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5th International Conference on Electronics and Sustainable Communication systems, ICESC 2024

作者： Revathy, G. Maheswari, P. Uma Madhavi, S. Vijay Anand, P. Begum, R. Vajubunnisa Jasmin, H. Department of Computer Science and Engineering Chennai India K. Ramakrishnan College of Engineering Department of Computer Science and Business Systems Trichy India Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Information Technology Chennai India Cmr Institute of Technology Department of Artificial Intelligence and Data Science Bengaluru India Justice Basheer Ahmed Sayeed College for Women Affiliated by University of Madras Department of Electronics and Communication Science Teynampet Chennai India

ISBN: (纸本)9798350379945

Ovarian cancer is the fifth most common cause of death from cancer in women. This is mainly because it is often diagnosed at a late stage, as the earliest symptoms are unclear and inconsistent. Existing diagnostic techniques, such as biomarkers, biopsies, and imaging tests, have notable drawbacks such as reliance on subjective interpretation, inconsistency across different observers, and time-consuming testing procedures. This research study introduces an innovative deep learning framework that employs a convolutional neural network (CNN) algorithm to accurately predict and diagnose ovarian cancer, thereby overcoming the existing constraints. The CNN was trained using a dataset of histopathology images. The dataset was partitioned into test and training subsets to enhance the model's performance. The algorithm demonstrated an impressive accuracy rate of 96%, successfully detecting 97.12% of malignant instances and precisely categorizing 95.02% of healthy cells. This method effectively mitigates the challenges related to human expert evaluation, including elevated rates of misclassification and variability across different observers, while also reducing the time required for analysis. The results underscore the capability of this CNN-based technique to offer a more precise, effective, and dependable strategy for forecasting and detecting ovarian cancer. Subsequent investigations will prioritize the integration of new breakthroughs in deep learning to further amplify the efficacy of the suggested approach. © 2024 IEEE.

关键词： Lung cancer

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Prediction Aided by Surrogate Training

arXiv

引用

arXiv 2024年

作者： Xia, Eric Wainwright, Martin J. Laboratory for Information and Decision Systems Statistics and Data Science Center Department of Electrical Engineering & Computer Sciences Massachusetts Institute of Technology CambridgeMA United States Laboratory for Information and Decision Systems Statistics and Data Science Center Department of Mathematics Massachusetts Institute of Technology CambridgeMA United States

We study a class of prediction problems in which relatively few observations have associated responses, but all observations include both standard covariates as well as additional "helper" covariates. While the end goal is to make high-quality predictions using only the standard covariates, helper covariates can be exploited during training to improve prediction. Helper covariates arise in many applications, including forecasting in time series;incorporation of biased or mis-calibrated predictions from foundation models;and sharing information in transfer learning. We propose "prediction aided by surrogate training" (PAST), a class of methods that exploit labeled data to construct a response estimator based on both the standard and helper covariates;and then use the full dataset with pseudo-responses to train a predictor based only on standard covariates. We establish guarantees on the prediction error of this procedure, with the response estimator allowed to be constructed in an arbitrary way, and the final predictor fit by empirical risk minimization over an arbitrary function class. These upper bounds involve the risk associated with the oracle data set (all responses available), plus an overhead that measures the accuracy of the pseudo-responses. This theory characterizes both regimes in which PAST accuracy is comparable to the oracle accuracy, as well as more challenging regimes where it behaves poorly. We demonstrate its empirical performance across a range of applications, including forecasting of societal ills over time with future covariates as helpers;prediction of cardiovascular risk after heart attacks with prescription data as helpers;and diagnosing pneumonia from chest X-rays using machine-generated predictions as helpers. © 2024, CC BY.

