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Activation Layers Implication of CNN Sequential Models for Facial Expression Recognition

IOP Conference Series: Materials science and engineering 2021年第1期1074卷

作者： M. Shyamala Devi Ankita Sagar Karan Thapa Mreenmoy Hazarika P. Swathi Professor Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Final Year B.Tech Student Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai

Facial Expression Recognition is the critical part in the human emotional detection in the field of image processing. The application tends to soft or hard real time application based on the power of expression detection of idle image or videos. The Social communication with any object can be done by verbal or non-verbal format. Expression and Emotion detection completely rely on the non-verbal communication and facial expression. Machine Learning play an important role in the recognition of facial expression. An attempt is made in this paper to analyse the performance of Convolutional neural network models with diverse activation layer for the performance ***, the facial expression dataset is extracted from the website http://***/ckagree/, http://***/database/faces/ is subjected with the data processing. Secondly, the data analysis is done for the distribution of expression image in the training and testing dataset. Thirdly, the facial expression images are detected with HAAR cascade and then the images are cropped with (350, 350). Fourth, the facial expression images is applied with normalized and the bottleneck features are created for training and testing data. Fifth, the training dataset is fitted with convolutional sequential neural network models with various activation layers like Sigmoid, Elu, Relu, Selu, Tanh, Softsign and Softplus. Sixth, the performance analysis is done with loss and accuracy for all the epoch of all CNN models for all the activation layers. Experimental results show that CNN sequential model with Relu activation layer is found to have the accuracy of 100%.

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Prevention from Gas Devastation

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Journal of Physics: Conference Series 2021年第1期1797卷

作者： Sanghita Datta Ankita Sah Sidharth Gautam Upamita Das Dr. Saikat Maity B. Tech Computer Science Engineering JIS University

LPG leakages are the most common issues found in household and gas plants. This issue is life threatening not only for the humans in the vicinity but also for the surrounding areas. The idea behind our project is to save the lives of those innocents who can get affected due to the carelessness of certain people. So, as soon as the gas gets detected by the system, the concerned people around will receive messages as well as the alarm rings.

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Extracting Facial Features in Filter Modified Images using Neural Networks

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IOP Conference Series: Materials science and engineering 2021年第1期1074卷

作者： M Sumithra M Shyamala Devi A Poornima Shatadru Sarkar Harish Patel Preethi Mahankali boggula Navya Assistant Professor Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Professor Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Final Year B.Tech Student Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai

Facial identification plays a vital role in this digital era. Recent advancements in technology provide various filtering effects while capturing the images. Extracting the facial features from the filter modified image becomes challenging. To identify the important facial attribute values from those images, special features extraction techniques needs to be applied. Facial points are the key factors to recognize the face. When these factors are altered, facial detection becomes crucial. Initially apply the image pre-processing techniques to reduce the noise level in the images then perform feature selection. To overcome the curse of dimensionality, Occam's razor approach is followed to limit the selection of attributes. In different stages the features are going to be filtered by replacing the weaker attributes. Later Pearson Correlation, a filter based correlation technique is applied to evaluate the deviated numerical value from the target. The deviated features can be treated with Lasso effect to suppress the poor weighted features. Finally through recursive feature elimination, highest influencing attribute factor can be identified. In this approach some of the facial feature which is used in training the neural network can be discarded based on the deviation rates. In terms of accuracy the proposed system can recognize the dataset with 70% of accuracy with filter effected images.

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High resolution spatial modelling of soil parent material: An initial investigation study using random forest machine learning techniques

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Journal of South American Earth sciences 2025年 162卷

作者： Vijayakumar, A. Alshardan, Amal Alghamdi, Abdulaziz G. Durai, S. Department of Civil Engineering V.S.B. Engineering College Tamilnadu Karur 639111 India Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University (PNU) P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Soil Sciences College of Food and Agricultural Sciences King Saud University P.O. Box 2460 Riyadh 11451 Saudi Arabia Department of CSE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai 600062 India

