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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Jamunadevi, C. Prarthana, V. Arun Prasath, D. Pandikumar, S. Manimaran, M. Kongu Engineering College Department of CT-PG Erode Tamil Nadu Perundurai India Bengaluru India School of Computing Science and Engineering Vit Bhopal University Madhya Pradesh Bhopal India

ISBN: (纸本)9798350313987

Echocardiography is a non-invasive imaging technique for visualizing the heart and its functions. Accurate classification of myocardial tissue in echocardiography images is crucial for diagnosing and treating heart diseases. This research work suggests a new technique for categorizing cardiac tissue by utilizing the Naive Bayes (NB) and Support Vector Machine (SVM) algorithms. Preprocessing extracts texture, shape, and intensity-based features, followed by classification using NB and SVM. Evaluation metrics include accuracy, sensitivity, specificity, and precision. The results demonstrate high accuracy, with NB slightly outperforming SVM. The proposed method offers an efficient and accurate approach for clinical myocardial tissue classification, benefiting heart disease diagnosis and treatment. © 2023 IEEE.

关键词： Echocardiography Myocardial Naive Bayes SVM

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Augmenting Clinical Decisions with Deep Learning Lung Cancer Image Abnormality Segmentation

Augmenting Clinical Decisions with Deep Learning Lung Cancer...

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International Conference on Confluence The Next Generation Information Technology Summit (Confluence)

作者： K Venkatraman Sirigiri Naga Pavan Sathvik Reddy Computer Science and Engineering Amrita Vishwa Vidyapeetham School of Computing Chennai India

Lung cancer is a major global cause of death, highlighting the critical need for quick and accurate detection methods. The exploration of computational alternatives arose from the standard way of manually processing CT images, which is time-consuming and error-prone. In this work, we combine the advantages of Support Vector Machine (SVM) and VGG16 classifiers to provide a novel method for enhancing lung cancer diagnosis. By analyzing the “IQ-OTH/NCCD” dataset, our hybrid model-which combines the VGG 16 and SVM algorithms-performs admirably in differentiating between aggressive, benign, and normal lung cancer cases. This combination of traditional machine learning with deep learning tackles accuracy and efficiency issues, which is a promising development over current diagnostic methods. We conduct a comprehensive comparative analysis with prominent architectures to select the optimal model based on accuracy, efficiency, and resource requirements. In addition to introducing the VGG 16+SVM model, our research provides valuable insights into deep learning architectures, with the ultimate goal of advancing precise and efficient diagnosis of lung cancer, which is crucial in combating this global health challenge in the future.

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Optimizing Transformer Training Based on Computation and Accessing Memory Features with Deep Learning Processor 29

Optimizing Transformer Training Based on Computation and Acc...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Lei, Zhou Fang, Qin Qian, Jingfeng Chen, Shengbo Xu, Qingguo Yang, Ninghua Shanghai University School of Computer Engineering and Science Shanghai200444 China Ningbo Zhongke Intelligent Computing Technology Co. Ltd Zhejiang315100 China

ISBN: (纸本)9798350330717

The Transformer model, which has significantly advanced natural language processing and computer vision, overcomes the limitations of recurrent neural networks and convolutional neural networks. However, it faces challenges with computational efficiency and memory management due to complex computations and variable-length inputs. Despite research efforts, these issues persist. This paper presents a novel optimization of the Transformer model, following an indepth analysis of its computational graph structure. Firstly, we utilize Deep computing Unit (DCU) as our hardware platform. Secondly, we optimize element-wise and reduction operators through operator fusion and rewriting. Thirdly, we develop a fine-grained memory management algorithm using a greedy strategy. As a result, the training speed of the Transformer model increases by 1.2x - 1.4x without compromising accuracy. © 2023 IEEE.

关键词： computation optimization DCU memory management Transfomer

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Enhancing Object Detection Using Information Theory and Network Coding 1

Enhancing Object Detection Using Information Theory and Netw...

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1st International Conference on Data, Computation and Communication, ICDCC 2024

作者： Alam, Sultan Dhingra, Veronika Raghuvanshi, Diya Sharma, Srishti Sengar, Disha Singh, Anshika School of Computing Science and Engineering VIT Bhopal University Sehore466114 India

ISBN: (纸本)9798331532956

This research explores the challenge of dynamic environment object detection. Changing environment factors adversely affect the feature detection and robustness of the model. The approach proposed homogenizes the principles from Information Theory and Network Coding to enhance the overall final matching accuracy and robustness of the model. The effectiveness is improved by systematically using the concepts of entropy, mutual information, Principal Component Analysis, Random Linear Network Coding and Support Vector Machines. The developed framework maintains distinctive feature recognition and classification performance with rapid environment changes. The proposed and the baseline model is run in python using OpenCV for obtaining the experimental result. By including these robust feature extraction techniques along with Scale-Invariant Feature Transform, in dynamic environments, the model achieves a final matching accuracy of 72.4% and an F1-score of 76.1%. In contrast, the baseline model achieves a final matching accuracy of 43.8% and an F1-score of 44%. The improvement in performance shows the model's enhanced resilience and robustness with the changing environmental conditions, contributing valuable prospects for real-time applications where environmental variability is a factor to be considered. © 2024 IEEE.

