Article
Computer Science, Information Systems
Mustafa M. Al-Sayed
Summary: This paper proposes an attention-based Seq2Seq technique for predicting cloud resources' workloads and validates its effectiveness and performance using a real-world dataset. The results show that the proposed technique outperforms other techniques in terms of accuracy and computational time.
JOURNAL OF GRID COMPUTING
(2022)
Article
Computer Science, Theory & Methods
Ashutosh Kumar Singh, Deepika Saxena, Jitendra Kumar, Vrinda Gupta
Summary: This work introduces a novel EQNN-based workload prediction model for Cloud datacenters, utilizing quantum computing efficiency and SB-ADE algorithm for optimization. Results show that the quantum neural network approach substantially improves prediction accuracy.
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
(2021)
Article
Computer Science, Information Systems
Wiem Matoussi, Tarek Hamrouni
Summary: The proposed method aims to predict the number of requests at a SaaS service to achieve precise forecasting results and optimized response time, striking a balance between execution time and prediction accuracy.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Dawei Chen, Xiaoqin Zhang, Li Wang, Zhu Han
Summary: This paper introduces a novel deep learning model HPFDNN for predicting the quantity of cloud services, which combines the methods of fuzzy logic and neural networks to better interpret original data, provide useful information, and achieve economical predictions.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2021)
Article
Computer Science, Information Systems
Zaakki Ahamed, Maher Khemakhem, Fathy Eassa, Fawaz Alsolami, Abdullah S. Al-Malaise Al-Ghamdi
Summary: Proactive resource management in Cloud Services is important for cost effectiveness and addressing issues such as SLA violations and resource provisioning. Workload prediction using Deep Learning (DL) is popular for analyzing cloud environment data, but the quality of the training data influences the model's performance. Existing works in this domain often lack uniformity in data sources, leading to decreased efficacy of DL models. In this study, DL models are used to analyze real-world workloads from SWF, and the LSTM model exhibits the best performance. The paper also addresses the lack of literature on DL in workload prediction in cloud computing environments.
Article
Computer Science, Hardware & Architecture
Liang Bao, Jin Yang, Zhengtong Zhang, Wenjing Liu, Junhao Chen, Chase Wu
Summary: This study proposes an ensemble framework for cloud workload prediction using adaptive pattern mining. By combining a two-step method and error-based weights aggregation, it is able to capture both low frequency and high frequency characteristics, resulting in improved accuracy in predicting highly variable workloads.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Chemistry, Multidisciplinary
Sania Malik, Muhammad Tahir, Muhammad Sardaraz, Abdullah Alourani
Summary: Cloud computing has revolutionized computing, but also faces challenges such as power consumption, dynamic resource scaling, and resource provision. This research focuses on multi-resource utilization prediction using FLNN, GA, and PSO, with experimental results showing better accuracy compared to traditional techniques.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Information Systems
Qazi Zia Ullah, Gul Muhammad Khan, Shahzad Hassan, Asif Iqbal, Farman Ullah, Kyung Sup Kwak
Summary: Cloud computing is on the rise with Industry 4.0 technologies, but the energy consumption of data centers has become a global challenge. This paper introduces a new neural network model, CGPNN, which trains both parameters and topology to enhance performance and achieves significant prediction accuracy.
Article
Engineering, Biomedical
Hasan Turker, Bekir Aksoy, Koray Ozsoy
Summary: This study investigated the production of dental guides using additive manufacturing stereo lithography (SLA) technology and utilized artificial intelligence to analyze the dimensional aperture values. The results showed that the SLA-produced dental guides were compatible with the mandible bone, and the most suitable guide design was determined. Through the use of artificial neural network models, an accuracy rate of 99% was achieved.
JOURNAL OF THE MECHANICAL BEHAVIOR OF BIOMEDICAL MATERIALS
(2022)
Article
Computer Science, Hardware & Architecture
Seyed Soroush Nezamdoust, Mohammad Ali Pourmina, Farbod Razzazi
Summary: This study proposes a modified gated recurrent unit (MGRU) model and a dropout method for predicting future prices using the Spot price history of Amazon EC2. The test results show that the proposed method performs superior and more accurately compared to other sophisticated methods.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Engineering, Biomedical
Shuangyue Yu, Jianfu Yang, Tzu-Hao Huang, Junxi Zhu, Christopher J. Visco, Farah Hameed, Joel Stein, Xianlian Zhou, Hao Su
Summary: This paper presents a high-accuracy gait phase estimation and prediction algorithm based on a two-stage artificial neural network. The algorithm uses a portable controller with only two IMU sensors to estimate and predict the gait cycle in real time. It can detect and classify gait phases in unrhythmic conditions, and also predict future intra-and inter-stride gait phases.
