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检索条件"任意字段=2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025"

共 517 条记录，以下是81-90 订阅

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Enhancing Master data Management Within Enterprise Resource Planning Systems Using Artificial Intelligence for Optimized Business Processes

Enhancing Master Data Management Within Enterprise Resource ...

引用

visual analytics and data visualization (icvadv), international conference on

作者： Sachin More Sampath Kumar Mucherla SME in ERP Architect Independent researcher Atlanta USA SAP Supply Chain Architect Independent researcher Texas USA

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Master data Management has been at the heart of the streamlining of data governance and its integrity, and the penetrations even into the Enterprise Resource Planning systems. This paper proposes, from the perspective of ERPs, how the integration of Artificial intelligence into MDM procedures and processes can leverage Business workflows. Through deployment of AIenabled algorithms, the enterprises can execute an entire set of operations including data validation, cleansing as well as enrichment at the same moment. This improves real time decision-making and optimal resource management. In the paper, AI techniques such as machine learning, natural language processing, and predictive analytics are used to enrich data quality, eliminate redundancies, and remove inconsistencies. It portrays a new architecture that embodies AI capabilities within existing ERP frameworks, with specific emphasis on modular adaptability and scalability. Experimental results suggest that data processing speed and accuracy improvements, which are the potential of AI to revolutionize MDM in ERP systems, are substantial. This research concludes with a discussion of challenges and limitations, and further directions toward smooth AI adoption in enterprise environments.

关键词： data integrity visual analytics Scalability Redundancy Enterprise resource planning Real-time systems Resource management Artificial intelligence Predictive analytics Business

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Review on the Real-Time Mapping of Autonomous Vehicles: Technologies and Challenges

Review on the Real-Time Mapping of Autonomous Vehicles: Tech...

引用

visual analytics and data visualization (icvadv), international conference on

作者： C Paveithra U Jaivasanthan K Kirubasankar Department of Electronics and Communication Engineering Chennai Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Autonomous vehicles rely on real-time mapping systems to ensure effective navigation, avoid obstacles, and plan their paths accurately. This paper attempts a detailed review of the technologies, and challenges involved in real-time mapping for AVs. The core technologies comprise Lidar, radar, cameras, GNSS, and IMU as made use of in aggregate to build high-definition maps of the environment. Techniques such as SLAM and high-definition mapping can update the map continuously when deployed over dynamic environments. It is precisely this capability that qualifies AVs as appropriately responsive to reality changes. Yet, such is an extremely challenging task that falls within the broad scope of latent issues, resource utilization through computation, adaptability, and sensor reliability, then discussed several promising future directions for improving the real-time mapping capabilities of AVs, namely, edge computing and vehicle to everything (V2X) communication. These can bring a great advantage to improving the performance of AVs by processing vast quantities of real-time data extremely quickly.

关键词： Simultaneous localization and mapping Laser radar Reviews Navigation visual analytics Real-time systems Vehicle dynamics Autonomous vehicles Vehicle-to-everything Edge computing

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A Novel Approach for Authenticating Churna Medicine Using Transfer Learning

A Novel Approach for Authenticating Churna Medicine Using Tr...

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visual analytics and data visualization (icvadv), international conference on

作者： S Gandhimathi Alias Usha P Uma Maheswari A Archita K Bharathi Department of Electronics and Communication Engineering (Autonomous) Velammal College of Engineering and Technology Madurai India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

In the field of Siddha medication system, Churna is a fine herbal powder used in Indian medicine. Identifying the specific type of Indian siddha medicine (churna) based on visual characteristics is a challenging task. This paper proposes an efficient method for churna identification using transfer learning techniques A manual dataset of 1555 churna images was created in which 1120 images are given for training, 195 images are given for validation and 240 images are given testing purposes. Experimentation has been carried out using transfer learning techniques such as VGG16, ResNet50, DenseNet121, MobileNetV2, and ResNet101. The experimental analysis shows that ResNet101 achieved the highest accuracy of 84% compared to the other pre trained models. This approach offers a promising solution for accurate churna identification even for the individuals who does not have a prior knowledge about Churna.

关键词： Training Adaptation models Accuracy visual analytics Transfer learning Manuals data models Biomedical imaging Testing Residual neural networks

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Deep Learning for Real-Time Malware Detection and Classification

Deep Learning for Real-Time Malware Detection and Classifica...

