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Jellyfish Search Optimization with Deep Learning Driven Autism Spectrum Disorder Classification

computers, Materials & Continua 2023年第1期74卷 2195-2209页

作者： S.Rama Sree Inderjeet Kaur Alexey Tikhonov E.Laxmi Lydia Ahmed A.Thabit Zahraa H.Kareem Yousif Kerrar Yousif Ahmed Alkhayyat Department of Computer Science&Engineering Aditya Engineering CollegeSurampalemAndhra PradeshIndia Department of Computer Science&Engineering Ajay Kumar Garg Engineering CollegeGhaziabadUttar PradeshIndia Department of Human Resource Management Moscow Aviation InstituteMoscowRussia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Computer Communication Engineering Al-Rafidain University CollegeBaghdadIraq Department of Medical Instrumentation Techniques Engineering Al-Mustaqbal University CollegeHillah51001Iraq Department of Computer Technical Engineering Al-Hadba University CollegeMosulIraq College of Technical Engineering The Islamic UniversityNajafIraq

Autism spectrum disorder(ASD)is regarded as a neurological disorder well-defined by a specific set of problems associated with social skills,recurrent conduct,and *** ASD as soon as possible is favourable due to prior identification of ASD permits prompt interferences in children with *** of ASD related to objective pathogenicmutation screening is the initial step against prior intervention and efficient treatment of children who were ***,healthcare and machine learning(ML)industries are combined for determining the existence of various *** article devises a Jellyfish Search Optimization with Deep Learning Driven ASD Detection and Classification(JSODL-ASDDC)*** goal of the JSODL-ASDDC algorithm is to identify the different stages of ASD with the help of biomedical *** proposed JSODLASDDC model initially performs min-max data normalization approach to scale the data into uniform *** addition,the JSODL-ASDDC model involves JSO based feature selection(JFSO-FS)process to choose optimal feature ***,Gated Recurrent Unit(GRU)based classification model is utilized for the recognition and classification of ***,the Bacterial Foraging Optimization(BFO)assisted parameter tuning process gets executed to enhance the efficacy of the GRU *** experimental assessment of the JSODL-ASDDC model is investigated against distinct *** experimental outcomes highlighted the enhanced performances of the JSODL-ASDDC algorithm over recent approaches.

关键词： Autism spectral disorder biomedical data deep learning feature selection hyperparameter optimization data classification machine learning

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Detection of Polycystic Ovary Syndrome Using Clinical Data and Ultrasound Images 7

Detection of Polycystic Ovary Syndrome Using Clinical Data a...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Thomson, Esther Sara Ram, Ajai College of Engineering Trivandrum Department of Computer Science and Engineering India

ISBN: (纸本)9798350372816

Polycystic Ovary Syndrome (PCOS) is a hormonal issue that occurs in women of adulthood. The World Health Organization (WHO) has identified PCOS as a prevalent endocrine illness that affects around 10% of women globally. It can result in various health complications, which includes infertility, metabolic complications such as insulin resistance, obesity, as well as cardiovascular problems, sleep apnea, endometrial cancer, and psychological disorders like anxiety and depression. Hence early diagnosis of PCOS is crucial. One of the diagnosis methods used for its detection is the Rotterdam criteria or Consensus. This diagnostic approach includes three criteria: Oligovulation or anovulation, presence of hyperandrogenism, and the identification of polycystic ovaries through ultrasound examination. Patients who meet two or more of these criteria can be diagnosed with PCOS. Cysts may indicate Polycystic Ovarian Disease (PCOD), a condition similar to PCOS. In PCOD, the ovaries release numerous immature or partially-mature eggs, which can develop into cysts over time. Among the numerous available techniques in the machine learning domain, only one criterion is typically assessed at a time, either clinical data or ultrasound, but not both simultaneously. The proposed system considers both and has two sections to aid in this process - one for detection through images and the other through clinical data. The dataset for the system includes 781 PCOS and 1143 Non-PCOS images, as well as clinical data from 541 patients, including 177 with PCOS and 43 features collected from open sources. Several models and techniques are used for the detection individually. A novel feature selection approach for CS-PCOS is employed, utilizing an optimized chi-squared mechanism. Additionally, overfitting is assessed using ten-fold cross-validation. Different pre-trained models are tried out for ultrasound images and the best is taken. Random forest is considered the best model for clinical data with

