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Deep Learning Model for Identification and Classification of Web based Toxic Comments

Deep Learning Model for Identification and Classification of...

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Advances in Power, Signal, and Information technology (APSIT), International Conference in

作者： Anubhav Shukla Deepak Arora Department of Computer Science & Engineering Amity School of Engineering and Technology Lucknow Amity University Uttar Pradesh India

Everyday, many individuals face online trolling and receive hate on different social media platforms like Twitter, Instagram to name a few. Often these comments involving racial abuse, hate based on religion, caste are made by anonymous people over the internet, and it is quite a task to keep these comments under control. So, the objective was to develop a Machine Learning Model to help identify these comments. A Deep Learning Model (a sequential model) was made and it was trained to identify and classify a comment based on whether it is an apt comment or not. LSTM (Long Short-Term Memory) is a type of recurrent neural network (RNN) that is particularly well-suited for modeling sequential data, such as text. LSTMs are capable of modeling long-term dependencies in sequential data. In the case of text classification, this means that LSTMs can take into account the context of a word or phrase within a sentence, paragraph, or even an entire document. LSTMs can learn to selectively forget or remember information from the past, which is useful for filtering out noise or irrelevant information in text. LSTMs are well-established in the field of natural language processing (NLP) and have been shown to be effective for various NLP tasks, including sentiment analysis and text classification. Binary cross-entropy is a commonly used loss function in deep learning models for binary classification problems, such as predicting whether a comment is toxic or not. Binary cross-entropy is designed to optimize the model's predictions based on the binary nature of the classification task. It penalizes the model for assigning a low probability to the correct class and rewards it for assigning a high probability to the correct class. The loss function is differentiable, which allows gradient-based optimization methods to be used during training to minimize the loss and improve the model's performance. Binary cross-entropy is a well-established loss function that has been extensively used

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CallerId Application Design using Amazon Web Services

CallerId Application Design using Amazon Web Services

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

作者： Shourya Chawla Mohd. Abuzer Abhinav Singh Dolly Sharma Department of Computer Science Amity School of Engineering and Technology Amity University Noida India

A phone number is a personal unique number which can identify its owner. Personal information can be exploited through a person’s phone number. Many methods such as phishing and spam attacks have been used to exploit a person’s personal information. Nowadays, a phone number has many linked important information such as bank accounts, and passwords which make it more vulnerable to attacks. The growing demand for accessibility and connectivity is creating demand for an efficient solution. Hence, a caller identification app which can identify caller ID and spam protection of the incoming call or SMS is required. It protects users from attending unwanted calls and messages. In this work, the design and working of a caller identification app using AWS services is explored. Many researchers have conducted studies and surveys to identify the process of caller identification and develop caller identification methods and have published their research about the same. Most of these methods are conventional. What makes the proposed model novel, is that it uses a cloud-based new age model with complete backend design to develop a strong caller identification application with its roots in the AWS cloud. Though other models have been successful, this model is better because of the benefits provided by the cloud, which include high availability, scalability, elasticity, security, and cost-friendliness.

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AGSQTL: Adaptive Green Space Quality Transfer Learning for Urban Environmental Monitoring 8

AGSQTL: Adaptive Green Space Quality Transfer Learning for U...

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8th IET Smart Cities Symposium, SCS 2024

作者： Chundru, Swathi Balantrapu, Siva Subrahmanyam Maroju, Praveen Kumar Alam, Naved Dutta, Pushan Kumar Whig, Pawan Motivity labs INC QA Automation Engineer United States United States TX United States Department of computer science and engineering Jamia hamdard New Delhi India School of Engineering and Technology Amity India Threws VIPS-TC New Delhi India

ISBN: (纸本)9781837242672

This study presents a novel machine learning approach, Adaptive Green Space Quality Transfer Learning (AGSQTL), for assessing urban green space quality. Unlike traditional transfer learning methods, AGSQTL incorporates domain-specific adaptations and dynamic feature analysis tailored to green space monitoring. The algorithm utilizes a pre-trained source model and adapts it to the target domain of green space images, categorizing them as Healthy, Dried, or Contaminated. AGSQTL introduces several innovative components: domain-specific layers for capturing unique green space features, an adaptive domain discrepancy adjustment mechanism, dynamic feature pruning based on Grad-CAM importance analysis, and a self-attention mechanism for spatial context understanding. The model is trained on a dataset of 986 green space images, augmented to balance class distribution. Performance evaluation includes accuracy, precision, recall, F1-score, Cohen's Kappa, and ROC-AUC, as well as practical considerations such as model file size and inference time. The algorithm demonstrates superior adaptability to green space features compared to standard transfer learning approaches, achieving 99.2% accuracy on the test set. Grad-CAM visualizations confirm the model's ability to identify relevant areas for classification. AGSQTL was deployed as an interactive web application using Streamlit, allowing real-time green space quality assessment. This research contributes a more adaptive and efficient methodology for urban green space monitoring, with potential applications in environmental management and urban planning. © The Institution of engineering & technology 2024.

