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15th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2023

作者： Ayodele, Oludayo C. Yerima, Suleiman Y. School of Computer Science and Informatics De Montfort University Leicester United Kingdom British University in Dubai Faculty of Engineering and Information Technology Dubai United Arab Emirates

ISBN: (纸本)9798350324433

This study presents a comprehensive comparative analysis of the effectiveness of word-level and character-level embeddings in the context of machine learning-based detection of malicious URLs and DGA-generated domains. Utilizing distinct datasets comprising DGA-generated domains and Spam URLs, we systematically evaluate various machine learning models coupled with word-level and character-level tokenization techniques. Our findings indicate that character-level tokenization yields superior results in identifying DGA-generated domains, particularly due to the random character composition of these URLs. Conversely, both word-level and character-level embeddings exhibit comparable success rates in classifying Spam URLs, owing to the non-random nature of their URL structures. The study sheds light on the importance of tailoring tokenization strategies based on the unique characteristics of the data. We recommend character-level embeddings for detecting DGA-generated domains characterized by random characters. In contrast, the choice between word-level and character-level embeddings is less critical when dealing with Spam URLs, as both approaches yield effective results. © 2023 IEEE.

关键词： Embeddings

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Sequential generation of self-starting diverse operations in all-fiber laser based on thulium-doped fiber saturable absorber

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Chinese Physics B 2022年第6期31卷 87-90页

作者： Pei Zhang Kaharudin Dimyati Bilal Nizamani Mustafa M.Najm S.W.Harun School of Electrical and Information Engineering Huaihua UniversityHuaihua 418008China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua UniversityHuaihua 418008China Department of Electrical Engineering Faculty of EngineeringUniversity of MalayaKuala Lumpur 50603Malaysia Faculty of Advanced Technology and Multidiscipline Airlangga UniversitySurabaya 60115Indonesia Institute of Computer Science and Digital Innovation UCSI UniversityKuala LumpurMalaysia

Self-starting Q-switching,Q-switched mode-locking and mode-locking operation modes are achieved sequentially in an all-fiber erbium-doped fiber laser with thulium-doped fiber saturable absorber for the first *** central wavelengths of Q-switching,Q-switched mode-locking and mode-locking operation modes are 1569.7 nm,1570.9 nm,and 1572 nm,*** mode-locking operation of the proposed fiber laser generates stable dark soliton with a repetition rate of 0.99 MHz and signal-to-noise ratio of 65 *** results validate the capability of generating soliton pulse by doped fiber saturable ***,the proposed fiber laser is beneficial to the applications of optical communication and signal processing system.

关键词： all-fiber laser Q-switched mode-locking dark soliton fiber saturable absorber

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Object detection of linear structures on Mars based on YOLO network 22

Object detection of linear structures on Mars based on YOLO ...

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Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence

作者： Jingkun Xu Jiarui Liang Pengcheng Yan Xiaolin Tian School of Computer Science and Engineering/Faculty of Innovation Engineering Macau University of Science and Technology China

ISBN: (纸本)9781450398336

The geographic feature of terrestrial planets is a critical and important reference that could help researchers have a further understanding of planetary history and its evo-lution. Traditionally, the detection of specific landforms and their geographic parameter extraction basically relies on manual marking, these types of approach may consume a lot of labor and time costs. On the other side, with the development of convolutional neural networks (CNNs),it is able to handle more complicated tasks such as object detection and semantic segmentation with high efficiency and accuracy. In order to solve this problem, this paper presents an introduction about using neural network to do object detection of the linear structures on Mars, like dorsum, fossa and so on. Based on the DEM data of Mars, this paper makes a linear structure data set. The neural network be used is YOLO-v5. In the test of 300 iterations, the algorithm can get the best detection results when it iterates 200 times, the accuracy of the object detection can reach 81% when mAP = 0.5. The results show that the method proposed in this paper can effectively judge whether there is a linear structure in the graph and mark it, which can be used to assist scientists to reduce the time cost required for detection.

