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Geometric Transformations and Tensor Product 20

Geometric Transformations and Tensor Product

引用

20th IEEE Jubilee World Symposium on Applied Machine Intelligence and Informatics, SAMI 2022

作者： Skala, Vaclav University of West Bohemia Faculty of Applied Sciences Dept. of Computer Science and Engineering Pilsen Czech Republic

ISBN: (纸本)9781665497046

This contribution describes new useful geometric transformations using the tensor product. The geometric transformations are used widely in many applications, especially in CAD/CAM systems, systems for Civil engineering, computer graphics, virtual and augmented reality, etc. The basic geometric transformations, e.g. rotation, translation, scaling, etc., are usually applied on points represented in homogeneous coordinates and described in many relevant books. However, there are also other primitives, e.g. lines, planes, normal 'vector' of a line, plane, triangle, etc., on which some geometric transformations can be applied. © 2022 IEEE.

关键词： Tensors Graphics processing units computer graphics Informatics Machine intelligence Civil engineering Augmented reality

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Performance Analysis of Hybrid Quantum-Classical Convolutional Neural Networks for Audio Classification

Performance Analysis of Hybrid Quantum-Classical Convolution...

引用

International Conference on Computing and Networking Technology (ICCNT)

作者： Yash Thakar Bhuvi Ghosh Vishma Adeshra Kriti Srivastava Dept. of Computer Science and Engineering (Data Science) Dwarkadas J. Sanghvi College of Engineering Mumbai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Audio signals being high-dimensional and complex pose challenges for classical machine learning techniques in terms of computation and generalization on real-world data. This paper evaluates the use of hybrid quantum-classical convolutional neural networks (QC-CNN) that leverage quantum effects like superposition and entanglement for audio classification using mel-spectrograms obtained from audio data. Evaluated on both small-sized and large-sized datasets, the proposed QC-CNN model gave comparable training accuracy with classical CNN (Convolutional Neural Network) on the smaller dataset but outperformed classical CNN on test accuracy ($\mathbf{95.04 \%}$ vs $\mathbf{92.88 \%}$) for a larger birdsong dataset and reduced overfitting, thus highlighting the potential advantages of QC-CNNs for audio data. The QC-CNN exhibited higher cross-entropy loss in case of the small-sized dataset which was further significantly reduced when evaluated on the large-sized birdsong dataset. The work demonstrates the application of QC-CNN for audio classification.

关键词： Training Accuracy Quantum computing Quantum entanglement Neural networks computer architecture Data models Performance analysis Convolutional neural networks Testing

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Development of an All-In-One Smart Home and Irrigation System Prototype

Development of an All-In-One Smart Home and Irrigation Syste...

引用

2022 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2022

作者： Hasan, Hadi Madi, Mervat Rahi, Mahmood American University of Beirut Dept. of Electrical and Computer Engineering Beirut Lebanon Dept. of Science Technology and Engineering Higher Colleges of Technology Abu Dhabi United Arab Emirates

ISBN: (数字)9781665456005

ISBN: (纸本)9781665456005

The advancement of automation technologies made lives simpler and easier in all aspects. In today's world, automated systems [1] are replacing manual systems progressively. In this paper, an overview of current and emerging home automation systems is discussed. Then, the developed system is presented that is a one-stop-shop for all the use cases with the novelty of transforming traditional homes into smart homes through merging Artificial Intelligence (AI) [2] and Internet of Things (IoT) [3] thus providing the freedom to connect, integrate, monitor, and control houses remotely, securely, and at a cheaper cost. The use of AI allows monitoring and analyzing data through dashboards. Hence, sending realtime notifications to the user to ensure home management and security remotely. Also, the novelty in introducing IoT in this system is through incorporating different modalities and functions in one system controlled remotely for example the automation and monitoring system [4], the wireless [5] irrigation system, and the security system. All the components are controlled and monitored using a central web and mobile application. On the hardware components side, the system consists of the Raspberry Pi, the RFID Card Reader, the 8-channel relay module, and some sensors. Hence, the presented system is wholesome and scalable, so the user can start small and grow on demand, as this solution is loosely coupled, and tightly integrated. In brief, the solution is a secure, reliable, cheap, and all-inclusive smart home automated system. © 2022 IEEE.

关键词： Irrigation

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Detection of Cataracts from Retinal Fundus Image Using Ensemble Machine Learning Approach

Detection of Cataracts from Retinal Fundus Image Using Ensem...

