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5th International Conference on Recent Developments in Control, Automation and Power engineering, RDCAPE 2023

作者： Vianny, Maria Manuel Bhardwaj, Rajat Bhardwaj, Vivek Sharma, Vikrant Virender Manikandan, S. Department of Artificial Intelligence and Data Science Panimalar Engineering College Tamilnadu Chennai India Department of Computer Science and Engineering ASET Amity University Karnataka Bengaluru India School of Computer Science and Engineering Manipal University Jaipur Jaipur India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Chandigarh University Punjab Mohali India Department of Information Technology E.G.S. Pillay Engineering College Tamil Nadu Nagapattinam India

ISBN: (纸本)9789819600465

Cloud computing is a world view appropriately characterized for IT contributions where data and resources are aggregated by web-based cloud administration organizations using planned hardware and packages, primarily based on the web. People could use at a reasonable cost. Combining resources may affect the ease with which those resources are accessed, which might result in an issue. Load control is the process of distributing network traffic among several servers. No server will be overburdened as a result of this. In order to increase user responsiveness, load regulation equitably distributes the tasks. Additionally, it improves client accessibility to projects and websites. This archive is being kept in order to better understand charge control. It is connected to other structures via correspondence bodies like the Internet. Load balancing is an important distributed computing space to stay away from work beyond the workload and provide equally important support. Different calculations are used to determine the complexity of the system. Our suggested technique selects 100 virtual computers and, with 92% accuracy, determines where an ideal storage facility is for use in real time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud storage

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Zero-Day Vigilance: Proposing a Machine Learning Framework for DDoS Detection in Cloud Computing

Zero-Day Vigilance: Proposing a Machine Learning Framework f...

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Artificial Intelligence and Emerging Technology (Global AI Summit), International Conference on

作者： Anukriti Naithani Shailendra Narayan Singh Pradeep Kumar Sharma Computer Science and Engineering ASET Amity University Noida Maulana Azad National Urdu University Hyderabad

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

The seemingly exponential growth of cloud computing has introduced menacing and crucial threats that are specifically DDoS attacks. These types of attacks are hard to counter because they exploit as yet unknown vulnerabilities thereby making it difficult to counter the zero-day DDoS attacks. Such threats are rarely detected by traditional anomaly-based systems because they do not rely on anomalies, but rather use real-time detection and response methodologies with the help of sophisticated frames. This paper provides a machine learning-based integrated framework to fight against zero-day DDoS attacks in the cloud context, whose goal is to enhance the detection accuracy and reaction speed including anomaly detection, deep learning, and feature engineering as well as collecting reliable data from cloud honeypots. The purpose of this framework is to become proactive against the increasing number of zero-day DDoS attack threats.

关键词： Deep learning Cloud computing Predictive models Denial-of-service attack Reliability engineering Feature extraction Real-time systems Safety Security computer crime

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Unravelling the Potential of Knowledge Graphs and Graph Neural Networks: Techniques and Applications

Unravelling the Potential of Knowledge Graphs and Graph Neur...

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Signal Processing and Advance Research in Computing (SPARC), International Conference on

作者： Shreyas Om Anika Dogra Ananya Singh Ashwani Kumar Dubey Puneet Sharma Computer Science and Engineering ASETAmity University Uttar Pradesh Noida UP India Electronics and Communication Engineering ASETAmity University Uttar Pradesh Noida UP India School of Computer Science & Artificial Intelligence SR University Warangal Telangana India

ISBN: (数字)9798350385205

ISBN: (纸本)9798350385212

In today’s digital economy, organisations must manage an excessive volume of unstructured data. Traditional data warehouses are frequently underutilised due to inconsistencies in how to successfully evaluate such massive amounts of information. Knowledge graphs have developed as an effective solution, giving a structured representation of interrelated facts to improve data organisation, retrieval, and inference across several disciplines. Knowledge graphs, which capture interactions between elements such as people, locations, and concepts, provide a complete framework for interpreting and identifying hidden patterns in data. This study investigates the evolution of knowledge graphs, from simple data integration tools to complex systems that support semantic searches. These searches go beyond standard keyword searches, providing context-aware results that increase the relevance and accuracy of information retrieval. In addition, the study investigates the use of knowledge graphs in recommendation systems, emphasising their capacity to personalise user experiences through personalised suggestions. This research highlights the potential of knowledge graph technology to improve data management and utilisation, promoting innovation and efficiency across a variety of sectors.

