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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Ganesh, D. Chaudhary, Chetan Verma, Pradeep Kumar Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India College of Engineering Teerthanker Mahaveer University Uttar Pradesh Moradabad India

ISBN: (纸本)9798350383348

Exploring Low Voltage and coffee strength strategies for high-performance ASIC layout is a study mission that focuses on building and developing new and modern strategies that allow for the optimization of semiconductor design systems. Low Voltage and low strength (LVLP) techniques are increasingly utilized to reap the ultimate intention of ASIC designs: maximum overall performance with minimum power requirements. If you want to attain this, numerous interrelated parameters have to be considered. These parameters are chip length, circuit efficiency, device power dissipation, area/overall performance alternate-offs, and clock velocity. This research assignment explores various LVLP design techniques that may be used to optimize ASIC designs and bring about progressed overall performance. Those techniques consist of dynamic voltage scaling, clock gating, lively electricity control, and adaptive body biasing. These strategies can be evaluated in opposition to today's enterprise practices, and the handiest one might be followed in subsequent tiers of this study's task. The output of this research task will permit ASIC designers to construct complex designs that have the maximum overall performance at the lowest feasible power consumption as compared to the existing counterparts. This stepped-forward efficiency will allow ASIC designers to build merchandise that might be of greater value and powerful in addition to being less complicated to manufacture and debug. © 2024 IEEE.

关键词： Adaptive Construct Consumption Counterparts Maximum Optimization Strategies

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INTELLIGENT AUTONOMOUS PARKING SYSTEM INTEGRATING RFID AND IoT FOR SMART CITIES

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2024年第2期83卷 65-77页

作者： Pushgara Rani, D. Prasanna, R. Surenderanath, S. Kalaiselvi, V.K.G. Vaishnavi, V. Rithika, P. Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Ramapuram Campus Chennai India Department of Information Technology Sri Sai Ram Engineering College Chennai India

An autonomous parking system is the essential innovation for stress free car parking in smart cities. The efficient use of resources is a key goal of smart cities, and their integration with technology has improved productivity and dependability for urban infrastructure. In this paper, an efficient automated parking system integrating Internet of Things (IoT) and radio frequency identification (RFID) technologies is proposed. The proposed system is integrated with IoT in which the consumers can book the parking spots before arriving to the destination—drastically reducing the time taken by the consumers in searching for the parking slot. The proposed system effectively saves time for the consumers and also significantly provides solutions for all the parking-related issues faced by the management of flats, theaters, malls, hospitals, etc. In management perspective, this system will provide better use of their resources and reduce the amount of labor needed to maintain these parking lots. The automated parking system overcomes the important issue of jamming near the gates of parking spots by improving the check-in and check-out process of vehicles. The RFID tags given to the drivers reduce the time for vehicle identification as it is done automatically. The proposed system avoids long queues at the gates, unnecessary traffic jams near the gates, reduces the waiting time, and ensures security by allowing only the registered users identified through RFID tags. Thus, the proposed system will be a sophisticated system for the consumers, drivers, and a boon to mall owners and management. © 2024 by Begell House, Inc.

关键词： Internet of things

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Enhancing Road Safety: An Intelligent Drowsiness Detection System Based on Deep Neural Networks 7

Enhancing Road Safety: An Intelligent Drowsiness Detection S...

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7th IEEE Pune Section International Conference, PuneCon 2024

作者： Nalavade, Jagannath E. Sachdeo, Rajneeshkaur Kale, Dattatray R. Buchade, Amar Subhedar, Mansi Shinde, Swapnil K. Mit Art Design and Technology University Computer Science and Engineering Pune India BRACT's Vishwakarma Institute of Information Technology Department of Artificial Intelligence and Data Science Pune India Pillai Hoc College of Engineering and Technology Electronics and Computer Science Rasayani India

