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2nd International Conference on Advances in data-driven Computing and Intelligent Systems, ADCIS 2023

作者： Prathyuma, V. Hareesh Teja, S. Suganeshwari, G. Divya, S. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai600127 India Centre for Advanced Data Science Vellore Institute of Technology Tamil Nadu Chennai6000127 India

ISBN: (纸本)9789819995233

Tip burn is a common calcium deficiency symptom found in strawberry leaves, which can deform, fold over, and discolor the tips or edges of budding leaves. To help farmers detect diseases early and manage their crops more effectively, this work aims to build a solution that employs image processing and machine learning techniques to detect tip burn in strawberry leaves on a farm. The proposed work utilizes a dataset of 1431 images of ‘diseased’ and ‘healthy’ strawberry leaves. A Random Forest classifier, which is an ensemble learning model built on decision trees, CNN, or convolutional neural networks, and VGG-16, a transfer learning-based approach were all fitted in the PySpark environment to assess the feasibility and scalability of using Spark for distributed computing. The results indicate the deep learning approaches outperformed the traditional machine learning approach in accuracy and execution speed, making them a promising tool for identifying tip burn diseases in strawberry leaves. Overall, this work provides farmers with a fast, feasible, and reliable method for detecting tip burn, aiding in crop management, and reducing crop loss. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Big data

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Agricultural Water Conservation through AI-Enhanced Solar IoT Networks 3

Agricultural Water Conservation through AI-Enhanced Solar Io...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Maniraj, S.P. Monisha, J. Amutha, R. Kavitha, R. Shriidhar, P.J. Srinivasan, C. Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Kattankulathur Tamilnadu Chennai India Tamil Nadu Chennai India Ultra College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Madurai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Vinayaka Missions Kirupananda Variyar Engineering College Department of Computer Science and Engineering Tamil Nadu Salem India Saveetha Universityi Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509675

With the increasing water shortage and climatic uncertainty, creative strategies for effective water management in agriculture are necessary. This research investigates the integration of Artificial Intelligence (AI), particularly Artificial Neural Networks (ANN), with solar-powered Internet of Things (IoT) networks to enhance water utilization in agricultural activities. The proposed system facilitates accurate irrigation scheduling using real-time data from soil moisture sensors, meteorological stations, and crop health monitoring devices. The ANN model analyses this data to predict ideal irrigation schedules and volumes, minimizing water waste while improving crop yields. The solar-powered IoT framework provides sustainability and reduces dependence on traditional energy sources. Field trials indicate that the AI-enhanced system may promote water saving by as much as 30% relative to traditional irrigation techniques. Moreover, using AI enables adaptive learning, enabling the system to enhance its irrigation tactics using historical and real-time data. This novel method conserves water resources and fosters sustainable agriculture practices, coinciding with global initiatives to address water shortages and assure food security. The findings underscore the potential of technology-based solutions to revolutionize agricultural water management for a sustainable future. © 2025 IEEE.

关键词： Smart agriculture

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SConE: Simplified Cone Embeddings with Symbolic Operators for Complex Logical Queries 61

SConE: Simplified Cone Embeddings with Symbolic Operators fo...

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Findings of the Association for Computational Linguistics, ACL 2023

作者： Nguyen, Chau Duc Minh French, Tim Liu, Wei Stewart, Michael ARC Centre for Transforming Maintenance through Data Science Department of Computer Science and Software Engineering School of Physics Mathematics and Computing The University of Western Australia

ISBN: (纸本)9781959429623

Geometric representation of query embeddings (using points, particles, rectangles and cones) can effectively achieve the task of answering complex logical queries expressed in first-order logic (FOL) form over knowledge graphs, allowing intuitive encodings. However, current geometric-based methods depend on the neural approach to model FOL operators (conjunction, disjunction and negation), which are not easily explainable with considerable computation cost. We overcome this challenge by introducing a symbolic modeling approach for the FOL operators, emphasizing the direct calculation of the intersection between geometric shapes, particularly sector-cones in the embedding space, to model the conjunction operator. This approach reduces the computation cost as a non-neural approach is involved in the core logic operators. Moreover, we propose to accelerate the learning in the relation projection operator using the neural approach to emphasize the essential role of this operator in all query structures. Although empirical evidence for explainability is challenging, our approach demonstrates a significant improvement in answering complex logical queries (both non-negative and negative FOL forms) over previous geometric-based models. © 2023 Association for Computational Linguistics.

关键词： computer circuits

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Evaluating data-Driven Models to Enhance Employee Retention and Performance

Evaluating Data-Driven Models to Enhance Employee Retention ...

