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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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Emotion based Music Player Through Speech Recognition 13

Emotion based Music Player Through Speech Recognition

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

作者： Malik, Mihika Gupta, Sumita Amity University Amity School of Engineering and Technology Department of Computer Science and Engineering Noida India

ISBN: (纸本)9781665462631

Music is a crucial part of our everyday lives;it provides a sense of stability and calmness to the mind and body giving birth to the concept of Music therapy. Conceptualizing the aspect of an emotion-based music player for the use case of Music Therapy pertains to be an innovative solution for Mental Health Issues. This paper aims to recognize variations in data for human emotion detection through their vocal parameters along with highlighting aspects of SER i.e., speech emotion detection. © 2023 IEEE.

关键词： Emotion Recognition

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Multilingual Text Recognition System 1

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7th International Conference on Microelectronics, Electromagnetics and Telecommunication, ICMEET 2022

作者： Maithani, Mansi Meher, Dibyasha Gupta, Sumita Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Noida India

ISBN: (数字)9789811988653

ISBN: (纸本)9789811988646

Most of us take our reading and writing abilities for granted until the onset of visual impairment. After all, literacy is a key to personal independence and access to information. Tasks that require little thinking on our part, such as reading lables and business cards, can prove to be very challenging for visually impaired people. The proposed approach suggests the use of OCR techniques to extract information from printed and electronic documents and convert this information into a voice output for the benefit of the visually impaired. This paper considers the functioning of two of the common OCR engines, Tesseract OCR and EasyOCR. We have used these tools for the text recognition of Indian languages, Bengali and Tamil along with English text. We conclude this paper with a comparative study of these two tools by considering business cards as an input. From business cards we have tried to extract all legible data by using Tesseract and EasyOCR and compared these tools based on their accuracy parameter. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Optical character recognition

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Electroencephalogram-based deep learning framework for the proposed solution of e-learning challenges and limitations

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International Journal of Intelligent Information and Database Systems 2022年第3期15卷 295-310页

作者： Pathak, Dharmendra Kashyap, Ramgopal Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Chhattisgarh Raipur492001 India

There is a high surge in usage of online e-learning platforms due to the current ongoing COVID-19 scenario. There are specific problems that persist in the current e-learning online models, i.e., validations and tracking of students’ learning curves, validation of presented course material, content-based personalisation as per the requirements of the students, identification of learning disabilities among students, etc. Our paper proposes the deep learning model to solve the issues related to existing machine learning models of manual feature extraction and training on limited data. Also, real-time e-learning data will be collected from students wearing EEG-headband while taking online classes. It solves the issues associated with conventional machine learning models and historical data. The proposed CNN model will classify the students on different grades and help in the development of an automated framework for the tracking of a student learning curve, providing recommendations for the betterment of e-learning course materials. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Feature extraction

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A Sentiment Analysis Based Approach for Customer Segmentation

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Recent Patents on engineering 2022年第2期16卷 32-42页

作者： Bhatnagar, Anisha Bhatia, Madhulika Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Noida India

Background: Customer Segmentation is the process of dividing customers into groups based on some demographic factors in order to get an idea of the targeted audience for a product and to best market said product. Objective: Sentiment Analysis on customer reviews is one way that this process can be enhanced to get not just demographic information but subjective information and preferences as well. Methods: In this study, Long Short-Term Memory model, a deep learning technique, has been applied for Sentiment Analysis and its results have been used to perform Customer Segmentation on demographic data containing information such as age and gender. Segmentation was performed using Spectral Clustering. Cluster Labels were extracted to perform supervised classification using different supervised algorithms, such as Support Vector Machines, Random Forests, Decision Trees and Logistic Regression. Results: An accuracy of 90.9% was achieved by the LSTM model. An accuracy of 100% was achieved by the Random Forest and Decision Tree Classifiers. © 2022 Bentham science Publishers.

关键词： Decision trees

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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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Momentary sensor based cricket ball design for bowlers performance analysis

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Multimedia Tools and Applications 2024年 1-34页

作者： Balbudhe, Pravin Sharma, Rika Solanki, Sachin Department of Computer Science & amp Engineering Amity School of Engineering and Technology Amity University Raipur Chhattisgarh India Directorate of Technical Education Government Polytechnic Campus Maharashtra Sadar Nagpur India

With rapid development in gaming technology, we have seen different apparatuses helping players, coaches, umpires, refries, and students for better decision-making and performance enhancement. This research work focused on development of the momentary sensors-based cricket ball for capturing minimal details of ball activity which cannot be reviewed or tracked with normal human eyes of cameras. This is more challenging because changes in ball motions are at the highest speed and need a high-speed sensor to track the details. We are presenting the custom-modified cricket ball having a perfect weight proportioned microcontroller and momentary sensor-based logic board inside. This hardware inside is capable of recording every minor rotational, traveler, and geographical information of the ball activity so that the data can be utilized for performance analysis. The experimental result shows that not all the information could be gathered from the single or similar working principal type of sensor, rather it needs the fusion of different sensors and still needs the right time synchronization between all sensor modules to capture the right statistical information. We achieved the result and presented it to the best coaches in the town, to have their opinions. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Decision making

