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arXiv 2024年

作者： Puglisi, Lemuel Alexander, Daniel C. Ravì, Daniele Dept. of Math and Computer Science University of Catania Italy Centre for Medical Image Computing University College London United Kingdom School of Physics Engineering and CS University of Hertfordshire United Kingdom

In this work, we introduce Brain Latent Progression (BrLP), a novel spatiotemporal disease progression model based on latent diffusion. BrLP is designed to predict the evolution of diseases at the individual level on 3D brain MRIs. Existing deep generative models developed for this task are primarily data-driven and face challenges in learning disease progressions. BrLP addresses these challenges by incorporating prior knowledge from disease models to enhance the accuracy of predictions. To implement this, we propose to integrate an auxiliary model that infers volumetric changes in various brain regions. Additionally, we introduce Latent Average Stabilization (LAS), a novel technique to improve spatiotemporal consistency of the predicted progression. BrLP is trained and evaluated on a large dataset comprising 11,730 T1-weighted brain MRIs from 2,805 subjects, collected from three publicly available, longitudinal Alzheimer’s Disease (AD) studies. In our experiments, we compare the MRI scans generated by BrLP with the actual follow-up MRIs available from the subjects, in both cross-sectional and longitudinal settings. BrLP demonstrates significant improvements over existing methods, with an increase of 22% in volumetric accuracy across AD-related brain regions and 43% in image similarity to the ground-truth scans. The ability of BrLP to generate conditioned 3D scans at the subject level, along with the novelty of integrating prior knowledge to enhance accuracy, represents a significant advancement in disease progression modeling, opening new avenues for precision medicine. The code of BrLP is available at the following link: https://***/LemuelPuglisi/BrLP. © 2024, CC BY.

关键词： Brain

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RAINER: A Robust Ensemble Learning Grid Search-Tuned Framework for Rainfall Patterns Prediction

arXiv

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arXiv 2025年

作者： Li, Zhenqi Zhong, Junhao Wang, Hewei Xu, Jinfeng Li, Yijie You, Jinjiang Zhang, Jiayi Wu, Runzhi Dev, Soumyabrata The Fu Foundation School of Engineering and Applied Science Columbia University United States Biomedical Engineering Department Pratt School of Engineering Duke University United States School of Computer Science University College Dublin Ireland Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Robotics Institute School of Computer Science Carnegie Mellon University United States Faculty of Science and Engineering University of Nottingham Ningbo China China College of Intelligence and Computing Tianjin University China The ADAPT SFI Research Centre

Rainfall prediction remains a persistent challenge due to the highly nonlinear and complex nature of meteorological data. Existing approaches lack systematic utilization of grid search for optimal hyperparameter tuning, relying instead on heuristic or manual selection, frequently resulting in sub-optimal results. Additionally, these methods rarely incorporate newly constructed meteorological features such as differences between temperature and humidity, to capture critical weather dynamics. Furthermore, there is a lack of systematic evaluation of ensemble learning techniques and limited exploration of diverse advanced models introduced in the past one or two years. To address these limitations, we propose a Robust ensemble LeArning grId search-tuNed framEwoRk (RAINER) for rainfall prediction. RAINER incorporates a comprehensive feature engineering pipeline, including outlier removal, imputation of missing values, feature reconstruction, and dimensionality reduction via Principal Component Analysis (PCA). The framework integrates novel meteorological features to capture dynamic weather patterns and systematically evaluates non-learning mathematical-based methods and a variety of machine learning models, from weak classifiers (e.g., KNN, LASSO, and Random Forest) to advanced neural networks such as Kolmogorov-Arnold Networks (KAN). By leveraging grid search for hyperparameter tuning and ensemble voting techniques, RAINER achieves state-of-the-art results within real-world dataset. Extensive experiments demonstrate the framework’s superior performance across diverse metrics, such as Accuracy, Precision, Recall, F1-score, and AUC, highlighting the pivotal role of feature engineering, grid search, and ensemble learning in rainfall prediction tasks. © 2025, CC BY.

关键词： Dimensionality reduction

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Fuzzy-Based Weighted Federated Machine Learning Approach for Sustainable Energy Management with IoE Integration

Fuzzy-Based Weighted Federated Machine Learning Approach for...

