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Endoscopic Image Processing: Evaluating the Robustness of Deep Neural Networks 5

Endoscopic Image Processing: Evaluating the Robustness of De...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Gireesh Babu, C.N. Babu, Kaparapu Satish Suneel, Sajja Rao, K.Srinivasa Muralitharan, Divya Aarthi, R. Bms Institute of Technology and Management Dept.of CSE/ISE Karnataka Bengaluru India Geetanjali College of Engineering and Technology Department of Electronics and Communication Engineering Telangana Hyderabad India Hyderabad Telangana Dundigal500043 India K.S.R.M. College of Engineering Department of Ai&ml Andhra Pradesh Kadapa516003 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India R.M.D Engineering College Department of Ece R. S. M Nagar Gummidipoondi601206 India

ISBN: (纸本)9798331504403

The Hybrid CNN-Transformer Model (HCNNT) is a novel model which exploits Convolutional Neural Networks (CNN) for spatial feature extraction and Transformers for capturing contextual and long range dependencies in order to carry complex image classification. In order to evaluate the algorithm proposed, a new model has been introduced and trained on a data set of gastrointestinal (GI) image classification, which is derived from a balanced data set with 226 images available on Kaggle. Although the cascaded CNN part is to learn elaborate visual characteristics, the improved understanding of complex patterns and relationships is achieved by the Transformer component to process these features. When both are integrated, we gain a powerful system that has high accuracy in distinguishing GI bleeding condition from normal condition. Extensive training and optimization of the hybrid model using binary cross-entropy loss and Adam optimizer has achieved state-of-the-art performance. Through the experiments evaluated on the HCNNT model, it is evident that an accuracy of 97.34% proves to be significantly beneficial, its practicality in terms of reliability for medical image classification that essentially improves the accuracy of diagnosis in gastrointestinal healthcare. © 2024 IEEE.

关键词： Convolutional neural networks

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Building an Emotion Detection System in Python Using Multi-Layer Perceptrons for Speech Analysis

Building an Emotion Detection System in Python Using Multi-L...

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Regula Srilakshmi Vidya Kamma Shilpa Choudhary Sandeep Kumar Munish Kumar Computer Science and Engineering Neil Gogte Institute of Technology Hyderabad India Department of Computer Science and Engineering (AI&ML) Neil Gogte Institute of Technology Hyderabad India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India

Human speech emotion recognition analyses a speaker's speech to determine their emotional state. Included are several applications in psychology, medicine, and human-computer interaction. Automated speech emotion identification techniques have been widely used in recent years thanks to machine learning algorithms. A number of speech processing libraries are available in Python, a popular programming language for system research. This focuses on creating a Python-based full speech popularity system that uses mlP classifiers. The goal of applications in psychology, healthcare, and human-computer interaction is to routinely discern the emotional state of a speaker based on their speech. With the aid of a multi-layer perceptron (mlP) classifier, we intend to improve a speech emotion reputation device written in Python. The proposed system will derive features from speech recordings, including mal-frequency cepstral coefficients (MFCCs) and prosodic functions. We will train and evaluate our model using an open-supply speech emotion popularity dataset, including RAVDESS. The extracted features may be supplied to an mlP classifier that has been trained using the classified emotional speech statistics dataset. Once mastered, the device can accurately categorise the speaker's emotional condition into a variety of categories, including happiness, despair, anger, and fear. Finally, we can evaluate the performance of our model using existing techniques on a distinct check out dataset.