关键词： Technological innovation Semantic search Signal processing algorithms Knowledge graphs Machine learning Signal processing Graph neural networks Question answering (information retrieval) Recommender systems Standards

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MetaEd: Metaverse-Assisted Virtual Classroom and Content Generation Framework Underlying 5G

MetaEd: Metaverse-Assisted Virtual Classroom and Content Gen...

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IEEE International Conference on Communications Workshops, ICC

作者： Pronaya Bhattacharya Aparna Kumari Ishan Budhiraja Sudeep Tanwar Department of Computer Science and Engineering ASET Amity University Kolkata India Department of Computer Science and Engineering Institute of Technology Nirma University Gujarat India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (数字)9798350304053

ISBN: (纸本)9798350304060

In this paper, we propose MetaEd, which is a metaverse enabled virtual classroom and content generation framework, with networking services employed over fifth generation (5G) network. The framework is designed for Education 5.0 space, and presents two key functions- a virtual classroom setup, where the teacher and the student avatars can interact (like in physical classroom) in near real time latency, which is assisted by ultra-reliable-low latency service (5G-uRLLC). Secondly, we present the digital content generation on meta-verse space, where digital assets (contents) with non-fungible tokens (NFTs) are stored. The entire data is stored in offline interplanetary file system (IPFS) ledgers, and the content hash reference is stored in blockchain. The framework is operational over a Web 3.0 platform, which allows AI-based cognition to users to enhance personalization. The framework is deployed via simulated metaverse environment (Unity 3D) space, where avatar creation is done through Blender. Performance metrics shows a significant improvement of 59.93% in user-experienced bandwidth, lowered transaction size by 44.07% in blockchain, 44.37% in jitter rates. and 66.31% improvement in object rendering time for avatars, which signifies the viability of the framework in real Education 5.0 scenarios.

关键词： Semantic Web Three-dimensional displays Metaverse 5G mobile communication Avatars Conferences Rendering (computer graphics)

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Analysis of Global Finance Using Web Scraping and Topic Modeling 3

Analysis of Global Finance Using Web Scraping and Topic Mode...

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

作者： Sidhu, Anmol Singh Misra, Neeti Kaushik, Vaibhav Shankar, Achyut Joshi, Kapil Singh, Rajesh Amity University ASET Department of Computer Science and Engineering Uttar Pradesh India Uttaranchal University UIM Dehradun India Amity University ASET Uttar Pradesh India Uttarakhand University UIT Department of Computer Science Dehradun India Uttarakhand University UIT Division of Research & Innovation Dehradun India

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467568

At present there is a humongous amount of knowledge that is present all around us for every field and it may be used to derive predictions to get better outcomes and profit. Organizations can use this data to improve their user experience and learn from user trends. This paper aims to exploit these databases thereby giving out a processed knowledge base and forming the outcome of topics and subtopics related to Global Finance. It focuses on providing a modeled output in the form of visits, clicks, likes, dislikes and more, these parameters can help organizations to grow immensely. Sentiment analysis is one of the branches of NLP, which is, in lay man's terms is a text mining operation to find the positive and negative sentiment of the data source, so if we could apply this to financial news, social media feed or use opinion mining for detecting the opinion of the masses on social media this will act as an excellent tool for individual investors to analyze market in no time. In addition, a machine learning model is developed to predict the stock prices. The model is based on support vector regression. The major challenge in this scheme was to detect the taxonomy into which the data is to be categorized. With IAB Taxonomy, a list of subtopics and topics was formulated to arrange the content in groups. We will use raw structured Data of URLs as an input, the Proposed Model will use this input to power on the program and produce an output by dividing the website data into topics and subtopic. In addition, various other parameters like visits, likes, dislikes and score will also be displayed. The analysis and implementation of the model presented in our study was completed using modeling and scraping methods keeping the kind of data we have in mind. © 2022 IEEE.

