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3rd International Conference on Digital Transformation and Applications, ICDXA 2024

作者： Lee, Nicholas Mahmud, Rohana Universiti Malaya Faculty of Computer Science and Information Technology Kuala Lumpur Malaysia

ISBN: (纸本)9798350373424

The role of automatic generated playlists and recommendations in music streaming services has played a significant role in driving the steady growth of the music industry since 2015. With the evolving landscape of music, Music Emotion Recognition (MER) has gained importance in this era. Contemporary solutions today focus solely on four emotions and popular music genres, neglecting emerging styles and artistes where their music evokes a danceable rhythm, but lyrics convey negativity. This study considers both lyrics and audio features for the classification of eight emotions with the aim to identify key features influencing music emotion, develop a classification model and evaluate its performance. A dataset was created by expanding a base dataset with additional lyrics and audio features from Genius and Spotify API respectively. This study employs established algorithms like K-Nearest Neighbours (KNN), Decision Trees (DT), and Random Forests (RF), with Random Forests emerging with a notable 73% accuracy. A web application named Emotion was developed, allowing data scientists and music researchers to explore the dataset and access details of this study. This study's impact extends to various applications, including mental health and media, enabling tailored music-based interventions for emotional well-being, and improving music selection in media and entertainment. © 2024 IEEE.

关键词： Music

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Blockchain-Enabled Data Model for Medical Diagnoses: Supporting Physician Collaboration 17

Blockchain-Enabled Data Model for Medical Diagnoses: Support...

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17th IEEE International Scientific Conference on Informatics, INFORMATICS 2024

作者： Siedlecka-Lamch, Olga Czestochowa University of Technology Faculty of Computer Science and Artificial Intelligence Czestochowa Poland

ISBN: (纸本)9798350387674

In the dynamic healthcare setting, accurate and timely medical diagnoses are crucial. This paper presents a novel hybrid database model optimized for medical applications, facilitating diagnostics by a consortium of physicians. Combining traditional relational databases with blockchain technology, the model ensures a secure, immutable record of medical decisions. Featuring a unique voting algorithm, it allows physicians to collectively validate diagnostic decisions on the blockchain, creating a transparent, tamper-proof data management system. This hybrid approach not only improves the reliability of patient treatment, but also promotes physician collaboration in complex diagnostics. The ongoing work will focus on refining the model and exploring its practical implementation in clinical settings. © 2024 IEEE.

关键词： Electronic health record

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Harmful Brain Activity Classification Using Ensemble Deep Learning 66

Harmful Brain Activity Classification Using Ensemble Deep Le...

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66th International Symposium ELMAR, ELMAR 2024

作者： Habijan, Marija Sojo, Robert Tolic, Ivana Hartmann Galic, Irena Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (纸本)9798350375411

Electroencephalography (EEG) is a crucial tool for monitoring electrical brain activity and diagnosing neurological conditions. Manual analysis of EEG signals is time-consuming and prone to variability, necessitating automated methods for accurate and efficient interpretation. This paper presents an ensemble learning approach for the automated classification of harmful brain activity from EEG data. The model combines the strengths of ResNet1d GRU, EfficientNetB0, and EfficientNetB1 to predict brain activity from spectrograms and raw EEG signals. By leveraging the unique capabilities of each model and employing a weighted averaging scheme, the ensemble achieves a competitive Kullback-Liebler divergence score of 0.3 on the testing dataset. This approach demonstrates the potential of ensemble learning to improve the accuracy and robustness of automated EEG signal classification, offering a valuable tool for early detection and diagnosis of neurological conditions. © 2024 IEEE.

