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intelligent Gen: An Efficient On-Demand Access Protocol in MANETs using Network Simulator - 2 18

Intelligent Gen: An Efficient On-Demand Access Protocol in M...

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18th INDIAcom;11th international conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Ganesan, T. Murali, N. Murugan, S. Palani Peter, M. Chandrakumar Raj, P. Anand Manikandan, S. E.G.S. Pillay Engineering College Department of Computer Science and Engineering Tamil Nadu Nagapattinam India E.G.S. Pillay Engineering College Department of Artificial Intelligence & Data Science Tamil Nadu Nagapattinam India Periyar Maniammai Institute of Science & Technology Department of Software Engineering Tamil Nadu Thanjavur India E.G.S. Pillay Engineering College Department of Computer Science and Business Systems Tamil Nadu Nagapattinam India E.G.S. Pillay Engineering College Department of Information Technology Tamil Nadu Nagapattinam India

ISBN: (纸本)9789380544519

Mobile Ad-Hoc network is a self-configuring and multi-hop collection of mobile devices for making successful communication. the wireless and mobile communication process are encountered based on bandwidth, process control and energy conception. Energy conception is considered and dynamically changed to rely on life time of batteries and power. the life time of battery get exhausted very quickly the nodes are moved and changed rapidly across the network. this paper noticed the problem of life time of power, batteries and energy consumption problem in various routing stages. the proposed intelligent Gen Algorithm is used to get optimal solution for ensuring high robustness, flexibility and Energy consumption factor. It presents a common framework to balance various routing requirements in MANETs dynamically. the following are the main contributions includes, i. introducing intelligent GEN Algorithms to classify the throughout bandwidth, delay, data ratio. ii. Various routing performance are evaluated and viewed by speed, packetize and simulation time. the comparison evaluated by select routing algorithm using NS-2. the simulation results are recorded and the performance is monitored by using on demand access protocl. the result clearly demonstrated the proposed intelligent Gen algorithm used in self-organized manner by wireless channel. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： AOMDV Protocol intelligent Gen Algorithm MANET Network Simulator NS-2

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Machine learning based Assessment of Mental Stress using Wearable Sensors 18

Machine Learning based Assessment of Mental Stress using Wea...

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18th INDIAcom;11th international conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Sadruddin, Safia Khairnar, Vaishali D. Vora, Deepali R. Terna Engineering College Nerul Navi Mumbai India Anjuman i Islam Kalsekar Technical Campus Panvel India Pune India

ISBN: (纸本)9789380544519

Stress is recognized as a strong factor linked to severe health conditions like hypertension, cardiovascular diseases, and diabetes. With the growing emphasis on wearable health monitoring, numerous investigations have been conducted into the feasibility of leveraging diverse physiological markers to detect stress. this research endeavors to conduct classification using physiological data, drawing from the readily accessible WESAD (Wearable Stress and Affect Detection) dataset. the primary goal is to employ this dataset to develop models capable of predicting stress based on physiological indicators. In this paper, a model is designed to enhance the accuracy of stress level detection through the application of the Synthetic Minority Oversampling Technique (SMOTE). the purpose of SMOTE is to rectify the issue of imbalanced datasets by oversampling the minority class. To handle the imbalanced nature of the data, this study adopted the SMOTE technique to effectively balance the dataset groups. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： artificial intelligence machine learning mental health physiological signal stress detection WESAD

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Wheat Powdery Mildew Automatic Identification through YOLOv8 Instance Segmentation 11

Wheat Powdery Mildew Automatic Identification Through YOLOv8...

