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A Hybrid Approach Towards Data optimization for Cloud Based IoT Environment 4

A Hybrid Approach Towards Data optimization for Cloud Based ...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Pal, Souvik Jhanjhi, N.Z. Akila, D. Abdullah, Monir Humayun, Memona Kumar, Sandeep Kumar, Rahul Saveetha Institute of Medical & Tech. Sciences Dept. of Management IS Saveetha College of Liberal Arts & Sciences Chennai602105 India Sister Nivedita University Dept. of CS and Engineering Kolkata700156 India School of Computer Science SCS Taylor's University Subang Jaya47500 Malaysia SIMATS Deemed University Saveetha College of Liberal Arts & Sciences Dept. of Computer Applications Chennai602105 India University of Bisha Department of CS and AI College of Computing & IT Bisha67714 Saudi Arabia School of Arts Humanities & Social Sciences University of Roehampton Department of Computing LondonSW15 5PJ United Kingdom India KKR and KSR Institute of Technology and Sciences Division of Research and Development Lovely Professional University Phagwara Andhra Pradesh Guntur144411 India

ISBN: (纸本)9798350353266

An innovative method for data optimization in the Internet of Things (IoT) assister sensing devices is presented in this research. The many elements of the cloud platform with IoT assistance are addressed. A fresh design for IoT-aided sensor networks is introduced to monitor the environment. A Data Optimisation in Cloud Internet of Things (DO-CIoT) is proposed in this paper to optimize the data stored in the cloud and presents methods to access the data in end devices. The suggested technique uses a membership function to improve data allocation while considering all node data. The suggested model is assessed regarding performance ratios, reliability percentage, coverage ratios, and sensing error and contrasted with two-stage efficiency enhancement. A mean performance ratio of 89%, reliability of 91%, coverage of 89 © 2024 IEEE.

关键词： Cloud platforms

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INVESTIGATION of GIS BASED MOBILE ROUTE PLANNING-NAVIGATION APPLICATIONS for TOURISM ACTIVITIES 6

INVESTIGATION of GIS BASED MOBILE ROUTE PLANNING-NAVIGATION ...

引用

6th International Conference on Smart City Applications

作者： Karas, Y.K. Atay, L. Sevinc, H.K. Duru, A. Aksaray University Dept. of Tourism Guidance Faculty of Tourism Aksaray Turkey Canakkale Onsekiz Mart University Dept. of Travel Management and Tourism Guidance Faculty of Tourism Canakkale Turkey Sinop University Dept. of Computer Technologies Ayanclk Vocational School Sinop Turkey Karabuk University Dept. of Biomedical Engineering Faculty of Engineering Karabuk Turkey

Mobile devices can be used in many areas such as shopping, entertainment and education. The number of mobile applications which provides information about a city or region, helps to find route by navigation and makes accommodation plans with food and beverage suggestions, is also increasing. Since these devices are portable and have access to internet, they emerges as a new and important potential for tourism sector. In particular, the increase in the use of route planning and navigation applications, which are location based services within the scope of Geographic Information Systems, has also affected the tourism sector. Which route planning and navigation applications are used extensively by tour operators, tourist guides and individual visitors ? What are the strengths and weaknesses about these applications? Answers to such questions were sought in this study. © Author(s) 2021. CC BY 4.0 License.

关键词： Geographic information systems

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Expression of Concern for: Identification of Texts Using ML Algorithm for The Accurate Recognition

Expression of Concern for: Identification of Texts Using ML ...

