检索结果-内蒙古大学图书馆

7th International Conference on Inventive Communication and Computaional technologies, ICICCT 2023

作者： Duraivelu, Vivek Deepa, S. Suguna, R. Arunkumar, M.S. Sathishkumar, P. Aswinraj, S. Department of Computer Science and Engineering B V Raju Institute of Technology Telangana Narsapur502313 India Department of Computer Technology Kongu Engineering College Tamil Nadu Perundurai638060 India Department of CSE Bannari Amman Institute of Technology Tamil Nadu Sathyamangalam638401 India Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai62 India

ISBN: (纸本)9789819951659

As a rigid human organ, bone supports the human body and helps in the formation of shapes;it also protects many important parts of the human body. A healthy bone is crucial for humans for three main reasons: It promotes mobility, protects internal organs and blood cells formulation, and stores nutrients. As a result, researchers discovered stable bone mineral density (bMD) as an indicator for bone health. As bMD plays a key role in maintaining bone health, many clinical studies are proposed to perform the prediction of bMD and fracture risk level based on the gender and age descriptions. bMD is not only related with bone metabolism (bM), and it also correlates with Endocrine Diseases (EDs). Various hormone imbalance and biominerals are estimated to measure the deficiency of endocrine disorder, which mainly causes Diabetes Mellitus type 1, type 2 (T1DM, T2DM), hyperthyroidism, and hypothyroidism. Factors associated with bone mineral density and endocrine disorder are recognized with various proven medical studies. Therefore, the relationship between bMD and ED is linked and categorized for performing further analysis. Comparison of different prediction and classification algorithms used in the correlation of bMD and ED is discussed with the help of Artificial Intelligence techniques (AITs). The above discussion on AIT is summarized with a dataset (image, biological data) related to bMD and ED. Henceforth, the evaluation metrics of different models are categorized and compared to show the requirement of AIT in the prediction of bone health. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

AI-Assist tool to enhance the reliability and accuracy of automated speech and writing support for students with disabilities in learning languages 11

AI-Assist tool to enhance the reliability and accuracy of au...

引用

11th IEEE International Conference on Cloud Computing in Emerging Markets, CCEM 2023

作者： Surana, D. Amruta Devika, Mrs K. Prema, S. Dhannaseth, Nilesh H. Gopinath, N. Tamilarasan, P. Department of Computer Engineering Sinhgad Institute of Technology Lonavala410401 India Computer Scienceand Business Systems Sri Manakula Vinayagar Engineering College Pudhucherrry India Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India St. Vincent Pallotti College of Engineering and Technology Maharashtra Nagpur441108 India Department of CSE B. S. Abdur Rahman Crescent Institute of Science and Technology Tamil Nadu India Department of English and Foreign Languages SRM College of engineering and Technology SRMIST Kattankulathur Chennai India

ISBN: (纸本)9798350360059

Innovative educational technology have made it possible to communicate with children who have special needs (SEN) in new ways. Artificial intelligence (A.I.)-based approaches have been among the most effective during the past ten years. The efficient application of artificial intelligence tools is seen as a means of improving SEN students' quality of life. As a result, the establishment of systems for both diagnosis and intervention calls for the adoption of A.I. techniques. Today, people from all socioeconomic backgrounds and walks of life concur on the value of education. Everyone needs to have a top-notch education in order to advance and succeed in today's society of information. It is now feasible to communicate with children with special needs (SEN) in new ways thanks to innovative educational technology. In the past ten years, artificial intelligence (A.I.)-based strategies have been among the most successful. The effective use of artificial intelligence tools is viewed as a way to enhance the quality of life for SEN students. As a result, the use of A.I. techniques is necessary for the creation of systems for both diagnosis and intervention. Today, people from all socioeconomic levels and walks of life agree that education is important. To advance and be successful in today's information culture, everyone needs to have a top-notch education. Then, it will display the statement that goes with that specific term. The learner will then get it in both text and audio versions after that. This makes it simple to assist any student with a disability in having good communication in a foreign language. ©2023 IEEE.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Jellyfish Search Optimizer with Deep Learning Driven Cervical Cancer Recognition and Classification Model on biomedical Images 2

Jellyfish Search Optimizer with Deep Learning Driven Cervica...

