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Multimodal emotion recognition model via hybrid model with improved feature level fusion on facial and EEG feature set

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Multimedia Tools and Applications 2025年第1期84卷 1-36页

作者： Singh, Pratima Tripathi, Mukesh Kumar Patil, Mithun B. Shivendra Neelakantappa, Madugundu Department of Computer Science and Engineering Galgotias University Gautam Buddh Nagar Uttar Pradesh India Department of Computer Science and Engineering Vardhaman College of Engineering Telangana Hyderabad India Department of Computer Science and Engineering N K Orchid College of Engineering & Technology Maharastra Solapur India Department of Computer Application D. K. College Dumraon Bihar Buxar India Department of Information Technology Vasavi College of Engineering Telangana Hyderabad India

In recent years, academics have placed a high value on multi-modal emotion identification, as well as extensive research has been conducted in the areas of video, text, voice, and physical signal emotion detection. This paper proposes a novel multimodal emotion recognition model that employs a hybrid model with AMIG-based feature fusion on facial and EEG feature sets. The EEG signal and facial image are subjected to preprocessing to remove unwanted background noises with the Butterworth filter and Viola Jones Algorithm, respectively. While considering the pre-processed EEG signal, features such as EWFS-transform, wavelet features, and CSP-based features are extracted. In the proposed EWFS-transform, the window function is modified by using the frequency function and updated STFT. Conversely, the features including SE-AMM-EST-based features;LGXP and GLCM are extracted while considering the preprocessed face image. In the proposed SE-AMM-EST-based features, the mean of shape is updated and covariance in PCA for extracting texture-based features. In order to extract redundant free essential features, AMIG-based feature fusion is proposed. Further, the fused features are fed into a proposed hybrid model that includes LSTM and AML-CNN models. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Textures

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APD - ML: Air Pollution Detection Using Machine Learning Algorithms 2

APD - ML: Air Pollution Detection Using Machine Learning Alg...

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2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Sofia, A. Sathya Shrinee Venisha, J. Sowmiya, K. Soundarya, K. Theepiga, M. PSNA College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Dindigul India

ISBN: (纸本)9798350347982

To analyze the air quality of any country, a machine learning technique is being developed and an air quality indicator is proposed for a particular area. Air Quality Index is considered to be a basic measure which can indicate the levels of SO2, NO2. etc. over a particular amount of time. We technologically put forward a model to determine the air quality index in view of historical data of preceding years and computing the same for the forthcoming year considering it as a gradient decent attached boosted multivariable regression problem. We enhance the proposed model's effectiveness by relating cost estimation on behalf of the problem to be a predictive one. Thus this proposed system resolve successfully and work well to envisage the air quality indicator of any entire country or state or any bounded region furnished with enough historical data about contaminants in air. In the proposed model, subsequently machine learning technique is assimilated, upright enactment with performance is accomplished further than the standard regression model. The implementation of envisaging air quality index is prepared for our country India as well as accurateness of 96% is attained via XG Boost Algorithm joined with LightBGM algorithm to find an accurate solution that is in adjacent proximity to the ideal solution. © 2023 IEEE.

关键词： Air quality

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Profile and Rating Similarity Analysis for Recommendation Systems Using Deep Learning

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computer Systems science & engineering 2022年第6期41卷 903-917页

作者： Lakshmi Palaniappan K.Selvaraj Department of Computer Science and Engineering Veerammal Engineering CollegeK.SingarakottaiDindigul624708India Department of Information Technology PSNA College of Engineering and TechnologyDindigul624622India

Recommendation systems are going to be an integral part of any E-Business in near *** in any other E-business,recommendation systems also play a key role in the travel business where the user has to be recommended with a restaurant that best suits *** general,the recommendations to a user are made based on similarity that exists between the intended user and the other *** similarity can be calculated either based on the similarity between the user profiles or the similarity between the ratings made by the *** phase of this work concentrates on experimentally analyzing both these models and get a deep insight of these *** the lessons learned from the insights,second phase of the work concentrates on developing a deep learning *** model does not depend on the other user's profile or rating made by *** model is tested with a small restaurant dataset and the model can predict whether a user likes the restaurant or *** model is trained with different users and their *** system learns from it and in order to predict whether a new user likes or not a restaurant that he/she has not visited earlier,all the data the trained model needed is the rating made by the same user for different *** model is deployed in a cloud environment in order to extend it to be more realistic product in *** evaluated with dataset,it achieves 74.6%is accurate prediction of results,where as existing techniques achieves only 64%.

