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An improved particle swarm optimization algorithm for task scheduling in cloud computing

Journal of Ambient intelligence and Humanized Computing 2023年第4期14卷 4313-4327页

作者： Pirozmand, Poria Jalalinejad, Hoda Hosseinabadi, Ali Asghar Rahmani Mirkamali, Seyedsaeid Li, Yingqiu Hebei Key Laboratory of Machine Learning and Computational Intelligence Hebei University Baoding071002 China Department of Mathematics and Computer Science Bandar Abbas Branch Islamic Azad University Bandar Abbas Iran Department of Computer Science University of Regina Regina Canada Tehran Iran

In the context of cloud computing, the task scheduling issue has an immediate effect on service quality. Task scheduling is the process of assigning work to available resources based on requirements. The objective of this NP-hard problem is to identify the ideal timetable for resource allocation so that more tasks can be done in less time. Several algorithms have been presented thus far to solve the problem of work scheduling. In this paper proposes an Improved Particle Swarm Optimization (IPSO) algorithm to address the aforementioned issue. In order to shorten the execution time of the original Particle Swarm Optimization (PSO) algorithm for task scheduling in the cloud computing environment, a multi-adaptive learning strategy is employed. In its initial population phase, the proposed Multi Adaptive learning for Particle Swarm Optimization (MALPSO) defines two sorts of particles: ordinary particles and locally best particles. During this phase, the population's variety is reduced and the likelihood of reaching the local optimum rises. This study compares the proposed approach to various algorithms based on four criteria: makespan, load balancing, stability, and efficiency. Additionally, we examine the proposed technique using the CEC 2017 benchmark. Compared to what is currently known, the suggested method can solve the problem in less time and get the best answer for most of the criteria. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Cloud computing

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A Performance Analysis of Supervised machine learning Models for Thyroid Disease Detection 4

A Performance Analysis of Supervised Machine Learning Models...

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4th International Conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Gaurkhede, Sanskruti Burle, Rushikesh Puri, Chetan Datta Meghe Institute of Higher Education and Research Computer Science and Design Faculty of Engineering and Technology Mahartashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Mahartashtra Wardha442001 India

ISBN: (纸本)9798331523923

Thyroid disease is a major causes of the main reasons for medical diagnosis and prediction is thyroid illness, whose growth is a interesting topic in medicinal study. One of our body's utmost dynamic body part is the thyroid secreter. Thyroid hormone issues are in control for adaptable metabolism. Two predominant thyroid disorders that issue thyroid hormones to controller the body's breakdown are thyrotoxicosis and hypothyroidism. Thyrotoxicosis and hypothyroidism are two mutual thyroid situations that impair the thyroid's capacity to generate thyroid hormones and regulate the build's absorption. Data cleaning actions remained utilised to kind the data simple sufficient for analytics to demonstration the likelihood of affected role having thyroid illness. Thirteen clinicopathologic features from the dataset occupied from UCI machine learning source are employed In order to prediction the reappearance of well-differentiated thyroid cancer, this article analyses and classifies thyroid disease models. machine learning is a key component in the sickness prediction process. For complex knowledge activities, like medical diagnostic and prediction jobs, it is crucial to take a tough information base that can be combined and used as a cross model. We too optional a quantity of machine learning methods for thyroid preventative diagnosis in this work. machine learning methods such decision trees, logistic regression, random forest, and gradient boosting remained used to evaluate the likelihood that a patient will acquire thyroid disease. © 2025 IEEE.

关键词： Endocrinology

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Ensemble machine learning Based Blockchain Model for Privacy Preserving of Medical data in Fog Computing

Ensemble Machine Learning Based Blockchain Model for Privacy...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Sheetal, V.K. Vidya Sagar, S.D. Pareek, Piyush Kumar Nitte Meenakshi Institute of Technology Department of Master of Computer Applications Bengaluru India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9798350313413

