检索结果-内蒙古大学图书馆

2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Evangelin Sonia, S.V. Nedunchezhian, R. Rajalakhmi, M. Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Computer Science and Engineering Coimbatore Institute of Technology Coimbatore India Information Technology Coimbatore Institute of Technology Coimbatore India

ISBN: (纸本)9798350348217

Many researchers have spent years trying to create an effective algorithm for identifying cardiac arrhythmias. However, despite the fact that they have yielded satisfactory outcomes, they have only been used on either relatively small or uniform data sets. In this work, we provide the results of a study designed to detect cardiac arrhythmias in 12-lead ECG signals of varying lengths, as collected as part of the PhysioNet/Computing in Cardiology Challenge. We came up with a method that begins with looking at the database's labels. Following this, a number of signal processing methods were used on the 12-lead data to remove noise. Next, the data were segmented and filtered to determine which beats were most crucial in demonstrating the arrhythmia. Then, a 1DCNN+BiLSTM model was trained and evaluated on the training set and not only on the training set but also on the hidden validation and testing sets, all using a 10-fold cross-validation scheme. In the validation set, our method scored 0.854 as accuracy, and in the entire test set, it scored 0.812. © 2023 IEEE.

关键词： Electrocardiograms

来源：评论

学校读者我要写书评

暂无评论

Fog-Assisted Blockchain-IoMT Healthcare Framework with Role-Based Access Control for Critically Ill Patients

引用

SN computer science 2024年第6期5卷 658页

作者： Mallick, Soubhagya Ranjan Lenka, Rakesh Kumar Tripathy, Pradyumna Kumar Rao, D. Chandrasekhar Sharma, Suraj Ray, Niranjan Kumar School of Technology Woxsen University Telangana Hyderabad 502345 India Department of Computer Science Central University Odisha Koraput 764021 India Computer Science and Engineering Silicon University Odisha Bhubaneswar 751024 India Department of Computer Science and Engineering Biju Patnaik University of Technology Odisha Rourkela 769015 India Computer Science and Engineering Guru Ghasidas Vishwavidyalaya Chhattisgarh Bilaspur 495009 India School of Computer Science and Engineering Kalinga Institute of Industrial Technology Odisha Bhubaneswar 751024 India

Hospitals, clinics, pharmaceutical firms, diagnostics centres, pathology labs, insurance companies, and emergency vehicles must be connected in a unified system to better serve patients worldwide with cutting-edge medical care. Most previously existing systems rely on cloud-based platforms, which face several technological challenges, including data availability, data accessibility, processing delays, scalability concerns, problems gathering real-time data, and difficulties monitoring patients. Information about a person’s health is extremely private and should be protected with the utmost care. To address these issues, We propose a decentralised, secure, and efficient framework incorporating the internet of things, fog computing, and blockchain technology to collect, process, manage, and store healthcare data. A unique role-based access control is used in the proposed framework to prioritise critically ill patients while maintaining confidentiality, scalability, data availability, and system security. Finally, we evaluate the efficiency of our proposed framework by conducting experiments on device registration time, transaction latency, transaction throughput, and access control in medical environments using realistic healthcare scenarios. We then compare the results to existing fog models to determine the system’s performance. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blockchain Cloud computing Fog computing Healthcare IoMT Scalability

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

Group Teaching Optimization Algorithm with Machine Learning Model for Big Data Analytics

Group Teaching Optimization Algorithm with Machine Learning ...

引用

2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Prabaharan, G. Sesha Vidhya, S. Venkatesh, K. Mary Sundararajan, Suma Christal Shobana, G. Srimathi, S. Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Department of Computer Science and Engineering Veltech University Avadi Chennai India Rmk College of Engineering and Technology Department of Ece India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering India Panimalar Engineering College Department of Information Technology Poonamalle Chennai India College of Science and Humanities Srm Institute of Science and Technology Department of Computer Applications SRM Nagar Kattankulathur603 203 India Saveetha School of Engineering Simats Department of Bio Technology Tamilnadu Chennai India

