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1st Serbian international conference on Applied Artificial Intelligence, SICAAI 2022

1st Serbian International Conference on Applied Artificial I...

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1st Serbian international conference on Applied Artificial Intelligence, SICAAI 2022

ISBN: (纸本)9783031297168

The proceedings contain 22 papers. The special focus in this conference is on Applied Artificial Intelligence. The topics include: Application of MCDM DIBR-Rough Mabac Model for Selection of Drone for Use in Natural Disaster Caused by Flood;improving the Low Accuracy of Traditional Earthquake Loss Assessment systems;SecondOponionNet: A Novel Neural Network Architecture to Detect Coronary Atherosclerosis in Coronary CT Angiography;ontology-Based Exploratory Text Analysis as a Tool for Identification of Research Trends in Polish Universities of Economics;improved Three-Dimensional Reconstruction of Patient-Specific Carotid Bifurcation Using Deep Learning Based Segmentation of Ultrasound Images;seat-to-Head Transfer Functions Prediction Using Artificial Neural Networks;a Review of the Application of Artificial Intelligence in Medicine: From data to Personalised Models;digital Platform as the Communication Channel for Challenges in Artificial Intelligence;Mathematical Modeling of COVID-19 Spread Using Genetic Programming Algorithm;implementation of Deep Learning to Prevent Peak-driven Power Outages Within Manufacturing systems;liver Tracking for Intraoperative Augmented Reality Navigation System;intelligent Drug Delivery systems;cost Effectiveness Analysis of Real and in Silico Clinical Trials for Stent Deployment Based on Decision Tree;Reproductive Autonomy Conformity Assessment of Purposed AI System;baselines for Automatic Medical Image Reporting;HR Analytics: Serbian Perspective;ontology-Based Analysis of Job Offers for Medical Practitioners in Poland;synergizing Four Different computing Paradigms for Machine Learning and Big data Analytics;pose Estimation and Joint Angle Detection Using Mediapipe Machine Learning Solution;Application of AI in Histopathological Image Analysis.

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VIBE: A data-driven Approach to Career Guidance and Skill Development

VIBE: A Data-Driven Approach to Career Guidance and Skill De...

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advances in data Engineering and intelligent computing systems (ADICS), international conference on

作者： Sathya A Naveen K T Narendran M Basker S P Nirudeeshwar B Yuvaraj S K Department of Information Technology KCG College of Technology Chennai Tamil Nadu India Senior Software Engineer Centennial Toronto Canada

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

Embarking on the journey to choose a career presents a widespread challenge, often leaving individuals uncertain about their future paths, despite possessing valuable skills. Our project, underpinned by machine learning, aims to empower students by helping them identify the most suitable career based on their distinct abilities and academic backgrounds. Leveraging innovative algorithms, particularly a Recurrent Neural Network (RNN), we meticulously analyze personal and academic information to provide tailored career recommendations. This methodology offers students a clear sense of direction, facilitating informed decision-making and bridging the gap between confusion and clarity. The application of cutting-edge technologies ensures a seamless and user-friendly experience, making our tool readily accessible to students seeking valuable guidance.

关键词： Recurrent neural networks Machine learning algorithms Engineering profession Employment Decision making Entrepreneurship Machine learning

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Business Decision making using data Science

Business Decision making using Data Science

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2022 international conference on Innovative computing, intelligent Communication and Smart Electrical systems, ICSES 2022

作者： Singh Yadav, Nagendra Goar, Vishal Singh Yadav, Pallavi Chowdhury, Subrata Ninh Bui, Trung Thi Thu, Nguyen Vijayakumar, K. Government Engineering College Rajasthan Bikaner India Maharaja Ganga Singh University Faculty of Commerce Rajasthan Bikaner India Computer Science Department Chittoor India VNU University of Engineering and Technology Hanoi Viet Nam Hanoi University of Industry Department Faculty of Electronic Engineering Hanoi Viet Nam St. Joseph's Institute of Technology OMR Department of Computer Science and Engineering Chennai India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

