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An NLP Framework to Detect Ailment based on Summary of Patient Symptoms Supporting Multilinguality for Rural Area 3

An NLP Framework to Detect Ailment based on Summary of Patie...

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3rd international conference on Advances in Computing, Communication control and Networking, ICAC3N 2021

作者： Shelat, Nishee D Shiveshwar, Prachi K Srivastava, Tanay Modi, Hiral NMIMS University Computer Engineering MPSTME Mumbai India

ISBN: (纸本)9781665438117

India is home to approximately 1.4 billion people, has a diverse mixture of both urban and rural regions. Compared to the medical development in the urban sector, the rural sector is trailing behind by a lot. The advancement of technology in the 21st century has brought various sectors close to one another. With the help of this technology, we can bridge the gap between the two sectors in the medical domain. The medical sector is a sector that requires human intervention irrespective of however advance the technology has become. To have a balanced amount of technology and human mediation and to utilize both their knowledge efficiently and feasibly, we have come up with a solution that integrates both these aspects and tries to give the best possible accurate solution. Our module gives a probable detection of ailment based on symptoms which helps speed up doctor's analysis. It also takes into consideration, the diversity of languages that India has, and it takes care that the communication with the patient is in a language that he/she understands. Sentiment Analysis is also performed to detect mental health issues among rural areas to create more awareness and to create normalcy regarding the same. © 2021 IEEE.

关键词： Rural areas

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A Novel Approach for Detection of Cyber Attacks in Microgrid SCADA System

A Novel Approach for Detection of Cyber Attacks in Microgrid...

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Sustainable Energy and Future Electric Transportation (SEFET), international conference on

作者： Manoj Tripathy Rajdeep Niyogi Peddoju Sateesh Kumar Ganesh Balu Kumbhar Ram Singh Vivek Thakur Department of Electrical Engineering IIT Roorkee Roorkee India Department of Computer Science and Engineering IIT Roorkee Roorkee India

Modern electrical microgrid incorporates Supervisory control and Data Acquisition (SCADA) system, Intelligent Electronic Devices (IEDs), and other smart devices to monitor, and control the flow of power in electrical networks. SCADA employs various communication protocols such as Modbus, Ethernet/IP, DNP3, IEC61850, etc. to send and receive the data and control commands. These protocols have several known vulnerabilities and pose a threat to SCADA systems from cybersecurity perspective and thus the microgrid. This paper attempts to assess the susceptibility of Modbus and Ethernet/IP protocols to potential cyber-attacks. To carry out the analysis a cyber-physical hardware test-bed for a hybrid microgrid SCADA system is developed. Further, a novel intrusion detection algorithm involving both network traffic-based and power system data-based is proposed. The proposed technique uses traffic signatures for detecting Denial of Service (DoS) attack, and power system anomaly-based approach to identify False Data Injection (FDI) attack. The accuracy and effectiveness of the technique have been tested and results show proof of high accuracy and effectiveness.

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Glaucoma Insights: a Visual Comparative Analysis of Fundus Images Across Diverse Datasets

Glaucoma Insights: a Visual Comparative Analysis of Fundus I...

引用

Smart Systems for applications in Electrical Sciences (ICSSES), international conference on

作者： Chunduri Yoshitha G Vishwa Prakashini Neha S Aiswariya Milan K Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru India

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

Glaucoma is a major global health issue that can lead to irreversible vision loss if untreated. Early diagnosis and continuous monitoring are crucial to mitigating its impacts. Fundus images are necessary to evaluate, diagnose glaucoma and offer an in-depth perception of retinal structures and features indicative of the condition. The project aims to compare the features of fundus images from three widely recognized datasets, they are REFUGE, ODIR, and DRONIS-DB focusing on analyzing critical metrics such as pixel intensity, brightness, image channel analysis, edge mean, and saturation means. Advanced visualizations are employed to ensure a comprehensive analysis of the features. The main objective of the work is to provide a structured data-driven approach for feature extraction and visualization, enhancing our understanding. This project contributes to advancing diagnostic precision, supporting clinical decision-making, and enabling researchers to explore innovative solutions in ophthalmology by bridging the gap between medical imaging data and actionable insights.