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IEEE Symposium on Systems and Information engineering Design, SIEDS

作者： Taher M. Ghazal Jamshaid Iqbal Janjua Sagheer Abbas Areej Fatima Muhammad Saleem Muhammad Adnan Khan Abdulrahman Alqarafi Computer Science Department Centre for Cyber Physical Systems Khalifa University Abu Dhabi UAE Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Selangor Malaysia School of Computer Sciences National College of Business Administration & Economics Lahore Pakistan Al-Khawarizmi Institute of Computer Science University of Engineering & Technology (UET) Lahore Pakistan Department of Computer Science Bahria University Lahore Pakistan Department of Computer Science LGU Lahore Pakistan School of Computer Science Minhaj University Lahore Pakistan School of Computing Skyline University College Sharjah UAE Riphah School of Computing and Innovation Riphah International University Lahore Pakistan College of Computer Science and Engineering Taibah University Medina Saudi Arabia

ISBN: (数字)9798350385144

ISBN: (纸本)9798350385151

The considerable surge in energy consumption due to population growth and the use of new technology has posed significant challenges to energy security and the environment. Managing energy consumption on the consumer side is crucial as the demand for energy continues to increase. Consumers need to monitor their daily usage and understand consumption standards to prepare themselves for energy efficiency and cost savings. To address these challenges, a Smart Energy Management System (SEMS) based on the Internet of Energy (IoE) has been developed. This system uses day-ahead planning and accurate energy availability forecasting to manage energy demand in a smart environment with high sustainable energy penetration. The Fuzzy based Weighted Federated Learning (FWFML) approach is integrated into the system to optimize energy management. The SEMS system monitors energy usage at the user level, and a review of management plans has been introduced to improve energy efficiency in terms of 93.5% accuracy, and 6.5% miss-rate. This research work also highlights future challenges in developing SEMS to increase energy efficiency in a more robust manner.

关键词： Energy consumption Costs Smart buildings Sociology Energy efficiency Energy management systems Surges

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SYSTEMATIC LITERATURE REVIEW ON APPLICATION OF MACHINE LEARNING IN CONTINUOUS INTEGRATION

arXiv

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arXiv 2023年

作者： Arani, Ali Kazemi Le, Triet Huynh Minh Zahedi, Mansooreh Babar, Muhammad Ali CREST-The Centre for Research on Engineering Software Technologies School of Computer Science University of Adelaide AdelaideSA Australia School of Computing and Information Systems University of Melbourne MelbourneVIC Australia

Context: Machine learning (ML) is a field that involves analysing raw data and extracting useful information from it through specific phases. As continuous practices become more prevalent in software projects, there is a need to explore how ML methods can be trained to enhance the Continuous Integration (CI) pipeline. Moreover, the growing utilization of ML algorithms in CI, combined with the surge of literature on the subject, highlights the significance of establishing a comprehensive body of knowledge to support future researchers in conducting high-quality research and bridging any existing gaps. Objective: The objective of this research is to conduct a systematic review and analysis of the existing literature on ML-based methods employed in the CI domain. This study aims to identify and describe the various techniques employed in the literature and present the key characteristics of the training phases of ML-based solutions in the CI context. Method: To achieve this objective, we conducted a Systematic Literature Review (SLR) of 48 primary studies that were selected after searching relevant literature published over the past 22 years (2000–November 2022). We used statistical and thematic analysis to examine the composition phase of CI, data engineering techniques and data source types, feature engineering methods and extracted features, the employed hyper-parameter tuning methods and types of the ML models, and the evaluation methods and metrics used in the selected studies. Additionally, this article aims to present the relationship between these concepts. Results: In this paper, we have depicted the phases of CI testing, the connection between them, and the employed techniques in training the ML method phases. We presented nine types of data sources and four taken steps in the selected studies for preparing the data. Also, we identified four feature types and nine subsets of data features through thematic analysis of the selected studies. Besides, five method

关键词： Machine learning

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Wind speed prediction and insight for generalized predictive modeling framework: a comparative study for different artificial intelligence models

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Neural computing and Applications 2024年第23期36卷 14119-14150页

