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3rd International Conference on Neural Networks, information and Communication engineering, NNICE 2023

作者： Zheng, Xuecong Chen, Li Chen, Yaojie School of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan430065 China Wuhan Institute of Marine Electric Propulsion Hubei Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Hubei Wuhan430065 China

ISBN: (纸本)9798350335972

To solve the problem that the flame area of small targets on ships can not be easily segmented and the flame boundary is obviously affected by other background such as smoke, this experiment improves DeepLabV3+ algorithm from the following three aspects. Firstly, Dense Atrous Spatial Pyramid Pooling (DASPP) was improved to enhance the extraction and utilization of flame boundary and small target flame features. Secondly, in order to avoid the influence of background such as smoke around the flame, the high-level flame features and shallow flame features obtained by DASPP were combined with the improved Attention Mechanism (PE-SCAM) to obtain more context information in spatial and channel dimensions, so as to enhance the processing effect of the model on the flame and its boundaries. Finally, the traditional cross loss function is optimized to reduce the influence caused by the imbalance of positive and negative samples between the flame and the background. Experimental results show that the MIou of the proposed algorithm on the ship flame data set reaches 88.43%, which can realize the accurate segmentation of small target flames and flame boundaries on the basis of ensuring high real-time performance. © 2023 IEEE.

关键词： Ships

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Development of China's spaceborne SAR satellite,processing strategy,and application:take Gaofen-3 series as an example

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地球空间信息科学学报（英文版） 2024年第1期27卷 221-236页

作者： Guo Zhang Shunyao Wang Zhenwei Chen Yuzhi Zheng Ruishan Zhao Taoyang Wang Yu Zhu Xinzhe Yuan Wei Wu Weitao Chen State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhanChina School of Law Wuhan UniversityWuhanChina School of Geomatics Liaoning Technical UniversityFuxinChina School of Remote Sensing and Information Engineering Wuhan UniversityWuhanChina Institute of Spacecraft System Engineering China Academy of Space TechnologyBeijingChina National Disaster Reduction Center of China Ministry of Emergency ManagementBeijingChina School of Computer Science China University of GeosciencesWuhanChina Key Laboratory of Space Ocean Remote Sensing and Application Ministry of Natural Resources of the People's Republic of ChinaBeijingChina

After mastering the key technologies of manufacturing spaceborne Synthetic Aperture Radar(SAR),China's SAR satellites have been successfully launched into *** the only civil microwave satellite listed in the"National High-resolution Earth Observation system Major Project,"the Gaofen-3(GF-3)01 satellite is the first C-band multi-polarization SAR satellite with a resolution of 1 ***-3 series satellites stand out among civil SAR satellites worldwide because of their high resolution,wide swath,high geometric radiation quality,multiple imaging modes,and long operation *** GF-3 series satellites as an example,this study introduces the development of China's civil SAR satellites,as well as their processing strategies and *** success of the GF-3 series satellites shows that China's SAR remote sensing technology has stepped into a new era of high-quality and high-precision Earth observations.

关键词： High-resolution Synthetic Aperture Radar(SAR) GaoFen-3(GF-3)satellites earth observation

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Binary and Multi-Class Classification on the IoT-23 Dataset

Binary and Multi-Class Classification on the IoT-23 Dataset

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Advances in Science and engineering technology International Conferences (ASET)

作者： Alia Ahli Ayesha Raza Kevser Ovaz Akpinar Mustafa Akpinar Computing Security Department Rochester Institute of Technology Dubai UAE Faculty of Computer Information System Higher Colleges of Technology Sharjah UAE

In the recent years, Internet of Things (IoT) technology has become widespread and is commonly used by organizations for many different purposes. With the increase in the demand for this technology, new security challenges arise. It becomes difficult for cybersecurity experts to administer security measures and prevent the IoT network from malicious attacks. This paper proposes the use of machine learning algorithms – Random Forest (RF), Multi- layer Perceptron (MLP) and Gradient Boosting (GB) to detect malicious traffic flows and anomalies in the IoT network. For this purpose, Aposemat IoT-23 dataset is used, and the models are built, trained and tested on it. Two types of classification are considered – binary and multi-label. Random Forest and Gradient Boosting classifier were able to achieve high accuracies of 98.6% and 97.7% respectively.

