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Implementing Robust Security Protocols for Virtual Images in Cloud Computing Platform

Implementing Robust Security Protocols for Virtual Images in...

2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Tiwari, Shiv Kumar Gupta, Manish Neogi, Subhrendu G. Amity School of Engineering and Technology Amity University Department of Computer Science Engineering Gwalior India The Neotia University Department of Computer Science Engineering West Bengal India

ISBN: (纸本)9798331517953

The new technology, with the aid of newly emerging knowledge known as cloud computing, can provide resources remotely and on demand. With the use of cloud computing, users can operate in settings where they are not dependent on physical infrastructure. Numerous protocols facilitate the utilization of cloud computing resources. Cloud computing is Internet-based technology that is made available to consumers of its resources and services and is billed depending on demand and usage. All cloud servers cannot see the precise place where the user is to be configured. User-generated secure keys are employed in the proposed work for both virtual machine (VM) switching and authentication. Every time a valid user is authenticated, a secure pass key is utilized to guarantee security and authentication between the user and the system. © 2024 IEEE.

关键词： Cloud platforms

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Analysis and Impact of Electromagnetic Field Leakage in WPT on Central Nervous System: A Neural Network Approach 4

Analysis and Impact of Electromagnetic Field Leakage in WPT ...

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4th IEEE International Conference on Sustainable Energy and Future Electric Transportation, SEFET 2024

作者： Shukla, Garima Awasthi, Vanshaj Goswami, Prafull Singh, Sofia Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Maharashtra Mumbai India Indian Institute of Information and Technology Department of Computer Science and Engineering Sonipat India Amity School of Engineering and Technology Amity University Department of Artificial Intelligence Uttar Pradesh Noida India

ISBN: (纸本)9798350383997

The potential for wireless power transfer (WPT) technologies to transform the charging capabilities of a wide range of applications, from consumer electronics to medical devices has attracted a lot of attention. But uncertainties remain about how these technology's electromagnetic field (EMF) leakage affects human health, especially the central nervous system (CNS). To find a way to assess and forecast all of these consequences, this study explores at the possible effects of EMF leakage from WPT systems on the central nervous system (CNS) and suggests a distinctive approach that employs the applications for neural networks. Using real- world data and computational models, this study will produce an accurate model of EMF absorption and propagation in biological tissues, with a specific focus on the central nervous system. By focusing insights into potential risks to health and informing the emergence of requirements which assure the safe implementation of wireless power technologies, the research will enhance our understanding of the connections between EMF exposure and the central nervous system. The ultimate objective of this study is to encourage safer and more sustainable wireless charging technologies by enhancing the accuracy and efficiency of risk assessment in circumstances of EMF exposure from WPT systems through the implementation of Machine Learning (ML) strategies © 2024 IEEE.

关键词： Inductive power transmission

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Object Detection Using TensorFlow 2 and Amazon SageMaker

Object Detection Using TensorFlow 2 and Amazon SageMaker

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International Conference on Recent Developments in Cyber Security, ReDCySec 2023

作者： Goel, Vartika Arora, Deepak Rizvi, Sheenu Department of Computer Science & Engineering Amity School of Engineering and Technology Lucknow Amity University Uttar Pradesh Noida India

ISBN: (纸本)9789819998104

Many frameworks with packaged pre-guided models have been created to give users fast access to transfer learning considering the rapidly expanding field of object detection techniques. For instance, three well-known computer vision systems with trained models are GluonCV, Detectron2, and the Object Detection API for TensorFlow, as well. The TensorFlow2 Object Detection API is an update to the TensorFlow Object Detection API. One of the cutting-edge object identification algorithms that can be trained using the TensorFlow2 Object Detection API is EfficientDet from Google Brain (Implemented here). Authors created and implemented an EfficientDet model with the help of TensorFlow Object Detection API using Amazon SageMaker. It is constructed on top of TensorFlow 2, which facilitates its creation, training, and deployment. Because it is designed on top of TensorFlow 2, creating, training, and deploying object detection models is simple. SageMaker is a completely managed tool that lets data scientists and developers quickly build, direct, and implement ML models. To make it simpler to create high-quality models, SageMaker takes the labour-intensive tasks out of each stage of the ML process. Transfer learning on numerous pre-guided models accessible in TensorFlow Hub is made possible by object detection with TensorFlow in SageMaker. The head of the TensorFlow model that handles object detection is replaced based on the amount of class labels present in the guiding data. Based on fresh guiding data, either the entire network—including pre-guided model—or just the top layer (object recognition head) can be fine-tuned. Authors trained using a smaller dataset in this transfer learning method. Authors have talked over each step-in detail, including data collection and labelling with Ground Truth, making, and converting the data to TFRecord format, training as well as launching a special object detection model with the TensorFlow Object Detection API, and ultimately deploying th

