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Facial Recognition and Machine Learning-based Student Attendance Monitoring System 3

Facial Recognition and Machine Learning-based Student Attend...

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3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Narkhede, Namrata Menon, Anupama Mathane, Itisha Nikam, Sayali Dange, Suchita A.P. Shah Institute of Technology Department of Computer Engineering Thane India

ISBN: (纸本)9798350338607

AI-based student monitoring system provides an innovative approach that enables schools and institutions to monitor student attendance performance and behavior. By utilizing cutting-edge technologies like face recognition, the system makes it easier for teachers to keep track of students' attendance while also giving administrators useful information. The technology automatically logs student attendance, doing away with the necessity for manual human attendance taking. Each student's attendance rate is determined, and the attendance analysis is made available. Administrators can more easily identify students with strong or poor attendance records thanks to the visualization module of the system, which presents attendance data as a graphical representation. Administrators can use this function to ensure that students attend classes on a regular basis. Additionally, the system notifies students and their parents through email when a particular level of attendance is not met, encouraging students to improve their attendance by taking appropriate action and fostering accountability. The AI-based student monitoring system is a useful tool for teachers and students. It helps to raise academic attainment by promoting an accountability and attendance culture in organizations and institutions. The system also provides administrators with useful data and minimizes the effort for educators. © 2023 IEEE.

关键词： Face recognition

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Analysis and Estimation of Discounts on Product Prices in E-Commerce Sites 5

Analysis and Estimation of Discounts on Product Prices in E-...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Julian, Anitha Maheswar, Challa Ramyadevi, R. Murali Dhar, M.S. Selvi, S. Saveetha Engineering College Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Department of Computer Science and Engineering Chennai India R.M.K Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383522

Sales on e-commerce platforms are significantly influenced by discounts. When a discount is offered, 80% of buyers who initially show disinterest end up making a purchase, per a Retail Me-not survey. This increases revenue and draws in new clients who might not have otherwise thought about making a purchase. Another survey by Accenture reveals that 75% of consumers are more inclined to choose a brand that provides personalized discounts. To retain customers, e-commerce websites can implement tailored discount strategies based on customers' past purchases or preferences. The decision-making process for discounts involves various factors. Pricing strategies and discounts, as identified in the study, are pivotal in influencing consumer behavior and encouraging purchases, particularly among new customers. This research delves into the complexities of discounting practices and their impact on the online retail ecosystem. Employing a comprehensive methodology that combines quantitative and qualitative techniques, the study investigates the frequency, trends, and effectiveness of discounts across different e-commerce platforms. Through extensive data collection, including details on discount percentages, durations, and consumer feedback, the research aims to reveal insights into the strategic implementation of discounts. The study's main objective is to analyze discounts that are given on a variety of products by gathering information from an online store and using the characteristics, reviews, and ratings of the products as factors to calculate the discount value. © 2024 IEEE.

关键词： data analysis discount analysis EDA estimation techniques feedback and reviews predictive modelling

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A Deep Learning Approach for Gaussian Noise-Level Quantification

A Deep Learning Approach for Gaussian Noise-Level Quantifica...

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International Conference on Robotics, Control, and computer Vision, ICRCCV 2022

作者： Yadav, Rajni Kant Singh, Maheep Kumain, Sandeep Chand Department of Computer Science and Engineering National Institute of Technology Uttarakhand Srinagar India

ISBN: (纸本)9789819902354

Image noise and its reduction have been of active interest over the last few years due to the increasing application of Image processing in various domains such as medical, video processing, robotics, etc. This paper presents a Deep Learning (Convolutional Neural Network) approach to classify an image into different levels of Gaussian noise it has. Most of the image-denoising models are based on the assumption that the images to be processed have some level of noise in them. The proposed model evaluates whether the image has noise present in it and also quantifies the amount of noise the image has. The MSRA 10K dataset has been used to train, test, and validate the model. Ten levels of Gaussian noise have been introduced to the dataset which represent the ten classes for the model and one class with a clean image. The model has achieved an accuracy of 96%. The proposed model has proven to be very effective in identifying the level of Gaussian Noise present in the image. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image denoising

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Enhancing Human Action Recognition through advanced Motion Descriptors and Machine Learning 1

Enhancing Human Action Recognition through advanced Motion D...

