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检索条件"机构=Student of Computer Science and Engineering Amity School of Engineering and Technology"

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Application of Data Mining Methods and Techniques for Diabetes Prediction Using Pima Indian Dataset 3rd

Application of Data Mining Methods and Techniques for Diabet...

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3rd International Conference on Smart Computing and Cyber Security—Strategic Foresight, Security Challenges and Innovation, SMARTCYBER 2023

作者： Arora, Aditi Sehgal, Chirag Sharma, Shilpi Nayak, Soumya Ranjan Kumar, Raghvendra Amity School of Engineering and Technology Amity University Uttar Pradesh India School of Computer Engineering KIIT Deemed to Be University Odisha Bhubaneswar751024 India Department of Computer Science and Engineering GIET University Gunupur India

ISBN: (纸本)9789819705726

Diabetes is a health condition that only occurs when the body either does not use insulin effectively or produces an insufficient amount of insulin from the pancreas. Insulin, a hormone that regulates blood sugar levels, is crucial. Untreated diabetes can lead to hyperglycaemia, a condition characterized by elevated levels of glucose in the bloodstream. This can have a profound impact on several physiological systems such as blood vessels and neurons. computer vision is a crucial component in the realm of human health as it offers precise outcomes and eliminates any human biases. This research aims to improve diabetes classification. The study focused on analysing how data mining and ML techniques compare to traditional algorithms in predicting diabetes accurately. The "Pima Indians Diabetes Dataset" Standard was used, and feature selection was conducted to improve its potential. To assess the performance of the models, Naive Bayes, SVM, and KNN techniques were utilized, and evaluation criteria such as precision, recall, specificity, and mean absolute error were taken into account. Cross-validation was used to enhance accuracy across all models. The results indicated that the SVM method had an accuracy rate of 78%, making it the best classifier for the sample dataset. Thus, this analysis could help predict diabetes more effectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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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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Design and Implementation of Real Time Image Retrieval System for De-Noising Space Images in Chandrayaan 3 Lunar Mission Using Autoencoders 7

Design and Implementation of Real Time Image Retrieval Syste...

引用

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

作者： Shubham, Prince Banerjee, Pallab Thakur, Kanika Kumar, Biresh Mitra, Dipta Gaurav, Kumar Amity University Department of Computer Science Engineering Jharkhand Ranchi India Amity University Amity School of Information Technology Jharkhand Ranchi India

ISBN: (纸本)9798350350067

In the context of the Chandrayaan 3 Lunar Mission, this research paper introduces a real-time image retrieval and denoising system powered by autoencoders, designed to tackle the challenge of noisy space imagery. Leveraging advanced deep learning techniques, our system employs autoencoders to extract essential features from the noisy lunar images and subsequently retrieves and integrates similar, noise-free reference images from a comprehensive database. By doing so, it achieves real-time denoising, ensuring that the mission's acquired lunar images are of high quality, thus facilitating more accurate scientific analysis. The paper details the architecture of the autoencoder-based denoising system, its training process using a meticulously curated lunar image dataset, and its seamless integration into the mission's image processing pipeline. Experimental results underscore the system's remarkable noise reduction capabilities, thereby playing a pivotal role in enhancing the Chandrayaan 3 mission's scientific contributions, ultimately advancing our understanding of the lunar environment and bolstering the success of lunar exploration endeavours. © 2024 IEEE.

关键词： Noise abatement

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A Robust Protocol Design to Perform Data Communication over Wireless Sensor Networks with Enhanced Privacy Preserving Principles 4

A Robust Protocol Design to Perform Data Communication over ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Sathyavani, Bandela Kumar Yadav, Rakesh John Shiny, J. Revathi, R. Bulakh, Ashish Kumar Jain, Shilpa School of Engineering Sr University Department of Eee Telangana Warangal India Amity School of Engineering and Technology Amity University Bihar Patna India Velammal College of Engineering and Technology Madurai India Psgr Krishnammal College for Women Department of Computer Science-Information Technology Coimbatore India Cdgi Department of Computer Science and Engineering Indore India Ies College of Technology Department of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798331523923

the growth of wireless sensor networks has transformed all automation and industrial systems. Nonetheless, guaranteeing safe and privacy-preserving connectivity in sensor networks continues to be a difficult task. Current authentication and routing techniques frequently jeopardize privacy, entail significant design intricacy, also vulnerable to malware assaults. To mitigate these restrictions, we present an innovative secure protocol, the Asymmetric Cryptography-based Modified Cuckoo Search Optimization Protocol (MCuSO) for wireless sensor networks. Our proposed technique impacts asymmetric credentials with belief supervision for the privacy preservation of WSN data. This method attains an equilibrium in the middle of security and efficiency. Through comprehensive simulations utilizing the MATLAB platform, we illustrate that MCuSO attains an average network throughput of 94% and a network residual energy of 89%, in comparison to leading other algorithms. Moreover, the protocol efficiently reduces malware attacks. These findings underscore MCuSO as a viable outcome of resource limited WSN devices, providing an advantageous equilibrium of information security, data integrity and data efficacy. © 2025 IEEE.

