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Ensemble Attention Guided Multi-SEANet Trained with Curriculum Learning for Noninvasive Prediction of Gleason Grade Groups from MRI

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Journal of Shanghai Jiaotong university(science) 2024年第1期29卷 109-119页

作者：沈傲胡冀苏金鹏飞周志勇钱旭升郑毅包婕王希明戴亚康 School of Computer Science and Technology Changchun University of Science and TechnologyChangchun 130022China Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of SciencesSuzhou 215163JiangsuChina School of Biomedical Engineering(SooChow) University of Science and Technology of ChinaSuzhou 215163JiangsuChina Department of Radiology The First Afiliated Hospital of Soochow UniversitySuzhou 215006JiangsuChina

The Gleason grade group(GG)is an important basis for assessing the malignancy of prostate can-cer,but it requires invasive biopsy to obtain *** noninvasively evaluate GG,an automatic prediction method is proposed based on multi-scale convolutional neural network of the ensemble attention module trained with curriculum ***,a lesion-attention map based on the image of the region of interest is proposed in combination with the bottleneck attention module to make the network more focus on the lesion ***,the feature pyramid network is combined to make the network better learn the multi-scale information of the lesion ***,in the network training,a curriculum based on the consistency gap between the visual evaluation and the pathological grade is proposed,which further improves the prediction performance of the ***-perimental results show that the proposed method is better than the traditional network model in predicting GG *** quadratic weighted Kappa is 0.4711 and the positive predictive value for predicting clinically significant cancer is 0.9369.

关键词： prostate cancer Gleason grade groups(GGs) bi-parametric magnetic resonance imaging deep learn-ing curriculum 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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Normalized category travel personality by considering explicit and implicit feedback (NCTP): approach for improving travel recommender systems search result

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International Journal of Information technology (Singapore) 2023年第7期15卷 3689-3708页

作者： Kumar, Niranjan Hanji, Bhagyashri R. Department of Computer Science and Engineering Global Academy of Technology Visvesvaraya Technological University Belagavi 590018 India Department of Computer Science and Engineering Dayananda Sagar Academy of Technology and Management Visvesvaraya Technological University Belagavi 590018 India

Large volumes of end-user-generated textual data are assembled every day which leads to the evolution of social media in the form of reviews/feedback, and brief description messages. As a consequence, end-user often see it difficult to understand more concerning the subject being discussed or appropriate knowledge from such material. The enormous amount of text-oriented data that is accessible via the online platform is analyzed using machine learning and natural language processing algorithms, including topic modeling techniques that have been more prevalent in current years. The novel approach is proposed to represent travel categories called the Normalized Category Travel Personality (NCTP). The main purpose of the technique is to construct the semantics of category feedback to model travelers’ interests to create the category travel personality (CTP) representation. Likewise, we normalize the CTP to obtain our proposed model. The NCTP category model will apprehend the explicit and implicit aspects of categories shown by the social-circle groups to find sentiment scores. The TripAdvisor dataset was considered to evaluate the performance of the NCTP model based on the topic-model quality, implicit and explicit characteristics, and some legacy statistical evaluation metrics, like Recall, Mean Average Precision, and Mean Reciprocal Rank. © 2023, The Author(s), under exclusive licence to Bharati vidyapeeth's Institute of computer Applications and Management.

关键词： Probability distribution Rating and reviews Recommendation system Sentiment score

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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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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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TwinNet: Twin Structured Knowledge Transfer Network for Weakly Supervised Action Localization

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Machine Intelligence Research 2022年第3期19卷 227-246页

作者： Xiao-Yu Zhang Hai-Chao Shi Chang-Sheng Li Li-Xin Duan Institute of Information Engineering Chinese Academy of SciencesBeijing 100093China School of Computer Science Beijing Institute of TechnologyBeijing 100081China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China

Action recognition and localization in untrimmed videos is important for many applications and have attracted a lot of attention. Since full supervision with frame-level annotation places an overwhelming burden on manual labeling effort, learning with weak video-level supervision becomes a potential solution. In this paper, we propose a novel weakly supervised framework to recognize actions and locate the corresponding frames in untrimmed videos simultaneously. Considering that there are abundant trimmed videos publicly available and well-segmented with semantic descriptions, the instructive knowledge learned on trimmed videos can be fully leveraged to analyze untrimmed videos. We present an effective knowledge transfer strategy based on inter-class semantic relevance. We also take advantage of the self-attention mechanism to obtain a compact video representation, such that the influence of background frames can be effectively eliminated. A learning architecture is designed with twin networks for trimmed and untrimmed videos, to facilitate transferable self-attentive representation learning. Extensive experiments are conducted on three untrimmed benchmark datasets (i.e., THUMOS14, ActivityNet1.3, and MEXaction2), and the experimental results clearly corroborate the efficacy of our method. It is especially encouraging to see that the proposed weakly supervised method even achieves comparable results to some fully supervised methods.

