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Enhancing Nucleus and Cytoplasm Segmentation in Cervical Cytology Using Deep Learning

Enhancing Nucleus and Cytoplasm Segmentation in Cervical Cyt...

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IEEE Punecon

作者： Soham Mhatre Arya Singh Swati Shinde Department of Computer Science and Engineering (AI&ML) Pimpri Chinchwad College of Engineering Pune India Department of Computer Science and Engineering Pimpri Chinchwad College of Engineering Pune India

ISBN: (数字)9798331527822

ISBN: (纸本)9798331527839

Cervical cancer, with over 660,000 cases in 2022, ranks as the fourth most frequent malignancy in women globally, with low- and middle-income nations accounting for approximately 94% of the 350,000 annual deaths. The challenging diagnosis of cervical cancer, often detected at advanced stages, has been addressed by recent advancements in high-throughput quantitative imaging of 2D cell cultures. Image segmentation automates the traditionally manual process of Pap smear cell diagnosis. While conventional approaches are effective, they face limitations in underrepresented cellular regions, noise, and blurriness in microscopy. Semantic segmentation causes inter-class competition, which causes performance issues for popular segmentation algorithms such as U-Net and Mask R-CNN in multi-class contexts. Our novel approach addresses these challenges by treating nucleus and cytoplasm segmentation as separate tasks, significantly enhancing overall segmentation performance. The nucleus, characterized by higher density and darker appearance, presents a more readily learnable target for the U-Net algorithm. While watershed and similar methods partially mitigate the issue of overlapping and occluded cells, challenges persist. A viable approach is to use pre-trained open-source models like Cellpose, which have been trained on thousands of images. Fine tuning these models through transfer learning for cytoplasm segmentation has produced remarkable outcomes. Pairing segmented nuclei and cytoplasm via centroid distance analysis enhances segmentation accuracy and resolves cellular overlaps. With an accuracy of 99.3%, an IoU of 83.91%, a Dice Coefficient of 91.04%, and precision of 89.88% for nucleus segmentation, and 98.81%, 95.62%, and 91.81% for cytoplasm segmentation, our method demonstrated remarkable accuracy across several parameters. These findings show how our method may help with cervical cancer diagnosis and detection.

关键词： Accuracy Semantic segmentation Microscopy Noise Transfer learning Watersheds Manuals Cervical cancer Tuning Optimization

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EAP Based Certificateless Authentication Technique to Access Cloud Services in Openstack 8th

EAP Based Certificateless Authentication Technique to Access...

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8th International Conference on Cognition and Recognition, ICCR 2021

作者： Raghavendra, K. Ramesh, B. Chandrika, J. Computer Science and Engineering AI-ML Department Dayananda Sagar University Kudlu Gate Hosur Main Road Karnataka Bangalore560068 India Department of Computer Science and Engineering Malnad College of Engineering Karnataka Hassan India

ISBN: (纸本)9783031224041

Nowadays cloud services have gained more interest. The main advantage of the cloud is, it reduces management costs and efficient usage of resources. The major role of efficient authentication means not only providing authentication but it should also reduce the authentication traffic and provide security. There are many authentication mechanisms most of them were only for a web application. Here in this paper, we focus on both web and non-web applications authentication in cloud environment to access SaaS service. For non-web application services of cloud, ABFAB has created an architecture. ABFAB also defines how to use existing EAP/AAA and GSS-EAP for both web and non-web-based applications. In this paper, we mainly focus on efficient AAA to access cloud services. To demonstrate the proposed system, we use the moonshot from Github, freeradius an opensource, and Openstack for cloud service with our proposed authentication method. We have done the performance analysis of our authentication method compared with the other authentication mechanism to access cloud services. This analysis shows a significant reduction in the computation time required for authentication and reduction in the authentication traffic. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Authentication

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The Future of Smart Factories: Real-Time Automation and Big Data Analytics on the Cloud 15

The Future of Smart Factories: Real-Time Automation and Big ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Singh, K. Ranjith Arora, Arpit Goud, Dilip Ambika, Krovvidi S B Sandeep, V. More, Priyanka Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India NNRG Engineering College India ADITYA COLLEGE of ENGINEERING and TECHNOLOGY Department of CSE-AI&ML & DS Surampalem India School of Engineering and Technology Mechaical Engineering Karnataka Bangalore India Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350370249