关键词： Encoding (symbols)

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Video Frame Interpolation Based on Lightweight Convolutional Unit and Three-scale Encoder 23

Video Frame Interpolation Based on Lightweight Convolutional...

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15th International Conference on Digital Image Processing, ICDIP 2023

作者： Liu, Weijing Yang, Xiaohui Feng, Zhiquan Xu, Tao Guo, Qingbei School of Information Science and Engineering University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan250022 China

ISBN: (纸本)9798400708237

Video frame interpolation (VFI) achieves temporal super-resolution by synthesizing intermediate frame between two original adjacent frames. This paper proposes a lightweight VFI network based on lightweight convolutional unit and three-scale encoder. We first introduce a three-scale encoding-decoding structure with two-level attention cascades to represent the multi-scale motion information. Then, recurrent convolutional layer (RCL) and residual operation are adopted to design the recurrent residual convolutional unit (RRCU) to refine the three-scale structure. Finally, we propose a lightweight unit by combining the depth separable convolution and RRCU, and introduce the local lightweight idea to reduce the model parameters. Experimental results show that the proposed method achieves a better performance against the state-of-the-art methods with fewer parameters. © 2023 ACM.

关键词： Convolution

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Predictive Analysis of Consumer Behavior Using Supervised Learning Techniques 2

Predictive Analysis of Consumer Behavior Using Supervised Le...

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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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An Innovative Method for Fake News Classification using LSTM-RF Approach 4

An Innovative Method for Fake News Classification using LSTM...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Yadav, Prashant S Subba Reddy, K. Ashwini, S. Santhosh, J. Nirmala Devi, M. Singh, Kamlesh Mmit Hathras Computer Science and Engineering Uttar Pradesh India Prakasam Engineering College Science and Engineering Andhra Pradesh Kandukur India Srm Institute of Science and Technology Kattankulathur Campus School of Computing College of Engineering and Technology Department of Computing Technologies Chennai India Sri Krishna Adithya College of Arts and Science Department of Computer Applications Tamilnadu Coimbatore India St.Martin's Engineering College Telangana Secunderabad India Graphic Era Hill University Department of Computer Science and Engineering Dehradun Campus Dehradun India

ISBN: (纸本)9798350300093

The dissemination of news and coverage of events, both domestically and internationally, is now mostly the responsibility of print, web, and broadcast media. Because of the explosion of information available online, verifying the veracity of claims is more difficult than ever. It is extremely difficult to verify the veracity of fake news using conventional data processing techniques without seriously misinforming the public. Indeed, the difficulty for the government and the public is a matter of case by case argument. Several sites were built specifically for this purpose, each using its own unique logic and algorithm to determine which stories were more likely to be true or false. There needs to be a system in place for verifying the veracity of online claims, especially those that garner thousands of shares and clicks before being disproved by credible sources. In this proposed approach to an LSTM-RF classifier was used to determine whether or not a news story was fabricated. The three-step process that makes up the suggested method is as follows: preprocessing;feature extraction and feature selection;and LSTM-RF model training. In preprocessing, you'll clean the data, get rid of stop words, tokenize it, and stem it. The TF-IDF and Extra Tree Classifier are used for feature selection. The model is trained with LS TM-RF. The proposed method achieves superior results compared to LSTM and RF. © 2023 IEEE.

关键词： Feature extraction

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A new DEVS-based network simulator for online routing protocol education

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Multimedia Tools and Applications 2024年第21期83卷 60903-60919页

作者： Zengin, Ahmet Altunkaya, Cevat Kara, Şahin Sarjoughian, Hessam Computer Engineering Sakarya University Esentepe Campus Sakarya Serdivan54187 Turkey Computer Programming Sakarya Applied Science University Sakarya Kaynarca54650 Turkey School of Computing and Augmented Intelligence Arizona State University 1151 S Forest Ave TempeAZ85281 United States