关键词： Knowledge transfer weakly supervised learning self-attention mechanism representation learning action localization

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Symptoms-based Classification of Disease Using Various ML Algorithms and Interpretation using LIME and SHAP KERNELS 4

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

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4th IEEE Asian Conference on Innovation in technology, ASIANCON 2024

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

ISBN: (纸本)9798350354218

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. © 2024 IEEE.

关键词： Adversarial machine learning

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Discriminatively Constrained Semi-Supervised Multi-View Nonnegative Matrix Factorization with Graph Regularization

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Big Data Mining and Analytics 2024年第1期7卷 55-74页

作者： Guosheng Cui Ye Li Jianzhong Li Jianping Fan Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology Shenzhen 518055China University of Chinese Academy of Sciences Beijing 100049China School of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Nonnegative Matrix Factorization(NMF)is one of the most popular feature learning technologies in the field of machine learning and pattern *** has been widely used and studied in the multi-view clustering tasks because of its *** study proposes a general semi-supervised multi-view nonnegative matrix factorization *** algorithm incorporates discriminative and geometric information on data to learn a better-fused representation,and adopts a feature normalizing strategy to align the different *** specific implementations of this algorithm are developed to validate the effectiveness of the proposed framework:Graph regularization based Discriminatively Constrained Multi-View Nonnegative Matrix Factorization(GDCMVNMF)and Extended Multi-View Constrained Nonnegative Matrix Factorization(ExMVCNMF).The intrinsic connection between these two specific implementations is discussed,and the optimization based on multiply update rules is *** on six datasets show that the effectiveness of GDCMVNMF and ExMVCNMF outperforms several representative unsupervised and semi-supervised multi-view NMF approaches.

关键词： multi-view semi-supervised clustering discriminative information geometric information feature normalizing strategy

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Privacy Preservation of Clusters with Distance as Sensitivity Measure 16

Privacy Preservation of Clusters with Distance as Sensitivit...

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16th International Conference on Developments in eSystems engineering, DeSE 2023

作者： Sowmya, S.R. Manjunath, S.S. Dayananda Sagar Academy of Technology and Management Department of Information Science and Engineering Bangalore India Academy of Technology and Management Department of Computer Science and Engineering Mysore India

ISBN: (纸本)9798350381344

Preserving privacy in data mining is critical to protecting sensitive information while gaining valuable insights. Clustering algorithms using Euclidean distance measures often face privacy challenges due to the potential disclosure of sensitive information. This paper explores the application of micro aggregation as a protection method to improve privacy within clusters. The study focus on distance measure as Euclidian measure to identify sensitive data. The study evaluates the effectiveness of micro aggregation in mitigating privacy risks while maintaining clustering accuracy. © 2023 IEEE.

关键词： Data mining

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Grocery Purchase through Blockchain System 7

Grocery Purchase through Blockchain System

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7th IEEE Pune Section International Conference, PuneCon 2024

作者： Kumar, Boddu Manoj Rajeswari, G. Thanush, B.V. Krishna, Vamsi Sree, E. Divija Balakrishnan, Arunkumar Sathyabama Institute of Science and Technology School of Computer Science Engineering with Blockchain Technology Tamil Nadu Chennai600119 India Sathyabama Institute of Science and Technology School of Computer Science Engineering Tamil Nadu Chennai600119 India VIT-AP University School of Computer Science Engineering India Crm Institute of Technology School of Computer Science Engineering Karnataka Bengaluru India Sree Vidyanikethan Engineering College Rangampeta School of Computer Science Engineering with Artificial Intelligence Tirupati517102 India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Information Technology Manipal India

ISBN: (纸本)9798331527822

The integration of blockchain technology into the grocery purchasing process offers a transformative approach to enhancing transparency, security, and efficiency. This paper presents a comprehensive framework for a blockchain-based grocery system, detailing each step from user registration and identity verification to product listing, smart contract creation, and real-time inventory management. Consumers can place orders with confidence, knowing that payments are securely processed and supply chain logistics are fully transparent. The use of IoT devices ensures quality control during transportation, while smart contracts automate delivery and receipt confirmation. Additionally, the system supports consumer feedback and dispute resolution through immutable records and predefined arbitration mechanisms. This innovative application of blockchain technology aims to revolutionize the grocery sector, fostering trust and accountability among all stakeholders involved. Future research will explore the potential for AI integration and address scalability and interoperability challenges. © 2024 IEEE.

关键词： Smart contract

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