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2nd International Conference computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Rekha, K. Sashi Amutha, T. Usharani, R. Pushparani, S. Sivagami, V.M. Saveetha Institute of Medical and Technical Sciences Simats Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Artificial Intelligence & Data Science Tamil Nadu Trichy India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computational Intelligence School of Computing Chennai India Meenakshi College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Venkateswara College of Engineering Department of Information Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350364699

The study presented here consists of an entire framework for prediction of consumers behavior through the machine training of supervised learning methods. The strategy involves the following: data collection, preprocessing, exploration, feature selection, model selection, training, evaluation, interpretation and deployment. The systematic process is tailored to clear the pathway for combining data about customer taste, sophisticated habits, and purchasing trends. Primary components include data preprocessing, training of machine learning models, feature selection, and ethical issues regarding data privacy and transparency. The outcomes of the implementation are a high accuracy in consumers clustering, efficient classification of customers' behavior, as well as unveiling a bunch of crucial predictors that affect the customers mood and behavior. As a result, the report stresses the necessity of integrated monitoring and redirection strategies to realize the potential of the created models. The idea of predictive analysis as an evolving tool that enables marketers to tailor their campaigns more closely, and improve the overall customer experience therefore also receives attention. The proposed system methodology constitutes a solid process framework for business defined to aim at using predictive analytics to gain competitive edge in the current dynamic marketplace. © 2024 IEEE.

关键词： Self-supervised learning

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Infer and Adapt: Bipedal Locomotion Reward Learning from Demonstrations via Inverse Reinforcement Learning

Infer and Adapt: Bipedal Locomotion Reward Learning from Dem...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Feiyang Wu Zhaoyuan Gu Hanran Wu Anqi Wu Ye Zhao School of Computational Science and Engineering Georgia Institute of Technology Atlanta GA USA Woodruff School of Mechanical Engineering Georgia Institute of Technology Atlanta GA USA College of Computing Georgia Institute of Technology Atlanta GA USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Enabling bipedal walking robots to learn how to maneuver over highly uneven, dynamically changing terrains is challenging due to the complexity of robot dynamics and interacted environments. Recent advancements in learning from demonstrations have shown promising results for robot learning in complex environments. While imitation learning of expert policies has been well-explored, the study of learning expert reward functions is largely under-explored in legged locomotion. This paper brings state-of-the-art Inverse Reinforcement Learning (IRL) techniques to solving bipedal locomotion problems over complex terrains. We propose algorithms for learning expert reward functions, and we subsequently analyze the learned functions. Through nonlinear function approximation, we uncover meaningful insights into the expert’s locomotion strategies. Furthermore, we empirically demonstrate that training a bipedal locomotion policy with the inferred reward functions enhances its walking performance on unseen terrains, highlighting the adaptability offered by reward learning.

关键词： Legged locomotion Training Robot motion Imitation learning Reinforcement learning Approximation algorithms Robot learning

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Implementing Asynchronous Jacobi Iteration on GPUs 13

Implementing Asynchronous Jacobi Iteration on GPUs

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13th IEEE/ACM Workshop on Latest Advances in Scalable Algorithms for Large-Scale Heterogeneous Systems, ScalAH 2022

作者： Tsai, Yu-Hsiang Mike Nayak, Pratik Chow, Edmond Anzt, Hartwig Karlsruhe Institute of Technology Steinbuch Centre for Computing Germany Georgia Institute of Technology School of Computational Science and Engineering United States University of Tennessee Innovative Computing Laboratory United States

ISBN: (纸本)9781665475709

Computation on architectures that feature fine-grained parallelism requires algorithms that overcome load imbalance, inefficient memory access, serialization, and excessive synchronization. In this paper, we explore an algorithm for iteratively solving systems of linear equations that allows for asynchronous updates by different execution units and completely removes the need for synchronization. Methods of this type have been identified as potentially competitive for computations on Exascale machines, but practical implementations for GPU platforms have scarcely been studied. We present an asynchronous Jacobi iteration optimized for high-end GPUs, demonstrate the superiority of the algorithm over a highly tuned synchronous Jacobi iteration, and deploy the algorithm as production-ready implementation in the Ginkgo open source library. The ideas presented here on the algorithm design, implementation and performance can help guide the design of other asynchronous iterative methods on GPUs. © 2022 IEEE.

