This paper examines the environmental impact of green assets using machine learning and impulse responses by local projections.A series of 87 green assets from various classes are considered,namely firms providing ren...This paper examines the environmental impact of green assets using machine learning and impulse responses by local projections.A series of 87 green assets from various classes are considered,namely firms providing renewable energy and carbon offset solutions,carbon and sustainable investing ETFs and green cryptocurrencies.The dataset spans the period from 2015 to 2022 and comprises globally sourced environmental and financial data.The current study examines whether asset prices,returns and trading volumes have an impact on environmental indicators such as temperature(global mean and anomalies)and greenhouse gas concentration.The results indicate that adoption of these green assets does not have a significant environmental impact,suggesting that they should not be used as substitutes for real climate action.This work serves as a cautionary tale on the nexus between green assets and environmental indicators and the results can be used by governments and corporations when formulating climate and ESG strategies.展开更多
Underwater acoustic target recognition(UATR)has become increasingly prevalent for ocean detection,localisation,and identification.However,due to the complexity and variability of underwater environments,especially in ...Underwater acoustic target recognition(UATR)has become increasingly prevalent for ocean detection,localisation,and identification.However,due to the complexity and variability of underwater environments,especially in multi ship event environments,where multiple acoustic signals coexist,practical applications face significant challenges.These challenges hinder single-category acoustic recognition algorithms,particularly in extracting time series features and achieving fine-grained or multi-scale feature fusion.This paper innovatively introduce the SKANN framework,which achieve precise submarine sound recognition in underwater mixed ship events environments through timing data enhancement and sampling training module and selective kernel feature extraction module.The timing data enhancement and sampling training module improves time sequence feature extraction through progressive acoustic sampling.The selective kernel feature extraction module effectively fuses multi-scale features by integrating selective kernel(SK)technology.To simulate concurrent ship events,we constructed the mixed ship noise dataset(MDeepShip),providing an experimental basis and test platform for underwater mixed ship event detection.This dataset ensures that the model encounters diverse audio samples during training and validation,improving its ability to extract temporal features.Experimental results show that SKANN achieves a 93.6%recognition rate on the M-DeepShip dataset,demonstrating its effectiveness in recognising underwater mixed ship events.Given the complexity of real underwater environments,this work lays a crucial foundation for the sound recognition of submarine vessels.Future research will focus on real marine environments to validate and refine the models and methods for practical applications.展开更多
Vertical Federated Learning(VFL),which draws attention because of its ability to evaluate individuals based on features spread across multiple institutions,encounters numerous privacy and security threats.Existing sol...Vertical Federated Learning(VFL),which draws attention because of its ability to evaluate individuals based on features spread across multiple institutions,encounters numerous privacy and security threats.Existing solutions often suffer from centralized architectures,and exorbitant costs.To mitigate these issues,in this paper,we propose SecureVFL,a decentralized multi-party VFL scheme designed to enhance efficiency and trustworthiness while guaranteeing privacy.SecureVFL uses a permissioned blockchain and introduces a novel consensus algorithm,Proof of Feature Sharing(PoFS),to facilitate decentralized,trustworthy,and high-throughput federated training.SecureVFL introduces a verifiable and lightweight three-party Replicated Secret Sharing(RSS)protocol for feature intersection summation among overlapping users.Furthermore,we propose a(_(2)^(4))-sharing protocol to achieve federated training in a four-party VFL setting.This protocol involves only addition operations and exhibits robustness.SecureVFL not only enables anonymous interactions among participants but also safeguards their real identities,and provides mechanisms to unmask these identities when malicious activities are performed.We illustrate the proposed mechanism through a case study on VFL across four banks.Finally,our theoretical analysis proves the security of SecureVFL.Experiments demonstrated that SecureVFL outperformed existing multi-party VFL privacy-preserving schemes,such as MP-FedXGB,in terms of both overhead and model performance.展开更多
Cardiovascular disease prediction is a significant area of research in healthcare management systems(HMS).We will only be able to reduce the number of deaths if we anticipate cardiac problems in advance.The existing h...Cardiovascular disease prediction is a significant area of research in healthcare management systems(HMS).We will only be able to reduce the number of deaths if we anticipate cardiac problems in advance.The existing heart disease detection systems using machine learning have not yet produced sufficient results due to the reliance on available data.We present Clustered Butterfly Optimization Techniques(RoughK-means+BOA)as a new hybrid method for predicting heart disease.This method comprises two phases:clustering data using Roughk-means(RKM)and data analysis using the butterfly optimization algorithm(BOA).The benchmark dataset from the UCI repository is used for our experiments.The experiments are divided into three sets:the first set involves the RKM clustering technique,the next set evaluates the classification outcomes,and the last set validates the performance of the proposed hybrid model.The proposed RoughK-means+BOA has achieved a reasonable accuracy of 97.03 and a minimal error rate of 2.97.This result is comparatively better than other combinations of optimization techniques.In addition,this approach effectively enhances data segmentation,optimization,and classification performance.展开更多
The successful penetration of government,corporate,and organizational IT systems by state and non-state actors deploying APT vectors continues at an alarming pace.Advanced Persistent Threat(APT)attacks continue to pos...The successful penetration of government,corporate,and organizational IT systems by state and non-state actors deploying APT vectors continues at an alarming pace.Advanced Persistent Threat(APT)attacks continue to pose significant challenges for organizations despite technological advancements in artificial intelligence(AI)-based defense mechanisms.While AI has enhanced organizational capabilities for deterrence,detection,and mitigation of APTs,the global escalation in reported incidents,particularly those successfully penetrating critical government infrastructure has heightened concerns among information technology(IT)security administrators and decision-makers.Literature review has identified the stealthy lateral movement(LM)of malware within the initially infected local area network(LAN)as a significant concern.However,current literature has yet to propose a viable approach for resource-efficient,real-time detection of APT malware lateral movement within the initially compromised LAN following perimeter breach.Researchers have suggested the nature of the dataset,optimal feature selection,and the choice of machine learning(ML)techniques as critical factors for detection.Hence,the objective of the research described here was to successfully demonstrate a simplified lightweight ML method for detecting the LM of APT vectors.While the nearest detection rate achieved in the LM domain within LAN was 99.89%,as reported in relevant studies,our approach surpassed it,with a detection rate of 99.95%for the modified random forest(RF)classifier for dataset 1.Additionally,our approach achieved a perfect 100%detection rate for the decision tree(DT)and RF classifiers with dataset 2,a milestone not previously reached in studies within this domain involving two distinct datasets.Using the ML life cycle methodology,we deployed K-nearest neighbor(KNN),support vector machine(SVM),DT,and RF on three relevant datasets to detect the LM of APTs at the affected LAN prior to data exfiltration/destruction.Feature engineering presented four critical APT LM intrusion detection(ID)indicators(features)across the three datasets,namely,the source port number,the destination port number,the packets,and the bytes.This study demonstrates the effectiveness of lightweight ML classifiers in detecting APT lateral movement after network perimeter breach.It contributes to the field by proposing a non-intrusive network detection method capable of identifying APT malware before data exfiltration,thus providing an additional layer of organizational defense.展开更多