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Transfer Learning based Analysis of Chest X-rays for Accurate Lung Disease Detection and Interpretation

Transfer Learning based Analysis of Chest X-rays for Accurat...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Someswari Perla Sudhakar Veledendi Pooja Jaiswal Satish Muppidi Jyothi Mandala Balajee Maram Department of CSE - AI&ML GMR Institute of Technology Rajam Andhra Pradesh India School of Computer Science and Artificial Intelligence SR University Warangal Galgotias University Departnent of Computer Science and Engineering GMR Institute of Technology Rajam India Department of Computer Science and Engineering Christ(Deemed to be) University Banglore India School of Computer Science and Artificial Intelligence SR University Warangal Telangana

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

This is a research paper based on a transfer learning approach with a primary aim at the analysis of chest Xrays for accurate detection and interpretation of lung diseases. The proposed method relies heavily on the use of pretrained deep learning models to enhance diagnostic accuracy and reduce the time and computational resources taken during training. Applying transfer learning to a large chest X-ray dataset, the model successfully detects key patterns associated with common lung diseases, such as pneumonia and tuberculosis. The manuscript encompasses data preprocessing, model finetuning, and performance evaluation and demonstrates huge improvements over the traditional methods both in terms of accuracy and interpretability. It has been experimentally proven that the model is competent enough to provide localization of disease areas, as it can be visualized through heatmaps obtained from predictions, which might further help the radiologists perform their diagnosis tasks. This work advocates for medical imaging automation for the early and efficient detection of lung disease.

关键词： Deep learning Training Accuracy Tuberculosis Pulmonary diseases Computational modeling Transfer learning X-ray imaging Biomedical imaging Residual neural networks

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Diabetes Prediction in Teenagers using Machine Learning Algorithms

Diabetes Prediction in Teenagers using Machine Learning Algo...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Jayavrinda Vrindavanam Raye Haarika Sindhu MG Kilari Sumanth Kumar Department of Computer Science & Engineering (AI & ML) School of Engineering Dayananda Sagar University Bengaluru India

Diabetes Mellitus (DM) is a hazardous condition that can lead to worldwide health problems owing to an increase in blood glucose levels, age, lack of movement, high blood pressure, poor nutrition, and other factors. The aim of this study is to develop a system that can predict diabetes in individuals aged 10 to 30 by merging the results of different machine-learning algorithms. Machine learning is used for increasing performance and forecasting accuracy. Some of the methods used to detect diabetes early include Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), K Nearest Neighbours (KNN), and XGBoost. In comparison to the other methods, the random forest and XGBoost Classifiers algorithm outperformed all other ml algorithms, with a maximum accuracy of 86

关键词： Support vector machines Radio frequency Machine learning algorithms Computational modeling Forestry Prediction algorithms Feature extraction

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Student Data Analysis using Hadoop

Student Data Analysis using Hadoop

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International Conference on ent Computing and Control Systems (ICICCS)

作者： N. Shirisha G. Divyajyothi A. Prashanthi G. Sowmya Department Of Computer Science & Engineering MLR Institute Of Technology Hyderabad India Department Of Computer Science & Engineering Nalla Narasimha Reddy Education Society’s Group of Institutions Hyderabad India Department Of AI&ML MLR Institute Of Technology Hyderabad India

The amount of data produced at educational institutions today is growing quickly. It is challenging to analyze the student performance from the vast amount of data using conventional data processing methods. One can quickly analyze the vast amount of data by using big data. Previously, different databases are used for students. So that it takes more time to analyze the performance of a student. By using this application, it takes less time to analyze the data, as it is just like a centralized database where the entire profile of a student is found. In this study, data are analyzed using map-reduce techniques, which is a programming model suitable for processing large amounts of data, and a Hadoop cluster for ease of processing. This helps educational institutions analyze the students’ data better and focus on their weak spots.

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Enhancing Legal Decision Making: WRIT Case Outcome Prediction with LegalBERT Embeddings and AdaBoost Classifier 2

Enhancing Legal Decision Making: WRIT Case Outcome Predictio...

