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A Well Structured Design for Individualized Healthcare System via Fuzzy Logic System (FLS) 4

A Well Structured Design for Individualized Healthcare Syste...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Raman, Ramakrishnan Verma, Apurv Kumar, Vikram Rastogi, Shailesh Pillai, Biju G. Meenakshi, R. India Parul Institute of Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology India Srv Media Pvt. Ltd India Pune India Faculty of Management It & Business Analytics India Institute of Technology Computer Science and Engineering Chennai India

ISBN: (纸本)9798350360165

Personalized medicine is driving the need for new and customized biopharmaceuticals using proteins that have been changed. As such, it is critical to evaluate these items for allergenicity before introducing them as treatments. Even though there are a number of techniques available to predict the allergenicity of proteins, it may still be difficult to discriminate between allergens and nonallergens, particularly when the allergens have a considerable sequence resemblance to their known counterparts. To address this, we provide 'FuzzyApp,' a novel fuzzy rule-based system that evaluates a query protein's potential for allergenicity. FuzzyApp uses fuzzy IF-THEN rules that come from five different modules to determine how allergenic a protein is. FuzzyApp outperforms the state-of-the-art techniques on a variety of datasets, attaining a pleasing trade-off between specificity and sensitivity, as shown by an upbeat Mathew's correlate coefficient. FuzzyApp's capacity to distinguish allergens from allergen-like potential nonallergens (APN) is highlighted by its notable high specificity in this regard. The efficacy and uniformity of this suggested approach are further supported by error analysis and analysis of the whole proteome dataset. Furthermore, Tropomyosin from a variety of allergic and nonallergenic sources is properly predicted by FuzzyApp. The companion online service helps users comprehend the reasoning behind predictions by facilitating batch sequence filing and presenting findings in coherent words. © 2024 IEEE.

关键词： Personalized medicine

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Leveraging Transformer-Based Topic Modeling using BERTopic for Publication Venue Recommendation 11

Leveraging Transformer-Based Topic Modeling using BERTopic f...

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11th International Conference on Electrical Engineering, computer Science and Informatics, EECSI 2024

作者： Badieah Chaerul Haviana, Sam Farisa Mulyono, Sri Salim, Channa Abdullah Adli, Muhammad Fatih Febriansah, Moh Taufik Wiktasari Sulaiman, Noor Suhana Yaqob, Azliza Universitas Islam Sultan Agung Informatics Engineering Department Semarang Indonesia Politeknik Negeri Semarang Computer Engineering Technology Semarang Indonesia University College of TATI Faculty of Computer Media and Technology Management Terengganu Malaysia

ISBN: (纸本)9798350355314

Topic modeling has become essential in a variety of text mining applications, such as document clustering and recommendation systems. This study investigates the potential of BERTopic, a transformer-based method that leverages BERT embeddings to recommend publication venues. Traditional topic modeling methods are made better by BERTopic, which uses the deep contextual embeddings that BERT (Bidirectional Encoder Representations from Transformers) offers. This investigation assesses the efficacy of BERTopic in clustering academic papers according to their content in order to suggest appropriate publication venues. We also compare the performance between different kinds of pre-trained sentence embedding models and input types (title, abstract, and title+abstract). The experimental results indicate that BERTopic performs well with the GARUDA dataset, which comprises 5272 records from three different research subjects. The results emphasize the transformative influence of the transformer-based model. The best performance of all cases was 99.17% mean average precision in the Top 3 position, using Title+Abstract as input data and all-mpnet-base-v2 as the sentence embedding model. © 2024 IEEE.

关键词： Recommender systems

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Optimizing Routes for Improved Quality of Service in Wireless Sensor Networks through an Energy-Aware Routing Protocol for Maximizing Lifetime

Optimizing Routes for Improved Quality of Service in Wireles...

