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Virtual Surveillance Assistant: Converging computer Vision, NLP, and Chatbot Technologies via Azure Cognitive Services 23

Virtual Surveillance Assistant: Converging Computer Vision, ...

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

作者： Garima Srivastava Sachin Kumar Kunal Yadav Computer Science Amity University Uttar Pradesh Lucknow Campus India Electronics & Communication Engineering Amity University Uttar Pradesh India Amity School of Engineering and Technology Amity University Uttar Pradesh India

ISBN: (纸本)9798400709418

In the ever-evolving landscape of heightened security concerns, traditional surveillance systems are revealing their limitations in effectively addressing the complexities of modern security challenges. This paper presents a pioneering solution in the form of a sophisticated Virtual Surveillance Assistant, a revolutionary system that seamlessly integrates cutting-edge technologies. By harnessing the power of computer vision, Natural Language Processing (NLP), and chatbot technologies, this project stands as a testament to innovation in security enhancement. Bolstered by the formidable capabilities of Azure Cognitive Services, the Virtual Surveillance Assistant endeavors to combat security vulnerabilities through a harmonious fusion of automated processes, real-time monitoring, multilingual communication, and intelligent threat detection. The work proposed offers a unique and effective combination of chatbot integrated with the computer vision, NLP, and speech/text processing components to leverage their functionalities. Integration points are established to enable seamless communication between the chatbot and other system components. computer vision component achieves an accuracy of 99.7 % and precision of 94.4%. The results obtained were analyzed to identify any potential limitations or areas for improvement, such as handling occlusions, variations in lighting conditions, and complex backgrounds. Virtual Surveillance Assistant charts a course towards an intelligent, proactive, and vigilant security ecosystem.

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Towards Accurate Heart Disease Diagnosis: An Overview of Machine Learning Approaches

Towards Accurate Heart Disease Diagnosis: An Overview of Mac...

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International Conference on Recent Trends in Advance Computing (ICRTAC)

作者： Varun Deshmukh Sunil Pathak Pronaya Bhattacharya Ashwin Verma Department of Computer Science & Engineering Amity University Jaipur India Department of Computer Science and Engineering Amity School of Engineering and Technology and Research and Innovation Cell Amity University Kolkata India Department of Computer Science & Engineering Nirma University Ahmedabad India

cardiovascular diseases, especially heart ailments, pose substantial health challenges globally, often exacerbated by unhealthy lifestyle habits. Recent technological advances in machine learning (ML) have emerged as a beacon of hope, presenting avenues for swift and efficient heart disease detection. Despite the voluminous health data accumulated daily, its vast analytical potential is frequently overlooked, creating a pronounced knowledge disparity. Thus, it becomes imperative to study recent ML approaches for accurate heart diagnosis, which allows to fetch critical insights. In this paper, we discussed a comparative analysis of ML algorithms, datasets for heart diseases, and compared ML algorithms in terms of metrics like precision, recall, F1 and accuracy. We considered the benchmark Cleveland Heart Dataset for analysis. We observed an accuracy of 84% by Artificial Neural Network (ANN), while Support Vector Machine (SVM) has the highest recall of 95%. The presented findings and analysis indicate the efficacy of the analysis in heart prediction using ML in real-world setups.

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Development of Emotion Recognition via Federated Learning: A Multi-modal Approach with Hybrid Convolutional Network

Development of Emotion Recognition via Federated Learning: A...

引用

Signal Processing and Advance Research in Computing (SPARC), International Conference on

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

ISBN: (数字)9798350385205

ISBN: (纸本)9798350385212

Human emotion recognition is the crucial process for performing effective communication and intelligent interaction. Identifying human emotion through discrete technologies is an essential step. Affective computing is one machine learning process in which multi-task learning has gathered a great deal of attention. It leverages shared data across associated activities to enhance performance. The physiological results from the aforementioned devices may also constitute a privacy risk because they reveal extremely sensitive information. To address this issue, we develop a federated learning approach with multi-task learning to precisely recognize facial emotion from the human. The required face images and Electroencephalogram (EEG) signals are collected from online sources. The face images are considered as feature set 1. From the EEG signals, the deep features are extracted through the Restricted Boltzmann Machine (RBM) and these features are considered as feature set 2. Both feature set 1 and feature set 2 are applied to the federated learning network, where the Hybrid Convolution-based Convolutional Neural Network (HC-CNN) is adopted for the recognition of human facial emotions. The face images are applied to the 2D layer and the extracted deep features from the EEG signal are applied to the 1D layer of the hybrid convolution network to provide an effective face recognition outcome. The recognition outcome of the proposed model is compared with the traditional face emotion recognition models concerning various measures. The recommended model successfully recognizes the emotion of the human with an accuracy of ${9 5. 7 5 \%}$ and precision of ${9 5. 9 7 \%}$, respectively.