关键词： Glaucoma Visualization Decision making Data visualization Feature extraction Smart systems Retina Ophthalmology Biomedical imaging Resistance heating

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Potato Plant Leaf Disease Detection and Recognition Using R-CNN Model

Potato Plant Leaf Disease Detection and Recognition Using R-...

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Pervasive Computing and Social Networking (ICPCSN), international conference on

作者： Minu Balakrishnan Raja M P.Vinoth Kumar Suvitha Subramaniam Latha R Department of Information Technology Sri Eshwar College of Engineering Coimbatore India Department of Computer Science and Engineering CMR Institute of Technology Bengaluru India Depatment of Mechatronics Chennai Institute of Technology Chennai India Department of English KPR Institute of Engineering and Technology Coimbatore India Saveetha school of Engineering Saveetha Institute of Medical and Technical Science- (SIMATS) Chennai India

A smart farming system that makes use of the appropriate infrastructure is an example of a new technology that aids in the improvement of the quality and quantity of the country's agricultural goods, such as potatoes. Diseases cannot be prevented since developing potato plants requires consideration of factors such as the environment, soil, and quantity of sunlight. Deep learning has resulted in recent advances in computer vision. These advancements have enabled Potato to utilize a camera to diagnose disease. This study developed an effective new method for detecting infections in potato plants. Yukon Gold, a kind of potato, served as the trial subject. Specifically, Alternaria, Blackleg, and Target Spot were targeted for detection using this method. Using 5,615 images of diseased and healthy Potato plant leaves taken in controlled environments, deep CNN is trained to identify between the presence and absence of three illnesses. To identify the Potato illnesses affecting the tracked plants, the system used a CNN. Anomaly detection using the F-RCNN-trained model achieved a confidence score of 80%, while illness identification using the Transfer Learning model achieved an accuracy of 97.85%. The effectiveness of the automated image-capturing system for diagnosing diseases in Potato plant leaves was determined to be 93.33%.

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EAFNet: Feature Enhancement and Self-Adaptive Guided Feature Fusion for Object Detection in Haze Conditions

EAFNet: Feature Enhancement and Self-Adaptive Guided Feature...

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Electronic Information Engineering and computer Technology (EIECT), international conference on

作者： Xinxin Hu Yuanbo Zhou Xu Kuang Hao Chen Xiaoyong Lin Fang Liu Hongjun Liu Xiaofei Wang Tong Tong Qinquan Gao Fuzhou University Fuzhou China Fujian Provincial Key Laboratory for Network Audiovisual Application Innovationand Xiamen University of Technology Fujian China Chongqing Dadukou District Archives Chongqing China Imperial Vision Technology Fuzhou Fujian China

Recently, there is a significant success in the current object detection task. However, existing research has demonstrated that detectors trained on regular images experience a significant decrease when facing to wild scences such as haze scence. Although enhancing detection performance is possible through multi-stage adaptive parameter-guided image enhancement, this method fails to consistently enhance downstream object detection performance. To address this issue, we propose an end-to-end object detection framework that utilizes weakly supervised parameter-guided feature fusion to improve detection performance. Specifically, we introduce the Self-Adaptive Feature Fusion Module (SAFFM) that dynamically guides feature fusion in the detection network through the haze image information encoded by the Prior Knowledge (PK) components. By the joint learning of the PK components and the detection network in an end-to-end manner, we ensure that the PK components weakly supervise the feature fusion process to enhance detection performance. Additionally, we propose a Downsampling Feature Enhancement Module (DFEM) that utilizes multibranch down-sampling operators and reparameterization to enhance the feature extraction capability of the network, allowing it to capture more latent information. Comprehensive experiments on synthetic datasets and real datasets, demonstrated that the detection performance of the proposed network significantly outperforms other state-of-the-art detection methods.

