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Real-Time Sentiment Analysis of Domestic Violence Tweets: Informing Intervention Strategies 13

Real-Time Sentiment Analysis of Domestic Violence Tweets: In...

引用

13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Itham, Vinitha Mande, Ramesh Prathyusha, K.P.S.J. Velagapudi Ramakrishna Siddhartha Engineering College Dept. of Information Technology Kanuru India

ISBN: (纸本)9798350380583

Intimate partner violence is still a relevant topic in modern societies all over the world. which requires constant activities for the purpose of treatment and prevention. It is equally important to grasp attitudes of the public towards domestic violence as well as the overall attitude that is spread in the society. vital when it comes to identifying specific approaches to address the concerns and influence the population. discourse. Based on the context defined above, this paper aims to present a sentiment analysis solution. designed especially for the purpose of analyzing the posts that fall under the category of domestic. violence. By using the never-ending stream of data in the use of business applications, it is possible to increase accountability of employee productivity. For this purpose, we follow our method on Twitter to offer the corresponding insights into public. concerning this sensitive issue, the ideas and feelings people have towards it are as follows: topic. First of all, we gather a large amount of tweets containing tweets containing the relevant keywords about domestic violence employing the Twitter Application Programming Interface. Next, in order to manipulate the text data we have, we require processing it to remove noise, tokenizetion, and apply techniques such as stemming, lemmatization or both. to standardize the text. Subsequently, we employ stateof-the-art analysis - which is performed with the help of some specific algorithms for sentiment analysis maybe based on deep learning. models like, for instance, recurrent neural networks or transformers. architectures as BERT (Bidirectional Encoder Representations derived from Transformers to transcribe the tweet into a certain category of 'positive', 'negative' or 'neutral' sentiment. positive, negative, or neutral © 2024 IEEE.

关键词： Tweets

来源：评论

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Identification of Non-Vehicle and Vehicle Density at Intersections Based on GPS Data

Identification of Non-Vehicle and Vehicle Density at Interse...

引用

2024 International Conference on information technology and Computing, ICITCOM 2024

作者： Aziz, Qodri Rakhman, Arkham Zahri Pratama, Azkario Rizky Widyawan Universitas Gadjah Mada Dept. of Electrical Engineering and Information Technology Yogyakarta Indonesia

ISBN: (纸本)9798350379839

This study aims to identify the density of vehicles and non-vehicles at the Zero Point Yogyakarta intersection based on GPS data. The data in this study comes from one of the telecommunication operator companies in Indonesia with active MPD data criteria. The data was collected from telecommunication service users who consciously activated their location services, during the period from November 2021 to January 2022. Zero-Point Intersection in Yogyakarta was chosen because of its high density, historical value, and location in the center of Yogyakarta and as a tourist destination. In this study, GPS data was processed, using a rules-based approach to categorize users into vehicle and non-vehicle groups based on speed, distance, mobility time, and duration of time at the intersection. The results of the analysis show that the highest density occurred in November 2021, with a higher number of vehicles and non-vehicles during the day than at night. The decrease in density was seen in December 2021 and continued until January 2022, due to movement restrictions during the COVID-19 pandemic. The data shows that the number of nonvehicle users is higher than that of vehicles, especially during the day. The rule-based method used in this study succeeded in distinguishing between vehicles and non-vehicles, as well as providing an understanding of mobility patterns at the Zero Point intersection in Yogyakarta. The study shows that the use of GPS technology and rule-based data analysis can enhance ongoing research on traffic management efficiency in congested urban areas. © 2024 IEEE.

关键词： Global positioning system

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Multivariate Analysis and Detection of Concept Drift in Longitudinal COVID-19 Data: Implications for Adaptive Healthcare Strategies 2

Multivariate Analysis and Detection of Concept Drift in Long...