ISBN: (纸本)9798350313987

Big Data An analytics (BDA) plays a significant role in the process of decision-making over several domains. Effective evaluation and processing of enormous data amounts need influential techniques and Machine Learning (ML) methods. BDA by implementing ML and Feature Selection (FS) is an influential model in extracting valued patterns and vision from huge and convolutional datasets. The process encompasses implementing modern ML methods and models for interpreting, processing, and analyzing data on a scale that cannot be handled effectively by conventional models. This research study proposes a design of Group Teaching Optimization Algorithm with Machine Learning model for Big Data Analytics (GTOA-MLBDA) technique. The GTOA-MLBDA technique classifies the big data and it can be managed by Map Reduce tool. The GTOA-MLBDA technique uses GTOA to determine the important features, reduce dimensionality and enhance classifier results. In addition, the classification of big data takes place using functional link neural network (FLNN) model. Finally, the parameter tuning of the FLNN model takes place using Adam optimizer. Experimental analysis has been carried out by using big data datasets and the results highlight that the combination of the GTOA with the FLNN classifier of the GTOA-MLBDA technique resulted in improved results over classical approaches. © 2023 IEEE.

关键词： Big Data Classification Data Analytics Feature Selection Machine Learning Map Reduce

来源：评论

学校读者我要写书评

暂无评论

Enhancing Video Anomaly Detection Using Spatio-Temporal Autoencoders and Convolutional LSTM Networks

引用

SN computer science 2024年第1期5卷 1-13页

作者： Almahadin, Ghayth Subburaj, Maheswari Hiari, Mohammad Sathasivam Singaram, Saranya Kolla, Bhanu Prakash Dadheech, Pankaj Vibhute, Amol D. Sengan, Sudhakar Department of Networks and Cybersecurity Faculty of Information Technology Al Ahliyya Amman University Country Amman Jordan School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai 600127 India Department of Networks and Cybersecurity Information Technology Al Ahliyya Amman University Amman Jordan Department Computing Technology School of Computing SRM Institute of Science and Technology Kattankulathur Campus Tamil Nadu Chennai 603203 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur 522302 India Department of Computer Science and Engineering Swami Keshvanand Institute of Technology Management and Gramothan (SKIT) Rajasthan Jaipur 302017 India Symbiosis Institute of Computer Studies and Research (SICSR) Symbiosis International (Deemed University) MH Pune 411016 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli 627152 India

Identifying suspicious activities or behaviors is essential in the domain of Anomaly Detection (AD). In crowded scenes, the presence of inter-object occlusions often complicates the detection of such behaviors. Therefore, developing a robust method capable of accurately detecting and locating anomalous activities within video sequences becomes crucial, especially in densely populated environments. This research initiative aims to address this challenge by proposing a novel approach focusing on AD behaviors in crowded settings. By leveraging a spatio-temporal method, the proposed approach harnesses the power of both spatial and temporal dimensions. This enables the method to effectively capture and analyze the intricate motion patterns and spatial information embedded within the continuous frames of video data. The objective is to create a comprehensive model that can efficiently detect and precisely locate anomalies within complex video sequences, specifically those featuring human crowds. The efficacy of the proposed model will be rigorously evaluated using a benchmark dataset encompassing diverse scenarios involving crowded environments. The dataset is designed to simulate real-world conditions where millions of video footage need to be continuously monitored in real time. The focus is on identifying anomalies, which might occur within short time frames, sometimes as brief as five minutes or even less. Given the challenges posed by the massive volume of data and the requirement for rapid AD, the research emphasizes the limitations of traditional Supervised Learning (SL) methods in this context. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Anomaly detection Autoencoders LSTM Machine learning Spatio-temporal features Suspicious activities

来源：评论

学校读者我要写书评

暂无评论

Jatropha leaf disease classification Using Improved Inception ResNetV2 2

Jatropha leaf disease classification Using Improved Inceptio...

引用

2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Sri Sathya, B.K. Akila, M. Logesh, D.G. Navaneethan, R. Perumal, P. Sarun, R. KPR Institute of Engineering and Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350347982

Agriculture is an essential for feeding the world, providing resources, and supporting livelihoods. However, crop disease identification is a prevalent challenge for farmers. To address this issue, a deep learning model was developed to classify Jatropha leaf disease. As the World moving to Bio Diesel and Electrical as that jatropha leaf is a main yield for generating Bio fuels like the Crop Fertilizer and Bio-Diesel with a High Quality of Bio-Diesel Production. The model used deep learning model is InceptionResNetV2 and augmented binary healthy and unhealthy Jatropha leaf images to increase dataset variability for collection from the various resources. The proposed transfer learning algorithm, InceptionResNetV2, achieved a remarkable accuracy of 97%, demonstrating the potential of deep learning in agriculture for effective disease detection and management. In this transfer learning algorithm can fine tune and classify the Jatropha Leaf by an automated classification System. © 2023 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Food Identification and Waste Reduction Solution with Built-in Nutritional Tracking using Machine Learning 8

A Comprehensive Food Identification and Waste Reduction Solu...

