ACM Transactions on Internet Technology最新文献_第3页

ML-Based Identification of Neuromuscular Disorder Using EMG Signals for Emotional Health Application 基于 ML 的神经肌肉失调识别技术（使用肌电信号）在情感健康领域的应用

IF 5.3 3区计算机科学

ACM Transactions on Internet Technology Pub Date : 2023-12-14 DOI: 10.1145/3637213

Abdelouahad Achmamad, Mohamed Elfezazi, Abdellah Chehri, Imran Ahmed, Atman Jbari, Rachid Saadane

{"title":"ML-Based Identification of Neuromuscular Disorder Using EMG Signals for Emotional Health Application","authors":"Abdelouahad Achmamad, Mohamed Elfezazi, Abdellah Chehri, Imran Ahmed, Atman Jbari, Rachid Saadane","doi":"10.1145/3637213","DOIUrl":"https://doi.org/10.1145/3637213","url":null,"abstract":"Abstract: The electromyogram (EMG), also known as an EMG, is used to assess nerve impulses in motor nerves, sensory nerves, and muscles. EMS is a versatile tool used in various biomedical applications. It is commonly employed to determine physical health, but it also finds utility in evaluating emotional well-being, such as through facial electromyography. Classification of EMG signals has attracted the interest of scientists since it is crucial for identifying neuromuscular disorders (NMDs). Recent advances in the miniaturization of biomedical sensors enable the development of medical monitoring systems. This paper presents a portable and scalable architecture for machine learning modules designed for medical diagnostics. In particular, we provide a hybrid classification model for NMDs. The proposed method combines two supervised machine learning classifiers with the discrete wavelet transform (DWT). During the online testing phase, the class label of an EMG signal is predicted using the classifiers’ optimal models, which can be identified at this stage. The simulation results demonstrate that both classifiers have an accuracy of over 98%. Finally, the proposed method was implemented using an embedded CompactRIO-9035 real-time controller.","PeriodicalId":50911,"journal":{"name":"ACM Transactions on Internet Technology","volume":"7 1","pages":""},"PeriodicalIF":5.3,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138629200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

An IoT and Deep Learning-Based Smart Healthcare Framework for Thyroid Cancer Detection 基于物联网和深度学习的甲状腺癌检测智能医疗框架

IF 5.3 3区计算机科学

ACM Transactions on Internet Technology Pub Date : 2023-12-11 DOI: 10.1145/3637062

Rohit Sharma, Gautam Kumar Mahanti, Chinmay Chakraborty, Ganapati Panda, Adyasha Rath

{"title":"An IoT and Deep Learning-Based Smart Healthcare Framework for Thyroid Cancer Detection","authors":"Rohit Sharma, Gautam Kumar Mahanti, Chinmay Chakraborty, Ganapati Panda, Adyasha Rath","doi":"10.1145/3637062","DOIUrl":"https://doi.org/10.1145/3637062","url":null,"abstract":"A world of healthcare possibilities has been opened with the development of the Internet of Medical Things and related machine learning, deep learning, and artificial intelligence approaches. It has a broad range of uses: when linked to the Internet, common medical equipment and sensors may gather important data; deep learning and artificial intelligence algorithms use this data to understand symptoms and patterns and allow remote healthcare. There are a large number of people affected by thyroid disorders across the world. The ultrasound-based thyroid nodule detection using traditional methods increased the burden on the expertise. Therefore, alternate methods are required to overcome this problem. In order to facilitate early thyroid disorder detection, this research aims to offer an IoT-based ensemble learning framework. In the proposed ensemble model, three pre-trained models DeiT, Mixer-MLP and Swin Transformer, are used for feature extraction. The mRMR technique is used for relevant feature selection. A total of 24 machine learning models have been trained, and weighted average ensemble learning is employed using the Improved Jaya optimization algorithm and Coronavirus Herd Immunity optimization algorithm. The ensemble model with the improved Jaya optimization algorithm achieved excellent results. The best value for accuracy, precision, sensitivity, specificity, F2-score and ROC-AUC score are 92.83%, 87.76%, 97.66%, 88.89%, 0.9551 and 0.9357, respectively. The main focus of this research is to increase the specificity. A poor value of specificity can lead to a high false positive rate. This situation can increase anxiety and emotionally weaken the patient. The proposed ensemble model with the Improved Jaya optimization algorithm outperformed state-of-the-art techniques and can assist medical experts.","PeriodicalId":50911,"journal":{"name":"ACM Transactions on Internet Technology","volume":"68 1","pages":""},"PeriodicalIF":5.3,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138567161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 Softwarized Intrusion Detection System for IoT-Enabled Smart Healthcare System 面向物联网智能医疗系统的软件入侵检测系统