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2024 International Conference on Progressive Innovations in Intelligent Systems and data science, ICPIDS 2024

作者： Mishra, Rashmi Tyagi, Neha Tyagi, Shobha Amity University M. Tech (Data Science) Noida India Amity University Computer Science and Engineering Noida India Manav Rachna International Institute of Research and Studies Computer Science and Engineering Faridabad India

ISBN: (纸本)9798331534691

Employee attrition presents considerable challenges for organizations, affecting productivity, employee morale, and financial health. This research seeks to create and evaluate data-driven models to more accurately forecast employee turnover. By analyzing various employee data, such as demographic details, job-related factors, and performance metrics, machine learning techniques are used to uncover the primary drivers of attrition. The goal is to identify the most effective model for prediction, of-fering practical insights to shape employee retention strategies. In conclusion, this study contributes to the development of proactive measures aimed at reducing attrition and enhancing workforce management. This study employs Logistic Regression and Ran-dom Forest, each bringing distinct advantages to HR analytics over conventional approaches. Logistic Regression is appreciated for its ease of interpretation, making it highly applicable for HR insights. Conversely, the ensemble nature of Random Forest enhances prediction accuracy by effectively identifying non-linear trends commonly found in employee behavior. By combining these models, the approach provides both robust predictive capabilities and valuable insights into major attrition factors, which are essential for crafting targeted retention strategies. © 2024 IEEE.

关键词： attrition mitigation data-driven models demographic analysis Employee attrition employee turnover job characteristics machine learning performance metrics predictive modeling retention strategies work-force management

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Point of Sale Fraud Detection Methods via Machine Learning 17

Point of Sale Fraud Detection Methods via Machine Learning

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17th International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2023

作者： Begen, Emre Sayan, Ismail Utku Tugrul Bayrak, Ahmet Yildiz, Olcay Taner Ata Technology Platforms Data Science and Innovation İstanbul Turkey Özyeǧin University Computer Engineering İstanbul Turkey

ISBN: (纸本)9798350338904

Restaurant cash registers frequently experience fraudulent transactions, leading to substantial financial losses for operators. Despite several methods aimed at preventing fraud at the cash register, addressing this issue remains an ongoing concern. In this study, machine learning methods are used to detect fraudulent transactions at the cash register in fast-food restaurants. By using POS logs, transactions in restaurants are recorded and these logs are analyzed to detect fraudulent transactions on an unbalanced dataset. Random forest, XGBoost and LGBM algorithms are used in the study and different resampling techniques (ADASYN etc.) are applied to improve the performance of these algorithms. In addition, it is aimed to find the best parameters with the randomized search method. In conclusion, this study offers a solution for detecting fraudulent transactions at the cash register in fast-food restaurants. The results of the study are promising in its current state. © 2023 IEEE.

关键词： Cash registers

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Aggregating Structural Features for Computational Analysis of Mutations in BRCT Domains of BRCA1 Protein

Aggregating Structural Features for Computational Analysis o...

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2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Hidayat, Alam Ahmad Nirwantono, Rudi Isnan, Mahmud Trinugroho, Joko Pebrianto Pardamean, Bens Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Mathematics Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Bioinformatics and Data Science Research Center Biotechnology Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Binus Graduate Program Bioinformatics and Data Science Research Center Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350307306

Understanding the mechanistic interpretability of mutation effects in a protein can help predict the clinical implications of the genetic variants. Hence, computational variant effect predictions that involve protein structural features of the protein mutations might be suitable in this case. In this work, we focus on BRCT domains of BRCA1 gene that is widely studied in breast cancer studies. We retrieved 88 selected missense variants found in BRCT domains annotated in both ClinVar and gnomAD databases. To computationally characterize the pathogenic property of the mutations we used two types of features extracted from protein structures: a change in free Gibbs energy and a set of features derived from molecular dynamics simulations of each mutant. Using a dimensional reduction and Gaussian mixture model (GMM)-based clustering we demonstrate that the variants are segregated into two regions that may correspond to their pathogenic status. This method can be a potential computational pipeline for providing the preliminary mechanistic interpretation of mutation effects in terms of their thermodynamic and structural features. © 2023 IEEE.

关键词： Proteins

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Network Traffic Anomaly Detection Using Quantile Regression with Tolerance

Network Traffic Anomaly Detection Using Quantile Regression ...

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2023 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2023

作者： Alsan, Huseyin Fuat Guler, Ali Kerem Yildiz, Ekrem Kilinc, Sena Camlidere, Bora Arsan, Taner Kadir Has University Department of Computer Engineering Istanbul Turkey Turknet Department of Data Science Istanbul Turkey

ISBN: (纸本)9798350337822

Network traffic anomaly detection describes a time series anomaly detection problem where a sudden increase or decrease (called spikes) in network traffic is predicted. data is modeled with the trend and heteroscedastic noise component. Traditional autoregressive models struggle to capture data changes effectively, making anomaly detection difficult. Our approach is to generate upper and lower limits by using quantile regression. We use a deep learning based multilayer perceptron model to predict five data quantiles 1, 25, 50, 75, and 99. The upper and lower limits are calculated as differences between the quantile-1 and quantile-99. Any data that is outside these limits are considered as an anomaly. We also add tolerance to these limits to add flexibility to anomaly detection. Anomalies and non-anomalies are labeled to get a binary classification task. Anomaly detection is class imbalanced by nature;therefore, precision, recall, and F-1 score are computed to evaluate the proposed anomaly detection method. We conclude that choosing tolerance is a tradeoff between false alarms and missing anomaly detections. © 2023 IEEE.

