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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Gupta, Aanya Rana, Ajay Rajput, Namita Kumar, Bhupendra Independent Research New Delhi India Amity School of Engineering & Technology Department of Computer Science Amity University Uttar Pradesh Greater Noida India Sri Aurobindo College University of Delhi Department of Commerce Delhi India Galgotias University Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350304480

Accurate forecasting is crucial in the dynamic world of economics for directing investments, policy choices, and long-term planning. A new era of economic forecasting has evolved with the rise of artificial intelligence, providing previously unheard-of potential to glean insightful information from complicated and copious data. This in-depth research article explores the mutually beneficial interaction between AI and economic forecasting. In order to offer distinctive viewpoints on economic developments, the study investigates AI approaches. Deep learning architectures and hybrid models, two advancements in these methodologies, have paved the way for enhanced forecasting accuracy and decision-making procedures. This survey demonstrates the revolutionary potential of AI in economic forecasting across multiple indices, revealing deep patterns that impact economies through real-world applications. This study emphasizes the need for open, strong, and morally sound AI-driven economic insights by addressing the issues and ethical concerns inherent to AI. This study provides a compass for academics, decision-makers, and economists alike as we navigate the growing AI landscape, pointing us in the direction of fully utilizing AI to shed light on the future of economic forecasting. © 2023 IEEE.

关键词： AI ARIMA Decision Tree economic forecasting Linear Regression ML NLP Random Forest RNN Sentiment Analysis SVM

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Jelly Bot: Revolutionizing Beach Safety

Jelly Bot: Revolutionizing Beach Safety

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computer, Electronics & Electrical engineering & their Applications (IC2E3), International Conference on

作者： Karan Allagh Mohammad Aadil Sehrawat Bhupendra Singh Amity School of Engineering and Technology Amity University Noida New Delhi Delhi India Department Of Computer Science and Engineering Amity University Noida Noida Uttar Pradesh India

Drowning is a major problem in most of the world. It is the 3rd leading cause of accidental demise globally and accounts for 7% of all injury deaths. There are approximately 236000 deaths worldwide every year due to drowning, but it is still believed that this number underestimates the actual public health problem associated with drowning. This paper proposes a solution to address this issue by introducing autonomous Jelly Bots equipped with drowning detection capabilities for deployment in high-risk areas such as beaches and ghats. The Jelly Bot offers improved rescue capabilities in uncertain situations and is computationally efficient. The proposed model utilizes a Convolutional Neural Network (CNN) for real-time video analysis to identify drowning activity with high speed and accuracy. The implementation of underwater drones, visual surveillance, and search and rescue techniques contributes to enhancing beach safety and reducing drowning incidents.

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AES-Driven Image Encryption: Overcoming Security and Transmission Challenges

AES-Driven Image Encryption: Overcoming Security and Transmi...

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Delhi Section Conference (DELCON), IEEE

作者： Harshil Bodat Harshil Patel Monika Arora Dhruti Ambekar Sonu Bodat Sonia Computer Science & Engineering Science Pandit Deendayal Energy University Gandhinagar India Computer Science & Engineering Charotar University of Science and Technology (CHARUSAT) Gujrat India IT/Operations Amity Business School Amity University Haryana India Yogananda School of AI computers and Data Science Shoolini University Solan India

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

Continuous demand for digitization of data necessitates world to take robust measures for securing messages sent over the insecure channels. Thus, the presented research gives an innovative image encryption scheme that focuses on encrypting the RGB components of each pixel individually, rather than the entire pixel. The encryption process employs a secondary image as a key, which must be equal to or larger than the original image being encrypted. By utilizing chaotic systems, this method improves security while also reducing the time required for encryption and decryption. Simulation results indicate that this approach offers a high level of both security and efficiency. Additionally, a Command Line Interface (CLI) tool has been developed, allowing users to encrypt and decrypt PNG, JPG, and JPEG images, while providing a key for future decryption, thus ensuring complete control over their images. This study highlights the critical need for image protection in today's digital landscape, where the surge of image data demands innovative solutions to safeguard privacy and integrity. The Advanced Encryption Standard (AES) is employed to enhance both performance and security. This research contributes to the ongoing advancement of secure and efficient image encryption techniques, addressing the increasing concerns surrounding data security in the digital world.

