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International Conference on Inventive Computation Technologies (ICICT)

作者： Lavanya M C Akshatha M P. Radhakrishnan Kshatriya Vinaya Sree Bai S. Vijayalakshmi Jarabala Ranga Dept of CSE Vidyavardhaka College of Engineering Mysuru Dept of CSE. Vidvavardhaka College of Engineering Mysuru Dept. of Electronics & communication Engineering Tagore Engineering College Artificial Intelligence & Data Science Lakireddy Bali Reddy College of Engineering Department of Computer Science and Engineering Park College of Engineering and Technology Coimbatore Tamilnadu Department of EEE Ramachandra college of engineering Eluru AP

In particular, the emphasis of our study is on the most important difficulties associated with finding data-driven representations of nonlinear systems that allow for linear analysis is a key step towards deriving dynamics from data. Many issues plaguing modern dynamical systems are explored and the innovative methods being created in data science and machine learning to address them. Nonlinear dynamics, on the one hand, and unknown or only partially understood dynamics, on the other, are the two greatest challenges. Machine learning is providing cutting-edge, effective answers to all of these issues. Methods of reducing the number of dimensions are used to visualize dynamical processes in a more compact format. It is computationally efficient to use these methods on real-world data. Finding the fundamental equations fundamental principles of physics requires models that are themselves driven by evidence. By using deep learning techniques, dynamical systems are identified correctly, and that in turn enables accurate inference about physical systems. Several cutting-edge and very successful new approaches to nonlinear dynamics haw emerged from the area of machine learning. Autoencoders, recurrent neural networks, convolutional neural networks, and reinforcement learning are only some of the cutting-edge deep learning approaches used to simulate dynamical systems.

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Usage of Computer Aided Methods for Detection and Evaluation of Breast Cancer in Mammograms 5

Usage of Computer Aided Methods for Detection and Evaluation...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Rao, M. Nagabhushana Reddy, L. Venkateswara Ganesh, D. Reddy, Lakkireddy Udanth Mohana, R Madana Marri Laxman reddy institute of Technology & Management Cse Hyderabad India Joginapally B. R. Engineering College Department of Cse RR Dist Yenkepally Telangana Hyderabad501504 India Cse Andhra Pradesh Tirupati India VIT-AP University Dept. of Ece Amaravath India Artificial Intelligence & Data Science Chaitanya Bharathi Institute of Technology Telangana Hyderabad500075 India

ISBN: (纸本)9798331504403

Breast cancer accounts for an extremely high annual death rate. Many tools have been created to facilitate earlier diagnosis of breast cancer, which is the most common cancer among females. According to the research, cancer of the breast is also regarded as the second most deadly form of the disease. It is the most prominent kind of the disease and the top reason for death among females worldwide. Any progress made in the early detection and identification of cancer is crucial to maintaining good health. Therefore, it is essential to improve both the therapeutic aspect and the survival standard of patients by utilizing high precision in cancer prognosis. A reliable, effective, and speedy reaction from medical professionals is provided by an automatic disease identification system, which also reduces the danger of death. Recently, breast cancer screening methods that utilise deep learning have shown promising results, allowing for earlier identification and thereby enhancing patients' chances of survival thanks to the introduction of artificial intelligence (AI). Deep learning reduces the need for manual intervention while extracting features, in comparison to traditional machine learning methods. We provide a brief overview of deep learning techniques, data availability, and the many breast cancer screening options, such as mammography, thermography, ultrasound, and MRI. Using demographic, laboratory, & mammographic data, this investigation sought to forecast breast cancer using various Deep learning algorithms. Ultimately, we apply artificial intelligence to breast cancer clinical trials and compare the proposed approach to current algorithms. © 2024 IEEE.