引用

2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Anand, M. Suresh Venkatramulu, S. Vidhya, Sesha S. Chembian, W.T. Yashaswini, b.M. Kumar, Magesh b. Department of Computing Technologies School of Computing SRM Institute of Science & Technology Kattankulathur603203 India Department of CSE Kakatiya Institute of Technology and Science Warangal India Department of ECE RMK College of Engineering and Technology India Department of CSE Vel Tech High Tech Dr Rangarajan Dr Sakuntala Engineering College Avadi Morai India Electronics & Communication Engineering JSS Academy of Technical Education JSSATE-B Campus Dr. Vishnuvardhan Rd Uttarahalli-Kengeri Main Road JSS Campus Rd Srinivaspura Karnataka Bengaluru560060 India Department of Computer Science and Engineering Bannari Amman Institute of Technology Erode Sathyamangalam India

ISBN: (纸本)9798350383867

The emergence of Cervical Cancer (CC) Recognition and Classification Model for biomedical Images is a main advancement in fields of medical imaging and diagnostics. Leveraging state-of-the-art image processing and machine learning algorithms, this model can accurately detect and classify CC-related abnormalities in medical images, namely colposcopy or Pap smear images. by providing automated and precise assessments, the model empowers medical experts to make timely and informed decisions, which leads to earlier interference and potential life-saving treatment. With its potential to improve diagnostic accuracy and reduce human error, the model holds great promise in the fight against CC, especially in resource-constrained healthcare settings where medical practitioners may be limited. This study presents a Jellyfish Search Optimizer with Deep Learning Driven CC Recognition and Classification (JSODL-CCRC) model on biomedical Images. The JSODL-CCRC technique is mainly developed to investigate the medical images for detection and classification of CC. The JSODL-CCRC approach comprises median filtering (MF) for noise reduction, followed by feature extraction utilizing Inceptionv3 to derive complex image patterns. besides, the JSO based hyperparameter selection process is performed for the Inception v3 model. At last, Extreme Learning Machine (ELM) algorithm is applied to categorize the images which validates efficacy in handling complex biomedical image data. The experimental results stated that the JSODL-CCRC model enables effective detection results over other models. © 2024 IEEE.

关键词： Median filters

来源：评论

学校读者我要写书评

暂无评论

AI-based Deep Learning Chatbot for Career and Personal Mentorship

AI-Based Deep Learning Chatbot for Career and Personal Mento...

引用

technology, engineering, Management for Societal impact using Marketing, Entrepreneurship and Talent (TEMSMET), IEEE International Conference on

作者： Muskan Sharma Anita Kumari Jyotsna Department- Computer Science and Engineering B-Tech CSE (AIML) Sharda University Greater Noida Uttar Pradesh India

Mentoring is an inseparable part of any student’s life. As the level of education increases, every student requires support and guidance regarding career choice, program and major selection, as well as individual and personal counseling. The proposed system has been made to meet this facility, where a student can express themselves anonymously, and without any criticism and partiality. The chat-bot will be used to interact with the student regarding their professional and personal life both, which will solve their issues and doubts based on the questions they have asked. The student will input their query through text, and the bot will generate an output best suited in the student’s interest. The proposed system created can provide the students with an alternative to hectic and physical mentoring sessions.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Development of a Current-Position Control Strategy for Motion Systems Utilizing Nonlinear Reluctance Actuators 63

Development of a Current-Position Control Strategy for Motio...

引用

63rd IEEE Conference on Decision and Control, CDC 2024

作者： Al Saaideh, Mohammad Al-Rawashdeh, Yazan M. Alatawneh, Natheer Aljanaideh, Khaled boker, Almuatazbellah Al Janaideh, Mohammad Memorial University St. John's Department of Electrical and Computer Engineering NLA1B 3X5 Canada University of Jordan Electrical Engineering Department at Al-Zaytoonah 130 Amman 11733 Jordan Memorial University St. John's Department of Mechanical and Mechatronics Engineering NLA1B 3X5 Canada Jordan University of Science and Technology Faculty of Engineering Department of Aeronautical Engineering Irbid22110 Jordan Virginia Tech Department of Electrical and Computer Engineering BlacksburgVA24060 United States University of Guelph School of Engineering GuelphON N1G 2W1 Canada

ISBN: (纸本)9798350316339

Reluctance actuators (RA) can replace the current Lorentz actuators in the next generation of positioning and scanning motion systems, such as the wafer stage in lithography machines. However, the nonlinear output force characteristic and the gap dependency of the RA are the main challenges in using the RA to drive motion systems. In this paper, we design a two-loop control approach for a reluctance actuator motion system (RAMS) to achieve tracking performance for a desired motion profile. First, the current control loop linearizes the RA under different conditions. Next, the position control loop is designed using a PID control based on an extended-high gain observer to achieve a desired motion profile considering unknown dynamics in the system. The simulation results show the efficiency of the proposed current control in linearizing the dynamic behavior under different desired forces and nominal air gaps and achieving a frequency response similar to the spring-mass-damper system. Moreover, the position control can achieve different long-stroke and short-stroke motion profiles with different amplitudes and ranges of motion. © 2024 IEEE.

