Deep Convolutional Neural Networks (CNNs) have achieved impressive performance in
edge detection tasks, but their large number of parameters often leads to high memory and energy
costs for implementation on lightweight devices. In this paper, we propose a new architecture, called
Efficient Deep-learning Gradients Extraction Network (EDGE-Net), that integrates the advantages of Depthwise Separable Convolutions and deformable convolutional networks (DeformableConvNet) to address these inefficiencies. By carefully selecting proper components and utilizing
network pruning techniques, our proposed EDGE-Net achieves state-of-the-art accuracy in edge
detection while significantly reducing complexity. Experimental results on BSDS500 and NYUDv2
datasets demonstrate that EDGE-Net outperforms current lightweight edge detectors with only
500k parameters, without relying on pre-trained weights.
A deep learning based stereo matching model for autonomous vehicleIAESIJAI
Autonomous vehicle is one the prominent area of research in computer
vision. In today’s AI world, the concept of autonomous vehicles has become
popular largely to avoid accidents due to negligence of driver. Perceiving the
depth of the surrounding region accurately is a challenging task in
autonomous vehicles. Sensors like light detection and ranging can be used
for depth estimation but these sensors are expensive. Hence stereo matching
is an alternate solution to estimate the depth. The main difficulties observed
in stereo matching is to minimize mismatches in the ill-posed regions, like
occluded, texture less and discontinuous regions. This paper presents an
efficient deep stereo matching technique for estimating disparity map from
stereo images in ill-posed regions. The images from Middlebury stereo data
set are used to assess the efficacy of the model proposed. The experimental
outcome dipicts that the proposed model generates reliable results in the
occluded, texture less and discontinuous regions as compared to the existing
techniques.
Machine learning based augmented reality for improved learning application th...IJECEIAES
Detection of objects and their location in an image are important elements of current research in computer vision. In May 2020, Meta released its state-ofthe-art object-detection model based on a transformer architecture called detection transformer (DETR). There are several object-detection models such as region-based convolutional neural network (R-CNN), you only look once (YOLO) and single shot detectors (SSD), but none have used a transformer to accomplish this task. These models mentioned earlier, use all sorts of hyperparameters and layers. However, the advantages of using a transformer pattern make the architecture simple and easy to implement. In this paper, we determine the name of a chemical experiment through two steps: firstly, by building a DETR model, trained on a customized dataset, and then integrate it into an augmented reality mobile application. By detecting the objects used during the realization of an experiment, we can predict the name of the experiment using a multi-class classification approach. The combination of various computer vision techniques with augmented reality is indeed promising and offers a better user experience.
Massive parallelism with gpus for centrality ranking in complex networksijcsit
Many problems in Computer Science can be modelled using graphs. Evaluating node centrality in complex
networks, which can be considered equivalent to undirected graphs, provides an useful metric of the
relative importance of each node inside the evaluated network. The knowledge on which the most central
nodes are, has various applications, such as improving information spreading in diffusion networks. In this
case, most central nodes can be considered to have higher influence rates over other nodes in the network.
The main purpose in this work is developing a GPU based and massively parallel application so as to
evaluate the node centrality in complex networks using the Nvidia CUDA programming model. The main
contribution of this work is the strategies for the development of an algorithm to evaluate the node
centrality in complex networks using Nvidia CUDA parallel programming model. We show that the
strategies improves algorithm´s speed-up in two orders of magnitude on one NVIDIA Tesla k20 GPU
cluster node, when compared to the hybrid OpenMP/MPI algorithm version, running in the same cluster,
with 4 nodes 2 Intel(R) Xeon(R) CPU E5-2660 each, for radius zero
Stochastic Computing Correlation Utilization in Convolutional Neural Network ...TELKOMNIKA JOURNAL
In recent years, many applications have been implemented in embedded systems and mobile Internet of Things (IoT) devices that typically have constrained resources, smaller power budget, and exhibit "smartness" or intelligence. To implement computation-intensive and resource-hungry Convolutional Neural Network (CNN) in this class of devices, many research groups have developed specialized parallel accelerators using Graphical Processing Units (GPU), Field-Programmable Gate Arrays (FPGA), or Application-Specific Integrated Circuits (ASIC). An alternative computing paradigm called Stochastic Computing (SC) can implement CNN with low hardware footprint and power consumption. To enable building more efficient SC CNN, this work incorporates the CNN basic functions in SC that exploit correlation, share Random Number Generators (RNG), and is more robust to rounding error. Experimental results show our proposed solution provides significant savings in hardware footprint and increased accuracy for the SC CNN basic functions circuits compared to previous work.
Ameliorate the performance using soft computing approaches in wireless networksIJECEIAES
Wireless sensor networks are an innovative and rapidly advanced network occupying the broad spectrum of wireless networks. It works on the principle of “use with less expense, effort and with more comfort.” In these networks, routing provides efficient and effective data transmission between different sources to access points using the clustering technique. This work addresses the low-energy adaptive clustering hierarchy (LEACH) protocol’s main backdrop of choosing head nodes based on a random value. In this, the soft computing methods are used, namely the fuzzy approach, to overcome this barrier in LEACH. Our approach’s primary goal is to extend the network lifetime with efficient energy consumption and by choosing the appropriate head node in each cluster based on the fuzzy parameters. The proposed clustering algorithm focused on two fuzzy inference structures, namely Mamdani and Sugeno fuzzy logic models in two scenarios, respectively. We compared our approach with four existing works, the conventional LEACH, LEACH using the fuzzy method, multicriteria cluster head delegation, and fuzzy-based energy efficient clustering approach (FEECA) in wireless sensor network. The proposed scenario based fuzzy LEACH protocol approaches are better than the four existing methods regarding stability, network survivability, and energy consumption.
IGeekS Technologies is a company located in Bangalore, India. We have being recognized as a quality provider of hardware and software solutions for the student’s in order carry out their academic Projects. We offer academic projects at various academic levels ranging from graduates to masters (Diploma, BCA, BE, M. Tech, MCA, M. Sc (CS/IT)). As a part of the development training, we offer Projects in Embedded Systems & Software to the Engineering College students in all major disciplines.
A deep learning based stereo matching model for autonomous vehicleIAESIJAI
Autonomous vehicle is one the prominent area of research in computer
vision. In today’s AI world, the concept of autonomous vehicles has become
popular largely to avoid accidents due to negligence of driver. Perceiving the
depth of the surrounding region accurately is a challenging task in
autonomous vehicles. Sensors like light detection and ranging can be used
for depth estimation but these sensors are expensive. Hence stereo matching
is an alternate solution to estimate the depth. The main difficulties observed
in stereo matching is to minimize mismatches in the ill-posed regions, like
occluded, texture less and discontinuous regions. This paper presents an
efficient deep stereo matching technique for estimating disparity map from
stereo images in ill-posed regions. The images from Middlebury stereo data
set are used to assess the efficacy of the model proposed. The experimental
outcome dipicts that the proposed model generates reliable results in the
occluded, texture less and discontinuous regions as compared to the existing
techniques.
Machine learning based augmented reality for improved learning application th...IJECEIAES
Detection of objects and their location in an image are important elements of current research in computer vision. In May 2020, Meta released its state-ofthe-art object-detection model based on a transformer architecture called detection transformer (DETR). There are several object-detection models such as region-based convolutional neural network (R-CNN), you only look once (YOLO) and single shot detectors (SSD), but none have used a transformer to accomplish this task. These models mentioned earlier, use all sorts of hyperparameters and layers. However, the advantages of using a transformer pattern make the architecture simple and easy to implement. In this paper, we determine the name of a chemical experiment through two steps: firstly, by building a DETR model, trained on a customized dataset, and then integrate it into an augmented reality mobile application. By detecting the objects used during the realization of an experiment, we can predict the name of the experiment using a multi-class classification approach. The combination of various computer vision techniques with augmented reality is indeed promising and offers a better user experience.
Massive parallelism with gpus for centrality ranking in complex networksijcsit
Many problems in Computer Science can be modelled using graphs. Evaluating node centrality in complex
networks, which can be considered equivalent to undirected graphs, provides an useful metric of the
relative importance of each node inside the evaluated network. The knowledge on which the most central
nodes are, has various applications, such as improving information spreading in diffusion networks. In this
case, most central nodes can be considered to have higher influence rates over other nodes in the network.
