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Facial Emotion Recognition Using Swarm Optimized Multi-Dimensional DeepNets with Losses Calculated by Cross Entropy Function

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computer Systems Science & engineering 2023年第9期46卷 3285-3301页

作者： A.N.Arun P.Maheswaravenkatesh T.Jayasankar Department of Computer Science&Engineering Sri Venkateswara Institute of Science and TechnologyTiruvallurTamilnaduIndia Department of Electronics and Communication Engineering Anna UniversityTrichyTamilnaduIndia

The human face forms a canvas wherein various non-verbal expressions are *** expressional cues and verbal communication represent the accurate perception of the actual *** many cases,a person may present an outward expression that might differ fromthe genuine emotion or the feeling that the person *** when people try to hide these emotions,the real emotions that are internally felt might reflect as facial expressions in the form of micro *** micro expressions cannot be masked and reflect the actual emotional state of a person under *** expressions are on display for a tiny time frame,making it difficult for a typical person to spot and recognize *** necessitates a place for Machine Learning,where machines can be trained to look for these micro expressions and categorize them once they are on *** study’s primary purpose is to spot and correctly classify these micro expressions,which are very difficult for a casual observer to *** research improves upon the accuracy of the recognition by using a novel learning technique that not only captures and recognizes multimodal facial micro expressions but also has features for aligning,cropping,and superimposing these feature frames to produce highly accurate and consistent results.A modified variant of the deep learning architecture of Convolutional Neural Networks combined with the swarm-based optimality technique of the Artificial Bee Colony Algorithm is proposed to effectively get an accuracy of more than 85%in identifying and classifying these micro expressions in contrast to other algorithms that have relatively less *** of the main aspects of processing these expressions from video or live feeds is aligning the frames homographically and identifying these concise bursts of micro expressions,which significantly increases the accuracy of the *** proposed swarm-based technique handles this in the research to precisely alig

关键词： Facial micro expression recognition deep learning CNN artificial bee colony

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Software design and FPGA implementation of optimized medical image fusion techniques

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Multimedia Tools and Applications 2025年 1-31页

作者： Elshazly, E.A. El-Shafai, Walid El-Hoseny, Heba El-Rabaie, El-Sayed M Zahran, O. Abdelwahab, Safey A. S. El-Halawany, M.M. Fikry, R.M. Elaraby, S.M. Faragallah, Osama S. Mohamed, Wael A. Mahmoud, Korany R. Abd El-Samie, Fathi E. Cairo Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Faculty of Computer Studies Arab Open University 11681 Riyadh Saudi Arabia Department of Information Technology College of Computers and Information Technology Taif University 11099 Taif21944 Saudi Arabia Department of Electrical Engineering Faculty of Engineering Benha University Benha Egypt Electronics and Communications Engineering Department Faculty of Engineering Helwan University Helwan Egypt Giza Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Nowadays, medical image fusion plays a crucial role in enhancing the diagnosis accuracy and the clinical decision-making process in various healthcare applications. This research work presents a comprehensive study of the design and implementation of optimized medical image fusion techniques using a combination of software and Field-Programmable Gate Array (FPGA) technologies. The proposed medical image fusion strategy is based on the utilization of Discrete Wavelet Transform (DWT) and Modified Central Force Optimization (MCFO). The implementation of the proposed technique as well as the traditional medical image fusion techniques is considered using an appropriate software design and FPGA. The presented techniques aim to overcome the limitations of traditional fusion techniques by integrating advanced image processing algorithms, optimization algorithms, and parallel computing capabilities offered by FPGA platforms. The first step in the proposed framework is to match the histogram of one of the images with that of the other, so that both images will have the same dynamic range. After that, the DWT is used to decompose the images that should be fused together. Based on some constraints, the MCFO optimization algorithm is used to evaluate the optimum level of decomposition and the optimum parameters for the best fusion quality. Finally, to improve the obtained visual quality and reinforce the information in the fusion result, an additional contrast enhancement step using adaptive histogram equalization is applied to the fusion result. Comparative study between the proposed optimized DWT-based fusion framework, the traditional Principal Component Analysis (PCA), Additive Wavelet Transform (AWT), and DWT-based fusion techniques is presented. Various metrics of fusion quality are considered, including average gradient, standard deviation, local contrast, entropy, edge strength, Peak Signal-to-Noise Ratio (PSNR), Qab/f, and processing time. The proposed optimized DWT-ba

