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5th International Conference on data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Kour, Kanwal Preet Singh, S. Kanwal Deep Bali, Malvinder Singh Ghantasala, G. S. Pradeep Rajesh Sharma, R. Ananthanagu, U. Manipal University School of Computer Science and Engineering Jaipur India Federation University Department of Computer Science Australia Manipal University Department of Data Science and Engineering Rajasthan Jaipur India Alliance College of Engineering and Design Alliance University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350389609

In recent times, blockchain has evolved the security of traditional supply chain systems. Different issues of supply chain management like flexibility and reliability can be easily addressed using blockchain. Meat Packing Industry involves the complete process of processing, packaging and supplying the meat and its products throughout the world for human consumption. The animals used for the meat supply chain include pigs, sheep, cattle etc. Due to the enormous increase in the number of meat-eaters worldwide, its packing and timely supply are of utmost importance. The meat packing industry has revolutionized a lot by using the latest supply chain methods based on new technologies like blockchain. These include adulteration, long-term delays leading to spoilage of the product, mixing of other similar products which are lower in price etc. All these issues can be easily handled by using blockchain and IoT for meat supply chain management. Using these technologies, the challenges ranging from timely transportation of good quality meat to the best quality meat delivered to the customer can be easily addressed. In this paper, an effective blockchain and IoT-based model has been proposed which will counter the above challenges and improve the reliability and security factor. © 2024 IEEE.

关键词： Supply chain management

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Enhanced Identification of Care Preference Documentation in Patients’ Discharge Summaries Using Pre-trained Large Language Models 1

Enhanced Identification of Care Preference Documentation in ...

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1st IEEE International Conference on Artificial Intelligence for Medicine, Health and Care, AIMHC 2024

作者： Arora, Saksham Khayal, Inas S. Department of Computer Science Dartmouth College HanoverNH United States Dartmouth Institute & Biomedical Data Science Geisel School of Medicine at Dartmouth Dept. of Computer Science Dartmouth College HanoverNH United States

ISBN: (纸本)9798350371987

Improving patient-centered care necessitates accurate documentation of care preferences, a crucial aspect often underrepresented in administrative data. Most studies apply care documentation to specific patient populations, rather than the more appropriately broad population of ‘seriously ill’ patients. This study addresses this gap, first by matching decedent patients to non-decedent counterparts by using propensity score matching, accounting for important variables like age, gender, primary diagnoses, and patient co-morbidities, and then by leveraging transformer-based machine learning models, exhibiting an improvement over traditional keyword-based search methods. We trained and fine-tuned Bio_ClinicalBERT and ClinicalLongformer models on a large dataset of patient discharge summaries, enhancing their signal-to-noise ratio by focusing on key textual aspects, thereby capturing contextually nuanced mentions of care preference documentation. These models demonstrated high sensitivity and specificity compared to industry-standard keyword-search methods, proving adept at interpreting complex clinical concepts. This study highlights the potential of transformer-based models specifically trained for clinical domain tasks. Our findings not only contribute to enhancing end-of-life communication and aligning treatment with patients’ care objectives but also pave the way for future research in this promising domain, with potential implications for improving patient care quality. ©2024 IEEE.

关键词： Large datasets

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A Novel Approach for Heart Disease Classification using Cascaded XGBoost Algorithm 3

A Novel Approach for Heart Disease Classification using Casc...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Rajitha, M. Megala, S. Santhana School of Computer Studies RVS College of Arts and Science Sulur Coimbatore India Artificial Intelligence and Data Science SNS College of Engineering Coimbatore India Department of Computer technology Hindustan College of Arts and Science Coimbatore India

ISBN: (纸本)9798331532420

Heart disease remains a leading cause of mortality worldwide. Accurate and timely diagnosis is crucial for effective treatment and prevention. This research proposes a novel approach using a cascaded XGBoost model to improve the accuracy and efficiency of heart disease diagnosis. With gradient boosting, the cascaded XGBoost model effectively handles complex interactions within the dataset, leading to superior performance compared to traditional machine learning techniques like logistic regression, support vector machines, and decision trees. The proposed model was evaluated on a standard heart disease dataset, demonstrating significant improvements in terms of accuracy, precision, and recall. Furthermore, the model's ability to identify key factors contributing to heart disease provides valuable insights for clinical decision-making. This research highlights the potential of machine learning techniques, particularly cascaded XGBoost, in advancing the field of heart disease diagnosis and improving patient outcomes. © 2024 IEEE.