Several organizations started to adopt the strategy to have data scientist professionals to help them identify the true value of the cost vs benefits and since data science is on fire for a quite long time, adopting the same comes in handy for several factors. i.e., investment, net profit, etc. At the same time, the clarity is missing around what data science is and this tends to introduce the concept of unambiguous theory or assumptions. In this research, we are aiming to pin down the fact of what data science is and how it can lead to crucial decision-making using a data-driven approach. We firmly believe that the accurate limitations of data science scope cannot be defined due to its vast possibilities. If the business wants to align themselves using the data science by understanding the true potential, the following needs to be underlined: 1) understand the importance of related concepts using the relationship approach, 2) identify how fundamental principles of data science can be useful for the different business use case, 3) Identify what the data science can offer depending on our requirement. In this research, we offer a decision-making solution for businesses using the underlying data science principles. © 2022 IEEE.

关键词： Software testing

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A Communication data Layer for Distributed Neuromorphic systems 18th

A Communication Data Layer for Distributed Neuromorphic Syst...

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18th international conference on Artificial Intelligence Applications and Innovations (AIAI)

作者： Veres, Andras Haga, Peter Racz, Andras Borsos, Tamas Kenesi, Zsolt Ericsson Res Budapest Hungary

ISBN: (纸本)9783031083372;9783031083365

The proliferation of AI into everyday devices is a major trend today. This trend combined with the increasing amount of different AI hardware architectures and software frameworks imposes significant challenges when we want to interconnect such AI-based devices into single, large AI-driven distributed system. This paper addresses one key challenge which is around the problem of sharing AI encoded information among components of vastly heterogeneous nature. For that end we propose a new concept called Neuromorphic data Layer, which can bridge various internal AI data representations in a communication channelfriendly way. The proposed methods are also stress tested in a distributed industrial robotic control & training use-case where all components are state-of-the-art devices, have some form of AI computation and they are interconnected over wireless technologies using the proposed Neuromorphic data Layer.

关键词： Neuromorphic computing Distributed AI IoT Wireless networks Vector symbolic architectures

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Learning to Parameterize a Stochastic Process Using Neuromorphic data Generation 22

Learning to Parameterize a Stochastic Process Using Neuromor...

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international conference on Neuromorphic systems (ICONS)

作者： Severa, William Smith, J. Darby Aimone, James B. Lehoucq, Richard Sandia Natl Labs Albuquerque NM 87123 USA

ISBN: (纸本)9781450397896

Deep learning is consistently becoming more integrated into scientific computing workflows. These high-performance methods allow for data-driven discoveries enabling, among other tasks, classification, feature extraction, and regression. In this paper, we present a unique approach to solving an inverse problem-determining the initial parameters of a system from observed data-using not only deep learning-powered AI but also simulation data generated using neuromorphic, brain-inspired hardware. We find this approach to be both scalable and energy efficient, capable of leveraging future advancements both in AI algorithms and neuromorphic hardware. Many high performing deep learning approaches require large amounts of training data. And, while great progress is being made in new techniques, current methods suggest that data-heavy approaches are still best-suited for maintaining critical generalization required for an inverse problem. However, that data comes at a cost, often in the form of expensive high-fidelity numerical simulations. Instead, we make use of recent advances in spiking neural networks and neural-inspired computing wherein we can use Intel's Loihi to compute hundreds of thousands of random walk trajectories. Statistics from these random walkers effectively simulate certain classes of physical processes. Moreover, the use of neuromorphic architectures allows these trajectories to be generated quickly and at drastically lower energy cost. This generated data can then be fed into a deep learning regression network, modified to incorporate certain known physical properties. We find the resulting networks can then determine the initial parameters and their uncertainties, and we explore various factors that impact their performance.

关键词： scientific deep learning heterogeneous HPC neuromorphic computing

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Brain-Inspired Hyperdimensional computing for Ultra-Efficient Edge AI

Brain-Inspired Hyperdimensional Computing for Ultra-Efficien...

