Predicting Market Performance Using Machine and Deep Learning Techniques

 Today, forecasting the stock market has been one of the most challenging issues for the ‘‘artificial intelligence’’ AI research community. Stock market investment methods are sophisticated and rely on analyzing massive volumes of data. In recent years, machine-learning techniques have come under increasing scrutiny to assess and improve market pred...

Predicting Market Performance Deep Learning Stock Market Prediction Predicting Economic Trends Forecasting Stock Market Prices

Client Selection for Federated Learning in Vehicular Edge Computing: A Deep Reinforcement Learning Approach

 Vehicular edge computing (VEC) has emerged as a solution that places computing resources at the edge of the network to address resource management, service continuity, and scalability issues in dynamic vehicular environments. However, VEC faces challenges such as task offloading, varying communication conditions, and data security. To tackle thes...

Federated Learning in Vehicular Edge Computing Vehicular Edge Computing Deep Learning Reinforcement Learning machine learning

Improving open source software security using Fuzzing

 Background: Fuzzing is an automated process of identifying software vulnerabilities by supplying unexpected and faulty inputs to the software. The main aim of fuzzing is to identify the crucial edge cases where a software might fail. Therefore, fuzzing provides a crucial insight into the stability and security of the software. The process of fuzzin...

OSS-Fuzz - continuous fuzzing open source software security using Fuzzing Open Source Security Fuzz CNCF fuzzing open source projects Fuzzing

Credit card fraud detection using artificial neural network

 Frauds in credit card transactions are common today as most of us are using the credit card payment methods more frequently. This is due to the advancement of Technology and increase in online transaction resulting in frauds causing huge financial loss. Therefore, there is need for effective methods to reduce the loss. In addition, fraudsters f...

decision-making Credit card fraud detection money fraud detection Online fraud detection ATM fraud detection

A Human-Machine Agent Based on Active Reinforcement Learning for Target Classification in Wargame

 To meet the requirements of high accuracy and low cost of target classification in modern warfare, and lay the foundation for target threat assessment, the article proposes a human-machine agent for target classification based on active reinforcement learning (TCARL_H-M), inferring when to introduce human experience guidance for model and how to au...

Active reinforcement learning adaptive learning wargaming environments machine learning algorithms and human input cutting-edge technologies

TRAFFIC PREDICTION FOR INTELLIGENT TRANSPORTATION SYSTEM USING MACHINE LEARNING

 The most vital information about the electrical activities of the brain can be obtained with the help of Electroencephalography (EEG) signals. It is quite a powerful tool to analyze the neural activities of the brain and various neurological disorders like epilepsy, schizophrenia, sleep related disorders, parkinson disease etc. can be investigated ...

DEEP LEARNING TRAFFIC PREDICTION

SASDL AND RBATQ: SPARSE AUTOENCODER WITH SWARM BASED DEEP LEARNING AND REINFORCEMENT BASED Q-LEARNING FOR EEG CLASSIFICATION

 The most vital information about the electrical activities of the brain can be obtained with the help of Electroencephalography (EEG) signals. It is quite a powerful tool to analyze the neural activities of the brain and various neurological disorders like epilepsy, schizophrenia, sleep related disorders, parkinson disease etc. can be investigated ...

DEEP LEARNING SASDL AND RBATQ

Multi-Agent Deep Reinforcement Learning-Empowered Channel Allocation in Vehicular Networks

 With the rapid development of vehicular networks, vehicle-to-everything (V2X) communications have huge number of tasks to be calculated, which brings challenges to the scarce network resources. Cloud servers can alleviate the terrible situation regarding the lack of computing abilities of vehicular user equipment (VUE), but the limited resources, t...

DEEP LEARNING Multi-Agent Deep Reinforcement Learning

A DEEP LEARNING APPROACH FOR TASK OFFLOADING IN MULTI-UAV AIDED MOBILE EDGE COMPUTING

 A number of electric devices in buildings can be considered as important demand response (DR) resources, for instance, the battery energy storage system (BESS) and the heat, ventilation, and air conditioning (HVAC) systems. The conventional model-based DR methods rely on efficient ondemand computing resources. However, the current buildings suffer ...

DEEP LEARNING AIDED MOBILE EDGE COMPUTING

A DEEP LEARNING APPROACH FOR TASK OFFLOADING IN MULTI-UAV AIDED MOBILE EDGE COMPUTING

 A number of electric devices in buildings can be considered as important demand response (DR) resources, for instance, the battery energy storage system (BESS) and the heat, ventilation, and air conditioning (HVAC) systems. The conventional model-based DR methods rely on efficient ondemand computing resources. However, the current buildings suffer ...

