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15th International Conference on Innovations in Information Technology, IIT 2023

作者： Hireche, Abdelhadi Damseh, Rafat Sirpal, Parikshat Belkacem, Abdelkader Nasreddine United Arab Emirates University College of Information Technology Department of Computer and Network Engineering United Arab Emirates United Arab Emirates University College of Information Technology Department of Computer Science and Software Engineering United Arab Emirates University of Oklahoma School of Electrical and Computer Engineering Gallogly College of Engineering United States

ISBN: (纸本)9798350382396

Epilepsy is a prevalent neurological disorder and has been studied through the analysis of Electroencephalogram (EEG) signals. However, the identification and classification of epileptic seizure patterns remains challenging due to the non-stationary nature of EEG signals and the presence of artifacts. In this paper, we investigate the applicability of a transformer-based deep learning model to classify seizure patterns observed in epileptic patients. We employed the self-Attention mechanism inherent in transformers to capture complex temporal relationships in the EEG recordings. By prepossessing the EEG signals into suitable input sequences and adapting the transformer architecture, we achieved 78.11% in distinguishing between different epileptic seizure patterns. Our findings indicate that the transformer model, with its ability to manage long-range dependencies, offers a robust approach to EEG-based seizure pattern classification. This work is important for building advanced automated diagnostic tools for epilepsy and related neurological disorders. © 2023 IEEE.

关键词： Electroencephalography

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Edge-Cloud Enabled Smart Sensing Applications with Personalized Federated Learning in IoT

Edge-Cloud Enabled Smart Sensing Applications with Personali...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Mao, Yingchi Rong, Yi Wang, Jiajun Wang, Zibo He, Xiaoming Wu, Jie Hohai University College of Computer Science and Software Engineering Nanjing China Nanjing University of Posts and Telecommunication College of Internet of Things Nanjing China Temple University Center for Networked Computing Philadelphia United States

ISBN: (纸本)9798350351255

The rapid development of deep learning technologies and the widespread deployment of sensing devices have brought considerable attention to Internet of Things (IoT). The smart sensing application is one of the popular applications in IoT. Personalized Federated Learning (pFL) is a replacement to traditional Federated Learning (FL) to tackle the statistical heterogeneity of clients' private datasets (e.g., non-Independent and Identically (non-IID) data). However, existing pFL methods encounter two challenges in smart sensing applications: a) the global model preference, causing poor global model performance for minority classes on sensing device data. b) the dynamic role differences in each hierarchy of the layer-stacked deep learning model that needs to be considered. Jointly considering these challenges, we present a novel edge-cloud enabled pFL framework named pFL-Sensing for smart sensing applications. Specifically, the sensing device serves as an edge server. Each edge server produces a customized model through two phases: a model training phase and a model aggregation phase. In the model training phase, we design a novel loss function to alleviate the issue of the global model preference. In the model aggregation phase, hierarchical aggregation and an Adaptive Weight Calculation (AWC) mechanism are proposed to capture the dynamic role differences of model hierarchies. We perform simulation experiments on real image and text classification tasks. Experimental results show that pFL-Sensing demonstrates higher classification accuracy than advanced pFL baselines. © 2024 IEEE.

关键词： Federated learning

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A Monte Carlo Based COVID-19 Detection Framework for Smart Healthcare

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computers, Materials & Continua 2022年第2期70卷 2365-2380页

作者： Tallat Jabeen Ishrat Jabeen Humaira Ashraf Nz Jhanjhi Mamoona Humayun Mehedi Masud Sultan Aljahdali Research Center for Modelling and Simulation(RCMS)NUST Islamabad44000Pakistan Department of Computer Science and Software Engineering International Islamic UniversityIslamabad44000Pakistan School of Computer Science and Engineering SCE Taylor’s UniversitySubang Jaya47500Malaysia College of Computer Science and Information Technology Jouf UniversityAl JoufSaudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

