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Irrigation Control System for Seedlings Based on the Internet of Things 1

International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： de Carvalho, André Kusuma, Gede Putra Handoyo, Alif Tri Department of Computer Science BINUS Graduate Program—Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

The agricultural activities in the Vocational school of Agricultural Engineering (ESTVA) in Timor-Leste requires large quantities of water with high availability for the production and cultivation of plants. However, the crop results sometimes decrease due to challenges problem faced by the management of the water resources. This study aims to design an Internet of Things (IoT) based automatic control system to control water supply and maintain soil moisture conditions effectively. The evaluation of manual and automatic irrigation systems during the 21 days trial shows that the automatic system provides stable utilization of water with watering frequency of 274 occurs only in 12 days, with an amount of total watering of 35.3 L. Meanwhile, the manual system uses up to 49.8 L of water, which is 14.5 L more than the water spent by the automatic system. The results of the evaluation also indicate that automatic system obtains the average moisture value of 509 with a standard deviation of 63, which is smaller than the manual system that obtains an average moisture value of 559 and a significant standard deviation of 126. From these results, the automatic system is more effective in maintaining the soil moisture level and yet requires less water compared to the manual watering system. Furthermore, the quality and productivity of the growth of mustard seedlings with automatic system prove that the seeds are more stable and feasible to continue transplanting since the automatic system produces 12 more mustard trees compared to the manual system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Soil moisture

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Estimation of Neurological Diseases and Age Using Brain MRI 4th

Estimation of Neurological Diseases and Age Using Brain MRI

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4th International Conference on Smart Systems: Innovations in Computing, SSIC 2023

作者： Ashokkumar, K. Dharshan, K. Ragupathi, T. Pritam, Anem Sagar, Amujuru Gnana Department of Computer Science Engineering Sathyabama Institute of Science and Technology Chennai India School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Data Science and Business systems School of Computing SRM Institute of Science and Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9789819736898

Leveraging D-NN trained on neuroimaging data, we can effectively estimate the chronological ages of normal persons;this projected brain age has potential as a biomarker for identifying age-related disorders. The suggested technique employs a CNN cascade network from DL and the Support Vector Machine (SVM) algorithm from ML (SVM). These techniques have been employed to train MRI scans of the brain, classifying them into regular (unaffected by illness), Alzheimer’s disorder (AD), and neurocognitive disability. That we also have the ability to determine ages thanks to the age classifications on the photos. The brain MRI collection is trained using CNN and SVM, on which the classifications and age estimate will be carried out. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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Functional Connectivity Disruptions in Alzheimer’s Disease: A Maximum Flow Perspective 12th

Functional Connectivity Disruptions in Alzheimer’s Disease...

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12th International Conference on Computational Advances in Bio and Medical sciences, ICCABS 2023

作者： Stubby, Emma T. Razavi, Seyed Majid Khanmohammadi, Sina School of Computer Science University of Oklahoma Norman73019 United States Data Science and Analytics Institute University of Oklahoma Norman73019 United States

ISBN: (纸本)9783031827679

Alzheimer’s disease is a neurological disorder characterized by functional and structural atrophy, leading to symptoms like memory loss and cognitive decline. This study seeks to analyze the disruptions of functional connectivity pathways within the brain caused by Alzheimer’s disease from the maximum flow perspective. More specifically, we computed the maximum flow pathways within the functional brain networks, and compared it between healthy controls and Alzheimer’s patients. Our results suggest that the Alzheimer’s patients utilize pathways related to the default mode network (DMN) more frequently and display significant alterations in the usage of paths connected to the striate cortex (SC). The increased usage of DMN pathways might point to a compensation mechanism that facilitates interregional communications in Alzheimer’s patients. Understanding the nature of such a compensation mechanism could help develop new treatment options for Alzheimer’s patients. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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MCTS: A Multi-Reference Chinese Text Simplification dataset 30

MCTS: A Multi-Reference Chinese Text Simplification Dataset

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Chong, Ruining Lu, Luming Yang, Liner Nie, Jinran Liu, Zhenghao Wang, Shuo Zhou, Shuhan Li, Yaoxin Yang, Erhong School of Information Science Beijing Language and Culture University China Department of Computer Science and Technology Northeastern University China Department of Computer Science and Technology Institute for AI Tsinghua University China

ISBN: (纸本)9782493814104

Text simplification aims to make the text easier to understand by applying rewriting transformations. There has been very little research on Chinese text simplification for a long time. The lack of generic evaluation data is an essential reason for this phenomenon. In this paper, we introduce MCTS, a multi-reference Chinese text simplification dataset. We describe the annotation process of the dataset and provide a detailed analysis. Furthermore, we evaluate the performance of several unsupervised methods and advanced large language models. We additionally provide Chinese text simplification parallel data that can be used for training, acquired by utilizing machine translation and English text simplification. We hope to build a basic understanding of Chinese text simplification through the foundational work and provide references for future research. All of the code and data are released at https://***/blcuicall/mcts/. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Chinese Text Simplification Language Resource Multi-Reference

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Analyzing the Parallel Computing Performance of Unsupervised Machine Learning 1

Analyzing the Parallel Computing Performance of Unsupervised...

