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An Effective Sensor Filtering Model for Mobile Devices Based on Long Short-Term Memory Technique in Sensor Archives

An Effective Sensor Filtering Model for Mobile Devices Based...

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Gilda, A. Jasmine Suresh, G. Sivamrugan, S. Kumar, R. Suresh Sushmitha, K. Prabu, R. Thandaiah R.M.K. Engineering College Department of Computer Science and Engineering Chennai India Sona College of Technology Department of Electrical and Electronics Engineering Salem India Sri Sairam Engineering College Department of Artificial Intelligence and Data Science Chennai India Vel Tech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Electronics and Communication Engineering Chennai India R.M.K. Engineering College Department of Electronics and Communication Engineering Chennai India Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350334357

It is expected that the Internet of Things (IoT) would be capable to deliver smart applications by aggregating data through many detectors spread across the natural world. A sensor registration system (SRS) is used by a mobile device in order to provide information obtained from ambient sensors. After this, the mobile device connects directly to get useful data. The Sensor Reporting System (SRS) has to offer information for sensors that have a chance of successfully connecting. This procedure is also known as sensor filtering, and it is dependent on the location of the user. In practice, the GPS may sometimes display an incorrect location, which results in anunable to connect. To improve the completion rate of interactions among mobile devices and environmental detectors, we provide a double collaboration approach that simultaneously collects GPS data and predictions from prior trajectories. Furthermore, we update the procedure for assessing detector filtering in SRS by integrating a Monte Carlo-based modeling stream within the procedure of gauging the resource supply rate. Realistic research shows that LSTM-based path forecasting can efficiently remove position irregularities and make up for the lack of position information. © 2023 IEEE.

关键词： Forecasting

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Double duality: variational primal-dual policy optimization for constrained reinforcement learning

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 18431-18473页

作者： Zihao Li Boyi Liu Zhuoran Yang Zhaoran Wang Mengdi Wang Department of Electrical and Computer Engineering Princeton University Princeton NJ Department of Industrial Engineering and Management Sciences Northwestern University IL Department of Statistics and Data Science Yale University CT

We study the Constrained Convex Markov Decision Process (MDP), where the goal is to minimize a convex functional of the visitation measure, subject to a convex constraint. Designing algorithms for a constrained convex MDP faces several challenges, including (1) handling the large state space, (2) managing the exploration/exploitation tradeoff, and (3) solving the constrained optimization where the objective and the constraint are both nonlinear functions of the visitation measure. In this work, we present a model-based algorithm, Variational Primal-Dual Policy Optimization (VPDPO), in which Lagrangian and Fenchel duality are implemented to reformulate the original constrained problem into an unconstrained primal-dual optimization. The primal variables are updated by model-based value iteration following the principle of Optimism in the Face of Uncertainty (OFU), while the dual variables are updated by gradient ascent. Moreover, by embedding the visitation measure into a finite-dimensional space, we can handle large state spaces by incorporating function approximation. Two notable examples are (1) Kernelized Nonlinear Regulators and (2) Low-rank MDPs. We prove that with an optimistic planning oracle, our algorithm achieves sublinear regret and constraint violation in both cases and can attain the globally optimal policy of the original constrained problem.

关键词： online learning function approximation reinforcement learning constrained optimization duality theory

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Two-Way Approach for Improved Real-Time Transmission in Fog-IoT-Based Health Monitoring System for Critical Patients

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computer Systems science & engineering 2023年第9期46卷 3815-3829页

作者： Abeera Ilyas Saeed Mahfooz Zahid Mehmood Gauhar Ali Muhammad ElAffendi Department of Computer Science University of PeshawarPeshawar25120Pakistan Department of Computer Engineering University of Engineering and TechnologyTaxila47050Pakistan The FAMLIR Group The University of LahoreLahore54000Pakistan EIAS Data Science and Blockchain Lab College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia

Health monitoring systems are now required,particularly for essential patients,following the COVID-19 pandemic,which was followed by its variants and other epidemics of a similar *** procedures and strategies are required,though,to react promptly to the enormous volume of real-time data offered by monitoring *** fog-based designs for IoT health systems typically result in enhanced services,they also give rise to issues that need to be *** this paper,we propose a two-way strategy to reduce network latency and usewhile increasing real-time data transmission of device gateways used for sensors by making educated judgments for connection setup with BS and task *** this,a simulation using iFogSim in the Eclipse IDE showed how effective the suggested strategy for massive IoT healthmonitoring systems *** algorithm is analyzed for network usage and latency,and the results reveal 20%–25%improvements compared to the existing methods regarding network usage and latency.

