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

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A Study of Various Classification Algorithms to Reveal Anomaly during Fingerprint Presentation on Biometric Sensor Based on Naïve Feature Vector On Two Separate Region 2

A Study of Various Classification Algorithms to Reveal Anoma...

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2nd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Chaudhary, Riya Verma, Akhilesh Ajay Kumar Garg Engineering College Department of Computer Science and Engineering UP Ghaziabad India

ISBN: (纸本)9781665437899

Fingerprint biometric-based application suffers from a problem, in which no genuine biometric sample known as a spoof sample is classified as a live presentation. To overcome this problem the research community is proposing new algorithms or adopting existing state-of-art. In continuation, this work proposes a naïve feature vector computed from two independent image regions to analyze various classifiers' performance to validate feature vector design. In this work, the first step is to calculate the region of interest (ROI) using factorized directional band-pass (FBD) segmentation algorithm to remove unwanted regions. The two regions proposed are region-I and region-II. The (region-is composed of four (non-overlapping) sub-regions calculated using the Otsu thresholding algorithm. The second region (region-comprises six sub-regions (overlapping) using activity mask, noticeable artifacts mask, noise mask, and their image negatives. For feature selection, this work proposes to use five different no-reference image quality assessments (IQA) metrics. These are perception-based image quality evaluator (PIQE), naturalness image quality evaluator (NIQE), and its model blind/reference less image spatial quality evaluator (BRISQUE) and its model. The two feature design consists of a total of 38 and 24 features respectively. To test the accuracy of the proposed methodology all 25 binary classifier algorithm available in MATLAB has experimented with 10-fold cross-validation. This experiment uses cross-sensor and cross-material experimental validation on two different datasets LivDet2013 and LivDet2015. The proposed method achieved 99.8% reasonable accuracy with the Bagged Tree Ensemble learning classifier with cross-sensor and cross-material combinations using 38 features. Further, another classifier performs fairly well with 24 features in a lower combination of datasets which is comparable with existing state-of-art methods. The experimental result verifies the effectiveness

关键词： Image quality

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Developing an Effective Model for Skin Disease Detection: Techniques and Approaches

Developing an Effective Model for Skin Disease Detection: Te...

引用

Advancement in Electronics & Communication engineering (AECE), International Conference on

作者： Nikhil kumar Yadav Amit Tiwari Gaurav Harsh kumar Verma Arpna Saxena Ajay Kumar Garg Engineering College Varanasi India Ajay Kumar Garg Engineering College Muzaffarpur India Ajay Kumar Garg Engineering College Meerut India Ajay Kumar Garg Engineering College Ghaziabad India Master of Computer Application Ajay Kumar Garg Engineering College Ghaziabad India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

This research highlights the critical role of the skin as the body's primary defense against damage and infection. Despite its importance, the skin can be vulnerable to serious infections caused by fungi, viruses, or even dust, leading to conditions ranging from acne to eczema. Some skin diseases are highly contagious and can be spread by simple contact, underscoring the need for proper diagnosis and treatment to alleviate suffering and prevent transmission. The study focuses on developing a prototype for identifying skin diseases using convolutional neural networks (CNNs), a choice based on their effectiveness in image processing tasks. This research uses CNNs to classify specific skin conditions, including hand dermatitis, hand eczema, subacute eczema, lichen simplex, stasis dermatitis, and ulcers are all different skin conditions.. The methodology integrates image processing techniques with machine learning and uses the processed images to train a CNN model. The training data consists of five classes of skin conditions from the Dermnet dataset, which includes 19600 images. The prototype achieved an accuracy of 92%, highlighting the system's potential. Research suggests that with further improvements and a larger data set, the system could significantly improve early detection and treatment of skin ailments representing a substantial advance in dermatology care.

