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Women Infertility: A Systematic Review of Effects and Causes

SN computer science 2023年第2期4卷 103页

作者： Sahana Devi, K.J. Yendapalli, Vamsidhar Department of Computer Science and Engineering GITAM School of Technology Bengaluru GITAM University Karnataka Bengaluru India

Recently, infertility has been affecting a large number of women than men. It is the male or female reproductive system’s disease. Consequently, after 12 months or more of usual insecure sexual intercourse, if they fail to attain pregnancy then that failure is defined as infertility. As per the National Institute of Child Health and Human Development, people in the USA have infertility issues that are found in 11% of women along with 9% of men. Women are more fertile in their 20s, while in their 30s it reduces to half of it. After 35 years, the probability of getting pregnant is diminished in women. For the woman to get pregnant, the proper functioning of the ovaries, fallopian tubes, and uterus is required. So, infertility occurs due to any issues with the above body parts. Women's infertility is caused by many factors, but the most vital cause is polycystic ovary syndrome (PCOS), which is a hormonal disorder commonly found among reproductive-aged women. So, women infertility, effects of women infertility, causes of women infertility, PCOS and detection of PCOS by general and machine learning (ML) techniques had been discussed in this paper. The accuracy attained in the detection of PCOS utilizing ML techniques is analyzed. Among people as of the top five responding countries that incorporate the USA, UK, Australia, India, together with the Philippines, the percentage of women with physician-assured PCOS against women exclusive of PCOS is analyzed. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Causes Detection of PCOS Effects ML techniques PCOS Women infertility

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Convolutional Neural Network driven Electroencephalogram Characterization for Robust and Efficient Schizophrenia Diagnosis 3

Convolutional Neural Network driven Electroencephalogram Cha...

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3rd International Conference on Emerging Trends in Electrical, Control, and Telecommunication engineering, ETECTE 2024

作者： Iqbal, Haseeb Khan, Salaar Tahir, Anoosha Ramzan, Hamza Biomedical Engineering Department University of Engineering & Technology Pakistan School of Science & Engineering Lahore University of Management Sciences Pakistan Buch International Hospital Multan Pakistan Computer Science Department University of Engineering & Technology Pakistan

ISBN: (纸本)9798331504861

Complex psychiatric disorders such as Schizophrenia show symptoms like disorganized thinking and hallucinations, typically rooted in abnormal brain activity. Early detection of such abnormal brain activity significantly improves diagnosis and subsequent treatment. To that end, Electroencephalogram (EEG), a non-invasive technique that records electrical activity in the brain is often used to record information about these cerebral anomalies. EEG data can be analyzed using various techniques to facilitate Schizophrenia diagnosis. In that respect, traditional machine learning techniques have been extensively used. However, traditional machine learning techniques necessitate pre-processing and extensive feature extraction, which can often considerably vary from one dataset to another. In contrast, deep learning techniques are better suited to classify between normal and schizophrenic cases, owing to their sophisticated network architecture and their ability to resolve complex data. In this work, we leverage a convolutional neural network (CNN) to characterize EEG data from two cohorts to facilitate schizophrenia diagnosis. We report a 96.4% accuracy of classification with fewer network parameters which beats the current gold standard and shows promise for effective and robust detection and subsequent Schizophrenia diagnosis based on EEG data. ©2024 IEEE.

关键词： Electroencephalography

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Deeply‐Recursive Attention Network for video steganography

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CAAI Transactions on Intelligence technology 2023年第4期8卷 1507-1523页

作者： Jiabao Cui Liangli Zheng Yunlong Yu Yining Lin Huajian Ni Xin Xu Zhongfei Zhang College of Computer Science and Technology Zhejiang UniversityHangzhouChina School of Software Technology Zhejiang UniversityNingboChina College of Information Science and Electronic Engineering Zhejiang UniversityHangzhouChina Shanghai SUPREMIND Technology Co. Ltd.ShanghaiChina College of Intelligence Science and Technology National University of Defense TechnologyChangshaChina Department of Computer Science Binghamton UniversityBinghamtonNew YorkUSA

Video steganography plays an important role in secret communication that conceals a secret video in a cover video by perturbing the value of pixels in the cover *** is the first and foremost requirement of any steganographic *** by the fact that human eyes perceive pixel perturbation differently in different video areas,a novel effective and efficient Deeply‐Recursive Attention Network(DRANet)for video steganography to find suitable areas for information hiding via modelling spatio‐temporal attention is *** DRANet mainly contains two important components,a Non‐Local Self‐Attention(NLSA)block and a Non‐Local Co‐Attention(NLCA)***,the NLSA block can select the cover frame areas which are suitable for hiding by computing the correlations among inter‐and intra‐cover *** NLCA block aims to effectively produce the enhanced representations of the secret frames to enhance the robustness of the model and alleviate the influence of different areas in the secret ***,the DRANet reduces the model parameters by performing similar operations on the different frames within an input video *** results show the proposed DRANet achieves better performance with fewer parameters than the state‐of‐the‐art competitors.

