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检索条件"机构=Computer Science and Engineering Ajay Kumar Garg Engineering College"

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High Degree Dependency Study of Ensemble Models in Credit Card Fraud Analysis 2

High Degree Dependency Study of Ensemble Models in Credit Ca...

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Rani, Shalini Tiwari, Mayank Tyagi, Shiva Department of Computer Science and Engineering Ajay Kumar Garg Engineering College Ghaziabad India

ISBN: (纸本)9798331530389

Credit card fraud detection is an increasingly critical issue due to the growth of digital transactions and the sophistication of fraudulent activities. This study proposes a hybrid framework combining Graph Neural Networks (GNNs), ensemble methods (e.g., XGBoost, Random Forest), and polynomial feature engineering to address key challenges, including class imbalance, non-linear data relationships, and real-time applicability. The system achieved a detection accuracy of 96.7%, precision of 96.9%, recall of 96.4%, and a false-negative rate of 3.6% on benchmark datasets. These improvements are attributed to the integration of advanced sampling techniques, polynomial feature transformations, and the hybrid architecture. The findings demonstrate the effectiveness of the proposed approach in enhancing fraud detection systems and provide a scalable solution for real-world applications. © 2025 IEEE.

关键词： Graph neural networks

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Integration of computer Vision Systems in Robotics and Industry 4.0 2

Integration of Computer Vision Systems in Robotics and Indus...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Vikas Prasad, Rajesh Upadhaya, Santosh kumar kumar, Amit Ajay Kumar Garg Engineering College Department of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350368109

computer vision is the field that focuses on automating and combining various processes and representations used for visual perception. The subject encompasses numerous approaches that provide individual use, such as image processing. Image processing is converting, transforming and transferring an image. This method is the statistical categorization of patterns based on applying statistical decision theory. computer vision systems are deployed in various environments, from the shop floor and hospital suite to Mars and surgical suits. Incorporating computer vision in manufacturing is a groundbreaking step towards elevating Industry 4.0 by enhancing efficiency, accuracy and connectivity. This is driven by the compelling benefits realized when computer vision can be used in a factory to create a safe environment for all stakeholders and operators, affording machines with visual perception and enabling them to process and act on the high-richness of environmental data given input by a camera. computer vision integrated into manufacturing is a drastic step towards more intelligent, efficient and connected production methods as part of Industry 4.0. This paper discusses different features of the Industrial Revolution, the role of computer vision in Industrial Revolution 4.0, and how computer vision affected the Industrial Revolution in future industry development. As computer vision debuts firsthand in commercial use in manufacturing, so is the industry about to explode with unprecedented creativity and productivity. © 2024 IEEE.

关键词： computer integrated manufacturing

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Exploring the Cloud and Fog: Addressing Security Risks in AI Applications 2

Exploring the Cloud and Fog: Addressing Security Risks in AI...

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2nd IEEE International Conference on Advancements and Key Challenges in Green Energy and Computing, AKGEC 2024

作者： Jain, Jaishree Kaur, Inderjeet Ajay Kumar Garg Engineering College Department of Computer Science & Engineering Ghaziabad India

ISBN: (纸本)9798350390179

There are various security risks with cloud- or fog-based machine learning services. Because machine learning applications rely on these services, it is imperative to secure the underlying cloud or fog services to prevent serious disruptions to the applications. We distinguish based on whether Artificial Intelligence applications are employed in a fog computing network or the cloud because the needs for these applications can also vary. This consequently gives rise to various threats or avenues for attack. Security responsibilities for cloud platforms can be split up amongst several parties. Even though fog computing networks have fewer responsibilities, we still need to safeguard services from physical access to the devices at the network's edge because they have been moved there. Lastly, we go into specific information security requirements for AI-related applications. © 2024 IEEE.

关键词： Cloud security

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Hybrid Model for Predicting Used Car Prices: Integrating Natural Language Processing with Random Forest Regressor 2

Hybrid Model for Predicting Used Car Prices: Integrating Nat...

引用

2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Tyagi, Shiva Sirohi, Shagun Singh, Yash Siddhant Vishwakarma, Akash Department of Computer Science and Engineering Ajay Kumar Garg Engineering College Ghaziabad India

ISBN: (纸本)9798350383867

The automotive sector has a notable upswing in the sales of pre-owned vehicles, hence necessitating the development of precise pricing models to enable knowledgeable decision-making for both purchasers and vendors. In order to predict used automobile prices, we provide a novel hybrid methodology in this work that combines Natural Language Processing (NLP) methods with the Random Forest regressor. Our method leverages structured and unstructured data sources to extract valuable contextual information that is hidden in textual listings for automobiles. Preprocessing a heterogeneous dataset with unstructured textual descriptions and structured attributes (make, model, year, mileage) is part of the methodology. We leverage natural language processing (NLP) approaches to extract informative features from textual representations, improving the model's comprehension of subtleties in the conditions and attributes linked to individual car listings. ©2024 IEEE.

