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21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2023

作者： Saibharath, S. Mishra, Sudeepta Arora, Gazal Sharma, Anamika Hota, Chittaranjan Dept. of Computer Science and Information systems BITS Pilani Hyderabad Campus India Dept. of Computer Science and Engineering IIT Ropar Punjab India

ISBN: (纸本)9783903176553

5G operates in the unlicensed spectrum to expand its capacity. This spectrum provides more uplink and downlink frequency bands in 5G to satisfy ultra-dense and scalability requirements. Wi-Fi is a prominent wireless technology that operates in unlicensed bands. Due to the proximity of cellular and Wi-Fi channels, utilization of both Wi-Fi and 5G New Radio (NR) can cause interference. The co-existence of 5G NR and Wi-Fi devices lacks the means of communication for negotiation and coordination among them. Listen-Before-Talk and Duty-cycling are standard Medium Access Control (MAC) mechanisms to enable co-existence. Due to a lack of coordination, network utilization becomes unfair with the existence of selfish devices. The selfish users could maximize throughput and affect other Wi-Fi and NR users. Here, we investigate the effects of selfish nodes on this Wi-Fi Co-existence with 5G. These nodes disobey exponential backoff to boost their channel acquisition. We study the effect of misbehavior through the Order Gain metric. Through standard CSMA/CA settings with overall channel occupancy of 99.75%, we demonstrate that selfish users profoundly impact fair co-existence. We study the side effects through throughput, channel sensing, and acquisition for various backoff window access patterns. Finally, we go through counteraction methods to overcome the selfish user impact. © 2023 IFIP.

关键词： Wi-Fi

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Survey on Ethical Challenges of Implementing AI in Healthcare 7

Survey on Ethical Challenges of Implementing AI in Healthcar...

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7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Lalwani, Nikita Ur Rahim, Muhammad Abdul Basit Anand, Aravind Shaik, Sumair Pasha Moghe, Gauri Shashank Datta, Debarati California State University Long Beach Dept. of Computer Engineering and Computer Science Long Beach United States

ISBN: (纸本)9798350350890

In today's world, Artificial Intelligence (AI) and its usage concerning human tasks have become an integral part of our daily lives. Humans depend upon AI to provide faster and more efficient solutions. In the world of medicine, where every decision made by a doctor, physician, or consultant directly impacts the lives of those being treated or diagnosed, AI is making its way to help efficiently provide such results as the intelligence of a human being. Healthcare systems around the world are now making use of AI to help provide humane solutions using algorithms and Machine Learning (ML). Fallacies or inaccuracies in the arena of healthcare can have horrendous outcomes for the patient on whom the procedure is being carried out. Sometimes, certain ethics could be traded off by the system, such as confidentiality, autonomy, justice, and biased decisions leading to false results and victim blaming, which directs the masses into not trusting AI-related healthcare solutions. This situation can lead to mishaps like 'AI winter,' where the public could start disbelieving the results of AI solutions. This survey review makes an effort to analyze and address these pertinent ethical issues underlying the requirement for algorithmic translucency, concealment, and safeguarding the interests of all those involved. © 2024 IEEE.

关键词： Adversarial machine learning

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Advancements in Environmental data Analysis for Climate-Resilient Agriculture Using Remote Sensing and Deep Learning

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 670-680页

作者： Nirmal, Khyati R. Reddy, Chilukala Mahender P, Jose Tulasi, Rajesh Maheswari, G. Uma Sapthami, I. SNJB’S Late Sau KBJ CoE Maharashtra Nashik India Department of Artificial Intelligence and Data Science GITAM Deemed to Be University Hyderabad India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology Chennai Avadi India Dept. of Computer Science and Engineering Koneru Lakshmaiah Education Foundation A.P Vaddeswaram Guntur Dist. India Department of Computer Science and Engineering RMK College of Engineering and Technology Chennai Pudhuvoyal India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Weather significantly influences agricultural productivity. Plant biotic and abiotic stressors are primarily induced by climate change, resulting in a detrimental effect on worldwide agricultural productivity. These two components are closely interconnected. This paper presents an innovative approach in smart agriculture for climate change combining remote sensing and a deep learning algorithm. The input is obtained as a multispectral environmental image and then subjected to noise reduction and normalisation processing. The image has been retrieved using a primary convolutional component with a stacked encoder model. The retrieved features are identified using ResNet graph reinforcement neural networks. The resulting classification output displays environmental imagery depicting climate variations. The agriculture sector has been analysed based on the classified climate analysis. The experimental results have been conducted on diverse farm datasets related to climate change, evaluating the detection accuracy, recall, mean average precision, normalized correlation, and F-1 score. The proposed method achieved a detection accuracy of 98%, a normalised correlation of 95%, a mean average precision of 92%, a recall of 97%, and an F-MEASURE of 94%. Machine learning can assist in monitoring and predicting the impact of climate change on food security, as indicated by the findings. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Climate change Deep learning model Remote sensing Smart agriculture Stacked encoder model

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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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2023 IEEE International Conference on Network, Multimedia and Information Technology, NMITCON 2023

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

ISBN: (纸本)9798350300826

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. © 2023 IEEE.

