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检索条件"机构=Dept. Computer Science and Engineering IIIT"

共 34612 条记录，以下是1-10 订阅

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Leveraging Name Entity Recognition and Text Classification for Talent Mapping 2

Leveraging Name Entity Recognition and Text Classification f...

引用

2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Charan, Annepu Sai Moirangthem, Dennis Singh Iiit Manipur Dept. of Computer Science and Engineering Imphal India

ISBN: (纸本)9798350360523

The paper presents a dual-model approach that can be used for categorizing the work experience data from resumes or other textual copra. The application tracking system (ATS), which is the main tool used to screen resumes, presents operational issues in the quickly changing software sector. Although this technique matches resumes with job descriptions effectively, it requires separate scans for every job posting, which could result in errors and inefficiencies. This initiative is motivated by the need to expedite the hiring process for tech companies and potential employees. The suggested approach presents a cutting-edge tool to improve and expedite job matching and resume analysis. The work attempts to improve the software industry's hiring process efficiency by overcoming present system restrictions. This study presents a novel solution - a text classification model intended for resume analysis and shortlisting to address the operational difficulties with the modern resume screening process. Even though the Application Tracking System (ATS) is currently used, it requires separate scans for every job posting, which could lead to inefficiencies. We propose dual-model architecture which includes our custom Name Entity Recognition (NER) Model which is trained on annotated work experience data and a Multi-Layer Perceptron (MLP) model for multi label classification, aiming to streamline the resume screening process. This dual-model approach enhances accuracy and efficiency in resume classification, benefiting both aspirants and tech companies. This research is a pivotal step in optimizing recruitment in the dynamic software industry. The complete work can be found on GitHub1 © 2024 IEEE.

关键词： Mapping

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Unravelling the Enigma of Polycystic Ovary Syndrome (PCOS): Using MLAlgorithms 2

Unravelling the Enigma of Polycystic Ovary Syndrome (PCOS): ...

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Juneja, Sonam Bhati, Bhoopesh Singh Atwal, Shikha Anand, Monika Maiti, Souvik Chandigarh University Dept. of Computer Science and Engineering Gharuan India Iiit Dept. of Computer Science and Engineering Sonepat India

ISBN: (纸本)9798350371055

The difficulties and ramifications of PCOS, which affects a sizable portion of women who are of reproductive age, are discussed in this review. The article covers the various clinical manifestations of PCOS, how it affects both reproductive and non-reproductive health, and how it's linked to psychological distress and metabolic disorders. It highlights how critical lifestyle modifications, early detection, and accurate diagnosis are. Additionally, the study presents machine learning techniques for PCOS detection, demonstrating the effectiveness of techniques like Random Forests, CNN, and SVM. An innovative CDSS that uses the Red Deer Algorithm shows encouraging accuracy. The necessity of continued research, diversified datasets, and cooperative efforts to enhance PCOS detection at the nexus of technology and healthcare is highlighted in the abstract's conclusion. © 2024 IEEE.

关键词： Deep Learning KNN Machine Learning PCOS Random Forest SVM

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BeTS: Abstractive Text Summarization with Transfer Learning 2

BeTS: Abstractive Text Summarization with Transfer Learning

引用

2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Bhardwaj, Meher Ethari, Hrishikesh Charan, Annepu Sai Singh Moirangthem, Dennis Iiit Manipur Dept. of Computer Science and Engineering Imphal India

ISBN: (纸本)9798350360523

We introduce BeTS, which stands for Bert Transformer Summarizer, an abstractive summarization model that is built using transfer learning. Our model consists of a pre-trained BERT encoder and a generative transformer decoder to produce abstract summaries. The model is trained by a simple two step process, where the decoder is first trained and then the entire model is fine-tuned end-to-end. We train the model with CNN/Daily Mail summarization dataset and use the trained weights for transfer learning on the AMI dialog summarization dataset. We successfully demonstrate that our model outperforms the baseline models by at least 7.65 ROUGE points on the AMI data. We also demonstrate that our model shows good generalization performance by testing on different unseen input data and compare the generated results. We are able to generate significantly better summaries on inputs such as dialog compared to the current state-of-the-art model. © 2024 IEEE.

关键词： Decoding

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Maskify: Precision Detection and Fit Assessment for Mask Compliance 6

Maskify: Precision Detection and Fit Assessment for Mask Com...

