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Emotion State Detection Using EEG Signals-A Machine Learning Perspective 8th

Emotion State Detection Using EEG Signals-A Machine Learning...

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8th Smart Trends in Computing and Communications (SmartCom)

作者： Naidu, P. Geethika Adhitya, C. M. Jayanth Harshita, S. Bashpika, T. Manikumar, V. S. S. S. R. Muthulakshmi, M. Amrita Vishwa Vidhyapeetham Amrita Sch Comp Dept Comp Sci & Engn Chennai Tamil Nadu India Amrita Vishwa Vidhyapeetham Amrita Sch Engn Dept Elect & Commun Engn Chennai Tamil Nadu India

ISBN: (纸本)9789819713257;9789819713264

Feelings are the foundation of the human experience which influences choices, interpersonal interactions, and overall well-being. Emotional intelligence is necessary for mental health, emotional growth, and effective communication. In the modern world, the study of human emotions is becoming more and more popular. Because the signals produced by the brain are unstable, developing electronic models to identify emotional states from EEG data is challenging. In this study, we propose a deep learning framework-based efficient technique for EEG data analysis developed and collected from the DEAP dataset. We also use feature selection approaches to identify characteristics relevant to the cognitive search task, based on discoveries from neuroscientific research. The multimodal DEAP dataset consists of audiovisual recordings, physiological signals, and self-reported emotional assessments obtained from exposure to a range of multimedia stimuli includes a huge quantity of data. The data set was written by the scientific community and includes the EEG recordings of 32 participants. Each subject's data was obtained from 32 EEG sensors, while the remaining 8 sensors were obtained from pupils and electromyography. The suggested study takes into account "Support Vector Machines (SVMs), Random Forests, Convolutional Neural networks (CNNs), and Long Short-Term Memory (LSTM) networks" in addition to a range of supervised and unsupervised learning techniques. The advantages and disadvantages of each approach are explored, with a focus on how crucial it is to select the best approach depending on the type of EEG data and the particular emotional awareness task at hand. Our established model effectively categorized emotions into two main groups: arousal (the strength of the emotion) and valence (the pleasantness of the emotion). Surprisingly, our model was able to identify between these high and low valence, as well as high and low arousal states, with an average accuracy of over 92%.

关键词： Emotion perception Cognitive function Long-term memory (LSTM) networks Feature selection

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Deep Learning Approaches for Understanding Adverse Drug Reaction: Short Literature Review 4th

Deep Learning Approaches for Understanding Adverse Drug Reac...

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4th International Conference on Digital Technologies and Applications (ICDTA)

作者： Zyani, Chaimaa Nfaoui, El Habib Sidi Mohamed Ben Abdellah LISAC Lab Fac Sci Dhar El Mahraz Fes Morocco

ISBN: (纸本)9783031686740;9783031686757

An adverse drug reaction (ADR) is a harmful disarray that occurs as a result of taking a drug at doses commonly used in humans for the prevention, diagnosis, treatment of disease, or to change physiological function. The detection of ADRs is critical to our understanding of risk-benefit profiles. Reporting ADRs has become an essential part of the monitoring and evaluation activities carried out in hospitals. Spontaneous reporting of ADRs has long been the standard method of reporting. However, this approach is known to have high underreporting rates, a problem that limits pharmacovigilance efforts. Deep Learning (DL) techniques are now widely used for drawing useful information from unstructured textual data. The review begins with an explanation of ADRs. It also underscores how traditional methods are inadequate to deal with the large volume of unstructured information. The following parts describe major methods of DL applied in ADR detection.

关键词： Adverse drug reaction Deep learning Pharmacovigilance databases gene-gene interactions drug-related attributes

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Data Imputation Using Artificial Neural Network for a Reservoir System 4th

Data Imputation Using Artificial Neural Network for a Reserv...

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4th Congress on Intelligent systems (CIS)

作者： Shrinivas, Chintala Rahulsai Bhatia, Rajesh Wadhwa, Shruti Punjab Engn Coll Chandigarh 160012 India

ISBN: (纸本)9789819990368;9789819990375

This study provides a comprehensive comparison of the different algorithms implemented on a reservoir system, and the results are statistically analyzed from the results of other machine learning algorithms. Different weights and activation methods have been used to obtain the results. The algorithms implemented on the data of reservoir system are generative adversarial networks, synthetic model, non-dominated sorting genetic algorithm 2. Later on, we have done comparisons and visualization on the data obtained We have attempted to implement generative adversarial networks on a reservoir system that is in the time series representation and the data values are from June 1, 1989, to May 1, 2016. Data was collected from the reservoir authorities, and they did not have the records for some of the months. The target is to regenerate that empty values and find out what could be the next data value in the upcoming months.

关键词： Generative adversarial network (GAN) Data imputation Synthetic model Non-dominated sorting algorithm 2 (NSGA2)

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Artificial Neural Network for Extraction and Analysis of Consumer Opinions in Tourism 4th

Artificial Neural Network for Extraction and Analysis of Con...