引用

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

Comparative Study on Cassava Leaf Disease Classification 2

Comparative Study on Cassava Leaf Disease Classification

引用

2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Mohanraj, G. Prabakaran, K. Rajkumar, M. School of Information Technology and Engineering Vellore Institute of Technology Vellore India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350328202

Manihot esculenta, the scientific name for cassava, is an important staple crop that feeds millions of people in tropical countries and provides a substantial amount of carbohydrates. To minimize these losses and guarantee food security, it is essential to identify plant illnesses as early as possible. Using innovative technology in image processing and machine learning, this comparative study explores the field of classifying illnesses of cassava leaves. The need for efficient and mechanized disease detection methods is pressing, and it might have a significant impact on food security and agricultural sustainability. our is the driving force behind our research. Code examples for processing data and machine learning model implementation are included in the research, together with the compilation and labelling of an extensive dataset. The findings and outcomes from the tests on disease classification are carefully explained. Accompanying them is a comprehensive examination of the assessment metrics, encompassing ROC curves, cross-validation outcomes, recall, accuracy, and a F1 score. These results not only highlight the effectiveness of the suggested methodology but also provide a comparative evaluation of various models and techniques. The study's comparison assessment and investigation findings highlight the possibility for more precise and scalable illness detection methods, providing a foundation for further research in this area. © 2024 IEEE.

关键词： Food supply

来源：评论

学校读者我要写书评

暂无评论

MLModeler5 @ Causal News Corpus 2023: Using RoBERTa for Casual Event Classification 6

MLModeler5 @ Causal News Corpus 2023: Using RoBERTa for Casu...

引用

6th Workshop on Challenges and Applications of Automated Extraction of Socio-Political Events from Text, CASE 2023

作者： Bhatia, Amrita Thomas, Ananya Jain, Nitansh Bedi, Jatin Computer Science and Engineering Department Thapar Institute of Engineering and Technology India

ISBN: (纸本)9789544520892

Identifying cause-effect relations plays an integral role in the understanding and interpretation of natural languages. Furthermore, automated mining of causal relations from news and text about socio-political events is a stepping stone in gaining critical insights, including analyzing the scale, frequency and trends across timelines of events, as well as anticipating future ones. The Shared Task 3, part of the 6th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE @ RANLP 2023), involved the task of Event Causality Identification with Causal News Corpus. We describe our approach to Subtask 1, dealing with causal event classification, a supervised binary classification problem to annotate given event sentences with whether they contained any cause-effect relations. To help achieve this task, a BERT based architecture - RoBERTa was implemented on four different datasets with the main difference being the inclusion and exclusion of both stopwords and various abbreiviations/acronyms present in the dataset. The results of this model are validated on the dataset provided by the organizers of this task. We achieved a rank of 8 with an F1 Score of 0.7475 on Dataset 1 (Removed Stop Words and Replaced Abbreviations). © CASE 2023 - Proceedings of the 6th Workshop on Challenges and Applications of Automated Extraction of Socio-Political Events from Text, associated with 14th International Conference on Recent Advances in Natural Language Processing, RANLP 2023.

关键词： Extraction

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Meteorological variables for Air Pollution Detection in Various Cities using ZigBee

Comparative Analysis of Meteorological variables for Air Pol...

引用

2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Rishi, Priti Sathiya, R. Akila, V. Srm Institute of Science and Technology Department of Electronics and Communication Engineering Chennai Vadapalani600026 India

ISBN: (纸本)9798350300857

A ZigBee module plays an efficient role to monitor air pollutants. The design of a low cost wireless sensor network for air pollution monitoring using ZigBee connectivity is proposed in this work. The proposed system is made up of a network of low cost sensors that measures Particulate Matter (PM2.5), Particulate Matter (PM10). To obtain a precise reading a reference gas analyzer is utilized to calibrate the sensors. The training and validation have been made for the period of December 2020-January 2021 for various cities. The proposed system ensures the system precision and comparative analysis made for various cities and validated with Meteorological recorded values. © 2023 IEEE.

关键词： Wireless sensor 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：