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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Ghule, Vijaykumar R. Agrawal, Ankur Kumar Kumar, Jambi Ratna Raja Mondal, Dipannita Hemelatha, S. Preetha, M. Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Mangalayatan University Department of Management Aligarh India Genba Sopanrao Moze College of Engineering Balewadi Department of Computer Engineering Maharashtra Pune India Dr. D.Y. Patil College of Engineering and Innovation Artificial Intelligence and Data Science Department Talegaon India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350319125

The emergence of synthetic intelligence (AI) has substantially revolutionized the way records is accessed and optimized. With AI-powered equipment and algorithms, groups are capable of efficiently method and manipulate large amounts of data, main to upgrades in overall performance and decision-making. This paper pursuits to evaluate the impact of AI on statistics get entry to optimization performance, discussing both the potential blessings and challenges that include its integration. One of the major blessings of using AI for information get entry to optimization is its capability to automate and streamline obligations. AI-powered structures can analyze and categorize massive datasets at a quicker and extra correct rate than human beings, liberating up time and assets for different obligations. This results in stronger efficiency and productivity in statistics control methods. Further, AI also can enhance the accuracy and best of facts access. thru system learning and predictive analytics, AI structures can identify styles and correlations in information, providing treasured insights for decision-making. This now not simplest improves the speed at which records is accessed, however also the reliability and relevance of the data. However, there are also demanding situations associated with the combination of AI in facts get admission to optimization. one of the fundamental worries is the potential bias in algorithms, which can result in erroneous or discriminatory selection-making. it's far vital for groups to carefully display and review the algorithms utilized by AI structures. © 2023 IEEE.

关键词： accuracy discriminatory predictive structures treasured

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Robust and Interpretable Vector Borne Disease Prediction in IoT-Enabled Edge Networks

Robust and Interpretable Vector Borne Disease Prediction in ...

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2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Doulani, Khushbu Sharma, Samarth Adhikari, Mainak Vijayalata, Y. Indian Institute of Information Technology Lucknow Department of Computer Science Lucknow India Indian Institute of Information Technology Lucknow Department of Computer Science and Artificial Intelligence Lucknow India School of Data Science Indian Institute of Science Education and Research Thiruvananthapuram Thiruvananthapuram India Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350364866

Vector-borne diseases have created critical world-wide public health hazards that require innovative approaches for prevention and management. Within the context of IoT-enabled edge networks, this study offers a unique framework to build a reliable and straightforward distant prediction model for vector-borne diseases. The suggested strategy incorporates the use of edge computing capabilities to predict the onset and spread of diseases caused by vectors by using real-time data streams from mobile applications. Employing widely used techniques from machine learning, including interpretable models, our system not only achieves high prediction accuracy but also provides insight into the most important factors influencing the spread of disease. Moreover, this approach overcomes the resource constraints essential to edge networks, ensuring the prediction model's dependability and scalability. © 2024 IEEE.

关键词： Prediction models

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Edge-Guided Human Depth Image Inpainting Based on Diffusion Models 4

Edge-Guided Human Depth Image Inpainting Based on Diffusion ...

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4th IEEE International Conference on data Science and computer Application, ICDSCA 2024

作者： Xia, Shiji Du, Yi Zhang, Jingyi Yang, Zuopeng Wang, Yu Fu, Yue Liu, Ming Ding, Xie School of Computer and Information Engineering Shanghai Polytechnic University Shanghai China Data Intelligence Product Department Wonders Information Co. Ltd Shanghai China

