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Design and Implementation of a GPS-GSM Based Women Safety Device for Combating Sexual Assaults 5

Design and Implementation of a GPS-GSM Based Women Safety De...

引用

5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Haque, Md Arfanul Rahman, Md Ashikur Ferdows, Rubayea Ahmed, Fuad IUBAT-International University of Business Agriculture and Technology Dept of Computer Science and Engineering Bangladesh Dept of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350394290

In recent times, women have been facing a multitude of challenges, particularly the alarming rise in sexual assaults. Such acts of violence have a profound impact on the lives of the victims, affecting not only their physical well-being but also their psychological balance. To address this pressing issue, this work proposes a compact and portable women's safety solution that addresses the rising concern of sexual assaults. It utilizes GPS and GSM technology to provide a reliable and cost-effective means of protection. The device, which incorporates a GPS receiver, Microcontroller, and GSM Modem, allows women to activate an alarm in case of danger. By pressing a dedicated button on the device, it sends an SMS with the woman's location to a predefined contact using GPS and GSM. It also features a shock function as an added layer of protection if someone attempts harm. Furthermore, the system enables users to send distress messages to law enforcement services, enhancing their safety. An SMS reply system is integrated for user convenience, allowing individuals to inquire about the device's location. This comprehensive solution empowers women to safeguard themselves and promotes their safety, making it a valuable asset in today's challenging times. © 2023 IEEE.

关键词： Accident prevention

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LiCT-Net: Lightweight Convolutional Transformer Network for Multiclass Breast Cancer Classification

LiCT-Net: Lightweight Convolutional Transformer Network for ...

引用

2024 IEEE Region 10 Conference, TENCON 2024

作者： Babita Nayak, Deepak Ranjan Malaviya National Institute of Technology Dept. of Computer Science and Engineering Jaipur India

ISBN: (纸本)9798350350821

Classification of multiclass breast cancer through histopathological images is indispensable and poses daunting challenges due to color inconsistencies, high appearance variations, and large inter-class similarities. Regardless of the success of traditional convolutional neural networks (CNNs) and vision transformers (ViTs), they often fail to capture intricate patterns and local contextual information, respectively, while demanding high computational resources and data requirements. To mitigate these issues, this paper proposes a lightweight convo-lutional transformer network named LiCT-Net for multiclass breast cancer classification. The LiCT-Net introduces a local-global spatially-aware transformer layer and integrates it with a pre-trained FastViT model to effectively capture global and local contextual features, thereby facilitating learning fine-grained and intricate lesion patterns from histopathological images. The LiCT-Net is validated on a benchmark dataset and the experimental results and comparative analysis demonstrate its effectiveness. In specific, it achieves a higher accuracy of 96.16 %, 95.62 %, 95.25 %, and 94.21 % on 40×, 100×, 200×, and 400× magniflcations respectively, © 2024 IEEE.

关键词： Convolution

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Transformer-based Self-Supervised Imputation and Attention GANs Oversampling for Medical Data Processing 31

Transformer-based Self-Supervised Imputation and Attention G...

引用

31st Annual IEEE International Conference on High Performance Computing, Data, and Analytics, HiPC 2024

作者： Singh, Aryan Kumar Saikia, Arpit Baro, Pranita Borah, Malaya Dutta Dept. of Computer Science and Engineering National Institute of Technology Silchar Silchar India

ISBN: (纸本)9798331509095

Dealing with missing data and class imbalance is a necessity for the development of a solid predictive model in medical research. This work presents a novel hybrid dynamic method devoted to addressing these dual challenges, specifically focusing on medical datasets. In this work, we investigate the incompleteness and imbalance of the dataset using two sophisticated measures: the Incompleteness Factor and the Imbalance Ratio. These metrics are used to guide customized approaches. A transformer-based imputation method is employed to learn complex data dependencies to impute missing data. Second, the oversampling method of Self-Attention Enhanced Conditional Generative Adversarial Network is implemented. This technique generates better model training results by accommodating self-attention mechanisms that create fake samples that follow the actual data distributions as well as class distributions. This paper introduces one potential solution to obtain model-level accuracy increase for tasks like disease prediction, patient prognosis, etc., over medical datasets. The proposed method aims to remove the measurement biases caused by incompleteness and class imbalance and preprocess the data to enhance the credibility and clinical relevance of the experimental results. © 2024 IEEE.

关键词： Generative adversarial networks

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Active Control for Resonance Mitigation in Electric Drive Applications 15

Active Control for Resonance Mitigation in Electric Drive Ap...