关键词： Deep learning Adaptation models Accuracy Computational modeling Dermatology Eczema Transformers Skin Convolutional neural networks Diseases

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OpportuNest - Campus Career Management System 3

OpportuNest - Campus Career Management System

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3rd International Conference on Disruptive Technologies, ICDT 2025

作者： Agarwal, Tanya Kalwani, Chandni Pathak, Divyanshu Sachan, Saumya Arora, Neerja Sahu, Shashank Ajay Kumar Garg Engineering College Department of Computer Science and Engineering Ghaziabad India Ajay Kumar Garg Engineering College Department of Cse Ghaziabad India

ISBN: (纸本)9798331519582

Becoming well settled in placements is quite difficult for a student in today's job market scenario as it is very competitive. Placement cells have a lot of data regarding the students as well as job openings to be collected. Most of these methods are traditional and very slow and tend to make mistakes, because of which there are missed opportunities for both students and companies. Being completely online, the OpportuNest project will build a very efficient platform that shall focus on streamlining processes, reviewing CV s with machine automation and making predictions on placements. Highly capable modern web technolo-gies would be employed to make the entire placement process simpler, from the sophisticated machine learning algorithms on the MERN stack to assistance for administrators and placement officers. Some of the additional benefits will include ATS scoring for resume optimization, real-time data analysis for displaying metrics of performance, AI-based job matching according to skills and qualifications of the user, and improved user experience with personalized dashboards. All of that can be achieved thanks to effective, transparent, and user-friendly components in OpportuNest's career placement environment. The practice aptitude tests score based on the Random Forest algorithm to determine the placement probability, and an interview mock test feature is available on the student dashboard. For predicting future trends like those pertaining to enrollments and placements, OpportuNest uses an LSTM neural network for analyses based on patterns in historical data. All these features in OpportuNest are meant to enhance efficiency, transparency, and user experience, thus facilitating the smooth and hassle-free placement process for both administrators and students. © 2025 IEEE.

关键词： Students

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Hy-PSO: Hybrid Algorithm for Lung Cancer Diagnosis and Prognosis

Hy-PSO: Hybrid Algorithm for Lung Cancer Diagnosis and Progn...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Sachi, Savya Jain, Jaishree Jain, Arpit Patel, Umesh kumar Bhatnagar, Amrita Jain, Abhishek Research Foundation of India India Ajay Kumar Garg Engineering College Ghaziabad201009 India Koneru Lakshmaiah Education Foundation Department of Computer Science Engineering A.P. Vaddeswaram India GNIOT Institute of Professional Studies U.P. Greater Noida201306 India Ajay Kumar Garg Engineering College U.P. Ghaziabad201009 India Uttaranchal University Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350394412

As one of the primary causes of cancer-related death globally, lung cancer demands the development of sophisticated diagnostic and prognostic instruments in order to enhance patient outcomes. Lung cancer stands as one of the malignancies with significant morbidity and mortality rates. Imaging serves as a crucial component throughout the entire spectrum of lung cancer management, facilitating everything from initial detection to evaluating response to treatment. Innovations in medical imaging and image processing have created new opportunities for enhancing the diagnosis and prognosis of lung cancer. This study introduces a hybrid algorithm, termed the Hy-PSO algorithm, which combines the strengths of particle swarm optimization and convolutional neural networks (CNNs) for classification tasks. Feature selection optimization significantly influences the accuracy of the classification model. Comparative analysis with other classification techniques, including support vector machines, artificial neural networks (ANNs), and CNNs, demonstrates the superiority of the proposed algorithm. Experimental results indicate its effectiveness in automatic lung tumor detection, surpassing contemporary methods in terms of accuracy (98.4%), sensitivity (99%), and specificity (97.65%). Hy-PSO combines many Machine Learning (ML) methods, including support vector machines (SVM), decision trees, and neural networks, to create prediction models after determining the ideal feature set. To find promising biomarkers for targeted medicines, estimate patient survival rates, and categorize lung cancer stages, these models are trained using the optimal characteristics. By utilizing the advantages of both PSO and machine learning, the hybrid technique makes sure that the prediction models are more robust and generalizable. © 2024 IEEE.

关键词： Lung cancer

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Enhancing OTP Generation Efficiency through Cryptographic Techniques 3

Enhancing OTP Generation Efficiency through Cryptographic Te...

