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4th International Conference on Communication, Information and Computing technology, ICCICT 2021

作者： Bhatia, Nishant Lalitkumar Shukla, Vinod Kumar Punhani, Ritu Dubey, Shish Kumar Bachelor of Information Technology Amity University Dubai United Arab Emirates Department of Engineering and Architecture Amity University Dubai United Arab Emirates Department of Information Technology ASET Amity University Sector – 125 Uttar Pradesh Noida India Department of Computer Science and Engineering Jaipur National University India

ISBN: (纸本)9781665404303

The world is witnessing a transition from in-store shopping to online shopping. E-commerce (Electronic commerce) giants such as Amazon, Alibaba, eBay etc. are leading the way towards this change. Much technological advancement are being made to ease the life of mankind with online shopping being the most notable. E-commerce is known to be a powerful instrument for transformation of business that gives companies the opportunity to upgrade their supply chain operations, improve their network, as well as provide better services to both customers and suppliers. Applying the techniques of online shopping that yield such advantages may not be possible without the presence of a well-organized approach to E-commerce security. Ecommerce organizations such as Amazon and Alibaba have also been using such techniques to ensure data protection. The most common of them all is the One Time Password (OTP), which is sent to a user when they make payments online for identity verification. On the other hand, Alibaba uses a unique Key Management System (KSM) which is a fully managed service that helps customers create, delete, and manage encryption keys to protect data. This system provides availability, reliability and elasticity alongside security and compliance. The paper also explore the importance of different security algorithms in Ecommerce domain. © 2021 IEEE.

关键词： Cryptography

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Advanced IOT Traffic Light Control System

Advanced IOT Traffic Light Control System

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International Conference on Advances in Computational Tools for Engineering Applications, ACTEA

作者： Martin Abou Hamad Antonio El Hajj Roy Abi Zeid Daou Ali Hayek Gaby Abou Haidar Biomedical Technologies Department Faculty of Public Health Lebanese German University Lebanon MART Learning Education and Research Center Lebanon Department of Computer Architecture and System Programming University of Kassel Kassel Germany Engineering Faculty American University of Science and Technology Zahle Lebanon

For several years, traffic congestion has been a major problem in big cities where the number of cars and different means of transportation has been increasing significantly. The problem of congestion is becoming more and more critical, and if not treated smartly this issue will negatively affect drivers by wasting time and fuel gas while waiting for hours in lanes. This paper presents a new and smart way to mitigate this issue in an affordable cost, minimum processing power, and low power consumption. This concept takes into consideration the majority of the cases that may cause congestion and presents a smart and accurate outputs to ease traffic flow leading to the prediction of the peak hours of traffic congestion for smarter control. A model is designed to study the case of a four lanes crossroad with two traffic lights and two LCD monitors. The strategy in reading data is divided into two parts: real data from sensors and pre collected data from google maps to create a kind of a predicted pattern over a certain time interval. The responsiveness of the system is analyzed thoroughly, and the accuracy of all possible cases is carefully considered and evaluated. Each part of the system was tested alone, and the overall system is still in an ongoing testing phase. The results have shown minimum faulty errors and accepted outputs that can lead to safe traffic control decisions. Finally, integrating more IoT devices and sensors between V2V, V2P, V2I with the help of artificial intelligence will definitely optimize this system with higher accuracy.

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Enabling VR/AR and Tactile through 5G Network 4

Enabling VR/AR and Tactile through 5G Network

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4th International Conference on Communication, Information and Computing technology, ICCICT 2021

作者： Kusuma, Henry Mathews Shukla, Vinod Kumar Gupta, Shaurya Bachelor of Information Technology Amity University Dubai United Arab Emirates Department of Engineering and Architecture Amity University Dubai United Arab Emirates School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun India

