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13th Mining Humanistic Data Workshop, MHDW 2024, 9th Workshop on 5G-Putting Intelligence to the Network Edge, 5G-PINE 2024 and 1st Workshop on AI in Applications for Achieving the Green Deal Targets, AI4GD 2024 held as parallel events of the IFIP WG 12.5 International Workshops on Artificial Intelligence Applications and Innovations, AIAI 2024

作者： Rompolas, Gerasimos Smpoukis, Athanasios Kafeza, Eleanna Makris, Christos Computer Engineering and Informatics Department University of Patras Patras26504 Greece College of Interdisciplinary Studies Zayed University Dubai144534 United Arab Emirates

ISBN: (纸本)9783031632266

In this paper, we delve into the intricate relationship between technology, music, and success. Our research focuses on leveraging the capabilities of machine learning algorithms to forecast the success and popularity of songs. By combining audio features, social media data, and emotion analysis, our machine learning-based model demonstrates its capability as a reliable tool for artists, amateurs, and music industry stakeholders to make informed and targeted decisions. Our analysis encompassed not only the inherent audio characteristics of each track but also the extent of audience engagement and emotional response of the fans. Through the integration of textual emotion analysis, we quantified the reactions of the audience and performed an emotion labelling, leveraging our social information to uncover the emotions surrounding each track’s release. The experimental results show that the feature data from which the classification algorithms were trained, unfolded to be qualitative and precise, while they also show that the use of the social media features improves the classifiers’ performance in predicting the popularity of the tracks. © IFIP International Federation for Information Processing 2024.

关键词： Emotion Recognition

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Cloud Based Automated Control System Workshops and Rooms for Controlling Parameters 1

Cloud Based Automated Control System Workshops and Rooms for...

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1st International Conference on Advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Kumar, Dinesh Kuntal, Ravinder Singh Deep, Pragya Chamoli, Anup Shekhar Singh, Pinki Mandal, Rishabh SRM Institute of Science & Technology Department of Computer Science and Engineering UP Ghaziabad India Nitte Meenakshi Institute of Technology Department of Mathematics Karnataka Bangalore India IIMT College of Engineering Department of AI & DS Greater Noida India Uttaranchal School of Computing Sciences Uttaranchal University Dehradun248001 India Accurate College of Engineering & Management Department of Computer Science and Engineering Greator Noida India Tula's Institute Department of Computer Science and Engineering UK Dehradun India

ISBN: (纸本)9798331518578

Across all industries, cloud-based automated control systems have completely changed how operational and environmental parameters are tracked and controlled. With cloud-integrated systems for real-time data collection, processing, and control, this concept presents intelligent, remote-controlled workshops and control rooms. To guarantee accurate control of crucial variables like temperature, humidity, pressure, and equipment efficiency, these systems make use of IoT devices, AI techniques, and big data analytics. Through sophisticated data perspectives, the cloud-based strategy enables predictive maintenance, improves flexibility, and lowers operating expenses. Stakeholders may make prompt, data-driven choices from anywhere with remote access to control rooms. These seminars provide practical experience with cloud-controlled settings and function as training and development centres. The system's flexibility to meet a range of industrial demands is further enhanced by its compatibility with external tools and equipment. The design, deployment, and possible uses of cloud-based control systems are covered in this paper, with an emphasis on how they might promote operational excellence, energy efficiency, and smart manufacturing. © 2025 IEEE.

关键词： Smart manufacturing

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Blockchain-based multi-diagnosis deep learning application for various diseases classification

Blockchain-based multi-diagnosis deep learning application f...

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作者： Rahal, Hakima Rym Slatnia, Sihem Kazar, Okba Barka, Ezedin Harous, Saad Computer Science Department LINFI Laboratory University of Mohammed Khider Biskra Biskra Algeria College of Information Technology University of the United Arab Emirates Al Ain United Arab Emirates Computer Science Department College of Computing and Informatics University of Sharjah Sharjah27272 United Arab Emirates

Misdiagnosis is a critical issue in healthcare, which can lead to severe consequences for patients, including delayed or inappropriate treatment, unnecessary procedures, psychological distress, financial burden, and legal implications. To mitigate this issue, we propose using deep learning algorithms to improve diagnostic accuracy. However, building accurate deep learning models for medical diagnosis requires substantial amounts of high-quality data, which can be challenging for individual healthcare sectors or organizations to acquire. Therefore, combining data from multiple sources to create a diverse dataset for efficient training is needed. However, sharing medical data between different healthcare sectors can be problematic from a security standpoint due to sensitive information and privacy laws. To address these challenges, we propose using blockchain technology to provide a secure, decentralized, and privacy-respecting way to share locally trained deep learning models instead of the data itself. Our proposed method of model ensembling, which combines the weights of several local deep learning models to build a single global model, that enables accurate diagnosis of complex medical conditions across multiple locations while preserving patient privacy and data security. Our research demonstrates the effectiveness of this approach in accurately diagnosing three diseases (breast cancer, lung cancer, and diabetes) with high accuracy rates, surpassing the accuracy of local models and building a multi-diagnosis application. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE.

