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9th International Symposium on computer and Information Processing Technology, ISCIPT 2024

作者： Zhang, Yunfei Xiao, Yonghao Chinese Academy of Engineering Physics Institute of Computer Application Mianyang China

ISBN: (纸本)9798350388404

Graphs, as fundamental data structures, are indispensable for delineating intricate interrelations among entities, exhibiting utility across diverse domains within the realm of the information society. Traversal algorithms, including the pivotal breadth-first search, are keystones in the realm of graph theory, constituting essential elements within broader algorithmic frameworks. With the expansion of graph scale, the imperative for expedient processing of graph data becomes increasingly pronounced. This paper aims to meticulously examine the execution efficiency of three distinct breadth-first traversal methodologies across varying node magnitudes: the traditional queue-based sequential approach, CPU parallelization leveraging OPENMP and GPU parallelization harnessing CUDA. Each method possesses its distinctive array of advantages and disadvantages contingent upon the scale of nodes. Moreover, this study aims to delve into the nuanced effect of thread count on the efficacy of OPENMP-based CPU parallel traversal, elucidating an inversely proportional relationship in scenarios with fewer nodes. Finally, we investigate the influence of graph sparsity on the traversal methodologies, acknowledging its pivotal role in connectivity and traversal time. © 2024 IEEE.

关键词： Graph algorithms

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CKD NET: Chronic Kidney Disorders Distinction Model 2

CKD NET: Chronic Kidney Disorders Distinction Model

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Prasad, Arpana Asha, V. Nirmala, A.P. Akash, V. Chethan, S.G. Naik, Chintan S New Horizon College of Engineering Master of Computer Application Bengaluru India

ISBN: (纸本)9798350327533

Habitual Renal complaint is a condition that causes a progressive drop in renal function. It refers to a clinical reality that causes renal dysfunction, indecorous complaint opinion, and treatment can affect in unrecoverable renal complaint with veritably short chances of survival. Artificial Intelligence (AI) technologies equip healthcare providers with a rich set of algorithms to prognosticate the habitual development of conditions. previous information helps to enable visionary corrective measures. This exploration study intends to develop an effective tool for prognosticating CKD using machine learning approaches. A machine literacy model is proposed and presented to address the on-uniform distribution of cases through class balancing, point ranking and analysis, and classifier training. © 2024 IEEE.

关键词： Kidney Disease Machine Learning Neural Network Prediction Module ROC Curve

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Herbaceous Plant Leaf Disease Identification 9

Herbaceous Plant Leaf Disease Identification

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Prasad, Arpana Asha, V. Sreeja, S.P. Dheeraj, P. Ajay Hussain, G. Saber New Horizon College of Engineering Master of Computer Application Bengaluru India

ISBN: (纸本)9798350394474

The precise identification and control of foliar diseases in herbaceous species play a crucial part in maintaining agricultural efficiency and environmental equilibrium. This research tackles the difficulties in pinpointing foliar diseases by merging sophisticated image analysis methods with artificial intelligence algorithms. In this study, a comprehensive collection of over 10,000 high-definition images was amassed, showcasing a wide variety of herbaceous plants afflicted by prevalent diseases such as leaf rust, blight, and powdery mildew. These images underwent a series of feature extraction processes to underscore the disease signatures. We employed and assessed several machine learning frameworks, particularly Convolutional Neural Networks and Support Vector Machines With regard to their precision and operational efficacy. The outcomes suggested that the CNN model excelled, achieving an impressive 95% accuracy rate in pinpointing diseases, thereby surpassing conventional diagnostic approaches. It not only provides a powerful solution for swift and precise disease detection in herbaceous plants but also paves the way for future advancements in automated plant health surveillance systems. The findings help farming methodologies, suggesting a pathway towards enhanced disease control and minimization of crop loss. © 2024 IEEE.