关键词： IoT fog computing delay healthcare network usage quality of service

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PAIL: Performance based Adversarial Imitation Learning Engine for Carbon Neutral Optimization

arXiv

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arXiv 2024年

作者： Ye, Yuyang Tang, Lu-An Wang, Haoyu Yu, Runlong Yu, Wenchao He, Erhu Chen, Haifeng Xiong, Hui Department of Management Science and Information Systems Rutgers Business School Rutgers University NewarkNJ United States Department of Data Science and System Security NEC Laboratories PrincetonNJ United States Department of Computer Science University of Pittsburgh PittsburghPA United States Department of Computer Science and Engineering The Hong Kong University of Science and Technology Guangzhou China

Achieving carbon neutrality within industrial operations has become increasingly imperative for sustainable development. It is both a significant challenge and a key opportunity for operational optimization in industry 4.0. In recent years, Deep Reinforcement Learning (DRL) based methods offer promising enhancements for sequential optimization processes and can be used for reducing carbon emissions. However, existing DRL methods need a pre-defined reward function to assess the impact of each action on the final sustainable development goals (SDG). In many real applications, such a reward function cannot be given in advance. To address the problem, this study proposes a Performance based Adversarial Imitation Learning (PAIL) engine. It is a novel method to acquire optimal operational policies for carbon neutrality without any pre-defined action rewards. Specifically, PAIL employs a Transformer-based policy generator to encode historical information and predict following actions within a multi-dimensional space. The entire action sequence will be iteratively updated by an environmental simulator. Then PAIL uses a discriminator to minimize the discrepancy between generated sequences and real-world samples of high SDG. In parallel, a Q-learning framework based performance estimator is designed to estimate the impact of each action on SDG. Based on these estimations, PAIL refines generated policies with the rewards from both discriminator and performance estimator. PAIL is evaluated on multiple real-world application cases and datasets. The experiment results demonstrate the effectiveness of PAIL comparing to other state-of-the-art baselines. In addition, PAIL offers meaningful interpretability for the optimization in carbon neutrality. Copyright © 2024, The Authors. All rights reserved.

关键词： Inverse problems

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EMPATHIC: Emulating Human-Like Multimodal Personality Architecture Through Thoughtful Human-AI Conversation

EMPATHIC: Emulating Human-Like Multimodal Personality Archit...

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International Conference on Confluence The Next Generation Information Technology Summit (Confluence)

作者： Varshini Devi I I. R. Oviya Kalpana Raja Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Section for Biomedical Informatics and Data Science School of Medicine Yale University New Haven Connecticut USA

In the evolving landscape of human-computer interaction, this paper introduces an innovative framework poised to revolutionize chatbot systems. Our framework, meticulously designed for emotionally aware multimodal chatbots, seamlessly integrates real-time face and emotion recognition with natural language processing. Currently in the developmental phase, the framework exhibits a distinctive proficiency in executing a diverse array of human instructions. These instructions span tasks such as generating detailed captions, object counting, and responding to general queries. Leveraging parameter-efficient fine-tuning from OpenFlamingo, augmented by the inclusion of the Low-rank Adapter (LoRA), our framework strategically prioritizes versatility and operational efficiency. The core of our approach lies in the establishment of instruction templates that intricately merge both vision and language data. This strategic integration forms the backbone of our multi-modality instruction tuning methodology, enhancing the adaptability of the chatbot. Recognizing the pivotal role of high-quality training data, we emphasize its significance in shaping the dialogue performance of the chatbot. As the framework progresses through its developmental stages, this research lays a robust foundation for the creation of a versatile conversational agent. The envisioned applications of this agent range from enhancing customer service experiences to providing valuable support in mental health scenarios.

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

Next-Gen Dermatology: Revolutionizing Dermatological Diagnos...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Yashas D M Shivani Kashyap Charan PR Hemanth Kumar GP Abhishek BK Deekshith C Dept of Artificial Intelligence and Machine Learning New Horizon College of Engineering Bengaluru Dept of Information Science and Engineering New Horizon College of Engineering Bengaluru Dept of Artificial Intelligence and Machine Learning BNM Institute of Technology Bengaluru Dept of Artificial Intelligence and Data Science Siddaganga Institute of Technology Tumkur Dept of Computer Science and Engineering Vidyavardhaka College of Engineering Mysore

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

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.