The advent of COVID-19 highlights the need for big data-driven medical applications, the Internet of Medical Things, and smart healthcare. The biological information collected is strictly private. This enormous quantity of biological information is, however, beyond the capacity of current health care systems. Therefore, cloud computing has become the norm for archiving and sharing data. The combined information is then put to use in a variety of ways, including research and the identification of previously unknown facts. Textual forms (such as test results, prescriptions, and diagnoses) are the norm for biological data. Unfortunately, there are a number of security dangers and assaults that may be made against such data, including infringements on privacy and confidentiality. The security of biological data has come a long way, yet the majority of current methods still cause considerable delays and cannot support real-time replies. To improve the healthcare system, this study suggests a unique fog-enabled blockchain based privacy-preserving approach that makes use of machine learning. The suggested model efficiently carries out Medical Entity Recognition as it is built on four different machine learning models. In terms of detection rate and accuracy, the suggested model is 97% and 98%, respectively, better than the state-of-the-art replicas, as shown by the experiments. The sanitization approach outdoes the state-of-the-art by 8 percentage points when it comes to preserving utilities. © 2023 IEEE.

关键词： Biomedical Data Blockchain machine learning Medical Entity Recognition Privacy-preserving model

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A Hybrid Deep learning Approach for Early Detection of Diabetic Retinopathy 3

A Hybrid Deep Learning Approach for Early Detection of Diabe...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Mary Vespa, M. Shajeena, J. Shiny, R.M. Gayathri Devi, K. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Tiruchirapalli Campus India St Joseph's College of Engineering Department of Computer Science and Engineering Chennai India Rmd Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509675

Diabetes is a malnutrition that results from elevated glucose levels. Diabetes eventually results in diabetic retinopathy (DR), a retinal condition that significantly impairs vision. It is an insulin-dependent difficulty that impacts ocular health. The receptive to cells at the back of the individual retina's veins gets injured, which is the cause of it. A favorable prognosis for this condition depends on its early identification. In this paper, the application of Convolutional Neural Network (CNN) with Long Short Term Memory (LSTM) on color fundus images is used for diabetic retinopathy is applied for understanding the task. The goal of the proposed DR identification method is to use deep learning to automatically identify the problem. An hybrid model that was trained and tested to distinguish between healthy and DR-affected fundi was able to achieve an accuracy of roughly 99.07% using fundus images from diabetic patients that were available in MESSIDOR. © 2025 IEEE.

关键词： Convolutional neural networks

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LSTM deep learning model for Alzheimer's disease prediction based on cost-effective time series cognitive scores 5

LSTM deep learning model for Alzheimer's disease prediction ...

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5th International IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2023

作者： Saleh, Hager El-Rashidy, Nora Abuhmed, Tamer El-Sappagh, Shaker South Valley University Faculty of Computers and Artificial Intelligence Hurghada Egypt Kafrelsheiksh University Faculty of Artificial Intelligence Machine Learning and Information Retrieval Department Egypt Sungkyunkwan University College of Computing and Informatics Korea Republic of Galala University Faculty of Computer Science and Engineering Suez435611 Egypt

ISBN: (纸本)9798350381030

Alzheimer's disease (AD) is a complex chronic neurodegenerative disease that propagates over time. Deep learning (DL) models can be used to learn time series data to extract deep temporal features and make robust decisions. The fusion of multimodal time series data has been proven to enhance model performance [1]. For instance, cognitive scores (CSs) including clinical dementia rating, and Alzheimer's disease assessment scores have been integrated with other modalities like MRI to predict the future status of AD patients [2]. They enhanced the performance of DL models. These scores can be collected easily and in a cost-effective way in hospitals. No study in the literature has built a deep learning model to predict AD based on the cost-effective and multimodal time series data of CSs. In this study, we propose an LSTM-based DL architecture to predict AD based on multiple time series CSs. The proposed model has been optimized using a Bayesian optimizer to select the best architecture, and it has been compared with multiple classical machine learning models like random forest and others. The proposed LSTM architecture achieved better results than other models and provided stable and robust performance. © 2023 IEEE.

关键词： Data fusion

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Predicting Stock Market Trends Using Deep learning: A Case Study of Pakistan Stock Exchange 18

Predicting Stock Market Trends Using Deep Learning: A Case S...