关键词： Time series

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data-Driven Power Flow Estimation for MVDC Distribution Systems Based on Physics-Embedded FCN

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IEEE Transactions on Smart Grid 2025年第4期16卷 2816-2829页

作者： Sun, Pingyang Wu, Rongcheng Shen, Zhiwei Wang, Hongyi Li, Gen Khalid, Muhammad Konstantinou, Georgios School of Electrical Engineering and Telecommunications UNSW Sydney Australia University of Technology Sydney Data Science Institute Australia Aalborg University Department of AAU energy Denmark Energy Technology and Computer Science Section Department of Engineering Technology Ballerup2750 Denmark King Fahd University of Petroleum and Minerals Electrical Engineering Department Interdisciplinary Research Center for Sustainable Energy Systems Dhahran31261 Saudi Arabia

Maintaining high prediction accuracy with varying grid topologies poses a significant challenge to adopting neural network (NN)-based approaches for power flow (PF) estimation in medium-voltage direct current (MVDC) distribution systems. This paper proposes a physics-embedded fully convolutional network (PEFCN) to improve the accuracy. Physics-embedded techniques are incorporated in the proposed PEFCN through 1) architecture reconstruction, and 2) loss function reformulation. Architecture is reconstructed by input channel conversion calculation in a new physics operation layer. This process offers physical connections among the three input matrix channels (voltage, current, and line conductance). Three new physics loss terms are included in the loss function to restrict the outliers violating the limits of converter power ratings and terminal MVDC voltages. The two operations enable the FCN to achieve improved prediction accuracy and strong generalization capabilities. Five MVDC distribution systems, characterized by different dc voltage levels and topology configurations, serve to validate the superiority of the proposed PEFCN over other NNs in the PF estimation for scenarios involving both fixed and varying system structures. Moreover, a modified IEEE 69-bus distribution system is further used to demonstrate the applicability of the proposed PEFCN for larger systems. © 2010-2012 IEEE.

关键词： DC distribution systems

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Classification of PC Baseband Signals from Wireless Egress 26

Classification of PC Baseband Signals from Wireless Egress

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26th International ITG Workshop on Smart Antennas, WSA 2023 and 13th Conference on Systems, Communications, and Coding, SCC 2023

作者： Leghari, Mohammad Ahmed Pralle, Sina Mei Peik, Sören F. Lütje, Sebastian Henkel, Werner Computer Science and Engineering Jacobs University Bremen Bremen Germany Electrical Engineering and Computer Science Bremen University of Applied Sciences Bremen Germany Development and TEMPEST Aerospace Data Security GmbH Bremen Germany

ISBN: (纸本)9783800760510

Being related to so-called TEMPEST activities, we show some possibilities to classify standard signal emissions from a PC environment, such as Ethernet, USB, or HDMI. Hereto, we make use of time-frequency analysis followed by deep learning or alternatively, by looking into individual signal properties, such as symbol rates, special fixed patterns, common-mode presence, or amplitude histograms. We will see that the distinction of such standard signals can be very efficient, even in the case of 100Base-T and 1000Base-T Ethernet that are designed to use the same frequency range and hence have a very similarly looking time-frequency representation. Under noise-free conditions, 100 % correct classification is achieved. © VDE VERLAG GMBH · Berlin · Offenbach.

关键词： Ethernet

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A Comprehensive Analysis of the Internet of Things (IOT) in the Context of 5G Wireless Systems 4

A Comprehensive Analysis of the Internet of Things (IOT) in ...

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4th International Conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Mohseni, Milad Kanwer, Budesh Singh, Navdeep Krishna, M Hari Rajalakshmi, B. Alkhafaji, Mohammed Ayad Islamic Azad University Department of Computer and Engineering Tabriz Iran Poornima Institute of Engineering & Technology Department of Artificial Intelligence & Data Science India Lovely Professional University Phagwara India Institute of Aeronautical Engineering Hyderabad India New Horizon College of Engineering Department of Computer Science Bangalore India National University of Science and Technology College of Technical Engineering Dhi Qar Iraq

ISBN: (纸本)9798350307757

The fast development of 5G wireless technologies (IoT) has been a major contributor to the rapid growth and expansion of the Internet of Things! This in-depth study investigates the mutually advantageous relationship between 5G and the Internet of Things (IoT), focusing on how the combination assists in the development of massive device networks, enhances connectivity, and enables the development of real-time applications. There are now new opportunities for innovative applications in smart cities, industrial automation, healthcare, and other industries owing to 5G, which enables Internet of Things devices to communicate with one another at a speed and efficiency that has never been seen before. In spite of the benefits, however, there are challenges to be faced, such as the construction of infrastructure to support the expansion of the Internet of Things and the assurance of robust security. This article provides an analysis of these components and a blueprint for leveraging the advantages of Internet of Things (IoT) with 5G while minimizing any potential downsides. © 2024 IEEE.

关键词： Internet of things

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