关键词： data privacy video processing

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Machine Learning Based Personality Classification for Carpooling Application

Machine Learning Based Personality Classification for Carpoo...

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2023 International Conference on Intelligent Systems for Communication, IoT and Security, ICISCoIS 2023

作者： Anas, Mohd Gunavathi, C. Kirubasri, G. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Sona College of Technology Department of Computer Science and Engineering Tamil Nadu Salem India

ISBN: (纸本)9798350335835

Sharing a ride in vehicle is known as carpooling and many businesses are adapting this system. Carpooling is an effective way to control air pollution since it decreases the number of automobiles on busy roads. It also helps to reduce traffic roadways, allowing automobiles to drive faster. Carpooling reduces the pollution even further because vehicles use less gas when they travel without much stopping in the traffic. People may also drive their automobiles whenever they choose, with no need for cheaper maintenance and parking charges for each vehicle. According to studies, each car in a carpooling system takes up to 11 vehicles off the road. This cuts yearly pollution by 13 metric tonnes. In India, if carpooling done correctly, one single car will take around 25 automobiles off the road. To make carpooling easier, in this work, a machine learning (ML) model is utilized to identify the personality types of customers based on the tweets and to group them for the same car with similar personalities. The idea intends to break down this barrier between people who don't like to travel with the people who have different personality. In this model, Natural Language Processing (NLP) is used to deal with the dataset before passing it to the model to train. The proposed work is done using XG-Boost, Decision Tree Classifier, Support Vector Machine, and Stochastic Gradient Decent optimizer. Pre-processing the dataset employed the NLP techniques such as Tokenization, Lemmatization and Stemming. After evaluating all the algorithms, we found out that XG-Boost is outperforming all the remaining three algorithms by achieving 68% accuracy easily with 5-fold cross validation. © 2023 IEEE.

关键词： Stochastic systems

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Privacy Enhanced Mobile User Authentication Method Using Motion Sensors

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computer Modeling in engineering & sciences 2024年第3期138卷 3013-3032页

作者： Chunlin Xiong Zhengqiu Weng Jia Liu Liang Gu Fayez Alqahtani Amr Gafar Pradip Kumar Sharma Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesShenzhen518052China Sangfor Technologies Inc. Shenzhen518055China School of Data Science and Artificial Intelligence Wenzhou University of TechnologyWenzhou325035China College of Computer Science&Technology Zhejiang University of TechnologyHangzhou310023China Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Math&Computer Science Department Faculty of ScienceMenofia UniversityShebin El-KomEgypt Computing Science Department University of AberdeenAberdeenUK

With the development of hardware devices and the upgrading of smartphones,a large number of users save privacy-related information in mobile devices,mainly smartphones,which puts forward higher demands on the protection of mobile users’privacy *** present,mobile user authenticationmethods based on humancomputer interaction have been extensively studied due to their advantages of high precision and non-perception,but there are still shortcomings such as low data collection efficiency,untrustworthy participating nodes,and lack of *** this end,this paper proposes a privacy-enhanced mobile user authentication method with motion sensors,which mainly includes:(1)Construct a smart contract-based private chain and federated learning to improve the data collection efficiency of mobile user authentication,reduce the probability of the model being bypassed by attackers,and reduce the overhead of data centralized processing and the risk of privacy leakage;(2)Use certificateless encryption to realize the authentication of the device to ensure the credibility of the client nodes participating in the calculation;(3)Combine Variational Mode Decomposition(VMD)and Long Short-TermMemory(LSTM)to analyze and model the motion sensor data of mobile devices to improve the accuracy of model *** experimental results on the real environment dataset of 1513 people show that themethod proposed in this paper can effectively resist poisoning attacks while ensuring the accuracy and efficiency of mobile user authentication.