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1st International Conference on Optimization Techniques for Learning, ICOTL 2023

作者： Ramalingam, Vijay Saravanan, D. Vignesh, S. Rahin Batcha, R. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350328042

Understanding human activities from videos presents a formidable challenge in computer Vision. Intelligent video systems primarily specialize in automatically recognizing human actions within video sequences and annotating them accordingly;human action identification involves recognizing actions associated with one or more objects based on analysis of their activities and context. As technology has proliferated rapidly, huge amounts of human-centric video data have accumulated;it has become an obstacle in contemporary video processing systems due to identifying all these actions efficiently. Complex backgrounds compound these challenges, creating error margins that severely hamper many object detection algorithms and creating error margins that compromise performance - particularly with regard to occlusion and distortion issues. This work addresses these obstacles using Apache Spark with in-memory computing and distributed environment for improved recognition of human actions;specifically, a Local Motion Descriptor is employed for feature extraction that calculates intensities between frames that include both motion and appearance elements. Spark MLlib employs random forest to effectively recognize human behavior. Experimental studies are performed to evaluate its activity recognition capabilities and results are benchmarked against popular supervised classification techniques like Decision Trees;results demonstrate that our proposed model outshone other classification techniques, confirming its efficacy in activity recognition. © 2023 IEEE.

关键词： Decision trees

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Autonomous Search and Rescue Agent Using Deep Reinforcement Learning

Autonomous Search and Rescue Agent Using Deep Reinforcement ...

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2023 International Conference on Recent Advances in Science and engineering technology, ICRASET 2023

作者： Rampriya, R.S. Alam, Mohammed Junaid Jayan, Sri Vellore Institute of Technology School of Computer Science Engineering and Information Systems Tamilnadu Vellore India Madras Institute of Technology Department of Computer Technology Tamilnadu Chennai India

ISBN: (纸本)9798350306927

Disaster management is one of the areas of paramount importance for any government or organization. This process gets very tedious and tricky for some specific type of disasters. Deploying manpower for mitigating the disaster and saving the victims is a risky decision because the life of the people in the disaster management team is put to risk, which is a very unfortunate thing. So instead of using manpower, we can deploy intelligent machine agents, which are equivalent or sometimes more efficient than human beings for such highly risky tasks and avert the loss of lives. In our case, we have specifically considered the disaster wherein fire breaks out in a building and people get trapped inside due to the dangerous situation. In this case, where deploying manpower to rescue them requires a high quality protective gear and can even put their lives on risk, we have proposed to deploy intelligent machine agents that are trained to figure out the path on their own and find their way to the victims after which a rescue operation can be performed by safely escorting the victims to a nearby exit in the building. The agent in the proposed application has been evaluated by the metrics such as the time taken to reach the victims and come back to the exit, the total rewards gathered by the agent during its exploration in the environment and the number of correct decisions it made during its journey. The agent takes an average time of 145.6 seconds to complete its operation in the real-time environment. It gathered an average peak cumulative reward value of -0.2 in the real time environment. The agent on an average, records 67.3% chance of making a correct decision (that gets a positive reward) given a state vector. © 2023 IEEE.

关键词： Autonomous agents

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Preserving Service Availability Under DDoS Attack in Micro-Service Based Cloud Infrastructure 16

Preserving Service Availability Under DDoS Attack in Micro-S...

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16th International Conference on Security of Information and Networks, SIN 2023

作者： Kumar, Anmol Agarwal, Mayank Indian Institute of Technology Department of Computer Science & Engineering Patna801103 India

ISBN: (纸本)9798350343212

Distributed denial of service (DDoS) attacks target the availability of the victim's services. DDoS attacks, being resource-consumption attacks, create heavy resource contention. In the state of the art, we found that resource isolation for legitimate users assisted in maintaining service availability even in the presence of DDoS attacks. As the networks are moving towards micro-service architecture, DDoS attack on these architecture can lead to disruption of services. In this work, we implement a micro-service architecture using container based environment. We use the threshold connection and micro-service architecture to preserve service availability under DDoS attack. The threshold connection will check for the active connection of distinct web pages, and micro-service architecture helps in serving those different requests on different containers. We classify those users whose number of requests is greater than the threshold connection as attacker and the rest of them as benign users. Also, we classify the target web page into two categories: high resource consumption web pages and low resource consumption web pages based on their resource consumption. We serve the requests for both pages in different containers. Our experimental results show that even in the presence of a massive DDoS attack, our proposed mechanism is able to preserve the availability of the target service. The proposed methodology leads to failure of only 8 benign requests as compared to 499 under state-of-the-art. It is imperative to emphasize that the proposed technique should not be regarded as a DDoS detection instrument but rather as a supplementary component to an existing detection solutions. © 2023 IEEE.

关键词： Network security

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Utilizing Machine Learning Models to Determine the Security Level of Different Cryptosystems 7

Utilizing Machine Learning Models to Determine the Security ...

