检索结果-内蒙古大学图书馆

2nd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2023

作者： Vincent, Robin Rohit Sarkar, Swagata Irence, Shalom Prakash, R.B.R. Senthil Rama, R. Suganthi, D. School of Computer Science & Engineering and Information Science Computer Science & Engineering Presidency University Bengaluru560064 India Artificial Intelligence and Data Science Sri Sairam Engineering College Chennai 44 India EEE Karunya Kits / CBE India Koneru Lakshmaiah Education Foundation Electrical and Electronics Engineering Vaddeswaram522302 India DMI College of Engineering EEE Chennai India Saveetha College of Liberal Arts and Sciences Department of Computer Science Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (纸本)9798350397635

Due to fast expansion and improved lifestyle, the need for useable energy has skyrocketed in recent decades, notably in the construction industry. Several factors, such as local climate, building makeup, and energy consumption habits, affect a structure's efficiency in using energy. In this paper, we aim for forecasting the interior temperature of smart buildings by combining IoT with popular machine Learning algorithms. This prediction model has been developed using online learning techniques to increase its adaptability to novel inputs. This paper details a methodology for creating an analytical model that may be utilized for smart building interior temperature forecasting by merging the IoT with some popular machine learning methods. This prediction model was built with an online-learning strategy, so it may be used for a dataset with which the developers are unfamiliar. To ensure the precision of the method, this article uses Machine Learning to test it using real-world sensor data. In order to verify the methodology, the study conducts experiments using Machine Learning on collected, real-world sensor data. The study then proposes incorporating the following method into an Edge Computing based IoT architecture in order to make the building operational in an energy-efficient manner. The learning framework was validated with data collected from an Internet of Things network installed in a home over the course of a summertime. The learnt model's efficacy has been evaluated, and the model's predictability of interior temperature changes has been quantified. The paper demonstrates the viability of the proposed Learning framework for thermal models based on the Internet of Things and provides a pragmatic answer to the problem of supplying accurate thermal models for use in the temperature controls of smart buildings of the future. © 2023 IEEE.

关键词： Energy utilization

来源：评论

学校读者我要写书评

暂无评论

Adaptive Residual Attention Network for Handwritten Character and Digits Recognition with Improved Energy Valley Optimizer Algorithm

Adaptive Residual Attention Network for Handwritten Characte...

引用

2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Srinivasa Rao, N. Nelson Kennedy Babu, C. Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Department Of Computer Science And Engineering Chennai Tamil Nadu Thandalam602105 India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Department Of Cloud Computing Chennai Tamil Nadu Thandalam602105 India

ISBN: (纸本)9798350364828

One of the very significant issues in pattern recognition applications is handwritten character recognition. Digit recognition is used in a variety of applications, such as form data entry, postal mail sorting and bank check processing. The capacity to create an effective algorithm that can identify handwritten numbers that are submitted by users via digital devices, tablets, and scanners. Handwritten digit recognition in computer vision systems is a challenging task and it is essential for many emerging applications. Therefore, a handwritten character and digits recognition technique is developed. The images related to handwritten characters are collected through the internet. Then, the images are given to the recognition phase. The handwritten character and digit recognition are performed using the Adaptive Residual Attention Network (ARAN) model. Here, the parameter tuning is performed using the Improved Energy Valley Optimization Algorithm (IEVOA) for enhancing the recognition performance. Hence, the suggested handwritten character and digits recognition model effectively recognizes the handwritten digits and characters than the traditional systems. The experimental outcomes are compared with other models. © 2024 IEEE.

关键词： Digital devices

来源：评论

学校读者我要写书评

暂无评论

Optimizing Diabetic Kidney Disease Diagnosis with Machine Learning: A Novel Approach

Optimizing Diabetic Kidney Disease Diagnosis with Machine Le...

