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Smart Happy Device with IoT : For those who suffer anxiety, stress, and depression 12

Smart Happy Device with IoT : For those who suffer anxiety, ...

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12th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2024

作者： Yakub, Fitri Azizan, Azizul Dhamanti, Inge Khudzari, Ahmad Zahran Md. Govindan, Niranjana Muljono, Muljono UTM Malaysia-Japan International Institute of Technology Electronic System Engineering Department Kuala Lumpur Malaysia Universiti Teknologi Malaysia Cyberphysical Research Group Embedded AI & ML Science Lab Faculty of Artificial Intelligence Kuala Lumpur Malaysia Universitas Airlangga Faculty of Public Health Department of Health Policy and Administration Surabaya Indonesia UTM Faculty of Engineering Department of Biosciences & Medical Engineering Johor Bahru Malaysia SRMIST Faculty of Engineering & Technology Department of Computing Technologies Chennai India Universitas Dian Nuswantoro Faculty of Computer Science Semarang Indonesia

ISBN: (纸本)9798350340204

Due to the rising cases of Covid-19 in the world especially in Malaysia, the impact had been hit hard for many people. Due to this all people must be isolated in their home. It has affected the well-being and working life of millions of people across the world. Due to this disease, the mental health among people has increased significantly for the past 2 years. According to MoH, out of 983 Malaysian students, about 20.4%, 6.6%, and 2.8% are experienced minimal to moderate, marked to severe, and most extreme levels of anxiety, respectively. The main stressors are the remote online teaching, and uncertainty about the future regarding study and career which can affect mental health. Thus, the main objective of this project is to develop a product that may provide different medication methods to its users by helping people who suffer anxiety, stress, and depression. We used aromatherapy, positive quotes, and music therapy in order to release stress. The user can also check his or her pulse rate by only touching the fingerprint through the sensor. The microcontrollers used in this project are Arduino Uno and Arduino Mega to build the project circuit. The experimental results can improve body health, mind and spirit which enhances both physical and emotional health. © 2024 IEEE.

关键词： COVID-19

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Authentication of Multi-agent System with Verifiable Computation and Distributed Aggregation 63

Authentication of Multi-agent System with Verifiable Computa...

引用

63rd IEEE Conference on Decision and Control, CDC 2024

作者： Lee, Seungbeom Kim, Dongwoo Chung, Heewon Kim, Junsoo Shim, Hyungbo Seoul National University Asri Department of Electrical and Computer Engineering Korea Republic of Dongguk University Department of Ai · Sw Convergence Korea Republic of Desilo Inc Korea Republic of Seoul National University of Science and Technology Department of Electrical and Information Engineering Korea Republic of

ISBN: (纸本)9798350316339

We propose an authentication scheme for a multiagent system over integers, based on verifiable computation primitives. The naive approach, employing Freivalds' algorithm in centralized way, faces several challenges. Specifically, unreliability of the network introduces the risk of information tampering by other agents. To this end, it requires locally updating and merging the proofs of the individual states in a distributed manner. Our proposed method addresses these issues with assuming presence of a leader agent who is responsible for validating the correctness of all the states of the agents. This can be achieved by a distributed protocol that aggregates proofs from the individual agents, relying on the well-known knowledge-of-exponent assumption. By using this distributed protocol, computational complexity and communication cost are reduced compared to centralized verification. Furthermore, we incorporate a clustering technique to distribute storage requirements. © 2024 IEEE.

关键词： Authentication Protocol

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Oppositional Crow Search Algorithm Based Artificial Neural Network for Epileptic Seizure Classification

Oppositional Crow Search Algorithm Based Artificial Neural N...

引用

2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Swetha, K.R. Gayatri, P. Kumar, E. Shweta, N. Sundaram, S. Meenakshi Adichunchanagiri University Bgs Institute of Technology Dept of Cse India Malla Reddy Engineering College for Women Autonomous India Cmr Engineering College Department of Computer Science and Engineering Hyderabad India Cmr Technical Campus Department of Computer Science and Engineering Hyderabad India Nitte Meenakshi Institute of Technology Department of Ai and Ds Bengaluru India