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7th International Conference on Trends in Electronics and informatics, ICOEI 2023

作者： Pranathi, Kantipudi Priya, Bodepudi Lakshmi Felix, A. Yovan Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350397284

Due to recent developments in multimedia technology, digital data security has emerged as an important concern. To improve upon the state of the art in terms of security, researchers often recommend modifications to procedures that have previously been implemented. However, many suggested encryption algorithms have proved unsafe over the previous several decades, putting sensitive data at risk. It is crucial to use the most suitable encryption strategy to defend against such attacks;nevertheless, the type of data being protected might affect the method that is most appropriate for each given situation. However, systematically evaluating various cryptosystems to choose the optimal one may consume significant computational effort. To rapidly and reliably choose the appropriate algorithm, an SVM (support vector machine) is proposed as a security-level identification tool for photo encryption algorithms. In this research, a dataset was compiled with the help of common encryption security criteria, including Security, Peak signal to noise ratio, Homogeneity, Correlation, Contrast, Energy, Entropy, and Mean Square Error. These values are used as extracted characteristics from various cypher pictures. There are three tiers of security for dataset labels: strong, acceptable, and weak. For evaluating the effectiveness of the proposed model, the calculated accuracy and results demonstrate the value of this SVM system. © 2023 IEEE.

关键词： Support vector machines

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An Efficient way of Emotion and Gesture Recognition using Deep Learning Algorithms 5

An Efficient way of Emotion and Gesture Recognition using De...

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5th IEEE International Conference on Electrical, computer and Communication Technologies, ICECCT 2023

作者： Periyanayaki, M. Duraisamy, M. Duraisamy, Premkumar Natarajan, Yuvaraj KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Government Arts and Science College Department of Computer Science Tirupattur India

ISBN: (纸本)9781665493604

Researchers in psychology, computer science, linguistics, neurology, and allied fields have become more interested in a human-computer interface system for autonomous face recognition or facial expression recognition. An Automatic Facial Expression Recognition System (AFERS) has been suggested in this study. Three steps comprise the proposed methodology: face detection, feature extraction, facial expression recognition, and gesture recognition. Face detection's initial steps include YCbCr-color skin colour detection, illumination correction for uniformity on the face, and morphological procedures for maintaining the necessary face region. Using the AAM (Active Appearance Model) approach, the first phase's output is utilized to extract facial features such as the mouth, nose, and eyes. Automatic facial expression recognition, the third stage, entails straightforward. Using the AAM (Active Appearance Model) approach, the first phase's output is utilized to extract facial features such as the mouth, nose, and eyes. © 2023 IEEE.

关键词： Face recognition

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ACR2UNet: Semantic Segmentation of Remotely Sensed Images using Residual-Recurrent UNet and Asymmetric Convolutions

ACR2UNet: Semantic Segmentation of Remotely Sensed Images us...

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2023 IEEE Applied Sensing Conference, APSCON 2023

作者： Putty, Aarabhi Annappa, B. National Institute of Technology Karnataka Department of Computer Science and Engineering Surathkal India

ISBN: (纸本)9781665461634

Land-use and land-cover (LULC) mapping is one of the significant components in environmental monitoring. LULC mapping, necessary to manage the vital resource of land, has been achieved, in recent years, by segmenting remotely sensed images (RSIs). A standard paradigm for segmentation is UNet, and this paper proposes a novel asymmetric convolutional residualrecurrent UNet architecture, which utilizes the power of asymmetric convolutions as well as residual and recurrent techniques for mapping RSIs. The proposed methodology has a couple of additional advantages. First, asymmetric convolution operations strengthen the square kernels and enhance the semantic feature space. Further, a recurrent network assists in providing rich local contextual information with the help of residual inputs. The presented model is evaluated on the WHDLD dataset for LULC segmentation and is found to achieve an improvement of 1-2% in the mIoU score compared to state-of-the-art methods. © 2023 IEEE.

关键词： Convolution

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Image Classification of Ischemic Stroke Blood Clot Origin using Stacked EfficientNet-B0, VGG19 and ResNet-152 3

Image Classification of Ischemic Stroke Blood Clot Origin us...

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3rd International Conference on Secure Cyber Computing and Communications, ICSCCC 2023

作者： Rao, Masabathula V S Raghavendra Puligundla, Surya Ekkala, Nikhil Sai Chebrolu, Srilatha National Institute of Technology Department of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9798350300710

Stroke continues to be the second-leading cause of mortality globally. Over 700,000 Americans suffer from an ischemic stroke every year as a result of a blood clot clogging a brain artery. The chances of the patient surviving a second stroke where recurrent strokes account for 23% of all incidents, are decreased. The motivation of this work is to help physicians better understand the causes of blood clots in fatal strokes, making it simpler for doctors to recommend the most effective post-stroke therapeutic management and lowering the risk of a second stroke. Whole slide digital pathology images capture the blood clot of the patient who has suffered from ischemic stroke. The objective of this work is to classify the blood clot origin in the case of ischemic stroke. Its a binary classification problem where the classes are Cardioembolism and Large artery atherosclerosis. Deep learning models can be applied to classify the stroke origin efficiently. This work proposes a stacked deep learning model with VGG19, ResNet-152, EfficientNet-B0 to classify the stroke origin. Experiments have been conducted on Mayo Clinic - Strip AI dataset from Kaggle. Comparisons have been made with these individual models and also combinations of these models and found that the proposed model is achieving lower loss value. © 2023 IEEE.

关键词： computer vision

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