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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Tiwari, Nandini Rizvi, Sheenu Arora, Deepak Tiwari, Shivam Department of Computer Science and Engineering Amity School of Engineering and Technology Lucknow Amity University Uttar Pradesh Noida India Department of Computer Science and Engineering Sharda University Greater Noida India

ISBN: (纸本)9789819789450

An effective strategy for identifying and categorizing toxic comments is urgently needed due to the overwhelming number of such comments on various internet platforms. While previous research has addressed various forms of toxic behaviour, existing approaches often fall short. Our research fills this need by suggesting a novel method that combines ML with natural language processing (NLP). Building on previous work, we focus on recurrent neural networks (RNNs), specifically LSTM networks, renowned for their adeptness in capturing sequential patterns and contextual information within text. Also, we incorporate dropout layers to enhance model generalization and combat overfitting. Evaluating our models on a benchmark dataset reveals promising outcomes, with the LSTM model achieving an accuracy score of 0.9943 and a loss of 0.0493, while the LSTM model with dropout layers closely follows with an accuracy score of 0.9939 and a loss of 0.0493. These findings underscore how effectively our methodology detects toxic comments, thereby advancing automated moderation systems. Looking ahead, we aim to refine our approach and explore avenues for real-world implementation, aiming to foster healthier online discourse and enhance user safety. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Rule-based Classifiers for Suspect Detection from CCTV Footages

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Recent Advances in computer science and Communications 2022年第4期15卷 594-600页

作者： Pathak, Sunil Department Of Computer Science and Engineering Amity School of Engineering and Technology Amity University Rajasthan Jaipur India

Background: A significant work has been presented to identify suspects, gathering information and examining any videos from the CCTV Footage. This exploration work expects to recognize suspicious exercises, i.e., object trade, the passage of another individual, peeping into other's answer sheet and individual trade from the video caught by a reconnaissance camera amid examinations. This requires the procedure of face acknowledgment, hand acknowledgment and distinguishing the contact between the face and hands of a similar individual and that among various people. Methods: Segmented frames have been given as input to obtain a foreground image with the help of Gaussian filtering and background modeling method. Such foreground images have been given to Activity Recognition model to detect normal activity or suspicious activity. Result: Accuracy rate, Precision and Recall are calculated for activities detection, contact detection for Best Case, Average Case and Worst Case. Simulation results are compared with performance parameter such as Material Exchange, Position Exchange, and Introduction of a new person, Face and Hand Detection and Multi Person Scenario. Conclusion: In this paper, a framework is prepared for suspect detection. This framework will absolutely realize an unrest in the field of security observation in the training area. © 2022 Bentham science Publishers.

关键词： Face recognition

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A Cross Design for Breast Cancer Prediction 1

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6th International Conference on Communications and Cyber-Physical engineering, ICCCE 2023

作者： Saini, Hemant Kumar Preeti Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Rajasthan Jaipur India Department of Computer Science and Engineering Chandigarh University Punjab Gharuan India

ISBN: (数字)9789819971374

ISBN: (纸本)9789819971367

Breast cancer in women’s becoming the serious cause moving to the morbidity and the mortality worldwide. This paper aims to design the hybrid model using various machine learning classification algorithms like k-Nearest Neighbor (KNN), Support Vector Classifier (SVC), Logistic Regression (LR), and Gaussian Naïve Bayes to predict the breast cancer. Moreover, the accuracy has been increased with the varied classifiers and decides the precision using the f1- Score, and Jaccard index. The data set is of Kaggle is used to train the model and then test them using the machine learning algorithms. The proposed model is also prepared forum foreseen data in future by validating using K-fold Cross Validation Technique. By Random Forest Classifier (RFC) model exhibit the significant performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Nearest neighbor search

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Enhancing Literary Exploration: A Machine Learning Approach to Book Recommendations 7