The purpose of this article review is to update what is known about the role of diet on non-alcoholic fatty liver disease(NAFLD). NAFLD is the most common cause of chronic liver disease in the developed world and is c...The purpose of this article review is to update what is known about the role of diet on non-alcoholic fatty liver disease(NAFLD). NAFLD is the most common cause of chronic liver disease in the developed world and is considered to be a spectrum, ranging from fatty infiltration of the liver alone(steatosis), which may lead to fatty infiltration with inflammation known as non alcoholic steatohepatitis While the majority of individualswith risk factors like obesity and insulin resistance have steatosis, only few people may develop steatohepatitis. Current treatment relies on weight loss and exercise, although various insulin-sensitizing medications appear promising. Weight loss alone by dietary changes has been shown to lead to histological improvement in fatty liver making nutrition therapy to become a cornerstone of treatment for NAFLD. Supplementation of vitamin E, C and omega 3 fatty acids are under consideration with some conflicting data. Moreover, research has been showed that saturated fat, trans-fatty acid, carbohydrate, and simple sugars(fructose and sucrose) may play significant role in the intrahepatic fat accumulation. However, true associations with specific nutrients yet to be clarified.展开更多
This paper investigates the bit-interleaved coded generalized spatial modulation(BICGSM) with iterative decoding(BICGSM-ID) for multiple-input multiple-output(MIMO) visible light communications(VLC). In the BICGSM-ID ...This paper investigates the bit-interleaved coded generalized spatial modulation(BICGSM) with iterative decoding(BICGSM-ID) for multiple-input multiple-output(MIMO) visible light communications(VLC). In the BICGSM-ID scheme, the information bits conveyed by the signal-domain(SiD) symbols and the spatial-domain(SpD) light emitting diode(LED)-index patterns are coded by a protograph low-density parity-check(P-LDPC) code. Specifically, we propose a signal-domain symbol expanding and re-allocating(SSER) method for constructing a type of novel generalized spatial modulation(GSM) constellations, referred to as SSERGSM constellations, so as to boost the performance of the BICGSM-ID MIMO-VLC systems.Moreover, by applying a modified PEXIT(MPEXIT) algorithm, we further design a family of rate-compatible P-LDPC codes, referred to as enhanced accumulate-repeat-accumulate(EARA) codes,which possess both excellent decoding thresholds and linear-minimum-distance-growth property. Both analysis and simulation results illustrate that the proposed SSERGSM constellations and P-LDPC codes can remarkably improve the convergence and decoding performance of MIMO-VLC systems. Therefore, the proposed P-LDPC-coded SSERGSM-mapped BICGSMID configuration is envisioned as a promising transmission solution to satisfy the high-throughput requirement of MIMO-VLC applications.展开更多
This study examines the connectedness between the US yield curve components(i.e.,level,slope,and curvature),exchange rates,and the historical volatility of the exchange rates of the main safe-haven fiat currencies(Can...This study examines the connectedness between the US yield curve components(i.e.,level,slope,and curvature),exchange rates,and the historical volatility of the exchange rates of the main safe-haven fiat currencies(Canada,Switzerland,EURO,Japan,and the UK)and the leading cryptocurrency,the Bitcoin.Results of the static analysis show that the level and slope of the yield curve are net transmitters of shocks to both the exchange rate and its volatility.The exchange rate of the Euro and the volatility of the Euro and the Canadian dollar exchange rate are net transmitters of shocks.Meanwhile,the curvature of the yield curve and the Japanese Yen,Swiss Franc,and British Pound act mainly as net receivers.Our static connectedness analysis shows that Bitcoin is mainly independent of shocks from the yield curve’s level,slope,and curvature,and from any main currency investigated.These findings hint that Bitcoin might provide hedging benefits.However,similar to the static analysis,our dynamic analysis shows that during different periods and particularly in stressful times,Bitcoin is far from being isolated from other currencies or the yield curve components.The dynamic analysis allows us to observe Bitcoin’s connectedness in times of stress.Evidence supporting this contention is the substantially increased connectedness due to policy shocks,political uncertainty,and systemic crisis,implying no empirical support for Bitcoin’s safe-haven property during stress times.The increased connectedness in the dynamic analysis compared with the static approach implies that in normal times and especially in stressful times,Bitcoin has the property of a diversifier.The results may have important implications for investors and policymakers regarding their risk monitoring and their assets allocation and investment strategies.展开更多
Recently,video object segmentation has received great attention in the computer vision community.Most of the existing methods heavily rely on the pixel-wise human annotations,which are expensive and time-consuming to ...Recently,video object segmentation has received great attention in the computer vision community.Most of the existing methods heavily rely on the pixel-wise human annotations,which are expensive and time-consuming to obtain.To tackle this problem,we make an early attempt to achieve video object segmentation with scribble-level supervision,which can alleviate large amounts of human labor for collecting the manual annotation.However,using conventional network architectures and learning objective functions under this scenario cannot work well as the supervision information is highly sparse and incomplete.To address this issue,this paper introduces two novel elements to learn the video object segmentation model.The first one is the scribble attention module,which captures more accurate context information and learns an effective attention map to enhance the contrast between foreground and background.The other one is the scribble-supervised loss,which can optimize the unlabeled pixels and dynamically correct inaccurate segmented areas during the training stage.To evaluate the proposed method,we implement experiments on two video object segmentation benchmark datasets,You Tube-video object segmentation(VOS),and densely annotated video segmentation(DAVIS)-2017.We first generate the scribble annotations from the original per-pixel annotations.Then,we train our model and compare its test performance with the baseline models and other existing works.Extensive experiments demonstrate that the proposed method can work effectively and approach to the methods requiring the dense per-pixel annotations.展开更多
System logs record detailed information about system operation and areimportant for analyzing the system's operational status and performance. Rapidand accurate detection of system anomalies is of great significan...System logs record detailed information about system operation and areimportant for analyzing the system's operational status and performance. Rapidand accurate detection of system anomalies is of great significance to ensure system stability. However, large-scale distributed systems are becoming more andmore complex, and the number of system logs gradually increases, which bringschallenges to analyze system logs. Some recent studies show that logs can beunstable due to the evolution of log statements and noise introduced by log collection and parsing. Moreover, deep learning-based detection methods take a longtime to train models. Therefore, to reduce the computational cost and avoid loginstability we propose a new Word2Vec-based log unsupervised anomaly detection method (LogUAD). LogUAD does not require a log parsing step and takesoriginal log messages as input to avoid the noise. LogUAD uses Word2Vec togenerate word vectors and generates weighted log sequence feature vectors withTF-IDF to handle the evolution of log statements. At last, a computationally effi-cient unsupervised clustering is exploited to detect the anomaly. We conductedextensive experiments on the public dataset from Blue Gene/L (BGL). Experimental results show that the F1-score of LogUAD can be improved by 67.25%compared to LogCluster.展开更多