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Recommendation System Algorithms For Music Therapy 13

Recommendation System Algorithms For Music Therapy

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

作者： Singh, Anannmaya Gupta, Sumita Amity University Amity School of Engineering and Technology Department of Computer Science and Engineering Noida India

ISBN: (纸本)9781665462631

A recommendation system is an artificial intelligence or machine learning system that uses big data to suggest or recommend products to consumers. In this paper analysis of different types of recommendation system such as collaborative filtering & content filtering has been done through the help of implementations of different types of algorithms. Through those implementations an optimal algorithm which best suits our purpose of making a music recommendation system will be selected. Initially we will be looking at the different approaches implemented in the research papers and study them. In the paper implementations have been done for three algorithms i.e., Popularity based recommendation system, Item based recommendation system and Content filtering-based recommendation system. After the implementation of these approaches, it was found that item-based recommendation is the best model for the project and meets all our requirements. This paper also discusses most of the known approaches and gives a deep insight to their working & methodology. © 2023 IEEE.

关键词： Recommender systems

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Extensive Analysis of Deep Learning Algorithms for Automatic Detection of Liver Anomalies Using MRI and CT Scanned Images 4

Extensive Analysis of Deep Learning Algorithms for Automatic...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Chakraverti, Sugandha Sharma, Smita Shukla, Vijay Amity School of Engineering and Technology Amity University Uttar Pradesh Department of Computer Science and Engineering Greater Noida Campus India Amity School of Engineering and Technology Amity University Uttar Pradesh Greater Noida Campus India Greater Noida Institute of Technology Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350387490

The liver's segmentation during medical imaging is crucial for the diagnosis and prognosis of liver diseases., including fibroid cysts, malignant growths, and other pathological problems. The World Health Organization says that alcohol addiction, non-alcoholic fatty liver disease, chronic hepatitis infections, and genetic predispositions are among the risk factors that make liver cancer a primary cause of cancer-related death. Radiologists' traditional manual assessment of CT and MRI images takes a lot of time and is prone to mistakes. Medical picture segmentation has undergone a revolution because of developments in deep learning, namely convolutional neural networks (CNNs)., which automate feature extraction and increase accuracy. This comprehensive analysis investigates many deep learning techniques, such as CNNs, U-Net, autoencoders, GANs, and transformers, that are used for liver segmentation. The above-mentioned models have revealed a significant improvement in efficiency and accuracy in segmentation. These models' evaluation and assessment have been eased with datasets such as LiTS, CHAOS, MSD, SLIVER07, and many others. Some of the performance indices that are used to measure the effectiveness of these mathematical models. Despite this, there are still problems that persist such as the processing requirements, morphological variation, and dependency on labeled data. To improve model clinical relevance in the future, more work should be done to make them less sensitive to noise and to increase model interpretability. This analysis raises questions for further development to overcome the current limitations and reveal the potential of deep learning to transform the identification of liver abnormalities. © 2024 IEEE.

关键词： computerized tomography

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An AutoML Approach Integrated with Live Weather Data in Rain Forecasting System (RFS) 1

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8th International Conference on Inventive Communication and Computational Technologies, ICICCT 2024

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

ISBN: (数字)9789819777105

ISBN: (纸本)9789819777099

The increasing climate unpredictability highlights the critical need for accurate rain prediction methods. Traditional approaches often struggle to adapt and accurately forecast rain across varied climatic conditions. This research work investigates the fusion of automated machine learning (AutoML) with real-time weather data to tackle these challenges. Previous methodologies have relied on manual algorithm selection and optimization, leading to inefficiencies and suboptimal outcomes. In contrast, the proposed AutoML-based approach automates algorithm selection and optimization, resulting in heightened accuracy and efficiency. Integration of diverse meteorological data streams and advanced preprocessing techniques enhances data quality for model training. Evaluation against traditional methods showcases significant enhancements in rain prediction accuracy, with scores ranging from 80.60 to 86.09% across distinct algorithms. Specifically, the Random Forest Classifier achieved an accuracy score of 85.43% and an AUC score of 0.88, Gaussian NB achieved 80.60% accuracy and 0.82 AUC, K Neighbors Classifier achieved 82.89% accuracy and 0.82 AUC, and XGBoost achieved 86.09% accuracy and 0.89 AUC. The developed approach not only advances rain forecasting but also offers potential applications in agricultural planning, urban water management, and disaster preparedness. Future research avenues may explore further enhancements to the AutoML framework and its extension to other environmental prediction domains. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Rain

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