ANNALS OF BIOMEDICAL ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Umar Farooq, Muhammad Wasif Shabir, Muhammad Awais Javed, Muhammad Imran
Summary: This paper presents two energy prediction techniques for fog nodes, based on Recursive Least Square and Artificial Neural Network, to enable intelligent energy-aware task offloading. Simulation results show that the ANN-based technique has up to 20% less root mean square error compared to the RLS-based technique.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Hardware & Architecture
Peyman Yazdanian, Saeed Sharifian
Summary: This paper introduces a novel hybrid E2LG algorithm, using deep learning and ensemble GAN/LSTM architecture to predict cloud workload time-series, improving prediction accuracy, especially for high-frequency, noise-like components.
JOURNAL OF SUPERCOMPUTING
(2021)
Article
Multidisciplinary Sciences
Zhengcai Li, Xinmin Hu, Chun Chen, Chenyang Liu, Yalu Han, Yuanfeng Yu, Lizhi Du
Summary: This paper investigates the optimization algorithms based on machine learning for settlement prediction. By comparing the performance of different algorithms, the study finds that Sparrow Search Algorithm (SSA) significantly improves the optimization effect of the gradient descent model and enhances its stability to a certain degree.
SCIENTIFIC REPORTS
(2022)
Article
Computer Science, Information Systems
Amir Erfan Eshratifar, Mohammad Saeed Abrishami, Massoud Pedram
Summary: The paper introduces JointDNN, an engine for collaborative computation between mobile devices and the cloud for DNNs, which improves energy and performance efficiency for mobile devices while reducing the workload and communications for the cloud server. By processing some layers on mobile devices and others on the cloud server, JointDNN can adapt to battery limitations, server load constraints, and quality of service requirements, achieving significant reductions in latency and mobile energy consumption compared to traditional approaches.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2021)
Article
Chemistry, Analytical
Massimo Vecchio, Paolo Azzoni, Andreas Menychtas, Ilias Maglogiannis, Alexander Felfernig
Summary: The AGILE project has developed a modular hardware and software framework to address the fragmented market of IoT gateways, providing a low-cost solution to support proof-of-concept and rapid prototyping.
Article
Computer Science, Artificial Intelligence
Antonis Pardos, Andreas Menychtas, Ilias Maglogiannis
Summary: This work presents a novel methodology for creating exergames on an edge-native platform by integrating multiple deep neural networks, with a focus on training posture classifiers dynamically adapted to specific requirements for real-time event identification and game control. Ideal for individual consumers in a home environment, the system also allows communication with state-of-the-art hardware and enables the collection and analysis of game data for specialized use in rehabilitation centers and healthcare.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Engineering, Multidisciplinary
Dimitris K. Iakovidis, Melanie Ooi, Ye Chow Kuang, Serge Demidenko, Alexandr Shestakov, Vladimir Sinitsin, Manus Henry, Andrea Sciacchitano, Stefano Discetti, Silvano Donati, Michele Norgia, Andreas Menychtas, Ilias Maglogiannis, Selina C. Wriessnegger, Luis Alberto Barradas Chacon, George Dimas, Dimitris Filos, Anthony H. Aletras, Johannes Toger, Feng Dong, Shangjie Ren, Andreas Uhl, Jacek Paziewski, Jianghui Geng, Francesco Fioranelli, Ram M. Narayanan, Carlos Fernandez, Christoph Stiller, Konstantina Malamousi, Spyros Kamnis, Konstantinos Delibasis, Dong Wang, Jianjing Zhang, Robert X. Gao
Summary: Signal processing plays a crucial role in sensor-enabled systems and has various applications. The advancement in artificial intelligence and machine learning has shifted research focus towards intelligent, data-driven signal processing. This roadmap provides a critical overview of current methods and applications, aiming to identify future challenges and research opportunities for next generation measurement systems.