关键词： Object detection

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Adapting Federated Learning to Dynamic and Non-Stationary data: An Online Learning Approach

Adapting Federated Learning to Dynamic and Non-Stationary Da...

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7th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2025

作者： Kim, Jaemin Hur, Donghyeon Won, Dongwook Cho, Sungrae Chung-Ang University Dept. of Computer Science and Engineering Seoul Korea Republic of

ISBN: (纸本)9798331507022

Online learning-based optimization represents a novel approach that integrates the principles of federated learning and online learning to effectively handle dynamic data distributions and continuous learning scenarios. This paper offers a comprehensive survey of the latest developments in online federated learning, focusing on optimizing communication efficiency in federated learning environments through online learning techniques. We delve into foundational concepts, key methodologies, challenges, and diverse applications of this emerg-ina field. © 2025 IEEE.

关键词： Federated learning

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Deep Learning-based Image Classification Framework for Rice Leaf Disease Detection: A Systematic Implementation Approach with Comparative Analysis 8

Deep Learning-based Image Classification Framework for Rice ...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Bortty, Joy Chakra Chakraborty, Gouri Shankar Ahmed, Tamjid Polok, Sourav Das Taki, Tanvir Ahmed Saha, Partha Westcliff University Department of Computer Science IrvineCA United States Lovely Professional University Dept. of Computer Science and Engineering Punjab Phagwara India

ISBN: (纸本)9798350367904

Approximately half of the population of the world consumes rice every day as their daily diet. In Asia, rice is the main ingredient of the meal. However, various diseases influence the production of rice being harmed. Many factors are responsible for the issue such as climate change, pathogens and excessive usage of pesticides. Varieties of leaf diseases are there that may harm quality of rice production. To protect the crop from diseases, farmers are relying on their eyes to identify diseases. Most often human eye can possibly make mistake identifying the disease. In consequence, harvested rice gets harmed both in quality and in quantity as well. To prevent those diseases, researchers have found cutting-edge techniques such as deep neural network with image processing based frameworks to detect and classify rice plant diseases to increase crops production and to reduce losses. Recent works present all the possible techniques related to rice plant disease classification and detection. This work represents a state-of-art in disease identification approach for rice leaf that can efficiently classify several types of diseases analyzing the leaf images. By adopting the efficiency of deep learning models with hyperparameter tuning, the methodology achieved accuracy of 97.10% with ResNet50. Importantly, the experimental outcomes of the proposed framework highlight the remarkable performance comparing the results with existing state-of-the-art technique. The aim of this research is to provide a more promising future for crop management through increased productivity while addressing the urgent problem of rice plant disease detection. © 2024 IEEE.

关键词： Convolutional neural networks

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Less is More: Deep Learning Framework for Digital Forensics in Resource-Constrained Environments 11

Less is More: Deep Learning Framework for Digital Forensics ...

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11th International Symposium on Digital Forensics and Security, ISDFS 2023

作者： Rath, Suman Das, Tapadhir Astaburuaga, Ignacio Sengupta, Shamik University of Nevada Reno Dept. of Computer Science & Engineering Reno United States University of the Pacific Dept. of Computer Science Stockton United States

ISBN: (纸本)9798350336986

Machine learning (ML)-enabled tools and techniques are an unavoidable necessity for modern digital forensics. However, the performance of such tools often requires extensive training using powerful computing environments that may expend thousands of dollars and several training hours for a single model. Further, this process necessitates using high-volume datasets that are generally unavailable due to court-mandated restrictions. To overcome these problems, this paper proposes using a two-layered strategy that fuses several features of transfer learning and knowledge distillation-based techniques to build lightweight neural networks that can be trained using low-volume datasets to achieve high accuracy. Experimental results highlight we achieved an accuracy of 98.5% and 97.5% during training and testing of the heavier ML model, and an accuracy of 97.08% and 97.2% on the lightweight ML model (during training and testing respectively), therefore demonstrating the robustness and validity of the proposed technique. © 2023 IEEE.

关键词： Digital forensics

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Lung Cancer Image Identify and Categorization using Artificial Neural Networks

Lung Cancer Image Identify and Categorization using Artifici...

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2024 International Conference on Computational Intelligence for Green and Sustainable Technologies, ICCIGST 2024

作者： Lingappa, Ediga Begum, D Jareena Pachouri, Priya Rashmi, Kuderu Varalakshmi, Thokala Tharuna Kumar, Anoop Mallareddy College of Engineering Maisammaguda Dept of Computer Science and Engineering TS Secunderabad500100 India CMR Institute of Technology Dept. of Artificial Intelligence and Data Science Bengaluru560037 India Malla Reddy University Dept of Cyber Security Hyderabad India

