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Fusion Strategy for Improving Medical Image Segmentation

computers, Materials & Continua 2023年第2期74卷 3627-3646页

作者： Fahad Alraddady E.A.Zanaty Aida HAbu bakr Walaa M.Abd-Elhafiez Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Information System Department Faculty of Computers and InformationSohag UniversitySohagEgypt Mathematical and Computer Science Department Faculty of ScienceAswan UniversityAswanEgypt College of Computer Science&Information Technology Jazan UniversityJazanKingdom of Saudi Arabia Computer Science Department Faculty of Computers and Artificial IntelligenceSohag UniversitySohagEgypt

In this paper,we combine decision fusion methods with four metaheuristic algorithms(Particle Swarm Optimization(PSO)algorithm,Cuckoo search algorithm,modification of Cuckoo Search(CS McCulloch)algorithm and Genetic algorithm)in order to improve the image *** proposed technique based on fusing the data from Particle Swarm Optimization(PSO),Cuckoo search,modification of Cuckoo Search(CS McCulloch)and Genetic algorithms are obtained for improving magnetic resonance images(MRIs)*** algorithms are used to compute the accuracy of each method while the outputs are passed to fusion *** order to obtain parts of the points that determine similar membership values,we apply the different rules of incorporation for these *** proposed approach is applied to challenging applications:MRI images,gray matter/white matter of brain segmentations and original black/white images Behavior of the proposed algorithm is provided by applying to different medical *** is shown that the proposed method gives accurate results;due to the decision fusion produces the greatest improvement in classification accuracy.

关键词： Decision fusion particle swarmoptimization(PSO) cuckoo search algorithm CS McCulloch algorithm genetic algorithm CT and MRI

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Fingerprint Image Enhancement: Multi-Filter Approaches and Stage Timing Evaluation 4

Fingerprint Image Enhancement: Multi-Filter Approaches and S...

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4th International Conference on Sustainable Expert systems, ICSES 2024

作者： Bhargava, Neeraj Rathore, Pramod Singh Goswami, Ankur Chauhan, Kapil Choudhary, Suresh School of Engineering & System Sciences Mds University Ajmer India Manipal University Department of Computer and Communication Engineering Jaipur India Govt. Engineering College Department of Computer Applications Bikaner India Aryabhattt College of Engieering and research Centre India Mds University Ajmer India

ISBN: (纸本)9798331540364

The performance of fingerprint recognition and verification systems heavily depends on the quality of the input fingerprint images. This paper introduces a multi-filter image enhancement strategy tailored for enhancing low-quality fingerprint images. The objective is to enhance the visibility and definition of ridge patterns, tackling problems stemming from irregular skin contacts and contaminated sensor surfaces. Author incorporates a new normalization technique that adjusts for finger flexibility, improving the correction of anomalies and boosting overall image quality, especially in detecting fine details. Experimental evidence shows that technique surpasses previous methods in effectiveness. © 2024 IEEE.

关键词： Gabor filters

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The Impact of Stuttering Event Representation on Detection Performance 2

The Impact of Stuttering Event Representation on Detection P...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Al-Banna, Abedal-Kareem Fang, Hui Shubita, Ahmad F. Arafah, Mohammad Eqteeshat, Amani Abuaisheh, Saif B. University Of Petra Department Of Ai And Data Science Jordan Loughborough University Department Of Computer Science United Kingdom University Of Petra Department Of Software Engineering Jordan University Of Petra Department Of Computer Science Jordan Malaya University Department Of Information System Malaysia

ISBN: (纸本)9798350394962

Stuttering or stammering is a common neurogenic, psychogenic fluency disorder wherein people have uncontrolled blocks on the natural flow of speech. Automatic Stuttering Event Detection (SED) is a widely recognised challenge due to the heterogenic and overlapped nature of stuttering speech. Therefore, this comparative study investigates the impact of stuttering event representation on detection performance. The paper evaluates the performance of SED and shows the impact of employing ASR pre-trained feature extractions on each stuttering event. Moreover, the paper provides different groups of experiments that evaluate the impact of three acoustic features, the Zero Crossing Rate (ZCR), the Spectral Flux Onset Strength Envelope (SFO), and Fundamental Frequency (FF) features, on SED performance. The paper demonstrates that the fusion of spectral and ASR features improves SED performance across all stuttering events by 8%. The F1 score of the Block class increased by approximately 10%, from 15% to 25%, while the word repetition and interjection increased by 8%. In addition, our experiments proved that the fusion of temporal features increased the average F1 score of SED by 2% across all stuttering events. The F1 score of the prolongation and sound repetition classes increased by 6% and 3%, respectively. © 2024 IEEE.

