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International Conference on Computing and Networking Technology (ICCNT)

作者： Ayain John Navya Damodar N Mohith Bharathy Vijayan Dept. Artificial Intelligence and Machine Learning Dayananda Sagar University Bangalore India Dept. Computer Science Engineering Dayananda Sagar University Bangalore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The research work investigates the factors that influence the quality of red wine and how they contribute to positive outcomes. It uses machine learning techniques like Support Vector machines (SVM) and K-nearest neighbors (KNN) to classify and assess red wine quality data. Traditionally, red wine quality has been evaluated subjectively, which presents challenges. machine learning algorithms offer an objective alternative. This study compares SVM and KNN algorithms in assessing red wine quality and aims to determine the crucial attributes that affect its quality. To evaluate the classification performance of different machine learning algorithms, metrics like specificity, sensitivity, selectivity, and F1 measure are used. Additionally, grid search algorithms are employed for parameter optimization. In the red wine datasets, the Support Vector machine (SVM) model obtains an accuracy rate of 89%, surpassing the K-Nearest Neighbors (KNN) model’s accuracy rate of 87%, especially regarding predictive specificity. Furthermore, using grid search algorithms improves the overall performance of the classifiers. This study highlights the effectiveness of machine learning in assessing red wine quality, providing valuable information for producers to optimize wine production processes and ensure high-quality products for consumers.

关键词： Support vector machines machine learning algorithms Accuracy Sensitivity Production Nearest neighbor methods Predictive models Prediction algorithms Vectors Classification algorithms

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Indian Sign Language Recognition Using Scale-Invariant Feature Transform by dept. Sensor 5

Indian Sign Language Recognition Using Scale-Invariant Featu...

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5th IEEE International Conference on Cybernetics, Cognition and machine learning Applications, ICCCMLA 2023

作者： Gangrade, Jayesh Gangrade, Shweta Mishra, Ashish Kumar, Gautam Gunjan, Vinit Kumar Manipal University Jaipur Dept. of Artificial Intelligence & Machine Learning Jaipur India Manipal University Jaipur Dept of Information Technology Jaipur India Jaypee Institute of Information Technology Dept of Computer Science & Engineering Noida India CMR Institute of Technology Department of Computer Science & Engineering Hyderabad India

ISBN: (纸本)9798350338287

Deaf and hard of hearing people in the Indian community now have a way to communicate with society because to Indian Sign Language (ISL). The hand is accorded significant importance in the framework of the ISL. ISL is composed of both static and dynamic sign hand gestures. Both static and dynamic signs are made up of three components: hand movement, hand orientation, and hand position. The proposed method can recognize static ISL signs. The technique that was suggested makes use of the Microsoft Kinect sensor to locate and separate a hand from its background in a complex scene. SIFT, which stands for 'scale-invariant feature transform,' was used to extract the feature. The SIFT algorithm is invariant under changes in perspective, orientation, and lighting. Recognize the 10 ISL digit movements with an accuracy of 94.9% using a multi support vector machine (MSVM). © 2023 IEEE.

关键词： Support vector machines

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PAC-Bayes-Chernoff bounds for unbounded losses

arXiv

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arXiv 2024年

作者： Casado, Ioar Pérez, Aritz Ortega, Luis A. Masegosa, Andrés R. Machine Learning Group Computer Science Dept. - EPS Universidad Autónoma de Madrid Spain Department of Computer Science Aalborg University Denmark

We introduce a new PAC-Bayes oracle bound for unbounded losses that extends Cramér-Chernoff bounds to the PAC-Bayesian setting. The proof technique relies on controlling the tails of certain random variables involving the Cramér transform of the loss. Our approach naturally leverages properties of Cramér-Chernoff bounds, such as exact optimization of the free parameter in many PAC-Bayes bounds. We highlight several applications of the main theorem. Firstly, we show that our bound recovers and generalizes previous results. Additionally, our approach allows working with richer assumptions that result in more informative and potentially tighter bounds. In this direction, we provide a general bound under a new model-dependent assumption from which we obtain bounds based on parameter norms and log-Sobolev inequalities. Notably, many of these bounds can be minimized to obtain distributions beyond the Gibbs posterior and provide novel theoretical coverage to existing regularization techniques. © 2024, CC BY-NC-ND.

关键词： Cramer-Rao bounds

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UAV-based Multi-scale Features Fusion Attention for Fire Detection in Smart City Ecosystems 8

UAV-based Multi-scale Features Fusion Attention for Fire Det...

