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Computer, Electronics & Electrical Engineering & their Applications (IC2E3), International Conference on

作者： M. Karthiga D. Kavitha Dept of Artificial Intelligence and Data Science Coimbatore Institute of Technology Coimbatore India Dept. of Artificial Intelligence and Machine Learning Coimbatore Institute of Technology Coimbatore India

The number of reported COVID-19 cases has once again increased due to the recent spread of the H3N2 virus. Even with vaccinations and medications, social distancing remains the most effective way to prevent the spread of these viruses. To address this issue, this paper proposes an automated system for monitoring social distancing in public places. The system utilizes a YOLOv3 object detection model to detect pedestrians in video frames captured by a camera. The frames are then converted to a top-down perspective to measure the distances between individuals in a 2D plane. If any individuals are too close to each other, the system displays a warning by highlighting them with a red frame and a red line.

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Optimizing CNNs for Leaf Disease Detection: A Comparative Analysis of Algorithms, Activation functions, and Performance Metrics

Optimizing CNNs for Leaf Disease Detection: A Comparative An...

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Silchar Subsection Conference (SILCON), IEEE

作者： Nitisha S Mohith N Vinay R Monika Agarwal Aparajita Sinha Mokshitha M Dept. Artificial Intelligence and Machine Learning Dayananda Sagar University Bangalore India Dept. Computer Science Engineering National Institute of Technology Agartala India

ISBN: (数字)9798331540821

ISBN: (纸本)9798331540838

Agriculture plays an important role in ensuring food security. However, the emergence of plant diseases causes significant economic losses. The routine method of detecting crop diseases is time consuming, requires technical skill and can be inaccurate, especially when new diseases are introduced. To solve these problems, we present a new and effective method for the early identification of leaf diseases using computer vision and machine learning. Extensive testing has been done on the widely used Plant Village dataset to ensure accurate findings. Our plans focus on disease diagnosis and classification. This study demonstrates the pros and drawbacks of this method. The testing process includes data collection, data training, feature extraction, model testing, and classification. The main processing techniques include image acquisition, image preprocessing, extraction and the use of neural networks. Combining computer vision and machine learning, our approach holds great promise for efficient and reliable detection of crop diseases. This has important implications for improving disease control, reducing economic losses and increasing food security.

关键词： Economics Computer vision Accuracy Computational modeling Crops Food security Real-time systems Agriculture Diseases Testing

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Understanding Red Wine Quality with Feature Importance in machine learning and Explainable AI

Understanding Red Wine Quality with Feature Importance in Ma...

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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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LaCK: Lung Cancer Classification and Detection using Convolutional Neural Network-based Gated Recurrent Unit Neural Network Model

LaCK: Lung Cancer Classification and Detection using Convolu...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Thara, Machakanti Navya Chatterjee, Kalyan Raju, Muntha Rout, Sandip Priyadharshini, M. Prasad, Kokkula Shiva Suraj Kumar, Samala Reddy, Mandadi Sriya Nalla Malla Reddy Engineering College Artificial Intelligence & Machine Learning Hyderabad India Nalla Malla Reddy Engineering College Computer Science & Engineering Hyderabad India Government of Odisha Mathematics Government Autonomous College Dept. of Higher Education Odisha Angul India

ISBN: (纸本)9798350361537

Cancer instills fear in people worldwide, with millions succumbing to its grasp each year. Among the various types, lung cancer stands as a prominent contributor to this alarming statistic. Therefore, this novel research Lung Cancer Classification and Detection using Convolutional Neural Network-based Gated Recurrent Unit Neural Network Model (LaCK) has focused on classifying lung cancer, notably Non-Small Cell Lung Cancer, which constitutes the majority of cases. We employed five units of gated recurrent unit neural network (GRU-NN) together to achieve this classification, utilizing logistic regression (LR) and support vector classifier (SVC) models for comparisons. Among all models, the LaCK model demonstrated remarkable accuracy, achieving approximately 98% success rate, outperforming its counterparts. Implementing this model in clinical settings enables medical professionals to establish a precise and automated approach to diagnosing lung cancer. © 2024 IEEE.