关键词： Deep learning

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Authenticating Smartphone Users Continuously Even if the Smartphone is in the User’s Pocket 1

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1st International Conference on Advanced Computing, Machine Learning, Robotics and Internet Technologies, AMRIT 2023

作者： Dutta, Sandip Roy, Soumen Roy, Utpal Department of Computer and System Sciences Visva-Bharati West Bengal Santiniketan731235 India Department of Computer Science and Engineering University of Calcutta Acharya Prafulla Chandra Roy Siksha Prangan JD - 2 Sector - III Saltlake City West Bengal Kolkata7000106 India Department of Computer and System Sciences Visva-Bharati West Bengal Santiniketan731235 India

ISBN: (数字)9783031472213

ISBN: (纸本)9783031472206

Users often underestimate the power and utility of smartphones, as they are unaware of the wide range of intelligent sensors that are now built-in into them. Smartphones are equipped with a wide array of sensors, including accelerometers, gyroscopes, magnetometers, barometers, pedometers, etc. These sensors are continuously active whenever the phone is turned on. Our study examines the way in which a smartphone can authenticate a user in a continuous and automatic manner, without requiring their awareness. To achieve this goal, we have created a moderate-sized human gait database that comprises data from 63 volunteers, taking into account their ages, genders, educational levels, professions, and backgrounds. In order to create this database, a web-based application was used to utilize the sensors on smartphones to capture human gaits. To determine which algorithm is most effective at verifying users based on their gait patterns, we analyzed the dataset here using ten popularly known anomaly detection algorithms. We found that one-class support vector machines (OCSVMs) are the most powerful for verifying users based on gait, with an average Equal Error Rate (EER) of 0.0043. It has been also identified that the EER has been reduced from 0.0043 to 0.0013 when soft biometric traits like gender, education level, and age group information are incorporated. We have proposed research directions to develop and implement this technology, and we believe it has practical applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Smartphones

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Automatic Early Detection of Tomato Blight Disease Using Deep Neural Networks 4

Automatic Early Detection of Tomato Blight Disease Using Dee...

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4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Halder, Anangsha Neogi, Subhrendu Guha Dutta, Shibdas Chandra, Soumallya Chandra Majumdar, Pritha Guha Sarkar, Akash The Neotia University Department of Computer Science and Engineering West Bengal Sarisha India Brainware University Department of Computer Science and Engineering West Bengal Kolkata India Dcg Data-Core System India Pvt. Ltd West Bengal Kolkata India

ISBN: (纸本)9798350387490

Crop disease control is crucial for food security since plant illnesses diminish agricultural production. Crop productivity and quality depend on leaf diseases like tomatoes. Early plant disease identification utilizing computer vision and artificial intelligence can lessen disease severity and overcome the limitations of continuous human monitoring. Automatic Early detection and alert systems reduce disease and boost agricultural productivity by classifying tomato diseases from tomato leaf images using Deep Neural Network Models. Our study examined leaf photos to classify tomato diseases and healthy plants using AlexNet, ResNet-9, VGG-19, and a modified CNN model for tomato plant disease diagnosis using deep neural network architectures. The proposed CNN analyses images to identify edges, colours, and textures. The suggested model can recognize healthy and diseased tomato leaves with 98% accuracy, making it a valuable crop management tool that promotes sustainable agriculture and global food security. © 2024 IEEE.