关键词： Smart contract

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Snowfall Forecasting with Enhanced Accuracy: Leveraging Multi-Class SVM for Meteorological Predictions 7th

Snowfall Forecasting with Enhanced Accuracy: Leveraging Mult...

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7th IFIP TC 12 International Conference on Computational Intelligence in Data Science, ICCIDS 2024

作者： Ranjith Kumar, M. Sudeesh Kumar, V. Tharun Kaarthik, G.K. Chatiyode, Veda Srinivasan Anusha, Janani Revathi, P. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai600127 India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Computer Science and Engineering C Abdul Hakeem College of Engineering and Technology Tamil Nadu Melvisharam India

ISBN: (纸本)9783031699856

The use of multi-class Support Vector Machines (SVM) for snowfall prediction is an advanced meteorological application. The study emphasizes how valuable snowfall forecasts are in various contexts, including agriculture, aviation, transportation management, and climate reporting. This approach presents a novel methodology for classifying snowfall types by analyzing various meteorological factors such as temperature range, wind dynamics, and precipitation. A 400-entries of datasets were pulled from GitHub to demonstrate the methodology. The sample is selected to reduce the complexity and to increase the performance. The dataset is split into a testing set (30%) and a training set (70%), which aids in assessing the model’s performance in a wide range of parameters. When the algorithm was first tested, it could distinguish different types of snowfall with a remarkable 83% accuracy rate based on indicators such as Temperature (min & max), Precipitation, Wind Speed, and Wind Gust. By optimizing the SVM configuration through hyper-parameter tuning approaches such as grid search, we were able to further refine our model’s accuracy to an astounding 91%. This improvement in forecast accuracy signifies a significant move away from older, more expensive instrument-based methods and towards data-driven snowfall analysis. Our results show how machine learning can revolutionize weather prediction systems, providing a more effective and economical method of comprehending and predicting snowfall patterns. © IFIP International Federation for Information Processing 2024

关键词： Snow

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Estimating Uncertainty in Deep Learning Methods and Applications

Estimating Uncertainty in Deep Learning Methods and Applicat...

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2024 International Conference on computer and Applications, ICCA 2024

作者： Alqarafi, Abdulrahman Batool, Hina Abbas, Tahir Janjua, Jamshaid Iqbal Ramay, Sadaqat Ali Ahmed, Munir College of Computer Science and Engineering Taibah University Medina42353 Saudi Arabia Times Institute Department of Computer Science Multan Pakistan Lahore Pakistan College of Informatics Korea University Seoul02841 Korea Republic of

ISBN: (纸本)9798350367560

Uncertainty estimation in deep learning has emerged as a crucial area of research due to its significance in enhancing model reliability and decision-making in critical applications. This article explores various methods and applications of uncertainty estimation in deep learning, aiming to provide insights into its importance, methods, and potential impact. Through a comprehensive literature review and analysis, the study identifies key findings regarding the effectiveness, limitations, and ethical considerations associated with uncertainty estimation techniques. The results reveal the diverse range of methodologies employed, including Bayesian approaches, ensemble methods, and Monte Carlo sampling, each with its strengths and drawbacks. Furthermore, the article discusses the implications of uncertainty estimation in deep learning for fields such as healthcare, autonomous systems, and safety-critical domains. Overall, this study underscores the significance of uncertainty estimation in deep learning and provides valuable insights for researchers and practitioners in the field. © 2024 IEEE.

