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2024 IEEE International Conference on Cybernetics and Innovations, ICCI 2024

作者： Ruengdat, Sunisa Leklert, Panutchakorn Kongkachandra, Rachada Songmuang, Pokpong Thammasat University Data Science And Innovation College Of Interdisciplinary Studies Bangkok Thailand Thammasat University Department Of Computer Science Bangkok Thailand

ISBN: (纸本)9798350370096

Menstrual cycle prediction is a critical issue for many women, as it can help them plan their daily activities, and prepare for potential physical and emotional changes. However, current methods for predicting menstruation often rely on physiological data such as age, cycle length, and ovulation history, which may not be convenient or accurate for some *** paper presents a comparative study of menstruation prediction models using daily social media sentiment data. The underlying hypothesis is that social media posts can reflect the user's emotions and feelings, which may be related to the menstrual cycle. By tracking emotions from social media posts, it may be possible to monitor potential symptoms associated with the menstrual cycle, such as premenstrual syndrome (PMS) and premenstrual dysphoric disorder (PMDD).The proposed models are evaluated using a dataset of social media posts and self-reported menstruation data. The results demonstrate that the proposed models can effectively predict the onset of menstruation and identify potential symptoms of PMS and PMDD. This work has the potential to contribute to the development of new tools and interventions for women's health and *** experimental research utilized a dataset of posts on X from one woman over a 3-year period (2020-2023), covering both normal and abnormal menstrual cycles. The challenge of this research was to transform ordinary text that could be posted on social media but could not be used for prediction into text that could be used for in-depth prediction and analysis. This was achieved through two processes: 1) Sentiment Analysis using the WangchanBERTa model to determine the sentiment of the posts, which achieved an accuracy of 61.7%;2) the Time Series Forecasting process for predicting the date of menstruation using the Random Forest model was the most appropriate if compared to Linear Regression and SVR, which is considered to be the most accurate. Challenge new faces This is bec

关键词： Forecasting

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Beyond Precision and Recall: Measuring Search Engine Consistency Using Rank Stability

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IEEE Access 2025年 13卷 92242-92259页

作者： Lakshmi, S. Thinakaran, Rajermani Batri, Krishnan Sowrirajan, R. Murugan, Sivaram Jain University Department of Electronics and Communication Engineering Karnataka Bengaluru India INTI International University Faculty of Data Science and Information Technology Negeri Sembilan Malaysia Jain University School of Computer Science and Engineering Karnataka Bengaluru India Dr. N.G.P. Arts and Science College Department of Mathematics Tamil Nadu Coimbatore India Sivas University of Science and Technology Faculty of Engineering and Natural Sciences Department of Computer Engineering Sivas58000 Turkey

Traditional information retrieval metrics such as precision, recall, and F-measure assess document relevance but fail to capture the stability of search rankings. Search engines frequently update their algorithms, leading to variations in retrieved documents and their rankings across identical queries. This study introduces a novel rank correlation measure that quantifies search result consistency by analyzing rank fluctuations in repeated queries, serving as a proxy for temporally separated searches. The proposed approach distinguishes between overlapping and non-overlapping documents, providing a detailed measure of ranking stability. Empirical evaluations comparing Google and Bing reveal that Google exhibits greater rank fluctuations for non-overlapping documents, whereas both search engines maintain relatively stable rankings for overlapping documents. These findings highlight the importance of rank-based evaluation frameworks in search engine analysis, extending beyond conventional retrieval metrics. By incorporating stability assessments, this research enhances the methodology for evaluating search consistency in dynamic web environments, contributing to process innovation in information retrieval (SDG 9: Industry, Innovation, and Infrastructure) and promoting fair, reliable access to information (SDG 16: Peace, Justice, and Strong Institutions). © 2013 IEEE.

关键词： Online searching

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AI Based Criminal Detection and Recognition System for Public Safety and Security using novel CriminalNet-228 4th

AI Based Criminal Detection and Recognition System for Publi...