引用

Next-Generation Computing, IoT and Machine Learning (NCIM), International Conference on

作者： Md. Nahid Hasan Md. Ali Hossain Dept. of Computer Science and Engineering Rajshahi University of Engineering and Technology

Cataract is a common eye condition that causes clouding of the eye's natural lens, resulting in blurry vision and it is very common in older ages people. However, it can also occur in younger aged people due to various genetical effects, diabetes, smoking, and prolonged exposure to sunlight. Cataracts can be treated through surgery to remove the clouded lens and replaced by an artificial one, which can significantly improve the vision. This problem can be reduced through early detection, regular eye examinations etc. In this research, a Contrast Limited Histogram Equalization (CLAHE) of retinal fundus images is applied for a better presentation of the above cataract effects. These improved images are fed to a machine learning-based model for effective detection. Finally, the ensembling of the proposed machine learning-based classifiers is performed for the accurate detection of cataracts. The proposed model was tested experimentally on a real dataset and achieves excellent performance with accuracy, precision, and recall scores of 99.67%, 100%, and 99.25%, respectively which outperform the baseline methods. The robustness of the proposed ensemble model is evaluated through 5-fold cross-validation and achieved an average accuracy of 99.6%. The implementation code can be accessed from the GitHub repository: https://***/nahid-tech/cataract-detection.

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A Deep Learning Approach for Classification and Segmentation of Leafy Vegetables and Diseases

A Deep Learning Approach for Classification and Segmentation...

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Next-Generation Computing, IoT and Machine Learning (NCIM), International Conference on

作者： Ashraful Islam Syeda Rafiatus Sama Raisa Nahiyan Habib Khan Ariful Islam Rifat Dept. of Computer Science and Engineering Sylhet Engineering College Sylhet Bangladesh Dept. of Computer Science and Engineering American International University-Bangladesh Dhaka Bangladesh

Bangladesh's economy is heavily dependent on agriculture, making the region an exceedingly crucial sector that requires great vigilance. In comparison to other vegetables, leafy greens have yet to garner as much attention as other crops that this study's large-scale analysis comes up with, particularly when categorizing readily available leafy vegetables and identifying the affected area and whether they are affected or not. Plant diseases can cause substantial losses in agricultural productivity, and many researchers are working to address the difficulties farmers encounter in identifying and treating these conditions while diagnosing their diseases. The purpose of this study is to develop a dependable and effective approach for classifying seven varieties of leafy vegetables and detecting seven types of plant diseases precisely in Bangladesh using machine learning and deep learning CNN models. To this end, this paper analyzed and provided solutions using a total of eight models: five classification models (VGG16, VGG19, ResNet50, YOLOv5, and YOLOv8) and three instance segmentation models (YOLOv5, YOLOv7, and YOLOv8). Two distinct datasets, “LeafyVclassify7BD” with 3306 images and “LeafyVdisease7BD” with 4493 images, were developed to classify leaves, segment the diseased area, and classify and detect damaged leaves. The research outputs will help strengthen the region's ability to control plant diseases more effectively while also cutting diagnostic costs and enhancing disease detection accuracy in the country's agricultural surroundings.

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Analysis of Historical Weather of Karnataka, India for Insights in Climate Trends

Analysis of Historical Weather of Karnataka, India for Insig...

引用

Modeling, Simulation & Intelligent Computing (MoSICom), International Conference on

作者： B K Sanjana Anilloy Frank Dept. of Computer Science Engineering Presidency University Bangalore India Dept. of Electronics and Communication Engineering Presidency University Bangalore India

ISBN: (数字)9798331533311

ISBN: (纸本)9798331533328

Climate change in simpler words means the change in the long-term average weather parameters. Climate change is an important issue because its causing imbalance in the environment and affects the lives of all flora and fauna. The climate change is due to many factors, but the major factor is the human activities which are leading to global warming over the past decades. Numerous studies consider analysis of continental or global data, providing the overall insights in climatic changes continentally or globally respectively. The approach in this paper considers regional analysis to provide better insights to the local people needs so they get adapted to the changes that are taking place. This provides a warning to the people to take preventive and precautionary measures. The dataset of Karnataka region from 1951 to 2020 is used to study the regional climate change pattern. The main objective of the paper is to analyze the climate change and extending in the future. The results show that there is a gradual increase of average mean surface air temperature and gradual decrease of the precipitation over each decade between the years 1951 to 2020. The trend line extending to 2040 by analyzing the historical data gives insights about the climate change in the near future. Understanding the historical data and the future predictions are very important to the policy makers, people and researchers so that effective planning can be done for adapting and decreasing the climate change patterns.

关键词： Climate change Temperature distribution Precipitation Urban areas Data visualization Predictive models Market research Planning Spreadsheet programs Portals

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Faulty Classes Prediction in Object-Oriented Programming Using Composed Dagging Technique

Faulty Classes Prediction in Object-Oriented Programming Usi...

