PeerJ Computer Science最新文献_第4页

Towards an automated classification phase in the software maintenance process using decision tree 利用决策树实现软件维护过程中的自动分类阶段

IF 3.8 4区计算机科学

PeerJ Computer Science Pub Date : 2024-08-30 DOI: 10.7717/peerj-cs.2228

Sahar Alturki, Sarah Almoaiqel

引用次数: 0

Design of compensation algorithms for zero padding and its application to a patch based deep neural network 零填充补偿算法设计及其在基于补丁的深度神经网络中的应用

IF 3.8 4区计算机科学

PeerJ Computer Science Pub Date : 2024-08-29 DOI: 10.7717/peerj-cs.2287

Safi Ullah, Seong-Ho Song

引用次数: 0

Comprehensive analysis of clustering algorithms: exploring limitations and innovative solutions 全面分析聚类算法：探索局限性和创新解决方案

IF 3.8 4区计算机科学

PeerJ Computer Science Pub Date : 2024-08-29 DOI: 10.7717/peerj-cs.2286

Aasim Ayaz Wani

引用次数: 0

Optimizing agricultural data security: harnessing IoT and AI with Latency Aware Accuracy Index (LAAI) 优化农业数据安全：利用延迟感知准确度指数 (LAAI) 利用物联网和人工智能

IF 3.8 4区计算机科学

PeerJ Computer Science Pub Date : 2024-08-28 DOI: 10.7717/peerj-cs.2276

Omar Bin Samin, Nasir Ahmed Abdulkhader Algeelani, Ammar Bathich, Maryam Omar, Musadaq Mansoor, Amir Khan

引用次数: 0

A facial expression recognition network based on attention double branch enhanced fusion 基于注意力双分支增强融合的面部表情识别网络

IF 3.8 4区计算机科学

PeerJ Computer Science Pub Date : 2024-08-28 DOI: 10.7717/peerj-cs.2266

Wenming Wang, Min Jia

{"title":"A facial expression recognition network based on attention double branch enhanced fusion","authors":"Wenming Wang, Min Jia","doi":"10.7717/peerj-cs.2266","DOIUrl":"https://doi.org/10.7717/peerj-cs.2266","url":null,"abstract":"The facial expression reflects a person’s emotion, cognition, and even physiological or mental state to a large extent. It has important application value in medical treatment, business, criminal investigation, education, and human-computer interaction. Automatic facial expression recognition technology has become an important research topic in computer vision. To solve the problems of insufficient feature extraction, loss of local key information, and low accuracy in facial expression recognition, this article proposes a facial expression recognition network based on attention double branch enhanced fusion. Two parallel branches are used to capture global enhancement features and local attention semantics respectively, and the fusion and complementarity of global and local information is realized through decision-level fusion. The experimental results show that the features extracted by the network are made more complete by fusing and enhancing the global and local features. The proposed method achieves 89.41% and 88.84% expression recognition accuracy on the natural scene face expression datasets RAF-DB and FERPlus, respectively, which is an excellent performance compared with many current methods and demonstrates the effectiveness and superiority of the proposed network model.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142203483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Wormhole attack detection and mitigation model for Internet of Things and WSN using machine learning 利用机器学习检测和缓解物联网和 WSN 的虫洞攻击模型

IF 3.8 4区计算机科学

PeerJ Computer Science Pub Date : 2024-08-28 DOI: 10.7717/peerj-cs.2257

Asma Hassan Alshehri

{"title":"Wormhole attack detection and mitigation model for Internet of Things and WSN using machine learning","authors":"Asma Hassan Alshehri","doi":"10.7717/peerj-cs.2257","DOIUrl":"https://doi.org/10.7717/peerj-cs.2257","url":null,"abstract":"The Internet of Things (IoT) is revolutionizing diverse sectors like business, healthcare, and the military, but its widespread adoption has also led to significant security challenges. IoT networks, in particular, face increasing vulnerabilities due to the rapid proliferation of connected devices within smart infrastructures. Wireless sensor networks (WSNs) comprise software, gateways, and small sensors that wirelessly transmit and receive data. WSNs consist of two types of nodes: generic nodes with sensing capabilities and gateway nodes that manage data routing. These sensor nodes operate under constraints of limited battery power, storage capacity, and processing capabilities, exposing them to various threats, including wormhole attacks. This study focuses on detecting wormhole attacks by analyzing the connectivity details of network nodes. Machine learning (ML) techniques are proposed as effective solutions to address these modern challenges in wormhole attack detection within sensor networks. The base station employs two ML models, a support vector machine (SVM) and a deep neural network (DNN), to classify traffic data and identify malicious nodes in the network. The effectiveness of these algorithms is validated using traffic generated by the NS3.37 simulator and tested against real-world scenarios. Evaluation metrics such as average recall, false positive rates, latency, end-to-end delay, response time, throughput, energy consumption, and CPU utilization are used to assess the performance of the proposed models. Results indicate that the proposed model outperforms existing methods in terms of efficacy and efficiency.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142203484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A feature-enhanced knowledge graph neural network for machine learning method recommendation 用于机器学习方法推荐的特征增强型知识图谱神经网络

