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12th International Conference on Soft Computing for Problem Solving, SocProS 2023

作者： Singh, Piyush Bhushan Singh, Pawan Dev, Harsh Chaurasia, Brijesh Kumar Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Lucknow India Department of Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur India

ISBN: (纸本)9789819731794

The most common cause of blindness in the world is glaucoma. It can lead to a decline in eyesight and quality of life if not addressed within the stipulated time. In computer vision, convolutional neural networks (CNNs) have achieved a viable solution. The most standard glaucoma screening method is fundus imaging due to the compromises it makes between mobility, size, and price. In this paper, we utilized the RIM-1 DL dataset in two different versions: RIM-1 Hospital and RIM-1 Randomly. To enhance the image quality and increase accuracy, we used median filtering pre-processing techniques in this work. We also used fundus images to automatically diagnose glaucoma using six different Image-net-trained models (DenseNet121, DenseNet169, VGG19, InceptionV3, ResNet50, and MobileNetV2). The six CNN architectures mentioned above are modified using normalization, inception, and global pooling layers in the model with the pre-trained classification models. Results indicate that the DenseNet-121 is the most accurate model in this work, improving accuracy in the RIM-1 Hospital dataset from 70.68 to 80.13 and in the RIM-1 randomly generated dataset from 79.31 to 87.67 after the models were fine-tuned. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Neural network models

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Systematic analysis of on-premise and cloud services

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International Journal of Cloud Computing 2024年第3期13卷 214-242页

作者： Ali, Asif Laghari, Asif Ali Kandhro, Irfan Ahmed Kumar, Kamlesh Younus, Salman Department of Computer Science Sindh Madressatul Islam University Karachi Pakistan Department of Software Engineering Sindh Madressatul Islam University Karachi Pakistan

There are two key distinctions between cloud and on-premise (OP) software, the cost for each varies and so does the level of control. As organisations explore to reduce costs, many data and rules are migrating to multiple clouds like GCP, AWS, AZURE, and so on. Cloud service providers provide the extensibility, resilience, and quickness that traditional OP deployments usually lose. This paper presents a comparative analysis of on-premise and cloud computing differs by outline, arrangement, administration, and devices for associations and clients. This comparison shows that cloud computing provides more flexible infrastructure and better service of data processing on-premise. In today’s enterprise IT world, there are many factors that a business must consider when deciding whether a cloud infrastructure is the right choice. As there are many mid-level organisations that I personally know which fails to adapt cloud solutions, instead rely on their proven legacy and on-premises applications and software to do business. Therefore, it is very challenging to decide for business, if either a cloud infrastructure is right choice or not. In this article, which have covered many areas and analyses of on-premise and cloud, which will help mid-level organisations to make the decision of choosing cloud solutions for their running business applications. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Cloud computing

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A three-step SEM-Bayesian network approach for predicting the determinants of CloudIoT-based healthcare adoption

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International Journal of Information Technology (Singapore) 2024年 1-21页

作者： Altawaiha, Iyad Atan, Rodziah Yaakob, Razali Bin Abdullah, Rusli Bin Hj Department of Software Engineering and Information Systems Faculty of Computer Science and Information Technology Universiti Putra Malaysia (UPM) Serdang 43400 Malaysia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia (UPM) Serdang 43400 Malaysia

Adopting the CloudIoT-based healthcare paradigm provides various prospects for medical IT and considerably enhances healthcare services. However, compared to the advanced development of CloudIoT-based healthcare systems, their usage is relatively low among healthcare professionals in hospitals. Thus, a study on the healthcare professionals acceptance and adoption of CloudIoT-based healthcare is critical. This study explores factors influencing healthcare professionals adoption of CloudIoT-based healthcare solutions by extending the Unified Theory of Acceptance and Use of Technology 2 model. Data was collected from 300 healthcare professionals in Jordan through an online questionnaire developed using the Google^® form application. The Bayesian Network and Structural Equation Modeling techniques were used to analyze and validate the proposed model. The results revealed that the healthcare professional’s behavioral intention is directly affected by seven factors: performance expectancy, effort expectancy, facilitating conditions, habit, trust, privacy, and security. The results also indicate that trust mediates the influence of privacy, facilitating conditions, and performance expectancy on behavioral intention. The research results will aid CloudIoT service providers, healthcare organizations, designers, and developers by offering comprehensive knowledge of the significant factors of CloudIoT-based healthcare adoption. A better understanding of these factors will help these stakeholder groups enhance their services and speed up the adoption of CloudIoT in the healthcare area. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Adoption Bayesian network CloudIoT Healthcare professionals Structural equation modeling UTAUT2

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A rehabilitation framework based on motor imagery induced wheelchair movement using fuzzy vector quantization

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International Journal of Information Technology (Singapore) 2023年第6期15卷 3025-3036页

作者： Ghosh, Ahona Saha, Sriparna Ghosh, Lidia Department of Computer Science and Engineering Maulana Abul Kalam Azad University of Technology West Bengal India Department of Computer Science and Engineering Institute of Engineering and Management Kolkata India