引用

6th IEEE International Conference on Image Processing, Applications and Systems, IPAS 2025

作者： Pathak, Prakrit Garg, Ayush Kumar Chaurasia, Priyesh IIIT-Delhi Dept. of Computer Science India

ISBN: (纸本)9798331506520

We introduce Maskify, an innovative tool designed to automatically detect and evaluate the use of face masks. Amid the challenges of the COVID-19 pandemic, wearing masks has emerged as a crucial strategy to limit the virus's spread, especially in environments where mask mandates are enforced. Reliable systems for monitoring mask usage are therefore vital for public *** solution leverages deep learning to not only determine if a person is wearing a mask but also to assess how well the mask fits using a specialized metric called the "wearable score."This score quantifies the extent to which the mask properly covers essential areas of the face. Developing Maskify involved creating a diverse dataset that includes images of both masked and unmasked *** process begins with advanced pre-trained models that accurately identify faces in images. Once a face is detected, key landmarks on the lower face are extracted to assist in classifying the mask. We utilize ResNet-based architectures, known for their robust image classification performance, to enhance the detection accuracy. The wearable score is then computed by segmenting the mask region and calculating the percentage of the face that is effectively *** summary, Maskify offers a robust approach for monitoring mask compliance and ensuring proper fit, thereby supporting ongoing public health efforts to mitigate the spread of COVID-19. © 2025 IEEE.

关键词： COVID-19

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5G User Equipment (UE) Positioning and Localization Estimation using Machine Learning

5G User Equipment (UE) Positioning and Localization Estimati...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Survase, Sanket Diwan, Raman Lal, Kumari Nidhi Kumar, Shishupal VNIT Nagpur Dept. of Computer Science and Engineering Nagpur India IIIT Nagpur Dept. of Computer Science and Engineering Nagpur India

ISBN: (纸本)9798350378092

The emergence of 5G technology has ushered in a new era of connectivity, making communications faster and cheaper. One of the main challenges of 5G networks is accuracy and efficiency, which are important for many applications such as driver less cars, smart cities and virtual reality. This research paper explores the integration of machine learning techniques to improve the accuracy and reliability of 5G infrastructure. The plan uses rich data generated by 5G networks, combining various parameters such as signal strength, flight time and angle of arrival. Machine learning algorithms are used to process this data and create good models for the correct location. The system is designed to adapt to dynamic and complex urban environments where traditional methods often encounter limitations. The main contribution of this research includes the development of a new machine learning-based distribution algorithm suitable for 5G networks, a comprehensive evaluation of performance comparison with the existing system, and evaluation of recommendations for feasibility and implementation. The results showed significant improvements in accuracy and reliability, demonstrating the potential of machine learning to revolutionize the 5G workplace. © 2024 IEEE.

关键词： Adversarial machine learning

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Analysis of Deep Learning Approaches to Predict Cardiac Arrhythmia in ECG Signals 11

Analysis of Deep Learning Approaches to Predict Cardiac Arrh...

引用

11th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2023

作者： Padhy, Subham Kumar Mahalik, Saurav Nayak, Himanshu Patra, Sabyasachi Mohapatra, Anjali IIIT DST-FIST Bioinformatics Lab Dept. of Computer Science and Engineering Bhubaneswar India IIIT Dept. of Computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9798350326147

Abnormal heart rhythm or irregular heartbeat, often known as arrhythmia. It is a kind of cardiovascular illness that necessitates a precise and fast diagnosis. Because of its simplicity and non-invasive nature, an electrocardiogram (ECG) that detects the electric activity of the heart has been frequently used to identify cardiac disorders. Each heartbeat's electrical signal, the peak of action impulse waveforms produced by various specialised cardiac tissues, can be used to diagnose various heart defects. Deep learning has evolved as a significant technique in recent decades due to its ability to handle vast amounts of data. The use of hidden layers in the convolution layer have enhanced pattern recognition performance. Deep learning has aided in the automation of medical image analysis and can aid in detecting of any anomalies in the medical images. In this work, ECG-based automated irregular heartbeat prediction is conducted to determine to which arrhythmia class it belongs with greater accuracy and less data loss. This study is based on convolutional neural networks, which are used to evaluate ECG images. For the normal case and cases impacted by various arrhythmias and my-ocardial infarction, the signals correspond to electrocardiogram (ECG) forms of ***-CNN, ResNet34, ResNet50, vgg16, and vgg19 models are utilised to predict of cardiac arrhythmia. vgg16 performed the best and is chosen to be further tweaked to improve accuracy to 99.79 percent. © 2023 IEEE.