关键词： Performance evaluation Emotion recognition Analytical models Federated learning Convolution Face recognition Feature extraction Brain modeling Electroencephalography Convolutional neural networks

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Analyzing Customer Psychological and Behavioral Attributes in Corporate Social Responsibility in Supply Chain Management Using a Multilayer Perceptron Approach 2

Analyzing Customer Psychological and Behavioral Attributes i...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Agnihotri, Kuldeep Kamidi, Deepthi Ranjan, Radha Vasudevan Unni, Manu Babu K, Ramesh Vijaya Jayanthi, N. ISBA Group of Institutes Department of Commerce & Management Madhya Pradesh Indore India Vignan Institute of Technology and Science Department of Computer Science and Engineering Telangana Hyderabad India Amity University Law Amity Law School Bihar Patna India Department of Management Karnataka Bangalore India SRKR Engineering College Department of English and Foreign Languages Andhra Pradesh Bhimavaram India Periyar Maniammai Institute of Science and Technology Deemed to be University Department of Commerce Tamilnadu Thanjavur India

ISBN: (纸本)9798331534950

Despite the fact that CSR is currently important in the retail industry, academic research on its influence is sparse. Retailers have a great interest in understanding the influence of customer opinion on CSR initiatives because the system serves as a conduit between producers and consumers. Understanding the impact of CSR projects on store bottom lines is critical for contextualising the system's results. The suggested technique includes preprocessing, feature selection, and model training. This step involved cleaning the dataset with a text-processing method that assigned each document three numerical values: document length, token number, and token length. The best approach is to model feature selection using Latent Dirichlet Allocation (LDA). The MLP was used to train the model in the specified approach. The suggested model has an average accuracy rate of 94.35%, which is greater than cutting-edge alternatives like as SVM and CNN. © 2024 IEEE.

关键词： Sales

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Integrating Predictive Models and Remote Sensing for Sustainable Agriculture, Energy, and Environmental Management 4

Integrating Predictive Models and Remote Sensing for Sustain...

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

作者： Kumar, Raj Ather, Danish Sagar, Pinki Yusupov, Ibrohimbek Duggal, Sonia Jain, Vishal Department of Computer Applications Faridabad India Amity University Department of It and Engineering Tashkent Uzbekistan G.L. Bajaj Institute of Technology and Management Greater Noida India Tashkent University of Information Technologies Named after Muhammad ibn Musa al-Khwarizm Department of Artificial Intelligence Tashkent Uzbekistan Sharda School of Engineering and Technology Sharda University Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350387490

This paper focuses on a critical analysis of the application of integrated works of predictive models and remote sensing in agriculture and renewable energy, and environmental conservation. This method involves the use of deep learning approaches, UAV data collection and hyperspectral imagery which gives the researchers an understanding on the real world effectiveness of the systems and technologies analyzed;on predictive models and resource utilization efficiency. © 2024 IEEE.

关键词： Agriculture Deep Learning Energy Management Environmental Man-Agement Predictive Models Remote Sensing UAV

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Market Segmentation Using Machine Learning and Data Analysis: A Comprehensive Study

Market Segmentation Using Machine Learning and Data Analysis...

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International Conference on Computing and Networking technology (ICCNT)

作者： Ajay Singh Namrata Dhanda Sharda Tiwari Rajat Verma Amity School of Engineering and Technology Amity University Uttar Pradesh Lucknow India Department of Computer Science and Engineering Babu Banarasi Das Northern India Institute of Technology Lucknow India Department of Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This research explores the integration of machine learning into market segmentation, a strategy traditionally reliant on demographic, geographical, and psychographic traits. With the rise of big data, machine learning offers enhanced precision by uncovering intricate consumer behavior patterns often missed by conventional methods. The study involves reviewing relevant literature, preprocessing datasets, and developing machine learning models to evaluate their effectiveness in segmenting markets. Findings indicate that machine learning significantly improves segmentation accuracy, enabling businesses to predict behaviors and tailor marketing strategies more effectively. This research contributes by highlighting machine learning’s potential to transform market segmentation and drive business growth. Future work should focus on real-world implementation of these models to validate their practicality and impact.

关键词： Data analysis Consumer behavior Accuracy Computational modeling Machine learning Transforms Big Data Business

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MetaEd: Metaverse-Assisted Virtual Classroom and Content Generation Framework Underlying 5G

MetaEd: Metaverse-Assisted Virtual Classroom and Content Gen...

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IEEE International Conference on Communications Workshops, ICC

作者： Pronaya Bhattacharya Aparna Kumari Ishan Budhiraja Sudeep Tanwar Department of Computer Science and Engineering ASET Amity University Kolkata India Department of Computer Science and Engineering Institute of Technology Nirma University Gujarat India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (数字)9798350304053

ISBN: (纸本)9798350304060

In this paper, we propose MetaEd, which is a metaverse enabled virtual classroom and content generation framework, with networking services employed over fifth generation (5G) network. The framework is designed for Education 5.0 space, and presents two key functions- a virtual classroom setup, where the teacher and the student avatars can interact (like in physical classroom) in near real time latency, which is assisted by ultra-reliable-low latency service (5G-uRLLC). Secondly, we present the digital content generation on meta-verse space, where digital assets (contents) with non-fungible tokens (NFTs) are stored. The entire data is stored in offline interplanetary file system (IPFS) ledgers, and the content hash reference is stored in blockchain. The framework is operational over a Web 3.0 platform, which allows AI-based cognition to users to enhance personalization. The framework is deployed via simulated metaverse environment (Unity 3D) space, where avatar creation is done through Blender. Performance metrics shows a significant improvement of 59.93% in user-experienced bandwidth, lowered transaction size by 44.07% in blockchain, 44.37% in jitter rates. and 66.31% improvement in object rendering time for avatars, which signifies the viability of the framework in real Education 5.0 scenarios.