引用

International Conference on Electronics, Materials engineering and Nano-Technology (IEMENTech)

作者： Vinitha G Bandi Rambabu Sathish Kumar P. J Surendran R K S. Balamurugan Department of Computer Science and Engineering Karpaga Vinayaga College of Engineering and Technology Chengalpattu Tamil Nadu India Department of Computer Science and Engineering CVR College of Engineering Hyderabad India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

Diabetic kidney disease (DKD) is a diabetic condition in which elevated blood sugar levels harm the kidney's filtering units, leading to kidney damage and potentially, kidney failure. About 700 million people effected by Diabetic Kidney Disease of whom approximately four million patients require kidney replacement therapy (KRT). The existing methods for predicting Diabetic Kidney Disease (DKD) have several drawbacks that limit their effectiveness and accuracy. Current models often rely on traditional biomarkers, such as blood glucose and urine albumin levels, which can be insufficient for early detection as they typically indicate kidney damage only after it has progressed. Proposed a novel technique by utilizing machine learning algorithm such as navie Bayes to predict the DKD in early stage leads to decrease the mortality rate. Proposed invention enhances the accuracy in disease diagnosis, which result in the score of 96.03, 94.03, 95.08 and 95.09 with precision, accuracy recall and F1-Score respectively.

关键词： Accuracy Mortality Machine learning Predictive models Data models Diabetes Glucose Medical diagnosis Kidney Diseases

来源：评论

学校读者我要写书评

暂无评论

Cascade computational model for prediction impact of transient depth change on combustion parameters of certain timber species under continuous heating rate

引用

International Journal of Thermofluids 2024年 23卷

作者： Olimat, Abdullah N. Al-Shawabkeh, Ali F. Quran, Omar maaitah, Hussein Al-Najdawi, Nijad Al-Qa, Ziad A. Department of Mechanical Engineering Faculty of Engineering Technology Al-Balqa Applied University Amman Jordan Department of Scientific Basic Sciences Faculty of Engineering Technology Al-Balqa Applied University Amman Jordan Department of Computer Science College of computer and information sciences Prince Sultan University Riyadh Saudi Arabia Department of Computer and Networks Engineering Faculty of Engineering Technology Al-Balqa Applied University Amman Jordan

The purpose of this research is to examine the effects of depth variation and exposure duration on the combustion characterization of the Maple wood species (Acer platanoides L.) and Walnut (Juglans regia L.) according to ISO 5660–1 in case of exposing the specimens to a constant heating rate 50 kWm−2. The delivered experimental data by cone calorimeter apparatus is used to train, test, and validate the optimized cascade forward artificial neural network with topology 2–8–8–12–2. This structure is simulated to predict the target parameters: the heat release rate (HRR) and the mass burning rate (MBR). With real systems, it is possible to achieve a reasonable performance and the closest approximations for the cascade model. The experimental combustion parameters: time of flame out, average specific mass burning rate, total heat release, maximum average rate of heat emission, and fire growth index are considered to make a systematic analysis and assessment with change depth. Three regions are considered for investigation of output target curves, thermally thin (d ≤ 1.1 cm), thermally medium (1.1 cm © 2024 The Author(s)

关键词： Combustion

来源：评论

学校读者我要写书评

暂无评论

Cloud Computing Based POC Diagnostic Device: Rapid Infectious Disease Testing by Data Analytics 1

Cloud Computing Based POC Diagnostic Device: Rapid Infectiou...

引用

1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Basha, H. Anwer Ramalingam, L. Hasan, Md Ashfaqul Ananda, M.H. Velmurugan, S. Maniraj, S.P. Saveetha College of Liberal Arts & Sciences Simats University Department of Computer Science Tamil Nadu Chennai India Loyola Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Malla Reddy Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India Reva University School of Electrical and Electronics Engineering Karnataka Bangalore India Tjs Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350330915

To meet the growing need for affordable, efficient, and accurate diagnostic tools, the system has developed a Raspberry Pi-based Point-of-Care (POC) gadget for speedy and reliable detection of infectious illnesses. This groundbreaking gadget, built on the flexible and powerful Raspberry Pi computer, ushers in a new era of state-of-the-art POC diagnostics by combining molecular and image analysis methods. Complex testing for pathogens like COVID-19 and malaria may be carried out using this state-of-the-art system, which integrates a camera module and custom biosensors. The device's software parses sensor data, improves diagnostic precision using machine learning techniques, and presents a straightforward interface for clinicians and patients alike. This study takes a holistic approach by considering all relevant factors, including adherence to regulations, user education, upkeep, and data management. Our POC diagnostic gadget driven by a Raspberry Pi provides a low-cost, high-throughput method for diagnosing illness, which may have far-reaching implications for the future of healthcare delivery in both under-resourced and well-endowed regions. © 2023 IEEE.

