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Blockchain - A Secure and Transparent Solution to Detect Counterfeit Products 1

6th International Conference on Applied Machine Learning and Data Analytics, AMLDA 2023

作者： Tyagi, Ishaan Gupta, Rajat Upadhyay, Divya Dubey, Ashwani Kumar Department of Computer Science and Engineering ABES Engineering College UP Ghaziabad India Department of Electronics and Communication Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh UP Noida India

ISBN: (数字)9783031554865

ISBN: (纸本)9783031554858

Counterfeit products have had a massive impact on manufacturing industries in the past couple of years. This is affecting the company’s name, sales, and profit. Blockchain innovation has acquired an interest in the course recently. The most important issue about this is currency exchange, but its application is not restricted to only Digital currency. This technology has the potential to influence different business sectors. Blockchain has brought high transparency and ease in how transactions are dealt with. Blockchain technology can be used to identify real products from fake ones. Using this technology, customers or users need not rely on third-party services for the product’s safety. The proposed system’s Quick Response (QR) code provides a robust technique to stop counterfeiting the products. Fake products can be detected using a Quick Response scanner, where a QR code is attached. This QR code is linked to the Blockchain network to identify the fake product. A block will be created to store the product-related information and its QR code, like product details, and generate a unique code for each block in the blockchain database. The user uploads and then compares the unique code of the product he intends to purchase with that stored in the blockchain network. If the code matches the code that the manufacturer generated, it will notify the customer, saying the QR code is matched;otherwise, it will notify the customer that the QR code is not matched and the product is fake. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Smart contract

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A Novel Work on Analyzing STRESS and Depression level of Indian Population During COVID-19

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Recent Advances in computer science and Communications 2022年第6期15卷 822-831页

作者： Gupta, Amit Kumar Mathur, Priya Bijawat, Shruti Dadhich, Abhishek Department of Computer Science & Engineering Amity School of Engineering & Technology Amity University Rajasthan Jaipur India Department of Mathematics Poornima Institute of Engineering & Technology Rajasthan Jaipur India Department of Computer Science & Engineering Poornima Institute of Engineering & Technology Rajasthan Jaipur India

Objective: The world is facing the pandemic of COVID-19, which has led to a considerable level of stress and depression in mankind as well as in society. Statistical measurements can be made for early identification of the stress and depression level and prevention of the pre-vailing stressful conditions. Several studies have been carried out in this regard. The Machine learning model is the best way to predict the level of stress and depression in humankind by statistically analyzing the behavior of humans which helps in the early detection of stress and de-pression. This helps to prevent society from psychological pressures from any disaster like COVID-19. COVID-19 pandemic is one of the public health emergencies that are of great international concern. It imposes a great physiological burden and challenges on the population of the country facing the calamity caused by this disease. Methods: In this paper, the authors conducted a survey based on some questionnaires related to depression and stress and used the machine learning approach to predict the stress and depression level of humankind in the pandemic of COVID-19. The data sets were analyzed using the Multiple Linear Regression Model. The predicted score of stress and depression was mapped into DASS-21. The predictions have been made over different age groups, gender, and categories. The machine learning model is the best way to predict the level of stress and depression in humans by statistically analyzing their behavior which helps in the early detection of stress and depression. Results: Women, in general, were more stressed and depressed than men. Moreover, the people who are 45+ years of age were found to be more stressed and depressed, including male and fe-male students. The overall analysis showed that the people of India were stressed and depressed at "Serve" level due to COVID-19. It may be because students were more depressed about their study and career, women were stressed about their business as

关键词： Forecasting

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Development of Emotion Recognition via Federated Learning: A Multi-modal Approach with Hybrid Convolutional Network

Development of Emotion Recognition via Federated Learning: A...

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Kaur, Dilsheen Misra, Anuradha Vyas, O.P. Amity University Amity School of Engineering and Technology Department of Computer Science and Engineering Lucknow Campus Lucknow India Indian Institute of Information Technology Allahabad Department of Information Technology Prayagraj India

ISBN: (纸本)9798350385199

Human emotion recognition is the crucial process for performing effective communication and intelligent interaction. Identifying human emotion through discrete technologies is an essential step. Affective computing is one machine learning process in which multi-task learning has gathered a great deal of attention. It leverages shared data across associated activities to enhance performance. The physiological results from the aforementioned devices may also constitute a privacy risk because they reveal extremely sensitive information. To address this issue, we develop a federated learning approach with multi-task learning to precisely recognize facial emotion from the human. The required face images and Electroencephalogram (EEG) signals are collected from online sources. The face images are considered as feature set 1. From the EEG signals, the deep features are extracted through the Restricted Boltzmann Machine (RBM) and these features are considered as feature set 2. Both feature set 1 and feature set 2 are applied to the federated learning network, where the Hybrid Convolution-based Convolutional Neural Network (HC-CNN) is adopted for the recognition of human facial emotions. The face images are applied to the 2D layer and the extracted deep features from the EEG signal are applied to the 1D layer of the hybrid convolution network to provide an effective face recognition outcome. The recognition outcome of the proposed model is compared with the traditional face emotion recognition models concerning various measures. The recommended model successfully recognizes the emotion of the human with an accuracy of 95.75 % and precision of 95.97%, respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhancing Image Segmentation through an Innovative Hybrid Kernel: A Weighted Fusion of Sobel and Prewitt Operators Using Artificial Neural Networks 1

