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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Sicen Wang, Xiaolong Zhao, Xianbing Chen, Hao The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong The Shenzhen MSU-BIT University Department of Engineering Shenzhen China Department of Computer Science Shenzhen China Hong Kong University of Science and Technology Department of Chemical and Biological Engineering Hong Kong Hkust ShenzhenHong Kong Collaborative Innovation Research Institute Shenzhen China

ISBN: (纸本)9798350386226

Medication recommendation is a fundamental yet crucial branch of healthcare that presents opportunities to assist physicians in making more accurate medication prescriptions for patients with complex health conditions. Previous studies have primarily concentrated on deriving patient representations from electronic health records (EHRs) to recommend medications, often overlooking the effective integration of domain-specific prior knowledge. However, integrating domain knowledge with the patient's clinical manifestations can be challenging, particularly when dealing with complex clinical manifestations. Therefore, in this paper, we first identify comprehensive domain-specific prior knowledge, namely the Unified Medical Language System (UMLS), which is a comprehensive repository of biomedical vocabularies and standards, for knowledge extraction. Subsequently, we propose a knowledge injection module that addresses the effective integration of domain knowledge with complex clinical manifestations, enabling an effective characterization of the health conditions of the patient. Moreover, acknowledging the influence of historical medications on patients' current treatments, we propose a historical medication-aware patient representation module to capture the longitudinal influence of historical medication information on the representation of current patients. Extensive experiments on three publicly benchmark datasets verify the superiority of our proposed method, which outperformed other methods by a significant margin. The code is available at: https://***/sherry6247/DKINet. © 2024 IEEE.

关键词： Electronic health record

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Safety Wearable Devices for Monitoring and Predicting the Emergency of Civil Workers Using Mobile Application

Safety Wearable Devices for Monitoring and Predicting the Em...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Kiruthiga Devi, M. Parthasarathy, R. Tamilselvi, C. Dharani, V. Kaviya, V. Dr. M.G.R. Educational and Research Institute Department of Information Technology Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science & Engineering Chennai India Sathyabama Institute of Science and Technology Department of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350319200

In several industries, wearable sensing devices are assisting workers in maintaining their health and safety. However, because WSDs can record precise information that might be regarded as private and personal, workers, particularly in the construction industry, have demonstrated some resistance to their use. Nonetheless, this highly regarded data may offer some crucial insight that management needs in order to plan and enhance worksite safety and assist the implementation of technology in decision-making. This project aims to develop a wearable safety device for construction workers, integrating various sensors such as a flame sensor, max30100 sensor, neo-6m GPS module, and MPU6050 sensor. The wearable device is intended to track the worker's position, monitor heart rate and SpO2, and identify falls and fire mishaps. The MQTT protocol will be used to transfer the sensor data to an Android application, enabling for real-time monitoring and emergency warning. To detect falls at the edge, the device also includes a Raspberry Pi camera module. The ultimate purpose of this initiative is to increase safety for construction workers and lower the likelihood of accidents and injuries occurring on job sites. The findings show the development of wearable technology in construction safety research and shed light to reduce the risk of civil workers. The analysis also provides a dynamic platform for integrating future applications and describes the current state of wearable technology in the field of construction safety. © 2023 IEEE.

关键词： construction worker fall detection fire accidents MQTT protocol Raspberry Pi Wearable safety device Wearable Sensing Devices

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An Efficient ResNetSE Architecture for Smoking Activity Recognition from Smartwatch

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Intelligent Automation & Soft Computing 2023年第1期35卷 1245-1259页

作者： Narit Hnoohom Sakorn Mekruksavanich Anuchit Jitpattanakul Department of Computer Engineering Image Information and Intelligence LaboratoryFaculty of EngineeringMahidol UniversityNakhon Pathom73170Thailand Department of Computer Engineering School of Information and Communication Technology University of PhayaoPhayao56000Thailand Department of Mathematics Faculty of Applied ScienceKing Mongkut’s University of Technology North BangkokBangkok10800Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research InstituteKing Mongkut’s University of Technology North BangkokBangkok 10800Thailand

