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Marine Predators Algorithm with Deep Learning-Based Leukemia Cancer Classification on Medical Images

computer Modeling in engineering & sciences 2024年第10期141卷 893-916页

作者： Sonali Das Saroja Kumar Rout Sujit Kumar Panda Pradyumna Kumar Mohapatra Abdulaziz S.Almazyad Muhammed Basheer Jasser Guojiang Xiong Ali Wagdy Mohamed Department of Computer Science&Engineering Gandhi Institute for TechnologyBPUTBhubaneswar752054India Department of Information Technology Vardhaman College of Engineering(Autonomous)Hyderabad501218TelanganaIndia Department of Electronic&Communication Engineering Vedang Institute of TechnologyBhubaneswar752010India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computing and Information Systems School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Research Centre for Human-Machine Collaboration(HUMAC) School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11931Jordan

In blood or bone marrow,leukemia is a form of cancer.A person with leukemia has an expansion of white blood cells(WBCs).It primarily affects children and rarely affects *** depends on the type of leukemia and the extent to which cancer has established throughout the *** leukemia in the initial stage is vital to providing timely patient *** image-analysis-related approaches grant safer,quicker,and less costly solutions while ignoring the difficulties of these invasive *** can be simple to generalize computer vision(CV)-based and image-processing techniques and eradicate human *** researchers have implemented computer-aided diagnosticmethods andmachine learning(ML)for laboratory image analysis,hopefully overcoming the limitations of late leukemia detection and determining its *** study establishes a Marine Predators Algorithm with Deep Learning Leukemia Cancer Classification(MPADL-LCC)algorithm onMedical *** projectedMPADL-LCC system uses a bilateral filtering(BF)technique to pre-process medical *** MPADL-LCC system uses Faster SqueezeNet withMarine Predators Algorithm(MPA)as a hyperparameter optimizer for feature ***,the denoising autoencoder(DAE)methodology can be executed to accurately detect and classify leukemia *** hyperparameter tuning process using MPA helps enhance leukemia cancer classification *** results are compared with other recent approaches concerning various measurements and the MPADL-LCC algorithm exhibits the best results over other recent approaches.

关键词： Leukemia cancer medical imaging image classification deep learning marine predators algorithm

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Environmental water quality prediction based on COOT-CSO-LSTM deep learning

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Environmental science and Pollution research 2024年第42期31卷 54525-54533页

作者： Rajagopal, Sankarasubbu Ganesh, Sundaram Sankar Karthick, Alagar Sampradeepraj, Thangavel Department of Information Technology National Engineering College Tamilnadu Kovilpatti628503 India Department of Computer Science and Engineering R.M.K. College of Engineering and Technology Puduvoyal Tamilnadu Thiruvallur601206 India Renewable Energy Lab Department of Electrical and Electronics Engineering KPR Institute of Engineering and Technology Tamilnadu Coimbatore641407 India Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu Virudhunagar626126 India

Water resource management relies heavily on reliable water quality predictions. Predicting water quality metrics in the watershed system, including dissolved oxygen (DO), is the main emphasis of this work. The enhanced long short-term memory (LSTM) model was suggested to improve the model’s performance. Additionally, a hybrid model was employed to calculate the ideal parameter values for the LSTM model, which helped overcome the nonstationarity, unpredictability, and nonlinearity of the data about the water quality parameters. This model recruited the COOT method. The original weekly water quality values at the Vaigai River, Madurai, Tamil Nadu, India, were tested using the suggested hybrid model. An independent LSTM, the hybrid optimisation method takes its cues from the cuckoo bird’s reproductive strategy and a novel meta-heuristic optimisation technique dubbed COOT, which is based on the behaviour of a flock of coot birds. If implemented, the suggested hybrid model might serve as an alternate framework for water quality prediction, laying the groundwork for basin-wide efforts to manage water quality and control pollutants. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Macroinvertebrates

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Advancing Industrial Cybersecurity: Machine Learning-Based Detection and Mitigation of IIoT Attacks 5

Advancing Industrial Cybersecurity: Machine Learning-Based D...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Kumar, S Gouri Kiran Prasad, Raavi Satya College of Computing and Information Sciences University of Technology and Applied Sciences Muscat Oman Dhanekula Institute of Engineering & Technology Department of Computer Science & Engineering A.P. Vijayawada India