关键词： Electrocardiograms

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PsychSynth: Advancing Mental Health AI Through Synthetic Data Generation and Curriculum Training 9

PsychSynth: Advancing Mental Health AI Through Synthetic Dat...

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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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Statistical Analysis of the Properties of Geometric Network with Node Mobility 29

Statistical Analysis of the Properties of Geometric Network ...

引用

29th IEEE Asia Pacific Conference on Communications, APCC 2024

作者： Arquam, Md. Tiwari, Utkarsh Kumari, Suchi Iiit Dept. Computer Science and Engineering Haryana Sonepat India Shiv Nadar Institute of Eminence Dept. Computer Science and Engineering Delhi-NCR India

ISBN: (纸本)9798331517816

The movement changes the underlying spatial representation of the participated mobile objects or nodes. In real-world scenarios, such mobile nodes can be part of any biological network, transportation network, social network, human interaction, etc. The change in the geometry leads to the change in various desirable properties of real-world networks, especially in human interaction networks. In real life, human movement is concerned with a better lifestyle where they form their new connections due to the geographical changes. Therefore, in this paper, we design a model for geometric networks with mobile nodes (GNMN) and conduct a comprehensive statistical analysis of their properties. We analyze the effect of node mobility by evaluating key network metrics such as connectivity, node degree distribution, second-hop neighbors, and centrality measures. Through extensive simulations, we observe significant variations in the behavior of geometric networks with mobile nodes. © 2024 IEEE.

关键词： Complex networks

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Efficient Spectral Attention Block for Cascaded Hyperspectral Image Super-Resolution Architectures 14

Efficient Spectral Attention Block for Cascaded Hyperspectra...

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14th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2024

作者： Patibandla, Tarun Pv, Arun Porwal, Alok IIIT Sri City Computer Science Dept. India IIT Bombay Computer Science Dept. India

ISBN: (纸本)9798331513139

Hyperspectral image (HSI) super-resolution has seen significant advancements with the emergence of diffusion models, demonstrating impressive results in prior works. However, existing approaches often take up cascaded architectures for feature extraction, often suffering from high computational costs, gradient instability, and training difficulties. This paper introduces a novel Efficient Spectral Attention Block (ESAB) that addresses these challenges by leveraging atrous convolutional blocks and channel attention within a multiscale feature extraction strategy. ESAB enables effective spectral feature extraction while maintaining computational efficiency, leading to improved performance. Furthermore, a unique loss function is proposed to enhance spectral fidelity by comparing the spectral slopes of the ground truth and predicted image pixels. Experimental results demonstrate that the proposed approach achieves great performance in super-resolving HSIs, achieving high spectral fidelity with reduced model complexity. © 2024 IEEE.

关键词： Hyperspectral imaging

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Enhanced Predictive Modeling Techniques for Early Detection of COPD Utilizing 1D Convolutional Neural Networks 15

Enhanced Predictive Modeling Techniques for Early Detection ...

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

作者： Jha, Sneha Sahu, Prakash Kumar, Santosh CSVTU Dept. of Data Science Chhattisgarh India IIIT-Naya Computer Science & Engineering Raipur India

ISBN: (纸本)9798350370249

Artificial intelligence (AI) breakthroughs have created new opportunities in the field of medical diagnostics, especially for the early identification of respiratory conditions like Chronic Obstructive Pulmonary Disease (COPD), the most prevalent respiratory condition, is characterized by breathing difficulties, mucus (sputum) coughing, and wheezing. Early verification is critical for efficient administration. A one-dimensional convolutional neural network (1D CNN) model for COPD detection, optimized using Adam and RMSprop algorithms is proposed for this purpose. Utilizing the ICBHI 2017 dataset, the performance of each optimizer based on training and validation accuracy is evaluated. The Adam optimizer achieved a training accuracy of 9 4% and a validation accuracy of 9 0%. In comparison, the RMSprop optimizer yielded a training accuracy of 9 2% and a validation accuracy of 8 8%. These results demonstrated that the Adam optimizer surpassed RMSprop in terms of accuracy. It showcased the potential of the proposed 1D CNN model as a reliable diagnostic tool, contributing to the advancement of respiratory health analytics. © 2024 IEEE.

关键词： Convolutional neural networks

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