This study uses Random Forest machine learning to explore the distribution of soil parent material in Morelos, Mexico. Elemental composition was determined using a portable X-ray fluorescence (pXRF) spectrometer, and soil variability was examined using magnetic susceptibility (MS) measurements over the A and b horizons. The goal was to improve soil categorisation and mapping accuracy to promote sustainable land management and agricultural operations. The statistical study indicated substantial differences between soil horizons: Magnesium content ranged from 7.1 % to 15.7 % (mean: 11.4 %) in the A horizon and from 8.0 % to 20.9 % (mean: 10.4 %) in the b horizon;iron concentrations exhibited lower variability, with mean values of 8.9 % (A horizon) and 7.4 % (b horizon);calcium displayed the most significant variation, with a maximum of 28.5 % in the b horizon;silicon remained relatively stable, with mean values of 3.8 % (A horizon) and 4.0 % (b horizon);and magnetic susceptibility showed substantial dif The Random Forest model accurately reflected geographic variations in soil parent material, emphasizing important geochemical and mineralogical distinctions. The findings highlight machine learning's potential for high-resolution soil mapping and categorisation. This approach lays the groundwork for enhanced soil fertility assessments, erosion management, and land-use planning. Future study should include additional geographic and environmental factors to improve model accuracy and prediction capabilities. © 2025 Elsevier Ltd

关键词： Geochemical analysis Machine learning Magnetic susceptibility pXRF spectrometry Random forest Soil classification Soil parent material Spatial modelling

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A study on hybrid recommender system with deep learning and deployment in big data

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Test engineering and Management 2019年第11-12期81卷 1869-1875页

作者： Ramanujam, Srivats S. Sivaneshwar, P. Naren, J. Madhumitha, S. Vithya, G. B. Tech Computer Science and Engineering School of Computing SASTRA Deemed University Thanjavur India B. Tech Information and Communication Technology School of Computing SASTRA Deemed University Thanjavur India School of Computing SASTRA Deemed University Tirumalaisamudram ThanjavurTamil Nadu India B. Tech Electronics and Communication Engineering School of Computing SASTRA Deemed University Thanjavur India School of Computing KL University Vijayawada India

A recommender system is a filtering tool that provides customized suggestions of products to users by using various techniques. A hybrid recommender system combines the approaches of two or more techniques, primarily focusing on Content-based recommender systems and Collaborative recommender systems, thus proving better suggestions by exploiting the advantages of both the strategies. This paper provides a detailed review of the various ways to implement a Hybrid Recommender System, enabling the reader to get a bird’s eye view on the concept. The study allows the user to understand the relationship between data characteristics and the relative performance of various approaches to a Hybrid Recommender System. The flow of the research starts with a description of the initial models of Recommender Systems, followed by a study on the application of Machine Learning and Deep Learning algorithms to the Hybrid Recommenders. The contributions to the study are dichotomous: 1) to identify and discuss the various techniques for Hybrid Recommendation Systems via a Systematic literature review and 2) to compare the various techniques to get a clear view of the efficiency and the accuracy of each technique. At the end of the study, the reader understands the development flow of Hybrid Recommender Systems and gets a clear idea of how various Recommender Systems work. It is observed that the technique which incorporates Deep Learning provides better results. © 2019 JPR Solutions. All rights reserved.

关键词： Recommender systems

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Epoch interrogation for skin cancer detection using convolutional neural network models

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IOP Conference Series: Materials science and engineering 2021年第1期1074卷

作者： A Poornima M Shyamala Devi M Sumithra Mullaguri Venkata bharath Swathi S Sathishkumar K Yogesh Shubham Santosh Upadhyay Nitesh Kumar Sah Assistant Professor Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Professor Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Assistant Professor Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Final Year B.Tech Student Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai