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Teacher Assistance Android Application 3

Teacher Assistance Android Application

引用

3rd international conference on Advances in Computing, Communication control and Networking, ICAC3N 2021

作者： Muthusamy, P Singh, Anushka Bharadwaj, Arpit Shamsuddin, Ghulam Galgotias University Computer Science and Engineering Greater Noida India

ISBN: (纸本)9781665438117

Understudy's participation following is an essential issue so as to screen understudies exhibition in the study hall just as in their examinations. It turns into a key concern in light of the fact that the authority of educational institution keeps up a standard that one understudy can possibly go to the test if his/her participation is equivalent or higher to a few rates (60%, 70% or 80% and so on.) in any case not. This is pointlessly tedious to notice and imprint understudy's name on the participation sheet. This additionally happens a few understudies may incidentally or readily mark the understudy's name like as intermediary. The printed version of participation sheet may get lost. Utilizing Advanced cell like as Android Innovation the course instructor will have the option to gauge participation effectively by our planned versatile application and spare the participation in the phone. To evade these issues, this paper presents a portable application for understudy participation and imprint the board framework. This application is chiefly intended for the resources and other staff individuals from the association who keep up participation and stamps consistently. Utilizing this framework, the subject handlers, staffs or the specialists can check the quantity of understudies present or missing in the class meetings. The proposed application permits the clients to stamp participation through cell phones and to stay in contact with understudies. © 2021 IEEE.

关键词： Android (operating system)

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A Multi-Tenant Access control Method Based on Environmental Attributes and Security Labels 2021

A Multi-Tenant Access Control Method Based on Environmental ...

引用

3rd international conference on Information Technology and computer Communications, ITCC 2021

作者： Duan, Yubing Deng, Xiaolei Yang, Haosen Institute of Computer Application China Academy of Engineering Physics Mianyang China

ISBN: (纸本)9781450389884

Secure access control is a key technology to protect cloud data from unauthorized access and achieve safe sharing of user data. On the basis of introducing traditional access control models, this paper analyzed the problems and summarized the main research status of access control in cloud computing environment. Then we proposed a multi-tenant access control method based on environmental attributes and security labels. Experimental results show that the proposed method can achieve fine-grained access control and more flexible access control granularity. © 2021 ACM.

关键词： Access control

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A simple Design of Automatic Bag Valve Mask Ventilator using arduino

A simple Design of Automatic Bag Valve Mask Ventilator using...

引用

Sciences and Techniques of Automatic control and computer Engineering MI-STA, IEEE international Maghreb Meeting of the conference on

作者： Ahmed Salem Daw Alarga Hadya S. Hawedi Ibrahim Imbayah Abdussalam Ali Ahmed Abdulgader Alsharif Mohamed Mohamed Khaleel Computer Science Elmergib University Khums Libya Computer Science Al-Asmarya Islamic University Zliten Libya Department Energy Engineering College of Renewable Energy Tajoura Libya Mechanical Engineering Bani Waleed University Bani Waleed Libya Faculty of Engineering Universiti Teknologi Malaysia (UTM) Johor Malaysia Department of Electrical and Electronic Engineerin Karabuk University Karabuk Turkey