关键词： Fruits

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Prediction of Post-COVID-19 Pandemic Learning Mode Preferences Using Machine Learning 14

Prediction of Post-COVID-19 Pandemic Learning Mode Preferenc...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Pratondo, Agus Novianty, Astri Putro, Budi Laksono Komala Sari, Siska Telkom University School of Applied Sciences Department of Multimedia Engineering Bandung Indonesia Telkom University School of Electrical Engineering Department of Computer Engineering Bandung Indonesia Indonesia University of Education Department of Computer Science Education Bandung Indonesia Telkom University School of Applied System Diploma Program of Information System Bandung Indonesia

ISBN: (纸本)9798350335095

The COVID-19 epidemic has had a huge impact on the educational landscape, prompting the adoption of online and remote learning as viable alternatives to conventional in-person instruction. In order to create effective educational methods as institutions prepare for the post-pandemic period, it is essential to understand students' preferences for various learning modes. With the aid of machine learning techniques, this study seeks to forecast preferences for learning styles in post-pandemic higher education. The study made use of a dataset compiled from a wide range of college students, which included information on the institution type (public or private), program type (bachelor's or diploma), academic discipline (science or humanities), gender, year of study, internet use, communication costs, comorbidity status, vaccination history, gadget and laptop ownership, and preferred learning mode (online, in-person, or both). For the aim of prediction, a Support Vector Machine (SVM) was used. Its kernel functions included linear, polynomial, radial basis function (RBF), and sigmoid. Performance criteria like accuracy, precision, recall, and F1 score were used to assess the models after the dataset was randomly partitioned into training and testing sets. The findings showed that for identifying the preferred mode of learning, SVM with the RBF kernel had the highest prediction accuracy (91.67%). Further proving the RBF kernel's efficacy, the confusion matrix analysis showed that high values in the diagonal represented accurate predictions for each of the three classes (both online and onsite). The results imply that machine learning approaches, in particular SVM with the RBF kernel, may accurately forecast students' preferences for learning modes in the post-pandemic situation. In a post-pandemic society, the findings provide insightful information that may be used to build adaptable and inclusive learning models and put them into practice. © 2023 IEEE.

关键词： Support vector machines

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Pulsed ToF LiDAR-Based Depth Imaging: SPAD Circuit Considerations and Simulation Study 66

Pulsed ToF LiDAR-Based Depth Imaging: SPAD Circuit Considera...

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2023 IEEE 66th International Midwest Symposium on Circuits and systems, MWSCAS 2023

作者： Noyan, Utku Lu, Sheung Al-Shabili, Abdullah Dandin, Marc Chan, Stanley H. Abshire, Pamela University of Maryland Department of Electrical and Computer Engineering Institute of System Research College ParkMD United States Purdue University Department of Electrical and Computer Engineering West LafayetteIN United States Carnegie Mellon University Department of Electrical and Computer Engineering PittsburghPA United States

ISBN: (纸本)9798350302103

Pulsed Time-of-Flight (ToF) LiDAR is a crucial technology for acquiring depth information in various applications, including autonomous vehicles, robotics, and 3D mapping. Single-Photon Avalanche Diodes (SPADs) are widely employed as detectors in these systems due to their fast response and high sensitivity. This paper investigates pulsed ToF LiDAR-based depth imaging to clarify the hardware design considerations that affect depth estimation accuracy. We present a simulation study that investigates the performance of SPAD-based LiDAR systems under various conditions, offering insights into the design of SPAD circuits for improved depth imaging performance. We find that, as expected, accuracy and depth resolution depend strongly on the fill factor, with accuracy decreasing as the fill factor drops, gradually at first and then more steeply for fill characteristics of less than 50%. Lastly, we discuss the outcomes of our simulations and suggest directions for future research in this area. © 2023 IEEE.

关键词： Autonomous vehicles Circuit considerations Depth imaging Pulsed Time-of-Flight (ToF) LiDAR Single-Photon Avalanche Diodes (SPADs)

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Examining the Role of Natural Language Processing in Generating Topics from Web Content

Examining the Role of Natural Language Processing in Generat...

