147 research outputs found
Hybrid Whale-Mud-Ring Optimization for Precise Color Skin Cancer Image Segmentation
Timely identification and treatment of rapidly progressing skin cancers can
significantly contribute to the preservation of patients' health and
well-being. Dermoscopy, a dependable and accessible tool, plays a pivotal role
in the initial stages of skin cancer detection. Consequently, the effective
processing of digital dermoscopy images holds significant importance in
elevating the accuracy of skin cancer diagnoses. Multilevel thresholding is a
key tool in medical imaging that extracts objects within the image to
facilitate its analysis. In this paper, an enhanced version of the Mud Ring
Algorithm hybridized with the Whale Optimization Algorithm, named WMRA, is
proposed. The proposed approach utilizes bubble-net attack and mud ring
strategy to overcome stagnation in local optima and obtain optimal thresholds.
The experimental results show that WMRA is powerful against a cluster of recent
methods in terms of fitness, Peak Signal to Noise Ratio (PSNR), and Mean Square
Error (MSE)
On the Sensitivity of Deep Load Disaggregation to Adversarial Attacks
Non-intrusive Load Monitoring (NILM) algorithms, commonly referred to as load
disaggregation algorithms, are fundamental tools for effective energy
management. Despite the success of deep models in load disaggregation, they
face various challenges, particularly those pertaining to privacy and security.
This paper investigates the sensitivity of prominent deep NILM baselines to
adversarial attacks, which have proven to be a significant threat in domains
such as computer vision and speech recognition. Adversarial attacks entail the
introduction of imperceptible noise into the input data with the aim of
misleading the neural network into generating erroneous outputs. We investigate
the Fast Gradient Sign Method (FGSM), a well-known adversarial attack, to
perturb the input sequences fed into two commonly employed CNN-based NILM
baselines: the Sequence-to-Sequence (S2S) and Sequence-to-Point (S2P) models.
Our findings provide compelling evidence for the vulnerability of these models,
particularly the S2P model which exhibits an average decline of 20\% in the
F1-score even with small amounts of noise. Such weakness has the potential to
generate profound implications for energy management systems in residential and
industrial sectors reliant on NILM models
Deep Transfer Learning Applications in Intrusion Detection Systems: A Comprehensive Review
Globally, the external Internet is increasingly being connected to the
contemporary industrial control system. As a result, there is an immediate need
to protect the network from several threats. The key infrastructure of
industrial activity may be protected from harm by using an intrusion detection
system (IDS), a preventive measure mechanism, to recognize new kinds of
dangerous threats and hostile activities. The most recent artificial
intelligence (AI) techniques used to create IDS in many kinds of industrial
control networks are examined in this study, with a particular emphasis on
IDS-based deep transfer learning (DTL). This latter can be seen as a type of
information fusion that merge, and/or adapt knowledge from multiple domains to
enhance the performance of the target task, particularly when the labeled data
in the target domain is scarce. Publications issued after 2015 were taken into
account. These selected publications were divided into three categories:
DTL-only and IDS-only are involved in the introduction and background, and
DTL-based IDS papers are involved in the core papers of this review.
Researchers will be able to have a better grasp of the current state of DTL
approaches used in IDS in many different types of networks by reading this
review paper. Other useful information, such as the datasets used, the sort of
DTL employed, the pre-trained network, IDS techniques, the evaluation metrics
including accuracy/F-score and false alarm rate (FAR), and the improvement
gained, were also covered. The algorithms, and methods used in several studies,
or illustrate deeply and clearly the principle in any DTL-based IDS subcategory
are presented to the reader
Zipf distribution power allocation approach for NOMA Systems
L'accès multiple non orthogonal ( NOMA ) a reçu une attention considérable pour le développement des réseaux sans fil 5G et au-delà. Power-domain NOMA fonctionne sur le concept d'attribution de niveaux de puissance variables aux utilisateurs dans la même fréquence et le même temps bloc. Dans cet article, nous proposons une nouvelle approche d'allocation de puissance qui utilise la loi de distribution Zipf qui satisfait la condition de base d'un système NOMA. Le Zipf PA se caractérise par la simplicité et la facilité de mise en œuvre qui permet d'étendre la capacité du système à prendre en charge un grand nombre d'utilisateurs. Les résultats numériques montrent que le système atteint un débit et une efficacité énergétique élevés sans aucune contrainte d'optimisation des paramètres, ainsi qu'une capacité améliorée en augmentant le nombre d'utilisateurs par rapport au système NOMA avec les techniques d'allocation de puissance existantes
The Media industry in the era of artificial intelligence mechanisms and impacts
Abstract: Explore the role of artificial intelligence in enhancing the personalization of media content, improving the user experience, and increasing audience engagement. The study also discusses the potential ethical challenges that arise when using artificial intelligence in the media industry and provides recommendations for how to address them. Through an in-depth analysis of case studies and industry trends, this study provides valuable insights into how artificial intelligence can be leveraged to improve the efficiency and effectiveness of media content production and distribution. Overall, the findings of this study suggest that artificial intelligence has the potential to revolutionize the media industry, and that organizations that invest in this technology are likely to have a competitive advantage in the increasingly crowded media landscape.