关键词： MATLAB

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An AI-Driven Cybersecurity Framework for the Internet of Medical Things: A Hybrid LSTM-CNN-GAN Approach 2

An AI-Driven Cybersecurity Framework for the Internet of Med...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Loganathan, Sathyapriya Francis Raj, J. Relin Krishnan, R. Santhana Blessie, A. Alice Kumar, V. Vinoth Kavitha, S. University of Kentucky Department of Computer Science KY United States Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Government College of Engineering Department of Electronics and Communication Engineering Tirunelveli India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Department of Electronics and Communication Engineering Chennai India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9798331505745

The Internet of Medical Things (IoMT) is transforming healthcare by connecting medical devices for realtime patient monitoring. However, the security of these devices remains a significant concern. This paper presents a hybrid deep learning model that combines Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Generative Adversarial Networks (GAN) to enhance the security of IoMT systems. The proposed model ensures data integrity, detects anomalies, and protects against cyber threats in healthcare environments. Data for this study is collected from various IoMT devices, including wearable health trackers, medical sensors, and smart hospital equipment. It includes timeseries data (ECG, blood pressure, etc.), network traffic data, and medical images (X-rays, MRI scans). Cyberattack data, such as malware logs and denial-of-service attacks, is also incorporated to train the model. Preprocessing steps include normalization, imputation, and labeling to differentiate normal and attack scenarios. The hybrid model uses LSTM for timeseries anomaly detection, capturing irregularities in real-time health data, CNN for medical image security and malware detection, and GAN to generate synthetic attack data for detecting zero-day threats. This multi-layered approach ensures timely detection of health emergencies and cyberattacks, safeguarding both patient data and IoMT devices. Model training uses supervised and unsupervised learning techniques, with performance evaluated using metrics such as accuracy, precision, recall, and ROC-AUC. The hybrid model's effectiveness is validated by comparing it against existing IoMT security methods, demonstrating improvements in detection accuracy and robustness. This approach has critical applications in healthcare, providing real-time threat detection, data protection, and anomaly detection to secure IoMT devices, ensuring patient safety, and minimizing the risk of cyberattacks. © 2025 IEEE.

关键词： Electrotherapeutics

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Optimized Power Allocation in Downlink NOMA with Imperfect Successive Interference Cancellation

Optimized Power Allocation in Downlink NOMA with Imperfect S...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Biswas, Soumik Bera, Debasish Indian Institute of Information Technology Department of Electronics and Communication Engineering Kalyani India Indian Institute of Information Technology Department of Computer Science and Engineering Kalyani India

ISBN: (纸本)9798350350821

It is expected that physical-layer security will finally thrive to provide an extra layer of defence for a variety of devices in next-generation 6G wireless communication. In this work, physical-layer security using non-orthogonal multiple access (NOMA) with single-input-single-output (SISO) assumptions are considered where the users and eavesdroppers are spatially uniformly deployed. The channels between the transmitter (base station (BS)) and the NOMA users and eavesdroppers are considered to be independent Nakagami-m. We analyse the secrecy sum rate (SSR) of the NOMA system in the case of imperfect successive interfere cancellation (SIC) and propose an optimised power allocation scheme with minimised transmit power requirement to satisfy the Quality of Service (QoS) for all legitimate users. Closed-form solutions for the SSR are also derived. The performance of the proposed schemes has been evaluated both by analysis and by simulations. Results are evaluated for a wide range of the signal-to-noise ratio (SNR). © 2024 IEEE.

关键词： Channel state information

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Brain Tumor Diagnosis Using Sparrow Search Algorithm Based Deep Learning Model

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computer Systems Science & engineering 2023年第2期44卷 1793-1806页

