Glycans play an indispensable role in various bio-logical processes, such as cancer and autoimmune diseases. The function of glycan is closely determined by its structure. Due to the branch and nonlinear properties of...
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ISBN:
(数字)9781665468190
ISBN:
(纸本)9781665468206
Glycans play an indispensable role in various bio-logical processes, such as cancer and autoimmune diseases. The function of glycan is closely determined by its structure. Due to the branch and nonlinear properties of glycans, previous research treats the glycans graph structure as a topological graph to represent glycans data effectively. Graph neural networks (GNNs) are an efficient graph mining method and have many applications in bioinformatics. Therefore, researchers have successfully used handcrafted GNNs to predict glycan immunogenicity. However, a GNN architecture contains many different components, and designing GNN architectures for specific graphs in the bioinformatics field is time-consuming and expert-dependent. To address this challenge, we propose an efficient automatic graph neural network method called EAGNN that can efficiently and automatically construct GNN architecture for glycan immunogenicity prediction. We design an effective graph attention pooling (GAP) search space. We use differential architecture search to efficiently create the optimal GNN architecture in the search space to build the GNN model for glycan immunogenicity prediction. We test EAGNN on the data set SugarBase based on the glycan immunogenicity prediction task. The experiment results show that EAGNN can work more superiorly than the baseline model and achieve the best performance.
Software project management plays an important role in producing high-quality software, and effort estimation can be considered as a backbone for successful project management. Size is a very significant attribute of ...
Software project management plays an important role in producing high-quality software, and effort estimation can be considered as a backbone for successful project management. Size is a very significant attribute of software by being the only input to perform early effort estimation. Even though functional size measurement methods showed successful results in effort estimation of traditional data-centric architectures such as monoliths, they were not designed for today’s architectures which are more service-based and decentralized such as microservices. In these new systems, the event concept is highly used specifically for communication among different services. By being motivated by this fact, in this study, we looked for more microservice-compatible ways of sizing microservices using events and developed a method accordingly. Then, we conducted an exploratory case study in an organization using agile methods and measured the size of 17 Product Backlog Items (PBIs) to assess how this proposed method can be useful in effort estimation in microservices. The implication from the case study is that despite performing a more accurate effort estimation using the proposed size measurement than COSMIC, we were unable to significantly outperform using the total number of events. However, our suggested approach demonstrated to us a different way to use software size in terms of events, namely, to determine the coupling complexity of the project. This finding can be beneficial specifically when evaluating the change requests.
Universities can employ information technology as one means of achieving their goals and objectives. Universities can get advantages from information technology, such as effective resource management and information m...
Universities can employ information technology as one means of achieving their goals and objectives. Universities can get advantages from information technology, such as effective resource management and information management for decision-making. However, don't forget that the campus must consider what technology is appropriate to assist them achieve their goals, particularly in the current industrial era 4.0 where technology is available with many different choices. The campus requires an enterprise architecture in order to design, manage, and coordinate information technology infrastructure, applications, and processes strategically and thoroughly. The adoption of enterprise information system architecture (EA) is also intended to improve the quality of services provided to internal and external stakeholders. In this case, Enterprise Architecture can help an organization to match its information technology resources with business processes and strategies to achieve their goals. This research was conducted using TOGAF ADM, also known as the Open Group Architecture Framework Architecture Development Method. This method offers best practices for creating enterprise architecture and emphasizes several steps that include creating an architectural vision, information systems, business architecture modeling to help XYZ campus manage all their information technology.
A novel approach to the modeling and control of a subactuated aircraft is performed based on Geometric Algebra (GA) principles. The selected platform for analysis is a quad rotorcraft. The derived model leverages obje...
A novel approach to the modeling and control of a subactuated aircraft is performed based on Geometric Algebra (GA) principles. The selected platform for analysis is a quad rotorcraft. The derived model leverages objects from GA, such as the rotor, to perform rotations, replacing the need for Euler angles and quaternions. Controllers, which operate exclusively on GA objects, are developed to regulate the altitude, attitude, and translation of the quad rotorcraft. Numerical examples, including way-point navigation and trajectory tracking, illustrate the feasibility of the GA approach.
The OpenFlow protocol facilitates the communication between the control and forwarding planes within a software-defined networking (SDN) framework. In SDN, the control plane is housed in a distinct entity known as the...
