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Human Emotions Classification Using EEG via Audiovisual Stimuli and AI

computers, Materials & Continua 2022年第12期73卷 5075-5089页

作者： Abdullah A Asiri Akhtar Badshah Fazal Muhammad Hassan A Alshamrani Khalil Ullah Khalaf A Alshamrani Samar Alqhtani Muhammad Irfan Hanan Talal Halawani Khlood M Mehdar Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajranSaudi Arabia Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and TechnologyTopi23460Pakistan Department of Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan Department of Software Engineering University of MalakandDir LowerPakistan Department of Computer Science and Information Systems College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajranSaudi Arabia

Electroencephalogram(EEG)is a medical imaging technology that can measure the electrical activity of the scalp produced by the brain,measured and recorded chronologically the surface of the scalp from the *** recorded signals from the brain are rich with useful *** inference of this useful information is a challenging *** paper aims to process the EEG signals for the recognition of human emotions specifically happiness,anger,fear,sadness,and surprise in response to audiovisual *** EEG signals are recorded by placing neurosky mindwave headset on the subject’s scalp,in response to audiovisual stimuli for the mentioned *** a bandpass filter with a bandwidth of 1-100 Hz,recorded raw EEG signals are *** preprocessed signals then further analyzed and twelve selected features in different domains are *** Random forest(RF)and multilayer perceptron(MLP)algorithms are then used for the classification of the emotions through extracted *** proposed audiovisual stimuli based EEG emotion classification system shows an average classification accuracy of 80%and 88%usingMLP and RF classifiers respectively on hybrid features for experimental signals of different *** proposed model outperforms in terms of cost and accuracy.

关键词： Electroencephalography emotion classification signal processing multilayer perceptron random forest

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Quantum Resnet18 for Classifying and Predicting the Maize Leaf Diseases

Quantum Resnet18 for Classifying and Predicting the Maize Le...

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2024 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence, RAEEUCCI 2024

作者： Kujani, T. Bhuvanya, R. Sathya, T. Arivubrakan, P. Usha, V. Thangaselvi, R. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Computer Science and Engineering Tamilnadu Chennai India Sri Ramachandra Institute of Higher Education and Research Sri Ramachandra Faculty of Engineering and Technology Tamilnadu Chennai India Sri Sairam Engineering College Computer Science and Business Systems Tamilnadu Chennai India

ISBN: (纸本)9798350354539

The agriculture industry contributes one-third of the worldwide Gross Domestic Product (GDP). Moreover, a substantial number of developing countries rely on their agricultural output as it offers job prospects for a considerable portion of the impoverished population. This necessitates the use of techniques to guarantee the precise and effective identification of plant diseases to minimize any detrimental consequences on the crops. Recently, the application of quantum-enhanced techniques to machine learning tasks including recommendation systems, classification, and regression has increased significantly. For this reason, this work suggests using the hybrid quantum-based Resnet18 transfer learning model to categorize and diagnose diseases that impact the leaves of maize plants. The dataset employed consists of four categories including Leaf Blight, Grey Leaf spots, Common Rust, and Healthy leaves. When compared to Resnet18 without quantum, this method outperforms it in terms of recall, accuracy, precision, and f1-score. The validation accuracy of 87% is achieved using the model. In the future, the proposed effort can serve as a practical tool to aid farmers in recognizing the diseases and safeguarding their maize crops. © 2024 IEEE.

关键词： Crops

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Classification of Helpful and Unhelpful Online Customer Reviews Using XLNet and BERT Variants 1

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International Conference on Green Energy, Computing, and Intelligent Technology, GEn-CITy 2023

作者： Bilal, Muhammad Arshad, Muhammad Haseeb Ramzan, Muhammad Department of Computing and Information Systems School of Engineering and Technology Sunway University Petaling Jaya Malaysia Department of Computer Science National University of Computer and Emerging Sciences Chiniot-Faisalabad Campus Chiniot Pakistan Department of Software Engineering University of Sargodha Sargodha Pakistan School of Electronics and Computer Science University of Southampton Malaysia Iskandar Puteri Malaysia

ISBN: (数字)9789819998333

ISBN: (纸本)9789819998326

The majority of businesses have made public appearances on various social media platforms as a result of recent advances in e-commerce and the popularity of social media websites. Customers can share their experiences in the form of online customer reviews, which can assist potential customers in determining the quality of a company and making purchasing decisions. Due to the large volume of published reviews, it becomes difficult for customers to read all of the reviews and assess the quality of the business, resulting in the problem of information overload. Several solutions have been proposed in the literature by researchers using statistical and machine learning techniques to predict the helpfulness of online customer reviews. However, most of the existing solutions are based on the use of various business, review, and reviewer features, which lead to generalizability issues. Moreover, very limited studies have examined the effectiveness of state-of-the-art pre-trained language models for the classification of helpful and unhelpful reviews. Therefore, this study aims to examine the effectiveness of XLNet, Albert, DistilBert, and Roberta for review helpfulness prediction using textual features. The models were fine-tuned using a publicly available dataset of Yelp reviews. The results showed that XLNet achieved the highest F1-score of 0.730 compared to a benchmark of 0.717 achieved by the BERT base model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Sales

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The Pseudo-Reachability Problem for Diagonalisable Linear Dynamical systems 47

The Pseudo-Reachability Problem for Diagonalisable Linear Dy...