ISBN: (纸本)9781665404303

After the introduction of fifth generation network, there are several prospects and encounters for the evolving technologies like Virtual Reality (VR) and Augmented Reality (AR). This paper emphases on the possibilities of 5G network and how 5G will integrate with Virtual Reality and Augmented Reality and Tactile Internet. Tactile Internet in AR/VR is intended to generate a model alteration from the content-oriented interactions (through internet) towards remote based connections by empowering synchronized broadcast of tactile data like vibration, touch, gestures, surface texture over wireless network with the regular audio and visual data traffics which are also transferred over wireless network. Finally, this research paper intends to provide an overall opinion on the use of Tactile Internet in AR/VR, and to recognize and examine the involved drawbacks and technical problems, and to provide the possible resolutions and to suggest upcoming research guidelines. Initially, opening with the future of Tactile Internet in AR/VR and recent developments in this field, this paper presents a comprehensive outline for Tactile Internet in AR/VR in the past the 5G Era including a Tactile Internet planning, the VR/AR planning, the core technical problems, the important application studies, how it can be possibly enable ground-breaking ways to use Augmented Reality and Virtual Reality. Later, we can see a complete evaluation of the present Tactile Internet progress by generally classifying them into two main instances;specifically, haptic feedback and portable AR/VR device using 5G network. Finally, learning about both Tactile Internet and Augmented Reality/Virtual Reality, we will learn how 5G can change the way Tactile Internet integrates and works with AR/VR that can make an impact in the ever-growing IoT world. © 2021 IEEE.

关键词： 5G mobile communication systems

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Speaker's Emotions Recognition Module Based on the GoogleLeNet Neural Network

Speaker's Emotions Recognition Module Based on the GoogleLeN...

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2022 International Conference on Smart Information Systems and Technologies, SIST 2022

作者： Tereikovskyi, Ihor Tereikovska, Liudmyla Kryvoruchko, Olena Tereikovskyi, Oleh Tyshchenko, Dmytro Franchuk, Tamara National Technical University Of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Dept. Of System Programming And Specialized Computer Systems Kyiv Ukraine Kyiv National University Of Construction And Architecture Dept. Of Information Technology For Design And Applied Mathematics Kyiv Ukraine Kyiv National University Of Trade And Economics Dept. Of Software Engineering And Cyber Security Kyiv Ukraine National Technical University Of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Dept. Of Department Of Computer Engineering Kyiv Ukraine

ISBN: (数字)9781665467902

ISBN: (纸本)9781665467902

The article is devoted to the development of means for recognition of the emotions of the speaker, based on the neural network analysis of fixed fragments of the voice signal. The possibility of improving recognition tools through the use of a convolutional neural network of the GoogLeNet type is shown. The expediency of training the network on the examples of the TESS database is determined. A procedure for representing a voice signal in the form of a square grayscale image has been developed. Based on the use of the specified database, the modification of the GoogLeNet network was carried out. The architecture and software implementation of the recognition module have been developed. With the help of computer experiments, it has been established that after 150 epochs of training on a fairly limited training sample, the modified GoogLeNet makes it possible to achieve an accuracy of recognition of the speaker's emotions of approximately 0.97 on test examples. The obtained accuracy is comparable to the accuracy of the best modern systems of this purpose and confirms the promise of using neural network models like GoogLeNet for emotion recognition based on voice signal analysis. The need for further research aimed at reducing the resource intensity of the neural network model is shown. © 2022 IEEE.

关键词： Emotion Recognition

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Enhancing Visual Surveillance on Smart Ships Through Integrated Image Deblurring and Object Detection

SSRN

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SSRN 2024年

作者： Feng, Hui Guo, Jundong Xu, Haixiang Du, Zhe Zhang, Yingju He, Yijing Cao, Guilin Ge, Shuzhi Sam Key Laboratory of High-Performance Ship Technology Wuhan University of Technology Ministry of Education Wuhan China School of Naval Architecture Ocean and Energy Power Engineering Wuhan University of Technology Wuhan430063 China Wuhan University of Technology China Key Laboratory of High Performance Ship Technology of Ministry of Education Wuhan University of Technology Wuhan430063 China Department of Electrical & Computer Engineering National University of Singapore Singapore117576 Singapore

Smart ships commonly utilize visual measurement units, such as cameras, for environmental perception in the maritime domain, aimed at achieving advanced navigation capabilities. Characterized by the influence of wind, wave, and ocean current, smart ships navigating on maritime environment frequently encounter sway and motion disturbances, which subsequently introduce uncertainties to visual sensors, notably leading to motion blur in captured images. In order to mitigate the detrimental impact of motion blur on the visual obstacle detection algorithm employed in smart ships, we introduce IDD-GAN. Unlike conventional approaches, which treat motion deblurring and object detection as separate tasks, IDD-GAN integrates both processes, enabling end-to-end training and prediction through the use of generative adversarial networks. To address varying scales of motion blur caused by different levels of sensor interference, we have developed a multi-scale deblurring discriminator. Empirical results demonstrate the capability to achieve high-performance objectdetection even under conditions of motion blur. © 2024, The Authors. All rights reserved.

