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Global video object segmentation with spatial constraint module

Computational Visual Media 2023年第2期9卷 385-400页

作者： Yadang Chen Duolin Wang Zhiguo Chen Zhi-Xin Yang Enhua Wu Engineering Research Center of Digital Forensics Ministry of EducationSchool of Computer and SoftwareNanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Internet of Things for Smart City Department of Electromechanical EngineeringUniversity of MacaoMacao 999078China State Key Laboratory of Computer Science Institute of SoftwareUniversity of Chinese Academy of SciencesBeijing 100190China Faculty of Science and Technology University of MacaoMacao 999078China

We present a lightweight and efficient semisupervised video object segmentation network based on the space-time memory *** some extent,our method solves the two difficulties encountered in traditional video object segmentation:one is that the single frame calculation time is too long,and the other is that the current frame’s segmentation should use more information from past *** algorithm uses a global context(GC)module to achieve highperformance,real-time *** GC module can effectively integrate multi-frame image information without increased memory and can process each frame in real ***,the prediction mask of the previous frame is helpful for the segmentation of the current frame,so we input it into a spatial constraint module(SCM),which constrains the areas of segments in the current *** SCM effectively alleviates mismatching of similar targets yet consumes few additional *** added a refinement module to the decoder to improve boundary *** model achieves state-of-the-art results on various datasets,scoring 80.1%on YouTube-VOS 2018 and a J&F score of 78.0%on DAVIS 2017,while taking 0.05 s per frame on the DAVIS 2016 validation dataset.

关键词： video object segmentation semantic segmentation global context(GC)module spatial constraint

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Brain magnetic resonance image (MRI) segmentation using multimodal optimization

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Multimedia Tools and Applications 2025年第16期84卷 16971-17020页

作者： Akan, Taymaz Oskouei, Amin Golzari Alp, Sait Bhuiyan, Mohammad Alfrad Nobel Department of Medicine Louisiana State University Health Sciences Center ShreveportLA71103 United States Software Engineering Department Istanbul Topkapi University Istanbul34020 Turkey Faculty of Information Technology and Computer Engineering Azarbaijan Shahid Madani University Tabriz Iran Department of Software Engineering İstinye University İstanbul Turkey Department of Artificial Intelligence Engineering Trabzon University Trabzon61335 Turkey

One of the highly focused areas in the medical science community is segmenting tumors from brain magnetic resonance imaging (MRI). The diagnosis of malignant tumors at an early stage is necessary to provide treatment for patients. The patient’s prognosis will improve if it is detected early. Medical experts use a manual method of segmentation when making a diagnosis of brain tumors. This study proposes a new approach to simplify and automate this process. In recent research, multi-level segmentation has been widely used in medical image analysis, and the effectiveness and precision of the segmentation method are directly tied to the number of segments used. However, choosing the appropriate number of segments is often left up to the user and is challenging for many segmentation algorithms. The proposed method is a modified version of the 3D Histogram-based segmentation method, which can automatically determine an appropriate number of segments. The general algorithm contains three main steps: The first step is to use a Gaussian filter to smooth the 3D RGB histogram of an image. This eliminates unreliable and non-dominating histogram peaks that are too close together. Next, a multimodal particle swarm optimization method identifies the histogram’s peaks. In the end, pixels are placed in the cluster that best fits their characteristics based on the non-Euclidean distance. The proposed algorithm has been applied to a Cancer Imaging Archive (TCIA) and brain MRI Images for brain Tumor detection dataset. The results of the proposed method are compared with those of three clustering methods: FCM, FCM_FWCW, and FCM_FW. In the comparative analysis of the three algorithms across various MRI slices. Our algorithm consistently demonstrates superior performance. It achieves the top mean rank in all three metrics, indicating its robustness and effectiveness in clustering. The proposed method is effective in experiments, proving its capacity to find the proper clusters. © The Auth

关键词： Image segmentation

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MQTT based monitoring and control system for remote stations in automotive industry

MQTT based monitoring and control system for remote stations...