The OpenFlow protocol facilitates the communication between the control and forwarding planes within a software-defined networking (SDN) framework. In SDN, the control plane is housed in a distinct entity known as the OpenFlow controller, which assumes the responsibility of making high-level determinations regarding the forwarding of network traffic. With the OpenFlow controller, network administrators can centrally program and manage network behavior, incorporating functionalities like traffic engineering, load balancing, QoS enforcement, security policies, and more. By segregating the control plane from the data plane, SDN utilizing OpenFlow empowers flexible network management, automation, and programmability. Through the utilization of the OpenFlow protocol, the OpenFlow controller interacts with the forwarding plane, typically implemented in network switches or routers. This approach offers a centralized perspective of the network and grants dynamic control over the behavior of network devices by installing flow entries into their forwarding tables. In this research, we use a single-controller N-policy finite buffer waiting queue model to investigate the system performance metrics. Numerical examples and different indices are presented to illustrate the proposed model.
This paper proposes a prediction system of the effect of electrical defibrillation based on the wavelet transform with pseudo-differential operators. To construct this system first, we analyze and extract features fro...
This paper proposes a prediction system of the effect of electrical defibrillation based on the wavelet transform with pseudo-differential operators. To construct this system first, we analyze and extract features from pre-shock (before defibrillation) ECGs (ElectroCardioGrams) by using Gabor wavelet transform with pseudo-differential operators. Next, the effective feature parameters are selected based on the χ 2 test. Finally, this system predicts two states such as "Effective" and "Ineffective" defibrillation by using the SVM (Support Vector Machine). In this way, we show the effectiveness of the proposed feature extraction method and prediction system.
This paper proposes a new pre-processing technique to separate the most effective features from those that might deteriorate the performance of the machine learning classifiers in terms of computational costs and clas...
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This paper proposes a new pre-processing technique to separate the most effective features from those that might deteriorate the performance of the machine learning classifiers in terms of computational costs and classification accuracy because of their irrelevance,redundancy,or less information;this pre-processing process is often known as feature *** technique is based on adopting a new optimization algorithm known as generalized normal distribution optimization(GNDO)supported by the conversion of the normal distribution to a binary one using the arctangent transfer function to convert the continuous values into binary ***,a novel restarting strategy(RS)is proposed to preserve the diversity among the solutions within the population by identifying the solutions that exceed a specific distance from the best-so-far and replace them with the others created using an effective updating *** strategy is integrated with GNDO to propose another binary variant having a high ability to preserve the diversity of the solutions for avoiding becoming stuck in local minima and accelerating convergence,namely improved GNDO(IGNDO).The proposed GNDO and IGNDO algorithms are extensively compared with seven state-of-the-art algorithms to verify their performance on thirteen medical instances taken from the UCI *** is shown to be superior in terms of fitness value and classification accuracy and competitive with the others in terms of the selected *** the principal goal in solving the FS problem is to find the appropriate subset of features that maximize classification accuracy,IGNDO is considered the best.
In this paper, we study the latency minimization problem for a wireless federated learning (FL) system with heterogeneous computation capability, where different edge devices perform different numbers of local updates...
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In this paper, we study the latency minimization problem for a wireless federated learning (FL) system with heterogeneous computation capability, where different edge devices perform different numbers of local updates in each communication round. We formulate a total latency minimization problem, taking into account both the communication and computation latency in the whole FL procedure. We reveal that decoupling the resource allocation variables from the model convergence is essential to reduce the problem to a single-round latency minimization problem. To solve this simplified problem, we propose an alternating optimization scheme to jointly consider communication and computation resource allocation and mitigate the straggler effect. We prove that the resulting sub-problems, i.e., bandwidth and computation capacity allocation, are both convex and can be optimally solved in closed form, respectively. Simulations show that compared with the baseline scheme that allocates the communication and computation resources equally across edge devices, the proposed scheme can achieve single-round latency reduction.
The design of the camera and optical measurement is a crucial part of optimizing machine vision systems. However, camera designs are usually optimized to produce human-interpretable images. Moreover, camera optimizati...
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Effective and efficient cyber incident handling is crucial for maintaining the security of information systems and organizational data. This research aims to develop a priority-based cyber incident handling method by ...
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ISBN:
(数字)9798350355314
ISBN:
(纸本)9798350355321
Effective and efficient cyber incident handling is crucial for maintaining the security of information systems and organizational data. This research aims to develop a priority-based cyber incident handling method by adopting the NIST 2.0 framework and utilizing machine learning classification algorithms. This method enables organizations to quickly and accurately identify and respond to incidents based on threat levels and impact, focusing resources on the most critical incidents. The research employs the Support Vector Machine (SVM) classification algorithm to determine incident priorities. The study is still developing for handling cyber incidents, followed by testing and evaluating the system prototype within organizations. Initial results indicate that the priority-based approach using the SVM classification algorithm achieves a True Positive (TP) Rate of 0.999 and a False Positive (FP) Rate of 0.008, with a correlation of 98.88%, resulting in excellent performance in identifying positive cases and a low error rate.
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