引用

International Conference on Power Energy, Environment and Intelligent control (PEEIC)

作者： M Jahir Pasha K. Kajendran Shrikala Deshmukh G. Kumar N Sreekanth Sachin Pund Computer Science and Engineering G.Pullaiah College of Engineering and Technology Kurnool Dept. of Computer Science and Engineering Panimalar Engineering College Chennai Amity University Mumbai Bharati Vidyapeeth College of Engineering Pune India Faculty of Management SRM Institute of Science and Technology Kattankulathur Mallareddy Engineering College for Women Hyderabad Department of Industrial Engineering Shri Ramdeobaba College of Engineering and Management Nagpur Maharashtra India

ISBN: (数字)9798350357769

ISBN: (纸本)9798350357776

A Comparative evaluation of device mastering Algorithms for textual content classification textual content category is an vital assignment in herbal language processing, where the aim is to routinely assign a label or category to a given text file. With the increasing quantity of text facts available in diverse domain names, the need for efficient and accurate text class algorithms has turn out to be extra vital. In this paper, we gift a comparative analysis of machine mastering algorithms for text category. We experiment with one of a kind algorithms, such as aid Vector Machines, Naive Bayes, Random Forests, and Multilayer Perceptron, on numerous benchmark datasets. Our results reveal that the choice of set of rules considerably affects the overall performance of textual content classification, and there may be no unmarried high-quality algorithm that works well for all datasets. We also provide insights into the strengths and weaknesses of each set of rules and talk their applicability in specific eventualities. Our findings can guide researchers and practitioners in selecting the maximum suitable system gaining knowledge of algorithm for their text category duties. Textual content classification, device learning, assist vector machines, naive bays, random forests, multilayer perceptron, and comparative analysis.

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Identification of Texts Using ML Algorithm for The Accurate Recognition

Identification of Texts Using ML Algorithm for The Accurate ...

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International Conference on Power Energy, Environment and Intelligent control (PEEIC)

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Impact of blended education system on outcome-based learning and sector skills development

Impact of blended education system on outcome-based learning...

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Annual Information Technology, Electromechanical engineering and Microelectronics Conference (IEMECON)

作者： Suplab Kanti Podder Debabrata Samanta Benny Thomas Soumi Dutta Abhishek Bhattacharya Dept. of Commerce and Management Dayananda Sagar College of ArtsScience and Commerce Bangalore Karnataka India Dept. of Computing and Information Technologies RIT Kosovo (A.U.K) Rochester Institute of TechnologyRIT Global Campus Prishtina Kosovo Europe Dept. of CSE CHRIST University Bangalore Karnataka India Dept. of Computer Science & Applications Institute of Engineering & Management Kolkata India

An effective education system transforms the teaching and learning process into innovative idea generation and independent working ability. A blended education system is the representation of effective education that connects the teachers, students, and educational institutions for content development, delivery of effective teaching methods, and choice-based learning. The motive for initiating the research work was to address the demand for outcome-based learning in society that can fulfill the sector-wise human resource requirements and sector skill development. A blended education system helps to design effective courses and degrees with the capacity of choosing subjects, lectures, and teachers either in online or offline mode of education. The system may also assist in preparing the learning pattern like classroom-based learning, internship-based learning, or learning through project works. The researchers identified the dependent and independent variables with the help of expert opinion. The questionnaire was designed with all relevant questions based on the variables and refined through a pilot study. The research outcomes are described by understanding the nature of quantitative data using statistical tools like frequency distribution, t-test, and ANOVA test with the connectivity of qualitative data and the reality of social issues.

关键词： Education Transforms Microwave communication Microwave theory and techniques Internet of Things Analysis of variance

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Deep Learning: A New Way of Looking into Fashion

Deep Learning: A New Way of Looking into Fashion

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Advances in Computing, Communication control and Networking (ICAC3N), International Conference on

作者： Josh Hanley Thomas Roopashree S Jacob Charles Joy Haseeb Abdullah Kollarath Arun Siby Dept. CSE-AI and ML JAIN (Deemed-to-be University) Bengaluru India Dept. Computer Science and Engineering RV Institute of Technology and Management Bengaluru India