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2023 IEEE Zooming Innovation in Consumer Technologies Conference, ZINC 2023

作者： Ivanovic, Denis Job, Josip Puntaric, Marko Herceg, Marijan TTTech-Auto CX Osijek Croatia Faculty of Electrical Engineering Computer Science and Information Technology Department of Software Engineering Osijek Croatia Faculty of Electrical Engineering Computer Science and Information Technology Department of Communications Osijek Croatia

ISBN: (纸本)9798350347722

In this paper is presented implementation of a lightweight supervisory control and data acquisition system for remote test stations as a part of a larger test house in the automotive industry. It is necessary to allow remote set up and monitoring of the test stations, which means defining the structure of a system with numerous test stations as endpoints. The test stations are meant to be virtual machines but can be both virtual and physical machines. A communication protocol must be used that enables this within the organization's network, regardless of actual distance and location. The parameters of the test stations to be monitored can be defined according to the needs and purpose. Even when creating your own tool, this function is not unique, i.e. on one test station parameters of operating system resources such as memory usage can be monitored, while on another test station logged-in users or running programs, reported system errors and others must be monitored. Data storage is one of the mandatory functions of this system. This process allows viewing data throughout history and more detailed analysis of events. There are numerous benefits that can be drawn and used from the data stored in the database. It is necessary to pay attention to the data stored as well as the scope and retention throughout history. The correct and secure setup of a database is one of the prerequisites for any serious system that processes data. Appropriate views should be created for users involved in the data monitoring process using all the elements mentioned so far. Creating a visually acceptable and easy-To-use interface that serves the main purpose is the final presentation of the entire system to users. A complete system leads to higher productivity, profitability and better organization of the company. It also allows project managers to better plan the use and occupancy of test stations. At any time, they have insight into the status of individual test stations, their activitie

关键词： Automotive industry

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A new hybrid teaching learning based Optimization -Extreme learning Machine model based Intrusion-Detection system

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Materials Today: Proceedings 2023年 80卷 2701-2705页

作者： Qahatan Alsudani, Mustafa Abbdal Reflish, Salah H. Moorthy, Kohbalan Mundher Adnan, Myasar Computer Techniques Engineering Department Faculty of Information Technology Imam Ja'afar Al-sadiq University Najaf Iraq Faculty of Computer Systems & Software Engineering Universiti Malaysia Pahang Kuantan Malaysia Islamic University Najaf Iraq Faculty of Engineering School of Computing Universiti Teknologi Malaysia Johor Bahru81310 Malaysia

Currently, effective Intrusion-detection systems (IDS) still represent one of the important security tools. However, hybrid models based on the IDS achieve better results compared with intrusion detection based on a single algorithm. But even so, the hybrid models based on traditional algorithms still face different limitations. This work is focused on providing two main goals;firstly, analysis based on the main methods and limitations of the most-recent hybrid model-based on intrusion detection, secondly, to propose a novel hybrid IDS model called TLBO-ELM based on the Firefly algorithm and Fast Learning Network. © 2021

关键词： Optimization

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Enhanced Steganalysis for Color Images Using Curvelet Features and Support Vector Machine

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computers, Materials & Continua 2024年第1期78卷 1311-1328页

作者： Arslan Akram Imran Khan Javed Rashid Mubbashar Saddique Muhammad Idrees Yazeed Yasin Ghadi Abdulmohsen Algarni Faculty of Computer Science and Information Technology Department of Computer ScienceSuperior UniversityLahore54000Pakistan Machine Learning Code(MLC)Lab Meharban HouseHouse Number 209Zafar ColonyOkara56300Pakistan Information Technology Services University of OkaraOkara56310Pakistan Department of Computer Science&Engineering University of Engineering&Technology LahoreNarowal CampusNarowal51601Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain11671Abu Dhabi Department of Computer Science King Khalid UniversityAbha61421Saudi Arabia

Algorithms for steganography are methods of hiding data transfers in media *** machine learning architectures have been presented recently to improve stego image identification performance by using spatial information,and these methods have made it feasible to handle a wide range of problems associated with image *** with little information or low payload are used by information embedding methods,but the goal of all contemporary research is to employ high-payload images for *** address the need for both low-and high-payload images,this work provides a machine-learning approach to steganography image classification that uses Curvelet transformation to efficiently extract characteristics from both type of *** Vector Machine(SVM),a commonplace classification technique,has been employed to determine whether the image is a stego or *** Wavelet Obtained Weights(WOW),Spatial Universal Wavelet Relative Distortion(S-UNIWARD),Highly Undetectable Steganography(HUGO),and Minimizing the Power of Optimal Detector(MiPOD)steganography techniques are used in a variety of experimental scenarios to evaluate the performance of the *** WOW at several payloads,the proposed approach proves its classification accuracy of 98.60%.It exhibits its superiority over SOTA methods.

