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2023 International Seminar on Application for Technology of information and Communication, iSemantic 2023

作者： Priyo Utomo, Ade Cahyo Sari, Christy Atika Rachmawanto, Eko Hari Hadi, Heru Pramono Mostafa, Salama A. Al-Dayyeni, Wisam Subhi Talib University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia University of Dian Nuswantoro Study Program in Information Systems Semarang Indonesia Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Johor Malaysia Ada University School of Information Technologies and Engineering Baku Azerbaijan

ISBN: (纸本)9798350339215

Citrus Limon L. (Lemon) is a type of fruit that is currently widely consumed, because it contains abundant vitamin C, fiber, and antioxidants. This fruit have high potential in the agribusiness sector and is widely cultivated by traditional farmers. However, during harvest time, traditional farmers generally still use manual methods using the human eye in distinguishing the maturity level of lemons which is less efficient because it has a low accuracy. Digital image processing is one solution to this problem. In the research on the classification of lemon maturity levels using digital image processing with the feature extraction method of the Mean RGB, HSV, and LBP methods and the K-Nearest Neighbor classification algorithm in this study, a total of 120 lemon images were used which were divided into 80 training image data and 40 image data. testing. The results of model performance measurements in the form of the highest accuracy level in the Mean RGB method of 100%, the highest accuracy in the HSV method of 98%, and the highest accuracy in the LBP method of 82.5%. © 2023 IEEE.

关键词： Citrus fruits

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An improved longitude correction algorithm for fisheye image edge distortion 3

An improved longitude correction algorithm for fisheye image...

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3rd International Conference on Artificial Intelligence and computer engineering, ICAICE 2022

作者： Ji, Xiu Yang, Huamin Han, Cheng Wang, Hexin Lang, Chaoyue School of Computer Science and Technology Changchun University of Science and Technology Jilin Changchun130022 China School of Information and Control Engineering Jilin Institute of Chemical Technology Jilin132022 China

ISBN: (纸本)9781510663473

The fisheye camera is widely used in computer vision because of its large field of view. However, in optical theory, the large field of view is at the cost of distortion. It is impossible to obtain effective information directly from the fisheye image, so the original image needs to be distorted first to become a linear image without distortion. Aiming at the existence of traditional longitude correction in the image center, edge correction effect is different and edge distortion, introducing the repositioning center algorithm and stretch factor. Firstly, the effective area of fisheye image is obtained by row-by-row column scanning method. And then uses the repositioning center algorithm to obtain the new center and radius, according to the distortion principle to calculate the distortion principle. The mapping relationship between the target image and the original image is obtained, and finally modified by the bilinear interpolation method. Compared with the traditional longitude correction, the proposed algorithm can correct the fisheye image accurately and effectively, and improve the quality of correction. © 2023 SPIE.

关键词： Image enhancement

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Semi-supervised Object Detection for Remote Sensing Images via Pseudo Labeling and Consistency Learning: RS-PCL for SSOD in Remote Sensing Images 23

Semi-supervised Object Detection for Remote Sensing Images v...

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15th International Conference on Digital Image Processing, ICDIP 2023

作者： Li, Yuqiu Xue, Jian Lu, Ke School of Engineering Science University of Chinese Academy of Sciences China College of Computer and Information Technology China Three Gorges University China

ISBN: (纸本)9798400708237

Object detection for remote sensing images (RSI) has made significant progress in recent years. However, existing supervised methods typically require a large amount of labeled data, whose acquisition, and particularly that of RSI, can be quite expensive. For natural images, many semi-supervised methods have been proposed to tackle the lack of labeled data, while few studies have focused on semi-supervised object detection for RSI. To address this problem, this paper proposes RS-PCL, which is a semi-supervised method applying Pseudo labeling and Consistency Learning to RSI. The RS-PCL method uses the teacher-student training scheme to generate high-quality pseudo labels for self-training. To deal with biased pseudo boxes, Noisy Pseudo box Assignment (NPA) is proposed to consider the teacher predictions during the label assignments of the unlabeled student. To ensure scale invariance, Scale-Invariant Learning (SIL) is proposed to carry out feature-consistency training on multi-scale images. This study conducted experiments using the DOTA-v1.0 dataset as labeled data and newly added images in the DOTA-v2.0 dataset (DOTA-v2.0 was constructed by adding extra images to DOTA-v1.0) as unlabeled data. The results reveal that there is obvious improvement over the supervised baseline and state-of-the-art semi-supervised methods, which demonstrates the effectiveness of the proposed method. © 2023 Owner/Author.

