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Cognitive intelligent Transportation System for smart Cities 10

Cognitive Intelligent Transportation System for Smart Cities

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10th international conference on Advanced computing (ICoAC)

作者： Raja, Gunaskaran Ganapathisubramaniyan, Aishwarya Selvakumar, Madhumitha Sri Ayyarappan, Thiruveni Mahadevan, Karthikeyan Anna Univ Dept Comp Technol MIT Campus Chennai Tamil Nadu India

ISBN: (纸本)9781728103532

Recent advancement in hardware, software and communication technologies have culminated in improvisation of safety aspects in vehicular communication networks. Vehicular Ad hoc NETworks (VANETs) is used to establish network among the vehicles which paves way for intelligent Transportation System. In spite of myriad of benefits in VANETs, there exists enormous amount of concerns such as establishing privacy, safety monitoring, reliable connectivity, high bandwidth, less delay with guaranteed Quality of Service (QoS). Establishing security services in the VANET is the key to make any application successful in the safe driving condition. As part of intelligence in cognitive systems, Machine Learning algorithms make it possible for constantly mine data and acquire knowledge through advanced analytics. We use clustering, a machine learning algorithm for clustering the vehicles at intersection and grouping the messages in each cluster and filtering the redundant message. Naive Bayes algorithm is applied over the filtered message for classifying the messages and assigning priority before transmission. Further, our proposed Cognitive-intelligent Transportation System (CITS) model provides efficient channel utilization and the accuracy is improved compare to the existing methods of classification. Finally, performance and accuracy of proposed approach are evaluated.

关键词： Vehicular Ad hoc NETworks Machine Learning intelligent Transportation System Clustering Naive Bayes

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Association Rules Mining for Predictive analytics in IoT Cloud System 9th

Association Rules Mining for Predictive Analytics in IoT Clo...

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9th Annual international conference of the Biologically-Inspired-Cognitive-Architectures-Society (BICA) on Biologically Inspired Cognitive Architectures (BICA) held as Part of the Joint Multi-conference on Human-Level Artificial Intelligence (HLAI)

作者： Kireev, Vasiliy S. Guseva, Anna, I Bochkaryov, Pyotr, V Kuznetsov, Igor A. Filippov, Stanislav A. NRNU MEPhI Kashirskoe Shosse 31 Moscow 115549 Russia Konnekt Ltd Lazenki 6 St2 Moscow 119619 Russia

ISBN: (纸本)9783319993164;9783319993157

The Internet of Things is one of the fastest growing areas of research currently. A promising area for the introduction of this technology is housing and communal services, for which the reduction of accidents, increasing efficiency, in general, focus on transparency, personalization of services and payments for the end user are relevant. This article is devoted to the development and testing of predictive algorithm for predicting the need for repair of various units within the smart home, such as heating, ventilation and air conditioning. The basis for the algorithm is the association rules mining. The paper presents the results of experiments and the directions for further improvement of the algorithm.

关键词： IoT Association rules mining data mining Cloud technologies Predictive analytics

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Prevalence of HCV in Shopian: Using data Visualization (data Mining) 3rd

Prevalence of HCV in Shopian: Using Data Visualization (Data...

引用

3rd international conference on intelligent computing and Communication (ICICC)

作者： Ahmad, Zubair Seethlani, Jitendra Gul, Sheikh Babar Sri Satya Sai Univ Technol & Med Sci Dept Comp Sci Sehore 466001 MP India Mini Secretariat Chief Med Off Block B Shopian 192303 J&K India

ISBN: (纸本)9789811510847;9789811510830

data mining is an important subfield of data Science and is also called knowledge discovery within a database. In this paper, we actually use data mining visualization techniques to analyze the huge database of District Shopian and assume prevalence rate of HCV in Specific Area (Vehil) of District Shopian (J&K). Health Department of District Shopian uses different types of registers to maintain the data of patients. During survey, we go through the registers to collect data of symptomatic HCV patients analyze it by maintaining the disease trends. data mining analytical tools were being used to detect prevalence of HCV infection distribution and also identify the reasons that contributed to such widespread of HCV in the area of Vehil

关键词： Descriptive analytics Predictive analytics data visualization techniques hepatic C CASE STUDY

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Machine Learning Algorithm in smart Farming for Crop Identification 1

引用

international conference on Computational Vision and Bio-Inspired computing (ICCVBIC)

作者： Vindya, N. D. Vedamurthy, H. K. SIT Dept CSE Tumakuru India

ISBN: (数字)9783030372187

ISBN: (纸本)9783030372187;9783030372170

Agriculture is the main supporting sector of Indian economy and most of the rural population's livelihood depends on it. Crop yielding has been decreasing day by day because it's growth mainly depends on monsoon which is highly unpredictable in India. Further, the crop growth also depends on soil parameters, which varies due to ground contamination. For increasing the crop productivity, there is a need to incorporate newtechnologies in the field of agriculture. By utilizing new technologies like Machine Learning and Internet of Things applications in the field of agriculture will be enhance the financial growth of the country. Here, we have presented a framework which mainly focuses on agriculture in Tumakuru district of Karnataka that uses Naive Bayes classification method to suggest the farmers with the best suitable crop for sowing in that particular environmental condition.

