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Automated 20-20-20 Timers Using Facial Detection and Facial Recognition

Automated 20-20-20 Timers Using Facial Detection and Facial ...

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India Council International Subsections Conference (INDISCON), IEEE

作者： Saumodip Das Geetanjali Pal Somsubhra Manna Arnab Dhara Anindya Sen Department of Electronics and Communication Engineering Heritage Institute of Technology Kolkata INDIA Department of Computer Science and Engineering Heritage Institute of Technology Kolkata INDIA

This paper proposes two automated 20-20-20 timers using facial detection and facial recognition respectively. computer Vision Syndrome (CVS) is a common phenomenon these days, resulting in eye irritation and headaches. A possible prevention of CVS is adopting the 20-20-20 rule. Two types of 20-20-20 timer exist in the market, but both have scopes of improvement. While one type of timer needs to be manually set by the user like a stopwatch, the other type of timer detects the ON -screen time of monitors as a parameter. Both these models fail to detect whether the user is actually looking at the screen, and interrupt the work schedule of the users. The authors have developed a smart automated 20-20-20 timer that uses haar-cascade classifiers for detecting the face and accordingly adapt to the situation. This timer is able to detect the facial features in real time, and alert the user after 20 minutes of screen time. Furthermore, the authors have developed another smart 20-20-20 timer using YOLOv5s. This timer can recognise multiple users looking at the screen and run the timer independently for each person. While the haar-cascade enabled timer is preferred for homes and personal laptops, desktops and PCs; the YOLOv5s enabled timer is suited for offices, schools, colleges and libraries. This paper proposes an automated smart 20-20-20 timer system.

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XGC-VQA: A Unified Video Quality Assessment Model for User, Professionally, and Occupationally-Generated Content

XGC-VQA: A Unified Video Quality Assessment Model for User, ...

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2023 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2023

作者： Huang, Xinhui Li, Chunyi Bentaleb, Abdelhak Zimmermann, Roger Zhai, Guangtao Shanghai Jiao Tong University SJTU Paris Elite Institute of Technology Shanghai China Shanghai Jiao Tong University Department of Electronic Engineering Shanghai China Concordia University Department of Computer Science Montreal Canada National University of Singapore Department of Computer Science Singapore

ISBN: (纸本)9798350313154

With the rapid growth of Internet video data amounts and types, a unified Video Quality Assessment (VQA) is needed to inspire video communication with perceptual quality. To meet the real-time and universal requirements in providing such inspiration, this study proposes a VQA model from a classification of User Generated Content (UGC), Professionally Generated Content (PGC), and Occupationally Generated Content (OGC). In the time domain, this study utilizes non-uniform sampling, as each content type has varying temporal importance based on its perceptual quality. In the spatial domain, centralized downsampling is performed before the VQA process by utilizing a patch splicing/sampling mechanism to lower complexity for real-time assessment. The experimental results demonstrate that the proposed method achieves a median correlation of 0.7 while limiting the computation time below 5s for three content types, which ensures that the communication experience of UGC, PGC, and OGC can be optimized altogether. © 2023 IEEE.

关键词： Importance sampling

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Advanced Machine Learning Techniques for Early Diagnosis of Diabetic Macular Edema

Advanced Machine Learning Techniques for Early Diagnosis of ...

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International Conference on electronics, Materials engineering and Nano-Technology (IEMENTech)

作者： Muthuselvi M Suneel Kumar Duvvuri Geetha Rani K Surendran R K S. Balamurugan Department of Computer science and engineering Karpaga Vinayaga College of Engineering and Technology Chengalpattu Tamil Nadu India Department of Computer Science and Applications Government College (Autonomous) Rajahmundry Andhra Pradesh Department of Computer Science and Engineering Jain (Deemed-to-be University) Bangalore India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electronics and Communication Engineering Karpaga Vinayaga College of Engineering and Technology Chengalpattu Tamil Nadu India

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

One consequence of diabetic retinopathy is diabetic macular edema (DME), a condition that effects the eyes of individuals with diabetes. More than 28 million people been affecting by diabetic macular edema due to diabetes, late diagnose of diabetic macular edema leads to vision loss. Available method with deep learning algorithms and artificial intelligence (AI) for prediction of diabetic macular edema is not effective way for diagnosing in early. Proposed a method with XGBoost algorithm for the classification, pre-processing, extraction and detection of a disease by fundus samples. This method resulted in 0.992, 0.989, 0.991 and 0.990 for accuracy, Recall, precision and F1-score respectively.