关键词： Lung cancer

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AZTR: Aerial Video Action Recognition with Auto Zoom and Temporal Reasoning

AZTR: Aerial Video Action Recognition with Auto Zoom and Tem...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Xijun Wang Ruiqi Xian Tianrui Guan Celso M. de Melo Stephen M. Nogar Aniket Bera Dinesh Manocha Dept. of Computer Science University of Maryland College Park MD USA Dept. of Electrical and Computer Engineering University of Maryland College Park MD USA Computational and Information Sciences Directorate DEVCOM U.S. Army Research Laboratory Adelphi MD USA Dept. of Computer Science Purdue University West Lafayette IN USA

We propose a novel approach for aerial video action recognition. Our method is designed for videos captured using UAVs and can run on edge or mobile devices. We present a learning-based approach that uses customized auto zoom to automatically identify the human target and scale it appropriately. This makes it easier to extract the key features and reduces the computational overhead. We also present an efficient temporal reasoning algorithm to capture the action information along the spatial and temporal domains within a controllable computational cost. Our approach has been implemented and evaluated both on the desktop with high-end GPUs and on the low power Robotics RB5 Platform for robots and drones. In practice, we achieve $6.1-7.4 \%$ improvement over SOTA in Top-1 accuracy on the RoCoG-v2 dataset, 8.3-10.4% improvement on the UAV-Human dataset and 3.2% improvement on the Drone Action dataset.

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Machine Learning Models for Energy Consumption Forecasting in Power Generation

Machine Learning Models for Energy Consumption Forecasting i...

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IEEE International Conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Samuel-Soma M. Ajibade Angela Lee Siew Hoong Muhammed Basheer Jasser Adedotun O. Adetunla Anthonia Oluwatosin Adediran Department of Data Science and Artificial Intelligence School of Engineering and Technology Sunway University Selangor Malaysia Research Centre for Nanomaterials and Energy Technology (RCNMET) Sunway University Selangor Malaysia Dept. of Mechanical and Mechatronics Engineering Afe Babalola University Ado Ekiti Nigeria Dept. of Mechanical Engineering Science University of Johannesburg Johannesburg South Africa Department of Real Estate Faculty of Built Environment Universiti Malaya Kuala Lumpur

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

The application of machine learning for power-related purposes represents a novel notion in artificial intelligence for predicting power generation. While prediction models are employed to forecast various global situations, their application in predicting energy-related themes will be distinctive and specialized. Scholars and AI engineers, in collaboration with professionals in the energy sector, are conducting investigations to develop AI models that assist in their work. Consequently, due to the distinct nature of the situation and the diversity of datasets, several AI models are chosen and built to aid in their tasks. This study examines the application of AI models for enhancing energy efficiency in power generating in Malaysia. A Linear Regression model and an Artificial Neural Network are developed to forecast future power consumption across various industries in Malaysia, compute the growth rate and average growth rate of the projected energy consumption, and identify a viable area for investment. It also formulates a power strategy based on the aggregate anticipated energy usage. Both models are developed, analyzed, and compared to ascertain the most suitable model for this scenario.

关键词： Energy consumption computational modeling Linear regression Machine learning Artificial neural networks Predictive models Energy efficiency data models Forecasting Power generation

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Automated Detection of Poultry Farms from Aerial Images for Actionable AI System toward Biosecurity Applications

Automated Detection of Poultry Farms from Aerial Images for ...

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Applied Imagery Pattern Recognition Workshop (AIPR)

作者： Yu Li Rana Das Cuong Duong Teng-Teeh Lim Timothy Haithcoat Ilker Ersoy Chi-Ren Shyu Department of Electrical Engineering and Computer Science University of Missouri Columbia MO US Biological Engineering University of Missouri Columbia MO US Plant Science and Technology Extension University of Missouri Columbia MO US Institute for Data Science and Informatics University of Missouri Columbia US Institute for Data Science and Informatics University of Missouri Columbia MO US Electrical Eng. and Computer Science Dept. Institute for Data Science and Informatics University of Missouri Columbia MO US

Poultry productions have shifted towards larger farms and often cluster in certain regions. However, many of the smaller farms with a considerable amount of production are not considered concentrated animal feeding operation under regulation in many regions, and therefore are not required to obtain a permit. As a result, information of these operations is not being recorded and monitored, contributing to an increased risk for biosecurity issues. The development in computer vision and availability of high-resolution remote sensing imagery allows the possibility for automatic localization and operation estimation of the unregistered farms, filling in the missing information needed for biosecurity risk management. The farm information extracted from images can be incorporated with other available geospatial data, such as road networks, watersheds, etc., to build a comprehensive knowledge base for actionable plans. In the current study, we present a framework for automated localization, analysis, and visualization of poultry farms from remote sensing images. A robust machine learning method was developed to computationally link the visual features from remote sensing imagery to Missouri Department of Natural Resources Concentrated Animal Feeding Operation data, to identify the animal farm type, location, and potential capacity in the region. The developed process can be performed automatically on images of different timestamps to analyze temporal and spatial development of poultry production facilities of a region. This research contributes to the effective management and regulation of poultry and potentially other animal farms, providing insights into farm characteristics and capacities while facilitating informed decision-making.