The main purpose in this work is developing a GPU based and massively parallel application so as to
evaluate the node centrality in complex networks using the Nvidia CUDA programming model. The main
contribution of this work is the strategies for the development of an algorithm to evaluate the node
centrality in complex networks using Nvidia CUDA parallel programming model. We show that the
strategies improves algorithm´s speed-up in two orders of magnitude on one NVIDIA Tesla k20 GPU
cluster node, when compared to the hybrid OpenMP/MPI algorithm version, running in the same cluster,
with 4 nodes 2 Intel(R) Xeon(R) CPU E5-2660 each, for radius zero
Stochastic Computing Correlation Utilization in Convolutional Neural Network ...TELKOMNIKA JOURNAL
In recent years, many applications have been implemented in embedded systems and mobile Internet of Things (IoT) devices that typically have constrained resources, smaller power budget, and exhibit "smartness" or intelligence. To implement computation-intensive and resource-hungry Convolutional Neural Network (CNN) in this class of devices, many research groups have developed specialized parallel accelerators using Graphical Processing Units (GPU), Field-Programmable Gate Arrays (FPGA), or Application-Specific Integrated Circuits (ASIC). An alternative computing paradigm called Stochastic Computing (SC) can implement CNN with low hardware footprint and power consumption. To enable building more efficient SC CNN, this work incorporates the CNN basic functions in SC that exploit correlation, share Random Number Generators (RNG), and is more robust to rounding error. Experimental results show our proposed solution provides significant savings in hardware footprint and increased accuracy for the SC CNN basic functions circuits compared to previous work.
Ameliorate the performance using soft computing approaches in wireless networksIJECEIAES
Wireless sensor networks are an innovative and rapidly advanced network occupying the broad spectrum of wireless networks. It works on the principle of “use with less expense, effort and with more comfort.” In these networks, routing provides efficient and effective data transmission between different sources to access points using the clustering technique. This work addresses the low-energy adaptive clustering hierarchy (LEACH) protocol’s main backdrop of choosing head nodes based on a random value. In this, the soft computing methods are used, namely the fuzzy approach, to overcome this barrier in LEACH. Our approach’s primary goal is to extend the network lifetime with efficient energy consumption and by choosing the appropriate head node in each cluster based on the fuzzy parameters. The proposed clustering algorithm focused on two fuzzy inference structures, namely Mamdani and Sugeno fuzzy logic models in two scenarios, respectively. We compared our approach with four existing works, the conventional LEACH, LEACH using the fuzzy method, multicriteria cluster head delegation, and fuzzy-based energy efficient clustering approach (FEECA) in wireless sensor network. The proposed scenario based fuzzy LEACH protocol approaches are better than the four existing methods regarding stability, network survivability, and energy consumption.
IGeekS Technologies is a company located in Bangalore, India. We have being recognized as a quality provider of hardware and software solutions for the student’s in order carry out their academic Projects. We offer academic projects at various academic levels ranging from graduates to masters (Diploma, BCA, BE, M. Tech, MCA, M. Sc (CS/IT)). As a part of the development training, we offer Projects in Embedded Systems & Software to the Engineering College students in all major disciplines.
Reconfigurable intelligent surface passive beamforming enhancement using uns...IJECEIAES
Reconfigurable intelligent surfaces (RIS) is a wireless technology that has the potential to improve cellular communication systems significantly. This paper considers enhancing the RIS beamforming in a RIS-aided multiuser multi-input multi-output (MIMO) system to enhance user throughput in cellular networks. The study offers an unsupervised/deep neural network (U/DNN) that simultaneously optimizes the intelligent surface beamforming with less complexity to overcome the non-convex sum-rate problem difficulty. The numerical outcomes comparing the suggested approach to the near-optimal iterative semi-definite programming strategy indicate that the proposed method retains most performance (more than 95% of optimal throughput value when the number of antennas is 4 and RIS’s elements are 30) while drastically reducing system computing complexity.
Residual balanced attention network for real-time traffic scene semantic segm...IJECEIAES
Intelligent transportation systems (ITS) are among the most focused research in this century. Actually, autonomous driving provides very advanced tasks in terms of road safety monitoring which include identifying dangers on the road and protecting pedestrians. In the last few years, deep learning (DL) approaches and especially convolutional neural networks (CNNs) have been extensively used to solve ITS problems such as traffic scene semantic segmentation and traffic signs classification. Semantic segmentation is an important task that has been addressed in computer vision (CV). Indeed, traffic scene semantic segmentation using CNNs requires high precision with few computational resources to perceive and segment the scene in real-time. However, we often find related work focusing only on one aspect, the precision, or the number of computational parameters. In this regard, we propose RBANet, a robust and lightweight CNN which uses a new proposed balanced attention module, and a new proposed residual module. Afterward, we have simulated our proposed RBANet using three loss functions to get the best combination using only 0.74M parameters. The RBANet has been evaluated on CamVid, the most used dataset in semantic segmentation, and it has performed well in terms of parameters’ requirements and precision compared to related work.
Analysing Mobile Random Early Detection for Congestion Control in Mobile Ad-h...IJECEIAES
This research paper suggests and analyse a technique for congestion control in mobile ad hoc networks. The technique is based on a new hybrid approach that uses clustering and queuing techniques. In clustering, in general cluster head transfers the data, following a queuing method based on a RED (Random Early Detection), the mobile environment makes it Mobile RED (or MRED), It majorly depends upon mobility of nodes and mobile environments leads to unpredictable queue size. To simulate this technique, the Network Simulator 2 (or NS2) is used for various scenarios. The simulated results are compared with NRED (Neighbourhood Random Early Detection) queuing technique of congestion control. It has been observed that the results are improved using MRED comparatively.
Development of 3D convolutional neural network to recognize human activities ...journalBEEI
Human activity recognition (HAR) is recently used in numerous applications including smart homes to monitor human behavior, automate homes according to human activities, entertainment, falling detection, violence detection, and people care. Vision-based recognition is the most powerful method widely used in HAR systems implementation due to its characteristics in recognizing complex human activities. This paper addresses the design of a 3D convolutional neural network (3D-CNN) model that can be used in smart homes to identify several numbers of activities. The model is trained using KTH dataset that contains activities like (walking, running, jogging, handwaving handclapping, boxing). Despite the challenges of this method due to the effectiveness of the lamination, background variation, and human body variety, the proposed model reached an accuracy of 93.33%. The model was implemented, trained and tested using moderate computation machine and the results show that the proposal was successfully capable to recognize human activities with reasonable computations.
Vertex covering has important applications for wireless sensor networks such as monitoring link failures,
facility location, clustering, and data aggregation. In this study, we designed three algorithms for
constructing vertex cover in wireless sensor networks. The first algorithm, which is an adaption of the
Parnas & Ron’s algorithm, is a greedy approach that finds a vertex cover by using the degrees of the
nodes. The second algorithm finds a vertex cover from graph matching where Hoepman’s weighted
matching algorithm is used. The third algorithm firstly forms a breadth-first search tree and then
constructs a vertex cover by selecting nodes with predefined levels from breadth-first tree. We show the
operation of the designed algorithms, analyze them, and provide the simulation results in the TOSSIM
environment. Finally we have implemented, compared and assessed all these approaches. The transmitted
message count of the first algorithm is smallest among other algorithms where the third algorithm has
turned out to be presenting the best results in vertex cover approximation ratio.
AN EFFICIENT M-ARY QIM DATA HIDING ALGORITHM FOR THE APPLICATION TO IMAGE ERR...IJNSA Journal
Methods like edge directed interpolation and projection onto convex sets (POCS) that are widely used for image error concealment to produce better image quality are complex in nature and also time consuming. Moreover, those methods are not suitable for real time error concealment where the decoder may not have sufficient computation power or done in online. In this paper, we propose a data-hiding scheme for error concealment of digital image. Edge direction information of a block is extracted in the encoder and is embedded imperceptibly into the host media using quantization index modulation (QIM), thus reduces work load of the decoder. The system performance in term of fidelity and computational load is improved using M-ary data modulation based on near-orthogonal QIM. The decoder extracts the embedded
features (edge information) and those features are then used for recovery of lost data. Experimental results duly support the effectiveness of the proposed scheme.