关键词： Curvelets fast fourier transformation support vector machine high pass filters steganography

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Fusion of speech and handwritten signatures biometrics for person identification

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International Journal of Speech technology 2023年第4期26卷 833-850页

作者： Abushariah, Ahmad A. M. Abushariah, Mohammad A. M. Gunawan, Teddy Surya Chebil, J. Alqudah, Assal A. M. Ting, Hua-Nong Mustafa, Mumtaz Begum Peer Faculty of Engineering University of Malaya Kuala Lumpur50603 Malaysia Department of Computer Information Systems King Abdullah II School of Information Technology The University of Jordan Amman Jordan ECE Department Faculty of Engineering International Islamic University Malaysia Kuala Lumpur53100 Malaysia Department of Computer Science Faculty of Science and Information Technology Al-Zaytoonah University of Jordan Amman Jordan Department of Biomedical Engineering Faculty of Engineering University of Malaya Kuala Lumpur50603 Malaysia Department of Software Engineering Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia

Automatic person identification (API) using human biometrics is essential and highly demanded compared to traditional API methods, where a person is automatically identified using his/her distinct characteristics including speech, fingerprint, iris, handwritten signatures, and others. The fusion of more than one human biometric produces bimodal and multimodal API systems that normally outperform single modality systems. This paper presents our work towards fusing speech and handwritten signatures for developing a bimodal API system, where fusion was conducted at the decision level due to the differences in the type and format of the features extracted. A data set is created that contains recordings of usernames and handwritten signatures of 100 persons (50 males and 50 females), where each person recorded his/her username 30 times and provided his/her handwritten signature 30 times. Consequently, a total of 3000 utterances and 3000 handwritten signatures were collected. The speech API used Mel-Frequency Cepstral Coefficients (MFCC) technique for features extraction and Vector Quantization (VQ) for features training and classification. On the other hand, the handwritten signatures API used global features for reflecting the structure of the hand signature image such as image area, pure height, pure width and signature height and the Multi-Layer Perceptron (MLP) architecture of Artificial Neural Network for features training and classification. Once the best matches for both the speech and the handwritten signatures API are produced, the fusion process takes place at decision level. It computes the difference between the two best matches for each modality and selects the modality of the maximum difference. Based on our experimental results, the bimodal API obtained an average recognition rate of 96.40%, whereas the speech API and the handwritten signatures API obtained average recognition rates of 92.60% and 75.20%, respectively. Therefore, the bimodal API system is a

关键词： Biometrics

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DM-Net for Colorectal Polyp Detection Using Dual Feature and Multi-Layer Aggregation 12

DM-Net for Colorectal Polyp Detection Using Dual Feature and...

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12th International Conference on Advanced Cloud and Big Data, CBD 2024

作者： Wan, Jingjing Wang, Ling Zhu, Wenjie Chen, Bolun The Second People's Hospital of Huai'an Department of Gastroenterology Huaian China Huaiyin Institute of Technology Faculty of Computer and Software Engineering Huaian China

ISBN: (纸本)9798331511074

Early detection of colorectal polyps is crucial for preventing colorectal cancer. Although endoscopy is the current standard diagnostic method, it still faces challenges in terms of accuracy, efficiency, and patient comfort. To address these issues, this paper proposes a colorectal polyp detection model named DM-Net. This model utilizes the Dual Feature Integration Block (DFIB) to integrate channel and spatial features, enhancing feature extraction efficiency. Additionally, the model incorporates the Multi-Layer Path Aggregation Network (MLPAN) to handle the multi-scale variations of polyps. Experimental results demon-strate that DM-Net significantly outperforms existing methods in terms of accuracy and efficiency, offering high clinical applica-bility. © 2024 IEEE.

关键词： Endoscopy

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Success Factors of Enterprise Resource Planning: A State-of-the-Art Mini Review

Success Factors of Enterprise Resource Planning: A State-of-...