Skin cancer is very important notable disease and it is probable to everyone nowadays, it flourishes on the area of body where it exposed to ultraviolet rays. It leads anomalous gain in skin cells. It initiate on various parts of body like face, hand and bottoms of the feet as cautious hole or spot. The initial investigation of anomalous gain is essence to cure the disease at early stage, and it still remains a feasible challenge in the scientific improvements. From the analysis, this paper endeavour to inspect the category of disease with the following improvements. Initially, the skin dataset from ISIC machine archive is utilized for image processing. Secondly, the values of dataset images are normalized by dividing all the RGb values by 255. Thirdly, keras sequential API is used to add one layer at a time, initiating from the input. The CNN can extract the features that are useful for classifying the image, by using the kernel filter matrix. MaxPool reduce the computational cost by down-sampling the image, and the relu activation function is implemented to provide non linearity to the network. The flatten layer is utilized to remodel the final feature maps into 1D vector. CNN model provides accuracy of 94.83% with 3297 images and ResNet 50 model has attained accuracy of 90.78% due to less number of images used for classification. AlexNet model has attained accuracy of 81.8% with 1300 images and GoogleNet V3 inception has attained accuracy of 96% with 3374 images. Finally Vgg16 model has attained accuracy of 97.3% with 5636 samples.

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Synthesis and characterization of tritendate Schiff base rare earth nano metal complexes

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Materials Today: Proceedings 2021年 34卷 453-459页

作者： A. Kavitha D. Easwaramoorthy T. Thangeeswari G. Parthipan S. Shanmugan Thamean Ansari Department of Chemistry Vel Tech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Avadi Chennai Tamil Nadu 600 062 India Department of Chemistry B.S. Abdur Rahman University Vandalur Chennai 600 048 Tamil Nadu India Department of Physics Vel Tech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Avadi Chennai Tamil Nadu 600 062 India Department of Computer Science Engineering Vel Tech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Avadi Chennai Tamil Nadu 600 062 India

The tridentate Schiff base ligand (1-pyridin-3-ylimino-ethyl benzene-1,3-diol were synthesized using precursor source 3-aminopyridine with 2,4-dihydroxyacetophenone in methanol at 60 °C. The prepared Schiff base ligand doped with rare earth metal complex such as Ce(III), Pr(III) and Er (III) ion, respectively and it is denoted as [Ln(L)(NO 3 ) 2 (H 2 O)]·NO 3 . The Schiff base ligand and nano metal complexes have been characterized by UV–Visible, FTIR, 1 H NMR, fluorescence spectrum (FL). Also the thermal properties of synthesized rare earth metal complexes were analysed by using TGA & DTA thermal analysis. These characterisation results given exact structural elucidation and optical properties of synthesized rare earth metal ions doped Schiff base nano metal complex. In UV spectra of the cerium, praseodymium and erbium nitrate complexes, there is no noticeable absorption in the visible region (380–700 nm) due to the shielding effect of rare earth 4f orbital electrons. FTIR spectra prove that the various functional groups present in doped Schiff base metal complexes. The blue shift in the, fluorescence spectra of lanthanide (III) complex indicates that the doped lanthanum ions have an apparent influence on the energy absorption of the complex system through well co-ordination with ligand. The purity and molecular formulae of synthesized Schiff base ligands and Ce(III), Pr(III) and Er (III) metal complexes were identified by H 1 NMR, C 13 NMR spectroscopy. Thermal analysis (TGA & DTA) concludes that synthesized rare earth metal complexes with Schiff base ligand have adequate thermal stability.

关键词： Schiff Nano Metal complexes Antimicrobial activity benzene-1 3-diol

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Modeling and prediction of nonlinear cable slab dynamics using Koopman operators

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Mechatronics 2025年 110卷

作者： Michael Pumphrey Almuatazbellah M. boker Mohammad Al Saaideh Natheer Alatawneh Yazan M. Al-Rawashdeh Khaled Aljanaideh Mohammad Al Janaideh School of Engineering University of Guelph Guelph ON N1G 2W1 Canada Bradley Department of Electrical and Computer Engineering Virginia Tech Blacksburg VA USA Department of Electrical and Computer Engineering Memorial University St. John’s NL A1B 3X5 Canada School of Computer Science and Engineering Sacred Heart University 3135 Easton Turnpike Fairfield CT USA Department of Electrical Engineering Al-Zaytoonah University of Jordan Amman Jordan Department of Aeronautical Engineering Jordan University of Science and Technology Irbid 22110 Jordan University of Jordan Amman Jordan Fraunhofer Institute for Applied Optics and Precision Engineering Jena Germany