A Novel Weighted Clustering Based Approach for Improving the Wireless Sensor ...IJERA Editor
Great lifetime and reliability is the key aim of Wireless Sensor Network (WSN) design. As for prolonging
lifetime of this type of network, energy is the most important resource; all recent researches are focused on more
and more energy efficient techniques. Proposed work is Weighted Clustering Approach based on Weighted
Cluster Head Selection, which is highly energy efficient and reliable in mobile network scenario. Weight
calculation using different attributes of the nodes like SNR (Signal to Noise Ratio), Remaining Energy, Node
Degree, Mobility, and Buffer Length gives efficient Cluster Head (CH) on regular interval of time. CH rotation
helps in optimum utilization of energy available with all nodes; results in prolonged network lifetime.
Implementation is done using the NS2 network simulator and performance evaluation is carried out in terms of
PDR (Packet Delivery Ratio), End to End Delay, Throughput, and Energy Consumption. Demonstration of the
obtained results shows that proposed work is adaptable for improving the performance. In order to justify the
solution, the performance of proposed technique is compared with the performance of traditional approach. The
performance of proposed technique is found optimum as compared to the traditional techniques.
Particle Swarm Optimization–Long Short-Term Memory based Channel Estimation w...IJCNCJournal
Non-Orthogonal Multiple Access (NOMA) helps to overcome various difficulties in future technology wireless communications. NOMA, when utilized with millimeter wave multiple-input multiple-output (MIMO) systems, channel estimation becomes extremely difficult. For reaping the benefits of the NOMA and mm-Wave combination, effective channel estimation is required. In this paper, we propose an enhanced particle swarm optimization based long short-term memory estimator network (PSOLSTMEstNet), which is a neural network model that can be employed to forecast the bandwidth required in the mm-Wave MIMO network. The prime advantage of the LSTM is that it has the capability of dynamically adapting to the functioning pattern of fluctuating channel state. The LSTM stage with adaptive coding and modulation enhances the BER.PSO algorithm is employed to optimize input weights of LSTM network. The modified algorithm splits the power by channel condition of every single user. Participants will be first sorted into distinct groups depending upon respective channel conditions, using a hybrid beamforming approach. The network characteristics are fine-estimated using PSO-LSTMEstNet after a rough approximation of channels parameters derived from the received data.
PARTICLE SWARM OPTIMIZATION–LONG SHORTTERM MEMORY BASED CHANNEL ESTIMATION WI...IJCNCJournal
Non-Orthogonal Multiple Access (NOMA) helps to overcome various difficulties in future technology wireless communications. NOMA, when utilized with millimeter wave multiple-input multiple-output (MIMO) systems, channel estimation becomes extremely difficult. For reaping the benefits of the NOMA and mm-Wave combination, effective channel estimation is required. In this paper, we propose an enhanced particle swarm optimization based long short-term memory estimator network (PSOLSTMEstNet), which is a neural network model that can be employed to forecast the bandwidth required in the mm-Wave MIMO network. The prime advantage of the LSTM is that it has the capability of dynamically adapting to the functioning pattern of fluctuating channel state. The LSTM stage with adaptive coding and modulation enhances the BER.PSO algorithm is employed to optimize input weights of LSTM network. The modified algorithm splits the power by channel condition of every single user. Participants will be first sorted into distinct groups depending upon respective channel conditions, using a hybrid beamforming approach. The network characteristics are fine-estimated using PSO-LSTMEstNet after a rough approximation of channels parameters derived from the received data
AN ENERGY EFFICIENT DISTRIBUTED PROTOCOL FOR ENSURING COVERAGE AND CONNECTIVI...ijasuc
As wireless sensor networks (WSNs) continue to attract more and more researchers attention, new ideas for
applications are continually being developed, many of which involve consistent coverage with good
network connectivity of a given area of interest. For the successful operation of the wireless Sensor
Network, the active sensor nodes must maintain both coverage and also connectivity. These are two closely
related essential prerequisites and they are also very important measurements of quality of service (QoS)
for wireless sensor networks. This paper presents the design and analysis of novel protocols that can
dynamically configure a sensor network to result in guaranteed degrees of coverage and connectivity. This
protocol is simulated using NS2 simulated and compared against a distributed probabilistic coveragepreserving configuration protocol (DPCCP) with SPAN [1] protocol in the literature and show that it
activates lesser number of sensor nodes, consumes much lesser energy and maximises the network lifetime
significantly.
DYNAMIC NETWORK ANOMALY INTRUSION DETECTION USING MODIFIED SOMcscpconf
Detection of unexpected and emerging new threats has become a necessity for secured internet
communication with absolute data confidentiality, integrity and availability. Design and
development of such a detection system shall not only be new, accurate and fast but also
effective in a dynamic environment encompassing the surrounding network. In this paper, an algorithm is proposed for anomaly detection through modifying the Self – Organizing Map (SOM), by including new neighbourhood updating rules and learning rate dynamically in order to overcome the fixed architecture and random weight vector assignment. The algorithm initially starts with null network and grows with the original data space as initial weight vectors. New nodes are created using distance threshold parameter and their neighbourhood is identified using connection strength. Employing learning rule, the weight vector updation is carried out for neighbourhood nodes. Performance of the new algorithm is evaluated for using standard bench mark dataset. The result is compared with other neural network methods, shows 98% detection rate and 2% false alarm rate.
Depth-DensePose: an efficient densely connected deep learning model for came...IJECEIAES
Camera/image-based localization is important for many emerging applications such as augmented reality (AR), mixed reality, robotics, and self-driving. Camera localization is the problem of estimating both camera position and orientation with respect to an object. Use cases for camera localization depend on two key factors: accuracy and speed (latency). Therefore, this paper proposes Depth-DensePose, an efficient deep learning model for 6-degrees-of-freedom (6-DoF) camera-based localization. The Depth-DensePose utilizes the advantages of both DenseNets and adapted depthwise separable convolution (DS-Conv) to build a deeper and more efficient network. The proposed model consists of iterative depth-dense blocks. Each depth dense block contains two adapted DS-Conv with two kernel sizes 3 and 5, which are useful to retain both low-level as well as high-level features. We evaluate the proposed Depth-DensePose on the Cambridge Landmarks dataset, which shows that the Depth-DensePose outperforms the performance of related deep learning models for camera based localization. Furthermore, extensive experiments were conducted which proven the adapted DS-Conv is more efficient than the standard convolution. Especially, in terms of memory and processing time which is important to real-time and mobile applications.
CONFIGURABLE TASK MAPPING FOR MULTIPLE OBJECTIVES IN MACRO-PROGRAMMING OF WIR...ijassn
Macro-programming is the new generation advanced method of using Wireless Sensor Network (WSNs), where application developers can extract data from sensor nodes through a high level abstraction of the system. Instead of developing the entire application, task graph representation of the WSN model presents simplified approach of data collection. However, mapping of tasks onto sensor nodes highlights several problems in energy consumption and routing delay. In this paper, we present an efficient hybrid approach of task mapping for WSN – Hybrid Genetic Algorithm, considering multiple objectives of optimization – energy consumption, routing delay and soft real time requirement. We also present a method to configure the algorithm as per user's need by changing the heuristics used for optimization. The trade-off analysis between energy consumption and delivery delay was performed and simulation results are presented. The algorithm is applicable during macro-programming enabling developers to choose a better mapping according to their application requirements.
CONFIGURABLE TASK MAPPING FOR MULTIPLE OBJECTIVES IN MACRO-PROGRAMMING OF WIR...ijassn
Macro-programming is the new generation advanced method of using Wireless Sensor Network (WSNs),
where application developers can extract data from sensor nodes through a high level abstraction of the
system. Instead of developing the entire application, task graph representation of the WSN model presents
simplified approach of data collection.
Cross Layering using Reinforcement Learning in Cognitive Radio-based Industri...IJCNCJournal
The coupling of multiple protocol layers for a Cognitive Radio-based Industrial Internet of Ad-hoc Sensor Network, enables better interaction, coordination, and joint optimization of different protocols in achieving remarkable performance improvements. In this paper, network, and medium access control (MAC) layer functionalities are cross-layered by developing the joint strategy of routing and effective spectrum sensing and Dynamic Channel Selection (DCS) using the Reinforcement Learning (RL) algorithm. In an industrial ad-hoc scenario, the network layer utilizes the sensed spectrum and selected channel by MAC layer for next-hop routing. MAC layer utilizes the lowest known transmission delay of a channel for a single hop as computed by the network layer, which improves the MAC channel selection operation. The applied RLbased technique (Q learning) enables the CR Secondary Users (SUs) to sense, learn, and make the optimal decision on their environment of operations. The proposed RLCLD schemes improve the SU network performance up to 30% as compared to conventional methods.