关键词： Healthcare Infectious Disease Internet of Things, Cloud Computing Point-of-Care Diagnostics

来源：评论

学校读者我要写书评

暂无评论

LIT: Lightning-fast In-memory Temporal Indexing

引用

Proceedings of the ACM on Management of Data 2024年第1期2卷 1-27页

作者： George Christodoulou Panagiotis Bouros Nikos Mamoulis Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Institute of Computer Science Johannes Gutenberg University Mainz Mainz Germany Department of Computer Science and Engineering University of Ioannina Ioannina Greece

We study the problem of temporal database indexing, i.e., indexing versions of a database table in an evolving database. With the larger and cheaper memory chips nowadays, we can afford to keep track of all versions of an evolving table in memory. This raises the question of how to index such a table effectively. We depart from the classic indexing approach, where both current (i.e., live) and past (i.e., dead) data versions are indexed in the same data structure, and propose LIT, a hybrid index, which decouples the management of the current and past states of the indexed column. LIT includes optimized indexing modules for dead and live records, which support efficient queries and updates, and gracefully combines them. We experimentally show that LIT is orders of magnitude faster than the state-of-the-art temporal indices. Furthermore, we demonstrate that LIT uses linear space to the number of record indexed versions, making it suitable for main-memory temporal data management.

关键词： indexing query processing temporal data

来源：评论

学校读者我要写书评

暂无评论

Frilled Lizard Optimization: A Novel Bio-Inspired Optimizer for Solving engineering Applications

引用

computers, Materials & Continua 2024年第6期79卷 3631-3678页

作者： Ibraheem Abu Falahah Osama Al-Baik Saleh Alomari Gulnara Bektemyssova Saikat Gochhait Irina Leonova OmParkash Malik Frank Werner Mohammad Dehghani Department of Mathematics Faculty of ScienceThe Hashemite UniversityP.O.Box 330127Zarqa13133Jordan Department of Software Engineering Al-Ahliyya Amman UniversityAmman19328Jordan Faculty of Science and Information Technology Software EngineeringJadara UniversityIrbid21110Jordan Department of Computer Engineering International Information Technology UniversityAlmaty050000Kazakhstan Symbiosis Institute of Digital and Telecom Management Constituent of Symbiosis International Deemed UniversityPune412115India Neuroscience Research Institute Samara State Medical UniversitySamara443001Russia Faculty of Social Sciences Lobachevsky UniversityNizhny Novgorod603950Russia Department of Electrical and Software Engineering University of CalgaryCalgaryAB T2N 1N4Canada Faculty of Mathematics Otto-von-Guericke UniversityP.O.Box 4120Magdeburg39016Germany Department of Electrical and Electronics Engineering Shiraz University of TechnologyShiraz***Iran

This research presents a novel nature-inspired metaheuristic algorithm called Frilled Lizard Optimization(FLO),which emulates the unique hunting behavior of frilled lizards in their natural *** draws its inspiration from the sit-and-wait hunting strategy of these *** algorithm’s core principles are meticulously detailed and mathematically structured into two distinct phases:(i)an exploration phase,which mimics the lizard’s sudden attack on its prey,and(ii)an exploitation phase,which simulates the lizard’s retreat to the treetops after *** assess FLO’s efficacy in addressing optimization problems,its performance is rigorously tested on fifty-two standard benchmark *** functions include unimodal,high-dimensional multimodal,and fixed-dimensional multimodal functions,as well as the challenging CEC 2017 test ***’s performance is benchmarked against twelve established metaheuristic algorithms,providing a comprehensive comparative *** simulation results demonstrate that FLO excels in both exploration and exploitation,effectively balancing these two critical aspects throughout the search *** balanced approach enables FLO to outperform several competing algorithms in numerous test ***,FLO is applied to twenty-two constrained optimization problems from the CEC 2011 test suite and four complex engineering design problems,further validating its robustness and versatility in solving real-world optimization ***,the study highlights FLO’s superior performance and its potential as a powerful tool for tackling a wide range of optimization problems.