引用

2024 International Conference on Knowledge Engineering and Communication Systems, ICKECS 2024

作者： Raman, Ramakrishnan Kumar, Vikram Pillai, Biju G. Rabadiya, Dhaval Patre, Smruti Meenakshi, R. India SRV Media Pvt. Ltd. India Biib Faculty of Management It & Business Analytics India Parul Institute of Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology India Symbiosis Institute of Business Management Nagpur India Chennai Institute of Technology Computer Science and Engineering India

ISBN: (纸本)9798350359688

In the realm of Wireless Sensor Networks (WSNs), optimizing the Quality of Service (QoS) while ensuring the longevity of network operations remains a pivotal challenge, primarily due to the constrained energy resources of sensor nodes. This paper introduces a novel energy-aware routing protocol designed to enhance the QoS by optimizing routes within WSNs, thereby maximizing the network's operational lifetime. The proposed protocol employs a dual-focused approach, integrating energy efficiency with QoS parameters to select the most optimal routing paths. Through the deployment of a specialized routing algorithm alongside a comprehensive energy consumption model, the protocol strategically balances energy expenditure across the network, ensuring an equitable distribution of the energy load among sensor nodes. This methodology not only prolongs the operational lifespan of the network but also significantly improves the reliability and efficiency of data transmission. The efficacy of the proposed protocol is rigorously evaluated through a series of simulations, which demonstrate a marked improvement in both network lifetime and QoS, as compared to conventional routing protocols. The findings underscore the potential of integrating energy-aware considerations into routing decisions as a means to sustainably manage the critical resources of WSNs, thereby facilitating their broader application across various domains. © 2024 IEEE.

关键词： Sensor nodes

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The Design of Security System in Transmission of Healthcare Cloud Data System to Secure from Attacks 4

The Design of Security System in Transmission of Healthcare ...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Raman, Ramakrishnan Pillai, Biju G. Rastogi, Shailesh Kumar, Vikram Verma, Apurv Meenakshi India Faculty of Management It & Business Analytics India Symbiosis Institute of Business Management Nagpur Pune India Srv Media Pvt. Ltd. India Parul Institute of Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology India Chennai Institute of Technology Computer Science and Engineering India

ISBN: (纸本)9798350360165

Harmonizing both safety and ease of use in healthcare software is a top priority in current security research. The balance between security and usability is still elusive, despite the constant efforts of developers and security experts. More thorough research into practical approaches to assess and improve the usable security of medical technology is urgently needed. The main goal of this research is to evaluate healthcare software's usable security and, using empirical analysis as a basis, provide approaches for optimum security that don't sacrifice usability. Decision-makers in the development of software have a difficulty when it comes to effortlessly integrating exceptionally safe software with optimal usability. In order to evaluate usability and security, this study proposes merging the Analytic Network Process (ANP), Fuzzy Set (FS), and Technique for Priority of Priority by Similarity to the Ideal Response (TOPSIS). This innovative technique promotes the integration of user and safety qualities. The FSAHP-TOPSIS hybrid solution's effectiveness in assessing usable-security is shown by the study's application to a hospital management system in Mecca, Saudi Arabia. These results provide insightful advice for security management that doesn't sacrifice end-user usability. © 2024 IEEE.

关键词： Cloud security

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Novel Based Design of System for the Purpose of Supply Chain with Fuzzy Logic Algorithms and its Models 4

Novel Based Design of System for the Purpose of Supply Chain...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Raman, Ramakrishnan Pillai, Biju G. Rastogi, Shailesh Kumar, Vikram Verma, Apurv Meenakshi India Faculty of Management It & Business Analytics India Pune Symbiosis Institute of Business Management Nagpur India Srv Media Pvt. Ltd. India Parul University Computer Science and Engineering Faculty of Engineering and Technology Parul Institute of Technology India Chennai Institute of Technology Computer Science and Engineering India

ISBN: (纸本)9798350360165

Lean concepts and techniques have been adopted by a wide range of businesses, with the goal of increasing the effectiveness of company processes by reducing expenses, waste, intake and effort. But previous analyses haven't explicitly addressed how lean supply chains are in healthcare environments. This work presents a new way for assessing how well lean concepts and practices are used in healthcare chain of custody, and then it validates the methodology in a healthcare setting. An extensive literature overview on leanness assessment and multi-grade fuzzy logic is presented at the beginning of the study. Next, a conceptual model for measuring leanness is developed and validated by means of interviews with academic specialists, namely those who focus on leanness in healthcare settings. A healthcare business is chosen as an example after receiving helpful input from experts. This is due to the organization's desire to engage and dedication to putting lean concepts into practice, which guarantees a strong basis for projects with the potential to be transformational. Experts working for the case-study company confirm the estimated leanness index, which is the outcome of multi-grade fuzzy logic used in leanness assessment. In the end, the analysis pinpoints areas where the organization's procedures are lacking, providing guidance for future improvements. © 2024 IEEE.