CROSS LAYERING USING REINFORCEMENT LEARNING IN COGNITIVE RADIO-BASED INDUSTRI...IJCNCJournal
The coupling of multiple protocol layers for a Cognitive Radio-based Industrial Internet of Ad-hoc Sensor
Network, enables better interaction, coordination, and joint optimization of different protocols in achieving
remarkable performance improvements. In this paper, network, and medium access control (MAC) layer
functionalities are cross-layered by developing the joint strategy of routing and effective spectrum sensing
and Dynamic Channel Selection (DCS) using the Reinforcement Learning (RL) algorithm. In an industrial
ad-hoc scenario, the network layer utilizes the sensed spectrum and selected channel by MAC layer for
next-hop routing. MAC layer utilizes the lowest known transmission delay of a channel for a single hop as
computed by the network layer, which improves the MAC channel selection operation. The applied RLbased technique (Q learning) enables the CR Secondary Users (SUs) to sense, learn, and make the optimal
decision on their environment of operations. The proposed RLCLD schemes improve the SU network
performance up to 30% as compared to conventional methods.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
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Reconfigurable intelligent surfaces (RIS) is a wireless technology that has the potential to improve cellular communication systems significantly. This paper considers enhancing the RIS beamforming in a RIS-aided multiuser multi-input multi-output (MIMO) system to enhance user throughput in cellular networks. The study offers an unsupervised/deep neural network (U/DNN) that simultaneously optimizes the intelligent surface beamforming with less complexity to overcome the non-convex sum-rate problem difficulty. The numerical outcomes comparing the suggested approach to the near-optimal iterative semi-definite programming strategy indicate that the proposed method retains most performance (more than 95% of optimal throughput value when the number of antennas is 4 and RIS’s elements are 30) while drastically reducing system computing complexity.
Residual balanced attention network for real-time traffic scene semantic segm...IJECEIAES
Intelligent transportation systems (ITS) are among the most focused research in this century. Actually, autonomous driving provides very advanced tasks in terms of road safety monitoring which include identifying dangers on the road and protecting pedestrians. In the last few years, deep learning (DL) approaches and especially convolutional neural networks (CNNs) have been extensively used to solve ITS problems such as traffic scene semantic segmentation and traffic signs classification. Semantic segmentation is an important task that has been addressed in computer vision (CV). Indeed, traffic scene semantic segmentation using CNNs requires high precision with few computational resources to perceive and segment the scene in real-time. However, we often find related work focusing only on one aspect, the precision, or the number of computational parameters. In this regard, we propose RBANet, a robust and lightweight CNN which uses a new proposed balanced attention module, and a new proposed residual module. Afterward, we have simulated our proposed RBANet using three loss functions to get the best combination using only 0.74M parameters. The RBANet has been evaluated on CamVid, the most used dataset in semantic segmentation, and it has performed well in terms of parameters’ requirements and precision compared to related work.
Analysing Mobile Random Early Detection for Congestion Control in Mobile Ad-h...IJECEIAES
This research paper suggests and analyse a technique for congestion control in mobile ad hoc networks. The technique is based on a new hybrid approach that uses clustering and queuing techniques. In clustering, in general cluster head transfers the data, following a queuing method based on a RED (Random Early Detection), the mobile environment makes it Mobile RED (or MRED), It majorly depends upon mobility of nodes and mobile environments leads to unpredictable queue size. To simulate this technique, the Network Simulator 2 (or NS2) is used for various scenarios. The simulated results are compared with NRED (Neighbourhood Random Early Detection) queuing technique of congestion control. It has been observed that the results are improved using MRED comparatively.
Development of 3D convolutional neural network to recognize human activities ...journalBEEI
Human activity recognition (HAR) is recently used in numerous applications including smart homes to monitor human behavior, automate homes according to human activities, entertainment, falling detection, violence detection, and people care. Vision-based recognition is the most powerful method widely used in HAR systems implementation due to its characteristics in recognizing complex human activities. This paper addresses the design of a 3D convolutional neural network (3D-CNN) model that can be used in smart homes to identify several numbers of activities. The model is trained using KTH dataset that contains activities like (walking, running, jogging, handwaving handclapping, boxing). Despite the challenges of this method due to the effectiveness of the lamination, background variation, and human body variety, the proposed model reached an accuracy of 93.33%. The model was implemented, trained and tested using moderate computation machine and the results show that the proposal was successfully capable to recognize human activities with reasonable computations.
Vertex covering has important applications for wireless sensor networks such as monitoring link failures,
facility location, clustering, and data aggregation. In this study, we designed three algorithms for
constructing vertex cover in wireless sensor networks. The first algorithm, which is an adaption of the
Parnas & Ron’s algorithm, is a greedy approach that finds a vertex cover by using the degrees of the
nodes. The second algorithm finds a vertex cover from graph matching where Hoepman’s weighted
matching algorithm is used. The third algorithm firstly forms a breadth-first search tree and then
constructs a vertex cover by selecting nodes with predefined levels from breadth-first tree. We show the
operation of the designed algorithms, analyze them, and provide the simulation results in the TOSSIM
environment. Finally we have implemented, compared and assessed all these approaches. The transmitted
message count of the first algorithm is smallest among other algorithms where the third algorithm has
turned out to be presenting the best results in vertex cover approximation ratio.
AN EFFICIENT M-ARY QIM DATA HIDING ALGORITHM FOR THE APPLICATION TO IMAGE ERR...IJNSA Journal
Methods like edge directed interpolation and projection onto convex sets (POCS) that are widely used for image error concealment to produce better image quality are complex in nature and also time consuming. Moreover, those methods are not suitable for real time error concealment where the decoder may not have sufficient computation power or done in online. In this paper, we propose a data-hiding scheme for error concealment of digital image. Edge direction information of a block is extracted in the encoder and is embedded imperceptibly into the host media using quantization index modulation (QIM), thus reduces work load of the decoder. The system performance in term of fidelity and computational load is improved using M-ary data modulation based on near-orthogonal QIM. The decoder extracts the embedded
features (edge information) and those features are then used for recovery of lost data. Experimental results duly support the effectiveness of the proposed scheme.
A Novel Weighted Clustering Based Approach for Improving the Wireless Sensor ...IJERA Editor
Great lifetime and reliability is the key aim of Wireless Sensor Network (WSN) design. As for prolonging
lifetime of this type of network, energy is the most important resource; all recent researches are focused on more
and more energy efficient techniques. Proposed work is Weighted Clustering Approach based on Weighted
Cluster Head Selection, which is highly energy efficient and reliable in mobile network scenario. Weight
calculation using different attributes of the nodes like SNR (Signal to Noise Ratio), Remaining Energy, Node
Degree, Mobility, and Buffer Length gives efficient Cluster Head (CH) on regular interval of time. CH rotation
helps in optimum utilization of energy available with all nodes; results in prolonged network lifetime.
Implementation is done using the NS2 network simulator and performance evaluation is carried out in terms of
PDR (Packet Delivery Ratio), End to End Delay, Throughput, and Energy Consumption. Demonstration of the
obtained results shows that proposed work is adaptable for improving the performance. In order to justify the
solution, the performance of proposed technique is compared with the performance of traditional approach. The
performance of proposed technique is found optimum as compared to the traditional techniques.
Particle Swarm Optimization–Long Short-Term Memory based Channel Estimation w...IJCNCJournal
Non-Orthogonal Multiple Access (NOMA) helps to overcome various difficulties in future technology wireless communications. NOMA, when utilized with millimeter wave multiple-input multiple-output (MIMO) systems, channel estimation becomes extremely difficult. For reaping the benefits of the NOMA and mm-Wave combination, effective channel estimation is required. In this paper, we propose an enhanced particle swarm optimization based long short-term memory estimator network (PSOLSTMEstNet), which is a neural network model that can be employed to forecast the bandwidth required in the mm-Wave MIMO network. The prime advantage of the LSTM is that it has the capability of dynamically adapting to the functioning pattern of fluctuating channel state. The LSTM stage with adaptive coding and modulation enhances the BER.PSO algorithm is employed to optimize input weights of LSTM network. The modified algorithm splits the power by channel condition of every single user. Participants will be first sorted into distinct groups depending upon respective channel conditions, using a hybrid beamforming approach. The network characteristics are fine-estimated using PSO-LSTMEstNet after a rough approximation of channels parameters derived from the received data.