ISBN: (纸本)9798350313413

The Electroencephalogram (EEG) is a widely used diagnostic test for identifying epilepsy by analyzing the electrical activity in a person's brain. Epilepsy poses a significant social challenge due to its unpredictable nature, high treatment costs, and the urgent need for improved diagnostic methods. In this context, a novel approach is proposed namely, Oppositional Crow Search Algorithm (OCSA)-based Artificial Neural Network (ANN) for classifying epileptic seizure disorders. Data is gathered from the CHB-MIT dataset, subjected to signal pre-processing, and enhanced through Empirical Mode Decomposition (EMD) for increased training data volume. Features are extracted, including entropy, Hjorth parameters, fractal dimension, as well as Hurst exponent. Subsequently, the proposed OCSA-ANN method is applied to classify epileptic seizures through a training and testing process. The results demonstrate that the OCSA-ANN approach outperforms existing methods, achieving a remarkable sensitivity of 96.71%, an accuracy rate of 89.88%, and a specificity of 89.88%. © 2023 IEEE.

关键词： Artificial Neural Network Electroencephalogram Empirical Mode Decomposition Epileptic seizure Oppositional Crow Search Algorithm

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Empowering Fake News Detection Through Innovative Hybrid Deep Learning-Based Approach

Empowering Fake News Detection Through Innovative Hybrid Dee...

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Computing for Sustainability and Intelligent Future (COMP-SIF), International Conference on

作者： A Suresh R M Mallika S Gireesh G Hari Kiran Singh E S Jeevanandham A Hemalatha Department of Computer Science and Engineering Siddharth Institute of Engineering & Technology Puttur India Department of Computer Science and Engineering (AI&DS) Siddharth Institute of Engineering & Technology Puttur India

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538545

The essence of getting information to the populace in the shortest time possible through social media and other interactive technologies has made it easier for fake news to thrive hence creating a menace to any society as well as its decision-making systems. This research work presents a sound ai-based approach to classify fake news using DL algorithms embedded within a CNN-BiLSTM model. Using the ISOT dataset, the comparison of the outcomes achieved by the proposed model with the original models shows that the proposed model has the highest accuracy score of 94.07%, a precision of 93.50%, a recall of 94.60%, and an F1-score of 94.00%. The proposed hybrid model architecture of CNN for spatial feature extraction and BiLSTM for capturing contextual dependencies that are essential for text analysis and have relatively high accuracy compared to conventional ml algorithms, including CNN, RNN, LSTM, and other DL methods. The proposed approach is verified by a set of comprehensive measures, including ROC curve analysis and various training-validation metrics that reveal the effectiveness of the presented approach in overcoming the challenges of fake news identification.

关键词： Fake news Accuracy Feature extraction Bidirectional long short term memory Convolutional neural networks Data models computer science Vectors Computational modeling Adaptation models

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A Machine Learning Model for the Early Prediction of Cardiovascular Disease in Patients

A Machine Learning Model for the Early Prediction of Cardiov...

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Advances in Computational Intelligence and Communication (ICACIC), International Conference on

作者： Abhishek Hutashan Vishal Bhagat Manminder Singh Department of Computer Science and Engineering Sant Longowal Institute of Engineering and Technology Sangrur Department of AI &ML (CSE) B V Raju Institute of Technology Hyderabad India

Cardiovascular disease is a major health concern worldwide, with an estimated 17.9 million deaths each year. It encompasses a range of conditions affecting the heart and blood vessels, including coronary heart disease, stroke, and heart failure. These diseases are often associated with a range of modifiable and non-modifiable risk factors, including high blood pressure, high cholesterol, smoking, physical inactivity, obesity, diabetes, and genetic predisposition. Data analysis and machine learning have emerged as powerful tools for predicting and preventing cardiovascular disease. By analysing large and complex datasets of medical records, researchers can identify patterns and risk factors associated with cardiovascular disease. Machine learning models can help identify important features in the data that are predictive of heart disease outcomes, and can be used to develop accurate and reliable predictive models. Moreover, data analysis can help identify disparities and biases in cardiovascular disease outcomes across different populations and regions. This research aims to develop a machine-learning model for predicting cardiovascular disease risk using benchmark datasets. Various imputation techniques, including Mean, Median, Most Frequent, KNNI, and the proposed risk prediction model, are compared in terms of accuracy and efficiency. The CatBoost classification algorithm is used for developing the proposed model, achieving an accuracy of 91% for the Hungarian dataset. This research provides valuable insights into the use of machine learning and data analysis for predicting cardiovascular disease and improving healthcare outcomes.

