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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Kollovieh, Marcel Charpentier, Bertrand Zügner, Daniel Günnemann, Stephan School of Computation Information and Technology Technical University of Munich Germany Munich Data Science Institute Germany Munich Center for Machine Learning Germany Pruna AI Germany Microsoft Research AI for Science United States

Hierarchical clustering has usually been addressed by discrete optimization using heuristics or continuous optimization of relaxed scores for hierarchies. In this work, we propose to optimize expected scores under a probabilistic model over hierarchies. (1) We show theoretically that the global optimal values of the expected Dasgupta cost and Tree-Sampling divergence (TSD), two unsupervised metrics for hierarchical clustering, are equal to the optimal values of their discrete counterparts contrary to some relaxed scores. (2) We propose Expected Probabilistic Hierarchies (EPH), a probabilistic model to learn hierarchies in data by optimizing expected scores. EPH uses differentiable hierarchy sampling enabling end-to-end gradient descent based optimization, and an unbiased subgraph sampling approach to scale to large datasets. (3) We evaluate EPH on synthetic and real-world datasets including vector and graph datasets. EPH outperforms all other approaches quantitatively and provides meaningful hierarchies in qualitative evaluations. © 2024 Neural information processing systems foundation. All rights reserved.

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Hybrid Design for Privacy Preserved Image Representation in a Cloud Environment 1

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Vijay, K. Sorna Shanthi, D. Jaeyalakshmi, M. Vignesh, P. Yuvaraja, M. Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India Department of Artificial Intelligence and Data Science Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India

ISBN: (数字)9789819789467

ISBN: (纸本)9789819789450

The digital era has made seamless sharing and keeping of media such as images on cloud platforms an integral part of our lives. Still, there is a big issue about user privacy and data security in these repositories. We have thus created a very strong system that is able to do image encryption and decryption so as to protect the privacy of users while at the same time making their pictures accessible. Our system uses hybrid encryption where we try to prevent even cloud service providers from accessing the original files hence ensuring that only the owner who has been authenticated through password based authentication can decrypt or access them. In this way, it allows maintaining high privacy standards without sacrificing user convenience. It starts with preparing photos for encryption so that they are kept safe during transmission and storage. When needed by a user, images are decrypted by our system and become available only to authorized users. Consequently, users can comfortably leave their private photo albums on cloud storage systems knowing too well that their friends cannot access them thereby increasing trust in using cloud services for storing sensitive information. The aim of this work is to increase user privacy and data security in cloud repositories through employing this encryption technique. The users are guaranteed that their private details are not exposed to others enabling them to use cloud storage efficiently without interfering with their privacy at all. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud platforms

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Next-Gen Dermatology: Revolutionizing Dermatological Diagnostics with AI and Emergency Care Solutions 3

Next-Gen Dermatology: Revolutionizing Dermatological Diagnos...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Yashas, D. Kashyap, M. Shivani Charan, P.R. Hemanth Kumar, G.P. Abhishek, B.K. Deekshith, C. New Horizon College of Engineering Dept of Artificial Intelligence and Machine Learning Bengaluru India New Horizon College of Engineering Dept of Information Science and Engineering Bengaluru India Bnm Institute of Technology Dept of Artificial Intelligence and Machine Learning Bengaluru India Siddaganga Institute of Technology Dept of Artificial Intelligence and Data Science Tumkur India Vidyavardhaka College of Engineering Dept of Computer Science and Engineering Mysore India

ISBN: (纸本)9798331515911

Skin health conditions should be diagnosed and treated on time so as to prevent complications and promote better health results. This paper presents an AI-based system that makes use of Convolutional Neural Networks with the InceptionV3 Architecture for predicting skin diseases at higher levels. The model, which is based on the large DermaNet dataset, classifies skin disorders fast and effectively. The system is coupled with a simple Telegram based application for the users to get a complete solution. A user can upload a photo of the affected skin area and get a diagnosis within seconds, an explanation of the disease and how severe it is in percentage. The bot also has a location and contact sharing feature that assists users in finding nearby doctors and clinics that can provide further assistance. It further provides on the spot appropriate cures for the particular condition diagnosed. Moreover, this makes it possible for the customer to fill the healthcare void in particular the neglected ones by making it easy for them to access real time high quality services. By equipping users with relevant information and appropriate actions required at the right time, this project has showcased how AI has the ability to transform the dermatological field. © 2025 IEEE.

