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Recognition of data Breach Method from Story Using Advanced Deep Learning 3

Recognition of Data Breach Method from Story Using Advanced ...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Jeba, Samiha Maisha Aurpa, Tanjim Taharat Alom, Md Samiul Joy, Md. Wadud Mahmud Ahamed, Asm Shakil Dash, Satya Ranjan Ahmed, Md Shoaib International University of Business Agriculture and Technology Dept. of Computer Science and Engineering Bangladesh Bangabandhu Sheikh Mujibur Rahman Digital University Department of Data Science Bangladesh Kiit University School of Computer Applications Odisha India Boise State University Department of Computer Science BoiseID United States

ISBN: (纸本)9798350381931

We often open our eyes in the morning with the appalling news of data breaches of different popular companies. This is a significant threat not only to giant companies but also to the general people's privacy. Various methods are maneuvered for data breaches, and identifying these methods in a short can help understand the loss and take proper steps against these incidents. Researchers nowadays focus on the data breaches issue to reduce its threats to the tech industry. In this research, we have used advanced deep learning methods(e.g., BERT, XLNet, and Electra) to predict data breach methods using the data breach story. The proposed classifier has provided an accuracy of 92.86%. © 2024 IEEE.

关键词： Deep learning

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Image-Based Distance Estimation of a Target from a Camera using Color Checker Chart 2

Image-Based Distance Estimation of a Target from a Camera us...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Kamath, Vedavyasa Kurian, Ciji Pearl Shailesh, K.R. Suprabha Padiyar, U. Sontakke, Mangalam Manipal Institute of Technology Manipal Academy of Higher Education Dept. of Electrical and Electronics Engg. Karnataka Manipal India Manipal Institute of Technology Manipal Academy of Higher Education Dept. of Data Science and Computer Applications Karnataka Manipal India

ISBN: (纸本)9798350306415

In recent years, photography-based photometry has gained popularity and is being implemented widely. Under this approach, a camera is used as a sensor to estimate photometric quantities like illuminance or luminance. The absolute distance between the target surface and the camera can be crucial for photometric analysis. The technique proposed in this article employs a fixed focal length lens mounted on a DSLR camera. The standardised classic ColorChecker chart is placed over the target surface, and the scene image with the chart is captured. The intuition is that the average pixel area of the swatches present on the chart decreases as the camera is moved away from the chart. OpenCV libraries are used to calculate the average pixel area of the chart color swatches. Distance is measured using an industry-standard laser-based distance measurement tool. Curve fitting is used to establish a mathematical relationship between camera-chart distance and the average pixel area of the swatches. Distance varies from 650 cm to 2 m during the data collection process. The influence of the tilt of the chart plane with respect to the camera sensor plane is also studied. During testing, the proposed power trendline-based mathematical model performed with an error percentage of less than 1. This approach for distance measurement enables finer tuning of the machine learning-based model and, as a result, more accurate illuminance estimation for the work plane. © 2024 IEEE.

关键词： computer vision Curve fitting Distance measurement Image analysis Image edge detection Photometry

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Challenges and Opportunities of Symbiotic AI in Rare Disease Diagnosis

Challenges and Opportunities of Symbiotic AI in Rare Disease...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Lembo, Serena Barra, Paola Dash, Satya Ranjan Di Biasi, Luigi University of Salerno DISA-MIS Salerno Italy University of Naples Parthenope Dept. of Science and Technology Naples Italy Kiit University School of Computer Applications India University of Salerno Dept. of Computer Science Salerno Italy

ISBN: (纸本)9798350386226

Diagnosing rare diseases is difficult due to the complexity of the conditions, limited data, and a lack of specialized expertise. With over 10,000 rare diseases affecting more than 350 million people globally, diagnosis is often delayed or inaccurate, partly because traditional methods rely on fragmented and decentralized data. In this contribution, we highlight an issue similar to the curse of dimensionality that impacts the artificial intelligence training process, where too many features may lead to training failure. We named this issue the curse of heterogeneity: the need for massive interactions that slow down or lead to fail diagnosis process. Then, the contribution examines the challenges hidden behind rare disease diagnoses and discusses how SAI can improve it by combining AI-driven data analysis with human expertise. To do this, we use two real use-case scenarios. Finally, we discussed how SAI could optimize diagnosis processes and better use platforms like Orphanet, RareCare, and OMIM, which centralize rare disease data. The contribution aims to show how SAI offers a transformative approach to rare disease diagnosis by improving data integration, expert collaboration, and patient outcomes to expand the knowledge network as much as possible. © 2024 IEEE.

关键词： diagnosis healthcare predictive analytics rare diseases Symbiotic AI

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Hindi News Article's Headline Generation based on Abstractive Text Summarization

Hindi News Article's Headline Generation based on Abstractiv...

