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9th International Conference on computer science and engineering, UBMK 2024

作者： Vedanta, S.P. Rao, Madhav IIIT-Kottayam Dept. of Computer Science and Engineering Kottayam India IIIT-Bangalore Department of Electronics & Communication Engineering Bangalore India

ISBN: (纸本)9798350365887

The number of Mental-health help seekers are on rise over recent years, but the medical practitioners are limited due to which the healthcare system is heavily loaded and thereby the outcome is not promising. Advanced AI Technology is expected to ease this problem, however completely relying on the tools is reported to not work with the help seekers. Hence technology to assist help seekers and allow the healthcare system to screen the needy ones from the large pool of distress candidates should make the system efficient a nd effective.H owever for establishing the AI supportive system, two challenges exists: one is the large pool of dataset with diversity, and other is the AI model to respond to a context-aware situations. Expecting large dataset for training model from medical community needs further infrastructure support to make it digitally available with embedded annotations. Relying patiently for the dataset will miss the opportunity to serve the distress patients. Hence synthetic dataset generation and its validation from medical experts is an alternative for training robust and reliable model. Besides, context-aware curriculum inspired AI based summarizer model is found appropriate to adopt for this use-case where relevant features meant for diagnosing the problem is extracted from the improvised input text. The proposed curriculum trained AI model helps in transforming the improvised text inputs fed from the distress individuals to a summarized version representing domain expert form, embedded with symptoms related features for further classification. The synthetic data-set g eneration through OpenAI's GPT-40 models and Nemotron models are further evaluated with BERT based classifier m odels a nd curriculum based AI model. The training of the classifier m odels are also evaluated for synthetic and real-world dataset, which was scrapped from Reddit forum. Around 800 stream of real-world posts were evaluated from the medical experts and their findings related

关键词： Diagnosis

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Generative Artificial Intelligence: Principles, Potentials and Challenges 2

Generative Artificial Intelligence: Principles, Potentials a...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Routray, Sudhir K. Jha, Mahesh K. Sharmila, K.P. Javali, Abhishek Pappa, M. Singh, Monika Cmr University Department of Computer Science and Engineering Bangalore India Cmr Institute of Technology Department of Electronics and Communication Engineering Bangalore India

ISBN: (纸本)9798350368413

In recent years, we have witnessed the raise of several generative artificial intelligence (AI) products and services which have strong disruptive capabilities. Majority of these AI-based products and services are also very popular since their public availability. These generative AI products and services have a lot of potentials to do the common office works, writing codes, doing accounting tasks, predicting common patterns, generating creative contents, marketing products and services, increasing efficiencies in the systems, translating to different languages, designing products, and many such things. These effects of generative AI are phenomenal and it is going to start a large scale automation in the product design and service sectors. In addition to the benefits, several challenges are associated with generative AI. Starting from the bias to security risks to fake content creation, there are many challenges with these products and services. In this paper, we go through the basic principles of generative AI and then study its present and future prospects. We analyze its perilous effects and the common challenges with a few examples. Overall, generative AI can be beneficial tool if it is used positively under appropriate regulatory systems. © 2024 IEEE.

关键词： Generative adversarial networks

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The Firstever LTE Network on the Moon: Challenges and Prospects 5

The Firstever LTE Network on the Moon: Challenges and Prospe...

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5th IEEE International Conference on Recent Trends in computer science and Technology, ICRTCST 2024

作者： Routray, Sudhir K. Pappa, M. Singh, Monika Javali, Abhishek Sharmila, K.P. Tengshe, Richa CMR University Department of Computer Science and Engineering Bangalore India CMR Institute of Technology Department of Electronics and Communication Engineering Bangalore India

