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A novel optimised GWOAN algorithm for scheduling task and power consumption in MEC network

International Journal of Intelligent Systems Technologies and Applications 2023年第4期21卷 386-401页

作者： Sivakumar, P. Murugan, Murali Somasundaram, S.K. Prabhu, S. Nagendra School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai600127 India Engineering Retail Business Professional Georgia United States School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Computational Intelligence SRM Institute of Science and Technology Tamil Nadu Chennai632014 India

In recent decades, the 5G and internet of things (IoT) are occupied with several applications like face recognition, traffic control, video surveillance and telecommunication, etc. Mobile-edge computing (MEC) is a promising paradigm in wireless communication which is carried by the computational scheduling of mobile devices. To enhance the computation design, the AlexNet of the DL model is applied which is based on the convolutional neural network (CNN) used to train a large number of attributes. To provide an optimal solution, the metaheuristics algorithm of grey wolf optimisation (GWO) is combined with an AlexNet which is named a novel grey wolf optimisation-based AlexNet (GWOAN) algorithm. In the proposed GWOAN algorithm, the AlexNet hyperparameters (weights, biases and other parameters) are fine-tuned by GWO and then performed a classification. As a result, the GWOAN has achieved a higher scheduling task and low latency than the standard AlexNet, ResNet-18 and VGGNet-16 respectively. © 2023 Inderscience Enterprises Ltd.

关键词： Optimization

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Automatic Document Summarization of Unilingual Documents: A Review 6th

Automatic Document Summarization of Unilingual Documents: A ...

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6th International Conference on Intelligent Computing and Optimization, ICO 2023

作者： Anan, Sabiha Islam, Nazneen Ali, Mohammed Nadir Bin Bhuiyan, Touhid Bijoy, Md.Hasan Imam Reza, Ahmed Wasif Arefin, Mohammad Shamsul Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chittagong Bangladesh Department of Computer Science and Engineering Daffodil International University Birulia Bangladesh Department of Computer Science and Engineering East West University Dhaka Bangladesh

ISBN: (纸本)9783031503269

Automatic summarization of documents is the creation of short and accurate summary of a source document. Due to the abundance of information on various platforms of our social life, shortening of that information is greatly needed for us. Thus, document summarization automatically provides short, precise and informative abstract of a large document. In this era of information, as the necessity of document summarization is increasing continuously, the techniques of summarizing document(s) are also evolved throughout the years. In this paper, a review on summarization of unilingual documents has been presented. A detailed description of some prominent approaches for document summarization and their performances are also analyzed in this paper. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Abstracting

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Heuristics based Segmentation of Left Ventricle in Cardiac MR Images 3

Heuristics based Segmentation of Left Ventricle in Cardiac M...

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

作者： Gowthamani, R. Sasi Kala Rani, K. Rohini, M. Avinash, S. Deepakkumar, B. ArunKumar, L. Sri Krishna College of Engineering and Technology Department of Computer Science and Engineering India

ISBN: (纸本)9781665462167

In the development of clinical decision supporting system, the clinical image classification and disease prognosis with human organ plays a main role in health care systems. Especially in cardiac Magnetic Resonance Imaging (MRI) diagnosing abnormal condition will be very difficult. Segmentation of left and right ventricles predicts heart problems and abnormalities and can be helpful to diagnose various heart issues. Cardiovascular disease plays a vital role in humans' life. Diagnosis and prevention of left ventricular segmentation acts as an important part. Segmenting left ventricular is the complex and significant task to intensity and shape similarity with other organs in our body. In our proposed can be used to prevent cardiac disease effectively by automatically segmenting left ventricle with the help of MRI images, Improvised Convolution network and heuristic algorithm to detect the disease with high accuracy. The network is trained by considering the left ventricle's comparatively small percentage of pixels in the overall image,. In the post- processing stage, the regions are determined based on their shapes and thresholding on the result image of the fully convolutional network. The performance is measured using the accuracy and loss observed as 96.79 percentage and 0.0029. The input image size was processed to 256 x 256 and the mask fitted the accuracy parameters at this size to the optimized rate of result. © 2023 IEEE.