PARTICLE SWARM OPTIMIZATION–LONG SHORTTERM MEMORY BASED CHANNEL ESTIMATION WI...IJCNCJournal
Non-Orthogonal Multiple Access (NOMA) helps to overcome various difficulties in future technology wireless communications. NOMA, when utilized with millimeter wave multiple-input multiple-output (MIMO) systems, channel estimation becomes extremely difficult. For reaping the benefits of the NOMA and mm-Wave combination, effective channel estimation is required. In this paper, we propose an enhanced particle swarm optimization based long short-term memory estimator network (PSOLSTMEstNet), which is a neural network model that can be employed to forecast the bandwidth required in the mm-Wave MIMO network. The prime advantage of the LSTM is that it has the capability of dynamically adapting to the functioning pattern of fluctuating channel state. The LSTM stage with adaptive coding and modulation enhances the BER.PSO algorithm is employed to optimize input weights of LSTM network. The modified algorithm splits the power by channel condition of every single user. Participants will be first sorted into distinct groups depending upon respective channel conditions, using a hybrid beamforming approach. The network characteristics are fine-estimated using PSO-LSTMEstNet after a rough approximation of channels parameters derived from the received data
AN ENERGY EFFICIENT DISTRIBUTED PROTOCOL FOR ENSURING COVERAGE AND CONNECTIVI...ijasuc
As wireless sensor networks (WSNs) continue to attract more and more researchers attention, new ideas for
applications are continually being developed, many of which involve consistent coverage with good
network connectivity of a given area of interest. For the successful operation of the wireless Sensor
Network, the active sensor nodes must maintain both coverage and also connectivity. These are two closely
related essential prerequisites and they are also very important measurements of quality of service (QoS)
for wireless sensor networks. This paper presents the design and analysis of novel protocols that can
dynamically configure a sensor network to result in guaranteed degrees of coverage and connectivity. This
protocol is simulated using NS2 simulated and compared against a distributed probabilistic coveragepreserving configuration protocol (DPCCP) with SPAN [1] protocol in the literature and show that it
activates lesser number of sensor nodes, consumes much lesser energy and maximises the network lifetime
significantly.
DYNAMIC NETWORK ANOMALY INTRUSION DETECTION USING MODIFIED SOMcscpconf
Detection of unexpected and emerging new threats has become a necessity for secured internet
communication with absolute data confidentiality, integrity and availability. Design and
development of such a detection system shall not only be new, accurate and fast but also
effective in a dynamic environment encompassing the surrounding network. In this paper, an algorithm is proposed for anomaly detection through modifying the Self – Organizing Map (SOM), by including new neighbourhood updating rules and learning rate dynamically in order to overcome the fixed architecture and random weight vector assignment. The algorithm initially starts with null network and grows with the original data space as initial weight vectors. New nodes are created using distance threshold parameter and their neighbourhood is identified using connection strength. Employing learning rule, the weight vector updation is carried out for neighbourhood nodes. Performance of the new algorithm is evaluated for using standard bench mark dataset. The result is compared with other neural network methods, shows 98% detection rate and 2% false alarm rate.
Depth-DensePose: an efficient densely connected deep learning model for came...IJECEIAES
Camera/image-based localization is important for many emerging applications such as augmented reality (AR), mixed reality, robotics, and self-driving. Camera localization is the problem of estimating both camera position and orientation with respect to an object. Use cases for camera localization depend on two key factors: accuracy and speed (latency). Therefore, this paper proposes Depth-DensePose, an efficient deep learning model for 6-degrees-of-freedom (6-DoF) camera-based localization. The Depth-DensePose utilizes the advantages of both DenseNets and adapted depthwise separable convolution (DS-Conv) to build a deeper and more efficient network. The proposed model consists of iterative depth-dense blocks. Each depth dense block contains two adapted DS-Conv with two kernel sizes 3 and 5, which are useful to retain both low-level as well as high-level features. We evaluate the proposed Depth-DensePose on the Cambridge Landmarks dataset, which shows that the Depth-DensePose outperforms the performance of related deep learning models for camera based localization. Furthermore, extensive experiments were conducted which proven the adapted DS-Conv is more efficient than the standard convolution. Especially, in terms of memory and processing time which is important to real-time and mobile applications.
CONFIGURABLE TASK MAPPING FOR MULTIPLE OBJECTIVES IN MACRO-PROGRAMMING OF WIR...ijassn
Macro-programming is the new generation advanced method of using Wireless Sensor Network (WSNs), where application developers can extract data from sensor nodes through a high level abstraction of the system. Instead of developing the entire application, task graph representation of the WSN model presents simplified approach of data collection. However, mapping of tasks onto sensor nodes highlights several problems in energy consumption and routing delay. In this paper, we present an efficient hybrid approach of task mapping for WSN – Hybrid Genetic Algorithm, considering multiple objectives of optimization – energy consumption, routing delay and soft real time requirement. We also present a method to configure the algorithm as per user's need by changing the heuristics used for optimization. The trade-off analysis between energy consumption and delivery delay was performed and simulation results are presented. The algorithm is applicable during macro-programming enabling developers to choose a better mapping according to their application requirements.
CONFIGURABLE TASK MAPPING FOR MULTIPLE OBJECTIVES IN MACRO-PROGRAMMING OF WIR...ijassn
Macro-programming is the new generation advanced method of using Wireless Sensor Network (WSNs),
where application developers can extract data from sensor nodes through a high level abstraction of the
system. Instead of developing the entire application, task graph representation of the WSN model presents
simplified approach of data collection.
Cross Layering using Reinforcement Learning in Cognitive Radio-based Industri...IJCNCJournal
The coupling of multiple protocol layers for a Cognitive Radio-based Industrial Internet of Ad-hoc Sensor Network, enables better interaction, coordination, and joint optimization of different protocols in achieving remarkable performance improvements. In this paper, network, and medium access control (MAC) layer functionalities are cross-layered by developing the joint strategy of routing and effective spectrum sensing and Dynamic Channel Selection (DCS) using the Reinforcement Learning (RL) algorithm. In an industrial ad-hoc scenario, the network layer utilizes the sensed spectrum and selected channel by MAC layer for next-hop routing. MAC layer utilizes the lowest known transmission delay of a channel for a single hop as computed by the network layer, which improves the MAC channel selection operation. The applied RLbased technique (Q learning) enables the CR Secondary Users (SUs) to sense, learn, and make the optimal decision on their environment of operations. The proposed RLCLD schemes improve the SU network performance up to 30% as compared to conventional methods.
CROSS LAYERING USING REINFORCEMENT LEARNING IN COGNITIVE RADIO-BASED INDUSTRI...IJCNCJournal
The coupling of multiple protocol layers for a Cognitive Radio-based Industrial Internet of Ad-hoc Sensor
Network, enables better interaction, coordination, and joint optimization of different protocols in achieving
remarkable performance improvements. In this paper, network, and medium access control (MAC) layer
functionalities are cross-layered by developing the joint strategy of routing and effective spectrum sensing
and Dynamic Channel Selection (DCS) using the Reinforcement Learning (RL) algorithm. In an industrial
ad-hoc scenario, the network layer utilizes the sensed spectrum and selected channel by MAC layer for
next-hop routing. MAC layer utilizes the lowest known transmission delay of a channel for a single hop as
computed by the network layer, which improves the MAC channel selection operation. The applied RLbased technique (Q learning) enables the CR Secondary Users (SUs) to sense, learn, and make the optimal
decision on their environment of operations. The proposed RLCLD schemes improve the SU network
performance up to 30% as compared to conventional methods.
Similar to EDGE-Net: Efficient Deep-learning Gradients Extraction Network (20)
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
10th International Conference on Artificial Intelligence and Applications (AI...gerogepatton
10th International Conference on Artificial Intelligence and Applications (AI 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence and its applications. The Conference looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
May 2024 - Top 10 Read Articles in Artificial Intelligence and Applications (...gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
3rd International Conference on Artificial Intelligence Advances (AIAD 2024)gerogepatton
3rd International Conference on Artificial Intelligence Advances (AIAD 2024) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the area advanced Artificial Intelligence. It will also serve to facilitate the exchange of information between researchers and industry professionals to discuss the latest issues and advancement in the research area. Core areas of AI and advanced multi-disciplinary and its applications will be covered during the conferences.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
Information Extraction from Product Labels: A Machine Vision Approachgerogepatton
This research tackles the challenge of manual data extraction from product labels by employing a blend of
computer vision and Natural Language Processing (NLP). We introduce an enhanced model that combines
Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) in a Convolutional
Recurrent Neural Network (CRNN) for reliable text recognition. Our model is further refined by
incorporating the Tesseract OCR engine, enhancing its applicability in Optical Character Recognition
(OCR) tasks. The methodology is augmented by NLP techniques and extended through the Open Food
Facts API (Application Programming Interface) for database population and text-only label prediction.
