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Construction of hospital big data center for intelligent medical care

Construction of hospital big data center for intelligent med...

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ISCTT 2022;7th international conference on Information science, Computer Technology and Transportation

作者： Jun Wang Xiu-lai Wang Ai-Rong Yu Bo Deng

New technologies of Internet of Things and artificial intelligence are rapidly developing in the field of medical big health, and they are widely used in many fields such as text analysis of medical records, image recognition, and analysis of medical procedures. The construction of hospital big data center can give full play to the potential value of medical big data and realize the effective interaction among patients, medical care and treatment institutions through big data mining. By analyzing the classification, composition, characteristics and application scenarios of medical big data, adapting different storage and computing schemes and building hospital big data center according to the scheme, supporting the unified storage management of data resources, realizing the classification application, rapid retrieval, sharing and exchange and intelligent computing of data in different medical fields, maximizing the utilization of medical service resources, taking the construction of the eastern war zone general hospital big data as a typical case. It also promotes the scientific, professional, digital and refinement of hospital operation and management.

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STABA: Secure Trust Based Approach for Black-Hole Attack Detection

STABA: Secure Trust Based Approach for Black-Hole Attack Det...

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international conference on advances and Applications of Artificial Intelligence and Machine Learning, ICAAAIML 2021

作者： Dani, Virendra Kokate, Priyanka Umre, Jayesh Computer Science and Engineering Department Shivajirao Kadam Institute of Technology and Management Indore India Computer Science and Engineering Department Shri Govindram Seksaria Institute of Technology and Science Indore India

ISBN: (纸本)9789811948305

The rapid advancement of mobile computing and wireless networking technology has resulted in a massive increase in the number of mobile users around the world. This reality is at the heart of modern mobile ad-hoc network deployments and the delivery of reliable communication services to users. Owing to a lack of resources and a complex network topology, providing dependable service is difficult. Trust protocols for mobile ad hoc network have developed as an active research topic in modern years due to the development of trust protocols to enable mobile community-based applications. Since there is no central coordination and the wireless medium is shared, reliable communications in ad hoc wireless networks is much more complicated than in wired networks. The intend of Quality-of-Service provisioning is to accomplish more deterministic network operations, allowing for more effective distribution of network information and better use of network resources. In this paper, we use the weighted method to suggest a composite trust-based approach. The packet delivery ratio, buffer length and energy utilization rate must be considered when calculating the weighted trust. In this case, the computed weight serves as the system’s positive trust, while the negative trust is also computed. MANET has established a defense against black-hole attacks using both trusts. The provided technique’s experimental results demonstrate safe data communication and optimized efficiency of the network. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Mobile ad hoc networks

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A Proficient GK-KMA Based Segmentation and Lung Nodule Detection in CT Images Using PTRNN 5th

A Proficient GK-KMA Based Segmentation and Lung Nodule Detec...

引用

5th international conference on Smart computing and Informatics, SCI 2021

作者： Gugulothu, Vijay Kumar Balaji, Savadam Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Deemed To Be University Telangana Hyderabad500075 India Head of Computer Science Department of Computer Science & Engineering Government Polytechnic College Masab Tank Hyderabad500004 India

ISBN: (纸本)9789811696688

One among the prevalent diseases and the foremost causes of cancer-associated death worldwide is Lung Cancer (LC). The prognosis can be probably enhanced, and many lives can be saved every year if the Lung Nodules (LN) analysis is done early. Prevailing LN detection methods was complex as it shows extensive variation in appearance as well as spatial distribution. Here, a new Deep Learning (DL) approach, Parameter Tuned Recurrent Neural Network (PTRNN), is proposed aimed at LN detection. Preprocessing, lung segmentation, detection of nodule candidates, deep Feature Extraction (FE), together with classification are the ‘5’ phases of proposed LN detection. Primarily, from the openly available data sources, the CT lung images are amassed. Then, those data are preprocessed via carrying out filtering and contrast enhancement. Next, a Gaussian Kernel-based K-means algorithm (GK-KMA) segments the lung areas from the preprocessed image. Subsequently, nodule candidates are detected amongst every available object in the lung areas through a morphological process. After that, utilizing Residual Networks (ResNet 152), features are extracted as of the candidate nodules. Lastly, for nodule classification, the feature vectors (extracted ones) are inputted to PTRNN. In PTRNN, Cauchy Mutated Whale Optimization Algorithm (CMWOA) tune and optimize the weight and bias parameters of RNN. The outcomes exhibited that the PTRNN superiorly performs nodule detection with a high detection rate when weighed against prevailing classifiers. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Feature extraction

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Implementation Of Fake Review Detection Using Passive Aggressive Classifier

Implementation Of Fake Review Detection Using Passive Aggres...