关键词： Optimization engineering bio-inspired metaheuristic frilled lizard exploration exploitation

来源：评论

学校读者我要写书评

暂无评论

Opticurve: an optimized informer-curvelet framework for enhanced hyperspectral image segmentation and classification

引用

International Journal of Information Technology (Singapore) 2025年 1-16页

作者： Shaw, Kailash Wou Onn, Choo Li, Baihua Department of Computer Science and Engineering (AIML) Vishwakarma Institute of Technology Maharashtra Pune 411037 India Faculty of Data Science and Information Technology INTI International University Persiaran Perdana BBN Putra Nilai Negeri Sembilan Nilai 71800 Malaysia Department of Computer Science Loughborough University Loughborough LE11 3TU United Kingdom

Hyperspectral image (HSI) classification is crucial for applications in climate action, land use analysis, disaster risk reduction, and informed decision-making, given the complex spatial and spectral variations inherent in HSI data. Traditional methods struggle with accurately capturing these variations, necessitating more advanced techniques. This work introduces an Optimized Linformer-Curvelet (OptiCurve) Framework that integrates CNN-based feature extraction, Curvelet Transform for spatial detail capture, and Linformer for efficient feature representation. By combining these techniques, the model enhances HSI segmentation and classification, supporting improved outcomes in critical areas like environmental monitoring and disaster response. The framework is validated on four standard HSI datasets-Indian Pines, Pavia University, Kennedy Space Center, and Houston University-showing significant performance improvements over existing methods. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Climate action Disaster risk reduction Feature extraction Hyperspectral image classification Land use Linformer-Curvelet transform

来源：评论

学校读者我要写书评

暂无评论

THE INCLUSION-EXCLUSION PRINCIPLE AND RECURRENCES FOR PARTITION NUMBERS

引用

UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2025年第1期87卷 97-106页

作者： Ballantine, Cristina Merca, Mircea Department of Mathematics and Computer Science College of The Holy Cross WorcesterMA01610 United States Department of Mathematical Methods and Models Fundamental Sciences Applied in Engineering Research Center National University of Science and Technology Politehnica Bucharest BucharestRO-060042 Romania Academy of Romanian Scientists BucharestRO-050044 Romania

The inclusion-exclusion principle together with Legendre type theorems for number of distinct restricted partitions weighted by the parity of their length are used to give several recurrence relations for restricted partition numbers as well as for overpartition numbers. In the case of overpartitions, POD partitions and 3-color partitions, we give combinatorial proofs for the Legendre type theorems and arrive to combinatorial proofs for the recurrence relations. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Combinatorial mathematics

来源：评论

学校读者我要写书评

暂无评论

Revolutionising Tumour Diagnosis: How Clinical Application of Artificial Intelligence and Machine Learning Enhances Accuracy and Efficiency 1

引用

3rd International Conference on Mechanical and Energy Technologies, ICMET 2023

作者： Malathy, V. Pasupulla, Ajay Prakash Srivastava, Diksha Hymavathi, J. Pandian, P. Muthu Acharjee, Purnendu Bikash School of Engineering SR University Telangana Warangal India Oral and Maxillofacial Pathology School of Medicine College of Health Sciences Wachemo University Hosanna Ethiopia Department of Biotechnology and Chemical Engineering Manipal University Jaipur India Department of Computer Science and Engineering KLEF Andhra Pradesh Vaddeswaram India Department of Chemistry Saveetha Engineering College Chennai Thandalam India Computer Science CHRIST University Pune Lavasa Campus Lavasa India

ISBN: (数字)9789819727162

ISBN: (纸本)9789819727155

This research paper examines the transformative influence of Artificial Intelligence (AI) and Machine Learning (ML) on tumour diagnosis within clinical settings. The advent of AI and ML technologies has revolutionised the field of oncology, offering the unprecedented potential for more accurate, timely, and personalised cancer detection. By leveraging vast datasets of medical images, genomic information, and patient records, these intelligent systems enable the early identification of tumours, classification of cancer types, and prediction of patient outcomes with remarkable precision. This paper delves into the mechanisms through which AI and ML algorithms analyse complex data, highlighting their ability to detect subtle patterns and anomalies that may escape human perception. Moreover, we examine the successful integration of these technologies into clinical workflows, their potential to reduce diagnostic errors, and the implications for patient care and outcomes. As AI and ML continue to emerge, the synergy between technology and clinical expertise promises to enhance tumour diagnosis, ultimately contributing to more effective and personalised cancer treatments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：