引用

2nd IEEE International Conference on Networks, Multimedia and information technology, NMITCON 2024

作者： Chitla, Vikas Benhur Lakshminarasimhan, P.S. Manoj Kumar, Mv Prashanth, B.S. Nitte Meenakshi Institute of Technology Dept. of Information Science and Engineering Bengaluru India

ISBN: (纸本)9798350372892

The COVID-19 pandemic has unfolded with unprecedented complexity, marked by shifting epidemiological trends, evolving treatment protocols, and the emergence of new variants. Such inherent dynamism necessitates a thorough examination of data stability over time. In this study, our focus extends beyond univariate analysis, incorporating a multivariate approach to capture interdependencies among key health parameters of generated COVID-19 datasets over six years to provide critical insights into evolving symptoms and vital signs through the identification of concept drift. Our findings enhance the understanding of the pandemic's trajectory, informing healthcare practitioners, policymakers, and researchers about temporal variations in symptoms and vital signs. Concept drift implications in COVID-19 datasets include re-calibrating predictive models, refining treatment strategies, and enhancing decision support systems. This underscores the importance of adaptive analytics in navigating infectious disease dynamics amidst evolving global responses to the pandemic. © 2024 IEEE.

关键词： COVID-19

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Crop Yields Prediction in Bangladesh: A Hybrid Machine Learning and DNN Approach

Crop Yields Prediction in Bangladesh: A Hybrid Machine Learn...

引用

2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Meem, Rudaiba Haque Noor Turna, Taskin Pabna University of Science and Technology Dept. of Information and Communication Engineering Pabna Bangladesh

ISBN: (纸本)9798331529765

In todays world, machine learning and artificial intelligence are gaining popularity. In today's digital age, many tasks are being transformed through artificial intelligence and machine learning. Agriculture, as a vital component of human sustenance, remains The principal source of livelihood for many. Bangladesh is an agrarian economy due to its geographical location. Bangladeshs economy is heavily dependent on agriculture as it contributes a lot to the countrys GDP. Even though artificial intelligence and machine learning are being used to digitize every industry in this day and age, the agricultural sector is falling behind. In Bangladesh agricultural work is mostly done in old ways and manually. From crop selection to fertilizer selection, all work is done by farmers manually based on their experience. In addition, agricultural institutions in Bangladesh use manual data, which is not a proper solution to accurately select crops and yield prediction. To address these problems, there is a need for modern and innovative approaches to yield prediction and crop selection. This paper demonstrates the most efficient and cost-efficient approach to predicting crop yield utilizing deep neural networks (DNN) and machine learning techniques. Artificial Neural Network and Machine Learning are considered powerful tools for modeling and prediction. The effectiveness of the algorithms that were assessed in this study are Deep Neural Network- Gradient Boosting Regressor(DNN-GBR),Support vector machine(SVR), K-Nearest Neighbors Algorithm. The goal of this algorithm is to improve output and prediction. The performance of the proposed hybrid DNN-GBR model was evaluated using MSE and R-squared (coefficient of determination) metrics. In this paper, we have suggested using the hybrid DNN-GBR model for agricultural yield prediction and crop selection. © 2024 IEEE.

关键词： Adversarial machine learning

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Robust Fetal Head Circumference Calculation on Ultrasound Images Using Just-in-Time Compiled Ellipse Fitting

Robust Fetal Head Circumference Calculation on Ultrasound Im...

引用

2024 IEEE Region 10 Conference, TENCON 2024

作者： Zumarsyah, Pandega Abyan Solaeman, Achmad Alvindo, Fhareza Nugroho, Hanung Adi Universitas Gadjah Mada Dept. of Electrical Engineering and Information Technology Yogyakarta Indonesia

ISBN: (纸本)9798350350821

Calculation of fetal head circumference (HC) is essential for various purposes, particularly in evaluating fetal health during prenatal care. However, performing HC calculations manually is labor intensive, prone to errors, and requires specific expertise. Thus, there is a pressing demand for automated methods for HC calculation. Recent automation methods for head circumference calculation focus on using deep learning that is often slow and memory hog due to the high computational complexity. This study presents a novel approach for fast and efficient automatic HC calculation from 2D ultrasound images by leveraging just-in-time compiled ellipse fitting based on Randomized Hough Transform. Before ellipse fitting, there are downscaling, blurring, and clean edge detection to increase robustness. After ellipse fitting, there is circumference calculation using the Ramanujan approximation. Our approach achieved a remarkable speed of 0.41 s per image with less than 800 kB of memory. It demonstrated robust accuracy with a Hausdorff distance of 3.88 mm, a Dice score of 0.93, and an absolute difference of 7.24 mm, which is comparable to existing methods. © 2024 IEEE.