关键词： Object detection

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Early Detection of Cognitive Skill Impairments using Machine Learning 8

Early Detection of Cognitive Skill Impairments using Machine...

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8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Patil, Sunita Kukreja, Swetta Amity University Maharashtra Amity School of Engineering and Technology Department of Computer Science & Engineering Maharashtra Mumbai410206 India

ISBN: (纸本)9798350391770

Cognitive skills are essential for everyday tasks, encompassing mental abilities such as memory, problem-solving, and logical thinking. Conditions such as depression, anxiety, and schizophrenia can impair these skills, affecting memory, attention, and problem-solving abilities. The COVID pandemic and other stress-inducing events have a great impact on individuals, often leading to stress-related diseases. Depression often stems from heightened stress levels, yet individuals may struggle to recognize or seek help due to social stigma. Delayed treatment impairs outcomes. Surveys indicate low success rates in treating cognitive skill impairment. Early identification through routine cognitive testing is crucial, especially for vulnerable individuals due to age, family history, or health conditions. Regular screenings facilitate proactive healthcare management, leading to improved outcomes in patients with cognitive skill impairments. This study explores the application of Support Vector Machine (SVM) algorithms for the early detection of cognitive skill impairments, aiming to develop accurate prediction models for identifying individuals at risk of cognitive decline. By employing feature selection methods, the SVM achieved an 83% accuracy rate. This study aimed to advance the development of efficient cognitive assessment tools, enabling early intervention and personalized care for individuals vulnerable to cognitive impairment. © 2024 IEEE.

关键词： Support vector machines

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Eye Disease/Disorder Diagnosis Using Deep Learning 8th

Eye Disease/Disorder Diagnosis Using Deep Learning

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8th International Conference on Information and Communication technology for Intelligent Systems, ICTIS 2024

作者： Rastogi, Ishaan Singh, Shrey Tripathi, Suryansh Mittal, Mukund Bhulania, Paurush Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Noida Uttar Pradesh Noida India

ISBN: (纸本)9789819766772

Web-based interfaces for medical diagnostics and ophthalmology are being transformed by recent advances in web development. By properly analyzing medical pictures, particularly retinal imaging and fundus photos, convolutional neural networks (CNNs) and machine learning are revolutionizing illness prediction in optics. Precision, recall, and F1-score are essential measures for assessing the efficiency of models. The MERN stack (MongoDB, Express, React, and ***) distinguishes out in the development of websites for its flawless technology integration. ***, a crucial component, makes UI creation easier by emphasizing user experience improvement above peak performance with its effective Virtual DOM. *** selection is validated by its simple training arc, robust support from the community, and extensive guides, which make it easier to switch from more traditional frontend components. The influence that CNNs and transfer learning have on ophthalmology, as well as the effectiveness of the MERN stack in developing websites, are highlighted in this abstract’s conclusion. The need of precise medical evaluation and efficient interfaces for users is emphasized by these advances, which show great potential in developing both professions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Crime Data Analysis 8th

Crime Data Analysis

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8th International Conference on Information and Communication technology for Intelligent Systems, ICTIS 2024

作者： Srivastava, Divyanshi Jha, Ujjwal Kumar Bhulania, Paurush Department of Computer Science Engineering Amity School of Engineering and Technology Amity University Noida Uttar Pradesh Noida India

