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An Enhanced Integrated Method for Healthcare Data Classification with Incompleteness

computers, Materials & Continua 2024年第11期81卷 3125-3145页

作者： Sonia Goel Meena Tushir Jyoti Arora Tripti Sharma Deepali Gupta Ali Nauman Ghulam Muhammad Electrical and Electronics Engineering(EEE)Department Maharaja Surajmal Institute of TechnologyNew-Delhi110058India Information Technology(IT)Department Maharaja Surajmal Institute of TechnologyNew-Delhi110058India Chitkara University Institute of Engineering and Technology Chitkara UniversityRajpuraPunjab140401India Department of Computer Science and Engineering Yeungnam UniversityGyeongsan-si38541Republic of Korea Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11421Saudi Arabia

In numerous real-world healthcare applications,handling incomplete medical data poses significant challenges for missing value imputation and subsequent clustering or classification *** approaches often rely on statistical methods for imputation,which may yield suboptimal results and be computationally *** paper aims to integrate imputation and clustering techniques to enhance the classification of incomplete medical data with improved *** classification methods are ill-suited for incomplete medical *** enhance efficiency without compromising accuracy,this paper introduces a novel approach that combines imputation and clustering for the classification of incomplete ***,the linear interpolation imputation method alongside an iterative Fuzzy c-means clustering method is applied and followed by a classification *** effectiveness of the proposed approach is evaluated using multiple performance metrics,including accuracy,precision,specificity,and *** encouraging results demonstrate that our proposed method surpasses classical approaches across various performance criteria.

关键词： Incomplete data nearest neighbor linear interpolation imputation clustering classification

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Hybrid Optimization Based BERT Model for Drug Detection in NLP 3

Hybrid Optimization Based BERT Model for Drug Detection in N...

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3rd IEEE Mysore Sub Section International Conference, MysuruCon 2023

作者： Kruthika, C.G. Namitha Devi, N.N. Madlur, Vijayananda V Asha, K. Aruna, T.M. Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Vidya Vikas Institute of Engineering and Technology Department of Computer Science and Engineering Mysuru India Mangalore Institute of Technology and Engineering Department of Computer Science and Engineering Moodabidri India Reva University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350340358

When it comes to natural language processing (NLP), sentence embedding is a hot topic because of the knowledge it transfers to subsequent tasks. Recent studies have shown that sequential neural networks, which can be trained on large quantities of data, can be used to encode info through a one-way analysis of text sequences, making them a powerful tool for drug detection. The purpose of this study is to smear cutting-edge deep learning techniques to publicly available data in order to accurately detect and identify adverse medication reactions that have not been recorded by medical professionals (open data). This study presents a deep learning-based strategy employing Enhanced Encoder on a dataset of 10,000 reviews culled from WebMD and *** and labelled by hand (IBERT). The model is created by integrating the BERT with many parallel blocks of a single-layer deep Convolutional Neural Network (CNN) that use varying kernel sizes and filters. To deal with ambiguity, the biggest obstacle to NLP, such a combination is helpful. Cuckoo Search (CS) and Antlion Optimization (ALO) are coupled to create a hybrid optimization strategy for determining the best values for the proposed model's hyper-parameters. Results from a number of different classification methods demonstration that the suggested model meaningfully outdoes the state-of-The-Art models. © 2023 IEEE.

关键词： Embeddings

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Growing Insights: Federated Learning CNN's in Combatting Sunflower Leaf Diseases

Growing Insights: Federated Learning CNN's in Combatting Sun...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Suryavanshi, Ankita Mehta, Shiva Malhotra, Sonal Choudhary, Ankur Jain, Vishal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand Dehradun India Sharda University Computer Science and Engineering Sharda School of Engineering and Technology U.P. Greater Noida India

ISBN: (纸本)9798350361537

It is an issue of great significance in detecting sunflower leaf diseases. This research paper fills that gap using the collective intelligence of distributed data sources through a federated learning (FL) framework. The CNN classifier model is employed for training. With sunflower being an economically important crop and leaf diseases lowering the yield, our research proposes a novel approach where associations are made among data for each client (owning different datasets for five classes of leaf diseases) that are fused to enhance disease detection and produce actionable images. Our FL concept not only protects user privacy and ensures model consistency but also allows customers to support the development of a global model by delivering their data only to a centralized server. It was observed that the proposed approach has the potential to be successful and effective as the experimental results of the presented results confirmed it. The FL model demonstrates an impressive level of performance in the figures on the data on the performance and disease classification shown with macro, micro, and weighted averages, which depict a well-balanced approach. The macro average scores depict the model working well with precision, recall, and the F1-scores of 85.31 to 91.85, respectively, for customers vi-1 through vf-5. The weighted average (with the class imbalance included) also gives high scores, pointing out precision within a range of 85.32 to 91.86, similar to the micro-average values. The only difference is in the range of these values. © 2024 IEEE.