关键词： Proportional control systems

来源：评论

学校读者我要写书评

暂无评论

Cyber Security Challenges and Effective Security Measures for IOT-based Intelligent Healthcare Systems

引用

Recent Advances in Electrical and Electronic engineering 2025年第6期18卷 735-747页

作者： Somnathe, Abhijit T. Gopinath, N. Deena, G. Anandh, R. Kumar, Santosh Tiwari, Kanchan S. Electronics and Telecommunication Smt Kashibai Navale College of Engineering Savitribai Phule Pune University Pune India Computer Science and Engineering B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai India Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Electrical and Electronics Engineering Gaya College of Engineering Bihar India Electronics and Telecommunication Modern Education Society’s Wadia College of Engineering Pune India

Aims and background: In order to keep up with the rapid development of technology, cities must enhance their offerings in the areas of public security, medical care, transportation, and citizen well-being. Healthcare systems rely heavily on patient care. Patients' life and the services offered by doctors, nurses, clinicians, pharmaceutical companies, and governments stand to benefit from the implementation of Healthcare IoT. There has been a change in the medical industry, marked by the widespread adoption of wireless healthcare-monitoring-systems (HMS) in hospitals. Methodology: However, the Internet-of-Things (IoT) concept frequently ignores security-privacy for linked things. Security and privacy protections must be implemented systematically throughout the entire healthcare and remote health monitoring system, from the creation of equipment to their interconnection, communication, storage, and eventual destruction. This research investigates the risks associated with using smart health devices and provides recommendations for addressing those risks. The present security and privacy of IoT devices in healthcare systems, as well as the difficulties in adopting security frameworks, are discussed, and recommendations for addressing these issues are offered. Results: This study also contributes in two ways to the ongoing effort to investigate securityprivacy concerns in the circumstance of smart cities for healthcare purposes. Conclusion: Moreover, this article provides a summary of the many Internet of Things applications and the cyber threats they face. On the other hand, thorough assessments of methods to reduce cyber threats to 60% are presented. © 2025 bentham science Publishers.

关键词： Cyber attacks

来源：评论

学校读者我要写书评

暂无评论

Combination kernel support vector machine based digital twin model for prediction of dyslexia in distributed environment 2nd

Combination kernel support vector machine based digital twin...

引用

2nd International Conference on Next-Generation Computing and Information Systems, ICNGCIS 2023

作者： Kareemullah, H. Quraishi, Aadam Prashant, G.C. Shabaz, Mohammad Kollu, Archana Sandhu, Mukta B.S.A Crescent Institute of Science and Technology Tamil Nadu Chennai India Interventional Treatment Institute Houston Texas USA McAllenTX United States USA University Texas Tech University Lubbock StateTX United States Model Institute of Engineering and Technology J&K Jammu India Pimpri Chinchwad College of Engineering and Research Ravet Pune India Skill Department of Computer Science and Engineering Shri Vishwakarma Skill University Haryana Palwal India

ISBN: (纸本)9781032738659

Dyslexia is a learning disability that is widespread and marked with a persistent problem with word identification and spelling. It has an impact on a person’s capacity to decipher letters and words accurately and fluently. Dyslexia has emerged as one of the most widespread learning disabilities, though the medical experts have not yet come out explaining its core causes. In this research, the prediction and classification of dyslexia is effectively carried out with the help of brain images which acts as input to machine learning techniques towards the prediction of dyslexia. The proposed dyslexic prediction model uses a Combination digital twin Kernel-based Support Vector Machine (KSVM) optimized by Whales algorithm. The combination kernel SVM shows better accuracy and less computational time compared to single kernel of SVM in a distributed environment. The proposed combinational kernel is less complex than deep learning models. © 2025 The Author(s).

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

A novel CZP-bC-FLASH ADC using zero phase bubble error robust controller for reliable VLSI circuits

引用

International Journal of Information technology (Singapore) 2022年第1期14卷 155-163页

作者： Kalaiyarasi, M. Saravanan, S. Karunanithi, K. Raja, S.P. Department of Electronics and Communication Engineering V.S.B. Engineering College Karur India Department of Electrical and Electronics Engineering B V Raju Institute of Technology Telangana Narsapur India Department of Electrical and Electronics Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Avadi Tamilnadu Chennai India School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore 632014 India