关键词： Prediction models

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A New Tree Structure for Local Diagnosis

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Journal of information Science and engineering 2024年第2期40卷 231-244页

作者： Chen, Meirun Li, Xiao-Yan Lin, Cheng-Kuan Pai, Kung-Jui School of Mathematics and Statistics Xiamen University of Technology Xiamen361024 China College of Computer and Data Science Fuzhou University Fuzhou350108 China Department of Computer Science National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Undergraduate Degree Program of Systems Engineering and Technology National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Computer Science and Information Engineering Chung Cheng Institute of Technology National Defense University Taoyuan30010 Taiwan Department of Industrial Engineering and Management Ming Chi University of Technology New Taipei City243303 Taiwan

Diagnosability is an important parameter to measure the fault tolerance of a multiprocessor system. If we only care about the state of a node, instead of doing the global diagnosis, Hsu and Tan proposed the idea of local diagnosis. Chiang and Tan provided an extended star structure to diagnose a node under comparison model. In this work, we evaluate the local diagnosability better by proposing a tree structure around this node. We provide the corresponding algorithm to diagnose the node. Simulation results are presented for different failure probability of a node in the tree and different percentage of faulty nodes in the tree, showing the performance of our algorithm. © 2024 Institute of information Science. All rights reserved.

关键词： comparison model diagnosis algorithm fault diagnosis local diagnosability tree

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Health information Broadcast Distributed Pattern Association based on Estimated Volume 3

Health Information Broadcast Distributed Pattern Association...

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

作者： Dhanalakshmi, R. Kalpana, A.V. Umamageswaran, J. Kumar, B. Praveen Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Kattankulathur Department of Data Science and Business Systems Tamil Nadu Chennai India R.M.K Engineering College Department of Information Technology Tamil Nadu Chennai India K L University Department of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9781665462167

Any study on dataset and inadequate representation are featured in the conventional data mining process. Consequently, data mining findings reveal high chances of challenges and huge root-mean-square approximation errors besides long utilization. The proposed algorithm for associating distribution patterns in streams of medical data is based on density calculations. The distance approach will be utilized to identify any orphaned or questionable data within the data stream, and the gathering of medical data will be used to achieve the aim. data stream duplicates are compiled by sector match quality, and both the out-of-the-ordinary and the redundant data are eventually eliminated. The density of the information stream is then calculated based on histogram calculations. This investigates the flow of medical information from the viewpoints of concentration and dispersion, as well as the physical characteristics of information distribution, in combination with the results of data density estimate. The model is then built upon a neural network composite, the data distribution parameters in the clustering layer of the model are entered, and in-depth testing is carried out via the neural B.P. network (Back Propagation) on the mining layer of the model. Temporarily, all laws under the grouping of the hidden layer operation value besides the resulting output value are derived, and all rules under the grouping of the hidden layer value besides the conforming contribution value in the medical information stream pattern. The new findings show a curve of contour contiguous to the true curve of the likelihood density, a fair range of medical data dispersion and high medical data consistency, a lower probability of data redundancy, a low RMSEA for mining, a lower time for data recovery. © 2023 IEEE.

关键词： data mining

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Feature deformation network with multi-range feature enhancement for agricultural machinery operation mode identification

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International Journal of Agricultural and Biological engineering 2024年第4期17卷 265-275页

作者： Weixin Zhai Zhi Xu Jinming Liu Xiya Xiong Jiawen Pan Sun-Ok Chung Dionysis Bochtis Caicong Wu College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083China Key Laboratory of Agricultural Machinery Monitoring and Big Data Application Ministry of Agriculture and Rural AffairsBeijing 100083China Department of Smart Agricultural Systems Graduate SchoolChungnam National UniversityDaejeon 34134Republic of Korea Institute for Bio-Economy and Agri-Technology(iBO) Centre for Research and Technology-Hellas(CERTH)Thessaloniki 57001Greece

Utilizing the spatiotemporal features contained in extensive trajectory data for identifying operation modes of agricultural machinery is an important basis task for subsequent agricultural machinery trajectory *** the present study,to effectively identify agricultural machinery operation mode,a feature deformation network with multi-range feature enhancement was ***,a multi-range feature enhancement module was developed to fully explore the feature distribution of agricultural machinery trajectory ***,to further enrich the representation of trajectories,a feature deformation module was proposed that can map trajectory points to high-dimensional space to form feature ***,EfficientNet-B0 was used to extract features of different scales and depths from the feature map,select features highly relevant to the results,and finally accurately predict the mode of each trajectory *** validate the effectiveness of the proposed method,experiments were conducted to compare the results with those of other methods on a dataset of real agricultural *** the corn and wheat harvester trajectory datasets,the model achieved accuracies of 96.88%and 96.68%,as well as F1 scores of 93.54%and 94.19%,exhibiting improvements of 8.35%and 9.08%in accuracy and 20.99%and 20.04%in F1 score compared with the current state-of-the-art method.