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Integrating IoT, Blockchain, and Vision Transformers for Enhanced Security and Efficiency in Smart Home and Healthcare systems

Integrating IoT, Blockchain, and Vision Transformers for Enh...

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Computing and information technology (ICCIT-1441), 2020 International Conference on

作者： Salman Jan Toqeer Ali Syed Saad Said Alqahtany Sohail Iqbal Ijaz Khan Shahrulniza Musa Faculty of Computer Studies Arab Open University A'Ali Kingdom of Bahrain Faculty of Computer and Information System Islamic University Madinah Madinah Saudi Arabia Department of Information Technology Alburaimi University College Alburaimi Oman Malaysian Institute of Information Technology Universiti Kuala Lumpur Kuala Lumpur Malaysia

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

Internet of Things (IoT) and cloud computing have revolutionized various realms including healthcare, education, industries and businesses. Wearable health monitors, smart medical equipment continuously collect patients data that are sent to cloud computing that possess powerful analytical tools facilitating individuals and healthcare units in terms of personalized treatment, proactive real-time health management, remote patient monitoring. For an efficient processing, edge computing satiated in terms of analysis however this decentralized nature, edge computing is significantly targeted and exposed to security threats and cyber attacks due to inhibition of vulnerabilities and breaches in the IoT devices. This research study focuses on enhancing strengths of edge computing to insure the reliability and integrity of data by utilizing the Blockchain infrastructure. This research also utilizes the transformer models to identify the real and fake data that fosters trust in edge computing technologies.

关键词： computer vision Computational modeling Data security Medical services Smart homes Transformers Threat assessment Blockchains Internet of Things Edge computing

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Utilizing Genetic Algorithms for Detecting Congenital Heart Defects

Utilizing Genetic Algorithms for Detecting Congenital Heart ...

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E-mobility, Power Control and Smart systems (ICEMPS), International Conference on

作者： Sachin C. Patil Balaram Yadav Kasula Vazeer Ali Mohammed Ketan Gupta T. Thamaraimanalan Senior Software Engineer USA Dept of Information Technology University Of The Cumberlands Williamsburg KY USA Dept. of Computer & Information System Lewis University Illinois USA Department of ECE Sri Eshwar College of Engineering Coimbatore Tamil Nadu INDIA

ISBN: (数字)9798350394399

ISBN: (纸本)9798350394405

Genetic Algorithms have been more and more employed to stumble on congenital heart defects in the last variety of years. Gasoline is a computational technique that uses evolutionary concepts to optimize answers to complex problems. This study proposes the use of Genetic Algorithms as a novel approach for detecting Congenital Heart Defects a common and potentially life-threatening condition affecting newborns. By incorporating genetic principles and simulations, GAs have proven to be effective in solving complex medical diagnosis problems. The study aims to optimize the classification process by employing evolutionary techniques to identify key features and prioritize them based on their importance in diagnosing CHDs. Results from the GA model showed a significant improvement in accuracy and speed compared to existing methods, making it a promising tool for early detection and intervention of CHDs in newborns. But gas is more appropriate for this assignment because of its potential to pick out extra subtle variations between ordinary and abnormal cardiovascular anatomy. Gas has additionally been established to be promising within the identity and quantification of CHDs.

关键词： Heart Pediatrics Accuracy Power control Prediction algorithms Genetics Market research

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Smart Expiry Tracker and Product Recommender using Google ML Kit OCR Engine 2

Smart Expiry Tracker and Product Recommender using Google ML...

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2nd International Conference on Computational Intelligence and Network systems, CINS 2024

作者： Krishanamurthy, Vallidevi Kurumthottam, Aleena Bosco Ali, Shireen Aliza Anandan, Paul Danny Kavitha, Muthu Subash Karthikeyan, K. Kumaresh, V. Manikandan, S. School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Mechanical Electrical and Manufacturing Engineering Loughborough University United Kingdom School of Information and Data Sciences Nagasaki University Japan Senior Data Analyst Ninjacart Bangalore India Software Development Engineer Vegrow Chennai India iOS App Developer Facilio Chennai India