引用

15th International Symposium on Industrial Electronics and Applications, INDEL 2024

作者： Berglund, Emelie Borg, Johan Johansson, Jonny Luleå University of Technology Dept. of Computer Science Electrical and Space Engineering Sweden

ISBN: (纸本)9798350352320

The use of inverter-based motor controllers that can generate fast voltage level transitions is increasing in the field of electric drive systems because of their ability to achieve higher efficiency. However, the fast voltage transitions, dv/dt's, are prone to generate bearing currents and cause overvoltages that may harm the electric motor. An LC filter applied between the inverter and the motor terminals can reduce the dv/dt's. However, the LC filter will cause overvoltage due to resonance but this can be overcome by introducing an extra pulse at each pulse-width modulation (PWM) transition. The timing of this extra pulse is crucial for suppressing the resonance, but due to nonlinearities in a real system, the time instants may vary over time and are thus in need of control. In this study, an analytical expression for the optimal pulse location and duration is derived and we propose a control algorithm that continuously calculates the optimal pulse switch time instants based on the measured output voltage. The control algorithm can handle sudden system disturbances and a Monte Carlo simulation is performed to evaluate the performance of the algorithm. This hybrid filter successfully reduces dv/dt's at the motor terminals while simultaneously eliminating overvoltages due to the filter resonance. © 2024 IEEE.

关键词： Control nonlinearities

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A Novel Method to Detect Oral Carcinoma Using Box Annotation Based on YOLO Model

A Novel Method to Detect Oral Carcinoma Using Box Annotation...

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2024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems, iCACCESS 2024

作者： Laila, Shahara Ema, Romana Rahman Galib, Syed Md. Jamil, Abu Rafe Md Uddin, A. F. M. Shahab Jashore University of Science and Technology Dept. of Computer Science and Engineering Jashore Bangladesh

ISBN: (纸本)9798350350289

Oral carcinoma affects millions of individuals globally and is a major public health problem due to its high fatality rate. Treatment effectiveness depends on early detection and improves survival rates. An effective method for automated oral cancer screening is deep learning. The paper's primary goal is to use deep learning techniques to advance the state of oral cancer diagnosis, specifically YOLOv5 and YOLOv7. We aimed to achieve accurate detection by leveraging bounding box annotations for both two distinct types of data: collected clinical data and benchmark data of oral carcinoma. The proposed methodology encompasses data collection, preprocessing, expert-driven bounding box annotation and the use of cutting-edge deep learning models for lesion identification, YOLOv5 and YOLOv7. Bounding box serving as expert annotations are meticulously added to pinpoint cancerous regions within the images. Subsequently, a comprehensive evaluation is conducted, measuring the performance of both models based on precision, recall rate (sensitivity) and mean average precision (mAP). Our findings reveal that YOLOv5 outperforms YOLOv7 in terms of oral cancer detection, demonstrating superior precision and recall rates. In the evaluation of this research, the YOLOv5 model demonstrated strong performance metrics on both clinical and lip and tongue cancer datasets. For the lip and tongue cancer dataset, the YOLOv5 model achieved a precision of 97.2%, a mAP (mean Average Precision) of 97.7% and a recall of 96.3%. © 2024 IEEE.

关键词： Clinical research

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Comprehensive Women's Health Risk Assessment Platform 2

Comprehensive Women's Health Risk Assessment Platform

引用

2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Navya, S. Saha, Sharmistha Nagarajan, Deepa Sathyabama Institute of Science and Technology Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350349221

The goal of this research is to develop a machine learning-based method for predicting several diseases in women, with an emphasis on polycystic ovarian syndrome (PCOs), diabetes, heart disease, and breast cancer. Timely and accurate prediction of these disorders is essential for early intervention and prevention due to their impact on women's health and prevalence. Predictive models are trained using a wide variety of medical data, including genetic markers, lifestyle factors, demographic data, and clinical test findings. To find patterns and connections between these predictors and the incidence of diseases, machine learning methods are used, such as decision trees, random forests, and support vector machines. The models are able to forecast with accuracy the probability that a woman would develop one or more of these diseases by efficiently obtaining and evaluating data from these datasets. Metrics including accuracy, precision, recall, and F1-score are used to assess the models' performance;the outcomes demonstrate the models' promising levels of predictive power. The creation of a system like this can facilitate targeted screening and preventive measures, assist medical practitioners in identifying high-risk individuals, and eventually lead to better patient outcomes. © 2024 IEEE.

关键词： Diseases

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Architectural Design for AdhaarAssist - A Forward-Looking Lock-Free STM Solution for Adhaar

Architectural Design for AdhaarAssist - A Forward-Looking Lo...

引用

2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Jafar, Sana Pranveer Singh Institute of Technology Dept. of Computer Science and Engineering Kanpur India

ISBN: (纸本)9798350385199

Any parallel program is judged based on its efficiency in doing multiple tasks at the same time without getting stuck due to an underlying problem. The main purpose of a parallel program is to effectively and quickly solve a large and complicated problem at hand. This is why;parallel programs form the basis of many powerful supercomputers where parallel computing is supported through special hardware which is complemented with parallel software achieving hundreds of petaflop/s of performance. The biggest challenge for a parallel programmer is to build a program that has a minimum tradeoff for increased parallelism and good concurrency control among the parallel tasks. One of the biggest applications of parallelism is the huge, ever-increasing database of Adhaar biometric. Its crucial to have a robust, fast and efficient way of accessing and manipulating this data so that along with faster results, consistency of the database is also maintained. This paper proposes a schema AdhaarAssist which is a lock-free STM based algorithm that will work on a shared in-memory data structure. Some of its features are increased concurrency by lazy conflict detection, partial aborts that optimize the algorithm for maximum throughput when complex and lengthy transactions are employed, memory barriers and timestamping. The algorithm proposed in this paper will open the way for leveraging it for distributed-shared in-memory databases like the one used by Adhaar. © 2024 IEEE.

