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5th International Conference on Information Systems and computer Networks, ISCON 2021

作者： Goyal, Abhishek Garg, Rakesh Bhatia, Komal Kumar Amity School of Engineering and Technology Amity University Uttar Pradesh India J C Bose University of Science and Technology Department of Computer Science and Engineering Faridabad India

ISBN: (纸本)9781665403412

Cloud computing is a new paradigm in computing that enables the availability of various resources at one place on the user's demand. In cloud computing, a cloud is a unit containing various resources like hardware, software, etc. In contrast, computing refers to the way or mechanism to access the available resources from the cloud. Numerous challenges such as cloud security, performance, task scheduling etc., are observed during the adaption of cloud computing. Task scheduling aims at the optimized resource allocation to the various tasks. The performance of any system is directly dependent on the scheduling process. More effective task scheduling will undoubtedly lead to the high performance of the system. This paper highlights the various task scheduling algorithms and their advantages and disadvantages in the cloud environment. Further, A novel effort is made to categorize existing task scheduling algorithms based on their working concepts. © 2021 IEEE.

关键词： Cloud computing

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Heart Disease Prediction Using Machine Learning

Heart Disease Prediction Using Machine Learning

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Nahida Nazir Aditya Saraogi Kanksha Kaur Vinod Bera Akash Kumar Singh Omdev Dahiya Navlesh Kumar Singh Kapil Bhardwaj Suman Avdhesh Yadav School of Computer Science and Engineering Lovely Professional University Phagwara Punjab India Department of CSE Amity School of Engineering and Technology Amity University Uttar Pradesh India

One of the main reasons for death worldwide is heart disease, and early detection of the condition can help lower the risk of having a cardiac arrest. This research paper aims to suggest a machine learning-based method for estimating the risk of developing cardiac disease. First recent advancements in the field have been reviewed and then an ML model has been implemented to work on the limitations of the previous research work. To train and evaluate several ML algorithms, Framingham Heart Study (FHS) dataset has been used. The algorithms trained in this research paper are decision trees, random forest, SVM and ANN. It has been demonstrated that ML techniques can significantly improve the accuracy and timeliness of heart disease diagnosis, with SVM achieving the highest accuracy of 86.53%. This research paper provides valuable insights for clinicians, researchers, and data scientists working in the field of cardiovascular disease diagnosis and treatment.

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Strategic Green Itinerary for Supply Chain Delivery Planning Using Drones and Max-Min Ant System

SSRN

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SSRN 2024年

作者： Gaida, Itoua Wanck Eyika Mittal, Mandeep Amity Institute of Information Technology Amity University Uttar Pradesh Noida India School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India

Most of the distribution operations revolve around road transportation medium and majorly with non-green vehicles, which alone constitute a great threat for the environment by enormous pollution caused. This study tends to suggest a model of optimization to augment efficiency for a sustainable delivery approach using drones technology, to deliver by air, products to every customer’s locations, and Max-Min Ant system to help guide routing in the air based on customers’ locations. Routing with Max-Min Ant system is evaluated best with the evaporation concept as to simulate adaptation for real world dynamism prone to various changes. © 2024, The Authors. All rights reserved.

关键词： Drones

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A Hybrid Deep Learning Framework for IoT Security Enhancement and Anomaly Detection

A Hybrid Deep Learning Framework for IoT Security Enhancemen...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： D Baswaraj Abdur Rahman Deependra Pandey T Bhargavi Muslimbek Ismoilov Natha Deepthi Department of Computer Science and Engineering Vasavi College of Engineering Hyderabad India School of Business & Technology Emporia State University Emporia Kansas USA Department of Electronics and Communication Engineering Amity School of Engineering & Technology (ASET) Amity University Lucknow India Department of Information Technology St. Martins Engineering College Hyderabad India Department of Electrical Engineering Fergana Polytechnic Institute Fergana Uzbekistan Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Cyber threats targeting Internet-of-Things(IoT) devices have grown in frequency and severity alongside the IoT's fast uptake and usage in the past few years. Because of this, creating a model that can detect hostile attacks and lessen security risks in IoT-devices is becoming a very important subject. Botnets pose a significant risk because they disrupt the networks that Internet of Things (IoT) devices rely on to function. An method called GA-HDLAD, which stands for genetic-algorithm with hybrid deep learning(DL), was created in this study to advance security in the IoT environment by detecting botnets. Botnet detection makes use of hybrid deep learning, which combines attention concepts, feature-extraction-techniques(FETs), and recurrent-neural-networks(RNNs). The hybrid-DL approach can identify botnet attacks, which frequently use complicated patterns. In addition, the model's RNNs capture temporal dependencies, while FETs guarantee efficient feature extraction from geographical data. To choose the hyperparameters required by the HDL method, simulated annealing (SA) is employed. This paper compares the GA-HDLAD system to other detection approaches and finds that it outperforms them experimentally using a benchmark botnet dataset.