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FogMedX-Transform: A Transformer-Based Task Interoperability Framework for Energy-Efficient Fog-Enabled IoMT

FogMedX-Transform: A Transformer-Based Task Interoperability...

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International Conference on Inventive Computation Technologies (ICICT)

作者： R Jayakarthik Sajja Suneel Jayendra Gopal Thatipudi V. Jaganraja Minesh Vohra Gopinath D Dept of Computer science Saveetha College of Liberal Arts and Sciences SIMATS Department of CSE(Data Science) Institute of Aeronautical Engineering Dundigal Hyderabad Telangana India Dept. of Computer Science University of North Texas Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India School of Mechanical Engineering Lovely Professional University Phagwara Department of Computer Science Kristu Jayanti College (Autonomous) Bengaluru Karnataka India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

The increasing speed of Internet of Medical Things (IoMT) development requires framework approaches that secure, efficient, and scalable for healthcare data management. The study presents FogMedX-Transform as a Transformer-based task interoperability framework purposebuilt for energy-efficient fog-enabled IoMT systems. A customized Transformer design utilizing multi-head selfattention allows the framework to deliver timely critical healthcare data processing alongside optimized energy usage. This model demonstrated a 98.7% task interoperability success rate coupled with a 97.5% anomaly detection accuracy that exceeded results from conventional models based in fog environments and the cloud. The framework improved performance through latency reduction between 50% and 70% and energy consumption minimization reached between 25% to 40% thus proving its effectiveness in resource administration. The scalability tests proved steady system functionality when dealing with different device quantities and built-in security features blocked data breaches and had a false positive rate of 2.3% during testing. FogMedX-Transform demonstrates the capability for healthcare transformation through its union of fog computing and deep learning technology integration according to test results.

关键词： Deep learning Performance evaluation Internet of Medical Things data breach Transformers data processing Energy efficiency Real-time systems Interoperability Anomaly detection

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Learning Superconductivity from Ordered and Disordered Material Structures 38

Learning Superconductivity from Ordered and Disordered Mater...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Pin Peng, Luoxuan Jiao, Rui Mo, Qing Wang, Zhen Huang, Wenbing Liu, Yang Lu, Yutong National Supercomputer Center in Guangzhou School of Computer Science and Engineering Sun Yat-sen University China Dept. of Comp. Sci. & Tech. Institute for AI BNRist Center Tsinghua University China Institute for AIR Tsinghua University China Gaoling School of Artificial Intelligence Renmin University of China China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China

Superconductivity is a fascinating phenomenon observed in certain materials under certain conditions. However, some critical aspects of it, such as the relationship between superconductivity and materials' chemical/structural features, still need to be understood. Recent successes of data-driven approaches in material science strongly inspire researchers to study this relationship with them, but a corresponding dataset is still lacking. Hence, we present a new dataset for data-driven approaches, namely SuperCon3D, containing both 3D crystal structures and experimental superconducting transition temperature (Tc) for the first time. Based on SuperCon3D, we propose two deep learning methods for designing high Tc superconductors. The first is SODNet, a novel equivariant graph attention model for screening known structures, which differs from existing models in incorporating both ordered and disordered geometric content. The second is a diffusion generative model DiffCSP-SC for creating new structures, which enables high Tc-targeted generation. Extensive experiments demonstrate that both our proposed dataset and models are advantageous for designing new high Tc superconducting candidates. © 2024 Neural information processing systems foundation. All rights reserved.

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Space-Frequency Diversity based MAC protocol for IEEE 802.11 ah networks 15

Space-Frequency Diversity based MAC protocol for IEEE 802.11...