引用

International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Nagib Mahfuz Pintu Chandra Shill Dept. of Computer Science and Engineering North Western University Khulna Bangladesh Dept. of Computer Science and Engineering Khulna University of Engineering and Technology Khulna Bangladesh

Class is one of the fundamental concepts of the object-oriented paradigm and has been scrutinized since the developers moved on from procedural programming design. In software fault prediction, the legalization of software metrics is essential. As a handful of software metrics suites exist, it is a very hard task to predict the defective classes flawlessly using a particular set of metrics suites. However, it is a rational approach to use only the object-oriented metrics that are directly relatable to the class definitions in the code that helps the developers foresee the errors in defining the classes and minimize the errors as much as possible. This paper utilized twelve object-oriented metrics selected from various metrics suites. The dagging ensemble model is merged with three well-known classification algorithms (Naive Bayes, Multilayer Perceptron, J48 Decision Tree) individually and applied to twelve java projects. The study depicts that the proposed ensemble method gives improved outcomes that are statistically significant when merged with Naive Bayes and Multilayer Perceptron. The proposed ensemble method shows improvements up to 12.5% in accuracy and 15% in F-Score.

关键词： Measurement Software metrics Object oriented modeling Multilayer perceptrons Predictive models Software Classification algorithms

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ML-Based Trading Strategy for Short-Term Price Reactions on Earnings Announcement Reports

ML-Based Trading Strategy for Short-Term Price Reactions on ...

引用

2022 IEEE International Conference on Big Data, Big Data 2022

作者： Jin, Yiqun Bao, Siya Waseda University School of Commerce Japan Waseda University Dept. of Computer Science and Communications Engineering Japan

ISBN: (纸本)9781665480451

An earnings announcement report (EAR) contains the latest information about a company's financial situation and operating performance. Short-term stock price reacts strongly to such information. In this paper, to gain investment return from the short-term price reaction to EARs, we use 28 important variables from EARs and propose an ML-based trading strategy (MLTS) with random forest (RF). Results show that our strategy achieves the highest final investment return of 178.1%. © 2022 IEEE.

关键词： Profitability

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Prioritized Semi-supervised Deep Embedded Clustering

Prioritized Semi-supervised Deep Embedded Clustering

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2022 International Conference on Innovative Trends in Information Technology, ICITIIT 2022

作者： Saladi, Pranita Guntupalli, Rishi Manudeep Puppala, Sudheer Kumar Pulabaigari, Viswanath Indian Institute of Information Technology Sri City Dept. Computer Science Engineering Sri City India

ISBN: (纸本)9781665406703

Clustering, to group similar objects, is an important problem. Recently deep learning-based methods like Deep Embedded Clustering (DEC) [6] and its semi-supervised version called Semi-supervised Deep Embedded Clustering (SDEC) [12], where partially labeled data or data with constraints is available, are shown to give promising results. Both DEC and SDEC learn a latent space where similar objects are closer and dissimilar are away. While promising results are shown, the information present in constraints or a labeled subset of the data is not fully utilized. This paper proposes to use priorities for constraints so that important constraints are given more weightage than unimportant ones. Those constraints with points that are far away, but should be clustered into a group, gets more weight than other labeled points. Similarly, those in different groups which are very close get more weightage. The appropriate loss function is used in the learning process. The proposed method is called Prioritized Semi-supervised Deep Embedded Clustering (PSDEC). The results are compared using a few standard data sets against recent and classical similar methods. PSDEC is found to achieve a better result than un-prioritized constraints. © 2022 IEEE.

关键词： Interpolation

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Directed Adversarial Training for Comprehensive Robustification of CNNs

Directed Adversarial Training for Comprehensive Robustificat...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Bhuvi Ghosh Harsh Shetye Atharv Salian Nilesh Marathe Dept. of Computer Science and Engineering (Data Science) Dwarkadas J. Sanghvi College of Engineering Mumbai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Deep convolutional neural networks (CNNs), renowned for their high accuracy on clean datasets, remain notably vulnerable to adversarial attacks. This study assesses how the VGG16 and ResNet50 models withstand various white-box attacks when used for traffic sign classification. Despite their impressive standard accuracy, our findings reveal that these accuracies do not predict robustness against attacks. Techniques such as the Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) substantially lowered model accuracy, while DeepFool and Carlini & Wagner (C&W) attacks were even more disruptive. In particular, ResNet50 was more prone to these attacks, with its accuracy dropping to as low as $\mathbf{1 2. 8 5 \%}$ under DeepFool and $\mathbf{1 7. 4 2 \%}$ under C&W at specific confidence levels. Retraining models with adversarial examples did enhance their resilience to certain attacks but at the expense of reduced effectiveness on clean data. These results highlight the critical need for developing more robust defense mechanisms that protect CNNs against a wide array of adversarial threats without compromising performance on standard inputs.

关键词： Training Accuracy Noise Robustness Data models Iterative methods Standards Residual neural networks Glass box Resilience

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