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A novel feature selection approach with integrated feature sensitivity and feature correlation for improved prediction of heart disease

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Journal of Ambient Intelligence and Humanized computing 2023年第9期14卷 12005-12019页

作者： Saranya, G. Pravin, A. Department of Computer Science and Engineering School of Computing Sathyabama Institute of Science and Technology Chennai600119 India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai603203 India

This paper presents a random forest-feature sensitivity and feature correlation (RF-FSFC) technique for enhanced heart disease prediction. The proposed methodology is implemented using the Cleveland heart disease dataset which comprises a total of 120 heart disease patient records. Data imputation was utilized for missing values, and min–max normalization was utilized for data transformation. We attempted to construct an RF-based classifier for coronary heart disease in this paper by combining feature sensitivity and correlation analysis. The sensitivity-based feature selection process ranks features according to their value in assessing CHD risk, and the feature correlation analysis phase analyses if there are any correlations between features. The heart disease prediction accuracy of 81.16% was obtained using the proposed RF-FSFCA technique by omitting five features (sex, hemoglobin, TD, CRF, and cirrhosis). When compared to the Naïve Bayes, decision tree, regression analysis, and support vector machine models, the proposed model offered a higher accuracy of 86.141% without omitting any features. It also offered sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) scores of 87.321%, 87.364%, 91.23, and 91.02 respectively. Experiment findings demonstrated that the proposed RF-FSFC approach significantly improves prediction accuracy as compared to other approaches that do not use the integrated Feature selection method. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Sensitivity analysis

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Integral Sliding Mode Control for Automated Vehicles under Coding-Decoding Mechanisms with Constrained Bit Rate

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IEEE Internet of Things Journal 2025年第11期12卷 16214-16226页

作者： Zhang, Mingming Ding, Derui Shen, Bo Ge, Xiaohua University of Shanghai for Science and Technology Department of Control Science and Engineering Shanghai200093 China Donghua University College of Information Science and Technology Shanghai201620 China Swinburne University of Technology School of Science Computing and Engineering Technologies MelbourneVIC3122 Australia

In this paper, an integral sliding mode (ISM) control scheme is developed to tackle platooning control issues of vehicle systems under both an improved constant-Time headway (CTH) spacing strategy and a coding-decoding communication protocol (CDCP) with constrained bit rate. The utilization of CDCPs can effectively reduce communication burden and promote data security, while the improved CTH spacing policy based on relative velocities can ensure high traffic efficiency as well as platoon safety. First, an ISM surface is constructed to handle matched unmodeled dynamics by resorting to the improved CTH spacing strategy and the decoded information governed by CDCPs. Both an equivalent ISM law and a practical ISM controller are constructed in terms of such an ISM surface, reflecting the effect of both spacing strategies and decoded information. Then, a generally dynamical model of platoon tracking errors involving an unusual coupling with the adopted spacing strategy is derived by leveraging both the equivalent ISM law and a matrix transformation technique. Moreover, the reachability of the proposed ISM surface and the required platooning performance are profoundly discussed and sufficient conditions to determine the required gain parameters are deduced with essential inequality techniques. In terms of vehicle tracking errors, the upper boundary is determined by both the bit rate assigned and the spacing strategy employed. Finally, the effectiveness of the devised ISM control scheme is professionally evaluated through Carsim's simulation instances. © 2014 IEEE.

关键词： Decoding

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Optimised EfficientNet for multi-class skin cancer diagnosis: integrating chaotic grey wolf algorithms

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Current science 2025年第7期128卷 728-736页

作者： Rama, P. Angayarkanni, V. Theresa, W. Gracy Pandiaraj, A. Department of Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur603 203 India Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai600 123 India