ISBN: (纸本)9798350351378

Mobile cellular networks are very popular across the globe, and they are found at all places where human settlements are present. Long term evolution (LTE) is a popular standard for mobile wireless communications. It has been deployed in almost all the countries. These networks are deployed to support various services, such as voice, image, video, text, and data transmission. In 2024, NASA and Nokia together get ready to deploy an LTE network on the moon. This project was planned in 2020, and NASA selected Nokia as the vendor for this lunar LTE network deployment mission. The LTE network on the moon would help in the enhancement of lunar exploration. The common services of LTE are planned to be deployed on the lunar surface. In addition to that, some moon specific valueadded services will also be deployed to support the exploration tasks. The deployed network would send the pictures, videos, and other sensor measured data from the lunar surface back to earth. This lunar network will be supported by the orbiting satellites and other peripheral networks. In this paper, the basic features and characteristics of the first lunar LTE network and its utilities have been studied. © 2024 IEEE.

关键词： NASA

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A Deep Knowledge Distillation HeartCare Framework for Detection of Multi-label Myocardial Infarction from Multi-lead ECG Signals

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SN computer science 2025年第1期6卷 1-13页

作者： Borah, Bidyut Bikash Das, Khushboo Hazarika, Uddipan Roy, Soumik Department of Electronics and Communication Engineering Tezpur University Naapam Assam Tezpur 784028 India Department of Computer Science and Engineering Tezpur University Naapam Assam Tezpur 784028 India

Myocardial Infarction (MI) is the most frequent form of Coronary Heart Disease (CHD), which is the world’s leading cause of illness and mortality. More than 15% of deaths per year are caused by MI. An extensively utilized diagnostic technique for cardiovascular problems is the 12-lead Electrocardiogram (ECG). Deep learning algorithms have outperformed traditional feature engineering-based techniques in terms of performance. By analyzing ECG data, deep learning models can generate clinical assessments that are comparable to those of cardiologists. The article proposes a framework for detecting and localizing MI using a convolutional neural network. The method employs a knowledge distillation approach and accepts a 12-lead ECG as input. The framework has been developed and validated using the PTB-XL dataset. The framework achieved the ability to identify and locate five super-diagnostics classes included in the PTB-XL dataset. The framework has been validated successfully, with an average accuracy of 94.25% and a micro-averaged receiver operating characteristic (ROC) of 0.95. In addition, the model is validated using a combined dataset, prepared from the Shaoxing People’s Hospital (SPH) dataset, the China Physiological Signal Challenge (CPSC) 2018 dataset, and a private dataset containing ECG data from Indian patients. This allows the scientific community to assess the applicability of the proposed method to various patient groups and evaluate its performance in detecting MI. During the cross-validation process, the proposed structure achieves a mean accuracy of 95.80% and a sensitivity of 84.98%. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cardiovascular disease Convolutional neural network Electrocardiogram Knowledge distillation Myocardial infarction

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Human Stress Detection in and through Sleep using Machine Learning

Human Stress Detection in and through Sleep using Machine Le...

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2024 International Conference on Recent Advances in Electrical, electronics, Ubiquitous communication, and Computational Intelligence, RAEEUCCI 2024

作者： Harshini, V. Kuresan, Harisudha Kancharlawar, Omkar Madhan, V.M. Balamithra, S. Department of Computer Science Engineering Department of Electronics and Communication Engineering Tamilnadu Kattankulathur India SRM Institute of Science and Technology Department of Electronics and Communication Engineering Tamilnadu Kattankulathur India Sathyabama Dental College and Hospital Bachelor of Dental Surgery Chennai India

