检索结果-内蒙古大学图书馆

8th International Conference on Inventive Systems and Control, ICISC 202

作者： Jonathan, J. Benjamin, Ruth Moly Prasad, Gunji Prem Karunya Institute of Technology and Sciences Computer Science Engineering Coimbatore India Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Coimbatore India Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350386578

Food Solutions and Nutrition Checker is an innovative and comprehensive project aimed at providing users with a reliable tool to identify various foods and dishes, while also offering a custom-built database for detecting fresh and wasted food items. With this system, users can easily determine the nutritional value of different foods, making informed decisions about their dietary choices. This study incorporates advanced technology that enables the recognition of a wide range of food items. By simply capturing an image or specifying the dish, users can obtain accurate information about the food, including its ingredients and origin. This feature is particularly helpful for individuals with specific dietary needs or restrictions, allowing them to easily identify suitable menu options. Additionally, this study includes a custom-built data system that helps to identify fresh and wasted food. By analyzing factors such as expiration dates, storage conditions, and quality indicators, the proposed system can determine the freshness status of various food items. This capability promotes food waste reduction by enabling users to make informed decisions regarding the consumption or disposal of perishable goods. Moreover, this study incorporates a nutritional tracker that provides users with comprehensive information regarding the nutritional composition of different foods. This feature includes details such as calorie count, macronutrient breakdown, vitamins, and minerals, empowering users to make healthier food choices aligned with their dietary goals and requirements. Overall, the proposed model offers a holistic approach for food identification, waste reduction, and nutritional tracking. By incorporating advanced technology and a robust database, this study aims to facilitate informed decision-making and promote healthier dietary practices. © 2024 IEEE.

关键词： Food ingredients

来源：评论

学校读者我要写书评

暂无评论

Design,simulation,and testing of a tunable MEMS multi-threshold inertial switch

引用

Microsystems & Nanoengineering 2024年第2期10卷 37-46页

作者： Qiu Xu Rodrigo T.Rocha Yousef Algoos Eric Feron Mohammad I.Younis Faculty of Electronic Information Engineering Huaiyin Institute of TechnologyHuai’an223003PR China Computer Electrical and Mathematical Science and Engineering DivisionKing Abdullah University of Science and TechnologyThuwal23955-6900Saudi Arabia Piezoelectric Microsystem Technologies Silicon Austria LabsVillach9524Austria Physical Sciences and Engineering Division King Abdullah University of Science and TechnologyThuwal23955Saudi Arabia Mechanical Engineering Department State University of New YorkBinghamtonNY13902USA

This paper presents a tunable multi-threshold micro-electromechanical inertial switch with adjustable threshold *** demonstrated device combines the advantages of accelerometers in providing quantitative acceleration measurements and g-threshold switches in saving power when in the inactive state upon experiencing acceleration below the *** designed proof-of-concept device with two thresholds consists of a cantilever microbeam and two stationary electrodes placed at different positions in the sensing *** adjustable threshold capability and the effect of the shock duration on the threshold acceleration are analytically investigated using a nonlinear beam *** are shown for the relationships among the applied bias voltage,the duration of shock impact,and the tunable *** fabricated prototypes are tested using a shock-table *** analytical results agree with the experimental *** designed device concept is very promising for the classification of the shock and impact loads in transportation and healthcare applications.

关键词： tunable acceleration testing

来源：评论

学校读者我要写书评

暂无评论

Induced pulse voltage and hysteresis loss during magnetization reversal of Wiegand wire

引用

Journal of the Magnetics Society of Japan 2024年第1期48卷 1-4页

作者： Xue, Fei Yang, Chao Song, Zenglu Takemura, Yasushi Division of Electrical and Computer Engineering Yokohama National University Yokohama240-8501 Japan School of Electrical Engineering Nanjing Vocational University of Industry Technology Nanjing210023 China

When an alternating magnetic field is applied to a Wiegand wire, a series of pulse voltages are induced in a pickup coil due to fast magnetization reversal known as the large Barkhausen jump. This study aims to analyze the characteristics of the pulse voltage, including its height and area, which are determined from the intensity and frequency of the applied alternating magnetic field. We quantified the energy generated by magnetization reversal in a Wiegand wire. To achieve this, it was necessary to measure the energy consumed by a load resistor connected to the pickup coil. Additionally, we clarified the energy loss in the Wiegand wire. © 2024 by the Magnetics Society of Japan.