COVID-19 is a novel coronavirus disease that has been declared as a global pandemic in *** affects the whole world through personto-person *** virus spreads by the droplets of coughs and sneezing,which are quickly falling over the ***,anyone can get easily affected by breathing in the vicinity of the COVID-19 ***,vaccine for the disease is under clinical investigation in different pharmaceutical *** now,multiple medical companies have delivered health monitoring ***,a wireless body area network(WBAN)is a healthcare system that consists of nano sensors used to detect the real-time health condition of the *** proposed approach delineates is to fill a gap between recent technology trends and healthcare *** COVID-19 affected patient is monitored through WBAN sensors and network,a physician or a doctor can guide the patient at the right timewith the correct possible *** scenario helps the community to maintain social distancing and avoids an unpleasant environment for hospitalized patients Herein,a Monte Carlo algorithm guided protocol is developed to probe a secured cipher *** cipher helps to avoid wireless network issues like packet loss,network attacks,network interference,and routing *** Carlo based covid-19 detection technique gives 90%better results in terms of time complexity,performance,and *** indicate that Monte Carlo based covid-19 detection technique with edge computing idea is robust in terms of time complexity,performance,and efficiency and thus,is advocated as a significant application for lessening hospital expenses.

关键词： COVID-19 coronavirus cryptography Monte Carlo algorithm edge computing

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Design of a Smart and Green Industry 4.0 Architecture 24

Design of a Smart and Green Industry 4.0 Architecture

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24th International Arab Conference on Information Technology, ACIT 2023

作者： Hamdan, Khaled Kazar, Okba College of It Uaeu Dept. of Computer Science and Software Engineering United Arab Emirates College of It Uaeu Dept. of Information Systems & Security United Arab Emirates

ISBN: (纸本)9798350384307

The industrial sector has entered a phase of profound change which sees digital technologies being integrated into the heart of industrial processes. This fourth industrial era gives birth to a new generation of factory called 'Digital Factory', 'Industry 4.0'. This industrial change is the result of the integration of new digital technologies into manufacturing processes and the optimization of energy consumption. The commitment to the environment is also a key and success factor of Industry 4.0 and green in particular the reduction of carbon emissions and pollution. This is how smart green industry try to take in charge the problem of energy consumption and to reduce the Co2 pollution. The goal of this paper is to present the new model of smart green industry using distributed artificial intelligence based on multi-agent system (MAS). The motivation to use the intelligent approach based on MAS is based on the similarity between the fundamental concepts of the cyber-physical system and those contained in the architecture and operation of MAS such as communication, coordination, decision and cooperation. © 2023 IEEE.

关键词： environmental impact Industry 4.0 smart and green Industry 4.0

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IEEE 802.11 VANETs for Safety of Autonomous Driving: QoS Requirements & Benchmarking 100

IEEE 802.11 VANETs for Safety of Autonomous Driving: QoS Req...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Ma, Xiaomin Ding, Shengli Zhang, Tingyu Oral Roberts University School of Engineering TulsaOK74171 United States Purdue University Elmore Family School of Electrical and Computer Engineering United States Dalian University of Science and Technology College of Software Engineering China

ISBN: (纸本)9798331517786

It is anticipated that wireless vehicular ad hoc networks (VANETs) can further enhance the safety of autonomous vehicles. Recently, two major standards for the next generation of VANET technologies have been suggested and tested: IEEE 802.11p/bd and 3GPP NR-V2X. Compared with human driving VANET, autonomous driving VANET poses big challenges for more stringent requirements on reliability and delay of message transmissions. Up to date, the quality of service (QoS) requirements of VANETs for the safety services of autonomous driving vehicles have not been investigated systematically. Whether or not the current proposed communication systems can meet such high QoS requirements is unclear. This paper reviews the communication requirements for safety use cases, and the QoS requirements for level 5 autonomous driving VANETs are deduced. Then, the QoS of IEEE 802.11-based VANETs, including 802.11p, 802.11bd, and other 802.11 cutting-edge versions, are evaluated by simulations and analyses on a few selected safety use cases and benchmarked against the corresponding QoS requirements. The numerical results reveal that the current IEEE 802.11p/bd cannot meet the QoS requirements for some selected safety applications in autonomous driving vehicles. However, IEEE 802.11 systems with new enhancements can potentially support safety services with assured QoS. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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Deep Learning-Based Probability Model for Traffic Information Estimation