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2024 International Conference on Artificial Intelligence, Metaverse and Cybersecurity, ICAMAC 2024

作者： BaniMustafa, Ahmed Bulkrock, Olla Faculty of Computer Information Science Higher Colleges of Technology Abu Dhabi United Arab Emirates Software Engineering Department Princess Sumaya University for Technology Amman Jordan

ISBN: (纸本)9798350353488

Enterprise Resource Planning Systems (ERP) are vital for today’s businesses. However, the successful implementation of ERP systems faces several challenges, which can determine its success or failure. This paper provides a brief state-of-the-art review of the success factors of ERP implementation using various applications that span different contexts. The paper discusses and compares the aims defined and the techniques applied in these studies, as well as the data used and the results obtained from each of these studies. This review paper reviews published work to tackle these challenges, covering the literature published in 2020-2023. A special effort was also made to focus on high-quality papers published in top journals with high-impact factors and indexed in Scopus and Web of Science. The review process is repeatable, as special keywords were used to search the literature using publishers’ databases and three specialized tools. The paper concludes by discussing the future of the successful implementation of ERP systems. The paper argues that ERP systems will continue to have a major impact on the success of today’s businesses. Therefore, they must carefully consider various factors that influence their successful implementation. © 2024 IEEE.

关键词： Information use

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An Acceptance Model of Using Mobile-Government Services(AMGS)

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computer Modeling in engineering & Sciences 2022年第5期131卷 865-880页

作者： Ahmad Althunibat Mohammad Abdallah Mohammed Amin Almaiah Nour Alabwaini Thamer Ahmad Alrawashdeh Department of Software Engineering Faculty of Science and Information TechnologyAl-Zaytoonah University of JordanAmmanJordan Faculty of Computer Sciences and Information Technology King Faisal UniversityAl-AhsaSaudi Arabia

In recent years,the telecommunications sector is no longer limited to traditional communications,but has become the backbone for the use of data,content and digital applications by individuals,governments and companies to ensure the continuation of economic and social activity in light of social distancing and total closure inmost countries in the ***,electronic government(e-Government)andmobile government(m-Government)are the results of technological evolution and ***,it is important to investigate the factors that influence the intention to use m-Government services among Jordan’s *** paper proposed a new m-Government acceptance model in Jordan(AMGS);this model combines the Information System(IS)Success Factor Model and Hofstede Cultural Dimensions *** study was conducted by surveying different groups of the Jordanian *** questionnaire was used to collect data from203 *** regression analysis has been conducted to analyze the *** results indicate that the significant predictors of citizen intention to use m-Government services in Jordan are Information Quality,Service Quality,Uncertainty Avoidance,and Indulgence ***,the results also suggest that Power Distance is not a significant predictor of citizen intention to use m-Government services.

关键词： Acceptance test e-Government m-Government acceptance model IS success factor model Hofstede cultural dimensions theory

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Web Application to predict Skin Disease: An Approach Using Deep Learning Algorithm 6

Web Application to predict Skin Disease: An Approach Using D...

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6th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Thayalan, Eelanila Arumugavel, Ayingaran Ganeshan, Sharujan Dalaxshan, Makenthiran Fernando, Harinda Rajapakse, Kaushalya Department of Information Technology Faculty of computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Systems Engineering Faculty of computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Science and Software Engineering Faculty of computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350394634

The Disease Prediction System revolutionizes healthcare with advanced machine learning techniques for early detection of skin diseases, notably focusing on skin cancer. Through image processing and Transfer Learning, it excels in identifying potential malignancies, enabling timely interventions. Its capabilities extend to benign tumors and growth disorders, utilizing the ResNet50 Model for precise identification. Additionally, it predicts skin reactions associated with various dermatological conditions, such as Urticaria Hives and Warts, leveraging the efficientnetB0 Model. Integration of the VGG16 Model enhances diagnostic accuracy for inflammatory skin conditions. This holistic approach prioritizes patient-centric care, leveraging diverse datasets and intricate pattern recognition in medical images. The system's proactive nature embodies personalized solutions for early detection, timely intervention, and improved patient outcomes. Its versatility and accuracy underscore itstransformative potential in healthcare delivery. By harnessing diverse datasets and recognizing intricate patterns within medical images, it heralds a new era of personalized healthcare solutions. In essence, the Disease Prediction System exemplifies the transformative potential of machine learning in healthcare, ensuring the highest standards of diagnostic accuracy and efficacy, while prioritizing patient well-being and quality oflife. © 2024 IEEE.

关键词： Deep learning

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