关键词： Object detection

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Utilizing Machine Learning Algorithms for Effectively Detection IoT DDoS Attacks 1st

Utilizing Machine Learning Algorithms for Effectively Detect...

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1st International Conference on Advances in Computing Research, ACR’23

作者： Mughaid, Ala Alqahtani, Ali AlZu’bi, Shadi Obaidat, Ibrahim Alqura’n, Rabee AlJamal, Mahmoud AL-Marayah, Raid Department of Information Technology Faculty of Prince Al-Hussien Bin Abdullah for IT The Hashemite University Zarqa Jordan Department of Networks and Communication Engineering College of Computer Science and Information Systems Najran University Najran Saudi Arabia Computer Science Department Faculty of Science and Information Technology Al Zaytoonah University of Jordan Amman Jordan

ISBN: (纸本)9783031337420

One of the most critical features of the Internet of Things (IoT) is that it allows a massive number of devices to communicate with each other at the same time, for example (cars, smoke alarms, watches, glasses, web cameras, etc.). Given that they can connect to the Internet, the huge demand to convert systems into smart systems that can be controlled remotely increases the chances of cyber-attacks that threaten security and availability becoming more frequent, severe, and in a different form. Distributed Denial of Service (DDoS) attacks have become the most common threats in IoT. These attacks make IoT systems unreliable because complex DDoS attacks cannot be detected by traditional security countermeasures such as firewalls and intrusion detection systems (IDS). In this research, we proposed a model dedicated to work on prediction and detection of DDoS attacks on networks IoT, where we run this model on a NF-BoT-IoT-v2 dataset, which contains 37,763,497 records divided into five categories (Benign, Reconnaissance, DDoS, DoS, and Theft), and then we process our dataset to be more business friendly so it only has two categories (100,000 records broken down into 23,906 benign attacks and 76,094 DDoS attacks). This study used WEKA and MATLAB tools to perform the classification efficiently and find the most important features through feature selection. We used a decision tree (J48) machine learning classifier, a Naive Bayes (NB) classifier, and a random forest (RF) classifier. However, the results were as expected and improved accuracy and efficiency. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Denial-of-service attack

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Real-Time Anomaly Detection in Grinding Wheels Using a Multimodal Deep Learning Framework

Real-Time Anomaly Detection in Grinding Wheels Using a Multi...

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2024 International Conference on information Technology, Data science, and Optimization, I-DO 2024

作者： Zhang, Qiaoyun Yang, Wan-Chi Chang, Hsiang-Chuan Ho, Chia-Ling Chang, Chih-Yung Chuzhou University School of Computer and Information Engineering China National Taipei University of Nursing and Health Sciences General Education Center Taiwan Tamkang University Department of Transportation Management Taiwan Tamkang University Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798400709180

In the manufacturing process involving grinding wheels, challenges arise in fine-tuning grinding machines, typically addressed by craftsmen through subjective observations of sparks and sounds. This paper introduces a novel mechanism comprising two pivotal phases aimed at optimizing grinding wheel production line efficiency and accuracy. Firstly, an AutoEncoder is employed for spectrogram denoising, effectively isolating grinding sounds from environmental noise. Convolutional Neural Networks (CNNs) in the Encoder extract features across time and frequency domains, while deconvolution in the Decoder gradually restores features. ReLU activation ensures computational efficiency and effectively handles nonlinear features. Secondly, an AI-based assessment determines parameter adjustments using a combination of 3DCNN and CNN. By integrating classification results from both networks, features from video and audio data are identified, thereby enhancing classification effectiveness. Anomalies during grinding operations are detected through combined outputs, indicating the need for parameter adjustments. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Anomaly detection

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Bayesian Detection for Colocated MIMO Radar with Limited Training Data 2

Bayesian Detection for Colocated MIMO Radar with Limited Tra...