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18th International Conference on Open Source Systems and Technologies, ICOSST 2024

作者： Balouch, Maaz Hamayat, Faizan Abbasi, Humza Fazal Javed, Muhammad Qasim Mehrban, Ali Ahmad, Rana Fayyaz Department of Business Studies Namal University Mianwali Pakistan National Center for Physics Machine Learning and Deep Learning Lab Artificial Intelligence Technology Center Islamabad Pakistan Department of Computer Science Bahria University Islamabad Pakistan School of Electrical and Electronics Engineering Newcastle University Newcastle United Kingdom

ISBN: (纸本)9798331508692

This research investigates the short-term stock price prediction in the Pakistan Stock Exchange (PSX) using Deep learning (DL) techniques. By integrating historical stock data, economic indicators, and market news, the research aims to address the limitations of traditional stock analysis methods. Data was collected and pre-processed from various sources, including the PSX, economic indicators, and market news. Four techniques were implemented: ARIMA, LSTM, BI-LSTM, and a hybrid CNN-BI-LSTM. Their performance was evaluated using RMSE, MAE, and MAPE metrics. Comparative analysis revealed that BILSTM outperformed the other techniques, with RMSE, MAE, and MAPE scores of 0.9298, 0.6099, and 13.9443, respectively. Despite ARIMA's lower accuracy, it had the fastest inference time, followed by LSTM, CNN-BI-LSTM, and BI-LSTM. Results indicate that using comprehensive data with DL models significantly improves stock price forecast accuracy, highlighting the potential of technological innovation in enhancing financial market decision-making. These DL algorithms can provide valuable insights for investors and analysts in the PSX, leading to better decision-making and more accurate economic predictions. © 2024 IEEE.

关键词： Deep learning

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Utilising SkyScript for Open-Vocabulary Categorization, Extraction, and Captioning to Enhance Multi-Modal Tasks in Remote Sensing

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Remote Sensing in Earth Systems sciences 2024年第3期7卷 149-158页

作者： Nagaraj, Saranya Sivakumar, Shanmuga Priya Annabel, Lawrence Sherly Puspha Joshi, Vilas Ramrao Patil, Mithun Baswaraj Patil, Vishal Ratansing SRM Institute of Science and Technology School of Computing SRM Institute of Science and Technology Department of Artificial Intelligence and Machine Learning St. Joseph’s College of Engineering Department of Computer Engineering ISBM College of Engineering Department of Computer Science and Engg N K Orchid College of Engg and Technology Solapur Department of CSE (AIML) Pimpri Chinchwad College of Engineering Nigdi

The SkyScript dataset was developed by integrating large-scale remote sensing images from Google Earth Engine with geo-tagged semantic data from OpenStreetMap. This open-access dataset, consisting of 2.6 million image-text pairs covering 29,000 unique tags, facilitates various remote sensing tasks such as cross-modal retrieval, image captioning, and classification. The dataset ensures global representation and semantic diversity, although it exhibits a higher concentration of high-resolution images from the USA and Europe due to licencing constraints. The images, sourced from multiple collections with varying ground sampling distances, are paired with captions generated using a combination of rule-based methods and logistic regression models for tag classification. Experiments demonstrate that models pre-trained on SkyScript outperform those trained on other datasets in zero-shot classification and fine-grained attribute recognition, highlighting its potential for advancing vision-language models in remote sensing applications. Future improvements could involve enhancing geographic coverage and refining caption quality using advanced language models. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Image captioning Remote sensing Sampling distance Vision language

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Enhancing Prognostication in Colorectal Cancer with Integrated machine learning for Improved Survival Prediction

Enhancing Prognostication in Colorectal Cancer with Integrat...

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2025 International Conference on Multi-Agent Systems for Collaborative intelligence, ICMSCI 2025

作者： Velmurugan, R. Suresh, K. Thilagaraj, T. Sharma, Meenakshi Saranya, D. Bangalore India Department of Computer Science Bangalore India Moodlakatte Institute of Technology Department of Artificial Intelligence and Machine Learning Karnataka Kundapura India Global Group of Institutes Punjab Amritsar India Sri Eshwar College of Engineering Department of Information Technology Coimbatore India

ISBN: (纸本)9798331509828

machine learning methods are recently used to predict patient survival in colorectal cancer using such models as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), VGG16, and Support Vector machines (SVM). Taking advantage of a combination of CT, MRI scan images, and clinical records with drug recommendations, the study also checks to see how these models compare for distinguishing between patients in terms of their illness course-whether they are going to get better or worse over time. The results reveal VGG16 has better accuracy than CNN, RNN and SVM;as the highest-performing model tested, it also demonstrates superior precision, recall and F1-score. The research findings also validate these proposed models as they compare favorably with existing literature. This presents a promising proposition: a new, revolutionary approach to using artificial intelligence to boost prognostic accuracy. © 2025 IEEE.