ISBN: (纸本)9798350368239

In the context of depth images, fusing human segmentation images with depth background images provides critical data for human detection tasks, where accurate localization of the human head is a key indicator. However, in real-world scenarios with occlusions and complex backgrounds, human segmentation images often lack head pixels. This missing data leads to subpar performance in human recognition tasks for deep learning models. To address this issue, this paper proposes an efficient approach that combines edge detection, projection analysis, and template matching for upper-body localization and head identification. Additionally, a refined diffusion model is used to repair the human head edge mask obtained from the initial processing. The main advantage of this approach is its independence from complex deep learning model training. It integrates traditional image processing techniques, morphological analysis, and diffusion models, simplifying head localization and effectively avoiding interference from the shoulder and arm regions. This approach enables more accurate head localization and uses diffusion models to restore missing portions of the human head, thereby improving overall pose recognition accuracy. Experimental results demonstrate that the repaired depth images outperform the original images in human pose model training. When trained with the repaired images, detection accuracy (mAPVal50-95) and precision improved by 10.1% and 7.1%, respectively, significantly enhancing downstream object detection performance. These findings validate the effectiveness of the proposed method in repairing missing human head depth images. They further highlight its potential in improving pose recognition and object detection performance, offering reliable technical support for related tasks. ©2024 IEEE.

关键词： Template matching

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A Unified Framework for Convergence Analysis in Social Networks 63

A Unified Framework for Convergence Analysis in Social Netwo...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Lee, Ti-Chung Huang, Jun-Kai Su, Youfeng National Sun Yat-sen University Department of Electrical Engineering Kaohsiung80424 Taiwan Fuzhou University College of Computer and Data Science Fuzhou350116 China

ISBN: (纸本)9798350316339

This paper proposes a unified framework for the stability analysis of discrete-time nonlinear systems from social networks, including the Friedkin-Johnsen opinion model, two opinion dynamics models in the study of social power, and a general nonlinear polar opinion model. Three novel convergence results are proposed to treat various conditions based on LaSalle invariance principle. Several applications are provided to illustrate the power of the proposed framework. © 2024 IEEE.

关键词： Nonlinear simulations

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DASIV: Directional Acoustic Sensing based Intelligent Vehicle Interaction System

DASIV: Directional Acoustic Sensing based Intelligent Vehicl...

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2024 IEEE International Performance, Computing, and Communications Conference, IPCCC 2024

作者： Zhao, Dinghua Zhou, Juntao Ding, Dian Lu, Yu Li, Yijie Yang, Hang Chen, Yi-Chao Xue, Guangtao Shanghai Jiao Tong University Department of Computer Science and Engineering China Governance and Web3 Shanghai Key Laboratory of Trusted Data Circulation China

ISBN: (纸本)9798350367942

With the increase in motor vehicles, more convenient and accurate interactions are expected while retaining a high standard of safe driving. However, complex and dynamic vehicle environments challenge sensing tasks such as breathing monitor and hand gesture recognition. In this paper, we propose DASIV, which utilizes the highly directional nature of ultrasonic signals to achieve fine-grained directional acoustic sensing in vehicle environments. Due to air nonlinearity, the system enables synchronized directional acoustic communication to transmit information (e.g., navigation) to the driver without affecting other passengers. By optimizing the frequency of the Frequency Modulated Continuous Wave (FMCW) signals, DASIV avoids mutual interference between the sensing and communication signals and achieves breathing detection and hand gesture recognition for the driver. Specifically, the system extracts breathing-induced weak thoracic bullying through the signal phase, captures and analyses breathing patterns using bandpass and Gaussian filters, and develops a breathing model. Then, the system defines 10 interaction hand gestures to meet daily interaction needs, uses spectral features to mine complex and fast hand movement features, and proposes a hand gesture recognition model. Extensive experiments in real environments show that DASIV achieves high-precision breathing monitor (Pearson correlation coefficient of 0.89) and hand gesture recognition (Precision of 91.7%). © 2024 IEEE.

