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Advancing emotion recognition via EEG signals using a deep learning approach with ensemble model

Journal of Intelligent and Fuzzy Systems 2024年第1-2期47卷 143-154页

作者： Immanuel, Rajeswari Rajesh Sangeetha, S.K.B. Department of Computer Science and Engineering SRM Institute of Science and Technology Faculty of Engineering and Technology Vadapalani Campus Tamil Nadu Chennai India

Human emotions are the mind's responses to external stimuli, and due to their dynamic and unpredictable nature, research in this field has become increasingly important. There is a growing trend in utilizing deep learning and machine learning techniques for emotion recognition through EEG (electroencephalogram) signals. This paper presents an investigation based on a real-time dataset that comprises 15 subjects, consisting of 7 males and 8 females. The EEG signals of these subjects were recorded during exposure to video stimuli. The collected real-time data underwent preprocessing, followed by the extraction of features using various methods tailored for this purpose. The study includes an evaluation of model performance by comparing the accuracy and loss metrics between models applied to both raw and preprocessed data. The paper introduces the EEGEM (Electroencephalogram Ensemble Model), which represents an ensemble model combining LSTM (Long Short-Term Memory) and CNN (Convolutional Neural Network) to achieve the desired outcomes. The results demonstrate the effectiveness of the EEGEM model, achieving an impressive accuracy rate of 95.56%. This model has proven to surpass the performance of other established machine learning and deep learning techniques in the field of emotion recognition, making it a promising and superior tool for this application. © 2024 - IOS Press. All rights reserved.

关键词： Convolutional neural networks

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Cross-Layer Optimization Strategies for Energy-Efficient and Reliable Machine-Type Communication in LPWANs 2

Cross-Layer Optimization Strategies for Energy-Efficient and...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Amanullah, M. Manoranjini, J. Sathiya, V. Helen Prabbu Sankar, P. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science & Engineering Chennai India Rajalakshmi Engineering Department of Artificial Intelligence and Data Science Chennai India Panimalar Engineering College Department of Computer Science & Engineering Chennai India

ISBN: (纸本)9798331534950

In the massive Machine-Type Communication (mMTC), the exponential growth of Internet of Things (IoT) devices over Low Power Wide Area Networks (LPWANs) presents substantial issues regarding energy efficiency and stability. The existing frameworks frequently fails in the the intricate relationships among the MAC-layer and physical parameters. To enhanced cross-layer evaluation methodology that integrates adaptive data rate (ADR) optimization, real-time energy consumption monitoring, and dynamic downlink message handling to address the problems. To maintain stability and performance with the network situations, the framework makes use of a Control-Lyapunov function. To enable effective resource allocation uses the Successive Convex Approximation that improve the effects of various transmission systems under quasi-static and dynamic channel conditions. To verify the cross-layer interactions and it impacts the energy efficiency, the proposed method begins with the data collection and it moves to payload size and packet length, are regularly changed to examines the affect throughput and energy consumption. The findings show the high-density node situations, energy consumption that reduced while retaining reliable communication. Furthermore, the results show the interdependencies among network parameters, highlighting the necessity of an integrated strategy. This framework helps to ensure the mMTC scenarios that optimized by improving the long-term viability and dependability of IoT networks. © 2024 IEEE.

关键词： Resource allocation

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Per-user network access control kernel module with secure multifactor authentication

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Journal of Supercomputing 2024年第1期80卷 970-1008页

作者： Cheng, Sheng-Tzong Horng, Gwo-Jiun Hsu, Chih-Wei Su, Z.-Yu Department of Computer Science and Information Engineering National Cheng Kung University Tainan701 Taiwan Department of Computer Science and Information Engineering Southern Taiwan University of Science and Technology Tainan Taiwan

Network attacks, such as botnets stealing sensitive data, constitute a critical concern for administrators in the Internet area. Such attacks can be prevented using a network access control (NAC) scheme. However, existing NAC mechanisms all rely on the user account mechanism provided by the operating system. Herein, we propose a protocol involving a Linux security module for user-based NAC. The module requires neither user accounts nor secure user space;it loads signed rules and keys for a user from a USB security key, securely authenticates the user, and controls network permissions directly from the Linux kernel. Moreover, we present extensions for the protocol, developed using additional authentication methods and devices (such as a smartphone), to prevent man-in-the-middle, replay, and phishing attacks. The protocol can securely authenticate users from kernel space by using USB security keys and a user smartphone despite an insecure user space, is resistant to various attacks, and can guarantee that authorized users obtain their corresponding network privileges. At the same time, the analysis of Linux operating system security enhancement technology is proposed to achieve the goal of trusting machines. Through the use of empirical algorithm analysis, enhanced virtualization technology, and other methods, the risk of hacker intrusion on information platforms is reduced in multiple aspects, and management mechanisms are used to set and use resource permissions. On the other hand, after mastering the key technologies for network blocking development in the core layer of the system, there is also an opportunity to independently build modules for the security core of operating systems in special fields and future 5G/6G SDNs. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Network security