关键词： Wire

来源：评论

学校读者我要写书评

暂无评论

Breast-NET: a lightweight DCNN model for breast cancer detection and grading using histological samples

引用

Neural Computing and Applications 2024年第32期36卷 20067-20087页

作者： Saha, Mousumi Chakraborty, Mainak Maiti, Suchismita Das, Deepanwita Department of Computer Science and Engineering National Institute of Technology West Bengal Durgapur713209 India School of Innovation Design and Engineering Division of Intelligent Future Technologies Malardalen University Västerås Sweden Department of Information Technology Narula Institute of Technology West Bengal Kolkata700109 India Department of Computer Science and Engineering Narula Institute of Technology West Bengal Kolkata700109 India

Breast cancer is a prevalent and highly lethal cancer affecting women globally. While non-invasive techniques like ultrasound and mammogram are used for diagnosis, histological examination after biopsy is considered the gold standard. However, manual examination of tissues for abnormality is labor-intensive, expensive, and requires prior domain knowledge. Early detection, awareness, and access to specialized medical infrastructure in resource-constrained and remote areas are significant challenges but crucial for saving lives. In recent years, deep learning-based approaches have shown promising results in breast cancer detection, facilitated by advancements in GPU memory, computation power, and the availability of digital data. Motivated by these observations, we propose the Breast-NET deep convolutional neural network model for breast cancer detection and grading using histological images. Our model’s performance is evaluated on the BreakHis dataset, and we demonstrate its generalization ability on the Invasive Ductal Carcinoma (IDC) grading and IDC datasets. Extensive experimental and statistical performance analysis, along with an ablation study, validates the efficiency of our proposed model. Furthermore, we demonstrate the effectiveness of transfer learning with seven pre-trained convolutional neural networks for breast cancer detection and grading. Experimental results show that our framework outperforms state-of-the-art approaches in terms of accuracy, space, and computational complexity for the BreakHis, IDC grading, and IDC datasets. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Grading

来源：评论

学校读者我要写书评

暂无评论

A Deep Learning-Based Method for Crowd Counting Using Shunting Inhibition Mechanism

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5733-5745页

作者： Tivive, Fok Hing Chi Bouzerdoum, Abdesselam Phung, Son Lam Le, Hoang Thanh Baali, Hamza University of Wollongong School of Electrical Computer and Telecommunications Engineering WollongongNSW2522 Australia Hamad Bin Khalifa University Information and Computing Technology Division College of Science and Engineering Doha Qatar

Image-based crowd counting has gained significant attention due to its widespread applications in security and surveillance. Recent advancements in deep learning have led to the development of numerous methods that have achieved remarkable success in accurately counting crowds. However, many of the existing deep learning methods, which have large model sizes, are unsuitable for deployment on edge devices. This article introduces a novel network architecture and processing element designed to create an efficient and compact deep learning model for crowd counting. The processing element, referred to as the shunting inhibitory neuron, generates complex decision boundaries, making it more powerful than the traditional perceptron. It is employed in both the encoder and decoder modules of the proposed model for feature extraction. Furthermore, the decoder includes alternating convolutional and transformer layers, which provide local receptive fields and global self-attention, respectively. This design captures rich contextual information that is used for generating accurate segmentation and density maps. The self-attention mechanism is implemented using convolution modulation instead of matrix multiplication to reduce computational costs. Experiments conducted on three challenging crowd counting datasets demonstrate that the proposed deep learning network, which comprises a small model size, achieves crowd counting performance comparable to that of state-of-the-art techniques. Codes are available at https://***/ftivive/SINet. © 2024 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Criminal Activity Forecasting using Machine Learning 10

Criminal Activity Forecasting using Machine Learning

引用

10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Augustin Sam, N. Bamini, A. M. Anusha Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350384369

Crime analysis is an important topic that uses cutting-edge machine learning. This study focuses on using machine learning algorithms to examine crime trends and give law enforcement organizations useful information. The ML models may find patterns and trends by using past crime data, which makes it possible to identify possible crime hotspots and allocate the necessary resources. To find hidden patterns in crime data, Random forest algorithm was applied. The algorithm has the ability to automatically group criminal episodes into clusters, categorize them according to various attributes, and spot anomalies-that is, deviations from the norm. Furthermore, by using previous data, machine learning algorithms can be trained to support proactive crime prevention tactics. The accuracy and efficacy of crime analysis models are further improved by the integration of many data sources, such as information. The proposed algorithm makes it easier to monitor crimes in real time and permits prompt decision-making in emergency scenarios. Law enforcement organizations can use their resources more wisely, plan out patrol routes, and create focused crime prevention programs. By giving law enforcement agencies useful insights, machine learning turns crime analysis into a potent weapon. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Leveraging Sentiment Analysis and Emotion Detection for Mental Health Insights

Leveraging Sentiment Analysis and Emotion Detection for Ment...