Stroke and many other neural disorders affecting motor functions lead to a significant degradation in the quality of life. The solution to the problem can be found in integrating brain commands with a mind driven wheelchair system for the disabled persons leading to motor rehabilitation, for achieving efficient control in different environments. But, the performance issues like the lack of robustness and reliability of the existing prototypes motivated us to design a multimodal rehabilitation framework using brain-computer interfacing for people with movement complications with the help of an electroencephalogram and a mobile robot. The current framework utilizes Fuzzy Vector Quantization as a motor imagery-based movement classifier after channel selection from electroencephalographic signals and feature extraction from that. The proposed model of robot navigation based on movement classification achieves 96.45% accuracy and it outperforms existing works by a remarkable margin. After getting successful results, a mobile robot-based prototype of a wheelchair (here, using TurtleBot3) is designed and presented with real-time implementation proving its suitability in day-to-day rehabilitative platforms. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Electroencephalogram Fuzzy vector quantization Motor imagery Rehabilitation TurtleBot3 Wheelchair

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Is GPT-4 Conscious?

Journal of Artificial Intelligence and Consciousness

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Journal of Artificial Intelligence and Consciousness 2024年第1期11卷 1-16页

作者： Tait, Izak Bensemann, Joshua Wang, Ziqi Computer Science and Software Engineering Department Auckland University of Technology NAOInstitute The University of Auckland

GPT-4 (Generative Pre-Trained Transformer 4) is often heralded as a leading commercial AI offering, sparking debates over its potential as a steppingstone toward Artificial General Intelligence. But does it possess consciousness? This paper investigates this key question using the nine qualitative measurements of the Building Blocks theory. GPT-4's design, architecture, and implementation are compared to each of the building blocks of consciousness to determine whether it has achieved the requisite milestones to be classified as conscious or, if not, how close to consciousness GPT-4 is. Our assessment is that, while GPT-4 in its native configuration is not currently conscious, current technological research and development is sufficient to modify GPT-4 to have all the building blocks of consciousness. Consequently, we argue that the emergence of a conscious AI model is plausible in the near term. The paper concludes with a comprehensive discussion of the ethical implications and societal ramifications of engineering conscious AI entities. © 2024 World Scientific Publishing Company.

关键词： Artificial Intelligence Consciousness Ethics GPT-4 Philosophy of Mind Sentience

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An Empirical Analysis of Machine Learning Models with Performance Comparison and Insights for Heart Disease Prediction 3

An Empirical Analysis of Machine Learning Models with Perfor...

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3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Gulhane, Monali Kumar, Sandeep Borkar, Pradnya Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram India Department of Computer Science and Engineering Pune India

ISBN: (纸本)9798350342338

Globally, heart disease is a major health concern, and precise forecasting of its occurrence is essential for early diagnosis along with prevention. The lack of extensive comparison and assessment of various models using machine learning for implementing the predicting heart disease is a problem in comparable recent research, restricting the ability to identify the best approach for accurate predictions. The purpose behind the investigation and research is to compare the predictive accuracy of multiple models and determine which one is the most effective. In this paper, we present an empirical evaluation of six well-known models of the machine learning for predicting heart disease such as Random Forest(RF), Logistic Regression (LR), Decision Tree(DT), K-Nearest Neighbours (KNN), Gaussian Naive Bayes (GNB), and Support Vector Machine (SVM) are implemented the combination of this model is proposed as HD-M6M i.e. heart disease prediction using six machine learning models. Each model was developed and evaluated using 10-fold cross-validation and a dataset with relevant heart disease-predicting traits. The results of tests have shown that LR was the most accurate model, with a total accuracy of 90.164%. Our results give us important information about how to choose the correct model for predicting heart disease. © 2023 IEEE.

关键词： dataset reading feature selection health care intervention heart diseases prediction Machine learning

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VSNet: classification of pulmonary nodules in 3D using vision transformer and sequence spatial attention mechanism

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Multimedia Tools and Applications 2025年第14期84卷 13885-13903页

作者： Tang, Dongfang Xiao, Ting Yang, Fan Zhang, Conghao Wang, Zhe Gao, Wen Department of Thoracic Surgery Huadong Hospital Affiliated to Fudan University Shanghai200040 China Department of Computer Science and Engineering East China University of Science and Technology Shanghai200237 China

Accurate classification of benign and malignant nodules in Computed Tomography (CT) scans is crucial for the early detection of lung cancer and computer-Aided Diagnosis (CAD) systems. Despite significant advancements, challenges such as the interpretability of the reasoning process and the lack of fine-grained representations persist. To address these challenges, we propose a 3D VSNet. Our approach incorporates a Sequence Spatial Attention Module (SSAM) that automatically locates the sequence of the encoder’s output and the receptive field region required, achieving the acquisition of key features of pulmonary nodules. We leverage the characteristics of shallow and deep features based on 3D Vision Transformer (ViT) and Convolutional Neural Networks (CNNs) to obtain fine-grained representations and improve nodule classification. Additionally, we implement a new training strategy using Supervised Contrastive (SC) loss and Proxy-Anchor (PA) loss to optimize the embedding feature of similar samples and the direct cosine distance, and enhance the convergence speed of tuple sampling. Our experiments on the LIDC-IDRI dataset demonstrate the effectiveness of our proposed technique, achieving an accuracy of 90.28% and a precision of 91.63%. Furthermore, we conduct ablation experiments to analyze the contribution and influence of each component of our method. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