关键词： Contrastive Learning

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Estimation of Neurological Diseases and Age Using Brain MRI 4th

Estimation of Neurological Diseases and Age Using Brain MRI

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4th International Conference on Smart Systems: Innovations in Computing, SSIC 2023

作者： Ashokkumar, K. Dharshan, K. Ragupathi, T. Pritam, Anem Sagar, Amujuru Gnana Department of Computer Science Engineering Sathyabama Institute of Science and Technology Chennai India School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Data Science and Business systems School of Computing SRM Institute of Science and Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9789819736898

Leveraging D-NN trained on neuroimaging data, we can effectively estimate the chronological ages of normal persons;this projected brain age has potential as a biomarker for identifying age-related disorders. The suggested technique employs a CNN cascade network from DL and the Support Vector Machine (SVM) algorithm from ML (SVM). These techniques have been employed to train MRI scans of the brain, classifying them into regular (unaffected by illness), Alzheimer’s disorder (AD), and neurocognitive disability. That we also have the ability to determine ages thanks to the age classifications on the photos. The brain MRI collection is trained using CNN and SVM, on which the classifications and age estimate will be carried out. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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Asymmetric Self-Play for Learning Robust Human-Robot Interaction on Crowd Navigation Tasks 3

Asymmetric Self-Play for Learning Robust Human-Robot Interac...

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3rd International Conference on Electronics, Communications and Information Technology, CECIT 2022

作者： Ma, Jun Qiu, Quecheng Chen, Shuyan Yao, Shunyi Chen, Yuan Ji, Jianmin School of Data Science China USTC School of Computer Science and Technology China

ISBN: (纸本)9798350331974

A robot can make a sound to aware nearby pedestrians during its navigation, which often results in a more efficient and safer trajectory in a crowded environment. However, it is challenging to integrate such interaction capability into an existing robot navigation policy. In this paper, we propose a deep reinforcement learning (DRL) approach for integration, which results in effective interactive robot navigation policies in crowded environments. In particular, we specify the interaction capability by a set of high-level actions with flexible control. Then the navigation policy can be considered as a specific implementation of the 'move' action. Based on these high-level actions, we can train a robust human-robot interaction policy via asymmetric self-play, where the robot and some pedestrians, considered as naughty kids, play a game. Then the interaction policy can not only interact with pedestrians who cooperate with the robot but also with pedestrians who try to block the robot. We evaluate our approach in various simulation environments and compare it with multiple approaches. The experimental results show that our approach is robust and performs well in dense environments that are challenging for others. We also deploy the trained policy on a robot and evaluate its performance in multiple real-world crowded environments. A demonstration video is available online at https://***/x32YmivsIh0. © 2022 IEEE.

关键词： Reinforcement learning

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Building User-oriented Personalized Machine Translator based on User-Generated Textual Content

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Proceedings of the ACM on Human-computer Interaction 2022年第CSCW2期6卷 1–26页

作者： Zhang, Peng Guan, Zhengqing Liu, Baoxi Ding, Xianghua Sharon Lu, Tun Gu, Hansu Gu, Ning School of Computer Science Shanghai Key Laboratory of Data Science Fudan University Shanghai China School of Computing Science University of Glasgow Glasgow United Kingdom Seattle United States

Machine Translation (MT) has been a very useful tool to assist multilingual communication and collaboration. In recent years, by taking advantage of the exciting developments of neural networks and deep learning, the accuracy and speed of machine translation have been continuously improved. However, most machine translation methods and systems are data-driven. They tend to select a consensus response represented in training data, while a user's preferred linguistic style, which is important for translation comprehension and user experience, is ignored. For this problem, we aim to build a user-oriented personalized machine translation model in this paper. The model aims to learn each user's linguistic style from the textual content that is generated by her/him (User-Generated Textual Content, UGTC) in social media context and generate personalized translation results utilizing several state-of-The-Art deep learning techniques like Transformer and pre-Training. We also implemented a user-oriented personalized machine translator using Weibo as a case of the source of UGTC to provide a systematical implementation scheme of a user-oriented personalized machine translation system based on our model. The translator was evaluated by automatic evaluation in combination with human evaluation. The results suggest that our model can generate more personalized, natural and lively translation results and enhance the comprehensibility of translation results, which makes its generations more preferred by users versus general translation results. © 2022 ACM.

关键词： Computational linguistics

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Identification of Post Covid Symptoms in Long Haulers 3

Identification of Post Covid Symptoms in Long Haulers

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Jadhav, Aditi Bhamare, Mamta S School of Computer Engineering and Technology Mit Wpu Data Science and Analysis Pune India

ISBN: (纸本)9798350343274

Covid 19 is a disastrous infection that the whole world tackled for 2 years from 2020-to 2021. As this virus was new and doctors had no idea about it, they treated patients to save lives with all their possible experiences. This virus was contagious, and lockdown took place, people faced financial problems due to loss of jobs or businesses getting shut down for months, many people could not get admitted to hospitals and lost their life due to lack of facilities. While the whole world was dealing with these numerous issues, there was one kind of group of people who successfully recovered from Covid19 but, they faced some changes in their health like some inability they didn't have before infecting of Covid19 or worsened health issues that were mild in them pre Covid19 infection. These are called post Covid19 symptoms which are seen in post Covid19 patients, and such patients are termed as 'Long Haulers'. Long Covid19 symptoms are different in different people, some face mild symptoms like headache, fatigue to severe symptoms where we see an effect on vital organs. In some cases, the effect on the vital organ is irreversible. The conception behind this study is to rack up the data of post Covid19 patients and perform analysis on such data and upskill a ML model, this trained model then project what disease the post Covid19 patient is prone to by taking all his/her details. This idea will help users to get an idea beforehand and take prophylactic measures by conferring with a respective specialist. © 2023 IEEE.