关键词： Contrastive Learning

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Obstacle Detection Using Arduino Board and Bluetooth Control 8th

Obstacle Detection Using Arduino Board and Bluetooth Control

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8th International Conference on Information and Communication technology for Competitive Strategies, ICTCS 2023

作者： Pavitha, N. Dardige, Rohit Patil, Vaibhav Pawar, Ameya Shah, Bhavesh Department of Computer Engineering Faculty Science and Technology Vishwakarma University Pune India

ISBN: (纸本)9789819994885

An intelligent robotic vehicle with an ultrasonic sensor that can avoid obstacles in its path is the research idea. This sensor recognizes obstructions, permitting the vehicle to perform activities like halting, turning right or left, and going in reverse. The essential objective is to make a robot vehicle that can work independently or under client direction. The vehicle responds to avoid collision when the ultrasonic sensor detects an obstruction. The vehicle's development incorporates an Arduino UNO microcontroller, ultrasonic sensor, engines, and other electric parts. The microcontroller, which serves as the vehicle's brain, receives information about objects in the vehicle's path from the ultrasonic sensor. After that, the microcontroller issues commands to a servo motor that is connected to the wheels. This ensures that the vehicle comes to a stop in order to avoid colliding with anything. Moreover, the vehicle's usefulness incorporates Bluetooth availability for control. Bluetooth allows users to control the vehicle from a distance and even use voice commands to do so. The joining of a Bluetooth module empowers these control choices, upgrading the vehicle's flexibility and connection prospects. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Bluetooth

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Abnormal Corner of Mouth Fall Detection of Stroke Patient Using Camera 1

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19th International Conference on Computing and Information technology, IC2IT 2023

作者： Thirapanmethee, Piya Tancharoen, Jirayu Sae-Tang, Khananat Bootchai, Nilubon Nutphadung, Sirion Patsadu, Orasa Computer Science Faculty of Science and Technology Rajamangala University of Technology Krungthep Bangkok Thailand

ISBN: (数字)9783031304743

ISBN: (纸本)9783031304736

This paper proposes method to detect abnormal corner of mouth fall in stroke patient using camera. There are four steps of our proposed method. First, sample subject's face is captured. Next, face image is extracted for coordinate using image processing to obtain all coordinates. Then, appropriate coordinates are selected to compute degree of mouth corner (left and right). Finally, Multilayer Perceptron and Decision Tree are used to build model. From our experiment, the result shows that our proposed method for abnormality detection of corner of mouth fall in stroke patient using Decision Tree and Multilayer Perceptron achieve accuracy of 97.06% and 96.66%, respectively. From comparison of performance, Decision Tree achieves higher accuracy than Multilayer Perceptron. So, Decision Tree model is used to develop the prototype system for abnormality detection of corner of mouth fall in stroke patient. When the system detects abnormal corner of mouth fall in stroke patient, the system will send immediate notification to caregiver. Therefore, our proposed system could be the primary assistance, especially, in case the caregiver is not with the patient. So, caregiver can perceive symptoms and can make decisions in timely manner to refer a patient for further treatment. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision trees

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Recent metaheuristic algorithms for medical object localization using MSER detector in computer-aided diagnosis system

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Multimedia Tools and Applications 2025年第15期84卷 14433-14487页

作者： Ait Mehdi, Mohamed Belattar, Khadidja Souami, Feryel Faculty of Computer Science Data Science and Artificial Intelligence Department Laboratory for Research in Artificial Intelligence University of Science and Technology Houari Boumediene BP 32 El-Alia Algiers Bab-Ezzouar16111 Algeria Department of Computer Science University of Algiers Benyoucef Benkhedda Algiers16000 Algeria