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Improving Biomedical Image Segmentation: An Extensive Analysis of U-Net for Enhanced Performance 2

Improving Biomedical Image Segmentation: An Extensive Analys...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Ahmed, Zuhair Tanim, Sharia Arfin Prity, Fariya Sultana Rahman, Hasibur Maisha, Tasmia Binte Munir Department of Computer Science and Engineering Dhaka Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350328202

The U-Net architecture is the focus of this study, which optimizes biomedical picture segmentation. Improving performance in contexts with limited resources is the goal. The methodology uses GradCAM++, k-fold cross-validation, and kernel size modifications to conduct a systematic evaluation over many folds. Significant gains in accuracy, precision, recall, F1- score and accuracy are indicate in the results which reach 96.70%. By lowering the reliance on data augmentation, the suggested modifications improve the model's effectiveness. Under resource constraints, our work provides important new under- standings for biological picture segmentation. © 2024 IEEE.

关键词： Image enhancement

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Cardiovascular Disease Prediction with Convolutional Neural Networks and Hyperparameter Tuning 8th

Cardiovascular Disease Prediction with Convolutional Neural ...

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8th International Conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Hossain, Mohammad Alamgir Metwally, Abdelnasser Abdelwakil Khan, Asif Ali Amin, Sherif Tawfik Hamdan, Alfadil Ahmed Limkar, Suresh Department of Computer Science College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department ofComputer Science College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department ofComputer and Network Engineering College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department of Artificial Intelligence & Data Science AISSMS Institute of Information Technology Maharashtra Pune India

ISBN: (纸本)9789819765805

CVD continues to pose a substantial global health burden, demanding the need for the creation of precise predictive models that can aid in the timely identification and evaluation of risks. This research paper aims to provide a thorough examination of the prediction of CVD by utilizing CNN in aggregation with hyperparameter tuning techniques. This paper examines the effectiveness of three widely recognized CNN architectures, namely ResNet, VGGNet, and Xception CVD. The Tree-structured Parzen Estimator (TPE) is utilized as a method for tuning hyperparameters in order to optimize the CNN models. The study commences with a comprehensive examination of the dataset, encompassing a wide array of cardiovascular CT image data. The chosen CNN architectures serve as the fundamental framework for our predictive models. The selection of these architectures is based on their established track record in effectively addressing image analysis tasks and their capacity to accurately capture complex patterns and features present in medical images. The models undergo training using a dataset in order to acquire knowledge about distinctive features that are linked to various cardiovascular conditions. The experimental findings presented in the study indicate that the utilization of the Xception architecture, in aggregation with TPE for hyperparameter optimization, yields the most favorable predictive accuracy. Specifically, the achieved accuracy rate of 98.26% is notably remarkable. This result highlights the significance of not only choosing advanced CNN architectures but also optimizing their hyperparameters to fully exploit their potential in predicting cardiovascular disease. This establishes it as a promising tool for the early diagnosis and risk assessment of CVD. This study establishes a fundamental basis for future investigations in the advancement of precise and dependable predictive models applicable to various medical imaging contexts. © The Author(s), under exclusive license

关键词： Diseases

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Multi-objective Cluster-head-based Energy-aware Routing using a Separable Convolution Neural Network in a Wireless Sensor Network

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IEIE Transactions on Smart Processing and Computing 2024年第2期13卷 105-112页

作者： Ather, Danish Mary, J. Prisca Singh, Pooja Garg, Kanika Priyadharshini, T.R. Princy, B. Anni Tiwari, Mohit Department of IT and Engineering Amity University in Tashkent Uzbekistan Department of Computer Science and Engineering NPR College of Engineering and Technology Tamil Nadu Dindigul India Department of Physics Faculty of Science SGT University Haryana Gurugram India Department of Computer Science and Engineering SRM Institute of Science and Technology NCR Campus Ghaziabad Modinagar India Department of Information Technology Hindusthan College of Engineering and Technology Pollachi main road Coimbatore India Department of Computer and Communication Panimalar Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi India

