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A Novel Approach of Object Detection using Deep Learning for Animal Safety 12

A Novel Approach of Object Detection using Deep Learning for...

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12th International Conference on Cloud Computing, Data science and engineering, Confluence 2022

作者： Gandhi, Rashmi Gupta, Aakankshi Yadav, Ashok Kumar Rathee, Sonia Amity School Of Engineering Technology Department Of Computer Science And Engineering Noida India Maharaja Surajmal Institute Of Technology Ggsipu Department Of Computer Science And Engineering Delhi India

ISBN: (纸本)9781665437011

This paper uses convolutional neural networks (CNN) algorithm to detect animals. Four different Object Detection frameworks are implemented and compared their performance with our novel approach. Many algorithms of convolutional neural networks like RCNN, Faster RCNN, Single Shot detection, YOLOv3 are used in object detection tasks are compared. A customized Faster RCNN is designed with anchor manipulations This framework is found to be best suited technique for Animal Detection System giving the best time-accuracy trade-off. Experimental results shows that this method has reduced the best inference time significantly. © 2022 IEEE.

关键词： Animals

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Enhancing Deep Neural Network Convergence and Performance: A Hybrid Activation Function Approach by Combining ReLU and ELU Activation Function 2

Enhancing Deep Neural Network Convergence and Performance: A...

引用

2nd International Conference on Informatics, ICI 2023

作者： Maurya, Ritesh Aggarwal, Divyam Gopalakrishnan, T. Pandey, Nageshwar Nath Amity Centre for Artificial Intelligence Amity University Noida India Amity School of Engineering and Technology Amity University Noida India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Udupi India Computer Science and Engineering ITER Shiksha 'o' Anusandhan Odisha Bhubaneswar India

ISBN: (纸本)9798350343830

Activation functions play an important role in Deep Neural Networks. The activation function can learn nonlinearities present in the data;therefore, it can learn intricate patterns present in the data. Rectified Linear Unit (ReLU) is an activation function that helps in encountering the problem of vanishing gradient. However, it suffers from 'dying ReLU' problem for the negative values. Leaky ReLU can solve the problem of 'dying ReLU';though it still suffers from a vanishing gradient problem due to the small gradient at for negative values, which results in slow convergence. Therefore, in this work, a combination of ReLU and Exponential Linear Unit (ELU) has been proposed considering the smoother convergence of the ELU activation function for the values on the negative side. Evaluating the effectiveness of the developed hybrid activation function compared to previous ReLU activation function versions such as SeLU, Leaky ReLU, ELU, etc. using a toy multi-layer perceptron and convolution neural network (CNN) model on the FashinMNIST and MNIST datasets. The improvement in the performance of these toy models when used with the proposed hybrid activation function on given datasets suggests the effectiveness of the proposed hybrid activation function. © 2023 IEEE.

关键词： Deep neural networks

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Mitigating Cross-Site Request Forgery Vulnerabilities: An Examination of Prevention Systems

Mitigating Cross-Site Request Forgery Vulnerabilities: An Ex...

引用

International Symposium on Advanced Networks and Telecommunication Systems (ANTS)

作者： Yuvraj Singh Paranjay Goel Shubhani Aggarwal Rajat Chaudhary Ishan Budhiraja Department of Computer Science & Engineering Amity School of Engineering & Technology Amity University Punjab India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (数字)9798350391725

ISBN: (纸本)9798350391732

Cross-Site Request Forgery (CSRF) remains a pervasive vulnerability in web applications, appearing regularly in the OWASP Top 10 lists, posing significant threats to user data and system integrity despite the existence of numerous rectification methodologies. This study undertakes a thorough, comprehensive analysis of the present environment of CSRF prevention systems, investigating their operational mechanisms, effectiveness, and associated limitations. Hence, this paper explores the role of CSRF tokens in the prevention of cyberattacks, including the detection of token absence and the implementation of token-based countermeasures to mitigate CSRF risks. The system model utilizing simple JavaScript and Flask Applications that aim to utilize the token-based countermeasure to identify the existence of tokens and generate and append them to requests, which ensures security of user data and system integrity.

关键词： Analytical models Prevention and mitigation Forgery Data models Telecommunications Security computer crime Testing

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Data Visualization in Transforming Raw Data into Compelling Visual Narratives 2

Data Visualization in Transforming Raw Data into Compelling ...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Sarkar, Prithu Priya, Yadavalli Devi Patel, Pravina B. Chatterjee Biswas, Prasanta Arigela, Sreenivasulu Sallaram, Sravanthi Amity University Amity School of Communication Kolkata India Computer Science and Engineering Vignan's Institute of Information Technology Duvvada Andhra Pradesh Vishakapatanam India Ganpat University Department of Computer Science Gujarat Mehsana India Cdoe Parul University Vadodara India Mohan Babu University Andhra Pradesh Tirupati India St.Martin's Engineering College Department of Computer Science and Engineering India