关键词： Deep learning restricted boltzman machine profile based similarity rating based similarity item based similarity

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Building a Robust and Efficient Defensive System Using Hybrid Adversarial Attack

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4470-4478页

作者： Dhanaraj, Rachel Selva Sridevi, M. Goa College of Engineering Computer Engineering Department Goa 403401 India National Institute of Technology Department of Computer Science and Engineering Tiruchirappalli620015 India

Adversarial attack is a method used to deceive machine learning models, which offers a technique to test the robustness of the given model, and it is vital to balance robustness with accuracy. Artificial intelligence (AI) researchers are constantly trying to find a better balance to develop new techniques and approaches to minimize loss of accuracy and increase robustness. To address these gaps, this article proposes a hybrid adversarial attack strategy by utilizing the Fast Gradient Sign Method and Projected Gradient Descent effectively to compute the perturbations that deceive deep neural networks, thus quantifying robustness without compromising its accuracy. Three distinct datasets - CelebA, CIFAR-10, and MNIST - were used in the extensive experiment, and six analyses were carried out to assess how well the suggested technique performed against attacks and defense mechanisms. The proposed model yielded confidence values of 99.99% for the MNIST dataset, 99.93% for the CelebA dataset, and 99.99% for the CIFAR-10 dataset. Defense study revealed that the proposed model outperformed previous models with a robust accuracy of 75.33% for the CelebA dataset, 55.4% for the CIFAR-10 dataset, and 98.65% for the MNIST dataset. The results of the experiment demonstrate that the proposed model is better than the other existing methods in computing the adversarial test and improvising the robustness of the system, thereby minimizing the accuracy loss. © 2020 IEEE.

关键词： Deep neural networks

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From light sensing to adaptive learning:hafnium diselenide reconfigurable memcapacitive devices in neuromorphic computing

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Light(science & Applications) 2025年第1期14卷 215-226页

作者： Bashayr Alqahtani Hanrui Li Abdul Momin Syed Nazek El-Atab Electrical and Computer Engineering Program Computer Electrical Mathematical Science and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)ThuwalSaudi Arabia Electrical Engineering Department College of EngineeringPrincess Nourah Bint Abdulrahman University(PNU)RiyadhSaudi Arabia

Advancements in neuromorphic computing have given an impetus to the development of systems with adaptive behavior,dynamic responses,and energy efficiency *** charge-based or emerging memory technologies such as memristors have been developed to emulate synaptic plasticity,replicating the key functionality of neurons—integrating diverse presynaptic inputs to fire electrical impulses—has remained *** this study,we developed reconfigurable metal-oxide-semiconductor capacitors(MOSCaps)based on hafnium diselenide(HfSe2).The proposed devices exhibit(1)optoelectronic synaptic features and perform separate stimulus-associated learning,indicating considerable adaptive neuron emulation,(2)dual light-enabled charge-trapping and memcapacitive behavior within the same MOSCap device,whose threshold voltage and capacitance vary based on the light intensity across the visible spectrum,(3)memcapacitor volatility tuning based on the biasing conditions,enabling the transition from volatile light sensing to non-volatile optical data *** reconfigurability and multifunctionality of MOSCap were used to integrate the device into a leaky integrate-and-fire neuron model within a spiking neural network to dynamically adjust firing patterns based on light stimuli and detect exoplanets through variations in light intensity.

关键词： integrate volatile integrating

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Enhanced prediction of agricultural CO2 emission using ensemble machine learning-based imputation approach

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Neural Computing and Applications 2024年第36期36卷 22867-22887页

作者： Thendral, M. Sathya Abinaya, S. Devi, M. K. Kavitha Department of Computer Science and Engineering Thiagarajar College of Engineering Madurai625015 India School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India