Journal of Network Intelligence

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Journal of Network Intelligence 2022年第3期7卷 592-607页

作者： Sun, Zhaoshan Pan, Jeng-Shyang Pan, Tien-Szu Chen, Chi-Hua College of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China Collage of Computer Science and Engineering Shandong University of Science and Technology China Chaoyang University of Technology Taiwan Fujian University of Technology China Department of Electronic Engineering National Kaohsiung University of Science and Technology 415 Chien-Kung Road Kaohsiung807 Taiwan College of Computer and Data Science College of Software Fuzhou University Fuzhou350116 China

This study proposes a novel traffic information estimation method based on deep learning and cellular floating vehicle data (CFVD). In this paper, a probabilistic analysis model based on deep learning is proposed to consider the relationship between vehicle speed and communication behaviors. Then, a vehicle speed estimation method based on the proposed probabilistic analytical model is proposed to estimate vehicle speed. For traffic flow estimation, normal location update is adopted to estimate traffic flow. The estimated vehicle speed and the estimated traffic flow can be gathered to estimate traffic density. The proposed method is verified by the simulation tool and the experiment results revealed that the accuracies of estimated vehicle speed, estimated traffic flow, and estimated traffic density are 96.36%, 99.80%, and 96.45%, respectively. Thus, this method estimates traffic information accurately and helps improve the performance of the intelligent transportation system (ITS). © 2022, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Vehicles

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Distinguishing Sensitive and Insensitive Options for the Winograd Schema Challenge 28th

Distinguishing Sensitive and Insensitive Options for the ...

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Li, Dong Wang, Pancheng He, Liangliang Pang, Kunyuan Li, Shasha Tang, Jintao Wang, Ting College of Computer Science and Technology National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems Changsha China

ISBN: (纸本)9783031306747

The Winograd Schema Challenge (WSC) is a popular benchmark for commonsense reasoning. Each WSC instance has a component that corresponds to the mention of the correct answer option of the two options in the context. We observe that the answers of many instances are insensitive to the options. In this paper, based on this observation, we propose an approach based on fine-tuning the pre-trained language model for WSC by distinguishing sensitive and insensitive options. First, we split WSC instances into option-sensitive and insensitive categories, and use option expanding and option masking strategies to weaken the options so that the model does not pay attention to options when they are insensitive during fine-tuning. Second, we treat the two categories as intermediate-task of each other, and use transfer learning to improve the performance. We fine-tune BERT-Large and T5-XXL with our approach on WINOGRANDE, a new dataset of WSC, and the experiment shows our method outperforms baselines by a large margin, achieving state-of-the-art, which indicates the effectiveness of our instance-distinguishing strategy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Large dataset

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Robust graph‐based localization for industrial Internet of things in the presence of flipping ambiguities

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1140-1149页

作者： Mian Imtiaz ul Haq Ruhul Amin Khalil Muhannad Almutiry Ahmad Sawalmeh Tanveer Ahmad Nasir Saeed Faculty of Electrical Engineering Swedish College of Engineering and TechnologyWah CanttPakistan Department of Electrical Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and TechnologyPeshawarPakistan Department of Electrical Engineering Remote Sensing UnitNorthern Border UniversityArarSaudi Arabia Data Science and Artificial Intelligence Department‐College of Science and Information Technology Irbid National UniversityIrbadJordan Innovation Education and Research Center for On‐Device AI Software(Bk21) Department of Computer Science and EngineeringChungnam National UniversityDaejeonRepublic of Korea Department of Electrical and Communication Engineering United Arab Emirates University(UAEU)Al AinUnited Arab Emirates

