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Statistical Modeling of Temperature Prediction Using Residual Network 3rd

Statistical Modeling of Temperature Prediction Using Residua...

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3rd international conference on computing, Communication and learning, CoCoLe 2024

作者： Sharaff, Aakanksha Sonkusare, Ankit Pal, Ankita Pahadi, Sheetal Department of Computer Science and Engineering National Institute of Technology Raipur Raipur India

ISBN: (纸本)9783031790409

The worldwide change in earth’s temperature, particularly in tropical regions, has drawn the attention of experts due to its impact on human life. The availability of extensive weather observation data and the utilization of advanced data science tools have inspired researchers to investigate trends and minimize errors in massive volumes of data for accurate weather forecasting across various weather parameters. The goal of this analysis is to generate a reliable and flexible statistical model that accurately predicts the temperature of Delhi without any errors. The climate of Delhi is a combination of monsoon-influenced humid subtropical and droughty, characterized by significant fluctuations in both summer and winter temperatures. The prediction model being suggested utilizes Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) as the hidden layers within the residual network, which highly outperforms in terms of mean square error (MSE) as measure to other cutting-edge algorithms like LSTM, GRU, and ResNet. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Prediction models

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A Review of Deep-learning-Based Models for Afaan Oromo Fake News Detection on Social Media Networks 3rd

A Review of Deep-Learning-Based Models for Afaan Oromo Fake ...

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3rd international conference on Advancements in Smart computing and Information Security, ASCIS 2024

作者： Arega, Kedir Lemma Tune, Kula Kekeba Beyene, Asrat Mulatu Tariku, Wegderes Bune, Nurhussen Menza ICT Infrastructures and Administrator Team Leader Ambo University Ambo Ethiopia Department of Software Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia Department of Electrical and Computer Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia Department of Information Systems Mizan Tepi University Tepi Ethiopia Project Management System Support Expert Ethio Telecom Addis Ababa Ethiopia

ISBN: (纸本)9783031862953

This study explores modern fake news detection techniques in the Afan Oromo language, aiming to contribute to advancing research in this field. It explores deep learning methods for transformers, including Bidirectional Encoder Representations, Attention, and Generative Adversarial Networks, and their applications in various industries such as machine translation, email spam detection, information extraction, summarization, medical, and question-answering. The paper investigates false news identification, its categories, characteristics, and taxonomy, and compares machine learning and deep learning algorithms for fake news, liars, and corpus datasets. It proposes that future studies use picture data and deeper learning network topologies to decrease erroneous outcomes. The research endeavor aims to illuminate and advance the understanding of the complex difficulties and possibilities that exist in this particular sector, thereby acting as a crucial resource for decision-makers, academics, and professionals, while also encouraging the creation of more effective and trustworthy methodologies that can adequately address and alleviate the dissemination of false information shared on various social media channels. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Efficient Classification of Live Sensor Data on Low-Energy IoT Devices with Simple Machine learning Methods 19thth

Efficient Classification of Live Sensor Data on Low-Energy...

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19th international conference on Computer Aided Systems Theory, EUROCAST 2024

作者： Hanreich, Martin Krauss, Oliver Zwettler, Gerald Research and Development Research group AIST University of Applied Sciences Upper Austria Hagenberg4232 Austria Faculty for Informatics Communications and Media Department of Media Technology and Design University of Applied Sciences Upper Austria Hagenberg4232 Austria Faculty for Informatics Communications and Media Department of Software Engineering University of Applied Sciences Upper Austria Hagenberg4232 Austria

ISBN: (纸本)9783031829598

This paper addresses the implementation of time series classification on Internet of Things (IoT) devices with minimal resource usage while maintaining required accuracy levels. Although IoT devices have grown more powerful, optimizing resource usage remains essential to reduce costs and extend battery life. This work explores the practical aspects of developing and deploying time series classification models on IoT devices, emphasizing non-neural network models due to their lower resource demands. A detection system utilizing multiple predictions and a voting-based mechanism is constructed to enhance prediction stability. The input data comprises multivariate sensor streams of the same length. All computations are performed on the device, leveraging edge computing principles without external server dependency. This approach demonstrates how to balance accuracy and resource efficiency in IoT-based time series classification, offering a practical framework for similar applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Energy-Efficiency IoT-Devices Machine learning Time-Series-Classification

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Traffic Sign Recognition Using Convolutional Neural Networks with Transfer learning for Advanced Driving Assistance Systems (ADAS) 6th

Traffic Sign Recognition Using Convolutional Neural Networks...