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4th International Conference on Digital Technologies and Applications (ICDTA)

作者： Larhmouch, Rachid Merhraoui, Mohammed Moussaoui, Hanae El Akkad, Nabil Sidi Mohamed Ben Abdellah Univ Lab Engn Syst & Applicat ENSAF Fes Morocco

ISBN: (纸本)9783031686528;9783031686535

Exploring opinions from written or orally expressed data in sentiment classification has become an important decision-making tool that helps companies improve their profits using various machine-learning methods. In this article, we introduce a new method, termed the combined approach, for performing sentiment analysis on consumer opinions it uses a hybrid Artificial Neuron Network ANN algorithm which combines PSO swarm particle optimization and PB back propagation. The results obtained by the Artificial Neural Network model developed by the PSO-PBB algorithm show very good prediction, better performance, and convergence.

关键词： Sentiment Analysis Neural Network Backpropagation Particle Swarm Optimization

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Towards a KOS to Manage and Retrieve Legal Data 1

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11st World Conference on Information systems and Technologies (WorldCIST)

作者： Oliveira, Bruno Sousa, Cristovao Polytech Porto CIICESI ESTG Rua Curral P-4610156 Felgueiras Portugal INESC TEC Rua Dr Roberto Frias P-4200465 Porto Portugal

ISBN: (数字)9783031456459

ISBN: (纸本)9783031456442;9783031456459

Legislation is a technical domain characterized by highly specialized knowledge forming a large corpus where content is interdependent in nature, but the context is poorly formalized. Typically, the legal domain involves several document types that can be related. Amendments, past judicial interpretations, or new laws can refer to other legal documents to contextualize or support legal formulation. Lengthy and complex texts are frequently unstructured or in some cases semi-structured. Therefore, several problems arise since legal documents, articles, or specific constraints can be cited and referenced differently. Based on legal annotations from a real-world scenario, an architectural approach for modeling a Knowledge Organization System for classifying legal documents and the related legal objects is presented. Data is summarized and classified using a topic modeling approach, with a view toward the improvement of browsing and retrieval of main legal topics and associated terms.

关键词： Legal Knowledge Information Retrieval Ontology KOS

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Design and Implementation of a Secure Framework for Biometric Identification Based on Convolutional Neural Network Technique 2nd

Design and Implementation of a Secure Framework for Biometri...

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2nd International Conference on Forthcoming networks and Sustainability in the AIoT Era (FoNeS-AIoT)

作者： Najah, Tiba Abbas, Thekra Mustansiriyah Univ Coll Sci Dept Comp Baghdad Iraq

ISBN: (纸本)9783031628801;9783031628818

The implementation of robust authentication and identification methods has emerged as a pressing imperative to ensure the preservation of device integrity and the protection of sensitive data. Biometrics, especially electrocardiogram (ECG) technology, is presented as a promising solution due to its individualized and difficult-to-counterfeit nature. ECG signals offer advantages such as liveness detection and ubiquity. This study delves into ECG biometric recognition, leveraging Deep Learning advancements and employing wearable devices with ECG electrodes to ensure user-friendly scenarios. Samples were taken for 65 people, including males and females of varying ages. Six motivational cases were considered for each person. Subsequently, a model was constructed utilizing Sequential layers of a virtual convolutional neural network (CNN) and the Batch normalization layer, optimizing the training process for increased speed. The main contribution is a novel fingertip ECG identification system integrating Deep Learning and a deep convolutional neural network (CNN), trained on a large-scale database. The solution excels in speed and effectiveness, not requiring manual feature extraction or intricate model computations, and performs well with limited training data, achieving a good validation accuracy of 99.97%.

关键词： Biometric features counterfeiting Electrocardiogram (ECG) verify identity Deep Learning enhanced protection

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Automatic Detection of Optic Disc and Fovea from Colour Fundus Images

Automatic Detection of Optic Disc and Fovea from Colour Fund...

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Computing Conference

作者： Elena Martinez-Perez, M. Elze, Tobias Rauscher, Franziska G. Univ Nacl Autonoma Mexico Inst Invest Matemat Aplicadas & Sistemas Apdo 20-126 Ciudad De Mexico 01000 Mexico Harvard Med Sch Harvard Ophthalmol AI Lab Dept Ophthalmol Schepens Eye Res Inst Mass Eye & Ear Boston MA 02115 USA Univ Leipzig Inst Med Informat Stat & Epidemiol Haertelstr 16-18 D-04107 Leipzig Germany Univ Leipzig Med Ctr Dept Med Data Sci Med Informat Ctr Leipzig Germany