关键词： Decision making

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Hybrid Algorithm Generation for the Development of Medical System using FC, AI & ML 4

Hybrid Algorithm Generation for the Development of Medical S...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Raman, Ramakrishnan Kumar, Vikram Pillai, Biju G. Verma, Apurv Rastogi, Shailesh Meenakshi, R. India Srv Media Pvt.Limited. India Sri Balaji University Faculty of Management It Business Analytics Pune India Parul Institute of Technology Parul University Faculty of Engineering and Technology Computer Science and Engineering India Symbiosis Institute of Business Management Nagpur India Chennai Institute of Technology Computer Science and Engineering India

ISBN: (纸本)9798350360165

This study introduces a pervasive hospital (PH) infrastructure for denture fabrication that consists of many 3D printing facilities. In order to maximize efficiency, the PH system divides up the printing jobs across several 3D printing facilities as it handles orders from dental clinics. Interestingly, this system adds two novel components that set it apart from other models. In the first place, it plans ahead of time for the possibility of reprinting, which eliminates the need for later replanning. In order to save travel time, it also works with home delivery companies, and have become more popular after the COVID-19 epidemic. The work models both printing and shipping durations using interval type-II irregular fuzzy numbers, which are appropriate given the inherent uncertainty related to these unique elements. The PH system's activities are then strategically planned to use the interval type-II fuzzy blended integer-linear processing (FMILP) model that has been designed and optimized. A useful case study is provided to demonstrate how well the suggested technique works. The experimental findings reveal a significant decrease in order completion time, demonstrating that the system may improve efficiency in operation by up to 9%. © 2024 IEEE.

关键词： Fuzzy sets

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Integration of Fuzzy Model with the IOT Model to Achieve Better Health(Medical) Care Design System 4

Integration of Fuzzy Model with the IOT Model to Achieve Bet...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Raman, Ramakrishnan Kumar, Vikram Pillai, Biju G. Verma, Apurv Rastogi, Shailesh Meenakshi, R. India Srv Media Pvt. Limited India Sri Balaji University Faculty of Management It & Business Analytics Pune India Parul Institute of Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology India India Computer Science and Engineering Chennai Institute of Technology India

ISBN: (纸本)9798350360165

The Internet of Things is a pervasive network that utilizes sensor-equipped devices by the billions to observe the real-world environment. The Internet of Things technology presents considerable potential for the development of various industries, including medical treatment, where it can decrease the impact that the combination of old age and chronic ailments puts on health care systems. This paper tends to put forward a secure hierarchical routing approach, so-called Fuzzy Logic-based secure Hierarchical Routing using the Firefly Algorithm, to mitigate the issued challenges in IoT technologies for healthcare. A Fuzzy Trust Framework, an Inter-cluster Routing Framework, and a Firefly Algorithm-based Clustered Framework are the three main components of the proposed FSRF. The Fuzzy Security Framework identifies the trustworthiness of IoT devices in the network to resist routing attacks. The Firefly Algorithm-based Colony Framework selects IoT devices as cluster head nodes using the fitness function that measures the trust level, residual power hop count, connectivity radius, and centrality. FSRF goes further to apply an on-demand routing technique to ensure efficient data transmission. Simulation analysis compared with the current protocol was presented that showed that FSRF significantly improves the network lifetime, and energy efficiency, but security is the issue that needs further concern to improve the system. © 2024 IEEE.

关键词： Sensor nodes

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The Intensive Model Design using FL for Smart HC System Design 4

The Intensive Model Design using FL for Smart HC System Desi...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Raman, Ramakrishnan Verma, Apurv Kumar, Vikram Rastogi, Shailesh Pillai, Biju G. Meenakshi, R. India Parul Institute of Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology India Srv Media Pvt. Limited India India Sri Balaji University Faculty of Management It & Business Analytics Pune India Chennai Institute of Technology Computer Science and Engineering India