Smoking is a major cause of cancer,heart disease and other afflictions that lead to early *** effective smoking classification mechanism that provides insights into individual smoking habits would assist in implementing addiction treatment *** activities often accompany other activities such as drinking or ***,smoking activity recognition can be a challenging topic in human activity recognition(HAR).A deep learning framework for smoking activity recognition(SAR)employing smartwatch sensors was proposed together with a deep residual network combined with squeeze-and-excitation modules(ResNetSE)to increase the effectiveness of the SAR *** proposed model was tested against basic convolutional neural networks(CNNs)and recurrent neural networks(LSTM,BiLSTM,GRU and BiGRU)to recognize smoking and other similar activities such as drinking,eating and walking using the UT-Smoke *** different scenarios were investigated for their recognition performances using standard HAR metrics(accuracy,F1-score and the area under the ROC curve).Our proposed ResNetSE outperformed the other basic deep learning networks,with maximum accuracy of 98.63%.

关键词： Smoking activity recognition deep residual network smartwatch sensors deep learning

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Pre-Impact and Impact Fall Detection Based on a Multimodal Sensor Using a Deep Residual Network

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Intelligent Automation & Soft Computing 2023年第6期36卷 3371-3385页

作者： Narit Hnoohom Sakorn Mekruksavanich Anuchit Jitpattanakul Image Information and Intelligence Laboratory Department of Computer EngineeringFaculty of EngineeringMahidol UniversityNakhon Pathom73170Thailand Department of Computer Engineering School of Information and Communication TechnologyUniversity of PhayaoPhayaoThailand Department of Mathematics Faculty of Applied ScienceKing Mongkut’s University of Technology North BangkokBangkok 10800Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research Institute King Mongkut’s University of Technology North BangkokBangkok 10800Thailand

Falls are the contributing factor to both fatal and nonfatal injuries in the ***,pre-impact fall detection,which identifies a fall before the body collides with the floor,would be ***,researchers have turned their attention from post-impact fall detection to pre-impact fall ***-impact fall detection solutions typically use either a threshold-based or machine learning-based approach,although the threshold value would be difficult to accu-rately determine in threshold-based ***,while additional features could sometimes assist in categorizing falls and non-falls more precisely,the esti-mated determination of the significant features would be too time-intensive,thus using a significant portion of the algorithm’s operating *** this work,we developed a deep residual network with aggregation transformation called FDSNeXt for a pre-impact fall detection approach employing wearable inertial *** proposed network was introduced to address the limitations of fea-ture extraction,threshold definition,and algorithm *** training on a large-scale motion dataset,the KFall dataset,and straightforward evaluation with standard metrics,the proposed approach identified pre-impact and impact falls with high accuracy of 91.87 and 92.52%,*** addition,we have inves-tigated fall detection’s performances of three state-of-the-art deep learning models such as a convolutional neural network(CNN),a long short-term memory neural network(LSTM),and a hybrid model(CNN-LSTM).The experimental results showed that the proposed FDSNeXt model outperformed these deep learning models(CNN,LSTM,and CNN-LSTM)with significant improvements.

关键词： Pre-impact fall detection deep learning wearable sensor deep residual network

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Intelligent Accident Recognition and Notification System using Hybrid Deep Learning and IoT 4

Intelligent Accident Recognition and Notification System usi...

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4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Sathish Kumar, P. Singh Rawat, Ramesh Shrimali, Manish Parikh, Swapnil Patil, Harshal Maranan, Ramya United Institute of Technology Department of Robotics & Automation Tamil Nadu Coimbatore641020 India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248 002 India Department of Computer Science and IT Rajasthan Udaipur313 003 India Parul Institute of Engineering and Technology Parul University Department of Computer Science and Engineering Gujarat 391760 India Symbiosis Institute of Technology Symbiosis International Deemed University Computer Science and Engineering Maharashtra Pune412 115 India Saveetha School of Engineering SIMATS Department of Research and Innovation Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331515720