关键词： DEM data

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A Blockchain-based IoT-Verifiable Query Service for Efficient and Authentic Data Retrieval in Cloud Systems 2

A Blockchain-based IoT-Verifiable Query Service for Efficien...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Mabel Rani, A. Jaya Samuthira Pandi, V. Jackson, Beulah Kumar, K.Suresh Lakshmi Priya, J. Dickson Irudayaraj, V. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece India Panimalar Engineering College Department of Mba Poonamallee Chennai India Saveetha Engineering College Department of Ece Chennai India Dhanalakshmi College of Engineering Department of Ai&ds Chennai India

ISBN: (纸本)9798350388916

The integration of blockchain technology into various sectors has shown promise in enhancing security and data integrity, particularly in Internet of Things (IoT) systems. However, this amalgamation introduces challenges in data query efficiency and authenticity, especially in cloud-based settings. While blockchain offers operational streamlining and robust security, its inherent architecture involving distributed ledgers and consensus algorithms poses efficiency issues in data querying. This paper addresses the critical challenge of balancing query efficiency and data authenticity in Internet of Things (IoT)-based cloud systems. Existing solutions often lack scalability, real-time efficiency, and adaptability to the unique constraints of IoT environments. To overcome these limitations, this paper introduces the IoT-Verifiable Query Service (IoT-VQS), a scalable and secure architecture optimized for real-time data querying and verification in blockchains. This architecture demonstrates high throughput and verification accuracy, desirable for real-time cloud based applications. © 2024 IEEE.

关键词： Block chain cloud data authentication query verification

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Performance of different Machine Learning Techniques for the Prediction of Heart Diseases

Performance of different Machine Learning Techniques for the...

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2021 International Conference on Forensics, Analytics, Big Data, Security, FABS 2021

作者： Sarveshvar, M.R. Gogoi, Anindita Chaubey, Amrit Kumar Rohit, S. Mahesh, T.R. Department of Computer Science and Engineering Faculty of Engineering and Technology Bangalore India

ISBN: (纸本)9781665420051

Machine Learning (ML), which is the most well known sub area Artificial Intelligence, is revolutionizing the area of research. ML is primarily an subset of Artificial Intelligence(AI) that has been a crucial component of digitalization solutions that has gotten a lot of buzz in the digital world. In this work, ML is utilized to determine whether or not a person has cardiac disease. ML may be used to determine if a person has a cardiovascular illness based on particular characteristics such as chest discomfort, cholesterol levels, age, and other factors. Cardiovascular disease diagnosis can be simplified using ML classification algorithms that are based on supervised learning. To distinguish those with cardiac illness from those who do not, many ML algorithms are being used including Naïve Bayes Classifier, Logistic Regression Classifier and Random Forest.. The dataset contains certain irrelevant features that are removed during the data cleaning stage, and the data is also standardized for better results. The results and analyses of the publicly available ML Heart Disease dataset are being compared in this paper using different ML methods. The accuracy as well as confusion matrix are also being used to validate a good amount of promising outcomes. © 2021 IEEE.

关键词： Machine learning

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Fetal Health Classification using AI from Cardiotocography Features 3

Fetal Health Classification using AI from Cardiotocography F...

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Jyostna, G. Goyal, Himanshu Rai Soudagar, Manzoore Elahi M. Parikh, Swapnil Patil, Harshal Alaskar, Kamal Potti Sriramulu Chalavadi Mallikarjuna Rao College of Engineering & Technology Department of CSE-DS Andhra Pradesh Vijayawada520 012 India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of VLSI Microelectronics Tamil Nadu Chennai602105 India Parul Institute of Engineering and Technology Department of Computer Science and Engineering Parul University Post Limda Gujarat Waghodia391760 India Computer Science and Engineering Symbiosis Institute of Technology Maharashtra Pune412 115 India Department of Computer Application Institute of Management Maharashtra Kolhapur416003 India