关键词： Federated learning

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Extreme Learning Machine for Breast Cancer Diagnosis using cloud computing

Extreme Learning Machine for Breast Cancer Diagnosis using c...

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2024 International Conference on Recent Innovation in Smart and Sustainable Technology, ICRISST 2024

作者： Muller, Priya Shirley Prasad, Mudarakola Lakshmi Sikrant, Poornima A Reddy, Pundru Chandra Shaker Sharma, Swati Sharma, Nipun Sathyabama Institute of Science and Technology Department of Mathematics Chennai India Institute of Aeronautical Engineering Computer Science and Engineering Telangana Dundigal India Sri Ramachandra Institute of Higher Education and Research Sri Ramachandra Faculty of Management Sciences Tamilnadu Chennai India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India Presidency University Bangalore School of Computer Science Engineering and Information Science Karnataka India

ISBN: (纸本)9798350309140

Worldwide, breast cancer ranks high among women's leading causes of mortality. The likelihood of mortality from breast cancer can be decreased with early detection and rapid treatment. Among those residing in rural regions with limited access to healthcare, machine learning on the cloud is currently playing a crucial role in illness diagnosis. Machine learning-based diagnostic systems can supplement radiologists' work as secondary readers to ensure accurate illness diagnosis, while cloud-based systems can facilitate remote diagnostics and telehealth services. The potential of artificial neural networks (ANN)-based techniques for illness diagnosis has piqued the interest of numerous academics. Among the many ANN versions, extreme learning machine (ELM) stands out as a promising candidate for tackling a wide range of classification issues. Three distinct areas of study are brought together in the suggested framework of this work: The first area where ELM is used is in the detection of breast-cancer. Next, the gain-ratio feature selection strategy is utilized to remove features that are not significant. Finally, this research offers an ELM-based CC-approach for remote breast-cancer diagnostics. Several cutting-edge technologies for illness diagnosis are contrasted with the cloud-based ELM's performance. The outcomes obtained on the Wisconsin-Diagnostic-Breast-Cancer(WBCD) dataset demonstrate that the ELM technique hosted in the cloud surpasses alternative approaches. When comparing ELM's performance in both the standalone and cloud contexts, the former proved to be superior. © 2024 IEEE.

关键词： Adversarial machine learning

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HQF-CC: hybrid framework for automated respiratory disease detection based on quantum feature extractor and custom classifier model using chest X-rays

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International Journal of Information Technology (Singapore) 2024年第2期16卷 1145-1153页

作者： Eswara Rao, G.V. Rajitha, B. Computer Science and Engineering Department Motilal Nehru National Institute of Technology Allahabad UP Prayagraj 211004 India

In recent years, humans have been affected by a variety of respiratory diseases, such as dry cough, fever, pneumonia, and COVID-19. Respiratory diseases may severely damage the respiratory system of humans. The early diagnosis of it can help enable appropriate treatment immediately to reduce the abnormalities in the healthcare monitoring systems. A chest X-ray could be used as one of the medical tools to assess the severity of respiratory diseases. In order to predict or classify respiratory diseases, many researchers have proposed different deep learning-based models. But they consume more computational resources. This article proposes a hybrid framework for respiratory disease detection based on a quantum feature extractor and custom classifier model (HQF-CC) from Chest X-rays. Here, we propose a quantum machine learning algorithm named MMS (Multi-Multi-Single) for feature extraction and a custom classifier for classification. The experiments were performed on the COVID-19 Radiography Dataset (CRD), which includes 15,153 Chest X-ray images of COVID-19, viral, and Normal diseases, respectively. The proposed model had the highest training and testing accuracy of 97.2% and 98.8% on the CRD dataset and a training and testing loss of 0.02, 0.01. Upon various experiments, the proposed model has proven to be more accurate, portable, and memory-efficient than other deep and machine learning models for respiratory disease detection. © The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Classification Deep learning model Feature extraction Quantum transfer learning Qubit Respiratory disease

