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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Manjunath Managuli Sagar Pujar Sangamesh C Managuli Krishnamurthy K. T Rajesh Nandalike Pavan Kunchur Dept. of Electronics & Communication KLS Gogte Institute of Technology Belagavi India Dept. of Computer Science KLS Gogte Institute of Technology Belagavi India Dept. of Mechanical Engineering PES University Bangalore India Dept. of Electronics & Instrumentation B M S College of Engineering Bangalore India Dept. of Electronics & Communication Nitte Meenakshi Institute of Technology Bangalore India

Relevant data technology advancements have led to a massive growth in classifications. Deep learning systems, however, need help handling such a massive volume of data. This work aims to use the Evaluation of different classification algorithms for big data, Comparison of accuracy, precision, recall, and F1 score among various algorithms, and Identification of algorithms suitable for handling large-scale datasets and conventional deep learning to analyze a large amount of data. Classifiers allowed researchers to examine how various random sampling strategies affected the model's performance. Two methods of feature selection were employed in the tests. The Bayesian Network likewise employs a single deep learning classifier. By lowering the dimensionality of the data, this study seeks to maximize model performance. A recording dataset larger than 60 GB was made less dimensional to conduct the trials. The findings demonstrate that, when employing both sampled and non-sample data, overall performance varies between 78% accuracy and 82% accuracy, a minor variation in performance while utilizing both sampled and non-sample (all the data) datasets. The random subset approach and the Bayesian network classifier were employed in the trials, and the overall findings show that the variety experiment had the best performance out of all the combinations of experiments. Test Cradle Experimentation: Variation length, delay variation, and packet loss are used to assess the predicted scheme's efficiency.

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Towards Explainable Test Case Prioritisation with Learning-to-Rank Models

Towards Explainable Test Case Prioritisation with Learning-t...

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IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW

作者： Aurora Ramírez Mario Berrios José Raúl Romero Robert Feldt Dept. of Computer Science and Numerical Analysis University of Córdoba Córdoba Spain Dept. of Computer Science and Engineering Softw. Eng. Division Chalmers University of Technology Goteborg Sweden

Test case prioritisation (TCP) is a critical task in regression testing to ensure quality as software evolves. Machine learning has become a common way to achieve it. In particular, learning-to-rank (LTR) algorithms provide an effective method of ordering and prioritising test cases. However, their use poses a challenge in terms of explainability, both globally at the model level and locally for particular results. Here, we present and discuss scenarios that require different explanations and how the particularities of TCP (multiple builds over time, test case and test suite variations, etc.) could influence them. We include a preliminary experiment to analyse the similarity of explanations, showing that they do not only vary depending on test case-specific predictions, but also on the relative ranks.

关键词： Software testing Machine learning algorithms Conferences Software algorithms Machine learning Software Task analysis

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Comparative Study of Customer Churn Prediction Based on Data Ensemble Approach

Comparative Study of Customer Churn Prediction Based on Data...

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2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Kumar, S.Dinesh Soundarapandiyan, K. Meera, S. Sri Sairam Engineering College Department of Management Studies Tamil Nadu Chennai India B.S.Abdur Rahman Crescent Institute of Science & Technology Dept. of Commerce Tamil Nadu Chennai India Sri Sairam Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350394580

Customer Churn is the prima facie problem of any company irrespective of their size and also it has a direct influence on the revenue of the companies. So this makes every company to understand the rationale behind the customer churn and predict the potential customer churn and the reasons for the same. By identifying the factors of churn, the companies will be in adept.position to retain the customers. Thence the main intention of this work is to build-up a churn prediction model using machine learning that assists the companies to predict the customers who are most likely to churn. The models were measured by adopting Area Under Curve (AUC) measure, the confusion matrix (Accuracy, Recall, Precision) and F1 Score. The ensemble modeling was also adopted in order to compare and consolidate the each models for a common outcome. The models were run through Python (Jupyter Notebook) environment on the big dataset obtained from Kaggle. This research tried out with following model algorithms: Naive Bayes Model, Discriminant Analysis, Decision Tree, Random Forest, and Logistic Regression and further to attest the outcome of the data ensemble modeling of Ada boost, Gradient Boosting and Bagging has also been used. Out of all the models Random Forest model was considered to be the best fit model due to high accuracy, specificity, AUC and FI Score. So this model can be used as a classification churn prediction model. © 2023 IEEE.

关键词： Customer Churn Decision Tree Discriminant Analysis Ensemble modeling Logistic Regression ML algorithms Naive Bayes Random Forest

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Integrating Cloud And Edge Computing For Scalable Iot Applications 2

Integrating Cloud And Edge Computing For Scalable Iot Applic...

