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27th International Conference on Information Technology, IT 2023

作者： Lakovic, Luka Kara, Peter A. Ognjanovic, Ivana Bokor, Laszlo Sendelj, Ramo Reich, Christoph Roganovic, Milovan Zoulias, Emmanouil Erakovic, Jevto Radusinovic, Tanja Rakocevic, Nada Simon, Aniko University of Donja Gorica Faculty for Information Systems and Technologies Podgorica81000 Montenegro Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Networked Systems and Services Budapest1111 Hungary Faculty of Science Engineering and Computing Kingston University Wireless Multimedia and Networking Research Group Department of Computer Science School of Computer Science and Mathematics LondonKT1 2EE United Kingdom University of Donja Gorica Humanistic Studies Podgorica81000 Montenegro Furtwangen University Institute of Data Science Cloud Computing and It Security Furtwangen78120 Germany Clinical Centre of Montenegro Clinic for Neurology Podgorica81000 Montenegro National and Kapodistrian University of Athens Faculty of Nursing School of Health Sciences Goudi 11527 Greece Chamber of Economy of Montenegro Podgorica81000 Montenegro Sigma Technology Budapest1093 Hungary

ISBN: (纸本)9798350397512

In the era of global digitalization, digital innovation hubs have become essential, as they function as transforming forces. In this paper, we provide an analysis of the Montenegrin Academic Digital Innovation Hub by applying multi-criteria qualitative evaluation over several criteria: process flow of fully digitized operations of the Hub (with aspects of the innovation life-cycle, ethics, and security), resource management, strategic goals, and feasibility. The paper also proposes a model of comprehensive resource management via the perspective of smart specialization priorities of Montenegro. © 2023 IEEE.

关键词： Life cycle

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A Novel Framework for Deepfake Image Detection Using Deep Learning Approach

A Novel Framework for Deepfake Image Detection Using Deep Le...

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Learning and Technology Conference (L&T)

作者： Ambika Jaiswal Aruna Chamatkar Akash Prakash Kharat Bhisham Sharma Imed Ben Dhaou Suhashini Awadhesh Chaurasia P.G. Department of Computer Science Sant Gadge Baba Amravati University Amravati Maharashtra India Kamala Nehru College Nagpur Maharashtra India Department of Computer Science Saraswati College Shegaon Shegaon India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science Hekma School of Engineering Computing Jeddah Saudi Arabia Department of Computer Science RTM Nagpur University Nagpur

ISBN: (数字)9798331532215

ISBN: (纸本)9798331532222

Deepfake, fast growing field in the age of multimedia and AI, have been attracted attention in the recent years. Deep learning algorithms are used to create real digital content which are difficult to distinguish from authentic content. Deepfakes can serve multiples purposes, including educational content, academic research, social entertainment so it spread wrong information, information manipulation, damage to reputation, many frauds. Day to day deepfake crime is increasing. Detection of deepfake is big challenging issue in the digital forensics. A strong approach needs to be created from protecting against the media deepfake. The main work of this paper is to build a efficient framework for deepfake detection. The paper discusses deepfakes, detection techniques, datasets, proposed model. Novel proposed approach has been created that are based on the integration of CNN and VGG16. The deepfake dataset has been used to utilized the network architecture. Proposed model has been achieved the 95% accuracy and 94% precision score. The developed model is superior than the other state of art developed methods.

关键词： Deep learning Deepfakes Analytical models Accuracy Image processing Neural networks Network architecture Media Fraud Faces

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Assistive Technical Communication of Montenegrin eServices: a Case Study 27

Assistive Technical Communication of Montenegrin eServices: ...

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27th International Conference on Information Technology, IT 2023

作者： Simon, Aniko Lakovic, Luka Kovacevic, Pavle Kara, Peter A. Ognjanovic, Ivana Sendelj, Ramo Reich, Christoph Roganovic, Milovan Mantas, John Bokor, Laszlo Sigma Technology Budapest1093 Hungary University of Donja Gorica Faculty for Information Systems and Technologies Podgorica81000 Montenegro Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Networked Systems and Services Budapest1111 Hungary Kingston University Wireless Multimedia and Networking Research Group Department of Computer Science School of Computer Science and Mathematics Faculty of Science Engineering and Computing LondonKT1 2EE United Kingdom University of Donja Gorica Humanistic Studies Podgorica81000 Montenegro Furtwangen University Institute of Data Science Cloud Computing and It Security Furtwangen78120 Germany Clinical Centre of Montenegro Clinic for Neurology Podgorica81000 Montenegro National and Kapodistrian University of Athens Faculty of Nursing School of Health Sciences Goudi 11527 Greece

ISBN: (纸本)9798350397512

With the advancement of information technology in the recent decades, digital and online services have become essential components of everyday life in Europe. Montenegro is no different in this regard. However, as digital literacy in the country is still in a developing phase in comparison with most countries of the EU, a higher portion of its citizens may rely on assistive technical communication. In this paper, we provide a case study on the assistive technical communication of Montenegrin eServices. The work addresses the sectors of online banking, healthcare services, higher education, national administrative services, and utilities. Our findings highlight the diversity in terms of implemented assistive technical communication, and distinguish usage contexts on the level of adequacy. Yet as the functionalities of Montenegrin eServices are expected to gradually expand in the upcoming years, assistive technical communication must aim to handle the higher extents of service complexity. © 2023 IEEE.