关键词： Heart

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Bayesian Deep Learning for Dynamic Power System State Prediction Considering Renewable Energy Uncertainty

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Journal of Modern Power Systems and Clean Energy 2022年第4期10卷 913-922页

作者： Shiyao Zhang James J.Q.Yu Academy for Advanced Interdisciplinary Studies Southern University of Science and TechnologyShenzhen 518055China Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 518055China

Modern power systems are incorporated with distributed energy sources to be environmental-friendly and ***,due to the uncertainties of the system integrated with renewable energy sources,effective strategies need to be adopted to stabilize the entire power ***,the system operators need accurate prediction tools to forecast the dynamic system states *** this paper,we propose a Bayesian deep learning approach to predict the dynamic system state in a general power ***,the input system dataset with multiple system features requires the data pre-processing ***,we obtain the dynamic state matrix of a general power system through the Newton-Raphson power flow ***,by incorporating the state matrix with the system features,we propose a Bayesian long short-term memory(BLSTM)network to predict the dynamic system state variables *** results show that the accurate prediction can be achieved at different scales of power systems through the proposed Bayesian deep learning approach.

关键词： Bayesian deep learning data analytics NewtonRaphson power flow renewable energy source system state

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Early Prediction of Alzheimer’s Disease Using Ensemble Learning Models

Early Prediction of Alzheimer’s Disease Using Ensemble Lear...

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International Conference on Machine Learning and Big Data Analytics, ICMLBDA 2022

作者： Singh, Divjot Mishra, Ashutosh Computer Science and Engineering Department Thapar Institute of Engineering and Technology Patiala India

ISBN: (纸本)9783031151743

The human brain is the most complex part as it controls numerous operations of the human body. For the past few decades, works have been going on to study the working of brain and how to make models that can replicate its working. Artificial intelligence is one such advanced field that trains on data we collect and predicts the output like a human brain. On the other hand, there are many diseases related to brain that have been discovered over time. These diseases progress with the passage of time, and, due to their slow rate of progression, it becomes difficult to detect them at early stage. Alzheimer’s disease (AD) is one of the neurodegenerative diseases which damages the brain permanently and becomes untreatable at the higher stages. Thus, it becomes very important to detect AD at an early stage to increase the life span of the person detected. The main predicament is to detect AD at an early stage and the selection of features responsible for it. The objective of this study is to predict AD at an early stage and to identify the features that facilitate its early prediction using ensemble learning. The proposed multiple ensemble method uses the selected features to achieve an accuracy of 82.40% and with higher ROC. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Brain

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Automatic classification of normal/AD brain MRI slices using whale-algorithm optimized hybrid image features

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Journal of Ambient Intelligence and Humanized Computing 2023年第10期14卷 14237-14248页

作者： Kadry, Seifedine Jessy, V. Elizabeth Rajinikanth, Venkatesan Crespo, Rubén González Department of Applied Data Science Noroff University College Kristiansand4612 Norway College of Engineering and Information Technology Ajman University Ajman United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Division of Research and Innovation Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Tamil Nadu Chennai602105 India Department of Computer Science and Technology Universidad Internacional de La Rioja La Rioja Logroño Spain

In recent years, the prevalence of Age-Related Illnesses (ARL) has been increasing among older individuals, and early recognition and treatment will result in better living conditions. It is well known that Alzheimer's Disease (AD) is among the ARD, and severe cases may result in dementia as well. It is the purpose of this study to propose a technique for distinguishing normal/AD brain MRI slices with improved accuracy utilizing the T2-modality. This scheme consists following phases: (i) Brain MRI collection and preprocessing, (ii) Deep feature extraction with the chosen scheme, (iii) Handcrafted feature extraction, (iv) Whale Algorithm (WA) based feature reduction and serial integration, and (v) binary classification using five-fold cross-validation. A total of 2000 MRI slices (1000 normal and 1000 AD class) are examined during this task using images collected from Alzheimer’s Disease Neuroimaging Initiative (ADNI). This study confirms that the proposed scheme provides a classification accuracy of > 98% when applied with the K-Nearest Classifier. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Feature extraction

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Artificial Intelligence Techniques and Methodology Available for Lung Cancer Detection

Artificial Intelligence Techniques and Methodology Available...

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2023 International Conference on Sustainable Computing and Smart Systems, ICSCSS 2023

作者： Kanaga Priya, P. Sivakumar, T. Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350333602

Cancer is a term used to describe the body's aberrant cells growing out of control, which can occur in various body parts. With over a hundred types of cancer known to exist, delays in treatment can lead to severe health issues and, in some cases, loss of life. This study reviews the current methods used to detect lung cancer through image processing. The study suggests that artificial intelligence (AI) is effective in processing large datasets, providing accurate and efficient results for cancer detection. However, implementing these systems on a large scale presents several challenges, such as the capture of images, preparation of images, segmentation, and information management. Classification strategies must also be developed to ensure compatibility with AI. This study examines various imaging techniques and methodologies available for detecting cancer using a computer-aided user interface, which is essential to improve healthcare and meet the growing patient population's needs. © 2023 IEEE.

关键词： Diseases

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Predictions of Basketball Match Results using Autoencoder with Gated Recurrent Unit Algorithm 1

Predictions of Basketball Match Results using Autoencoder wi...