To process the data, accurate and reliable, analog to digital converter (ADC) is an essential element in most applications such as satellite communication, instrumentation and measurement, industrial automation, defence, and aerospace applications. In reliable VLSI circuits, to mitigate the wastage of power, high quality and high-resolution Clocked Flash ADCs are utilized. It converts an analog electrical signal into a digital discrete-valued signal using a parallel set of comparators and the transformation time is very fast in flash ADC. Even though many approaches were proposed to convert the analog signal to a digital signal, the analog signal could not correctly convert because of its error. This paper proposed a novel flash ADC namely Clocked Zero Phase bubble Error Controller Flash ADC (CZP-bC-Flash ADC) using Clocked-Improved Threshold Inverter Quantization (CITIQ) comparator and Zero Phase bubble Error Robust Controller (ZPbEC). CITIQ comparator uses two cascaded inverters, Preamplifier and two parallel sets of PMOS and NMOS transistors whereas ZPbEC calculates the transfer function of the system based on the changes of the input and locates the error. There is a noticeable basis for eliminating the circuit board that unnecessarily spoils energy. The proposed CZP-bC-FLASH is executed using a tanner EDA with 250 nm size. When compared with QVC ADC technique, the error detection accuracy of proposed method has increased by 43% with the help of ZPbEC Controller. The simulation results showed that the proposed Controlled ADC method reduces power consumption by 63.32% when compared with QVC ADC method as well as compared with modified QVC ADC, it decreases to 60.98%. While the error controller controls the error signal by 56% compared with previous F-ADC and corrects the bits up to 4 and gives an efficiency of 97%. © 2021, bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Clocked flash Comparator Error controller Neural compensation VLSI

来源：评论

学校读者我要写书评

暂无评论

Optimized Deep Learning Mechanism for Intrusion Detection: Leveraging RFE-based Feature Selection and PCA for Improved Accuracy

Optimized Deep Learning Mechanism for Intrusion Detection: L...

引用

Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： M. Rajkumar Lakshi VS Karthik R Pavithra S Department of Smart Computing School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu India B. Tech-Information Technology School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

With the growing demand for wireless networks, network traffic has surged, particularly involving large-scale data transmission. This results in an increased number of network security threats and the emergence of various cyber-attacks. To overcome these threats, the development of an efficient intrusion detection system (IDS) has become crucial. by leveraging the recent advancements in Artificial intelligence (AI), this project introduces an optimized deep learning mechanism utilizing a prevalent deep learning model, the Deep Neural Network (DNN) algorithm. This proposed method uses feature engineering techniques like feature selection and feature reduction. Given the high dimensionality of the selected datasets, these techniques are employed to prevent over-fitting. Recursive feature elimination performs feature selection followed by Principal Component Analysis for the feature reduction process, thus enhancing the model's efficiency. The system is assessed using two benchmark datasets: KDD and UNSW-Nb15. The experimental results indicate that the presented DNN model on the KDD dataset has achieved 92.45% accuracy and 91.35% accuracy on the UNSW-Nb15 dataset for intrusion classification.

关键词： Deep learning Accuracy Wireless networks Computational modeling Intrusion detection Artificial neural networks Feature extraction Data models Tuning Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

Enhancing Human-Robot Collaboration through 5G-Enabled Transformation and Image-Guided Control with TYOLOV5 Algorithms 7

Enhancing Human-Robot Collaboration through 5G-Enabled Trans...

引用

7th International Conference on Image Information Processing, ICIIP 2023

作者： Madasamy, Sridhar Ramesh, P.S. Roy, Priyanka Arun Raj, S.R. Yadav, Vinay Kumar balaram, Allam Cognizant Technology Solution 500 Frank W. Burr Boulevard TeaneckNJ07666 United States Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science And Technology Department of Computer Science And Engineering Tamilnadu Chennai India Vellore Institute of Technology Department of Electrical And Electronics Engineering Tamilnadu Vellore India U.B.D.T College of Engineering Department of Electronics & Communication Engineering Karnataka Davanagere577004 India Glocal University Glocal School of Science And Technology Uttar Pradesh India Mlr Institute of Technology Department of Computer Science And Engineering Hyderabad500043 India

ISBN: (纸本)9798350371406

Factories that prioritize digital transformation experience heightened production rates and gain a competitive advantage by enhancing control and operational efficiency. In this study, image processing techniques were employed to achieve digital transformation objectives, and the data obtained was transmitted via 5G communication to a collaborative robot arm, enabling remote control. The robot arm was fitted with a 3D-printed humanoid hand for bin picking, where each finger was controlled by five servo motors. To manipulate the fingers, a user donned a glove equipped with flex sensors, which conveyed the finger positions to the servo motors, facilitating precise pick and place operations. Additionally, image processing was utilized to accurately control the robot arm's position. The research involved the implementation of tuned YOLO V5 (TYOLOV5) algorithms, using Python software, for object detection within the user's gloves. by comparing this approach with other existing methods, the experiment achieved the highest detection accuracy, demonstrating the effectiveness of the proposed approach in achieving superior recognition and estimation compared to traditional and existing deep learning methods. To validate its efficacy, experiments were conducted in a laboratory setting, where a collaborative robot was deployed for a small part assembly task. The results conclusively highlight the potential of this approach to significantly enhance recognition and estimation capabilities, especially in the realm of human-robot collaboration. © 2023 IEEE.

关键词： Image enhancement

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：