关键词： Adaptive algorithms

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A Deep Learning Based Approach for Sugarcane Disease Detection

A Deep Learning Based Approach for Sugarcane Disease Detecti...

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Delhi Section Conference (DELCON), IEEE

作者： Vishan Kumar Gupta Garima Sharma Vidisha Mukesh Kumar Singh Amity School of Engineering & Technology Amity University Punjab Mohali India Department of Computer Science & Engineering Graphic Era (Deemed to be University) Dehradun Uttarakhand India Computer Science & Engineering Department Galgotias College of Engineering and Technology Greater Noida India

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

The world's largest producers of sugarcane, which is used to make both sugar and bioethanol, are Brazil and India. The crop is primarily grown in tropical and subtropical regions. These nations produce 40% of the world's bioethanol and 80% of its sugar. Sugarcane is susceptible to a wide range of diseases, including bacterial blight, mosaic, red rot, rust, and yellow, due to a multitude of environmental factors. An accurate and dependable automated disease detection system is needed for sugarcane disease diagnosis. In this work, we created the convolution neural network model EfficientNetB0 utilizing data augmentation, evaluated its effectiveness in identifying diseases affecting sugarcane plants using leaf pictures, and compared its performance. The precision, recall, and F1 score values of the EfficientNetB0 model are 0.9812, 0.9821, and 0.9816. This model is a helpful tool that growers may use to prevent sugarcane infections from interfering with the harvesting process, as it has an accuracy rate of 98.12%.

关键词： Accuracy Microorganisms Neural networks Crops Data augmentation Environmental factors Medical diagnosis Labeling Sugar Diseases

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Analytical Approach of Machine Learning for Prediction Models

Analytical Approach of Machine Learning for Prediction Model...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Mohit Kumar Sanjay Kumar Dubey Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Noida Uttar Pradesh India

With the increasing availability of data, machine learning (ML) predictive models have become a popular tool for making informed decisions in various fields. However, choosing suitable algorithms and techniques to develop accurate forecasting models is a challenging task. In this study, we aim to compare different ML algorithms and methods to develop accurate prediction models. We analyzed the results to determine the most effective approach to developing accurate forecasting models. In addition, we investigated the effect of hyperparameter tuning on the performance of this model. The results of this study can provide valuable insights for practitioners and researchers in choosing the most appropriate machine learning algorithms and methods to develop accurate prediction models. Our study shows the impact of choosing the right features and choosing the method of data pre-processing and hyperparameter tuning on model performance. Finally, our results can inform the design of more accurate prediction models and contribute to the development of the field of machine learning.

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Zero Knowledge based Authentication for Internet of Medical Things

Zero Knowledge based Authentication for Internet of Medical ...

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International Conference on Computing and Networking technology (ICCNT)

作者： Garima Misha Bramah Hazela Brijesh Kumar Chaurasia Computer Science and Engineering Amity School of Engineering and Technology Lucknow Amity University Uttar Pradesh India Computer Science and Engineering Pranveer Sing Institute of Technology Kanpur India

Internet of Things (IoT) applications, such as e-healthcare departments have grown tremendously where devices gather patient data and instantly transmit it over a distance to servers. Despite its huge advantages, IoT in the healthcare sector has acquired little consideration, largely because of the threats of unauthorized access to private health data made possible by the weak wireless communication channel. The economic complexity of the current security protocols makes them inappropriate, hence new security protocols must be developed for resource-constrained and diverse IoT networks. This work suggests an authenticated key agreement protocol for the Internet of medical things (IoMT) that is based on zero-knowledge proofs (ZKP). The research will include the comparison between computational delays in traditional protocols and ZKP protocol. This paper provides a protocol to obtain privacy of data in IoMT environment on the basis of zero knowledge phenomenon and anonymous communication by ZKP.

