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Feeling Recognition by Facial Expression Using Deep Learning

Journal of Physics: Conference Series 2021年第1期1717卷

作者： Dr. Shalini Gupta Dr. Shubha Jain Associate Professor Computer Science and Engineering Department Kanpur Institute of Technology KANPUR Associate Professor Computer Science and Engineering Department Axis Institute of Technology and Management KANPUR

The visible appearance of the emotion state, personality, purpose, psychological feature activity and psychopathology of an individual is the Facial expression. This plays an outgoing role in social affairs. Automatic recognition of facial expressions will be a vital part of natural human-machine interfaces. It should even be applied in activity science and in clinical apply. Fellows in nurturing automatic facial features Recognition system must perform detection and placement of faces during a disordered scene, facial feature extraction and facial features classification. Emotion recognition by facial features utilizes a Deep Learning system, which is enforced victimization Convolution Neural Network (CNN). The CNN model of the project relies on LeNet design. Kaggle facial features dataset with seven facial features labels as fear, anger, happy, surprise, sad, neutral and disgust is employed during this project. The system achieved 60.37 accuracy.

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Exploring the Needle Tip Interaction Force with Retinal Tissue Deformation in Vitreoretinal Surgery

Exploring the Needle Tip Interaction Force with Retinal Tiss...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Simon Pannek Shervin Dehghani Michael Sommersperger Peiyao Zhang Peter Gehlbach M. Ali Nasseri Iulian Iordachita Nassir Navab Department of Computer Science Technische Universität München München Germany Laboratory for Computational Sensing and Robotics Johns Hopkins University Baltimore MD USA Wilmer Eye Institute Johns Hopkins Hospital Baltimore MD USA Augenklinik und Poliklinik Klinikum Rechts der Isar der Technische Universität München München Germany Professor and the Chair for Computer Aided Medical Procedures & Augmented Reality Technical University of Munich Munich Germany Adjunct Professor at the Whiting School of Engineering Johns Hopkins University Baltimore MD USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Recent advancements in age-related macular degeneration treatments necessitate precision delivery into the subretinal space, emphasizing minimally invasive procedures targeting the retinal pigment epithelium (RPE)-Bruch’s membrane complex without causing trauma. Even for skilled surgeons, the inherent hand tremors during manual surgery can jeopardize the safety of these critical *** has fostered the evolution of robotic systems designed to prevent such tremors. These robots are enhanced by FBG sensors, which sense the small force interactions between the surgical instruments and retinal tissue. To enable the community to design algorithms taking advantage of such force feedback data, this paper focuses on the need to provide a specialized dataset, integrating optical coherence tomography (OCT) imaging together with the aforementioned force *** introduce a unique dataset, integrating force sensing data synchronized with OCT B-scan images, derived from a sophisticated setup involving robotic assistance and OCT integrated microscopes. Furthermore, we present a neural network model for image-based force estimation to demonstrate the dataset’s applicability.

关键词： Force Estimation Pigments Robot sensing systems Retina Needles Robot learning

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Improved clustering algorithm in student’s performance with high dimensional and large volume of educational data

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AIP Conference Proceedings 2023年第1期2855卷

作者： Rajdipsinh Vaghela Sailesh Iyer Research Scholar Computer Science Engineering Rai University Ahmedabad Gujarat India Professor & Dean Computer Science Engineering Department Rai School of Engineering Rai University Ahmedabad Gujarat India

Data Mining and Analytics have transformed the way business is conducted. Prominent techniques of Data Mining include Clustering, Classification and Association Rule Mining. There are a lot of algorithms for cluster analysis. But all algorithms have some limitations especially for high dimensional dataset. In this paper modified the k-mean algorithm for better cluster accuracy and minimized the computational time processing the dataset for student academic performance. Students undergo immense stress and pressure to outscore their competitors. Higher and Technical Education has many challenges owing to voluminous data and various dimensions for acquisition and processing. Various Clustering Algorithms provide partition accuracy, computational time, lead time but lack visual assessment post partition. This paper proposes a variant of k-Means algorithm using comparative criteria of Partition accuracy (PA) parameter and computational time.

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An approach for Protein Secondary Structure prediction using prediction-based language models

An approach for Protein Secondary Structure prediction using...