关键词： Ships

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AUTOMATION OF DISASTER RECOVERY AND SECURITY IN CLOUD COMPUTING 4

AUTOMATION OF DISASTER RECOVERY AND SECURITY IN CLOUD COMPUT...

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4th International Conference on Communication, Information and Computing technology, ICCICT 2021

作者： Kappukalar Nasurudeen, Tahsin Fasila Shukla, Vinod Kumar Gupta, Shaurya Bachelor of Information Technology Amity University Dubai United Arab Emirates Department of Engineering and Architecture Amity University Dubai United Arab Emirates School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun India

ISBN: (纸本)9781665404303

Cloud computing has become a vital part of our everyday life. A decade ago, proposing an idea on the improvement of cloud seemed like a reach. Just like with any other technology, cloud has its own drawbacks too. The way our life is moving at a fast pace, we can’t begin to think of checking our backups every other day. It exhausts most people to even give it a thought. Ransom ware attacks have become so much common which eventually gave rise to the creation of multiple software. Retrieving lost files and data will become much easier if it were to be stored elsewhere prior to an attack. The subject cloud in itself has been overlooked and the fact that the same is the base for further alleviation hasn’t been paid enough attention too. Improving an already existing technology almost always starts with going through its previous version’s capabilities. This paper will mainly focus on how certain techniques are favoured over the other due to it meeting the standards for both personal and official usage. In simpler words, how those said techniques help small businesses develop themselves with the help of dependable backup with low cloud cost. The combination of machine learning and artificial intelligence in automation of disaster recovery testing is the future of cloud computing. When all of this checks the box, security would still be an issue. In this paper, I propose a combined approach to the access of cloud with two major algorithms: Seed block and Rivest Shamir Adleman. ©2021 IEEE

关键词： Automation

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Affecting Factors of Efficiency in Photovoltaic Energy Systems and Productivity-Enhancing Suggestions

Affecting Factors of Efficiency in Photovoltaic Energy Syste...

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2022 Innovations in Intelligent Systems and Applications Conference, ASYU 2022

作者： Ay, Ilker Kademli, Murat Karabulut, Sener Savas, Serkan Resources Technology Program Hacettepe Ankara Chamber of Industry 1st Organized Industrial Zone Vocational School Hacettepe University Department of Alternative Energy Ankara Turkey Hacettepe Ankara Chamber of Industry 1st Organized Industrial Zone Vocational School Hacettepe University Department of Mechanical Program Ankara Turkey Kirikkale University Faculty of Engineering and Architecture Department of Computer Engineering Kirikkale Turkey

ISBN: (数字)9781665488945

ISBN: (纸本)9781665488945

In recent years, hazardous gases emission from fossil fuels has attracted public concerns due to its worse effects on the ecosystem and living conditions not only mankind but also all creatures living on earth. That's why solar energy has a vital role in alternative energy resources. Solar energy sources will gain more importance in the future. As it is known, the most needed type of energy today is electrical energy. Thus, in this study, the necessary conditions for the photovoltaic (PV) systems used in solar energy production to operate at maximum performance and which parameters are required to control these conditions are examined. The results show that four parameters that we need to measure. These are: maximum operating current of a panel/cell (Impp), maximum operating voltage of a panel/cell (Vmpp), panel surface temperature and light intensity falling on the panel. Except for the panel surface temperature, the rest of the parameters can be measured directly. However, affecting the panel surface temperature;we must not ignore parameters such as ambient temperature, wind speed, humidity and light intensity. Therefore, while determining the panel surface temperature, these parameters should also be measured and a surface temperature should be determined accordingly. © 2022 IEEE.

关键词： Surface temperature

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Enhanced Bone Fracture Diagnosis in X-rays Using Fine-Tuned DenseNet169 Deep Learning Model

Enhanced Bone Fracture Diagnosis in X-rays Using Fine-Tuned ...