关键词： Palmprint recognition

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A Fuzzy C-Means With Support Vector Regression for Calibration and Fault Detection in Modern Instruments 4

A Fuzzy C-Means With Support Vector Regression for Calibrati...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Habelalmateen, Mohammed I. Kasarla, Satish Reddy Sravanthi, Jajimoggala Kavitha, P. Sundaram, S. Meenakshi College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Hyderabad Institute of Technology And Management Department of Electronics and Communication Engineering Hyderabad India Gokaraju Rangaraju Institute of Engineering and Technology Department of Computer Science and Engineering Hyderabad India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Chennai India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Data Science Bengaluru India

ISBN: (纸本)9798350352931

From the past few decades, Calibration and Fault Detection in modern instruments are crucial for ensuring accuracy, reliability, and safety by configuring instruments to provide accurate measurements and identifying anomalies or malfunctions. These processes prevent accidents, reduce downtime, and increase productivity, but also have drawbacks, including being time-consuming and labor-intensive, requiring specialized expertise and equipment, and being expensive, while also being susceptible to environmental factors and requiring frequent recalibration. To overcome these problems, a Fuzzy C-Means with Support Vector Regression (FCM-SVR) is proposed for detecting calibration and fault in modern instruments using Predictive Electrical Fault Analysis (PEFA) dataset. The collected data is undergone preprocessing using Label Encoder (LE) to normalize data without reducing the dimension. Then, Empirical Mode Decomposition and Sample Entropy are considered for feature extraction for robust fault detection and to reduce false alarm. At last, FCM-SVR is employed as classifier for enhancing computational efficiency, flexibility, and overall performance. From the results, it clearly shows that the proposed FCM-SVR obtained higher values in the context of precision, recall, F1 score and accuracy when compared to existing method K-Nearest Neighbor (KNN). © 2024 IEEE.

关键词： Calibration

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Prediction and Comparative Analysis of the Stress Level of Humans by Using the Machine Learning Algorithms 4

Prediction and Comparative Analysis of the Stress Level of H...

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4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Sivaraj, C. Angeljulie, J. Hemamalini, D. Neerugatti, Vikram Panchal, Narayan Mahesh, T.R. Madanapalle Institute of Technology & Science Andhra Pradesh Madanapalle India Karpagam Academy of Higher Education Department of Computer Science and Engineering Tamilnadu Coimbatore India Arjun College of Technology Department of Artificial Intelligence and Data Science Tamilnadu Coimbatore India Department of Computer Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798350387490

Some of the techniques that have been used include Naive Bayes, K-Means Clustering, Support Vector Machine (SVM) and k-Nearest Neighbors (KNN). To provide useful information to researchers and practitioners, the study reflects the performance, strengths and weaknesses of each algorithm using several datasets. The ability of SVM to model complex decision boundaries is evaluated. It is widely applied in high-dimensional scenario. This paper focuses on the analysis of the advantages and disadvantages of using KNN for classification purposes, such as its simplicity of use, flexibility, and the fact that the classification accuracy and the sensitivity to the selection of parameters should not be overlooked. The performance of the unsupervised K-Means Clustering algorithm in partitioning data into clusters is assessed. The method of decision trees is considered, and the question of their capability to model non-linear dependencies is raised. Finally, the performance and relevancy of the probabilistic classifier Naive Bayes for text categorization is also evaluated. This research thus offers valuable guidance to practitioners regarding the strengths and weaknesses of the proposed algorithms for categorisation, based on a detailed comparative analysis. © 2024 IEEE.

关键词： k-nearest neighbors

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Distributionally Robust Reinforcement Learning with Interactive data Collection: Fundamental Hardness and Near-Optimal Algorithms 38

Distributionally Robust Reinforcement Learning with Interact...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Lu, Miao Zhong, Han Zhang, Tong Blanchet, Jose Department of Management Science and Engineering Stanford University United States Center for Data Science Peking University China Department of Computer Science University of Illinois Urbana-Champaign United States