关键词： Random forests

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Advancing Sentiment Understanding in social media through Dynamic Contextual Embedding 3

Advancing Sentiment Understanding in social media through Dy...

引用

3rd International Conference for Innovation in Technology, INOCON 2024

作者： Yadav, Aditi Chaudhary, Anu Gupta, Avdhesh Ajay Kumar Garg Engineering College Department of Computer Science & Engineering Ghaziabad India

ISBN: (纸本)9798350381931

In the current landscape of social media communication, believing sentiment expression is crucial for diverse applications, including brand management and public opinion analysis. The study explores ways to improvise sentiment analysis, in media by using contextual embeddings. It places its emphasis on well-settled models such as BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer). By tackling the force of these dynamic inserting, the study looks to beat the impediments of conventional feeling examination strategies in catching the nuanced and context-dependent nature of language prevalent in social media disclosure. Through a comprehensive examination of existing literature and recent developments, the paper systematically evaluates the impact of dynamic contextual embedding, emphasizing prominent models such as BERT and GPT. The main goal is to provide an in-depth overview of how these techniques have subsidized to the enhancement of sentiment analysis in the ever-evolving landscape of social media platforms. The research involves a comprehensive investigation into the fine-tuning and pre-training of these models on social media datasets. This paper provides an outline based on previous studies in the field of understanding sentiments in social media posts using algorithms like BERT and GPT. © 2024 IEEE.

关键词： Sentiment analysis

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Enhancing Blockchain Security through quantum key distribution and evaluating QKD network in QKDNetSim environment 3

Enhancing Blockchain Security through quantum key distributi...

引用

3rd International Conference on Power Electronics and IoT Applications in Renewable Energy and its Control, PARC 2024

作者： Singh, Sakshi Gupta, Avdhesh Chaudhary, Anu Ajay Kumar Garg Engineering College Department of Computer Science & Engineering Ghaziabad India

ISBN: (纸本)9798350309409

The theory of QKD was first introduced in the 1980s. Initially, classical cryptographic methods were used to secure communication, but it was not a secure approach. Quantum Key Distribution (QKD) represents high-tech solution in the arena of secure and reliable cryptography and communication. Quantum key distribution make the use of quantum superposition and quantum entanglement to facilitate the secure interchange of cryptographic keys between two parties, with a theoretical invulnerability resistant to many threats, also including the threats that classical computing faces. On the other hand, blockchain is secured through cryptographic measures. A wide range of block chain applications rely on digital signatures and hash function which is easily traceable by quantum computers. The progress in quantum computing poses a potential risk of compromising existing cryptographic algorithms, in response to this challenge, proposals for potential solutions, such as the development of quantum-resistant blockchains, are being presented. The motivation of this study is to secure blockchain with the help of QKD. Fundamentally, understanding quantum key distribution is challenging. Hence, this research employs a Quantum Key Distribution Network simulator to analyze key distribution. The study focuses on illuminating the security concerns within blockchain and explores a potential solution through the deployment of quantum key distribution. Additionally, the research delves into the examination of a quantum key distribution network using a dedicated simulator. © 2024 IEEE.

关键词： Quantum computers

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A comprehensive analysis and sensitivity model for emotion detection in text 1st

A comprehensive analysis and sensitivity model for emotion d...

引用

1st International Conference on Applications of AI in 5G and IOT, ICAAI5GI 2024

作者： Singh, Pancham Asthana, Amita Solanki, Beerbal Tomar, Swati Kansal, Mrignainy Tyagi, Gagan Department of Information Technology Ajay Kumar Garg Engineering College Ghaziabad India Department of Electronics and Communication Engineering Ajay Kumar Garg Engineering College Ghaziabad India Department of Computer Science and Engineering Ajay Kumar Garg Engineering College Ghaziabad India Technical Architect Tech Mahindra India

ISBN: (纸本)9781032874036

People use a variety of communication channels, including text, voice, and gestures, to convey a wide range of emotions in their daily interactions. The problem of automatically identifying these emotions from textual information has important practical ramifications. This study focuses on natural language processing (NLP) methods for textual emotion identification, particularly in Twitter tweets. The study uses machine learning, sentiment analysis, tokenization, and other techniques with a focus on using APIs such as the Twitter API. With an emphasis on the use of unsupervised learning algorithms and data expansion methodologies, the focal issue examines relevant work in sentiment analysis on social media. The suggested approach tackles issues including tokenization, sentiment analysis, and noise reduction in order to categorize tweets into distinct moods. The front-end development using Flask provides a user-friendly interface to visualize real-time Twitter data based on user-defined keywords. The study achieved an overall accuracy rate of 92% in classifying human moods across a diverse dataset. © 2025 the Author(s).