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Emerging Trends in Information Technology and engineering (ic-ETITE), Conference on

作者： Leo Dencelin Xavier Ramkumar Thirunavukarasu Department of Computer Science & Engineering Satyabhama Institute of Science and Technology Chennai India Associate Professor and Head School of Information Technology and Engineering VIT Vellore India

ISBN: (数字)9781728141428

ISBN: (纸本)9781728141435

Prediction based language models are considered as one of the major concepts in Natural Language processing which gains knowledge from unstructured text data. Extracting insights from sequential data such as biological sequences is an important problem in genomics, proteomics and classifying the secondary structures of protein, helps the researchers in aiding to understand protein functions. This is considered as one of the important preliminaries of Drug development. Traditional techniques such as sequential models, probabilistic techniques and statistical approaches were widely applied in structure prediction which extracts insights from sequence of amino acid. However, handheld feature extraction becomes a tedious task, which eventually leads to less accuracy. Our novel approach creates vectors using word embeddings which is assumed to consider contextual information of amino acids thereby improving the accuracy of secondary structure prediction approach. This is considered as an optimistic solution for secondary structure prediction problem. In this approach a variation of word embeddings - Continuous Bag of Words (CBOW) method is proposed which retains the sequential information of all amino acid in the protein chain. This vector is used as input features of Deep Neural Network classifier and class labels are classified into Helix, Sheet, Coil. We have tested this NLP based approach on GenBank dataset. The infrastructure required for this analysis was leveraged from Google Colab.

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Agro-waste derived β-Ca2SiO4:Nd3+ phosphors for thermal sensing and radiation dosimetry applications

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Materials science and engineering: B 2024年 305卷

作者： Mamatha, G.R. Radha Krushna, B.R. Malleshappa, J. Sharma, S.C. Krithika, C. George, Augustin Sobana, R. Mohapatra, Sathya Sunder Vanitha, Veera Manjunatha, K. Wu, Sheng Yun Nagabhushana, H. Department of Physics University College of Science Tumkur University Tumkur572 103 India Prof. C.N.R. Rao Centre for Advanced Materials Tumkur University Tumkur572 103 India Honorary Professor of Law and Forensic Materials Jain University Bangalore562112 India Pro Vice-Chancellor and Professor of Oral Medicine and Radiology Meenakshi Ammal Dental College Meenakshi Academy of Higher Education and Research Chennai600095 India Department of Computer Science Kristu Jayanti College Autonomous Karnataka Bengaluru560077 India Department of Physiology Mahatma Gandhi Medical College and Research Institute Puducherry 607402 India Department of Radiology IMS & SUM Hospital SOA University Odissa Bhubaneswar751030 India Department of Electronics and Communication Engineering Avinashilingam Institute for Home Science and Higher Education for Women Coimbatore641108 India Department of Physics National Dong Hwa University Hualien97401 Taiwan

A series of Nd3+ doped β-Ca2SiO4 (β-CSO:Nd3+) phosphors are synthesized using the solid-state reaction method, employing agricultural waste from egg shells and bamboo leaves as the source materials. XRD analysis confirmed the monoclinic structure of the resulting phosphors. The photoluminescence (PL) emission spectra display the distinct emission peaks associated with Nd3+ ions. Optimization of the Nd3+ ions concentration is carried out, revealing that 5 mol % yielded the highest intensity (β-CSO:5Nd3+). Further increases in concentration led to a reduction in emission intensity attributed to concentration quenching (CQ). The phosphors excitation spectra are characterized by a wide range, spanning from 450 to 900 nm. When excited at 581 nm, the phosphor emitted strong infrared light at 1053 nm, attributed to transitions of Nd3+ ions, specifically 4F3/2 → 4I9/2, 4F3/2 → 4I11/2, and 4F3/2 → 4I13/2. The near-infrared (NIR) down conversion emissions exhibited by the material suggest exciting prospects for leveraging the synthesized phosphors in the development of photonic devices engineered to emit within the NIR spectrum. The investigation of temperature-dependent photoluminescence (TDPL) within the range of 303–483 K revealed that emissions at elevated temperatures are significantly more intense compared to those observed at room temperature. TDPL investigations reveal outstanding thermal stability, with the emission intensity remaining at 92.7 % even at 423 K. Moreover, the phosphor demonstrates promising potential for temperature sensing applications, supported by its absolute sensitivity of 1.4 × 10−3 K−1 and relative sensitivity value of 0.14 % K−1. The Thermoluminescence (TL) glow curves derived from samples exposed to beta radiation displayed three discernible peaks at temperatures of 95 °C, 147 °C, and 194 °C. Moreover, the signal intensity exhibited a proportional augmentation corresponding to the escalation in irradiation dose, spanning from 1.58 to 100.86 Gy

关键词： Thermoluminescence

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Luminescent carbon dots encapsulating in Eu3+ doped gahnite spinel nanocomposite for boosting thermal sensing, advanced level III detection and intelligent anti-counterfeiting applications

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Materials Today Sustainability 2024年 27卷