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6th international conference on Advanced Informatics for computing Research, ICAICR 2023

作者： Tiwari, Aditya Upadhyay, Arsh Patel, Neel Goel, Parth Department of Computer Science and Engineering Devang Patel Institute of Advance Technology and Research Charotar University of Science and Technology Changa Gujarat Anand388421 India

ISBN: (纸本)9783031840616

Traffic Sign recognition is a crucial part of current systems of transportation and Advanced Driving Assistance Systems (ADAS). The safe operation of an autonomous vehicle and the effective management of road infrastructure depend on a traffic sign recognition system. Existing techniques to traffic sign identification often face difficulties to classify the traffic signs accurately and resulting in lower reliability in identifying critical traffic signs. This limitation compromises road safety and prevents ADAS from reaching its maximum potential. The research focuses to classify the Indian traffic signs using transfer learning with a pre-trained model. The pre-trained convolutional neural networks (CNNs) such as ResNet50, Xception, DenseNet121, and EfficientNetB5 are used to develop the convolutional and pooling layers that are used in the proposed framework. In order to help with accurate classification of traffic indicators, fully-connected layers as well as an output layer have also been developed. The proposed method is thoroughly tested using the Kaggle Indian Traffic Signal Prediction dataset, which contains 85 distinct types of traffic signals regularly noticed on Indian highways. The test outcomes show that ResNet50 outperformed among the four pre-trained CNNs, with an impressive testing accuracy of 86.47% and a training accuracy of 89.91%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Advanced driver assistance systems

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22nd international conference on Scientific computing and Bioinformatics, CSC 2024, and 25th international conference on Computational Biology, BIOCOMP 2024, held as part of the World Congress in Computer Science, Computer engineering and Applied computing, CSCE 2024

22nd International Conference on Scientific Computing and Bi...

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22nd international conference on Scientific computing and Bioinformatics, CSC 2024, and 25th international conference on Computational Biology, BIOCOMP 2024, held as part of the World Congress in Computer Science, Computer engineering and Applied computing, CSCE 2024

ISBN: (纸本)9783031859014

The proceedings contain 31 papers. The special focus in this conference is on Scientific computing and Bioinformatics,. The topics include: Austere Runway Simulated Identification;using Rhai to Create and Benchmark Bevy Entities;pyO3: Building Python Extension Modules in Native Rust with Performance and Safety in Mind;Leveraging Python Interpreters for Concurrency in SeQUeNCe;benchmark Performance of a Rust-Based Python Extension;quantum Error Correction in Repetition Codes;investigating Selective Reliability with the Laminar Networking Package;an Entanglement Swapping Throughput Analysis for Quantum Networks Using Linear Quantum Optics;notes on Symmetric Generalized Tent Map: Route to Chaos;scalable Service Model and Scheduler for Delay-Sensitive Services;regret-Minimization Heuristics for Identifying Monotone Boolean Functions;multi-class Classification of Satellite Orbits for Database Quality Control;real-Time Motion Planning for Autonomous Vehicles in Dynamic Environments;A Novel Deep learning Method for Solving PDE’s Applied to a Shallow Water Problem;exploring the Impact of Social Media on Mental Health and Well-Being: A Multi-dimensional Analysis;the Strategic Optimization of a Tabletop Role-Playing Game;Problem-Solving Using Logic and Reasoning, Mathematics, Algorithms, Python, and Generative AI: Part Two;acquisition and Analysis of Mobile Data Using Digital Forensics Tools and Techniques;structural Health Monitoring for Risk Assessment and Reliability of a Structure After Extreme Loads;scientific Constructing Adequate Statistical Decision Rules Under Parametric Uncertainty of Applied Mathematical Models via the Smart Use of Pivotal Quantities and Ancillary Statistics;a Smart Air Quality Analysis and Pollutant Diffusion Detection and Prediction System Based on Tree Canopy Shape Research Using Machine learning and Artificial Intelligence;a High Order Scheme for Modelling Viscous Incompressible Fluid Flow in a Channel with a Step;Feasibility Study of Neutron M

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Performance Evaluation of Medicinal Leaf Classification Using DeepLabv3 and ML Classifiers 1st

Performance Evaluation of Medicinal Leaf Classification Usin...

