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Unleashing Facial Expression Recognition for Stress Detection Using Deep CNN Model.

Procedia computer science 2025年 259卷 306-315页

作者： Geethu S Kumar Jo Cheriyan Aparna N Swathy J Research Scholar Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai- 600 119 Tamilnadu India Assistant Professor Department of Information Science and Engineering Cambridge Institute of Technology Bangalore-560 036 Karnataka India Assistant Professor Department of Computer Science and Engineering Saintgits College of Engineering Kottayam- 686 532 Kerala India Assistant Professor Department of Computer Science and Engineering Cambridge Institute of Technology Bangalore-560 036 Karnataka India

The technology and field of study known as Facial Emotion Recognition (FER) focuses on recognising and deciphering human facial expressions to ascertain emotions. It incorporates aspects of psychology, machine learning, and computer vision. Considering its ability to analyse facial expressions and spot signs of stress or emotional distress, FER plays a vital role in the detection of stress. Microexpressions are fleeting, uncontrollably expressed facial expressions that convey true feelings. These ephemeral expressions, which are frequently linked to stress, can be captured by FER systems. With its capacity to manage intricate patterns and variances in face expressions, FER utilising deep learning has grown in popularity. A novel FER system was designed to overcome issues that plague current FER systems, such as imbalanced datasets and robustness to noisy inputs. Large datasets are handled effectively by FER employing Deep CNN because of its architecture, which is built for high-dimensional input like images. Leveraging FER using Deep CNN achieves notable performance improvements, attaining an accuracy of 95.65 % and F1 score of 94.02%. Deep learning-based FER marks a substantial advancement in our ability to recognize and decipher facial expressions that convey emotions. FER technology has the ability to revolutionize several fields by improving intuition and sensitivity to human emotions through further development and thoughtful assessment of ethical ramifications. FER systems will advance in sophistication as science and technology develop, offering more profound understanding of human emotional states and promoting improved human-machine interactions.

关键词： Stress PPG Conv-XGBoost deep learning mental health

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Chord Rotation Capacity and Strength of Diagonally Reinforced Concrete Coupling Beams

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ACI Structural Journal 2023年第6期120卷 137-150页

作者： Lepage, A. Lequesne, R.D. Weber-Kamin, A.S. Ameen, S. Cheng, M.-Y. Professor at The University of Kansas Lawrence United States Chair’s Council Associate Professor University of Kansas United States Structural Engineering Department Caltrans Seismic Response Modification Device Test Facility University of California United States Project Consultant at Simpson Gumpertz Heger Inc. New YorkNY United States Professor in the Department of Civil Construction Engineering National Taiwan University of Science and Technology Taipei Taiwan

A database of results from 27 tests of diagonally reinforced concrete coupling beams was analyzed to develop improved force-deformation envelopes (backbone curves) for modeling and analysis of coupling beams. The database, which was selected from a larger set of 60 test results, comprises specimens that generally satisfy ACI 318-19 requirements. The analyses show that the chord rotation capacity of diagonally reinforced concrete coupling beams compliant with ACI 318-19 is closely correlated with beam clear span-to-overall depth ratio and, to a lesser extent, the ratio of hoop spacing to diagonal bar diameter. A simple expression is proposed for estimating beam chord rotation capacity. Coupling beam strength was shown to be more accurately estimated from flexural strength calculations at beam ends than other methods. Recommendations are made for obtaining more accurate backbone curves in terms of chord rotation capacity, strength, and stiffness. © 2023, American Concrete Institute. All rights reserved.

关键词： Aspect ratio

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Knowledge Extraction Using Auto Regression Method -A Tourist Information Extraction and Analytics

EAI Endorsed Transactions on Energy Web

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EAI Endorsed Transactions on Energy Web 2021年第35期8卷 1-7页

作者： Arun, Manicka Raja M Sumitha, T. Maria, Michael Visuwasam L Rejin, Paul N R Assistant Professor Department of Computer Science and Engineering R.M.K College of Engineering and Technology Chennai India Assistant Professor Department of Computer Science and Engineering R.M.K Engineering College Chennai India Assistant Professor Department of Computer Science and Engineering Rajalakshmi Institute of Technology Chennai India

Data analytics is played a vital role in Information Technology and Information Technology essential services ITeS for making effective decisions. The demand for tourism and prediction of tourist arrivals are important for tourism organisation. In this paper, we analyse tourist and extract information using data analytics process. The web data are processed using travellers' details and applying an aggregate function to calculate the searching index. Here, we use the autoregression analytics method for accurate prediction. The tourist information is recorded and creates a system log for processing and extracting information. The interaction between each record and their logs are used for data processing and analytics model. This paper uses a recommendation system for the data analytics process and compares the results with existing models. Our proposed method provides good and accurate results for tourism organisation.___________________________________________ © 2021. Arun Manicka Raja M et al.,. All Rights Reserved.

