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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Elangovan, Vetrimani Dhawaleswar Rao, C.H. Arul Prasath, A. Padmapriya, G. Presidency University Department of Computer Science and Engineering Karnataka Bangalore India Centurion University of Technology and Management Department of Computer Science and Engineering Odisha Gajapati Sitapur India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology School of Computing Department of Computing Technologies Tamil Nadu Chennai India

ISBN: (纸本)9781665462167

In Mobile Wireless Sensor Network (MWSN), sensor nodes build an active wireless network lacking existing structure. The MWSN contains several mobile sensor nodes that move randomly in the topology. In MWSN, Diminishing the packet drops is a significant challenge. Several approaches are introduced to handle packet losses. However, these approaches can't be able to manage the packet losses efficiently. To solve this issue, the Cooperative nodes for diminishing the packet drops in MWSN are used. In this approach, the cooperative nodes decrease packet loss during data transmission. In this approach, the suitable nodes observe every data transmission inside the communication range nodes. If suppose, any packet losses during data transmission, these cooperative nodes are captured the lost packets and then forward them to the near neighbor nodes. Finally, the data reach the destination via a regular route. As a result, avoids packet losses in the MWSN. Simulation results show that the proposed method performs better than the existing mechanisms. © 2023 IEEE.

关键词： Packet loss

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Enhancing Patient Care: A data-Driven Approach Using Decision Tree-ELM Models for Improved Safety and Quality 3

Enhancing Patient Care: A Data-Driven Approach Using Decisio...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Telsang, Supriya Shrikant Singh, Akhya Sanjay Kaur, Simarpreet Hazela, Bramah Singh, Shikha Chittapragada, Hemanand Vishwakarma Institute of Technology Bibwewadi Maharashtra Pune India Shah & Anchor Kutchhi Engineering College Mumbai India Amity University Amity School of Engineering and Technology Lucknow Department of Computer Science and Engineering Uttar Pradesh India Madanapalle Institute of Technology & Science Department of Computer Science & Engineering Andhra Pradesh Madanapalle India

ISBN: (纸本)9798350354171

In recent years, educational initiatives that seek to combine teaching medical students about patient safety and quality improvement (QI) have received funding from both the World Health Organization and the American Association of Medical Colleges. When developing curriculum, it is important to keep in mind the interests and values of the target audience. The proposed used a poll to find out how students felt about lessons on patient safety and quality improvement. It would be more efficient to incorporate patient safety and quality improvement into clinical education rather than teach it in separate computer modules or during pre-clinical lectures. This proposed approach consists of three stages, which are feature selection, model training, and preprocessing. Before doing thorough text processing, stop words are typically filtered out in conventional methods of natural language processing and information retrieval. Unigram and bigram selection are part of feature selection. During the entire model training process, a Decision Tree-ELM Ensemble was utilized. This innovative approach outperforms ELM and Decision Tree with an average accuracy of 94.36%. © 2024 IEEE.

关键词： Students

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A Novel Intelligent AI with Automated Defense Attack data Privacy System Design 5

A Novel Intelligent AI with Automated Defense Attack Data Pr...

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5th IEEE International Conference on Recent Trends in computer science and Technology, ICRTCST 2024

作者： Boggavarapu, Saikrishna Navale, Geeta S. Manikandan, G. Senthamilarasi, N. Lakshminarayana, Kodavali Goyal, Himanshu Rai University of Cumberlands Prospect HeightsIL United States Sinhgad Institute of Technology and Science Department of Computer Engineering Pune41 India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Electronics and Communication Engineering Thandalam India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai119 India Koneru Lakshmaiah Education Foundation Department of CSE Andhrapradesh Vaddeswaram India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350351378

In this study, we introduce a unique Intelligent AI system built specifically for protecting online privacy and sensitive information. To improve online safety, the system employs cutting-edge AI models, automatic defense mechanisms, and data privacy protections. The research shows that the system is reliable for detecting and preventing threats, with particular emphasis on its pinpoint accuracy and low incidence of false alarms. Furthermore, the automatic defence mechanisms significantly mitigate the effects of many forms of assault. Anonymization and encryption are two methods used to protect the privacy of user data. This study provides a sound method for better protecting online privacy and security by conforming to existing theoretical models and frameworks. The information and approaches given equip organizations to boost their security procedures and secure user data in today's changing digital ecosystem. © 2024 IEEE.