IF 3.8 4区计算机科学

PeerJ Computer Science Pub Date : 2024-08-28 DOI: 10.7717/peerj-cs.2284

Xin Zhang, Junjie Guo

引用次数: 0

Mechanical fault diagnosis of high voltage circuit breaker using multimodal data fusion 利用多模态数据融合诊断高压断路器的机械故障

IF 3.8 4区计算机科学

PeerJ Computer Science Pub Date : 2024-08-26 DOI: 10.7717/peerj-cs.2248

Tianhui Li, Yanwei Xia, Xianhai Pang, Jihong Zhu, Hui Fan, Li Zhen, Chaomin Gu, Chi Dong, Shijie Lu

引用次数: 0

Key derivation function: key-hash based computational extractor and stream based pseudorandom expander 密钥衍生功能：基于密钥哈希值的计算提取器和基于流的伪随机扩展器

IF 3.8 4区计算机科学

PeerJ Computer Science Pub Date : 2024-08-23 DOI: 10.7717/peerj-cs.2249

Chai Wen Chuah, Nur Ziadah Harun, Isredza Rahmi A. Hamid

{"title":"Key derivation function: key-hash based computational extractor and stream based pseudorandom expander","authors":"Chai Wen Chuah, Nur Ziadah Harun, Isredza Rahmi A. Hamid","doi":"10.7717/peerj-cs.2249","DOIUrl":"https://doi.org/10.7717/peerj-cs.2249","url":null,"abstract":"The key derivation function is a specific cryptographic algorithm that transforms private string and public strings into one or more cryptographic keys. The cryptographic keys are essential for protecting electronic data during transmission on the internet. This function is designed based on a computational extractor and pseudorandom expander and is typically constructed using various cryptography ciphers such as stream ciphers, keyed-hash message authentication codes, and block ciphers. Having secure and efficient key derivation function designs is essential in the development of numerous security systems. A vulnerable key derivation function could potentially give attackers the ability to compromise an otherwise secure cryptosystem. This research proposes a different approach by combining two different cryptography ciphers to develop key derivation functions. The findings demonstrate that a computational extractor utilizing keyed-hash message authentication codes and a pseudorandom expander using stream ciphers maintain the highest level of security while also providing efficiency benefits in terms of execution time compared to existing key derivation function schemes.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142203512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TSFF: a two-stage fusion framework for 3D object detection TSFF：三维物体检测的两阶段融合框架

IF 3.8 4区计算机科学

PeerJ Computer Science Pub Date : 2024-08-23 DOI: 10.7717/peerj-cs.2260

Guoqing Jiang, Saiya Li, Ziyu Huang, Guorong Cai, Jinhe Su

{"title":"TSFF: a two-stage fusion framework for 3D object detection","authors":"Guoqing Jiang, Saiya Li, Ziyu Huang, Guorong Cai, Jinhe Su","doi":"10.7717/peerj-cs.2260","DOIUrl":"https://doi.org/10.7717/peerj-cs.2260","url":null,"abstract":"Point clouds are highly regarded in the field of 3D object detection for their superior geometric properties and versatility. However, object occlusion and defects in scanning equipment frequently result in sparse and missing data within point clouds, adversely affecting the final prediction. Recognizing the synergistic potential between the rich semantic information present in images and the geometric data in point clouds for scene representation, we introduce a two-stage fusion framework (TSFF) for 3D object detection. To address the issue of corrupted geometric information in point clouds caused by object occlusion, we augment point features with image features, thereby enhancing the reference factor of the point cloud during the voting bias phase. Furthermore, we implement a constrained fusion module to selectively sample voting points using a 2D bounding box, integrating valuable image features while reducing the impact of background points in sparse scenes. Our methodology was evaluated on the SUNRGB-D dataset, where it achieved a 3.6 mean average percent (mAP) improvement in the mAP@0.25 evaluation criterion over the baseline. In comparison to other great 3D object detection methods, our method had excellent performance in the detection of some objects.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142203489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0