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Landscape approximation of low-energy solutions to binary optimization problems

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Physical Review A 2024年第1期109卷 012433-012433页

作者： Benjamin Y. L. Tan Beng Yee Gan Daniel Leykam Dimitris G. Angelakis Centre for Quantum Technologies National University of Singapore 3 Science Drive 2 Singapore 117543 Singapore School of Electrical and Computer Engineering Technical University of Crete Chania 73100 Greece AngelQ Quantum Computing 531A Upper Cross Street 04-95 Hong Lim Complex Singapore 051531 Singapore

We show how the localization landscape, originally introduced to bound low-energy eigenstates of disordered wave media and many-body quantum systems, can form the basis for hardware-efficient quantum algorithms for solving binary optimization problems. Many binary optimization problems can be cast as finding low-energy eigenstates of Ising Hamiltonians. First, we apply specific perturbations to the Ising Hamiltonian such that the low-energy modes are bounded by the localization landscape. Next, we demonstrate how a variational method can be used to prepare and sample from the peaks of the localization landscape. Numerical simulations of problems of up to ten binary variables show that the localization landscape-based sampling can outperform quantum approximate optimization algorithm (QAOA) circuits of similar depth, as measured in terms of the probability of sampling the exact ground state.

关键词： Quantum algorithms Quantum algorithms & computation Quantum computation

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Robust Signal Processing Approaches for Enhancing Communication Reliability in Ultra-Dense Massive MIMO Networks Facing Fading Challenges 3

Robust Signal Processing Approaches for Enhancing Communicat...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Moray, Rashmy Kumari, Savita Upadhyay, Deepak Sharma, Rashmi Ranswal, Aakash Singh Venu, Nookala Symbiosis International Deemed University India School of Computing Science and Engineering Galgotias University Greater Noida India Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Department of Cse Nagpur Campus Pune India Madhav Institute of Technology & Science Deemed University Centre for Internet of Things Gwalior India

ISBN: (纸本)9798331542375

The goal of this paper is to introduce robust signal processing techniques for realistic communication scenarios with fading in ultra-dense massive MIMO communications. The proposed techniques tackle major challenges like channel estimation accuracy, interference management, and energy efficiency, which are essential for sustaining high performance in highly dense and dynamic scenarios. The proposed system outperforms traditional algorithms through advanced techniques and pilot contamination mitigation, interference-to-signal ratio optimization, and hybrid relaying. It is shown by the simulation results those considerable gains in spectral efficiency, energy efficiency and packet errors are achieved, even in the presence of varying user mobility and hardware impairments. These approaches provide reliable communication and optimize the network performance and thus are desirable for future ultradense 6G networks. © 2025 IEEE.

关键词： Network coding

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Local and Global Guidance for Multi-Complementary Label Learning

Local and Global Guidance for Multi-Complementary Label Lear...

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Artificial Intelligence (CAI), IEEE Conference on

作者： Cheng Chen Ivor W Tsang The Australian Artificial Intelligence Institute (AAII) University of Technology Sydney Australia Centre for Frontier AI Research A*STAR Singapore Institute of High Performance Computing A*STAR Singapore School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

Complementary label learning involves instances associated with a candidate set of labels, which may contain multiple labels or a label that does not belong to the true label. Even though such weak supervision is easy to obtain, the setup inevitably introduces issues of label ambiguity and potential label distribution bias. Previous works have approached the task either based on the local characteristics of the dataset (label generation-wise), improving model robustness through mechanisms like transition matrices, or leveraging the global characteristics of the dataset (instance-wise), such as data augmentation and sample selection. However, these methods fail to simultaneously exploit both local and global aspects in addressing the issues of the multi-complementary label task. To tackle this issue, our paper presents a unified approach that considers both local and global guidance. For local guidance, we design homogeneous label regularisation using optimal transportation via the Sinkhorn-Knopp algorithm, ensuring a uniform label distribution across instances. For global guidance, we implement co-occurrence class embedding regularisation to enhance the model’s understanding of the underlying sample distribution. This dual approach mitigates the misleading effects of complementary labels and corrects errors introduced by less informative labels during training. Overall, our experiments have demonstrated that our method is particularly effective on large datasets like CIFAR-100.