This paper presents a ventilator that is characterized by simple construction and operation, as well as accurate execution of tasks due to the control system based on the Arduino board and servo motor. The device presented in this research is composed of two main parts: the mechanical part, represented by pliers, which are two opposite plates that can be moved at a specific angle and speed with the assistance of a servo motor connected to these two pliers by two iron rods, and the electronic part, which serves as the base for the ventilator described in this paper. The electronic component consists of a keypad, an Arduino board, and a servo motor. a display screen that shows the user-entered settings is also included. The main goal of this paper is to accurately adjust the amount of oxygen flowing to the patient through the ability of this device to determine the angle of the motor, and the number of breathing times that can be adjusted by adjusting the speed of the engine. A model of the proposed ventilator has been implemented and successfully tested in terms of modifying the motor angle and how rapidly the bag is pressed (motor speed). wherever the control system’s settings have all been accurately and completely applied. In comparison to other devices proposed in numerous studies, this device is characterized by the simplicity of its design, the affordability of its parts, as well as its light weight, which makes it easier to carry and move from one place to another. It is also distinguished by its accuracy in controlling the amount of oxygen required as well as the number of breathing cycles per minute.

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computer vision-Based Beef Grading for Marbling Beef Based on RGB-HSV Color Model and Python Software

Computer Vision-Based Beef Grading for Marbling Beef Based o...

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Signal Processing, Communications and Engineering Management conference (ISPCEM), international

作者： He Zhu Xinyuan Wang Tianxue Sun Yanfei Yang Chenhao He Dong Chen Li Zhang Tao Ma Yanxia Xing Fuqiang Li Fanchang Peng Shandong Agriculture and Engineering University Jinan Shandong China Shandong Aohang and Niu Pastoral Industry Co. Ltd Zibo Shandong China Shangdu Hengchang Co. Ltd Heze Shandong China Hunan Tianhua Industrial Co. Ltd Lianyuan Hunan China

In modern life, people have higher and higher requirements for material standards, high quality beef is sought by more and more diners, marbling beef is the best cow product in the world. Marbling density is a part of one of the important indices for evaluation quality of marbing beef, for these indicators usually adopt sensory evaluation, need professional evaluation personnel, it takes time and effort to train these professionals in the long run, Therefore, based on this situation, the development of a new type of computer evaluation system has a good prospect and research value. The main content of this study is based on the basic principle of RGB-HSV color model. The RGB values of the measured samples are then converted into HSV values for data processing to observe whether there is a rule in the data and summarize its linear equation. The results showed that different grades of beef had significant differences in RGB-HSV color model analysis, the linear relationship is, y(marbling standard)=0.018x( V value)+0.664 $(\mathbf{R^rd}=\boldsymbol{0.96533})$ ), with this data model, we checked our database for the marbling beef, the average accuracy is about 98.76, which also proved the effectiveness of the developed computer evaluation system and the feasibility on the method.

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Real Time Object Detection based on RCNN Technique

Real Time Object Detection based on RCNN Technique

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Pervasive Computing and Social Networking (ICPCSN), international conference on

作者： Ajay P Raja M A. Rizwanbasha Gokul P S M. Hema Latha R Department of Computer Electronics and Communication Engineering Rathinam Technical Campus Coimbatore India Department of Computer Science and Engineeting CMR Institute of Technology Bengaluru India Department of IT Jeppiaar Institute of Technology Chennai India Department of Mechatronics Engineering Chennai Institute of Technology Chennai India Department of English KPR Institute of Engineering and Technology Coimbatore India Saveetha School of Engineering Saveetha Institute of Medical and Technical Science - SIMATS Chennai India

This research provides a method for recognizing objects in real-time on any model-enabled device, regardless of the environment. In computer vision, object identification and training are thriving, all-encompassing, and elusive areas. CNN are used in this study to develop a multi-layered model for categorizing items into specified classes. Following object identification, the proposed algorithms assign a class label by assessing many photographs. These things are identified using more comprehensive feature maps. Recent advances in deep learning for image processing have made this possible. Such images might be found in the video frames used to train the model. Multi-scale feature maps are used for detection and filters, each of which has separate default boxes so that various aspect ratios can be accomodated. The training continues until there is a discernible reduction in the error rate. The performance of the trained model is used to evaluate test images. Proposed object recognition method employs a single-shot multi-box detector methodology and a faster region CNN architecture. This helps us to improve object detection method's computational efficiency and proposed model can identify and classify the given input image data more accurately.

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