ISBN: (纸本)9798350343632

Maternal health issues during pregnancy are a major global concern because they result in fetal death, which is more frequent in rising and poor countries. Cardiotocography (CTG) is the major non-invasive and cost-effective method for monitoring fetal health. CTG delivers critical parameters such as Fetal Heart Rate (FHR) and Uterine Contraction (UC), with a total of 21 features for accurate monitoring. These characteristics assist obstetricians in categorizing fetal health as normal, suspect, or pathological. However, differences in CTG interpretation among obstetricians frequently result in incorrect procedures. This work presents a unique way to detect deficient fetal states using a hybrid Machine Learning (ML) model that combines Support Vector Machine (SVM) and Random Forest (RF). The UCI repository dataset is pre-processed, which includes balancing, outlier reduction, and feature selection. The hybrid model's performance is then compared to that of classic ML models such as SVM, K-Nearest Neighbour (KNN), RF, and Decision Tree (DT). The hybrid model outperforms the other models in simulation, attaining the greatest accuracy rate of 98.83%. This novel ML-based technique has the potential to improve fetal health detection while reducing the influence of human CTG interpretation discrepancies across healthcare providers. © 2023 IEEE.

关键词： Cardiotocography Correlation Features Fetal Hybrid Machine Learning Outliers UCI Repository

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New In-Air Signature Datasets

New In-Air Signature Datasets

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2023 International Symposium on Networks, computers and Communications, ISNCC 2023

作者： Alobaidi, Abdulazeez R. Dhieb, Thameur Nuimi, Zeina N. Hamdani, Tarek M. Wali, Ali Alimi, Adel M. University of Sfax REGIM-Lab.: REsearch Groups in Intelligent Machines Enis Tunisia Imam ja'Afar Al-Sadiq University Faculty of Information Technology Department of Computer Techniques Engineering Iraq Higher Institute of Business Administration University of Gafsa Tunisia College of Engineering Department of Electrical Engineering University of Misan Iraq Higher Institute of Computer Science Mahdia University of Monastir Tunisia Higher Institute of Computer Science and Multimedia of Sfax University of Sfax Tunisia University of Johannesburg Faculty of Engineering and the Built Environment Department of Electrical and Electronic Engineering Science South Africa

ISBN: (纸本)9798350335590

Compared to traditional biometric systems, in-air signatures are considered more robust and secure than classical pen paper. A few datasets capturing in-air signatures have been introduced, utilizing various devices such as the Leap Motion and the Microsoft Kinect sensor camera. However, these devices are not exempt from shortcomings and exhibit certain limitations. The expenses associated with their implementation and the requirement for technical proficiency in operating them present notable challenges for in-air signature analysis. Additionally, users may encounter difficulties in adapting their finger movements to fit within the device's limited field of view, particularly if they lack familiarity with these devices. To address these concerns, this paper proposes the creation of three in-air signature datasets using solely the camera of a laptop or a smartphone, eliminating the need for any additional specialized equipment. Our datasets were collected in three ways. The first is the In-Air Signature dataset (IAS dataset) and the second is the In-Air Signature dataset using a transparent Glass Plate (IASGP dataset) while the third is the In-Air Signature dataset using Smart Phone (IASSP dataset). Forty volunteers participated in the construction of these datasets. Their ages ranged from 21 to 40 years. Each volunteer signs in the air five signatures and imitates five signatures of five other volunteers. Our in-air signatures datasets are publicly available and can be used for various research tasks like in-air signature verification and identification. © 2023 IEEE.

关键词： Cameras

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Classification of benign and malignant breast lesions in mammograms using dense-unified multiscale attention network and data-efficient image transformers

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The European Physical Journal Special Topics 2025年 1-19页

作者： Paavankumar, S. Karthik, R. Idayachandiran, Gangatharan Sri, Pulaparthi Penchala Deepthi Illakiya, T. School of Computer Science Engineering Vellore Institute of Technology Chennai India Centre for Cyber Physical Systems Vellore Institute of Technology Chennai India Department of Computational Intelligence Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Chennai India