关键词： Data Analytics

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A Novel Predictive Analysis and Classification of Land Subsidence Vulnerability Mapping based on GIS using Hybrid Optimized Machine Learning Techniques and computer Vision

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Procedia computer science 2024年 233卷 343-352页

作者： Senthil G. A R. Prabha Arun M A. Sahaya Anselin Nisha S. Prabu Associate Professor Department of Information Technology Agni College of Technology Chennai India Associate Professor Department of Electronics and Communication Engineering Sri Sairam Institute of Technology Chennai India Assistant Professor Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India Professor Department of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai India Assistant Professor Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India

Land subsidence is a natural disaster caused by the extraction of the earth's material strength on the surface that causes the earth's surface to settle and sink. Land subsidence can also result in significant financial losses since it causes structural damage and expensive maintenance costs for roads, trains, barriers, pipelines, ground water, oil, natural gas, mines, and buildings. The Geographic Information System (GIS) and Google Earth Platform (GEP) can be used to map this land subsidence. This provides a comprehensive view of land pattern subsidence and its interactions with other geographical elements. It applies to both progressive subsidence caused by fluid extraction (groundwater, oil, or natural gas) and rapid surface subsidence caused by the collapse of an underground mine. Risk mitigation is the most effective way to deal with the experimental model. This research study focuses on the use of Predictive Data Analysis (PDA), a powerful environment in data analytics that provides a set of tools that make it easier to evaluate optimal models. The proposed model makes use of a dataset that includes specific land subsidence circumstances as input. The prediction result is achieved using a hybrid machine learning technique that combines Stochastic Gradient Descent (SGD) with Long Short-Term Memory (LSTM) and is compared to K-Nearest Neighbor (KNN), Generative Adversarial Network (GAN), and Artificial Neural Network (ANN). The proposed system employs LSTM and SGD as optimizers, resulting in a hybrid model that improves accuracy. Among the four algorithms investigated the LSTM-SGD network has the highest accuracy (97%), followed by KNN (96%), GAN (90%), and ANN (89%). Due to their ability to capture complicated nonlinear correlations in data and manage temporal dependencies, optimized hybrid ML models LSTM-SGD and KNN models appear to be particularly well-suited for land subsidence prediction.

关键词： Land Subsidence Predictive Data Analysis (PDA) Geographic Information System Machine Learning computer Cision Susceptibility Mapping Stochastic Gradient Descent (SGD) Generative Adversarial Network (GAN) Long Short-Term Memory (LSTM) K-Nearest Neighbor (KNN) Artificial Neural Network (ANN) Google Earth Platform

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Lung cancer detection and classification with optimal feature selection and two-fold-deep-learning-classifiers

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Multimedia Tools and Applications 2025年 1-43页

作者： S., Babu Kumar Vinoth Kumar, M. Assistant Professor Department of AI ML & DS CHRIST University Bangalore India Associate Professor Department of Information Science and Engineering RV Institute of Technology and Management Bangalore India

The respiratory system is undoubtedly hampered by lung disorders. Also, one of the important reasons for death among people all around the world has been lung cancer. Early discovery can advance human survival probabilities. As a result, a unique ensemble-deep-learning paradigm for lung cancer detection and classification is established in the present research effort. The projected model includes five major phases: (a) image augmentation, (b) pre-processing, (c) segmentation, (d) feature extraction, (e) feature selection, and (f) lung cancer detection and classification, respectively. The collected raw CT images are augmentation with SMOTE. The augmented images are pre-processed via Median Filtering (for noise removal) and Contrast-limited adaptive histogram equalization (CLAHE) (for image contrast enhancement). Subsequently, from the pre-processed data, the ROI is identified via optimized U-NETS. The activation function (hyper-parameter) of U-NETS is optimized via a new hybrid optimization model-Digging Tunaswarm Optimizer (DTO). This DTO is the conceptual amalgamation of two standard meta-heuristic optimization models, namely “Honey Badger Algorithm (HBA)” and “Tuna Swarm Optimization (TSO)” models, respectively. Then, from the selected ROI area, the features like texture features (Manhattan Distance-based-GLCM, GLRM), Color features (Color Histogram), and Shape features (Moments, Area, Perimeter) are extracted. Among the extracted features, the optimal features are selected using DTO. This optimal feature selection reduces the computational complexity of the projected model. Finally, using these extracted optimal features, the two-fold-deep-learning-classifier framework is trained. This two-fold-deep-learning-classifiers framework encapsulates the “Bidirectional long-short term memory (Bidirectional LSTM)” and the “Recurrent Neural Network (RNN)” and the “Modified Convolutional Neural Network (M-CNN)”. In the first phase, the Bi-LSTM and RNN are clamped, and they