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2nd International Symposium on Computing and Artificial Intelligence, ISCAI 2023

作者： Liu, Weijian Zhou, Zhe Gong, Pengcheng Wu, Yuntao The First Department Wuhan Electronic Information Institute Wuhan China School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798400708954

Utilizing waveform diversity, colocated multiple-input multiple-output (MIMO) radar offers increased degrees of freedom (DOF) and potential enhancements in target detection and identification compared to traditional phased array radar. However, the expanded DOF in colocated MIMO radar systems also necessitates a significant increase in training samples for target detection, particularly in the presence of strong clutter. Acquiring a sufficient number of training samples in practical environments can pose challenges. This paper addresses the issue of target detection using colocated MIMO radar with limited training data. To overcome this challenge, a knowledge-based approach is employed. The unknown clutter covariance matrix is modeled as an inverse Wishart random distribution, with the DOF serving as an adjustable system parameter. The scalar matrix can be obtained through antenna configuration or historical data. Three effective Bayesian detectors are proposed based on three commonly used detection criteria, namely the Bayesian GLRT, Bayesian Rao test, and Bayesian Wald test. Through computer simulations, the effectiveness of these Bayesian detectors is validated, demonstrating their remarkable target detection capability even with limited training data. Furthermore, these proposed Bayesian detectors outperform existing detectors even when abundant training samples are available. Among the three detectors proposed, the Bayesian GLRT exhibits the most superior detection performance. © 2023 ACM.

关键词： Inverse problems

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Probabilistic Constellation Shaping for OFDM-Based ISAC Signaling

Probabilistic Constellation Shaping for OFDM-Based ISAC Sign...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Du, Zhen Liu, Fan Xiong, Yifeng Han, Tony Xiao Yuan, Weijie Cui, Yuanhao Yao, Changhua Eldar, Yonina C. School of Electronic and Information Engineering Nanjing University of Information Science & Technology Nanjing China Southern University of Science and Technology Department of Electronic and Electrical Engineering Shenzhen China School of Information and Electronic Engineering Beijing University of Posts and Telecommunications Beijing China Huawei Technologies Ltd. Shenzhen China Weizmann Institute of Science Faculty of Mathematics and Computer Science Rehovot Israel

ISBN: (纸本)9798350370218

Integrated Sensing and Communications (ISAC) has garnered significant attention as a promising technology for the upcoming sixth-generation wireless communication systems (6G). In pursuit of this goal, a common strategy is that a unified waveform, such as Orthogonal Frequency Division Multiplexing (OFDM), should serve dual-functional roles by enabling simultaneous sensing and communications (S&C) operations. However, the sensing performance of an OFDM communication signal is substantially affected by the randomness of the data symbols mapped from bit streams. Therefore, achieving a balance between preserving communication capability (i.e., the randomness) while improving sensing performance remains a challenging task. To cope with this issue, in this paper we analyze the ambiguity function of the OFDM communication signal modulated by random data. Subsequently, a probabilistic constellation shaping (PCS) method is proposed to devise the probability distributions of constellation points, which is able to strike a scalable S&C tradeoff of the random transmitted signal. Finally, the superiority of the proposed PCS method over conventional uniformly distributed constellations is validated through numerical simulations. © 2023 IEEE.

关键词： ambiguity function ISAC OFDM PCS

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Output Linearization of Single-Input Single-Output Fuzzy System to Improve Accuracy and Performance

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computers, Materials & Continua 2023年第5期75卷 2413-2427页

作者： Salah-ud-din Khokhar QinKe Peng Muhammad Yasir Noor Department of Automation Science and Technology(System Engineering Institute) School of Electronics and Information EngineeringXi’an Jiaotong UniversityXi’anShaanxi710049China Department of Computer Science G.C.UniversityLahore54000PunjabPakistan Department of Physics(Electronics) G.C.UniversityLahore54000PunjabPakistan

For fuzzy systems to be implemented effectively,the fuzzy membership function(MF)is essential.A fuzzy system(FS)that implements precise input and output MFs is presented to enhance the performance and accuracy of single-input single-output(SISO)FSs and introduce the most applicable input and output MFs protocol to linearize the fuzzy system’s *** a variety of non-linear techniques,a SISO FS is *** results of FS experiments conducted in comparable conditions are then *** simulated results and the results of the experimental setup agree fairly *** findings of the suggested model demonstrate that the relative error is abated to a sufficient range(≤±10%)and that the mean absolute percentage error(MPAE)is reduced by around 66.2%.The proposed strategy to reduceMAPE using an FS improves the system’s performance and control *** using the best input and output MFs protocol,the energy and financial efficiency of every SISO FS can be improved with very little tuning of *** proposed fuzzy system performed far better than other modern days approaches available in the literature.

