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6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Sharma, Priyanka Kumar, Tapas Tyagi, S.S. Department of Computer Science Engineering Faculty of Engineering MRIIRS Faridabad India Department of Computer Science Engineering Faculty of Engineering IIMT Greater Noida India

ISBN: (纸本)9789819933143

EHR was a statistical database of electronic health records for individuals or demographics that can be accessed through diverse healthcare environments. Planning for long-term retention and handling of electronic health information is an important factor in the planning process. The majority of commercial EHR models were designed to integrate data from vast ancillary facilities like radiology, pharmacy, and laboratory with different clinical care components. Electronic health records (EHRs) were digital representations of hospital records that were renovated in real time. An (electronic health record (EHR) was a comprehensive statement on a person's health. EHRs relief in the provision of evidence-based care, the tracing of a patient's treatment development, and the enabling of improved healthcare judgments. This paper aims to review the comparison of different EHR models that are being used throughout the world. The usage of various machine learning approaches in existing EHR has also been explored in this study. A thorough introduction of EHR is provided initially, followed by a focus on some related work that explains the adoption principles employed by various authors in EHR. In addition, discuss the different EHR models and the methodologies they have used and challenges in existing EHR models, their advantages, need, and then finally the challenges of EHR. This paper can be concluded in a way that to create an effective framework of potential data exchange agreements between health services and patients more public dialogue around electronic health records is needed. Quantitative research, such as polls of a wide variety of customers and doctors, may also be performed in order to obtain better outcomes. In next paper, we intend to propose framework of such EHR model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Patient treatment

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Covid-19 Classification using Fine-tuned EfficientNet Architecture 9

Covid-19 Classification using Fine-tuned EfficientNet Archit...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Sruthi, Srigiri Emadaboina, Siddharth MacHavarapu, Pradyumna Singh, Rimjhim Padam Kanchan, Sneha Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Bengaluru India Universiti Tunku Abdul Rahman Department of Internet Engineering And Computer Science Malaysia

ISBN: (纸本)9798350394474

This work utilizes an Efficient Net-based deep learning architecture, leveraging its efficiency and scalability for image classification tasks. We employ a Dataset of chest X-ray images, including COVID-19 cases as well as other relevant Pathology, to train and evaluate the model. The training process involves data augmentation, model optimization, and early stopping to ensure robust and generalizable performance. It specifies the image size, channels, and class count, and then proceeds to create a pre-trained model using the EfficientNetB0 architecture. The model is built upon the pre-trained EfficientNetB0 model, with additional layers including Layer Normalization, Dense, and Dropout layers for fine-tuning on the Covid-19 data. Subsequently, the model is rigorously evaluated using standard performance metrics, including precision, recall, F1-score, and accuracy, and has proven to outperform other state-of-art models by a 3% increase in F1-score value. © 2024 IEEE.

关键词： Deep learning

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Low Cost Active Learning Framework for Short Answer Scoring 20th

Low Cost Active Learning Framework for Short Answer Scoring

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20th Principle and Practice of Data and Knowledge Acquisition Workshop, PKAW 2024, held in conjunction with the 21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Hori, Tatsuya Yamauchi, Koichiro Graduate school of Engineering Department of Computer Science Chubu University Kasugai Japan

ISBN: (纸本)9789819600250

Short answer scoring (SAS) that automatically scores learner’ answers based on given rubric. Because SAS uses different rubrics for different prompts, it is necessary to generate training data for each prompt, which is time-consuming and labor-intensive. We propose a human-in-the-loop framework that achieves SAS with a minimal amount of training data through active data creation and efficient training. We fine-tuned the Sentence BERT model with a small dataset by using SetFit (Lewis Tunstall, et al. (2022).) and then apply active learning (AL) to an unlabeled data pool. The grader scores the data selected by AL, creating new training data that are then used to further fine-tune the models. For the experiments, we used six prompts from a Japanese SAS dataset. Our experimental results demonstrate that the proposed method outperforms previous studies in most cases. We also examined the potential challenges when applying this framework in practical scenarios. This framework significantly reduces the graders’ time and effort by enabling automatic scoring with minimal training data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Active learning

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OntoMG: a unique and ontological-based intelligent framework for early identification of myasthenia gravis (MG)

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International Journal of Information Technology (Singapore) 2024年第6期16卷 3847-3853页

作者： Mahajan, Prerna Agarwal, Trapty Vekariya, Daxa Gupta, Rajesh Malviya, Ashwini Anandaraj, S.P. Jain, Garima Anand, D. Department of Computer Science and Information Technology Jain (Deemed to Be University) Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Lucknow India Department of Computer Science and Engineering Faculty of Engineering and Technology Parul University Gujarat Vadodara India Department of Management Sanskriti University UP Mathura India Department of UGDX ATLAS SkillTech University Mumbai India Department of Computer Science Engineering Presidency University Bengaluru India Department of Computer Science and Business Systems (CBS) Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

