检索结果-内蒙古大学图书馆

5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Kasthuri, E. Subbulakshmi, S. Sreedharan, Rajasree Department of Computer Science and Engineering Amrita School of Computing Amritapuri India Department of Computer Science and Applications Amrita School of Computing Amritapuri India Division of Nephrology Department of Pediatrics Medical College of Wisconsin MilwaukeeWI United States

Epilepsy is a brain disorder that can affect people of all ages which is one of the most common neurological disorders affecting around 50 million individuals. Healthcare Chatbots schedule appointments, respond to patient inquiries, and offer relevant information, freeing medical staff to handle life crises. This paper implements a chatbot for epilepsy patients to increase awareness about epilepsy. Patients may easily access this AI-based healthcare chatbot whenever they have queries regarding precautions to be taken by epilepsy patients. Chatbot uses a rich set of epilepsy-related datasets collected from trusted websites which are preprocessed with NLP techniques to prepare the data for model ***, LSTM, and RNN models are created and evaluated to produce answers to user queries related to possible precautions to avoid seizures, safety measures to be taken by patients with seizure history, etc. Evaluation of models shows that the RNN model generates more accurate answers with 94% accuracy. Finally, the epilepsy question answering Chatbot for precaution is concluded with the RNN model. © 2024 The Authors. Published by Elsevier B.V.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

AI in Medical Imaging: Enhancing Diagnostic Accuracy with Deep Convolutional Networks

AI in Medical Imaging: Enhancing Diagnostic Accuracy with De...

引用

2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Shrivastava, Anurag Bhadula, Shuchi Kumar, Rakesh Kaliyaperumal, Gopal Rao, Bolleddu Devananda Jain, Alok Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India GLA University Department of Computer Engineering & Applications Mathura India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Lovely Professional University Phagwara India

ISBN: (纸本)9798331512965

The past has witnessed a time of astounding developments in AI, particularly notable deep convolutional networks, which has greatly facilitated medical imaging by opening up previously unattainable doors for both precision and effectiveness of diagnoses. This study is an investigation into how deep convolutional neural networks (CNNs) might be applied (using MRI, CT, and X-ray as examples) to medical imaging for the purposes of improving accuracy, consistency, and practicability of diagnostic procedures. Through automated feature extraction and categorization, convolutional neural networks (CNNs) enable earlier, and more accurate, detection of illnesses such as cancer, cardiovascular disease and neurological problems by detecting minor deviations that humans may miss. Here we evaluate the accuracy, sensitivity, and computing efficiency of several convolutional network (CNN) designs. ResNet and UNet are these architectures. The results indicate that CNN based models provide better performance compared to conventional diagnostic approaches with a substantial diminution in both false positives and false negatives. The article also covers combining CNNs with others AI methods such as transfer learning, ensemble methods, etc. to further improve diagnostic results. Although the encouraging outcomes, data protection, interpretability, and clinical acceptance continue to be obstacles. This study shows how AI driven technology can revolutionize medical imaging to enhance patient outcomes as well as tailored healthcare solutions. © 2025 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

A robust and consistent stack generalized ensemble-learning framework for image segmentation

引用

Journal of engineering and Applied science 2023年第1期70卷 74页

作者： Faska, Zahra Khrissi, Lahbib Haddouch, Khalid El Akkad, Nabil Laboratory of Engineering Systems and Applications ENSA Sidi Mohamed Ben Abdellah University Fez Morocco LIIAN Department of Computer Science FSDM of Fez Sidi Mohamed Ben Abdellah University Fez Morocco

In the present study, we aim to propose an effective and robust ensemble-learning approach with stacked generalization for image segmentation. Initially, the input images are processed for feature extraction and edge detection using the Gabor filter and the Canny algorithms, respectively;our main goal is to determine the most feature descriptions. Subsequently, we applied the stacking generalization technique, which is generally built with two main learning levels. The first level is composed of two algorithms that give good results in the literature, namely: LightGBM (Light Gradient Boosting Machine) and SVM (support vector machine). The second level is the meta-model in which we use a predictor model that takes the base-level predictions to improve the accuracy of the final prediction. In the stacked generalization process, we use the Extreme Gradient Boosting (XGBoost);it takes as input the sub-models’ outputs to better classify each pixel of the image to give the final prediction. Today, several research works exist in the literature using different machine learning algorithms;in fact, instead of trying to find a single efficient and optimal learner, ensemble-based techniques take the advantage of each basic model;they integrate their outputs to obtain a more consistent and reliable learner. The result obtained from the models of individuals and our proposed approach is compared using a set of evaluation measures for image quality such as IoU, DSC, CC, SSIM, SAM, and UQI. The evaluation and a comparison of the results obtained showed more consistent predictions for the proposed model. Thus, we have made a comparison with some recent deep learning-based unsupervised segmentation methods. The evaluation and a comparison of the results obtained showed more coherent predictions for our stacked generalization in terms of precision, robustness, and consistency. © 2023, 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