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2nd International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2024

作者： Nagarjuna, Pitty Sevuga Pandian, A. Sharma, Suresh Kumar Ramesh, T. Sohail, Mohammed Ali Dinesh Babu, M. Indian Institute of Science Karnataka Bengaluru India Department of Computer Science Kristu Jayanti College Karnataka Bengaluru India Dept. of Statistics Mathematics and Computer Science Sri Karan Narendra Agriculture University Jaipur Jobner India Department of CSE R.M.K Engineering College Chennai India Department of EEE Jazan University Jazan Saudi Arabia Department of Mechanical Rajalakshmi Institute of Technology Chennai India

ISBN: (纸本)9798331541217

The data from the Internet of Things (IoT) is essential in the modern data-driven digital economy since it inspires many new business models supplying a wide spectrum of services both ubiquitous and intelligent. The data transmitted using edge computing technologies is investigated in this paper. Under the framework of the trust model of the research, the data exchanged between the source block and the destination block generates direct, indirect, and reciprocal trust. Although cloud computing can serve as a paradigm for safe data flow, the results of the study revealed that a trust-based approach can avoid cloud intrusion. The simulation runs on numerous test beds using the presently in use cloud technique. The results clearly illustrate that the trust-based approach is effective in protecting data moved across edge computing. © 2024 IEEE.

关键词： Cloud platforms

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Sharpening Directivity of Beamforming based on Neural Networks using Virtual Observations 10

Sharpening Directivity of Beamforming based on Neural Networ...

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10th IEEE Global Conference on Consumer Electronics, GCCE 2021

作者： Kanekiyo, Yuki Mizumachi, Mitsunori Miyazaki, Ryoichi National Institute of Technology Tokuyama College Dept. of Computer Science And Electronic Engineering Yamaguchi Japan Kyushu Institute of Technology Dept. of Electrical Engineering And Electronics Kitakyushu Japan

ISBN: (纸本)9781665436762

Beamforming based on neural networks (NN) has been proposed as nonlinear beamforming. This method improves the performance with more microphones, but it may not be possible to easily increase the number of microphones due to hardware restrictions. Therefore, we focus on virtual observation, which is an estimation of the signal observed at a location where the microphone is not actually placed, using only actual observations. In this paper, we propose to apply virtual observation to NN-based beamforming. From the evaluation experiment, we confirmed that the proposed method can obtain sharper directivity than the case of using only actual microphones. © 2021 IEEE.

关键词： Beamforming

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A Framework Leveraging Robotics and Machine Learning Technologies for Early Disease and Pest Detection in Greenhouse Tomato Crops

A Framework Leveraging Robotics and Machine Learning Technol...

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International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO)

作者： Nikolaos Kounalakis Emmanouil Kalykakis Dimitrios Kosmopoulos John Fasoulas Michael Sfakiotakis Control Systems and Robotics Laboratory (CSRL) School of Engineering Hellenic Mediterranean University Heraklion Greece Dept. of Computer Engineering and Informatics University of Patras Patras Greece

We present an integrated framework for intelligent automated plant monitoring, towards early disease and pest detection in greenhouse tomato crops. The framework combines the use of a robotic mobile platform to autonomously collect multi-spectral images of the plants, with a tool that utilizes Faster R-CNN to detect regions that signify the presence of a disease or pest. The robot is based on a modified mobile vertical mast lift platform, and integrates a 6-dof robotic arm that is used to position the plant imaging multi-spectral camera. The robot can navigate autonomously inside the greenhouse via a magnetic guidance sensor. Results from a series of experiments demonstrate the validity and effectiveness of the implemented framework.

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MedEthicEval: Evaluating Large Language Models Based on Chinese Medical Ethics

arXiv

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arXiv 2025年

作者： Jin, Haoan Shi, Jiacheng Xu, Hanhui Zhu, Kenny Q. Wu, Mengyue Ant Group Hangzhou China SJTU Shanghai China SJTU: X-LANCE Lab Dept. of Computer Science and Engineering Shanghai Jiao Tong University China FDU: Institute of Technology Ethics for Human Future Fudan University China UTA: Dept. of Computer Science and Engineering University of Texas Arlington United States

Large language models (LLMs) demonstrate significant potential in advancing medical applications, yet their capabilities in addressing medical ethics challenges remain underexplored. This paper introduces MedEthicEval, a novel benchmark designed to systematically evaluate LLMs in the domain of medical ethics. Our framework encompasses two key components: knowledge, assessing the models’ grasp of medical ethics principles, and application, focusing on their ability to apply these principles across diverse scenarios. To support this benchmark, we consulted with medical ethics researchers and developed three datasets addressing distinct ethical challenges: blatant violations of medical ethics, priority dilemmas with clear inclinations, and equilibrium dilemmas without obvious resolutions. MedEthicEval serves as a critical tool for understanding LLMs’ ethical reasoning in healthcare, paving the way for their responsible and effective use in medical contexts. © 2025, CC BY.