引用

2024 IEEE International Conference on Future Machine Learning and Data Science, FMLDS 2024

作者： Jose, Joysh Jayaron Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (数字)9798350391213

ISBN: (纸本)9798350391213

This paper aims to establish a comprehensive analysis of mental health patient reviews using various NLP models, to derive insights and propose improvements that will lead to the enhancement of various mental health facilities. These NLP techniques were further used to categorize key themes such as doctor behaviour, communication and cost. A thorough correlation analysis was also conducted, understanding the relationship between various themes and how their frequency affects the overall patient satisfaction. The findings indicate that attentive listening, respectful staff behavior, and affordable services are critical to improving patient satisfaction. While speaking of mental health is still a taboo in various places, it is important to understand the frequency of the rising mental health related cases such as depression, anxiety, stress and social stigma associated with it. It is indeed much more important to provide better resources and lend understanding hand to the unaddressed mental health problems that are prevalent in various countries and places. © 2024 IEEE.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

Real Time Stock Inventory Management System 8

Real Time Stock Inventory Management System

引用

8th International Conference on Inventive Systems and Control, ICISC 202

作者： Roosevelt, Steve Caleb Veemaraj, Ebenezer Kirubakaran, Stewart Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350386578

For companies looking to stay competitive in the dynamic and fast-paced business world of today, effective stock inventory management has become critical. In order to provide organizations with an all-inclusive and flexible inventory control solution, this paper presents a Real-Time Stock Inventory Management System. The system provides real-time stock inventory tracking, monitoring, and management by utilizing cutting-edge technologies including cloud computing, data analytics, and the Internet of Things. Through the utilization of Internet of Things devices, the Real-Time Stock Inventory Management System's cloud-based infrastructure enhances scalability, accessibility, and flexibility while providing accurate real-time tracking of inventory quantities, locations, and statuses. © 2024 IEEE.

关键词： Cloud analytics

来源：评论

学校读者我要写书评

暂无评论

Cache Allocation in Multi-Tenant Edge Computing: An Online Model-Based Reinforcement Learning Approach

引用

IEEE Transactions on Cloud Computing 2025年第2期13卷 459-472页

作者： Ben-Ameur, Ayoub Araldo, Andrea Chahed, Tijani Dán, György Institut Polytechnique de Paris SAMOVAR Telecom SudParis Palaiseau91120 France KTH Royal Institute of Technology Division of Network and Systems Engineering School of Electrical Engineering and Computer Science Stockholm11428 Sweden

We consider a Network Operator (NO) that owns Edge Computing (EC) resources, virtualizes them and lets third party Service Providers (SPs) run their services, using the allocated slice of resources. We focus on one specific resource, i.e., cache space, and on the problem of how to allocate it among several SPs in order to minimize the backhaul traffic. Due to confidentiality guarantees, the NO cannot observe the nature of the traffic of SPs, which is encrypted. Allocation decisions are thus challenging, since they must be taken solely based on observed monitoring information. Another challenge is that not all the traffic is cacheable. We propose a data-driven cache allocation strategy, based on Reinforcement Learning (RL). Unlike most RL applications, in which the decision policy is learned offline on a simulator, we assume no previous knowledge is available to build such a simulator. We thus apply RL in an online fashion, i.e., the model and the policy are learned by directly perturbing and monitoring the actual system. Since perturbations generate spurious traffic, we thus need to limit perturbations. This requires learning to be extremely efficient. To this aim, we devise a strategy that learns an approximation of the cost function, while interacting with the system. We then use such an approximation in a Model-Based RL (MB-RL) to speed up convergence. We prove analytically that our strategy brings cache allocation boundedly close to the optimum and stably remains in such an allocation. We show in simulations that such convergence is obtained within few minutes. We also study its fairness, its sensitivity to several scenario characteristics and compare it with a method from the state-of-the-art. © 2013 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Retrospective Analysis of an Interpretable Skin Cancer Classification using Deep Learning Models 1

Retrospective Analysis of an Interpretable Skin Cancer Class...

引用

1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Abraham, Shaun Shaji Dinesh Peter, J. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350372748

Skin cancer, particularly malignant melanoma, presents major diagnostic challenges due to similarities with benign tumors. Automatic detection systems based on deep learning algorithms provide intriguing answers, but lack interpretability, limiting clinical application. This research presents an explainable artificial intelligence (XAI) approach to skin lesion classification, with the goal of improving diagnostic accuracy and clinician trust. The created algorithm accurately identifies types of skin lesions using the dataset. Using the local interpretable model-agnostic explanations (LIME) framework, the model creates visual explanations to assist dermatologists in rational diagnosis. Explainability is integrated into the model, increasing its usability in real-world clinical practice. Furthermore, the study emphasizes the importance of XAI in increasing the interpretability and trustworthiness of deep learning models for skin cancer diagnosis, focusing on crucial features such as asymmetry and pigment network classification. This study helps to address the critical need for transparent and accurate AI models in the medical realm, particularly in preventing the rising global prevalence of skin cancer. © 2024 IEEE.

关键词： Deep 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：