关键词： Convolutional neural networks

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A recurrent neural network architecture for android mobile data analysis for detecting malware infected data

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Soft Computing 2024年第21期28卷 12917-12928页

作者： Murugan, Prabhu Manimaran, A. Sundar, Ramesh Dakshinamoorthy, Prabakar Rajaram, Gnanajeyaraman Garg, Shruti Department of ECE Saveetha School of Engineering Simats Chennai602105 India Department of Artificial Intelligence and Machine Learning Saveetha School of Engineering Simats Chennai602105 India Department of Netwoking and Communication School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India Department of Data Science and Business System School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India Department of Applied Machine Learning Saveetha School of Engineering Saveetha Institute of Medical and Technological Sciences Tamilnadu Chennai India Birla Institute of Technology Mesra Ranchi India

One of the latest modern communication devices is a mobile device seriously affected by multiple malware. Malware is a virus software installed automatically by hackers on various computing devices. Malware corrupts the system software, kills *.exe files, and tries to access user-sensitive data from the device. Around the world, 80% of people use smartphones, and 70% use Android phones. All Android phones are installed with anti-virus software but not used properly. Thus, it creates a pathway for malware to enter Android mobile devices and deploy vulnerabilities. Several earlier methods have focused on using the malware detection method by analyzing static data, which cannot identify the dynamic behavior of the malware. Thus, this paper implemented one of the deep learning algorithms, RNN, for identifying and detecting malware activities in Android mobile devices statically and dynamically. An IoT network architecture is proposed for experimenting with the RNN algorithms verifying the performance. The results of the RNN method show that it is better than other models in malware detection in mobile devices. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Android malware

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Intelligent Posture Detection System for Improved Ergonomics

Intelligent Posture Detection System for Improved Ergonomics

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Patil, Jay Joshi, Suvarna Mulla, Rahesha Yadav, Kushal Mit School of Computing Mit Adt University Department of Computer Science and Eng. Pune India University Department of Artificial Intelligence and Machine Learning Pune India Indian Institute of Technology Madras Department of Data Science India

ISBN: (纸本)9798331527495

This paper introduces a cost-effective smart posture detection system aimed at promoting ergonomic sitting habits and preventing musculoskeletal disorders. The system integrates ultrasonic sensors for backrest proximity measurement and load cells for seat weight distribution analysis. By analyzing these inputs, it identifies posture irregularities and provides real-time feedback for corrective action. Testing results demonstrate the system's reliability, achieving a detection accuracy of 99% across users with varying body types and postures. The proposed hybrid approach ensures simplicity, affordability, and scalability, making it a viable solution for homes, workplaces, and schools. This research contributes to the growing field of smart health monitoring systems, offering a practical alternative to traditional posture correction methods. © 2025 IEEE.

关键词： Ultrasonic testing

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ML Powered Analytics for Sensing Demand in Consumer Industry 6

ML Powered Analytics for Sensing Demand in Consumer Industry

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6th IEEE Pune Section International Conference, PuneCon 2023

作者： Shanbhogue, Rahul Yashwanth, N.D. Paduri, Anwesh Reddy Ramakrishna, Panish Prabhu, Srikanth Dhulipati, Muralidhara Sarma Darapaneni, Narayana Pes University Data Science & Machine Learning Bangalore India Koneru Lakshmaiah Eduaction Foundation Department of Commerce and Management Hyderabad India Great Learning/Northwestern University Aiml IL United States

ISBN: (纸本)9798350324204

The consumer goods industry is facing significant challenges in meeting the consumer's everevolving demands and preferences. The e-commerce and online delivery brought in the ease of access to purchase products, while the popularity of social media brought the social sentiment angle which are impacting customer purchases patterns. These factors are leading to a rise in influencer marketing campaigns and have enabled businesses or brands to reach consumers faster. The goal of the study is to address these challenges and to explore the use of machine learning and big data analytic techniques to provide insights by understanding consumer purchase patterns, sales data, market and social media trends, news feed and stock levels to predict future demand for products in advance which will better enable inventory planning. © 2023 IEEE.

关键词： Big data

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Predicting Agricultural Crop Damage Caused by Unexpected Rainfall Using Deep learning

Predicting Agricultural Crop Damage Caused by Unexpected Rai...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Fulkar, Bhushan Patil, Pawan Srivastav, Gaurav Mahale, Promod Maharashtra Wardha India Artificial Intelligence and Data Science Faculty of Engineering and Technology Maharashtra Wardha India Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha India

ISBN: (纸本)9798350375237

Efficient allocation of resources and timely agricultural interventions depend on the precise identification of crop loss at the field parcel level. Using recent data from 2018 to 2023, this study investigates the integration of deep learning techniques with real-time field photography to classify agricultural field parcels into those experiencing crop loss and those not. We build and assess deep learning models specifically for crop loss classification using a dataset from the Finnish Food Authority (FFA) that combines on-the-ground photos taken during field visits with comprehensive field parcel information. In this work, we use convolutional neural networks (CNNs) to examine the visual characteristics that we have extracted from field photos in order to identify the subtleties that may indicate crop health or possible loss. Our goal is to improve the model's generalization capabilities over a variety of crop types and agricultural landscapes by training it on a combination of field parcel data and corresponding photographs. © 2024 IEEE.

关键词： Image segmentation

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Leveraging Patient Flow data to Reduce Hospital Readmissions: A Study Using Medical Dashboards 4

Leveraging Patient Flow Data to Reduce Hospital Readmissions...