关键词： Sentiment analysis

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Machine Learning-Based Counterfeit Currency Detection

Machine Learning-Based Counterfeit Currency Detection

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Advances in Computation, Communication and Information Technology (ICAICCIT), International Conference on

作者： Srishti Srivastava Anshika Panwar Jalaj Bhati Dolly Sharma Amity School of Engineering and Technology (ASET) Amity University Noida India Department of Computer Science and Engineering Amity University Noida India

ISBN: (数字)9798331541217

ISBN: (纸本)9798331541224

Counterfeit currency detection is a critical issue posing significant challenges to economies worldwide. Traditional methods relying on manual inspection are prone to error and inefficiency. To enhance detection accuracy, this study explores advanced machine learning techniques, specifically Support Vector Machine (SVM) and Convolutional Neural Network (CNN) models. Existing SVM models struggle with large-scale datasets, and CNN models require extensive training data. There is also a lack of diversity in counterfeit samples and limited use of augmented data in existing datasets. This research compares SVM and CNN models in identifying counterfeit ₹500 Indian banknotes. Using a dataset of genuine and counterfeit notes, features were extracted with a Histogram of Oriented Gradients (HOG) for SVM. ResNet50 architecture was used for CNN. The dataset was augmented to improve robustness. The CNN model achieved training and validation accuracy, while the SVM model also reached high accuracy. These results highlight the potential of both models in providing accurate and automated counterfeit detection, demonstrating their effectiveness and reliability in this application.

关键词： Support vector machines Training Measurement Accuracy Biological system modeling Feature extraction Robustness Convolutional neural networks Ensemble learning Currencies

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Enhancing CNN Weights for Improved Routing in UAV Networks for Catastrophe Relief with MSBO Algorithm

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Journal of Mobile Multimedia 2024年第5期20卷 1117-1152页

作者： Gupta, Sachin Kumar Ul Din, Suhail Mohi Upreti, Kamal Mahajan, Shubham Date, Saroj S. Department of Electronics and Communication Engineering Central University of Jammu Jammu Samba 181143 India Department of Electronics and Communication Engineering Indian Institute of Technology Chhattisgarh Bhilai 491002 India Department of Computer Science Christ University Delhi NCR 201003 India Amity School of Engineering and Technology (ASET) Amity University Gurugram Haryana Panchgaon 122412 India Department of Artificial Intelligence and Data Science CSMSS Chh Shahu College of Engineering Maharashtra Chh. Sambhajinagar India

UAVs have become key in various applications lately, from catastrophe relief to environmental monitoring. The plan of powerful and reliable directing protocols in UAV networks is seriously hampered by the dynamic and habitually eccentric mobility patterns of UAVs. This study proposes a novel technique to beat these challenges by utilizing the Modified Smell Bees Optimization (MSBO) algorithm to upgrade the weights of CNNs. This study’s principal objective is to further develop UAV network routing decisions by using CNNs’ ability for design recognition and the Modified SBO’s optimization abilities. Our methodology comprises of randomly relegating CNN weights to a populace of bees at start, evaluating their wellness by fitness of directing performance, and iteratively fine-tuning these weights utilizing local and global search procedures got from bee searching. Broad simulations and performance evaluations show that our recommended approach incredibly expands the general dependability of UAV’s, brings down communication latency, and improves directing productivity. Future exploration in UAV network improvement gives off an impression of being going in a promising direction with the integration of CNNs for pattern recognition and the Modified SBO for weight enhancement. In addition to progressing UAV routing conventions, this work sets out new open doors for machine learning applications of bio-inspired optimization algorithms. © 2024 River Publishers.

关键词： catastrophe relief CNN Modified smell bees optimization (MSBO) optimization algorithms routing protocols UAV networks

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A Selection-Based Framework for Building and Validating Regression Model for COVID-19 Information Management 5th

A Selection-Based Framework for Building and Validating Regr...