关键词： Mobile authentication blockchain federated learning smart contract certificateless encryption VMD LSTM

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Monolithic Fabrication of On-Paper Self-Charging Power Systems Through Direct Ink Writing 60

Monolithic Fabrication of On-Paper Self-Charging Power Syste...

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60th Annual Microelectronics and Packaging Conference and Exhibition, NordPac 2024

作者： Su, Yingchun Fu, Yujie Chen, Shiqian Li, Zheng Xue, Han Li, Jiantong School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Electrum 229 Kista16440 Sweden

ISBN: (纸本)9789189896925

The rapid development of emerging electronics requires power sources with the advantages of lightweight, high efficiency, and portability. Considering the use of critical raw materials (such as Li, Co, etc.) and the increasing global concern of battery waste, self-charging power systems (SCPSs) integrating energy harvesting, power management, and energy storage devices have been envisioned as promising solutions to replace traditional batteries to avoid the use of toxic materials and the need of frequent recharging/replacement. Up to date, the reported SCPSs still hold the problem of large form factor, unscalable fabrication, noble materials, and material complexity. In our work, a highly stable and eco-friendly organic conductive ink based on poly(3,4-ethylenedioxythiophene): poly(styrenesulfonate) (PEDOT:PSS) has been developed for monolithic fabrication on-paper SCPSs almost fully through a simple direct ink writing (DIW) process. The ink possesses multiple functions and enables to directly print almost all the key components in the SCPSs, including electrodes for triboelectric nanogenerators (TENGs, mechanical energy harvesters), electrodes for micro-supercapacitors (MSCs, energy storage devices), and interconnects, on the same paper substrate in a monolithic manner without the need for "post-integration". The monolithic printing process exhibits excellent upscaling capability for manufacturing. In particular, the direct patterning merit of the DIW process offers great flexibility in optimizing the system performance through adjusting the cell number, electrode dimension, and thickness of the MSC arrays. By adjusting the cell numbers, the MSC arrays attain high-rate capability up to 50 V/s to match the pulsing electricity produced from the TENGs. For small-size printed SCPSs (~ 2 cm × 3 cm ×1 mm), after continuous press and release of the TENGs for ~79000 cycles, the 3-cell series-connected MSC array can be charged to 1.6 V while 6-cell array to 3.0 V. For a lar

关键词： Electrodes

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Exploring Machine Learning Models for Duplicate Question Detection in Online Communities 5

Exploring Machine Learning Models for Duplicate Question Det...

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5th IEEE International Conference on Advances in Electronics, computers and Communications, ICAECC 2023

作者： Bharathi Mohan, G. Kumar, R. Prasanna Elakkiya, R. Veer, V. Druva Amrita Vishwa Vidyapeetham School of Computing Computer Science and Engineering Chennai India Institute of Technology and Science School of Computing Computer Science and Engineering Birla Dubai345055 United Arab Emirates

ISBN: (纸本)9798350305449

Duplicate question detection is a crucial task in question-and-answer platforms, aiming to improve user experience and minimize redundancy. This project utilizes NLP techniques and machine learning models to predict whether a pair of questions are duplicates accurately. The project involves comprehensive data pre-processing, including tokenization, stopword removal, and lemmatization, to prepare the textual data for analysis. Here we are using various feature engineering techniques, such as bag-of-words, TF-IDF, and word embedding, are applied to extract meaningful representations of the questions. Multiple machine learning models, including Logistic Regression, Random Forest, SVM, XGBoost, and a Combined model, are trained on labeled data. Evaluation metrics focus on precision and recall minimizing misclassification costs. New sample data is used to validate the models. Predictions are made to classify question pairs as duplicates or non-duplicates, showcasing the model's generalization capabilities. The results highlight the potential of NLP techniques and machine learning models in accurately identifying duplicate question pairs, improving knowledge sharing, and reducing redundancy. This project provides valuable insights and methodologies for future research in duplicate question detection, contributing to the field of NLP and enhancing user experiences on question-and-answer platforms. © 2023 IEEE.