DEEP LEARNING AIDED MOBILE EDGE COMPUTING

A HYBRID CLOUD AND EDGE CONTROL STRATEGY FOR DEMAND RESPONSES USING DEEP REINFORCEMENT LEARNING AND TRANSFER LEARNING

 A HYBRID CLOUD AND EDGE CONTROL STRATEGY FOR DEMAND RESPONSES USING DEEP REINFORCEMENT LEARNING AND TRANSFER LEARNING...

DEEP LEARNING TRANSFER LEARNING

AI EMPOWERED RIS-ASSISTED NOMA NETWORKS: DEEP LEARNING OR REINFORCEMENT LEARNING

 A novel reconfigurable intelligent surfaces (RISs)- based transmission framework is proposed for downlink nonorthogonal multiple access (NOMA) networks. We propose a quality-of-service (QoS)-based clustering scheme to improve the resource efficiency and formulate a sum rate maximization problem by jointly optimizing the phase shift of the RIS and t...

DEEP LEARNING REINFORCEMENT LEARNING

SASDL AND RBATQ: SPARSE AUTOENCODER WITH SWARM BASED DEEP LEARNING AND REINFORCEMENT BASED Q-LEARNING FOR EEG CLASSIFICATION

 The most vital information about the electrical activities of the brain can be obtained with the help of Electroencephalography (EEG) signals. It is quite a powerful tool to analyze the neural activities of the brain and various neurological disorders like epilepsy, schizophrenia, sleep related disorders, parkinson disease etc. can be investigated ...

DEEP LEARNING REINFORCEMENT BASED Q-LEARNING

ONLINE DEEP REINFORCEMENT LEARNING FOR COMPUTATION OFFLOADING IN BLOCKCHAIN-EMPOWERED MOBILE EDGE COMPUTING

 Offloading computation-intensive tasks (e.g., blockchain consensus processes and data processing tasks) to the edge/cloud is a promising solution for blockchain-empowered mobile edge computing. However, the traditional offloading approaches (e.g., auction-based and game-theory approaches) fail to adjust the policy according to the changing environ...

A vision-based teleoperation method for a robotic arm with 4 degrees of freedom

 The ability to remotely control a robotic arm through a human one is essential where human involvement is needed but physical presence is not possible. Control provided through vision-based approaches comes with advantage over non-vision schemes, as vision-based approaches are less intrusive. On the other hand, the problem of estimating the hand po...

Deep Reinforcement Learning Based Resource Allocation in Cooperative UAV-Assisted Wireless Networks

 We consider the downlink of an unmanned aerial vehicle (UAV) assisted cellular network consisting of multiple cooperative UAVs, whose operations are coordinated by a central ground controller using wireless fronthaul links, to serve multiple ground user equipments (UEs). A problem of jointly designing UAVs’ positions, transmit beamforming, as well ...

Dynamic Computation Offloading in Ultra-Dense Networks based on Mean Field Games

 In ultra-dense networks, the increasing popularity of computation intensive applications imposes challenges to the resource-constrained smart mobile devices (SMDs), which may be solved by offloading these computation tasks to the nearby mobile edge computing centers. However, when massive SMDs offload computation tasks in a dynamic wireless environ...

Multi-Agent Deep Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks with Imperfect Channels

 This paper investigates a futuristic spectrum sharing paradigm for heterogeneous wireless networks with imperfect channels. In the heterogeneous networks, multiple wireless networks adopt different medium access control (MAC) protocols to share a common wireless spectrum and each network is unaware of the MACs of others. This paper aims to design a...

Space-Air-Ground Integrated Multi-Domain Network Resource Orchestration Based on Virtual Network Architecture A DRL Method

 Traditional ground wireless communication networks cannot provide high-quality services for artificial intelligence (AI) applications such as intelligent transportation systems (ITS) due to deployment, coverage and capacity issues. The space-air-ground integrated network (SAGIN) has become a research focus in the industry. Compared with traditional...

Energy-Efficient Secure Video Streaming in UAV-Enabled Wireless Networks A Safe-DQN Approach

 Unmanned aerial vehicles (UAVs) are anticipated to be integrated into the next generation wireless networks as new aerial mobile nodes, which can provide various live streaming applications such as surveillance, reconnaissance, etc. For such applications, due to the dynamic characteristics of traffic and wireless channels, how to guarantee the qual...

Dynamic Age Minimization with Real-Time Information Preprocessing for Edge-Assisted IoT Devices with Energy Harvesting

 Age of information (AoI) is a newly proposed metric to quantify the freshness of system status. However, in many cases, the original raw data collected by IoT devices needs to be preprocessed in real-time to extract the hidden effective information, which is usually time consuming. To this end, we promote an edge computing assisted approach and aim...