关键词： Ultrasonic imaging

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Scale Invariant Feature Transform with Crow Optimization for Breast Cancer Detection

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Intelligent Automation & Soft Computing 2023年第5期36卷 2973-2987页

作者： A.Selvi S.Thilagamani Department of Computer Science and Engineering M.Kumarasamy College of EngineeringKarur639113India

Mammography is considered a significant image for accurate breast cancer ***-based image retrieval(CBIR)contributes to classifying the query mammography image and retrieves similar mammographic images from the *** CBIR system helps a physician to give better *** features must be described with the input images to retrieve similar ***-ing methods are inefficient and inaccurate by failing in local features ***,efficient digital mammography image retrieval needs to be *** paper proposed reliable recovery of the mammographic image from the data-base,which requires the removal of noise using Kalman filter and scale-invariant feature transform(SIFT)for feature extraction with Crow Search Optimization-based the deep belief network(CSO-DBN).This proposed technique decreases the complexity,cost,energy,and time *** the proposed model using a deep belief network and validation is ***,the testing pro-cess gives better performance compared to existing *** accuracy rate of the proposed work CSO-DBN is 0.9344,whereas the support vector machine(SVM)(0.5434),naïve Bayes(NB)(0.7014),Butterfly Optimization Algorithm(BOA)(0.8156),and Cat Swarm Optimization(CSO)(0.8852).

关键词： SIFT Kalman filter crow search optimization deep neural network noise removal

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Machine Learning based Early Detection of Ongoing Cyber-Attacks 3

Machine Learning based Early Detection of Ongoing Cyber-Atta...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Bhagyalakshmi, A. Sruthi Laya, C.D. Yoga Preethikaa, A.M. Varsha, V. Easwari Engineering College Computer Science and Engineering Department India

ISBN: (纸本)9798350375190

The need for sophisticated detection and response systems to counteract cyber threats has grown in importance as the cybersecurity landscape changes. In order to improve cyberattack detection and response, this study proposes a novel combination of a Security Orchestration, Automation, and Response (SOAR) framework with a machine learning (ML) model. A Random Forest classifier was created and trained to distinguish between benign and potentially dangerous files using a dataset that included a variety of executable file properties. The classifier demonstrated a notable level of accuracy and precision. Then, a prediction API was established to enable real-time threat analysis by seamlessly integrating the learned machine learning model into a SOAR system. Through this integration, possible threats can be continuously identified by evaluating real-time data that has been retrieved from network traffic and endpoints. The SOAR system is activated when a suspicious file is found, which sets off a sequence of automatic events that include notifying security staff and starting response procedures. For a comprehensive and strong defence strategy, the system also integrates with already-in-use security solutions like firewalls and intrusion detection systems (IDS). The integration strengthens the overall cybersecurity posture by improving response time and increasing the effectiveness of cyberattack detection. This study presents the integrated approach's methodology, system architecture, and effectiveness, showcasing its potential as a comprehensive cybersecurity solution. © 2024 IEEE.

关键词： Cybersecurity

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An Intrusion Detection System for SDN Using Machine Learning

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Intelligent Automation & Soft Computing 2023年第1期35卷 867-880页

作者： G.Logeswari S.Bose T.Anitha Department of Computer Science and Engineering College of EngineeringGuindyAnna UniversityChennaiTamilnaduIndia