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visual analytics and data visualization (icvadv), international conference on

作者： Shantanu Sudhir Gujar Department of Cyber Security Golisano College of Computing and Information Sciences Rochester Institute of Technology Rochester NY

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

The increased deep learning styles have been adopted to improve the identification and detection of malware in real time. This improves the detection and detection of malware. The research focuses on the utilization of Convolutional Neural Networks (CNNs) to recognize malware patterns. These techniques are utilized to accomplish the aforementioned goals. The incorporation of these styles results in an improvement in both the delicacy and the speed of operation when it comes to transmitting viruses. Additionally, the research highlights the application of DeepLearning4J (DL4J), which is an important framework for deep learning, to successfully deploy these models, which enables large-scale malware analysis operations. This is demonstrated by the research. During the research, significant advancements were made in the detection of malware in real-time, and strong results were obtained in the fight against developing cyber threats. One way in which these developments are displayed is through the combination of different approaches and technologies that are utilized.

关键词： Deep learning Recurrent neural networks visual analytics Heuristic algorithms Autoencoders Malware Real-time systems Pattern recognition Convolutional neural networks Long short term memory

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IoT-Driven Predictive analytics for Precision Harvest Timing with LightGBM Models

IoT-Driven Predictive Analytics for Precision Harvest Timing...

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2025 international conference on visual analytics and data visualization, icvadv 2025

作者： Lourdu Jame, S. Ranganathan, Chitra Sabapathy Saravanan, S.K. Tidke, Bharat Punarselvam, E. Rajmohan, M. Srm Institute of Science and Technology Department of Electrical and Electronics Engineering Tamil Nadu Kattankulathur Chennai India Mphasis Corporation Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Big Data and Network Security Tamil Nadu Chennai India University Pune India Department of Information Technology Tamil Nadu Namakkal India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331521394

In agriculture, precise harvest time is essential to maximize production and quality. A predictive analytics system powered by the Internet of Things (IoT) and using LightGBM models for accurate harvest time determination is presented in this study. The framework incorporates IoT sensors to gather environmental data, such as soil moisture, light intensity, temperature, and humidity, which are important markers of crop maturity. The data is analyzed using machine learning models, namely LightGBM, to estimate when to harvest accurately. The proposed framework seeks to improve agricultural efficiency by using real-time data analytics. This would allow farmers to make better crop output, waste, and resource utilization choices. Farmers may dynamically adjust their harvesting plans in response to changing crop conditions due to the IoT-driven strategy that enables constant monitoring of environmental variables. By reducing input waste and environmental effects, this capacity enhances agricultural operations' total productivity while supporting sustainable farming practices. The proposed model can transform harvest management using advanced predictive analytics and IoT technologies, as shown by case studies and simulations confirming its efficacy in several agricultural settings. © 2025 IEEE.

关键词： Prediction models

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Automatic Weed Detection and Removal Using AI

Automatic Weed Detection and Removal Using AI

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visual analytics and data visualization (icvadv), international conference on

作者： K. Kishore Babu D. Sharada Mani P. Seetha Lakshmi B. Suresh D. V. Ashok Mohana Prasad Mendu Dept. of CSE QIS College of Engineering and Technology (A) Ongole AP India Dept. of AIML QIS College of Engineering and Technology (A) Ongole AP India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Real-time weed detection and identification in autonomous weeding systems are obtained by combining computer vision and machine learning. In this research study, High-resolution images of the field are taken by cameras, which are then processed with convolutional neural networks (CNNs) to precisely detect various types of vegetation. The proposed system is trained on a large and varied dataset, providing high accuracy under different environmental conditions. Through accurate targeting and eliminating undesirable plants with mechanical claws or targeted sprayers, this method maximizes minimal weed presence while maintaining soil and crop integrity. The process improves sustainable agriculture, lowers operational expenditure, and maximizes overall efficiency.

关键词： Accuracy visual analytics Vegetation mapping Soil Robot sensing systems Cameras Real-time systems Agriculture Convolutional neural networks Artificial intelligence

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Quantum Based Grover's Algorithm and Graph Coloring in Unstructured Searches for Bit String Validation

Quantum Based Grover's Algorithm and Graph Coloring in Unstr...