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2023 IEEE International Conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Kumar, Jeetendra Shekhar, Shashi Gupta, Rashmi Atal Bihari Vajpayee University Dept. of Computer Science and Application Bilaspur India Gla University Dept. of Computer Engineering and Applications Mathura India

ISBN: (纸本)9798350300826

Text summarization is a technique that converts large text into shorter ones by keeping its important information. Most of the available summarizers, summarize English language text. The availability of summarizers in other languages is rare. In the proposed work, abstractive text summarization (ATS) for the Hindi language has been used to generate headlines from news text. To build the model, a Hindi news dataset has been used that contains different news articles related to sports, politics, crime, etc. After pre-processing the text, Word2Vec method has been used for word embedding. ATS model has been built using the Seq2Seq model with attention. After that, we have calculated evaluation metrics like ROUGE and BLEU scores of the result summaries. We have also fine-tuned two pre-trained models, indicBART and multi-lingual variant of T5. After result comparison, it was found that the indicBART perform better than the other two models, considered for the experiment. © 2023 IEEE.

关键词： Text processing

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Development of a Multimodal Machine Learning Model for Seizure Detection Using Wearable Devices 1

Development of a Multimodal Machine Learning Model for Seizu...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Singh Rathore, Saurabh Pratap Magotra, Sumedha Sruthy, K.V. Sharma, Sonu Kumar Kaur, Gaganpreet Singh, Sailendra ICAPSR Dept. of Management New Delhi India Chandigarh University Dept. of Computer Science Engineering Chandigarh India Dhanalakshmi Srinivasan College Dept. of Computer Science & Engineering Coimbatore India Mangalayatan University Dept. of Comp. Eng & Applications Aligarh India Lovely Professional University Punjab India J.S. University Dept. of Computer Science Shikohabad India

ISBN: (纸本)9798350356816

This research explores the transformative potential of wearable devices designed to automatically detect & predict epileptic seizures, offering continuous monitoring & early detection capabilities. The study focuses on developing a machine learning model tailored for seizure detection in such devices, leveraging multimodal sensors such as Electrodermal Activity (EDA) & Accelerometer (ACC). By measuring skin resistance and identifying irregular heartbeats, which are signs of upcoming seizures, these sensors allow for precise seizure detection. The study intends to demonstrate the effectiveness of this machine learning model in wearable technology with the goal of improving patient outcomes and seizure management. © 2024 IEEE.

关键词： Accelerometers

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Enhancing Emotional Support Chatbots Using Deep Learning & NLP: A Study on Personalization and Effectiveness 1

Enhancing Emotional Support Chatbots Using Deep Learning & N...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Kaushik, Priyanka Renuka Parihar, Sanjay Khalid, Md. Saifullah Sharma, Umesh Chandra Mary, J. Josphin Chandigarh University Dept. of Computer Science Chandigarh India Madhav University Dept. of Humanities & S.Science Pindwara India Usha Martin University Dept. of Bus. Management & Commerce Jharkhand India Mangalayatan University Dept. of Computer Eng. & Applications Aligarh India Meenakshi A.H.E.R Dept. of Computer Science Chennai India

ISBN: (纸本)9798350356816

This research focuses on utilizing deep learning and natural language processing (NLP) to design an AI-powered chatbot for emotional support. The primary objective is to establish a supportive platform for individuals dealing with emotional challenges. By leveraging NLP, the chatbot will identify and interpret users' emotional states, generating tailored and empathetic responses. Deep learning techniques will enable the system to learn from past interactions, adapting its responses to meet the unique needs of each user. The platform aims to deliver personalized, scalable support while incorporating features like sharing uplifting content, such as cute cat images. This study emphasizes the potential of AI-driven solutions in enhancing mental well-being through empathetic and meaningful interactions. © 2024 IEEE.

关键词： Contrastive Learning

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Revolutionizing Facial Feature Extraction with AI: An Innovative Approach 5

Revolutionizing Facial Feature Extraction with AI: An Innova...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Lakshmi, B. Alekhya, Jandhyam Lakshmi Parish Venkata Kumar, K. Kishore, B. Kamala Nyni, K. Likhita, K. V.R. Siddhartha Engineering Dept. of Computer Applications KanuruVijayawada 7 India V.R. Siddhartha Engineering College Dept of Computer Science and Engineering KanuruVijayawada 7 India Department of Computer Applications India

ISBN: (纸本)9798350385649

This paper provides a technique for detecting human faces & extracting their features based on segmentation of skin tone & facial structure. The system goes through several stages. The method first searches for regions where faces might be present by segmenting skin tone. After performing various morphological operations on skin, it uses aspect ratio of a face to find viable face blocks. face blocks are then used to extract additional features. Therefore, one may identify and validate a person's image using their face features by using this biometric attribute. To enhance facial recognition, numerous researchers have employed Support Vector Machine (SVM), Principal Component Analysis (PCA), a combination of SVM and PCA, PCA with an Artificial Neural Network, and even the conventional PCA-SVM. Due to PCA-ANN's limited dataset limitation, PCA-SVM is superior to PCA-ANN. To extract features, relative location & size are used. If algorithm is unable to identify even one feature in a prospective face block, it rejects face block. © 2024 IEEE.