Software Defined Networking(SDN)has emerged as a promising and exciting option for the future growth of the *** has increased the flexibility and transparency of the managed,centralized,and controlled *** the other hand,these advantages create a more vulnerable environment with substantial risks,culminating in network difficulties,system paralysis,online banking frauds,and *** issues have a significant detrimental impact on organizations,enterprises,and even ***,high performance,and real-time systems are necessary to achieve this *** a SDN to extend intelligent machine learning methodologies in an Intrusion Detection System(IDS)has stimulated the interest of numerous research investigators over the last *** this paper,a novel HFS-LGBM IDS is proposed for ***,the Hybrid Feature Selection algorithm consisting of two phases is applied to reduce the data dimension and to obtain an optimal feature *** thefirst phase,the Correlation based Feature Selection(CFS)algorithm is used to obtain the feature *** optimal feature set is obtained by applying the Random Forest Recursive Feature Elimination(RF-RFE)in the second phase.A LightGBM algorithm is then used to detect and classify different types of *** experimental results based on NSL-KDD dataset show that the proposed system produces outstanding results compared to the existing methods in terms of accuracy,precision,recall and f-measure.

关键词： Intrusion detection system light gradient boosting machine correlation based feature selection random forest recursive feature elimination software defined networks

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Cardiovascular Disease Prediction Using Deep Learning 11

Cardiovascular Disease Prediction Using Deep Learning

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Khanna, Vaneet Punjab Engineering College Department of Computer Science and Engineering India

ISBN: (纸本)9798350350357

Cardiovascular disease (CVD), ahead of all other causes of death worldwide in this era. There is an immediate need for accurate, reliable, and practically applicable ways of early detection and treatment of diseases, and it connects a number of hazards for cardiovascular disease. One common method for analyzing massive amounts of historical information in healthcare is data extraction. To assist physicians with CVD prediction, they employ a number of data mining and deep learning (DL) techniques to navigate complex medical data. This review paper critically examines the application of deep learning techniques in predicting cardiovascular diseases, emphasizing data collection, preprocessing, model selection, performance metrics, and challenges. The data-driven nature of DL models allows for the analysis of diverse patient information, contributing to more accurate risk assessments. The review achieves by discussing the challenges and limitations, emphasizing the importance of collaborative efforts to connect DL's potential in enhancing cardiovascular disease prediction and improving patient outcomes. © 2024 IEEE.

关键词： Forecasting

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Abuse Reporting System 6

Abuse Reporting System

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6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Sivadasan, Sreeram Mohamed Jisar, C. Rasmina Murshida Farsana, V. Shijaz, K. Mea Engineering College Department of Computer Science and Engineering India

ISBN: (数字)9798350381689

ISBN: (纸本)9798350381689

This project embarks on the development of an abuse reporting system designed to maintain user anonymity and employ advanced machine learning algorithms for genuineness assessment and fraud detection. In the contemporary digital landscape, safeguarding user privacy and ensuring the reliability of abuse reports are paramount. Our project addresses this challenge by integrating state-of-the-art machine learning techniques, enhancing the accuracy and efficiency of abuse reporting process, and mitigating false or malicious reporting. The technical underpinnings of the system involve the creation of a distributed, scalable, and fault-tolerant architecture capable of handling a wide range of abuse reports from diverse sources. We implement advanced natural language processing (NLP) models for textbased report analysis, while image and multimedia reports undergo deep learning-based content analysis. Anonymity preser-vation is achieved through advanced encryption techniques and decentralized data storage. Furthermore, we integrate adaptive machine learning algorithms that continuously evolve to detect novel abuse patterns and emerging fraud strategies, ensuring the system's adaptability to evolving threats. The project includes a comprehensive evaluation of the abuse reporting app's performance, precision, and scalability in handling a large volume of reports while preserving user anonymity. This project represents a significant advancement in the field of abuse reporting systems, offering a technologically robust and privacy-centric solution for contemporary society's increasing demand for secure and effective reporting mechanisms. © 2024 IEEE.