Wireless sensor network (WSN) applications are added day by day owing to numerous global uses (by the military, for monitoring the atmosphere, in disaster relief, and so on). Here, trust management is a main challenge. Sensor nodes are important in wireless sensor networks, but they are easily depleted because of their short lifespan from continuous sensing activity and low battery capacity. So efficient energy utilization is a challenging task in a WSN. To minimize energy loss, clustering with an optimum path selection process is needed to retain energy in sensor nodes. This manuscript proposes multi-objective Pelican Optimization Algorithm (POA) routing to maintain energy efficiency and minimize transmission distances in wireless sensor networks. A cluster head (CH) is selected by using a Separable Convolution Neural Network (SCNN). Simulation outcomes prove the proposed technique attains 22.3% and 25.04% improvements in energy consumption when compared to the Multi-Objective CH Energy-aware Optimized Routing Approach at WSN (MOCH-EORA-WSN) and Multiple Optimum Cluster Head Multi-Objective Grasshopper Optimization with Harmony-search at WSN (MOCH-MOGOH-WSN), respectively. © 2024 Institute of Electronics Engineers of Korea. All rights reserved.

关键词： Energy utilization

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A Comparative Study of Optimization Based Task Scheduling in Cloud Computing Environments Using Machine Learning 5

A Comparative Study of Optimization Based Task Scheduling in...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Kaur, Surinder Singh, Jaspreet Bharti, Vishal Chandigarh University Department of Computer Science & Engineering India Department of Computer Science & Engineering India

ISBN: (纸本)9798350385649

Cloud computing has brought about a significant transformation in the field of Information Technology (IT) by providing the capability to access a shared pool of resources over the internet in a scalable and on-demand manner. For this purpose, task scheduling and balancing play an impactful role in the cloud since the efficient scheduling of tasks encounters issues arising from the variability of workloads, the availability of resources, and to optimize both performance and cost. This study presents a comparative study of optimization-based task scheduling mechanisms in cloud computing with the help of supervised Machine Learning method. Here, we performed several experiments on several swarm-based optimization approaches to solve the scheduling process in a balanced manner with the help of Artificial Neural Network (ANN) as a supervised machine learning mechanism. Firstly, we perform the task scheduling using the traditional approach, and after that, we apply optimization mechanism to improve the efficiency of the model based on the problems that occur during the allocation of resources and balancing mechanisms. So, to analyze the complete scheduling mechanism and problem identification mechanism, the concept of Back-Propagation Algorithm (BPA) with ANN is used that is named as ANN-BPA. For the selection of optimal features to train the ANN -BP A, the concept of swarm-based optimization method is used for random simulation -based data. At the end, to validate the model efficiency, a comparative analysis is performed with several swarm algorithms like Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Cuckoo Search Optimization (CSA) and Moth Flame Optimization (MFO). Here, the combination of MFO with ANN-BPA performs better than the other in terms of task allocation, completion, execution time, andenergy consumption. © 2024 IEEE.

关键词： Resource allocation

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Integration of Blockchain and IPFS: healthcare data management & sharing for IoT Environment

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Multimedia Tools and Applications 2024年 1-22页

作者： Mishra, Rajiv Kumar Yadav, Rajesh Kumar Nath, Prem Department of Computer Science & amp Engineering Jaypee Institute of Information Technology Noida India Department of Computer Science & amp Engineering Delhi Technological University Delhi India Department of Computer Science HNB Garhwal University Srinagar India

The immense volume of data generated and collected by smart devices has significantly enhanced various aspects of our daily lives. However, safeguarding the sensitive information shared among these devices is crucial. Ensuring the security of the Internet of Things (IoT) ecosystem from unauthorized access is imperative. Blockchain technology emerges as a promising solution to address these security concerns. Nevertheless, the effectiveness of Blockchain in handling the extensive data generated by smart devices is challenged by the rapid pace of IoT data generation and the slower transaction validation speed within Blockchain networks. This research aims to resolve these issues by integrating Blockchain with the Inter-Planetary File System (IPFS), creating a robust framework for secure data recording on a distributed storage network while enabling authorized access to the stored data. The proposed mechanism involves defining and recording access policies and cryptographic hash content on the Blockchain network, while storing the actual IoT-generated data on IPFS to enhance the confidentiality, integrity, and availability (CIA) triad. Performance assessments of the proposed scheme demonstrate its security and practicality, validating its potential for real-world application. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Electronic health record