作者： Gagana, M. Krushna, B.R. Radha Sharma, S.C. J Salwe, Kartik George, Augustin Sanjana, M.R. kar, Bikash Archana, K.V. pasha, Sardar Manjunatha, K. Yun Wu, Sheng Nagabhushana, H. Prof. C.N.R. Rao Centre for Advanced Materials Tumkur University Tumkur572103 India Honorary Professor of Law and Forensic Materials Jain University Bengaluru562112 India Department of Pharmacology Mahatma Gandhi Medical College and Research Institute Pondicherry607402 India Department of Computer Science Kristu Jayanti College Autonomous Bengaluru560077 India Meenakshi Academy of Higher Education and Research Chennai600095 India Department of Dermatology IMS and SUM Hospital SOA University Odissa 751003 India Department of Electronics and Communication Engineering School of Engineering Avinashilingam Institute for Home Science and Higher Education for Women Coimbatore641 108 India Department of Physics Government First Grade College Gauribidanur561208 India Department of Physics National Dong Hwa University Hualien97401 Taiwan

The exceptional optical properties of carbon dots (CDs) position them as a highly promising category of multifunctional carbon-based nanomaterials. When hydrothermally prepared CDs are incorporated into europium-doped zinc aluminate ZnAl2O4 nanophosphors (ZAO:Eu3+ NPs) synthesized via ultrasonication method, the resulting CDs@ZAO:Eu3+ nanocomposite (NCs) exhibits a broad excitation band, with a prominent peak at approximately 393 nm, perfectly matching the emission wavelength range (500–750 nm) of near-ultraviolet (n-UV) LED chips. Significantly, the CDs (5 wt %)@ZAO:Eu3+ NCs exhibit a remarkable 12.5 folds enhancement in photoluminescence (PL) intensity compared to ZAO:Eu3+ alone, thanks to the Förster Resonance Energy Transfer (FRET) between CDs and Eu3+ ions. This enhancement in luminescence could stem from the CDs capturing electrons and facilitating energy transfer to the luminescent Eu3+ ions. Utilizing CDs to enhance fluorescence presents a straightforward and eco-friendly approach for boosting luminescence properties. Impressively, even under elevated temperatures reaching 420 K, the thermal stability of CDs (5 wt %)@ZAO:Eu3+ NCs remains outstanding, retaining 90 % of its initial luminescence intensity. This highlights its exceptional thermal stability, characterized by a high activation energy of 0.27 eV. With an impressive internal quantum efficiency (IQE) of 87.01 %, the optimized composite also achieves remarkable colour purity (CP), reaching 99.5 %. The relative sensitivity was 1.36 %, at 300 K. Thus, CDs@ZAO:Eu3+ is a promising material for non-contact optical thermometry applications. Utilizing the optimized NCs through a simple powder dusting technique effectively develops latent fingerprints (LFPs) on various surfaces, revealing fingerprints (FPs) under 365 nm UV light. Additionally, the study investigates diverse pore parameters including number, position, size, shape, and inter-spacing. Furthermore, optimized NCs are utilized to create a transpare

关键词： Nanocomposites

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Green dream: A deep learning based real-time model for automatic waste segregation from video streams

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AIP Conference Proceedings 2023年第1期2773卷

作者： Prince Puthussery Nikhita Mariam Cherian T. K. Gokul Kiran M. U. Sreeja Abin Oommen Philip Department of Computer Science & Engineering Toc H Institute of Science & Technology Ernakulam Kerala India Assistant Professor Department of Computer Science & Engineering Toc H Institute of Science & Technology Ernakulam Kerala India Assistant Professor Department of CSE-Cyber Security Indian Institute of Information Technology Kottayam Kerala India

In this fast-pacing world one of the substantial problems faced is the drastic increase in waste generation and ensuring efficient and rational management of waste. Recycling tasks reduce waste production, mitigate the environment and improve the whole nation’s prosperity in the future. Inappropriate handling and discarding of useless materials have led to the contamination of groundwater and defiled the land resources. Therefore, it is vital that the methods and processes involved in waste collection and segregation is examined as the present-day disposal system is inefficient, time consuming, cumbersome and not completely viable due to their large amount. The proposed model provides a way of solving the above stated problems by detecting, identifying and segregating the waste materials. The proposed system is an automated waste classifier which uses a deep learning-based object detection model to classify objects into different categories. Using YOLOv5, an object detection algorithm the waste can be identified successfully from the image taken with the help of a camera and classify them into categories. This result is then sent to a segregation unit which categorizes the waste into the designated bin by rotating the conveyor belt using a motor. The proposed model operated at a Mean Average Precision(mAP) of 0.726 and an F1-Score of 0.83. This method of segregation of waste will enable faster ways to recycle the waste and save time and resources.