ISBN: (纸本)9798350384277

One of the main ways to evaluate information is through visual learning, which traditionally combines textual reading with visual aids like graphical representations. However, separating the visual method of learning to distinguish between text and visuals has been suggested in light of advancements in learning styles. The ability to swiftly create complex visual information and inexpensively distribute it digitally is another way that technology has improved visual presentation. The purpose of this research paper is to conduct a detailed investigation of the Virtualization of data, storytelling, and Data Visualization's Narrative, understand how Instruments and methods are used in visualization, and select the appropriate tool for presenting visual data stories and upcoming projects. To address this purpose, this research has reviewed a wide range of different journals and articles to gather information about data visualization, storytelling, and data narratives. Based on the analysis, this research paper finds an all-encompassing perspective on the visual data narrative process, incorporating the phases of discovering insights (data exploration), developing these insights into a tale (creating a story), and sharing this story with others (telling a story). Through the pursuit of these novel research directions, visualizations can facilitate more potent data storytelling. © 2024 IEEE.

关键词： Spatio-temporal data

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Voting Based Ensemble Framework For Crop Recommendation 7

Voting Based Ensemble Framework For Crop Recommendation

引用

7th International Conference on Image Information Processing, ICIIP 2023

作者： Aryaman, Mayank Paliwal, Yashica Sharma, Aman Mohana, Rajni Jaypee University of Information Technology Computer Science And Engineering Department Waknaghat India Cse Jaypee University of Information Technology Waknaghat India Cse Amity School of Engineering Chandigarh India

ISBN: (纸本)9798350371406

Agriculture holds paramount importance for a significant portion of India's population, contributing significantly to the national economy. However, many Indian farmers rely on traditional senses rather than data-driven insights for crop selection, risking negative impacts on their mental and financial well-being. This article emphasizes the potential of Machine Learning models, utilizing a comprehensive dataset combining factors like rainfall, climate, humidity, and soil nutrients, to guide farmers in making informed decisions for optimal crop selection. To address the challenges faced by Indian farmers, the proposed methodology involves the application of Machine Learning models to a dataset encompassing crucial parameters such as sowing season, soil properties, climate, and location. By leveraging advanced algorithms, the models aim to recommend crops tailored to the farm's specific conditions, ensuring precision and accuracy in decision-making. This data-driven approach seeks to revolutionize traditional farming practices and enhance overall agricultural outcomes. The application of Machine Learning models on a comprehensive dataset demonstrated a robust recommendation system for crop selection, considering factors like soil health and weather conditions. This technology-driven approach empowers Indian farmers with informed decision-making, mitigating risks and fostering sustainable agricultural practices. © 2023 IEEE.

关键词： Crops

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Detection of Cyberbullying Using Modified Dense Framework 10

Detection of Cyberbullying Using Modified Dense Framework

引用

10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Malik, Varun Mittal, Ruchi Singh, Vikram Mittal, Amit Singh, S Vikram Diwvedi, Shashi Prakash Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Chaudhary Devilal University Department of Computer Science and Engineering Sirsa India Chitkara Business School Chitkara University Punjab India Amity School of Engineering and Technology Amity University Greater Noida India Lloyd Institute of Engineering & Technology Greater Noida India

ISBN: (纸本)9798350382471

Cyberbullying has emerged as a significant societal concern, with the proliferation of digital communication platforms. Detecting and mitigating cyberbullying is crucial to ensure online safety and promote positive digital interactions. This study proposes an innovative approach for the detection of cyberbullying using a Modified Dense Framework (MDF). The Modified Dense Framework (MDF) introduced in this research is a deep learning architecture specifically tailored for cyberbullying detection. Unlike traditional methods, MDF combines the power of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) in a novel way, enhancing the model's ability to capture intricate patterns within textual and multimedia content. The framework is trained on diverse datasets encompassing text, images, and videos, enabling it to effectively identify various forms of cyberbullying across multiple online platforms. The study demonstrates the effectiveness of MDF through rigorous experimentation and evaluation. Comparative analyses with existing state-of-the-art models highlight the superiority of MDF in terms of accuracy, precision, recall, and F1-score. Moreover, MDF exhibits remarkable resilience against adversarial attacks, ensuring robust cyberbullying detection even in challenging scenarios. © 2023 IEEE.

关键词： Cyberbullying Deep Learning Detection Modified Dense Framework Robust Detection

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Ensembled Multi-detector Aggregation For Disaster detection (EMAD) 13

Ensembled Multi-detector Aggregation For Disaster detection ...

引用

13th International Conference on Cloud Computing, Data science and engineering, Confluence 2023

作者： Gulati, Nancy Agarwal, Ajay Aggarwal, Apeksha Bhutani, Nikhil Kapur, Ritvik Amity University Amity School of Engineering and Technology Department of Computer Science and Engineering Uttar Pradesh Noida India Kiet Group of Institutions Department of Information Technology Uttar Pradesh Ghaziabad India Indian Institute of Technology Department of Computer Science and Engineering Uttarakhand Roorkee India Maharaja Surajmal Institute of Technology Affiliated to Ggsipu Department of Computer Science and Engineering Delhi India Maharaja Surajmal Institute of Technology Affiliated to Ggsipu Department of Information Technology Delhi India

ISBN: (纸本)9781665462631

The most popular method for analysing a text is sentiment analysis. It is quite helpful for social media monitoring since it enables us to get a broad sense of what the general population thinks about particular issues. However, it is also handy for business analysis and various other situations in which the text needs to be analyzed. In this paper we present a solution up to 90% correctly classify whether a tweet is associated with a disaster. Using a multi-detector ensembled approach, we create a reliable model that can predict accurately in situations powered by multiple CNNs and BERT detectors. © 2023 IEEE.