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2023 IEEE International Conference on Computing, ICOCO 2023

作者： Altarturi, Hamza H.M. Saadoon, Muntadher Anuar, Nor Badrul Universiti Malaya Department of Computer System and Technology Kuala Lumpur Malaysia Universiti Malaya Department of Software Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9798350302684

Natural Language Processing (NLP) is a major subfield of machine learning that focuses on the interaction between humans and computers through several techniques, including topic modeling, which extracts and generates topics from large amounts of unstructured data on the web. Topic modeling uncovers hidden patterns and contextual meanings in web pages, allowing for the categorization, clustering, recommendation of relevant content, detection of emerging trends, and improved information retrieval. The benefit of topic models relies on their performance to detect and generate topics;however, the evaluation of applying topic modeling on web content data remains lacking in current studies. The absence of examining such performance evaluations hinders understanding performance variability across different web pages, the development of refined models, and the ability to keep pace with emerging patterns in the dynamic online environment. This study presents a comprehensive performance evaluation of the benchmark topic models when applied to web content data. It presents a comparison of topic models using four topic coherence metrics, providing an in-depth performance analysis. The evaluation is conducted on a public dataset consisting of 2 million web pages, providing a substantial basis for assessing the strengths and limitations of these models in the context of web data. Additionally, this study examines the utilization of NLP techniques for topic generation within the context of web content and sheds light on the challenges and issues associated with generating topics from web content data, highlighting potential research prospects and the need for future work in the field of web topic modeling using natural language processing. © 2023 IEEE.

关键词： Websites

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Text to Image Generation Using Attentional Generative Adversarial Network 1

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1st International Conference on Advanced Computing, Machine Learning, Robotics and Internet Technologies, AMRIT 2023

作者： Rao, Supriya B. Shetty, Shailesh S. Singh, Chandra M, Srinivas P. Bekal, Anush Department of Artificial Intelligence and Machine Learning New Horizon College of Engineering Bangalore Karnataka Mangaluru India Department of Computer Science and Business System Srinivas Institute of Technology Karnataka Mangaluru India Department of Electronics & Communication Engineering Sahyadri College of Engineering & Management Karnataka Mangaluru India Department of Computer Science Engineering Sahyadri College of Engineering & Management Karnataka Mangaluru India

ISBN: (数字)9783031472213

ISBN: (纸本)9783031472206

AttnGan – Attentional Generative Adversarial Network (GAN) is a type of GANs that is used for text to image generation. Unlike other art models, it focuses on fine-grained word level information and once the model is trained by mapping the images and the corresponding text in the dataset, the model will generate image part-by-part using the image description given by the user as an input to the model. Current paper, focuses on the conversion of text into images based on the description provided by the user. This enables them to translate their ideas in the form of text into images of their required choices. The model is initially trained by the description of the text and images and when the user provides the text as an input to the system, it is used as a testing data. By analyzing the text word-by-word, the respective images pertaining to the text is generated part-by-part by considering the text description into account. This can heavily help the users in a applications such as in editing aids, documental archives, etc. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Generative adversarial networks

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FDDSGCN: Fractional Decoupling Dynamic Spatiotemporal Graph Convolutional Network for Traffic Forecasting

FDDSGCN: Fractional Decoupling Dynamic Spatiotemporal Graph ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Xu, Jinpeng Zhao, Chunna Yang, Jing Huang, Yaqun Yang, Yaoyuan Yee, Por Lip School of Information Science & Engineering Yunnan University Kunming China Department of Computer System and Technology Universiti Malaya Kuala Lumpur Malaysia

ISBN: (纸本)9798350368741

Urban traffic flow management faces increasing challenges due to accelerating urbanization. Traffic data collected from roadside sensors contain complex temporal and spatial dependencies that interact simultaneously. Although Graph Neural Networks and Recurrent Neural Networks have been successful in capturing these dependencies, two critical issues remain: 1) Treating all traffic signals equally fails to capture the nuanced spatiotemporal dependencies hidden in time series data;2) Dynamic traffic conditions hinder the accurate capture of local spatial dependencies, thereby limiting prediction accuracy and reliability. To address these challenges, we propose an innovative model, FDDSGCN. To resolve the first issue, we introduce Fractional Residual Decomposition, which effectively separates traffic data into spatial and temporal signals. For the second issue, we employ Dynamic Spatial-Temporal Graph Convolution with fractional-order weight adjustments to dynamically capture local dependencies. Additionally, the Long Short-Term Dependency module analyzes both long-term and short-term dependencies. Extensive experiments on three public datasets demonstrate the superior performance and practical value of our model. © 2025 IEEE.

关键词： dynamic graph learning Fractional Residual decomposition spatial-temporal graph Traffic prediction

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