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8th IEEE International Smart Cities Conference, ISC2 2022

作者： Hussain, Tanveer Dai, Hang Gueaieb, Wail Sicklinger, Marco De Masi, Giulia Mohamed Bin Zayed University of Artificial Intelligence Dept. of Computer Vision Abu Dhabi United Arab Emirates Mohamed Bin Zayed Univ. of Artificial Intelligence Dept. of Machine Learning Abu Dhabi United Arab Emirates Technology Innovation Institute Arrc Abu Dhabi United Arab Emirates

ISBN: (数字)9781665485616

ISBN: (纸本)9781665485616

Effective fire detection using vision sensors is a widely accepted challenge in smart cities and rural areas, where forest and building fires significantly contribute to the loss of human lives and properties. Early fire detection using deep learning techniques is emerged to be an effective solution using close-circuit television (CCTV) in smart cities, but it has limited coverage in huge building infrastructures and urban forests. Unmanned Aerial Vehicles (UAV) cover wide areas, but fire detection in visual data captured from UAVs is a challenging task. Therefore, we employ deep multi-scale features from a backbone model and apply attention mechanism for accurate fire detection. The deep features from intermediate layers capture fire regions using spatial object edges information and final layers extract image global representations. The features fusion ensures to represent the image effectively, where the fused features are enhanced using multi-headed self-attention to highlight the most important fire regions. Preliminary experimental results (https://***/tanveer-hussain/DMFA-Fire) using UAV fire detection dataset demonstrate effective performance of the proposed model against rivals and consequently present a new deep model's perspective to consider multi layer features for accurate detection performance, thereby providing effective applicability in smart cities environments. © 2022 IEEE.

关键词： Antennas

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Exploring the potential of collaborative UAV 3D mapping in Kenyan savanna for wildlife research

arXiv

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arXiv 2024年

作者： Shukla, Vandita Morelli, Luca Trybala, Pawel Remondino, Fabio Gan, Wentian Yu, Yifei Wang, Xin Trento Italy Computer Vision and Machine Learning Systems Group Institute for Geoinformatics University of Muenster Germany Dept. of Civil Environmental and Mechanical Engineering University of Trento Italy School of Geodesy and Geomatics Wuhan University China

UAV-based biodiversity conservation applications have exhibited many data acquisition advantages for researchers. UAV platforms with embedded data processing hardware can support conservation challenges through 3D habitat mapping, surveillance and monitoring solutions. High-quality real-time scene reconstruction as well as real-time UAV localization can optimize the exploration vs exploitation balance of single or collaborative mission. In this work, we explore the potential of two collaborative frameworks - Visual Simultaneous Localization and Mapping (V-SLAM) and Structure-from-Motion (SfM) for 3D mapping purposes and compare results with standard offline approaches. © 2024, CC BY-NC-SA.

关键词： Unmanned aerial vehicles (UAV)

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A Simple Heuristic for the Graph Tukey dept. Problem with Potential Applications to Graph Mining

A Simple Heuristic for the Graph Tukey Depth Problem with Po...

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2022 learning, Knowledge, Data, Analysis, LWDA 2022 - Workshops: Special Interest Group on Knowledge Management (FGWM), Knowledge Discovery, Data Mining, and machine learning (FGKD) and Special Interest Group on Database Systems (FGDB)

作者： Seiffarth, Florian Horváth, Tamás Wrobel, Stefan Dept. of Computer Science University of Bonn Bonn Germany Fraunhofer IAIS Schloss Birlinghoven Sankt Augustin Germany Fraunhofer Center for Machine Learning Sankt Augustin Germany

We study a recently introduced adaptation of Tukey dept. to graphs and discuss its algorithmic properties and potential applications to mining and learning with graphs. In particular, since it is NP-hard to compute the Tukey dept. of a node, as a first contribution we provide a simple heuristic based on maximal closed set separation in graphs and show empirically on different graph datasets that its approximation error is small. Our second contribution is concerned with geodesic core-periphery decompositions of graphs. We show empirically that the geodesic core of a graph consists of those nodes that have a high Tukey dept.. This information allows for a parameterized deterministic definition of the geodesic core of a graph. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Graphic methods

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Modified EfficientNetB3 Deep learning Model to Classify Colour Fundus Images of Eye Diseases 5

Modified EfficientNetB3 Deep Learning Model to Classify Colo...

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5th IEEE International Conference on Cybernetics, Cognition and machine learning Applications, ICCCMLA 2023

作者： Sharma, Riya Gangrade, Jayesh Gangrade, Shweta Mishra, Ashish Kumar, Gautam Kumar Gunjan, Vinit Manipal University Jaipur Dept of Computer Science & Engineering Jaipur India Learning Manipal University Jaipur Dept. of Artificial Intelligence & Machine Jaipur India Manipal University Jaipur Dept of Information Technology Jaipur India Jaypee Institute of Information Technology Dept of Computer Science & Engineering Noida India CMR Institute of Technology Department of Computer Science & Engineering Hyderabad India

ISBN: (纸本)9798350338287

The only way to prevent blindness from eye problems is by early detection and prompt treatment. Although colour fundus photography (CFP) is useful for fundus inspection, there is a need for computer-assisted automated diagnosis tools due to the similarities between the early symptoms of many eye disorders. The suggested approach uses cutting-edge deep learning model to categorize images into several disease categories by learning distinguishing features from the input images. The high-resolution fundus photos from individuals with diabetic retinopathy (DR), glaucoma, cataract, and healthy eyes make up most of the dataset used for this research. The experimental findings show that the suggested system achieve 97% accuracy with modified efficientNetB3 model and surpasses current approaches for categorizing eye diseases. This approach may help doctors diagnose and treat eye conditions earlier, leading to better patient outcomes. © 2023 IEEE.

