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Research on Dynamic Path Planning of Internet of Vehicles Based on Edge computing

Research on Dynamic Path Planning of Internet of Vehicles Ba...

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Networking, Communications and Information Technology (NetCIT), international conference on

作者： Mei Zhong Ying Wu Fang Wan QinHui Fu Hainan Vocational University of Science and Technology Haikou Hainan China

Vehicles in the Internet of Vehicles can be regarded as a mobility platform integrating perception, communication and computing capabilities. The vehicle can collect the surrounding environment data in real time through the vehicle-mounted sensor system, make intelligent decisions by using the vehicle computing resources, and realize the circular feedback of control decisions through the information interaction between workshops, thus forming an autonomous system to improve the traffic efficiency. Due to the limited local computing resources of vehicles, it is difficult to meet the performance requirements of real-time decision-making based on computation-intensive tasks such as image recognition and video processing in complex and highly dynamic scenes. As one of the key technologies of intelligent vehicles, path planning has a wide application prospect not only in intelligent vehicles, but also in similar mobile robots. In this paper, aiming at the dynamic path planning of vehicle navigation, a dynamic path planning algorithm of vehicle networking based on edge computing is proposed. In order to solve the shortage of local computing resources for vehicles, the mobile edge computing is used to unload the vehicle tasks to the mobile edge computing server, which reduces the overall network energy consumption and meets the task delay requirements. Intelligent vehicle is a hot research topic at home and abroad at present. It has made breakthrough progress in many key technologies, and will be officially launched in the next decade. However, in the Internet of Vehicles, the dissemination of road information lacks a trust mechanism, which easily leads to attacks. The vehicle can collect the surrounding environment data in real time through the vehicle-mounted sensor system, make intelligent decisions by using the vehicle computing resources, and realize the circular feedback of control decisions through the information interaction between workshops, thus forming an auton

关键词： Navigation Intelligent vehicles Roads conferences Heuristic algorithms Path planning Real-time systems

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Crypto currency portfolio allocation Using Machine Learning 3

Crypto currency portfolio allocation Using Machine Learning

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3rd international conference on advances in computing, Communication Control and Networking, ICAC3N 2021

作者： Rao, Pradhyumna Bhasin, Nishit Goswami, Puneet Aggarwal, Lakshita SRM University Computer Science Engg Haryana Delhi-NCR India

ISBN: (纸本)9781665438117

Objective: Unprecedented social and economic health patterns change or when a new normal arises, people prefer to seek safety and wealth preservation. Crypto is the buzzword nowadays where people worldwide are investing their money in a digital payment transaction and have ticked all the boxes when it comes to cases such as security, seemingly fast transactions, and has quickly emerged as one the world's fastest-growing financial markets. Bitcoin, the first cryptocurrency launched in 2009 showcased the proof of work of a decentralised system and what blockchain is capable of doing in a digital ecosystem. Scope: Our paper aims at suggesting currencies to future investors based on the sentiments on popular social media and predicting the overall growth of any coin to analyze its growth prospects. Method: In this paper, we have attempted a meticulous approach to predict the price movements in cryptocurrencies considering its fluctuating factors. Since no unique model can predict the market changes effectively, we focussed on creating a SETP (Sentiment Evaluation for Trend Prediction) model, which analyzes the sentiment surrounding the crypto market and the apprehensions that engulf an investor while investing. The statistical data analysis of the top 5 coins in the market has been undertaken effectively using IBM Watson services. Further enhancements have been made using the jupyter notebook to predict the market highs of the top trending coins. The sentimental evaluation has been done by extracting Twitter trends and hashtags thereby judging the sentiments of the microblogging site and paving a way to predict the exact emotion of investors all over the globe. Findings: Finally, we believe that this research has immense capability to predict the market speculation using the various technologies that are still in the rudimentary phase and has a lot of potential in invoking the interest of researchers and providing useful data/cognizance for future investors. © 2021 IEEE

关键词： Forecasting

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Temporal Facial Features for Depression Screening

Temporal Facial Features for Depression Screening

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Adjunct Proceedings of the 2022 ACM international Joint conference on Pervasive and Ubiquitous computing and the 2022 ACM international Symposium on Wearable Computers

作者： Ricardo Flores M. L. Tlachac Avantika Shrestha Elke Rundensteiner Data Science Worcester Polytechnic Institute United States Information Systems and Analytics Bryant University United States Computer Science/DAISY Lab Worcester Polytechnic Institute United States