引用

3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： kumar, Vishal Ahmareen, Shafaque kumar, Manoj Prajapati, Yogendra Narayan Pant, Bhasker Bohra, Sneha Chandigarh University Department of CSE India Ministry of Education Department of Computer Science United Arab Emirates Ajay Kumar Garg Engineering College Ghaziabad India Ajay Kumar Garg Engineering College CSE Ghaziabad India Graphic Era Deemed to Be University Department of Computer Science Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Department of Computer Science and Engineering India

ISBN: (纸本)9798350399264

As part of the verification procedure, credentials are typically hashed. Such cipher-text password schemes are often easy and quick to implement because to their concentration on text and reliance on conventional encryption. On the other hand, they could be susceptible to hacks carried out by rogue websites or password cracking programmes. If the original password is simple and obvious, attackers may recover it from the hash. Many cyber attacks have occurred as a consequence, often targeting specific cipher-text methods. In this research, we provide a more secure system for handling image-based passwords through cryptographic techniques (VC). Our approach takes a login name from a work of literature and transforms it into two VC-protected pictures, in addition to the standard hashes and words. Using SEED and certain hidden knowledge, the user can split a single image into two completely different pictures. The website just requires a login and one of his photos as authentication, rather than passwords. When a user signs in and uploads a new picture, the server may utilise Oct (Optical Character Recognition) to capture identifying information. Therefore, it may be able to identify the user by matching the ID it generates with the one it already has. Our idea allows for authentication without giving up sensitive data like ID numbers, requires less computing power, and precludes simulated efforts to break hashes. © 2023 IEEE.

关键词： Websites

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Automated Detection of Parkinson's Disease 4

Automated Detection of Parkinson's Disease

引用

4th International Conference on Recent Trends in computer science and Technology, ICRTCST 2021

作者： Sharma, Shivani Singh, Pratima Ajay Kumar Garg Engineering College Dept. Of Computer Science & Engineering Uttar Pradesh India

ISBN: (纸本)9781665466332

The prognosis of Parkinson's disease is one of the maximum critical hassles that must be investigated in the early levels of the graduation of the disorder to be able to lessen the disorder development rate of the various individuals. Tracking Parkinson's disease (PD) symptom development frequently makes use of UPDRS ratings, which calls for the affected person's presence in the clinic, and time-ingesting bodily examinations via way of means of the skilled medical team of workers. Thus, symptom tracking is high-priced and logistically inconvenient for the affected person. Here, for the primary time, we have made a system that could detect PD in few minutes rapidly with clinical accuracy by conducting speech tests, tremor tests and handwriting tests and hence further predict stage of the disease. This model will make a foundation in telemonitoring frameworks converting wide-ranging medical trials into rapid PD treatments. Thus, our study will focus on finding the best model to provide an automated method to extract the necessary biomarkers which will help in the prediction and classification of Parkinson's disease into stages. © 2022 IEEE.

关键词： Machine learning

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Enhancing Tourism Experiences and Preserving Cultural Heritage with AR and VR

Enhancing Tourism Experiences and Preserving Cultural Herita...

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Disruptive Technologies (ICDT), International Conference on

作者： Pancham Singh Neeti Pahuja Mrignainy Kansal Siddhant Gurung Utkarsh Shukla Sandeep Gupta Information Technology Ajay Kumar Garg Engineering College Ghaziabad India Computer Science and Engineering Ajay Kumar Garg Engineering College Ghaziabad India CSE (AI & ML) Ajay Kumar Garg Engineering College Ghaziabad India Ajay Kumar Garg Engineering College CSIT Ghaziabad India

