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2D-3D Feature Co-Embedding Network with Sparse Annotation for 3D Medical Image Segmentation

2D-3D Feature Co-Embedding Network with Sparse Annotation fo...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhu, Mingzhu Guo, Shuyu Wang, Ziwei Jiao, Jianhang Bai, Tian Jilin University College of Computer Science and Technology Changchun China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun China Jilin University College of Software Changchun China The Second Norman Bethune Hospital Jilin University Department of Orthopedics Changchun China

ISBN: (纸本)9798350386226

Supervised methods on 3D medical image segmentation need large amounts of annotated data, but annotating is time-consuming. Also, existing 3D segmentation methods capture more global structural information but overlook local detailed features, which negatively impacts the segmentation of small tissues. In this paper, we propose a novel weakly-supervised 2D-3D Feature Co-Embedding Network (2D-3D CoENet) that includes 2D and 3D encoding layers, simultaneously extracting 2D local detailed and 3D global structural features. To reduce annotation costs, we use fewer labeled slices as ground truth and pseudo-labels are generated by 2D-3D CoENet for other slices. Additionally, multi-view learning is introduced to capture more 2D local detailed information, and a Multi-view Semantic Consistency loss (MSC loss) is proposed to constrain features from multiple perspectives. To further enhance the local detailed texture features, we propose an Edge Enhancement Module (EEM) in the 3D segmentation network to enhance the edge detail features. Our experimental results on the SKI10 dataset and OAI ZIB dataset demonstrate that our method outperforms the SOTA weakly-supervised segmentation methods. Moreover, our approach achieves results that are comparable to the fully-supervised upper bound results. © 2024 IEEE.

关键词： Medical imaging

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Diagnosing Alzheimer Disease Using MRI Scan: A Deep Learning Approach

Diagnosing Alzheimer Disease Using MRI Scan: A Deep Learning...

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Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： S. Saravanan K. Muthumanickam N. Subha P.C.Senthil Mahesh Department of CSE-Data Science Dayananda Sagar Academy of Technology and Management Bengaluru India Department of Information Technology Kongunadu College of Engineering and Technology (Autonomous) Thottiyam Tamilnadu India Department of Computer Science and Engineering Knowledge Institute of Technology Salem Tamilnadu India Department of Computer Science and Engineering Excel Engineering College Komarapalayam Tamilnadu India

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

The neurodegenerative disease known as Alzheimer's disease (AD), which affects people worldwide, is complicated to treat and expensive. Conventional approaches, including manual Support Vector Machine (SVM) based methods with separate feature extraction and preprocessing, lack automation and pose limitations. This project recognizes the pressing need for efficient and automated solutions. Building on existing work that employed SVM and manual processing for Alzheimer's diagnosis, this work proposes a novel approach. Utilizing brain MRI scans and the VGG16 architecture, our automated system achieves high accuracy in predicting both labeled and unlabeled data. The integration of VGG16 ensures a streamlined process, eliminating manual intervention in feature extraction and preprocessing. This automation enhances accessibility, reduces costs, and offers a user-friendly interface for users. In contrast to traditional methods, our proposed system allows users to input MRI scans, facilitating prompt and accurate predictions. Upon analysis, the proposed system provides doctor recommendations and suggests prescriptions. The VGG16-based approach not only ensures superior diagnostic accuracy but also streamlines the entire process, emphasizing the importance of early detection for improved outcomes in Alzheimer's disease management.

关键词： Support vector machines Accuracy Automation Magnetic resonance imaging Manuals Pressing Streaming media

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STGTP: Enhancing Stance Detection with Graph and Text Embedding through the Post-Graph Model

STGTP: Enhancing Stance Detection with Graph and Text Embedd...

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IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES)

作者： Samineni Bhavani Hima Bindu Kommanti Department of Computer Science and Engineering National Institute of Technology Andhra Pradesh India Rajiv Gandhi University of Knowledge Technologies Andhra Pradesh India

ISBN: (数字)9798350376135

ISBN: (纸本)9798350376142

This paper presents STGTP, which combines graph and text-based techniques for stance detection of social media posts. This model learns from the text in the post and the relationships between the posts due to users conversations and interactions. The proposed model uses the embeddings constructed using the textual content and an interaction graph of the posts. The concatenated embeddings are fed to a CNN module for predicting the stance label. Results over the ConvinceMe and CreateDebate datasets show that our proposed STGTP surpasses existing stance detection models by up to 3% in terms of Macro F1-score.