关键词： Deep learning Integrated circuits Botnet Field effect transistors Simulated annealing Feature extraction Internet of Things Security Hardware design languages Anomaly detection

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Multimodal Sensor Data Fusion Based Cyberattack Detection in Industrial Internet of Things Environment 7

Multimodal Sensor Data Fusion Based Cyberattack Detection in...

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7th International Conference on Electronics, Communication and Aerospace technology, ICECA 2023

作者： Nithya, R. Sundari, J.Jerlin Adaikala Rajesh Kanna, B. Balamurugan, M.S. Sindhuja, R. Srivastava, Aviral School of Computing Department of Networking and Communications College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India Psg College of Arts and Science Department of Computer Technology Coimbatore641014 India Alagappa Chettiar College of Engineering and Technology Dept. of Electronics and Communication Engineering Karaikudi India Panimalar Engineering College Department of ECE Chennai India Saveetha Engineering College Department of Medical Electronics Chennai India Amity University Rajasthan Department of CSE Jaipur300202 India

ISBN: (纸本)9798350340600

The Industrial Internet of Things (IIoT) cites the usage of classical Internet of Things (IoT) in different applications and industrial fields. Smart grids, smart homes, supply chain management, connected cars, and smart cities are the different applications of IIoT. But this system is widely targeted by cyber-criminals. Big data analytics and Deep learning (DL) have considerable potential in developing and designing strong security systems for IIoT networks. This manuscript introduces Multimodal Sensor Data Fusion based Cyberattack Detection with glow worm swarm optimization (MSDF-CDGSO) technique in IIoT environment. The MSDF-CDGSO technique examines the IIoT data for identifying the existence of cyberattacks in the IIoT environment. In the presented MSDF-CDGSO technique, the IIoT devices collect data from different devices and fused them together. At the preliminary stage, the MSDF-CDGSO technique preprocesses the input data to scale them properly. For cyberattack detection, the MSDF-CDGSO technique uses class-specific cost regulation extreme learning machine (CCR-ELM) model. Finally, the GSO method is exploited to adjust the parameter values of the CCR-ELM algorithm. The simulation result analysis of the MSDF-CDGSO method is investigated on benchmark database. A widespread comparison results highlighted that the MSDF-CDGSO technique reaches improved cybersecurity in the IIoT environment. © 2023 IEEE.

关键词： Sensor data fusion

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IoT and Blockchain based Smart Agriculture Monitoring and Intelligence Security System 3

IoT and Blockchain based Smart Agriculture Monitoring and In...

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3rd International Conference on Computation, Automation and Knowledge Management, ICCAKM 2022

作者： Ali, Md. Akkas Dhanaraj, Rajesh Kumar Sharma, Vandana Balamurugan, B. Galgotias University School of Computing Science and Engineering U.P Greater Noida India Amity University Amity Institute of Information Technology U.P Greater Noida India Associate Dean-Student Engagement India

ISBN: (数字)9781665453196

ISBN: (纸本)9781665453196

Food security seems to be a more prevalent concern for all countries throughout the world due to global population growth, dwindling natural resources, agricultural land, and an increase in unfavorable environmental circumstances. These problems are the driving force behind agriculture industry's migration towards modern agriculture through the use of IoT and Blockchain technology for improving operations, productivity and maintaining, monitoring agricultural farms and creating fewer people involvement. We have demonstrated how IoT and Blockchain systems may be linked with agriculture's intelligent component to maximize benefits for farmers. Security essential not only for the resources but also essential for agricultural products need to be protected and protected in the first instance, as protection from rodents and pests in the large agricultural field or grain shops. As a result, these problems need to be addressed. So, we designed an IoT and Blockchain based Smart Agriculture monitoring and Blockchain oriented Intelligence Security systems for Smart Agriculture Infrastructure. © 2022 IEEE.

关键词： Blockchain

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Web Development and performance comparison of Web Development Technologies in *** and Python

Web Development and performance comparison of Web Developmen...

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2021 International Conference on Technological Advancements and Innovations, ICTAI 2021

作者： Challapalli, Sai Sri Nandan Kaushik, Prakarsh Suman, Shashikant Shivahare, Basu Dev Bibhu, Vimal Gupta, Amar Deep Amity School of Engineering and Technology Dept. of Computer Science Greater Noida India

ISBN: (纸本)9781665420877

From the first website being created in 1991 by Tim Berners-Lee to this day in 2021, the world of websites and their development has seen a rapid evolution. Every business nowadays has a website to make its online presence in this digital age. Website use is not only limited to this, but many people use them to build their online portfolio, apart from that there are many web-based applications/software, etc. This research paper discusses the process involved in developing a website in past and present, development of content delivery over the years, the website uses, a website for mobile devices, and performance comparison between two of the most used web backend development technologies, i. e, *** and Python. For comparing performance, we have used Locust-an open-source software and Autocannon and tested both of them under similar conditions. © 2021 IEEE.