Currently, Fashion-Fit using AI focuses on finding the perfect fit for users using GAN’s (General Adversarial Net¬works) and various methods proposed in the Artificial Intelligence Industry. However, due to changes in fashion trends and various preferences of the "type of fit" users want for their clothing the prior methodology is not meant for the perfect fit. These days a lot of people wear tighter or even get an oversized fit based on the user’s preference. In this paper we use Deep Learning techniques like CNNs and GANs to implement neural body fit by looking at various human body poses from large datasets and concatenating this data to make an accurate "desired" fit based on different human poses. This will set a new benchmark for virtual try-on. In the model being proposed, we are building Convolutional Neural Networks (CNNs) for body segmentation to make it easier to map a human pose. We will then map the human body using CNNs but not necessarily to find the "perfect fit". The CNNs will be used to make measurements that are similar to sizes available within the fashion industry allowing users to see how they would fit within those garments virtually. We will then use 2D renders, using uploaded pictures of the user from 4-5 different angles in order to allow the user to see how he/she would look in the outfit of their choice. These methods can give an accurate prediction helping every fashion e-commerce business. Personalized size and fit recommendations are critical for a fashion e-commerce business. Predicting the optimal fit increases consumer happiness while lowering expenses associated with size-related returns.

关键词： Industries Deep learning Biological system modeling Clothing Neural networks Size measurement Convolutional neural networks

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Improvement of Flexibility in the Integrated Energy Systems 15

Improvement of Flexibility in the Integrated Energy Systems

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15th International Scientific-Technical Conference on Actual Problems of Electronic Instrument engineering, APEIE 2021

作者： Bayasgalan, Zagdkhorol Bayasgalan, Tsetsaee Hardt, Wolfram Reva, Ivan L. Power Engineering School Mongolian University of Science and Technology Dept. of Electrotechnics Ulaanbaatar Mongolia School of Business Administration and Humanities Mongolian University of Science and Technology Dept. of Technology Management Ulaanbaatar Mongolia Chemnitz University of Technology Computer Engineering Professorship Faculty of Computer Science Chemnitz Germany Novosibirsk State Technical University Dept. of Data Security Novosibirsk Russia

ISBN: (纸本)9781665434089

This paper presents possibilities to improve flexibility for integrated energy systems using optimization techniques in a capacity market. Long-Term planning of the energy system is considered. Meeting the rapidly growing demand for electricity requires a complete integrated power system solution that must support consumers, stakeholders, and regional operations. The rapid change in the global economy is creating an energy transition towards the strategic future of power systems and energy markets-a new era of flexible grids, or the era of energy flexibility. The proposed capacity market model improves the competitiveness of the energy market system in accordance with the assessment and flexibility of electricity security. It also presents recommendations for evaluating the dimensions of the integrated energy system. All participants in the energy market can accept it as a useful tool to support decision-making and increase the sustainability of the energy system. The smart grids provide the reliability of power supply in an increasingly complex operating environment, achieved through observability, digitalization, and an increase in the number of measurements. Power supply and grid companies remain the main drivers of change. © 2021 IEEE.

关键词： Power markets

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A Secure Softwarized Blockchain-based Federated Health Alliance for Next Generation IoT Networks

A Secure Softwarized Blockchain-based Federated Health Allia...

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2021 IEEE Globecom Workshops, GC Wkshps 2021

作者： Das, Puja Singh, Moutushi Roy, Deepsubhra Guha HMM College for Women Dept. of Computer Science Dakshineswar Kolkata700076 India Institute of Engineering and Management Dept. of Information Technology Kolkata Salt Lake700091 India Indian Institute of Science Dept. of Computational and Data Sciences Bangalore560012 India

ISBN: (纸本)9781665423908

Medical health care centres are seen as a potential paradigm for dealing with large amounts of data utilizing artificial intelligence (AI). The fast growth in the massive volume of data created by connected devices in the health paradigm brings up new opportunities for data sharing to improve the quality of service for developing applications. Traditional AI approaches frequently need centralized data gathering and model training within a single company, which is a significant flaw owing to the lack of privacy and security of raw data transfer through mobile networks. The leakage of sensitive data can result in substantial consequences for the providers, in addition to financial loss. For that, we first build a blockchain-enabled safe data sharing architecture for distributed multiple parties in this paper. Then, by adding suggested privacy-preserved federated learning, we turn the data-sharing problem into a machine-learning challenge. Recommendations are based on the user's previous search values, and data privacy is protected by providing the data model rather than the raw figures. Finally, we include federated learning into the permissioned blockchain consensus process, allowing the consensus computing effort to be used for federated training. According to numerical findings generated from data sets, the suggested data sharing method delivers good accuracy, high efficiency, and increased security. © 2021 IEEE.