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1st international conference on Innovation and Emerging Trends in computing and Information Technologies, IETCIT 2024

作者： Bodla, Ashwin Kumar Damodara, Rama Krishna AP Visakhapatnam India Department of CSE CVR College of Engineering Telangana Hyderabad India

ISBN: (纸本)9783031808388

Identifying and classifying medicinal plants is pivotal in various fields, including healthcare, pharmaceuticals, and traditional healing practices. With the integration of advanced technologies, this study focuses on the performance evaluation of a novel approach for medicinal leaf classification. Leveraging the DeepLabv3 model for semantic segmentation, we propose a comprehensive methodology that combines deep learning with traditional machine learning (ML) classifiers. The study begins with developing a real-time identification system using the DeepLabv3 network, enabling swift and accurate classification of medicinal plant leaves from images or live video feeds. Semantic features extracted from the segmentation maps are utilized for feature representation. Subsequently, these features are fed into various ML classifiers, including Support Vector Machine (SVM), Random Forest, Logistic Regression, k-nearest Neighbors (KNN), and a Naïve Bayes classifier. The classifiers are trained and evaluated on a dataset comprising five distinct medicinal plant species: Basale, Betel, Guava, Hibiscus, and Tulsi. Performance indicators such as accuracy, precision, F1 score, and recall are extensively analyzed to assess the efficacy of each classifier in medicinal leaf classification. The DeepLabv3 model′s capability for semantic segmentation contributes valuable features, enhancing the discriminatory power of ML classifiers. The results demonstrate the potential of this integrated approach, providing insights into the strengths and limitations of each classifier in the context of medicinal leaf classification. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Medicinal chemistry

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Chat Bot for Crop Yield Prediction and Recommendation Through K-Nearest Neighbor Algorithm 1st

Chat Bot for Crop Yield Prediction and Recommendation Throug...

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1st international Research conference on computing Technologies for Sustainable Development, IRCCTSD 2024

作者： Surya, J. Prabha, B. Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai India SRM Institute of Science and Technology Chennai India

ISBN: (纸本)9783031823886

The agricultural sector is vital to the nation’s economy, providing food, employment, income, foreign exchange, and raw materials for manufacturing industries. However, farmers face significant challenges in maintaining quality and quantity controls over crop cultivation to meet market demands, requiring extensive knowledge and expertise. In India, a leading producer of various crops, the agricultural sector still suffers from insufficient yields due to the lack of advanced techniques and a reliable referral system for farmers. A critical gap exists in the decision-making process for Indian farmers, who struggle with selecting appropriate crops based on soil characteristics and assessing soil health. This issue has significantly impacted their productivity. Addressing this gap is essential for optimizing resource allocation and mitigating future risks. Accurate crop yield prediction and crop recommendation are crucial in agricultural decision-making, and recent advancements in machine learning algorithms have facilitated more precise predictions. This study focuses on employing the Decision Tree method for crop yield prediction. Once constructed, the Decision Tree model can predict crop yields for unknown data and be updated with new inputs such as soil measurements or weather forecasts. The interpretability of Decision Trees allows farmers to identify the factors that most significantly affect crop yield variations, leading to more informed decision-making. Furthermore, a chatbot has been developed to assist users in providing details and receiving crop predictions and recommendations. The study evaluates the performance of three machine learning algorithms—Random Forest, K-Nearest Neighbor, and Decision Tree—in developing the chatbot. By processing input data through these algorithms and comparing their outputs, the study determines the most accurate crop forecast and recommendation. This comparison also helps calculate the standard deviation of crop forecasts and

关键词： Chatbot Decision tree K-Nearest Neighbor Machine learning Random Forest

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Predicting Employee Job Satisfaction by Using Vector Space Model 6th

Predicting Employee Job Satisfaction by Using Vector Space M...

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6th international conference on Computational Intelligence in Communications and Business Analytics, CICBA 2024

作者： Sibikanna, P. Venkataraman, D. Dayanand, V. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9783031813382

In today’s business landscape, the sheer number of companies and organizations makes it challenging and time-consuming to assess and select the most suitable one. While various methods exist for rating these firms, there’s a genuine need for a comprehensive metric that accounts for the diverse factors shaping employee perceptions of these organizations. This study aims to address this challenge by generating aspect-based sentiment embeddings for businesses through the analysis of reliable employee evaluations. To accomplish this, we employ a state-of-the-art ensemble technique for aspect-level sentiment analysis, leveraging a substantial dataset of business appraisals sourced from the reputable platform ***. When it comes to predicting job satisfaction, assessments of employees can be performed manually or using advanced machine learning algorithms. In this study, we systematically evaluate commonly employed machine learning algorithms, including logistic regression, support vector classifier (SVC), Random Forest (RF), Stochastic Gradient Descent classifier (SGD), AdaBoost classifier, and gradient boosting. Furthermore, we propose a deep learning model for forecasting employee satisfaction. Our assessment of these models includes the examination of precision, accuracy, recall, and F1 score metrics, encompassing macro-average precision and weighted average precision. According to our analysis of Glassdoor reviews, the Long Short Term Memory (LSTM) model demonstrates superior performance compared to the other models, boasting the highest accuracy. This pioneering study stands as the first of its kind, despite the extensive research conducted in fields like film and music evaluations. Additionally, it provides valuable insights derived from aggregated embeddings, aiding consumers in making informed choices and selecting businesses that align with their personalized preferences. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Job satisfaction