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An AutoML Approach Integrated with Live Weather Data in Rain Forecasting System (RFS) 1

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8th International Conference on Inventive Communication and Computational Technologies, ICICCT 2024

作者： Mazahir Mehdi Zaidi, Syed Rizvi, Sheenu Arora, Deepak Tiwari, Shivam Department of Computer Science and Engineering Amity School of Engineering & Technology Lucknow Amity University Uttar Pradesh Lucknow India Department of Computer Science and Engineering Sharda University Greater Noida India

ISBN: (数字)9789819777105

ISBN: (纸本)9789819777099

The increasing climate unpredictability highlights the critical need for accurate rain prediction methods. Traditional approaches often struggle to adapt and accurately forecast rain across varied climatic conditions. This research work investigates the fusion of automated machine learning (AutoML) with real-time weather data to tackle these challenges. Previous methodologies have relied on manual algorithm selection and optimization, leading to inefficiencies and suboptimal outcomes. In contrast, the proposed AutoML-based approach automates algorithm selection and optimization, resulting in heightened accuracy and efficiency. Integration of diverse meteorological data streams and advanced preprocessing techniques enhances data quality for model training. Evaluation against traditional methods showcases significant enhancements in rain prediction accuracy, with scores ranging from 80.60 to 86.09% across distinct algorithms. Specifically, the Random Forest Classifier achieved an accuracy score of 85.43% and an AUC score of 0.88, Gaussian NB achieved 80.60% accuracy and 0.82 AUC, K Neighbors Classifier achieved 82.89% accuracy and 0.82 AUC, and XGBoost achieved 86.09% accuracy and 0.89 AUC. The developed approach not only advances rain forecasting but also offers potential applications in agricultural planning, urban water management, and disaster preparedness. Future research avenues may explore further enhancements to the AutoML framework and its extension to other environmental prediction domains. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Rain

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Detecting Communities Using Network Embedding and Graph Clustering Approach 6th

Detecting Communities Using Network Embedding and Graph Clus...

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6th International Conference on Soft Computing and Signal Processing, ICSCSP 2023

作者： Bhattacharya, Riju Nagwani, Naresh Kumar Tripathi, Sarsij Department of Computer Science and Engineering National Institute of Technology Raipur Raipur India Department of Computer Science and Engineering Motilal Nehru National Institute of Technology Allahabad Allahabad India

ISBN: (纸本)9789819984503

Complex network community structure has been shown effective in many fields, including biology, social media, health, and more. Researchers have explored many different techniques for studying complex networks and discovering communities within them. Most of them, however, lack the expressiveness necessary to learn the node and edge representations of complicated networks. This study has aimed to improve the performance of the Hierarchical Clustering algorithm by analyzing a framework for learning continuous feature representations using node embedding methods for nodes in networks. The proposed method improves upon previous methods by training a map from nodes to a feature space with fewer dimensions. The proposed approach employs the Hierarchical Clustering method to accomplish community identification in the benchmark networks by determining the level of similarity between any pair of node embeddings. Substantial exploratory research on a variety of real-world social networks has shown the efficacy of the proposed approach compared to existing state-of-the-art community discovery techniques. The proposed method has provided high-accuracy results when applied to graph datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Complex networks

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IoT eHealth Big Data Analytics Based on Machine Learning

IoT eHealth Big Data Analytics Based on Machine Learning

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International Conference on Data Analytics and Management, ICDAM 2023

作者： Mahdi, Mohammed A. Ahamad, Shahanawaj Department of Information and Computer Sciences College of Computer Science and Engineering University of Hail Hail City Saudi Arabia

ISBN: (纸本)9789819965465

The convergence of health care and technology has led to the emergence of numerous inventive solutions, among which the Internet of Things (IoT) in tandem with Big Data Analytics and Machine Learning appears to hold significant potential. In the previous research that has been conducted on the Internet of Things (IoT), a variety of big data solutions has been presented for the purpose of evaluating massive amounts of data that have been obtained from a wide variety of sources in the field of smart health care. This evaluation is intended to take place to provide better care for the patients. This has been done with the goal of enhancing the quality of medical treatment offered to patients. Machine learning (ML) is a cutting-edge technology in the industry that has the capacity to analyze huge amounts of data and come to judgements based on those analyses in unique ways. This ability makes ML one of the most important developments in this field in recent years. In this article, we propose to use three distinct machine learning algorithms, each of which serves as a learning engine for big data analytics. Our goal is to investigate how quickly data is processed and how well it can be used to make predictions. Python is used throughout the whole training and testing process, including the simulation that is used to determine whether the model is effective. The findings of the simulation indicate that the suggested technique has a high degree of accuracy in determining the medical histories of patients based on the data provided by IoT devices. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Internet of things

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