Simulation continues to be highly attractive and indispensable for studying networked systems. There remains the need for simulators that can support efficient execution in large-scale, complex models and can offer a theoretical underpinning with model abstractions. A qualified and effective simulator is still hard to find in education and research. To obtain a better solution in education and research, a collection of the parallel DEVS-based computer network model components is presented in this article. The Discrete Event System Specification (DEVS) is a mathematical modeling formalism for describing (discrete and continuous) dynamical systems. Among discrete event modeling approaches, it is well suited for formally describing concurrent processing and the event-driven nature of arbitrary configuration of nodes and links forming network systems. The suggested system is developed by emphasizing the Open Shortest Path First (OSPF) protocol. These expressive models are realized in the DEVS-Suite simulator as DEVS-Suite provides a robust environment with rich support for designing experiments and visualization. Visualization representation of a typical routing protocol logic with routing tables and control packet provides better support for active learning, particularly for distance education in COVID-19 disease. The resulting OSPF-DEVS simulation environment offers a model abstraction well-suited to educational environments. An exemplary computer network model is developed and evaluated in both simulators. The simulation models have proved very helpful in understanding the basics of the computer network’s principles, designs, routing protocols, etc. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Abstracting

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Multi-objective Optimization of Hybrid Microgrid System of Semau Island in Indonesia: Minimising Energy Cost and Carbon Emission Using Evolutionary Computation

Multi-objective Optimization of Hybrid Microgrid System of S...

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Power and Renewable Energy engineering (PREE), International Conference on

作者： Muhammad Elyan Andaswara Kit Yan Chan Dowon Kim Daryl Haris Antoni School of Electrical Engineering Computing and Mathematical Science Curtin University Perth Australia

ISBN: (数字)9798350366808

ISBN: (纸本)9798350366815

The optimisation of the power system to minimise cost-emission has been discussed and discussed in many studies. The early approach to optimisation is trial and error without using systematic and automated tools, resulting in not all solutions being explored. Other than that, optimising using only a single objective is ineffective since it produces one-sided advantages that could lead to disadvantages for the other parameters. Additionally, single-objective optimisation requires several constraints to be set up, which can be problematic if the limits are unfixed or complex. This paper proposes a novelty multi-objective formulation of the optimal power generation problem involving energy cost, emissions, and total voltage violations as objectives using the Evolutionary Algorithm to automate the process. The object used in this project is a Hybrid Microgrid System consisting of Diesel Generators and Solar Photovoltaic Systems with Battery Energy Storage located in Semau Island, Indonesia. This study aims to provide an alternative optimisation method that the grid operator can use to determine the most environmentally friendly and cost-efficient power generation combination in the operation of a hybrid microgrid system and its future development. The optimization on the grid includes 1,620 load flow simulations, indicating that the energy cost ranges from $202.54 to $208.82, and the emissions range from 0.12 to 0.225 tons per hour. These findings suggest that significant accumulations in costs and emissions can occur over extended periods.

关键词： Renewable energy sources Costs Optimization methods Microgrids Evolutionary computation Voltage Hybrid power systems Generators Batteries Load flow analysis

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ANSAN (Amrita Nethra Samrakshan System) A Mobile App to Assist Hospitals for Glaucoma Screening 8

ANSAN (Amrita Nethra Samrakshan System) A Mobile App to Assi...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Ashwin Narayanan, S. Viswa, A.V. Sharma, Ashwin R. Harinandan, N. Murugesh, J.L.P. Shanmugam, Selvanayaki Kolandapalayam Somasundaram, K. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Tamil Nadu Coimbatore India Ashland University Department of Mathematics and Computer Science United States Amrita Vishwa Vidyapeetham Amrita School of Physical Sciences Department of Mathematics Tamil Nadu Coimbatore India

ISBN: (纸本)9798350376425

In India, around 12 Million people suffer from glaucoma, and 1.5 million are blind due to it. However, more than 75% of glaucoma cases are undiagnosed. Millions of individuals worldwide experience blindness due to the unfortunate reality that they were unaware of their glaucoma diagnosis. As per the World Health Organization (WHO) [1], glaucoma is among the primary causes of blindness and visual impairment, particularly in high-income nations. This motivated us to develop and implement a system advantageous to both healthcare facilities and potential patients (i.e., the general public). We posit that this system will substantially mitigate the disease's severity, which will be discussed in this paper. Individuals can self-register for the application, or frontline workers, doctors, or hospital management personnel can add them. Upon registration, their medical history is collected, fundus images of their eyes are captured and uploaded, and an immediate prediction of the presence or absence of glaucoma is provided. A report of the findings is shared with the hospital's doctors, among other actions. Both the patient and the doctor can review the reports. Depending on the severity, doctors can add comments to the reports, which patients can view and respond to. There were apps like the Yanbao app [2], but it only works on iOS, and our app ANSAN works on both Android and iOS platforms and has more personalized features in comparison. The app was shortlisted for the Google Solutions Challenge [3] India Regional Bootcamp when submitted in 2024. We propose two models: one [4] wherein data is stored in a relational database model in the hospital's server, and another where data is stored in a NoSQL model [5] integrated by Google Firebase tools. The app is scalable on both ends naturally by design. ANSAN can be a real-world application to help doctors, potential patients, and the world in reducing cases where glaucoma is the cause of blindness through early detection. © 2024

关键词： Information management

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