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1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Baligodugula, Vishnu Vardhan Amsaad, Fathi Jhanjhi, Nz Wright State University Department of Computer Science Dayton United States SCS Taylors University School Computer Science Subang Jaya Malaysia

ISBN: (纸本)9798350373042

The popularity of unsupervised machine learning techniques is increasing drastically as they can generate clusters of data samples. It facilitates making critical decisions and overcoming challenges in various applications. In the realm of data analysis, clustering algorithms are commonly used to cat egorize large datasets into smaller groups. As datasets continue to grow in size, traditional clustering methods become impractical, and parallel computing has become an attractive option to improve performance. The proposed method investigates the use of unsupervised learning to classify data and determine the GDP of countries as low, medium, or high. Three clustering techniques are compared for their performance, scalability, and ease of use on large datasets: Minibatch K-Means, parallel Minibatch K-Means using Massage Passing Interface, and Parallel Minibatch K-Means using the cloud. The results highlight the importance of parallel computing in data analysis and demonstrate the effectiveness and efficiency of parallel clustering techniques. © 2024 IEEE.

关键词： Large datasets

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Viral Melodies: Exploring the Factors Influencing Music Virality in TikTok Engagement

Viral Melodies: Exploring the Factors Influencing Music Vira...

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science and Information Technology in Smart Administration (ICSINTESA), International Conference of

作者： Evelyn Zefanya Rahardjo Jasmine Mutia Alifa Sharon Zefanya Setiawan Alexander Agung Santoso Gunawan Karli Eka Setiawan Computer Science Department School of Computer Science Data Science Program Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350376111

ISBN: (纸本)9798350376128

In the digital era and the evolution of social media platforms like TikTok, understanding the factors influencing content virality has become increasingly crucial. Therefore, this research aims to delve into the musical variables contributing to the popularity of content on TikTok, including aspects such as artist, beat of music, and the number of shares. Focusing on content that successfully garnered over $\mathbf{5, 0 0 0}$ likes, this study employed a data analysis approach involving the librosa library for music tempo extraction and logistic regression to identify relationships among these variables. The research findings indicate that the most significant variable influencing the virality of music content on TikTok is the number of shares. This highlights that the phenomenon of virality is not solely dependent on musical characteristics like artist or beat but is more influenced by social interactions through video sharing among users. The findings suggest that when someone shares a video with others who may have similar music preferences, the video is likely to receive likes, even if it does not appear on the user’s For You Page (FYP). Thus, the virality of content on TikTok can be explained by the existence of social connections among friends that trigger the viral spread of the video. These results provide valuable insights into the mechanisms behind the success of conten/t on this platform.

关键词： Logistic regression Video on demand Social networking (online) Refining Music Focusing Libraries Web sites data mining Information technology

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HIV Inhibitory Activity Classification with Morgan Fingerprint and Integrated Graph-Based Deep Learning

HIV Inhibitory Activity Classification with Morgan Fingerpri...

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International Conference of Information and Communication Technology (ICoICT)

作者： Jocelyn Verna Siswanto Caroline Angelina Sunarya Agnes Calista Alexander Agung Santoso Gunawan Karli Eka Setiawan Computer Science Department Data Science Program School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350363432

ISBN: (纸本)9798350363449

This research offers a new perspective on predicting the activity of the HIV virus from the Drug Therapeutics program (DTP) Antiviral Screen by using the molecular data represented in SMILES notation. The topic has significance as it focuses on a major global health issue using modern computational approaches and has the potential to uncover new antiviral drug candidates, which could eventually save lives and improve public health outcomes. The study addresses the data imbalance between two classes, active and inactive, and employs the Morgan Fingerprint method for feature extraction, along with the Graph Convolutional Network (GCN) and Graph Attention Network (GAT) as the baseline architectures and the fusion of GCN's and GAT's main features as the proposed architecture. The random oversampling technique is applied to alleviate dataset imbalances. However, even though it improved the training process, the performance of the model flopped when the test set was fed into the model. Combining the main features in GCN and GAT, the proposed model was able to do the classification task more accurately. The attention mechanism from GAT allows the model to focus more on the parts that are more relevant and ignore the irrelevant ones. It managed to outperform the baseline models. Despite a high overall accuracy of 94%, the fusion model exhibits significant disparities in precision, recall, and f1-score metrics, potentially due to class imbalance. Random oversampling led to improved training but compromised model performance on the test set.

