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3rd International Conference on Intelligent Sustainable Systems, ICISS 2020

作者： Kavitha, P. Jayalakshmi, V. Vels Institute of Science Technology Advanced Studies Department of Computer Science Chennai India Vels Institute of Science Technology Advanced Studies Department of Computer Applications Chennai India

ISBN: (纸本)9781728170893

Due to the increasing complexities in human perception difficulties and subjectivity, the dermatological disorders are still remaining as one of the greatest medical issues. In recent years, a melanocytic cancer is becoming as a most deadly cancer in the human kind. Dermatologists are expecting a computer aided system that can detect it in early stage. So, it is very important for the physicians to detect cancer in its early stage. This paper has been presenting a survey on readily accessible image processing techniques for melanoma detection as image processing plays a significant part on the images obtained from the digital clinic in detecting and classifying the diseases. This paper studies about the different available non-invasive techniques that are ought to provide a computerized image. Numerous classifiers performcertainly for the diagnosis of skin lesions is associated and the corresponding findings are also discussed. © 2020 IEEE.

关键词： Surveys

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Maestro: A Robust Multi-Head Attention Enhanced CNN Architecture for Heat-Induced Stress Recognition Using EEG Signals

Maestro: A Robust Multi-Head Attention Enhanced CNN Architec...

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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Sagnik De Sreenija Pavuluri Amaan Sayyad Anil Kumar Gupta Institute of Radio Physics and Electronics University of Calcutta Kolkata India Department of Computer Science Engineering GVPCE Visakhapatnam AP India Department of Computer Engineering MESWCOE Pune Maharashtra India HPC I&E Group Centre for Development of Advanced Computing Pune Maharashtra India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

Heat-induced stress impacts various physiological parameters in the body. Elevated temperature can cause tachycardia (an increase in heart rate), as the body attempts to dissipate heat through vasodilation, leading to dehydration and electrolyte imbalances. In addition, the hypothalamus triggers sweating in order to regulate body temperature; that can culminate in fluid and electrolyte loss, which could impact metabolic processes and blood pressure. A prolonged exposure to high temperatures can cause heat stroke, heat exhaustion, and other ailments including organ damage and systemic dysfunction. Existing electroencephalography (EEG)-based heat-induced stress detection often considers the entire EEG frequency range (delta to gamma), concealing redundant and lossy information and increasing the likelihood of false detection rates. To address the limitations of conventional handcrafted feature engineering approaches in heat stress detection, this paper introduces MAESTRO, a novel model comprising two blocks: Convolutional and Multi-head Attention. The Convolutional block extracts precise information from individual EEG frequency bands, while the Multi-head Attention block enhances feature representation through attention mechanism. Finally, two dense layers are employed to classify heat stress into three classes: Acute, Chronic, and Control. The proposed framework undergoes validation using EEG data obtained from 40 rodents in a simulated laboratory environment. The outcomes illustrate the viability of the method in classifying heat-induced stress, yielding remarkable results for overall accuracy, precision, recall, and F1 score of 98.88 %, 98.54 %, 98. 67 %, and 98.60 %, respectively.

关键词： Heating systems Accuracy Convolution Rodents Feature extraction Brain modeling Electrolytes Electroencephalography Data models Stress

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Analysis of U-Net and Modified VGG16 Technique for Mitosis Identification in Histopathology Images

Analysis of U-Net and Modified VGG16 Technique for Mitosis I...

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International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET)

作者： Jithy Lijo Saleema J. S Research Scholar Christ (Deemed to be University) Bengaluru Karnataka India Christ Academy Institute for Advanced Studies Bengaluru Karnataka India Department of Computer Science Christ Deemed to be University Bengaluru Karnataka India

ISBN: (数字)9798350350845

ISBN: (纸本)9798350350852

One of the most frequently diagnosed cancers in women is breast cancer. Mitotic cells in breast histopathological images are a very important biomarker to diagnose breast cancer. Mitotic scores help medical professionals to grade breast cancer appropriately. The procedure of identifying mitotic cells is quite time-consuming. To speed up and improve the process, automated deep learning methods can be used. The suggested study aims to conduct analysis on the detection of mitotic cells using U-Net and modified VGG16 technique. In this study, pre-processing of the input images is done using stain normalization and enhancement processes. A modified VGG16 classifier is used to classify the segmented results after the altered image has been segmented using U-Net technology. The suggested method's robustness is evaluated using data from the MITOSIS 2012 dataset. The proposed strategy performed better with a precision of 86%,recall of 75% and F1-Score of 80%.