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4th International Conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Deshmukh, Santoshi Pacharaney, Utkarsha Datta Meghe Institute of Higher Education and Research Department of Artificial Intelligence and Data Science Wardha India Datta Meghe Institute of Higher Education and Research Department of Artificial Intelligence and Machine Learning Wardha India

ISBN: (纸本)9798331523923

Transparent reporting of patient outcomes is becoming more and more common as the inequalities in health outcomes amongst groups are being more widely acknowledged. In addition, documenting health outcomes over time across subgroups will be necessary for pay-for-equity programs like those put forth by the Centers for Medicare and Medicaid. One way to visualize data in the context of healthcare is through dashboards, which may be used to identify important differences in clinical outcomes, direct focused efforts toward quality improvement, and eventually enhance health equity. We assess every study that has documented the effective creation of a disparity dashboard in this viewpoint, and we also publish the information gathered and any unexpected repercussions that have been mentioned. We offer a framework for the systematic enhancement of equity via reward systems to lessen health inequalities and incentives for gathering and reporting health data. essential elements of efficient hospital management. A sophisticated approach is necessary to address the impact that hospital leadership has on patient outcomes. Because they embody patient-centered care in their everyday lives, these executives serve as the company's role models. The beliefs, vision, and leadership style of healthcare professionals greatly influence their behavior and performance. Competent leaders who inspire and empower their staff foster a culture of responsibility, creativity, and cooperation. They exhort healthcare professionals to put their patients' safety, satisfaction, and well-being first. Strategic planning, resource allocation, and decision-making are all components of good leadership that support the hospital's goal of offering the highest quality of care. essential elements of efficient hospital management. A sophisticated approach is necessary to address the impact that hospital leadership has on patient outcomes. Because they embody patient-centered care in their everyday lives, these ex

关键词： Resource allocation

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A Comparative Analysis of Rule-based and AI-Driven Systems for Improving Customer Satisfaction and Engagement in E-Commerce using Chatbots Powered by Artificial Intelligence 6

A Comparative Analysis of Rule-based and AI-Driven Systems f...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Deshmukh, Santoshi Gundewar, Swapnil Department of Artificial Intelligence and Data Science Datta Meghe Institute of Higher Education and Research Wardha India Department of Artificial Intelligence and Machine Learning Datta Meghe Institute of Higher Education and Research Wardha India

ISBN: (纸本)9798331522667

Chatbots: Chatbots, as robots, are the most recent and intelligent live chat communication devices. Furthermore, these days, it is commonly one of the customer service methods as it is also perfect for providing an array o f services, like personalized offers, individual communication, responsive customer support, etc. The interaction between banks and other financial institutions and their consumers has been entirely transformed due to the marriage of financial technology and artificial intelligence (AI). This scientific investigation deals with artificial intelligence (AI) technology for better communication among people;its special focus is on the effective use of chatbots and natural language processing (NLP) in banking support. Banks are increasingly turning their efforts to AI-driven chatbots as valuable means of enhancing customer support while channeling communication effectively. We are now at the point where human creativity and technology will combine and reimagine finance principles. © 2025 IEEE.

关键词： Customer satisfaction

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Optimal bounds for p sensitivity sampling via 2 augmentation 41

Optimal bounds for p sensitivity sampling via 2 augmentation

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41st International Conference on machine learning, ICML 2024

作者： Munteanu, Alexander Omlor, Simon Dortmund Data Science Center Faculties of Statistics and Computer Science TU Dortmund University Dortmund Germany Faculty of Statistics TU Dortmund University Dortmund Germany Lamarr-Institute for Machine Learning and Artificial Intelligence Dortmund Germany

data subsampling is one of the most natural methods to approximate a massively large data set by a small representative proxy. In particular, sensitivity sampling received a lot of attention, which samples points proportional to an individual importance measure called sensitivity. This framework reduces in very general settings the size of data to roughly the VC dimension d times the total sensitivity S while providing strong (1 ± Ε) guarantees on the quality of approximation. The recent work of Woodruff & Yasuda (2023c) improved substantially over the general Õ(Ε−2Sd) bound for the important problem of p subspace embeddings to Õ(Ε−2S2/p) for p ∈ [1, 2]. Their result was subsumed by an earlier Õ(Ε−2Sd1−p/2) bound which was implicitly given in the work of Chen & Derezinski (2021). We show that their result is tight when sampling according to plain p sensitivities. We observe that by augmenting the p sensitivities by 2 sensitivities, we obtain better bounds improving over the aforementioned results to optimal linear Õ(Ε−2(S+d)) = Õ(Ε−2d) sampling complexity for all p ∈ [1, 2]. In particular, this resolves an open question of Woodruff & Yasuda (2023c) in the affirmative for p ∈ [1, 2] and brings sensitivity subsampling into the regime that was previously only known to be possible using Lewis weights (Cohen & Peng, 2015). As an application of our main result, we also obtain an Õ(Ε−2µd) sensitivity sampling bound for logistic regression, where µ is a natural complexity measure for this problem. This improves over the previous Õ(Ε−2µ2d) bound of Mai et al. (2021) which was based on Lewis weights subsampling. Copyright 2024 by the author(s)

关键词： Importance sampling

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