cost-efficient, and sustainable public management solutions.

关键词： Temperature sensors Wireless sensor networks Temperature distribution Technological innovation Zigbee User interfaces Environmental monitoring Security Sustainable development Standards

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Multi-Modal Sensing Aided RIS Communications

Multi-Modal Sensing Aided RIS Communications

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US National Committee of URSI National Radio Science Meeting (USNC-URSI NRSM)

作者： Tawfik Osman Aditya S. Shekhawat Ahmed Alkhateeb Georgios C. Trichopoulos Arizona State University Tempe AZ USA

ISBN: (数字)9781946815200

ISBN: (纸本)9798331513979

Reconfigurable intelligent Surfaces (RIS) have emerged as one of the key enabling technologies for beyond 5G and 6G networks due to their capability to create virtual line-of-sight (LOS) paths between transmitters and receivers in non-line-of-sight (NLOS) environments. However, to achieve narrow beams and significantly enhanced received power, RIS must incorporate a large number of elements. This, in turn, presents a challenge due to the substantial beam training overhead required for optimal beam selection, particularly when dealing with larger RIS arrays. In this work, we propose a novel approach that leverages multi-modal sensing data to guide the beam selection process in RIS, especially in mobile environments where frequent beam alignment is necessary. This approach is motivated by promising results from recent research (G. Charan, T. Osman, A. Hredzak, N. Thawdar and A. Alkhateeb, “Vision-Position Multi-Modal Beam Prediction Using Real Millimeter Wave Datasets,” 2022 IEEE Wireless Communications and Networking conference (WCNC), 2022, pp. 2727-2731; U. Demirhan and A. Alkhateeb, “Radar Aided 6G Beam Prediction: Deep Learning Algorithms and Real-World Demonstration,” 2022 IEEE Wireless Communications and Networking conference (WCNC), 2022, pp. 2655–2660), which demonstrated that multi-modal sensing, combined with machine learning, can effectively extract wireless channel information and map it to optimal mmWave beams in vehicle-to-infrastructure (V2I) communication scenarios. These findings suggest that sensing data from co-located sensors and mmWave systems can significantly reduce the beam training overhead in mmWave communication, with minimal impact on system performance.

关键词： Training 6G mobile communication Wireless communication Wireless sensor networks Vehicle-to-infrastructure System performance Reconfigurable intelligent surfaces sensor systems sensors Millimeter wave communication

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