检索结果-内蒙古大学图书馆

Deep learning-based multi-head self-attention model for human epilepsy identification from EEG signal for biomedical traits

引用

Multimedia Tools and Applications 2024年第33期83卷 80201-80223页

作者： Dutta, Ashit Kumar Raparthi, Mohan Alsaadi, Mahmood Bhatt, Mohammed Wasim Dodda, Sarath Babu Prashant, G.C. Sandhu, Mukta Patni, Jagdish Chandra Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Ad Diriyah Riyadh13713 Saudi Arabia Alphabet Life Science DallasTX7506 United States Department of Computer Science Al-Maarif University College Al Anbar31001 Iraq Model Institute of Engineering and Technology J&K Jammu India Software Engineer Central Michigan University DallasTX75009 United States Department of Computer Science Texas Tech University Lubbock79409 United States Skill Faculty of Engineering & amp Technology Shri Vishwakarma Skill University Haryana Palwal India CSE School of advance Computing Alliance University Bengaluru India

The neurological condition epilepsy is demanding and even fatal. Electroencephalogram (EEG)-based epilepsy detection still faces various difficulties. EEG readings fluctuate, and different patients have various seizure activity patterns. EEG signal detection is time-consuming and labour-intensive, which puts a strain on medical staff and raises the possibility of erroneous detections. Usually, electrodes are inserted into the scalp or inside the brain for a brief period of time in order to obtain EEG data. It is essential to research efficient cross-patient automatic epilepsy detection techniques. The multi-head self-attention mechanism recognises long-distance dependencies with the same proficiency as it does temporal dynamic correlations between short-term temporal pattern characteristics and sequential relationships. The contextual representations are inputted into a bidirectional long short-term memory network (BiLSTM) so that information can be extracted in both directions. Classification and training are carried out utilising the log SoftMax algorithm. The experiments utilised scalp EEG data from the CHB-MIT database. Sensitivity, specificity, F1-score, and accuracy were computed to be 96.5 percent, 97.04 percent, 96.6 percent, and 96.2 percent, respectively. The results of the experiment show how well the technique works for detecting seizures in several patients utilising multi-channel EEG recordings. The results also demonstrate the method's improved generalisation capabilities and resilience and consistency in collecting seizure patterns. This is particularly critical for the tertiary diagnosis of epilepsy, and the findings indicate that the proposed method significantly improves the accuracy of detection. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

An Efficient Detection and Classification of Plant Diseases using Deep Learning Approach

An Efficient Detection and Classification of Plant Diseases ...

引用

2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Jagadeesan, S. Deepakraj, E. Ramalingam, Venkadeshan Venkatachalam, Ilayaraja Vivekanandan, Manojkumar Manjula, R. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India University of Technology and Applied Sciences - Shinas Branch Information Technology Department Oman Vellore Institute of Technology School of Computer Science and Engineeing Vellore India School of Engineering and Applied Sciences Srm University-AP Department of Computer Science & Engineering Andhra Pradesh Amaravati India Srm Institute of Science and Technology College of Engineering and Technology Department of Computing Technology Kattankulathur India

ISBN: (纸本)9798350313413

Crop failure caused by disease-causing pests is an important problem. Farmers struggle with disease management and detection due to inadequate interventions. The goal is to create an automatic system to efficiently identify plant diseases from photos while reducing crop losses and increasing productivity. Machine learning algorithms offer a faster and cheaper alternative to visual inspection by experts. The main goal is to perform image analysis for early diagnosis and effective disease control. The use of CNN architecture for plant disease classification and detection offers a promising solution for plant health monitoring and risk mitigation. Given the threats to crop productivity and global food security, reliable methods for early detection and accurate classification are essential. CNNs enable efficient analysis of vast plant image databases, enabling accurate plant disease identification with speed and accuracy. The multi-layer CNN approach extracts feature and refines the representation, facilitating accurate prediction and accurate diagnosis. Transfer learning methods accelerate system development and allow adaptation to plant disease-specific databases. Combining computer vision algorithms with CNN architecture enables real-time monitoring, early disease detection and targeted intervention, reducing yield losses and improving crop management. This approach uses AI, image analysis, and plant pathology to solve the challenges of sustainable agriculture and plant diseases. System performance is measured by various performance metrics. © 2023 IEEE.