Enhancing Image Segmentation through an Innovative Hybrid Ke...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Verma, Ankush Dhanda, Namrata Yadav, Vibhash Amity Institute of Information Technology Amity University Uttar Pradesh Department of Information Technology Lucknow India Amity School of Engineering and Technology Amity University Uttar Pradesh Department of Computer Science & Engineering Lucknow India Rajkiya Engineering College Department of Information Technology Banda India

ISBN: (纸本)9798350353648

Image segmentation, a pivotal element in computer vision, relies on effective edge detection to delineate boundaries within images. Traditional methods, including Canny, Sobel, Prewitt, and Laplacian, while widely used, may exhibit limitations that necessitate pre- and post-processing techniques for optimal results. In response to this, our paper introduces an innovative hybrid kernel, constructed from the weighted average of Sobel and Prewitt operators, with weights determined through an Artificial Neural Network (ANN). The objective is to synergize the strengths of these operators, enhancing the precision of edge detection. The proposed hybrid kernel is rigorously evaluated against established techniques, such as Sobel and Canny, utilizing comprehensive metrics like Peak Signal-to-Noise Ratio (PSNR) and F-score. Results consistently demonstrate that the hybrid kernel outperforms previously published outcomes, showcasing notable improvements in edge detection quality and accuracy. This research positions the hybrid kernel approach as a promising alternative for image segmentation, offering advancements beyond conventional methods. © 2024 IEEE.

关键词： Image segmentation

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Production prediction using machine learning 10

Production prediction using machine learning

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10th International Conference on Signal Processing and Integrated Networks, SPIN 2023

作者： Batra, Rakshit Abbi, Prateek Sharma, Rohan Agarwal, Hardik Bhulania, Paurush Amity University Amity School of Engineering and Technology Department of Computer Science and Engineering Uttar Pradesh Gautam Budh Nagar India

ISBN: (纸本)9781665490993

Our project is inspired from the problems faced by Micro and Small-scale industry owners who are into food production and have to manufacture their goods on daily basis of demand and sale. To plan the production these business owners, predict their production based on several parameters, Day of week, festival, weather etc. but sometimes their prediction goes wrong. So, we are developing a machine model which will be trained on various parameters such as weather, festival, etc. and it will return production quantity as prediction. We are not only developing machine model, but also a full stack application in which a Micro and Small-Scale industry owner can feed the details and according to his/her details, machine will predict the quantity of the product and that predicted quantity will be visible to owner. © 2023 IEEE.

关键词： Forecasting

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EleKAN: Temporal Kolmogorov-Arnold Networks for Price and Demand Forecasting Framework in Smart Cities 4th

EleKAN: Temporal Kolmogorov-Arnold Networks for Price and ...

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4th International Conference on Frontiers of Electronics, Information and Computation Technologies, ICFEICT 2024

作者： Bhattacharya, Pronaya Mukherjee, Tamoghna Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University West Bengal Kolkata 700135 India

ISBN: (纸本)9789819653171

In smart cities, the demand forecasting problem is inherently dynamic and difficult to predict. A novel forecasting framework, EleKAN is proposed, that utilizes the Temporal Kolmogorov-Arnold Networks (TKANs), which are designed to leverage enhanced efficiency, accuracy, and reslience in the multistep time series predictions. TKANs integrate the interpretability and performance strengths of Kolmogorov-Arnold Networks (KANs) with the temporal dependency management capabilities of recurrent architectures, effectively addressing the limitations of existing models. The proposed framework is applied to a benchmarking Australian New South Wales (NSW) electricity market, which includes various features including total demand, demand delay, power consumption, and temporal factors such as weekdays, holidays, and time intervals. Experimental results demonstrate that EleKAN achieves a Root Mean Square Error (RMSE) of 0.0030, an R-squared (R²) score of 0.9757, significantly improving the precision and dependability of power demand estimates. A percentage improvement of 10. 9%, 6. 1%, and 9. 7% is obtained in the score of R² over LSTM and GRU respectively. This makes it particularly valuable for long-term energy management and planning in smart cities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Forecasting Model Kolmogorov-Arnold Networks Smart Cities Temporal Networks

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Comprehensive Framework for Analyzing Grammar Error Correction with GPT -3.5

Comprehensive Framework for Analyzing Grammar Error Correcti...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Munda, Rajni Kant Kumar, Abhishek Burman, Ravi Kumar Kumar, Biresh Alam, Md. Shoaib Sinha, Anurag Jharkhand University of Technology Computer Science and Engineering Ranchi India Amity University Computer Science and Engineering Jharkhand Ranchi India Ignou School of Computing and Information Science New Delhi India