关键词： Intrusion detection

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Mimo Systems with Improved Channel State Infrmations with ML and AI

Mimo Systems with Improved Channel State Infrmations with ML...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Thiruvenkadam, T. Hassan, Inzimam Ul Agarwal, Trapty Computer Science Engineering Department Bangalore India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Maharishi School of Engineeing & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350357769

The utilization of huge various information different result (MIMO) millimeter-wave (mmWave) frameworks is presently viewed as an expected choice for the improvement of fifth-age (5G) mobile phone organizations. Anyway, one major challenge in this field is to the high power consumption resulting from the many radio frequency (RF) links. By using not high-quality analog-to-digital converters (ADCs), this problem may be solved and a fair trade-off between performance and power efficiency can be made. This discussion investigates numerous significant points roused by the latest discoveries on mmWave immense MIMO frameworks utilizing low-goal ADCs. Our examination centers around significant issues connected with handset configuration, including channel gauges, signal distinguishing proof, criticism on channel data, and communicate precoding. Moreover, a clever methodology is presented looking like a blended ADC development, giving an alternate way to work on the general execution of the framework. Finally, we explore the complexities of deploying realistic 5G mmWave large MIMO systems with ADC quantizers, discuss related issues, and consider possible practical uses. Without using plagiarism, this synthesis seeks to provide a thorough summary of the relevant concerns surrounding this state-of-the-art technology. © 2023 IEEE.

关键词： channel assessment Channel State Information (CSI) enormous MIMO low-goal ADCs mixed-ADC architecture mmWave signal detection Signal-to-Noise Ratios (SNRs) and 5G networks transmit precoding

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Misaligned Visible-Thermal Object Detection: A Drone-based Benchmark and Baseline

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-12页

作者： Song, Kechen Xue, Xiaotong Wen, Hongwei Ji, Yingying Yan, Yunhui Meng, Qinggang School of Mechanical Engineering and Automation Northeastern University ShenyangLiaoning 110819 China Department of Computer Science Loughborough University LE11 3TU Loughborough U.K

Multispectral object detection has achieved remarkable results due to its ability to fuse information from visible and thermal modalities in recent years. However, the existing visible-thermal datasets are constructed based on manually aligned image pairs, which cannot fully represent the challenges of real-world scenarios where image pairs are often misaligned. Existing methods for visible-thermal object detection are based on aligned data and are limited by the accuracy of registration. To address the above issues, we propose a dataset, namely DVTOD, which is a misaligned visible-thermal object detection dataset captured by drones. DVTOD includes 16 challenging attributes and 54 capture scenes. Furthermore, we introduce a cross-modal alignment detector (CMA-Det) for misaligned visible-thermal object detection. Firstly, we design an alignment network to estimate the visible-to-thermal deformation field, which is used to correct for misalignment of the corresponding visible and thermal features. Secondly, we propose a strategy called Object Search Rectification (OSR) to improve the robustness of feature alignment. To better remove the interference of complex backgrounds, a bi-directional feature correction fusion module (BFCFM) is designed to calibrate bimodal features by exploiting the correlation of channel and spatial information between two modalities. CMA-Det outperforms existing methods on the DVTOD dataset and two other visible-thermal object detection datasets. The dataset and code will be published at https://***/VDT-2048/DVTOD. IEEE

关键词： Real time systems

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Reducing Task Scheduling Time in Cloud Computing using Novel Improved Whale Optimization Algorithm over Ant Colony Algorithm 2

Reducing Task Scheduling Time in Cloud Computing using Novel...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Priya, P. Sushma Thangaraj, S. John Justin Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350384277

To decrease Cloud Computing Task Scheduling Time utilising Novel Improved Whale Optimisation Algorithm over Ant Colony Algorithm. A Novel Improved Whale Optimisation Algorithm schedules tasks in cloud computing. Cloud computing involves internet-based distribution of data storage, servers, databases, networking, and applications. Task scheduling data is obtained using cloudsim. We analyse, create, and implement cloud computing application framework and cloud computing infrastructures. After data import, Improved Whale Optimisation and Ant Colony Algorithm are tried. Two algorithms have two groups. Each group has 120 samples with 80% G power, 0.05% threshold, and 95% CI. Task Scheduling Time (ms) is returned for inputs. The findings come from IBM SPSS. Results indicate a significant independent sample t test (p= 0.000, p © 2024 IEEE.

关键词： Ant colony optimization

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A Robust Asynchrophasor in PMU Using Second-Order Kalman Filter

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computers, Materials & Continua 2023年第2期74卷 2557-2573页

作者： Nayef Alqahtani Ali Alqahtani Electrical and Computer Engineering Department School of Engineering and Computer ScienceOakland UniversityRochesterMI48326USA King Faisal University Al-Ahsa31982Kingdom of Saudi Arabia Department of Networks and Communications Engineering Najran UniversityNajran61441Kingdom of Saudi Arabia

Phasor Measurement Units(PMUs)provide Global Positioning System(GPS)time-stamped synchronized measurements of voltage and current with the phase angle of the system at certain points along with the grid *** synchronized data measurements are extracted in the form of amplitude and phase from various locations of the power grid to monitor and control the power system condition.A PMU device is a crucial part of the power equipment in terms of the cost and operative point of ***,such ongoing development and improvement to PMUs’principal work are essential to the network operators to enhance the grid quality and the operating *** paper introduces a proposed method that led to lowcost and less complex techniques to optimize the performance of PMU using Second-Order Kalman *** is based on the Asyncrhophasor technique resulting in a phase error minimization when receiving the signal from an access point or from the main access *** MATLAB model has been created to implement the proposed method in the presence of Gaussian and *** results have shown the proposed method which is Second-Order Kalman Filter outperforms the existing *** results were tested usingMean Square Error(MSE).The proposed Second-Order Kalman Filter method has been replaced with a synchronization unit into thePMUstructure to clarify the significance of the proposed new PMU.