关键词： CNN-Based Approach Error Rate Plant disease Precision ROC

来源：评论

学校读者我要写书评

暂无评论

AI-Driven Mobile Healthcare: Societal Impact, Workforce Adaptation, and Enhanced Predictive Modeling for Diabetes Risk Assessment

AI-Driven Mobile Healthcare: Societal Impact, Workforce Adap...

引用

2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Hole, Shreyas Rajendra Kolluru, Vinothkumar Gangul, Ranita Kolekar, Sagar Chintakunta, Advaitha Naidu Mungara, Sudeep Bangalore India Institute of Technology Department of Data Science Stevens HobokenNJ07030 United States Delaware State University Computer Science & Engineering Bangalore India Department of Civil Engineering Pune India University of North Carolina at Charlotte Department of Computer Science CharlotteNC28223 United States Masters in Information Systems Stevens Institute of Technology HobokenNJ07030 United States

ISBN: (纸本)9798331508685

With advancements in artificial intelligence, it is natural for the health industry to focus on IOT devices. AI could promote a paradigm shift in how doctors and patients interact through immediate updates, predictive features, and remote access. The focus of this study is the outreach and employment aspects revolving around AI-enabled health care, with special attention to mHealth solutions. We searched through published data and came across 112 relevant studies identifying keywords such as accessibility, empowerment, and workforce. One of the most interesting findings was the fact that AI targeting mobile applications has the possibility of increasing access to health services by approximately 60 percent for rural areas, as well as increasing the patient cooperative rate by approximately 45%, where a predictive health tracking device has been utilized. We proposed two new measures: the Physical Activity and Nutrition Index (PANI) and Health Status Index (HSI), allowing for better diabetes risk predictions with a 0.82 AUC-ROC and better generalizability than traditional baselines, with an improvement of 12%. When identifying the best available ML models, including Logistic Regression, Random Forest, XGBoost, LightGBM, and Bayesian Neural Networks, we found that the hybrid Random Forest + Linear SVC model had the best performance with recall and accuracy of 0.77 and 0.72, respectively. The ability of the model to demonstrate both interpretability and specificity was evident. It was also observed that synthetic data augmentation (SMOTE) increased the recall for minority class predictions by 18% but did not reduce specificity. © 2025 IEEE.

关键词： Risk assessment

来源：评论

学校读者我要写书评

暂无评论

TSEML: A task-specific embedding-based method for few-shot classification of cancer molecular subtypes

TSEML: A task-specific embedding-based method for few-shot c...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Su, Ran Shi, Rui Cui, Hui Xuan, Ping Fang, Chengyan Feng, Xikang Jin, Qiangguo Tianjin University School of Computer Software College of Intelligence and Computing Tianjin China La Trobe University Department of Computer Science and Information Technology Melbourne Australia Shantou University School of Engineering Department of Computer Science Guangdong China Northwestern Polytechnical University School of Software Shaanxi China Yangtze River Delta Research Institute Northwestern Polytechnical University Taicang China

ISBN: (纸本)9798350386226

Molecular subtyping of cancer is recognized as a critical and challenging upstream task for personalized therapy. Existing deep learning methods have achieved significant performance in this domain when abundant data samples are available. However, the acquisition of densely labeled samples for cancer molecular subtypes remains a significant challenge for conventional data-intensive deep learning approaches. In this work, we focus on the few-shot molecular subtype prediction problem in heterogeneous and small cancer datasets, aiming to enhance precise diagnosis and personalized treatment. We first construct a new few-shot dataset for cancer molecular subtype classification and auxiliary cancer classification, named TCGA Few-Shot, from existing publicly available datasets. To effectively leverage the relevant knowledge from both tasks, we introduce a task-specific embedding-based meta-learning framework (TSEML). TSEML leverages the synergistic strengths of a model-agnostic meta-learning (MAML) approach and a prototypical network (ProtoNet) to capture diverse and fine-grained features. Comparative experiments conducted on the TCGA FewShot dataset demonstrate that our TSEML framework achieves superior performance in addressing the problem of few-shot molecular subtype classification. © 2024 IEEE.

