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Scan and Automated Test pattern Generation in VLSI 5

Scan and Automated Test Pattern Generation in VLSI

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5th international conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Yadav, Aniruddh Bahadur Udaykumar, Mudigonda VR Siddhartha Engineering College Department of ECE AP Vijayawada India

ISBN: (纸本)9798350367171

Integrated circuits (ICs) are becoming more and more complicated, which makes it difficult to guarantee their functioning and dependability. Automated testing techniques are crucial for identifying and resolving this issue by identifying and diagnosing IC problems. Among these approaches, test pattern generation and scan-based testing are essential for quickly locating and analyzing flaws. the creation of an automated system for test pattern generation and scan-based testing in the context of ICs is the main goal of this study. the suggested solution makes use of cutting-edge algorithms and methodologies to improve the efficacy and efficiency of the testing procedure. the system's initial component entails incorporating scan chains into the IC design. Scan chains offer observable and adjustable places in the circuit, making it possible to apply test patterns and track responses. this integration makes it possible to isolate and analyze particular IC portions, which enables thorough testing. the system's second part deals with the automatic creation of test patterns. Given the complexity of current ICs, traditional approaches for generating test patterns may be both time-consuming and not the best. the suggested method creates test patterns intelligently by utilizing sophisticated algorithms, including machine learning and artificial intelligence. these algorithms create effective and high-coverage test patterns by examining the circuit's architecture, functioning, and past test data. Moreover, the system integrates self-learning techniques to adjust to changing fault models and integrated circuit designs. Because of its flexibility, testing can continue to be efficient even with increasingly complex IC architectures. the suggested system's efficacy in terms of fault coverage, test time reduction, and adaptation to different IC designs is demonstrated by the experimental evaluation. the outcomes show a notable improvement over conventional testing methods, underscori

关键词： Chains

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Enabling Edge Devices Using Federated learning and Big data for Proactive Decisions 5th

Enabling Edge Devices Using Federated Learning and Big Data ...

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5th international conference on Recent Trends in Image Processing and pattern recognition (RTIP2R)

作者： Chowdhury, Abishi Swaminathan, A. Ashoka, Rajan R. Pal, Amrit Vellore Inst Technol Sch Comp Sci & Engn Chennai 600127 Tamil Nadu India

ISBN: (纸本)9783031235986;9783031235993

Big data and cloud computing lay a new paradigm of data analytics and pave an insight for new types of intelligent devices. these smart devices continuously capture, store, and transfer data to the centrally controlled devices. In recent years, there has been an exponential increase in smart devices, which has increased the amount of data generated by these devices. the communication of the generated data to the central nodes results in a high communication rate. this type of data communication is vulnerable to network intrusions and multiple security threats. Realtime processing of the data is also a huge challenge, and it is an arduous task to provide support to the applications working on realtime decision making. this paper proposes a message passing approach among intelligent edge devices, which leverages the advantages of the big data analytics and federated learning to tackle the challenges as mentioned above. the experimental analysis of several benchmark datasets supports the proposed approach's theoretical base. the proposed approach with suitable number of edge devices reduces the overall training time maximum by one third while maintaining a significant amount of accuracy.

关键词： Federated learning Big data Edge computing Prediction model Fault prediction

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Augmentation of Human Activity data: Convert, Generate, Transform 27th

Augmentation of Human Activity Data: Convert, Generate, Tra...

引用

27th international conference on pattern recognition, ICPR 2024

作者： Nair, Nilah Ravi Matei, Arthur Krön, Dennis Moya Rueda, Fernando Reining, Christopher Fink, Gernot A. Chair of Material Handling and Warehousing TU Dortmund University Dortmund Germany Department of Computer Science TU Dortmund University Dortmund Germany MotionMiners GmBH Dortmund Germany

ISBN: (纸本)9783031781919

data-driven neural network models trained on human motion data facilitate human activity recognition and identity verification applications. However, large annotated and processed human motion datasets are scarce, leading to the overfitting of models to training data. thus, it is important to investigate data augmentation techniques to generate additional data to facilitate model generalisation. In addition, the choice of augmentation techniques severely impacts the performance of self-supervised learning. thus, this work experiments and evaluates various augmentation techniques on seven sensor-based human activity datasets. three supervised neural network models and one self-supervised learning model were experimented with. We note that due to the high variability in the performance of algorithmic augmentation techniques on time-series human activity datasets, generative data is highly influential in this domain. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Self-supervised learning

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Proceedings of the 5th international conference on Recent Advances in Information Technology, RAIT 2023

Proceedings of the 5th International Conference on Recent Ad...

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5th international conference on Recent Advances in Information Technology, RAIT 2023

ISBN: (纸本)9798350335705

the proceedings contain 16 papers. the topics discussed include: deep ensemble architecture for knee osteoarthritis severity prediction and report generation;sweep coverage with faults;microarray data analysis for diagnosis of cancer diseases by machine learning algorithm;blockchain-based secure storage and management of electronic health record using a smart card;high throughput circuit design of flash type analog to digital converter;rad-former: structuring radiology reports using transformers;realistic benchmark datasets for team formation problem in social networks;an efficient speaker identification approach for biometric access control system;emotion recognition from masked faces using inception-v3;secrecy outage analysis of energy harvesting enabled two user cooperative NOMA;and high throughput circuit designs of digital to analog converter.