MEASUREMENT SCIENCE AND TECHNOLOGY
(2022)
Article
Chemistry, Analytical
Dionysios Koulouris, Andreas Menychtas, Ilias Maglogiannis
Summary: Augmented reality (AR) and Internet of Things (IoT) are core technological elements in modern information systems and applications, with extensive applications in healthcare. This work presents a prototype platform that combines AR and IoT for the development of serious games in the healthcare domain. The platform aims to promote user physical activities and monitor their health and cognitive statuses through challenges and quests in virtual and real world environments.
Article
Chemistry, Analytical
Athanasia Zlatintsi, Panagiotis P. Filntisis, Christos Garoufis, Niki Efthymiou, Petros Maragos, Andreas Menychtas, Ilias Maglogiannis, Panayiotis Tsanakas, Thomas Sounapoglou, Emmanouil Kalisperakis, Thomas Karantinos, Marina Lazaridi, Vasiliki Garyfalli, Asimakis Mantas, Leonidas Mantonakis, Nikolaos Smyrnis
Summary: This paper presents an innovative integrated system called e-Prevention, which utilizes wearable technologies and digital phenotyping to effectively monitor and prevent relapse in patients with mental disorders. By applying machine learning and deep learning techniques to the collected data, it is possible to detect and predict relapses.
Article
Chemistry, Analytical
Argyro Mavrogiorgou, Athanasios Kiourtis, Spyridon Kleftakis, Konstantinos Mavrogiorgos, Nikolaos Zafeiropoulos, Dimosthenis Kyriazis
Summary: Extracting useful knowledge from data analysis is crucial for timely decision-making in healthcare. This paper proposes a data analysis mechanism and constructs a catalogue of efficient machine learning algorithms for predicting disease onset based on healthcare scenarios.
Article
Computer Science, Information Systems
Panagiotis Karamolegkos, Argyro Mavrogiorgou, Athanasios Kiourtis, Dimosthenis Kyriazis
Summary: This paper proposes EverAnalyzer, a self-adjustable Big Data management platform that utilizes multiple frameworks to address different data processing and analysis scenarios. By collecting data and utilizing metadata, the platform is able to recommend the best framework for users. Experimental results demonstrate that EverAnalyzer correctly suggests the optimum framework in the majority of cases.
Article
Computer Science, Information Systems
Dimitris Gkoulis, Cleopatra Bardaki, George Kousiouris, Mara Nikolaidou
Summary: This paper focuses on IoT architectures and knowledge generation from streams of events for user-centric IoT services. A general symmetrical IoT architecture is proposed, enabling bidirectional communication between things and users. The Event Engine and Process Engine components implement parametric CEP to handle event transformation, from raw IoT data to processed information. The implementation includes a library of composite transformations for transforming basic IoT events into business events. The appropriateness and integration of the implementation in an IoT environment are demonstrated using scenarios in a smart farming application domain.
Article
Nursing
Etienne Paradis-Gagne, Dave Holmes, Emmanuelle Bernheim, Myriam Cader
Summary: The involvement of people with mental illness in the judicial process, known as judiciarization, is a growing phenomenon that can have negative impacts on their lives. A study was conducted to understand how this process affects individuals with mental illness and to explore their perceptions and experiences within the justice system. The research identified three categories: diversity of judicial trajectories, involuntary psychiatric admission process, and the complex experience of judiciarization. The results of this study are important for informing healthcare professionals and policymakers about the effects of judiciarization on mental illness and how to better support individuals with complex health needs.
ISSUES IN MENTAL HEALTH NURSING
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
George Marinos, Chrysostomos Symvoulidis, Dimosthenis Kyriazis
Summary: Traditional survival analysis estimates the instantaneous failure rate of an event and predicts survival probabilities distributions. However, in a set of censored data, there may exist several sub-populations with various risk profiles or survival distributions that are ignored by regular survival analysis approaches. Therefore, it is essential to discover such sub-populations with unambiguous risk profiles and survival distributions. In this study, a modified version of the K-Medoids algorithm is proposed to efficiently cluster censored data and identify diverse groups with distinct lifetime distributions.
ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, ICAISC 2022, PT II
(2023)
Review
Health Care Sciences & Services
Eshita Dhar, Umashankar Upadhyay, Yaoru Huang, Mohy Uddin, George Manias, Dimosthenis Kyriazis, Usman Wajid, Hamza AlShawaf, Shabbir Syed Abdul
Summary: Due to the challenges posed by the COVID-19 pandemic, technology and digital solutions have played a crucial role in providing necessary healthcare services, particularly in medical education and clinical care. This scoping review examined recent developments in the use of Virtual Reality (VR) for therapeutic care and medical education, with a focus on training medical students and patients. The findings showed significant improvements in medical education and clinical care through the use of VR, with participants endorsing its safety, engagement, and benefits. Collaboration between researchers, the VR industry, and healthcare professionals is needed to further enhance patient care and refine VR content and simulation development.
Proceedings Paper
Computer Science, Interdisciplinary Applications
Antonios Pardos, Parisis Gallos, Andreas Menychtas, Christos Panagopoulos, Ilias Maglogiannis
Summary: This paper presents a methodology for using intelligent recommendations and gamification functionalities in patients' remote monitoring platforms to support their adherence to care plans. By providing personalized recommendations, patients can receive valuable and safe coaching based on their recorded data.
CARING IS SHARING-EXPLOITING THE VALUE IN DATA FOR HEALTH AND INNOVATION-PROCEEDINGS OF MIE 2023
(2023)
Proceedings Paper
Computer Science, Software Engineering
Argyro Mavrogiorgou, Vasileios Koukos, Eleftheria Kouremenou, Athanasios Kiourtis, Alexandros Raikos, George Manias, Dimosthenis Kyriazis
Summary: This paper discusses the concept of a cross-domain Data Marketplace as a unified web-based platform that offers various ready-to-use data management solutions to users, supporting different kinds of cross-sector assets including datasets, software components, data science notebooks, and multimedia content.
PROCEEDINGS OF 2022 THE 3RD EUROPEAN SYMPOSIUM ON SOFTWARE ENGINEERING, ESSE 2022
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
George Manias, Athanasios Kiourtis, Argyro Mavrogiorgou, Dimosthenis Kyriazis
Summary: The widespread use of social media platforms has resulted in a large volume of real-time social texts and posts. Extracting valuable information from these multilingual data is a challenging task. This research compares two multilingual sentiment analysis approaches and highlights the importance of utilizing multilingual approaches in policy making.
ARTIFICIAL INTELLIGENCE APPLICATIONS AND INNOVATIONS, AIAI 2022, PART II
(2022)
Article
Computer Science, Information Systems
Chrysostomos Symvoulidis, George Marinos, Athanasios Kiourtis, Argyro Mavrogiorgou, Dimosthenis Kyriazis
Summary: In recent years, there has been increasing attention given to the integration of new technologies into the health sector. A citizen-centered storage cloud solution is proposed, allowing citizens to hold their health data and exchange it with healthcare professionals during emergencies. A context-aware prefetch engine with deep learning capabilities is also proposed to reduce health data transmission delay. The proposed solution is evaluated in various scenarios and shows significant improvement in download speed compared to other state of the art solutions.