关键词： Zero-shot learning

来源：评论

学校读者我要写书评

暂无评论

Robust Counting in Overcrowded Scenes Using Batch-Free Normalized Deep ConvNet

引用

computer systems science & engineering 2023年第9期46卷 2741-2754页

作者： Sana Zahir Rafi Ullah Khan Mohib Ullah Muhammad Ishaq Naqqash Dilshad Amin Ullah Mi Young Lee Institute of Computer Sciences and Information Technology(ICS/IT) The University of AgriculturePeshawar25130Khyber PakhtunkhwaPakistan Department of Convergence Engineering for Intelligent Drone Sejong UniversitySeoul05006Korea Collaborative Robotics and Intelligent Systems(CoRIS)Institute Oregon State UniversityORUSA Department of Software Sejong UniversitySeoul05006Korea

The analysis of overcrowded areas is essential for flow monitoring,assembly control,and *** counting’s primary goal is to calculate the population in a given region,which requires real-time analysis of congested scenes for prompt reactionary *** crowd is always unexpected,and the benchmarked available datasets have a lot of variation,which limits the trained models’performance on unseen test *** this paper,we proposed an end-to-end deep neural network that takes an input image and generates a density map of a crowd *** proposed model consists of encoder and decoder networks comprising batch-free normalization layers known as evolving normalization(EvoNorm).This allows our network to be generalized for unseen data because EvoNorm is not using statistics from the training *** decoder network uses dilated 2D convolutional layers to provide large receptive fields and fewer parameters,which enables real-time processing and solves the density drift problem due to its large receptive *** benchmark datasets are used in this study to assess the proposed model,resulting in the conclusion that it outperforms conventional models.

关键词： Artificial intelligence deep learning crowd counting scene understanding

来源：评论

学校读者我要写书评

暂无评论

ASSESSING THE ACCURACY AND ETHICAL IMPLICATIONS OF AUTOMATED CRIMINAL DETECTION USING A DEEP LEARNING APPROACH

ASSESSING THE ACCURACY AND ETHICAL IMPLICATIONS OF AUTOMATED...

引用

2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Jagadeesan, S. Sujigarasharma, K. Manjula, R. Vetriselvi, T. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India Department of IOT School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798350305708

This study examines the accuracy and ethical implications of using convolutional neural networks (CNN) for automated crime detection. A CNN model was trained on a dataset of criminal mugshots to identify potential criminal behaviour based on facial features. This study analyzed the performance of the model and achieved a high accuracy rate in identifying criminals. However, the ethical implications of automated criminal detection are also explored, including bias, privacy and human rights violations. The findings of this study highlight the need for caution and ethical considerations when implementing automated crime detection technologies. It is important to ensure that such technologies are not used to violate the rights of individuals or perpetuate societal biases. © 2023 IEEE.

关键词： CNN Criminal detection Face detection Machine learning

来源：评论

学校读者我要写书评

暂无评论

Cross-cultural contextualisation for recommender systems

引用

Journal of Ambient Intelligence and Humanized Computing 2024年第2期15卷 1659-1670页

作者： Hong, Minsung An, Sojung Akerkar, Rajendra Camacho, David Jung, Jason J. Big Data Research Group Western Norway Research Institute Vestlandsforsking Sogndal6851 Norway Department of Computer Science and Engineering Chung-Ang University 84 Heukseok-ro Heukseok-dong Dongjak-gu Seoul Korea Republic of Information Systems Department Technical University of Madrid Madrid28031 Spain

Cultural Heritage (CH) domain is rapidly moving from traditional heritage sites into smart cultural heritage environment through various technologies. As one of the important technologies in the smart space, Recommender systems (RSs) have been widely utilised to personalised services and matching visitors’ goals and behaviours. Whereas, cultural difference is often considered a barrier to technology transfer or adoption. However, few studies focus on how the cultural factor influences recommendation despite cultural difference largely affects user preferences in the RSs. Furthermore, existing researches have mainly analysed evaluation results of their recommendation to reveal cultural differences, rather than utilising the cross-cultural factors into RSs. In this paper, we propose a novel concept of cross-cultural contextualisation and a model to compute the cross-cultural factor affecting users (countries or cultures) preferences by using matrix factorisation and clustering techniques. In addition, we discuss how to apply the model to RSs in CH domain through cross-domain recommendation techniques. Note that the two computational techniques were used to analyse cross-cultural factors which impact to user preferences, rather than to recommend items. In other words, the proposed model and computing results capable of utilisation into the other RSs as well as various research fields. Results of experiments with a real-world dataset showed effectiveness of the proposed model and supported that there is cultural difference influencing users’ rating behaviours. Furthermore, a systematic analysis of dataset and the experimental results presented that individual users could be considered as country-wise groups for the model to analyse the cross-cultural factors. © Springer-Verlag GmbH Germany, part of Springer Nature 2019.