关键词： Concurrency control

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Design of Blockchain-based Secured Election Voting System

Design of Blockchain-based Secured Election Voting System

引用

2024 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2024

作者： Chinchamalatpure, Sumedh V. Kapse, Smita R. Balki, Aditya K. Rahangdale, Vaishnavi A. Fulzele, Vidisha K. Telrandhe, Shital Yeshwantrao Chavan College of Engineering Computer Technology Dept. Nagpur India India

ISBN: (纸本)9798350348460

The modern electoral process faces numerous challenges concerning security, transparency, and inclusivity. Traditional voting systems have struggled to keep pace with the demands of a rapidly evolving digital landscape. In response to these challenges, this paper proposes a novel approach to develop and implement an IoT and blockchain-based voting system (machine), aimed at providing unparalleled security and transparency to the electoral process. The proposed voting machine leverages the power of the Internet of Things (IoT) to enable secure and efficient voter authentication. Utilizing biometric data and unique identifiers associated with IoT devices, the system ensures accurate voter identification, mitigating the risk of fraudulent activities. By incorporating IoT technology, the voting process becomes more accessible, accommodating a broader range of citizens. The implementation of such a voting machine promises to transform the electoral landscape, ushering in a new era of secure and transparent elections. With real-time vote counting and results, citizens can have immediate access to election outcomes, bolstering public confidence in the integrity of the democratic process. This paper outlines the design, architecture, and security measures that underpin IoT and blockchain-based voting machines. © 2024 IEEE.

关键词： blockchain Electoral Voting System IoT IoT and Blockchain-based electronic voting Transparency

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Enhancing Brain Tumor Classification Using Data-Efficient Image Transformer

Enhancing Brain Tumor Classification Using Data-Efficient Im...

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2024 International Russian Smart Industry Conference, SmartIndustryCon 2024

作者： Yang, Jiafeng Rusak, Alena Belozubov, Alexander ITMO University Software Engineering and Computer Technology Dept. Saint-Petersburg Russia

ISBN: (纸本)9798350395044

Medical image analysis is essential in the diagnosis and treatment of many diseases, including brain tumors. This work investigates the effectiveness of transformer models in the multiclass classification of brain tumor MRI images. We use a combination of three publicly available brain tumor datasets classified into four categories and appropriately modify existing transformer variants, including adjustments to patch size, number of categories, dropout, etc. The results indicate that, due to the condition of insufficient data volume, most transformer variants are still inferior to the benchmark model VGG-16. The DeiT model with knowledge distillation has significantly improved various indices on the test set, verifying its effectiveness in the classification of brain tumor MRI images under limited data. The article supports the application of transformer-based models to medical image analysis. Follow-up work will verify the generalization performance of the model on other datasets and explore methods to reduce the dependence of the transformer on the data, making it more applicable to tasks such as medical images classification. © 2024 IEEE.

关键词： Distillation

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Tuberculosis Diagnosis in Chest X-Rays via Transfer Learning Techniques

Tuberculosis Diagnosis in Chest X-Rays via Transfer Learning...

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2024 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2024

作者： Keerthana, T. Jayakumar, K. Iyyappan, V. Shopika, S. Sowmiya, X.L. Ponnusamy, R. Chennai Institute of Technology Dept. of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (数字)9798331535476

ISBN: (纸本)9798331535476

Tuberculosis (TB) remains one of the world's major health problems. Detection with Chest X-rays is a mainstay tool in the early diagnosis of TB. Traditional methods used in the conventional detection of TB are very slow and susceptible to human errors. Thus, there is a need for an automated technique. The paper discusses the possibility of using advanced deep learning techniques, particularly techniques stemming from transfer learning, to enhance the detectability as well as efficiency of TB. Fine-tuned pre-trained convolutional neural networks, such as ResNet and VGG, on a dataset of chest X-ray images extracted high-level features relevant to the detection of TB. Superhuman performance was achieved with limited medical imaging data by transferring learned representations from large-scale image classification tasks. Experimental results illustrate high accuracy, precision, and recall rates, confirming that transfer learning is adequate for automating TB detection. This approach reduces the need for manual intervention and offers a scalable, cost-effective solution to enable rapid TB screening, particularly in low-resource settings. The novelty of the work comes from using transfer learning with fine-tuned pre-trained models (ResNet and VGG) in getting high generalization accuracy for tuberculosis detection from chest X-rays, even with limited data. © 2024 IEEE.

关键词： Convolutional neural networks

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