ISBN: (纸本)9798331508685

This investigate assesses the viability and exactness of an error-correction approach utilizing GPT-3.5 compared to routine instruments. Letter set rectification plays a vital part in making strides the quality of and improving clarity in different scholastic and proficient situations. The consider utilizes appraisal criteria such as precision, exactness, review, and F1 score to how well GPT-3.5 performs relative to other language structure redress devices. Also, the information incorporates client input and daze testing to gage the viable convenience of the adjustments. Discoveries demonstrate that GPT-3.5 outperforms conventional instruments in taking care of relevant blunders and client fulfilment, illustrating a momentous capacity to redress syntactic botches, indeed in complex sentences with inconspicuous subtleties. Be that as it may, it does confront a few challenges, such as affectability to input varieties and intermittent overcorrection. The think about recommends entering on moving forward relevant understanding and diminishing overcorrection to development these calculations assist. It too investigates potential applications of GPT-3.5 for upgrading composing back and languagelearning assets, highlighting conceivable future investigate ranges to optimize AI-driven language structure rectification models. © 2025 IEEE.

关键词： Instrument errors

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Sentimental Technique Implementation on Textual Data 6

Sentimental Technique Implementation on Textual Data

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

作者： Karthik, Baggam Sai Bharat, Dhavileswarapu N.S.S.V.V. Durga Alam, Md Afroz Sai, Bandari Tharun Rana, Mrinalini Sharma, Smita Yadav, Suman Avdhesh School of Computer Science and Engineering Lovely Professional University Punjab Kapurthala India Amity School of Engineering and Technology Department of Computer Science and Engineering Amity University Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350304480

The increasing amount of real-time or near-real-time data in the big data age provides organizations with opportunities to make data-driven decisions. However, social media data, such as that generated by Twitter, is typically unstructured and difficult to manage. This paper proposes an efficient text data pre-processing approach and develops an algorithm for training decision tree, random forest, SVM, and logistic regression classifiers on a dataset. The algorithm weights the sentiment score based on the hashtag and cleaned text weights, and the most efficient data pre-processing strategy is identified using a novel technology called the hash vectorizer. The effectiveness of the algorithm is tested against manually sorted sentiments and sentiments created before text data pre-processing, and the resultsshow its accuracy is comparable to human-annotated feelings. This research contributes to the existing body of knowledge by providing a precise method for cleaning social media data and a new approach to sentiment analysis. © 2023 IEEE.

关键词： Decision tree Hashing Vectorizer Hybrid model Logistic Regression Random Forest Sentimental Analysis SVM

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Enhancing Information Security for Text-Based Data Hiding Using Midpoint Folding Approach: A Comparative Analysis 4th

Enhancing Information Security for Text-Based Data Hiding Us...

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4th International Conference on Mobile Radio Communications and 5G Networks, MRCN 2023

作者： Allwadhi, Sachin Joshi, Kamaldeep Yadav, Ashok Kumar Department of Computer Science and Engineering University Institute of Engineering and Technology Maharshi Dayanand University Haryana Rohtak124001 India Amity School of Engineering and Technology Amity University Uttar Pradesh Noida201301 India

ISBN: (纸本)9789819706990

The activity of concealing information or data within a carrier entity in a way that is difficult for unauthorized users to notice is known as information hiding, sometimes known as data hiding. Different types of digital material, such as pictures, audio files, movies, text documents, and network protocols, can use information-concealing strategies. This paper proposes a text-based data-hiding approach using Huffman and the midpoint folding technique, which increases the payload capacity and reduces the embedding distortion. Results show that the average PSNR obtained is 57.23, which depicts the good quality of stego images, making them indistinguishable from the human visual system (HVS). Also, on concealing 2 KB of data in four considered dataset images, an average of PSNR = 57.23195, WPSNR = 55.31998, SSIM = 0.993238, and MSE = 0.125765, which proves the credibility of the proposed method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Steganography

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Prevention of Attacks on Spanning Tree Protocol 2nd

Prevention of Attacks on Spanning Tree Protocol

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2nd International Conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Adhikari, Manoj Singh Gupta, Ritika Raj, Dharm Astya, Rani Ather, Danish Agrawal, Ambuj Department of Computer Science & Engineering Sharda School of Engineering & Technology Sharda University Greater Noida India Amity University in Tashkent Tashkent Uzbekistan

ISBN: (纸本)9789819776023

Spanning Tree Protocol (STP) is an extensively used protocol in Ethernet networks that ensures loop-free paths by selectively blocking redundant links. However, STP is vulnerable to various attacks that can disrupt network operations, compromise network security, and lead to network downtime. This research proposes a novel approach to enhance the security and resilience of Ethernet networks by leveraging machine learning algorithms to detect and prevent attacks on the Spanning Tree Protocol. The core objective is to design a robust intrusion detection and prevention system that can adaptively safeguard the STP infrastructure in real-time. The first stage of the study involves collecting a comprehensive dataset of normal and attack scenarios in STP networks, including various attack types and their associated characteristics. Leveraging supervised learning techniques, machine learning models are trained on this dataset to learn the patterns and behaviors of different attacks on the STP. And finally, unsupervised learning algorithms are employed to analyze the network traffic and detect anomalies in real-time. This approach aims to identify any deviations from normal behavior, thereby indicating potential STP attacks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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