Since December 2019,a new pandemic has appeared causing a considerable negative global impact.The SARS-CoV-2 first emerged from China and transformed to a global pandemic within a short time.The virus was further obse...Since December 2019,a new pandemic has appeared causing a considerable negative global impact.The SARS-CoV-2 first emerged from China and transformed to a global pandemic within a short time.The virus was further observed to be spreading rapidly and mutating at a fast pace,with over 5,775 distinct variations of the virus observed globally(at the time of submitting this paper).Extensive research has been ongoing worldwide in order to get a better understanding of its behaviour,influence and more importantly,ways for reducing its impact.Data analytics has been playing a pivotal role in this research to obtain valuable insights into understanding and fighting against the spread of infection.However,this is time and resource intensive,making it difficult to observe and quickly identify the impact of mutations.Factors such as the spread or virulence could explain the three months delay in revealing the new virus variant in the UK.This paper presents an extensive correlation analysis of the effect caused by the different SARS-CoV-2 strains,and their influence on the population across diverse factors,such as propagation and fatality rates,during the peak of the pandemic,with a focus on two major countries in the Middle East,the United Arab Emirate(UAE)and the Kingdom of Saudi Arabia(KSA).This research aims to investigate the epidemiological behaviour of the Coronavirus’genomic variants over time in the UAE,compared with the KSA,where correlation analysis is carried out for a number of cases,deaths and their statistical deviations.The results of the analysis highlight very interesting insights into the epidemiological impact of the Covid-19 genomic behaviour in both countries,which could lead to important actions taken to minimize the impact on wider public health,possibly saving lives,and the economy.For instance,our method identifies a potential correlation between a spike in the number of deaths per case of 5.5 observed in the UAE by March 24th,with the emergence of new genomic variants of the Coronavirus(G0_c,G0_e1 and G0_e2).Our proposed methodology can be instrumental in identifying and classifying new variations of the virus earlier,and possibly predicting foreseeable mutations through pattern analysis,hence creating proactive measures to control its spread,such as the recent case of the new virus variant,recently discovered in the UK.展开更多
The Mare Moscoviense is an astonishing rare flatland multi-ring basin and one of the recognizable mare regions on the Moon's farside.The mineralogical,chronological,topographical and morphological studies of the m...The Mare Moscoviense is an astonishing rare flatland multi-ring basin and one of the recognizable mare regions on the Moon's farside.The mineralogical,chronological,topographical and morphological studies of the maria surface of the Moon provide a primary understanding of the origin and evolution of the mare provinces.In this study,the Chandrayaan-1 M^(3)data have been employed to prepare optical maturity index,FeO and TiO^(2)concentration,and standard band ratio map to detect the mafic indexes like olivine and pyroxene minerals.The crater size frequency distribution method has been applied to LROC WAC data to obtain the absolute model ages of the Moscoviense basin.The four geological unit ages were observed as 3.57 Ga(U-2),3.65 Ga(U-1),3.8 Ga(U-3)and 3.92 Ga(U-4),which could have been formed between the Imbrian and Nectarian epochs.The M^(3)imaging and reflectance spectral parameters were used to reveal the minerals like pyroxene,olivine,ilmenite,plagioclase,orthopyroxene-olivine-spinel lithology,and olivine-pyroxene mixtures present in the gabbroic basalt,anorthositic and massive ilmenite rocks,and validated with the existing database.The results show that the Moscoviense basin is dominated by intermediate TiO^(2)basalts that derived from olivine-ilmenite-pyroxene cumulate depths ranging from 200 to 500 km between 3.5 Ga and 3.6 Ga.展开更多
The unavailability of sufficient information for proper diagnosis,incomplete or miscommunication between patient and the clinician,or among the healthcare professionals,delay or incorrect diagnosis,the fatigue of clin...The unavailability of sufficient information for proper diagnosis,incomplete or miscommunication between patient and the clinician,or among the healthcare professionals,delay or incorrect diagnosis,the fatigue of clinician,or even the high diagnostic complexity in limited time can lead to diagnostic errors.Diagnostic errors have adverse effects on the treatment of a patient.Unnecessary treatments increase the medical bills and deteriorate the health of a patient.Such diagnostic errors that harm the patient in various ways could be minimized using machine learning.Machine learning algorithms could be used to diagnose various diseases with high accuracy.The use of machine learning could assist the doctors in making decisions on time,and could also be used as a second opinion or supporting tool.This study aims to provide a comprehensive review of research articles published from the year 2015 to mid of the year 2020 that have used machine learning for diagnosis of various diseases.We present the various machine learning algorithms used over the years to diagnose various diseases.The results of this study show the distribution of machine learning methods by medical disciplines.Based on our review,we present future research directions that could be used to conduct further research.展开更多
Underwater acoustic sensor networks(UWASNs)aim to find varied offshore ocean monitoring and exploration applications.In most of these applications,the network is composed of several sensor nodes deployed at different ...Underwater acoustic sensor networks(UWASNs)aim to find varied offshore ocean monitoring and exploration applications.In most of these applications,the network is composed of several sensor nodes deployed at different depths in the water.Sensor nodes located at depth on the seafloor cannot invariably communicate with nodes close to the surface level;these nodes need multihop communication facilitated by a suitable routing scheme.In this research work,a Cluster-based Cooperative Energy Efficient Routing(CEER)mechanism for UWSNs is proposed to overcome the shortcomings of the Co-UWSN and LEACH mechanisms.The optimal role of clustering and cooperation provides load balancing and improves the network profoundly.The simulation results using MATLAB show better performance of CEER routing protocol in terms of various parameters as compared to Co-UWSN routing protocol,i.e.,the average end-to-end delay of CEER was 17.39,Co-UWSN was 55.819 and LEACH was 70.08.In addition,the average total energy consumption of CEER was 9.273,Co-UWSN was 12.198,and LEACH was 45.33.The packet delivery ratio of CEER was 53.955,CO-UWSN was 42.047,and LEACH was 30.31.The stability period CEER was 130.9,CO-UWSN was 129.3,and LEACH was 119.1.The obtained results maximized the lifetime and improved the overall performance of the CEER routing protocol.展开更多
In this paper,a differential scheme is proposed for reconfigurable intelligent surface(RIS)assisted spatial modulation,which is referred to as RISDSM,to eliminate the need for channel state information(CSI)at the rece...In this paper,a differential scheme is proposed for reconfigurable intelligent surface(RIS)assisted spatial modulation,which is referred to as RISDSM,to eliminate the need for channel state information(CSI)at the receiver.The proposed scheme is an improvement over the current differential modulation scheme used in RIS-based systems,as it avoids the high-order matrix calculation and improves the spectral efficiency.A mathematical framework is developed to determine the theoretical average bit error probability(ABEP)of the system using RIS-DSM.The detection complexity of the proposed RIS-DSM scheme is extremely low through the simplification.Finally,simulations results demonstrate that the proposed RIS-DSM scheme can deliver satisfactory error performance even in low signal-to-noise ratio environments.展开更多
Radio frequency identification(RFID),also known as electronic label technology,is a non-contact automated identification technology that recognizes the target object and extracts relevant data and critical characteris...Radio frequency identification(RFID),also known as electronic label technology,is a non-contact automated identification technology that recognizes the target object and extracts relevant data and critical characteristics using radio frequency signals.Medical equipment information management is an important part of the construction of a modern hospital,as it is linked to the degree of diagnosis and care,as well as the hospital’s benefits and growth.The aim of this study is to create an integrated view of a theoretical framework to identify factors that influence RFID adoption in healthcare,as well as to conduct an empirical review of the impact of organizational,environmental,and individual factors on RFID adoption in the healthcare industry.In contrast to previous research,the current study focuses on individual factors as well as organizational and technological factors in order to better understand the phenomenon of RFID adoption in healthcare,which is characterized as a dynamic and challenging work environment.This research fills a gap in the current literature by describing how user factors can influence RFID adoption in healthcare and how such factors can lead to a deeper understanding of the advantages,uses,and impacts of RFID in healthcare.The proposed study has superior performance and effective results.展开更多