ISBN: (纸本)9789819766772

The number of criminal cases in India is growing quickly, which is also causing an increase in the backlog of pending cases. The persistent rise in criminal cases is making it more challenging to categorize and resolve them. It’s critical to identify a location’s trends of criminal activity in order to stop it from occurring. If the agencies responsible for solving crimes have a clear understanding of the trends in criminal activity in a certain area, they will be able to perform their jobs more effectively. This can be achieved by applying various algorithms and machine learning techniques to identify trends in the criminal activity within a specific region. This study expedites the categorization of criminal cases and moves forward appropriately by forecasting the sorts of crimes in a certain location using a crime dataset. Because they are inefficient and slow-moving, traditional crime-solving approaches cannot keep up with the present fast-rising crime rate. Therefore, it would lessen the load on police and aid in crime prevention if we could figure out how to precisely forecast crime before it happens or create a "machine" that can support police officers. It is advantageous to use computer vision algorithms and techniques as well as machine learning (ML) to accomplish this. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Crime

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Survey on Asclepius: A User-Centric Chatbot Bridging the Gap in Healthcare Integrated with VPN technology 4

Survey on Asclepius: A User-Centric Chatbot Bridging the Gap...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Shukla, Garima Sharma, Dolly Awasthi, Vanshaj Shekhawat, Deepika Shukla, Pavan Kumar Saranya, A. Amity University Mumbai Amity School of Engineering and Technology Department of Computer Science and Engineering Maharashtra India Graphic Era Hill University School of Computing Haldwani India Amity University Mumbai Department of Computer Science and Engineering Mumbai India Noida Institure of Engineering and Technology Department of Electronics and Communication Engineering Uttar Pradesh Greater Noida India Amity University Amity School of Engineering and Technology Department of Computer Science and Engineering Mumbai India

ISBN: (纸本)9798350364729

This groundbreaking paper introduces Asclepius, a revolutionary software solution aimed at transforming early healthcare assistance through the seamless integration of cutting-edge technologies. At its core lies a sophisticated chatbot system, augmented with machine learning capabilities, facilitating swift and efficient connections between users and local medical professionals. With a strong emphasis on user anonymity and security, particularly concerning sensitive mental health concerns, Asclepius ensures utmost privacy throughout the interaction process. Constructed with meticulous attention to healthcare technology and security, Asclepius has been developed following extensive research across various domains, incorporating insights from acclaimed research articles to optimize and build an effective model. Central to its functionality is the utilization of state-of-the-art facial recognition technology and secure authentication protocols, instilling confidence in the system's reliability by enabling accurate user verification upon login. As of the publishing date, Asclepius has an 89% accuracy for its diagnoses by making use of a Supervised Decision tree-based Machine Learning Model. Through the incorporation of integrated virtual private network (VPN) technology, the project further establishes a secure communication channel between users and healthcare professionals, guaranteeing the protection of sensitive information and fostering a safe, confidential environment for seeking assistance. By seamlessly integrating a vast array of advanced technologies, Asclepius provides users with a comprehensive and user-friendly interface, prioritizing convenience and security in healthcare interactions and heralding a transformative shift in the landscape of early healthcare assistance. © 2024 IEEE.

关键词： Diagnosis

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Glaucoma Classification Using Improved Pretrained Model 12th

Glaucoma Classification Using Improved Pretrained Model

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12th International Conference on Soft Computing for Problem Solving, SocProS 2023

作者： Singh, Piyush Bhushan Singh, Pawan Dev, Harsh Chaurasia, Brijesh Kumar Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Lucknow India Department of Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur India

