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12th International Conference on learning Representations, ICLR 2024

作者： Tang, Zeyu Wang, Jialu Liu, Yang Spirtes, Peter Zhang, Kun Department of Philosophy Carnegie Mellon University United States Computer Science and Engineering Department University of California Santa Cruz United States Machine Learning Department Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates ByteDance Research China

We reveal and address the frequently overlooked yet important issue of disguised procedural unfairness, namely, the potentially inadvertent alterations on the behavior of neutral (i.e., not problematic) aspects of data generating process, and/or the lack of procedural assurance of the greatest benefit of the least advantaged individuals. Inspired by John Rawls's advocacy for pure procedural justice (Rawls, 1971;2001), we view automated decision-making as a microcosm of social institutions, and consider how the data generating process itself can satisfy the requirements of procedural fairness. We propose a framework that decouples the objectionable data generating components from the neutral ones by utilizing reference points and the associated value instantiation rule. Our findings highlight the necessity of preventing disguised procedural unfairness, drawing attention not only to the objectionable data generating components that we aim to mitigate, but also more importantly, to the neutral components that we intend to keep unaffected. © 2024 12th International Conference on learning Representations, ICLR 2024. All rights reserved.

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LSTM deep learning model for Alzheimer's disease prediction based on cost-effective time series cognitive scores 5

LSTM deep learning model for Alzheimer's disease prediction ...

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5th International IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2023

作者： Saleh, Hager El-Rashidy, Nora Abuhmed, Tamer El-Sappagh, Shaker South Valley University Faculty of Computers and Artificial Intelligence Hurghada Egypt Kafrelsheiksh University Faculty of Artificial Intelligence Machine Learning and Information Retrieval Department Egypt Sungkyunkwan University College of Computing and Informatics Korea Republic of Galala University Faculty of Computer Science and Engineering Suez435611 Egypt

ISBN: (纸本)9798350381030

Alzheimer's disease (AD) is a complex chronic neurodegenerative disease that propagates over time. Deep learning (DL) models can be used to learn time series data to extract deep temporal features and make robust decisions. The fusion of multimodal time series data has been proven to enhance model performance [1]. For instance, cognitive scores (CSs) including clinical dementia rating, and Alzheimer's disease assessment scores have been integrated with other modalities like MRI to predict the future status of AD patients [2]. They enhanced the performance of DL models. These scores can be collected easily and in a cost-effective way in hospitals. No study in the literature has built a deep learning model to predict AD based on the cost-effective and multimodal time series data of CSs. In this study, we propose an LSTM-based DL architecture to predict AD based on multiple time series CSs. The proposed model has been optimized using a Bayesian optimizer to select the best architecture, and it has been compared with multiple classical machine learning models like random forest and others. The proposed LSTM architecture achieved better results than other models and provided stable and robust performance. © 2023 IEEE.

关键词： Data fusion

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Optimizing Semantic Segmentation for Enhanced Football Analytics: A Pixel-level Approach 2

Optimizing Semantic Segmentation for Enhanced Football Analy...

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2nd International Conference on machine learning and Data engineering, ICMLDE 2023

作者： Malakreddy, Bharathi Venkataraman, Sadanand Khan, Mohammed Sinan Nidhi Padmanabhuni, Srinivas Natarajan, Santhi Research & Consultancy Cell BMS Institute of Technology & Management Bengaluru India Department of Computer Science and Engineering Research Shiv Nadar University Chennai India Department of Artificial Intelligence & Machine Learning BMS Institute of Technology & Management Bengaluru India *** Bengaluru India

Semantic Segmentation, a pivotal technique in image analysis, is adeptly leveraged in this research to bolster sports analytics, with a concentrated focus on football. A comprehensive pipeline is unveiled for an in-depth analysis of a select portion of the IAUFD 100k dataset, encompassing 2030 manually annotated football images. The methodology entails a thorough evaluation and comparison of diverse semantic segmentation models, supplemented by the integration of advanced pre-processing strategies and optimal training techniques. Such a holistic approach culminates in a marked enhancement in model performance, as evidenced by a significant uptick in the mean Intersection over Union (mIoU). This research offers granular, object-oriented insights that substantially augment player tracking, action recognition, and event detection in football. The conclusive remarks of the study highlight prospective avenues for further research, emphasizing the potential incorporation of Explainable AI and advanced Metamorphic and Security Testing to fortify sports analytics. © 2024 Elsevier B.V.. All rights reserved.