关键词： Training Diabetic retinopathy Machine learning algorithms Accuracy Medical treatment Transforms Prediction algorithms Diabetes Classification algorithms Medical diagnostic imaging

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A Software Defect Avoidance Technique for E-commerce Website Development 6th

A Software Defect Avoidance Technique for E-commerce Website...

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6th International Conference on Emerging Research in Computing, Information, communication and Applications, ERCICA 2020

作者： Sudarshan, R. Srivatsa, S.K. Chaithra, K.N. Mohan Kumar, K.N. Department of Computer Science and Engineering VELS University Chennai600117 India Department of Electronics Engineering Anna University Chennai600044 India Department of Electronics and Communication Nitte Meenakshi Institute of Technology Bengaluru560064 India Department of Computer Science and Engineering MS Ramaiah University of Applied Sciences Bengaluru560058 India

ISBN: (纸本)9789811613418

As per the literature study, the development of e-commerce websites is done by adopting different methodologies, which does not give due importance to the logical sequencing of the functionality of each module to be developed. This work proposes a technique that helps in producing defect-free e-commerce applications by developing a module dependency functionality matrix, mainly on ATG (Art Technology Group) framework, using rational unified process (RUP). © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： electronic commerce

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Deep Learning-Based Diagnosis System for Liver Cancer Through Multimodal Determination for Early Treatment

Deep Learning-Based Diagnosis System for Liver Cancer Throug...

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Artificial Intelligence and Emerging Technology (Global AI Summit), International Conference on

作者： N. Sabiyath Fatima Ramendra Singh Akuthota Swathi Amar Choudhary Azhar Ahmed Chhaya Sharma Department of Computer Science and Engineering B.S. Abdur Rahman Crescent Institute of Science & Technology Chennai India Department of Computer Science and Engineering (IoT) Raj Kumar Goel Institute of Technology Ghaziabad India Department of Computer Science and Engineering CVR College of Engineering Mangalpalli Rangareddy India Department Electronics and Communication Engineering Alliance College of Engineering and Design (ACED) Alliance University Bengaluru India Department of Computer Science and Engineering KCC Institute of Technology and Management Greater Noida India Department of Computer Science and Engineering Raj Kumar Goel Institute of Technology Ghaziabad India

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

The present research provides an analysis of the development and implementation of a deep learning-based diagnosis system for liver cancer. The provided system was developed using a dataset encompassing 2300 sensor readings, including imaging, clinical, and genomic data. A range of deep learning features was implemented to improve the early detection of liver cancer such as CNN, VGG 16, VGG 19, and Inception V3. The data were divided into two categories: one is for training, and another used for testing. The results of the data analysis have shown that VGG 19 provides the highest diagnostic accuracy of 98.76 % , with VGG 16, Inception V3 and CNN displaying the levels of 94.5%, 93.4%, and 90.25%, respectively. The Model performance analysis was also augmented at the application of precision, recall, and F1 score indicators, as well as using AUC-ROC values which highlighted VGG 19 as the most effective algorithm. Another method of evaluation was the application of confusion matrices for plotting the results based on the number of true positive, true negative, false positive, and false negative detections, where all the samples from the dataset were added to each model calculation. The obtained results have shown the importance of multimodal data for the increase of diagnosis accuracy and revealed the promising potential of advanced computation techniques in medical analytics. In such a way, it can be stated that the developed deep learning models contribute to the ability of healthcare specialists in the definition of liver cancer at early stages, which allows timely interventions and facilitates the development of a range of personalized treatment protocols. The application of such algorithms has the potential to reshape liver cancer diagnostics, where major tasks are associated with further improvement and validation of the developed algorithms based on clinical case studies.

关键词： Deep learning Training Liver cancer Analytical models Accuracy Computational modeling Confusion matrices computer architecture Feature extraction Testing

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An Automated System Based on Informational Value of Images for the Purpose of Classifying Plant Diseases

An Automated System Based on Informational Value of Images f...