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47th International Symposium on Mathematical Foundations of computer science, MFCS 2022

作者： D'Costa, Julian Karimov, Toghrul Majumdar, Rupak Ouaknine, Joël Salamati, Mahmoud Worrell, James Department of Computer Science University of Oxford United Kingdom Max Planck Institute for Software Systems Saarland Informatics Campus Germany Max Planck Institute for Software Systems Kaiserslautern Germany

ISBN: (纸本)9783959772563

We study fundamental reachability problems on pseudo-orbits of linear dynamical systems. Pseudoorbits can be viewed as a model of computation with limited precision and pseudo-reachability can be thought of as a robust version of classical reachability. Using an approach based on o-minimality of Rexp we prove decidability of the discrete-time pseudo-reachability problem with arbitrary semialgebraic targets for diagonalisable linear dynamical systems. We also show that our method can be used to reduce the continuous-time pseudo-reachability problem to the (classical) time-bounded reachability problem, which is known to be conditionally decidable. © 2022 Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. All rights reserved.

关键词： Computability and decidability

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Comparison of Missing Data Imputation Methods in Time Series Forecasting

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computers, Materials & Continua 2022年第1期70卷 767-779页

作者： Hyun Ahn Kyunghee Sun Kwanghoon Pio Kim Division of Software Convergence Hanshin UniversityGyeonggi18101Korea Contents Convergence Software Research Institute Kyonggi UniversityGyeonggi16227Korea Division of AI Computer Science and Engineering Kyonggi UniversityGyeonggi16227Korea

Time series forecasting has become an important aspect of data analysis and has many real-world ***,undesirable missing values are often encountered,which may adversely affect many forecasting *** this study,we evaluate and compare the effects of imputationmethods for estimating missing values in a time *** approach does not include a simulation to generate pseudo-missing data,but instead perform imputation on actual missing data and measure the performance of the forecasting model created *** an experiment,therefore,several time series forecasting models are trained using different training datasets prepared using each imputation ***,the performance of the imputation methods is evaluated by comparing the accuracy of the forecasting *** results obtained from a total of four experimental cases show that the k-nearest neighbor technique is the most effective in reconstructing missing data and contributes positively to time series forecasting compared with other imputation methods.

关键词： Missing data imputation method time series forecasting LSTM

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Three-dimensional reconstruction of industrial parts from a single image

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Visual Computing for Industry,Biomedicine,and Art 2024年第1期7卷 340-351页

作者： Zhenxing Xu Aizeng Wang Fei Hou Gang Zhao School of Mechanical Engineering and Automation Beihang UniversityBeijing 100191China Key Laboratory of Aeronautics Smart Manufacturing Beihang UniversityBeijing 100191China State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China

This study proposes an image-based three-dimensional(3D)vector reconstruction of industrial parts that can gener-ate non-uniform rational B-splines(NURBS)surfaces with high fidelity and *** contributions of this study include three parts:first,a dataset of two-dimensional images is constructed for typical industrial parts,including hex-agonal head bolts,cylindrical gears,shoulder rings,hexagonal nuts,and cylindrical roller bearings;second,a deep learning algorithm is developed for parameter extraction of 3D industrial parts,which can determine the final 3D parameters and pose information of the reconstructed model using two new nets,CAD-ClassNet and CAD-ReconNet;and finally,a 3D vector shape reconstruction of mechanical parts is presented to generate NURBS from the obtained shape *** final reconstructed models show that the proposed approach is highly accurate,efficient,and practical.

关键词： Three-dimensional reconstruction Non-uniform rational B-splines Industrial parts Deep learning

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CapsiScan: Autonomous Robotic System for Monitoring and Managing Greenhouse-Based Capsicum Farms 5

CapsiScan: Autonomous Robotic System for Monitoring and Mana...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Jayakody, E.R.T. Rabel, W.T.T. Estrop, R.D.V. Herath, K.G.L.P. Harshanath, S.M.B. Abeygunawardhana, Pradeep K. W. Sri Lanka Institute Information Technology Dept. of Computer Systems Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept. of Software Engineering Colombo Sri Lanka

ISBN: (纸本)9798350358131

The automation of processes in greenhouses provides a significant advantage to the agricultural sector. Due to the rapid growth of the population, increasing demand for food and labor shortage make the autonomous monitoring and management of agricultural work more important. Capsicum is a largely consumed vegetable in South Asia, which can be efficiently grown in greenhouses. Although there are many capsicum farms, they lack quality and efficient production to match the increasing demand. This paper proposes a robotic system for use in capsicum greenhouses that can navigate and monitor the plants to identify diseases, track the yield, and take remediation actions. The robot navigates to each plant in the greenhouse autonomously by using the SLAM approach with the use of laser range sensor data. The 4 DoF robotic arm of the robot has a camera and a sprayer attached to its end to investigate plants and spray remedies to diseased plants. The robotic arm moves around the plant to capture the images to detect deceases and count the pods to estimate the yield. The sprayer of the robot sprays remedies precisely to targeted plants to control pests and diseases. The system was successfully implemented and tested with the proposed functions. © 2023 IEEE.