Localisation of machines in harsh Industrial Internet of Things(IIoT)environment is necessary for various ***,a novel localisation algorithm is proposed for noisy range measurements in IIoT *** position of an unknown machine device in the network is estimated using the relative distances between blind machines(BMs)and anchor machines(AMs).Moreover,a more practical and challenging scenario with the erroneous position of AM is considered,which brings additional uncertainty to the final position ***,the AMs selection algorithm for the localisation of BMs in the IIoT network is *** those AMs will participate in the localisation process,which increases the accuracy of the final location ***,the closed‐form expression of the proposed greedy successive anchorization process is derived,which prevents possible local convergence,reduces computation,and achieves Cramér‐Rao lower bound accuracy for white Gaussian measurement *** results are compared with the state‐of‐the‐art and verified through numerous simulations.

关键词： Cramér‐Rao lower bound greedy successive anchorization industrial internet of things localization

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Chinese Speech Emotion Recognition Based on Pre-Trained Model 5

Chinese Speech Emotion Recognition Based on Pre-Trained Mode...

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5th International Conference on Big Data and Artificial Intelligence and software engineering, ICBASE 2024

作者： Xu, Sukui Jiang, Nan Lian, Shun Pan, Jingmin Anhui Institute of Information Technology College of Computer and Software Engineering Wuhu China Fujian Polytechnic of Information Technology School of General Education Fuzhou China Anhui University School of Computer Science and Technology Hefei China

ISBN: (纸本)9798331506612

This paper proposes a method based on large-scale speech pre-trained models for the task of Chinese speech emotion recognition. Similar to the transfer learning approach in image classification task, speech pre-trained model is used as a fixed feature extractor to extract features from speech data, followed by a fully connected network for classification. Experimental results show that, on the seven-class speech emotion recognition task of the Chinese M3ED dataset, the proposed method achieves classification accuracies of 0.506 and 0.560 using base and large sized pre-trained models respectively, surpassing the baseline accuracy of 0.454 for the ECAPA-TDNN model. © 2024 IEEE.

关键词： Transfer learning

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K-Banhatti Sombor Invariants of Certain computer Networks

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computers, Materials & Continua 2022年第10期73卷 15-31页

作者： Khalid Hamid Muhammad Waseem Iqbal Abaid Ur Rehman Virk Muhammad Usman Ashraf Ahmed Mohammed Alghamdi Adel A.Bahaddad Khalid Ali Almarhabi Department of Computer Science Superior UniversityLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Mathematics University of SialkotSialkot51013Pakistan Department of Computer Science GC Women UniversitySialkot53310Pakistan Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia Department of Information System Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computing in Al-QunfudahUmm Al-Qura UniversityMakkah24381Saudi Arabia

Any number that can be uniquely determined by a graph is called a graph *** the last twenty years’countless mathematical graph invariants have been characterized and utilized for correlation ***,no reliable examination has been embraced to decide,how much these invariants are related with a network graph or molecular *** this paper,it will discuss three different variants of bridge networks with good potential of prediction in the field of computer science,mathematics,chemistry,pharmacy,informatics and biology in context with physical and chemical structures and networks,because k-banhatti sombor invariants are freshly presented and have numerous prediction qualities for different variants of bridge graphs or *** study solved the topology of a bridge graph/networks of three different types with two invariants KBanhatti Sombor Indices and its reduced *** deduced results can be used for the modeling of computer networks like Local area network(LAN),Metropolitan area network(MAN),and Wide area network(WAN),backbone of internet and other networks/structures of computers,power generation,bio-informatics and chemical compounds synthesis.

关键词： Bridge networks invariants k-banhatti sombor indices maple network graph molecular graph

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