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1st International Conference on Software, Systems and Information technology, SSITCON 2024

作者： Kavitha, K.R. Almusawi, Muntather Vybhavi, Gummagatta Yajaman Edwin Prabhakar, P.B. Saranya, N. Naga Sona College of Technology Department of Electronics and Communication Engineering Salem India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Department of Computer Science and Engineering Hyderabad India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Chennai India Saveetha College of Liberal Arts & Science Simats Department of Computer Applications Chennai India

ISBN: (纸本)9798350352931

This research explores basketball match predictions within the field of sports analytics utilized Deep Learning (DL) technique to analyse the outcomes of National Basketball Association (NBA) games based on player performance and team statistics. The challenge in predicting NBA games outcomes and performance analysis various factor, as the high dimensionality of the data and uncertainty surrounding player due to difficult to predictions of basketball game. This paper, proposed Autoencoder with Gated Recurrent Unit (AE-GRU) algorithm to efficiently analyse and predict NBA game outcomes. The data was initially collected from NBA games between 2009 - 2021, focusing on five teams and pre-processing involved min-max normalization to uniformly scale the feature vectors. The Principal Component Analysis (PCA) utilized for feature selection, effectively reducing the high dimensionality of the NBA game data across various teams. The AE-GRU technique then captured the complex feature of basketball game data and handled uncertainty data and enhances performance. The performance of DL model evaluated the various metrics such as accuracy, precision, recall and f1-score metrics on NBA game. © 2024 IEEE.

关键词： Deep learning

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Candlestick Chart Based Stock Analysis System using Ensemble Learning 1

Candlestick Chart Based Stock Analysis System using Ensemble...

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1st IEEE International Conference on Networking and Communications, ICNWC 2023

作者： Varghese, Angel Ann Krishnadas, J. Kumar, R. Satheesh Sahrdaya College of Engineering and Technology Department of Computer Science and Engineering Thrissur India

ISBN: (纸本)9798350336009

The 18^{\mathrm{t}\mathrm{h}}-century candlestick charts, which were first utilized in the Japanese rice market, are now frequently used in trading tactics across all financial markets. Candlestick charts make it possible to comprehend an asset's opening, high, low, and closing values all in one image. In addition to these benefits, the abundance of candlestick chart patterns makes practical application challenging. A software framework that employs candlestick charts to forecast trend direction was built for this study. There are four stages of the project. A system that can identify candle patterns is developed in the first stage. The second stage involves executing training and testing procedures on data sets with labeled candlestick chart types and trend directions in order to assess the model's performance. During the machine learning phase, open-source techniques like xgboost were applied. In the project's final stage, it was discovered that the strategy focused solely on identifying candlestick patterns and taking positions in line with the trend based on the suggested methodology produced larger profits in 11 global indices than the Buy and Hold strategy. In comparison to the current accuracy of 53.8%, the model's average forecast accuracy is 59.42% © 2023 IEEE.

关键词： Commerce

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Analysis and Prediction of COVID-19 Disease using Machine Learning 14

Analysis and Prediction of COVID-19 Disease using Machine Le...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Parmar, Shabnam Rani, Rinkle Kalra, Nidhi Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala India

In this research, the symptoms and other factors of a patient are utilized to train machine learning algorithms to predict whether the patient would die from or recover from (COVID19). It is probable that the coronavirus (COVID19) will create the highly infectious Coronavirus illness COVID19 (SARSCoV2). By coughing, sneezing, speaking, or inhaling, this virus can be transmitted from an infected person's lips and nose to small liquid particles. The size of these tiny atoms' inhalation droplets and aerosols vary, with bigger droplets being larger than smaller atoms. COVID19 is transferred by inhalation or by touching your eyes, nose, or mouth with your fingers after meeting a contaminated surface. When a big number of people are present, the COVID-19 virus can spread rapidly. We will need to examine the COVID19 dataset to see which models are the most accurate in estimating fatality rates for the virus's most vulnerable victims. Machine learning is used to compute and evaluate the performance of a variety of prediction models. We have used K-nearest neighbor (KNN), Support Vector Machine (SVM) classifier, Gaussian naive Bayesian (GNB), Decision tree (DT), and Logistic regression (LR) for the prediction of death and recovery of symptomatic patients. In this research, a variety of feature selection and extraction strategies were used, and prediction accuracy for feature selection methods for the KNN model and feature extraction methods for the GNB model both reached up to 96 percent. The k-nearest neighbor has performed and predicted high accuracy of 96% in both feature selection and extraction techniques. © Grenze Scientific Society, 2023.

关键词： Support vector machines

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