A novel approach for modeling the nonlinear dynamics of cable slabs using Koopman operator theory is presented. Cable slab dynamics are a critical challenge in precision motion systems, as the cables can induce undesired vibrations and disturbances on motion stages. To address this, a higher-dimensional state-space model with nonlinear observable functions is developed to approximate the cable slab dynamics. The proposed model achieves a prediction error within ∼ 1 % over the specified motion range and demonstrates robustness in predicting untrained, randomized, acyclic cable slab motions. A systematic evaluation of various observable functions was conducted to minimize the modeling errors, leading to an optimized model with fractional-order exponents. When compared with a neural network-based state-space model (NN-SS), the Koopman approach demonstrated faster training and better performance. For force prediction, the Koopman approach achieved a reduction of three-quarters in maximum error when compared with the NN-SS method. This work offers a concise and experimentally validated analytical framework specifically for developing accurate predictive models of nonlinear cable slab dynamics.

关键词： Koopman operator Cable slab Motion control Nonlinear dynamics Data-driven modeling System identification

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Mining social network data for predictive personality modelling by employing machine learning techniques

Mining social network data for predictive personality modell...

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International Conference on Computational Advancement in Communication Circuit and System, ICCACCS 2018

作者： Sengupta, Arjun Ghosh, Anupam Tata Consultancy Services Ltd Candor Kolkata One Hi-Tech Structures Pvt. Ltd IT/ITES SEZ Block G III 5th Floor Tower B-II New Town Kolkata700160 India Department of Computer Science & Engineering Netaji Subhash Engineering College Techno City Garia Kolkata700152 India

ISBN: (纸本)9789811386862

Facebook, Twitter, LinkedIn and Tumblr are online social networking platforms where the users send and receive messages on the topic of their choice and express their sentiments. The usage of these sites has exponentially increased over the last few years, thereby increasing the information posted on online social media sites. The quantity of information/tweets keeps increasing on a daily basis. Twitter has become a stable platform to identify personality-related indicators and encrypted in user profiles and pages related to a subject. In this proposed work, we present a scalable real-time system for sentiment analysis of Twitter data. This work will collect tweets of the users in real time and thus provide a basis to identify each tweet into either positive or negative based on the mind-set of the user, thereby providing a real-time analysis of the users regarding a certain topic. The system relies on feature extraction from the tweets generated in real time. A supervised learning approach based on ensemble learning is used to train various classifiers based on the features extracted. A design and implementation in Flask and Celery has been carried out which contains the feature extraction and classification tasks. The system is scalable with respect to the size of the input data and the rate of data arrival. The merits of the proposed system in terms of scalability, performance and classification accuracy was evaluated experimentally. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Real time systems

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Android Malware Detection Amid COVID-19

Android Malware Detection Amid COVID-19

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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： Rohit Srivastava R.P. Mishra Vivek Kumar Himanshu Kumar Shukla Neha Goyal Chandrabhan Singh Computer Science and Engineering F.O.E.T. University Lucknow India Electronics and Communication Dr R.M.L. Awadh University Ayodhya India Computer Science and Engineering Teerthanker Mahaveer University Moradabad India Computer Science and Engineering K.M. C. Language University Lucknow India Computer Science and Engineering B.N. College of Engg. and Tech. Lucknow India

ISBN: (数字)9781728189086

ISBN: (纸本)9781728189093

Mobile is a rapidly growing web environment that attracts malware developers around the world. Smart phones, especially android phones are widely used and are the most popular new target for malware attacks. Most common type of malware found to attack android users was an unauthorized app repackaged as a normal app through a third party, unofficial app store. New apps found in the app store are hard to identify as malicious. Our work develops a malware detector and analyzer. This paper also links insights about malware attacks in COVID-19 on mobile devices. To meet the objectives, a model is implemented that extracts the inherent features of android application file and analyzes them for quick and accurate analysis. The model classifies the apps more accurately as benign or malicious.

关键词： COVID-19 XML Static analysis Machine learning Feature extraction Malware Smart phones

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