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Women in Computing (InCoWoCo), International Conference for

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

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

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.

关键词： Support vector machines Measurement Analytical models Accuracy Computational modeling Mortality Pressing Nearest neighbor methods Breast cancer Medical diagnostic imaging

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Automated Detection of Poultry Farms from Aerial Images for Actionable AI System toward Biosecurity Applications

Automated Detection of Poultry Farms from Aerial Images for ...

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2023 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2023

作者： Li, Yu Das, Rana Duong, Cuong Lim, Teng-Teeh Haithcoat, Timothy Ersoy, Ilker Shyu, Chi-Ren University of Missouri Department of Electrical Engineering and Computer Science ColumbiaMO United States University of Missouri Biological Engineering ColumbiaMO United States University of Missouri Plant Science and Technology Extension ColumbiaMO United States University of Missouri Institute for Data Science and Informatics Columbia United States University of Missouri Institute for Data Science and Informatics Electrical Eng. and Computer Science Dept. ColumbiaMO United States

ISBN: (纸本)9798350359527

Poultry productions have shifted towards larger farms and often cluster in certain regions. However, many of the smaller farms with a considerable amount of production are not considered concentrated animal feeding operation under regulation in many regions, and therefore are not required to obtain a permit. As a result, information of these operations is not being recorded and monitored, contributing to an increased risk for biosecurity issues. The development in computer vision and availability of high-resolution remote sensing imagery allows the possibility for automatic localization and operation estimation of the unregistered farms, filling in the missing information needed for biosecurity risk management. The farm information extracted from images can be incorporated with other available geospatial data, such as road networks, watersheds, etc., to build a comprehensive knowledge base for actionable plans. In the current study, we present a framework for automated localization, analysis, and visualization of poultry farms from remote sensing images. A robust machine learning method was developed to computationally link the visual features from remote sensing imagery to Missouri Department of Natural Resources Concentrated Animal Feeding Operation data, to identify the animal farm type, location, and potential capacity in the region. The developed process can be performed automatically on images of different timestamps to analyze temporal and spatial development of poultry production facilities of a region. This research contributes to the effective management and regulation of poultry and potentially other animal farms, providing insights into farm characteristics and capacities while facilitating informed decision-making. © 2023 IEEE.

关键词： Object detection

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Symptoms-based Classification of Disease Using Various ML Algorithms and Interpretation using LIME and SHAP KERNELS

Symptoms-based Classification of Disease Using Various ML Al...

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Innovation in Technology (ASIANCON), Asian Conference on

作者： Soujanya KJ Ashwini Kodipalli Sanjeev Gosai Sneha BJ Trupthi Rao Dept. of Artificial Intelligence & Data Science Global Academy of Technology Bangalore Karnataka India Dept. of Computer Science & Engineering Global Academy of Technology Bangalore Karnataka India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

Using a variety of machine learning techniques, this research study suggests a unique method for classifying diseases using symptom-based analysis. To improve model transparency and comprehension, the study makes use of the interpretability tools, SHAP kernels and LIME. The research attempts to close the gap between interpretability and prediction accuracy in medical diagnosis by combining two approaches. A thorough assessment of symptom-based categorization is ensured by the use of many machine learning methods. The application of LIME and SHAP kernels aids in the clarification of model predictions and provides information on the primary symptoms that impact illness categorization. This study advances the creation of accurate and comprehensible illness categorization models, promoting confidence in the use of machine learning methods in healthcare.

关键词： Technological innovation Machine learning algorithms Accuracy Machine learning Predictive models Prediction algorithms Vectors Classification algorithms Kernel Diseases

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Detection of PCOS Leveraging Machine Learning and Interpretation Using Explainable AI

Detection of PCOS Leveraging Machine Learning and Interpreta...

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Women in Computing (InCoWoCo), International Conference for

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

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

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.

关键词： Accuracy Machine learning algorithms Ultrasonic imaging Computational modeling Medical services Transforms Boosting Prediction algorithms Bagging Tuning

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Revolutionizing Jewellery Design through Stable Diffusion Model: Bridging the Gap between Consumer Preferences and Designer Creativity

Revolutionizing Jewellery Design through Stable Diffusion Mo...