Distributionally robust reinforcement learning (DRRL), often framed as a robust Markov decision process (RMDP), seeks to find a robust policy that achieves good performance under the worst-case scenario among all environments within a prespecified uncertainty set centered around the training environment. Unlike previous work, which relies on a generative model or a pre-collected offline dataset enjoying good coverage of the deployment environment, we tackle robust RL via interactive data collection, where the learner interacts with the training environment only and refines the policy through trial and error. In this robust RL paradigm, two main challenges emerge: managing the distributional robustness while striking a balance between exploration and exploitation during data collection. Initially, we establish that sample-efficient learning without additional assumptions is unattainable owing to the curse of support shift;i.e., the potential disjointedness of the distributional supports between training and testing environments. To circumvent such a hardness result, we introduce the vanishing minimal value assumption to RMDPs with a total-variation distance robust set, postulating that the minimal value of the optimal robust value function is zero. Such an assumption effectively eliminates the support shift issue for RMDPs with a TV distance robust set, and we present an algorithm with a provable sample complexity guarantee. Our work makes the initial step to uncovering the inherent difficulty of robust RL via interactive data collection and sufficient conditions for sample-efficient algorithms with sharp sample complexity. © 2024 Neural information processing systems foundation. All rights reserved.

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Machine Learning Algorithms used in Crop Yield Prediction for Precision Agriculture 4

Machine Learning Algorithms used in Crop Yield Prediction fo...

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4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Umamaheswari, R. Shanmugapriya, S. Vimala, P. Ganesan, R. Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vel Tech High Tech Dr.Rangarajan Dr. Sagunthala Engineering College Department of Computer Science and Engineering Chennai India Saveetha Engineering College Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798331515720

Agriculture is a crucial sector that plays a vital role in producing food for human consumption. However, predicting crop yield accurately can be a challenging task due to various natural factors, such as soil quality, climate, rainfall, and resource availability. In the past, farmers relied on their expertise and judgment to determine crop yield, but their traditional methods have become less effective due to the unpredictable nature of these factors. To address this challenge, researchers have developed various algorithms and models for predicting crop yield based on different parameters, such as soil quality, climate, and image analysis. These algorithms can help farmers make informed decisions about when and where to grow their crops, leading to improved productivity and higher yields. Among these algorithms, neural networks are showing great promise as they can analyze vast amounts of data and identify patterns that may not be immediately apparent to human observers. By training neural networks on historical crop yield data, researchers can develop models that accurately predict crop yield based on a variety of factors. Ensuring accurate crop yield prediction is critical to ensuring food security and preventing famine, particularly in developing countries such as India. As technology continues to advance, we can expect even more advanced algorithms and models for predicting crop yield that will help farmers make better decisions and ultimately lead to increased productivity and better outcomes for agriculture. © 2024 IEEE.

关键词： Agriculture Crop yield prediction (CYP) Machine learning Neural Network (NN)

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Object-Centric World Model for Language-Guided Manipulation

arXiv

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arXiv 2025年

作者： Jeong, Youngjoon Chun, Junha Cha, Soonwoo Kim, Taesup Graduate School of Data Science Department of Electrical and Computer Engineering

A world model is essential for an agent to predict the future and plan in domains such as autonomous driving and robotics. To achieve this, recent advancements have focused on video generation, which has gained significant attention due to the impressive success of diffusion models. However, these models require substantial computational resources. To address these challenges, we propose a world model leveraging object-centric representation space using slot attention, guided by language instructions. Our model perceives the current state as an object-centric representation and predicts future states in this representation space conditioned on natural language instructions. This approach results in a more compact and computationally efficient model compared to diffusion-based generative alternatives. Furthermore, it flexibly predicts future states based on language instructions, and offers a significant advantage in manipulation tasks where object recognition is crucial. In this paper, we demonstrate that our latent predictive world model surpasses generative world models in visuo-linguo-motor control tasks, achieving superior sample and computation efficiency. We also investigate the generalization performance of the proposed method and explore various strategies for predicting actions using object-centric representations. Copyright © 2025, The Authors. All rights reserved.

关键词： Robot programming

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