ISBN: (纸本)9781450394239

Depression is a common and debilitating mental illness. Given the shortage of mental health professionals, there are delays in depression detection. Interviews conducted by virtual agents could expedite depression screenings. While the interview audio and transcript have received more attention, facial features offer an attractive privacy-preserving screening modality. Thus, we conduct a comprehensive comparative evaluation of the effectiveness of temporal facial features to screen for depression. We extract time series of eye gaze, landmark, and action unit features from video responses to 15 clinical interview questions. We input them into CNN, LSTM, and recurrent convolutional neural network (RCNN) models. An extra attention layer proved critical for CNN and LSTM performance. For a general wellbeing question, eye gaze features screened for depression with an F1 of 0.81. Our study informs the use of temporal facial features in future digital mental illness screening technologies.

关键词： Recurrent Neural Network Convolutional Neural Network Time Series Classification Digital health Recurrent Convolutional Neural Network

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Computational Aspects of Solving Grid Equations in Heterogeneous computing Systems 16th

Computational Aspects of Solving Grid Equations in Heterogen...

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16th international conference on Parallel computing technologies, PaCT 2021

作者： Sukhinov, Alexander Litvinov, Vladimir Chistyakov, Alexander Nikitina, Alla Gracheva, Natalia Rudenko, Nelli Don State Technical University Rostov-on-Don Russia Azov-Black Sea Engineering Institute of Don State Agrarian University Zernograd Russia Southern Federal University Rostov-on-Don Russia

ISBN: (纸本)9783030863586

The prediction of environmental disasters, both technogenic and natural, is currently based on advances in mathematical modeling. The high cost and costly maintenance of computing clusters actualizes the research in the field of heterogeneous computing. One of the directions of them is to maximize the use of all available hardware resources, including the central processor and the video adapters (GPU). The purpose of the research is to develop an algorithm and a software module that implements it for solving a system of linear algebraic equations (SLAE) by the modified alternating-triangular iterative method (MATM) (self-adjoint and non-self-adjoint cases) for the hydrodynamics problem of shallow water using NVIDIA CUDA technology. The conducted experiment with the flow distribution along the Ox and Oz axes of the computational grid at a fixed value of the grid nodes along the Oy axis allowed reducing the implementation time of one step of the MATM on the GPU. A regression equation was obtained at the experimental data processing in the Statistica program, on the basis of which it was found that the implementation time of one step of the MATM on the GPU is affected only by the number of threads along the axis Oz. The optimal two-dimensional configuration of threads in a computing unit executed on a single thread multiprocessor is determined, in which the calculation time on the GPU for one step of the MATM is minimal. © 2021, Springer Nature Switzerland AG.

关键词： Iterative methods

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An Efficient Techniques For Disease Prediction From Medical data Using data Mining And Machine Learning

An Efficient Techniques For Disease Prediction From Medical ...

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international conference on Contemporary computing and Informatics (IC3I)

作者： Bharat Bhushan Agrawal Husam H. Abdulmughni Ali A. Al-Bakhrani Pranoti Durgadas Nage Sushma Jaiswal Vikas Tripathi Department of Computer Science & Engineering IFTM University Moradabad Uttar Pradesh Department of Information Technology Faculty of computer and IT Sana'a University Sana'a Yemen Department of Computer Science Technique Leaders College Sana’a Yemen Faculty of Administrative and Computer Sciences Albaydha University Yemen Usha Mittal Institute of Technology SNDT Women's University Mumbai Juhu Campus Santacruz (W) Mumbai Department of Computer Science & Information Technology (CSIT) Guru Ghasidas Vishwavidyalaya (A Central University) Bilaspur (C.G.) India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

Individuals are susceptible to numerous ailments in today's global setting, with people living highly automated lives under tremendous job pressure, both at home and at work. Such disorders have recently been on the rise at an alarming pace. As a result, the healthcare business must assume a prominent position soon, accountable for people's health, a better society, and a successful country at large. Healthcare prices are rising as the demand for health amenities grows. Healthcare facilities with improved detection, diagnostics, and treatment procedures are desperately needed. With growing digitization and computing techniques in place, a massive amount of data is being generated and used for diagnostic and detection approaches. This data might be used to obtain data for anticipating disease, commencing preventative measures, and improving treatment procedures long before the diseases progress. Powerful computing technologies must be used to build the finest intelligence of forecasting and decision-making abilities evaluated by an expert. The purpose of this paper is to examine the effectiveness of various classification techniques, such as Naive Bayes (NB), J48, REF Tree, Metaheuristic Optimization (SMO), and Linear regression (LR), on data sets pertaining to cardiovascular disease (CVD). Employing the WEKA tool, an analysis was designed to test the efficacy of several strategies using illness data sets obtained from UCI respiratory. Different categorization methods are used to compare the values of various metrics, such as the percentage of right classifications, the recall, the F-measure, and the time required.