ISBN: (纸本)9798331527822

The aim of this research is to create an intelligent vehicle system that can effectively mitigate the risks associated with drowsy driving, thereby preventing road accidents. Various factors like driver exhaustion, challenging road conditions, and adverse weather contribute to motor vehicle crashes, leading to over 1.35 million deaths annually, as estimated by entities such as the World Health Organization (WHO). To achieve this, the development of a robust warning system is crucial. In this study, we proposed an alert system to detect drowsy drivers. It utilizes a video stream process (VSP) to analyze the gaze's eye-aspect ratio (EAR) and geometric distance through an innate reflex model. Additionally, a facial landmark-based algorithm is employed for accurate eye recognition. When driver drowsiness is detected, the system issues a warning message, along with crash impact and location information, and activates the voice surveillance system. The suggested technique establishes a non-invasive approach for evaluating driver weariness in connection with accident severity. The camera, the fundamental element of this system, continuously acquires facial landmarks recognized by designated locations and analyzes them. Drivers who are distracted and have poor lighting may show signs of sleepiness. © 2024 IEEE.

关键词： Deep neural networks

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Comparing Boosting and Bagging Algorithms for Image Classification

Comparing Boosting and Bagging Algorithms for Image Classifi...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Chaudhary, Chetan Gupta, Arun Murugan, R. Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India College of Engineering Teetthanker Mahaveer University Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

Boosting and bagging are famous ensembles gaining knowledge of algorithms for photo classes. Each algorithm depends upon combining a couple of classifiers to enhance generalization accuracy, at the same time as using specific techniques to generate a final model and boosting works by using training more than one vulnerable classifier iteratively, deciding on new classifiers that concentrate on miscategorized examples from earlier rounds. Bagging then uses the same classifier with extraordinary subsets of the education dataset to generate more than one classifiers that are then blended. In phrases of generalization accuracy, boosting algorithms outperform bagging due to their capability to focus more on tough-to-classify examples. However, boosting can be more computationally intensive in phrases of education time than bagging because it continually updates and trains new vulnerable classifiers. Furthermore, boosting algorithms can also be afflicted by low bias and low variance problems, making them vulnerable to overfitting. In conclusion, boosting and bagging are effective strategies for picture type, with boosting algorithms presenting superior generalization accuracy and bagging algorithms being computationally greener. Relying on the task, the perfect method may be determined. © 2024 IEEE.

关键词： algorithm computationally generalization miscategorized vulnerable

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NAARI- A Portable Sanitary Napkin Vending Machine 2

NAARI- A Portable Sanitary Napkin Vending Machine

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Shetty, Chinmai Malathi, S.Y. Sarojadevi, H. Ranjan Kumar, H.S. Suchetha, G. Debnath, Sanjoy Kumar Dept of Information Science and Engineering Nitte India Kle Institute of Technology Dept of Computer Science and Engineering Hubballi India Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India Shri Madhwa Vadiraja Institute of Technology and Management Bantakal Udupi India Sahyadri College of Engineering and Management-Affiliated to Vtu Department of Information Science and Engineering Mangalore India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

ISBN: (纸本)9798350368413

In an effort to address the challenges of menstrual hygiene management in developing countries, a portable sanitary pad vending machine was developed. The vending machine is powered by an Arduino Uno microcontroller and a power source that ranges between 5 to 12 volts. It features an liquid-crystal display (LCD) keypad, token acceptor with an Radio Frequency Identification (RFID) scanner for validation, and a L298d motor driver that powers two DC Servo motors to turn the spring coil system that delivers the sanitary pads or disposable bags to the user. The entire system is enclosed in a metal box for protection against damage. The LCD keypad serves as an input and output device, displaying information about the vending machine and providing instructions for its use. With the token acceptor and RFID scanner, only authorized users are granted access to the vending machine. The scanner reads the user's RFID card, which has a unique identification number, to confirm their authorization. The portable vending machine is designed to be cost-effective and easy to use, providing an innovative solution to menstrual hygiene management challenges. It is also portable and can be relocated to various locations. Overall, the vending machine serves as a critical resource in promoting menstrual hygiene and addressing the challenges that come with it. Its use ensures that individuals have access to essential sanitary products that they need to live healthy lives. © 2024 IEEE.