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A new algorithm for detection of nodes failures and enhancement of network coverage and energy usage in wireless sensor networks

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Materials Today: Proceedings 2023年 80卷 1717-1722页

作者： Satyanarayana, P. Mahalakshmi, T. Sivakami, R. Alahmari, Saad Ali Rajeyyagari, Sivaram Asadi, Srinivasulu ECE V. R. Siddhartha Engineering College Vijayawada India Department of ECE Prasad V Potluri Siddhartha Institute of Technology India Associate Professor CSE Sona College of Technology TN Salem 5 India Department of Computer Science Shaqra University Saudi Arabia Data Analytics Research Laboratory BlueCrest University College Monrovia Liberia

Present natural lives shows rising attention in various applications of the wireless sensor networks (WSNs). Significant applications are isolated and stiff regions where human involvement is hazardous or unfeasible. Some examples are space investigation, battlefield observation, costal, and edge security;nowadays, such networks are needful in several manufacturing and customer applications. A WSN is a device system, indicated as nodes;these nodes intellect the atmosphere and transfer the details collected from the ground through a wireless connection. Statistics are sent, probably via many hops, to a target that can use it nearby or is linked to various networks using the gateway. The sensor nodes might be stationary or changeable. Every node has clear data about the neighborhood or not. Communication over WSN over ecological hazards is a key issue. These constraints might impact the performance of the sensors/direction finding protocols and resource utilization;thus, they may show the way to the node crash situation, i.e., software/hardware breakdown, safety threats, extreme power utilization, etc. It is essential to examine the collision of breakdown over system performance. In the projected method, relay nodes are used as secure nodes for the positioned sensor nodes. The projected method is separated into two phases: the intra segregation stage and inter segregation stage. In the intra segregation stage, the unnecessary nodes reach nearer to the segregation border, and dispersion slowly enlarges the coverage. The projected model is examined through simulations and compares the results with existing methods. © 2021

关键词： Sensor nodes

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Computational Intelligence to Progress in WMN 3rd

Computational Intelligence to Progress in WMN

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3rd International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Revathy, G. Senthilkumar, C. Rajesh, A. Selvi, C. P. Thamil Department of CSE Srinivasa Ramanujam Centre SASTRA Deemed University Tamilnadu Kumbakonam India Department of CSE Erode Sengunthar Engineering College Erode India Associate Professor Department of Artificial Intelligence & Data Science Rathinam Technical Campus Coimbatore India

ISBN: (纸本)9789819783540

Wireless mesh networks are been prominently developing in the past two decades and have received a lot of accomplished tasks in various applications. Machine learning is an art of computational intelligence that enhances development day by day in a rapid manner. The planned work deals with the combination of machine learning techniques to increase the Eminence of Amenities in wireless mesh networks. The prominent ideas of machine learning are to enhance the performance in Wireless Mesh Networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Wireless mesh networks (WMN)

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Experimental study of hardness and corrosion properties of AZ91 magnesium alloy after electroless nano composite coating

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Materials Today: Proceedings 2022年 62卷 2926-2929页

作者： Sayyad, Dr Farook Sayyad, Dr Shabnam Ansari, Dr. Md. Imran Mechanical Engineering Department. Dr. D. Y. Patil School of Engineering Pune India Associate Professor Computer Engineering Department. AISSMS College of Engineering Pune India Robotics & Automation Department. Bharati Vidyappeth Pune India

Magnesium and its numerous alloys have excellent rigidity, low density, and electromagnetic protection properties, making them ideal for usage in numerous industrial components. Magnesium alloys have a significant disadvantage in terms of corrosion and hardness, which restricts their practical uses. One of the most recent ways for improving the corrosion and hardness of Mg alloys is electro-less nano-coating. The experimental research of corrosion and hardness of AZ91 Mg Alloy due to nano composite coating procedures of ENi-B-TiO2is examined in this study. It would be useful to conduct a thorough examination, including experimental analysis, into the possibilities of increasing the deposit efficiency and characteristics of composite deposits by designing a suitable bath composition and operating circumstances. It has been discovered that as the concentration of titania particles increases, so does the surface hardness of coatings. The optimal concentration of second-phase (titania) particles and bath agitation are also examined in order to get superior surface properties following nano coating. Following this nano coating, the materials may be employed for a variety of materials engineering applications. © 2022

关键词： Surface properties

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An Efficient Method for Detecting Hate Speech in Tamil Tweets Using an Ensemble Approach 6th

An Efficient Method for Detecting Hate Speech in Tamil Tweet...