关键词： Disasters

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Sentiment Analysis Using Machine Learning Methods

Sentiment Analysis Using Machine Learning Methods

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2024 International Conference on Intelligent and Innovative Practices in engineering and Management, IIPEM 2024

作者： Badholia, Abhishek Diwan, Tarun Dhar Narooka, Preeti Khatkale, Pravin B Vishnoi, Ankit Kaushik, Keshav Shri Shankaracharya Institute of Professional Management and Technology Department of Data Science Raipur India Atal Bihari Vajpayee University Bilaspur India Manipal University Artificial Intelligence and Machine Learning School of Computer and Engineering Jaipur India Sanjivani University Maharashtra Kopargaon India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun India Amity University Punjab Amity School of Engineering and Technology Mohali India

ISBN: (纸本)9798350390049

Machine learning (ML) will be utilized to evaluate sentiment analysis in this project. This study aims to do this. Sentiment analysis is a popular natural language processing area. This is a natural language processing subfield. This section identifies textual senses. Due to the growing availability of digital text across platforms, sentiment analysis is now vital for many applications. This is because sentiment analysis has become essential. This technology might be used for market research, political analysis, and consumer feedback. A few instances. This study analyzes several machine learning methods, to name a few. This category includes SVM, Naive Bayes, Random Forest, and RNNs. To clarify, several datasets are used to assess these methodologies. To get more information. Preprocessing and feature engineering are crucial to sentiment analysis model construction, as this paper shows. The research emphasises these two phases. In addition, it provides insights into how these approaches are used in constantly changing digital situations. © 2024 IEEE.

关键词： Adversarial machine learning

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Fault Tolerance and Error Handling Techniques in Apache Kafka 23

Fault Tolerance and Error Handling Techniques in Apache Kafk...

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Proceedings of the 5th International Conference on Information Management & Machine Intelligence

作者： Shubham Vyas Rajesh Kumar Tyagi Shashank Sahu Information Technology Amity Institute of Information Technology India Computer Science and Engineering Amity School of Engineering and Technology Amity University Haryana India Computer Science and Engineering Ajay Kumar Garg Engineering College India

ISBN: (纸本)9798400709418

Real Time streaming framework enables the availability of data for users in near real time. For achieving the high throughput, low latency and to avoid loss of any events at runtime, real time streaming frameworks has their own methods to accomplish this task of message handling, but there are scenarios when due to huge volume of data at the source or due to other environment or Infrastructure related issues, some of the critical data might get lost in the process. This article evaluates methods to handle data rejections in conjunction with a fault-tolerant error handling recommendations for real time data streaming. There are many frameworks available for streaming the events in real time, in this article Apache Kafka is used to examine most frequent situations when an error can occur. This article will evaluate the key performance factors in terms of capability of the framework to recover when downtime occurs. Apache Kafka [15] is a distributed real time data streaming platform; it was developed by Linked inn and currently used by many applications across the industry. It works in producer consumer fashion and with correct configuration and set up, high Throughput with low latency can be achieved. This article will explain the configurations provided with the framework that can be utilized to design and develop optimized event streaming applications. This article is explaining the different parameters that can be used to design and develop the stream processing applications and what are the available options to handle the errors during the streaming to avoid any negative impact to the application.

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Deploying Mobile Application on Cloud

Deploying Mobile Application on Cloud

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2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing, COM-IT-CON 2022

作者： Kharbanda, Jayant Madan, Rishi Passi, Vansh Sharma, Dolly Amity University Amity School of Engineering and Technology Uttar Pradesh Noida India Amity University Department of Computer Science and Engineering Uttar Pradesh Noida India

ISBN: (数字)9781665496025

ISBN: (纸本)9781665496025

Today is the era of cloud-based applications. Over a span of time cloud has emerged as nascent term to new era of computing that gives access to anything, anywhere. This paper involves the study on the topic 'Mobile Application Deployment on Cloud'. The paper has been split into 3 major domains that are: Creating the front-end of the application, Creating and maintaining the back end of the application and finally deploying the application on cloud. The application to be deployed on cloud was built on 'Flutter'. The main reason to choose flutter as a platform is that, using a single codebase one can make and maintain an application on both the major mobile operating systems namely, Android and iOS. After the dummy application was built, we started the process of hosting the application on AWS cloud. At first, a reference architecture taken into contemplation for beginning the process. And at various stages the architecture was altered according to the use case and services were modified. After we were ready with the cloud architecture, we finally deployed our application with dummy data on the AWS cloud. Once the application got hosted on the cloud, we started to change the test data with actual data and as an outcome of which we successfully accomplished our goal. © 2022 IEEE.

关键词： Flutter (aerodynamics)

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