关键词： Convolutional neural networks

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A Comprehensive Analysis of Secure Attack Detection on Virtual machines in Cloud Computing 4

A Comprehensive Analysis of Secure Attack Detection on Virtu...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Nemirajaiah, Naveen Kumar Adalagere Raju, Channa Krishna Sri Siddhartha Institute of Technology Sri Siddhartha Academy of Higher Education Department of Computer Science and Engineering Tumakuru India Sri Siddhartha Institute of Technology Sri Siddhartha Academy of Higher Education Department of Artificial Intelligence and Machine Learning Tumakuru India

ISBN: (纸本)9798350352931

Cloud computing provides various computing services including processing power, storage, networking, software, analytics, and databases. A Virtual machine (VM) is a software-based simulation of a physical computer that runs an operating system and applications. The VM can run multiple functions on a single physical machine. Secure attack detection on VMs utilizes implementation techniques to detect and monitor malicious activities in the cloud environment. This detection involves Intrusion Detection Systems (IDS), anomaly detection methods, and real-time monitoring tools that analyse network traffic, system logs, and user behaviour. Optimization techniques are used to enhance the efficiency of the detection models. Swarm-based algorithms such as Intelligent Water Drop (IWD), Enhanced Shark Smell Optimization Algorithm (ESSOA), Brownian movement-centered gravitation search algorithm (BMGSA), Dual Conditional Moth Flame Algorithm (DC-MFA), Bat Optimization Algorithm (BOA), and Ant Colony Algorithm (ACO) are employed for this purpose. Additionally, Evolution-based algorithms such as Multi-objective Whale Optimization algorithm-based Differential Evolution (M-WODE), Gene Expression Programming (GEP), Genetic Algorithm (GA), and DragonFly-based Genetic Deep Belief Network (DGDBN) have also been used for anomaly detection. The performance metrics of makespan, energy consumption, Degree of Imbalance (DOI), and execution cost are used to evaluate these approaches. © 2024 IEEE.

关键词： Genetic algorithms

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Deep learning based RAGAE-SVM for Chronic kidney disease diagnosis on internet of health things platform

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Multimedia Tools and Applications 2024年 1-39页

作者： Kandukuri, Prabhakar Abdul, Ashu Kumar, Kuchipudi Prasanth Sreenivas, Velagapudi Ramesh, G. Gundu, Venkateswarlu Department of Artificial Intelligence and Machine Learning Chaitanya Bharathi Institute of Technology Telangana Hyderabad500075 India Department of Computer Science and Engineering SRM University Andhra PradeshNeerukonda Mangalagiri 522503 India Department of Computer Science & amp Engineering Koneru Lakshmaiah Education Foundation Andhra PradeshVaddeswaram 522302 India Department of Computer Science and Engineering SRK Institute of Technology Andhra Pradesh Vijayawada521108 India Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology TelanganaBachupally Hyderabad500090 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra PradeshVaddeswaram 522302 India

Chronic kidney disease (CKD) is a prominent disease that causes loss of functionality in the kidney. Doctors can now more easily gather patient health status data due to the growth of the Internet of Health Things (IoHT). The IoHT data contains a huge number of redundant data, making it challenging to predict CKD disease quickly and accurately. In healthcare applications like feature-based classification, a variety of disease diagnosis systems were used to address this problem. Current disease detection algorithms suffer from imbalanced dataset processing, low-accuracy feature learning, and high computational power requirements. Thus, deep learning-based clinical decision support systems have been developed to solve these complexities. To remove outliers from medical data, data collected with IoHT devices is first pre-processed using an enhanced K-means clustering technique. The Synthetic Minority over Sampling Technique is used to balance data because the IoHT dataset is highly imbalanced. The classifier detects anomalies in less time because of the implemented processing step. The balanced CKD dataset is then presented for use with a novel classifier called Residual Attention-Gated Autoencoder with Support Vector machine. In order to improve accurate detection, the adopted classifier can learn and extract features. The proposed method results in an increased accuracy of 99.43% within 70.1 s of computation time. The mean intersection of the union and kappa coefficient of the proposed method is 98.15% and 98.1%, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Classification (of information)

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