The CRNN model is trained on encoded labels and evaluated for accuracy on a dedicated test set.
Importantly, our approach enables visually impaired individuals to access essential information on
product labels, such as directions and ingredients. Overall, the study highlights the efficacy of deep
learning and OCR in automating label extraction and recognition.
10th International Conference on Artificial Intelligence and Applications (AI...gerogepatton
10th International Conference on Artificial Intelligence and Applications (AI 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence and its applications. The Conference looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
Research on Fuzzy C- Clustering Recursive Genetic Algorithm based on Cloud Co...gerogepatton
Aiming at the problems of poor local search ability and precocious convergence of fuzzy C-cluster
recursive genetic algorithm (FOLD++), a new fuzzy C-cluster recursive genetic algorithm based on
Bayesian function adaptation search (TS) was proposed by incorporating the idea of Bayesian function
adaptation search into fuzzy C-cluster recursive genetic algorithm. The new algorithm combines the
advantages of FOLD++ and TS. In the early stage of optimization, fuzzy C-cluster recursive genetic
algorithm is used to get a good initial value, and the individual extreme value pbest is put into Bayesian
function adaptation table. In the late stage of optimization, when the searching ability of fuzzy C-cluster
recursive genetic is weakened, the short term memory function of Bayesian function adaptation table in
Bayesian function adaptation search algorithm is utilized. Make it jump out of the local optimal solution,
and allow bad solutions to be accepted during the search. The improved algorithm is applied to function
optimization, and the simulation results show that the calculation accuracy and stability of the algorithm
are improved, and the effectiveness of the improved algorithm is verified
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
10th International Conference on Artificial Intelligence and Soft Computing (...gerogepatton
10th International Conference on Artificial Intelligence and Soft Computing (AIS 2024) will
provide an excellent international forum for sharing knowledge and results in theory, methodology, and
applications of Artificial Intelligence, Soft Computing. The Conference looks for significant
contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical
aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from
both academia as well as industry to meet and share cutting-edge development in the field.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
Employee attrition refers to the decrease in staff numbers within an organization due to various reasons.
As it has a negative impact on long-term growth objectives and workplace productivity, firms have
recognized it as a significant concern. To address this issue, organizations are increasingly turning to
machine-learning approaches to forecast employee attrition rates. This topic has gained significant
attention from researchers, especially in recent times. Several studies have applied various machinelearning methods to predict employee attrition, producing different resultsdepending on the employed
methods, factors, and datasets. However, there has been no comprehensive comparative review of multiple
studies applying machine-learning models to predict employee attrition to date. Therefore, this study aims
to fill this gap by providing an overview of research conducted on applying machine learning to predict
employee attrition from 2019 to February 2024. A literature review of relevant studies was conducted,
summarized, and classified. Most studies agree on conducting comparative experiments with multiple
predictive models to determine the most effective one.From this literature survey, the RF algorithm and
XGB ensemble method are repeatedly the best-performing, outperforming many other algorithms.
Additionally, the application of deep learning to employee attrition prediction issues also shows promise.
While there are discrepancies in the datasets used in previous studies, it is notable that the dataset
provided by IBM is the most widely utilized. This study serves as a concise review for new researchers,
facilitating their understanding of the primary techniques employed in predicting employee attrition and
highlighting recent research trends in this field. Furthermore, it provides organizations with insight into
the prominent factors affecting employee attrition, as identified by studies, enabling them to implement
solutions aimed at reducing attrition rates.
10th International Conference on Artificial Intelligence and Applications (AI...gerogepatton
10th International Conference on Artificial Intelligence and Applications (AIFU 2024) is a forum for presenting new advances and research results in the fields of Artificial Intelligence. The conference will bring together leading researchers, engineers and scientists in the domain of interest from around the world. The scope of the conference covers all theoretical and practical aspects of the Artificial Intelligence.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
THE TRANSFORMATION RISK-BENEFIT MODEL OF ARTIFICIAL INTELLIGENCE:BALANCING RI...gerogepatton
This paper summarizes the most cogent advantages and risks associated with Artificial Intelligence from an
in-depth review of the literature. Then the authors synthesize the salient risk-related models currently being
used in AI, technology and business-related scenarios. Next, in view of an updated context of AI along with
theories and models reviewed and expanded constructs, the writers propose a new framework called “The
Transformation Risk-Benefit Model of Artificial Intelligence” to address the increasing fears and levels of
AIrisk. Using the model characteristics, the article emphasizes practical and innovative solutions where
benefitsoutweigh risks and three use cases in healthcare, climate change/environment and cyber security to
illustrate unique interplay of principles, dimensions and processes of this powerful AI transformational
model.
13th International Conference on Software Engineering and Applications (SEA 2...gerogepatton
13th International Conference on Software Engineering and Applications (SEA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Software Engineering and Applications. The goal of this conference is to bring together researchers and practitioners from academia and industry to focus on understanding Modern software engineering concepts and establishing new collaborations in these areas.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...ssuser7dcef0
Power plants release a large amount of water vapor into the
atmosphere through the stack. The flue gas can be a potential
source for obtaining much needed cooling water for a power
plant. If a power plant could recover and reuse a portion of this
moisture, it could reduce its total cooling water intake
requirement. One of the most practical way to recover water
from flue gas is to use a condensing heat exchanger. The power
plant could also recover latent heat due to condensation as well
as sensible heat due to lowering the flue gas exit temperature.
Additionally, harmful acids released from the stack can be
reduced in a condensing heat exchanger by acid condensation. reduced in a condensing heat exchanger by acid condensation.
Condensation of vapors in flue gas is a complicated
phenomenon since heat and mass transfer of water vapor and
various acids simultaneously occur in the presence of noncondensable
gases such as nitrogen and oxygen. Design of a
condenser depends on the knowledge and understanding of the
heat and mass transfer processes. A computer program for
numerical simulations of water (H2O) and sulfuric acid (H2SO4)
condensation in a flue gas condensing heat exchanger was
developed using MATLAB. Governing equations based on
mass and energy balances for the system were derived to
predict variables such as flue gas exit temperature, cooling
water outlet temperature, mole fraction and condensation rates
of water and sulfuric acid vapors. The equations were solved
using an iterative solution technique with calculations of heat
and mass transfer coefficients and physical properties.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
1. EDGE-Net: Efficient Deep-learning Gradients
Extraction Network
Nasrin Akbari and Amirali Baniasadi
Department of Electrical and Computer Engineering, University of Victoria,
Victoria, Canada
Abstract. Deep Convolutional Neural Networks (CNNs) have achieved impressive performance in
edge detection tasks, but their large number of parameters often leads to high memory and energy
costs for implementation on lightweight devices. In this paper, we propose a new architecture, called
Efficient Deep-learning Gradients Extraction Network (EDGE-Net), that integrates the advan-
tages of Depthwise Separable Convolutions and deformable convolutional networks (Deformable-
ConvNet) to address these inefficiencies. By carefully selecting proper components and utilizing
network pruning techniques, our proposed EDGE-Net achieves state-of-the-art accuracy in edge
detection while significantly reducing complexity. Experimental results on BSDS500 and NYUDv2
datasets demonstrate that EDGE-Net outperforms current lightweight edge detectors with only
500k parameters, without relying on pre-trained weights.
Keywords: Efficient edge detection, lightweight deep neural network, Enhanced receptive field
1 Introduction
Edge detection refers to the process of identifying significant transitions in an im-
age by detecting discontinuities in texture, color, and brightness, among other at-
tributes. The detected edges provide the boundaries between different regions in the
image, and this step serves as the initial stage for numerous computer vision appli-
cations, including edge-based face recognition, edge-based target recognition, scene
understanding, image segmentation, fingerprint matching, license plate detection,
object proposal, and object detection [1]. The widespread use of edge detection in
various fields, such as fingerprint recognition in mobile devices [2], satellite image
localization to suppress noise and create realistic edge maps, self-driving vehicles
that adjust the steering wheel angle based on road images [3], and the identifica-
tion of pathological objects in medical images [4], underscores the importance of
developing effective neural networks for edge detection.