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4th International Conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Murthy, Jamuna S. Siddesh, G.M. Department of Computer Science and Engineering Ramiah Institute of Technology Bengaluru India Department of Artificial Intelligence and Data Science Ramiah Institute of Technology Bengaluru India

ISBN: (纸本)9789819726103

The recent surge in public space criminal activities underscores the need for an efficient system to promptly detect, recognize, and track criminals. Existing AI-based criminal detection literature, while insightful, has limitations such as the complexity to analyze video data, time, and speed of accuracy when it comes to training the algorithms that call for further advancements. Hence proposed AI-driven system addresses this demand, aiming to automate criminal identification, equipping law enforcement with a potent tool for proactive crime prevention and resolution. The system utilizes the innovative "CriminalNet-228" Convolutional Neural Network (CNN) architecture, meticulously trained on a vast criminal image dataset for enhanced detection accuracy. To bolster face detection, additional computational resources like parallel processing and distributed computing are employed, enabling real-time analysis of extensive CCTV footage. Notably, the system tracks identified criminals, providing law enforcement with real-time situational awareness. CriminalNet-228 achieves an impressive overall Mean Average Precision (mAP) of 0.65 and excels in detecting facial features as small as 4 × 4 pixels, demonstrating its detail-oriented recognition capabilities. When evaluating Proposed CriminalNet-228 in comparison to established state-of-the-art techniques like Fast-RCNN, Yolov7, and AlexNet, it surpassed them in terms of precision, recall, f-measure, and accuracy, achieving an impressive accuracy rate of 99.2%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Face recognition

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EmoCNN: Unleashing Human Emotions with Customized CNN Using Different Optimizers 2

EmoCNN: Unleashing Human Emotions with Customized CNN Using ...

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2nd International Conference on Machine Learning and data Engineering, ICMLDE 2023

作者： Sahana, M. Umesh, Praneetha Kodipalli, Ashwini Rao, Trupthi Department of Computer Science & Engineering Global Academy of Technology Bangalore India Department of Artificial Intelligence & Data Science Global Academy of Technology Bangalore India

A key development towards enhancing computer-human interaction is emotion recognition. This publication describes a technique called EmoCNN, which uses deep learning techniques to precisely identify and classify human emotions, emphasizing improving model performance using different optimizers. Our research intends to contribute to the creation of more effective systems that improve computer-human interaction by solving the problems associated with emotion recognition. By bridging the gap between humans and robots, accurate emotion detection enables systems to perceive emotions for customized and responsive interactions. AI-powered assistants, chatbots, and social robots all benefit from emotion recognition by providing more responsive, empathic and interesting user experiences. Emotion-aware technologies can also enhance user feedback analysis, human-centered design, and monitoring of mental health. Using a human emotion detection dataset, we carried out comprehensive experiments focusing on the happy, sad, and neutral emotion classes. Constructing a customized EmoCNN model with convolutional layers, a hidden layer, ReLU activation, and max-pooling was the focus of our computational work. We investigated various optimizers and evaluated how they affected accuracy, convergence speed and loss minimization. The results demonstrated that the EmoCNN model, which had been trained using the Adam optimizer, gave the best accuracy in distinguishing between emotions. Our paper provides a comparative analysis, highlighting the superiority of EmoCNN over existing models, showcasing its ability to achieve higher validation accuracy (89%) and more efficient emotion recognition when compared to previous approaches with minimal loss. Our research advances the field of emotional computing by demonstrating how well EmoCNN can identify and categorizes various human emotions. This discovery has significant ramifications for the creation of emotion-aware computers, which can better und

关键词： Emotion Recognition

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Routing with Massive Trajectory data 40

Routing with Massive Trajectory Data

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Jensen, Christian S. Yang, Bin Guo, Chenjuan Hu, Jilin Torp, Kristian Aalborg University Department of Computer Science Aalborg Denmark School of Data Science & Engineering East China Normal University Shanghai China

ISBN: (纸本)9798350317152

The unprecedented availability of new types of data coupled with the invention of new technologies combine to enable entirely new or higher-resolution services that in turn enable more rational and data-driven processes. We consider the overall process of vehicular transportation and, more specifically, the process of deciding which route to follow when having to reach a destination. Early solutions modeled a road work as a graph, used sparse in-road sensor data to assign weights to graph edges, and then applied improved versions of Dijkstra's algorithm to find routes with the lowest sums of edge weights. Since then, massive vehicle trajectory data has become available. When coupled with new technologies, this data enables entirely new and higher-resolution routing services that in turn enable better routing. For more than a decade, the authors have engaged in research aimed at exploiting trajectory data to enable better routing. The resulting technologies were developed outside a DBMS. Here, we cover aspects of this research. Further, we challenge the community to develop DBMS support for these and other aspects of routing. © 2024 IEEE.