Federated Learning(FL)enables collaborative and privacy-preserving training of machine learning models within the Internet of Vehicles(IoV)realm.While FL effectively tackles privacy concerns,it also imposes significan...Federated Learning(FL)enables collaborative and privacy-preserving training of machine learning models within the Internet of Vehicles(IoV)realm.While FL effectively tackles privacy concerns,it also imposes significant resource requirements.In traditional FL,trained models are transmitted to a central server for global aggregation,typically in the cloud.This approach often leads to network congestion and bandwidth limitations when numerous devices communicate with the same server.The need for Flexible Global Aggregation and Dynamic Client Selection in FL for the IoV arises from the inherent characteristics of IoV environments.These include diverse and distributed data sources,varying data quality,and limited communication resources.By employing dynamic client selection,we can prioritize relevant and high-quality data sources,enhancing model accuracy.To address this issue,we propose an FL framework that selects global aggregation nodes dynamically rather than a single fixed aggregator.Flexible global aggregation ensures efficient utilization of limited network resources while accommodating the dynamic nature of IoV data sources.This approach optimizes both model performance and resource allocation,making FL in IoV more effective and adaptable.The selection of the global aggregation node is based on workload and communication speed considerations.Additionally,our framework overcomes the constraints associated with network,computational,and energy resources in the IoV environment by implementing a client selection algorithm that dynamically adjusts participants according to predefined parameters.Our approach surpasses Federated Averaging(FedAvg)and Hierarchical FL(HFL)regarding energy consumption,delay,and accuracy,yielding superior results.展开更多
The end-to-end delay in a wired network is strongly dependent on congestion on intermediate nodes.Among lots of feasible approaches to avoid congestion efficiently,congestion-aware routing protocols tend to search for...The end-to-end delay in a wired network is strongly dependent on congestion on intermediate nodes.Among lots of feasible approaches to avoid congestion efficiently,congestion-aware routing protocols tend to search for an uncongested path toward the destination through rule-based approaches in reactive/incident-driven and distributed methods.However,these previous approaches have a problem accommodating the changing network environments in autonomous and self-adaptive operations dynamically.To overcome this drawback,we present a new congestion-aware routing protocol based on a Q-learning algorithm in software-defined networks where logically centralized network operation enables intelligent control and management of network resources.In a proposed routing protocol,either one of uncongested neighboring nodes are randomly selected as next hop to distribute traffic load to multiple paths or Q-learning algorithm is applied to decide the next hop by modeling the state,Q-value,and reward function to set the desired path toward the destination.A new reward function that consists of a buffer occupancy,link reliability and hop count is considered.Moreover,look ahead algorithm is employed to update the Q-value with values within two hops simultaneously.This approach leads to a decision of the optimal next hop by taking congestion status in two hops into account,accordingly.Finally,the simulation results presented approximately 20%higher packet delivery ratio and 15%shorter end-to-end delay,compared to those with the existing scheme by avoiding congestion adaptively.展开更多
Alzheimer’s disease(AD)is a neurodegenerative disorder,causing the most common dementia in the elderly peoples.The AD patients are rapidly increasing in each year and AD is sixth leading cause of death in USA.Magneti...Alzheimer’s disease(AD)is a neurodegenerative disorder,causing the most common dementia in the elderly peoples.The AD patients are rapidly increasing in each year and AD is sixth leading cause of death in USA.Magnetic resonance imaging(MRI)is the leading modality used for the diagnosis of AD.Deep learning based approaches have produced impressive results in this domain.The early diagnosis of AD depends on the efficient use of classification approach.To address this issue,this study proposes a system using two convolutional neural networks(CNN)based approaches for an early diagnosis of AD automatically.In the proposed system,we use segmented MRI scans.Input data samples of three classes include 110 normal control(NC),110 mild cognitive impairment(MCI)and 105 AD subjects are used in this paper.The data is acquired from the ADNI database and gray matter(GM)images are obtained after the segmentation of MRI subjects which are used for the classification in the proposed models.The proposed approaches segregate among NC,MCI,and AD.While testing both methods applied on the segmented data samples,the highest performance results of the classification in terms of accuracy on NC vs.AD are 95.33%and 89.87%,respectively.The proposed methods distinguish between NC vs.MCI and MCI vs.AD patients with a classification accuracy of 90.74%and 86.69%.The experimental outcomes prove that both CNN-based frameworks produced state-of-the-art accurate results for testing.展开更多
Cardiac diseases are one of the greatest global health challenges.Due to the high annual mortality rates,cardiac diseases have attracted the attention of numerous researchers in recent years.This article proposes a hy...Cardiac diseases are one of the greatest global health challenges.Due to the high annual mortality rates,cardiac diseases have attracted the attention of numerous researchers in recent years.This article proposes a hybrid fuzzy fusion classification model for cardiac arrhythmia diseases.The fusion model is utilized to optimally select the highest-ranked features generated by a variety of well-known feature-selection algorithms.An ensemble of classifiers is then applied to the fusion’s results.The proposed model classifies the arrhythmia dataset from the University of California,Irvine into normal/abnormal classes as well as 16 classes of arrhythmia.Initially,at the preprocessing steps,for the miss-valued attributes,we used the average value in the linear attributes group by the same class and the most frequent value for nominal attributes.However,in order to ensure the model optimality,we eliminated all attributes which have zero or constant values that might bias the results of utilized classifiers.The preprocessing step led to 161 out of 279 attributes(features).Thereafter,a fuzzy-based feature-selection fusion method is applied to fuse high-ranked features obtained from different heuristic feature-selection algorithms.In short,our study comprises three main blocks:(1)sensing data and preprocessing;(2)feature queuing,selection,and extraction;and(3)the predictive model.Our proposed method improves classification performance in terms of accuracy,F1measure,recall,and precision when compared to state-of-the-art techniques.It achieves 98.5%accuracy for binary class mode and 98.9%accuracy for categorized class mode.展开更多
文摘This paper examines the environmental impact of green assets using machine learning and impulse responses by local projections.A series of 87 green assets from various classes are considered,namely firms providing renewable energy and carbon offset solutions,carbon and sustainable investing ETFs and green cryptocurrencies.The dataset spans the period from 2015 to 2022 and comprises globally sourced environmental and financial data.The current study examines whether asset prices,returns and trading volumes have an impact on environmental indicators such as temperature(global mean and anomalies)and greenhouse gas concentration.The results indicate that adoption of these green assets does not have a significant environmental impact,suggesting that they should not be used as substitutes for real climate action.This work serves as a cautionary tale on the nexus between green assets and environmental indicators and the results can be used by governments and corporations when formulating climate and ESG strategies.