关键词： Machine learning Gaussian Naive Bayes data analytics Prediction

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UAV Deployment Design for Maximizing Effective data with Delay Constraint in a smart Farm

UAV Deployment Design for Maximizing Effective Data with Del...

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IEEE/CIC international conference on Communications in China (ICCC)

作者： Zhao, Junwei Wang, Ying Fei, Zixuan Wang, Xue Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

ISBN: (纸本)9781728173276

data transmission and real-time processing of Internet of Things (IoT) devices in remote unmanned area are becoming a challenging issue. In this paper, an Unmanned Aerial Vehicle (UAV) enabled computing system is investigated in a smart farm, where multiple UAVs are deployed to make intelligent decisions based on data collected by sensors. First, considering the delay constraint of data processing, the concept of effective data is introduced. Next, the effective data of farm monitoring devices (FMDs) is maximized by jointly optimizing computing and communication resources allocation and the deployment of UAVs. The formulated problem is a combinatorial optimization problem that is hard to tackle. Furthermore, this problem is transformed into two sub-problems and a two-layer iterative optimization algorithm is proposed to get an approximate optimal solution. Finally, simulation results demonstrate that the proposed algorithm has a significant increase in terms of effective data.

关键词： smart farm delay constraint effective data maximization multi-UAVs deployment mobile edge computing

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smart Retail Store Surveillance and Security with Cloud-Powered Video analytics and Transfer Learning Algorithms 2

Smart Retail Store Surveillance and Security with Cloud-Powe...

引用

2nd international conference on intelligent Cyber Physical Systems and Internet of Things (ICoICI)

作者： Sivalakshmi, P. Kavitha, U. Usha, R. Pattanaik, Ojasvi Maniraj, S. P. Srinivasan, C. RMK Coll Engn & Technol Dept Elect & Commun Engn Chennai Tamil Nadu India Veltech Rangarajan Dr Sagunthala R&D Inst Sci & T Sch Commerce Chennai Tamil Nadu India SA Engn Coll Dept Math Chennai Tamil Nadu India JNTU Dept Comp Sci & Engn Vignan Inst Management & Technol Women Hyderabad India SRM Inst Sci & Technol Fac Engn & Technol Dept Data Sci & Business Syst Chennai Tamil Nadu India Saveetha Univ Saveetha Sch Engn Saveetha Inst Med & Tech Sci Dept Comp Sci & Engn Chennai Tamil Nadu India

ISBN: (纸本)9798331540661;9798331540678

Retail security and surveillance systems have transformed into data-driven smart solutions thanks to the fast development of technology. This paper presents a new method for improving retail shop security and surveillance by combining transfer learning (TL) algorithms with cloud-powered video analytics. It is now possible to remotely interpret, analyze, and store massive amounts of video data produced by security cameras using cloud computing capabilities. In addition, TL methods are used to customize pre-trained deep learning models for retail settings, allowing for precise event and anomaly identification. This method drastically reduces the computing load on local devices and makes surveillance systems more accurate and responsive. One of the main advantages of the proposed system is that it can monitor the store premises in real time. It can also automatically identify suspicious actions like stealing or damage and inform staff or security about possible risks. By using cloud infrastructure, the system can also adapt to changing security needs and expand dynamically to handle increasing data volumes. Retailers may find useful information for refining shop layouts, product placements, and marketing tactics by integrating cloud-powered video analytics, which allows advanced functions like crowd analysis, heat mapping, and customer behavior analysis. A thorough plan for smart retail store security and surveillance, using cloud computing, video analytics, and TL algorithms to build a strong and effective system for protecting digital retail spaces.

关键词： Real-time monitoring Anomaly detection Proactive alerting Crowd analysis Customer behavior Retail optimization

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Big data Processing and Artificial Intelligence at the Network Edge 22

Big Data Processing and Artificial Intelligence at the Netwo...

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22nd international conference on Transparent Optical Networks (ICTON)

作者： Olmos, J. J. Vegas Cugini, Filippo Buining, Fred O'Mahony, Niamh Truong, Thuy Liss, Liran Oved, Tzahi Binshtock, Zac Goldenberg, Dror Mellanox Technol Hakidma 23 IL-2069200 Yokneam Israel Conzorcio Nazl Interuniv Telecommunicaz CNIT Natl Lab Photon Networks Via G Moruzzi 1 I-56124 Pisa Italy Hiro MicroDataCtr Voorburg Netherlands Dell EMC Cork Ireland

ISBN: (纸本)9781728184234

The delivery of computing services including storage, databases, networking, software, analytics, intelligence and more is moving from the cloud towards the edge;among all technical requirements, the edge needs to be able to natively support artificial intelligence processes to enable new technological paradigms, such as smart Factory 4.0, healthcare assisted living, smart cities and advanced robotics, for example In this paper, we will present the enabling technologies to will be developed within the "Big data processing and artificial intelligence at the network edge" (BRAINE) project, one of the largest joint activities in Europe in the area of edge computing enabling artificial intelligence. BRAINE covers the full stack of a system solution, from subsystem integration up to artificial intelligence service provisioning, and includes four use cases of direct impact in the European ecosystem.