关键词： Social networking (online) Oral communication knowledge graphs Signal processing Predictive models Transformers SPICE Informatics

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An Exhaustive Meta-Analysis on Consumer Retention Forecasting Using Advanced Machine Learning Techniques

An Exhaustive Meta-Analysis on Consumer Retention Forecastin...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Lakhanpal, Sorabh Alekhya, V. Rajalakshmi, B. Khan, Nida Khan, Irfan Mohammed, Saad Khudhur Lovely Professional University Phagwara India Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Computer Science Bangalore India GyanGanga Institute of Technology and Sciences DoCSE M.P Jabalpur India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India National University of Science and Technology College of Pharmacy Dhi Qar64001 Iraq

ISBN: (纸本)9798350373783

In this meta-analysis, we present a robust approach for consumer retention forecasting by integrating advanced machine learning algorithms. The method comprises three core algorithms: Consumer Behavior Neural Network (CBNN), Retention Decision Trees (RDT), and Long ShortTerm Memory for Retention (LSTM-R). Our objective is to model intricate relationships within consumer behaviors and accurately predict their retention probabilities. Consumer Behavior Neural Network (CBNN): CBNN, a key component, adeptly captures intricate patterns within consumer behavior data. It involves multiple hidden layers with activation functions, enabling accurate prediction of retention probabilities based on input features. Retention Decision Trees (RDT): This method utilizes decision tree algorithms to predict consumer retention decisions. By recursively splitting the dataset based on information gain, RDT efficiently models consumer behavior, as depicted in the flowchart. Information gain is maximized to create a robust predictive model. Long Short-Term Memory for Retention (LSTM-R): LSTM-R, tailored for temporal data in consumer retention prediction, leverages specialized LSTM architecture. It effectively processes sequential consumer interactions, crucial for accurate prediction of consumer retention probabilities. The proposed method integrates these algorithms, leveraging historical consumer data, and conducts a comprehensive meta-analysis to evaluate their predictive performance. © 2024 IEEE.

关键词： Adversarial machine learning

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Navigating the Generative AI Project Ecosystem with a Focus on Addressing Data Architecture Complexities and Strategic Model Selection for Optimal Outcomes

Navigating the Generative AI Project Ecosystem with a Focus ...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Gupta, Priyanka Kumari, I. Vasantha Rajalakshmi, B. Nijhawan, Ginni Praveen Mazaal, Zahraa Sahib Institute of Aeronautical Engineering Hyderabad Dundigal India Siddhartha Institute of Technology and Sciences Medchal Malkajgiri District Hyderabad Telangana India New Horizon College of Engineering Department of Computer Science Bangalore India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India National University of Science and Technology Collage of Dentist Dhi Qar64001 Iraq

ISBN: (纸本)9798350373783

This article discusses the Generative Artificial Intelligence (AI) project's complex ecology and how to pick the optimal models and data design. Creative AI in many commercial and academic fields presents new issues and opportunities. Knowing your way around generative AI projects is crucial. Choose models carefully since data design is hard to grasp. This research provides comprehensive guidance on how to address these areas and ensure project success. Data architecture study is the first step towards generative artificial intelligence since it identifies common issues and viable solutions. Next, to determine which generative models - in terms of complexity, scalability, and ethics - are best suited for a range of tasks, we must evaluate and compare many of them. Researchers were able to validate these findings using case studies and expert interviews. The results suggest that an ordered data infrastructure is required for creative AI. This study indicated the need to boost generational artificial intelligence programs by making practitioners and researchers more aware of the challenges. The study argues that further research and development are necessary to keep up with the fast progress of creative artificial intelligence. © 2024 IEEE.