ISBN: (纸本)9798350360165

Using cutting-edge technology like wearables, internet access of Things, and smartphones, smart healthcare has become a crucial component of health policy services. This revolutionary method enables seamless connection and ongoing information access between patients, medical equipment, the facilities, allowing for intelligent responses to a variety of demands within the medical ecosystem. No matter when or where they are, patients may get urgent assistance, diagnostic services, and monitoring via the digital platform of the Smart Health care management Systems (SHMS). This research emphasizes innovation with direct healthcare services and suggests the use of the Smart the management of healthcare Examination using Fuzzy Decision-Taking (SHME-FDM) technique to measure the effectiveness of technology integration. Furthermore, the Fuzzy Asymmetric Hierarchy Process-Technique for Orders of Preference by The sameness to Ideal Solution (Fuzzy AHP-TOPSIS) is used to evaluate the privacy protection and healthcare data inside the SHMS. In order to make predictions about healthcare, the research uses a fuzzy-based neural network. Through experimental analysis, reliability, accuracy, and error rates are evaluated. Notably, a security risk analysis highlights the proposed fuzzy model's potential for protecting healthcare data by demonstrating its improved risk assessment performance when compared to current methods. © 2024 IEEE.

关键词： Risk assessment

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Fog Computing Integrated with and Blockchain technology for Accurate Disease Prediction 4

Fog Computing Integrated with and Blockchain Technology for ...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Raman, Ramakrishnan Verma, Mr Apurv Kumar, Vikram Rastogi, Shailesh Pillai, Biju G. Meenakshi, R. India Parul University Computer Science and Engineering Faculty of Engineering and Technology Parul Institute of Technology India Srv Media Pvt. Limited. India Symbiosis Institute of Business Management Nagpur India Business Analytics Sri Balaji University Faculty of Management It Pune India Computer Science and Engineering Chennai Institute of Technology India

ISBN: (纸本)9798350360165

A modern computational paradigm known as fog computing provides distributed end users with very scalable and low-latency services. Because local fog nodes enable the quick storage and processing of data close to data sources, it has an inherent safety benefit over cloud computing. Simultaneously, the Blockchain (BC) technology represents a significant advancement, prioritizing data security, anonymity, and integrity via a consensus-building process. While BC was first widely used in finance, it is now finding its way into other industries, like as healthcare. In order to forecast diseases, this research suggests a safe healthcare service that uses Blockchain and fog computing. It focuses on diabetes and cardiovascular conditions. Health data about patients is collected from the fog nodes and safely kept on a blockchain. After grouping patient health data using a unique rule-based clustering approach, a feature selection-based neuro-fuzzy inference system with adaptive capability (FS-ANFIS) is used to forecast diseases. Significant real-world healthcare data trials are used to test the suggested technique. Performance measures, such as purity as normalized mutual information (NMI), are used to measure the effectiveness of rule-based clustering, as prediction accuracy is used to evaluate the performance of illness prediction. Comparing the testing results to other neural network methods, the suggested methodology achieved over 81% prediction accuracy, demonstrating its effectiveness. © 2024 IEEE.

关键词： Electronic health record

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Brain Image Denoising and Segmentation Using Advanced Mixed Thresholding and Edge Preservation Analysis Technique

Brain Image Denoising and Segmentation Using Advanced Mixed ...

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2024 International Conference on Knowledge Engineering and Communication Systems, ICKECS 2024

作者： Raman, Ramakrishnan Kumar, Vikram Pillai, Biju G. Rabadiya, Dhaval Patre, Smruti Meenakshi, R. India Srv Media Pvt. Ltd India India Parul Institute of Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology India Symbiosis Institute of Business Management Nagpur India Chennai Institute of Technology Computer Science and Engineering India

ISBN: (纸本)9798350359688

In the realm of medical imaging, particularly brain imaging, the clarity and accuracy of images are paramount for effective diagnosis and treatment planning. However, the inherent presence of noise in imaging modalities such as MRI often degrades the quality of these crucial images, complicating the segmentation process and potentially leading to inaccurate diagnoses. Addressing this challenge, our research introduces an innovative approach that synergizes advanced mixed thresholding with edge preservation analysis to significantly enhance brain image denoising and segmentation. This technique leverages a novel mixed thresholding method that dynamically adjusts to the specific noise characteristics of brain images, effectively reducing noise while retaining critical image details. Concurrently, our edge preservation analysis ensures that essential structural information is maintained, preventing the loss of vital features during the denoising process. The combination of these methods provides a robust solution to the persistent issue of noise in brain imaging, promising to improve the accuracy of segmentation and, by extension, the reliability of medical diagnoses derived from these images. By integrating advanced algorithms and mathematical models, our approach not only outperforms existing denoising and segmentation techniques but also establishes a new benchmark for image processing in medical diagnostics. © 2024 IEEE.

关键词： Image denoising

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