关键词： Deep learning YOLO Wireless communication Image segmentation Histopathology Signal processing algorithms Signal processing

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AI for Disaster Response: Developing Intelligent Systems for Real-Time Emergency Management

AI for Disaster Response: Developing Intelligent Systems for...

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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： T.K. Lakshmi Naresh Kumar Mangalapu Mary Fathima Anuja Joseph Swaminathan S. Chandravadhana R. Pari T. Ranjana Department of Computer Science and Engineering Malla Reddy University Hyderabad Telangana India Department of Orthodontics & Dentofacial Orthopedics Kamineni Institute of Dental Sciences Narketpally Nalgonda Telangana Department of Computer Science Prince Shri Venketeshwara Arts and Science College Chennai Tamil Nadu India Department of Mechatronics Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering VELS Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India Master of Business Administration S.A. Engineering College Thiruverkadu Tamil Nadu India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

The research introduces the design of an AI-based disaster response system that subscribes to enhance real-time emergency management. Manual processing, slow communication and siloed data sources are common downsides of traditional-approach systems, which ultimately translate into slower response times or insufficient resource spend. The system deals with them by exploiting ML, predictive analytics, and automatic decision-making so that these can consolidate pre-existing real-time information from social media, satellite images or IoT sensors. Several experiments show better-than-previous: response times decrease from an average of 11 hours to 3.6 and prediction accuracy increased from 62.5% up to cover %85 Average efficiency also improved including +23% in resource allocation. The innovative solution shifts disaster response from being entirely reactive to proactive, creating faster and more informed responses during times of crisis, thereby assuring public safety for all in the face of calamities.

关键词： Training Accuracy Disasters Emergency services Real-time systems Public security Time factors Resource management Predictive analytics Intelligent systems

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Approximate nearest-neighbor search for line segments 37

Approximate nearest-neighbor search for line segments

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37th International Symposium on Computational Geometry, SoCG 2021

作者： Abdelkader, Ahmed Mount, David M. Oden Institute for Computational Engineering and Sciences The University of Texas AustinTX United States Department of Computer Science Institute of Advanced Computer Studies University of Maryland College ParkMD United States

ISBN: (纸本)9783959771849

Approximate nearest-neighbor search is a fundamental algorithmic problem that continues to inspire study due its essential role in numerous contexts. In contrast to most prior work, which has focused on point sets, we consider nearest-neighbor queries against a set of line segments in Rd, for constant dimension d. Given a set S of n disjoint line segments in Rd and an error parameter Ε > 0, the objective is to build a data structure such that for any query point q, it is possible to return a line segment whose Euclidean distance from q is at most (1 + Ε) times the distance from q to its nearest line segment. We present a data structure for this problem with storage O((n2/Εd) log(∆/Ε)) and query time O(log(max(n, ∆)/Ε)), where ∆ is the spread of the set of segments S. Our approach is based on a covering of space by anisotropic elements, which align themselves according to the orientations of nearby segments. © Ahmed Abdelkader and David M. Mount;licensed under Creative Commons License CC-BY 4.0 37th International Symposium on Computational Geometry (SoCG 2021).