ISBN: (纸本)9798350354539

The effects of stress on the body and mind may be devastating, and it is a prevalent health problem. The quality of one's sleep is an important health indicator, and stress is a known disruptor of that quality. As a result, gauging a person's stress levels and health risks based on their sleep patterns may be quite informative. One's capacity for making decisions, paying attention, learning, and solving problems are all significantly impacted by stress. Both the disciplines of psychology and computer science have recently dedicated significant resources to the study of stress detection and modelling. Affective states, or the actual experience of having that emotion, are the measuring stick that psychologists use to assess the magnitude of stress. The majority of the progress in stress classification has been made using user-dependent models that cannot generalize to a new user. This results in substantial time investment from new users in training the algorithm to forecast their emotional states. Common mental health issues in children need to be addressed immediately since they may escalate into more serious issues if left untreated. The analysis and diagnosis of medical data are areas where machine learning techniques now excel. Different machine learning algorithms have had their accuracy across the chosen attribute set compared. Data on sleep-related factors, such as heart rate variability and sleep architecture, will be collected by wearable devices and analyzed using machine learning methods such support vector machine (SVM), decision tree (DT), MLP, and Ada Boost classifier. By analyzing a person's sleep patterns with the help of a machine learning model, researchers have shown they can effectively identify signs of stress in people. © 2024 IEEE.

关键词： Support vector machines

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Enhancing Sugarcane Disease Diagnosis: UNET Architecture for Red Rot Identification 2

Enhancing Sugarcane Disease Diagnosis: UNET Architecture for...

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2nd International Conference on Computational Intelligence, communication Technology and Networking, CICTN 2025

作者： Kumar, Arvind Singh, Jitendra Sharma, Rohit Department of Computer Science and Engineering Srm Institute of Science and Technology Delhi Ncr Campus Up Ghaziabad India Department of Electronics and Communication Engineering Abes Engineering College Up Ghaziabad India

ISBN: (纸本)9798331530389

Image segmentation is an important part of many computer vision problems and existing approaches such as pixel by pixel classification and manual annotation can be either in efficient or inaccurate. This research explored the way UNET architecture can circumvent these constraints to yield accurate segmentation. A diverse image dataset was applied to the UNET, and then its performance compared to conventional segmentation methods. Its capability to capture both the local and global characteristics contributes to the model's improved performance, especially in complex settings. To detect red rot disease in sugarcane, these characteristics were used with an accuracy of 97%. UNET can be further improved by integrating it with other deep-learning techniques and its future research should focus on optimizing UNET for special use cases. This technology can also be used by farmers and agricultural experts to better pinpoint and tackle crop diseases, which could lead to better harvest yields and less financial baggage. © 2025 IEEE.

关键词： Image segmentation

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Comparative Study of Enactment Scrutiny on Sheltered DSSS Multiuser Recognition Under Near- Far Atmosphere 3

Comparative Study of Enactment Scrutiny on Sheltered DSSS Mu...

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3rd IEEE International Conference of Electron Devices Society Kolkata Chapter, EDKCON 2024

作者： Manna, Tanajit Mohankumar, N. Banerjee, Dhrubojyoti Mukherjee, Shibabrata Bhattacharjee, Bidrohi Maity, Heranmoy Pailan College of Management & Technology Electronics & Communication Engineering Kolkata India Computer Science & Engineering Puna India Pailan College of Management & Technology Electrical & Electronics Engineering Kolkata India

ISBN: (纸本)9798350374643

The study offers a workable, low-cost solution to the near-far delinquent in CDMA systems by enhancing communication in Direct Sequence Spread Spectrum (DSSS) multiuser systems as opposed to single users. The existing telecommunications system's hear ability problem, which requires improvement, has several security applications. This study uses the Multi Operator Finding Decorelator (MUD-Decorrelator) & MUD-Decision Feedback approach to offer a comparison analysis of Bit Error Rate (BER) vs. Signal to Noise Ratio in near-far situations. A MUD-Decision Feedback system is used to alter the modulated signal's BER rather than relying on a single user and a MUD-Decorelator. The communicated and established Power Spectral Densities (PSD) of mutually systems show no change in the signal characteristics. © 2024 IEEE.