关键词： road-field trajectory classification efficientNet feature deformation network multi-range feature enhancement agricultural machinery operation mode recognition

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A Design of Hybrid Model and Bayesian Neural Networks for Smart Grid Stability Prediction 1

A Design of Hybrid Model and Bayesian Neural Networks for Sm...

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1st IEEE International Conference on Smart and Sustainable Developments in Electrical engineering, SSDEE 2025

作者： Hole, Shreyas Rajendra Kolluru, Vinothkumar Salotagi, Shreekant Challagundla, Yagnesh Mungara, Sudeep Sriramkumar, R. Dayananda Sagar University AI & ML Bangalore India Stevens Institute of Technology Department of Data Science HobokenNJ07030 United States Dayananda Sagar University Computer Science & Engineering Bagalore India Herbert Wertheim College of Engineering University of Florida Department of Computer Science GainesvilleFL32611 United States Stevens Institute of Technology Masters in Information Systems HobokenNJ07030 United States

ISBN: (纸本)9798331542108

The latest evolution in power systems, is 'smart grids'that offers real time monitoring, control features as well as effective management of renewable energy sources. Nonetheless, with the increase in the system complexity, the chances for insta- bility also increase and thus it is crucial to forecast the stability accurately for the safety of the grid. Weather it is in terms of Gradient Boosting Tree Algorithm or any other traditional algorithm, these machine learning models are successful in achieving the objectives of the task but they lack a forthcoming metric to assist in high stake situations, uncertainty quantification. The paper is devoted to the analysis of the reliability of smart grid systems based on Bayesian Neural Networks (BNN) taking into account both the reliability of predictive modeling and forecasting uncertainty. The smart grid stability augmented dataset was used to benchmark the efficacy of using BNNs or BNNs combined hybrid models, one with XGBoost, for feature extraction and the other with LightGBM. The present study shows that the BNN used with the above mentioned smart grid system achieved an accuracy of 92% with precision of 0.89, recall of 0.91 and F1 of 0.90. These metrics are appending with the help of the additional features of BNN where they are capable of addressing the key questions around uncertainty and thus the metrics confirming the predictions are quite strong in terms of real-time grid stability evaluation. This article encapsulates a detailed study of BNN architecture, the role of uncertainty in machine learning models and the natural application of such models in the design of dependable and fault-tolerant smart grids. © 2025 IEEE.

关键词： Smart power grids

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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...

引用

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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Application of Random Search Methods in the Determination of Learning Rate for Training Container Dwell Time data Using Artificial Neural Networks

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Intelligent Control and Automation 2024年第4期15卷 109-124页

作者： Justice Awosonviri Akodia Clement K. Dzidonu David King Boison Philip Kisembe Accra Institute of Technology Advanced School of Systems and Data Studies (ASSDAS) Accra Ghana Accra Institute of Technology Accra Ghana Knowledge Web Centre Accra Ghana Department of Computer Engineering Ghana Communication Technology University Accra Ghana