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Research on the closed-loop supply chain of intelligent products considering government subsidies in the context of the internet of things

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Discover Internet of Things 2025年第1期5卷 1-20页

作者： Zhao, Jinghua Cui, Shaoyun Xu, Zheng School of Business University of Shanghai for Science and Technology Shanghai China School of Computer and Information Engineering Institute for Artificial Intelligence Shanghai Polytechnic University Shanghai201209 China

The rapid development of Internet of Things technology and the continuous improvement of consumer demand have spawned the emergence of intelligent products based on Internet of Things technology. The existing research on the closed-loop supply chain of intelligent products under the background of the Internet of Things still lacks consideration of different subsidy objects and recyclers. Therefore, for different subsidy objects in the three-stage closed-loop supply chain of intelligent products, a pricing decision model involving manufacturers, recyclers, retailers and consumers under the minimum standard supervision of recycling technology is constructed, and different recyclers are distinguished according to the characteristics of intelligent products. By comparing the profits of each subject in different models, the optimal model of government subsidy is obtained. It is found that under the four modes, subsidizing recyclers without self-recovery technology makes the total profit of the supply chain reach the optimal solution;capital recyclers can obtain the largest number of old intelligent product recycling by using self-recovery technology;when subsidizing manufacturers, the total profit and product recovery rate of the supply chain are at the bottom. In addition, recycling subsidies, subsidy impact coefficients and technical standards will affect the best profits of enterprises. Through the analysis of the closed-loop supply chain of intelligent products, the research provides theoretical support and decision-making basis for the supply chain members to formulate relevant strategies and the government to take relevant measures to promote resource utilization and environmental protection. © The Author(s) 2025.

关键词： Smart manufacturing

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Fake News Detection on Social Media Platforms Using Deep Learning Hybrid CNN-BiLSTM Technique

Fake News Detection on Social Media Platforms Using Deep Lea...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Nandanwar, Amit Kumar Mahor, Vinod Choudhary, Jaytrilok Singh, Dhirendra Pratap Gupta, Amit Vishwakarma, Gagan Iiit Department of Computer Science and Engineering Madhya Pradesh Bhopal462003 India Maulana Azad National Institute of Technology Department Centre of Artificial Intelligence Bhopal462001 India Maulana Azad National Institute of Technology Department of Computer Science and Engineering Bhopal462001 India National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung Taiwan

ISBN: (纸本)9798331529833

Most Smartphone users prefer their phones to read news via various social platforms on the Internet. The news site publishes news and provides the source of identity verification. Humans are ineffective in distinguishing between true and false facts and make false news a threat to the logical truth, undermining the credibility of democracy, journalism, and government agencies. With the development of new technologies, finding a strategy to reduce the spread of false news or rumors that could harm society in any way is critical. Online clients are usually vulnerable to attack, and all content they run on web-based network media is generally considered reliable. Therefore, mechanized identification of fake news is essential to maintain a significant online media and informal organization. Believing in rumors and pretending to be news is harmful to society. A lot of effort is required to stop the rumors and focus on correct, verifiable news pieces, especially in developing countries. As a result, we offer a model for detecting fake news, a computational style analysis based on natural language processing that can effectively recognize text retrieved from social media Fake News using hybrid Hybrid model-based deep learning techniques. © 2025 IEEE.

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Interactive Learning Environment for Networking Topics based on Augmented Reality

Interactive Learning Environment for Networking Topics based...