关键词： Training Accuracy Transportation Data augmentation Robustness Data models Classification algorithms

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Emergency Vehicle Navigation in Connected Autonomous Systems Using Enhanced Traffic Management System

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IEEE Internet of Things Journal 2025年

作者： Raja, Gunasekaran Anbalagan, Sudha Gurumoorthy, Sugeerthi Jegathesan, Darshini Girivel, Niveditha Subramanian Sundaram, Varsha Mani Shanmuga Khowaja, Sunder Ali Dev, Kapal Anna University NGNLab Department of Computer Technology MIT Campus Chennai600044 India Vellore Institute of Technology Chennai Centre for Smart Grid Technologies School of Computer Science and Engineering Chennai600127 India Citicorp Services Private Ltd Software Development Chennai600113 India Dublin City University School of Computing Faculty of Engineering and Computing Glasnevin Campus DublinD09 V209 Ireland Munster Technological University Department of Computer Science Bishopstown CorkT12 P928 Ireland University of Johannesburg Department of Institute of Intelligent Systems Johannesburg2006 South Africa Chitkara University Institute of Engineering and Technology Centre for Research Impact Outcome Chitkara University Punjab Rajpura140401 India

Autonomous Vehicle (AV) usage has become predominant in the rapidly evolving landscape of urban transportation. Integrating AVs and non-AVs in the existing traffic infrastructure has significantly increased the complexity of traffic patterns. This research work primes the enhanced Traffic Management System (e-TMS), a solution implemented to expedite Emergency Vehicle (EV) travel in the context of Connected AVs (CAVs) and ensured secured communication employing Public Key Infrastructure (PKI) among the Internet of Vehicles (IoV) framework. When an EV is detected, the IoV system verifies the EV's signal using PKI, ensuring its authenticity and integrity by encrypting the communication between the EV and Road Side Units (RSUs). Further, the e-TMS activates the platooning process where CAVs in the EV's lane shift to the adjacent lanes and dynamically form platoons to create a dedicated lane for the EV. To further optimize the platooning process, an Adaptive Smart Leader Selection (ASLS) algorithm is employed to select a leader vehicle spontaneously among the AVs based on the proximity to the EV, communication reliability, and lane position. Radio Detection and Ranging devices aid this platooning process by providing distance and target velocity, which are needed to maintain the required distance between the AVs and ensure safe platoon formation. The e-TMS enhances EV response times and overall traffic flow efficiency and resulted in 27.8% increase in mean speed compared to the traditional methods. © 2014 IEEE.

关键词： Emergency vehicles

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Darcy-Forchheimer mangetized flow based on differential type nanoliquid capturing Ohmic dissipation effects

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Propulsion and Power Research 2023年第3期12卷 443-455页

作者： M.Waqas Yunjie Xu M.Nasir Md Mottahir Alam Amjad Ali Pasha Kashif Irshad Bandar M.Fadhl M.S.Kausar NUTECH School of Applied Science and Humanities National University of TechnologyIslamabad 44000Pakistan Department of Mechanical Engineeing Lebanese American UniversityBeirutLebanon School of Engineering Huzhou UniversityHuzhou 313000China Faculty of Informatics and Computing University Sultan Zainal AbidinBesut Campus22200 BesutTereng-ganuMalaysia Department of Electrical and Computer Engineering King Abdulaziz UniversityJeddah 21589Saudi Arabia Aerospace Engineering Department King Abdulaziz UniversityJeddah 21589Saudi Arabia Interdisciplinary Research Centre for Renewable Energy and Power System(IRC-REPS) Research InstituteKing Fahd University of Petroleum and Minerals(KFUPM)Dhahran 31261Saudi Arabia Mechanical Engineering Department College of Engineering and Islamic ArchitectureUmm Al-Qura UniversityP.O.Box 5555Makkah 21955Saudi Arabia

Hydromagnetic nanoliquid establish an extraordinary category of nanoliquids that unveil both liquid and magnetic *** interest in the utilization of hydromagnetic nanoliquids as a heat transporting medium stem from a likelihood of regulating its flow along with heat transportation process subjected to an externally imposed magnetic *** analysis reports the hydromagnetic nanoliquid impact on differential type(second-grade)liquid from a convectively heated extending *** well-known Darcy-Forchheimer aspect capturing porosity characteristics is introduced for nonlinear *** conditions elaborating heat-mass transportation effect are *** addition,Ohmic dissipation and suction/injection aspects are also a part of this *** analysis is done by implementing the basic relations of fluid *** modeled physical problem is simplified through order *** resulting systems(partial differential expressions)are rendered to the ordinary ones by utilizing the apposite *** solutions are constructed employing homotopy *** and numeric result are addressed comprehensively to elaborate the nature of sundry parameters against physical *** velocity profile is suppressed with increasing Hartmann number(magnetic parameter)whereas it is enhanced with increment in material parameter(second-grade).With the elevation in thermophoresis parameter,temperature and concentration of nanoparticles are accelerated.