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Job Resumes Recommendation using Integration of Fuzzy Discernibility Matrix Feature Selection and Convolutional Neural Network Multi-label Text Classification

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第13期49卷 49-58页

作者： Hayami, Regiolina Yusoff, Nooraini Daud, Kauthar Mohd Fatma, Yulia Department of Informatics Engineering Faculty Computer Science Universitas Muhammadiyah Riau Indonesia Faculty of Data Science and Computing Universiti Malaysia Kelantan 16100 Malaysia Center for Artificial Intelligence Technology Faculty of Information Science and Technology Universiti Kebangsaan Malaysia 43600 Malaysia

The manual analysis of job resumes poses specific challenges, including the time-intensive process and the high likelihood of human error, emphasizing the need for automation in content-based recommendations. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNN) for Multi-label Text Classification (MLTC), offer a promising solution for addressing these challenges through artificial intelligence. While CNN is renowned for its robust feature extraction capabilities, it faces specific challenges such as managing high-dimensional data and poor data interpretability. To address these limitations, this study employs the Fuzzy Discernibility Matrix (FDM) feature selection technique to determine the relevance of skills for each job vacancy. FDM assigns weights to the features of each job category and ranks their relevance based on the highest scores, which are then utilized in the MLTC CNN model. The integration of FDM and MLTC CNN serves as the foundation for generating content-based recommendations derived from key features in job resumes. This study produces a content-based job recommendation system that displays the top three job categories, accompanied by explanations of the skills supporting each selected category. These recommendations also consider cosine similarity values between analyzed items and the integrated results of FDM and MLTC CNN. The application of FDM for feature weighting has been proven to enhance multi-label text classification outcomes by providing better insights during the feature selection process. With a recall of 97.26%, precision of 94.81%, and accuracy of 98.58%, the MLTC model integrating FDM feature selection and CNN demonstrates robust performance characteristics in content-based job recommendation tasks. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Convolutional neural networks

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Deformable Dual Graph Aggregation Transformer Convolutional Networks with Spider Wasp Optimizer for Ovarian Tumor Classification Using Magnetic Resonance Imaging

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Biomedical Materials and Devices 2025年 1-19页

作者： Shanmugaveni, V. Jotheeswari, M. Abarnaswara, R. VijayaKumar, M. Manojanani, A. Department of Computer Science and Engineering (Cyber Security) Dr. Mahalingam College of Engineering and Technology Tamil Nadu Pollachi India Department of Computer Science and Engineering PSNA College of Engineering and Technology Tamil Nadu Dindigul India Department of Artificial Intelligence & Data Science Dr. Mahalingam College of Engineering and Technology Tamil Nadu Pollachi India Department of Computer Science and Engineering RVS Technical Campus Tamil Nadu Coimbatore India

The diagnosis of Ovarian Tumor (OT) remains a significant challenge as there is presently no practical non-invasive technique to determine true benign or malignant lesions before treatment. This study proposes a unique Deformable Dual Graph Aggregation Transformer Convolutional Network (DeDGATCNet) with Spider Wasp Optimizer (SWO) for the classification of ovarian tumors acquired from magnetic resonance imaging (MRI). In contrast to traditional deep learning models, DeDGATCNet + SWO directly integrates bending graph-based forming transformer convolutional learning and improves representation for better classification. The SWO is combined to optimize the parameters of the network, thereby improving its performance. The procedures include pre-processing the MRI data from the study of ovarian tumor TCGA-OV datasets with an Adaptive Bilateral Texture Filter (ABTF) for removing artifacts and normalization of intensity, accompanied by a segment using a Masked-Attention Mask Transformer (MAMT). Feature extraction is achieved through a One-Dimensional Quaternion Discrete Fourier Transform (1D-QDFT), which captures features from both the spatial and frequency domains. DeDGATCNet + SWO performance is way ahead and sets a new accuracy record of 99.9% for state-of-the-art models. This research introduces a new type of hybridization between deformable graph transformers, quaternion-based spectral analysis, and nature-inspired optimization techniques, setting non-invasive OT at a new height for classification and improving early diagnosis. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Adaptive bilateral texture filter Deformable dual graph aggregation transformer convolutional networks Masked-attention mask transformer One-dimensional quaternion discrete fourier transform

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