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6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Shibly, F.H.A. Sharma, Uzzal Naleer, H.M.M. South Eastern University of Sri Lanka Oluvil Sri Lanka Assam Don Bosco University Guwahati India Associate Professor Department of Computer Science Birangana Sati Sadhani Rajyik Viswavidyalaya Assam Golaghat India Department of Computer Science Faculty of Applied Sciences South Eastern University of Sri Lanka Oluvil Sri Lanka

ISBN: (纸本)9789819940707

People have converged on a worldwide level because of advancements in communication technologies. They are critical in ensuring freedom of speech by allowing individuals to openly express their thoughts, behaviors, and opinions. Although this presents an excellent chance for racism, trolling, and exposure to a flood of nasty online content. As a result, social media’s exponential rise in hate speech is profoundly affecting society. In this study, we used machine learning and deep learning algorithms to identify hate speech, and we compared their performance to create an ensemble model. Researchers gathered and integrated two distinct datasets of hateful tweets in Tamil that were produced by Bharathi Raja Chakravarthi et al. Tweets in this dataset fall into two categories: non offensive and offensive. This dataset contains 10,129 tweets. In addition, the researchers used six selected machine and deep learning algorithms for this study namely. Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), Bidirectional LSTM, Multi-layer Perceptron (MLP) and Multilingual BERT. When it comes to detecting hate speech, SVM (82%) and LR (82%) have the best Accuracy. Furthermore, researchers developed two ensemble algorithms to construct a most efficient model. First ensemble model was created by combining SVM, LR and NB and the second ensemble was developed using SVM and LR. Four algorithms including the two ensemble models were obtained same accuracy. Therefore, the researchers compared the F1 score and found that the ensemble model 02 outperformed other classifiers. The results of this study are significant because they can be used as a benchmark for future research on Tamil language hate speech using various machine learning algorithms to detect hate speech more effectively and accurately. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Support vector machines

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Bird Swarm Algorithm with Fuzzy Min-Max Neural Network for Financial Crisis Prediction

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computers, Materials & Continua 2022年第10期73卷 1541-1555页

作者： K.Pradeep Mohan Kumar S.Dhanasekaran I.S.Hephzi Punithavathi P.Duraipandy Ashit Kumar Dutta Irina V.Pustokhina Denis A.Pustokhin Department of Computing Technologies Associate ProfessorSRM Institute of Science and TechnologyKattankulathur603203India Department of Information Technology Kalasalingam Academy of Research and Education626126India Department of Computer Science and Engineering Sphoorthy Engineering CollegeHyderabadTelangana501510India Department of Electrical and Electronics Engineering J B Institute of Engineering and TechnologyHyderabadTelangana500075India Department of Computer Science and Information System College of Applied SciencesAlMaarefa UniversityRiyadh11597Kingdom of Saudi Arabia Department of Entrepreneurship and Logistics Plekhanov Russian University of Economics117997MoscowRussia Department of Logistics State University of Management109542MoscowRussia

Financial crisis prediction(FCP)models are used for predicting or forecasting the financial status of a company or financial *** is considered a challenging issue in the financial *** and machine learning(ML)models can be employed for the design of accurate FCP *** numerous works have existed in the literature,it is needed to design effective FCP models adaptable to different *** study designs a new bird swarm algorithm(BSA)with fuzzy min-max neural network(FMM-NN)model,named BSA-FMMNN for *** major intention of the BSA-FMMNN model is to determine the financial status of a firm or *** presented BSA-FMMNN model primarily undergoes minmax normalization to transform the data into uniformity ***,k-medoid clustering approach is employed for the outlier removal ***,the classification process is carried out using the FMMNN model,and the parameters involved in it are tuned by the use of *** utilization of proficient parameter selection process using BSA demonstrate the novelty of the *** experimental result analysis of the BSA-FMMNN model is validated using benchmark dataset and the comparative outcomes highlighted the supremacy of the BSA-FMMNN model over the recent approaches.

关键词： Financial crisis predictive model machine learning outlier removal clustering metaheuristics

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