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IEEE International Conference on Multi Topic

作者： Ali Orangzeb Panhwar Shahzad Memon Lachhman Das Dhomeja Mukesh Prasad Asghar Ali Chandio Dr. A. H. S. Bukhari Postgraduate Centre of Information and Communication Technology University of Sindh Jamshoro Pakistan SZABIST University Gharo Department of Computer Science and Digital Technologies Scool of Architecture Compuing and Engineering University of East London United Kingdom Faculty of Engineering and Information Technology University of Technology Sydney Australia Department of Artificial Intelligence Science and Technology (QUEST) Nawabshah Quaid-e-Awam University of Engineering Pakistan

ISBN: (数字)9798331507213

ISBN: (纸本)9798331507220

The classification of bone fractures from radiographs is an important yet challenging task in clinical diagnosis. Diagnosing fractures through X-rays remains difficult for orthopedic specialists due to image quality issues, which can result in errors, misalignments, and potential harm to patients. However, recent advancements in artificial intelligence (AI) and deep learning have revolutionized medical imaging, with state-of-the-art methods now capable of handling 2D and 3D images. This study focuses on deep-learning approaches for the classification and detection of bone fractures in radiograph images and aims to analyze and compare various deep-learning algorithms and techniques used in fracture detection. It also highlights current cutting-edge approaches in this field, providing insights and guidance for future research and practical applications. In this paper, the application of Fine-tuned DenseNet169 for the automated classification of bone fractures in X-ray images is explored. By using deep learning approaches, our method seeks to enhance the accuracy and efficiency of fracture detection. We trained and evaluated the DenseNet169 model on the MURA Stanford dataset and achieved 83% accuracy in distinguishing fractured and non-fractured elbow bones. The model’s performance highlights the potential of DenseNet169 to assist radiologists in clinical settings, promoting better patient outcomes through prompt and reliable fracture diagnosis.

关键词： Deep learning Accuracy Three-dimensional displays X-rays Bones Data models Convolutional neural networks Diagnostic radiography Artificial intelligence Load modeling

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An Improved Weighted Ensemble Clustering Based on Two-Tier Uncertainty Measurement

SSRN

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SSRN 2022年

作者： Gu, Qinghua Wang, Peipei Li, Xuexian Chen, Lu Xiong, Neal N. School of Management Xi’an University of Architecture and Technology Xi’an China Xi’ an Key Laboratory for Intelligent Industrial Perception Calculation and Decision Xi'an University of Architecture and Technology Xi’an China Department of Mathematics and Computer Science Northeastern State University OK United States

Existing local weighted ensemble clustering algorithms strive to weight on each cluster, which take into account the differences among all clusters. However, the base clustering label on each cluster is ignored. The purpose of this paper is to combine the influence of cluster level and base clustering level in a unified ensemble clustering framework. A novel two-level weighted ensemble cluster method (TWEC) is proposed, which inserts global weighting strategy into a locally ensemble cluster learning framework. First, the cluster uncertainty based an entropy criterion is refined by considering the base clustering labels for each cluster. Then, two-level uncertainty is converted to cluster reliability via improved ensemble-driven cluster validity measure. Finally, two novel consensus functions are developed. The effectiveness of the proposed framework TWEC is verified against fourteen real-world datasets and ten comparison algorithms. The results demonstrate that the TWEC framework can improve the robustness and stability of the clustering. © 2022, The Authors. All rights reserved.

关键词： Clustering algorithms

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Data-Driven Representation Model of Urban Movement Space 2020

Data-Driven Representation Model of Urban Movement Space

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3rd International Conference on Geoinformatics and Data Analysis, ICGDA 2020

作者： Arregui, Harbil Otaegui, Oihana Arbelaitz, Olatz Vicomtech Donostia-San Sebastian Spain Computer Architecture and Technology Department UPV/EHU Donostia-San Sebastian Spain

ISBN: (纸本)9781450377416

Tracking urban mobility with current heterogeneous sensing capabilities has opened a wide research area on analytical and predictive data-driven models for improvements in transport operations and planning. These improvements are applicable for individual users, service providers and decision-makers. People, vehicles and goods move along the city according to the physical resources (roads, bike-lanes, side-walks...) and non-physical resources (such as scheduled public transportation services). We present this set of resources as the Urban Movement Space (UMS). We collect the main challenges and research foundations that geoinformatic approaches need to cope when tackling transportation resources and mobility data. The work presented in this paper proposes a conceptual modelling framework to represent the urban movement space, in order to match observed tracking data accordingly, and allow further analytical queries. Our approach combines an open free-space and network-based space to model the time-varying urban movement space, considering seasonality and uncertainty of multimodal travel options. © 2020 ACM.

关键词： Decision making

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