作者： Bhagat, Suraj Kumar Tiyasha, Tiyasha Shather, A.H. Jamei, Mehdi Kumar, Adarsh Al-Khafaji, Zainab Goliatt, Leonardo Shafik, Shafik S. Alawi, Omer A. Yaseen, Zaher Mundher Faculty of Civil Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam Department of Computer Engineering Technology Al Kitab University Altun Kopru Kirkuk00964 Iraq Faculty of Civil Engineering and Architecture Shahid Chamran University of Ahvaz Ahvaz Iran Canadian Centre for Climate Change and Adaptation University of Prince Edward Island St PetersPE Canada Building and Construction Techniques Engineering Department College of Engineering and Engineering Techniques Al-Mustaqbal University Hillah Babylon51001 Iraq Department of Computational and Applied Mechanics Federal University of Juiz de Fora Juiz de Fora Brazil Experimental Nuclear Radiation Research Group Scientific Research Center Al-Ayen University Thi-Qar64001 Iraq Johor Bahru Skudai81310 Malaysia Civil and Environmental Engineering Department King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Dehradun India Dept of Environmental Science Gitam School of Science GITAM University Visakhapatnam India

Wind speed (WS) has played a vital role in local urban and sub-urban weather, agriculture, and ecosystem. Several meteorological parameters are influencing WS such as relative humidity (at 2 m, %), surface pressure (kPa), maximum temperature (at 2 m, °C), minimum temperature (at 2 m, °C), average temperature (at 2 m, °C), and all sky insolation incident on a horizontal surface (kW-h/m2/day). The current research was conducted to predict WS at different locations at Vietnam using the feasibility of computer aid models (i.e., multivariate adaptive regression splines (MARS), extreme gradient boosting (XGBoost) and random forest generator (Ranger)). Pearson correlation (PC) was investigated to select the high significant predictors to predict the WS at 10 m high. All inputs (maximum number, 6) are chosen by the PC approach for PhuongNinh, DaNang, and HaNoi;and for minimum number of inputs i.e four, are selected for PhuongHung, CanTho, and SaPa city;that exhibit the relationship with WS, citywise. The sequence selection of input parameters differed in each station as per the PC analysis. Based on the statistical evaluation and graphical presentation, MARS model attained the best prediction results, followed by XGBoost and Ranger. MARS predictive model remains at the top performance among others based on 95% confidence interval. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Forecasting

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ExplainableDetector: Exploring Transformer-based Language Modeling Approach for SMS Spam Detection with Explainability Analysis

arXiv

引用

arXiv 2024年

作者： Uddin, Mohammad Amaz Islam, Muhammad Nazrul Maglaras, Leandros Janicke, Helge Sarker, Iqbal H. Department of Computer Science and Engineering BGC Trust University Bangladesh Chittagong4381 Bangladesh Department of Computer Science and Engineering Military Institute of Science and Technology Dhaka1216 Bangladesh School of Computing Edinburgh Napier University United Kingdom Centre for Securing Digital Futures Edith Cowan University PerthWA6027 Australia

SMS, or short messaging service, is a widely used and cost-effective communication medium that has sadly turned into a haven for unwanted messages, commonly known as SMS spam. With the rapid adoption of smartphones and Internet connectivity, SMS spam has emerged as a prevalent threat. Spammers have taken notice of the significance of SMS for mobile phone users. Consequently, with the emergence of new cybersecurity threats, the number of SMS spam has expanded significantly in recent years. The unstructured format of SMS data creates significant challenges for SMS spam detection, making it more difficult to successfully fight spam attacks in the cybersecurity domain. In this work, we employ optimized and fine-tuned transformer-based Large Language Models (LLMs) to solve the problem of spam message detection. We use a benchmark SMS spam dataset for this spam detection and utilize several preprocessing techniques to get clean and noise-free data and solve the class imbalance problem using the text augmentation technique. The overall experiment showed that our optimized fine-tuned BERT (Bidirectional Encoder Representations from Transformers) variant model RoBERTa obtained high accuracy with 99.84%. We also work with Explainable Artificial Intelligence (XAI) techniques to calculate the positive and negative coefficient scores which explore and explain the fine-tuned model transparency in this text-based spam SMS detection task. In addition, traditional Machine Learning (ML) models were also examined to compare their performance with the transformer-based models. This analysis describes how LLMs can make a good impact on complex textual-based spam data in the cybersecurity field. © 2024, CC BY.

