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检索条件"任意字段=International Conference on Computer Engineering and Technology"

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Unmasking Hypertension Through a Fusion of Human Lifestyle Data using Machine Learning Algorithms 6

Unmasking Hypertension Through a Fusion of Human Lifestyle D...

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6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Vishnu Priya, V. Nisanth, G. Nivethaa, R.P. Vijay, Srinidhi Department of Computer Science and Engineering Kongu Engineering College Erode Perundurai India Department of Computer Applications Kongu Engineering College Erode Perundurai India Department of Computer Technology UG Kongu Engineering College Erode Perundurai India

ISBN: (纸本)9798331522667

Hypertension, also referred to as high blood pressure, is a condition arising from the consistently high blood pressure against artery walls. The volume and output of blood from the heart primarily control blood pressure under stiff or tight arterial conditions. High pulse pressure leads to increased blood pressure, since the pumping of more blood makes the arteries inactive. The World Health Organization has a comprehensive guideline in which an evidence-based pharmacological approach has been recommended for managing adult patients with hypertension, not neglecting to mention lifestyle modification in order to support medicinal treatment. Machine learning may revolutionize the way hypertension treatment is projected for the management of personalized treatment routines for patients based on their characteristics. ML provides great promise in seeking ideal medication and predicting responses thereof against massive sets of data for hypertensive patients. The techniques allow for the determination of the individual risk for hypertension and ensure that treatment protocols are optimal. Compared to methods used to predict hypertension in the past, ML algorithms show a marked improvement in performance. © 2025 IEEE.

关键词： Personalized medicine

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Cloud Based Virtual Reality Training Platform for Occupational Safety 2

Cloud Based Virtual Reality Training Platform for Occupation...

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2nd international conference on Advances in Computation, Communication and Information technology, ICAICCIT 2024

作者： Kirti Talwandi, Navjot Singh Thakur, Rakesh Singh, Kalpana Department of APEX Computer Science and Engineering AIT-CSE Chandigarh University Mohali India Department of APEX Computer Science and Engineering AIT-CSE UCRD Chandigarh University Mohali India

ISBN: (纸本)9798331541217

The impartial of this schoolwork remained to appraise how effectively simulated realism (VR) can be cast-off aimed at care exercise. user carried out a systematic examination of the works using a variety of information analysis techniques and resources, including Scopus, Google Scholar (via Harzing's "Publish or Perish"), and Web of Science. The results of our investigation, which was approved obtainable utilizing technologies like VOSviewer, MAXQDA, BibExcel, and CiteSpace, showed that VR-based safety instruction is significantly more successful than conventional approaches and actively engages learners. We also investigated drawbacks such HMD accessibility and linguistic hurdles, and we suggested future study areas. In addition to highlighting VR's significant potential in safety training, this work offers a model for systematic reviews. © 2024 IEEE.

关键词： Virtual environments

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DonorHarbor: Orchestrating Organ Giving with Centralized Wallet Infrastructure

DonorHarbor: Orchestrating Organ Giving with Centralized Wal...

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2024 international conference on computer, Electronics, Electrical engineering and their Applications, IC2E3 2024

作者： Jadhav, Ranjana Barhate, Jignesh Joshi, Ojas Nandeshwar, Arman Gujar, Manthan Kundu, Basun Vishwakarma Institute of Technology Department of Information Technology Pune India

ISBN: (纸本)9798350388534

Addressing the pressing issue of organ shortages for transplantation is paramount, as countless families endure the agonizing wait for suitable donors. Current obstacles, including a limited donor pool and convoluted intermediary processes, impede progress in organ allocation. This paper proposes a decentralized web application, leveraging the transparency, privacy, and decentralization features of blockchain technology to streamline communication among hospitals, organ recipients, and potential donors. The innovative design ensures user-friendly interaction with blockchain-based smart contracts, eliminating concerns about key management and ensuring security. This approach significantly improves transaction traceability and eliminates the need for cumbersome middleman networks, addressing key healthcare industry issues. Importantly, the economic efficiency of this system empowers patients to navigate transplantation without incurring exorbitant costs, providing a promising solution to a pressing healthcare challenge. DonorHarbor represents a promising solution to the organ shortage crisis, offering hope to those in need of life-saving transplants. © 2024 IEEE.

关键词： Electronic health record

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Revolutionizing Kidney Disease Diagnosis: A Comprehensive CNN-Based Framework for Multi-Class CT Classification

Revolutionizing Kidney Disease Diagnosis: A Comprehensive CN...