The objective of this research is to understand how virtual reality (VR), augmented reality (AR), and mixed reality (MR) are transforming the travel and tourism sectors as well as preserving cultural heritage. We are living in a digital interaction world where new immersive experiences have been induced by the interplay between technological breakthroughs, societal needs, and commercial imperatives. Given that tourism is about cultural heritage sites worldwide, this study examines the current challenges associated with their conservation within the globalization of culture. The objective is to understand how AR and VR technology can change the way people think about tourism and protect the world's cultural heritage. With this aim in mind, the paper explores different applications of AR and VR technologies while focusing on how these can offer contemporary tourists immersive and educational experiences. The authors suggest that the advantages of accessibility should be thoroughly examined; virtual visits could be used to bypass logistical, financial, or physical barriers; and the implications of such technologies for digital preservation will also be considered under this approach. It was found out that AR and VR serve as effective means of access, enabling a larger number of people from around the world to reach cultural heritage sites. Besides, it is through these technologies that significant changes in the field of preservation can be achieved, ensuring the survival of cultural assets for future generations. Lastly, this research paper will be helpful to researchers within academic circles as well as industry practitioners and policymakers who want to know how AR/VR technology is changing the travel industry or contributing to cultural heritage protection efforts. Essential for unlocking the potential of emerging digital channels fully is the need to appreciate such intricate linkages in our rapidly changing digital world.

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

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

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Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

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

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

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.

关键词： Accuracy Scalability Credit cards Feature extraction Polynomials Real-time systems Graph neural networks Fraud Ensemble learning Random forests

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Blockchain Integrated Cloud Architecture for Surveillance Systems 3

Blockchain Integrated Cloud Architecture for Surveillance Sy...

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3rd International Conference on Disruptive Technologies, ICDT 2025

作者： kumar, Vinay Raghuvanshi, Varun Singh Sharma, Shashwat Yadav, Yash Goutam, Arvind Ajay Kumar Garg Engineering College Department of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798331519582

The speedy growth of data-pushed programs, especially in surveillance and IoT systems, has underscored the need for steady, green, and privateness-retaining records control answers. While cloud computing gives scalability, it poses sizeable safety risks, making it liable to records breaches and unauthorized access. Blockchain technology, with its decentralized and immutable ledger, offers promising answers to these challenges, yet it struggles with scalability and computational demands while implemented to high-frequency, actual-time applications. This research paper proposes an technique to address those limitations through an integration of blockchain generation with cloudprimarily based surveillance systems. Through a hybrid version of selective storage and optimized consensus mechanisms, the proposed solution addresses the demanding situations of latency, scalability, and privacy maintenance at the same time while enhancing real-time access control. © 2025 IEEE.

关键词： Real time control

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Secure UPI: Machine Learning-Driven Fraud Detection System for UPI Transactions 2

Secure UPI: Machine Learning-Driven Fraud Detection System f...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Rani, Rupa Alam, Adnan Javed, Abdul Ajay Kumar Garg Engineering College Ghaziabad Department of Computer Science & Engineering U.P. Ghaziabad India Banasthali Vidyapith Department of Computer Science & Engineering Rajasthan India Abes Institute of Technology Department of Computer Science & Engineering-Data Science U.P. Ghaziabad India

ISBN: (纸本)9798350371055

Secure UPI specializes in developing an advanced fraud detection gadget the usage of the effective XGBoost device getting to know set of rules to create an advanced fraud identity device. XGBoost is a properly-proper alternative for enhancing the precision of fraud detection fashions because of its reputation for dealing with tricky datasets with efficiency and its music record of success throughout multiple industries. In order to extract pertinent records, like transaction quantity, frequency, and place, our approach preprocesses UPI transaction facts. This article makes use of a labelled dataset to train the XGBoost version in order that we may also take gain of its sturdy prediction talents and capacity to handle imbalanced datasets. To help create a system that is less difficult to apprehend and use, feature importance evaluation is used to discover essential symptoms of feasible fraud. After training, the model is covered right into a real-time UPI transaction tracking device, in which it maintains an eye fixed out for any suspicious traits in incoming transactions. In order to lessen the results of fraudulent activity, the system is constructed with 98.2 % accuracy to send out instant notifications and take preventive steps. This challenge allows in improving UPI transaction security and advancing economic era are accomplished through demonstrating the performance of machine learning in fraud detection. © 2024 IEEE.

关键词： Dimensionality Reduction Fraud Detection Imbalanced Datasets Machine Learning PCA SMOTE UPI XGBoost

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