关键词： Color

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A Validity Perspective on Evaluating the Justified Use of Data-driven Decision-making Algorithms

A Validity Perspective on Evaluating the Justified Use of Da...

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Secure and Trustworthy machine learning (SaTML), IEEE Conference on

作者： Amanda Coston Anna Kawakami Haiyi Zhu Ken Holstein Hoda Heidari Machine Learning Dept. Heinz College & Carnegie Mellon University Pittsburgh USA Human-Computer Interaction Institute Carnegie Mellon University Pittsburgh USA Machine Learning Dept. Carnegie Mellon University Pittsburgh USA

Recent research increasingly brings to question the appropriateness of using predictive tools in complex, real-world tasks. While a growing body of work has explored ways to improve value alignment in these tools, comparatively less work has centered concerns around the fundamental justifiability of using these tools. This work seeks to center validity considerations in deliberations around whether and how to build data-driven algorithms in high-stakes domains. Toward this end, we translate key concepts from validity theory to predictive algorithms. We apply the lens of validity to re-examine common challenges in problem formulation and data issues that jeopardize the justifiability of using predictive algorithms and connect these challenges to the social science discourse around validity. Our interdisciplinary exposition clarifies how these concepts apply to algorithmic decision making contexts. We demonstrate how these validity considerations could distill into a series of high-level questions intended to promote and document reflections on the legitimacy of the predictive task and the suitability of the data.

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Revolutionizing Obstetric Care: IoT, AI-Enabled, and Data-Driven Partograph System

Revolutionizing Obstetric Care: IoT, AI-Enabled, and Data-Dr...

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International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Dishant Zaveri Vividha Jagtap Jasleen Gill Krishi Jain Shivam Sheth Narendra Shekokar Dept. of Computer Engineering DJSCE Mumbai India Dept. of Artificial Intelligence and Machine Learning DJSCE Mumbai India Dept. of Computer Science and Engineering (ICB) DJSCE Mumbai India

This research paper introduces a comprehensive real-time labor and delivery monitoring system that uses various hardware devices like ultrasound transducers, cardiotocographs, sphygmomanometers, and IoT devices. It creates a database accessible via a dedicated mobile app for doctors, nurses, and receptionists. The system includes features to identify and notify medical staff of potential risks such as preterm labor or fetal distress, using SMS alerts and LED lights for immediate attention. In emergencies, it notifies relevant medical personnel and receptionists for urgent interventions like cesarean sections. If needed facilities aren't available nearby, the system searches for hospitals within an 8-kilometer radius and sends alerts to check availability, arranging for an ambulance if necessary. This system allows real-time access to patient data, leading to faster responses and improved outcomes, enhancing decision-making and reducing maternal and child mortality through timely interventions and improved maternal care quality.

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A New Intrinsic Complexity Metric for Air Traffic De-Conflicting 43

A New Intrinsic Complexity Metric for Air Traffic De-Conflic...

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43rd AIAA DATC/IEEE Digital Avionics Systems Conference, DASC 2024

作者： Zombré, Patrice Delahaye, Daniel Tapamo, Jules-Raymond Olanweraju, Oludolapo A. Mora-Camino, Felix Air Navigation Asecna Dakar Senegal Optimization and Machine Learning Lab. Ecole Nationale de l' Aviation Civile-ENAC Toulouse France University of KwaZulu-Natal-UKZN Computer Eng. Dept. Durban South Africa Durban University of Technology-DUT Industrial Eng. Dept. Durban South Africa Universidade Federal Fluminense-UFF Dut and Enac Industrial Eng. Dept. Niteroi Brazil

ISBN: (纸本)9798350349610

This study in in the field of Air Traffic Flow Management and considers mainly the pre-tactical planning level of air traffic flows where their global balance is already fixed (flows structures and levels, control sectors and capacity) and where flights are treated on an individual basis in the space between the different departure/arrival airports. Then, for each flight, decisions such as departure rescheduling and/or rerouting may be taken by the Air Traffic Flow Management to cope with predicted traffic congestion situations resulting in conflicts. The adopted objective is the global reduction of interactions between flights while maintaining as much as possible the original efficiency of flight plans computed by airlines. A measure of the criticality of the separation between two aircrafts is introduced as a basis to construct new air traffic complexity measures, either local or global, either instantaneous or integrated over periods of time. It is shown in this study how the resulting metric can be used to perform through standard maneuvers the de-confliction of enroute air traffic over a given area and during a given time window. Two approaches are considered, one which is global and leads to the resolution of a large nonlinear optimization problem, and another one which is decentralized and makes use of directional components of the aircraft centric and flight centric complexity indexes. Then, in the case of flight centric traffic control, using the new flight centric complexity measure, the assignment of flights to the different controllers in charge of the monitoring of an extended area is formulated as an optimal partitioning problem where interactions between controllers must be minimized. To cope quickly with this combinatorial problem, a heuristic approach is developed. © 2024 IEEE.

关键词： Traffic congestion

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