关键词： Measurement Metaheuristics Linear regression Medical services Machine learning Internet Informatics

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Topic Modelling of ongoing conflict between Russia and Ukraine

Topic Modelling of ongoing conflict between Russia and Ukrai...

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Trends in Quantum computing and Emerging Business technologies (TQCEBT), international conference on

作者： Pradhan Nayak J V N Lakshmi Vandana V. Bhagat Liverpool John Moores University Liverpool UK Patel Institute of Science and Management Sunstone Bangalore India CHRIST (Deemed to be University) India

ISBN: (纸本)9781665453622

Online news sites provide hotspots to extract popular ratings and opinions on a wide range of topics. Realizing what individuals are referring to and understanding their concerns and suppositions is exceptionally significant to organizations and political missions. Furthermore, it is incredibly difficult to physically peruse such enormous volumes of data and gather the themes. Keeping in mind the prevailing plight of war-torn nations such as the recent conflict between Russia and Ukraine. This study performs aims to perform topic modelling using LDA (Latent Dirichlet Allocation) and text analysis on datasets collected from various online news websites. To increase the accuracy and efficacy of the topic modelling, a comparative analysis is proposed that elevates the performance of machine learning models. This study also develops an algorithm where the entire process can be automated from the point of data collection to finding optimum array of topics in the given dataset. Searching for insights from the collected information can therefore become very tedious and time-consuming. Topic modelling was designed as a tool to organize, search, and understand vast quantities of textual information. The topic model using LDA was utilized to do a text analysis for this research. In the beginning, researchers have scraped a total of 1178 articles that covered the war conflict between Russia and Ukraine from December 1, 2021, to May 16, 2022. After that, researcher built the LDA model and modified hyper parameters based on the coherence score Cv that was used for the model evaluation technique. When using the most effective model, prominent topics, and representative documents pertaining to each topic, topic allocation among the documents, and potential enhancements are covered in the last section.

关键词： Analytical models Text analysis Quantum computing Machine learning algorithms Computational modeling Organizations Machine learning

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Establish a smart healthcare system with AIoT for Chinese Medicine

Establish a smart healthcare system with AIoT for Chinese Me...

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IEEE international conference on Orange technologies (ICOT)

作者： Chun-Cheng Wei Ching-Wen Chen Lon-Chen Hung School of Software and Big Data Changzhou College of Information Technology Department of Multimedia & Game Science Lunghwa University of Science and Technology Taoyuan Taiwan R.O.C Department of Electronic Engineering Lunghwa University of Science and Technology

ISBN: (纸本)9781665454209

This study establishes an intelligent medical system for diagnosis and treatment of Chinese medicine. Temperature and heart rate were measured using a variety of Internet of things technologies, including the WeMos D1 Mini with ESP8266 integrated and thermistors, and then the resistance was learned to relate to temperature via a neural network. In addition, the AD8232 biomedical sensor and the WeMos D1 Mini were used for ECG measurements and heart rate conversion. A self-written mobile application designed with multiple units to collect data from a variety of patients. The tongue diagnosis unit can recognize the characteristics of tongue substance and coating. The pulse examination unit adopts the five layer pulse method of Yaowangmai pulse. The inquiry unit adopts the syndrome differentiation method of Dr. Shaogong Shen. These IoT data, information if the patient's tongue diagnosis, five-layer pulse examination data and various inquiry data can all be read and transmitted to the cloud. The intelligent medical system of Chinese medicine in this study can also make preliminary treatment recommendations based on a variety of patient data and provide corresponding treatment drugs

关键词： Temperature measurement Temperature sensors Heart rate Drugs Cloud computing Temperature distribution Tongue

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Detailed analysis of Machine Learning and Deep Learning technologies for Web Security

Detailed analysis of Machine Learning and Deep Learning tech...