Editorial Material
Computer Science, Theory & Methods
Kiho Lim, Christian Esposito, Tian Wang, Chang Choi
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Editorial Material
Computer Science, Theory & Methods
Jesus Carretero, Dagmar Krefting
Summary: Computational methods play a crucial role in bioinformatics and biomedicine, especially in managing large-scale data and simulating complex models. This special issue focuses on security and performance aspects in infrastructure, optimization for popular applications, and the integration of machine learning and data processing platforms to improve the efficiency and accuracy of bioinformatics.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Renhao Lu, Weizhe Zhang, Qiong Li, Hui He, Xiaoxiong Zhong, Hongwei Yang, Desheng Wang, Zenglin Xu, Mamoun Alazab
Summary: Federated Learning allows collaborative training of AI models with local data, and our proposed FedAAM scheme improves convergence speed and training efficiency through an adaptive weight allocation strategy and asynchronous global update rules.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Qiangqiang Jiang, Xu Xin, Libo Yao, Bo Chen
Summary: This paper proposes a multi-objective energy-efficient task scheduling technique (METSM) for edge heterogeneous multiprocessor systems. A mathematical model is established for the task scheduling problem, and a problem-specific algorithm (IMO) is designed for optimizing task scheduling and resource allocation. Experimental results show that the proposed algorithm can achieve optimal Pareto fronts and significantly save time and power consumption.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Editorial Material
Computer Science, Theory & Methods
Weimin Li, Lu Liu, Kevin I. K. Wang, Qun Jin
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Mohammed Riyadh Abdmeziem, Amina Ahmed Nacer, Nawfel Moundji Deroues
Summary: Internet of Things (IoT) devices have become ubiquitous and brought the need for group communications. However, security in group communications is challenging due to the asynchronous nature of IoT devices. This paper introduces an innovative approach using blockchain technology and smart contracts to ensure secure and scalable group communications.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Robert Sajina, Nikola Tankovic, Ivo Ipsic
Summary: This paper presents and evaluates a novel approach that utilizes an encoder-only transformer model to enable collaboration between agents learning two distinct NLP tasks. The evaluation results demonstrate that collaboration among agents, even when working towards separate objectives, can result in mutual benefits.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Hebert Cabane, Kleinner Farias
Summary: Event-driven architecture has been widely adopted in the software industry for its benefits in software modularity and performance. However, there is a lack of empirical evidence to support its impact on performance. This study compares the performance of an event-driven application with a monolithic application and finds that the monolithic architecture consumes fewer computational resources and has better response times.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Haroon Wahab, Irfan Mehmood, Hassan Ugail, Javier Del Ser, Khan Muhammad
Summary: Wireless capsule endoscopy (WCE) is a revolutionary diagnostic method for small bowel pathology. However, the manual analysis of WCE videos is cumbersome and the privacy concerns of WCE data hinder the adoption of AI-based diagnoses. This study proposes a federated learning framework for collaborative learning from multiple data centers, demonstrating improved anomaly classification performance while preserving data privacy.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Maruf Monem, Md Tamjid Hossain, Md. Golam Rabiul Alam, Md. Shirajum Munir, Md. Mahbubur Rahman, Salman A. AlQahtani, Samah Almutlaq, Mohammad Mehedi Hassan
Summary: Bitcoin, the largest cryptocurrency, faces challenges in broader adaption due to long verification times and high transaction fees. To tackle these issues, researchers propose a learning framework that uses machine learning to predict the ideal block size in each block generation cycle. This model significantly improves the block size, transaction fees, and transaction approval rate of Bitcoin, addressing the long wait time and broader adaption problem.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Rafael Duque, Crescencio Bravo, Santos Bringas, Daniel Postigo
Summary: This paper introduces the importance of user interfaces for digital twins and presents a technique called ADD for modeling requirements of Human-DT interaction. A study is conducted to assess the feasibility and utility of ADD in designing user interfaces, using the virtualization of a natural space as a case study.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Xiulin Li, Li Pan, Wei Song, Shijun Liu, Xiangxu Meng
Summary: This article proposes a novel multiclass multi-pool analytical model for optimizing the quality of composite service applications deployed in the cloud. By considering embarrassingly parallel services and using differentiated parallel processing mechanisms, the model provides accurate prediction results and significantly reduces job response time.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Seongwan Park, Woojin Jeong, Yunyoung Lee, Bumho Son, Huisu Jang, Jaewook Lee
Summary: In this paper, a novel MEV detection model called ArbiNet is proposed, which offers a low-cost and accurate solution for MEV detection without requiring knowledge of smart contract code or ABIs.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Sacheendra Talluri, Nikolas Herbst, Cristina Abad, Tiziano De Matteis, Alexandru Iosup
Summary: Serverless computing is increasingly used in data-processing applications. This paper presents ExDe, a framework for systematically exploring the design space of scheduling architectures and mechanisms, to help system designers tackle complexity.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Chao Wang, Hui Xia, Shuo Xu, Hao Chi, Rui Zhang, Chunqiang Hu
Summary: This paper introduces a Federated Learning framework called FedBnR to address the issue of potential data heterogeneity in distributed entities. By breaking up the original task into multiple subtasks and reconstructing the representation using feature extractors, the framework improves the learning performance on heterogeneous datasets.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)