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Amresh Kumar Manish Kumar Anandhan. K Ajay Shanker Singh School of Computing Science and Engineering Galgotias University Greater Noida Uttar Pradesh India

In the recent year we have been experiencing a huge surge on internet, due to many people have started using internet. Nowadays there are many paid and fake reviews flooding the e-commerce websites like Amazon, Flipkart and many other e-commerce websites. In which many customers make decisions based on these fake reviews or comments provided by others who had similar experiences. In today's competitive environment, anyone may write anything, which has resulted in an increase in the number of bogus reviews. So, to overcome this problem of fake reviews we will propose a machine learning model. The model will be trained with 40000 datasets to predict the fake reviews. Machine learning is the one of the trending techniques to predicts the output based on the experience. Furthermore, we can test the model using testing data which is also a part of the data set. We are using Passive Aggressive Classifier and we have achieved an accuracy of 83.53%.

关键词： Training Machine learning algorithms Machine learning Predictive models Prediction algorithms Libraries Classification algorithms

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An Enhanced MIN-MIN algorithm for workflow scheduling in Cloud computing

An Enhanced MIN-MIN algorithm for workflow scheduling in Clo...

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Gifty Gupta Neeraj Mangla Computer Science and Engineering Department M.M. Engineering College MMDU Mullana Ambala

In Cloud computing environment, scheduling of Cloudlets and resource allocation of Cloudlets are two major issues that need to be considered. To improve the system performance, it is necessary that the Cloudlets scheduled on the resources available must be efficiently executed. For scientific applications, workflow scheduling is a significant issue. To handle this issue, a new mechanism called Enhanced Min-Min (E-Min-Min) is introduced inspired by the Min-Min algorithm. This scheduling algorithm not only schedules the jobs according to their completion time but considers the data transfer time too with equal priority and makes an aggregated function for scheduling of jobs to the best suitable resources. During the scheduling process, the main focus is on the minimization of Makespan, maximize the resource utilization and load balance level and also minimize the cost of scheduling. Workflow scientific applications such as Montage, Cybershake, InSpiral, SIPHT are considered. In workflow the tasks are represented as nodes and their dependencies represents the relationships among them. WorkflowSim simulator with Net Beans IDE is used to create the simulation environment and the results are validated over it. The proposed algorithm shows the better results as compared with the basic Min-Min and Modified Min-Min algorithm.

关键词： Cloud computing Schedules Costs System performance data transfer Scheduling Resource management

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Role of Artificial Intelligence in Object Detection: A Review

Role of Artificial Intelligence in Object Detection: A Revie...

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Aradhya Singh Bisht Amit Kumar Jha Abhinav Sachdeva Nikhil Sharma Department of Computer Science & Engineering Delhi Technological University Delhi India

Over the last 20 years, the field of object detection has seen a remarkable progress been made. In its 20-year history, this literature review not only goes through some landmark detectors (e.g., VJ detector, HOG detector, DPM, Faster-RCNN, YOLO, SSD, and others), key technologies, speed up methods, detection applications, datasets, and metrics, but also the 28 difficulties currently faced by the community are being discussed and further enhancement and continuation of these detectors are also discussed. We had shallow trainable structures and Handcrafted features which are the fundamentals of conventional object identification systems. With the spontaneous advancement of deep learning technology, additional effective tools are now competent of learning semantic, high-level, and deeper characteristics. In this study, deep learning-based object detection system has been reviewed. We also go through a few specific tasks, such as prominent item detection, face identification, and pedestrian detection.

关键词： Measurement Computational modeling Semantics Detectors Object detection Predictive models Object recognition

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A Steadfast Approach to perform Face Detection using Feed Forward Neural Networks

A Steadfast Approach to perform Face Detection using Feed Fo...

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Gaurav Gupta Sushant N. Suresh Kumar K Anandhan School of Computing Science and Engineering Galgotias University Greater Noida India

Now Presently every day with the monstrous expansion in video and picture data sets there’s a superb requirement for programmed comprehension and assessment of data by canny frameworks as physically it’s becoming far off. Step by step face recognition is turning into a test on account of its expanding use in a few applications. the essential advance for face acknowledgment, face investigation, and location of different elements of the face. Detection of dark-coloured faces and poorly illuminated faces don't seem to be well studied within the literature thanks to several challenges. the foremost critical challenge is that there’s inadequate contrast among facial expressions. The face detection algorithms are supported by either grey-level template matching or the computation of geometric relationships among face expressions. The technology nowadays isn't capable enough to acknowledge the face with or without beards or some general changes to the identical. This research illustrates a strong algorithm for pre-processing the face image with and without the beard. This research is implemented on a secured application where the robustness of the face recognition algorithms is enhanced.