Ensuring the safety of individuals should be a top priority. Everyone wants to return home safely. Many riders continue to disregard safety constraints, increasing the chance of accidents that result in death. It is critical to incorporate the latest technology into road monitoring systems to make cities smarter and to ensure rider safety. For this research, Deep Learning (DL) and the Internet of Things (IoT) were chosen for effective road surveillance and alert systems. The primary goal of the study is to detect and inform riders about accidents that occur along their path and to provide details about the location of the detected accident. Additionally, the system provides the vehicle's speed. To identify automobile accidents in the footage, a hybrid DL model that includes Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-Bi-LSTM) is suggested. For the model's training, both accident and non-Accident data were collected. The model is evaluated using three metrics: Accuracy, loss, and execution time. The next step is to upload the recommended model to the ThingSpeak cloud. A Graphical User Interface (GUI) is being created in the ThingSpeak cloud. The GUI displays the vehicle's speed, accident images, and the precise location of the accident scene using the Raspberry Pi, speed sensor, Pi-camera, and Global Positioning System (GPS) module. An LED is also integrated into the GUI to alert the user. A real-world test is conducted on the designed system. The proposed solution has many advantages and will ensure driver safety. © 2024 IEEE.

关键词： Graphical user interfaces

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A Federated Named Entity Recognition Model with Explicit Relation for Power Grid

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computers, Materials & Continua 2023年第5期75卷 4207-4216页

作者： Jingtang Luo Shiying Yao Changming Zhao Jie Xu Jim Feng State Grid Sichuan Economic Research Institute ChengduChina School of Computer Science Chengdu University of Information TechnologyChengduChina School of Information and Communication Engineering University of Electronic Science and Technology of China ChengduChina Amphenol Global Interconnect Systems San JoseCA 95131USA

The power grid operation process is complex,and many operation process data involve national security,business secrets,and user ***,labeled datasets may exist in many different operation platforms,but they cannot be directly shared since power grid data is highly *** to use these multi-source heterogeneous data as much as possible to build a power grid knowledge map under the premise of protecting privacy security has become an urgent problem in developing smart ***,this paper proposes federated learning named entity recognition method for the power grid field,aiming to solve the problem of building a named entity recognition model covering the entire power grid process training by data with different security *** decompose the named entity recognition(NER)model FLAT(Chinese NER Using Flat-Lattice Transformer)in each platform into a global part and a local *** local part is used to capture the characteristics of the local data in each platform and is updated using locally labeled *** global part is learned across different operation platforms to capture the shared NER *** local gradients fromdifferent platforms are aggregated to update the global model,which is further delivered to each platform to update their global *** on two publicly available Chinese datasets and one power grid dataset validate the effectiveness of our method.

关键词： Power grid named entity recognition federal learning

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research on Cross-Domain Heterogeneous Information Interaction System in Complex Environment Based on Blockchain technology 1

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7th International Conference on Smart Computing and Communication, SmartCom 2022

作者： Ma, BaoQuan Cheng, YeJian Zhang, Ni Wang, Peng Lei, XuHua Huai, XiaoYong Li, JiaXin Jia, ShuJuan Wang, ChunXia National Computer System Engineering Research Institute of China Beijing102200 China School of Computer Science and Technology Xidian University Xi’an710071 China

ISBN: (数字)9783031281242

ISBN: (纸本)9783031281235

In the field of information interaction, when a project involves a large amount of cross-domain heterogeneous information, it is difficult to transmit and update the required information in a timely, accurate, reliable and safe manner in such a complex environment, thereby maintaining synchronization. The decentralization and immutability of blockchain technology provide new ideas for solving this problem. Therefore, this paper takes this as a starting point and proposes a cross-domain heterogeneous information exchange system based on blockchain, which calls smart contracts to realize data transmission and other functions. This paper states the design idea of the system, introduces the system software framework and network topology in detail, shows the flow chart of the system operation, and conducts simple software testing and verification. Finally, this work is summarized, and the development direction of future work is prospected, hoping to provide inspiration for the solution of such problems. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Smart contract

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NLP using Wordnet Database for Monitor the Social Platform in Digital Forensic Investigation 14

NLP using Wordnet Database for Monitor the Social Platform i...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Senthil, P. Alagumuthukrishnan, S. Kannan, K.S. CMR College of Engineering & Technology Department of Computer Science and Engineering Kandlakoya Medchal Road Hyderabad501401 India CMR Institute of Technology Department of Computer Science and Engineering Kandlakoya Medchal Road Hyderabad501401 India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Krishnankoil626126 India