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visual analytics and data visualization (icvadv), international conference on

作者： S Vinodhkumar P Pravin P Kumar Department of CSE Rajalakshmi Engineering College Thandalam Chennai

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Quantum computing based Grover's algorithm performs unstructured search problems in unsorted data to structured data in validating bit string through graph coloring. The quantum Grover's algorithm and graph theory was looked at in graph coloring. In graph coloring algorithm can be leveraged to validate coloring assignments for graph with arbitrary structures. It's accuracy and efficiency has been achieved by the iteration of square root (N) By adapting Grover's search mechanism, we aim to optimize the process of finding valid coloring in a graph, which is a crucial NP-complete problem in classical computation. This algorithm determines shortest node to see if the gates can be colored rightly for detect validation bit string. To get a validated a significant advantage of the hybrid classical-quantum approach reduces time complexity and exhaustive search. Grover's algorithm reduces the number of iterations needed for a solution, providing a quantum advantage in efficiency.

关键词： Quantum advantage visual analytics Quantum mechanics Search problems Graph theory Registers Resource management Time complexity Quantum circuit Radio spectrum management

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Design, Analysis and Verification of AMBA AXI4.0

Design, Analysis and Verification of AMBA AXI4.0

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visual analytics and data visualization (icvadv), international conference on

作者： Aashima Lalit Mohan Saini School of VLSI and Embedded System Design National Institute of Technology Kurukshetra Haryana India Department of Electrical Engineering National Institute of Technology Kurukshetra Haryana India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

AMBA AXI was developed by ARM in 2003 upgrading it to AMBA AXI 4.0 in 2014. AMBA AXI4.0 is a fast, efficient, high frequency, high performance, high bandwidth SOC (system-on-chip) protocol that is used for communication between CPU (central processing unit) and FPGA (field programmable gate arrays). It has five communication channels for reading and writing between master and slave. It supports burst transactions and has handshake mechanism, which makes it suitable for high data transfers rate and for pipeline processing of instructions. It finds applications in various ARM (advanced RISC (reduced instruction set computer) machine) based FPGAs like Zynq-7000, Zynq Ultrascale+, Spartan 7, Arria 10 etc. This work implements the AXI4.0 protocol for communication between master and slave for 32 bits of data transfer and verification of using protocol checker.

关键词： Reduced instruction set computing visual analytics Communication channels Writing data transfer Main-secondary Central Processing Unit System-on-chip Field programmable gate arrays Pipeline processing

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Explainable AI for Gastrointestinal Disease Detection using Ensemble Deep Learning Techniques

Explainable AI for Gastrointestinal Disease Detection using ...

引用

visual analytics and data visualization (icvadv), international conference on

作者： Sale George Gideon P. Joyce Beryl Princess Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

This is a GI disease and a global threat, with treatment that doesn't take long when diseases are rightly identified. This study is introducing a novel approach to use multiple AI deep learning systems, to use for GI diseases identification and to offer explanations. In order to distinguish a few details amid the medical images based on endoscopy images then an identifying framework of such a type of deep learning techniques incorporating CNNs, RNNs, & the transformers. It indicates to doctors which parts of the image are responsible for the most salient results, all while keeping the results in the form of a explainability tools Grad-CAM, SHAP, and LIME. That is all the more true because the system's decision-making process is displayed in a framework and that's how healthcare providers trust AI predictions. That is, clearly predictive properties of the system, confirmed by the test result, accompanied with additional information of interpretative nature that can be utilized by physicians. The system enhances diagnostic processes with minimal error, which indicates that more doctors will trust in artificial intelligence medical solutions.

关键词： Deep learning Explainable AI visual analytics Medical services Predictive models Transformers Gastrointestinal tract Reliability Medical diagnostic imaging Diseases

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Smart Energy Forecasting: Energy Consumption Prediction in data Centers

Smart Energy Forecasting: Energy Consumption Prediction in D...

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visual analytics and data visualization (icvadv), international conference on

作者： Kanapareddy Nithin Kumar Chethan C S. Priya Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

The rapid expansion of data centers has been driven by the growing demand for digital services. As a consequence, the energy usage follows the real challenges in terms of cost and environmental effect. As a result, this study proposes a hybrid technique for predicting energy consumption in data centers that combines machine learning approaches, notably Long Short-Term Memory (LSTM) networks and Auto-Regressive Integrated Moving Average (ARIMA) models. This approach builds a model capable of accurately capturing both short-term variations and long-term trends in energy consumption. As a model for optimizing energy use in data centers, the suggested approach would save costs while potentially contributing to sustainability through improved operational efficiency. This research also shows the need for real-time prediction techniques in energy management while making decisions that support processes with a visual dashboard and alert system for mitigating risks of overconsumption of energy.

关键词： data centers Energy consumption Costs Predictive models Market research data models Real-time systems Forecasting Sustainable development Long short term memory

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