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Learning Salient Features for Speech Emotion Recognition using Attention-based Residual Bidirectional LSTM with Federated Learning 4

Learning Salient Features for Speech Emotion Recognition usi...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

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

ISBN: (纸本)9798350343670

In human communication, expressing human emotion plays an essential role in transferring information to the other individual. The expression forms of human communication are very rich, in different patterns like facial expressions, voice tone, eye contact, body language and laughter. Moreover, the languages used by individuals in the entire world are different, yet without understanding the communication language, the individuals can realize the message that other individuals require to convey as emotional expressions. Among all the human emotional expressions, the conveyed expressions through human voice are experimented with in different studies. However, identifying the emotional situation of the presenter is complicated for the machine learning techniques, which gained more attraction in Speech Emotion Recognition (SER). Here, the SER framework plays an efficient role in different real-world applications in observing human actions, virtual reality, emergency centers and robot communication to observe the emotional phase of the speakers. Thus, an innovative federated learning-based SER is presented in this paper. The essential speech signals are initially collected from the standard database. After that, the collected signal is fed to the feature extraction stage. Here, the deep features in the input signal are obtained by the Variational Autoencoder (VAE). Further, the deep features obtained from the VAE are passed to the "Attention-based Residual Bidirectional Long Short-Term Memory (A-RBiLSTM)"to identify the emotions in the speech signal. At last, different experimental analyses are executed to observe the effectualness of the implemented framework. © 2024 IEEE.

关键词： Attention-based Residual Bidirectional Long Short-Term Memory Deep Features Feature Extraction Federated Learning Speech Emotion Recognition Variational Autoencoder

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Network Intrusion Detection for IoT Devices Using Deep Learning 10

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

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

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

ISBN: (纸本)9798350382471

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. © 2023 IEEE.

关键词： accuracy convolutional neural network deep knowledge acquisition gated recurrent unit intrusion detection IoT long short-term memory

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SEEMA: An Automation Framework for Vulnerability Assessement and Penetration Testing 2

SEEMA: An Automation Framework for Vulnerability Assessement...

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2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Tyagi, Yash Shekhar, Shubham Abhishek, P. Bhardwaj, Shweta Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Uttar Pradesh Noida India

ISBN: (纸本)9798350347982

The world has witnessed an exponential increase in the number of Internet Connected computers due to various causes. This exponential increase and the resulting dependence have had far reaching cybersecurity implications. The primary implication being exponential rise in cybercrime which necessitates the vulnerability assessment and penetration testing (VAPT). The vulnerability assessment and penetration testing aids in offsetting any liability associated with a successful attack on an organization's asset. The current frameworks and tool available for VAPT require very high expert knowledge and are predominantly manual. Moreover, some tools available automate only certain phases of VAPT but not the whole process. This mismatch provides the opportunity to create a solution by automating the whole process while lowering the knowledge threshold. Thus, this paper provides a revamped automated framework for a novice individual and a cybersecurity professional's use. It also provides a framework for commercial use which leverages multi-agent system and certain aspects of full stack monitoring. © 2023 IEEE.

关键词： Multi agent systems

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Enhancing Driver Safety with Transfer Learning based Deep Learning Models for Drowsiness Detection

Enhancing Driver Safety with Transfer Learning based Deep Le...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

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

ISBN: (纸本)9798350378092

In response to the escalating concern over road accidents due to driver drowsiness, this research delves into enhancing drowsiness detection systems using advanced deep learning techniques and transfer learning. Prior investigations have explored conventional methods, yet they often lack the precision demanded by real-world scenarios. Leveraging convolutional neural networks (CNNs) alongside model fine-tuning and transfer learning, our methodology aims to optimize drowsiness detection accuracy. Through rigorous experimentation, we achieved compelling results, showcasing significant enhancements in accuracy across various CNN architectures. For instance, with Max Pooling and 2 Dense layers, one of our CNN models achieved an accuracy score of 1 at epoch 2, with a remarkably low loss of 6.622934e-12, all while taking only 2.6648 seconds for training. These outcomes underscore its potential to revolutionize driver safety systems, offering promising avenues for future research and implementation in real-world contexts. © 2024 IEEE.

关键词： Convolutional neural networks

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Detection and Classification of Bone Fracture Using Digital Image Processing 10

Detection and Classification of Bone Fracture Using Digital ...

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

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

ISBN: (纸本)9798350382471

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. © 2023 IEEE.

关键词： classification Digital Image processing fracture Machine learning SIFT X-ray

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Creativity: Mining of Innovative Thinking Using Educational Data

Creativity: Mining of Innovative Thinking Using Educational ...

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2023 International Conference on Disruptive Technologies, ICDT 2023

作者： Ritambhara Singh, Shailendra Narayan Amity University Computer Science & Engineering Utter Pradesh Noida India Amity University Amity School of Engineering and Technology Department of Computer Science & Engineering Utter Pradesh Noida India

ISBN: (纸本)9798350323887

Innovation i.e., the process of new ideas that are useful or effective in different types of domains. Innovation should be taught in schools, homes, and other extra places. Innovative will be increased or improved with the help of Education Data Mining (EDM), which will improve the quality of Information. By employing BERT- type, objectives are to train representation from sequence process data, then fine-tunes these representations on subsequent prediction tasks, by WEKA and machine learning algorithms. In addition, we investigated students' performance in exams using machine learning and symmetry-based learning algorithms. This article highly predicts female students pass more than boys students. © 2023 IEEE.