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Intelligent Technologies (CONIT), International Conference on

作者： Shobha Gupta Shubham Vishwakarma Kriti Srivastava Dept. of Electronics Dwarkadas J. Sanghvi College of Engineering Mumbai India Dept. of Computer and Engineering (Data Science) Dwarkadas J. Sanghvi College of Engineering Mumbai India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

This study offers a novel approach that uses generative AI models such as stable diffusion models to produce a large number of jewellery designs, improve the design process, and increase consumer involvement. The jewellery business, which relies on designers’ expertise and creativity, is on the verge of a dramatic shift with the integration of AI-driven technologies. Our proposed approach serves as a creative middleman, combining consumer preferences and artistic ideas to provide a wide range of jewellery pictures. By allowing customers to choose from these AI-generated designs, the technology not only meets their expectations but also dramatically streamlines the designer’s productivity. This synergy between AI-generated solutions and human ingenuity not only reduces the time and effort required in the initial design phase, but also guarantees that the final product resonates more strongly with customer preferences. We go into the technical details of stable diffusion models, highlighting their ability to generate and customise high-quality images. Furthermore, we discuss how this technology affects efficiency, satisfaction, and creative expression. Our findings indicate that this method not only promotes a more collaborative and consumer-centric design process, but it also creates new opportunities for innovation in the jewellery sector. This study attempts to show how the combination of artificial intelligence and human creativity may reshape the paradigms of jewellery creation, making it more inclusive, efficient, and responsive to consumer demands.

关键词： Productivity Industries Technological innovation Generative AI Design methodology Merging Collaboration

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

Enhancing Hospital Mortality Prediction for Heart Failure Pa...

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Women in Computing (InCoWoCo), International Conference for

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

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

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.

关键词： Logistic regression Accuracy Machine learning algorithms Hospitals Computational modeling Mortality Predictive models Boosting Prediction algorithms data models

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The Evolution of Spatial data Infrastructures with Policy, Integration, and Metadata Management for Effective Geospatial data Handling

The Evolution of Spatial Data Infrastructures with Policy, I...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Mohammad, Duaa Jasim Anjimoon, Shaik Anandhi, R.J. Singh, Navdeep Parmar, Ashish Bhasha, A. National University of Science and Technology College of Health and Medical Technology Dhi Qar64001 Iraq Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Information Science Engineering Bangalore India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India St. Martin's Engineering College Dept. of It Telangana Secunderabad India

ISBN: (纸本)9798350373783

Because of the critical role that spatial data infrastructures (SDIs) play in the efficient management of geospatial data, it is necessary for them to undergo continuous development in order to meet the difficulties that are always developing. Temporal Policy-Driven Integration with Dynamic Metadata augmentation (TPDIME) is a revolutionary approach that is presented in this paper. TPDIME blends temporal policy modeling, adaptive data integration, and semantic metadata augmentation within its framework. The Evolutionary Framework (TEF), Spatial Harmony Protocol (SHP), and Geo Integrator Pro (GIP) are among the six classic approaches that represent the benchmarks against which the suggested method is evaluated. In addition to adaptability, interoperability, usability, governance impact, resilience, and semantic interoperability, the assessment criteria include more. © 2024 IEEE.

关键词： Spatio-temporal data

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Forecasting Air Pollution of Urban Chennai Localities using Artificial Intelligence

Forecasting Air Pollution of Urban Chennai Localities using ...