关键词： Nearest neighbor search

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Quantum query complexity with matrix-vector products 48

Quantum query complexity with matrix-vector products

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48th International Colloquium on Automata, Languages, and Programming, ICALP 2021

作者： Childs, Andrew M. Hung, Shih-Han Li, Tongyang Joint Center for Quantum Information and Computer Science Department of Computer Science Institute for Advanced Computer Studies University of Maryland College ParkMD United States Center for Theoretical Physics MIT CambridgeMA United States

ISBN: (纸本)9783959771955

We study quantum algorithms that learn properties of a matrix using queries that return its action on an input vector. We show that for various problems, including computing the trace, determinant, or rank of a matrix or solving a linear system that it specifies, quantum computers do not provide an asymptotic speedup over classical computation. On the other hand, we show that for some problems, such as computing the parities of rows or columns or deciding if there are two identical rows or columns, quantum computers provide exponential speedup. We demonstrate this by showing equivalence between models that provide matrix-vector products, vector-matrix products, and vector-matrix-vector products, whereas the power of these models can vary significantly for classical computation. © 2021 Andrew M. Childs, Shih-Han Hung, and Tongyang Li.

关键词： Quantum computers

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Finding of Maturity Level of Crops for Harvesting using IOT and ML Based Structured Framework

Finding of Maturity Level of Crops for Harvesting using IOT ...

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Advance computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： R Swaranambigai R. Mythili Anurag Aeron P Thilakavathy Zainab Alassedi Waleed Al-Azzawi Department of ECE Jaishriram Engineering College Tiruppur Department of Information Technology SRM Institute of Science and Technology Ramapuram Chennai Department of CSE MIET Meerut Meerut Department of Computer science and engineering Vels Institute of Science Technology and Advanced Studies Chennai Collage of Nursing National University of Science and Technology Dhi Qar Iraq Al-Farahidi university Baghdad Iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

IoT designs make it possible to collect data for vast and remote agricultural regions, which may then be used to use machine learning algorithms to anticipate crops. In order to establish the suggested crop, recommendations are based on factors including temperature, humidity, rainfall, pH, N, P, and K. The dataset includes 2200 occurrences and 8 characteristics. For different combinations of these attributes, about 22 alternative crops are suggested. Using specific machine learning algorithms in WEKA, supervised learning techniques are used to create an ideal model. The rules-based classifier JRip, the decision table classifier, and the multilayer perceptron are the machine learning methods selected for classification. Establishing a model that can forecast high-yield crops and facilitate precision agriculture is the main goal of this case study. IoT technologies and crucial agricultural metrics are included in the suggested system modeling. A weighted average receiver operator characteristic of one and an accuracy of 98.2273% are shown by the performance evaluation of the chosen classifiers. 8.05 seconds is the maximum amount of time needed to construct the model.

关键词： Precision agriculture Machine learning algorithms Temperature Rain Supervised learning Crops Receivers

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Multi-modal Image and Radio Frequency Fusion for Optimizing Vehicle Positioning

arXiv

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arXiv 2024年

作者： Huan, Ouwen Luo, Tao Chen, Mingzhe Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing100876 China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States

In this paper, a multi-modal vehicle positioning framework that jointly localizes vehicles with channel state information (CSI) and images is designed. In particular, we consider an outdoor scenario where each vehicle can communicate with only one BS, and hence, it can upload its estimated CSI to only its associated BS. Each BS is equipped with a set of cameras, such that it can collect a small number of labeled CSI, a large number of unlabeled CSI, and the images taken by cameras. To exploit the unlabeled CSI data and position labels obtained from images, we design an meta-learning based hard expectation-maximization (EM) algorithm. Specifically, since we do not know the corresponding relationship between unlabeled CSI and the multiple vehicle locations in images, we formulate the calculation of the training objective as a minimum matching problem. To reduce the impact of label noises caused by incorrect matching between unlabeled CSI and vehicle locations obtained from images and achieve better convergence, we introduce a weighted loss function on the unlabeled datasets, and study the use of a meta-learning algorithm for computing the weighted loss. Subsequently, the model parameters are updated according to the weighted loss function of unlabeled CSI samples and their matched position labels obtained from images. Simulation results show that the proposed method can reduce the positioning error by up to 61% compared to a baseline that does not use images and uses only CSI fingerprint for vehicle positioning. Copyright © 2024, The Authors. All rights reserved.