ISBN: (纸本)9798350381887

Lung cancer that is made up of small cells is called small cell lung cancer (SCLC). Smoking tobacco in any form-cigarettes, cigars, or pipes-is by far the most significant risk factor for developing lung cancer, though it is only one of many. Cancer of the lungs is known as lung cancer. When cells in the body multiply uncontrollably, cancer develops. If cancer cells enter the lymphatic system or the bloodstream, they can travel to other organs and spread the disease. Cheap and fast cancer diagnostics could save millions. Latestage cancer treatment is hard. Lung cancer is diagnosed non-invasively. Biopsy requires careful ANN architecture selection. This study proposes a diagnosis ANN model. ANN research is 'hot' in cardiology, cancer, and radiology. Medical research uses ANNs. Lung cancer diagnosis was tested. MR images assess lung health for the neural network. MATLAB created ANNs and processed images. Training data was extracted from MR images using Harlick texture features. MATLAB made ANNs. Elman Network exceeds ANNs with 90.30 %. Lung cancer can develop. MRIs detect lung cancer. MRIs show lung development. Many lung tumour detection research use machine learning and deep learning. MRI algorithms swiftly detect lung malignancies, improving patient therapy. Radiologists use these forecasts. The study evaluates a self-defined ANN for lung tumour identification. The radiologist needs time to find and remove the cancer from the MR. Technology improves performance and segmentation. Machines always predict and detect. Neural networks outperformed SVM, Random forest, regression, and others. © 2024 IEEE.

关键词： Lung cancer

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Priority-Considered Channel Access for Registration-Backoff-Time (RBT) -based IEEE 802.11ah IOT Network 17

Priority-Considered Channel Access for Registration-Backoff-...

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IEEE Congress on Cybermatics: 17th IEEE International Conference on Internet of Things, iThings 2024, 20th IEEE International Conference on Green Computing and Communications, GreenCom 2024, 17th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE International Conference on Smart data, Smartdata 2024

作者： Huang, Chung-Ming Chen, Chia-Hsiang Ma, Jianhua National Cheng Kung University Dept. of Computer Science and Information Engineering Tainan Taiwan Hosei University Faculty of Computer and Information Science Tokyo Japan

ISBN: (纸本)9798350351637

This work devises a High Priority Reservation - centric Registered Backoff Time -based (HPR-RBT) channel access method to enhance the performance of IEEE 802.11ah IoT network. The proposed HPR-RBT method allows the STAs that have high priority's uplinked data to reserve, which is through the claiming action, the privilege of channel access. HPR-RBT adopts the Trigger RAW mode of IEEE 802.11ah and the RBT mechanism. In HPR-RBT, the Restricted Access Window (RAW) of IEEE 802.11ah is divided into (1) a claiming RAW that contains multiple claiming slots and (2) a data RAW that contains multiple data slots. Each claiming slot is further divided into many tiny slots, each of which is for one STA to claim its channel access's reservation for its high priority's uplinked data. Each data slot is further divided into a scheduled sub-slot, in which the channel access of the high priority's uplinked data is scheduled, and an unscheduled sub-slot, in which the channel access of the regular priority's uplinked data is allowed. Furthermore, this work proposes a grouping mechanism to balance the load of high priority's uplinked data across RAW's data slots to prevent excessive high priority's uplinked data in any particular RAW's data slot. Through the grouping mechanism, the IEEE 802.11ah Access Point (AP) can assign the data slot that has the lowest load of high priority's uplinked data to a newly associated STA of the AP. Comparing with the legacy 802.11ah method, the experiment result shows that the proposed HPR-RBT method has (i) the shorter transmission latency and (ii) the better throughput when the number of STA becomes bigger. © 2024 IEEE.

关键词： Internet of things

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A New Fault Diagnosis Approach Based on Extreme-Point Symmetric Modal Decomposition and Energy Operator Demodulation 5

A New Fault Diagnosis Approach Based on Extreme-Point Symmet...

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5th International Conference on Electronic Communication and Artificial Intelligence, ICECAI 2024

作者： Fu, Sicheng Liu, Baqiao Chen, Zhongzhe Cao, Shuchen Syracuse University Dept. of Electrical Engineering & Computer Science SyracuseNY United States Uiuc Dept. of Computer Science Urbana United States School of Mechanical and Electrical Engineering Uestc Chengdu China Bayer Crop Science Chesterfield Missouri United States

ISBN: (纸本)9798350386943

In this paper, the Extreme-point Symmetric Modal Decomposition (ESMD) was firstly introduced to the fault diagnosis field of rotating machinery, and a new fault diagnosis approach of rotating machinery based on ESMD and Energy Operator Demodulation (EOD) is proposed. Firstly, the gear fault vibration signal is decomposed into the Finite Intrinsic Mode Function (IMF) components though ESMD. Secondly, these IMF components were demodulated by the EOD to form envelope signals. Lastly, the fault feature information is extracted by analyzing IMF components and envelope signals. The ESMD is employed to decompose a simulation signal and made comparison with the Empirical Mode Decomposition (EMD) and the Local Mean Decomposition (LMD). The result shows that the ESMD can overcome the problems of end effect of EMD. Moreover, it avoids low efficiency of LMD. The method is applied to the fault diagnosis of a gear with broken teeth. The results indicated that this gear fault can be effectively identified, which demonstrated that the proposed method is an effective and practical method for fault diagnosis of rotating machinery. © 2024 IEEE.

关键词： EOD ESMD Fault Diagnosis

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