The evolving future of manufacturing will be all about intelligent factories featuring state-of-the-art technologies. Smart factories combine connected automation and big data in the cloud to streamline production, increase productivity, and lower operational costs. Real-time automation utilizes state-of-the-art sensors, internet of Things (IoT) devices, and artificial intelligence (ai) technology to monitor and control machines or processes in real time independently. This results in faster potential decision-making and bright maintenance works, thus decreasing downtime risk and increasing overall productivity. Big data analytics in the cloud enables real-time collection and analysis of extensive amounts of production processes, machine performance, and supply chain management-related information. The data can then be utilized to discover areas that need improvement, predict maintenance requirements, and even optimize the level of inventories in the sense that it would save costs while at the same time becoming more efficient. Also, real-time automation combined with significant cloud data analytics results in improved communication and collaboration from one production stage to another, hence smooth operations. In other words, it helps create an even more agile and adaptable manufacturing environment where processes can be fine-tuned on the fly to deal with dynamic market shifts. © 2024 IEEE.

关键词： Smart manufacturing

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MedLogic-AQA: Enhancing Medical Question Answering with Abstractive Models Focusing on Logical Structures

MedLogic-AQA: Enhancing Medical Question Answering with Abst...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Zafar, aizan Mishra, Kshitij Ekbal, Asif Department of Computer Science and Engineering Indian Institute of Technology Patna India School of AI and Data Science Indian Institute of Technology Jodhpur India

ISBN: (纸本)9798891761681

In Medical question-answering (QA) tasks, the need for effective systems is pivotal in delivering accurate responses to intricate medical queries. However, existing approaches often struggle to grasp the intricate logical structures and relationships inherent in medical contexts, thus limiting their capacity to furnish precise and nuanced answers. In this work, we address this gap by proposing a novel Abstractive QA system MEDLOGIC-AQA that harnesses First Order Logic (FOL) based rules extracted from both context and questions to generate well-grounded answers. Through initial experimentation, we identified six pertinent first-order logical rules, which were then used to train a Logic-Understanding (LU) model capable of generating logical triples for a given context, question, and answer. These logic triples are then integrated into the training of MEDLOGIC-AQA, enabling effective and coherent reasoning during answer generation. This distinctive fusion of logical reasoning with abstractive QA equips our system to produce answers that are logically sound, relevant, and engaging. Evaluation with respect to both automated and human-based demonstrates the robustness of MEDLOGIC-AQA against strong baselines. Through empirical assessments and case studies, we validate the efficacy of MEDLOGIC-AQA in elevating the quality and comprehensiveness of answers in terms of reasoning as well as informativeness. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Harnessing Tech Innovations for Diversified Multigenerational Work Force and Work Life Balance

Harnessing Tech Innovations for Diversified Multigenerationa...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Mittal, Mudit Sharma, Vishal Varshney, Neeraj Kumar, Sunil Prashanth Boob, Nandini Shirish Reddy, R. Akhilesh Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lovely Professional University Phagwara India GLA University Department of Computer Engineering and Applications Mathura India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India MLR Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India

ISBN: (纸本)9798331512965

This research aims to examine how technologically innovative breakthroughs may help a multigenerational heterogeneous workforce to maintain a good work and life balance. We investigate how technology can be used to strategically raise up the rate of participation and align with the particular needs of different age groups. The research findings show how ai powered personal learning, collaborative digital communication tools and flexible remote working solutions promote a healthy and mature culture in the workplace. The findings explain how key it is to utilize technology in a refined manner such that all workers can thrive and maintain a sustainable balance between their work and home life. © 2025 IEEE.