关键词： Sports

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Unveiling Customer Segmentation Patterns in Credit Card Data using K-Means Clustering: A machine learning Approach

Unveiling Customer Segmentation Patterns in Credit Card Data...

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2023 IEEE International Conference on Modelling, Simulation and Intelligent Computing, MoSICom 2023

作者： Priyadarshni, Sakshi Fathima, Rakshan Urolagin, Siddhaling Bongale, Anupkumar M. Dharrao, Deepak Sudhakar Birla Institute of Technology and Science Dept. of Computer Science Pilani Dubai United Arab Emirates Symbiosis Institute of Technology Dept. of Artificial Intelligence and Machine Learning Pune India Symbiosis Institute of Technology Dept. of Computer Science & Engineering Pune India

ISBN: (纸本)9798350393415

Customer segmentation plays a crucial role in business development by providing deep insights into customer behaviors and preferences, facilitating effective and personalized marketing strategies. However, traditional segmentation methods often fall short of accurately analyzing large and complex datasets. In recent years, the application of machine learning techniques has transformed the field of customer segmentation, enabling automated analysis, enhanced accuracy, and the discovery of intricate patterns and key trends. This research paper provides a detailed review of the application of K-Means clustering in credit card companies for effective customer segmentation. It explores the benefits, challenges, and practical considerations of utilizing K-Means clustering, along with real-world case studies highlighting trends and potential advancements for using K-Means clustering in credit card customer segmentation. © 2023 IEEE.

关键词： K-means clustering

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Early Detection of Black Fungus Using Deep learning Models for Efficient Medical Diagnosis 4

Early Detection of Black Fungus Using Deep Learning Models f...

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4th IEEE International Conference on Emerging Techniques in Computational intelligence, ICETCI 2024

作者： Hassan, Esraa Saber, Abeer El-Kenawy, El-Sayed M. Bhatnagar, Roheet Shams, Mahmoud Y. Kafrelsheikh University Faculty of Artificial Intelligence Dept. of Machine Learning Kafrelsheikh33516 Egypt Damietta University Faculty of Computers and Artificial intelligence Information technology Dept. Damietta34517 Egypt Delta Higher Institute of Engineering and Technology Dept. of Communications and Electronics Mansoura35111 Egypt Manipal University Department of Computer Science and Engineering Rajasthan Jaipur India

ISBN: (纸本)9798350387063

Mucormycosis, also known as black fungus, is a rare infection caused by mould that can affect the lungs, brain, skin, and sinuses. People with weakened immune systems due to underlying health conditions (e.g., organ transplant, uncontrolled diabetes, cancer, neutropenia, AIDS, and COVID-19) or drug use are more likely to contract the infection. Recent reports have indicated that a small number of COVID-19 survivors with co-morbidities are particularly vulnerable to black fungus. Therefore, recovered COVID-19 patients should seek medical attention immediately if they experience any mucormycosis symptoms. Patients with COVID-19 have numerous concerns regarding this life-threatening infection, particularly those with immunocompromised states such as solid organ transplant (SOT) and hematopoietic stem cell transplant (HSCT) or tumors, who are more likely to be infected. Early diagnosis and treatment are essential to prevent patient mortality. In this paper, we present the Multi-Class Black Fungus (MCBF) dataset, which consists of three compressed folders containing images. The first folder includes 66 images of eye black fungus after data preprocessing, while the second folder contains 63 images of mouth black fungus after data preprocessing. The third contains 62 images of the mouth after applying data preprocessing, and the last one contains 79 images of black skin fungus after applying data preprocessing. The MCBF dataset contains images that aid in training and validation when using deep learning algorithms to recognise and classify black fungus diseases, then we apply a resnet(50) pretrained model by using MCBF accuracy reaches 96.12%. © 2024 IEEE.

关键词： COVID-19

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Future Prospects of Agriculture Using IoT and machine learning 1

Future Prospects of Agriculture Using IoT and Machine Learni...