关键词： Python

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An Efficient Hybrid Sequence of Retargeting Operators to Minimize Structural Deformities in Image 1

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2nd International Conference on Nonlinear Dynamics and Applications, ICNDA 2024

作者： Garg, Ankit Garg, Ajay Singh, Anuj Kumar Maram, Balajee Apex Institute of Technology-CSE Chandigarh University Punjab Mohali140413 India Department of CSE Galgotias College of Engineering & Technology Greater Noida201310 India Amity University Madhya Pradesh Gwalior India School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India

ISBN: (数字)9783031691461

ISBN: (纸本)9783031691454

Researchers have proposed several techniques to alter image aspect ratios in order to preserve visual information while retargeting. However, researchers discovered that no single retargeting approach consistently produces the optimum outcomes. As a result, several retargeting operators are combined to form a hybrid sequence that takes use of the current algorithms. In this paper a hybrid set of image retargeting operators is proposed that combines five different retargeting operators. The addition of a switching point among these operators is an attempt to maximize the benefits of each algorithm via the retargeting operation. In addition, an image morphological procedure is used to minimize abnormalities in crucial image regions and increase the effectiveness of the suggested approach. To identify prominent parts in the image, an importance map is created by integrating a saliency map, gradient map, edge map, skin map, shadow map, and object map. These maps are generated using MATLAB's built-in functions and libraries. The suggested technique's effectiveness is tested using a structural similarity index measure (SSIM) that is based on three parameters: luminance comparison, contrast comparison, and structure comparison. The suggested hybrid sequence's superiority is compared with single image retargeting operators using the generated SSIM index. The SSIM index results show that the suggested technique achieves 86.51% similarity between the original and retargeted images. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Energy map Image retargeting Multi-operator Saliency detection Scaling Seam carving Warping

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Copy-move Forgery Detection using SIFT and DWT detection Techniques 3

Copy-move Forgery Detection using SIFT and DWT detection Tec...

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3rd International Conference on Intelligent engineering and Management, ICIEM 2022

作者： Singh, Richa Verma, Sandeep Yadav, Suman Avdhesh Vikram Singh, S. Dr. B. R. Ambedkar National Institute of Technology Department of ECE Punjab Jalandhar India Indian Institute of Technology G. S. Sanyal School of Telecommunications Kharagpur India Amity University Department of Computer Science & Engineering Uttar Pradesh India Amity University Amity School of Engineering & Technology Uttar Pradesh India

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467568

Copy-Move forgery practices are defamatory tampering processes, affecting digital images. Such practices have become much more common nowadays because of the advent of significantly effective image processing tools being readily available. In copy-move forgery technique, an area from the original image is copied and pasted over another image area. This technique provides a way to conceal the important details and information in a digital image without leaving much traces behind to observe the tampering or manipulation of the image. Such practices question the authenticity of an image to forensic. In this paper, the techniques are proposed for the detection of cloning or copy-move forgery that involve both block based extraction using Discrete Wavelet Transform (DWT) and feature point extraction based detection technique i.e., Scale Invariant Feature Transform (SIFT) to extract the tampered regions more precisely. The proposed algorithm mainly involves tentacle matching of features of same features, which can be extracted from each of the blocks by calculating the dot products between their respective unit vectors. The proposed algorithm is able to achieve an efficiency of 98.12%, precision factor of 97% of and recall factor of 100%. © 2022 IEEE.

关键词： Discrete wavelet transforms

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Feature Extraction, Essential Dimension Estimation and Dimension Reduction Method of Colony Images

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2021年第2期48卷 1-13页

作者： Sun, Wei-Zhong Guo, Fu-Jun Wang, Jie-Sheng Chen, Lin Wei, Dong Du, Xin-Feng A lecturer of School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China A postgraduate student of School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China A professor of School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China An associate professor of School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China

A colony is a group of microorganisms produced by the growth and reproduction of a single microorganism species. A method of colony image feature extraction, essential dimension estimation and dimension reduction based on digital image processing technology was proposed. Firstly, based on the HSI image color moments (first, second and third order moments), nine color characteristics of the colony images were extracted. Based on gray-level co-occurrence matrix (GLCM), twenty texture features of colony images were obtained. Then, three essential dimension estimation methods, namely, the correlation dimension estimator, the maximum likelihood estimator and the packing numbers estimator, are used to estimate the dimension of the inner low-dimensional structure of the high-dimensional data of the colony images. The results of different dimension estimators were combined with six different dimension reduction techniques (PCA, LDA, MDS, ISOMAP, SNE and NCA) respectively to form 18 different data dimension reduction methods. Based on the distance criterion function, an optimal dimension reduction method for the high-dimensional feature data of the colony images corresponding to the maximum distance criterion function was obtained. © 2021. All Rights Reserved.

关键词： Feature extraction

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