IF 5.3 3区计算机科学

ACM Transactions on Internet Technology Pub Date : 2023-11-27 DOI: 10.1145/3634748

Danish Javeed, Tianhan Gao, Muhammad Shahid Saeed, Prabhat Kumar, Randhir Kumar, Alireza Jolfaei

{"title":"A Softwarized Intrusion Detection System for IoT-Enabled Smart Healthcare System","authors":"Danish Javeed, Tianhan Gao, Muhammad Shahid Saeed, Prabhat Kumar, Randhir Kumar, Alireza Jolfaei","doi":"10.1145/3634748","DOIUrl":"https://doi.org/10.1145/3634748","url":null,"abstract":"The Internet of Things-enabled Smart Healthcare System (IoT-SHS) is a networked infrastructure of intelligent wearables, software applications, health systems, and services that continuously monitors and transmits patient-sensitive data using an open wireless channel. The conventional security mechanisms are unsuitable for detecting attacks in the dynamic IoT-SHS context due to resource limitations and heterogeneity in low-cost healthcare devices. Deep Learning (DL) solutions for Intrusion Detection System (IDS) and softwarization of the network has the potential to achieve secure network services in the IoT-SHS environment. Motivated by the aforementioned discussion, we propose an intelligent softwarized IDS for protecting the critical infrastructure of the IoT-SHS ecosystem. Specifically, the DL-based IDS is designed using a hybrid cuda Long Short-Term Memory Deep Neural Network (cuLSTM-DNN) algorithm to assist network administrators in efficient decision-making for the generated intrusions. To further bolster the system’s resilience, we suggest a deployment architecture for the proposed CUDA-powered IDS using OpenStack Tacker in a real SDN environment, ensuring that virtual machines can directly utilize the host’s NVIDIA GPU, thereby streamlining and enhancing the network’s operational efficiency. The experimental results using the CICDDoS2019 dataset confirm the effectiveness of the proposed framework over some baseline and recent state-of-the-art techniques.","PeriodicalId":50911,"journal":{"name":"ACM Transactions on Internet Technology","volume":"363 4","pages":""},"PeriodicalIF":5.3,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138507027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

EtherShield: Time Interval Analysis for Detection of Malicious Behavior on Ethereum EtherShield:检测以太坊恶意行为的时间间隔分析

IF 5.3 3区计算机科学

ACM Transactions on Internet Technology Pub Date : 2023-11-23 DOI: 10.1145/3633514