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11th international conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Sharma, Jatin Kumar, Deepak Chattopadhay, Saumitra Kukreja, Vinay Verma, Aditya Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350350357

the global production of wheat is seriously endangered by wheat powdery mildew, which can be brought on by Blumeria graminis f. sp. tritici (Bgt). the illness produces large annual losses and threatens food security. the labourintensive and time-consuming characteristics of conventional detection methods emphasizes the need for elegant, automated surveillance systems. Deep learning techniques, particularly the YOLOv8 instance segmentation model, were employed in this study to enhance wheat powdery mildew detection and classification. Results indicate the YOLOv8 model is efficient;it can recognize wheat leaves affected by powdery mildew with a precision of 99.37%, recall of 96%, and an F1-score of 97.67%. this approach works superior to before methods, making it a promising solution for early detection of illnesses in wheat crops. In addition, the research's next steps are examined, highlighting the potential of improving the YOLOv8 model in an array of environmental situations and disease stages. Effective disease detection could be enhanced by merging new data sources as drone-based monitoring systems and hyperspectral imaging. Developing and setting up autonomous disease tracking systems in real agricultural settings involves collaboration among data scientists, technologists, and agronomic teams, in addition to user-friendly interfaces tailored to farmer needs. When everything is taken into account, this study promotes the development of successful tools to control diseases, promoting sustainable crop production and assuring food safety around the globe. © 2024 IEEE.

关键词： Hyperspectral imaging

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An automated Railway Unguarded Level Crossing using Lab View 5

An Automated Railway Unguarded Level Crossing using Lab View

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5th international conference on IoT Based Control Networks and intelligent Systems, ICICNIS 2024

作者： Mohandass, M.P. Dhanush Nigandan Sri, N. Saron Raj, Y. Stephan, A. KIT-KalaignarKarunanidhi Institute of Technology Department of Electrical and Electronics Engineering Coimbatore India

ISBN: (纸本)9798331518097

this research study introduces an advanced automated system for unguarded railway level crossings, developed using LabView,with the primary objective of enhancing safety and mitigating accident risks. the TDS employs vibration and inductive proximity sensors to reliably detect the approach of trains, providing timely alerts. In parallel, the LCS utilizes a camera module that integrates sophisticated image processing and machine learning algorithms to identify and track vehicles at the crossing accurately. Secure wireless communication between the TDS and LCS is achieved through the AES algorithm, ensuring robust data transmission while minimizing potential vulnerabilities. this automated solution not only delivers real-time alerts for train operators and road users but also enhances the overall efficiency of railway operations. By tackling the significant safety issues posed by unguarded level crossings, the design suggested provides an economical and scalable approach this may greatly enhance public safety and foster sustainable development within the railway infrastructure. © 2024 IEEE.

关键词： Proximity sensors

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Federated deep transfer learning for EEG decoding using multiple BCI tasks 11

Federated deep transfer learning for EEG decoding using mult...

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11th international IEEE EMBS conference on Neural engineering (IEEE/EMBS NER)

作者： Wei, Xiaoxi Faisal, A. Aldo Imperial Coll London Dept Comp Brain & Behav Lab London England Imperial Coll London Brain & Behav Lab London England Univ Bayreuth Digital Hlth & Data Sci Bayreuth Germany

ISBN: (纸本)9781665462921

Deep learning is the state-of-the-art in BCI decoding. However, it is very data-hungry and training decoders requires pooling data from multiple sources. EEG data from various sources decrease the decoding performance due to negative transfer [1]. Recently, transfer learning for EEG decoding has been suggested as a remedy [2], [3] and become subject to recent BCI competitions (e.g. BEETL [4]), but there are two complications in combining data from many subjects. First, privacy is not protected as highly personal brain data needs to be shared (and copied across increasingly tight information governance boundaries). Moreover, BCI data are collected from different sources and are often with different BCI tasks, which has been thought to limit their reusability. Here, we demonstrate a federated deep transfer learning technique, the Multidataset Federated Separate-Common-Separate Network (MFSCSN) based on our previous work of SCSN [1], which integrates privacy-preserving properties into deep transfer learning to utilise data sets with different tasks. this framework trains a BCI decoder using different source data sets from different imagery tasks (e.g. some data sets with hands and feet, vs others with single hands and tongue, etc). therefore, by introducing privacypreserving transfer learning techniques, we unlock the reusability and scalability of existing BCI data sets. We evaluated our federated transfer learning method on the NeurIPS 2021 BEETL competition BCI task. the proposed architecture outperformed the baseline decoder by 3%. Moreover, compared with the baseline and other transfer learning algorithms, our method protects the privacy of the brain data from different data centres.