关键词： Darcy-Forchheimer magnetized flow Differential type nanoliquid Ohimc dissipation Robin conditions Homotopy algorithm

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Hybrid BiGRU-CNN Model for Load Forecasting in Smart Grids with High Renewable Energy Integration

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IET Generation, Transmission and Distribution 2025年第1期19卷

作者： Ullah, Kaleem Shakir, Daniyal Abid, Usama Alahmari, Saad Aslam, Sheraz Ullah, Zahid US-Pakistan Centre for Advanced Studies in Energy University of Engineering and Technology Peshawar Peshawar Pakistan Sr. Business Analyst S&P Global Islamabad Islamabad Pakistan Department of Computer Science and Information Technology School of Computing Engineering and Mathematical Sciences La Trobe University MelbourneVIC Australia Department of Computer Science Applied College Northern Border University Arar Saudi Arabia Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus Department of Computer Science CTL eurocolege Limassol Cyprus Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Lombardia Italy

Integrating renewable energy sources into smart grids increases supply and demand management because renewable energy sources are intermittent and variable. To overcome this type of challenge, short-term load forecasting (STLF) is essential for managing energy, demand-side flexibility, and the stability of smart grids with renewable energy integration. This paper presents a new model called BiGRU-CNN to improve the operation of STLF in smart grids. The BiGRU-CNN model integrates bidirectional gated recurrent units (BiGRUs) to model temporal dependencies and convolutional neural networks (CNNs) to extract spatial patterns from energy consumption data. The newly developed BiGRU captures past and future contexts through bidirectional processing, and the CNN component extracts high-level features to enhance the accuracy of load demand prediction. The BiGRU-CNN model is compared with two other hybrid models, CNN-LSTM and CNN-GRU, on real-world data from American electric power (AEP) and ISONE datasets. Simulation results show that the proposed BiGRU-CNN outperforms other models, with single-step forecasting yielding root mean square error (RMSE) of 121.43 (AEP) and 123.57 (ISONE), mean absolute error (MAE) of 90.95 (AEP) and 62.97 (ISONE), and mean absolute percentage error (MAPE) of 0.61% (AEP) and 0.41% (ISONE). For multi-step forecasting, the model yields RMSE of 680.02 (AEP) and 581.12 (ISONE), MAE of 481.12 (AEP) and 411.20 (ISONE), and MAPE of 3.27% (AEP) and 2.91% (ISONE). The results show that the BiGRU-CNN model can generate accurate and reliable STLF, which is useful in managing massive renewable energy-integrated smart grids. © 2025 The Author(s). IET Generation, Transmission & Distribution published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Convolutional neural networks

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IoT data analytic algorithms on edge-cloud infrastructure:A review

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Digital Communications and Networks 2023年第6期9卷 1486-1515页

作者： Abel E.Edje M.S.Abd Latiff Weng Howe Chan School of Computing Faculty of EngineeringUniversiti Teknologi Malaysia81310SkudaiJohorMalaysia Department of Computer Science Delta State UniversityAbrakaP.M.B 01 AbrakaDelta StateNigeria UTM Big Data Centre Ibnu Sina Institute of Scientific and Industrial ResearchUniversiti Teknologi Malaysia81310SkudaiJohorMalaysia

The adoption of Internet of Things(IoT)sensing devices is growing rapidly due to their ability to provide realtime ***,it is constrained by limited data storage and processing *** offloads its massive data stream to edge devices and the cloud for adequate storage and *** further leads to the challenges of data outliers,data redundancies,and cloud resource load balancing that would affect the execution and outcome of data *** paper presents a review of existing analytics algorithms deployed on IoT-enabled edge cloud infrastructure that resolved the challenges of data outliers,data redundancies,and cloud resource load *** review highlights the problems solved,the results,the weaknesses of the existing algorithms,and the physical and virtual cloud storage servers for resource load *** addition,it discusses the adoption of network protocols that govern the interaction between the three-layer architecture of IoT sensing devices enabled edge cloud and its prevailing challenges.A total of 72 algorithms covering the categories of classification,regression,clustering,deep learning,and optimization have been *** classification approach has been widely adopted to solve the problem of redundant data,while clustering and optimization approaches are more used for outlier detection and cloud resource allocation.

关键词： Internet of things Cloud platform Edge Analytic algorithms Processes Network communication protocols

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