关键词： Greenhouses

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A Unified Information Diffusion Prediction Model Based on Multi-task Learning 9th

A Unified Information Diffusion Prediction Model Based on M...

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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Shang, Yingdan Zhou, Bin Zeng, Xiang Chen, Kai College of Computer Science and Technology National University of Defense Technology Changsha China Key Lab of Software Engineering for Complex Systems School of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9783031466731

The prediction of online information diffusion trends on social networks is crucial for understanding people’s interests and concerns, and has many real-world applications in fields such as business, politics and social security. Existing research on information diffusion prediction has predominantly focused on either macro level prediction of the future popularity of online information or micro level user activation prediction. However, information diffusion prediction on micro or macro level only may lead to one-sided prediction results, and there is a lack of research on implementing micro and macro level diffusion prediction tasks at the same time. Since micro and macro level diffusion prediction tasks are related to each other, we propose a unified information diffusion model which can jointly predicting the micro level user activation probability and macro level information popularity based on multi-task learning framework. We utilize graph neural network to learn user representation from both the information cascades and social network structure. Comparing with micro and macro level baseline methods separately, the prediction results of our proposed model outperform all baseline methods and proves the effectiveness of the proposed method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Forecasting

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GPDCCL: Cross-Domain Named Entity Recognition with Span-Based Domain Confusion Contrastive Learning 9th

GPDCCL: Cross-Domain Named Entity Recognition with Span-Base...

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9th International Conference of Pioneering computer Scientists, Engineers and Educators, ICPCSEE 2023

作者： Wang, Ye Shi, Chenxiao Li, Lijie Guo, Manyuan College of Computer Science and Technology Harbin Engineering University Harbin China Harbin Institute of Technology Software Engineering Co. Ltd. Harbin China

ISBN: (纸本)9789819959709

The goal of cross-domain named entity recognition is to transfer models learned from labelled source domain data to unlabelled or lightly labelled target domain datasets. This paper discusses how to adapt a cross-domain sentiment analysis model to the field of named entity recognition, as the sentiment analysis model is more relevant to the tasks and data characteristics of named entity recognition. Most previous classification methods were based on a tokenwise approach, and this paper introduces entity boundary information to prevent the model from being affected by a large number of nonentity labels. Specifically, adversarial training is used to enable the model to learn domain-confusing knowledge, and contrastive learning is used to reduce domain shift problems. The entity boundary information is transformed into a global boundary matrix representing sentence-level target labels, enabling the model to learn explicit span boundary information. Experimental results demonstrate that this method achieves good performance compared to multiple cross-domain named entity recognition models on the SciTech dataset. Ablation experiments reveal that the method of introducing entity boundary information significantly improves KL divergence and contrastive learning. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

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Strengthening Authentication: The Role of Multi-Level Hashing in OSPA Protocol 5

Strengthening Authentication: The Role of Multi-Level Hashin...

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5th International Conference on IoT Based Control Networks and Intelligent systems, ICICNIS 2024

作者： Hariharasudhan, S. Varghese, Vineetha Malarvizhi, K. Santhoshkumar, S.P. Nalini, T. Vikram, D. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai Avadi India Rathinam Technical Campus Department of Computer Science and Engineering Coimbatore India Jct College of Engineering and Technology Department of Computer Science Al Business Systems Coimbatore India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai Avadi India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Saveetha University Department of Computer Science and Engineering Chennai India Rathinam Technical Campus Department of Artificial Intelligence Data Science Coimbatore India

ISBN: (纸本)9798331518097

Authentication is a crucial step in the cyber security process that confirms user identities. Even though they are widely used, traditional password based techniques are frequently vulnerable to attacks like guessing and password theft. This study presents the Optimal Strong Password Authentication (OSPA) protocol, which uses USB sticks to provide safe user authentication in order to address these issues. The Multi-Level Hashing Algorithm (MLHA), which dynamically picks hash functions during authentication to generate many layers of hashed passwords, is a key component of OSPA's effectiveness. By preventing eavesdropping efforts and reducing the possibility of unwanted access through guessing attacks, this method strengthens security. This protocol provides strong authentication procedures, protecting confidential data and improving overall cyber security posture by integrating MLHA into the OSPA architecture. © 2024 IEEE.

关键词： Authentication Protocol

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