关键词： Deep learning Transfer learning Domain Adaptation Brain-Computer-Interfaces (BCI) Electroencephalography (EEG) Privacy-preserving AI Federated Machine learning

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Hilbert-encoded Cooperative Coevolutionary Genetic Algorithm for Few-shot learning 11

Hilbert-encoded Cooperative Coevolutionary Genetic Algorithm...

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11th international conference on Machine Intelligence theory and Applications, MiTA 2024

作者： Zhang, Xinyuan Yang, Boda Ou Hu, Yue Jinan University School of Intelligent Systems Sciences and Engineering Guang Zhou China Jinan University School of Humanities Guang Zhou China

ISBN: (纸本)9798350385670

In recent years, the advancement of artificial intelligence algorithms, particularly in the domain of semantic segmentation within computer vision, has been noteworthy. this progress is attributed to the availability of diverse large-scale datasets and the continual improvement of computational resources, with deep learning serving as a representative paradigm. Consequently, numerous sophisticated segmentation models have emerged for visual tasks. However, the efficacy of these models hinges on the availability of substantial annotated datasets for training. A notable challenge arises in scenarios where few-shot datasets are prevalent due to inherent limitations. the sparse data volume in these instances often stems from difficulties in obtaining a sufficiently large and high-quality dataset. the process of image annotation is resource-intensive, demanding considerable time and human effort. Addressing the consequential shortage of sample size in few-shot datasets becomes a paramount concern. this paper tackles the aforementioned issues related to few-shot datasets and introduces the Hilbert encoded cooperative coevolutionary genetic algorithm (HCCGA), a novel image enhancement algorithm designed for the automated generation of 3D-Lookup Tables (3D LUTs). HCCGA leverages 3D LUTs generated automatically through the genetic algorithm to map the original few-shot dataset, thereby obtaining a more extensive set of high-quality samples. the methodology employed in this research encompasses the generation and optimization of 3D LUTs using a divide-and-conquer strategy based on genetic algorithms. Additionally, it involves the mapping of new images using 3D LUTs in conjunction with sampling and interpolation. the evaluation of the proposed approach encompasses comparisons based on various image binary classification evaluation metrics applied to images before and after mapping through the results of model inference. Experimental results obtained using HCCGA on diverse publ

关键词： Semantic Segmentation

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Damage evaluation of concrete beams using forced vibration testing and machine learning 11th

Damage evaluation of concrete beams using forced vibration t...

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11th international conference on Bridge Maintenance, Safety and Management, IABMAS 2022

作者： Fujisaku, Yusuke Naito, Hideki Japan, Sendai Inaba, Kohko Department of Civil and Environmental Engineering Graduate School of Engineering Tohoku University Japan Railway Technical Research Institute Tokyo Japan

ISBN: (纸本)9781032345314

A decrease in skilled infrastructure inspectors and the cost of maintenance are big issues in Japan. thus, an effective, automated inspection system is much needed. In this study, local vibration testing, a nondestructive testing method, coupled with machine learning, was utilized. Local vibration testing has the benefit of detecting damage that exists in deep layers because the waves travel through-thickness of concrete members. Using a portable vibrator, local vibration testing was conducted on reinforced concrete (RC) beams to detect the location of damages within. the RC beam specimens had two types of damages: flexural cracks and shear cracks. Results showed that the damaged conditions' frequency responses varied from that of the intact condition. Additionally, high abnormality degrees were detected around the crack region. this implies that the location of damages can be identified from the data produced by local vibration testing. though this method alone yields results, introducing automation would further increase the diagnostic efficiency. therefore, machine learning was introduced. A non-supervised learning method was applied. Using this method, the damages were identified with a very high accuracy rate. this indicates that the combination of local vibration testing and non-supervised learning can produce an effective, automated inspection system for the integrity diagnosis of RC beams. © 2022 the Author(s).

关键词： Frequency response

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VNCESM: VGG-Net Based Conjunctive Eyes Segmentation Model with Transfer learning 11

VNCESM: VGG-Net Based Conjunctive Eyes Segmentation Model wi...