关键词： Disease control

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Software Defect Classification and Analysis Study 5

Software Defect Classification and Analysis Study

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5th International Conference on Information Science, Parallel and Distributed Systems, ISPDS 2024

作者： Zheng, Liyuan Xie, Yang Hou, Boyuan Xie, Xin China Academy of Engineering Physics Institute of Computer Application Mianyang China

ISBN: (纸本)9798350352719

This study proposes a set of improved defect categorization criteria and analysis strategies based on a synthesis of a variety of commonly used software defect categorization methods, combined with the actual software development and testing process. By exploring the defect analysis process, this paper discusses the application of this standard in practice. Finally, basic methods and processes are proposed for software defect prevention, providing theoretical and practical guidance for quality management in the software development process. © 2024 IEEE.

关键词： Software quality

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application of adaptive guard channel reservation under hard handoff constraint in wireless cellular network

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International Journal of Wireless and Mobile Computing 2025年第2期28卷 170-185页

作者： Sahu, Promod Kumar Pati, Hemanta Kumar Pradha, Sateesh Kumar Department of Computer Science and Application Utkal University Vani Vihar Odisha Bhubaneswar India Department of Computer Science and Engineering International Institute of Information Technology Bhubaneswar Odisha Bhubaneswar India

Voice is the king of communication in wireless cellular network (WCN). Again, WCNs provide two types of calls, i.e., new call (NC) and handoff call (HC). Generally, HCs have higher priority than NCs because call dropping gives negative impact to users as compared to call blocking. So, to handle this problem some channels are reserved for HCs. If more channels are reserved for HCs, then handoff call dropping probability (HCDP) decreases at the same time as new call blocking probability (NCBP) increases;if fewer channels are reserved for HCs, HCDP increases and NCBP decreases. If channels are reserved for HCs by considering target HCDP then HCDP will be below the target and NCBP will be minimum for that target. So, keeping this in mind in this paper, we propose a mathematical model that estimates optimal number of guard channels (GCs) for HCs by considering target HCDP and keeping NCBP minimum. The mathematical model proposed assume static traffic parameters. However, in reality, the user traffic is dynamic and usually inherited through its testbed implementation. To obtain optimal performance we propose an adaptive GC reservation scheme by considering hard handoff constraint while implementing this model through testbed. Finally, we have verified testbed results with those of the model-based approach applying the parameters of different Global System for Mobile Communication (GSM) standards to evaluate its applicability to real systems. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Testbeds

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Networked Vehicle Practices Based on Networked Firing Ranges 9

Networked Vehicle Practices Based on Networked Firing Ranges

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Zheng, Wenqi Ren, Hao Yang, Siqi Chen, Zhi Xie, Jiajun Pan, Wenqiang China Academy of Engineering Physics Institute of Computer Application Mianyang China

ISBN: (纸本)9798350391367

In recent years, with the rapid development of network and computer technologies, the Governments of the United States, Japan, the United Kingdom and the European Union, as well as influential organizations, have successively carried out the construction of cyberspace ranges targeting the verification of new network technologies. New technology validation based on cyberspace ranges can solve the problems of high cost, difficult to manage iterative versions, and difficult to measure the accuracy of results. Based on the above advantages, this paper, in order to solve the problem of autonomous driving of cars in the park, which is difficult to drive safely under the influence of environmental uncertainties, completes the hardware design of intelligent cars in the cyberspace target range and proposes a Bayesian network-based design method of steering redundancy for hardware circuits, which divides the steering data of the two cars under different environmental factors into training set and test set. The Bayesian network parameters are predicted by the training set, and the prediction effect of the Bayesian network is tested by the test set. The prediction results show that the steering accuracy prediction of the smart car under different environmental factors is within the 85% confidence interval of the Bayesian network prediction, which provides a feasible way to design a safe and efficient underlying controller for smart cars. © 2024 IEEE.