Bofeng Pan, Natalia Stakhanova, Zhongwen Zhu

{"title":"EtherShield: Time Interval Analysis for Detection of Malicious Behavior on Ethereum","authors":"Bofeng Pan, Natalia Stakhanova, Zhongwen Zhu","doi":"10.1145/3633514","DOIUrl":"https://doi.org/10.1145/3633514","url":null,"abstract":"Advances in blockchain technology have attracted significant attention across the world. The practical blockchain applications emerging in various domains ranging from finance, healthcare, and entertainment, have quickly become attractive targets for adversaries. The novelty of the technology coupled with the high degree of anonymity it provides made malicious activities even less visible in the blockchain environment. This made their robust detection challenging. This paper presents EtherShield, an novel approach for identifying malicious activity on the Ethereum blockchain. By combining temporal transaction information and contract code characteristics, EtherShield can detect various types of threats and provide insight into the behavior of contracts. The time-interval based analysis used by EtherShield enables expedited detection, achieving comparable accuracy to other approaches with significantly less data. Our validation analysis, which involved over 15,000 Ethereum accounts, demonstrated that EtherShield can significantly expedite the detection of malicious activity while maintaining high accuracy levels (86.52% accuracy with 1 hour of transaction history data and 91.33% accuracy with 1 year of transaction history data).","PeriodicalId":50911,"journal":{"name":"ACM Transactions on Internet Technology","volume":"364 3","pages":""},"PeriodicalIF":5.3,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138507026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Special Section on “Advances in Cyber-Manufacturing: Architectures, Challenges, & Future Research Directions” 网络制造的进展：架构、挑战和未来研究方向

IF 5.3 3区计算机科学

ACM Transactions on Internet Technology Pub Date : 2023-11-17 DOI: 10.1145/3627990

Gautam Srivastava, Jerry Chun‐Wei Lin, Calton Pu, Yudong Zhang

引用次数: 0

DxHash: A Memory Saving Consistent Hashing Algorithm DxHash:一个内存保存一致哈希算法

3区计算机科学

ACM Transactions on Internet Technology Pub Date : 2023-11-03 DOI: 10.1145/3631708

Chao Dong, Fang Wang, Dan Feng

引用次数: 0

Positional Encoding-based Resident Identification in Multi-resident Smart Homes 多居民智能家居中基于位置编码的居民身份识别

3区计算机科学

ACM Transactions on Internet Technology Pub Date : 2023-11-01 DOI: 10.1145/3631353

Zhiyi Song, Dipankar Chaki, Abdallah Lakhdari, Athman Bouguettaya

引用次数: 0

Polarized Communities Search via Co-guided Random Walk in Attributed Signed Networks 带属性签名网络中基于联合引导随机漫步的极化社区搜索

3区计算机科学

ACM Transactions on Internet Technology Pub Date : 2023-10-07 DOI: 10.1145/3613449

Fanyi Yang, Huifang Ma, Cairui Yan, Zhixin Li, Liang Chang

{"title":"Polarized Communities Search via Co-guided Random Walk in Attributed Signed Networks","authors":"Fanyi Yang, Huifang Ma, Cairui Yan, Zhixin Li, Liang Chang","doi":"10.1145/3613449","DOIUrl":"https://doi.org/10.1145/3613449","url":null,"abstract":"Polarized communities search aims at locating query-dependent communities, in which mostly nodes within each community form intensive positive connections, while mostly nodes across two communities are connected by negative links. Current approaches towards polarized communities search typically model the network topology, while the key factor of node, i.e., the attributes, are largely ignored. Existing studies have shown that community formation is strongly influenced by node attributes and the formation of communities are determined by both network topology and node attributes simultaneously. However, it is nontrivial to incorporate node attributes for polarized communities search. Firstly, it is hard to handle the heterogeneous information from node attributes. Secondly, it is difficult to model the complex relations between network topology and node attributes in identifying polarized communities. To address the above challenges, we propose a novel method Co-guided Random Walk in Attributed signed networks (CoRWA) for polarized communities search by equipping with reasonable attribute setting. For the first challenge, we devise an attribute-based signed network to model the auxiliary relation between nodes and a weight assignment mechanism is designed to measure the reliability of the edges in the signed network. As to the second challenge, a co-guided random walk scheme in two signed networks is designed to explicitly model the relations between topology-based signed network and attribute-based signed network so as to enhance the search result of each other. Finally, we can identify polarized communities by a well-designed Rayleigh quotient in the signed network. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed CoRWA. Further analysis reveals the significance of node attributes for polarized communities search.","PeriodicalId":50911,"journal":{"name":"ACM Transactions on Internet Technology","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135252728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Malicious Account Identification in Social Network Platforms 社交网络平台中的恶意账户识别