关键词： Embeddings

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Grape Guard:A YOLO-based mobile application for detecting grape leaf diseases

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Journal of Electronic science and technology 2025年第1期23卷 60-75页

作者： Sajib Bin Mamun Israt Jahan Payel Md.Taimur Ahad Anthony S.Atkins Bo Song Yan Li Department of Computer Science and Engineering Daffodil International UniversityDhaka1207Bangladesh Department of Computer Science University of Southern QueenslandToowoomba4350Australia Faculty of Digital TechnologyInnovationand BusinessStaffordshire UniversityStoke-on-TrentST42DEUnited Kingdom School of Engineering University of Southern QueenslandToowoomba4350Australia School of Mathematics Physics and ComputingUniversity of Southern QueenslandToowoomba4350Australia

Grape crops are a great source of income for *** yield and quality of grapes can be improved by preventing and treating *** farmer’s yield will be dramatically impacted if diseases are found on grape *** detection can reduce the chances of leaf diseases affecting other healthy *** studies have been conducted to detect grape leaf diseases,but most fail to engage with end users and integrate the model with real-time mobile *** study developed a mobile-based grape leaf disease detection(GLDD)application to identify infected leaves,Grape Guard,based on a TensorFlow Lite(TFLite)model generated from the You Only Look Once(YOLO)v8 model.A public grape leaf disease dataset containing four classes was used to train the *** results of this study were relied on the YOLO architecture,specifically YOLOv5 and *** extensive experiments with different image sizes,YOLOv8 performed better than ***8 achieved 99.9%precision,100%recall,99.5%mean average precision(mAP),and 88%mAP50-95 for all classes to detect grape leaf *** Grape Guard android mobile application can accurately detect the grape leaf disease by capturing images from grape vines.

关键词： Bacterial diseases Grape Guard Mobile-based application YOLOv5 YOLOv8

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Augmentative Fusion Network for Robust RGBT Tracking 16

Augmentative Fusion Network for Robust RGBT Tracking

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2024 16th International Conference on Graphics and Image Processing, ICGIP 2024

作者： Hong, Fanghua Wang, Jinhu Lu, Andong Wang, Qunjing School of Electronic and Information Engineering Anhui University Hefei230601 China School of Computer Science and Technology Anhui University Hefei230601 China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

RGBT tracking aims to take full advantage of the complementary advantages of RGB and thermal infrared (TIR) modalities to achieve robust tracking in complex scenes. However, current approaches face limitations when dealing with the quality-imbalanced problem. In this paper, we introduce a novel augmentative fusion learning framework that aims to maximize the potential of existing fusion modules in modality quality imbalanced scenarios. In particular, we design a modality stochastic degradation strategy to improve the robustness of the fusion module in modality quality imbalance scenarios. Meanwhile, to further enhance the fusion performance with the modality quality imbalance inputs, a self-supervised constraint is introduced to reconstruct the modality features before degradation by combining high-quality modality and degraded modality information. Finally, the effectiveness of the proposed method is verified by evaluating it on two standard RGBT datasets and two state-of-the-art algorithms. And the results indicate that our method achieved superior performance without adding any parameters or computational complexity. © 2025 SPIE.

关键词： RGB color model

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BPCoach: Exploring Hero Drafting in Professional MOBA Tournaments via Visual Analytics

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Proceedings of the ACM on Human-computer Interaction 2024年第CSCW1期8卷 1-31页

作者： Liu, Shiyi Ma, Ruofei Zhao, Chuyi Li, Zhenbang Xiao, Jianpeng Li, Quan School of Information Science and Technology ShanghaiTech University Shanghai China Department of Computer Science Tufts University MedfordMA United States Guangzhou Qucheng Culture Media Co. Guangzhou China School of Information Science and Technology ShanghaiTech University Shanghai Engineering Research Center of Intelligent Vision and Imaging Shanghai China

Hero drafting for multiplayer online arena (MOBA) games is crucial because drafting directly affects the outcome of a match. Both sides take turns to "ban"/"pick" a hero from a roster of approximately 100 heroes to assemble their drafting. In professional tournaments, the process becomes more complex as teams are not allowed to pick heroes used in the previous rounds with the "best-of-N" rule. Additionally, human factors including the team's familiarity with drafting and play styles are overlooked by previous studies. Meanwhile, the huge impact of patch iteration on drafting strengths in the professional tournament is of concern. To this end, we propose a visual analytics system, BPCoach, to facilitate hero drafting planning by comparing various drafting through recommendations and predictions and distilling relevant human and in-game factors. Two case studies, expert feedback, and a user study suggest that BPCoach helps determine hero drafting in a rounded and efficient manner. © 2023 Copyright held by the owner/author(s).

关键词： Visualization

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