关键词： Collaborative learning

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ABLE: Personalized Disability Support with Politeness and Empathy Integration

ABLE: Personalized Disability Support with Politeness and Em...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Mishra, Kshitij Burja, Manisha Ekbal, Asif Department of Computer Science and Engineering Indian Institute of Technology Patna India School of AI and Data Science Indian Institute of Technology Jodhpur India

ISBN: (纸本)9798891761643

In today's dynamic world, providing inclusive and personalized support for individuals with physical disabilities is imperative. With diverse needs and preferences, tailored assistance according to user personas is crucial. In this paper, we introduce ABLE (Adaptive, Bespoke, Listen and Empathetic), a Conversational Support System for Physical Disabilities. By tracking user personas, including gender, age, and personality traits based on the OCEAN model, ABLE ensures that support interactions are uniquely tailored to each user's characteristics and preferences. Moreover, integrating politeness and empathy levels in responses enhances user satisfaction and engagement, fostering a supportive and respectful environment. The development of ABLE involves compiling a comprehensive conversational dataset enriched with user profile annotations. Leveraging reinforcement learning techniques and diverse reward mechanisms, ABLE trains a model to generate responses aligned with individual user profiles while maintaining appropriate levels of politeness and empathy. Based on rigorous empirical analysis encompassing automatic and human evaluation metrics based on persona-consistency, politeness accuracy, empathy accuracy, perplexity, and conversation coherence, the efficacy of ABLE is assessed. Our findings underscore ABLE's success in delivering tailored support to individuals grappling with physical disabilities. To the best of our knowledge, this is the very first attempt towards building a user's persona-oriented physical disability support system. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Imaging – based Detection of Bone Injuries using Hybrid Models

Imaging – based Detection of Bone Injuries using Hybrid Mod...

引用

International Conference on ent Computing and Control Systems (ICICCS)

作者： Vineela Madireddy Bhagya Lakshmi Medam Sai Vardhan Reddy Manne Navaneeth Yadav Madhugani Dept. Computer Science and Engineering -AI&ML Vignan Institute of Technology and Science Nalgonda India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Bone fractures are among the most common physical injuries, affecting individuals across all age groups and requiring prompt and precise diagnosis. However, radiologists often face challenges in accurate fracture identification due to variations in image quality and complexity. The present work investigates a robust system for the automatic detection and classification of fractures in X-radiation (X-ray) images. The proposed model integrates Convolutional Neural Networks (CNN) and MobileNet with a hybrid approach that combines MobileNet and Random Forest to improve classification accuracy. The system is designed to be user-friendly, incorporating modules for registration, data processing, and real-time classification of fractures into 11 distinct types, including avulsion fracture, impacted fracture, longitudinal fracture, comminuted fracture, greenstick fracture, oblique fracture, pathological fracture, spiral fracture, and others. Experimental results demonstrate that the hybrid model significantly enhance diagnostic accuracy, underscoring its potential application in medical settings.

关键词： Pathology Accuracy Spirals Computational modeling Bones Real-time systems Convolutional neural networks X-ray imaging Random forests Injuries

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Assessing Secure Cloud Information Sharing Through Authentication and Encoded Indexing 8

Assessing Secure Cloud Information Sharing Through Authentic...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Pandiaraja, P. Karthik, K. Rajesh Kanna, P. Ushapriya, C. Mahesh, P.C.Senthil Muthumanickam, K. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai600 062 India M. Kumarasamy College of Engineering Department of Computer Science and Engineering Karur Tamilnadu Thalavapalayam639113 India Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamilnadu Erode638401 India St. Martin's Engineering College Department of Ai & Ml Telangana Secunderabad500100 India Excel Engineering College Department of Computer Science and Engineering Tamilnadu Komarapalayam637303 India Kongunadu College of Engineering and Technology Department of Information Technology Tamil Nadu Trichy621215 India

ISBN: (纸本)9798350376425

The high usage of storage in cloud, ensuring the privacy and security of information stored in the cloud has come up with a difficult concern. One approach to address this issue is the use of techniques which are encrypted, where data is already encrypted being uploaded to the storage of cloud. However, traditional encryption methods hinder the ability to find and retrieve particular information from the secured information, as the information needs to be decrypted first. This limitation hampers the usability and efficiency of storage of cloud systems. To overcome the challenge, this research says a novel approach for keyword search on secured information in storage of cloud using an AES approach. The algorithm of AES is a maximum adopted symmetric encryption method familiar for efficiency and robustness. By leveraging the AES encryption approach, the solution assures the confidentiality of information stored while enabling efficient keyword-based search operations without the need for decryption. The proposed approach utilizes a combination of cryptographic techniques such as AES encryption and index structures to access efficient information findings on the secured information. A secure index is created during encryption process that enables for potent retrieval of encrypted data related to specific keywords. The encrypted data remains confidential, as only authorized users possess the necessary decryption keys to retrieve the plaintext information. During data outsourcing, data owner can set the access permission based on their user identity. Users should have specific identity with decryption key for accessing data from cloud. Here data manager can also provide particular period for accessing information from cloud. The results demonstrate that the keyword findings on secured information in storage of using the AES approach achieve a balance between data privacy and search functionality. The solution offers an effective means to securely store and retrieve data