DNNOff Offloading DNN-based Intelligent IoT Applications in Mobile Edge Computing

 Deep neural network (DNN) has become increasingly popular in industrial IoT scenarios. Due to high demands on computational capability, it is hard for DNN-based applications to directly run on intelligent end devices with limited resources. Computation offloading technology offers a feasible solution by offloading some computation-intensive tasks t...

An Opportunistic Approach for Cloud Service based IoT Routing Framework Administering Data, Transaction, and Identity Security

 There has been an asymmetric shift towards harnessing cloud-based technologies as the world focuses on shifting operations remotely. Data security for remote operations is crucial for the protection and preservation of critical infrastructure. Furthermore, there has been an emerging trend to integrate IoT based devices with the expanding cloud infr...

Multi-Objective Optimization for UAV-Assisted Wireless Powered IoT Networks Based on Extended DDPG Algorithm

 This paper studies an unmanned aerial vehicle (UAV)-assisted wireless powered IoT network, where a rotary-wing UAV adopts fly-hover-communicate protocol to successively visit IoT devices in demand. During the hovering periods, the UAV works on full-duplex mode to simultaneously collect data from the target device and charge other devices within its...

Trustworthy Target Tracking with Collaborative Deep Reinforcement Learning in EdgeAI-Aided IoT

 In this paper, we propose DRL Track, a framework for target tracking with a collaborative deep reinforcement learning (C-DRL) in Edge-IoT with the aim to obtain high quality of tracking (QoT) and resource-efficient performance. In DRLTrack, a huge number of IoT devices are employed to collect data about a mobile target. One or two edge devices coor...

Multi-Agent DRL for Task Offloading and Resource Allocation in Multi-UAV Enabled IoT Edge Network

 The Internet of Things (IoT) edge network has connected lots of heterogeneous smart devices, thanks to unmanned aerial vehicles (UAVs) and their groundbreaking emerging applications. Limited computational capacity and energy availability have been major factors hindering the performance of edge user equipment (UE) and IoT devices in IoT edge networ...

A Deep Reinforcement Learning Approach for Composing Moving IoT Services

 We develop a novel framework for efficiently and effectively discovering crowdsourced services that move in close proximity to a user over a period of time. We introduce a moving crowdsourced service model which is modelled as a moving region. We propose a deep reinforcement learning-based composition approach to select and compose moving IoT servi...

Federated Tensor Decomposition-Based Feature Extraction Approach for Industrial IoT

 Data in modern industrial applications and data science presents multidimensional progressively, the dimension and the structural complexity of these data are becoming extremely high, which renders existing data analysis methods and machine learning algorithms inadequate to the extent. In addition, high-dimensional data in actual scenarios often sh...

Endogenous Trusted DRL-Based Service Function Chain Orchestration for IoT

 With the development of the Internet of Things, trust has become a limited factor in the integration of heterogeneous IoT networks. In this regard, we use the combination of blockchain technology and SDN/NFV to build a heterogeneous IoT network resource management model based on the consortium chain. In order to solve the efficiency problem caused ...

Resource Allocation in Uplink NOMA-IoT Networks A Reinforcement-Learning Approach

 Non-orthogonal multiple access (NOMA) exploits the potential of power domain to enhance the connectivity for Internet of Things (IoT). Due to time-varying communication channels, dynamic user clustering is a promising method to increase the throughput of NOMA-IoT networks. This paper develops an intelligent resource allocation scheme for uplink NOM...

Leveraging Graph Convolutional-LSTM for Energy Efficient Caching in Blockchain-based Green IoT

 Nowadays, adopting blockchain technology to Internet of Things has become a trend and it is important to minimize energy consumption while providing a high quality of service (QoS) in Blockchain-based IoT networks. Pre-caching popular and fresh IoT content avoids activating sensors frequently, thus effectively reducing network energy consumption. H...

DORY Automatic End-to-End Deployment of Real-World DNNs on Low-Cost IoT MCUs

 The deployment of Deep Neural Networks (DNNs) on end-nodes at the extreme edge of the Internet-of-Things is a critical enabler to support pervasive Deep Learning-enhanced applications. Low-Cost MCU-based end-nodes have limited on-chip memory and often replace caches with scratchpads, to reduce area overheads and increase energy efficiency – requiri...

Deep Reinforcement Learning Based Resource Management for DNN Inference in Industrial IoT

 Performing deep neural network (DNN) inference in real time requires excessive network resources, which poses a great challenge to the resource-limited industrial Internet of things (IIoT) networks. To address the challenge, in this paper, we introduce an end-edge-cloud orchestration architecture, in which the inference task assignment and DNN mode...