关键词： Face recognition Neural networks Turning Robustness Face detection Feeds

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Smart Home Automation System Using Cloud computing Based Enhancement Security and Environment

Smart Home Automation System Using Cloud Computing Based Enh...

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advances in Engineering science and Technology (AEST), international conference on

作者： Anwer Sattar Hamzah Laith Ali Abdul-Rahaim Department of Electrical Engineering University of Babylon Babylon Iraq

In this paper, a smart home automation system is proposed can detect any leakage of gases, smoke, and fires with a prior warning sent via the Telegram application to the owner. When any danger is detected, the ventilation fan will work immediately to reduce the amounts of gases. Another mechanism that achieves a suitable climate for the home and reduces energy consumption is by connecting heating and cooling devices to the motion sensor. A two-threshold value has been added to avoid the machines turning on and off continuously when the temperature fluctuates at a certain value, as will be clarified in this paper. Regarding the security aspect of the house, a sensor has been placed inside the house on doors and windows to detect any movement, and sends instant photos of the place to the cloud and alerts via Telegram at the same time. These sensors are also connected to a microcontroller, which transmits data and values that will be processed, stored, and displayed in fog computing via a user interface, and then over Wi-Fi to the cloud. The time of sending data from the sensors to the graphical user interface (GUI) is instantaneous, with one second between reading and another, while images are sent from the internal camera with a time of 30 milliseconds, which is the processing time only. The system has been implemented in practice with good results in terms of reading accuracy and response time.

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Tobacco Plant Disease dataset 14

Tobacco Plant Disease Dataset

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14th international conference on Digital Image Processing, ICDIP 2022

作者： Lin, Hong Tse, Rita Tang, Su-Kit Qiang, Zhenping Ou, Jinliang Pau, Giovanni Faculty of Applied Sciences Macao Polytechnic University China Engineering Research Centre of Applied Technology on Machine Translation and Artificial Intelligence of Ministry of Education Macao Polytechnic University China College of Big Data and Intelligent Engineering Southwest Forestry University KunMing China Department of Computer Science and Engineering-DISI University of Bologna Italy Samueli Computer Science Department University of California Los Angeles United States

ISBN: (纸本)9781510657564

Tobacco is a valuable plant in agricultural and commercial industry. Any disease infection to the plant may lower the harvest and interfere the operation of supply chain in the market. Image-based deep learning methods are cutting-edge technologies that can facilitate the diagnosis of diseases efficiently and effectively when large-scale dataset is available for training. However, there is not a public dataset about tobacco currently. A comprehensive dataset is appealed to take advantage of deep learning methods in tobacco cultivation urgently. In this paper, we propose to create a specific dataset for tobacco diseases, called Tobacco Plant Disease dataset (TPDD). 2721 tobacco leaf images are taken in field. The dataset serves for two purposes: disease classification and leaf detection. For classification, we identify 12 classes and provide two types of disease annotations: 1) Whole Leaf Section;2) Disease Fragment Section. For leaf detection, we provide two kinds of bounding box: rectangle bounding box and polygon bounding box. In addition, we conduct baseline experiments to illustrate the usefulness of TPDD: 1) using deep learning model to detect single disease and multiple diseases;2) using YOLO-v3 and Mask-RCNN to detect leaves. We hope that the dataset could support the tobacco industry, also be a benchmark in fine-grained vision classification. © 2022 SPIE.

关键词： Computer vision

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Voice based translation in fitness band for tourists

Voice based translation in fitness band for tourists

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Manish Kumar Shruti Kapoor Pragya Tewari Department of Computer Science and Engineering Galgotias University

People all over the world speak in a variety of languages. Despite the fact that English appears to be the most suited language for most people and is taught all around the globe, many individuals find it difficult to learn, and some think that working or studying one's home language is more pleasurable than learning other languages. As a result, communication is tough while travelling throughout the world. As we see in our every-day lives, accomplishing activities, managing things, and so on, technology has the capacity to affect the world in new ways. It has been proved to make things easier for travellers. There are a variety of technologies that might help travellers who travel a long distance. We have included a new feature in today's fitness watches/bands that will assist travellers or anybody else who travels from one location to another and is having difficulty with language translation. Apart from language translation, it has many other features that other smart watches have, and they don't have to pay more for these services because they are included in their budget and are easy to use for everyone. For this, we employed voice recognition and a variety of other modules.

关键词： Speech recognition

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