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International Conference on computer Vision and Internet of Things 2023, ICCVIoT 2023

作者： Kanimozhi, S. Sairamesh, L. School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering St. Joseph's Institute of Technology Chennai India

ISBN: (纸本)9781839539848

Augmented Reality (AR) implemented on mobile devices has emerged as a prominent subject of study within the field of mobile applications and human-machine interaction. The mobile augmented reality (AR) technique integrates intelligent display, registration tracking, virtual and reality convergence, and human-computer interactions using portable devices or intelligent terminals. This enables the seamless integration of the 3D virtual object with the customer's real-life environment, thereby enhancing the perceived depth and breadth of the experience. Augmented reality has been extensively utilized across diverse domains, including the field of education. In the domain of education, augmented reality is employed to enhance the level of engagement and appeal in the learning process. Beginning with the issue of acquiring knowledge about computer networks, it is observed that the conventional approach involves the introduction of network devices, TCP/IP models, and OSI models. This research addresses the problem by creating a learning media solution that utilizes augmented reality (AR) technology. AR technology integrates two-dimensional or three-dimensional virtual objects into a real environment and displays these virtual objects in real-time. The novelty of this work implies on the design of augmented reality (AR) applications for teaching the OSI model in the field of computer networks. The aim is to enhance comprehension, stimulate motivation, and generate student interest in learning. © The Institution of engineering & Technology 2023.

关键词： Augmented reality

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Reliable Informational Data and Secured Deviation Notification over Networks Using IOT

Reliable Informational Data and Secured Deviation Notificati...

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2023 IEEE International Conference on Recent Trends in Electronics and Communication, ICRTEC 2023

作者： Vyshali Rao, K.P. Ramisetty, Srividya Dhanalakshmi, M. Cmr Institute of Technology Department of Information Science and Engineering Bangalore India New Horizon College of Engineering Department of Computer Science and Engineering India

ISBN: (纸本)9798350396195

The need of uninterrupted services from servers, networks and databases in the current environment relying completely on computing technologies. This paper implements Automatic database duplication with one master database server and multiple slave database servers. The proposed system ensures slave database server replicates data of master server which can be used to restore services in case master server fails. Existing method does this with human intervention which is a time consuming process. Therefore, the proposed system ensures to implement automatic failover capabilities with relatively low downtime and notifying the same using smart alert system. The alert mechanism includes sending an encrypted message notifying the master failure, any deviation from normal working of automatic up gradation software. The messages exchanged are AES encrypted using Message Queuing Telemetry Transport (MQTT) as IOT protocol. The proposed system implemented with Arduino, Sensors and java keycards. Results prove automatic failover utilizes less resources and system downtime is almost zero compared to manual failover. Analysis of performance, System downtime, time estimation is carried out to check the time required for key generation and encryption. © 2023 IEEE.

关键词： Database systems

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Secure Rotation Invariant Face Detection System for Authentication

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computers, Materials & Continua 2022年第1期70卷 1955-1974页

作者： Amit Verma Mohammed Baljon Shailendra Mishra Iqbaldeep Kaur Ritika Saini Sharad Saxena Sanjay Kumar Sharma Department of Computer Science&Engineering Chandigarh Group of CollegesMohali140317India Department of Computer Engineering College of Computer&Information ScienceMajmaah University11952Saudi Arabia Department of Computer Science and Engineering Thapar Institute of Engineering&TechnologyPatiala147004India Department of Computer Centre&Campus Network Facility Science Complex PO Central UniversityGachibowliHyderabad500046TelanganaIndia

Biometric applications widely use the face as a component for recognition and automatic *** rotation is a variable component and makes face detection a complex and challenging task with varied angles and *** problem has been investigated,and a novice algorithm,namely RIFDS(Rotation Invariant Face Detection System),has been *** objective of the paper is to implement a robust method for face detection taken at various *** to achieve better results than known algorithms for face *** RIFDS Polar Harmonic Transforms(PHT)technique is combined with Multi-Block Local Binary Pattern(MBLBP)in a hybrid *** MBLBP is used to extract texture patterns from the digital image,and the PHT is used to manage invariant rotation *** this manner,RIFDS can detect human faces at different rotations and with different facial *** RIFDS performance is validated on different face databases like LFW,ORL,CMU,MIT-CBCL,JAFFF Face Databases,and Lena *** results show that the RIFDS algorithm can detect faces at varying angles and at different image resolutions and with an accuracy of 99.9%.The RIFDS algorithm outperforms previous methods like Viola-Jones,Multi-blockLocal Binary Pattern(MBLBP),and Polar HarmonicTransforms(PHTs).The RIFDS approach has a further scope with a genetic algorithm to detect faces(approximation)even from shadows.

关键词： Pose variations face detection frontal faces facial expressions emotions

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