关键词： Semantic Web Three-dimensional displays Metaverse 5G mobile communication Avatars Conferences Rendering (computer graphics)

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Leveraging Digital Technologies and System Dynamics for Achieving Sustainable Development Goals Across Diverse Sectors 4

Leveraging Digital Technologies and System Dynamics for Achi...

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

作者： Duggal, Sonia Ather, Danish Yusupov, Ibrohimbek Sagar, Pinki Kumar, Raj Jain, Vishal Department of Computer Applications Faridabad India Amity University Department of It and Engineering Tashkent Uzbekistan Tashkent University of Information Technologies Named after Muhammad Ibn Musa al-Khwarizm Department of Artificial Intelligence Tashkent Uzbekistan G.L. Bajaj Institute of Technology and Management Greater Noida India Sharda School of Engineering and Technology Sharda University Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350387490

This paper aims at arguing the impact of digital technologies and system dynamics models towards the achievement of the United Nations - Sustainable Development Goals (UN-SDGs). Interrelated with numerous fields, this research focuses on the application on intelligent systems in big data environment and sensors networks to increase efficiency of resources, improve transparence of processes as well as to make sound decisions according to SDGs requirements. Based on findings of ten recent pieces of research, the paper integrates lessons and best practices with the aim of illustrating how technology can open new avenues for addressing complex problems. © 2024 IEEE.

关键词： Sustainable development goals

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Comprehensive Framework for Analyzing Grammar Error Correction with GPT -3.5

Comprehensive Framework for Analyzing Grammar Error Correcti...

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： Rajni Kant Munda Abhishek Kumar Ravi Kumar Burman Biresh Kumar Md. Shoaib Alam Anurag Sinha Computer Science and Engineering Jharkhand University of Technology Ranchi India Computer Science and Engineering Amity University Ranchi Jharkhand India School of Computing and Information Science IGNOU New Delhi India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

This investigate assesses the viability and exactness of an error-correction approach utilizing GPT-3.5 compared to routine instruments. Letter set rectification plays a vital part in making strides the quality of and improving clarity in different scholastic and proficient situations. The consider utilizes appraisal criteria such as precision, exactness, review, and F1 score to how well GPT-3.5 performs relative to other language structure redress devices. Also, the information incorporates client input and daze testing to gage the viable convenience of the adjustments. Discoveries demonstrate that GPT-3.5 outperforms conventional instruments in taking care of relevant blunders and client fulfilment, illustrating a momentous capacity to redress syntactic botches, indeed in complex sentences with inconspicuous subtleties. Be that as it may, it does confront a few challenges, such as affectability to input varieties and intermittent overcorrection. The think about recommends entering on moving forward relevant understanding and diminishing overcorrection to development these calculations assist. It too investigates potential applications of GPT-3.5 for upgrading composing back and languagelearning assets, highlighting conceivable future investigate ranges to optimize AI-driven language structure rectification models.

关键词： Performance evaluation Reviews Instruments Computational modeling Syntactics Forward error correction Grammar Appraisal Artificial intelligence Testing

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Advancements in Deep Learning for Early Detection and Diagnosis Across Multiple Disease Domains 4

Advancements in Deep Learning for Early Detection and Diagno...

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

作者： Ather, Danish Yusupov, Ibrohimbek Duggal, Sonia Kumar, Raj Sagar, Pinki Jain, Vishal Amity University Department of It and Engineering Tashkent Uzbekistan Tashkent University of Information Technologies Named after Muhammad Ibn Musa al-Khwarizm Department of Artificial Intelligence Tashkent Uzbekistan Department of Computer Applications Faridabad India G.L. Bajaj Institute of Technology and Management Greater Noida India Sharda School of Engineering and Technology Sharda University Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350387490

Identification and diagnosis of diseases in its early stages is one of the biggest determinants of improving patient health and needs, as well as decreasing general health care costs. Hence, with the application of deep learning, there has been a total change in the methods of medical diagnosis. This scoping review provides an employment of the current research progress of deep learning in different disease types, such as cancer, neurodegenerative diseases, and ophthalmologic diseases. This paper intends to compare different deep learning architectures, preprocessing methodologies used for data, and how data is incorporated into the framework, with a view of giving a glimpse of what researchers are currently doing in this fast-growing field, difficulties that are being encountered, as well as the future direction of the research domain being considered in this paper. © 2024 IEEE.

关键词： Cancer Detection Convolutional Neural Networks Deep Learning Diagnosis Early Detection Machine Learning Medical Imaging Neurodegenerative Diseases Ophthalmology

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