关键词： Generative adversarial networks

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A review of Communication and Data Networks Algorithms Shaping Modem Connectivity 7

A review of Communication and Data Networks Algorithms Shapi...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Srinath, M.K. Gatla, Anitha Lakhanpal, Sorabh Sharma, Saurabh Reddy, B.Ravali Al-Rubaye, Taqi Mohammed Khattab New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Institute of Aeronautical Engineering Department of Information Technology Dundigal Hyderabad500043 India Lovely Professional University Phagwara India Lloyd Law College Plot No. 11 Knowledge Park II Uttar Pradesh Greater Noida201314 India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India The Islamic University College of Medical Technologies Department of Medical Laboratory Technology Najaf Iraq

ISBN: (纸本)9798350350067

The world of today is highly interconnected and undergoes rapid changes. Algorithms are essential for data networks and communication systems. This study looks at how the complexity of algorithms affects network design. It focusses on the GROA (Global Routing Optimisation Algorithm), LBEA (Load Balancing and Efficient Allocation), and QKD-NS systems in particular. We aim to educate individuals on the impact of various methods on security, route completion time, and resource constraints. Based on the results, GROA creates variable paths, QKD-NS boosts safety with quantum-based innovations, and LBEA efficiently divides up resources based on what the network requires. According to the study, putting these many technologies together could lead to strong partnerships that can handle security breaches, traffic jams, and changes in the network. This study focusses on how advances in technology have led to better computer design, which in turn has made the internet safer and easier to use. The paper elucidates the application of these novel mathematical discoveries to enhance the performance and durability of data networks. These new details help communication networks improve by ensuring that they are safe and can meet today's link standards. © 2024 IEEE.

关键词： Traffic congestion

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Pancreatic Cancer Prediction Machine Learning using Multi Fusion Model

Pancreatic Cancer Prediction Machine Learning using Multi Fu...

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Distributed Systems, computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： V. Sathiyapriya K. Nethra Devi S. Deepa M. Marimuthu P. Manikandan S. Prabu Department of Computer Science and Engineering Knowledge Institute of Technology Kakapalayam India Department of Artificial Intelligence and Data Science Gnanamani College of Technology Namakkal India School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Computing SRM Institute of Science and Technology Tiruchirappalli India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Pancreatic cancer poses a significant challenge in early detection and treatment due to its malignant nature within the digestive tract. Recent studies, such as those conducted by the Pancreatic Cancer Action Network, predict that pancreatic cancer will rank as the world's second leading cause of cancer-related deaths in 2020. Various methods have been explored in the literature to segment pancreatic tumors and detect lesions from CT images, crucial for assessing severity. One such method is the Min-Max segmentation algorithm, which effectively removes the jaundiced sclera region from affected eyes by analysing color gradients, offering more feasible and discriminative solutions. Additionally, the utilization of Whale Optimized feature-based selection aids in extracting optimal features for optimization purposes. Through an analysis of different classification algorithms and the implementation of a suggested technique for achieving optimum solutions and maximum accuracy, the proposed work is structured into two phases. Phase I involves pre-processing and artifact removal, followed by segmentation, while Phase II focuses on feature extraction, feature selection, and classification. The initial step of pre-processing the image facilitates pixel location using intensity measures, crucial for accurate tumor segmentation and minimizing false detection. Ultimately, the proposed approach aims to design and develop an optimal feature selection strategy for classifying pancreatic cancer tumors in PET/CT images, resulting in increased classification efficiency 92% and improved cancer tumor detection rates with compare other existing approaches.

关键词： Image segmentation Accuracy Soft sensors Pancreatic cancer Medical services Feature extraction Prediction algorithms Whale optimization algorithms Classification algorithms Ensemble learning

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A Real-Time Hand Sign Language Recognition System for Threatening Situations Using Deep Learning

A Real-Time Hand Sign Language Recognition System for Threat...

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Zardasht Abdulaziz Abdulkarim Shwany Shayda Khudhur Ismail Karwan Hoshyar Khalid Khoshnaw Twana Saeed Mustafa Murat Karabatak Nashwan Adnan Othman Physiotherapy Department Erbil Polytechnic University Erbil Iraq Network Department Ministry of Higher Education and Scientific Research Erbil Iraq Department of Information and Communication Technology Engineering Erbil Polytechnic University Erbil Iraq Department of Software Firat University Elazig Turkey Department of Computer Science College of Science Knowledge University Erbil Iraq Department of Computer Engineering Knowledge University Erbil Iraq Department of Computer Engineering Al-Kitab University Altun Kupri Iraq

ISBN: (数字)9798350330366

ISBN: (纸本)9798350330373

Hand sign language has been done as physical movements in natural languages that humans have used since ancient times, along with letters, words, and spoken language. This paper presents a real-time method to identify threatening signs by criminals during interrogation, which increases their criminal rank and helps to reach conclusions. The proposed method is to install a camera of appropriate quality in front of the offender, record hand gestures in a specific area of the hand, apply some image processing techniques, such as contrast enhancement techniques, to the image to facilitate recognition as input, and then classify the images using a convolutional neural network (CNN) for a specific problem with high specifications for that topic by and using AlexNet. The accuracy of the presented method is more than 94.2% in real-time testing.