关键词： database systems

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A Real-Time computer Vision-Based Green Intensity Estimation Method for the Arecanut Tree Pesticide Spraying System

A Real-Time Computer Vision-Based Green Intensity Estimation...

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International Conference on VLSI, Signal Processing, Power Electronics, IoT, Communication and Embedded Systems, VSPICE 2023

作者： Nagaraja Hebbar, N. Sandeep, Bhat Artificial Intelligence and Data Science Department Srinivas Institute of Technology Mangaluru India Computer Science and Engineering Department Srinivas Institute of Technology Mangaluru India

ISBN: (纸本)9789819746569

In India, one of the commercial crops is arecanut. The majority of arecanut growers depend on the arecanut production. However, they are also having a great deal of difficulty finding skilled workers to do pesticide spraying jobs. This effort uses computer vision technologies to address the aforementioned problem. This work compares the HSV estimation with the green pixel estimate approach. This makes it easier to position the robot at the right height, where the pesticide spray will work best. In this case, the HSV estimation identified a better way to manage the robot’s motion. Using a wireless camera, the experimental setup achieves an incredibly innovative performance to capture an image of the arecanut crop. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Pixels

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Diabetes Prediction using Hybrid Machine Learning Techniques

Diabetes Prediction using Hybrid Machine Learning Techniques

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Naik, Bhukya Devendar Spoorthy, Anumula Baherji, Sakshi Rout, Saroja Kumar Sethi, Bijaya Kumar Vardhaman College of Engineering Department of Information Technology Hyderabad India Vardhaman College of Engineering Department of Computer Science & Engineering Data Science Hyderabad India

ISBN: (纸本)9798350373783

The rise in blood glucose levels is the primary factor contributing to the development of diabetes. Given the significance of preventing diabetes or delaying its onset, despite numerous efforts utilizing machine learning for medical diagnostics, there remains a notable gap in research concerning long-term disease prediction, especially for type 2 diabetes. However, the traditional method of diagnosing diabetes involves patients undergoing blood glucose tests administered by doctors, which can be limited by clinical resources. Many patients consequently encounter delays in getting a diagnosis. To create a predictive model for diabetes diagnosis, this study used six traditional machine learning models: boosting, neural networks, decision trees, random forests, logistic regression, and support vector machines. The study employed machine learning (ML) algorithms to predict diabetes using an authentic dataset from Safety Pressure Primary Health Care. With a validation accuracy of 84%, the study offers important new information on who is most likely to develop type 2 diabetes. By precisely predicting the type of diabetes and examining the importance of each indication in the prediction process, the goal of this study is to improve the accuracy of diabetes prediction. © 2024 IEEE.

关键词： Prediction models

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A Survey on Deep Learning Techniques for Secure and Accurate Mobile Sink Position Prediction in Vehicular Pattern WSN 8

A Survey on Deep Learning Techniques for Secure and Accurate...

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8th International Conference on Inventive Systems and Control, ICISC 202

作者： Shareef, D.K. Jyothsna, V. School of Computing Department of Computer Science and Engineering Andhra Pradesh Tirupati India School of Computing Department of Data Science Andhra Pradesh Tirupati India