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international conference on Contemporary computing and Informatics (IC3I)

作者： Chhaya Nayak Ramakrishnan Raman P Dileep Anita Gehlot Gogula Laxmi Priyanka Dhiraj Kapila G. H. Raisoni College of Engineering and Management Pune Maharashtra India Symbiosis Institute of Business Management Pune & Symbiosis International (Deemed University) Pune Maharashtra India Department of Computer Science and Engineering Malla Reddy College of Engineering and Technology Kompally Hyderabad Telangana India Uttaranchal Institute of Technology Uttaranchal University Dehradun Uttarakhand India Electronics and Communication Engineering St Martin's Engineering College Secunderabad India Department of Computer Science & Engineering Lovely Professional University Phagwara Punjab India

As the Internet has expanded, web attacks have become more sophisticated, and web security is currently in a poor position. Websecurity technology is used to protect networks, programmes, and systems from webattacks. Webattacks are capable of getting access to delete or alter sensitive data, demand payment from users, and hinder company activities as usual. Currently, many technologies are becoming smarter than hackers and people, which make it challenging to put websecurity precautions into practise. Notwithstanding the primary writing surveys on ML and DL methods for network investigation and interruption recognition, this review report contains a concise instructional exercise portrayal of every ML/DL strategy. In light of the sequential or warm connections between's the works that addressed every strategy, they were listed, checked out, and summed up. We feature probably the most generally utilized network datasets; make sense of the troubles utilizing ML/DL for websecurity, and make ideas for future review since information are so essential to ML/DL strategies.

关键词： Deep learning Correlation Computational modeling Intrusion detection Writing data models Internet

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Best Fit Radial Kernel Support Vector Machine for Intelligent Crop Yield Prediction Method 2nd

Best Fit Radial Kernel Support Vector Machine for Intelligen...

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2nd international conference on Machine Learning and Information Processing, ICMLIP 2020

作者： Hanuman, Vijay Pinnamaneni, Krishna Vamsi Singh, Tripty Department of Computer Science and Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9789813348585

Farming is the foundation of a country’s economy and it’s essential to meet the growing demand of the market. Therefore, it’s important to develop technologies that enhance the agricultural yield. The yield of a crop depends on various factors like climate, soil attributes, and so on, which forms a huge volume of data. Machine learning is one of the efficient technologies that helps in the identification of patterns and rules in large datasets. This paper proposed a method named Intelligent Crop Yield Prediction Method (ICYPM) to solve crop selection problem, and maximize net yield rate of crop. The authors have implemented and analyzed ICYPM with regression algorithms like SVM and RF. RMSE with 4 other parameters were used to compare the algorithms. In this study SVM fared the best as it efficiently predicted the climate and yield. This research has been carried out in collaboration with Indian Meteorological Department of India. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Crops

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Seamless Service Evolution: Enhancing Customer Satisfaction Using AWS-Driven AI Chatbots for Restaurant Ordering

Seamless Service Evolution: Enhancing Customer Satisfaction ...

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Sustainable Expert Systems (ICSES), international conference on

作者： Mohammed Jaffer Ali M R V Vyshnavi Konda V S Harshith Kumar Sravya Vishnubhatla Shinu M Rajagopal Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

In the current restaurant business environment, factors have emerged in the manner in which consumers are attended to and the ways orders are placed. As good as these traditional systems may be, they do not offer the optimum flexibility and consumer convergence that today's client demands for fast and convenient services. Recent approaches using only basic form of automation and rule-based chatbots do offer some enhancements but lack adequate capabilities to deal with huge volumes of orders, custom recommendations and scalability according to demand. Further, the existing systems have problems with scalability, versatility to different customers' trends and needs, and real-time interactions. To overcome these challenges our work, will utilize modern advances in artificial intelligence and cloud technology. The proposed system uses Amazon Lex for NLU, A WS Lambda for processing multiple booking requests simultaneously without any intervention from the users and Amazon Polly for making interaction more interactive with personalized audio. In addition, Amazon Rekognition and A WS S3 for images will be used for analysis of images and proper storage of data respectively, enhancing the customer satisfaction by recommending products based on a past order. All these technologies integrated with A WS ecosystem in our solution will provide convenience, efficiency and passion to restaurant business proprietors to fulfill the increasing demands of modern customer. The system also supports linkage with some commonly used applications such as WhatsApp, guaranteeing that the customers receive timely updates. Thus, this solution is designed to create an improved status of restaurant ordering and eliminate the problematic experiences placed in current schemes.

关键词： Scalability Customer satisfaction Chatbots Market research Real-time systems Image storage Artificial intelligence Internet telephony Optimization Business

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