Multi-Modal Emotion and Gesture Recognition System for Humans and Animals Using computer Vision and Deep Learning 5

Multi-Modal Emotion and Gesture Recognition System for Human...

引用

5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Nair, Rekha R Babu, Tina Kishore, S. Shivaswamy, Smitha Aruna, S. Bhargavi, Manchikalapudi Alliance School of Advanced Computing Alliance University Department of Computer Science and Engineering Bengaluru India Department of Computer Application Karnataka Mangaluru India Acharya Institute of Technology Department of Computer Applications Bengaluru India School of Science and Computer Studies CMR University Bangalore India Hyderabad India

ISBN: (纸本)9798331518097

Emotion recognition through facial expressions and gesture analysis has emerged as a key area of research in computer vision and human-computer interaction. The ability to automatically detect and classify human emotions has wide-ranging applications, from enhancing user experiences to improving mental health monitoring and surveillance systems. This project builds on this background to develop a comprehensive system for recognizing emotions and gestures in both humans and animals. The system integrates facial emotion recognition, hand gesture interpretation, and animal emotion detection into a single platform. Using advanced deep learning models and computer vision libraries, the system classifies facial expressions into categories such as happiness, sadness, anger, fear, surprise, and neutral. The gesture recognition component interprets hand signals to convey messages like 'need help' or 'need water.' An innovative aspect of this work is the extension of emotion recognition to animals, particularly focusing on species like dogs and monkeys. Implemented primarily in Python, the system leverages libraries such as OpenCV and DeepFace. By combining these different modalities of nonverbal communication analysis, the project presents a versatile solution with potential applications in human-computer interaction, mental health monitoring, security, and animal welfare. © 2024 IEEE.

关键词： Gesture recognition

来源：评论

学校读者我要写书评

暂无评论

IoT-Based Crop Yield Prediction System in Indian Sub-continent Using Machine Learning Techniques

引用

Remote Sensing in Earth Systems sciences 2023年第3-4期6卷 156-166页

作者： Nithya, V. Josephine, M.S. Jeyabalaraja, V. Department of Computer Applications Dr. M.G.R. Educational and Research Institute Tamil Nadu Chennai India Department of Computer Science Engineering Velammal Engineering College Tamil Nadu Chennai India

The agricultural sector holds significant importance in the socio-economic structure of India. The inability of farmers to make informed decisions regarding the optimal crop selection for their land, based on non-scientific and traditional methods, poses a significant challenge for a nation where farming constitutes the livelihood of nearly 58% of the population. At times, farmers have experienced challenges in selecting appropriate crops that align with the soil conditions, sowing season, and geographical location. By collecting data from sensors in the fields, ranchers can easily ascertain the extent of the crops, which helps preserve scarce resources and lessens the negative effects of weeds on the environment. The majority of Indian farmers choose conventional farming methods when deciding how their crops should be grown. Regardless, ranchers do not jeopardize agricultural yields, which are sensitive to abiotic factors such as soil type. In this proposed method, two types of datasets have been collected from the southern and northern parts of the country through IoT-based sensors. The crop recommendation system has been processed with various machine learning algorithms like logistic regression, KNN, decision tree, and support vector machine through the collected dataset. The accuracy of 97% and 96% has been achieved for processed datasets in the southern and northern parts of India. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Accuracy Crop recommendation Decision tree IoT KNN Logistic regression Machine learning Precision farming SVM Training

