Published: 14 Feb 2025
Volume 2
Mircea Țălu
talu.s.mircea@gmail.com
Published: 15 Jan 2025
Volume 2
Tianming Zhu
tianming.zhu@nie.edu.sg
Carol Anne Hargreaves
carol.hargreaves@nus.edu.sg
Yi Xin Chen
yixin_97@hotmail.com
Christopher Chen Li-Hsian
phccclh@nus.edu.sg
Saima Hilal
saimahilal@nus.edu.sg
Dementia is a decline in cognitive function and is typically diagnosed when acquired cognitive impairment is severe enough to affect social and/or occupational functioning. Between no cognitive impairment (NCI) and dementia, there are many intermediate states. Predictive cognitive impairment can be useful for initiating treatment to prevent further brain damage. Several deep learning-based approaches have been proposed for the classification of Magnetic Resonance Imaging (MRI) to diagnose Alzheimer’s disease (AD)..
Published: 31 Dec 2024
Volume 1
Senthil Kumar Jagatheesaperumal
senthilkumarj@mepcoeng.ac.in
Mohamed Rahouti
mrahouti@fordham.edu
Mohammed Aledhari
mohammed.aledhari@unt.edu
Abdelatif Hafid
a.hafid@esisa.ac.ma
Diogo Oliveira
dko5179@psu.edu
Hamza Drid
h.drid@univ-batna2.d
Ruhul Amin
mamin17@fordham.edu
The explosive growth of the Internet of Things (IoT) has significantly increased networked devices within distributed and heterogeneous networks. Due to these networks’ inherent vulnerabilities and diversity, the proliferation of IoT devices presents substantial security challenges. Traditional security solutions face challenges in keeping up with the constantly changing threats in dynamic situations. This article reviews the application of distributed Reinforcement Learning approaches to enhance IoT security in dispersed and heterogeneous..
Published: 25 Dec 2024
Volume 1
Moussa Ayyash
msma@ieee.org
Welcome to the inaugural volume of the Computing & AI Connect (CAIC) Journal, a peer-reviewed, open-access journal committed to fostering advancements and innovation in computing sciences and technologies, and artificial intelligence. CAIC is a platform that is dedicated to exploring the profound and transformative impacts of computing and artificial intelligence on the different aspects of life. In recent years, AI has become the focus of researchers from different fields, working to..
Published: 28 Nov 2024
Volume 1
Nijat Taghizade
nijattagizade83@gmail.com
Pietro Lio
pl219at@cam.ac.uk
Modern electrical systems rely heavily on sensors and relays for fault detection in three-phase transmission lines and distribution transformers. However, these devices often need more time complexity and are prone to false alarms (erroneous signals). The study was guided by the PRISMA guidelines and methodology. The study's objective was to compare the levels of accuracy of fault detection presented by different models studied between 2023 and 2024. The general objective..
Published: 15 Nov 2024
Volume 1
Muhammad Asif Ayub
asifayub836@gmail.com
Muhammad Tayyab Zamir
mzamir2023@cic.ipn.mx
Imran Khan
imran.cse@uetpeshawar.edu.pk
Hannia Naseem
hanninaseem836@gmail.com
Nasir Ahmad
nasir.ahmad@mtu.ie
Kashif Ahmad
kashif.ahmad@mtu.ie
Crowd-sourcing has been widely explored for monitoring and feedback on infrastructure and services, such as air and water quality analysis. However, the traditional methods of crowd-sourcing for feedback and analysis of water quality, such as offline and online surveys, have several limitations, such as the limited number of participants and low frequency due to the labor involved in conducting such surveys. Social media analytics could overcome these challenges by providing..
Published: 27 Oct 2024
Volume 1
Denis S. Pashchenko
This article presents a complex vision on software production practices aimed at enhancing the Scrum methodology within software project management. The recommended best practices are closely aligned with contemporary trends in the IT sector, including the total digitalization and virtualization of production processes, the transition to fully remote software development models, the incorporation of artificial intelligence technologies, and the implementation of cost-effective models in team organization. The modifications to software..
Published: 28 Aug 2024
Volume 1
Vishnu Vardhan Baligodugula
baligodugula.2@wright.edu
Ashutosh Ghimire
ghimire.18@wright.edu
Fathi Amsaad
fathi.amsaad@wright.edu
Network segmentation is a very important approach in enhancing network security. The approach involves breaking down the network into smaller, more manageable segments, each with its own specific security requirements. This strategy supports maintaining stable perimeters and effective access control while safeguarding critical resources, such as database servers, from unauthorized access. The relevance of network segmentation in IIoT comes right with the state-of-the-art and interconnected nature of many devices that..
Published: 02 Aug 2024
Volume 1
Amjad Yousef Majid
amjad.y.majid@email.com
Tomas van Rietbergen
t.l.vanrietbergen@student.tudelft.nl
R Venkatesha Prasad
r.r.venkateshaprasad@tudelft.nl
Effective indoor navigation in the presence of dynamic obstacles is crucial for mobile robots. Previous research on deep reinforcement learning (DRL) for robot navigation has primarily focused on expanding neural network (NN) architectures and optimizing hardware setups. However, the impact of other critical factors, such as backward motion enablement, frame stacking buffer size, and the design of the behavioral reward function, on DRL-based navigation remains relatively unexplored. To address this..
Published: 29 Jul 2024
Volume 1
Mariia Baidachna
2828197b@student.gla.ac.uk
Haneen Fatima
h.fatima.1@research.gla.ac.uk
Rahaf Omran
rahaf.omran@hotmail.com
Nour Ghadban
nour.ghadban@glasgow.ac.uk
Muhammad Ali Imran
muhammad.imran@glasgow.ac.uk
Ahmad Taha
ahmad.taha@glasgow.ac.uk
Lina Mohjazi
lina.mohjazi@glasgow.ac.uk
This paper presents a smart diagnostic framework for dental smile analysis. To accurately and efficiently identify esthetic issues from a single image of a smile, a convolutional neural network (CNN) was trained. To overcome the limitations of scarce data, a diffusion model was employed to generate dental smile images in addition to manually curated data. The CNN was trained and evaluated on three datasets: all real images, all generated images,..
Published: 13 Jun 2024
Volume 1
Lossy compression has become essential an important technique to reduce data size in many domains. This type of compression is especially valuable for large-scale scientific data, whose size ranges up to several petabytes. Although Autoencoder-based models have been successfully leveraged to compress images and videos, such neural networks have not widely gained attention in the scientific data domain. Our work presents a neural network that not only significantly compresses large-scale..