The agricultural sector contributes significantly to greenhouse gas emissions, which cause global warming and climate change. Numerous mathematical models have been developed to predict the greenhouse gas emissions from agriculture. However, the database utilized for prediction has thousands of missing values and imbalanced data records due to various factors, such as environmental disasters, sensor failure, maintenance issues, and budgetary constraints. Many researchers have either completely discarded records with missing values or have only partially addressed the issue, leading to less precise predictions. This study proposes a machine learning-based ensemble approach that uses multiple imputation by chained equations (MICE), K-nearest neighbors (KNN), and MissForest techniques to impute the missing values by reducing uncertainties and improves robustness in predictions. Furthermore, Synthetic Minority Oversampling Technique (SMOTE R) addresses data imbalance in CO2 emissions prediction by generating synthetic data to balance the target variable's distribution in the regression problem. Thereby the proposed SMOTE R and ensemble imputation approach aims to improve prediction accuracy and reliability by tackling data completeness and distribution issues simultaneously. The experiments are conducted on the GRACEnet database shows that the proposed approach outperforms the existing work in measure of R2, MAE, and RMSE metrics. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Greenhouse gas emissions

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Ant lion optimization based inertia weight optimized KGMO for mobility Management in Heterogeneous LTE cellular networks

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Multimedia Tools and Applications 2025年第17期84卷 17907-17928页

作者： Venkata Narasimha Reddy, G. Jayudu, T Venkata Naga Komarolu, Janardhan Rajesh, Nichenametla Reddy, B Lakshmi Narayana Department of Computer Science and Engineering St. Johns College of Engineering and Technology Yerrakota Yemmiganur India Anantapur India Nandyal India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Department of Computer Science and Engineering SV College of Engineering Kadapa India

This study proposes a hybrid optimization-based mobility management strategy employing Kinetic Gas Molecular Optimization (KGMO) and Ant Lion Optimization (ALO). Initially, KGMO calculates particle properties, such as gas molecule position and velocity, based on kinetic energy principles. While KGMO demonstrates expedited convergence in large-dimensional spaces, its limitations prompt the hybridization with ALO. ALO modifies the inertia weight of KGMO to mitigate these issues. The proposed KGMO-ALO approach is implemented and validated in a MATLAB environment, compared against other meta-heuristic techniques like Seagull and Monarch Butterfly Optimizations across various performance metrics including throughput, bit error rate, end-to-end delay, and HO. Existing techniques such as PSO, MBO, and SOA exhibit superior performance primarily in scenarios with high user counts. The underperformance of ALO is attributed to its reliance on the best antlion solution in each optimization round. By leveraging the hybrid KGMO-ALO approach, this study aims to overcome these limitations and advance mobility management strategies in heterogeneous LTE cellular networks. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Heterogeneous networks

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Metaheuristics Based Node Localization Approach for Real-Time Clustered Wireless Networks

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computer Systems science & engineering 2023年第1期44卷 1-17页

作者： R.Bhaskaran P.S.Sujith Kumar G.Shanthi L.Raja Gyanendra Prasad Joshi Woong Cho Department of Information Technology PSNA College of Engineering and TechnologyDindigul624622India Department of Computer Science and Engineering MEA Engineering CollegeMallapuram679325KeralaIndia Department of Electronics and Communication Engineering Rajalakshmi Institute of TechnologyKuthambakkamChennai600124India Department of Electronics and Communication Engineering Sri Eshwar College of EngineeringCoimbatore641202India Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Software Convergence Daegu Catholic UniversityGyeongsan38430Korea

In recent times,real time wireless networks have found their applicability in several practical applications such as smart city,healthcare,surveillance,environmental monitoring,*** the same time,proper localization of nodes in real time wireless networks helps to improve the overall functioning of *** study presents an Improved Metaheuristics based Energy Efficient Clustering with Node Localization(IM-EECNL)approach for real-time wireless *** proposed IM-EECNL technique involves two major processes namely node localization and ***,Chaotic Water Strider Algorithm based Node Localization(CWSANL)technique to determine the unknown position of the ***,an Oppositional Archimedes Optimization Algorithm based Clustering(OAOAC)technique is applied to accomplish energy efficiency in the ***,the OAOAC technique derives afitness function comprising residual energy,distance to cluster heads(CHs),distance to base station(BS),and *** performance validation of the IM-EECNL technique is carried out under several aspects such as localization and energy efficiency.A wide ranging comparative outcomes analysis highlighted the improved performance of the IM-EECNL approach on the recent approaches with the maximum packet delivery ratio(PDR)of 0.985.