关键词： Lung cancer

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Next-Gen Dermatology: Revolutionizing Dermatological Diagnostics with AI and Emergency Care Solutions

Next-Gen Dermatology: Revolutionizing Dermatological Diagnos...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Yashas D M Shivani Kashyap Charan PR Hemanth Kumar GP Abhishek BK Deekshith C Dept of Artificial Intelligence and Machine Learning New Horizon College of Engineering Bengaluru Dept of Information Science and Engineering New Horizon College of Engineering Bengaluru Dept of Artificial Intelligence and Machine Learning BNM Institute of Technology Bengaluru Dept of Artificial Intelligence and Data Science Siddaganga Institute of Technology Tumkur Dept of Computer Science and Engineering Vidyavardhaka College of Engineering Mysore

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

Skin health conditions should be diagnosed and treated on time so as to prevent complications and promote better health results. This paper presents an AI-based system that makes use of Convolutional Neural Networks with the InceptionV3 Architecture for predicting skin diseases at higher levels. The model, which is based on the large DermaNet dataset, classifies skin disorders fast and effectively. The system is coupled with a simple Telegram based application for the users to get a complete solution. A user can upload a photo of the affected skin area and get a diagnosis within seconds, an explanation of the disease and how severe it is in percentage. The bot also has a location and contact sharing feature that assists users in finding nearby doctors and clinics that can provide further assistance. It further provides on the spot appropriate cures for the particular condition diagnosed. Moreover, this makes it possible for the customer to fill the healthcare void in particular the neglected ones by making it easy for them to access real time high quality services. By equipping users with relevant information and appropriate actions required at the right time, this project has showcased how AI has the ability to transform the dermatological field.

关键词： Deep learning Dermatology Medical services Transforms Chatbots Skin Real-time systems Convolutional neural networks Artificial intelligence Diseases

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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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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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Fostering Sparsity in Sparse Convolution Networks for Efficient 3D Perception via Feature-Guidance

Fostering Sparsity in Sparse Convolution Networks for Effici...

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IEEE SENSORS

作者： Nico Leuze Henry Schaub Maximilian Hoh Samed Doğan Nicolas R.-Peña Nikolas Voss Alfred Schöttl Dept. of Electrical Engineering and Information Technology Institute for Applications of Machine Learning and Intelligent Systems University of Applied Science Munich Munich Germany

ISBN: (数字)9798350363517

ISBN: (纸本)9798350363524

3D Point clouds have unique computational challenges due to their unstructured sparse nature and non-uniform density. Recent 3D object detectors usually apply sparse convolutional networks for feature extraction owing to its efficiency. However, sparse CNNs suffer from the submanifold dilation problem of regular sparse convolutions yielding more and more active sites in feature maps. Thus, leading reduced feature sparsity and higher computational burden. We find that 3D object detector's learned underlying geometric and spatial distributions in deeper feature maps can serve as an important cue to determine crucial areas at runtime to eliminate the extra computations in sparse convolution networks. A Bayesian feature selection approach is proposed which adds zero training overhead while streamlining active sites at the inference stage. We reveal inherent divergences between object and background distributions in high-dimensional feature space and exploit this to classify features and prune non-essential regions. It is demonstrated that solely leveraging these learned distributions, our approach achieves significant memory savings with only minor performance drawbacks.

关键词： Training Point cloud compression Three-dimensional displays Runtime Quantization (signal) Network architecture Feature extraction Bayes methods Sensors Optimization

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Enhanced Vision Transformer with Lion Optimizer for Accurate Classification of Monkeypox Skin Images

Enhanced Vision Transformer with Lion Optimizer for Accurate...

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Roheet Bhatnagar Esraa Hassan Abeer Saber Mahmoud Y. Shams Samar Elbedwehy Department of Computer Science and Engineering Manipal University Jaipur Rajasthan India Dept. of Machine Learning Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Information Technology Dept. Faculty of Computers and Artificial Intelligence Damietta University Damietta Egypt Dept. of Data Science Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

Monkeypox is a contagious viral infection caused by the monkeypox virus (Mpoxvirus). Characterized by skin lesions, fever, respiratory difficulties, swollen lymph nodes, and neurological complications, MPX can be fatal. Recent outbreaks have spread the disease across Europe, Australia, the United States, and Africa. Traditionally diagnosed through PCR testing of skin lesions, this process poses significant risks to medical personnel due to potential exposure to the highly contagious virus. However, the usage artificial intelligence (AI) is revolutionizing diagnostics, providing a safer and more efficient approach to MPX detection. In this paper, a new AI technique based on vision transformer and lion optimizer for classifying Monkeypox Skin Images. The presented model used for classifying the dataset and results reached accuracy of 99.6%.

关键词： Computer vision Accuracy Measles Feature extraction Transformers Skin Lesions Artificial intelligence Viruses (medical) Testing

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