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11th international conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Pahwa, Seema Kaur, Amandeep Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350350357

the future of healthcare is being altered by technology;non-invasive medical techniques assisted by technology are preferred for medical diagnostics. One of the common illnesses harming people's health worldwide is conjunctivitis, and using cutting-edge technology to treat this condition would greatly lower its prevalence. the goal of this work is to use deep learning to do conjunctiva region segmentation for non-invasive conjunctive eye detection applications. the suggested VGG-net Based Conjunctive Eye Segmentation Model (VNCESM) effectively segments conjunctiva from digital eye pictures taken in an uncontrolled environment by using fine-tuned CNN architecture. Pre-processing and image augmentation were done to boost the model's performance and data volume. UNBCSM demonstrated strong segmentation performance, with segmented masks for training and validation of 97.47%, and 95.49% respectively. © 2024 IEEE.

关键词： Diagnosis

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AutoConsis: Automatic GUI-driven data Inconsistency Detection of Mobile Apps 44

AutoConsis: Automatic GUI-driven Data Inconsistency Detectio...

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ACM/IEEE 46th international conference on Software engineering - Software engineering in Practice (ICSE-SEIP)

作者： Hu, Yongxiang Jin, Hailiang Wang, Xuan Gu, Jiazhen Guo, Shiyu Chen, Chaoyi Wang, Xin Zhou, Yangfan Fudan Univ Sch Comp Sci Shanghai Peoples R China Meituan Shanghai Peoples R China Meituan Beijing Peoples R China Fudan Univ Sch Comp Sci Shanghai Key Lab Intelligent Informat Proc Shanghai Peoples R China

ISBN: (纸本)9798400702174

In industrial practice, many bugs in commercial mobile apps manifest as self-conflicts of data presented in the GUI (Graphical User Interface). Such data inconsistency bugs can bring confusion to the users and deteriorate user experiences. they are a major target of industrial testing practice. However, due to the complication and diversity of GUI implementation and data presentation (e.g., the ways to present the data in natural language), detecting data inconsistency bugs is a very challenging task. It still largely relies on manual efforts. To reduce such human efforts, we proposed AutoConsis, an automated data inconsistency testing tool we designed for Meituan. one of the largest E-commerce providers with over 600 million transacting users. AutoConsis can automatically analyze GUI pages via a multi-modal deep-learning model and extract target data from textual phrases leveraging LLMs (Large Language Models). With these extracted data, their inconsistencies can then be detected. We evaluate the design of AutoConsis via a set of ablation experiments. Moreover, we demonstrate the effectiveness of AutoConsis when applying it to real-world commercial mobile apps with eight representative cases.

关键词： Automatic Testing Mobile Apps Functional Bug In-context learning

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MODELING MULTIMODAL ALEATORIC UNCERTAINTY IN SEGMENTATION WIth MIXTURE OF STOCHASTIC EXPERTS 11

MODELING MULTIMODAL ALEATORIC UNCERTAINTY IN SEGMENTATION WI...

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11th international conference on learning Representations, ICLR 2023

作者： Gao, Zhitong Chen, Yucong Zhang, Chuyu He, Xuming ShanghaiTech University Shanghai China Lingang Laboratory Shanghai China Shanghai Engineering Research Cente of Intelligent Vision and Imaging Shanghai China

Equipping predicted segmentation with calibrated uncertainty is essential for safety-critical applications. In this work, we focus on capturing the data-inherent uncertainty (aka aleatoric uncertainty) in segmentation, typically when ambiguities exist in input images. Due to the high-dimensional output space and potential multiple modes in segmenting ambiguous images, it remains challenging to predict well-calibrated uncertainty for segmentation. To tackle this problem, we propose a novel mixture of stochastic experts (MoSE) model, where each expert network estimates a distinct mode of the aleatoric uncertainty and a gating network predicts the probabilities of an input image being segmented in those modes. this yields an efficient two-level uncertainty representation. To learn the model, we develop a Wasserstein-like loss that directly minimizes the distribution distance between the MoSE and ground truth annotations. the loss can easily integrate traditional segmentation quality measures and be efficiently optimized via constraint relaxation. We validate our method on the LIDC-IDRI dataset and a modified multimodal Cityscapes dataset. Results demonstrate that our method achieves the state-of-the-art or competitive performance on all metrics. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Stochastic systems

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