Enhancing Literary Exploration: A Machine Learning Approach ...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Singh, Kritika Gupta, Sumita Dhir, Saru Gambhir, Sapna Amity University Amity School of Engineering & Technology Department of Computer Science & Engineering Uttar Pradesh Noida India Amity University Amity School of Engineering & Technology Department of Information Technology Uttar Pradesh Noida India George Mason University College of Engineering and Computing Department of Computer Science United States

ISBN: (纸本)9798350350067

In today's era of rapid internet expansion, the need for efficient information retrieval tools is paramount. Recommendation systems serve as indispensable instruments, aiding users in navigating vast datasets swiftly and accessing relevant information. This paper presents a novel approach to book recommendation systems, offering readers a quick and intuitive method to discover their next read. Implementations encompass three algorithms: Popularity-based, Contentbased, and Collaborative-based recommendation systems. The primary focus lies in proposing a swift book idea system, facilitating users in promptly discovering fitting reading materials. By harnessing the power of these algorithms, readers can explore a diverse range of book options tailored to their preferences, enhancing their overall reading experience and satisfaction. Through this study, we aim to contribute to the advancement of recommendation systems, providing valuable insights and practical solutions for optimizing the book discovery process in the digital age. © 2024 IEEE.

关键词： Collaborative filtering

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Deep Learning Approach to Emotion Recognition by Facial Expressions: A Review Paper 5

Deep Learning Approach to Emotion Recognition by Facial Expr...

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5th IEEE International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Singh, Shubhanshi Shukla, Shipra Amity School Of Engineering And Technology Computer Science And Engineering Noida India

ISBN: (数字)9798350330861

ISBN: (纸本)9798350330861

Emotion recognition by facial expression is a challenging task that has gotten much attention in recent years. Deep neural networks are used to extract pertinent information from facial photographs and categorise them into various emotional categories in deep learning approaches, which have proven their potential in this area The article examines the most recent advances in deep learning for recognising emotions from facial expressions. The model architecture and training methods are explored, and their effectiveness on benchmark datasets is assessed. Also discuss the challenges and prospects for this field, including the need for large and diverse datasets, the interpretability of models, and the requirement to eliminate any bias in training data. Overall, deep learning algorithms for emotion recognition using facial expressions have shown considerable promise and can be applied in a variety of applications, such as virtual assistants, entertainment, and healthcare. © 2023 IEEE.

关键词： Emotion Recognition

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A Deep Learning Based Approach for Sugarcane Disease Detection 3

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

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Gupta, Vishan Kumar Sharma, Garima Vidisha Singh, Mukesh Kumar Amity School of Engineering & Technology Amity University Punjab Mohali India Department of Computer Science & Engineering Uttarakhand Dehradun India Galgotias College of Engineering and Technology Computer Science & Engineering Department Greater Noida India

ISBN: (纸本)9798331518592

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%. © 2024 IEEE.

关键词： Plant diseases

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Advanced Machine Learning Techniques for Fake News Detection: A Comprehensive Analysis Using the LIAR Dataset 3rd

Advanced Machine Learning Techniques for Fake News Detectio...

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3rd International Conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Paul, Subrata Ghosh, Shivnath Mitra, Anirban Bhattacharya, Pronaya Department of CSE-AI Brainware University West Bengal Barasat India Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University West Bengal Kolkata India Department of Computer Science and Engineering Amity School of Engineering and Technology and Research and Innovation Cell Amity University West Bengal Kolkata India

ISBN: (纸本)9789819624676

In today’s digital world, fake news presents a serious challenge to social cohesiveness, trust among individuals, and the functioning of democracy. Overcoming this issue demands novel solutions that make efficient use of machine learning (ML) for identifying and battle disinformation. This paper examines the important problem of fake news, including its meaning, its consequences, and the significance of social media in its rapid spread. The study emphasises the complexities of identifying fake news, focusing on sophisticated techniques like AI-generated content and the widespread dissemination of misinformation. In order to tackle such obstacles, the paper uses ML approaches, particularly the LIAR dataset and the XGBoost model, to create a reliable fake news detection system. The outcomes show that these methods have been successful at successfully recognising false information, highlighting ML’s prospective in reducing misinformation. This research contributes to the broader discourse on media literacy and the need for reliable information in the digital age. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Role of Smali Opcodes in Identifying Android Malware Obfuscation Variants 4

Role of Smali Opcodes in Identifying Android Malware Obfusca...