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Detection and Classification of Bone Fracture Using Digital Image Processing

Detection and Classification of Bone Fracture Using Digital ...

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IEEE UP Section Conference on Electrical computer and Electronics (UPCON)

作者： Simran Makwane Anjali Kiran Shweta Bhardwaj Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Noida Uttar Pradesh India

This research paper presents an innovative approach for automated bone fracture discovery and bracket using digital image processing ways and the Scale Invariant point Transform (SIFT) algorithm. The proposed system excerpts distinctive original features from radiographic images, enabling robust identification of fracture patterns. Through a multi-step process, including pre-processing, point birth, and bracket, the system achieves high delicacy in differencing between colourful fracture types. Experimental results demonstrate the effectiveness of the system on a different dataset, showcasing its eventuality for enhancing clinical diagnostics and expediting treatment planning. This exploration contributes to the advancement of computer- backed medical imaging systems, offering a promising tool for accurate and effective fracture opinion.

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Leveraging Natural Language Processing and Machine Learning for Efficient Fake News Detection 3

Leveraging Natural Language Processing and Machine Learning ...

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3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Kumar, Naresh Malhotra, Meetu Aggarwal, Bharti Rai, Dinesh Aggarwal, Gaurav Msit Department of Computer Science & Engineering Janakpuri Delhi India Harrisburg University of Science and Technology United States School of Eng & Tech Sushant University Gurugram India Amity University Tashkent Uzbekistan

ISBN: (纸本)9798350342338

In today's digital world, the internet deeply ingrains in all facets of our lives. People highly depend on myriad of online sources for news. The proliferation of fake news is increasingly recognized as a significant peril to democracy, journalism, and the principles of freedom of expression. As the use of social media platforms such as Facebook, Twitter, and others accelerates, any news can promptly circulate among thousands of users within a remarkably short interval of time. Additionally, spammers use enticing news headlines as click-baits to generate profits, using advertisements. To address fake news detection, certain endeavors have been put to develop tools, which prove to be helpful. Yet, it remains crucial to handle the more challenging scenarios, where trustworthy sources heedlessly propagate fake news. This calls for the development of a comprehensive end-to-end solution. The objective of this research is to perform binary classification, using Natural Language Processing and Machine Learning concepts on different news articles to categorize them into fake and real. The findings of this research demonstrate the impact of machine learning techniques in performing this task efficiently. © 2023 IEEE.

关键词： Binary classification Natural language processing and Machine Learning

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Network Intrusion Detection for IoT Devices Using Deep Learning

Network Intrusion Detection for IoT Devices Using Deep Learn...

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IEEE UP Section Conference on Electrical computer and Electronics (UPCON)

作者： Mohit Kumar Sanjay Kumar Dubey Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Noida Uttar Pradesh India

Cybersecurity has seen widespread adoption across various domains, encompassing critical business infrastructure, residential settings, personal devices, and machinery. This has led to the development of innovative capabilities to effectively address security challenges within the realm of Internet of Things (IoT) devices. Notably, new security measures, including the application of deep learning for intrusion detection, have been introduced. Recent research primarily focuses on enhancing algorithms for a deeper understanding of IoT security. This research explores intrusion detection techniques employing deep knowledge mining. It involves a comprehensive analysis of various deep knowledge mining methods, comparing their overall performance. The objective is to identify a precise approach for implementing intrusion detection in the IoT context. The research leverages deep learning methodologies, specifically convolutional neural networks (CNN), long short-term memory (LSTM), and gated recurrent units (GRU). Furthermore, it incorporates an extensive dataset tailored for IoT intrusion detection. The empirical results of this study are thoroughly examined and compared against existing IoT intrusion detection techniques. Notably, the proposed approach demonstrates superior accuracy when compared to other methods.

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Building a Chatbot using Natural Language Processing

Building a Chatbot using Natural Language Processing

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Informatics (ICI), International Conference on

作者： Uday Sehgal Shweta Bhardwaj Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Noida India

Chatbots are applications which simulate human conversational patterns, engage in human-to-human communication, and respond to user queries using data from their training set. There are two categories of chatbots: rule-based and self-learning. One of the beneficial things a company or organisation may have is a chatbot. They can be utilised for customer service and query resolution in many different industries. In this paper, research is done on NLP and a model is designed to build a chatbot using NLP techniques. The chatbot built using this model can be used as a normal conversation bot or as a customer support tool.

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