3区计算机科学

ACM Transactions on Internet Technology Pub Date : 2023-09-20 DOI: 10.1145/3625097

Loredana Caruccio, Gaetano Cimino, Stefano Cirillo, Domenico Desiato, Giuseppe Polese, Genoveffa Tortora

{"title":"Malicious Account Identification in Social Network Platforms","authors":"Loredana Caruccio, Gaetano Cimino, Stefano Cirillo, Domenico Desiato, Giuseppe Polese, Genoveffa Tortora","doi":"10.1145/3625097","DOIUrl":"https://doi.org/10.1145/3625097","url":null,"abstract":"Nowadays, people of all ages are increasingly using Web platforms for social interaction. Consequently, many tasks are being transferred over social networks, like advertisements, political communications, and so on, yielding vast volumes of data disseminated over the network. However, this raises several concerns regarding the truthfulness of such data and the accounts generating them. Malicious users often manipulate data in order to gain profit. For example, malicious users often create fake accounts and fake followers to increase their popularity and attract more sponsors, followers, and so on, potentially producing several negative implications that impact the whole society. To deal with these issues it is necessary to increase the capability to properly identify fake accounts and followers. By exploiting automatically extracted data correlations characterizing meaningful patterns of malicious accounts, in this paper, we propose a new feature engineering strategy to augment the social network account dataset with additional features, aiming to enhance the capability of existing machine learning strategies to discriminate fake accounts. Experimental results produced through several machine learning models on account datasets of both the Twitter and the Instagram platforms highlight the effectiveness of the proposed approach towards the automatic discrimination of fake accounts. The choice of Twitter is mainly due to its strict privacy laws, and because its the only social network platform making data of their accounts publicly available.","PeriodicalId":50911,"journal":{"name":"ACM Transactions on Internet Technology","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136313989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

UNION: Fault-Tolerant Cooperative Computing in Opportunistic Mobile Edge Cloud UNION:机会移动边缘云中的容错协同计算

3区计算机科学

ACM Transactions on Internet Technology Pub Date : 2023-09-20 DOI: 10.1145/3617994

Wenhua Xiao, Xudong Fang, Bixin Liu, Ji Wang, Xiaomin Zhu

{"title":"UNION: Fault-Tolerant Cooperative Computing in Opportunistic Mobile Edge Cloud","authors":"Wenhua Xiao, Xudong Fang, Bixin Liu, Ji Wang, Xiaomin Zhu","doi":"10.1145/3617994","DOIUrl":"https://doi.org/10.1145/3617994","url":null,"abstract":"Opportunistic Mobile Edge Cloud in which opportunistically connected mobile devices run in a cooperative way to augment the capability of single device has become a timely and essential topic due to its widespread prospect under resource-constrained scenarios (e.g., disaster rescue). Because of the mobility of devices and the uncertainty of environments, it is inevitable that failures occur among the mobile nodes. Being different from existing studies that mainly focus on either data offloading or computing offloading among mobile devices in an ideal environment, we concentrate on how to guarantee the reliability of the task execution with the consideration of both data offloading and computing offloading under opportunistically connected mobile edge cloud. To this end, an optimization of mobile task offloading when considering reliability is formulated. Then, we propose a probabilistic model for task offloading and a reliability model for task execution, which estimates the probability of successful execution for a specific opportunistic path and describes the dynamic reliability of the task execution. Based on these models, a heuristic algorithm UNION (Fa u lt-Tolera n t Cooperat i ve C o mputi n g) is proposed to solve this NP-hard problem. Theoretical analysis shows that the complexity of UNION is (mathcal {O}(|mathcal {I}|^2+|mathcal {N}|) ) with guaranteeing the reliability of 0.99. Also, extensive experiments on real-world traces validate the superiority of the proposed algorithm UNION over existing typical strategies.","PeriodicalId":50911,"journal":{"name":"ACM Transactions on Internet Technology","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136308036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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