关键词： Iterative methods

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Positive definiteness of real quadratic forms resulting from the variable-step L1-type approximations of convolution operators

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science China Mathematics 2024年第2期67卷 237-252页

作者： Hong-Lin Liao Tao Tang Tao Zhou School of Mathematics Nanjing University of Aeronautics and AstronauticsNanjing 211106China Division of Science and Technology BNU-HKBU United International CollegeZhuhai 519087China SUSTech International Center for Mathematics Shenzhen 518055China NCMIS&LSEC Institute of Computational Mathematics and Scientific/Engineering ComputingAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China

The positive definiteness of real quadratic forms with convolution structures plays an important rolein stability analysis for time-stepping schemes for nonlocal operators. In this work, we present a novel analysistool to handle discrete convolution kernels resulting from variable-step approximations for convolution *** precisely, for a class of discrete convolution kernels relevant to variable-step L1-type time discretizations, weshow that the associated quadratic form is positive definite under some easy-to-check algebraic conditions. Ourproof is based on an elementary constructing strategy by using the properties of discrete orthogonal convolutionkernels and discrete complementary convolution kernels. To our knowledge, this is the first general result onsimple algebraic conditions for the positive definiteness of variable-step discrete convolution kernels. Using theunified theory, we obtain the stability for some simple nonuniform time-stepping schemes straightforwardly.

关键词： discrete convolution kernels positive definiteness variable time-stepping orthogonal convolution kernels complementary convolution kernels

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Detection of Military Vehicles based on Edge Devices from Unmanned Aerial Vehicle 2

Detection of Military Vehicles based on Edge Devices from Un...

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2nd International Conference on Distributed computing and Optimization Techniques, ICDCOT 2024

作者： Umasankar, L. Anitha, D. Trisheela, S. Al-Jawahry, Hassan M. Borra, Subba Reddy S.A.Engineering College Electrical and Electronics Engineering Tiruvallur Poonamallee India School of Computing Srm Institute of Science and Technology Department of Computational Intelligence Tamilnadu Kattankulathur India Nitte Meenakshi Institute of Technology Dept of Cse Bengaluru India The Islamic University Najaf Iraq Computer Science and Engineering Maisammaguda Hyderabad India

ISBN: (纸本)9798350382952

The recent technology in surveillance for military vehicle parts forms the basis of intelligence warfare through information realized through monitoring and tracking operations in complex situations. Therefore, the process of identifying and classifying vehicles by an aerial vehicle installed with a resource-limited device and an intelligent object detection algorithm significantly assists security agencies. It is possible to make use of this vehicle either manually or autonomously. To overcome these issues, edge device based military vehicle detection from Unmanned Aerial Vehicle (UAV) is proposed. At present, no publicly available dataset exists that includes different military classes. Therefore, the proposed approach use a dataset consisting of 6772 images labeled as Tanks, Military Trucks, Helicopters, Aircraft, Civilian Cars and Civilian Aircraft. This data set is used to train deep learning models such as Quantized SSD Mobilenetv2 and Tiny Yolo v5 that are then compared with resource limited edge devices. The results reveal that Tiny Yolo v5 outperforms other models, showing high efficiency and suitability for edge-based devices due to its lightweight design. © 2024 IEEE.