ISBN: (纸本)9798331504106

Expiration dates play a crucial role in both personal and business contexts, influencing everything from health management to operational efficiency. Track Expiry Dates (TED) is a comprehensive mobile application designed to address two key needs: document management and expiry tracking. We designed a secure app which offers users to store personal documents such as driving licenses, passports, general documents, product warranty cards, and bills, ensuring that critical information is readily accessible and safeguarded against loss or damage. The proposed system provides a seamless and efficient solution for tracking expiry dates and storing essential documents such as Aadhar cards and PAN cards. It automatically sends reminders to users and their nominated family members prior to document expiration, ensuring timely renewals. From a business perspective, the platform enables companies to leverage data from uploaded documents to offer targeted, content-driven recommendations for products and brands. With its intuitive user interface and robust back-end infrastructure, the app delivers a comprehensive system for document management and expiry tracking, effectively meeting the diverse requirements of both individual users and businesses. ©2024 IEEE.

关键词： Driver licensing

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Modelling and Simulation of COVID-19 Outbreak Prediction Using Supervised Machine Learning

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computers, Materials & Continua 2021年第3期66卷 2397-2407页

作者： Rachid Zagrouba Muhammad Adnan Khan Atta-ur-Rahman Muhammad Aamer Saleem Muhammad Faheem Mushtaq Abdur Rehman Muhammad Farhan Khan Department of Computer Information System College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Computer Science Riphah International UniversityLahore CampusLahore54000Pakistan Hamdard Institute of Engineering and Technology Hamdard UniversityIslamabad44000Pakistan Department of Information Technology Khwaja Fareed University of Engineering and Information TechnologyRahim Yar Khan64200Pakistan School of Computer Science NCBA&ELahore54000Pakistan Department of Forensic Sciences University of Health SciencesLahore54000Pakistan

Novel Coronavirus-19(COVID-19)is a newer type of coronavirus that has not been formally detected in *** is established that this disease often affects people of different age groups,particularly those with body disorders,blood pressure,diabetes,heart problems,or weakened immune *** epidemic of this infection has recently had a huge impact on people around the globe with rising mortality *** levels of mortality are attributed to their transmitting behavior through physical contact between *** is extremely necessary to monitor the transmission of the infection and also to anticipate the early stages of the disease in such a way that the appropriate timing of effective precautionary measures can be *** latest global coronavirus epidemic(COVID-19)has brought new challenges to the scientific *** Intelligence(AI)-motivated methodologies may be useful in predicting the conditions,consequences,and implications of such an *** forecasts may help to monitor and prevent the spread of these *** article proposes a predictive framework incorporating Support Vector Machines(SVM)in the forecasting of a potential outbreak of *** findings indicate that the suggested system outperforms cutting-edge *** method could be used to predict the long-term spread of such an outbreak so that we can implement proactive measures in *** findings of the analyses indicate that the SVM forecasting framework outperformed the Neural Network methods in terms of accuracy and computational *** proposed SVM system model exhibits 98.88%and 96.79%result in terms of accuracy during training and validation respectively.

关键词： Coronavirus outbreak machine learning artificial intelligence

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A Hybrid Deep Learning Framework for Stock Price Forecasting with Sentimental Analysis

A Hybrid Deep Learning Framework for Stock Price Forecasting...

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International Conference on system Modeling & Advancement in Research Trends (SMART)

作者： Shobhan Banoth Sucharitha Yadala Vice President-Data/Information Management Lead Engineer CITI Bank School of Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering & Technology Hyderabad Telangana India

ISBN: (数字)9798350380583

ISBN: (纸本)9798350380590

Social media electronic word-of-mouth data affects stock market confidence and trading. As a result, stock market sentiment research of comments is essential for stock market forecasting. Unfortunately, English is becoming the de facto language for sentiment analysis. This study translates writings from places where English is not the local language into English so that multilingual sentiment analysis can be performed. In order to predict stock market movements, this research combined social media's unstructured data with structured data, such as transaction records and technical indicators. Parameter determination has a significant impact on the effectiveness of forecasting models; in recent years, deep-learning(DL) and machine-learning(ML) models have developed as effective means of dealing with anticipating issues. In order to anticipate stock market indices and business stock prices using hybrid data in places where English is not the native language, this research utilized Long-Short-Term Memory(LSTM) models that utilized the geneticalgorithm(GA) to choose parameters. The created LSTMGA model outperformed the other ML-strategies with different types of data in terms of accuracy when it came to generating forecasts using hybrid multilingual sentiment data. As a result, the stock market forecasting method that combines the suggested LSTMGA model with hybrid sentiment analysis is both practical and encouraging.