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ACM/IEEE international conference on Hardware/Software Codesign and System Synthesis part of the Embedded systems Week (ESWEEK)

作者： Amrouch, Hussam Imani, Mohsen Jiao, Xun Aloimonos, Yiannis Fermuller, Cornelia Yuan, Dehao Ma, Dongning Barkam, Hamza E. Genssler, Paul R. Sutor, Peter Univ Stuttgart Chair Semicond Test & Reliabil STAR Stuttgart Germany UC Irvine Bio Inspired Architecture & Syst Irvine CA USA Villanova Univ Dependable Efficient & Intelligent Comp Lab DETAIL Villanova PA USA Univ Maryland Percept & Robot Grp College Pk MD USA

ISBN: (数字)9781665472944

ISBN: (纸本)9781665472944

Hyperdimensional computing (HDC) is rapidly emerging as an attractive alternative to traditional deep learning algorithms. Despite the profound success of Deep Neural Networks (DNNs) in many domains, the amount of computational power and storage that they demand during training makes deploying them in edge devices very challenging if not infeasible. This, in turn, inevitably necessitates streaming the data from the edge to the cloud which raises serious concerns when it comes to availability, scalability, security, and privacy. Further, the nature of data that edge devices often receive from sensors is inherently noisy. However, DNN algorithms are very sensitive to noise, which makes accomplishing the required learning tasks with high accuracy immensely difficult. In this paper, we aim at providing a comprehensive overview of the latest advances in HDC. HDC aims at realizing real-time performance and robustness through using strategies that more closely model the human brain. HDC is, in fact, motivated by the observation that the human brain operates on high-dimensional data representations. In HDC, objects are thereby encoded with high-dimensional vectors which have thousands of elements. In this paper, we will discuss the promising robustness of HDC algorithms against noise along with the ability to learn from little data. Further, we will present the outstanding synergy between HDC and beyond von Neumann architectures and how HDC opens doors for efficient learning at the edge due to the ultra-lightweight implementation that it needs, contrary to traditional DNNs.

关键词： Hyperdimensional computing Embedded systems Energy-efficient computing Adversarial Attack Voltage Scaling In-memory computing Security Graphs Robotics Computer Vision

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A Review of Advanced Technologies for intelligent Sensing, Diagnostics, and Personalized Healthcare Solutions

A Review of Advanced Technologies for Intelligent Sensing, D...

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Ubiquitous computing and intelligent Information systems (ICUIS), international conference on

作者： G S Pradeep Ghantasala Rajesh Sharma R Akey Sungheetha Anitha P Selvalakshmi B Pradeepa K Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bengaluru Karnataka India Department of ECE Sri Sai Renganathan Engineering College Coimbatore Tamil Nadu India Department of Computer Science and Engineering Tagore Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering St. Michael College of Engineering & Technology Sivaganga Tamil Nadu India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

intelligent Health is a revolutionary approach to modern healthcare that uses the Internet of Things (IoT), artificial intelligence (AI), and sensor technologies to transform patient-centric solutions. This study examines how these technologies are combining to change patient monitoring, illness management, personalized treatment, and healthcare delivery. Wearable sensors, medical equipment, and IoT-enabled infrastructure allow for real-time, continuous monitoring of patient health data, enabling the early identification of anomalies and the implementation of personalized interventions. IoT platforms enable remote connectivity and data-driven insights by easily integrating sensor data into healthcare systems. AI algorithms analyses large amounts of sensor data to maximize clinical decision-making, forecast health outcomes, and extract insightful information. intelligent health solutions offer many advantages, such as better patient outcomes and a better patient experience. However, to fully utilize these technologies, issues like data privacy, interoperability, and regulatory compliance need to be resolved. Future directions in intelligent health include the establishment of standards and regulatory frameworks, patient-centric design, integration with electronic health records, and advances in AI and machine learning. With individualized, data-driven solutions, intelligent health holds great promise for revolutionizing the delivery of healthcare and enabling people to take charge of their own health and well-being

关键词： Patient monitoring data privacy Reviews data security Medical services Transforms Ubiquitous computing Internet of Things Wearable sensors Interoperability

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Wave Manipulating RIS for Enhanced Tomographic Imaging: Concept and Recent advances

Wave Manipulating RIS for Enhanced Tomographic Imaging: Conc...