关键词： Military vehicles

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MetaHap: A Low Cost Haptic Glove for Metaverse 6th

MetaHap: A Low Cost Haptic Glove for Metaverse

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6th IFIP International Conference on Internet of Things, IFIP IoT 2023

作者： Sibi Chakkaravarthy, S. John, Marvel M. Vimal Cruz, Meenalosini Arun Kumar, R. Anitha, S. Karthikeyan, S. and School of Computer Science and Engineering VIT-AP University Andhra Pradesh India Allen E. Paulson College of Engineering and Computing Georgia Southern University Statesboro United States Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India

ISBN: (纸本)9783031458774

This paper presents the design and development of a low cost haptic glove equipped with a range of affordable sensors, including gyroscopes, accelerometers, GPS, servos, and encoders, for use in metaverse environments. The primary focus of this research is to create an immersive and interactiveVirtual Reality (VR) experience by incorporating haptic feedback into the glove. This research provides a detailed overview of the glove’s design and construction, highlighting the integration of Arduino micro-controllers with the various sensors and actuators. The techniques employed to ensure accurate and synchronized data capture are also discussed. Furthermore, the haptic feedback system integrated into the glove is thoroughly explained, including the mechanisms for generating the haptic feed back, which will allow the user to determine the shape and size of the objects in the virtual environment. By utilizing the servos and encoders, the glove can provide users with a tactile experience by simulating the sensation of touching virtual objects or environments. The potential applications of the haptic glove in gaming, virtual training, and medical simulations are explored, emphasizing the benefits of incorporating haptic feedback for enhanced user immersion and engagement. The glove’s versatility and affordability make it a viable solution for a wide range of VR applications. In conclusion, this research presents an Small Board Computer (SBC) - based haptic glove that combines affordable sensors with haptic feedback capabilities, providing users with an immersive and tactile VR experience. The findings contribute to the advancement of VR technology, particularly in the field of haptic interfaces, and open avenues for further exploration and customization of haptic glove applications. © 2024, IFIP International Federation for Information Processing.

关键词： Virtual reality

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Measuring COVID-19 Opinion in the Online Debate using an Unsupervised Model 1

Measuring COVID-19 Opinion in the Online Debate using an Uns...

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1st IEEE International Conference on Automation, computing and Renewable Systems, ICACRS 2022

作者： Priyadarsini, K. Jeba Sonia, J. Vijayalakshmi, K. Anbazhagu, U.V. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai603203 India Department of Computational Intelligence School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai603203 India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai603203 India

ISBN: (纸本)9781665460842

A large quantity of potentially threatening COVID-19 false information is available online. In this article, machine learning approach is adopted to assess COVID-19 materials in online health advice adversaries, particularly those who oppose immunizations like (anti-vaccine). Pro-vaccination (pro-vaccine) group is emerging a more attentive conversation regarding COVID-19 above its corresponding portion, the anti-vaccine group. However, the anti-vaccine group presents a wide series of flavors of COVID-19-relatedtopics, andas a result, can demandto a wider cross-section of entities searching for COVID-19 assistance online, such as those who may be wary of receiving a COVID-19 vaccine as a condition of employment or those looking for alternative medications. Later, the anti-vaccine group appears to be better positioned than the pro-vaccine side to obtain complete support moving forward. This is important because if the COVID-19 vaccine is not widely used, the world will not be able to produce herd immunity, parting countries exposed to a COVID-19 comeback in the future. An automatic supervision machine learning model is provided that clarifies these results andcan be used to evaluate the efficacy of intervention efforts. Our method is adaptable and capable of addressing the crucial problem that social media platforms face when analyzing the vast amounts of online health misinformation. © 2022 IEEE

关键词： COVID-19

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Enhancing Medical Image Diagnosis with Convolutional Neural Networks 2

Enhancing Medical Image Diagnosis with Convolutional Neural ...

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Mahadevan, K. Pushparani, S. SashiRekha, K. Sivagami, V.M. Usharani, R. Department of Computer Science and Engineering CARE College of Engineering Tamil Nadu Trichy India Department of Computer Science and Engineering Meenakshi College of Engineering Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of Information Technology Sri Venkateswara College of Engineering Tamil Nadu Chennai India Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9798350383867