ISBN: (纸本)9798331518097

A threatening entry or exploitation of vulnerabilities within an industrial network or system connected to IoT devices is an IIoT (Industrial Internet of Things) attack. Due to manufacturing delays, broken equipment, lost confidential data, and fines from the government, cyberattacks on IIoT systems can cause significant financial losses. Rapid reaction and recovery are made possible by efficient detection systems, which can lessen these losses. Many researchers have applied machine learning (ML) approaches to predict IIoT attacks in advance. Industrial networks can be highly effectively and dynamically protected against various cyber threats by utilizing MLthrough IIoT attack distinguishing. This ML methodology is a vital component of today's cybersecurity toolkit for IIoT contexts owing to its capacity for handling massive info, adjusting for novel threats, & delivering insights in real-time. Industrial networks can be highly effectively and dynamically protected against cyber threats by utilizing ML to IIoT attack identification. ML is a vital component of today's cybersecurity toolkit for IIoT contexts for its capacity to handle massive quantities of info, adjust to emerging risks, & deliver insights in real time. IIoT data can contain noise, missing values, or irrelevant information, negatively affecting model performance. Identifying and extracting relevant features from IIoT data was highly complex and time-consuming. The methodology's effectiveness is heavily related to features with high quality utilized. This paper discusses the various techniques, their limitations, and the gaps studied during the literature survey. Among the machine learning approaches, the model should adapt to the new infrastructure attacks, which is effectively possible only through meta-heuristic learning algorithms. © 2024 IEEE.

关键词： Cyber attacks

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Semantic Analysis Using Deep Learning for Predicting Stock Trends 2

Semantic Analysis Using Deep Learning for Predicting Stock T...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Galphade, Manisha Nikam, V.B. Yedurkar, Dhanalekshmi Singh, Prabhishek Stephan, Thompson Department of Computer Science & Engineering School of Computing MIT Art Design & Technology University Pune India Department of Computer Engineering & IT Veermata Jijabai Technological Institute Mumbai India Department of Computer Science & Engineering School of Computing MIT Art Design & Technology University Pune India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science & Engineering Graphic Era Deemed to Be University Uttarakhand Dehradun India

Company Investors and financial professionals mostly rely on quarterly reports to help them decide the ways to invest in stocks and assess the company's current performance. Quarterly company reports offer an abstracted perspective of the company's overall past performance, as well as its present situation and the market value of its market share. Financial text streams in quarterly report are unstructured naturally, but they represent cooperative expressions that are of important in any financial decision for stake holder. It will be both daunting and necessary to procedure intelligence of unstructured textual data. In this study, we address important queries related with the explosion of interest in a method to extract useful information from unstructured data and the way to work out if such insight provides any hints regarding the trends of financial markets. There is a lack of availability in the labeled dataset for financial sentiment analysis applications. The pre-trained language model employs very little labeled parameters that is used for a variety of domain specific corpora including financial sentiment analysis. In this paper, FinBERT, a model built on the BERT framework, to address linguistics challenges in the financial domain. The proposed work uses twelve transformer layers and twelve attention layers with several million parameters. The design of encoder and decoder comprises of several attention layers along with RNN. This arrangement aids to recognize instances processing the strongest relation between the words within a particular sentence. The experimentation results shows that the presented method surpass the state-of-the-art methods for financial datasets. The results are also compared with other existing models using the same financial dataset. It is observed that the FinBERT attains an accuracy of 84.77% on quarterly reports despite using a lesser training set. © 2024 Elsevier B.V.. All rights reserved.

关键词： Sentiment analysis

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Deep-CNWO: a deep-chaotic nature whale optimization algorithm for early prediction of blood pressure disorder in smart healthcare settings

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Neural computing and Applications 2024年第24期36卷 15117-15136页

作者： Motwani, Anand Shukla, Piyush Kumar Pawar, Mahesh Arya, Monika Jain, Paras School of Computing Science and Engineering VIT Bhopal University Bhopal-Indore Highway Kothrikalan MP Sehore466114 India Department of Computer Science & amp Engineering University Institute of Technology RGPV MP Bhopal462033 India Department of Computer Science & amp Engineering CMR Engineering College Kandlakoya Medchal Telangana Hyderabad501401 India Department of Information Technology University Institute of Technology RGPV MP Bhopal462033 India

The integration of cloud and edge computing, along with machine learning, plays a vital role in the development of efficient healthcare systems in smart cities. However, machine and deep learning (DL) models are prone to delayed convergence and Type-I and Type-II errors due to data vastness and high degree imbalance. To overcome the shortcomings of previous frameworks, this work aims to propose an optimization method with DL, ‘Deep-Chaotic Nature Whale Optimization’ (Deep-CNWO) for early prediction of Blood Pressure disorders among patients under at-home supervision. A simplex search algorithm is integrated to improve the update mechanism of whale optimization algorithm (WOA), thereby creating a CNWO algorithm. The purpose of this hybrid optimization is to increase the accuracy and efficiency of DL models. Leveraging the power of DL and CNWO, this method (Deep-CNWO) provides an effective solution for early detection and proactive management of a chronic disease in at-home healthcare settings. We collected relevant data from clinical studies, including vital signs and patient contextual information, to train and evaluate the deep-CNWO model. The CNWO optimization approach has been used to improve the predictive performance and convergence of DL models. Experiments performed on imbalanced datasets using deep-CNWO have given 99.90% accuracy. The average F-score for emergency cases has improved by 22%, while the average accuracy has increased by 5.72% across all three classes, compared to the results reported in previous related work. Deep-CNWO improves the convergence of DL and reduces Type-I and Type-II errors. The experimental results demonstrate the efficacy of our proposed method for remote patient monitoring and highlight its potential for quick intervention during emergencies. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Optimization