ISBN: (纸本)9789819731794

The most common cause of blindness in the world is glaucoma. It can lead to a decline in eyesight and quality of life if not addressed within the stipulated time. In computer vision, convolutional neural networks (CNNs) have achieved a viable solution. The most standard glaucoma screening method is fundus imaging due to the compromises it makes between mobility, size, and price. In this paper, we utilized the RIM-1 DL dataset in two different versions: RIM-1 Hospital and RIM-1 Randomly. To enhance the image quality and increase accuracy, we used median filtering pre-processing techniques in this work. We also used fundus images to automatically diagnose glaucoma using six different Image-net-trained models (DenseNet121, DenseNet169, VGG19, InceptionV3, ResNet50, and MobileNetV2). The six CNN architectures mentioned above are modified using normalization, inception, and global pooling layers in the model with the pre-trained classification models. Results indicate that the DenseNet-121 is the most accurate model in this work, improving accuracy in the RIM-1 Hospital dataset from 70.68 to 80.13 and in the RIM-1 randomly generated dataset from 79.31 to 87.67 after the models were fine-tuned. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Neural network models

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ViT-SENet-Tom: machine learning-based novel hybrid squeeze–excitation network and vision transformer framework for tomato fruits classification

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Neural Computing and Applications 2025年第9期37卷 6583-6600页

作者： Swapno, S M Masfequier Rahman Nobel, S. M. Nuruzzaman Islam, Md Babul Bhattacharya, Pronaya Mattar, Ebrahim A. Department of CSE Bangladesh University of Business and Technology Dhaka1216 Bangladesh Department of Computer Modeling Electronic and System Engineering UNICAL Rende Italy Department of Computer Science and Engineering Amity School of Engineering and Technology and Research and Innovation Cell Amity University West Bengal Kolkata India Robotics - Cybernetic College of Engineering University of Bahrain Manama Bahrain

Tomatoes are essential fruits in numerous nations for their vast demand. It is very important to maintain the freshness of tomatoes. One of the primary challenges in the recent culinary landscape is accurately identifying healthy tomatoes while effectively eliminating damaged or rejected ones. Existing approaches employ various strategies for categorizing tomato fruit, but they often suffer from inaccuracies, slow detection, and suboptimal performance. Thus, motivated by this gap, in this paper, we propose a novel machine learning (ML) framework, ViT-SENet-Tom, which is a hybrid vision transformer (ViT) model with squeeze and excitation (SENet) block network for fast, accurate, and efficient tomato fruit classification. The framework works on three tomato classes, respectively, the ripe, unripe, and reject. In developing the proposed model, we utilized advanced and newly designed layers and functions. This integration created a more complex and sophisticated neural network, significantly enhancing efficiency and contributing to the model’s novelty. Our chosen dataset was small initially, but we implemented augmentation techniques to increase its size. This approach made our system more reliable, efficient, and effective. The hybrid ViT-SENet framework employs encoders and self-attention networks with squeeze and excitation channel functions to allow precise, robust, fast, and efficient tomato classification. In simulation, the framework achieves a training accuracy of 99.87% and validation accuracy of 93.87%, indicating the precise classification of tomatoes. Besides, this work tests accuracy using fivefold cross-validation. The highest accuracy seen at fold-5 is 99.90%. These testing results demonstrate the efficacy of the proposed framework in real-deployment scenarios. The implementation has the potential to provide enhanced and more sustainable food security and safety in future. © The Author(s) 2025.

关键词： Fruits

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Empowering students and Enabling Accessibility- A survey on Personal Voice Assistant for students Education and Enhanced Accessibility 15

Empowering Students and Enabling Accessibility- A survey on ...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Yadav, Shreya Dhingra, Gauri Gupta, Sarang Paulraj, Betty Department of Computer Science and Engineering Amity School of Engineering and Technology Uttar Pradesh Noida India

ISBN: (纸本)9798331300579

Personal voice assistants have revolutionized how people interact with technology by enabling effortless access to a variety of services by making the user experience natural and more effective using Artificial Intelligence (AI) and Natural language processing (NLP) technologies to understand spoken instructions and reply the information with the most appropriate information or actions that are appropriate to the current context. The voice assistant can now be the ultimate time saver thanks to its wide range of functionalities, which range from basic tasks like setting alarms and playing music on YouTube to more advanced ones like understanding text through paragraphs and responding to any questions inside it using NLP. Personal voice assistants have shown great promise in changing human-computer interaction and promoting a more comfortable, personalized, and natural experience with computing. © Grenze Scientific Society, 2024.

关键词： students

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