Purpose: This study aimed to enhance the prediction of container dwell time, a crucial factor for optimizing port operations, resource allocation, and supply chain efficiency. Determining an optimal learning rate for training Artificial Neural Networks (ANNs) has remained a challenging task due to the diverse sizes, complexity, and types of data involved. Design/Method/Approach: This research used a RandomizedSearchCV algorithm, a random search approach, to bridge this knowledge gap. The algorithm was applied to container dwell time data from the TOS system of the Port of Tema, which included 307,594 container records from 2014 to 2022. Findings: The RandomizedSearchCV method outperformed standard training methods both in terms of reducing training time and improving prediction accuracy, highlighting the significant role of the constant learning rate as a hyperparameter. Research Limitations and Implications: Although the study provides promising outcomes, the results are limited to the data extracted from the Port of Tema and may differ in other contexts. Further research is needed to generalize these findings across various port systems. Originality/Value: This research underscores the potential of RandomizedSearchCV as a valuable tool for optimizing ANN training in container dwell time prediction. It also accentuates the significance of automated learning rate selection, offering novel insights into the optimization of container dwell time prediction, with implications for improving port efficiency and supply chain operations.

关键词： Container Dwell Time Prediction Artificial Neural Networks (ANNs) Learning Rate Optimization RandomizedSearchCV Algorithm and Port Operations Efficiency

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Interruptions detection in video conferences

arXiv

引用

arXiv 2023年

作者： Horowitz, Shmuel Kagan, Dima Alpert, Galit Fuhrmann Fire, Michael The Data4Good Lab Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Israel

In recent years, video conferencing (VC) popularity has skyrocketed for a wide range of activities. As a result, the number of VC users surged sharply. The sharp increase in VC usage has been accompanied by various newly emerging privacy and security challenges. VC meetings became a target for various security attacks, such as Zoombombing. Other VC-related challenges also emerged. For example, during COVID lockdowns, educators had to teach in online environments struggling with keeping students engaged for extended periods In parallel, the amount of available VC videos has grown exponentially. Thus, users and companies are limited in finding abnormal segments in VC meetings within the converging volumes of data. Such abnormal events that affect most meeting participants may be indicators of interesting points in time, including security attacks or other changes in meeting climate, like someone joining a meeting or sharing a dramatic content. Here, we present a novel algorithm for detecting abnormal events in VC data. We curated VC publicly available recordings, including meetings with interruptions. We analyzed the videos using our algorithm, extracting time windows where abnormal occurrences were detected. Our algorithm is a pipeline that combines multiple methods in several steps to detect users' faces in each video frame, track face locations during the meeting and generate vector representations of a facial expression for each face in each frame. Vector representations are used to monitor changes in facial expressions throughout the meeting for each participant. The overall change in meeting climate is quantified using those parameters across all participants, and translating them into event anomaly detection. This is the first open pipeline for automatically detecting anomaly events in VC meetings. Our model detects abnormal events with 92.3% precision over the collected dataset. Copyright © 2023, The Authors. All rights reserved.

关键词： Video conferencing

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Iterative Feature Boosting for Explainable Speech Emotion Recognition 22

Iterative Feature Boosting for Explainable Speech Emotion Re...

引用

22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Nfissi, Alaa Bouachir, Wassim Bouguila, Nizar Mishara, Brian L. Data Science Laboratory Montreal Canada Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Centre for Research and Intervention on Suicide Ethical Issues and End-of-Life Practices Montreal Canada University of Quebec at Montreal Psychology Department Montreal Canada

ISBN: (纸本)9798350345346

In speech emotion recognition (SER), using predefined features without considering their practical importance may lead to high dimensional datasets, including redundant and irrelevant information. Consequently, high-dimensional learning often results in decreasing model accuracy while increasing computational complexity. Our work underlines the importance of carefully considering and analyzing features in order to build efficient SER systems. We present a new supervised SER method based on an efficient feature engineering approach. We pay particular attention to the explainability of results to evaluate feature relevance and refine feature sets. This is performed iteratively through feature evaluation loop, using Shapley values to boost feature selection and improve overall framework performance. Our approach allows thus to balance the benefits between model performance and transparency. The proposed method outperforms human-level performance (HLP) and state-of-the-art machine learning methods in emotion recognition on the TESS dataset. The source code of this paper is publicly available at Iterative-Feature-Boosting-for-Explainable-Speech-Emotion-Recoanition. © 2023 IEEE.

关键词： Acoustic analysis Explainable AI Feature selection Speech emotion recognition Supervised learning

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