The detection of skin cancer holds paramount importance worldwide due to its impact on global health. While deep convolutional neural networks (DCNNs) have shown potential in this domain, current approaches often struggle with fine-grained variability in skin lesion features, imbalanced datasets and inadequate augmentation. The objectives of our study encompass building a model that not only enhances accuracy but also reduces training time and cost, improves dimensionality reduction during classification and segmentation, and is evaluated on larger datasets for robustness. We enhance the learning process using an improved nature-inspired optimisation algorithm tailored specifically for skin cancer classification tasks. This algorithm optimises model parameters to maximise classification accuracy while minimising computational overhead. Our proposed architecture leverages a gold standard dermatological image dataset meticulously curated and validated by experts. This dataset spans eight distinct classes, including actinic keratosis, basal cell carcinoma, melanoma, nevus, and others, ensuring a diverse representation of skin conditions. The balanced class distribution in the training and test sets enables fair evaluation and robustness assessment of our deep learning models. The developed algorithm showcases novelty in its approach, contributing to advancements in early skin cancer detection technologies. This study introduces an optimised deep learning model designed for the early detection of skin cancer, achieving a notable accuracy of 97.48%. Our study aims to contribute significantly to the medical field by providing a reliable and efficient tool for dermatologists and healthcare practitioners to assist in diagnosing skin conditions accurately and promptly, ultimately improving patient outcomes and healthcare efficiency. © (2025), (Indian Academy of sciences). All rights reserved.

关键词： Convolutional neural networks

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Amazon Web Services Cloud Compliance Automation with Open Policy Agent 4

Amazon Web Services Cloud Compliance Automation with Open Po...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Paul, Alen Manoj, Rishi Udhayakumar, S. Amrita School of Computing Department of Computer Science and Engineering India

ISBN: (纸本)9798350385793

The security challenges posed by Infrastructure as code (IaC) are outgrowing established security procedures in an era of rapidly adopting cloud computing and DevOps methodologies. Using security principles to be integrated into the CI/CD pipeline for continuous validation and remediation, this research work proposes a DevSecOps approach to enable cloud environment IaC security. The proposed architecture evaluates CloudFormation Template file to the specified security policy by utilizing Open Policy Agent (OPA). Any violations are flagged by OPA, which may force the deployment process to stop. By incorporating security and compliance considerations into the development pipeline, this method guarantees that vulnerabilities are kept out of production systems. Organizations can improve the overall security posture of their cloud systems by proactively identifying and remediating security problems by implementing a DevSecOps approach. By ensuring that IaC deployments adhere to specified security policies based on the compliance requirements, OPA's continuous security validation reduces the possibility of misconfigurations and security flaws. © 2024 IEEE.

关键词： Cloud security

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Emotion detection: Facial emotion detection using ML 4th

Emotion detection: Facial emotion detection using ML

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4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Sharma, Sharad Chauhan, Ritik Vajpayee, Pooja School of Computing Science and Engineering Galgotias University Greater Noida India

ISBN: (纸本)9781032911571

Cognitive perception of images is an intense task, like guessing the truth of a thought or a mystery. In this process, we use different methods to solve the need to know the job. In recent years, emotional intelligence has received widespread attention due to its important role in improving human-computer interaction and emotional analysis. Because of its processes for extracting visual features, deep neural networks-particularly convolutional neural network (CNN)-are frequently employed in FER. A lot of research has been done on CNN, but only a few areas have solved the facial recognition (FER) problem. After many reports and studies, the shortcoming of most existing models is that they often consider the front profile and ignore the side profile of the current image, although different angular views are also important for the perspective model. This helps you make informed decisions about personal needs, providing support, or security threats. However, this proved to be too complex for computers, and feature extraction needed a wide range of image processing methods. Numerous machine learning techniques are appropriate for this particular purpose. To use machine learning for any kind of detection, an algorithm must first be trained and then tested on relevant data. This article examines some machine learning algorithms and strategies for extraction that aid in the analysis of human emotion. This review contains a comprehensive review of a variety of materials, methods, and measures commonly used in psychology research. It also highlights the challenges and limitations of current models, including dealing with uncertainties and cultural differences. Face recognition in large datasets poses additional challenges due to the diversity of images as well as the differences between side and front views. © 2025 the Author(s).