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Artificial Intelligence and Knowledge Discovery in Concurrent engineering (ICECONF), International Conference on

作者： B. Jeyapoornima P. Suganthi Shankar K Vadlapati Venkateswara Chowdary Vaddaram R Umesh Sai Bollu Naga Venkat Department of Electronics and Communication Engineering R.M.K. Engineering College Chennai Department of Computer Science and Engineering Sri Sai Ram Institute of Technology Chennai Department of Electronics and Communication Engineering Easwari Engineering College Chennai

The diagnosis of plant illnesses in conjunction with the use of a deep learning algorithm may provide a cost-effective preventative impact, and this method has seen widespread application. Nevertheless, the area of intelligent plant disease detection still struggles with issues such as inadequate data and erroneous categorization. In the current study, an image data contribution assessment approach that is based on the assessment of inter-class resemblance is proposed with the intention of resolving the issues that have been raised. When used in conjunction with an active learning Image selection approach, this method may provide useful guidance for the creation and annotation of database of plant diseases. This can increase the recognition impact while simultaneously lowering the associated costs. Both the inter-classes similarity (ICS) assessment module and the image data contribution Evaluation module are included into the suggested technique. As a result, the categorization of plant diseases will benefit from the additional information they supply. Experiments were performed using a dataset including fine-grained classifications of tomato illnesses so that it could be determined whether or not this strategy is successful. The results of the experiments demonstrate that this strategy is much more effective than others. This study is being done in the realm of classifying different plant diseases. Additional study is recommended for both the detection and the categorization processes.

关键词： Integrated circuits Deep learning Plant diseases Image resolution Costs Databases Knowledge discovery

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Future IoT sensing: Silvaco simulation of graphene-gated p-i-n photosensor on SOI for blue and ultraviolet light

Future IoT sensing: Silvaco simulation of graphene-gated p-i...

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Information Technology (ACIT)

作者： Shonak Bansal Arnav Bansal Mohammad Aljaidi Krishna Prakash Department of Electronics and Communication Engineering Chandigarh University Gharuan India Department of Computer Science and Engineering Chandigarh University Gharuan India Department of Computer Science Zarqa University Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of Electronics and Communication Engineering NRI Institute of Technology Vijayawada Andhra Pradesh India

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

The Internet of Things (IoT) revolutionizes how devices interact and communicate, creating a vast network of interconnected systems with diverse applications, including healthcare, environmental monitoring, and smart cities. A critical component of these IoT systems is the ability to detect and respond to various environmental stimuli, such as light. In this context, photosensors play a pivotal role, particularly in detecting blue and ultraviolet (UV) light, essential for applications like UV index monitoring, flame detection, and secure communications. This paper proposes a lateral p-i-n photosensor design incorporating a single-layer graphene gate, built using silicon-on-insulator (SOI) technology. The device aims to enhance the detection of blue and ultraviolet light. The sensor achieves a larger depletion region with minimal gate bias voltage by employing a graphene gate, leading to superior quantum efficiency. The photosensor exhibits a photocurrent-to-dark current ratio of 1.6×10 5 , a rise time of 2.2 ns, a fall time of 2.0 ns, and an external quantum efficiency of 51.66%. Leveraging graphene's unique optical and electrical characteristics, this gated structure outperforms conventional alternatives. Its compatibility with modern integrated circuit fabrication techniques makes it a promising candidate for future IoT system integration. Simulations conducted using the Silvaco ATLAS platform, a widely used simulation software for semiconductor device modeling and optimization, provide critical insights into the device's operation. The results demonstrate the potential for superior sensitivity and efficiency in IoT-driven blue and UV light detection.

关键词： PIN photodiodes Smart cities Graphene Silicon-on-insulator Voltage Logic gates Software Optical sensors Integrated circuit modeling Optimization

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Smart LPG Monitoring System: Enhanced Safety with Leakage Detection

Smart LPG Monitoring System: Enhanced Safety with Leakage De...