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4th International Conference on computer, Communication, Control and Information technology, C3IT 2024

作者： Anand, Abhishek Singh, Jyoti Prakash Amity School of Engineering and Technology Amity University Patna Patna India Computer Science and Engineering National Institute of Technology Patna Patna India

ISBN: (纸本)9798350389128

With the widespread adoption of Android and its applications, obfuscated malware poses significant challenges to the platform's security. Addressing these challenges requires automated, lightweight, and scalable models capable of understanding obfuscation variants and distinguishing obfuscated malware from non-obfuscated malware. This paper proposes a lightweight model based on static analysis, utilizing reverse engineering techniques to extract Smali opcodes. For efficient, fast, and accurate analysis, we employ the SelectKBest feature selection method to identify the most important opcodes critical for recognizing obfuscation variants. The machine learning classifier Random Forest is used for the classification tasks, distinguishing between obfuscated and non-obfuscated malware and identifying specific obfuscation variants with high precision and accuracy. Our approach achieves an impressive accuracy of up to 99.32% in detecting obfuscation and an accuracy of 83.56% in classifying different obfuscation variants. This demonstrates the potential of our lightweight model to significantly enhance the security of the Android platform by effectively identifying and classifying obfuscated malware. © 2024 IEEE.

关键词： Adversarial machine learning

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ETL Data Pipeline to Analyze Scraped Data 3rd

ETL Data Pipeline to Analyze Scraped Data

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3rd International Conference on Information technology, InCITe 2023

作者： Gandhi, Rashmi Khurana, Sparsh Manchanda, Harsh Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Noida India

ISBN: (纸本)9789819959969

In this day and age, corporations are moving towards data driven decision making. They are utilizing data to make applications that can assist them with business intelligence. Data assists them with working on their efficiency and income by chipping away at the business challenges. By utilizing data, corporations can generate market patterns, improve client experience and enable improved business decisions. The key problem is the abundance of data and its form. Data needs to be in a form that can be effectively be used to generate tangible insights. The complete cycle of taking data and then generating insights from it is a long one but there are modern solutions to this problem which simplify and improve this process. The scope of this literary research is to examine the use of Data Pipelining to enable better data insights. Data pipelines can be utilized to move data starting with one spot then onto the next using ETL (Extract Transform Load), ELT (Extract Load Transform), data improvement, and continuous data examination. An automated data pipeline is a relatively new solution which can provide impressive results without the need for manual intervention every time. The result of automated pipelines is automatic processing of bigdata from source to destination without any user input. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision making

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A Proposed Framework for Robot-Mediated Healthcare and Response Systems 5

A Proposed Framework for Robot-Mediated Healthcare and Respo...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Khan, Hafsah Arora, Deepak Rizvi, Sheenu Amity School of Engineering and Technology Amity University Lucknow Department of Computer Science & Engineering India

ISBN: (纸本)9798350321425

The study proposes an experimental model for robot-mediated systems in healthcare. The framework outline and required modules are discussed successively alongside the methodology needed to achieve the system's base functioning. The concept of response systems and their importance in building an efficient agent for healthcare support are also discussed. The study looks at the concept of human-machine interfaces, medical robotic systems, methods of implementation and design to achieve the conception, the underlying effects, and the public's opinion on technology in healthcare. The proposed system derives from the issues faced in conveying the full capabilities of a healthcare network. The idea behind the proposal is to provide a means to leverage different components of healthcare systems to attain a holistic model capable of serving various patient and staff needs. © 2023 IEEE.

关键词： Health care

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