基金funded by The National Natural Science Foundation of China under Grant(Nos.62273108 and 62306081)The Youth Project of Guangdong Artificial Intelligence and Digital Economy Laboratory(Guangzhou)(PZL2022KF0006)+6 种基金The National Key Research and Development Program-Research on Key technology of High Frequency broadband mobile communication credit Filter and its Industrialization application-Subproject Circuit Design and Simulation of high frequency broadband Filter(2022YFB3604502)‘New Generation Information Technology’Major Science and Technology Project of Guangzhou Key Field R&D Plan(202206070001)the Special Fund Project of Guangzhou Science and Technology Innovation Development(202201011307)the Guangdong Provincial Department of Education Key construction discipline Scientific research ability Improvement Project,Introduction of Talents Project of Guangdong Polytechnic Normal University of China(99166990222)the Special Projects in Key Fields of General Colleges and Universities in Guangdong Province(2021ZDZX1016)the Natural Science Foundation of Guangdong Province(2024A1515010120)the Special Fund Project of Guangzhou Science and Technology Innovation Development(202201011307).
文摘Underwater acoustic target recognition(UATR)has become increasingly prevalent for ocean detection,localisation,and identification.However,due to the complexity and variability of underwater environments,especially in multi ship event environments,where multiple acoustic signals coexist,practical applications face significant challenges.These challenges hinder single-category acoustic recognition algorithms,particularly in extracting time series features and achieving fine-grained or multi-scale feature fusion.This paper innovatively introduce the SKANN framework,which achieve precise submarine sound recognition in underwater mixed ship events environments through timing data enhancement and sampling training module and selective kernel feature extraction module.The timing data enhancement and sampling training module improves time sequence feature extraction through progressive acoustic sampling.The selective kernel feature extraction module effectively fuses multi-scale features by integrating selective kernel(SK)technology.To simulate concurrent ship events,we constructed the mixed ship noise dataset(MDeepShip),providing an experimental basis and test platform for underwater mixed ship event detection.This dataset ensures that the model encounters diverse audio samples during training and validation,improving its ability to extract temporal features.Experimental results show that SKANN achieves a 93.6%recognition rate on the M-DeepShip dataset,demonstrating its effectiveness in recognising underwater mixed ship events.Given the complexity of real underwater environments,this work lays a crucial foundation for the sound recognition of submarine vessels.Future research will focus on real marine environments to validate and refine the models and methods for practical applications.
基金supported by Open Research Projects of Zhejiang Lab(No.2022QA0AB02)Natural Science Foundation of Sichuan Province(2022NSFSC0913)Sichuan Province Selected Funding for Postdoctoral Research Projects(TB2022032).
文摘Vertical Federated Learning(VFL),which draws attention because of its ability to evaluate individuals based on features spread across multiple institutions,encounters numerous privacy and security threats.Existing solutions often suffer from centralized architectures,and exorbitant costs.To mitigate these issues,in this paper,we propose SecureVFL,a decentralized multi-party VFL scheme designed to enhance efficiency and trustworthiness while guaranteeing privacy.SecureVFL uses a permissioned blockchain and introduces a novel consensus algorithm,Proof of Feature Sharing(PoFS),to facilitate decentralized,trustworthy,and high-throughput federated training.SecureVFL introduces a verifiable and lightweight three-party Replicated Secret Sharing(RSS)protocol for feature intersection summation among overlapping users.Furthermore,we propose a(_(2)^(4))-sharing protocol to achieve federated training in a four-party VFL setting.This protocol involves only addition operations and exhibits robustness.SecureVFL not only enables anonymous interactions among participants but also safeguards their real identities,and provides mechanisms to unmask these identities when malicious activities are performed.We illustrate the proposed mechanism through a case study on VFL across four banks.Finally,our theoretical analysis proves the security of SecureVFL.Experiments demonstrated that SecureVFL outperformed existing multi-party VFL privacy-preserving schemes,such as MP-FedXGB,in terms of both overhead and model performance.
基金supported by the Research Incentive Grant 23200 of Zayed University,United Arab Emirates.
文摘Cardiovascular disease prediction is a significant area of research in healthcare management systems(HMS).We will only be able to reduce the number of deaths if we anticipate cardiac problems in advance.The existing heart disease detection systems using machine learning have not yet produced sufficient results due to the reliance on available data.We present Clustered Butterfly Optimization Techniques(RoughK-means+BOA)as a new hybrid method for predicting heart disease.This method comprises two phases:clustering data using Roughk-means(RKM)and data analysis using the butterfly optimization algorithm(BOA).The benchmark dataset from the UCI repository is used for our experiments.The experiments are divided into three sets:the first set involves the RKM clustering technique,the next set evaluates the classification outcomes,and the last set validates the performance of the proposed hybrid model.The proposed RoughK-means+BOA has achieved a reasonable accuracy of 97.03 and a minimal error rate of 2.97.This result is comparatively better than other combinations of optimization techniques.In addition,this approach effectively enhances data segmentation,optimization,and classification performance.
基金Rabdan Academy for funding the research presented in the paper.
文摘The successful penetration of government,corporate,and organizational IT systems by state and non-state actors deploying APT vectors continues at an alarming pace.Advanced Persistent Threat(APT)attacks continue to pose significant challenges for organizations despite technological advancements in artificial intelligence(AI)-based defense mechanisms.While AI has enhanced organizational capabilities for deterrence,detection,and mitigation of APTs,the global escalation in reported incidents,particularly those successfully penetrating critical government infrastructure has heightened concerns among information technology(IT)security administrators and decision-makers.Literature review has identified the stealthy lateral movement(LM)of malware within the initially infected local area network(LAN)as a significant concern.However,current literature has yet to propose a viable approach for resource-efficient,real-time detection of APT malware lateral movement within the initially compromised LAN following perimeter breach.Researchers have suggested the nature of the dataset,optimal feature selection,and the choice of machine learning(ML)techniques as critical factors for detection.Hence,the objective of the research described here was to successfully demonstrate a simplified lightweight ML method for detecting the LM of APT vectors.While the nearest detection rate achieved in the LM domain within LAN was 99.89%,as reported in relevant studies,our approach surpassed it,with a detection rate of 99.95%for the modified random forest(RF)classifier for dataset 1.Additionally,our approach achieved a perfect 100%detection rate for the decision tree(DT)and RF classifiers with dataset 2,a milestone not previously reached in studies within this domain involving two distinct datasets.Using the ML life cycle methodology,we deployed K-nearest neighbor(KNN),support vector machine(SVM),DT,and RF on three relevant datasets to detect the LM of APTs at the affected LAN prior to data exfiltration/destruction.Feature engineering presented four critical APT LM intrusion detection(ID)indicators(features)across the three datasets,namely,the source port number,the destination port number,the packets,and the bytes.This study demonstrates the effectiveness of lightweight ML classifiers in detecting APT lateral movement after network perimeter breach.It contributes to the field by proposing a non-intrusive network detection method capable of identifying APT malware before data exfiltration,thus providing an additional layer of organizational defense.