关键词： Internet of things

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Quasi-Oppositional Satin Bowerbird with Deep Learning based Content based Image Retrieval 6

Quasi-Oppositional Satin Bowerbird with Deep Learning based ...

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6th International Conference on Computing Methodologies and Communication, ICCMC 2022

作者： Singh, Devesh Pratap Joseph, Susheel George Selvi, V. Tamil Karunakaran, S. Appasami, G. Jegajothi, B. Graphic Era Deemed to be University Department of Computer Science Engineering Dehradun248002 India Kristu Jyoti College of Management and Technology Dept. of Computer Application Kottayam India R.M.D. Engineering College Department of ECE Tamil Nadu 601206 India Vardhaman College of Engineering Department of ECE Hyderabad501218 India NIT Department of Computer Applications Tamilnadu Tiruchirappalli India Chennai Institute of Technology Centre for Energy Research Tamil Nadu Chennai India

ISBN: (纸本)9781665410281

Content-based image retrieval (CBIR) is commonly employed to retrieve images from a massive set of unlabeled images. The design of CBIR model faces several limitations, as it is mainly based on the extraction of image features to calculate the similarity amongst the query image (QI) and database images. The recent advances of deep learning (DL) models help to attain remarkable retrieval outcomes. In this view, this paper presents a novel quasi-oppositional satin bowerbird optimizer with Densely Connected Networks (QOSBO-DCN) for CBIR. The proposed QOSBO-DCN technique aims to properly retrieve the images related to the QI in an effective and automated manner. The proposed QOSBO-DCN technique derives a DenseNet-77 model as a feature extractor to derive feature vectors from the QI and database images. Besides, the QOSBO algorithm is utilized to adjust the hyperparameter values of the DenseNet-77 model in such a way that the retrieval performance can be improved. Additionally, Euclidean distance is used as a similarity measurement approach to determine the highly resembling images and retrieve them. The simulation analysis of the QOSBO-DCN technique is performed using Corel10K dataset and the results reported the betterment of the QOSBO-DCN technique over the existing techniques. © 2022 IEEE.

关键词： Deep learning

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Ontological Analysis of the Digital Crime Conceptual Model

Ontological Analysis of the Digital Crime Conceptual Model

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International computer Sciences and Information Technologies (CSIT)

作者： Lidiia Vlasenko Nataliia Lutska Nataliia Zaiets Vitaliy Lysenko dept. of Software Engineering and Cyber Security State University of Trade and Economics Kyiv Ukraine dept. of Automation and Computer Technologies of Control Systems National University of Food Technologies Kyiv Ukraine dept. of Automation and Robotic Systems National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine

A conceptual ontological model of Digital Crime has been developed, consisting of five non-empty classes. Identification and classification were carried out according to the experience of domestic and foreign experts. The central place of the model is occupied by objects of the Digital Crime class, which have their own attributes and are connected by various links with other objects. Objects of some classes are filled in by users, others are formed by knowledge engineers. Based on the ontological analysis, SWRL-rules are constructed that automatically form links between objects of the custom classes Digital crime and Digital evidence with objects of the Type of crime and Criminal Liability classes. The last class contains articles of the Criminal Code of Ukraine on criminal liability for digital crimes. The ontological model is developed taking into account possible requests to it. Such queries can be formed on the basis of direct relationships between human and automatic concepts. And also contain logical chains to concepts that do not contain an explicit link in the ontology. It is assumed that the model can be expanded and supplemented. The ontological model of Digital Crime can be used to share information, develop new systems and applications, improve information retrieval and ensure its accordance.

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