关键词： Cloud storage

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Deep Reinforcement Learning for Optimizing Route Planning in Urban Traffic

Deep Reinforcement Learning for Optimizing Route Planning in...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Mittal, Mudit Sehgal, Archana Varshney, Neeraj Kumar, Sunil Prashanth Boob, Nandini Shirish Reddy, R. Akhilesh Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lovely Professional University Phagwara India GLA University Department of Computer Engineering and Applications Mathura India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India MLR Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India

ISBN: (纸本)9798331512965

Urban traffic networks are efficient route design problems due to the intricacy and dynamic character of contemporary cities. Conventional methods hamper real time adjustment to continually changing traffic conditions, thereby providing suboptimal routes, a greater burden on the roadways, and greater air pollution. This thesis presents a novel deep reinforcement learning (DRL) method to optimize the route planning for urban traffic networks. With this rendering of real time traffic data, we use DRL to make autonomous decisions to reduce travel time and congestion of routes. One advantage of using sophisticated neural networks for feature extraction and policy learning is that, since the model is so adaptable to changing traffic patterns, the model guarantees that. And, in comparison to common routing algorithms, the results of simulations using real world traffic data show that this method is able to greatly enhance route efficiency and overall traffic flow management. The suggested DRL based system is feasible as a smart city program based on intelligent transportation system which can scale to grand metropolitan regions. The research presents that DRL can enhance the commuter run experience, reduce the environmental effect, and alter urban transportation. Future study will also investigate how this system can be better optimized when integrated with traffic infrastructure enabled by the Internet of Things. © 2025 IEEE.

关键词： Deep reinforcement learning

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Efficient Liver and Tumor identification from CT Images and Clinical Data Using a Hybrid Deep Learning and Machine Learning Model

Efficient Liver and Tumor identification from CT Images and ...

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Advancement in Electronics & Communication engineering (AECE), International Conference on

作者： Vinish Kumar Virat Raj Saxena Mahima Tayal Department of Computer Science Engineering (AI&ML) Raj Kumar Goel Institute of Technology Ghaziabad India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

The present research investigates the integration of machine learning and artificial intelligence models for precise liver and tumour segmentation from CT images, with the goal of increasing diagnostic accuracy in liver cancer. A full dataset size of 2000 patients’ records were used, with standardised dosage information, treatment, as well as CT scans. The comprehensive preprocessing of the data, including normalisation, noise reduction, contrast enhancement, and segmentation, provided an opportunity not only to improve the image quality but also to select the desired features. The tracking of the tumor responses to the treatment was completed with the help of such machine learning models as Support Vector Machines, deep learning models such as VGG 16, VGG 19, Inception V3. The SVM + Inception V3 hybrid model was chosen as the most appropriate one, showing 98.76% of accuracy in detecting the correct responses to liver cancer. The SVM ensured the proper temporal data handling, and the Inception V3 made use of multi-scale feature extraction capabilities to localise and classify cancerous regions within CT scans accurately. The VGG 19 achieved an accuracy of 94.5%, and VGG 16 achieved an accuracy of 91.2%. Both VGG 16 and VGG19 performed commendably because of their image analysis capabilities. SAR values further emphasise that SVM + Inception V3 achieves the max precision and recall rates, while VGG displays slight limitations in this regard. The implications for this study are that hybrid models can provide a practical implementation of machine learning/deep learning artificial intelligence applications for medical imaging.

关键词： Support vector machines Deep learning Liver cancer Accuracy Computed tomography Liver Predictive models Feature extraction Data models Tumors

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