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1st IEEE International Conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Hazra, Sudipta Mondal, Arindam Dey, Prasenjit Prabhakar, Santosh Jha, Amit Kumar Rakshit, Nilendu Asansol Engineering College Computer Science and Engineering Burdwan Asansol India Dr. B. C. Roy Engineering College Electrical Engineering Burdwan Durgapur India National Institute of Technology Computer Science and Engineering Odisha Rourkela India Asansol Engineering College Artificial Intelligence and Machine Learning Burdwan Asansol India Asansol Engineering College Information Technology Burdwan Asansol India Nshm Knowledge Campus Computer Science and Engineering Burdwan Durgapur India

ISBN: (纸本)9798350377002

Agriculture balances both the world's food supply and the raw resources required by numerous businesses. The combination of Internet of Things (IoT) and machine learning (ML) technologies has revolutionized the agriculture industry, enabling efficient and data-driven decision-making processes. This paper explores the future prospects of using IoT and ML in agriculture, highlighting their potential applications, benefits, and challenges. It examines how IoT devices, such as sensors, drones, and smart irrigation systems, can collect present data on soil situations, weather patterns, crop condition, and resource utilization. ML algorithms can then analyze this data to provide actionable insights for optimizing agricultural practices, improving crop yield, and reducing resource wastage. The paper discusses emerging trends, such as precision agriculture (PA), smart farming, and autonomous farming, which leverage the combined power of IoT and ML. It also addresses the challenges of data security, privacy, and scalability associated with implementing IoT and ML in agriculture. By understanding the future prospects of IoT and ML in agriculture, stakeholders in the agricultural sector can harness these technologies to enhance productivity, sustainability, and profitability. © 2024 IEEE.

关键词： Smart agriculture

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An Effective Method for Distributing Workloads in Smart City using Sensor Networks Enabled by Fog

An Effective Method for Distributing Workloads in Smart City...

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Nithiyanandam Velvizhi, R. Priyadharshini, S.P. Poongavanam, N. SRM Institute of Science and Technology Kattankulathur Campus Department of Computing Technologies Chengalpattu Kattankulathur India Sathyabama Institute of Science and Technology OMR Department of Computer Science and Engineering Chennai India St' Joseph College of Engineering OMR Department of Artificial Intelligence and Machine Learning Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331522988

By linking sensor nodes to fog nodes in critical locations, the Distributing Workloads in Fog Enabled Smart City (DW-FESC) technique efficiently handles the problem of distribution of workloads in smart cities. To get over the processing limits of IoT devices, this setup makes sure that complicated tasks are sent off to neighbouring fog nodes. The plan uses a load balancer and an updated greedy algorithm to evenly distribute work, which speeds up execution and keeps loads from becoming too heavy. Additionally, it has a deadline-based task execution mechanism that makes sure the most important jobs are done on time. The simulation results show that DW-FESC keeps the variance from the mean execution times to a minimum while considerably improving task execution times in comparison to other methods. With the use of fog computing, DW-FESC improves the dependability, efficiency, and responsiveness of smart city applications, leading to greener city life. © 2024 IEEE.

关键词： Fog computing

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Enhancing the Prediction Accuracy of Brain Tumor Detection by using Fuzzy C-Means Segmentation and Support Vector machine 5

Enhancing the Prediction Accuracy of Brain Tumor Detection b...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Revathi, M. Kalaiarasi, P. Jeevitha, S. Akilandasowmya, G. St. Joseph's Institute of Technology Artificial Intelligence & Data Science Chennai India Kalasalingam University Department of Machine Learning Krishnankoil India Jerusalem College of Engineering Information Technology Chennai India Sri Sairam Institute of Technology Computer and Communication Engineering Chennai India

ISBN: (纸本)9798331518097

Brain is an important organ of the human body that helps in the proper functioning of the all the organs. Without brain, the functions of other organs will not happen since they cannot run without the commands of brain. Brain tumor refers to a situation where an abnormal group of cells are found in some part of the brain. There are many types of brain tumor that are identified. The tumors in brain is independent of age and it can grow at anytime. The symptoms that is exhibited by the brain tumor depends on the size of the tumor cell as well as its location. machine learning algorithms helps in the earlier identification of the disease which can be used to give proper treatment. In this paper, a fuzzy c means based segmentation of image is done. Then classification of the segmented image is done by using Support Vector machine. The results shows 98% percentage of accuracy. © 2024 IEEE.