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15th International Conference on Advanced Technologies for Communications, ATC 2022

作者： Minh Dang, Duc Ngoc Tran, Van Thau Nguyen, Hoang Lam Pham, Nhat Truong Tran, Anh Khoa Dang, Ngoc-Hanh Fpt University Dept. of Computing Fundamental Ho Chi Minh City Viet Nam Ton Duc Thang University Dept. of Electrical and Electronics Eng. Ho Chi Minh City Viet Nam Institute for Computational Science Ton Duc Thang University Ho Chi Minh City Viet Nam Ho Chi Minh City University of Technology Dept. of Telecommunications Engineering Ho Chi Minh City Viet Nam

ISBN: (数字)9781665451888

ISBN: (纸本)9781665451888

IEEE 802.11ah is a sub-GHz communication technology to offer longer range and low power connectivity for the Internet of Things (IoT) applications. A Restricted Access Window (RAW) is specified to decrease the collision probability. Stations are divided into groups and stations from each group attempt to access the channel by employing the Distributed Coordination Function during their assigned RAW slots. However, the network throughput is limited by a single channel MAC protocol. In this paper, Space-Frequency Diversity-based MAC protocol for the IEEE 802.11ah network (SF-MAC protocol) is proposed to allow stations of different sectors to transmit packets on different channels with the help of Forwarders. The proposed SF-MAC protocol improves the packet delivery ratio and aggregate throughput of the network. © 2022 IEEE.

关键词： Internet of things

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MEIL-NeRF: Memory-Efficient Incremental Learning of Neural Radiance Fields

arXiv

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arXiv 2022年

作者： Chung, Jaeyoung Lee, Kanggeon Baik, Sungyong Lee, Kyoung Mu Dept. of Electrical and Computer Engineering ASRI Seoul National University Seoul Korea Republic of Dept. of Data Science Hanyang University Seoul Korea Republic of

Hinged on the representation power of neural networks, neural radiance fields (NeRF) have recently emerged as one of the promising and widely applicable methods for 3D object and scene representation. However, NeRF faces challenges in practical applications, such as large-scale scenes and edge devices with a limited amount of memory, where data needs to be processed sequentially. Under such incremental learning scenarios, neural networks are known to suffer catastrophic forgetting: easily forgetting previously seen data after training with new data. We observe that previous incremental learning algorithms are limited by either low performance or memory scalability issues. As such, we develop a Memory-Efficient Incremental Learning algorithm for NeRF (MEIL-NeRF). MEIL-NeRF takes inspiration from NeRF itself in that a neural network can serve as a memory that provides the pixel RGB values, given rays as queries. Upon the motivation, our framework learns which rays to query NeRF to extract previous pixel values. The extracted pixel values are then used to train NeRF in a self-distillation manner to prevent catastrophic forgetting. As a result, MEIL-NeRF demonstrates constant memory consumption and competitive performance. © 2022, CC BY.

关键词： Learning algorithms

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ANFIS Model for Cost Analysis in a Dual Source Multi-Destination System

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Procedia Computer science 2023年 217卷 1266-1279页

作者： M.O. Okwu L.K. Tartibu E.O. Ojo S. Adume J.O. Gidiagba J. Fadeyi Department of Mechanical and Industrial Engineering Tech. University of Johannesburg South Africa Department of Mechanical Engineering Federal University of Petroleum Resources Effurun Nigeria Mathematics and Data Science Dept. Computing Science and Mathematics Division Faculty of Natural Science University of Stirling UK Faculty of Engineering and Applied Sc. Memorial University Newfoundland Canada Department of Industrial and Systems Engineering University of Buffalo USA

Managers face uncertainties while making allocation decisions, especially in dual or multi-source multi-destination inventory systems. Regrettably, many studies focus on classical methods as a technique for product distribution, which has never guaranteed a satisfactory solution. Real-life problems are non-deterministic polynomial-time hard (NP-hard), and solving such problems is relatively challenging. Such complicated problems need an efficient and robust computational hybrid algorithm. This study emphasises the need for a hybrid intelligent technique for effective product distribution. Soft computing hybrid algorithm, ANFIS was applied to product distribution in a double source multi-destination system. Rules were developed from available input datasets. Distributing products from dual manufacturing plants to fifteen available depots using the creative algorithm resulted in an overall 13.5% decrease in cost compared to the existing method adopted by the company. The result showed that the proposed method is relatively satisfactory and adequate for cost modelling. In addition, it is easy to use and outperforms the classical approach.

关键词： Adaptive Neuro-Fuzzy Inference System Dual Source Multi-Echelon Multi-Destinations

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