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D^(2)PAM:Epileptic seizures prediction using adversarial deep dual patch attention mechanism

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 755-769页

作者： Arfat Ahmad Khan Rakesh Kumar Madendran Usharani Thirunavukkarasu Muhammad Faheem Department of Computer Science College of ComputingKhon Kaen UniversityKhon KaenThailand Department of Computer Science and Engineering Rajalakshmi Engineering CollegeChennaiIndia Department of Biomedical Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical Sciences(Deemed to be University)ChennaiIndia School of Technology and Innovations University of VaasaVaasaFinland

Epilepsy is considered as a serious brain disorder in which patients frequently experience *** seizures are defined as the unexpected electrical changes in brain neural activity,which leads to *** researches made an intense effort for predicting the epileptic seizures using brain signal ***,they faced difficulty in obtaining the patients'characteristics because the model's distribution turned to fake predictions,affecting the model's *** addition,the existing prediction models have severe issues,such as overfitting and false positive *** overcome these existing issues,we propose a deep learning approach known as Deep dual‐patch attention mechanism(D^(2)PAM)for classifying the pre‐ictal signals of people with Epilepsy based on the brain *** neural network is integrated with D^(2)PAM,and it lowers the effect of differences between patients to predict *** multi‐network design enhances the trained model's generalisability and stability ***,the proposed model for processing the brain signal is designed to transform the signals into data blocks,which is appropriate for pre‐ictal *** earlier warning of epilepsy with the proposed model obtains the auxiliary *** data of real patients for the experiments provides the improved accuracy by D2PAM approximation compared to the existing *** be more distinctive,the authors have analysed the performance of their work with five patients,and the accuracy comes out to be 95%,97%,99%,99%,and 99%***,the numerical results unveil that the proposed work outperforms the existing models.

关键词： artificial intelligence techniques classification learning(artificial intelligence)

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Automatic Modulation Recognition with Breast Cancer Classification for Medical Internet of Things Applications 1

Automatic Modulation Recognition with Breast Cancer Classifi...

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1st IEEE International Conference on Emerging research in Computational science, ICERCS 2023

作者： Nirmalrani, V. Poornima, D. Gowri, S. Chandrasekaran, Kumaresan Sathyabama Institute of Science & Technology Department of Computer Science and Engineering Chennai India University of Technology and Applied Sciences Department of Computer Science and Engineering Oman

ISBN: (纸本)9798350359756

Medical Internet of Things (MIoT) and healthcare have lately converged, which has led to novel techniques to identifying and treating medical troubles. This research gives a completely unique method that utilizes the Internet of Things (IoT) to combine Automatic Modulation Recognition (AMR) with Breast Cancer (BC) categorization. The loss of included answers arises from the reality that most current research ignores BC type in desire for both AMR or BC type. A unique Medical Internet of Things-based totally Breast Cancer (MIoT-BC) category method addresses the project. In order to facilitate automatic modulation identity, the proposed gadget first statistics indicators from clinical gadget and wireless verbal exchange channels. After that, it uses DBNs (deep belief networks) to categorize BCs. The MIoT-primarily based strategy ensures the secure transmission and instantaneous analysis of scientific facts, which improves the precision and timeliness of diagnosis. There are some of blessings to the use of MIoT for BC categorization, such as less difficult get entry to to health information, faster diagnostic instances, and the option for faraway monitoring. In addition, the potential for misclassification and human errors is reduced, making this a useful tool inside the realm of medical diagnostics. DBNs are used to give a dependable and correct type version for BC categorization. Several healthcare packages have found achievement with DBNs due to their suitability for processing excessive-dimensional, complicated scientific facts. Differentiating benign from malignant breast lesions is an area where their capability to extract sizeable facts from enter facts robotically shines. MIoT-primarily based processes the usage of DBNs enhance BC classification accuracy in comparison to traditional techniques, consistent with this paper. Evaluation criteria, such as sensitivity, specificity, accuracy, overall performance and F1 rating, measure the effectiveness of the suggeste

关键词： Internet of things

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Application of a three-phase unified power quality conditioner in a microgrid

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IET Smart Grid 2024年第6期7卷 872-890页