作者： G.Ignisha Rajathi R.Ramesh Kumar D.Ravikumar T.Joel Seifedine Kadry Chang-Won Jeong Yunyoung Nam Department of Computer Science and Business Systems Sri Krishna College of Engineering and TechnologyCoimbatore641008India Department of Information Technology Sri Krishna College of TechnologyCoimbatore641008India Department of Electronics and Communication Engineering Kings Engineering CollegeChennai602117India Department of Electronics and Communication Engineering R.M.K.Engineering CollegeChennai601206India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Medical Convergence Research Center Wonkwang UniversityIksanKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Recently,Internet of Medical Things(IoMT)has gained considerable attention to provide improved healthcare services to *** earlier diag-nosis of brain tumor(BT)using medical imaging becomes an essential task,auto-mated IoMT and cloud enabled BT diagnosis model can be devised using recent deep learning *** this motivation,this paper introduces a novel IoMT and cloud enabled BT diagnosis model,named *** IoMTC-HDBT model comprises the data acquisition process by the use of IoMT devices which captures the magnetic resonance imaging(MRI)brain images and transmit them to the cloud ***,adaptive windowﬁltering(AWF)based image preprocessing is used to remove *** addition,the cloud server executes the disease diagnosis model which includes the sparrow search algorithm(SSA)with GoogleNet(SSA-GN)*** IoMTC-HDBT model applies functional link neural network(FLNN),which has the ability to detect and classify the MRI brain images as normal or ***ﬁnds useful to generate the reports instantly for patients located in remote *** validation of the IoMTC-HDBT model takes place against BRATS2015 Challenge dataset and the experimental analysis is car-ried out interms of sensitivity,accuracy,and speciﬁ*** experimentation out-come pointed out the betterment of the proposed model with the accuracy of 0.984.

关键词： Internet of medical things healthcare brain tumor disease classiﬁcation deep learning metaheuristics

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Adaptive Intrusion Detection in Cyber-Physical Systems using Reinforcement Learning-Based Autoencoders 2

Adaptive Intrusion Detection in Cyber-Physical Systems using...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Rajathi, N. Divya, S. Anitha Elavarasi, S. Saritha, G. Jeya ramya, V. Department of Information Technology Kumaraguru College of Technology Coimbatore India Department of Computer Science and Business System Bannari Amman Institute of Technology Erode India Department of Computer Science and Engineering Sona College of Technology Salem India Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India

ISBN: (纸本)9798331504960

The increasing integration of Cyber-Physical Systems (CPS) in critical infrastructure presents unique challenges for ensuring robust cybersecurity. Traditional Intrusion Detection Systems (IDS) often struggle with the dynamic, complex nature of CPS environments, making adaptive solutions essential. This paper presents an Adaptive Intrusion Detection System (AIDS) that leverages Reinforcement Learning-Based Autoencoders (RL-AE) to detect and respond to cyber intrusions in CPS environments. The proposed model combines the power of autoencoders for unsupervised anomaly detection with reinforcement learning to continuously adapt and improve detection accuracy. Experimental results on benchmark CPS datasets demonstrate that the RL-AE-based IDS achieves a detection accuracy of 98.3%, a precision rate of 96.8%, and a recall rate of 97.5%, effectively minimizing false positives to 2.4%. The system also demonstrated rapid adaptability, with a convergence time of less than 50 iterations in response to new attack patterns. These results highlight the model's ability to enhance system resilience and promote secure and efficient CPS operations in industrial and critical infrastructure applications. This approach provides a scalable and flexible IDS solution, promoting secure and resilient CPS operations in real-time, high-stakes environments. © 2024 IEEE.

关键词： Network intrusion

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Hybrid CNN-LSTM Model for Enhanced Weather Forecasting: Leveraging Spatial and Temporal Dependencies 4

Hybrid CNN-LSTM Model for Enhanced Weather Forecasting: Leve...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Vallileka, N. Rajkumar, G.Vinoth Krishnan, R. Santhana Shankar, S. Vijay Francis Raj, J. Relin Karthikeyan, M. Saravana Psna College of Engineering and Technology Department of Computer Applications Dindigul India J. P. College of Engineering Department of Electronics and Communication Engineering Tenkasi India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Sona College of Technology Department of Electrical and Electronics Engineering Salem India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India

ISBN: (纸本)9798331523923

Accurate weather forecasting is critical for decision-making in agriculture, disaster management, urban planning, and numerous other domains. Traditional forecasting methods often struggle to capture the intricate spatial and temporal dependencies in weather data. This work proposes a hybrid deep learning model that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to address these challenges. The CNN component efficiently extracts spatial features from satellite images, such as cloud formations and precipitation zones, while the LSTM network models the temporal dependencies inherent in meteorological data, such as temperature, humidity, and wind speed. The model is trained on a dataset comprising historical weather records and satellite imagery, spanning several years. PyTorch, a dynamic deep learning framework, is used to implement and train the hybrid architecture. Data preprocessing involves normalization, image augmentation, and sequence structuring to prepare inputs for the model. The CNN layers, built with convolutional and pooling operations, extract spatial features, which are combined with temporal weather data and passed to the LSTM network. The LSTM layers capture long-term dependencies and trends in weather patterns. Fully connected layers map the learned features to final predictions, including temperature, rainfall, and other key weather parameters. The hybrid model is evaluated using metrics such as Root Mean Square Error (RMSE) for numerical predictions and accuracy for classification tasks. Results indicate that the proposed hybrid CNN-LSTM model outperforms standalone models and traditional statistical approaches, demonstrating its ability to leverage spatial and temporal dependencies for accurate weather forecasting. This work highlights the potential of hybrid deep learning models in advancing weather forecasting capabilities, offering a scalable and reliable solution for real-world applications such as e

关键词： Convolutional neural networks

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computer Vision based Panoptic Driving Perception under Various Weather Conditions

Computer Vision based Panoptic Driving Perception under Vari...