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Enhancing IoT Security Through Hierarchical Message-Passing Graph Neural Networks: A Trust-Driven Strategy for Identifying Malicious Nodes 1st

Enhancing IoT Security Through Hierarchical Message-Passing ...

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1st international conference on Innovation and Emerging Trends in computing and Information Technologies, IETCIT 2024

作者： Senthil Kumar, C. Vijay Anand, R. School of Computer Science Engineering & Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9783031808388

Internet of Things (IoT) gadgets connect and simplify life in several ways. The independence of these technologies poses some issues, such as the protection of security and privacy from malicious and compromised nodes inside the network. To overcome this complication, Enhancing IoT Security Through Hierarchical Message-Passing Graph Neural Networks: A Trust-Driven Strategy for Identifying Malicious Nodes (HMPGNN-Trust-IoT) is proposed. These issues are addressed by the proposed HMPGNN-Trust-IoT solution, which develops a mechanism for recognizing malicious and compromised nodes utilizing trust-based deep learning. With the help of the dataset provided, HMPGNN-Trust develops a worldwide model to predict the IoT node’s irrational behavior. A global dataset comprising 19 trust parameters on three main categories including knowledge, experience, and reputation are used in the proposed HMPGNN-Trust-IoT technique. The data are fed to pre-processing. In preprocessing, it cleans and normalizes the data in the dataset by utilizing a Nanoplasmonic Ultra Wideband Band Pass Filter (NUWBF). HMPGNN-Trust uses the idea of communities using dedicated servers to divide the dataset into smaller portions for effective training to lessen the computational strain. Finally, it classifies the nodes by utilizing a Hierarchical Message-Passing Graph Neural Network (HMPGNN) into benign and malicious nodes. The proposed HMPGNN-Trust-IoT is implemented using Python. To identify Trust Management in IOT performance metrics like precision, accuracy, and F1-score are considered. Performance of the HMPGNN-Trust-IoT approach attains 24.11%, 28.56%, and 22.73% high accuracy, 21.89%, 23.04%, and 9.51% high precision, 25.289%, 15.35%, and 19.91% higher F1-Score compared with existing methods such as the Securing IoT along Deep Federated learning: A Trust-based Malicious Node Identification method (TM-ANN -IoT), the Trust-driven reinforcement selection technique in federated learning on Internet of Thin

关键词： Graph neural networks

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A Longitudinal Study to Investigate the Impact of a Service-learning Course on Its Participants 20th

A Longitudinal Study to Investigate the Impact of a Serv...

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20th international conference on Foundations of Computer Science, FCS 2024, and 20th international conference on Frontiers in Education, FECS 2024, held as part of the World Congress in Computer Science, Computer engineering and Applied computing, CSCE 2024

作者： Xu, Xin Jin, Wei Barakat, Rahaf Dekhane, Sonal Gunay, Cengiz Doloc-Mihu, Anca Robertson, Cindy School of Science and Technology Georgia Gwinnett College LawrencevilleGA30024 United States Robinson College of Business Georgia State University AtlantaGA30303 United States

ISBN: (纸本)9783031859298

The Technology Ambassador’s Program (TAP) was established in 2012 as an extra-curricular program and has been offered as a service learning course since spring 2016. To investigate the impact on program participants, we launched a longitudinal study in fall 2022 and surveyed the students who completed the course from spring 2016 to spring 2021. Analysis of the survey results discovered that students strongly agree that this program has provided them opportunities to conduct research, to network with other professionals in the field, and apply technical skills. Further analysis also revealed a strong correlation of these opportunities with improving soft skills and career readiness among participants. Overall, this program increased the confidence of the students and prepared them to learn new skills on their own. This paper describes the overall structure of the service learning program and presents the details of this study including the process and survey results. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Curricula

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