关键词： Convolutional neural networks

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Automatic Sentimental Analysis by Firefly with Levy and Multilayer Perceptron

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Computer Systems science & engineering 2023年第9期46卷 2797-2808页

作者： D.Elangovan V.Subedha School of Computing Computer Science and EngineeringSathyabama Institute of Science and TechnologyChennai600118India Computer Science and Engineering Panimalar Institute of TechnologyChennai600069India

The field of sentiment analysis(SA)has grown in tandem with the aid of social networking platforms to exchange opinions and *** people share their views and ideas around the world through social media like Facebook and *** goal of opinion mining,commonly referred to as sentiment analysis,is to categorise and forecast a target’s *** on if they provide a positive or negative perspective on a given topic,text documents or sentences can be *** compared to sentiment analysis,text categorization may appear to be a simple process,but number of challenges have prompted numerous studies in this area.A feature selection-based classification algorithm in conjunction with the firefly with levy and multilayer perceptron(MLP)techniques has been proposed as a way to automate sentiment analysis(SA).In this study,online product reviews can be enhanced by integrating classification and feature *** firefly(FF)algorithm was used to extract features from online product reviews,and a multi-layer perceptron was used to classify sentiment(MLP).The experiment employs two datasets,and the results are assessed using a variety of *** account of these tests,it is possible to conclude that the FFL-MLP algorithm has the better classification performance for Canon(98%accuracy)and iPod(99%accuracy).

关键词： Firefly algorithm feature selection feature extraction multi-layer perceptron automatic sentiment analysis

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Advanced Congestion Management System 1

Advanced Congestion Management System

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1st IEEE International Conference on Advances in computing, Communication and Networking, ICAC2N 2024

作者： Rajakumar, P. Baliyan, Akshay Chaudhary, Deepanshi Panwar, Sachin Kumari, Richa Galgotias University School of Computing Science & Engineering UP India

ISBN: (纸本)9798350356816

Congestion is poised to become a significant problem in numerous Asian cities and other countries. The causes of traffic congestion include signal malfunctions, inadequate law enforcement, and risky traffic management. The limited expansion possibilities of existing infrastructure in Indian cities present a major challenge, making effective traffic management the only viable option. The negative impact of congestion on the economy, environment, and overall quality of life necessitates prompt action. Various traffic management technologies are available, such as video investigation, infrared, inducible loop detection, and sensor networks, all of which contribute to efficient traffic management. Nevertheless, these systems are costly to install and maintain and take considerable time to implement. In this regard, a cost-effective alternative technology called radio frequency identification (RFID) is introduced, offering real-time traffic control capabilities comparable to the current signal system. Compared to other congestion management methods, this innovative technology requires less time and cost for deployment. By identifying bottlenecks early on, this groundbreaking technology enables proactive measures, saving drivers time and money while alleviating traffic congestion. © 2024 IEEE.

关键词： Radio frequency identification (RFID)

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The real estate time-stamping and registration system based on Ethereum blockchain

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Blockchain(Research and Applications) 2024年第1期5卷 119-132页

作者： Liyuan Zhang Limian Ci Yonghong Wu Benchawan Wiwatanapataphee School of Electrical Engineering Computing&Mathematical ScienceCurtin UniversityKent StreetPerth6102WAAustralia

In recent years,there has been a growing interest in real estate investments that utilize blockchain *** real estate investments usually involve third-party intermediaries for verifying and recording informal real estate *** paper proposes a blockchain-based real estate investment model and presents a detailed description of the real estate register authentication aspect of the *** model uses blockchain technology to create tamper-evident records of real estate transactions and provide secure authentication and verification for informal real estate ***,each real estate transaction is recorded in a block,and all transaction records are kept on the *** means that inventors can access these transaction records and verify their authenticity and *** system can also use smart contracts to automate the process of real estate transactions,which further improves transaction efficiency and reduces ***,the model’s timestamp and authentication mechanism can eliminate third-party intermediaries and ensure the authenticity and validity of real estate transactions through distributed ledgers and verification ***,blockchain-based real estate systems offer advantages of security,transparency,efficiency,and cost *** ongoing blockchain advancements,these systems are expected to play a crucial role in future real estate investment transactions.