关键词： Anonymity

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Optimised fuzzy C-means clustering based cluster indexing towards incremental learning integrated text categorisation

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International Journal of Intelligent Information and Database Systems 2023年第2期16卷 196-219页

作者： Kayest, Mamta Department of Computer Science and Engineering Punjab Engineering College Sector 12 Chandigarh160012 India

Text classification is the most significant task in the data retrieval process through classifying text into various groups depending on the document’s content. The quick progression of electronic documents may produce various issues, such as unstructured data, which requires more effort and time for searching appropriate documents. Text categorisation has high importance in information retrieval and processing, wherein unstructured documents are arranged into a predefined group. In addition, incredible growth in online documents obtains ability with the development of the internet needs a highly precise and effective retrieval approach. Thus, in this paper, Dingo Monarch butterfly optimisation (DMBO) approach and Tversky index-based indexing are developed for incremental learning-enabled text categorisation. Moreover, text categorisation is done based on incremental learning along with a Bayesian classifier. This text classification approach achieved better performance with a precision of 0.9136, recall of 0.9173, F-measure of 0.9051, and accuracy of 0.8461. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Classification (of information)

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Machine Learning-Based Routing Protocol in Flying Ad Hoc Networks: A Review

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computers, Materials & Continua 2025年第2期82卷 1615-1643页

作者： Priyanka Manjit Kaur Deepak Prashar Leo Mrsic Arfat Ahmad Khan Department of Computer Science and Engineering(CSE) Lovely Professional UniversityPhagwara144411India Department for Program Engineering Algebra UniversityGradiscanska 24Zagreb10000Croatia Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand

“Flying Ad Hoc Networks(FANETs)”,which use“Unmanned Aerial Vehicles(UAVs)”,are developing as a critical mechanism for numerous applications,such as military operations and civilian *** dynamic nature of FANETs,with high mobility,quick node migration,and frequent topology changes,presents substantial hurdles for routing protocol *** the preceding few years,researchers have found that machine learning gives productive solutions in routing while preserving the nature of FANET,which is topology change and high *** paper reviews current research on routing protocols and Machine Learning(ML)approaches applied to FANETs,emphasizing developments between 2021 and *** research uses the PRISMA approach to sift through the literature,filtering results from the SCOPUS database to find 82 relevant *** research study uses machine learning-based routing algorithms to beat the issues of high mobility,dynamic topologies,and intermittent connection in *** compared with conventional routing,it gives an energy-efficient and fast decision-making solution in a real-time environment,with greater fault tolerance *** protocols aim to increase routing efficiency,flexibility,and network stability using ML’s predictive and adaptive *** comprehensive review seeks to integrate existing information,offer novel integration approaches,and recommend future research topics for improving routing efficiency and flexibility in ***,the study highlights emerging trends in ML integration,discusses challenges faced during the review,and discusses overcoming these hurdles in future research.

关键词： FANET protocol machine learning

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Predicting heart disease based on an intelligent healthcare monitoring system using HPM-NIA

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Multimedia Tools and Applications 2025年第13期84卷 11475-11501页

作者： Alharbi, Meshal Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia

Before a heart attack happens, treating cardiac patients effectively depends on precise heart disease prediction. A heart disease prediction system for the determination of whether the patient has a heart disease condition or not is to be developed. The paper employs a diverse array of models within the heart disease prediction system, each serving a specific purpose to enhance accuracy and efficiency. Data pre-processing techniques, including normalization, standardization, and missing value removal, ensure the quality and consistency of the dataset. Feature extraction methods such as mean, median, standard deviations, and higher-order statistical features contribute to extracting informative features crucial for prediction. The Multi-Objective Forest Particle Swarm Optimization (MOFPSO) algorithm (hybrid version of Forest Optimization (FO) and Particle Swarm Optimization (PSO)) is introduced for efficient feature selection, balancing predictive accuracy and model complexity. Finally, the prediction model, Naïve Bayes trained with Meliorated Ant Colony Optimization algorithm (NB-MACO), is implemented for its simplicity and effectiveness in handling medical datasets. This integration fine-tunes the Naïve Bayes classifier’s hyperparameters, optimizing its performance and resulting in an accuracy of 91.974%. The collective utilization of these models and techniques ensures the development of a robust and accurate heart disease prediction system. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Forecasting

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