In recent years, the advent of deep learning techniques has significantly boosted
edge detection research. While traditional approaches on the BSDS500 dataset of-
ten achieve a 0.59 ODS F-measure, DL-based methods can achieve an impressive
0.828 ODS [5]. However, these recently proposed architectures may be computa-
tionally inefficient, emphasizing the need for lightweight networks that can reduce
the number of parameters while maintaining detection accuracy. Figure 1 provides
DOI: 10.5121/ijaia.2023.14207 85
International Journal of Artificial Intelligence and Applications (IJAIA), Vol.14, No.2, March 2023
2. insight into the detection accuracy and complexity (model size) of several well-
known deep learning-based methods. The orange dot on the graph represents how
well our model aligns with human perception in terms of accuracy, given its few
parameters.
Many deep learning-based edge detectors use VGGNet (Visual Geometry Group)
[6] as their feature-based extractor due to its impressive performance. However, the
network’s extensive backbone and high parameter count make it more appropriate
for complex tasks such as object recognition and image segmentation. Our moti-
vation for this study arises from the fact that edge detection is a low-level image-
processing task that does not require complex networks for feature extraction.
To decrease the number of parameters and floating point operations (FLOPs),
we leverage depthwise separable convolutions [7], which disentangle the spatial and
channel interaction that occurs during regular convolution operations. However, this
technique can reduce performance compared to conventional convolution methods.
To compensate for this reduction, we increase the receptive field by selecting ap-
propriate lightweight components for edge detection purposes. The details of this
approach are discussed in section 3.
Fig. 1. Comparison of complexity and accuracy performance among various edge detection
schemes. Our proposed methods (orange).
The remainder of this manuscript is arranged as follows: Section 2 provides
a comprehensive review of pertinent studies and their associated challenges. Sec-
tion 3 explicates the proposed network architecture. In Section 4, the outcomes
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3. of the experiments are presented, and a comparison is made between the proposed
model and state-of-the-art edge detector networks using the Berkeley Segmentation
Dataset 500 (BSDS500) [8] and NYUDv2 [9] datasets. Finally, Section 5 presents
concluding remarks and outlines future research directions.
2 Related Work
n recent years, a plethora of edge-detection techniques has been proposed. These
approaches can be broadly classified into three groups, namely traditional edge de-
tection, learning-based techniques utilizing handcrafted features, and deep learning
networks. In this section, we will provide an overview of some of the techniques
developed in the past few years.
Early pioneer edge detection methods primarily focused on intensity and color
gradients. For instance, the Sobel operator [10] measures the 2-D spatial gradient of
an image, emphasizing regions of high spatial frequency that correspond to edges.
The Canny algorithm [11] is another well-known multi-stage edge detector that
computes the intensity of the gradients by applying a filter based on the derivative
of a Gaussian. By removing non-maximum pixels of the gradient magnitude, pos-
sible edges are decreased to 1-pixel curves. However, traditional approaches have
limitations, including their focus solely on the changes of local intensity, while fail-
ing to recognize and remove non-edge textures.
The introduction of learning-based edge detectors partially overcame some of
the challenges associated with texture detection in traditional approaches. In this
group of detectors, hand-crafted features are initially extracted, and classifiers
trained using these features are applied to identify edges. The first data-driven
approaches were proposed by Konishi et al. [12], who used images to learn the
probability distributions of responses that correspond to the two sets of edge fil-
ters. In another work [13], random decision forests were applied to identify the
structure presented in local image patches, where color and gradient features were
used to obtain high-quality output edges.
Although the aforementioned techniques were developed using handcrafted fea-
tures, these features are limited in providing high-level information for semantically
meaningful edge detection and have a limited capability of capturing edges at dif-
ferent scales. To address these issues, several CNN-based algorithms with strong
learning capabilities have been proposed in recent years. HED [14], one of the most
influential DNN-based edge detectors, uses fully convolutional neural networks and
deeply-supervised nets to determine the edge probability for every pixel. HED uses
VGGNet [6] for feature extraction and fuses all side outputs of VGGNet features to
minimize the weighted cross-entropy loss function. Since then, various extensions
based on HED and VGGNet have been developed, including CED [15], AMH-Net
[16], RCF [17], LPCB [18], and BDCN [19].
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4. Although CNN is a successful model, it often requires significant computational
power and resources. Therefore, the current trend is to design efficient CNN struc-
tures that overcome these issues. Fined [20], dense extreme inception network [21],
and TIN [5] have proposed lightweight architectures for edge detection. However,
these networks sacrifice detection accuracy for speed. To achieve a better trade-off
between accuracy and efficiency for edge detection, it is necessary to optimize the
architecture and initial parameters of deep learning models, so that they consume
fewer resources while maintaining accuracy. In this paper, we propose a simplified
model for feature extraction by simplifying the backbone and selecting appropriate
components. This model achieves good edge quality with much simpler architecture
compared to other studies.
3 Lightweight Edge Detection Network
This section presents our proposed EDGE-Net, a lightweight neural network that
provides high running efficiency. It offers a solution to the efficiency concerns of
the models discussed in the previous section. Figure 2 illustrates the architecture
of EDGE-Net. We train the network from scratch to optimize its performance. In
the following paragraphs, we provide a detailed review of the components utilized
in EDGE-Net.
3.1 Efficient Backbone
The majority of deep learning-based edge detectors, such as those proposed in [15–
17], utilize VGGNet as their feature extraction backbone. However, we posit that
edge detection is a task that can be accomplished using a less complex backbone. We
achieve this by incorporating lightweight components that maintain high efficiency.
In order to achieve a pyramid structure, we employ three stages, with a max-
pooling operation for downsampling the features between stages. This results in a
decrease in the dimension of output feature maps as we progress through the stages.
As the complexity of the patterns increases in the subsequent stages, we increase
the number of feature channels (i.e., the number of filters) to capture a greater
number of combinations. The channel numbers for stages 1, 2, and 3 are 16, 64,
and 256, respectively. Our backbone comprises mainly deformable and customized
depthwise separable convolutions. To create the fused output, we use standard
bilinear interpolation to upsample the low-resolution features. The fused output is
then formed by concatenating all of the stage outputs. In the following sections, we
provide detailed explanations of the layers and components used in EDGE-Net.
Deformable convolution The presence of geometric transformations and vari-
ations in natural images poses a significant challenge to feature extraction tasks.
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5. Fig. 2. EDGE-Net architecture
Fig. 3. Convt1 block - Convt2 block
Standard convolution kernels, with their fixed structure, have limitations in captur-
ing such transformations. Deformable convolutions, on the other hand, offer a more
efficient solution to this problem. These convolutions possess the ability to adapt
their kernel shape and parameters to the image content, thereby accommodating
geometric variations. By incorporating 2D offset kernels to the regular sampling lo-
cation in the standard convolution, deformable convolutions enable the network to
have different receptive fields, depending on the scale of the objects. The 2D offset
kernels are learned from the preceding feature maps using additional convolutional
layers and can be trained end-to-end using normal back-propagation functions. In
order to keep the network light in terms of parameters and computation, we add
this module at the end of each stage to strengthen the features before transferring
them to the next stage [22].
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6. Fig. 4. Visual Representation of deformable Convolution Operation [22]
Depthwise Separable Convolution The conventional convolution operation
performs computation across both channel and spatial dimensions simultaneously.
In contrast, the Depthwise Separable Convolution (DSC) method [7] partitions the
computation into two sequential steps: 1) depthwise convolution, which involves
applying a single convolutional filter per input channel, and 2) pointwise convo-
lution, which combines the outputs from the depthwise convolution with a linear
combination. While this approach reduces the number of parameters, it also leads
to a decline in accuracy. To mitigate this issue, we incorporate auxiliary side blocks
to enhance the features while maintaining a minimal number of parameters.
Fig. 5. Visual Representation of depthwise separable convolution [7]
In order to introduce non-linearity and enable complex decision-making in our
model (Figure 3 - Convt1), we utilize the Rectified Linear Unit (RELU) activation
after each pointwise convolution. To enhance the model’s accuracy while minimizing
the number of parameters, we have made modifications to Convt1 resulting in
Convt2. This new modification involves adding a pointwise convolution with a
1 × 1 kernel that iterates through every point between two RELU activations.