关键词： Bit error rate

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Advancing Medical Imaging: A Focus on Efficient Net for Brain Tumor Classification 2

Advancing Medical Imaging: A Focus on Efficient Net for Brai...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Shashidhar, R. Manasa, R. Megha, K.M. Priyanga, P. Manjunath, A.S. Roopa, M. Jss Science and Technology University Department of Electronics and Communication Engineering Mysuru India Dayananda Sagar College of Engineering Department of Electronics and Communication Engineering Bengaluru India Bengaluru India Jss Science and Technology University Department of Computer Applications Mysuru India

ISBN: (纸本)9798350372892

In the field of medical diagnosis, the increasing occurrence of brain tumors calls for creative approaches to detect and intervene at early stages. This proposed work focus on the Efficient Net (EffNet) architecture to address the challenges associated with brain tumour recognition and sorting from Magnetic Resonance Imaging (MRI) scans. The main objectives are outlined to develop a robust model capable of discriminating between various tumour types and normal brain tissues. Four distinct classes - glioma tumour, pituitary tumour, meningioma tumour, and no tumour compose the dataset, providing diversity for training and evaluation. Using projected method got the accuracy of 98.08% accuracy. Our proposed method detects the all the four classes effectively. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Hybrid Deep Learning for International Trading: Integrating LSTM and Transformer Models 4

Hybrid Deep Learning for International Trading: Integrating ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Akhila, T.S. Relin Francis Raj, J. Kani Jesintha, D. Santhana Krishnan, R. Vinoth Kumar, V. Sundaravadivel, P. Dhanalakshmi College of Engineering Department of Electronics and Communication Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Scad College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331523923

This research proposes a hybrid deep learning model combining Long Short-Term Memory (LSTM) networks and Transformers to enhance decision-making in international trading. The model leverages advanced techniques such as RoBERTa for text tokenization, FinBERT for sentiment analysis, and XLM-Roberta for multilingual language processing. LSTM is employed to analyze sequential numerical data like historical prices and trading volumes, providing accurate price and volatility forecasts. The Transformer component processes textual data to extract sentiment classifications and event-driven market impacts, facilitating a comprehensive understanding of market conditions. A fully connected fusion layer integrates insights from both components, enabling the model to classify market conditions holistically. The optimization process utilizes the Adam optimizer with learning rate scheduling to ensure efficient and effective training. Loss functions like Mean Squared Error (MSE) and Cross-Entropy Loss are employed to optimize numerical predictions and categorical outcomes, respectively. By combining temporal analysis with sentiment-driven insights, this hybrid model offers improved price forecasting, advanced multilingual sentiment analysis, and event-driven market condition predictions. The proposed approach aims to provide traders with actionable insights, enabling more informed decision-making in the highly dynamic and multilingual environment of international trading. © 2025 IEEE.

关键词： Costs

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Data-Driven Cloud Based Soil Nutrient Prediction System for Precision Agriculture

Data-Driven Cloud Based Soil Nutrient Prediction System for ...

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2024 IEEE International Conference on Modeling, Simulation and Intelligent Computing, MoSICom 2024

作者： Mishra, Amit Dass, Jeevesh Saxena, Siddhant Prakash, Ishan Satya Sargun Garg, Arshia Verma, Karun Bhattacharya, Jhilik Thapar Institute of Engineering & Technology Department of Electronics and Communication Engineering Punjab Patiala India Thapar Institute of Engineering & Technology Department of Computer Science Engineering Punjab Patiala India Thapar Institute of Engineering & Technology Department of Electronics and Computer Engineering Punjab Patiala India

ISBN: (纸本)9798331533311

Decreasing arable acreage and a growing world population are pushing for new farming systems. Achieving high crop production requires maintaining a balanced nutrient level in the soil. This study offers a machine learning-based model that will predict the NPK fertilizer to achieve the higher crop yields. Field sensor data along with meteorological data and remote sensing data from various test fields are integrated. A random forest and decision tree machine learning model are applied to the integrated data to develop a software tool for precision agriculture, enabling accurate estimation of nutrient requirements. A mobile application is used to share the nutrient prediction with farmers, enabling them to make well-informed choices regarding resource utilization efficiency and environmental friendliness. This research can transform agricultural practices globally and contribute towards food security on earth. © 2024 IEEE.

关键词： Smart agriculture

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