关键词： Authentication Protocol

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Real-Time Prediction of Diabetes Complications Using Regression-Based Machine Learning Models 5th

Real-Time Prediction of Diabetes Complications Using Regress...

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5th International Conference on Trends in Computational and Cognitive engineering, TCCE 2023

作者： Tripathi, Abhay Kumar Mishra, Sumita Vasudevan, Shriram Kris Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Lucknow India Department of Electronics and Communication Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Lucknow India Intel India Pvt. Ltd. Bengaluru India

ISBN: (纸本)9789819719228

Diabetes, frequently identified as prolonged sickness, is a set of metabolism sicknesses caused through constantly high blood sugar levels. If correct early prediction is viable, the chance of risk and bareness of diabetic patient can be significantly compact. According to several studies, diseases at early stages are more likely to be cured and prevented from becoming a life-threatening illness. Diabetes is the same in this case, with proper monitoring and controlled daily routine not only it can be prevented but also can be kept under control and avoid any serious impact on patient’s health. Proposed solution leverages various capabilities of machine learning techniques to develop a web app interface which can allow people to easily draw insights about their current likelihood of being a diabetes patient and also provides data about the factors which are affecting this prediction so that the patient can act accordingly. The web app uses a machine learning model as a backend which takes the user inputs and generates the prediction;the framework itself is trained for vast publicly available datasets and uses machine learning techniques to generate prediction. Though model also checks for the parameter which has maximum influence over the prediction and generates the recommendations using Large Language Model such as ChatGPT, the patient can adapt to the lifestyle accordingly and hence control the further decline in health. The objective of the project is to generate insights on real-time data through an interface which is easily accessible and can cater for a wide audience hence contributing toward increasing diabetes awareness and promoting healthy lifestyle which helps people to reduce the risk of being diabetic due to irregularities in the modern chaotic lifestyle. Such a system will also increase the integration of advanced computing tools in daily life allowing everyone to leverage the power of machine learning to monitor and track diabetes effectively. © The A

关键词： Forecasting

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Enhanced image segmentation for cancer images using sparse based classification framework 2

Enhanced image segmentation for cancer images using sparse b...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Rai, Atul Kumar Singh, Archana Yadav, Satya Prakash Research Scholar Amity School of Engineering and Technology Amity University Uttar Pradesh Campus Greater Noida India Amity School of Engineering Technology Amity University UP Noida India Affiliated to Aktu Department of Computer Science and Engineering Greater Noida201306 India

ISBN: (纸本)9798350371055

The correct identification and class of cancerous regions in scientific pictures is vital for diagnosis and treatment planning. This examination recommends an enhanced image segmentation approach with a sparse-based total-type framework for most cancer pictures. Conventional segmentation techniques rely on hand-made functions and require guide tuning, leading to restrained accuracy and generalizability. Our proposed approach uses a sparse representation method, which mechanically learns discriminative functions from the statistics to enhance the accuracy of cancer place segmentation. Moreover, we include a classification framework, primarily based on a help vector device, to refine the segmentation consequences. © 2024 IEEE.

关键词： Identification Mechanically Planning Representation Segmentation

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Reinforcement Learning in Process Scheduling 13

Reinforcement Learning in Process Scheduling

引用

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

作者： Sarkar, Darothi Singal, Swati Institute of Engineering and Management Department of Computer Science and Engineering Kolkata India Amity University Uttar Pradesh Amity School of Engineering and Technology Department of Computer Science and Engineering Noida India

ISBN: (纸本)9781665462631

An efficient scheduling algorithm for a CPU has always been on the lookout by researchers and engineers since the invention of computers. Today most of the tasks are running online facilitated by servers. So, the requirement for fast, efficient, and reliable scheduling algorithms is the need of the hour. However, the traditional heuristic algorithms are having issues like complexity and uncertainty, so it is important to apply innovative technology to improve the task scheduling to ensure the efficient performance of these heavyduty systems. This study aims at developing a new scheduling model that integrates both optimized task scheduling and resource utilization using deep reinforcement learning algorithms. © 2023 IEEE.