关键词： Ultrasonic imaging

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Smart Traffic Management System using Background Subtraction 4

Smart Traffic Management System using Background Subtraction

引用

4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Kottha, Bharath Reddy Anuradha, T. Penumacha, Naveen Kumar Gadde, Harsha Vardhan Velagapudi Ramakrishna Siddhartha Engineering College dept. of Information Technology Kanuru India

ISBN: (纸本)9798350385793

The development of an intelligent and adaptive traffic signal management system lies in the challenges associated with urban traffic congestion and the need for efficient emergency vehicle passage. Urbanization and population growth have led to increased vehicular traffic, resulting in congestion during peak hours. An automated traffic monitoring system at the junctions with the count of the vehicles can help to achieve a dynamic allocation of timing for vehicles for achieving a smooth and congestion - free traffic movement. The proposed system can help in adjusting traffic signal timings based on the density of vehicles in each lane while prioritizing emergency vehicles. A common approach for vehicle detection is background subtraction. Background subtraction is a popular computer vision technique used to extract the foreground objects from a given video or image sequence. The main goal of this process is to differentiate the moving objects from a relatively static background. Detecting the approach of emergency vehicles can be done using RFID reader and tags. The primary goal of this system is to reduce traffic congestion by allocating time slots for each lane based on their current density, thereby optimizing overall traffic flow. Additionally, the system is designed to prioritize the passage of emergency vehicles, like ambulances, through intelligent detection mechanisms, ensuring swift and unobstructed access to critical locations. © 2024 IEEE.

关键词： Traffic congestion

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Identification of Visitors at Tourist Destinations in Yogyakarta Using GPS Data

Identification of Visitors at Tourist Destinations in Yogyak...

引用

2024 International Conference on information technology and Computing, ICITCOM 2024

作者： Nisa, Khairun Zahri Rakhman, Arkham Rizky Pratama, Azkario Widyawan Universitas Gadjah Mada Dept. of Electrical Engineering and Information Technology Yogyakarta Indonesia

ISBN: (纸本)9798350379839

This study utilized GPS data from telecommunications operators to analyze tourist behavior in Yogyakarta, Indonesia. A rule-based identification technique was developed to differentiate domestic tourist activity. The analysis revealed significant visitor pattern variations and highlighted priority destinations such as Gembira Loka and Parangtritis Beach. Preprocessing methods and rule-based classification categorized visitors into tourists (individuals from different sub-districts at tourist locations), working residents (spending 40 hours a week at tourist locations), visitors (spending an average of 3.3 hours at tourist sites), and ordinary residents (not fitting the previous categories). This study offers an essential reference for accurately assessing tourism's economic impact, emphasizing that non-local tourists contribute more significantly to local revenue through spending on accommodation, food, and attractions. The findings assist in planning visits and developing effective tourism policies. The study underscores the importance of precise visitor identification techniques to understand travel patterns and enhance tourism development. The results demonstrate GPS data's potential in tourism research, informing marketing strategies and product development. Future research could integrate additional data sources, such as social media check-ins or transaction records, to improve visitor profiling accuracy. Moreover, studies could investigate the long-term impacts of tourism on local economies and the environment. In Indonesia, working hours regulated by Law No. 13 of 2003 ensure a balance between productivity and leisure, influencing tourist behavior. This study advances the understanding of visitor behavior in Yogyakarta and promotes sustainable tourism development. © 2024 IEEE.

关键词： Product development

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T-type Inverter in both Continuous and Discontinuous Current Mode for Active Power Decoupling Capability

T-type Inverter in both Continuous and Discontinuous Current...