关键词： Wireless networks real time applications clustering node localization energy efficiency metaheuristics

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Spatial attention-based hybrid VGG-SVM and VGG-RF frameworks for improved cotton leaf disease detection

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Neural Computing and Applications 2025年第14期37卷 8309-8329页

作者： Pandiyaraju, V. Anusha, B. Senthil Kumar, A.M. Jaspin, K. Venkatraman, Shravan Kannan, A. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Marian Engineering College Trivandram Kerala Trivandrum India Department of Computer Science and Engineering St. Joseph’s Institute of Technology Tamil Nadu Chennai India Department of Information Science and Technology College of Engineering Guindy Anna University Tamil Nadu Chennai India

The agriculture industry's production and food quality have been impacted by plant leaf diseases in recent years. Hence, it is vital to have a system that can automatically identify and diagnose diseases at an initial stage for enhancing the quality of agricultural output and also for stopping the overall plant extinction. Most state-of-the-art methods do not provide appropriate accuracy of identification when the similarity among different diseases is higher and the input leaf images have complex background. In this work, a transfer learning-based automated crop disease recognition system is proposed to handle this problem. The cotton leaf disease detection framework being proposed here includes two key stages: (1) the feature extraction stage, where the convolutional neural network (CNN) model from the visual geometry group (VGG-16) is employed to obtain the deep features from cotton leaf images with complex background. (2) Cotton leaf disease detection stage: Here, the extracted optimal features are input to the machine learning classifiers, namely random forest (RF) and nonlinear support vector machine (SVM), for the initial classification of the diseases. For providing an optimal cotton crop disease detection performance, Kaggle’s four-class cotton disease dataset with complex background is chosen in this work. For the final decision making and analysis, two hybrid models named spatial attention-based hybrid VGG-RF and hybrid VGG-SVM have been proposed in this work for enhancing the decision accuracy. These hybrid models offered an average accuracy of approximately 98.29% and 99.31%, respectively, which is higher than the accuracy provided by the related work. This proved that the proposed hybrid model, namely spatial attention-based VGG-SVM and VGG-RF models, with spatial attention outperformed the state-of-the-art classifiers. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Convolutional neural networks

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Formability and Strength Enhancement of AA7075 Sheets by Cryorolling and Post-Treatment

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Journal of The Institution of Engineers (India): Series D 2024年 1-10页

作者： Radha Krishnan, B. Sudarsan, D. Sanjeevi, R. Parthiban, A. Eswara Prasath, N. Arun Prasath, K. Department of Mechanical Engineering Nadar Saraswathi College of Engineering and Technology Tamilnadu Theni India Department of Mechanical Engineering PSNA College of Engineering & ampTechnology Dindigul India SSM Institute of Engineering and Technology Tamil Nadu Dindigul India Department of Mechanical Engineering Vels Institute of Science Technology & Advanced Studies Tamil Nadu Chennai India Department of Mechanical Engineering Velammal College of Engineering and Technology Tamilnadu Madurai India Department of Mechanical Engineering PSN College of Engineering and Technology Tamilnadu Tirunelveli India

Cryorolling, an approach involving severe plastic deformation, offers a means to fabricate aluminum alloy sheets (AA7075) with exceptionally fine grains, conferring greater strength and hardness than those achieved through conventional cold rolling methods. Diverging from established techniques, we introduced a post-cryorolling heat treatment to enhance both the strength and formability of the cryorolled sheets. The formability within the temperature range of warm working was evaluated using parameters such as limiting dome height (LDH) and forming limit diagrams for cryorolled AA7075 alloy sheets. We conducted an analysis of strain distributions and thickness alterations resulting from biaxial stretching. To infer the mechanical attributes of the produced specimens, we established a correlation between their hardness and ultimate tensile strength. Employing this hybrid approach led to elevated limit stresses and LDH compared to the traditional processing route involving annealing, cold rolling, and room temperature forming. As a result, this method enables the fabrication of aluminum alloy sheet metal components with exceptional formability and high strength. Notably, this alloy exhibits optimal performance for warm forming at 250 °C due to its enhanced formability and post-forming strength when contrasted with annealed sheets and the conventional cold-rolling process. © The Institution of Engineers (India) 2024.

关键词： Hardness

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