关键词： Scheduling algorithms

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Gesture Controlled Mouse Navigation : Hand Landmark Approach 13

Gesture Controlled Mouse Navigation : Hand Landmark Approach

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

作者： Guliani, Shaurya Nagpal, Renuka Sehgal, Rajni Singhal, Abhishek Amity University Uttar Pradesh Amity School of Engineering and Technology Department of Computer Science and Engineering Uttar Pradesh India

ISBN: (纸本)9781665462631

Euclidean Distance has been an important metric for the calculation of distances between points on a scaled space. Recent times have seen an increased use of this metric for the purposes of pattern recognition. Hand Landmarks are a group of twenty-one points which define the skeletal structure of a human's hand. Calculation of distances between Hand Landmarks using Euclidean Distance can cater to the need of an efficient algorithm which could recognize patterns using mathematical calculations. The last few years have witnessed the introduction of technologies which promoted virtualization of important hardware thereby, increasing the portability and handling of the systems. The Mouse is an important aspect of the computer which helps to easily interfacing with the system. History has seen the use of trackballs, laser sensors and track pads as means of controlling the system however, each one of them consisted of costly hardware equipment be it the material or the sensors used. This paper proposes a virtual mouse system which can be controlled and navigated using gestures made using any one hand, whether left or right, without the involvement of any external hardware or sensor. All this is done by means of Euclidean distances and hand landmarks. It allows the user to control all the functioning of the mouse including its movement, mouse clicks and drag and drop without physically touching any hardware. It uses only the system's inbuilt camera to carry-out the functioning of the mouse. The system is carried out using Python along with OpenCV, Mediapipe and the PyAutoGui libraries. All the gestures become visible on the camera screen with appropriate marking to facilitate representing the working of the system. © 2023 IEEE.

关键词： Cameras

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A Disaster Prediction Ensemble Classifier Using Artificial Intelligence

A Disaster Prediction Ensemble Classifier Using Artificial I...

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2024 IEEE International Conference on Modeling, Simulation and Intelligent Computing, MoSICom 2024

作者： Bharathi, V. Saranya, A. Shukla, Garima Shekhawat, Deepika Awasthi, Vanshaj Kumar, C.N.S. Vinoth Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Amity University Amity School of Engineering and Technology Department of Computer Science and Engineering Mumbai India Amity University Department of Computer Science and Engineering Mumbai India Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur Chennai India

ISBN: (纸本)9798331533311

Natural and man-made disasters may have severe impacts, but disaster prediction can help lessen such effects. The natural catastrophes that strike India on a regular basis cause immense devastation in terms of human lives and material possessions. Predicting the start and development of disasters in real-time is essential for reducing their effect. For more precise and trustworthy catastrophe predictions, this research presents an AI-powered catastrophe Ensemble B-Classifier network (DE BCN). The DPEC ensemble technique improves forecast accuracy over individual models by integrating various predictive outputs from numerous machine learning models that specialise in distinct kinds of disasters. This is where the data was originally pulled from Kaggle. To finish off the preprocessing, the norma clash filter was used. The next step is to use the optimisation method known as mustard twin swarm to extract characteristics that are connected to the tragedy. With any luck, the calamity Ensemble-B Classifier network will be able to forecast the calamity. Python and the Kaggle database were the environments used to carry out the investigation. According to the results of the simulations, the suggested approach is better than the current standard for predicting the catastrophic state. © 2024 IEEE.

关键词： Prediction models

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Self-improved COOT Algorithm for Resource Allocation in Cloud Data Centers 4th

Self-improved COOT Algorithm for Resource Allocation in Clou...

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4th International Conference on Computing, Communications, and Cyber-Security, IC4S 2022

作者： Singh, Shubham Singh, Pawan Tanwar, Sudeep Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Lucknow India Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India

ISBN: (纸本)9789819914784

Cloud computing has been developed as a front-line platform for consumer and commercial potential. Additionally, it enables users to access data and apps from any location. Businesses will use cloud services to borrow storage as well as other operations, which significantly lowers the cost of technology. In this aspect, resource allocation is more important to assure the efficiency of the cloud service. The primary goal of this study is to suggest a new resource allocation model that uses optimization to help maximize sales while minimizing cost. An improved k-means clustering with an optimal tuned centroid is used to cluster all workload (the jobs that have been allotted). Here, tasks are clustered as per the consideration of QoS (trust) and task execution time. Subsequent to this, the resource allocation is done optimally under the consideration of power utilization effectiveness (PUE), CPU usage, and execution time, respectively. For solving the optimization problem specified, this work proposes a new self-upgraded coot optimization (SUCO) algorithm. Finally, the proposed SUCO algorithm was evaluated over the other models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： K-means clustering

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