来源：评论

学校读者我要写书评

暂无评论

IoT Based Smart Home Using Virtual Key 1

IoT Based Smart Home Using Virtual Key

引用

1st Asian Conference on Communication and Networks, ASIANComNet 2024

作者： Edna Elizabeth, N. Jeffrey Isaac, S. Janit, Sophia Sri Sivasubramaniya Nadar College of Engineering Kalavakkam Department of Electronics and Communication Chennai India Hindustan Institute of Technology and Science Department of Computer Applications Chennai India

ISBN: (纸本)9798350367003

In this comprehensive documentation, we embark on an enlightening journey into the dynamic realm of Internet of Things (IoT) home automation. From the seamless integration of smart devices to the complex orchestration of automated processes, IoT home automation redefines the very essence of domesticity. We explore the landscape of cutting-edge advancements, delving into the intricate interplay of sensors, actuators and connectivity protocols that underpin the functionality of smart homes. Furthermore, we delve into the practical applications of IoT in various aspects of home management. This includes optimizing the energy efficiency, monitoring the environment, controlling personalized comfort and enhancing security measures. As we navigate through real-world case studies and exemplary implementations, we gain invaluable insights into the practical challenges and opportunities inherent in deploying IoT solutions within residential settings. In our proposed work, we have developed a prototype for a smart IoT-based smart home to control and monitor various systems such as light, lamp, socket and fan using ESP32 controller. The various controls are performed through relays. The data are sent to the firebase cloud through Wi-Fi and stored in the cloud. Later, the appliances are switched ON/OFF through virtual key using mobile APP. By using virtual keys, we address pertinent ethical and privacy considerations, shedding light on the importance of responsible data stewardship and robust cybersecurity measures. These are crucial in safeguarding the integrity of smart home ecosystems. By combining theoretical frameworks with empirical observations, we aim to provide a comprehensive understanding of IoT home automation. © 2024 IEEE.

关键词： Smart homes

来源：评论

学校读者我要写书评

暂无评论

Enabling Smart Home Energy Management through Gesture-Based Control and IoT Technology

Enabling Smart Home Energy Management through Gesture-Based ...

引用

2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Rambe, Khairul Hawani Ou, Chia-Ho International Master Program in Information Technology and Applications Sweden National Pingtung University Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350324174

The Internet of Things (IoTs) have become ubiquitous in all aspects of public needs today. The application of IoT technology is crucial for promoting energy-saving behavior. This paper presents a control system used for smart home devices based on hand gesture control and Android applications. The hardware design uses the ESP32 microcontroller, which has an in-chip Wi-Fi module, making it very suitable for creating IoT application systems. The software was designed using the Arduino IDE, and the application display design was developed using Kodular Creator, which is used to control the lamps and switches. The experiment results show that the electrical equipment can be activated using the Android application with gesture sensors by waving down, up, left, and right. © 2023 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Blockchain Technology's Potential in Supply Chain management

Blockchain Technology's Potential in Supply Chain management

引用

2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Rakhra, Manik Nagpal, Rakhi Singh, Pritpal Angra, Prikshat Kumar Cheema, Gagandeep Singh Kumar, Ashwani Lovely Professional University School of Computer Science Engineering Phagwara India Lovely Professional University Mittal School of Business Phagwara India Lovely Professional University School of Computer Applications Phagwara India

ISBN: (纸本)9798350378092

This exploratory study examines the potential of blockchain technology to revolutionize supply chain management (SCM). By leveraging a comprehensive literature review and expert interviews, this paper identifies key benefits of blockchain integration in SCM, including increased transparency, enhanced security, and improved traceability. The study also addresses technical challenges and implementation barriers, such as scalability issues and the need for standardization across various stakeholders. Comparative analysis of current SCM systems and potential blockchain-based solutions is conducted, highlighting significant efficiency gains and cost reductions. The findings suggest that while blockchain presents a transformative potential for SCM, strategic frameworks and collaborative efforts among industry players are crucial for successful adoption. Future research directions are proposed, focusing on pilot projects and real-world application case studies to further validate the technology's practical benefits and scalability in diverse supply chain contexts. © 2024 IEEE.