关键词： Feature extraction

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Enhancing Detection of Breast Cancer Through a Novel Framework: A Machine Learning Approach with Explainable AI Interpretation 1

Enhancing Detection of Breast Cancer Through a Novel Framewo...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Vidyashree, R. Kodipalli, Ashwini Rao, Trupthi Global Academy of Technology Dept. of Computer Science & Engineering Karnataka Bengaluru India Global Academy of Technology Dept. of Artificial Intelligence & Data Science Karnataka Bangalore India

ISBN: (纸本)9798331518943

Breast cancer, the most prevalent cancer among women, significantly contributes to increased mortality rates and ranks as the second leading cause of cancer-related deaths in women. Early detection is crucial for effective treatment, yet traditional diagnostic approaches often involve multiple laboratory tests supervised by medical experts. To mitigate human error and expedite breast cancer detection, there is a pressing need for automated systems capable of accurate and timely diagnosis. This study aims to investigate the application of machine learning (ML) techniques in classifying breast cancer using features from digitized images of fine-needle aspiration (FNA) breast mass samples. Machine learning methods are instrumental in addressing this imperative. The study conducts a comparative analysis of various ML classification methods, including Logistic Regression, Support Vector Machine (SVM), K Nearest Neighbor (KNN), Decision Trees, Bagging-Boosting, and ensemble models, using a breast cancer dataset to establish a novel framework. The performance of each algorithm is assessed based on metrics such as accuracy, precision, recall, and F1 score. The findings underscore the effectiveness of ensemble classifier models, achieving a notable accuracy rate of 98.87%. Additionally, the integration of explainable Artificial Intelligence enhances the interpretability of the models by providing valuable insights into their decision-making processes. © 2024 IEEE.

关键词： Contrastive Learning

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Enhancing Hospital Mortality Prediction for Heart Failure Patients through Hyperparameter Optimization and Interpretability Analysis with LIME and SHAP 1

Enhancing Hospital Mortality Prediction for Heart Failure Pa...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Meena, S. Murthy, Monisha Kodipalli, Ashwini Global Academy of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India Global Academy of Technology Dept. of Artificial Intelligence & Data Science Karnataka Bengaluru India

ISBN: (纸本)9798331518943

Heart failure is one of the primary causes for deaths caused in the hospital. Predicting mortality rate of such patients is extremely important for the efficient use of health care resources. This research aims to estimate the hospital mortality rate of Heart Failure(HF) patients using machine learning(ML). Using computational technique, the obtained model has given the most efficient accuracy. This paper uses the classification model to detect two outcomes (0 and 1, i.e., if the patient will survive or not). The classification models used for the hospital mortality prediction are, single classifiers such as Logistic regression, SVM, KNN, Decision tree (Ginni and Entropy);with Logistic regression obtaining highest accuracy of 88.9%, Bagging and boosting models such as Randomforest, Adaptive boosting, Gradient boosting, XG boosting, Categorical boosting;with CatBoost obtaining highest accuracy of 90.2%. To achieve the best accuracy, ensemble models were used out of which ensemble classifier ensemble gave 91.1% accuracy. To understand the features playing important role in decision making, explainable AI(XAI) is used. LIME and SHAP are considered. LIME generated local explanations, it explained the decision of a complex model for a specific instance or observation. In contrast, SHAP provided global explanations, it explained the overall behavior of the model across all instances. © 2024 IEEE.

关键词： Adaptive boosting

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Detection of PCOS Leveraging Machine Learning and Interpretation Using Explainable AI 1

Detection of PCOS Leveraging Machine Learning and Interpreta...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Madhavi, Bindu V. Firdouse, Asfiya Kodipalli, Ashwini Global Academy of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India Global Academy of Technology Dept. of Artificial Intelligence & Data Science Karnataka Bengaluru India

ISBN: (纸本)9798331518943

Polycystic Ovary Syndrome (PCOS) is a widespread endocrine disorder impacting women globally. This research aims to early predict and detect PCOS which is needed to reduce long-term complications. Since it is considered to be a hard-to-diagnose disorder, machine learning is utilized for this purpose. This study leveraged the power of computational algorithms trained on patient data for model prediction where Decision tree with criterion Gini index outperformed with 88.07% accuracy, 85.29% precision, 78.37% recall and 81.69% F1 Score. In addition to this, Bagging and boosting algorithms were used to monitor their performance metrics where Gradient Boost stood out with a remarkable accuracy of 91.74%, 91.00% precision, 97.00% recall and 94.00% F1 Score. By optimizing the chosen parameters through Hyperparameter tuning, a notable increase in the model's accuracy was observed. Besides, three ensemble models were proposed out of which the ensemble classifier ensemble model involving bagging, boosting and single classifiers brought a significant difference of 95.41% accuracy. Additionally, Explainable (XAI) methodologies like LIME (Local Interpretable Model-Agnostic Explanations) and SHAP (SHapley Additive exPlanations) were used for model interpretability. © 2024 IEEE.

关键词： Adversarial machine learning

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