作者： Wu, Tsung-Hsun Huang, Shu-Syuan Chen, Pei-Yin Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Department of Power Electronics Industrial Technology Research Institute Tainan Taiwan

In power distribution systems, common issues such as voltage fluctuations, voltage instability, current harmonics, and power imbalances often arise, negatively impacting the stability and power quality (PQ) of the power system. As an advanced power electronics device, the unified power quality conditioner (UPQC) can effectively address these problems. Therefore, the integration of UPQC into power distribution lines can enhance the stability and reliability of the power system, providing higher-quality power supply to meet the needs of both users and suppliers for PQ. The authors utilise the TI TMS320F28377D chip as the control core to construct a simple yet highly efficient UPQC system. Through simulation, the correctness of the voltage and current compensation algorithms has been verified, and the overall system feasibility has been validated using a 100-kVA series-shunt inverter. The experimental results show that the voltage compensation range of the UPQC is 380V ± 10%. During sudden current drops, the imbalance rate can be reduced from 85.5% to 1.32% after controlled compensation. During sudden current rises, the imbalance rate can be reduced from 13.1% to 1.56% after controlled compensation. These findings demonstrate the effectiveness of the UPQC system in improving PQ and system stability in power distribution networks. © 2024 The Author(s). IET Smart Grid published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Power quality

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Generative Adversarial Networks (GANs) for Artistic Style Transfer in Images

Generative Adversarial Networks (GANs) for Artistic Style Tr...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Jamalpur, Bhavana Kiruthiga, S. Ranjitha, B. Lefty Joyson, J. Jayaram, K. Soundar, K. Ruba S R University Department of Computer Science and Artificial Intelligence India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Chennai India Mohan Babu University Department of Mathematics India Jp College of Engineering Department of Computer Science and Engineering Ayikudi India College of Engineering Adoor Department of Computer Science and Engineering Pathanamthitta India Department of Computer Science and Engineering Sivakasi India

ISBN: (纸本)9798350375442

Many in computer vision and computational imaging consider Generative Adversarial Networks (GANs) a big advance. Style transfer converts semantic visual content into creative styles. Recently, GANs for style transfer have gained popularity. Adversarial training of GANs to create realistic counterfeits has yielded encouraging results. High-quality style transfer results have been achieved using this method. Mode collapse is a common problem in classical GANs. This causes unsteady training and poor style transmission. Additionally, the GAN generator only supports one style. Therefore, numerous GANs must be trained to allow users to shift styles. This study examines artistic style transfer in photographs using depth extraction generative adversarial network (DE-GAN). This research aims to improve photo aesthetics by adding famous artworks' distinctive qualities. This study investigates DE-GANs' mechanics and how they modify text while keeping a style's creative qualities. By studying adversarial training, this work hopes to understand how DE-GAN achieves this dual goal. This research analyzes DE-GAN-based style transfer models to evaluate their effectiveness, identify their limits, and recommend areas for improvement. The experiments show that DE-GAN creates realistic and attractive stylized visuals. This study also discusses DE-GAN's practical applications, problems, and future possibilities in artistic style transfer. © 2024 IEEE.

关键词： Generative adversarial networks

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DDoS Attacks Detection in IoT Networks using Naive Bayes and Random Forest 3

DDoS Attacks Detection in IoT Networks using Naive Bayes and...

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3rd International Conference on applied Artificial Intelligence and computing, ICAAIC 2024

作者： Srivastava, Animesh Tiwari, Shweta Rawat, Bhupender Singh Dhondiyal, Shiv Ashish Graphic Era Hill University Department of Computer Science & Engineering Dehradun India Institute of Engineering Chandigarh University Department of Computer Science & Engineering Department / University Punjab Gharuan Mohali India Coer University College of Smart Computing Roorkee India Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350375190

The proliferation of Internet of Things (IoT) devices has resulted in numerous benefits, including streamlined tasks and improved connectivity but has also raised concerns regarding security vulnerabilities. Distributed Denial of Service (DDoS) attacks targeting IoT systems pose a significant threat, potentially disrupting operational activities. This study conducts a comparative analysis of existing methodologies to detect DDoS attacks in IoT environments, focusing on the efficacy of machine learning algorithms. Specifically, this study evaluates the performance of Random Forest and Naive Bayes classifiers in detecting DDoS attacks using an IoT dataset. The evaluation involves training and testing these classifiers and assessing their accuracy as a key performance metric. Empirical findings demonstrate enhanced precision in identifying DDoS attacks in IoT environment, indicating the effectiveness of the proposed approach. By leveraging machine learning algorithms, particularly Random Forest and Naive Bayes, traditional techniques for detecting and mitigating DDoS attacks in IoT environments are accelerated, thus enhancing the security of IoT systems. © 2024 IEEE.

关键词： Internet of things

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