关键词： Adversarial machine learning

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Deep transfer learning technique to detect white blood cell classification in regular clinical practice using histopathological images

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Multimedia Tools and Applications 2025年第9期84卷 5699-5723页

作者： Davamani, K. Anita Jawahar, Malathy Anbarasi, L. Jani Ravi, Vinayakumar Al Mazroa, Alanoud Robin, C. R. Rene Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Leather Process Technology Division CSIR-Central Leather Research Institute Chennai India School of Computing Science and Engineering Vellore Institute of Technology Vellore India Computer Science and Engineering Sri Sairam Engineering College Sirukalathur India Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia

Leukemia, a malignant disease characterized by the rapid proliferation of specific types of white blood cells (WBC), has prompted increased interest in leveraging automatic WBC classification system. This study presents the design of a robust WBC classification system, integrating deep learning with microscopic image analysis. The proposed classification model is designed to categorize blood cells into four distinct groups: eosinophil, lymphocyte, monocyte, and neutrophil. The study employs a Kaggle image dataset for comprehensive learning. The dataset comprises 12,500 microscopic images used to categorize the four blood cell groups. To enhance performance and reduce the likelihood of overfitting, transfer learning techniques are employed. Six deep transfer learning models namely VGG16, ResNet50, AlexNet, MobileNet, GoogLeNet, and EfficientNetBO are used for feature extraction and classification. The results of the experiments show that VGG16 and MobileNet demonstrated enhancements compared to other pre-trained models when evaluated on both augmented and original datasets. A diverse set of evaluation criteria was employed to thoroughly assess the effectiveness of medical image classification. Notably, the VGG16 and MobileNet models accurately predicted Lymphocytes and Monocytes with a 100% accuracy rate, and Eosinophil and Neutrophil with rates of 99.35% and 99.81%, respectively. This research work empower medical practitioners, particularly in the field of pathology and hematology by providing advanced tools for accurate blood cell classification. The proposed VGG16 and MobileNet models hold promise for simplifying WBC classification, improving decision-making processes, and ultimately advancing patient care in the medical domain. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

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Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning

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IEEE Transactions on Mobile computing 2025年第7期24卷 5731-5748页

作者： Sun, Geng Xiao, Jian Li, Jiahui Wang, Jiacheng Kang, Jiawen Niyato, Dusit Mao, Shiwen the College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China the College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore the School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore the School of Automation Guangdong University of Technology Guangzhou510641 China the Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

—Unmanned aerial vehicles (UAVs) have emerged as the potential aerial base stations (BSs) to improve terrestrial communications. However, the limited onboard energy and antenna power of a UAV restrict its communication range and transmission capability. To address these limitations, this work employs collaborative beamforming through a UAV-enabled virtual antenna array to improve transmission performance from the UAV to terrestrial mobile users, under interference from non-associated BSs and dynamic channel conditions. Specifically, we introduce a memory-based random walk model to more accurately depict the mobility patterns of terrestrial mobile users. Following this, we formulate a multi-objective optimization problem (MOP) focused on maximizing the transmission rate while minimizing the flight energy consumption of the UAV swarm. Given the NP-hard nature of the formulated MOP and the highly dynamic environment, we transform this problem into a multi-objective Markov decision process and propose an improved evolutionary multi-objective reinforcement learning algorithm. Specifically, this algorithm introduces an evolutionary learning approach to obtain the approximate Pareto set for the formulated MOP. Moreover, the algorithm incorporates a long short-term memory network and hyper-sphere-based task selection method to discern the movement patterns of terrestrial mobile users and improve the diversity of the obtained Pareto set. Simulation results demonstrate that the proposed method effectively generates a diverse range of non-dominated policies and outperforms existing methods. Additional simulations demonstrate the scalability and robustness of the proposed CB-based method under different system parameters and various unexpected circumstances. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Markov processes

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Supervised learning applied to electrocardiogram statistical features for the detection of premature ventricular contraction

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Research on Biomedical engineering 2025年第1期41卷 1-12页

作者： Issa, Khouloud Rammal, Abbas Assaf, Rabih Ghandour, Ahmad Electrical and Communication Engineering Department College of Engineering Phoenicia University Zahrani South Lebanon Statistics and Informatics Department Faculty of Science Lebanese University Beirut Lebanon Mathematics and Computer Sciences Department Faculty of Arts and Sciences Lebanese American University Beirut Lebanon Department of Mathematics Faculty of Arts and Sciences Holy Spirit University of Kaslik Jounieh Lebanon Biomedical Engineering Department Faculty of Engineering Islamic University of Lebanon Wardenieh30014 Lebanon