文摘The purpose of this article review is to update what is known about the role of diet on non-alcoholic fatty liver disease(NAFLD). NAFLD is the most common cause of chronic liver disease in the developed world and is considered to be a spectrum, ranging from fatty infiltration of the liver alone(steatosis), which may lead to fatty infiltration with inflammation known as non alcoholic steatohepatitis While the majority of individualswith risk factors like obesity and insulin resistance have steatosis, only few people may develop steatohepatitis. Current treatment relies on weight loss and exercise, although various insulin-sensitizing medications appear promising. Weight loss alone by dietary changes has been shown to lead to histological improvement in fatty liver making nutrition therapy to become a cornerstone of treatment for NAFLD. Supplementation of vitamin E, C and omega 3 fatty acids are under consideration with some conflicting data. Moreover, research has been showed that saturated fat, trans-fatty acid, carbohydrate, and simple sugars(fructose and sucrose) may play significant role in the intrahepatic fat accumulation. However, true associations with specific nutrients yet to be clarified.
基金supported in part by the NSF of China under Grant 62322106,62071131the Guangdong Basic and Applied Basic Research Foundation under Grant 2022B1515020086+2 种基金the International Collaborative Research Program of Guangdong Science and Technology Department under Grant 2022A0505050070in part by the Open Research Fund of the State Key Laboratory of Integrated Services Networks under Grant ISN22-23the National Research Foundation,Singapore University of Technology Design under its Future Communications Research&Development Programme“Advanced Error Control Coding for 6G URLLC and mMTC”Grant No.FCP-NTU-RG-2022-020.
文摘This paper investigates the bit-interleaved coded generalized spatial modulation(BICGSM) with iterative decoding(BICGSM-ID) for multiple-input multiple-output(MIMO) visible light communications(VLC). In the BICGSM-ID scheme, the information bits conveyed by the signal-domain(SiD) symbols and the spatial-domain(SpD) light emitting diode(LED)-index patterns are coded by a protograph low-density parity-check(P-LDPC) code. Specifically, we propose a signal-domain symbol expanding and re-allocating(SSER) method for constructing a type of novel generalized spatial modulation(GSM) constellations, referred to as SSERGSM constellations, so as to boost the performance of the BICGSM-ID MIMO-VLC systems.Moreover, by applying a modified PEXIT(MPEXIT) algorithm, we further design a family of rate-compatible P-LDPC codes, referred to as enhanced accumulate-repeat-accumulate(EARA) codes,which possess both excellent decoding thresholds and linear-minimum-distance-growth property. Both analysis and simulation results illustrate that the proposed SSERGSM constellations and P-LDPC codes can remarkably improve the convergence and decoding performance of MIMO-VLC systems. Therefore, the proposed P-LDPC-coded SSERGSM-mapped BICGSMID configuration is envisioned as a promising transmission solution to satisfy the high-throughput requirement of MIMO-VLC applications.
文摘This study examines the connectedness between the US yield curve components(i.e.,level,slope,and curvature),exchange rates,and the historical volatility of the exchange rates of the main safe-haven fiat currencies(Canada,Switzerland,EURO,Japan,and the UK)and the leading cryptocurrency,the Bitcoin.Results of the static analysis show that the level and slope of the yield curve are net transmitters of shocks to both the exchange rate and its volatility.The exchange rate of the Euro and the volatility of the Euro and the Canadian dollar exchange rate are net transmitters of shocks.Meanwhile,the curvature of the yield curve and the Japanese Yen,Swiss Franc,and British Pound act mainly as net receivers.Our static connectedness analysis shows that Bitcoin is mainly independent of shocks from the yield curve’s level,slope,and curvature,and from any main currency investigated.These findings hint that Bitcoin might provide hedging benefits.However,similar to the static analysis,our dynamic analysis shows that during different periods and particularly in stressful times,Bitcoin is far from being isolated from other currencies or the yield curve components.The dynamic analysis allows us to observe Bitcoin’s connectedness in times of stress.Evidence supporting this contention is the substantially increased connectedness due to policy shocks,political uncertainty,and systemic crisis,implying no empirical support for Bitcoin’s safe-haven property during stress times.The increased connectedness in the dynamic analysis compared with the static approach implies that in normal times and especially in stressful times,Bitcoin has the property of a diversifier.The results may have important implications for investors and policymakers regarding their risk monitoring and their assets allocation and investment strategies.
基金supported in part by the National Key R&D Program of China(2017YFB0502904)the National Science Foundation of China(61876140)。
文摘Recently,video object segmentation has received great attention in the computer vision community.Most of the existing methods heavily rely on the pixel-wise human annotations,which are expensive and time-consuming to obtain.To tackle this problem,we make an early attempt to achieve video object segmentation with scribble-level supervision,which can alleviate large amounts of human labor for collecting the manual annotation.However,using conventional network architectures and learning objective functions under this scenario cannot work well as the supervision information is highly sparse and incomplete.To address this issue,this paper introduces two novel elements to learn the video object segmentation model.The first one is the scribble attention module,which captures more accurate context information and learns an effective attention map to enhance the contrast between foreground and background.The other one is the scribble-supervised loss,which can optimize the unlabeled pixels and dynamically correct inaccurate segmented areas during the training stage.To evaluate the proposed method,we implement experiments on two video object segmentation benchmark datasets,You Tube-video object segmentation(VOS),and densely annotated video segmentation(DAVIS)-2017.We first generate the scribble annotations from the original per-pixel annotations.Then,we train our model and compare its test performance with the baseline models and other existing works.Extensive experiments demonstrate that the proposed method can work effectively and approach to the methods requiring the dense per-pixel annotations.
基金funded by the Researchers Supporting Project No.(RSP.2021/102)King Saud University,Riyadh,Saudi ArabiaThis work was supported in part by the National Natural Science Foundation of China under Grant 61802030+2 种基金Natural Science Foundation of Hunan Province under Grant 2020JJ5602the Research Foundation of Education Bureau of Hunan Province under Grant 19B005the International Cooperative Project for“Double First-Class”,CSUST under Grant 2018IC24.