关键词： Machine learning

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Integrating AI and Semi-Immersive AR Technology for Brain Tumor Diagnosis

Integrating AI and Semi-Immersive AR Technology for Brain Tu...

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International Conference on engineering and Emerging Technologies (ICEET)

作者： Kahina Amara Mohamed Amine Guerroudji Oussama Kerdjidj Meriem Khedir Nadia Zenati Shadi Atalla Wathiq Mansoor Naeem Ramzan Centre for Development of Advanced Technologies Algiers Algeria College of Engineering and IT University of Dubai Dubai UAE Higher School of Computer Science Sidi Bel Abbes Algeria School of Computing Engineering and Physical Sciences University of the West of Scotland Paisley Scotland United Kingdom

ISBN: (数字)9798331532895

ISBN: (纸本)9798331532901

Brain tumours, characterised by abnormal cell development inside the brain, pose a significant risk to individuals due to their high mortality rate. Early identification is essential for accurate diagnosis and successful treatment to enhance patient outcomes. However, diagnosing small tumours can be difficult and error-prone, as it largely depends on the experience and qualifications of medical personnel. Therefore, an automated diagnostic approach that reduces diagnostic time while increasing accuracy is desperately needed. This study addresses the challenges of manual diagnosis by applying deep learning algorithms to brain tumour segmentation. Additional challenges in applying deep learning for tumour segmentation include handling low-quality scans, variations in tumour features, and insufficient data. We present an emerging concept combining Artificial Intelligence and Augmented Reality as a valuable tool, providing deep learning model deployment, immersive visualisation, and interactive functionalities to support diagnosis and surgical interventions for brain tumours. The achieved performance is competitive with the literature, and feedback from medical professionals after deployment evaluation is satisfactory.

关键词： Deep learning Accuracy Three-dimensional displays Magnetic resonance imaging Surgery Artificial intelligence Medical diagnostic imaging Augmented reality Tumors Qualifications

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Improving 6G Network Safety, Privacy, and Resource Efficiency via the Application of Machine Learning and Network Data Analytics 2

Improving 6G Network Safety, Privacy, and Resource Efficienc...

引用

2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Rai, Supriya Lohani, Manoj Chandra Singh, K. Ranjith Ghumman, Sukhman Patil, Swati Bula Mythili, V. School of Management - UG Management Karnataka Bangalore India Graphic Era Hill University School of Computing Haldwani India Graphic Era Deemed to be University Uttarakhand Dehradun248002 India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Vishwakarma Institute of Technology Pune India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India

ISBN: (纸本)9798350388916

The 6G era of networks is tightly tied to intelligent network orchestration while administration, but the cloudification of networks using a micro-services-oriented architecture is a well-known element of 5G. It follows that the proposed 6G paradigm heavily relies on AI, ML, and DL. Proactive threat identification, smart mitigation tactics, and assurance that 6G networks would be self-sustaining are essential for future end-to-end network automation. In order to solidify and enhance AI's position in 6G network security, this article delves at potential AI applications in 6G security. To get there, our research reworked an existing 6G network anomaly detection system to make it operate with ensemble learning (EL) to communicate networks. In order to protect 6G user data from unauthorized access, resale, intoxication, and free-riding attacks, we offer a thorough framework for detecting data attacks that is optimized for secure aggregation using the Deep Reinforcement learning - PPO algorithm. To guard against data poisoning, unauthorized data sources, and mistakes in data aggregation, we use a set of encrypted data auditing tools. We have also performed analysis, evaluated many prospective security strategies, and suggested the best security measures for certain 6G situations. the Krull Herd-Advanced Encryption Standard (KH-AES) algorithm for secure data transmissions. To use IDS, this study makes use of the NSL-KDD dataset. Probing, normal, U2R, DoS, R2L, and unidentified were the six assaults used to categorize the data. Classification results are heavily affected by the weights in HCNN layers. When compared to well-known methods, the suggested system outperforms them when it comes of FS, classifications, and data sharing security. © 2024 IEEE.