Purpose: The development of an automated premature ventricular contraction (PVC) detection system has significant implications for early intervention and treatment decisions. This study aims to develop a novel approach using various supervised machine learning (ML) methods for detecting PVCs in electrocardiogram (ECG) recordings. Methodology: To achieve this, we extracted ten distinct statistical and temporal features from 33 long-term ECG recordings from the benchmark MIT-BIH arrhythmia database (MIT-BIH-AD), capturing significant characteristics from the signals. We then investigated the effectiveness of traditional ML algorithms in identifying PVCs based on these features, namely support vector machine (SVM), decision tree (DT), naïve Bayes (NB), k-nearest neighbor (KNN), linear discriminant analysis (LDA), and artificial neural network (ANN). Results: Among these classifiers, the SVM, KNN, ANN, and DT classifiers demonstrated exceptional discriminatory power and classification performance, yielding near-perfect area under the receiver operating characteristic curve (AUC-ROC) values of 97.3%, 95.4%, 95%, and 94.1%, respectively. On the other hand, the SVM classifier emerged as the most accurate, achieving an overall accuracy of 97.39%, indicative of making correct predictions. Conclusion: These findings underscore the generalization of the proposed approach, especially since we worked on an extended dataset encompassing different types of heartbeats to discriminate PVCs from other heartbeat types, not only normal ones, and providing closer alignment with real-world scenarios. Subsequently, the robustness and accuracy of these models highlight their suitability for clinical applications and their potential for efficient implementation and deployment in the diagnosis of cardiovascular diseases (CVDs), as they demand minimal computational resources and time. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2025.

关键词： Decision trees

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Work-in-Progress: Security of Public-Key Schemes in the Quantum computing Era - A Literature Review

Work-in-Progress: Security of Public-Key Schemes in the Quan...

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2023 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2023

作者： Redkins, Boriss Kuzminykh, Ievgeniia Ghita, Bogdan King's College London Department of Informatics London United Kingdom School of Engineering Computing and Mathematics University of Plymouth Plymouth United Kingdom

ISBN: (纸本)9798350337822

Quantum computers, although still in the theoretical stage, conceptually have the power to break many current cryptosystems, including public-key schemes., for example, RSA (Rivest, Shamir., Adleman), elliptic-curve cryptography (ECC), and ElGamal. NIST specialists and national governments believe that existing cryptography algorithms will become vulnerable to brute force analysis by the end of the decade and agree that measures should be taken before then to ensure the secrecy of current and future algorithms. In this context, information security, including confidentiality and integrity, must be considered. This paper provides a systematic literature review on whether existing public-key schemes have the necessary complexity and solution space to resist a quantum computer. The discussion will focus on a quantum computer's negative 'real-world' impact and examine existing post-quantum algorithms, then provide analysis across two selected dimensions. Finally, we summarise the latest trends and gaps in post-quantum cryptography and provide the security analysis of NIST's last round finalists. © 2023 IEEE.

关键词： Public key cryptography

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A Probabilistic Public-Key Encryption with Ensuring Data Integrity in Cloud computing 4

A Probabilistic Public-Key Encryption with Ensuring Data Int...

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4th International Conference on Control, Artificial Intelligence, Robotics and Optimization, ICCAIRO 2023

作者： Kara, Mostefa Karampidis, Konstantinos Papadourakis, Giorgos Laouid, Abdelkader Alshaikh, Muath University of El Oued LIAP Laboratory El Oued El Oued39000 Algeria Hellenic Mediterranean University Department of Electrical and Computer Engineering Greece College of Computing and Informatics Saudi Electronic University Computer Science Department Riyadh11673 Saudi Arabia

ISBN: (纸本)9798350300918

Researchers are working hard to face various challenges in the fast-moving field of network security and data exchange. When it comes to encryption techniques, efforts vary to address multiple issues such as computational speed, data size requirements, application scope, etc. that are driven by the purposes of collecting and sharing sensitive data that must be stored and processed securely. This paper proposes an efficient asymmetric cryptosystem that bases on the multiplication of plaintext by a variable public key. The origin of this key is constant, but for each encryption, a random number must be added to it. Therefore, a new public key each time is generated;in other words, a simulation of a one-time pad system. For each message, the sender generates a new secret twin of the public key;The receiver can decrypt all messages encrypted by different twins with a single private key. The proposed probabilistic scheme is linear coding and hence is lightweight, easy to execute, and practical in many areas. We consider the cloud to be an untrusted part that tries to disclose data when decoded by systems, the proposed method provides the additive homomorphic property and ensures data integrity when a homomorphic operation is performed by the cloud;thus, we realize verifiable partial homomorphic encryption. © 2023 IEEE.

关键词： Network security

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