Furthermore, to address the issue of overfitting, we apply batch normalization as a
regularizer after each RELU activation.
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7. 3.2 Efficient Side Structure
MAxout Layer Before transferring the inputs to the side output layers (from
left to right) at each stage, we perform a Maxout operation rather than using
the standard concatenation block. The Maxout activation reduces the number of
parameters significantly compared to classical dense blocks by inducing competition
between feature maps and accelerating network convergence. Instead of stacking
the outputs of previous layers on top of each other at each stage, we keep only the
maximum value at each position. This approach reduces the number of parameters
and improves the model’s performance.
Fig. 6. Visual Representation of Maxout Layer [23]
Dilated Residual Convolution Module To improve the feature extraction pro-
cess utilizing depth-wise separable convolution in the backbone, we establish a con-
nection between each feature extraction layer and the dilated convolution module,
as proposed in [5]. Various dilation sizes are utilized to capture different levels of
receptive fields in the image. Specifically, we adopt a dilation sequence of 4, 8,
12, and 16 for all layers, each with 32 filters. Following pixel-wise aggregation, we
incorporate hierarchical residual-like connections to enhance the multi-scale repre-
sentation ability at a more granular level. Importantly, this module can be readily
incorporated into state-of-the-art backbones. Figure 10 illustrates the design of the
proposed Dilated Residual Convolution (DDR) module.
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8. Fig. 7. Visual Representation of dilated residual convolution module
Convolutional Block Attention Module (CBAM) We incorporate a lightweight
spatial and channel attention module, as originally proposed by Woo et al. [24],
subsequent to the dilated residual convolution block. This module selectively ac-
centuates pertinent features while dampening others.
Fig. 8. Visual Representation of attention module
The spatial attention mechanism captures the inter-spatial relationships of fea-
tures to determine the informative regions within the image, and is achieved through
a series of steps. First, average pooling and max pooling are applied to summarize
the presence of features and their activations, respectively. Next, a convolutional
layer is utilized in conjunction with a concatenated feature descriptor to generate a
spatial attention map that specifies the locations to accentuate or diminish features,
as described by Woo et al. [24].
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9. Fig. 9. Visual Representation of spatial attention module
The channel attention block redistributes the channel feature responses to en-
hance the importance of specific channels while attenuating others. To calculate the
channel attention, the spatial dimension of the input feature map is first reduced,
a process known as squeezing, as proposed by Woo et al. [24].
Fig. 10. Visual Representation of channel attention module
3.3 Loss Function
In an image, the distribution of edge and non-edge pixel data is often imbalanced.
While CNN models may achieve high accuracy by predicting the majority class,
they may overlook the minority class, leading to a misleading accuracy estimate.
To address this issue, we adopt the weighted Cross-Entropy loss function proposed
by Liu et al. [17].
During network training, we compare all stages and fused outputs to the ground
truth. Specifically, we use the following equation to compare each pixel of each image
to its corresponding label.
L xi; W) =
α.log 1 − P xi; W
ifyi = 0
0 if ≤ yi ≤ η
β.logP xi; W
otherwise,
(1)
in which
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10. α = λ.
|Y +|
|Y +| + |Y −|
β =
|Y −|
|Y +| + |Y −|
(2)
The variables X, P(X), Y, W, and η represent features extracted from the
CNN network, the output of the standard sigmoid function, the ground truth edge
probability, all the parameters learned in the CNN network, and the percentage of
non-edge and edge pixels, respectively. The hyper-parameter η is used to balance the
number of positive and negative samples. Since each image is labeled by multiple
annotators, and human cognition varies, a predefined threshold is employed to
distinguish between edge and non-edge pixels in the edge probability map. If a
pixel is labeled by fewer than η of the annotators, it is considered a non-edge pixel.
To generalize the loss function to all pixels in the image (I) across each stage
(k) and fused layer, the following loss function is used.
L(W) =
|I|
X
i=1
|K|
X
k=1
L xk
i ; W) + L xfuse
i ; W)
(3)
4 Experiments And Discussions
4.1 Implementation Details
We implemented our backbone networks using PyTorch and initialized their stages
with a zero-mean Gaussian distribution with a standard deviation of 0.01. The
learning rate was set to 0.01 initially and then updated using a linear scaling factor
by multiplying 0.1 for every two epochs. We used stochastic gradient descent as the
optimizer, and we terminated the training process after eight epochs. We conducted
all the experiments on a single GPU, NVIDIA GeForce 2080Ti, which has 11G
memory.
4.2 Dataset
To ensure a fair comparison with other published works in tables 1 and 2, we
evaluated our proposed network on the same datasets as those studies. Specifically,
for the Berkeley Segmentation Dataset (BSDS500) [8] and NYUDv2 [9], we used the
same evaluation procedure. The BSDS500 dataset contains 500 images, with 200
for training, 100 for validation, and 200 for testing. We combined the training and
validation sets to create our training set, and applied the same data augmentation
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11. techniques as in RCF [17], including using the PASCAL VOC dataset [25] and its
flipped images.
The NYUD dataset consists of 1449 densely labeled pairs of aligned RGB and
depth images (HHA), captured by Microsoft Kinect cameras in various indoor
scenes. The dataset includes 381 training, 414 validation, and 654 testing images.
To augment the dataset, we rotated the images and corresponding annotations to
four different angles (0, 90, 180, and 270 degrees) and flipped them at each angle,
following the approach in RCF [17].
4.3 Performance Metrics
It is noteworthy that the proportion of edge pixels in an image is typically only
around 10%, while the remaining 90% of pixels are non-edge. This substantial
class imbalance renders accuracy an inadequate metric for evaluating edge detec-
tion performance, as a model could achieve high accuracy simply by predicting the
majority class of non-edges. To address this, we employ F-Score as a more appropri-
ate evaluation metric. F-Score accounts for both precision and recall, with optimal
performance occurring at a score of one and the poorest possible score at zero.
– Recall = TruePositives / (TruePositives + FalseNegatives)
– Precision= TruePositives / (TruePositives + FalsePositives)
– F-Measure = (2 * Precision * Recall) / (Precision + Recall)
There are two methods to calculate the optimal threshold and the corresponding
F-score for binarizing the output of the CNN network to make it comparable to
the binarized ground truth.
– Optimal Dataset Scale: Iterates over all possible thresholds and set one threshold
for the entire dataset. The threshold that gives the best F-score for the dataset
is used to calculate ODS score.
– Optimal Image Scale: Finds the best threshold and corresponding F-score for
each image. The OIS F-score is calculated by averaging all of the F-scores for
all images.
4.4 Comparison with State-of-the-arts.
On BSDS500 dataset - In terms of F-score and number of parameters, we com-
pare our methods to prior edge detection approaches, including both traditional
and recently proposed CNN-based models. As shown in Table 1 and Figure 11,
our baseline model achieves outstanding results (ODS of 0.792 and OIS of 0.805)
while using significantly fewer parameters, which are equal or better than most
recent lightweight CNN models such as BDCN2, TIN1, TIN2, FINED3-Inf, and
FINED3-Train [20].
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12. Fig. 11. Precision-Recall curves of our models and some competitors on BSDS500 dataset.
On NYUD dataset - Table 2 presents the comparison results for the NYUD
dataset, while figure 12 depicts the precision-recall curves. To test our model on
NYUD, we adopt network settings similar to those used for BSDS500. Some studies
employ two separate models to train RGB images and HHA feature images of
NYUD and report the evaluation metrics on the average of the models’ outputs.
However, our network is only tested on RGB images. Therefore, to ensure a fair
evaluation, we compare our model’s output with those of models that were also
tested solely on RGB images.
Fig. 12. Precision-Recall curves of our models and some competitors on NYUD dataset.
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14. 5 Conclusion
Efficient architecture design is crucial for the implementation of edge detection,
which has practical applications in various fields. Most deep neural networks for
edge detection use transfer learning from pre-trained models like VGG16, which
have a large number of parameters and are trained for high-level tasks. However,
edge detection requires a simple set of features and does not necessitate a large
number of convolutional layers for feature extraction. In this study, we introduce
a new lightweight architecture that achieves state-of-the-art performance. Our net-
work leverages customized depth-wise separable and deformable convolutions for
edge detection and incorporates lightweight components to increase the receptive
field of our model, resulting in high-quality edges. Our network architecture is ver-
satile and has the potential to be extended for other vision tasks, such as salient
object detection and semantic segmentation.
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