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Significant CC400 Functional Brain Parcellations Based LeNet5 Convolutional Neural Network for Autism Spectrum Disorder Detection 5th

Significant CC400 Functional Brain Parcellations Based LeNet...

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5th international conference on Recent Trends in Image Processing and pattern recognition (RTIP2R)

作者： Othmani, Alice Bizet, thibaut Pellerin, Tanguy Hamdi, Badr Bock, Marc-Antoine Dev, Soumyabrata Univ Paris Est LISSI UPEC F-94400 Vitry Sur Seine France EFREI Paris Engn Sch F-94800 Villejuif France Univ Coll Dublin Sch Comp Sci Dublin Ireland

ISBN: (纸本)9783031235986;9783031235993

machine learning and computer vision have opened new pathways to investigate imaging data captured from different sensors. Numerous application areas are getting benefit from these advancements and one of these areas is medical imaging. Despite rapid advancements in machine learning based medical condition diagnosis systems (CADs), some ailments and disorders are hard to diagnose/classify due to the absence or the lack of consensus on biomarkers for specific disorders, like the Autism Spectrum Disorder (ASD). In this study, the challenging problem of classification of ASD using the magnetic resonance imaging (MRI) data is tackled. Hence, we propose an interpretable deep neural network based approach for ASD detection from MRI images. Our proposed explanation method is based on the selection of four regions of interest from the MRI images. the four significant CC400 functional brain parcellations are then concatenated and fed to a LeNet-5-based convolutional neural network to predict ASD. the performances of the proposed approach are evaluated on ABIDE dataset and promising results are achieved. three augmented datasets are considered and an accuracy of 95% is achieved by using LeNet-5 which outperforms VGG16 and ResNet-50. the achieved accuracy outperforms also the existing deep neural networks based approaches on ABIDE dataset. the use of the four significant CC400 functional brain parcellations makes our approach more interpretable and more accurate.

关键词： Deep learning Convolutional Neural Network Autism Spectrum Disorder ABIDE I MRI images

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Efficient Unsupervised learning for Plankton Images 26

Efficient Unsupervised Learning for Plankton Images

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Alfano, Paolo Didier Rando, Marco Letizia, Marco Odone, Francesca Rosasco, Lorenzo Pastore, Vito Paolo Univ Genoa MaLGa DIBRIS Genoa Italy INFN Sez Genova Genoa Italy Italian Inst Technol Genoa Italy MIT CBMM 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Monitoring plankton populations in situ is fundamental to preserve the aquatic ecosystem. Plankton microorganisms are in fact susceptible of minor environmental perturbations, that can reflect into consequent morphological and dynamical modifications. Nowadays, the availability of advanced automatic or semi-automatic acquisition systems has been allowing the production of an increasingly large amount of plankton image data. the adoption of machine learning algorithms to classify such data may be affected by the significant cost of manual annotation, due to both the huge quantity of acquired data and the numerosity of plankton species. To address these challenges, we propose an efficient unsupervised learning pipeline to provide accurate classification of plankton microorganisms. We build a set of image descriptors exploiting a two-step procedure. First, a Variational Autoencoder (VAE) is trained on features extracted by a pre-trained neural network. We then use the learnt latent space as image descriptor for clustering. We compare our method with state-of-the-art unsupervised approaches, where a set of pre-defined hand-crafted features is used for clustering of plankton images. the proposed pipeline outperforms the benchmark algorithms for all the plankton datasets included in our analysis, providing better image embedding properties.

关键词： Image segmentation Microorganisms Pipelines Neural networks Sociology Computer architecture Feature extraction

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Sentiment Analysis of Amazon Alexa Product Reviews: A Comprehensive Comparative Study of learning Algorithms 5th

Sentiment Analysis of Amazon Alexa Product Reviews: A Compre...

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5th international conference on data Science, machine learning and Applications, ICDSMLA 2023

作者： Rao, Gouravelli Akshith Prakash, L.N.C.K. Suryanarayana, G. Joshua, Pathi Varun Reddy, Katta Nithin Kumar Karnati, Ramesh Computer Science and Engineering Vardhaman College of Engineering Hyderabad India CVR College of Engineering Hyderabad India Information Technology Vardhaman College of Engineering Hyderabad India

ISBN: (纸本)9789819780426

Sentiment analysis is part of text mining, which means extracting data from comments on specific products and people’s opinions or sentiments. It involves considering opinions, analysis, and suggestions to customers about Amazon Alexa products by classifying a review as positive, negative, or neutral. this work is to explore different learning algorithms to solve sentiment analysis on Amazon products. the proposed work is a comparative analysis of existing learning algorithms that can help businesses in real-time decision-making by providing insights into customer preferences, needs, and choices which in turn results in the growth of the company and enhancement of customer satisfaction. the existing works provided the accuracy of classification to a level of 80%. Algorithms used in previous projects are SVM, Naïve Bayes, Random Forest, and other learning algorithms. In this work, logistic regression, Gradient boosting, Multinominal NB, and Gaussian NB are used along with previously implemented algorithms for sentiment analysis. Deep learning algorithms such as Long Short-Term Memory (LSTM) and ANN with dropout and without dropout layers are also implemented and compared with machine learning algorithms. Word2Vec is employed with LSTM to increase the accuracy of the representation. Based on the evaluation of their metrics, accuracies are compared. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Logistic regression

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Prediction of Covid-19 Recovery Chances Using Supervised machine learning Classification 5th

Prediction of Covid-19 Recovery Chances Using Supervised Mac...