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Breast Cancer Prediction using Feature Selection and Classification with XGBoost

Breast Cancer Prediction using Feature Selection and Classif...

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Recent Trends in Electronics and Communication (ICRTEC), International Conference on

作者： Raye Haarika Tina Babu Rekha R Nair Rajesh T M Department of Computer Science and Engineering (AI & ML) School of Engineering Dayananda Sagar University Bengaluru India Department of Computer Science and Engineering School of Engineering Dayananda Sagar University Bengaluru India Department of Computer Applications School of Engineering Dayananda Sagar University Bengaluru India

As a result of high population growth, medical research and early sickness identification has become a responsibility. As the inhabitants grows, the likelihood of succumbing to breast cancer rises. A illness detection system that's automated reduces the danger of mortality, provides dependable, efficient, and rapid reaction, and assists doctors in disease diagnosis. to reinforce the predictive efficacy of current models and To estimate the risk of breast cancer, proposed model used Extreme Gradient Boosting (XGBoost) and compared it to Logistic Regression (LR), The Random Forest (RF), and Support Vector Machines (SVM). In this work, target picking the most weighted features using the XGBoost method and training the model with those features was performed. This approach yielded the identical accuracy as training the model with all features, however training the model with all features takes longer. The suggested approach has produced findings that are comparable and may help radiologists provide opinions

关键词： Training Support vector machines Costs Sociology Predictive models Breast cancer Reliability

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Modified Glowworm Swarm Optimization Algorithm based on Task Scheduling for Cyber-Physical System 2

Modified Glowworm Swarm Optimization Algorithm based on Task...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Anitha, D. Al-Farouni, Mohammed Shilpa, N. Revanasiddappa Lekshmy, P.L. Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Kattankulathur India The Islamic University Najaf Iraq Sr University School of Engineering Department of Ece Telangana India Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India Lbs Institute of Technology for Women Department of CSE Trivandrum India

ISBN: (纸本)9798350317558

The extensive application of Cyber-Physical Systems (CPS) requires efficient optimization of computational units as well as physical plant. Task scheduling (TS) is serious to enlightening resource utilization and system efficacy within CPS. Standard task schedulers in embedded real-time systems fall short of achieving CPS performance criteria due to task diversity and system heterogeneity. This research proposes a novel task-scheduling technique called Multi objective based Modified Glowworm Swarm Optimization algorithm (MMGSO) for TS that is specifically designed for the CPS context. MMGSO involves assigning number of autonomous tasks to various resources during the TS process. The objective function focuses on reducing task completion time and optimizing resource use. A series of simulations were run to demonstrate MMGSO improved performance. The comparison analysis demonstrated that the MMGSO system performed better across a variety of parameters. The proposed approach attains the better performance in Task Execution (ms) of 347ms and Average Load Balancing Ratio (%) of 69.91% from 100 iterations. © 2024 IEEE.

关键词： Cyber-Physical Systems Glowworm Swarm Optimization Multi-Objective Task Diversity Task Scheduling

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NeuKron: Constant-Size Lossy Compression of Sparse Reorderable Matrices and Tensors 23

NeuKron: Constant-Size Lossy Compression of Sparse Reorderab...

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2023 World Wide Web Conference, WWW 2023

作者： Kwon, Taehyung Ko, Jihoon Jung, Jinhong Shin, Kijung Kim Jaechul Graduate School of Ai Korea Advanced Institute of Science and Technology Seoul Korea Republic of Department of Computer Science and Engineering Jeonbuk National University Jeonju Korea Republic of