关键词： Blockchian technology Smart contract Real estate investment Time-stamping and registration system

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A Deep Learning Approach for Landmines Detection Based on Airborne Magnetometry Imaging and Edge computing

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Computer Modeling in engineering & sciences 2024年第5期139卷 2117-2137页

作者： Ahmed Barnawi Krishan Kumar Neeraj Kumar Bander Alzahrani Amal Almansour Faculty of Computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyDeemed to be UniversityPatialaIndia

Landmines continue to pose an ongoing threat in various regions around the world,with countless buried landmines affecting numerous human *** detonation of these landmines results in thousands of casualties reported worldwide ***,there is a pressing need to employ diverse landmine detection techniques for their *** effective approach for landmine detection is UAV(Unmanned Aerial Vehicle)based AirborneMagnetometry,which identifies magnetic anomalies in the local terrestrial magnetic *** can generate a contour plot or heat map that visually represents the magnetic field *** the effectiveness of this approach,landmine removal remains a challenging and resource-intensive task,fraughtwith *** computing,on the other hand,can play a crucial role in critical drone monitoring applications like landmine *** processing data locally on a nearby edge server,edge computing can reduce communication latency and bandwidth requirements,allowing real-time analysis of magnetic field *** enables faster decision-making and more efficient landmine detection,potentially saving lives and minimizing the risks involved in the ***,edge computing can provide enhanced security and privacy by keeping sensitive data close to the source,reducing the chances of data exposure during *** paper introduces the MAGnetometry Imaging based Classification System(MAGICS),a fully automated UAV-based system designed for landmine and buried object detection and *** have developed an efficient deep learning-based strategy for automatic image classification using magnetometry dataset *** simulating the proposal in various network scenarios,we have successfully detected landmine signatures present in themagnetometry *** trained models exhibit significant performance improvements,achieving a maximum mean average precision value of 97.8%.

关键词： CNN deep learning landmine detection magnetometer mean average precision UAV

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The Design and Development of web3.0: The Internet's Future Architecture 1

The Design and Development of web3.0: The Internet's Future ...

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1st IEEE International Conference on Advances in computing, Communication and Networking, ICAC2N 2024

作者： Kumar, Gautam Dut, Yagya Kumar, Rajneesh Rajakumar, P. Indervati Galgotias University School of Computing Science & Engineering UP India

ISBN: (纸本)9798350356816

The internet is regarded as the most vital international data media. It allows consumers to browse and write information about the world around them. The development of the web has been continuously increasing over the years. In the past couple of years, it has been able to develop various versions. In 1991, the concept of the exemplary net was confirmed. This means that web1.0 is tied to statistics and is used to gather information. The rise of net 2.0 allowed individuals to create and distribute content. Web 3.0 is regarded as the most significant update to the internet. The goal of the semantic internet is to create and think about new information in a way that is most appropriate for humans. The rise of artificial intelligence (AI) is expected to make people more interested in facts. The goal of the Metaverse is to consolidate various virtual spaces. Through the utilization of elevated reality, people can indulge in various diversions and undertakings. The rise of the Internet has created various new obstacles and opportunities for education and online learning. The previous generation of the Internet was known as Web 1.0, which was initially designed as a Read-Only medium. Web 3.0, which is currently in development, is a more technologically advanced version of the web that enables people to perform various tasks and read and write. The development of Web 3.0 has led to the creation of new tools and technologies that can help support online learning and education. This essay aims to provide a brief overview of the various aspects of Web 3.0. The goal of Web 3.0 is to provide a more personalized and comprehensive education and learning experience. It also supports knowledge building through the use of the Semantic Web. © 2024 IEEE.

关键词： Traffic congestion

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