This paper is a comparative analysis of medical image diagnosis algorithms with Convolutional Neural Networks (CNNs) and other methods;such as Support Vector Machine (SVM), Random Forest, and k-nearest Neighbors (k-NN) The study performs evaluations on datasets with multimodalities like medical images and diagnostic and various imaging modalities. Results shows that CNNs have the upper hand over traditional, static algorithms across several metrics that assesses the performance of the system such as accuracy, precision, recall, and F1. In particular, CNN precision was 90.5% while the recall was 93.7%, and the accuracy was 92.1% as well as F1-score 92.0%. In addition, the high area under the Receiver Operating Characteristic curve (AUC-ROC) of over 0.95 for the CNN also signed for its excellent discriminatory power. These outcomes only exemplify CNN usefulness in the diagnostic area because it is an exact and accurate method for analyzing medical images. Although the nets CNNs are deployed in clinical cases may help address concerns regarding interpretability, compliance with regulations, and ethical opinions, such issues still exist and require further exploration. Overall, this research takes the first steps in directing algorithmic approach towards medical diagnosis and show the feasibility in deployment of CNNs as inseparable pieces of equipment in modern healthcare. © 2024 IEEE.

关键词： Random forests

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Methodical Tamil Character Recognition Using Fabricated CNN Model

Methodical Tamil Character Recognition Using Fabricated CNN ...

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2023 International Conference on Computer Communication and Informatics, ICCCI 2023

作者： Bhavani, M. Ravikumar, S. Prithi, S. Arockia Raj, Y. Rajendiran, Babu Rajalakshmi Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India School of Computing Department of Computational Intelligence SRM Institute of Science and Technology Chennai India PSNA College of Engineering and Technology Department of Computer Science and Engineering Dindigul India

ISBN: (纸本)9798350348217

Recognizing text is one of the most challenging problems in computer vision. To put it simply, handwritten character recognition is the process of recognising text in photographs, papers, and other media and converting it into a form that a computer can read. You can solve this issue by employing the image classification method. We will be able to recognise the user's typed Tamil characters in this paper. We designed a brand new model, MVGG16, to handle character recognition and classification. Its basic structure is analogous to that of VGG16, but we added a few more layers to improve accuracy. So far, 156 Tamil characters have been learnt, with 350 images each class. In experimental settings, this model achieves an accuracy of 91.8%. As an input method, this work makes use of a whiteboard where the user can scribble the Tamil character to be recognised. The character will be sent for verification after the user has written it and the image has been exported. Once the model identifies the character, it will show it to the user. © 2023 IEEE.

关键词： Character recognition

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Actual Facial Mask Recognition Utilizing YOLOv3 and Regions with Convolutional Neural Networks 1

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2nd International Conference on Emerging Trends in Expert Applications and Security, ICETEAS 2023

作者： Thilagavathy, A. Naveen Raju, D. Priyanka, S. RamBalaji, G. Gopirajan, P.V. Sureshkumar, K. Department of Computer Science and Engineering R.M.K. Engineering College Kavaraipettai India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai603203 India Department of Information Technology Saveetha Engineering College Chennai India

ISBN: (数字)9789819919093

ISBN: (纸本)9789819919086

With the Coronavirus Disease (COVID-19) outbreak in 2019, it appears as though time has stopped. The World Health Organization (WHO) determined that wearing a face mask was essential for limiting virus transmission to combat pathogen transmission. Nevertheless, physically choosing how often individuals wear face masks in public is a momentary operation. The need to track people using respirators required creating an automated process. Nowadays, different deep and machine learning techniques can be used productively. All of the requirements for this type of model have been addressed in this work. The necessity and architectural layout of the suggested method have been thoroughly examined, followed by a rigorous inspection of several methodologies and their related comparison analyses. We investigate optimum settings for the sequential convolutional neural network model to accurately identify the existence of masks avoiding overfitting. Face identification categorically deals with distinguishing a specific group of entities, i.e., face. It has numerous applications, such as autonomous driving, education, surveillance. The prevalent practice reconfigures the images before fitting them into the network to surmount the inhibition. Some face collections are head turn, tilt, and slant with multiple faces in the frame and different types of masks with different colors. The proposed method consists of a cascade classifier and a pre-trained Convolutional Neural Network (CNN), which contains two 2D convolution layers connected to layers of dense neurons. As grayscale rationalizes the algorithm and diminishes the computational requisites, it is utilized for extracting descriptors instead of working on color images instantaneously. It has categorical representation, and the data is converted to categorical labels. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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