ISBN: (纸本)9783031622830;9783031622816;9783031622809

Diabetic Retinopathy (DR) is a chronic eye disease. More specifically, it is the manifestation of diabetes mellitus in the retina. DR is diagnosed by ophthalmoscopy or fundus imaging. Photographs of the retina are obtained using a fundus camera consisting of a low magnification microscope to which a digital camera is mounted. Three important anatomical areas are of great importance for the automated detection of DR: blood vessels, optic nerve head (ONH) region, and the central macular area termed fovea (fov). This work presents a methodology to detect and segment the ONH based on multispectral analysis and its amount of entropy, and a method to localise the fov centre based on level contour maps. As a pilot study, this methodology is tested using two different databases: APEC and MESSIDOR, with a total of 93 fundus images. Based on these we conclude that ONH and fov centres were successfully detected automatically, with low errors in average of 0.17% and 0.30% for ONH and fov respectively. An average p value of 0.5189 from a paired t-student test shows that the manual vs the automatic distance between ONH and fov centers is not statistically significantly different. These preliminary results show that the used methodology is promising. A more thorough evaluation has to be made using larger databases.

关键词： Optic disc Fovea Fundus photography Automatic detection

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Investigating the Impact of Artificial Intelligence on the Performance and Sustainability of Transportation in the Retail Industry: A Structural Equation Modeling Approach 7th

Investigating the Impact of Artificial Intelligence on the P...

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7th International Conference on Logistics Operations Management (GOL)

作者： Alhammadi, Aisha Cheaitou, Ali Alrhaimi, Salem Univ Sharjah Ind Engn & Engn Management Dept Sharjah U Arab Emirates Police Sci Acad Business Adm Sharjah U Arab Emirates

ISBN: (纸本)9783031686276;9783031686283

The recent advancements in artificial intelligence (AI) present new opportunities for improving the performance of various industries, including retail. In supply chain transportation, AI is being used to tackle challenges, including on-time deliveries, delivery flexibility, lead time, capacity utilization, shipping errors, and CO2 emissions. This study aims to explore the impact of adopting AI to improve transportation performance and sustainability. For this, structural equation modeling (SEM) was employed. We developed two hypotheses, created a conceptual model, and validated it through a survey with feedback from 73 respondents. It was clear that supply chain professionals in the consumer goods retail industry widely acknowledge the benefits of AI in enhancing transportation efficiency and sustainability. The model demonstrates AI's positive effect on transportation and sustainability measures. It has been found that the model can help businesses facilitate AI adoption in the consumer goods retail industry to improve transportation performance and sustainability in the United Arab Emirates.

关键词： Supply Chain Transportation Sustainability Artificial Intelligence

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A Fault Diagnosis Technique with the Combined DNN and CNN Using Motor Current Data 13th

A Fault Diagnosis Technique with the Combined DNN and CNN Us...

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13th Computer Science Online Conference

作者： Choi, YuRim Joe, Inwhee Hanyang Univ 222 Wangsimni Ro Seoul South Korea

ISBN: (纸本)9783031702846;9783031702853

With the global demand for energy efficiency and safety increasing, the need to monitor the condition of electric motors and diagnose faults is being emphasized. Motor failures have a significant impact on operational downtime, economy, and social trust, thereby making effective diagnostic methods crucial. Non-contact methods have been primarily used for fault diagnosis, while Motor Current Signal Analysis (MCSA) is a widely used fault detection method today because it can easily detect common mechanical defects such as rotor shorts, bar cracks/damages, and bearing degradation. In this study, we propose a new type of fault diagnosis method using an architecture that combines DNN and CNN. This technique deeply analyzes complex patterns in current data, extracts sophisticated features, and accurately determines faults by considering nonlinear and temporal characteristics. The experimental results show that our proposed method achieved the improved performance for motor faults compared to existing methods.

关键词： Motor Fault Diagnosis Predictive Maintenance DNN (Deep Neural Network) CNN (Convolutional Neural Network) Spectrum Domain Transformation Current Data Analysis

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Smart Agriculture: Machine Learning Approach for Tea Leaf Disease Detection 1

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5th International Conference on Communication and Intelligent systems (ICCIS)

作者： Rajagopal, Manikandan Sivasakthivel, Ramkumar Pandey, Megha Christ Univ Sch Business & Management Bangalore Karnataka India Christ Univ Sch Sci Dept Comp Sci Bangalore Karnataka India

ISBN: (数字)9789819720538

ISBN: (纸本)9789819720521;9789819720538

Across the globe, plant infections from pathogens such as fungi, bacteria and viruses are the major issues in the agricultural sector. Agricultural productivity is one of the most important things on which the nations' economy highly depends. The detection of diseases in plants plays a major role in the agricultural field. This study proposes a multi-stage network involving Convolutional neural network, Pattern identification and Classification using Siamese network. The main objective behind this study is to enhance the disease detection technique performance. The image data of Tea leaves chosen for this study will be gathered. The algorithms based on techniques of image processing would be designed. The proposed algorithm was tested on the following diseases namely Red rust, Blister blight, Twig dieback, Stem canker, Grey Blight, Brown Blight, Brown root rot disease and Red root rot disease in Tea leaves.

关键词： Machine learning Agriculture Tea leafs Convolutional neural network Pattern identification

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