Myasthenia Gravis (MG) is a neuromuscular disease causing extreme muscular fatigue, triggering problems with vision, swallowing, speech, mobility, dexterity, and breathing. However, early detection and prediction of MG is a crucial and quite challenging task for medical practitioners. So, in our proposed work, we are focused on introducing an ontology-based intelligent system for the prognosis of patients affected by this neuromuscular disease. The given paper presents a novel framework that employs the concepts of ontology, semantic web rules, and reasoner for deriving whether the patient is tested positive or negative. The suggested framework's effectiveness is assessed by comparing it to machine learning classifiers using parameters from the NIH repository for Myasthenia Gravis. The experimental results show that the ontological-based framework is able to achieve higher accuracy (80.85%), higher precision (78.37%), and higher recall (76.55%) than the existing classifiers used in the recent studies. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Machine learning (ML) Myasthenia gravis disease Ontology Reasoners Semantic web rules language (SWRL)

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Prediction of Cardiac Arrest using Generalized Soft Computing Techniques 18

Prediction of Cardiac Arrest using Generalized Soft Computin...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Hota, Radhanath Dash, Sachikant Mishra, Sujogya Das, Subhalaxmi Lenka, S. Pradhan, G. Giet University Department of Computer Science Engineering Odisha Gunupur India Gandhi Institute for Education and Technology Department of Computer Science Engineering Odisha Gunupur India Odisha University of Technology and Research Department of Mathematics Bhubaneswar India Odisha University of Technology and Research Dept. of Computer Science and Engineering Bhubaneswar India Utkal University Department of Computer Science and Application Bhubaneswar India

ISBN: (纸本)9789380544519

Cardiac-related diseases always lead to fatal consequences around the globe. This research includes vague data analysis of the data related to cardiac anomalies. This study proposes a method to develop a dimension reduction model using vague data related to cardiac diseases using rough set theory (RST). The overall research is divided into two different sections. The first section of this study proposes analyses of the raw cardiac-related data using RST, and the second section calculates the performance accuracy using Principal Component Analysis (PCA) applied to the universal data set;the performance measures also include a combination of two Universal data sets. The above claims are verified using statistical tests © 2024 Bharati Vidyapeeth, New Delhi.

关键词： PCA Performance and accuracy Rough Set Theory Statistical method Vague data

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Hyperparameter optimization: Classics, acceleration, online, multi-objective, and tools

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Mathematical Biosciences and engineering 2024年第6期21卷 6289-6335页

作者： Tan, Jia Mian Liao, Haoran Liu, Wei Fan, Changjun Huang, Jincai Liu, Zhong Yan, Junchi Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China College of Systems Engineering National University of Defense Technology Changsha China

Hyperparameter optimization (HPO) has been well-developed and evolved into a well-established research topic over the decades. With the success and wide application of deep learning, HPO has garnered increased attention, particularly within the realm of machine learning model training and inference. The primary objective is to mitigate the challenges associated with manual hyperparameter tuning, which can be ad-hoc, reliant on human expertise, and consequently hinders reproducibility while inflating deployment costs. Recognizing the growing significance of HPO, this paper surveyed classical HPO methods, approaches for accelerating the optimization process, HPO in an online setting (dynamic algorithm configuration, DAC), and when there is more than one objective to optimize (multi-objective HPO). Acceleration strategies were categorized into multi-fidelity, bandit-based, and early stopping;DAC algorithms encompassed gradient-based, population-based, and reinforcement learning-based methods;multi-objective HPO can be approached via scalarization, metaheuristics, and model-based algorithms tailored for multi-objective situation. A tabulated overview of popular frameworks and tools for HPO was provided, catering to the interests of practitioners. © 2024 the Author(s), licensee AIMS Press.

关键词： Deep neural networks

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computer Vision for Agricultural Automation - Algorithmic Solutions for Crop Yield Predictions

Computer Vision for Agricultural Automation - Algorithmic So...

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2024 IEEE International Conference on Recent Advances in science and engineering Technology, ICRASET 2024

作者： Ramachandran, A. Ganesh Saravanan, S.K. Bhanumathi, M. Sangeetha, M. Fernandez, F. Mary Harin Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science Chennai India Saveetha School of Engineering Simats Department of Computers Science and Engineering Chennai India Department of Computer Science and Engineering Chennai India University of Technology and Applied Sciences Computer Engineering Department Nizwa Oman Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350388602

computer vision is defined in this study and one of the applications demonstrated is crop yield prediction in agricultural automation. Here we have emphasized on its use for computer vision for the agricultural domain. Using multispectral images that can be obtained from drones satellites and pairs with ground truths of average crop yield, weather conditions and parts of the soil treated with practices performed by an agronomist the proffered methodology would take advantage of the uniqueness of the concept to predict crop production. Further, elaborate machine learning methods such as line regression, decision trees, Random Forest, Support Vector Machine and CNN models are applied in order that the ultimate selection of the model would be contingent on the accuracy in relation to a crop yield. The DL based approaches as seen majority of this work and particularly the CNN exhibited improved results over regression models since there are more of classification than regression;In addition DL models are more appropriate for spatial relations and variations characteristic of crop data. Besides that, data fusion methods which map the accuracy of the images, weather and fields data develops a more accurate forecast as proven by the above crop modeling that involves multidimensional data. While transferring on fresh test data, it also demonstrates that the trained models have the capability to use the collected data in order to not only provide intelligent advices to the farmers on resource goodwill and crop productivity that will pave way for better results. Most importantly therefore for the hear of it the research show that with computer visions and machine learning the agricultural process can be reinvented this is how food security can be improved as the environment evolves. © 2024 IEEE.