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2023 International Conference on Industry Sciences and computer Science Innovation, iSCSi 2023

作者： Sridevi, M. Harish, M. Department of Computer Science & Engineering National Institute of Technology Tiruchirappalli620015 India Department of Electrical and Electronics Engineering National Institute of Technology Tiruchirappalli620015 India

Learning models that have been known to multi-task are gaining too much attention in various fields. One such area of work where Multi-tasking models are extracting hopeful outcomes is Autonomous Driving. Panoptic Driving Perception has become an inevitable aspect of autonomous driving systems which demands a very good accuracy and speed. Over a decade, various multi-tasking models have been proposed to simultaneously carry out traffic object detection, drivable area segmentation, and lane detection. Among all of them, YOLOP, YOLOPv, and HybridNets are the 3 models that have accomplished the above tasks using the BDD100K Dataset with high excellent accuracy and efficiency, thus achieving the new State-Of-The-Art (SOTA) performance. This paper focuses mainly on comparing the performance of the above-mentioned models in various weather conditions based on accuracy, robustness, and computational speed. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Object detection

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Unveiling Sentiments: Exploring Tweet and Text Analysis for Deeper Insights

Unveiling Sentiments: Exploring Tweet and Text Analysis for ...

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2024 International Conference on Signal Processing, Computation, electronics, Power and Telecommunication, IConSCEPT 2024

作者： Ranjan, Ritik Karthik, N. Vani, V. National Institute of Technology Puducherry Department of Electrical and Electronics Engineering Karaikal India National Institute of Technology Puducherry Department of Computer Science and Engineering Karaikal India

ISBN: (数字)9798331540685

ISBN: (纸本)9798331540685

This work presents a comprehensive exploration of sentiment analysis on social media data, with a specific focus on Twitter. The study evaluates three state-of-the-art classifiers-Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest (RF)-to categorize sentiments (positive, negative, neutral) based on specific criteria. Data cleansing and classification methods are applied to extract meaningful insights from unstructured social media data. Results show that SVM achieves the highest accuracy (92.67%) among the classifiers. It also discusses the strengths and limitations of each classifier in capturing the nuances of sentiment expressions. This work contributes to the understanding of sentiment analysis techniques on social media platforms © 2024 IEEE.

关键词： Tweets

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Transformer Internal and Inrush Current Fault Detection Using Machine Learning

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Intelligent Automation & Soft Computing 2023年第4期36卷 153-168页

作者： R.Vidhya P.Vanaja Ranjan N.R.Shanker Department of Electronics and Communication Engineering Loyola-ICAM College of Engineering and TechnologyChennai600034India Embedded System Technologies Department of Electrical and Electronics EngineeringCollege of EngineeringGuindyChennai 600025India Department of Computer Science and Engineering Aalim Muhammed Salegh College of EngineeringChennai 600055India

Preventive maintenance in the transformer is performed through a dif-ferential relay protection system,and it protects the transformer from internal and external ***,the Current Transformer(CT)in the differential protec-tion system mal-operates during inrush *** saturates due to magnetizing inrush currents and causes false tripping of the differential ***,iden-tiﬁcation of tripping in protection relay either due to inrush current or internal faults needs to be *** the above problem,continuous monitoring of transformer breather and CT terminals with thermal camera helps detect the trip-ping in relay due to inrush or internal *** transformer’s internal fault leads to high breathing process in the transformer breather,never for inrush *** inrush currents,CT temperature is *** monitoring of breather and CT of the transformer through thermal imaging and radiometric pix-els detect the causes of CT saturation and differentiates *** wavelet threshold image analytics(HWT-IA)based radiometric pixels analysis of the transformer breather and CT after de-noising provides an accurate result of about 95%for identiﬁcation of the false tripping of differential protection system of transformer.

关键词： Wavelet threshold inrush transformer breather current transformer thermal image

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