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2024 IEEE international conference on Information technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Sharma, Gunjan Anand, Vatsala Chauhan, Rahul Pokhariya, Hemant Singh Gupta, Sheifali Sunil, G. 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 School of Computer Science & Artificial Intelligence Sr University Warangal India

ISBN: (纸本)9798350382693

A major global health concern, kidney infections are linked to rising death rates, especially when they worsen and cause the breakdown of the kidneys. Kidney function is seriously threatened by common kidney disorders such as nephrolithiasis, kidney tumors, and cyst development. Kidney failure, which can be brought on by conditions including tumors, stones, and cysts, can be avoided with prompt diagnosis and treatment. computer-aided diagnostics are essential due to the rising incidence of chronic renal illness, the lack of specialists, and the increased need for evaluation and monitoring. Though artificial intelligence (AI) methods, such as machine and deep learning, have been investigated for the identification of renal illness, their effectiveness is still lacking. In order to fill this gap, this study implements a deep learning-based Convolutional Neural Network (CNN) model for kidney illness prognosis and classification using a benchmark kidney dataset from Computed Tomography. CNN uses data reprocessing to extract features from the CT images. The results show how effective the suggested method is in correctly classifying renal illness, with a noteworthy accuracy of 99.88%, precision of 99.8%, recall of 99.7%, and an F1-score of 0.98. This work represents a potential development in the field of computer-assisted renal health diagnostics by supporting the use of the refined CNN model as a trustworthy instrument for kidney illness identification. This research can be applied in the medical field for the diagnosis of kidney diseases. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhance Energy Efficiency based Automobile Wheels by using Piezoelectric Energy Harvesting technology and Sustainable Power Generation 2

Enhance Energy Efficiency based Automobile Wheels by using P...

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2nd IEEE international conference on Networking and Communications, ICNWC 2024

作者： Dadi, Pradeep Surya Meera, R. Rajkumar, K. Surendran, R. Chennai Institute of Technology Department of Mechanical Engineering Chennai India St. Joseph's College of Engineering and Technology Department of Electronics and Communication Engineering Thanjavur India Guru Nanak Institute of Technology Department of Emerging Technologies Hyderabad India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering New Delhi India Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350365269

This paper investigates the feasibility of integrating piezoelectric energy harvesting technology into automobile wheels to improve energy efficiency and promote sustainable power generation. By strategically placing piezoelectric sensors around the circumference of the wheel, the rotational motion and pressure from the road are utilized to generate electricity. Through comprehensive analysis and simulations, optimal sensor placement, resonant frequency, and impedance are examined to maximize energy harvesting performance. The study evaluates the power output, current, and voltage at common Indian road speeds of 60 km / h and 80 km / h, considering the efficiency of a charge pump energy harvesting system. The results demonstrate significant potential for power generation, contributing to the development of self-powered automotive systems, and advancing sustainable energy solutions in the automotive industry. © 2024 IEEE.

关键词： Automotive industry

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Dynamic Graph-Enhanced Sequential LSTM for Context-Aware Point-of-Interest Recommendation 21

Dynamic Graph-Enhanced Sequential LSTM for Context-Aware Poi...

引用

21st international computer conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2024

作者： Yuwen, Zhang Shaobo, Ren School of Computer Science and Engineering School of Cyber Security University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798331519254

Next Point-of-Interest (POI) recommendation is a critical task in location-based services, aiming to predict the user's next POI based on historical check-in data. Existing approaches often rely on static graphs or sequential models to capture user preferences, which are limited by the sparsity of check-in data and the inability to dynamically model spatio-temporal transitions. To address these challenges, we propose the Dynamic Graph-Enhanced LSTM (DGEL) model for next POI recommendation. DGEL constructs dynamic POI transition graphs by combining Transformer-based graph learning with embedding techniques, capturing implicit relationships between POIs. Additionally, a Customized Sequential LSTM (CS-LSTM) is introduced to model long-term and short-term dependencies, incorporating spatial, temporal, and relational signals. Experimental results on Gowalla and Foursquare datasets demonstrate that DGEL achieves significant performance improvements over state-of-the-art baselines, highlighting its effectiveness in leveraging dynamic graphs and structured sequential learning for POI recommendation. © 2024 IEEE.

关键词： Knowledge graph

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Method of fruit and vegetable classification based on fusion pruning

Method of fruit and vegetable classification based on fusion...

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2024 international conference on Image, Signal Processing, and Pattern Recognition, ISPP 2024

作者： Jin, Songtao Zhang, Qiliang Qian, Yu School of Computer Jiangsu University of Science and Technology Zhenjiang212003 China School of Biotechnology Jiangsu University of Science and Technology Zhenjiang212003 China

ISBN: (纸本)9781510680425

Object detection is an important direction of computer vision, and small Object detection has long been a difficult point in computer vision. With the progress of satellite and remote sensing technology, there are a large number of high-definition remote sensing images every day. Because the target's feature map will shift after the target passes through the deep convolution network, which has a great impact on small target detection. In order to solve this problem, we introduce the receptive field module RFB in the relevant algorithm to enhance the discrimination of small target features. We further introduced the ASPP spatial pyramid module into the backbone network to expand the receptive field of the backbone network, so as to strengthen the detection of small and medium-sized targets. In order to evaluate its effectiveness, we carried out experiments in YOLOX target detection algorithm. The results show that this improvement can significantly improve the detection accuracy of small targets without affecting the reasoning speed. © 2024 SPIE.