关键词： Mean absolute percentage error membership functions relative error fuzzy system

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Optimal Task Offloading in Edge Cloud Environment

Optimal Task Offloading in Edge Cloud Environment

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2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, 2023 International Conference on Pervasive Intelligence and Computing, 2023 International Conference on Cloud and Big Data Computing, 2023 International Conference on Cyber science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Huang, Tao Chen, Fengmei Ji, Guohua Jiao, Feng Jing, Wei Yuan, Zhidan School of Computer Science and Technology Silicon Lake College Suzhou China College of Information Engineering Jiangsu Maritime Institute Nanjing China

ISBN: (纸本)9798350304602

Although offloading tasks to remote cloud servers can reduce the computation time of tasks, the long distance between user devices and cloud servers also raises the data transfer time and introduces unpredictable network latency. The introduction of edge servers further improves the execution efficiency of user tasks and relieves the pressure on cloud servers. However, the explosive growth of tasks also gradually puts pressure on the edge servers with limited computing resources, which not only affects the execution efficiency of each task, but also causes unbalanced load on all edge servers, thus affecting the efficiency and stability of the edge servers. In addition, with the increasing awareness of user privacy protection, it has become a challenge to maximize the privacy of user data while improving the execution efficiency of task and ensuring the load balance of edge servers. To address this challenge, we design a task offloading method in edge cloud environment, namely NTO. First, the task offloading problem in an edge cloud environment is modeled as a multi-objective optimization model with task execution time, load balancing of edge servers, and privacy conflict of user data as three key optimization objectives. Then, the model is optimized by the Non-dominated Sorting Differential Evolution (NSDE) algorithm. Finally, We conducted experiments to demonstrate the effectiveness and superiority of NTO. © 2023 IEEE.

关键词： Efficiency

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Incomplete intuitionistic fuzzy behavioral group decision-making based on multigranulation probabilistic rough sets and MULTIMOORA for water quality inspection

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Journal of Intelligent and Fuzzy systems 2023年第3期44卷 4537-4556页

作者： Bai, Wenhui Zhang, Chao Zhai, Yanhui Sangaiah, Arun Kumar School of Computer and Information Technology Key Lab. of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Shanxi Taiyuan China International Graduate Institute of Artificial Intelligence National Yunlin University of Science and Technology Taiwan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

Water quality inspection (WQI) is one of the primary ways to ensure the safe utilization of water resources, and complicated data modeling, fusion and analysis play a significant role in seeking the resource with the best water quality. Nevertheless, the challenges of missing data, relatively large differences in decision results and bounded rationality owned by decision-makers (DMs) in terms of WQI still exist nowadays. Thus, from the aspect of stable and behavioral decision-making in multi-granularity incomplete intuitionistic fuzzy information systems (MG-IIFISs), the paper investigates a comprehensive multi-attribute group decision-making (MAGDM) approach for the application of WQI. First, the concept of MG-IIFISs is built by modeling MAGDM problems with intuitionistic fuzzy numbers (IFNs), then a new transformation scheme is constructed for transforming MG-IIFISs into multi-granularity intuitionistic fuzzy information systems (MG-IFISs) based on the similarity principle. Second, three types of multigranulation intuitionistic fuzzy probabilistic rough sets (MG IF PRSs) are developed by referring to the MULTIMOORA (Multi-Objective Optimization by Ratio Analysis plus the full MULTIplicative form) method. Afterwards, attribute weights are objectively calculated based on the best-worst method (BWM), and a new stable and behavioral MAGDM approach is constructed by means of the TODIM (an acronym in Portuguese for interactive multi-criteria decision-making) method. At last, a case study in the setting of WQI is conducted with the support of a UCI data set, and sensitivity analysis, comparative analysis and experimental analysis are investigated to display the validity of the proposed approach. In general, the proposed approach improves the stability of decision results via MULTIMOORA and BWM, and also fully considers the bounded rationality of DMs' psychological behaviors from the aspect of the TODIM method, which has certain advantages in the community of MAGDM studie

关键词： Water quality

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