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5th international conference on Innovative Product Design and Intelligent Manufacturing Systems, ICIPDIMS 2023

作者： Dhanalakshmi, R. Nivashini, A. Vijayaraghavan, N. Centre for Cyber Physical Systems School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering MNM Jain Engineering College Chennai India Department of Mathematics KCG College of Technology Chennai India

ISBN: (纸本)9789819767311

Predictive analytics if combined with machine learning approaches have the potential to play a significant role in forecasting the spread of respiratory infections. machine learning approaches aid in the mining of data to better assess and forecast the presence or absence of covid-19 infection. In this paper, XGBoost Ensemble Classification using a fine-tuned approach is being used. Various machine learning algorithms are being analyzed and XGBoost Ensemble Classification algorithm is used to forecast and predict the covid-19 infected patient recovery chances that is the possible outcome of a covid-19 infected patient along with the prediction probability of recovery. the suggested categorization model is applied to a variety of situations. When compared to other ensemble models like random forest, hybrid ensemble and other individual classifiers such as decision tree, logistic regression, K-nearest neighbor, support vector machine and Naive Bayes, the XGBoost Ensemble Classification model gave the highest accuracy. Predicting the correct class is important. thus, the proposed system discusses the possibility of automated prediction for covid-19 infected patient recovery chances along with the probability of recovery and the effectiveness of such predictions. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： COVID-19

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5th international conference on Inventive Computation Technologies, ICICT 2022 - Proceedings

5th International Conference on Inventive Computation Techno...

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5th international conference on Inventive Computation Technologies, ICICT 2022

ISBN: (纸本)9781665408370

the proceedings contain 243 papers. the topics discussed include: provably secure pseudonyms based authentication protocol for wearable ubiquitous computing environment;analysis of dc motor speed control and interfacing it with drowsiness detection;height and weight estimation of an individual from virtual visuals;projective synchronization of uncertain chaotic systems with different dimensions and external noise disturbance;design and implementation of a rescue and surveillance robot using cross-platform application;design of voice controlled multifunctional computer numerical control (CNC) machine;Bangla font recognition using transfer learning method;electroencephalography (EEG) based home automation for physically challenged people using brain computer interface (BCI);optimization of redirection algorithm based on cache technology in programming implementation of college students' mental health education information platform;a resistance detection device for microelectronic semiconductors: from signal detection to fusion analysis;and research on positioning accuracy of welding robot based on visual data mining and pattern fusion analysis algorithm.

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A Deep learning Approach to Ringworm and Vitiligo Identification in Skin Imagery 5

A Deep Learning Approach to Ringworm and Vitiligo Identifica...

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5th international conference on Smart Electronics and Communication, ICOSEC 2024

作者： Kamasani, Kavya Venna, Deepa Badavathula, Harshitha Ranga, Namratha Velagapudi Ramakrishna Siddhartha Engineering College Dept of Computer Science & Engineering Vijaywada India

ISBN: (纸本)9798331504403

Diagnosing skin illnesses such as vitiligo and ringworm can be difficult because of their similarity to other dermatological conditions and their different looks. the suggested approach combines Local Binary patterns and Grey Level Co-occurrence Matrix, two potent feature extraction techniques. While Local Binary patterns depicts the local texture patterns inside an image, Grey Level Co-occurrence Matrix analyses the spatial relationships between pixels to collect textural information. these traits are successfully used to depict the feature extraction with the unique qualities of vitiligo and ringworm lesions with accuracy 98%, having been retrieved from dermatological photographs. However, previous machine learning research produced inaccurate results. then, for classification we tried different deep learning models like Google Net, Mobile Net, ResNet50, ResNet101, Xception, CNN, and VGG19. Among these, we found that VGG19 provided good results for us with an accuracy of about 99.19%. the pre-trained VGG19 model is improved by utilising deep learning and the extracted characteristics to reliably identify and categorise vitiligo and ringworm lesions. the model's discriminative power is increased by integrating Grey Level Co-occurrence Matrix and Local Binary patterns features, and it maintains strong classification performance with less training data by utilising VGG19. All things considered, the suggested method presents a User Interface for the automated diagnosis of vitiligo and ringworm, giving medical practitioners a useful instrument for early identification and treatment, eventually enhancing patient outcomes and care quality. © 2024 IEEE.

关键词： Local binary pattern

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