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International Conference on Networking, Embedded and Wireless Systems (ICNEWS)

作者： Varsha P Hotur Prianka R R Lynsha Helena Pratheeba H P Padmavathi M Kottaimalai Ramaraj Thilagaraj M Department of Electronics and Communication Engineering MVJ College of Engineering Bengaluru India Department of Artificial Intelligence and Machine Learning New Horizon College of Engineering Bengaluru India Department of Computer Science and Engineering CMR Institute of Technology Bengaluru India Department of Electronics and Communication Engineering Dayananda Sagar College of Engineering Bengaluru India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India Department of Industrial Internet of Things MVJ College of Engineering Bengaluru India

ISBN: (数字)9798350367461

ISBN: (纸本)9798350367478

Gas leaks can lead to significant accidents causing both material damage and harm to individuals. Two significant factors that contribute to gas leaks are inadequate upkeep of systems and inadequate public awareness. Therefore, the detection of Liquefied Petroleum Gas (LPG) leaks is crucial in order to prevent accidents and safeguard human lives. LPG cylinders are so important to our everyday existence, making it essential for users to keep track of their gas levels to manage consumption effectively. Monitoring usage not only helps users stay informed about their LPG levels but also ensures timely booking of a new cylinder when needed. Given the high flammability of LPG, gas leakage poses serious risks, leading to accidents with potential loss of lives and property damage. To address these concerns, this project introduces a comprehensive LPG level monitoring system with leakage detection. These systems often utilize advanced sensors that can quickly and accurately identify the release of LPG. Additionally, smart systems may integrate with other technologies, such as communication modules, to alert users or authorities in real-time when leakage of gas is identified. Employing a load cell, the equipment constantly monitors the gas content of the cylinder and uses a GSM module to notify the consumer when the replacement needs to be scheduled. Upon receiving confirmation, the device takes appropriate action. Additionally, the system features gas sensors to detect LPG leakage, notifying the user via a GSM module, while activating alarms and exhaust mechanisms for added safety.

关键词： GSM Smart systems Sensor systems Real-time systems Safety Monitoring Petroleum Intelligent sensors Gas detectors Accidents

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Influence of Prestrained Graded InGaN interlayer on the Optical Characteristics of InGaN/GaN MQW-based LEDs

Influence of Prestrained Graded InGaN interlayer on the Opti...

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2022 International Conference on Numerical Simulation of Optoelectronic Devices, NUSOD 2022

作者： Das, Samadrita Lenka, Trupti Ranjan Talukdar, Fazal Ahmed Velpula, Ravi Teja Nguyen, Hieu Pham Trung Crupi, Giovanni National Institute of Technology Silchar Department of Electronics and Communication Engineering Assam788010 India New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States University of Messina Biomorf Department Messina98125 Italy

ISBN: (数字)9781665478991

ISBN: (纸本)9781665478991

In this work, an InGaN/GaN multi-quantum well light emitting diode is designed with different kinds of prestrain layers (InGaN) inserted between the active region and n-GaN layer to demonstrate the effects of piezoelectric polarization on GaN-based LEDs. The device describes a GaN buffer layer which promotes charge injection by minimizing energy barrier between electrode and active layers. Compared to the conventional LED, more than 48.47% enhancement in the efficiency of the LED with prestrain layer can be observed which is attributed to the reduction of polarization field within MQW regions. The proposed device attains a high-efficiency of 81.94%, and minimized efficiency droop of 3.848% at an injection current of 10 mA with 16% In composition and has high-luminous power in the spectral range. © 2022 IEEE.

关键词： Light emitting diodes

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Multichannel One-Time Length Encoding: Exploiting Channel Redundancy

Multichannel One-Time Length Encoding: Exploiting Channel Re...

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2023 IEEE Virtual Conference on communications, VCC 2023

作者： Yin, Hoover H. F. Wang, Xishi Cao, Qi Liu, Shiqiu The Chinese University of Hong Kong Department of Information Engineering Hong Kong The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong N-hop Technologies Ltd Hong Kong Guangzhou Institute Xidian University China Peng Cheng Laboratory China

ISBN: (纸本)9798350318807

In some applications, communication would be terminated shortly due to reasons like security, secrecy, or energy saving. The time interval between two messages can be very long so that the idling interval is longer than sufficient to distinguish the messages. On the other hand, when there are multiple channels available, the channel-use allocation on the channels carries some information. In this paper, we exploit the use of the information carried by the channel-use allocation, specifically, the number of bits sent via each channel, so that we can transmit some extra bits implicitly at no extra cost. We propose a concrete scheme to make use of this advantage. © 2023 IEEE.

关键词： Energy conservation

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