关键词： Learning algorithms

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Building a Chatbot using Natural Language Processing 2

Building a Chatbot using Natural Language Processing

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2nd International Conference on Informatics, ICI 2023

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

ISBN: (纸本)9798350343830

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. © 2023 IEEE.

关键词： Natural language processing systems

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Medi-Connect: An End-to-End Intelligent Framework to Facilitate New Era Online Consultations Toward Sustainable Healthcare 5th

Medi-Connect: An End-to-End Intelligent Framework to Facilit...

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5th International Conference on Entrepreneurship, Innovation, and Leadership, ICEIL 2023

作者： Yadav, Jayash Raj Singh Jain, Paras Sharma, Shanu Dubey, Ashwani Kumar Department of Computer Science and Engineering ABES Engineering College Ghaziabad India Department of Electronics and Communication Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Noida India

ISBN: (纸本)9789819716814

The healthcare industry has undergone a significant transformation in recent years, with the integration of information technology and online platforms revolutionizing the way medical services are delivered. One such development is the emergence of online consultations, enabling patients to seek medical advice from doctors without the need for in-person visits. The increasing popularity of online consultations in the healthcare industry has highlighted the need for efficient systems to support remote medical services. In this paper, an intelligent framework is proposed to assist patients and doctors during online consultations. The framework offers end-to-end services starting from patient registration, doctors’ availability, the online appointment of doctors, etc., required during an online consultation. The proposed framework is also capable of assisting the doctors during diagnosis of diseases and drug recommendation through intelligent decision-making capability based on the symptoms provided by the patient. The use of the Naïve Bayes algorithm is explored here to perform classification on patient data and the determination of the probability of specific diseases. In addition to accurate diagnoses, the system recommends appropriate medicinal treatments and provides a database of specialist doctors for further consultation. This integration of technology and health expertise seeks to streamline the healthcare process, saving time and reducing costs for both patients and doctors. By presenting a streamlined framework for online consultations, the main aim of this work is to contribute to the advancement of virtual healthcare services and to bridge the gap between traditional healthcare practices and the evolving digital landscape, ultimately improving healthcare accessibility and outcomes for patients worldwide. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Diagnosis

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Efficient Vulnerability Assessment and Penetration Testing: A Framework for Automation

Efficient Vulnerability Assessment and Penetration Testing: ...

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2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Tyagi, Yash Bhardwaj, Shweta Shekhar, Shubham Abhishek, P. Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Uttar Pradesh Noida India

ISBN: (纸本)9798350323917

Due to a variety of factors, the number of computers connected to the internet has been growing exponentially. The dependence that resulted from this exponential growth has profound cybersecurity consequences. The main implication is that cybercrime is increasing exponentially, making vulnerability assessment and penetration testing (VAPT) necessary. Penetration testing and vulnerability assessment can balance any responsibility connected with a successful attack on an organization's assets. The present frameworks and tools for VAPT are primarily manual and demand very high specialist knowledge. Additionally, several systems on the market only automate specific VAPT processes, not the entire procedure. This discrepancy offers the chance to develop a solution by fully automating the procedure while minimizing the required level of knowledge. As a result, this paper offers an updated automated system for usage by both beginners and cybersecurity experts. Additionally, it offers a framework for business use that makes use of multi-agent systems and some elements of full stack monitoring. © 2023 IEEE.

关键词： Multi agent systems

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Maximizing user retention with machine learning enabled 6G channel allocation

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International Journal of Information technology (Singapore) 2023年第4期15卷 2225-2231页

作者： Singh, Pushpa Agrawal, Rajeev Singh, Krishna Kant Department of Computer Science and Engineering GL Bajaj Institute of Technology and Management Greater Noida India Department of Computer Science & Engineering Lloyd Institute of Engineering and Technology Greater Noida India Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Noida India

The paper proposes a machine learning-based user retention technique for the 6G network by identifying and classifying loyal users using supervised machine learning algorithms such as Decision Tree, K-Nearest Neighbor, and Support Vector Machine. The study also suggests a threshold-based channel allocation method to allocate network resources primarily to loyal users. The performance of the proposed algorithm is evaluated using SimPy simulation, and the results show that loyal users experience minimum average waiting time and no call drops compared to normal and recent users. The paper highlights the need for new schemes and policies to retain valuable users and proposes a novel approach that leverages machine learning techniques and 6G aspects to achieve this objective. The proposed algorithm's effectiveness is demonstrated through simulation, which provides useful insights into its performance under different network conditions. The research contributes to the ongoing efforts to enhance user experience in the rapidly evolving field of mobile communication networks. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： 6G Channel Loyal user Machine learning Retention Wireless network

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