Energy-Efficient Trajectory Optimization for UAV-Assisted IoT Networks

 In this paper, we propose and study an energy-efficient trajectory optimization scheme for unmanned aerial vehicle (UAV) assisted Internet of Things (IoT) networks. In such networks, a single UAV is powered by both solar energy and charging stations (CSs), resulting in sustainable communication services, while avoiding energy outage. In particular,...

BEHAVE Behavior-Aware, Intelligent, and Fair Resource Management for Heterogeneous Edge-IoT Systems

 Data-driven approaches are envisioned to build future Edge-IoT systems that satisfy IoT devices demands for edge resources. However, significant challenges and technical barriers exist which complicate resource management of such systems. IoT devices can demonstrate a wide range of behaviors in the devices resource demand that are extremely difficu...

Artificial Intelligence-Aided Minimum Reactive Power Control for the DAB Converter Based on Harmonic Analysis Method

 With the aim of reducing the reactive power for the dual-active-bridge (DAB) converter, this letter proposes an artificial intelligence (AI) aided minimum reactive power control scheme based on the harmonic analysis method. Specifically, as an advanced algorithm of the deep reinforcement learning (DRL), the deep deterministic policy gradient (DDPG)...

EIHDP Edge-Intelligent Hierarchical Dynamic Pricing Based on Cloud-Edge-Client Collaboration for IoT Systems

 Nowadays, IoT systems can better satisfy the service requirements of users with effectively utilizing edge computing resources. Designing an appropriate pricing scheme is critical for users to obtain the optimal computing resources at a reasonable price and for service providers to maximize profits. This problem is complicated with incomplete infor...

An Adaptive Machine Learning Framework for Behind-the-Meter LoadPV Disaggregation

 A significant amount of distributed photovoltaic (PV) generation is invisible to distribution system operators since it is behind the meter on customer premises and not directly monitored by the utility. The generation essentially adds an unknown varying negative demand to the system, which causes additional uncertainty in determining the total loa...

Quantum Learning Enabled Green Communication for Next Generation Wireless Systems

 Next generation wireless systems have witnessed significant R&D attention from academia and industries to enable wide range of applications for connected environment around us. The technical design of next generation wireless systems in terms of relay and transmit power control is very critical due to the ever-reducing size of these sensor enabled ...

Tuatara Location-driven Power-adaptive Communication for Wireless Body Area Networks

 Radio links in Wireless Body Area Networks (WBANs) suffer from both short-term and long-term variations due to the dynamic network topology and frequent blockage caused by body movements, making it challenging to achieve reliable, energy-efficient and real-time data communication. Through experiments with TelosB motes, we observe a strong positive ...

Embedded Feature Selection Based on Relevance Vector Machines With an Approximated Marginal Likelihood and Its Industrial Application

  Feature selection is of great importance to make prediction for process variables in industrial production. An embedded feature selection method, based on relevance vector machines with an approximated marginal likelihood function, is proposed in this study. By setting hierarchical prior distributions over the model weights and the parameters o...

Machine-Learning Beam Tracking and Weight Optimization for mmWave Multi-UAV Links

  Millimeter-wave (mmWave) hybrid analog-digital beamforming is a promising approach to satisfy the low-latency constraint in multiple unmanned aerial vehicles (UAVs) systems, which serve as network infrastructure for flexible deployment. However, in highly dynamic multi-UAV environments, analog beam tracking becomes a critical challenge. The ove...

Deep Correlation Mining Based on Hierarchical Hybrid Networks for Heterogeneous BigData Recommendations

 The advancement of several significant technologies, such as artificial intelligence, cyber intelligence, and machine learning, has made big data penetrate not only into the industry and academic field but also our daily life along with a variety of cyber-enabled applications. In this article, we focus on a deep correlation mining method in heterog...

Detection of leukemia in human blood sample based on microscopic images

 At the moment, identification of blood disorders is through visual inspection of microscopic images of blood cells. From the identification of blood disorders, it can lead to classification of certain diseases related to blood. This paper describes a preliminary study of developing a detection of leukemia types using microscopic blood sample images...

Dynamic Scheduling for Stochastic Edge Cloud Computing Environments using A3C learning and Residual Recurrent Neural Networks

 The ubiquitous adoption of Internet-of-Things (IoT) based applications has resulted in the emergence of the Fog computing paradigm, which allows seamlessly harnessing both mobile-edge and cloud resources. Efficient scheduling of application tasks in such environments is challenging due to constrained resource capabilities, mobility factors in IoT, ...