关键词： Recommender systems

来源：评论

学校读者我要写书评

暂无评论

EDUZONE - A Educational Video Summarizer and Digital Human Assistant for Effective Learning 7

EDUZONE - A Educational Video Summarizer and Digital Human A...

引用

7th International Conference on information Technology Research, ICITR 2022

作者： Wangchen, Tandin Tharindi, P. Navodya Chaveena De Silva, K.C.C. Thushan Sandeepa, W.D. Kodagoda, Nuwan Suriyawansa, Kushnara Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Sri Lanka

ISBN: (数字)9798350332032

ISBN: (纸本)9798350332032

The availability of technology and the expansive nature of the internet have created a surge in the demand for online learning. Despite so many advantages, there are some existing drawbacks related to online learning. The lengthy recorded video lectures of different subjects and modules in a static manner, are extremely tedious for the learner to understand the contents available. and lack of assistance for academic-related problems of students is also stated as a major issue that comes with online education. EDUZONE provides a reliable solution to mitigate and overcome these challenges. This tool is educational assistance that generates a summarized version of the video lectures which depicts the overall idea of the whole video with the capability of a lecture notes generator along with a digital human which helps to clarify students' problems and build an efficient conversational flow. The summarized video content can be used by the learners for revisions and as a quick reference before any examinations. In addition to generating short and precise content, EDUZONE also indexes any specific topics to make it easier to find content and generate class notes, highlighting all the important content. Overall EDUZONE can be considered a time-efficient educational assistant which helps students with their studies. © 2022 IEEE.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

GARDENPRO-Android Mobile Application for Home Garden Management Using Machine Learning Augmented Reality and IOT 10

GARDENPRO-Android Mobile Application for Home Garden Managem...

引用

10th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2022

作者： Netthikumarage, Dilshika Liyanage, L.B. Samarakoon, S.M.H.N. Ramkumar, P.M. Harshanath, S.M.B. Rajapaksha, U.U.Samantha SriLanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Department of Computer Science and Software Engineering SriLanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9781665401562

ISBN: (纸本)9781665401562

IoT is an important technology for Agriculture. When it comes to machine learning-based decisions related to agriculture the farmers can take precise decisions according to the sensor data collected from IoT. But most people who live in urban areas of Sri Lanka were adopted to agriculture because of COVID 19 pandemic situation. So most of the people have limited space for gardening, and newcomers haven't proper knowledge of what are the crops suitable for their area of garden space and relevant instructions to grow the crops which they like and environment factors needed. And they haven't understood the type of fertilizers that need for the crop. Almost nowadays people haven't much time for watering and care the plants that they like to grow in their garden. Garden Pro Android Application had introduced a feature Machine Learning based crop recommendation system for newcomers to home gardening. By using this feature of the app user can get an idea about crops that are suitable for their garden area, gardening instructions. And soil moisture also fertilizers and the application shows real-time temperature, soil moisture, and humidity, water level required for the crop that collected from IoT sensors and additionally the 3D View of the plants that predicted according to the soil conditions visualize to the user by Augmented reality technology. © 2022 IEEE.

关键词： Crops

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-Based Smart Infotainment System for Taxi Vehicles 4

Deep Learning-Based Smart Infotainment System for Taxi Vehic...

引用

4th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2022

作者： Dewalegama, M.P. De Zoysa, A.D.S. Kodikara, L.M Dissanayake, D.M.J.C Kuruppu, Thilmi Anuththara Rupasinghe, Shalini Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Sri Lanka

ISBN: (纸本)9781665468350

Nowadays, people are more intent to use IoT to ease their day-to-day work. As a result of that, the transportation industry is being adopted to more IoT-based approaches rather than traditional methods. When it comes to taxi services, companies need to keep up the competition with their rivals. Along with the high demand, there are also being reporting number of problems around the taxi industry daily. Taking some of the most common problems into consideration, such as 'Smart Infotainment System' will help to resolve most of those problems. Deep learning models such as CNN (Convolutional Neural Network), YOLO and SKLearn used to develop the proposed system. As a result of that, the reputation of the taxi service will be increased, and the taxi service consumers will be able to get a comfortable and safer journey to the end of the day. © 2022 IEEE.

关键词： Convolutional neural networks

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：