Object localization is a critical task in image analysis, often facilitated by artificial intelligence techniques. While the Maximally Stable Extremal Regions (MSER) detection algorithm is a popular choice for local detection, its exhaustive approaches can be algorithmically complex and prone to suboptimal results with improper parameter selection. Various metaheuristic algorithms have been proposed for medical object localization to address this. In this context, four contributions are presented. Firstly, recent metaheuristics, including the Slime Mould Algorithm (SMA), Marine Predators Algorithm (MPA), Heap-based Optimizer (HBO), and Gradient-based Optimizer (GBO), are adapted to tackle the MSER localization problem. These algorithms are rigorously evaluated across diverse medical image datasets using multiple metrics, with their performance statistically validated through the Friedman mean rank test. The second contribution introduces a novel objective function to improve the localization process. The third contribution involves a comparative analysis of the recent algorithms against seven standard metaheuristics specifically designed for MSER localization. Lastly, we present an improved computer-aided diagnosis system that integrates an SVM-based model with Local Binary Patterns (LBP) descriptors extracted from each MSER alongside DenseNet-121 features. Experimental results demonstrate that the HBO and SMA optimizers outperform conventional algorithms, showing a higher diversity, improved exploitation and exploration capabilities, a well-balanced exploration-exploitation trade-off, elevated fitness values, faster convergence speeds, and enhanced computational efficiency. Additionally, we integrate these findings into an improved computer-aided diagnosis system, yielding classification results surpassing state-of-the-art models. These findings highlight the potential advantages of this approach, especially in object detection applications. © The Author(s), under

关键词： Object detection

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Behavior Based Group Recommendation from Social Media Dataset by Using Deep Learning and Topic Modeling

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SN computer science 2024年第6期5卷 712页

作者： Mukta, Md. Saddam Hossain Ahmed, Jubaer Raiaan, Mohaimenul Azam Khan Fahad, Nur Mohammad Islam, Muhammad Nazrul Imtiaz, Nafiz Islam, Md. Adnanul Ali, Mohammed Eunus Azam, Sami LUT School of Engineering Sciences Lappeenranta-Lahti University of Technology Department of Computer Science and Engineering United International University Department of Computer Science and Engineering Military Institute of Science and Technology Department of HCC Action Lab Monash University Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Faculty of Science and Technology Charles Darwin University

In this digital era, users frequently share their thoughts, preferences, and ideas through social media, which reflect their Basic Human Values. Basic Human Values (aka values) are the fundamental aspects of human behavior, which define what we consider important, and worth having and pursue them. Existing studies identify the values of individuals from different social network usages such as Facebook and Reddit. However, discovering the similarity (or diversity) of value priorities among the members in a group is important since we can reveal many interesting insights such as finding a set of target customers, identifying the chain of misdeed groups, searching for similar acquaintances in workplaces, etc. In this paper, a graph dataset is compiled using the strongest correlation among the features and then we apply a graph clustering technique to identify a suitable hedonist group (i.e., one dimension of values) for users’ recommendations. Then, we also propose a behavior based (i.e., value) group recommendation technique by analyzing users’ contextual and psychological attributes. Finally, we validate those group members in real life by introducing two hypotheses. In particular, we analyze the tweets of a total of 1140 users collected from Twitter. We obtain a substantial intra-cluster correlation coefficient (ICC) and silhouette clustering coefficient (SCC) scores of 65% and 76%, respectively, among the members in our discovered group. © The Author(s) 2024.

关键词： Graph neural network (GNN) Group recommendation Psychological attributes Social media Topic modeling Twitter Values

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Visualizing Adidas and Nike Customer Segments Using K-Means Clustering 8

Visualizing Adidas and Nike Customer Segments Using K-Means ...

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8th International Conference on Electrical, Telecommunication and computer Engineering, ELTICOM 2024

作者： Br Ginting, Dewi Sartika Maulana Manru, Muhammad Azi Sachio, Wilbert Neilson Tarigan, Abiyan Agitia Marianto, Ivanny Putri Universitas Sumatera Utara Faculty of Computer Science and Information Technology Medan Indonesia