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2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： George, Angelina Jose, Alphonsa Ashik, F. Prabhakar, Priyanka Pati, Peeta Basa Parida, Shantipriya Amrita School of Computing Amrita Vishwa Vidyapeetham India Department of Computer Science & Engineering Bengaluru India Silo AI Helsinki Finland

ISBN: (纸本)9798350383652

Legal decision-making, the process of determining the outcome of court cases, is a pivotal aspect of the justice system. It involves evaluating evidence, interpreting laws, and ensuring justice is served. However, this process is challenging due to the complexity of legal cases, varying interpretations of laws, and the potential for lengthy court proceedings. In addition to these reasons, India also has one of the world's lowest ratios of judges to population. For swift and efficient disposal of justice, it is important to fast-track this process as much as possible. A traditional solution to this problem would take a considerable amount of time to show any meaningful results considering the current backlog of cases and the rate at which new cases are being filed each day. In order to overcome these difficulties, we employ text embeddings from LLM's with statistical classifiers to effectively and precisely forecast the outcomes for writ petitions. Our approach delivers an accuracy of 54% and a F1-score of 0.42 with LegalBERT embedder and AdaBoost classifier. © 2024 IEEE.

关键词： Embeddings

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Intelligent Predictive Maintenance RAG framework for Power Plants: Enhancing QA with StyleDFS and Domain Specific Instruction Tuning

Intelligent Predictive Maintenance RAG framework for Power P...

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

作者： Hong, Seongtae Shin, Joongmin Seo, Jaehyung Lee, Taemin Park, Jeongbae Cho, Manyoung Choi, Byeongho Lim, Heuiseok Department of Computer Science and Engineering Korea University Korea Republic of Human-inspired AI Research Korea Republic of GaonPlatform Korea Republic of

ISBN: (纸本)9798891761667

Process plants are complex large-scale industrial facilities that convert raw materials or intermediate products into final products, requiring continuous processes with high safety and efficiency standards. In particular, in nuclear process plants, Predictive Maintenance System (PMS) plays a critical role in predicting equipment anomalies and performing preventive maintenance. However, current PMS relies heavily on the experience of a few experts, leading to knowledge loss upon their retirement and difficulty in swift response. Existing off-premise Question-Answering (QA) systems based on Large Language Models (LLM) face issues such as data leakage and challenges in domain-specific tuning. To address these problems, this study proposes an on-premise intelligent PMS framework utilizing a new chunking method, StyleDFS, which effectively reflects the structural information of documents. Additionally, we demonstrate that Instruction tuning using relevant domain-specific data improves LLM performance even under limited data conditions. © 2024 Association for Computational Linguistics.

关键词： Predictive maintenance

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INDICVOICES-R: Unlocking a Massive Multilingual Multi-speaker Speech Corpus for Scaling Indian TTS 38

INDICVOICES-R: Unlocking a Massive Multilingual Multi-speake...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Sankar, Ashwin Anand, Srija Varadhan, Praveen Srinivasa Thomas, Sherry Singal, Mehak Kumar, Shridhar Mehendale, Deovrat Krishana, Aditi Raju, Giri Khapra, Mitesh AI4Bharat Department of Computer Science & Engineering Indian Institute of Technology Madras Tamil Nadu Chennai600036 India

Recent advancements in text-to-speech (TTS) synthesis show that large-scale models trained with extensive web data produce highly natural-sounding output. However, such data is scarce for Indian languages due to the lack of high-quality, manually subtitled data on platforms like LibriVox or YouTube. To address this gap, we enhance existing large-scale ASR datasets containing natural conversations collected in low-quality environments to generate high-quality TTS training data. Our pipeline leverages the cross-lingual generalization of denoising and speech enhancement models trained on English and applied to Indian languages. This results in IndicVoices-R (IV-R), the largest multilingual Indian TTS dataset derived from an ASR dataset, with 1,704 hours of high-quality speech from 10,496 speakers across 22 Indian languages. IV-R matches the quality of gold-standard TTS datasets like LJSpeech, LibriTTS, and IndicTTS. We also introduce the IV-R Benchmark, the first to assess zero-shot, few-shot, and many-shot speaker generalization capabilities of TTS models on Indian voices, ensuring diversity in age, gender, and style. We demonstrate that fine-tuning an English pre-trained model on a combined dataset of high-quality IndicTTS and our IV-R dataset results in better zero-shot speaker generalization compared to fine-tuning on the IndicTTS dataset alone. Further, our evaluation reveals limited zero-shot generalization for Indian voices in TTS models trained on prior datasets, which we improve by fine-tuning the model on our data containing diverse set of speakers across language families. We open-source code and data3 for all 22 official Indian languages. © 2024 Neural information processing systems foundation. All rights reserved.