关键词： Training Measurement Drugs Attention mechanisms Computational modeling computer architecture Fingerprint recognition Predictive models Feature extraction data models

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A Federated Learning Approach Towards a Privacy-Preserving Technique for Brain Tumor Classification 2nd

A Federated Learning Approach Towards a Privacy-Preserving T...

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2nd International Conference on data science and Network Engineering, ICDSNE 2024

作者： De, Anurag Pal, Gautam Shyam, Karnam Tej, Kalakanti Pawan School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India Computer Science and Engineering Department TIT Narsingarh Tripura Agartala India

ISBN: (纸本)9789819783359

The brain is one of the main organs in the human body. It controls almost all the actions of a human being. Any problem with the brain can lead to even fatal consequences. One of the most dangerous problems faced by the human brain is a brain tumor. If it is not treated properly at early stages, it may lead to severe conditions. However, finding a brain tumor is a very challenging task as the brain structure is very complex. Also, a lot of image data is required to train the model to detect brain tumors. However, the hospitals are not ready to share patient data to train the model. Hence, an approach to train the model by seeing the actual dataset or simply using decentralized data from multiple clients needs to be followed. So, a federated learning methodology is utilized to train the model. The trained federated learning model has achieved a training accuracy of 91% and a test accuracy of 88%. The federated learning and transfer learning approach stands out in preserving privacy, optimizing model performance, and leading to good collaboration among healthcare entities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Sentimental Analysis of Amazon Reviews using Map Reducer Technique in Hadoop 18

Sentimental Analysis of Amazon Reviews using Map Reducer Tec...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Byali, Manasi S. Kanakaraddi, Suvarna Badagi, Tulsa Chikaraddi, Ashoka Kle Technological University Department of Computer Science Engineering Hubli India Kle Technological University School of Computer Science Engineering Hubli India

ISBN: (纸本)9789380544519

Sentiment analysis (SA) is an active and dynamic aspect of text mining that focuses on the automated analysis of subjectivity, opinions, and sentiments in textual material. The study explores new SA domains such as resource development, emotion recognition, and transfer learning. The study combines rule-based classification, machine learning, and supervised learning in a novel hybrid technique. The usefulness of this hybrid method is demonstrated through experiments on multiple datasets, including MySpace comments, product reviews, and movie reviews, resulting in improved classification efficacy as judged by both macro and micro-averaged criteria. The study also supports an additional semi-automatic approach in which classifiers cooperate to enhance overall performance. In this article, we have applied SVM, Decision tree, and Linear SVM to the Amazon review dataset and got the output in the form of performance metrics such as CPU-time and maximum memory usage. and we found that Depending on the linearity of the data, a linear SVM or a Decision Tree is more appropriate;select a linear SVM for clearly defined separations and a Decision Tree for intricate, non-linear interactions, customizing the model to the features of the dataset and the goals of the research. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Hadoop Linear Regression MapReduce RDBMS (Relational database Management System) SVM (Support Vector Machine)

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Dual Attention and Question Categorization-Based Visual Question Answering

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第1期4卷 81-91页

作者： Mishra, Aakansha Anand, Ashish Guha, Prithwijit Department of Computer Science & Engineering Guwahati781039 India Department of Electronics and Electrical Engineering Guwahati781039 India Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Guwahati Guwahati781039 India

Visual question answering (VQA) aims at predicting an answer to a natural language question associated with an image. This work focuses on two important issues pertaining to VQA, which is a complex multimodal AI task: First, the task of answer prediction in a large output answer space, and second, to obtain enriched representation through cross-modality interactions. This work aims to address these two issues by proposing a dual attention (DA) and question categorization (QC)-based visual question answering model (DAQC-VQA). DAQC-VQA has three main network modules: First, a novel dual attention mechanism that helps toward the objective of obtaining an enriched cross-domain representation of the two modalities;second, a question classifier subsystem for identifying input (natural language) question category. The second module of question categorizer helps in reducing the answer search space;and third, a subsystem for predicting answer depending on the question category. All component networks of DAQC-VQA are trained in an end-to-end manner with a joint loss function. The performance of DAQC-VQA is evaluated on two widely used VQA datasets, viz., TDIUC and VQA2.0. Experimental results demonstrate competitive performance of DAQC-VQA against the recent state-of-the-art VQA models. An ablation analysis indicates that the enriched representation obtained using the proposed dual-attention mechanism helps improve performance. © 2022 IEEE.

关键词： Forecasting

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