关键词： Distributed generation asynchrophasor kalman filter phasor estimation phasor measurement unit state variables mean square error and signal to noise ratio

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Investigation of Task Scheduling in Cloud Computing by using Imperialist Competitive and Crow Search Algorithms 3

Investigation of Task Scheduling in Cloud Computing by using...

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3rd International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2023

作者： Ravi, Jayavadivel Rajkumar, N. Viji, C. Loganathan, D. Sushma, K.S.N. Stalin, M. Department of Computer Science & Engineering Alliance College of Engineering and Design Alliance University Karnataka Bangalore India Department of Information Science & Engineering Cambridge Institute of Technology Karnataka Bangalore India School of Computer Science and Engineering REVA University Karnataka Bangalore India

Cloud Storage is a complex method that is a method of processing and data of a cloud built by duplication of thousands of related devices in a complex manner. The main function of the data processing server is to show how many users are being investigated and provide accurate, efficient and efficient information. Important Algorithm Editing Players in the Cloud Defines the virtual machine (VM) required for this purpose. The role of editing the algorithm reduces the effect of the schedule. Naturally affected algorithms have recently been used to quickly comply with the recent traditional algorithms. Given many consumers of many cloud computing services, many researchers may have a serious explanation that many researchers take and discuss the complex themes of NP. Some sites use imperialist algorithms (ICA) and birds. The purpose of the proposed project is to develop intelligent scientific algorithms that focus on the integration of ICA and CSA to obtain data. CSA is concentrated on the corner of food habits. The crow is looking for his friends to get enough food for today's food. This will help the CSA find suitable VMs for these machines and complete the equipment. Cloud Sim is used to calculate CSA output with minmin and ant algorithms. The simulation results show that the CSA is extra powerful than the MinMin and Ant procedures. © 2023 Elsevier B.V.. All rights reserved.

关键词： Cloud computing

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Improving Robustness to Multiple Spurious Correlations by Multi-Objective Optimization 41

Improving Robustness to Multiple Spurious Correlations by Mu...

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41st International Conference on Machine Learning, ICML 2024

作者： Kim, Nayeong Kang, Juwon Ahn, Sungsoo Ok, Jungseul Kwak, Suha Department of Computer Science and Engineering POSTECH Pohang Korea Republic of Graduate School of Artificial Intelligence POSTECH Pohang Korea Republic of

We study the problem of training an unbiased and accurate model given a dataset with multiple biases. This problem is challenging since the multiple biases cause multiple undesirable shortcuts during training, and even worse, mitigating one may exacerbate the other. We propose a novel training method to tackle this challenge. Our method first groups training data so that different groups induce different shortcuts, and then optimizes a linear combination of group-wise losses while adjusting their weights dynamically to alleviate conflicts between the groups in performance;this approach, rooted in the multi-objective optimization theory, encourages to achieve the minimax Pareto solution. We also present a new benchmark with multiple biases, dubbed MultiCelebA, for evaluating debiased training methods under realistic and challenging scenarios. Our method achieved the best on three datasets with multiple biases, and also showed superior performance on conventional single-bias datasets. Copyright 2024 by the author(s)

关键词： Multiobjective optimization

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A Reinforcement Learning Approach for Intelligent Conversational Chatbot for Enhancing Mental Health Therapy 2

A Reinforcement Learning Approach for Intelligent Conversati...

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2nd International Conference on Machine Learning and data Engineering, ICMLDE 2023

作者： Abubakar, Abdulqahar Mukhtar Gupta, Deepa Parida, Shantipriya Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Silo AI Helsinki Finland

Mental health therapy plays a crucial role in addressing the growing challenges of mental health disorders. However, barriers such as limited accessibility and social stigma often hinder individuals from seeking timely and effective support. Advancement in the field of Artificial Intelligence (AI) and Natural Language Processing (NLP) has enabled the development of Chatbots which can simulate counseling conversations and provide support to those who seek help. However, existing chatbots often lack contextual understanding, limit user responses to multiple-choice questions, and face challenges in generating human-like responses. In this work, we present an Intelligent Conversational Agent using Reinforcement Learning with Human Feedback (RLHF) for mental health therapy support. The proposed system employs reinforcement learning to optimize its responses and adapts its conversational strategies to better understand user's responses, analyze the emotions expressed, and provide personalized therapeutic interventions to enhance Mental Health Therapy. © 2024 Elsevier B.V.. All rights reserved.

关键词： Reinforcement learning

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