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5th International Conference on Smart Computing and Informatics, SCI 2021

作者： Landge, Pravinkumar B. Bhise, Dhiraj V. Nagwanshi, Kapil Kumar Patra, Raj Kumar Durugkar, Santosh R. Department of Information Technology SVKM’s NMIMS—MPSTME Shirpur Shirpur India Department of Computer Science and Engineering ASET Amity University Rajasthan Jaipur India Department of Computer Science and Engineering CMR Technical Campus Hyderabad India

ISBN: (纸本)9789811696688

The world is facing pandemic situation, i.e., COVID-19, all the researchers and scientist are working hard to overcome this situation. Being human it is everyone’s duty to take care of family and the society. In this case study, an attempt has been made to find the relation between various variables by dividing them into the independent and dependent variables. A dataset is selected for analysis purpose which consists of variables like location (countries across the globe, date, new cases, new deaths, total deaths, smoking habits washing habits, diabetic prevalence, etc. Approach is to identify the impact of independent variable on the dependent variable by applying the regression modeling. Hence, proposed case study is based on selection-based framework for validating the regression modeling for COVID-19 data analysis. Regression modeling is applied, and few representations are shown to understand the current pandemic situation across the world. In the end, using regression modeling interceptor and coefficient values for different approaches (using different variables) is computed. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data mining

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Early Prediction of Rice Leaf Disease Using Deep Neural Network Models

Early Prediction of Rice Leaf Disease Using Deep Neural Netw...

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International Conference on Confluence The Next Generation Information Technology Summit (Confluence)

作者： Nandita Arunima Jaiswal Nitin Sachdeva Department of Computer Science and Engineering Indira Gandhi Delhi Technical University for Women New Delhi India Department of Computer Science & Engineering (ASET) Amity University Noida Uttar Pradesh India

The global significance of rice as a staple food for over half of the world's population underscores the need for robust measures to safeguard its production. Rice crops face threats from various diseases, necessitating swift and precise detection methods. Leveraging advancements in deep learning and computer vision, this study delves into the application of convolutional neural networks (CNNs) for automated rice leaf disease detection. The research emphasizes the urgency of timely identification to ensure consistent rice production, which is crucial for meeting escalating global food demands. By employing sophisticated models, particularly CNN s, this approach proves highly effective in accurately identifying disease-induced anomalies in digital representations of rice leaves. The study covers key aspects such as the training process, data preparation, model architecture, and evaluation metrics essential for crafting efficient disease detection models. Highlighting real-world implications, the paper underscores the transformative potential of deep learning in rice leaf disease detection, citing benefits like accuracy, early detection, automation, scalability, and data-driven insights. Ultimately, this integration of deep learning techniques presents a promising avenue to fortify global food security through early, precise, and scalable automated detection systems. This study explores disease detection in rice leaves by employing self-constructed and pre-trained DL models on two distinct verified datasets. The research compares the performance of five models, revealing that CNN-SVM achieves the highest accuracy in this experiment followed by ResNet50, VGG19, VGG16, and AlexNet.

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Cloud Cryptography: Mechanism of Different Encryption Standards

Cloud Cryptography: Mechanism of Different Encryption Standa...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Ravikumar Lanke A. M. J. Md Zubair Rahman Rajat Bhardwaj D Somashekhara Reddy Parth Jain T R Mahesh Dept. of Computer Science and Engineering JAIN (Deemed-to-be-University) Bangalore India Al-Ameen Engineering College Karundevanpalayam Erode India Department of Computer Science and Engineering ASET Amity University Bengaluru India

The term “cloud cryptography” describes the application of cryptographic methods and protocols to protect communications and data in cloud computing settings. The provision of computing services, including storage, processing power, and applications, via the internet is known as cloud computing. Although cloud computing has numerous advantages, such as cost-effectiveness and scalability, it also poses security risks because sensitive data is processed and stored outside of the conventional network borders of an organization. Cloud cryptography plays a pivotal role in cloud security by safeguarding against unauthorized access, data disclosure, modification, or destruction. Keeping up with the most recent cryptographic methods and protocols is crucial to addressing new threats and weaknesses in this industry, which is always changing. Organizations can safeguard their data from unauthorized access, modification, and destruction by utilizing encryption techniques. This article explores the cloud cryptography along with its different encryption standards.

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