文摘System logs record detailed information about system operation and areimportant for analyzing the system's operational status and performance. Rapidand accurate detection of system anomalies is of great significance to ensure system stability. However, large-scale distributed systems are becoming more andmore complex, and the number of system logs gradually increases, which bringschallenges to analyze system logs. Some recent studies show that logs can beunstable due to the evolution of log statements and noise introduced by log collection and parsing. Moreover, deep learning-based detection methods take a longtime to train models. Therefore, to reduce the computational cost and avoid loginstability we propose a new Word2Vec-based log unsupervised anomaly detection method (LogUAD). LogUAD does not require a log parsing step and takesoriginal log messages as input to avoid the noise. LogUAD uses Word2Vec togenerate word vectors and generates weighted log sequence feature vectors withTF-IDF to handle the evolution of log statements. At last, a computationally effi-cient unsupervised clustering is exploited to detect the anomaly. We conductedextensive experiments on the public dataset from Blue Gene/L (BGL). Experimental results show that the F1-score of LogUAD can be improved by 67.25%compared to LogCluster.
基金This work is funded by RIF project,from Zayed University,the UAE.AB www.zu.ac.ae.
文摘Since December 2019,a new pandemic has appeared causing a considerable negative global impact.The SARS-CoV-2 first emerged from China and transformed to a global pandemic within a short time.The virus was further observed to be spreading rapidly and mutating at a fast pace,with over 5,775 distinct variations of the virus observed globally(at the time of submitting this paper).Extensive research has been ongoing worldwide in order to get a better understanding of its behaviour,influence and more importantly,ways for reducing its impact.Data analytics has been playing a pivotal role in this research to obtain valuable insights into understanding and fighting against the spread of infection.However,this is time and resource intensive,making it difficult to observe and quickly identify the impact of mutations.Factors such as the spread or virulence could explain the three months delay in revealing the new virus variant in the UK.This paper presents an extensive correlation analysis of the effect caused by the different SARS-CoV-2 strains,and their influence on the population across diverse factors,such as propagation and fatality rates,during the peak of the pandemic,with a focus on two major countries in the Middle East,the United Arab Emirate(UAE)and the Kingdom of Saudi Arabia(KSA).This research aims to investigate the epidemiological behaviour of the Coronavirus’genomic variants over time in the UAE,compared with the KSA,where correlation analysis is carried out for a number of cases,deaths and their statistical deviations.The results of the analysis highlight very interesting insights into the epidemiological impact of the Covid-19 genomic behaviour in both countries,which could lead to important actions taken to minimize the impact on wider public health,possibly saving lives,and the economy.For instance,our method identifies a potential correlation between a spike in the number of deaths per case of 5.5 observed in the UAE by March 24th,with the emergence of new genomic variants of the Coronavirus(G0_c,G0_e1 and G0_e2).Our proposed methodology can be instrumental in identifying and classifying new variations of the virus earlier,and possibly predicting foreseeable mutations through pattern analysis,hence creating proactive measures to control its spread,such as the recent case of the new virus variant,recently discovered in the UK.
基金the Indian Space Research Organization,Bangalore,for funding under the Ch-1 AO Research Project(ISRO/SSPO/CH-1/2016–2019)to carry out this research work。
文摘The Mare Moscoviense is an astonishing rare flatland multi-ring basin and one of the recognizable mare regions on the Moon's farside.The mineralogical,chronological,topographical and morphological studies of the maria surface of the Moon provide a primary understanding of the origin and evolution of the mare provinces.In this study,the Chandrayaan-1 M^(3)data have been employed to prepare optical maturity index,FeO and TiO^(2)concentration,and standard band ratio map to detect the mafic indexes like olivine and pyroxene minerals.The crater size frequency distribution method has been applied to LROC WAC data to obtain the absolute model ages of the Moscoviense basin.The four geological unit ages were observed as 3.57 Ga(U-2),3.65 Ga(U-1),3.8 Ga(U-3)and 3.92 Ga(U-4),which could have been formed between the Imbrian and Nectarian epochs.The M^(3)imaging and reflectance spectral parameters were used to reveal the minerals like pyroxene,olivine,ilmenite,plagioclase,orthopyroxene-olivine-spinel lithology,and olivine-pyroxene mixtures present in the gabbroic basalt,anorthositic and massive ilmenite rocks,and validated with the existing database.The results show that the Moscoviense basin is dominated by intermediate TiO^(2)basalts that derived from olivine-ilmenite-pyroxene cumulate depths ranging from 200 to 500 km between 3.5 Ga and 3.6 Ga.
基金supported in part by Zayed University,office of research under Grant No.R17089.
文摘The unavailability of sufficient information for proper diagnosis,incomplete or miscommunication between patient and the clinician,or among the healthcare professionals,delay or incorrect diagnosis,the fatigue of clinician,or even the high diagnostic complexity in limited time can lead to diagnostic errors.Diagnostic errors have adverse effects on the treatment of a patient.Unnecessary treatments increase the medical bills and deteriorate the health of a patient.Such diagnostic errors that harm the patient in various ways could be minimized using machine learning.Machine learning algorithms could be used to diagnose various diseases with high accuracy.The use of machine learning could assist the doctors in making decisions on time,and could also be used as a second opinion or supporting tool.This study aims to provide a comprehensive review of research articles published from the year 2015 to mid of the year 2020 that have used machine learning for diagnosis of various diseases.We present the various machine learning algorithms used over the years to diagnose various diseases.The results of this study show the distribution of machine learning methods by medical disciplines.Based on our review,we present future research directions that could be used to conduct further research.
基金supported by the National Research Foundation of Korea-Grant funded by the Korean Government(MSIT)-NRF-2020R1A2B5B02002478)supported by the Cluster grant R20143 of Zayed University,UAE.
文摘Underwater acoustic sensor networks(UWASNs)aim to find varied offshore ocean monitoring and exploration applications.In most of these applications,the network is composed of several sensor nodes deployed at different depths in the water.Sensor nodes located at depth on the seafloor cannot invariably communicate with nodes close to the surface level;these nodes need multihop communication facilitated by a suitable routing scheme.In this research work,a Cluster-based Cooperative Energy Efficient Routing(CEER)mechanism for UWSNs is proposed to overcome the shortcomings of the Co-UWSN and LEACH mechanisms.The optimal role of clustering and cooperation provides load balancing and improves the network profoundly.The simulation results using MATLAB show better performance of CEER routing protocol in terms of various parameters as compared to Co-UWSN routing protocol,i.e.,the average end-to-end delay of CEER was 17.39,Co-UWSN was 55.819 and LEACH was 70.08.In addition,the average total energy consumption of CEER was 9.273,Co-UWSN was 12.198,and LEACH was 45.33.The packet delivery ratio of CEER was 53.955,CO-UWSN was 42.047,and LEACH was 30.31.The stability period CEER was 130.9,CO-UWSN was 129.3,and LEACH was 119.1.The obtained results maximized the lifetime and improved the overall performance of the CEER routing protocol.
基金supported by National Natural Science Foundation of China(No.61801106).