关键词： Liquid crystal displays

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Performance Analysis of Machine Learning Based On Optimized Feature Selection for Type II Diabetes Mellitus

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Multimedia Tools and Applications 2025年第8期84卷 4945-4964页

作者： Bhat, Salliah Shafi Ansari, Gufran Ahmad Ansari, Mohd Dilshad B. S. Abdur Rahman Crescent Institute of Science and Technology Chennai48 India Department of Computer Science and Software Engineering University of Hail Hail55424 Saudi Arabia Department of Computer Science & Engineering SRM University Delhi-NCR Harayana Sonepat131029 India

Type 2 Diabetes Mellitus (T2DM) is a common chronic illness caused by variations in the secretion of insulin. T2DM will be treated early with the help of an early diagnosis reducing the risk of early death and controlling is course of illness. In this Paper Machine Learning Algorithms (MLA) for T2DM diagnosis are proposed. The major objective of this research is to assess use of MLA and its methods to forecast different illnesses at an initial stage. In this research, we examine most recent proposed strategies with some limitations and potential improvements for future work. The data is collected from Kaggle (University of California) *. Researchers highlight how important is to identify important features that enhance outcomes suggested by recent methods. Six MLA were used in this paper such as Logistic Regression (LR), Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT), Random Forest (RF) and K-Nearest Neighbour (KNN) were applied in Jupiter Notebook. Feature sets are selected using three feature selection methodologies such as information gain-based selection, correlation-based selection and sequential feature selection. On these feature subsets a variety of MLA are applied and optimal feature subset is chosen based on their performance. Finally a DT is suggested as the top model among the top six performances. The suggested method provides an improved performance label with an average accuracy of 96.10%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature Selection

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A Comprehensive Review Analysis on Osteoarthritis Using Deep Learning and Machine Learning Technologies for Arthritis Detection 7

A Comprehensive Review Analysis on Osteoarthritis Using Deep...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Thomas, Tinu Prince Mary, S. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India St. Joseph's College of Engineering and Technology Department of Electronics and Computer Engineering Palai India

ISBN: (纸本)9798350372816

Osteoarthritis is a prevalent degenerative knee joint disease, causing considerable pain and mobility issues, thus impacting the independence and quality of life of millions. Deep learning and Machine Learning techniques have emerged as promising tools for enhancing the diagnosis, prognosis, and treatment of osteoarthritis (OA). These technologies allow for the extraction of valuable insights from medical images, clinical data, and genetic information, thereby improving patient care and management strategies. By employing advanced algorithms, healthcare professionals can improve early detection, and treatment plans, and accurately predict disease progression, ultimately leading to better outcomes for OA patients. This paper proposes a comprehensive review of various DL-based and ML-based methods for OA, spanning the years 2020 to 2024. It aims to provide valuable insights for researchers interested in exploring different models and potentially inspiring the development of integrated approaches for even more effective OA detection. © 2024 IEEE.

关键词： Joints (anatomy)

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Hybrid Method for Monitoring Sundarbans Mangrove Forest Using Satellite Images 2

Hybrid Method for Monitoring Sundarbans Mangrove Forest Usin...