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Anomaly Detection in Social Networks using Improved Local Outlier Factor

Anomaly Detection in Social Networks using Improved Local Ou...

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Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： Rana Veer Samara Sihman Bharattej R Muntader Mhsnhasan Sowmya M Prabhakaran M V K. Ghamya Department of Computer Science Saint Leo University FL USA Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Artificial Intelligence and Data Science Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering (Cyber Security) Dhanalakshmi Srinivasan College of Engineering and Technology Mamallapuram India Department of Computer Science and Engineering (AI&ML) Mohan Babu University Tirupati India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

In the present era, Anomaly Detection (AD) aims to identify malicious activities in social networks. Even though previous researchers suggest effective methods for AD still there are limitations such as a lack of labeled data and struggled to handle high dimensional data. In this research, Improved Local Outlier Factor (ILOF) is proposed to detect anomalies and effectively handles the high dimensional data by using distance metric to calculate local density. Primarily, the data is collected from CERT insider threat and then preprocessed with truncation to remove extreme values for effective feature extraction. After that, the Principal Component Analysis (PCA) is incorporated to extract most informative features such as principal components to identify anomalies as data points. Finally, the extracted features are fed into proposed ILOF uses density-based approach to calculate the local density effectively for detecting anomalies in social networks. From the results, proposed ILOF model attained better outcomes compared to existing Bidirectional Long Short-Term Memory (BiLSTM) in terms of accuracy, precision, recall and F1-score as 98.50%, 97.08%, 98.25% and 97.66% respectively.

关键词： Measurement Accuracy Social networking (online) High dimensional data Transfer learning Feature extraction Real-time systems Intelligent systems Anomaly detection Principal component analysis

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Deep Neural Pretraining for Improving Diagnostic and Therapeutic Entity Recognition in Electronic Medical Records

Deep Neural Pretraining for Improving Diagnostic and Therape...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Enquero, Shreya Patchala Sunil, G. Kumar, Suvarna Sunil Lavanya, Chunduri Siddhanti, Prateeksha Global-A Genpact Company Bangalore India School of Computer Science & Artificial Intelligence Sr University Warangal India Malla Reddy Engineering College for Women It Department Dhulapally Hyderabad India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaadeswaram India Nitte Meenakshi Institute of Technology Department of Ai and Ml Yelahanka Bengaluru India

ISBN: (纸本)9798350313413

The predominance of compound agency, a lack of utterance components, and unclear entity boundaries all contribute to difficulties in distinguishing diagnostic and treatment entities in EMRs. Furthermore, obtaining electronic medical records in China may be difficult. A deep learning strategy that uses deep neural pretraining to improve diagnostic and therapeutic entity recognition in EMRs is proposed. A BERT-CRF model example is shown using a dataset of 10,000 EMRs. This paradigm combines the benefits of pretraining with the adaptability of CRF. The outcomes of the study show that deep brain pretraining might be used to improve the performance of NLP applications in the healthcare business. The use of a domain-specific corpus learning technique and an entity recognition model trained on a training dataset improves word embeddings. The suggested system achieved an accuracy (0.973), precision (0.909), recall (0.896), f1-score (0.902) and MCC (0.893) respectively. © 2023 IEEE.

关键词： BERT-CRF Model BiLSTM Model Cardiovascular Records Deep Learning Investigation Electronic Medical Records (EMRs) Entity Recognition Model Transformer-Conditional Random Fields

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