关键词： Blockchain Blockchain framework Logistics and supply chain Qualitative analysis

来源：评论

学校读者我要写书评

暂无评论

An Improved Multi-Objective Particle Swarm Optimization Routing on MANET

引用

computer Systems science & engineering 2023年第2期44卷 1187-1200页

作者： G.Rajeshkumar M.Vinoth Kumar K.Sailaja Kumar Surbhi Bhatia Arwa Mashat Pankaj Dadheech Department of Computer Science and Engineering Erode Sengunthar Engineering CollegePerunduraiErode638057Tamil NaduIndia Department of Information Science and Engineering Dayananda Sagar Academy of Technology and ManagementBangalore560082India Department of Master of Computer Applications M.S.Ramaiah Institute of TechnologyBangalore560054India Department of Information Systems College of Computer Science and Information TechnologyKing Faisal UniversityRiyadh11533Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityRabigh21911Saudi Arabia Department of Computer Science and Engineering Swami Keshvanand Institute of TechnologyManagement&GramothanJaipur302017RajasthanIndia

A Mobile Ad hoc Network(MANET)is a group of low-power con-sumption of wireless mobile nodes that conﬁgure a wireless network without the assistance of any existing infrastructure/centralized *** primary aim of MANETs is to extendﬂexibility into the self-directed,mobile,and wireless domain,in which a cluster of autonomous nodes forms a MANET routing *** Intrusion Detection System(IDS)is a tool that examines a network for mal-icious behavior/policy violations.A network monitoring system is often used to report/gather any suspicious attacks/*** IDS is a software program or hardware system that monitors network/security trafﬁc for malicious attacks,sending out alerts whenever it detects malicious *** impact of Dynamic Source Routing(DSR)in MANETs challenging blackhole attack is investigated in this research *** Cluster Trust Adaptive Acknowledgement(CTAA)method is used to identify unauthorised and malfunctioning nodes in a MANET *** system is active and provides successful delivery of a data packet,which implements Kalman Filters(KF)to anticipate node ***,KF is used to eliminate synchronisation errors that arise during the sending and receiving *** order to provide an energy-efﬁcient solution and to minimize network trafﬁc,route optimization in MANET by using Multi-Objective Particle Swarm Optimization(MOPSO)technique to determine the optimal num-ber of clustered MANET along with energy dissipation in *** to the researchﬁndings,the proposed CTAA-MPSO achieves a Packet Delivery Ratio(PDR)of 3.3%.In MANET,the PDR of CTAA-MPSO improves CTAA-PSO by 3.5%at 30%malware.

关键词： MANET intrusion detection system cluster kalmanﬁlter dynamic source routing multi-objective particle swarm optimization packet delivery ratio

来源：评论

学校读者我要写书评

暂无评论

Model-based recognition in robot vision for monitoring built environments

引用

Multimedia Tools and applications 2025年第12期84卷 10137-10154页

作者： Khan, Asif Varish, Naushad Pandey, Dhirendra Rizvi, Syed Qasim Afser Mehrotra, Shashi Parveen, Nikhat Department of Computer Applications Integral University Dasauli Bas-ha Kursi Road Uttar Pradesh Lucknow226026 India Hyderabad Campus Telangana Hyderabad502329 India Department of Information Technology Babasaheb Bhimrao Ambedkar University Lucknow Lucknow India Guangdong Guangzhou510006 China Department of Computer Science and Engineering Teerthanker Mahaveer University Uttar Pradesh Moradabad244001 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Gunur522302 India

In contrast to the machine, people have strong perception to determine the multiple objects easily in various environments under certain conditions. It is evident that the objects may exist in different orientations, scales and/or shapes, however it does not affect our ability to correct object recognition accuracy. The computer vision based object recognition is one of the most tedious tasks. So, objective is to design and development of the robust object recognition system to recognize the multiple objects effectively since different variations in the natural images are existed. In this paper, authors have presented comparative analysis and studies of some state of art object-recognition models for robot vision based problems, where the main goal is to recognize position, orientations and the identity of objects/parts, where parts have been taken from the industry. In the dynamic environment problem, the parts are recognized in a complex scenario. This paper presented based on the object representations using the recognition algorithms. Three steps are discussed and examined in details which are common to each and every category, namely, feature extraction, modeling, and matching. Test results confirmed that proposed model-based recognition in robot vision is fast and provides 93.00% accuracy. The proposed algorithm for object-recognition model has been compared with existing industrial part-recognition models and has, provided insights for progress toward future robot vision systems. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Object recognition

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：