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Optimized Bit Matrix based Power Aware Load Distribution Policy for Federated Cloud

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Procedia computer science 2020年 167卷 1771-1790页

作者： B S Rajeshwari M Dakshayini Assistant Professor Department of Computer Science and Engineering B M S College of Engineering Bangalore 560019 India Professor Department of Information Science and Engineering B M S College of Engineering Bangalore 560019 India

Federated Cloud is a next generation cloud computing model which works towards a new dynamic scenario in smart ecosystems. It consolidates individual clouds together to form single large cloud to satisfy all on-demand requirements of the users. Federated cloud is currently facing many challenges including: monitoring of individual cloud services, maintaining service level agreement, fair load distribution, power usage etc. Hence, to address these challenges this paper presents a brokerage model “ Optimized Bit Matrix based Power Aware Load Distribution Policy for Federated Cloud (OBMPLP) ”. Presented model includes two novel concepts: Bit Matrix and Load Distribution Factor. Bit Matrix constructed by individual cloud members representing resource availability status and environment in executing the user’s request. Load Distribution Factor representing load distribution level at individual cloud. OBMPLP policy dispenses the incoming requests among multiple clouds by analyzing bits pattern and load distribution factor that not only performs fair load distribution among multiple clouds, but also improves response time, alleviate power consumption at each cloud and achieve better quality service. The performance of the proposed policy is evaluated and results demonstrate reduction in response time, optimized power consumption and fair distribution of load compared to existing approaches.

关键词： Cloud Computing Load Distribution Quality Service Power Consumption Federated Cloud

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A novel prediction of remaining useful life time of rolling bearings using convolutional neural network with bidirectional long short term memory

Journal of Engineering Research

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Journal of engineering Research 2024年

作者： R. Balamurugan Dattatray G. Takale M. Muzammil Parvez S. Gnanamurugan Associate Professor Department of Mechanical Engineering M.Kumarasamy College of Engineering Karur India Assistant Professor Department of Computer Engineering Vishwakarma Institute of Information Technology Pune Maharashtra India Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Assistant Professor/ ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology (Deemed to be University) Avadi Chennai India

Detection of bearing problems increases the importance of the service life of rotating machinery. Convolutional neural networks (CNNs) are often used in current research, and databases built on deep learning (DL) models have improved capabilities in the field of defect diagnosis. We use the publicly available Case Western Reserve University (CWRU) dataset to compare the classification accuracy and gain more adaptive knowledge and insights about the proposed approach. Extensive tests and evaluations are performed on the dataset to verify the diagnostic effectiveness of the recommended method in different situations. To demonstrate the superiority of the proposed method, we compare multiple views of the same dataset with similar tasks. CNN supports degraded index sequences to reduce noise and stop temporal oscillations. A new CNN-BiLSTM model is used to capture current and historical inspection data and predict the RUL's service life and supported power levels. Regarding production, we follow health rates. The proposed method was evaluated by accelerating the bearing motion to failure, and the results demonstrated its advantages in terms of more accurate RUL prediction. According to the experimental results, the proposed center distance measurement method is a new and valuable means for intelligent bearing diagnosis. Experimental results using 48 K and 12 K CWRU datasets show that the overall accuracy of the BiLSTM method is 99.80% and 98.3%, respectively, which is better in diagnosis than some popular models.

关键词： Rolling bearings life time CWRU dataset CNN BiLSTM

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Real-time GB pattern convolution neural network-based brain image classification

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AIP Conference Proceedings 2024年第1期3075卷

作者： Amarendra Reddy Panyala M. Baskar Research Scholar Department of Computer Science and Engineering School of Computing SRM Institute of Science and Technology Kattankulathur Chengalpattu Chennai Tamil Nadu 603 203 India Assistant Professor Department of Information Technology MLR Institute of Technology Hyderabad Telangana 500043 India Associate Professor Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chengalpattu Chennai Tamil Nadu 603 203 India

Medical image classification has been identified as a critical task in several medical solutions. Brain image classification has been identified as a challenging task that has been approached with different methods. The existing methods use different features like shape, texture, and gray to classify brain images. However, the methods need to improve performance in categorizing the brain image and detecting the presence of tumors. An efficient GB Pattern Convolution Neural Network (GBP-CNN) is presented to handle this issue. The method involves preprocessing the brain image given with Adaptive Feature Distribution Normalizer (AFDN) algorithm. The Grey Canvas Segmentation (GCS) technique was used to segment the normalized image. The feature vector is created by extracting the binary and grayscale patterns from the image that has been segmented. A convolution neural network is trained to recognize the features that were extracted. Two convolution layers in the CNN’s design are used to convolve the image’s features. Additionally, a pooling layer is being incorporated into the network’s design, which reduces the data to a single dimension. The neuron predicts support values for several attributes during the assessment stage. The approach then calculates the Class-Based Fitness value (CBFV) in order to categories the image against various tumour classes. The proposed GBP-CNN algorithm improves the performance of brain image classification with less time complexity.

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