Breast cancer is a common and a serious health problem and it is the major cause of morbidity and mortality for women. Early detection of the disease is particularly challenging because abnormalities such as masses and microcalcifications exhibit subtle and diverse characteristics that are often difficult to identify in mammograms. In recent years, advancement in artificial intelligence, particularly deep learning (DL), has shown to improve diagnostic accuracy and early-stage tumor detection. This study aims to improve performance of DL models by considering both masses and microcalcifications in the proposed work to classify breast cancer abnormalities. The proposed work introduces a novel dual-track network that employs a combination of dense-unified multiscale attention fusion (UMAF) track and data-efficient image transformer (DeiT). The DeiT track processes the entire image simultaneously using patch embeddings, enabling them to capture multiscale representations and dependencies across the entire image. Simultaneously, the Dense-UMAF track focuses on extracting localized features while utilizing connectivity of DenseNet architecture to enable effective feature reuse. This approach generates relevant input features through residual connections of varying lengths, thereby effectively addressing the vanishing gradient problem. The UMAF improves feature extraction by capturing multiscale information, resulting in a better representation of the input data. This dual-track architecture is specifically designed to capture the characteristics of mass and calcification abnormalities in mammograms, which display both localized features and global contextual patterns. The proposed network was evaluated on the Curated Breast Imaging Subset of Digital Database for Screening Mammography dataset, obtaining a classification accuracy of 88.69%.

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Assessing the Effectiveness of Machine Learning Algorithms in Vehicle Insurance Fraud Prediction: A Comparative Approach

Assessing the Effectiveness of Machine Learning Algorithms i...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Aditya P. Satone Subodh Daronde Department of Computer Science and Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Department of Biomedical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Due to the wealth of information accessible on many insurance issues, including travel and auto insurance, individuals are becoming more and more impacted by these kinds of insurance in today's society. But this tendency also makes room for dishonest practices. Insurance fraud is harmful and unlawful, regardless of who commits it—the vendor or the customer. Fraud can come from both the customer and the vendor. The consumer may commit fraud by filing exaggerated claims or using out-of-date policies, while the vendor may market insurance from phoney firms or neglect to submit premiums. This paper presents a comparative analysis of various classification algorithms, including Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Linear Regression (LR), and Polynomial Regression, for detecting insurance fraud. The performance of these algorithms is evaluated using key metrics such as Precision, Recall, and F1-Score. The analysis reveals that the bestperforming model is the Random Forest, with a Mean Squared Error (MSE) of 6.50, outperforming the other techniques.

关键词： Support vector machines Analytical models Machine learning algorithms Insurance Predictive models Data science Data models Fraud Classification algorithms Random forests

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IoT-Enabled LoRa Wireless Sensor Network for Real-Time Air Quality Monitoring with Geographic Information System Mapping

IoT-Enabled LoRa Wireless Sensor Network for Real-Time Air Q...

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2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Maulana, Nurhuda Matheus Edward, Ian Joseph Universitas Pembangunan Nasional Veteran Jakarta Faculty of Computer Science Jakarta12450 Indonesia Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung40132 Indonesia

ISBN: (纸本)9798350307306

The Internet of Things (IoT) can potentially change how humans interact with the environment. This technology is able to be applied in various fields, such as urban management, agriculture, environmental monitoring, health, and transportation. Conventional air quality monitoring systems are complex sensing equipment and they can measure various parameters of air pollutants. This system requires very high costs and maintenance. Therefore, a low-cost air quality measurement system is required and is able to measure the surrounding air pollutants. By using wireless sensor network technology as the acquisition of environmental monitoring sensor data. A system design was created in the form of geographic information capable of describing air quality in a particular area. The air parameters used are carbon monoxide, dust particulate, light intensity, temperature, and relative humidity. The wireless network uses the LoRa module connected to the gateway. The web server provides a database and geographic information system interface from the gateway through the MQTT broker. Data transmission using a radio frequency of 915 Mhz and able to send data sensors from various points. Outdoor testing at many buildings is capable of transmitting sensor data with a packet loss of 0% at a distance of fewer than 111 meters and the largest packet loss of 51.28% at a distance of 442 meters. © 2023 IEEE.

关键词： Internet of things

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