关键词： Tweets

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Deep Defense: A Hybrid LSTM-ANN Architecture for Intelligent Intrusion Detection System 2

Deep Defense: A Hybrid LSTM-ANN Architecture for Intelligent...

引用

2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Shadeja, Mohit Singh, Shashwat Prakash, Ayushi Dept. of Computer Science and Engineering Ajay Kumar Garg Engineering College Uttar Pradesh Ghaziabad India

ISBN: (纸本)9798331530389

The rapidly evolving cyber threat landscape requires innovative and adaptive intrusion detection solutions. Traditional signature-based intrusion detection systems, despite their high accuracy, are inherently inflexible and susceptible to evasion tactics, making them ineffective against novel and sophisticated attacks. In contrast, machine learning-powered anomaly-based intrusion detection systems demonstrate improved detection capabilities, but their advantages are often outweighed by substantial drawbacks, including significant computational resource requirements when the dataset and its features for behavior analysis increase. Also, there are high power consumption and latency issues in communication that compromise network performance. This study proposes a hybrid ANN-LSTM approach for performing deep packet inspection and efficiently detecting malicious network packets. By analyzing binary container data, our model reduces time and space complexity, thereby mitigating the computational resources and power consumption challenges but also increasing the efficiency of intrusion detection by 3%, making it more intelligent than various established IDS. © 2025 IEEE.

关键词： Network intrusion

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Optimizing Agricultural Health: Early Detection and Classification of Crop Diseases Through Hyperspectral Imaging and Convolutional Neural Networks 6th

Optimizing Agricultural Health: Early Detection and Classifi...

引用

6th International Conference on Intelligent Communication, Control and Devices, ICICCD 2024

作者： Tyagi, Shiva Yadav, Aman Gupta, Sankalp Jaiswal, Ujjwal Maurya, Vagish Department of Computer Science and Engineering Ajay Kumar Garg Engineering College Ghaziabad Uttar Pradesh Ghaziabad India

ISBN: (纸本)9789819783281

One of the most difficult jobs in agriculture is identifying plant diseases early on. Early disease detection is crucial for increasing agricultural yield. With the application of machine learning and deep learning techniques, this issue has been resolved. Large crop farms can now detect plant illnesses automatically, which is advantageous as it cuts down on monitoring time. Prediction model is developed using convolutional neural network (CNN) which is machine learning technique. Moreover, using Hyper Spectral Imaging (HSI) technique early disease or even asymptomatic diseases can be detected. Based on the real-time leaf photos that are gathered, a K-mean clustering process is also utilized to detect disease. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Blockchain-Powered Food Supply Chain Tracking: A Paradigm Shift for Transparency, Accountability, and Quality Assurance Through BlockTrackers 2

Blockchain-Powered Food Supply Chain Tracking: A Paradigm Sh...

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Kansal, Mrignainy Singh, Pancham Chaurasia, Prateek Dwivedi, Anupam Ali, Shahbaz Information Technology Ajay Kumar Garg Engineering College Uttar Pradesh Ghaziabad India Computer Science and Information Technology Ajay Kumar Garg Engineering College Uttar Pradesh Ghaziabad India

ISBN: (纸本)9798350371055

This research presents a revolutionary solution designed to usher in a new era for the food supply chain, setting standards for transparency, accountability, and quality assurance. In response to the multifaceted challenges afflicting the contemporary food industry, including an absence of accessible and dependable data on product origin, authenticity concerns, and equitable compensation for farmers, this project harnesses the transformative potential of blockchain technology. At its core, the work introduces a blockchain-based food supply chain tracking system, seamlessly integrated with the BlockTrackers application, which meticulously documents and traces the entire lifecycle of food products. This comprehensive system provides a robust foundation for stakeholders across the supply chain, offering numerous benefits, and it promises to bring about a paradigm shift in the food supply chain. Key elements include Blockchain-Enabled Transparency, BlockTrackers Application, QR Code Integration, Modular Hardware Device, Real- Time Data Logging and Update, and Consumer Confidence. This research represents a pioneering stride towards a brighter, more sustainable future for the global food supply chain. It addresses the long-standing challenges faced by the food industry, ensures fair compensation for farmers, and creates new revenue opportunities. Ultimately, the project fosters a more equitable and reliable ecosystem for all participants in the food supply chain, setting new standards for accountability and quality assurance in the industry. © 2024 IEEE.

关键词： Accountability Blockchain BlockTrackers Application Consumer Confidence Modular Hardware Device QR Code Integration Quality Assurance Real-Time Data Logging and Update Sustain ability Transparency

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