关键词： Data privacy Simulation Transform coding Surge protection Streaming media Encryption Data communication Surges Protection Standards

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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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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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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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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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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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Study and prediction of prostate cancer using fuzzy inference system

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Materials Today: Proceedings 2022年 56卷 157-164页

作者： Boadh, Rahul Aarya, Deo Datta Dahiya, Mamta Rathee, Rakesh Rathee, Sarita Kumar, Ajay Jain, Sarika Rajoria, Yogendra Kumar Department of the Mathematics School of Basic and Applied Science K. R. Mangalam University Sohna Road Haryana Gurugram122103 India Department of the Mathematics Acharya Narendra Dev College University of Delhi India Department of Computer Science & Engineering Faculty of Engineering and Technology Shree Guru Govind Singh Tricentenary University Haryana Gurugram122103 India University Institute of Engineering & Technology Maharshi Dayanand University Haryana 124001 Rohtak India Department of Electrical Engineering DPG Institute of Technology and Management Haryana Gurgaon122001 India Department of Mechanical Engineering Faculty of Engineering and Technology Shree Guru Govind Singh Tricentenary University Haryana Gurugram122103 India Department of Mathematics Amity School of Applied Sciences Amity University Haryana Gurugram122413 India

The essential driver of passing among men on the planet is prostate disease. In the chief stage, for early identification of the prostate disease by applying Fuzzy Inference System (FIS) has been depicted in this study. The age of the patient, Prostate Volume (PV), % Free PSA (FPSA) and Prostate Specific Antigen (PSA) are utilized as input parameters in FIS for computing the risk factor of the prostate cancer. In this study Mamdani method is used for evaluation and analysis of the Prostate Cancer Risk (PCR). The present FIS additionally required the base estimation decreases horribleness lessens dismalness, mortality and more dependable from other system. It can likewise be considered in medicine to decrease manual assignments and human mistake. This review advocate that, this FIS might consequence on work on the productivity to recognized the distinctive kind of the malignant growths just as lessening the expense of care of the patients. © 2022

关键词： Risk assessment

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Fedchain: Decentralized Federated Learning and Blockchain-Assisted System for Sustainable Irrigation

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 1-1页

作者： Bera, Somnath Dey, Tanushree Mukherjee, Anwesha Bhattacharya, Pronaya De, Debashis Maulana Abul Kalam Azad University of Technology West Bengal India Mahishadal Raj College Mahishadal West Bengal India Department of Computer Science and Engineering Amity School of Engineering and Technology and Research and Innovation Cell Amity University Kolkata West Bengal India

The conventional Internet of Things-based soil moisture monitoring system for irrigation decision making suffers from huge network traffic, high latency and energy consumption, and compromise in data security. To overcome these challenges, this paper proposes a blockchain-assisted decentralized federated learning strategy, Fedchain for irrigation decision making based on edge-cloud computing. A peer-to-peer network is formed among the edge servers. The edge servers have their local datasets, which are analyzed locally using Long Short-Term Memory (LSTM) network, and the model parameters are exchanged between the peer nodes. The model is updated accordingly. For data security purposes, blockchain is used. The LSTM model updates are recorded in the InterPlanetary File System by the local user, and a unique Content Identifier is generated for data retrieval, and it is stored in the blockchain as a transaction. A Distributed Hash Table in the file system maps the Content Identifier in the blockchain to the stored data in the file system, ensuring effective retrieval. The results show that Fedchain achieves above ~99% prediction accuracy, and reduces latency and energy consumption by 78% than the edge-cloud framework without federated learning. The use of blockchain reduces the mining cost by ~78% than the competing methods. IEEE

关键词： Blockchain

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