ISBN: (纸本)9798350386578

This research study presents a comprehensive survey of deep learning methods applied in order to improve the security along with accuracy of the mobile sink position prediction in Vehicular Pattern Wireless Sensor Networks (V-WSNs). V-WSNs are pivotal in modern wireless communication, especially in dynamic environments like urban traffic systems. The survey begins with an overview of V-WSNs, highlighting their unique characteristics, applications, and challenges, with a particular focus on mobile sink position prediction. It then delves into the nuances of routing protocols in V-WSNs, discussing the advantages and limitations of existing methods, and underscores the necessity for deep learning-based solutions. The paper further explores the integration of deep learning in WSNs, examining its applications, benefits, and inherent challenges. The significance of accurate and secure routing is analyzed, followed by an in-depth discussion on the integration of deep learning for enhancing these aspects in V-WSN routing. The paper also addresses the current challenges and open issues in mobile sink prediction and secure, accurate routing, paving the way for future research directions. A comparative analysis of various deep learning approaches and their practical applications in real-world scenarios is presented, offering insights into the effectiveness of these techniques. The survey concludes by summarizing the key findings and reaffirming the importance of integrating deep learning for secure and accurate mobile sink position prediction in V-WSNs. © 2024 IEEE.

关键词： Wireless sensor networks

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Machine Learning in Healthcare: Prognosis Heart Disease Prediction 4

Machine Learning in Healthcare: Prognosis Heart Disease Pred...

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4th International Conference on Advancement in Electronics and Communication Engineering, AECE 2024

作者： Ramnath, M. Revathi, B. Selva Birunda, S. Usharani, C. Ramana, R. Tyson Masillamani, J. Ramco Institute of Technology Department of Artificial Intelligence and Data Science Virudhunagar India Thamirabharani Engineernig College Department of Computer Science and Engineering Tirunelveli India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364729

The heart, being a crucial organ, necessitates meticulous care. Accurate information is essential for identifying heart-related disorders. Precise patient data is vital for hospitals to effectively predict and treat cardiac issues. Utilizing advanced Machine Learning algorithms, such as Logistic Regression, Random Forest, decision trees, K-Nearest Neighbours, Support Vector Machine, Naive Bayes along with statistical models, makes the accurate prediction of heart diseases feasible and accuracy comparison has been made. Finally, a better model has been developed to predict heart disease predictions. Medical practitioners can efficiently detect and address cardiac problems by leveraging these sophisticated algorithms, thereby maximizing the benefits of current medical technology. The application of these algorithms has notably improved patient health and the overall quality of care. Furthermore, hospitals to allocate the resources more precisely perform the predictive analytics more efficiently and enhance patient outcomes. By adopting these technological advancements, medical professionals can stay ahead of potential heart-related issues and deliver better care to their patients. © 2024 IEEE.

关键词： Logistic regression

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Robustness of Explainable AI Algorithms for Disease Biomarker Discovery from Functional Connectivity datasets

Robustness of Explainable AI Algorithms for Disease Biomarke...

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2024 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2024

作者： Girish, Deepank Chan, Yi Hao Gupta, Sukrit Xia, Jing Rajapakse, Jagath C. College of Computing and Data Science Nanyang Technological University Singapore Indian Institute of Technology Department of Computer Science and Engineering Ropar India

ISBN: (纸本)9798350351552

Machine learning (ML) models have been used in functional neuroimaging for wide-ranging tasks, ranging from disease diagnosis to disease prognosis. There have been successive functional connectivity-based ML studies focused on improving model performances for disease detection. An increasing number of such studies use their trained models to detect and evaluate salient features that could be potential biomarkers of these neuro-logical conditions. The evaluation of these salient features is often qualitative and limited to cross-referencing existing literature for similar findings. In this study, we present objective quantitative metrics to evaluate the robustness of these salient features. Building upon existing generic evaluation metrics, we propose metrics that capture topological properties known to be characteristic of brain functional connectomes. Using existing and newly proposed measures on a set of baselines and state-of-the-art graph neural networks (GNN) models, we found that when GNNExplainer is used with models that incorporate attention, the scores produced are relatively more robust than other combinations. On datasets of patients with Autism Spectrum Disorder (ASD) or Attention-deficit Hyperactivity Disorder (ADHD), our proposed metrics highlighted that salient features identified in both disorders are highly involved in functional specialization, while salient ASD features expressed stronger functional integration than ADHD. We package these existing and novel metrics together in the RE-CONFIRM framework that holds promise to set the foundations for the quantitative evaluation of salient features detected by future studies. © 2024 IEEE.

关键词： Graph neural networks

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