关键词： Steering

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Automated Landslide Detection using Ensemble Learning

Automated Landslide Detection using Ensemble Learning

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Jain, Tanishka Agrawal, Subhash GLA University Dept. of Computer Engineering & Application Mathura India

ISBN: (纸本)9798350378092

Landslides pose significant threats to human life, infrastructure, and the environment, making timely detection crucial for effective disaster management. This research paper introduces a novel approach to landslide detection utilizing Convolutional Neural Networks (CNNs) in conjunction with ensemble learning techniques. Leveraging the inherent capabilities of CNNs to extract discriminative features from remote sensing imagery, multiple CNN models are trained and aggregated into an ensemble. Through extensive experimentation on diverse satellite imagery datasets, the effectiveness of the ensemble model is evaluated against standalone CNNs and traditional methods. Results demonstrate that the ensemble approach outperforms individual CNN models and baseline methods regarding accuracy and robustness across varying environmental conditions, enhancing landslide detection capabilities and contributing to more effective disaster management strategies. Our Model achieves 95% Precision, 91% Recall, and 93% f1-score which makes it better than all comparative methods. After classification, we make predictions through GUI by uploading images, in which we are using the Nepal dataset. © 2024 IEEE.

关键词： Convolutional neural networks

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A SSCPS safety assessment method based on mission risk 24

A SSCPS safety assessment method based on mission risk

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24th IEEE International Conference on Software Quality, Reliability and Security Companion, QRS-C 2024

作者： Shen, Yafeng Qi, Lianzhi Li, Shiyong Ma, Siyou Deng, Fei Institute of Computer Application China Academy of Engineering Physics Mianyang China

ISBN: (纸本)9798350365658

Cyber physical system (CPS) is a complex system that deeply integrates computing, network communication and control equipment. It has broad application prospects in aerospace, industrial production, transportation and other fields, and it also faces very severe security challenges. Especially for some safety-critical CPS (SSCPS), security issues will have serious consequences. How to carry out security assessment for safety-critical CPS systems has always been a hot issue in CPS research. This paper proposes a task risk-based SSCPS security assessment method. By decomposing the key tasks of the SSCPS system, identifying system risk elements based on task characteristics, and establishing the impact relationship between tasks and risk factors such as assets, vulnerabilities, and threats, construct a security assessment index system based on mission risk, and propose a security fusion method to achieve multilevel and multidimensional security assessment of SSCPS. © 2024 IEEE.

关键词： Risk assessment

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An Optimization Method of Federated Database Join Query Based on Computational Push-Down 2

An Optimization Method of Federated Database Join Query Base...

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2nd IEEE International Conference on Control, Electronics and computer Technology, ICCECT 2024

作者： Gao, Wenjie Wen, Yong Zhang, Hao Institute of Computer Application Chinese Academy of Engineering Physics Mianyang China

ISBN: (纸本)9798350380958

Nowadays, in order to solve the problem of information sharing in heterogeneous systems, federated database is a mature solution. However, the distributed database system also faces many performance bottlenecks. For example, a large amount of data is queried case, and the network transmission cost of data between nodes greatly affects the efficiency of the query. Therefore, how to improve the efficiency of distributed query becomes a problem that needs to be solved. This paper presents a query optimization method based on computational push-down for join query in federated database. The method select a proper query optimization policy based on global statistics, reducing the amount of data transmitted between nodes and greatly improving query efficiency. © 2024 IEEE.

关键词： Distributed database systems

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A deep learning approach using modified Xception net for oral malignancy detection using histopathological images of oral mucosa

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International Journal of Applied Decision Sciences 2025年第2期18卷 168-188页

作者： Das, Madhusmita Dash, Rasmita Department of Computer Application Siksha ‘O’ Anusandhan Deemed to be University Odisha Bhubaneswar India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan Deemed to be University Odisha Bhubaneswar India

The early detection of oral malignancy by physicians is a strenuous task. The analysis of histopathological oral malignancy images using image processing and deep learning techniques can be an add-on facility for doctors to diagnose oral cancer. In this work, a deep learning model is used, designing a modified Xception net with swish activation function and generalised mean pool for the detection of oral malignancy. To prove the superiority of the model, three stages of comparative analysis are carried out. In the first stage, the model is compared with a few advanced models explicitly Alexnet, Resnet50, Resnet101, VGG16, VGG19, Inception net and original Xception. In the second stage, loss and accuracy graphs analysis is done and in the third stage, the proposed model’s accuracy is compared with other model’s accuracy available in the literature. It is found that the modified Xception net got upgraded performance by an accuracy of 98.97%. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Lung cancer

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