关键词： Fruits

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A Disaster Prediction Ensemble Classifier Using Artificial Intelligence

A Disaster Prediction Ensemble Classifier Using Artificial I...

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2024 IEEE international conference on Modeling, Simulation and Intelligent Computing, MoSICom 2024

作者： Bharathi, V. Saranya, A. Shukla, Garima Shekhawat, Deepika Awasthi, Vanshaj Kumar, C.N.S. Vinoth Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Amity University Amity School of Engineering and Technology Department of Computer Science and Engineering Mumbai India Amity University Department of Computer Science and Engineering Mumbai India Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur Chennai India

ISBN: (纸本)9798331533311

Natural and man-made disasters may have severe impacts, but disaster prediction can help lessen such effects. The natural catastrophes that strike India on a regular basis cause immense devastation in terms of human lives and material possessions. Predicting the start and development of disasters in real-time is essential for reducing their effect. For more precise and trustworthy catastrophe predictions, this research presents an AI-powered catastrophe Ensemble B-Classifier network (DE BCN). The DPEC ensemble technique improves forecast accuracy over individual models by integrating various predictive outputs from numerous machine learning models that specialise in distinct kinds of disasters. This is where the data was originally pulled from Kaggle. To finish off the preprocessing, the norma clash filter was used. The next step is to use the optimisation method known as mustard twin swarm to extract characteristics that are connected to the tragedy. With any luck, the calamity Ensemble-B Classifier network will be able to forecast the calamity. Python and the Kaggle database were the environments used to carry out the investigation. According to the results of the simulations, the suggested approach is better than the current standard for predicting the catastrophic state. © 2024 IEEE.

关键词： Prediction models

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Internet of Things Based Agribot for Smart Farming and farm Monitoring

Internet of Things Based Agribot for Smart Farming and farm ...

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2024 international conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Srivastava, Vivek Singh, Gulbir Aggarwal, Ritu Kumar, Suneet Haryana Ambala India Graphic Era Hill University Haldwani Campus Department of Computer Science and Engineering Uttarakhand Nanital India Seth Jai Parkash Mukand Lal Institute of Engineering and Technology Department of Computer Science and Engineering Haryana Yamunanagar India Mullana Department of Computer Science and Engineering Haryana Ambala India

ISBN: (纸本)9798350375237

The agricultural business of today is data-centered, precise, and wiser than it has ever been before. This is in contrast to the impression that some people may have about the agricultural process. Almost every sector, including 'smart farming,' was rebuilt as a result of the fast rise of technology built on the Internet of Things (IoT), which shifted the industry away from statistical techniques and towards quantitative ones. These revolutionary developments are shaking up the many agricultural practices that are now in use and bringing forth new possibilities of Agribots along with a variety of obstacles. Within the context of agriculture, this article present the promise of IoT Agribot, as well as the obstacles that are expected to be encountered when integrating it with the conventional agricultural operations. This paper conduct an in-depth analysis of IoT Agribot methods related with wireless sensors that are used in smart farming and farm monitoring. The explanation of how Agribot technology assists producers throughout the many phases of agricultural production, beginning with planting and continuing through harvesting, packaging, shipping and monitoring. In addition, this paper takes into consideration the use of Unmanned Aerial Vehicles (UAV) for the purpose of farm monitoring as well as other advantageous applications such as the crop yield optimization. On the basis of this comprehensive assessment, it conclude by identifying present and future trends of the IoT Agribot and highlighting prospective research problems. © 2024 IEEE.

关键词： Internet of things

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Blockchain in Project Management for Information Security, Transparency and Accountability

Blockchain in Project Management for Information Security, T...

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2025 international conference on Electronics, Information, and Communication, ICEIC 2025

作者： Rahman, Tahmid Noor Abedin Khan, Mohammad Fasiul Bin Shahed, Labib Kafi, Abdulla Hil BRAC University Department of Electrical and Electronic Engineering Dhaka Bangladesh BRAC University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331510756

Blockchain technology, first developed for Bitcoin, offers transformative potential for project management. We present ChainManager, a conceptual blockchain-based project management platform developed with Flutter. Built on Ethereum, the blockchain network is deployed using OCI (Oracle Cloud Infrastructure), offering adaptability to varying project needs. This platform for managing projects makes use of smart contracts and immutable activity tracking in order to ensure the safety of activities such as monitoring work hours and controlling access. According to our findings, blockchain technology has the capability to solve long-standing problems, such as maintaining the integrity of data and gaining the trust of stakeholders. On the other hand, limitations such as scalability and high energy consumption continue to exist, which calls for additional study and innovation. Although there are obstacles to overcome, the purpose of this study is to investigate how blockchain technology can revolutionize project management by securing project data, automating processes, enhancing overall operational efficiency, and guaranteeing responsibility to stakeholders. © 2025 IEEE.

关键词： Ethereum

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