关键词： Sentiment analysis Accuracy Translation Social networking (online) Predictive models Writing Data models Multilingual Stock markets Forecasting

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Design of 12-Lead Electrocardiogram Smart Clothing Based on Capacitive Sensing Technique

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IEEE Sensors Journal 2024年第10期24卷 16826-16835页

作者： Chang, Wei-Ting Lin, Bor-Shing Tsai, Wei-Lun Chen, Heng-Yin Liu, Chengyu Lin, Bor-Shyh Chi Mei Medical Center The Department of Cardiology Tainan71004 Taiwan National Taipei University Department of Computer Science and Information Engineering New Taipei City237303 Taiwan Institute of Imaging and Biomedical Photonics National Yang Ming Chiao Tung University Tainan71150 Taiwan Industrial Technology Research Institute System Design and Integration Department Hsinchu31057 Taiwan Southeast University School of Instrument Science and Engineering Nanjing210096 China

A 12-lead electrocardiogram (ECG) is one of the most commonly used tools for evaluating cardiovascular diseases (CVDs). In clinical, the conventional Ag/AgCl electrodes with conductive gel are usually used to monitor ECG, but they may encounter gel drying and skin allergic reaction problems for long-term ECG monitoring. In order to improve the above issue, several dry electrodes, made by conductive materials, was proposed in the design of wearable devices, but they still encounter the problems of electrode shifting and stretching, and the risk of skin irritation. Capacitive electrode can access biopotential without contacting skin directly and can completely avoid skin irritation. However, for capacitive electrode, how to maintain a good electrode-skin contacting condition is still a challenge due to electrode fixation problem, in particular in multichannel ECG measurement. In this study, a 12-lead ECG smart clothing based on capacitive sensing technology was proposed to monitor ECG in daily life. To improve the electrode-skin contacting condition, elastic conductive foams were used as the electrode plate to effectively fit the rough body contours. Moreover, a specific wearable belt mechanism was also designed to improve the convenience of use, and it could also provide good friction and proper pressure to reduce the electrode slippage and maintain a good electrode-skin contact condition. The experimental results showed that the proposed smart clothing could stably access the good ECG signal quality under different motion levels, and the specific ECG features corresponding to different CVDs could also be reflected on the measured ECG signal in clinical experiment. © 2001-2012 IEEE.

关键词： Electrocardiograms

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The Side-Channel Vulnerability in Network Protocol 11

The Side-Channel Vulnerability in Network Protocol

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11th International Conference on Communication and Network Security, ICCNS 2021

作者： Ru, Kaiqi Zheng, Yaning Feng, Xuewei Wang, Dongxia National Key Laboratory of Science and Technology on Information System Security Institute of System Engineering AMS PLA China Department of Computer Science and Technology Tsinghua University China National Key Laboratory of Science and Technology on Information System Security China

ISBN: (纸本)9781450386425

Some recent studies have found that there are some side-channel vulnerabilities in the operating system. Attackers would exploit the side-channel vulnerability for malicious purpose, such as hijack connections, denial of service attacks, etc. Currently, most attacks are detected manually. In this paper, we found that the reason for the existence of network protocol side-channel vulnerability is the use of shared resources. Since the state of shared resource affects all connections, when a connection uses a shared resource, information about that connection can be inferred by observing the usage of the shared resource. In order to find the shared resources, we implemented a tool called TASR which is a method of static analysis. The first is to find out what shared resources are available by the definition of shared resources in static analysis. Then, the data packet is used as the taint source to search the tainted shared resources. The second step is to analyze the taint-transmission-path according to the acquired tainted shared variable. Then it can find the side-channel vulnerability. By using this method on TCP, UDP and ICMP protocols, we find the following four shared variables: challenge_count, tcp_memory_allocated, tcp_memory_pressure, sysctl_icmp_msg_per_sec. It is difficult for tcp_memory allocated and tcp_memory pressure to exploit, because they will go through multiple strict checks. Using challenge_count can hijack the connection and inject malicious packets. Using sysctl_icmp_msg_per_sec can assist in DNS cache poisoning attack. © 2021 ACM.

关键词： Static analysis

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