引用

2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Higashide, Ryohei Kusui, Rintaro Watanabe, Hiroki Nakata, Yuki Itoh, Jun-Ichi Nagaoka University of Technology Dept. of Electrical Electronics and Information Engineering Nagaoka Japan

ISBN: (纸本)9798350376067

This paper proposes an active power decoupling (APD) method with both continuous and discontinuous current modes for a T-type inverter, which does not require additional components. The operation in continuous current mode (CCM) has uncontrollable periods of neutral point current for APD. On the other hand, the operation in discontinuous current mode (DCM) decreases efficiency due to an increase in the RMS value of the inductor current. The proposed method switches from CCM to DCM in the uncontrollable periods of the neutral current to reduce the inductor current RMS. However, the efficiency improvement effect is limited because the losses are too large in the DCM period. Therefore, the efficiency improvement method is applied to the CCM/DCM operation. The experimental result indicates that the efficiency improvement method improves the efficiency from 90.1% to 92.5%. In addition, the proposed CCM/DCM control reduces the second harmonic components of the input current by 90.1% compared with not using the APD operation. © 2024 IEEE.

关键词： Active power decoupling Continuous current mode Discontinuous current mode T-type inverter

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Real-Time Clickstream Data Processing and Visualization Using Apache Tools 7

Real-Time Clickstream Data Processing and Visualization Usin...

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2023 7th International Conference On Computing, Communication, control And Automation, ICCUBEA 2023

作者： Patil, T.B. Anand, Keshav Bhateja, Aditya Jamal, Kashif Sawant-Patil, S.T. Paygude, Priyanka College of Engineering Dept. of Information Technology Pune India

ISBN: (纸本)9798350304268

In the modern digital generation, clickstream data analytics is a crucial component of online selling and purchasing platforms. It offers useful information for enhancing user experience, conversion rates, website design, and keeping track of the success of digital marketing efforts. A data warehouse is required to store and analyze clickstream data, which requires a lengthy and complex ETL process. Two of the leading large data processing tools and distributed streaming systems are Spark and Kafka. This paper presents a comprehensive examination of recent research pertaining to clickstream data analytics, real-time data visualization, data lifecycle management utilizing Apache technologies. Based on the review, the paper proposes an architecture that can aid organizations and local merchants in effectively visualizing the performance of their products. This paper provides a valuable resource for researchers and practitioners seeking to explore the latest developments in their future work. © 2023 IEEE.

关键词： click streaming continuous data integration data lifecycle data transform data warehouse extract load real-time data visualization transform

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Active Element Arrangement and Prediction Model for Channel Estimation in IRS-Assisted Systems

Active Element Arrangement and Prediction Model for Channel ...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Tsuchiya, Yoshihiko Suga, Norisato Uruma, Kazunori Fujisawa, Masaya Tokyo University of Science Dept. of Information and Computer Technology Tokyo Japan Shibaura Institute of Technology Dept. of Information and Communication Engineering Tokyo Japan Kogakuin University Dept. of Computer Science Tokyo Japan

ISBN: (纸本)9798350350821

Intelligent reflecting surfaces (IRS) that can dynamically control the phase of radio waves and reflect them are attracting attention to realize non line-of-sight communication in the high-frequency band. Channel state information (CSI) is estimated to properly control the phase, however the overhead of the estimation reduces communication efficiency. To reduce the overhead, IRS systems with active elements that perform channel estimation are considered. One approach treats the partial CSI obtained from the active elements as an image and utilizes a super-resolution technique to predict the entire CSI. However, because the active elements are arranged in grid, prediction is difficult when the active elements spacing and the CSI fluctuation period match. In this study, we propose a new arrangement that gathers the active elements in the center of the IRS. Furthermore, we regard prediction as an image-inpainting problem that recovers missing pixels from known pixel values, and we propose applying the U-Net to solve this problem. Numerical experiments show that the proposed method outperforms conventional methods in prediction accuracy and spectral efficiency. © 2024 IEEE.

关键词： Channel estimation

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