关键词： Agricultural automation computer vision Crop yield prediction Deep learning Machine learning Multispectral imagery

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Enhancing Arabic Text Classification with a Hybrid Word Embedding Method 16

Enhancing Arabic Text Classification with a Hybrid Word Embe...

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16th International Conference on Developments in eSystems engineering, DeSE 2023

作者： Aljohani, Eman Taibah University College of Computer Science and Engineering Department of Computer Science Madinah Saudi Arabia

ISBN: (纸本)9798350381344

In this work, we propose a novel hybrid neural network architecture that combines the contextual sensitivity of Bidirectional Gated Recurrent Units (BiGRUs) with the semantic power of pre-trained FastText word embeddings. By incorporating Term Frequency-Inverse Document Frequency (TF-IDF) weighted features, our method improves feature representation by emphasizing both term significance and document-specific word usage. This combination makes it possible to represent text data in a more nuanced way, particularly for morphologically rich languages. Prior to classification via a dense layer, our model captures both the distinct contextual importance of terms and the deep semantic relationships encoded in the embeddings by concatenating the output of the BiGRU layers with TF-IDF features. The effectiveness of combining different machine learning techniques is highlighted by our results, which show a significant improvement in classification performance over traditional models. The results show that our hybrid approach, which achieved an impressive accuracy result of 0.97, can be a useful tool for difficult language tasks. It provides excellent accuracy as well as insightful information about how algorithmic text analysis is developing. © 2023 IEEE.

关键词： Embeddings

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A Study on E-Learning and Recommendation System

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Recent Advances in computer science and Communications 2022年第5期15卷 748-764页

作者： Madhavi, A. Nagesh, A. Govardhan, A. Department of Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad India Department of Computer Science and Engineering Mahatma Gandhi Institute of Technology Hyderabad India Department of Computer Science and Engineering Jawaharlal Nehru Technological University Hyderabad India

Introduction: Today, technology and internet both are proliferating due to which information access is becoming easier, and is creating new challenges and opportunities in all fields, especially when working in the field of education. For example, the e-learning education system can be personalized in order to acquire knowledge level and learner’s requirements in a learning process. The learning experience, as per the individual learner’s goals, should be adopted. Background: In the current educational environment, e-learning plays a significant role. For many researchers, it has become one of the most important subjects, as through the use of e-learning, the whole education system would revolutionize. There are many areas of e-learning in which research work is being carried out, such as Mass Communication, Information and Technology (IT), Education and Distance Education. Objectives: To meet the various needs of the learners such as talents, interests and goals an e-learning system needs to be designed as a personalized learning system by considering various educational experiences. Many methods such as ontologies, clustering, classification and association rules have been used along with filtering techniques to enhance the personalization and performance of the learner. Methods: This paper presents a detailed review of the literature of previous work that has been conducted in e-learning area, especially in the recommendation system. Current research works on e-learning has been discussed in this work in order to discover the research developments in this discipline. Conclusions: One of the vital functions of the current e-learning system is creating a personalized resource recommendation system. In this paper, we reviewed some crucial papers on both e-learning and recommendation systems. Future research work of this paper would be designing efficient and precise e-learning and recommendation system to deal with the problem of substantial personalized information

关键词： Recommender systems

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Identification of Credit Card Frauds Using Machine Learning Techniques 9

Identification of Credit Card Frauds Using Machine Learning ...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Jain, Shaily Rani, Neetu Srivastava, Prakash Prabha, Chander Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Chandigarh University Department of Computer Science and Engineering Punjab Ghruan India Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350377972

Nowadays, most of the transactions are online due to the high involvement of online sales and purchase purposes. Although these transactions provide convenience to their end user, meanwhile these are also the main cause of fraud in online transactions of credit cards. Nowadays, online fraud transactions hiked in some cities generating interest in this topic of utmost security concern and needs to be addressed. Recent research studies proposed many machine learning techniques to resolve such kinds of issues but still their accuracy needs to be improved. In this research, the Kaggle dataset was used to solve the textual data problem. On the dataset downloaded from Kaggle, few machine learning algorithms have been implemented for research validation. An accuracy of 99.87% was achieved by the proposed research work. The proposed model detects the fraud transaction in earlier stages and will be more beneficial for business firms. © 2024 IEEE.

关键词： Adversarial machine learning

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