关键词： COVID-19

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Image and Signal processing to detect violent content in social media videos 23

Image and Signal processing to detect violent content in soc...

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15th International Conference on Machine Learning and Computing, ICMLC 2023

作者： Mejhed Mkhinini, Meriem Sidibe, Aboubacar Sidiki Benali, Khaoula Bentaarit, Nouha Khelifi, Aymen Kaisens Data Paris France ENSI National School of Computer Science Tunis Tunisia

ISBN: (纸本)9781450398411

The amount of violent content shared on social networks makes it a very unhealthy space. Hence the birth of a growing research domain that involves filtering social media content using Artificial Intelligence-powered violence detection systems. In this paper, we propose a new approach based on deep learning to address this issue. We use a two layered model: First, a deep representation-based model that uses transfer learning concept to recognize violent content in a video. Second a text classifier to detect verbal violence using the audio cue. The result reports show that our approach is outperforming state-of-the art accuracies by learning most discriminating features, achieving 90% accuracy on the test set for physical violence detection and 89% for verbal violence detection. © 2023 ACM.

关键词： computer vision

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Automatic extraction of lightweight and efficient neural network architecture of heavy convolutional architectures to predict microsatellite instability from hematoxylin and eosin histology in gastric cancer

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Neural Computing and Applications 2024年第25期36卷 15295-15321页

作者： Rostami, Habib Ashkpour, Maryam Behzadi-Khormouji, Hamed Mokhtari, Maral Khayati, Armin Keshavarz, Ahmad Talatian Azad, Saeed Tabesh, Yahya Computer Engineering Department Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr75168 Iran Artificial Intelligence and Intelligent Healthcare Lab Artificial Intelligence and Data Mining Research Group Faculty of Intelligent Systems Engineering and Data Science ICT Research Institute Persian Gulf University Bushehr75168 Iran Faculty of New Science and Technologies University of Tehran Tehran Iran Department of Computer Science Imec-IDLab University of Antwerp Sint-Pietersvliet 7 Antwerp2000 Belgium Shahid Faghihi Hospital Shiraz University of Medical Sciences Shiraz Iran Department of Computer Science Engineering and IT School of Electrical and Computer Engineering Shiraz University Shiraz Iran Electrical Engineering Department Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr75168 Iran IoT and Signal Processing Research Group Faculty of Intelligent Systems Engineering and Data Science ICT Research Institute Persian Gulf University Bushehr75168 Iran Department of Mathematical Sciences Sharif University of Technology Tehran Iran

Cancers have emerged as a significant concern due to their impact on public health and society. The examination and interpretation of tissue sections stained with Hematoxylin and Eosin (H&E) play a crucial role in disease assessment, particularly in cases like gastric cancer. Microsatellite instability (MSI) is suggested to contribute to the carcinogenesis of specific gastrointestinal tumors. However, due to the nonspecific morphology observed in H&E-stained tissue sections, MSI determination often requires costly evaluations through various molecular studies and immunohistochemistry methods in specialized molecular pathology laboratories. Despite the high cost, international guidelines recommend MSI testing for gastrointestinal cancers. Thus, there is a pressing need for a new diagnostic modality with lower costs and widespread applicability for MSI detection. This study aims to detect MSI directly from H&E histology slides in gastric cancer, providing a cost-effective alternative. The performance of well-known deep convolutional neural networks (DCNNs) and a proposed architecture are compared. Medical image datasets are typically smaller than benchmark datasets like ImageNet, necessitating the use of off-the-shelf DCNN architectures developed for large datasets through techniques such as transfer learning. Designing an architecture proportional to a custom dataset can be tedious and may not yield desirable results. In this work, we propose an automatic method to extract a lightweight and efficient architecture from a given heavy architecture (e.g., well-known off-the-shelf DCNNs) proportional to a specific dataset. To predict MSI instability, we extracted the MicroNet architecture from the Xception network using the proposed method and compared its performance with other well-known architectures. The models were trained using tiles extracted from whole-slide images, and two evaluation strategies, tile-based and whole-slide image (WSI)-based, were employed and comp

关键词： Network architecture

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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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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

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

ISBN: (纸本)9798350375442

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. © 2024 IEEE.

关键词： computerized tomography

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