Cyberbullying has made people too vulnerable to hatred and abuse which makes cyberbullying an important subject of matter all over the world. We propose a framework that prevents the use from using abusive words and predict user crime probability in social application with the help of one of the machine learning technique Natural Language Processing (NLP). NLP plays an important role in analyzing the given words and find out the alternative meaning and provide which category these words belongs to with the help of the WordNet database. In the previous system, social application user information is processed and kept aside and required is action not taken. We proposed an application that solves that existing system issue and provides a solution by making a complaint on the user in the social application. The proposed system consists of three application social book, social admin, crime reporting. First social book is social media application that provides activities like adding friends posting status and etc. Second social admin has the control of the whole social media application admin has all details of the user and so on Third crime application contains two-part public and police this application acts as a compliant system to our application where public user can raise a complaint against a person in social application to take some action. © Grenze Scientific Society, 2023.

关键词： Crime

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Heterogeneous System Data Storage and Retrieval Scheme Based on Blockchain 7th

Heterogeneous System Data Storage and Retrieval Scheme Based...

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7th International Conference on Smart Computing and Communication, SmartCom 2022

作者： Zhang, Ni Ma, BaoQuan Wang, Peng Lei, XuHua Cheng, YeJian Li, JiaXin Huai, XiaoYong Shen, ZhiWei Song, NingNing Wang, Long National Computer System Engineering Research Institute of China Beijing102200 China School of Computer Science and Technology Xidian University Xi’an710071 China

ISBN: (纸本)9783031281235

In the field of information interaction, when a project involves a large amount of heterogeneous information, it is difficult to transmit and update the required information timely, accurately, reliably and securely in such a complex environment to maintain synchronization. At present, blockchain itself has problems such as high storage pressure of nodes, low access efficiency, and simple query. Therefore, this paper takes this as a starting point and proposes a data mapping method of physical resources based on node attributes and heterogeneous nodes, which provides a general method for the data mapping from actual physical resources to information domain. This method can define the attributes of heterogeneous physical information nodes and support unified expression of various physical information resources on the same platform in the real physical world, then connect heterogeneous physical information nodes and improve the sharing ability of data resources between nodes. When accessing data, the corresponding content can be found in the off-chain database by obtaining the index information of the off-chain location stored on the chain. This method takes advantage of the large space and high access efficiency of the off-chain storage system to share the pressure of on-chain data storage. This paper expounds the design idea of the system, introduces the design objective and method in detail, gives the flow chart of the system operation, and carries out a simple software test and verification. Finally, this paper summarizes the work and prospects the development direction of future work, hoping to provide inspiration for solving such problems. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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Design of Provably Secure Authentication Protocol for Edge-Centric Maritime Transportation System

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IEEE Transactions on Intelligent Transportation Systems 2023年第12期24卷 14536-14545页

作者： Mahmood, Khalid Shamshad, Salman Ayub, Muhammad Faizan Ghaffar, Zahid Khan, Muhammad Khurram Das, Ashok Kumar National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu610054 China National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Douliu64002 Taiwan King Saud University Center of Excellence in Information Assurance Riyadh11653 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500032 India

The epidemic growth of the Internet of Things (IoT) objects have revolutionized Maritime Transportation Systems (MTS). Though, it becomes challenging for the centralized cloud-centric framework to fulfil the application requirements such as low latency and power utilization. The introduction of the distributed edge-centric framework has recently helped the IoT-enabled MTS to meet these requirements by manipulating the tasks at the edge of the networks. Despite the fact that MTS leverages mobile subscribers by overcoming inherent cloud computing limitations, data security and user privacy requirements in establishing the MTS setup are still non-trivial challenges. In this article, we develop a key agreement solution for mobile users to realize mutual authentication in a single round. Our protocol offers user anonymity to maintain user privacy, and it can prevent physical attacks by physically unclonable functions. Initially, the security analysis is conferred to substantiate our protocol's security persistence or strength. Later, its performance correlation is observed under the assumption of diverse metrics in a predefined empirical setup. The meticulous performance correlation endorses the precedence of our protocol over specified related protocols. © 2000-2011 IEEE.

关键词： Hash functions

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