Keywords: Artificial intelligence, mechanisms, impacts, media wor
Appliance identification using a histogram post-processing of 2D local binary patterns for smart grid applications
Identifying domestic appliances in the smart grid leads to a better power
usage management and further helps in detecting appliance-level abnormalities.
An efficient identification can be achieved only if a robust feature extraction
scheme is developed with a high ability to discriminate between different
appliances on the smart grid. Accordingly, we propose in this paper a novel
method to extract electrical power signatures after transforming the power
signal to 2D space, which has more encoding possibilities. Following, an
improved local binary patterns (LBP) is proposed that relies on improving the
discriminative ability of conventional LBP using a post-processing stage. A
binarized eigenvalue map (BEVM) is extracted from the 2D power matrix and then
used to post-process the generated LBP representation. Next, two histograms are
constructed, namely up and down histograms, and are then concatenated to form
the global histogram. A comprehensive performance evaluation is performed on
two different datasets, namely the GREEND and WITHED, in which power data were
collected at 1 Hz and 44000 Hz sampling rates, respectively. The obtained
results revealed the superiority of the proposed LBP-BEVM based system in terms
of the identification performance versus other 2D descriptors and existing
identification frameworks.Comment: 8 pages, 10 figures and 5 table
Analyses of a composite functionally graded material beam with a new transverse shear deformation function
In the present paper, we offer a higher-order shear deformation theory for bending of functionally graded beam. A new polynomial shear function is used which satisfies the stress-free boundary conditions (exact boundary conditions on the stress) at both, top and bottom surfaces of the beam. Hence, the shear correction factor is not necessary. Additionally, the present theory has strong similarities with Timoshenko beam theory in some concepts such as equations of movement, boundary conditions and stress resultant expressions. The governing equations and boundary conditions are derived from the principle of minimum potential energy. Functionally graded material FGM beams have a smooth variation of material properties due to continuous (unbroken) change in micro structural details. The variation of material properties is along the beam thickness and assumed to follow a power-law of the volume fraction of the constituents. Finite element numerical solutions obtained with the new polynomial shear function are presented and the obtained results are evaluated versus the existing solutions to verify the validity of the present theory. At last, the influences of power law indicator and the new shear deformation polynomial function on the bending of functionally graded beams are explored
Artificial Intelligence based Anomaly Detection of Energy Consumption in Buildings: A Review, Current Trends and New Perspectives
Enormous amounts of data are being produced everyday by sub-meters and smart
sensors installed in residential buildings. If leveraged properly, that data
could assist end-users, energy producers and utility companies in detecting
anomalous power consumption and understanding the causes of each anomaly.
Therefore, anomaly detection could stop a minor problem becoming overwhelming.
Moreover, it will aid in better decision-making to reduce wasted energy and
promote sustainable and energy efficient behavior. In this regard, this paper
is an in-depth review of existing anomaly detection frameworks for building
energy consumption based on artificial intelligence. Specifically, an extensive
survey is presented, in which a comprehensive taxonomy is introduced to
classify existing algorithms based on different modules and parameters adopted,
such as machine learning algorithms, feature extraction approaches, anomaly
detection levels, computing platforms and application scenarios. To the best of
the authors' knowledge, this is the first review article that discusses anomaly
detection in building energy consumption. Moving forward, important findings
along with domain-specific problems, difficulties and challenges that remain
unresolved are thoroughly discussed, including the absence of: (i) precise
definitions of anomalous power consumption, (ii) annotated datasets, (iii)
unified metrics to assess the performance of existing solutions, (iv) platforms
for reproducibility and (v) privacy-preservation. Following, insights about
current research trends are discussed to widen the applications and
effectiveness of the anomaly detection technology before deriving future
directions attracting significant attention. This article serves as a
comprehensive reference to understand the current technological progress in
anomaly detection of energy consumption based on artificial intelligence.Comment: 11 Figures, 3 Table
Deep Transfer Learning for Automatic Speech Recognition: Towards Better Generalization
Automatic speech recognition (ASR) has recently become an important challenge
when using deep learning (DL). It requires large-scale training datasets and
high computational and storage resources. Moreover, DL techniques and machine
learning (ML) approaches in general, hypothesize that training and testing data
come from the same domain, with the same input feature space and data
distribution characteristics. This assumption, however, is not applicable in
some real-world artificial intelligence (AI) applications. Moreover, there are
situations where gathering real data is challenging, expensive, or rarely
occurring, which can not meet the data requirements of DL models. deep transfer
learning (DTL) has been introduced to overcome these issues, which helps
develop high-performing models using real datasets that are small or slightly
different but related to the training data. This paper presents a comprehensive
survey of DTL-based ASR frameworks to shed light on the latest developments and
helps academics and professionals understand current challenges. Specifically,
after presenting the DTL background, a well-designed taxonomy is adopted to
inform the state-of-the-art. A critical analysis is then conducted to identify
the limitations and advantages of each framework. Moving on, a comparative
study is introduced to highlight the current challenges before deriving
opportunities for future research
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