关键词： Adversarial machine learning

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Massive MIMO Joint Communications and Sensing with MRT Beamforming

Massive MIMO Joint Communications and Sensing with MRT Beamf...

引用

IEEE National Conference on Radar

作者： Nhan T. Nguyen V.-Dinh Nguyen Hieu V. Nguyen Hien Q. Ngo A. L. Swindlehurst Markku Juntti Centre for Wireless Communications University of Oulu Finland Collese of Engineering and Computer Science VinUniversity Hanoi Vietnam Faculty of Electronics and Telecommunication Engineering University of Danang Vietnam School of Electronics Electrical Engineering and Computer Science Queen's University Belfast UK Center for Pervasive Communications & Computing University of California Irvine CA USA

ISBN: (数字)9798350329209

ISBN: (纸本)9798350329216

Joint communications and sensing (JCAS) is en-visioned as a key feature in future wireless communications networks. In massive MIMO-JCAS systems, the very large number of antennas causes excessively high computational complexity in beamforming designs. In this work, we investigate a low-complexity massive multiple-input-multiple-output (MIMO)-JCAS system employing the maximum-ratio transmission (MRT) scheme for both communications and sensing. We first derive closed-form expressions for the achievable communications rate and Cramer-Rao bound (CRB) as functions of the large-scale fading channel coefficients. Then, we develop a power allocation strategy based on successive convex approximation to maximize the communications sum rate while guaranteeing the CRB con-straint and transmit power budget. Our analysis shows that the introduction of sensing functionality increases the beamforming uncertainty and inter-user interference on the communications side. However, these factors can be mitigated by deploying a very large number of antennas. The numerical results verify our findings and demonstrate the power allocation efficiency.

关键词： Wireless communication Closed-form solutions Uncertainty Array signal processing Massive MIMO Radar Interference

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Flowmind2digital: The First End-to-End Flowmind Recognition and Conversion Approach

SSRN

引用

SSRN 2023年

作者： Liu, Huanyu Cai, Jianfeng Zhang, Tingjia Li, Hongsheng Wang, Siyuan Zhu, Guangming Shah, Syed Afaq Ali Bennamoun, Mohammed Zhang, Liang School of Artificial Intelligence Xidian University China School of Computer Science and Technology Xidian University China School of Science Centre for AI and Machine Learning Edith Cowan University ECU Australia School of Physics Maths and Computing Computer Science and Software Engineering The University of Western Australia-UWA Australia

Flowcharts and mind maps, collectively referred to as flowmind, play an important role in our daily, and many companies have developed dedicated tools. Hand-drawn flowminds offer significant advantages for real-time and collaborative communication. However, there is an increasing demand to convert them into a digital format for further processing. Automated conversion methods are crucial in addressing the challenges associated with manual conversion, such as the cost of time and learning. Previous works have proposed diverse sketch recognition methods. However, these methods face significant limitations in practical situations. Firstly, most methods are designed for specific fields, making it challenging to extend their usage to other fields. Additionally, none of these methods address the critical step of digital conversion after recognition, which is essential to users. Moreover, existing datasets exhibit significant biases relative to actual data, hindering the methods’ generalization *** paper proposes the Flowmind2digital method and hdFlowmind dataset to address the aforementioned challenges. Flowmind2digital is an end-to-end recognition and conversion method for flowminds, utilizing a neural network architecture and keypoint detection technology to enhance overall recognition accuracy. Our hdFlowmind dataset consists of 1,776 hand-drawn and manually annotated flowminds, covering 22 scenarios and surpassing existing datasets in size. Our experiments showcase the effectiveness of our method, with an accuracy rate of 87.3% on the hdFlowmind dataset, surpassing the previous state-of-the-art work by 11.9%. Additionally, our dataset demonstrates effectiveness, with a 2.9% increase in accuracy after pre-training and fine-tuning on Handwritten-diagram-dataset. We also highlight the importance of simple graphics for sketch recognition, which can improve accuracy by 9.3%. © 2023, The Authors. All rights reserved.

关键词： Network architecture

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