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2024 United States National Committee of URSI National Radio Science Meeting, USNC-URSI NRSM 2024

作者： Tortoriello, Carlo Erricolo, Danilo Oliveri, Giacomo University of Illinois Chicago ChicagoIL60607 United States DICAM Trento Italy

ISBN: (纸本)9781946815194

The use of Reconfigurable intelligent Surfaces (RIS) in RF-based Imaging methods is showing promising results to overcome the limitations of the current generation of communications and sensing systems. The easy integration into modern systems and the ubiquity of the WiFi technology call for a deeper exploration into the possibility to exploit such a technology for advanced imaging applications. In this context, the possibility to integrate RISs in RF-based imaging applications must be carefully analyzed due to the intrinsic limitations of the propagation control enabled by such class of reconfigurable metasurfaces. In this work, the problem of performing tomographic imaging based on the integration of existing WiFi systems and RISs is addressed, and the proper tradeoff between methodological approach complexity and resulting imaging quality is discussed. © 2024 IEEE. All rights reserved.

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Soil-Based Crop Recommendation System Using Machine Learning

Soil-Based Crop Recommendation System Using Machine Learning

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advances in data Engineering and intelligent computing systems (ADICS), international conference on

作者： Kristuboyina Abhinov Kornepati Sai Saranya Mannem Mahendra Ch Suresh Babu Siva Satya Sreedhar P Department of Information Technology Seshadri Rao Gudlavalleru Engineering College Andhra Pradesh India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

The crop recommendation systems in agriculture frequently depend on general guidelines or personal experience, with little regard for important soil factors such as pH and nutrient concentrations. It also results in poor outcomes and resource inefficiencies. In response, the study introduces a Soil Based Crop Recommendation System (SBCRS), which integrates machine learning techniques to deliver tailored crop recommendations based on soil parameters. The SBCRS collects extensive soil and crop performance data, extensively preprocesses it, gathers relevant features, and then trains and evaluates models using machine learning methods. The system exceeds existing systems by achieving 93% accuracy, 89% precision, 93% recall, and a 90% F1 score, as opposed to the existing system's 78% accuracy and 86% F1 score. Furthermore, the study emphasizes the significance of soil pH levels in evaluating crop compatibility, providing precise information about soil conditions and their implications for agricultural activities. Overall, the SBCRS offers a data-driven approach to crop selection that improves farm productivity and sustainability while resolving the limitations of existing recommendation systems.

关键词： Soil properties Crops Machine learning Transforms Soil data collection Agriculture

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User Information Enhanced Knowledge Graph Convolutional Networks for Recommender systems 14

User Information Enhanced Knowledge Graph Convolutional Netw...

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14th international conference on Measuring Technology and Mechatronics Automation, ICMTMA 2022

作者： Hao, Junyu Li, Xiaoge School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an71012 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi'an710121 China Xi'an Key laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China

ISBN: (纸本)9781665499781

To deeply excavate the information contained in user data and better alleviate cold start problem, we propose KGCN++, a user information enhanced knowledge graph convolutional networks model for recommender system, which simulates nerves' influence diffusion of information of user and items in a unified framework. The model combines user information and user-item interaction information into a user knowledge graph, which advances KGCN by injecting high-order user potential information reflected in user graph and high-order influence of items reflected in item knowledge graph. In addition, we have designed the influence factors of different items on users, which are used to characterize the weight of items on the user's information dissemination, so as to distinguish users with different information. Through extensive experiments on real-world datasets, we demonstrate that KGCN++ achieves substantial gains in a variety of ecenarios, including movie and book recommendation, better performance compared to the other methods. © 2022 IEEE

关键词： Recommender systems

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