关键词： Support vector machines

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Exploring Deep Neural Networks for Accurate Diabetic Retinopathy Prediction

Exploring Deep Neural Networks for Accurate Diabetic Retinop...

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2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Kujani, T. Arivubrakan, P. Raju, K. Sankar, P. Mani Reddy, B. Surekha Bhavana, E. Computer Science And Engineering Vel Tech Rangarajan Dr. Sagunthala R&d Institute Of Science And Technology Chennai India Artificial Intelligence And Machine Learning Vel Tech Rangarajan Dr. Sagunthala R&d Institute Of Science And Technology Chennai India Artificial Intelligence And Data Science Vel Tech Rangarajan Dr. Sagunthala R&d Institute Of Science And Technology Chennai India

ISBN: (纸本)9798350352689

Diabetic Retinopathy (DR) emerges as a serious complication associated with diabetes mellitus, causing damage to retinal blood vessels, and resulting in vision loss. The critical need for early detection and effective management to curb the progression towards vision impairment and potential blindness underscores the significance of this issue. To address these challenges, have propose a novel deep learning system utilizing a Densely connected Convolutional Network (DenseNet-201) for the efficient detection of DR. This model is specifically trained to classify fundus images into six distinct categories: no DR, mild, moderate, and severe, proliferative DR, and hard exudates. The datasets utilized in this study include the publicly available Diabetic Retinopathy Detection 2015 and Aptos 2019 blindness detection datasets from Kaggle. The proposed system comprises multiple stages, including Data Collection, Preprocessing, Augmentation, and Modeling. During the preprocessing phase, normalization techniques are to eliminate biases or distortions present in the fundus images. Furthermore, the augmentation of data is employed to enhance the diversity and quantity of the training dataset. In the modeling stage, have designed and implemented the deep learning model, with the robust and high-performing DenseNet-201 architecture selected as the model's backbone. The model exhibits a significant accuracy of 87.7% in its classification task. Additionally, have introduced a Deep Neural Network (DNN) model, achieving an impressive accuracy rate of 95.8%. These results underscore the effectiveness of our proposed system in accurately and efficiently detecting DR. The primary objective of this research is to develop a reliable and automated system for Diabetic Retinopathy detection, with the overarching goal of improving the quality of life for individuals affected by diabetes. © 2024 IEEE.

关键词： Deep neural networks

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Skin Diseases Detection Using LBP and WLD: An Ensembling Approach

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SN computer science 2024年第1期5卷 72页

作者： Banerjee, Arnab Sarkar, Somenath Nasipuri, Mita Das, Nibaran Department of Computer Science and Engineering Jadavpur University Jadavpur West Bengal Kolkata 700032 India Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning) Dr. B. C. Roy Engineering College Jemua Road Fuljhore West Bengal Durgapur 713206 India Department of Dermatology Venereology and Leprosy Calcutta School of Tropical Medicine West Bengal Kolkata 700073 India

In all developing and developed countries globally, skin diseases are becoming a prevalent health problem for humans of all age groups. Skin diseases create anxiety, affect mental health, worsen working life, and sometimes cause social isolation. Detecting skin diseases in the early stage is very important because early treatment can be helpful in such cases. There are very few dermatologists in rural and semi-urban areas in a country like India. To overcome the problem, a machine learning technique has been developed to detect three prevalent skin diseases—Leprosy, Tinea versicolor, and Vitiligo from the images of the skin lesions. The proposed approach extracts the texture from the images of the skin regions affected by such diseases using Weber’s local descriptor and Local binary pattern. Then the representations are ensembled to prepare the feature vector. This ensemble technique achieved 91.38% accuracy using a multilevel support vector machine, using the features extracted from different regions of the skin lesions area utilising the centre of gravity based rotation invariant Weber local descriptor and Local binary pattern. Some popular deep learning networks such as MobileNet, ResNet152, GoogLeNet, DenseNet121, and ResNet101 were used to compare the performance of the proposed approach with the deep learning models. ResNet101 achieved 89% accuracy, i.e. best among all the deep learning models used in this study. The proposed ensemble technique outperformed the top performing deep learning model by 2.38%. This technique will be helpful for the early screening of these skin diseases. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： LBP Leprosy Multi-SVM Pattern recognition STM Tinea versicolor Vitiligo WLD WLDRI

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