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Applied Artificial Intelligence and Computing (ICAAIC), International Conference on

作者： Rayaan Faiz Tisha Agarwal D Rajeswari Dept. of Data Science and Business Systems School of Computing College of engineering and Technology SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

India’s air quality deteriorated significantly in 2023, ranking third worst globally, highlighting the urgency for effective monitoring and mitigation measures. To comprehend past trends and predict future patterns, historical air pollution data from seven urban areas in Chennai was analyzed using the XGBoost Algorithm. This study aims to gain insights into Chennai’s air quality, identify abnormalities, and issue timely warnings to the public and governing authorities. XGBoost was employed to forecast AQI, NO2, PM2.5, NOx, PM10, CO, SO2, NH3, and Ozone levels, outperforming traditional LSTM, Prophet, and ARIMA models. Evaluation using RMSE and MAE scores revealed XGBoost’s efficacy, with scores of 20.65 and 14.35, respectively. The research highlights regions like Alandur, Arumbakkam, and Manali with notable concentrations of contaminants, emphasizing the need for immediate action. These findings highlight the significance of utilizing advanced algorithms for accurate air quality forecasting and proactive pollution management.

关键词： Gases Atmospheric modeling Urban areas Lead Predictive models Air pollution Prediction algorithms

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Online 3D Bin Packing Using a Combination of Tree Structures and Deep Reinforcement Learning

Online 3D Bin Packing Using a Combination of Tree Structures...

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IEEE International Conference on Robotics and Biomimetics

作者： Kenji Fukuda Masaki Yamakita Hiroyuki Oyama Dept. of Systems and Control Engineering Institute of Science Tokyo Tokyo Japan Data Science Laboratories NEC Corporation Kanagawa Japan

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

The online 3D bin packing problem (online 3D-BPP) is crucial for optimizing packing efficiency in various industrial applications. In this work, we introduce two external areas: an item buffer for the selection of packing items, and a temporary backup space to allow the rearrangement of items already packed in the bin. We propose a novel method that combines a deep reinforcement learning-based placement position planner with a movement sequence planner structured as a tree with the use of these external areas. This approach enables efficient planning of item placement and movement sequences without expanding the learning space, regardless of the size of external areas. Through numerical experiments, our method demonstrates a consistent improvement in spatial filling rates and robustness against constraints like physical stability. Notably, by utilizing these external areas, we elevated the performance of existing DRL methods by approximately 15 % in the scenario involving complex constraints such as physical stability. These results suggest that our approach is highly effective for complex, real-time packing scenarios in logistics and warehousing.

关键词： Three-dimensional displays Warehousing Filling Robustness Real-time systems Planning Thermal stability Numerical stability Robots Logistics

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SELFORMER: MOLECULAR REPRESENTATION LEARNING VIA SELFIES LANGUAGE MODELS

arXiv

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arXiv 2023年

作者： Yüksel, Atakan Ulusoy, Erva Ünlü, Atabey Dogan, Tunca Biological Data Science Lab Dept. of Computer Engineering Hacettepe University Turkey

Automated computational analysis of the vast chemical space is critical for numerous fields of research such as drug discovery and material science. Representation learning techniques have recently been employed with the primary objective of generating compact and informative numerical expressions of complex data, for efficient usage in subsequent prediction tasks. One approach to efficiently learn molecular representations is processing string-based notations of chemicals via natural language processing (NLP) algorithms. Majority of the methods proposed so far utilize SMILES notations for this purpose, which is the most extensively used string-based encoding for molecules. However, SMILES is associated with numerous problems related to validity and robustness, which may prevent the model from effectively uncovering the knowledge hidden in the data. In this study, we propose SELFormer, a transformer architecture-based chemical language model that utilizes a 100% valid, compact and expressive notation, SELFIES, as input, in order to learn flexible and high-quality molecular representations. SELFormer is pre-trained on two million drug-like compounds and fine-tuned for diverse molecular property prediction tasks. Our performance evaluation has revealed that, SELFormer outperforms all competing methods, including graph learning-based approaches and SMILES-based chemical language models, on predicting aqueous solubility of molecules and adverse drug reactions, while producing comparable results for the remaining tasks. We also visualized molecular representations learned by SELFormer via dimensionality reduction, which indicated that even the pretrained model can discriminate molecules with differing structural properties. We shared SELFormer as a programmatic tool, together with its datasets and pre-trained models. Overall, our research demonstrates the benefit of using the SELFIES notations in the context of chemical language modeling and opens up new possibilities fo

关键词： Molecules

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