关键词： Expectation maximization algorithm

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Air Quality Index Prediction Using Support Vector Regression Based on African Buffalo Optimization 9th

Air Quality Index Prediction Using Support Vector Regression...

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9th International Conference on computing and Informatics, ICOCI 2023

作者： Yusof, Yuhanis Maijama’a, Inusa Sani School of Computing Institute for Advanced and Smart Digital Opportunities Universiti Utara Malaysia UUM Sintok Kedah Bukit Kayu Hitam06010 Malaysia Department of Computer Science Hussaini Adamu Federal Polytechnic Kazaure Kazaure Nigeria

ISBN: (纸本)9789819995912

Support Vector Regression (SVR) is one of the machine learning models widely used in regression analysis. As an alternative for fitting a line to the data points like typical linear regression algorithms, it finds a hyperplane that is effective in fitting data points in a continuous space. The kernel type and hyperparameters significantly influence the performance and effectiveness of SVR. Determination of the optimal values is crucial in ensuring the success of prediction, regardless of the application domain. This study adapts the African Buffalo Optimization (ABO) algorithm to determine SVR’s regularization and kernel parameters. The ABO algorithm mirrors African buffaloes’ hunting and defensive behavior, offering ability to track the best position and extensive memory capacity to discover the best solution for problems under analysis. Evaluation is then performed on the air quality index benchmark dataset, and the prediction results of SVR-ABO are compared against other optimized SVR prediction models. The results show that SVR-ABO is a better algorithm because it produces smaller errors and best fits the data. Such an outcome indicates that the proposed SVR optimized by ABO is a competitive prediction model in data analytics. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Air quality

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Addressing Non-stationarity with Stochastic Trend in the Context of Limited Time Series Data: An Experimental Survey

Research Square

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Research Square 2025年

作者： Batoure Bamana, Apollinaire Sokdou Bila Lamou, Yannick Fotsa-Mbogne, David Jaures Shafiee Kamalabad, Mahdi Department of Computer Engineering University Institute of Technology The University of Ngaoundere Cameroon Department of Robotic and Industrial Computing School of Chemical Engineering and Mineral Industries The University of Ngaoundere Cameroon Department of Mathematics and Computer Science Faculty of Science The University of Ngaoundere Cameroon Laboratory of Mathematics Computer Science and Applications Department of Mathematics and Computer Science National Higher School of Agro-Industrial Sciences The University of Ngaoundéré Cameroon Laboratory of Mathematics and Computer Science Department of Mathematics Statistics and Computer Science Faculty of Science The University of Bertoua Cameroon Department of Methodology and Statistics Faculty of Social and Behavioural Sciences Utrecht University Utrecht Netherlands Centre for Complex Systems Studies Utrecht University Utrecht Netherlands

Stationarity in time series is a key property for practical data analysis, inferences, and predictions particularly in biosciences. Stationarity can be either deterministic or stochastic. If a time series data is not stationary, it can be rendered stationary through detrending or differentiation techniques. Differentiation can be performed in either an integer or fractional form. This paper investigates non-stationarity in time series data, focusing specifically on stochastic trends characterised by a unit root. It hypothesises that fractional differentiation can enhance forecast accuracy for datasets with limited volume. For experiments, 24 series corresponding to weekly Malaria and Typhoid Fever from Adamawa Region (Cameroon) are analysed and forecasted. Each series comprises 156 observations covering January 2021 to December 2023. After collecting, checking and classifying the data, four forecasting models, Auto Regressive Integrated Moving Average (ARIMA), Fractional ARIMA (ARFIMA), Long Short-Term Memory (LSTM), and the proposed Fractional Differencing and LSTM (FD-LSTM), are implemented. The models' performances are evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of determination (R2) metrics, which inform further analysis and recommendations. Results reveal that ARFIMA outperformed the ARIMA by 93% in training and 100% in testing. Similarly, FD-LSTM achieved a 100% improvement over LSTM in training and testing. These findings underscore the value of achieving stationarity through fractional differentiation, enhancing model performance and providing a robust framework for forecasting in datasets with limited observations. © 2025, CC BY.

关键词： Time series

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