ISBN: (纸本)9798331531249

This study explores the application of the K-Means Clustering algorithm to categorize Adidas and Nike customers based on their behavior and preferences. The analysis was conducted on a dataset of 3,268 products from Adidas and Nike, focusing on grouping customer behavior into actionable segments. The K-Means algorithm identified key features, with Listing Price and Sale Price as significant variables. The Elbow Method determined the optimal number of clusters as three (k=3), and the Silhouette Score confirmed the model's effectiveness. The results show that the K-Means algorithm effectively distinguishes customer segments within the Adidas vs. Nike shoe dataset, represented through scatter plots and silhouette diagrams that reveal well-separated clusters. These findings are valuable for refining marketing and pricing strategies, enabling Adidas and Nike to better target specific customer groups. Future research could enhance these findings by experimenting with additional features, exploring alternative clustering algorithms like hierarchical clustering or DBSCAN, and fine-tuning parameters such as the number of clusters or preprocessing techniques to further optimize results. © 2024 IEEE.

关键词： Costs

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Enabling IoT Service Classification: A Machine Learning-Based Approach for Handling Classification Issues in Heterogeneous IoT Services

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IEEE Access 2023年 11卷 89024-89037页

作者： Abbasi, Mohammad Asad Chen, Yen-Lin Khan, Abdullah Ayub Memon, Zulfiqar A. Durrani, Nouman M. Yang, Jing Ku, Chin Soon Por, Lip Yee Benazir Bhutto Shaheed University Lyari Department of Computer Science and Information Technology Karachi75660 Pakistan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Sindh Madressatul Islam University Department of Computer Science Karachi74000 Pakistan -FAST Department of Computer Science Islamabad44000 Pakistan Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia

The Internet of Things (IoT) is a form of Internet-based distributed computing that allows devices and their services to interact and execute tasks for each other. Consequently, the footprint of the IoT is increasing and becoming more complex to the highest degree. This has also given birth to new IoT-enabled applications and services. Efficient service interaction and management also call for understanding and analyzing the nature of IoT services. Further, IoT services must be characterized into various classes, and different service-related attributes must be considered for the classification. This article assesses the requirements of heterogeneous IoT services by examining their interactions. Principally, heterogeneous IoT and their service interactions are targeted. The research work performs classification of IoT services into various classes. Services are classified on the basis of various attributes. The attributes reflect different characteristics of the services. This research enables improved utilization of IoT services through efficient classification of available resources using machine learning methods. To demonstrate service classification applicability, the SVM, voting classifier, and decision tree have been applied in a service-oriented environment along with different types of services. All the services in the data set were analyzed and divided into five classes. Moreover, the decision tree performed well and achieved higher accuracy values in all classes. However, the overall prediction and classification of the decision tree model were observed to be good and satisfactorily high. © 2013 IEEE.

关键词： Decision trees

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Security to text (S2T): multi-layered based security approaches for secret text content

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Multimedia Tools and Applications 2025年第15期84卷 15151-15171页

作者： Kashid, Shamal Awasthi, Lalit K. Berwal, Krishan Department of Computer Science and Engineering National Institute of Technology Uttarakhand IN Srinagar India Faculty of Communication Engineering Military College of Telecommunication Engineering IN Mhow India

In the digital world, text data is produced in an unstructured manner across various communication channels. Extracting valuable information from such data with security is crucial and requires the development of techniques in text mining, information retrieval, and natural language processing (NLP). To solve this issue, we introduce two novel approaches: keyword extraction (KE) and a multi-layered secret sharing scheme (MLSS) to provide security to extracted keywords rather than overall text documents. The KE approach encompasses a sequence of text pre-processing procedures, including tokenization, stopword removal, stemming, and bag of words representation, followed by indexing. This methodology aims to efficiently extract keywords from text datasets. For this research work, we have proposed three datasets, including text messages, whatsapp messages, and electronic mail. MLSS enhances the security of extracted textual data by leveraging text pre-processing steps. This scheme ensures better confidentiality and the non-revealment of sensitive information. Additionally, we evaluate our KE model on our dataset as well as on standard datasets. Experimental results demonstrate the effectiveness of our proposed security to text (S2T) model, which outperform existing state-of-the-art approaches. The model obtains a 100% correlation between the reconstructed text and the original text. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Extraction

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