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2nd International Conference on information and Communication technology, ICICT 2024

作者： Al Naim, M.D. Abdullah Sarar, Shihab Hasan, Mahdi Tasin, Md Maruf Hossain Farrok, Omar Department of Civil Engineering Ahsanullah University of Science and Technology Bangladesh Department of Computer Science and Engineering Ahsanullah University of Science and Technology Bangladesh Department of Electrical and Electronic Engineering Ahsanullah University of Science and Technology Bangladesh

ISBN: (纸本)9798331508227

A hybrid method for monitoring Sundarbans mangrove forest using satellite images with machine learning techniques is presented in this paper. The Random Forest algorithm, data scaling, cloud, and shadow masking are used to enhance the precision of land cover classification over the Sundarbans. The study utilizes Landsat 8 imagery to classify land into deep forest, light forest, barren land, and water bodies. Significant fluctuations in land cover types over the analyzed period, with notable variations in forest density, barren land, and water bodies are identified. Normalized Difference Vegetation Index and Normalized Difference Water Index are two popular methods for observing vegetation health and water bodies in remote sensing. But it fails to classify different objects. This paper focuses on solving this issue using machine learning technique by combining the two methods along with the other band’s reflectance as a feature to improve the classification and show a great f1 and recall. This study uses a hybrid model to monitor land cover changes in the Sundarbans mangrove forest from 2013 to 2022, achieving an overall accuracy of 0.98. Significant findings include a deep forest cover increase from 52.82% to 55.735%, fluctuations in barren land from 2.04% to a peak of 8.38% in 2021, and a decrease in water bodies from 32.18% to 28.82%, indicating possible changes in river dynamics and hydrological conditions. ©2024 IEEE.

关键词： Landsat

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An Extended Deep Learning and Feature Pyramid Based Network for Screening CT-Scans Images 6

An Extended Deep Learning and Feature Pyramid Based Network ...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Pandey, Dhiraj Pandey, Kavita JSS Academy of Technical Education Department of Information Technology Noida India Jaypee Institute of Information Technology Department of Computer Science Engineering & IT Noida India

ISBN: (纸本)9798350304480

Deep convolution neural Network is basically using a methodology to teach computer systems in the same way as humans learn by using examples and through experiences. It is a method of solving exhaustive image processing problems by using a heuristic-based approach. The objective of this research is to develop an extended deep learning-based hybrid method to detect abnormal symptoms using CT-scan Images. The detection of abnormal in any infected people is the most challenging issue due to less medical facility and time required to confirm its correctness based on the test conducted. Many AI-based approaches are suggested by researchers but most of the work is not integrated as a working model because of its correctness and accuracy level. The proposed work is implemented using Python and TensorFlow library. For experimentation, the CT-scan dataset is used with two different classes like normal or abnormal in nature. The proposed hybrid approach takes an input of CT-SCAN images from the selected data sets, converts it into grayscale, and then into. tiff format to get a more specific feature suitable for preprocessing step of the deep convolution network. Feature pyramid network has also been used on the selected images which provides better results in terms of low-level features in the deeper convolution networks. This hybrid methodology is helping us to get better results in comparison to state of art available. The implemented extended deep CNN network can accurately recognize varieties of real test cases CT-scans images. The learning accuracy of the hybrid method of RESNET101V2 with Feature pyramid network (FPN) and preprocessed dataset is up to 98.68 percent and it is maximizing the standard available state of art technique by a good margin in terms of result accuracy. © 2023 IEEE.

关键词： Abnormal computer Vision Deep Convolution Neural Network Feature pyramid network Image classification Residual Network

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Temperature-dependent wakeup behavior in back-end-of-line compatible ultra-thin HfxZr1-xO2 ferroelectric film

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science China(information sciences) 2025年第6期68卷 216-217页

作者： Xiaopeng LI Lu TAI Xiaoyu DOU Yang FENG Dong ZHANG Xuepeng ZHAN Jixuan WU Xiao GONG Jiezhi CHEN School of Information Science and Engineering Shandong University Beijing Superstring Academy of Memory Technology Department of Electrical and Computer Engineering National University of Singapore

HfO2-based ferroelectric（FE） devices have emerged as promising candidates for next-generation non-volatile memories（NVMs）, owing to their excellent CMOS compatibility, robust scalability, and low operating voltage requirements [1]. Among them, ultra-thin HfxZr1-xO2（HZO） FE films are particularly attractive for back-end-of-line（BEOL）integration in monolithic 3D memory architectures.

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