文摘In this paper,a differential scheme is proposed for reconfigurable intelligent surface(RIS)assisted spatial modulation,which is referred to as RISDSM,to eliminate the need for channel state information(CSI)at the receiver.The proposed scheme is an improvement over the current differential modulation scheme used in RIS-based systems,as it avoids the high-order matrix calculation and improves the spectral efficiency.A mathematical framework is developed to determine the theoretical average bit error probability(ABEP)of the system using RIS-DSM.The detection complexity of the proposed RIS-DSM scheme is extremely low through the simplification.Finally,simulations results demonstrate that the proposed RIS-DSM scheme can deliver satisfactory error performance even in low signal-to-noise ratio environments.
基金This work was supported by the Institute for Social and Economic Research(ISER),Zayed University,Under Policy Research Incentive Plan,2017。
文摘Radio frequency identification(RFID),also known as electronic label technology,is a non-contact automated identification technology that recognizes the target object and extracts relevant data and critical characteristics using radio frequency signals.Medical equipment information management is an important part of the construction of a modern hospital,as it is linked to the degree of diagnosis and care,as well as the hospital’s benefits and growth.The aim of this study is to create an integrated view of a theoretical framework to identify factors that influence RFID adoption in healthcare,as well as to conduct an empirical review of the impact of organizational,environmental,and individual factors on RFID adoption in the healthcare industry.In contrast to previous research,the current study focuses on individual factors as well as organizational and technological factors in order to better understand the phenomenon of RFID adoption in healthcare,which is characterized as a dynamic and challenging work environment.This research fills a gap in the current literature by describing how user factors can influence RFID adoption in healthcare and how such factors can lead to a deeper understanding of the advantages,uses,and impacts of RFID in healthcare.The proposed study has superior performance and effective results.
基金supported by the UAE University UPAR Research Grant Program under Grant 31T122.
文摘Federated Learning(FL)enables collaborative and privacy-preserving training of machine learning models within the Internet of Vehicles(IoV)realm.While FL effectively tackles privacy concerns,it also imposes significant resource requirements.In traditional FL,trained models are transmitted to a central server for global aggregation,typically in the cloud.This approach often leads to network congestion and bandwidth limitations when numerous devices communicate with the same server.The need for Flexible Global Aggregation and Dynamic Client Selection in FL for the IoV arises from the inherent characteristics of IoV environments.These include diverse and distributed data sources,varying data quality,and limited communication resources.By employing dynamic client selection,we can prioritize relevant and high-quality data sources,enhancing model accuracy.To address this issue,we propose an FL framework that selects global aggregation nodes dynamically rather than a single fixed aggregator.Flexible global aggregation ensures efficient utilization of limited network resources while accommodating the dynamic nature of IoV data sources.This approach optimizes both model performance and resource allocation,making FL in IoV more effective and adaptable.The selection of the global aggregation node is based on workload and communication speed considerations.Additionally,our framework overcomes the constraints associated with network,computational,and energy resources in the IoV environment by implementing a client selection algorithm that dynamically adjusts participants according to predefined parameters.Our approach surpasses Federated Averaging(FedAvg)and Hierarchical FL(HFL)regarding energy consumption,delay,and accuracy,yielding superior results.
基金This work was supported by Institute for Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.2019-0-01343,Training Key Talents in Industrial Convergence Security)and Research Cluster Project,R20143,by Zayed University Research Office.
文摘The end-to-end delay in a wired network is strongly dependent on congestion on intermediate nodes.Among lots of feasible approaches to avoid congestion efficiently,congestion-aware routing protocols tend to search for an uncongested path toward the destination through rule-based approaches in reactive/incident-driven and distributed methods.However,these previous approaches have a problem accommodating the changing network environments in autonomous and self-adaptive operations dynamically.To overcome this drawback,we present a new congestion-aware routing protocol based on a Q-learning algorithm in software-defined networks where logically centralized network operation enables intelligent control and management of network resources.In a proposed routing protocol,either one of uncongested neighboring nodes are randomly selected as next hop to distribute traffic load to multiple paths or Q-learning algorithm is applied to decide the next hop by modeling the state,Q-value,and reward function to set the desired path toward the destination.A new reward function that consists of a buffer occupancy,link reliability and hop count is considered.Moreover,look ahead algorithm is employed to update the Q-value with values within two hops simultaneously.This approach leads to a decision of the optimal next hop by taking congestion status in two hops into account,accordingly.Finally,the simulation results presented approximately 20%higher packet delivery ratio and 15%shorter end-to-end delay,compared to those with the existing scheme by avoiding congestion adaptively.
基金supported by the Researchers Supporting Project(No.RSP-2021/395),King Saud University,Riyadh,Saudi Arabia.
文摘Alzheimer’s disease(AD)is a neurodegenerative disorder,causing the most common dementia in the elderly peoples.The AD patients are rapidly increasing in each year and AD is sixth leading cause of death in USA.Magnetic resonance imaging(MRI)is the leading modality used for the diagnosis of AD.Deep learning based approaches have produced impressive results in this domain.The early diagnosis of AD depends on the efficient use of classification approach.To address this issue,this study proposes a system using two convolutional neural networks(CNN)based approaches for an early diagnosis of AD automatically.In the proposed system,we use segmented MRI scans.Input data samples of three classes include 110 normal control(NC),110 mild cognitive impairment(MCI)and 105 AD subjects are used in this paper.The data is acquired from the ADNI database and gray matter(GM)images are obtained after the segmentation of MRI subjects which are used for the classification in the proposed models.The proposed approaches segregate among NC,MCI,and AD.While testing both methods applied on the segmented data samples,the highest performance results of the classification in terms of accuracy on NC vs.AD are 95.33%and 89.87%,respectively.The proposed methods distinguish between NC vs.MCI and MCI vs.AD patients with a classification accuracy of 90.74%and 86.69%.The experimental outcomes prove that both CNN-based frameworks produced state-of-the-art accurate results for testing.
文摘Cardiac diseases are one of the greatest global health challenges.Due to the high annual mortality rates,cardiac diseases have attracted the attention of numerous researchers in recent years.This article proposes a hybrid fuzzy fusion classification model for cardiac arrhythmia diseases.The fusion model is utilized to optimally select the highest-ranked features generated by a variety of well-known feature-selection algorithms.An ensemble of classifiers is then applied to the fusion’s results.The proposed model classifies the arrhythmia dataset from the University of California,Irvine into normal/abnormal classes as well as 16 classes of arrhythmia.Initially,at the preprocessing steps,for the miss-valued attributes,we used the average value in the linear attributes group by the same class and the most frequent value for nominal attributes.However,in order to ensure the model optimality,we eliminated all attributes which have zero or constant values that might bias the results of utilized classifiers.The preprocessing step led to 161 out of 279 attributes(features).Thereafter,a fuzzy-based feature-selection fusion method is applied to fuse high-ranked features obtained from different heuristic feature-selection algorithms.In short,our study comprises three main blocks:(1)sensing data and preprocessing;(2)feature queuing,selection,and extraction;and(3)the predictive model.Our proposed method improves classification performance in terms of accuracy,F1measure,recall,and precision when compared to state-of-the-art techniques.It achieves 98.5%accuracy for binary class mode and 98.9%accuracy for categorized class mode.