关键词： edge computing artificial intelligence computing infrastructure extreme scale analytics

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Integration and Implementation of intelligent Circuit System in Internet of Things Equipment 3

Integration and Implementation of Intelligent Circuit System...

引用

3rd international conference on data analytics, computing and Artificial Intelligence, ICDACAI 2024

作者： Sang, Yike Han, Xintong Zhao, Songling Chang, Mingsheng Weifang Engineering Vocational College Shandong Qingzhou262500 China

ISBN: (纸本)9798331519032

The integration and implementation of intelligent circuit system in Internet of Things (IoT) devices is one of the key technologies in the current digital era. With the continuous improvement of the intelligence of IoT equipment, intelligent circuit system, as its core component, undertakes the heavy responsibility of data acquisition, processing and transmission, and gives the equipment the ability of independent learning and decision-making. However, there are still many challenges to effectively integrate the intelligent circuit system into IoT devices and achieve efficient and stable operation. In this paper, the integration method and implementation technology of intelligent circuit system in IoT equipment are deeply discussed. In the aspect of hardware integration, the research emphasizes the importance of standardization and universal interface design, and how to reduce energy consumption through intelligent sleep technology and energy management strategy. Software integration involves selecting the appropriate operating system, communication protocol and data processing strategy to ensure that the intelligent circuit system can seamlessly interface with IoT devices and realize efficient data processing. In addition, the paper also discusses the selection and application of embedded system and microcontroller, as well as the selection and optimization of communication technology to meet the needs of different IoT devices. Through case analysis, this paper shows the integration and realization process of intelligent circuit system in smart home system, including hardware selection, software development, application of communication technology and application of data analysis and processing technology. The analysis of performance, stability and security shows that the smart home system performs well in real-time control, data processing and personalized experience. At the same time, it also points out the potential risks of the system in extreme cases and puts

关键词： Decision making

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Hybrid Method for Cluster Analysis of Big data 1st

Hybrid Method for Cluster Analysis of Big Data

引用

1st international conference on intelligent computing Techniques for smart Energy Systems (ICTSES)

作者： Dabas, Chetna Nigam, Gaurav Kumar Jaypee Inst Informat Technol Noida India

ISBN: (纸本)9789811502149;9789811502132

In big data analytics, deep interest in a communication known as computer-mediated has cropped up. While using traditional techniques, it is difficult to handle the data which is magnanimous. Hence, there exists a need for improved methods to handle this data since the past methods do not fit properly in all kinds of situations. Normally, there are various steps for the handling of big data like acquisition, preprocessing, and processing and analysis of this data in order to retrieve proper semantics out of that amount of data. In a similar context, clustering has evolved as a popular approach for organizing and analysis of big data. In the present research work, a hybrid method for analysis of big data is proposed. The hybrid approach consists of the blending of K-means, Ward hierarchical along with the interpolation technique. The evaluation of and validation of the proposed approach has been carried out for the city dataset in R language. In the present work, the number of clusters and the size of the data get varied while carrying out the results. The results of the proposed work reflect impressive execution times of the proposed method over the existing ones. The proposed method also presents possible recommendation for extracting specific semantics for providing insights to business recommendations.

关键词： Analysis Big data Clustering Hybrid method

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data Model Logger - data Discovery for Extract-Transform-Load 19

Data Model Logger - Data Discovery for Extract-Transform-Loa...

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19th IEEE international conference on High Performance computing and Communications (HPCC) / 15th IEEE international conference on smart City (smartCity) / 3rd IEEE international conference on data Science and Systems (DSS)

作者： Madhikermi, Manik Buda, Andrea Dave, Bhargav Framling, Kary Aalto Univ Sch Sci POB 15400 FI-00076 Espoo Finland

ISBN: (纸本)9781538625880

Information Systems (ISs) are fundamental to streamline operations and support processes of any modern enterprise. Being able to perform analytics over the data managed in various enterprise ISs is becoming increasingly important for organisational growth. Extract, Transform, and Load (ETL) are the necessary pre-processing steps of any data mining activity. Due to the complexity of modern IS, extracting data is becoming increasingly complicated and time-consuming. In order to ease the process, this paper proposes a methodology and the results of a pilot implementation, that aims to simplify data extraction process by leveraging the end-users' knowledge and understanding of the specific IS.

关键词： ETL database Trigger Reverse Engineering data Warehouse Information System Information Retrieval Process Mapping data Discovery

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