ISBN: (纸本)9781450394161

Many real-world data are naturally represented as a sparse reorderable matrix, whose rows and columns can be arbitrarily ordered (e.g., the adjacency matrix of a bipartite graph). Storing a sparse matrix in conventional ways requires an amount of space linear in the number of non-zeros, and lossy compression of sparse matrices (e.g., Truncated SVD) typically requires an amount of space linear in the number of rows and columns. In this work, we propose NeuKron for compressing a sparse reorderable matrix into a constant-size space. NeuKron generalizes Kronecker products using a recurrent neural network with a constant number of parameters. NeuKron updates the parameters so that a given matrix is approximated by the product and reorders the rows and columns of the matrix to facilitate the approximation. The updates take time linear in the number of non-zeros in the input matrix, and the approximation of each entry can be retrieved in logarithmic time. We also extend NeuKron to compress sparse reorderable tensors (e.g. multi-layer graphs), which generalize matrices. Through experiments on ten real-world datasets, we show that NeuKron is (a) Compact: requiring up to five orders of magnitude less space than its best competitor with similar approximation errors, (b) Accurate: giving up to 10 × smaller approximation error than its best competitors with similar size outputs, and (c) Scalable: successfully compressing a matrix with over 230 million non-zero entries. © 2023 ACM.

关键词： Matrix algebra

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A Classification Technique for Online Shoppers' Purchasing Intention 1

A Classification Technique for Online Shoppers' Purchasing I...

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1st International Conference on Logistics, ICL 2024

作者： Mostafa, Salama A. Al-Dayyeni, Wisam Subhi Fadel, Ali Hussein Abbas, Ali Hashim Jaber, Mustafa Musa Ali, Rabei Raad Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Johor86400 Malaysia School of Information Technologies and Engineering ADA University BakuAZ1008 Azerbaijan University of Diyala Department of Computer Science Diyala32001 Iraq Imam Ja'afar Al-Sadiq University Department of Computer Engineering and Information Technology AI-Muthanna Iraq Al-Farahidi University Department of Medical Instruments Engineering Techniques Baghdad10021 Iraq Northern Technical University Department of Computer Engineering Technology Mosul41000 Iraq

ISBN: (纸本)9798331518844

Online shopping has become a recent outburst using electronic devices that allow consumers to shop online, as there are factors that influence shopping intention. The factors include consumers' attitudes, subject norms, behavior control, perception of usefulness, considered risks, and trust. This study aims to classify the intent of online consumers' shopping. Subsequently, based on these measures, this classification can provide fundamental and prioritized suggestions for performance improvements in online shopping. The classification is performed by using four data mining algorithms, which are random forest (RF), decision tree (DT), support vector machines (SVMs), and k-nearest neighbors (k-NN). Classification of online shoppers has been implemented based on the online shopping behavior dataset and critical performance metrics, including accuracy, precision, recall, and F1-measure. Essential factors have been considered in the online shopping behavior dataset that was used. The dataset has been preprocessed to check missing values and change the data to an appropriate numerical type. Then, the classification algorithms are applied to extract the required results. The results of detecting consumer shopping behavior showed that RF has outperformed other algorithms and achieved an average accuracy of 90.24%. The average classification accuracies of the DT, SVM, and k-NN are 89.59%, 89.38%, and 87.06%, respectively. The result of this work is hoped to assist in identifying online shoppers' behavior categories in practical aspects. © 2024 IEEE.

关键词： Support vector machines

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Automated Online Examination System using Hybrid Particle Swarm Optimization and Grey Wolf Optimization Algorithm 2

Automated Online Examination System using Hybrid Particle Sw...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Rana Veer Samara Sihman Bharattej, R. Rupavath, Rana Veer Samara Sihman Brahmatej Ramadan, Ghazi Mohamad Khanam, P Katheeja Sharma, Neeraj Saint Leo University Department of Computer Science St Leo United States Indiana Wesleyan University Marion United States Ahl Al Bayt University College of Mlt Karbala Iraq Mohan Babu University School of Computing Dept. of Cse Tirupati India Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Department of Ai&ml Hyderabad India

ISBN: (纸本)9798350372892

Artificial intelligence has been successfully used in many fields, conserving an enormous quantity of money and human resources. System of intelligent examination is a common application case that enables teachers to develop further research plans with the examination system's assistance along with analyzing each candidate's study conditions at any given time. This work implements a hybrid particle swarm optimization with grey wolf optimization (HPSOGWO) based self-optimization examination system. Two challenges that the traditional examining system overcame in its development can be addressed by the intelligent examination system: maintaining the examination question database and identifying attributes of examination questions. Data pre-processing is performed by utilizing data cleaning, and data integration for the evaluation of filtered text effectively. To prevent generating challenge an examination paper, a mathematical model is performed. The experiment demonstrate that the new approach can intelligently optimize the properties of exam questions in database and efficiently maintain examination database through extensive training. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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