关键词： Deep learning Dermatology Medical services Transforms Chatbots Skin Real-time systems Convolutional neural networks Artificial intelligence Diseases

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Enhanced Edges Detection from Different Color Space 22

Enhanced Edges Detection from Different Color Space

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Proceedings of the 4th International Conference on Information Management & Machine Intelligence

作者： Sandeep Kumar Sharma Anil Kumar Uday Pratap Singh Computer and Communication Engineering Manipal University Jaipur India Department of Artificial Intelligence & Data Science Poornima Institute of Engineering & Technology India

ISBN: (纸本)9781450399937

In this article, a Cellular Automata based method has been introduced to process different types of color images i.e., RGB, HSV, CMYK, and YUV for detecting enhanced edges. Generally, Color models are represented with different channels; to maintain uniformity in the method, the proposed methodology converts the source data into gray scale data. The proposed methodology analysis all the pixels of the source image and maps a threshold. The threshold has been mapped with a range of values. The proposed method is experimented on number of data sets and claim accuracy 87% to 98%.

关键词： Color space Digital image Edge detection Threshold

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Anytime Incremental ρPOMDP Planning in Continuous Spaces

arXiv

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arXiv 2025年

作者： Benchetrit, Ron Lev-Yehudi, Idan Zhitnikov, Andrey Indelman, Vadim Department of Computer Science Israel Israel Department of Aerospace Engineering Israel Department of Data and Decision Sciences Technion - Israel Institute of Technology Haifa32000 Israel

Partially Observable Markov Decision Processes (POMDPs) provide a robust framework for decision-making under uncertainty in applications such as autonomous driving and robotic exploration. Their extension, ρPOMDPs, introduces belief-dependent rewards, enabling explicit reasoning about uncertainty. Existing online ρPOMDP solvers for continuous spaces rely on fixed belief representations, limiting adaptability and refinement - critical for tasks such as information-gathering. We present ρPOMCPOW, an anytime solver that dynamically refines belief representations, with formal guarantees of improvement over time. To mitigate the high computational cost of updating belief-dependent rewards, we propose a novel incremental computation approach. We demonstrate its effectiveness for common entropy estimators, reducing computational cost by orders of magnitude. Experimental results show that ρPOMCPOW outperforms state-of-the-art solvers in both efficiency and solution quality. © 2025, CC BY.

关键词： Markov processes

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Deep Learning-Driven Black Box Approach For Image Segmentation

Deep Learning-Driven Black Box Approach For Image Segmentati...

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Nuthalapati Sudha Sai Bhargav Kasetty K. Sai Madhuri Jajala Nikitha Issac Neha Margret K. Rajakumar School of Engineering Mahindra University Hyderabad Telangana India School of Computer Science and Engineering V.I.T – University Vellore Tamilnadu India Dept. of Computer Science and Data Science BVRIT Narsapur Hyderabad Telangana India Dept. of CSE – AIML & IoT VNR VIET Hyderabad Telangana India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

The diagnosis of a range of eye disorders needs to categorize the retinal vessels. computerized implementation of this process is becoming increasingly essential for automated screening systems for retinal diseases. To achieve a more accurate extraction of the retinal vessels, a new pre-processing step is proposed. These proposed pre-processes are also compared to other algorithms to assess their impact. The proposed pre-processing process consists of two phases. The first phase is the implementation and validation of the pre-processing modules, and the second phase is the implementation of these pre-processes onto the retinal vessels that were to be extracted. To achieve a significantly improved segmented vessel image, the proposed pre-process phase employs a common image-processing technique. In recent years, there has been a great deal of focus on retinal vessel identification studies, and the importance of assessing and confirming the findings of retinal vessel segmentation.

关键词： Image segmentation Analytical models Accuracy Computational modeling Retinal vessels Diseases

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Emergency vs Non-Emergency Vehicle Classification: Enhancing Intelligent Traffic Management Systems

Emergency vs Non-Emergency Vehicle Classification: Enhancing...

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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： Madan S Kishan Shetty Ashwini Kodipalli Trupthi Rao Kumaraswamy S Dept. of Computer Science and Engineering Global Academy of Technology Bengaluru Karnataka India Dept of Artificial Intelligence and Data Science Global Academy of Technology Bengaluru Karnataka India Department of Computer Science and Engineering Global Academy of Technology Bengaluru Karnataka India

Due to its dense population, India frequently experiences traffic congestion, which puts lives in danger by trapping vehicles like ambulances, police cars, and fire trucks which run on road for emergency purpose. It becomes crucial to give these vehicles priority and allow for their easy passage. However, it becomes challenging or even impossible for traffic police to effectively handle such circumstances. Therefore, there is a requirement for an automated system that can locate emergency vehicles in high-traffic areas, alert the controller, or drive itself to direct other vehicles to make room. This study suggests an automated method for identifying emergency vehicles from CCTV footage that makes use of deep convolutional neural networks (CNN). The objective is to effectively identify and classify emergency vehicles in real-time, leveraging the power of advanced object detection techniques. With an accuracy of 91.73% and a loss of 0.2120, the suggested technique outperformed existing optimizers in accurately recognizing and classifying emergency vehicles.

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