关键词： Deep learning Sign language Image recognition Natural languages Digital forensics Cameras Real-time systems

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Performance Evaluation of Application Layer IoT Protocols: MQTT and CoAP

Performance Evaluation of Application Layer IoT Protocols: M...

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International Conference on computer, Communication, Control and Information Technology (C3IT)

作者： Manika Debnath Subrata Modak Dhrubasish Sarkar Department of Computer Science & Engineering Supreme Knowledge Foundation Group of Institutions Mankundu West Bengal India Computer Science & Information Technology Supreme Institute of Management and Technology Mankundu West Bengal India

ISBN: (数字)9798350389128

ISBN: (纸本)9798350389135

The Internet of Things (IoT) is one of the most intriguing technological revolutions of the last few years due to its rapid expansion. The advancement of IoT applications is dependent on standard and real-time communication protocols. The IoT protocols are designed to enable reliable messaging. However, the selection of a standard and efficient messaging protocol is a difficult and intimidating task for any organization. One of the main challenging issues facing the loT is wireless sen-sor networks (WSNs). The devices that make up these networks have low levels of memory, processing power, and energy. Certain restricted protocols must be in place to function in WSNs due to their constrained nature. In the context of a business application for monitoring, this paper shows the experimental performance of two protocols: Message Queue Telemetry Transport (MQTT) and Constrained Application Protocol (CoAP).

关键词： Performance evaluation Wireless communication Wireless sensor networks Technological innovation Protocols Power demand Throughput Internet of Things Telemetry Testing

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Deep Learning for Customer Relationship Management in E-commerce 6

Deep Learning for Customer Relationship Management in E-comm...

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

作者： Ahmed, Saadaldeen Rashid ALhashmi, Lubna Qassim Hussein, Ali Jabbar Radhi, Ahmed Dheyaa Radhi, Ahmed A. Ahmed, Mohammed Rashid Hussain, Abadal-Salam T. Tawfeq, Jamal Fadhil Computer Science Department Bayan University Kurdistan Erbil Iraq Artificial Intelligence Engineering Department College of Engineering Al-Ayen University Thi-Qar Iraq Department of information and knowledge technologies College of Arts Basrah University Basrah Iraq Al Hikma University College Baghdad Iraq College of Pharmacy University of Al-Ameed Karbala PO Box 198 Iraq Department of Computer Techniques Engineering Al-Ma’moon University College Al-Washash / Baghdad Iraq College of Technical Engineering National University of Science and Technology Dhi Qar Iraq Department of Medical Instrumentation Techniques Engineering Technical Engineering College Al-Kitab University Altun Kupri Kirkuk Iraq Department of Medical Instrumentation Technical Engineering Medical Technical College Al-Farahidi University Baghdad Iraq

ISBN: (纸本)9798350394634

Customer Relationship Management (CRM) plays a pivotal role in the success of e-commerce businesses by fostering stronger connections with customers. De-spite its importance, existing CRM approaches often fall short in effectively leveraging the vast amounts of data available in digital environments. This paper addresses this gap by proposing a novel framework that harnesses the power of deep learning techniques to enhance CRM in e-commerce. Building upon a comprehensive review of previous literature, we identify the need for advanced analytical methods to unlock insights from complex data patterns. Our approach in-volves the development and evaluation of artificial neural networks (ANNs) and deep neural networks (DNNs) tailored to CRM tasks such as customer segmentation, churn prediction, and recommendation systems. Leveraging a meticulously curated dataset encompassing diverse customer attributes and transaction history, we rigorously train and assess our models. Our results demonstrate the efficacy of deep learning in capturing nuanced customer behaviors and improving predictive accuracy in CRM tasks. Our highest achieved accuracy stands at 90%, showcasing the potential of our approach in driving superior CRM performance in e-commerce environments. Through this study, we contribute to bridging the research-practice gap in CRM by offering practical insights and methodologies for leveraging deep learning in e-commerce environments. © 2024 IEEE.

关键词： Deep neural networks

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