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A Comparative Analysis between Sentiment Analysis Methods in Stock Prediction of IoT-based companies

A Comparative Analysis between Sentiment Analysis Methods in...

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IEEE World Symposium on Communication engineering (WSCE)

作者： Georgia E. Pasoi Kyriacos A. Talattinis Vasileios A. Memos Sotirios K. Goudos Panagiotis G. Sarigiannidis Yutaka Ishibashi Konstantinos E. Psannis Dept. of Applied Informatics University of Macedonia Thessaloniki Greece Dept. of Physics Aristotle University of Thessaloniki Thessaloniki Greece Dept. of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Dept. of Business Management Faculty of Business Administration Aichi Sangyo University Aichi Japan

ISBN: (数字)9798331542825

ISBN: (纸本)9798331542832

Predictions in financial markets have attracted much of scholars’ interest. Since the stock market is generally defined as dynamic and volatile due to the affect of the well-known "Internet of Things" (IoT). It is a fact that IoT significantly impacts the financial market and stocks in various ways. Therefore, numerous researchers have attempted to detect patterns regarding the way financial markets react to various external factors. In the last decade, the integration of machine learning and sentiment analysis has appeared to be an effective approach for improving IoT-based stock price predictions in the financial field. This paper intends to propose an approach involving financial news as a source of information for forecasting the stock movement of noteworthy IoT-related companies, employing Big Data Analytics (BDA), Machine Learning, and Sentiment Analysis methods as well. A comparative analysis is conducted between lexicon-based and machine learning-based methods of sentiment analysis about their performance, particularly in the domain of IoT-related stock market prediction.

关键词： Measurement Sentiment analysis Recurrent neural networks Companies Lexicon Big Data Internet of Things Reliability Stock markets Software development management

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Federated Learning-based Techniques for Healthcare IoV: Challenges and Future Directions -A Knowledge Review

Federated Learning-based Techniques for Healthcare IoV: Chal...

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International Conference for Convergence of technology (I2CT)

作者： Pallati Narsimhulu Rashmi Sahay Dept. of CSE Faculty of Science and Technology (IcfaiTech) ICFAI Foundation for Higher Education Hyderabad India Department of Computer Engineering and Technology Chaitanya Bharathi Institute of Technology Hyderabad India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

Federated Learning (FL) is a cutting-edge method for creating Machine Learning (ML) models using networked data sets. It supports advanced Artificial Intelligence (AI), keeps information neighborhoods, protects personal information, and allows for ownership. FL’s features make it a good candidate for IoT networks due to its inherent distributed infrastructure. On the other hand, FL provides several challenges, the most significant of which is training on IoT devices with mostly heterogeneous data sources. The diversity of devices and algorithms in complex Internet of Vehicles (IoV) networks significantly impacts the FL training process and renders traditional FL unsuitable for direct deployment. While numerous recent publications claim to lessen the negative impacts of variety in FL networks, the efficacy of these proposed ways has never been tested and assessed. In this study, we examine the impact of heterogeneity in FL in dept. and provide an overview of the actual challenges imposed by the system and statistical heterogeneity. We researched on cutting-edge algorithms, emphasizing their practical application in IoT networks. We also compared the popular FL algorithms on a dynamic IoV network. Our study shows that existing methods fail to alleviate the issue appropriately, emphasizing the significance of considering system and statistical diversity while developing FL systems. To properly investigate the consequences of the network heterogeneity, we tested 10%, 30%, 50%, 70%, and 90% stragglers. These conditions serve as a training ground, allowing us to see how various degrees of network heterogeneity impact a technique’s efficiency. The test-bed results show that the Scafold technique outperforms existing techniques in terms of training time reduced to 18% and accuracy increased to 21%

关键词： Training Reviews Heuristic algorithms System performance Soft sensors Time measurement Stability analysis

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Enhanced Healthcare Through Metaverse Integration: Explainable AI and Blockchain for Trust, Security, and Immersive Experiences

Enhanced Healthcare Through Metaverse Integration: Explainab...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Bilal Ibrahim Sowan Hazem Qattous Mohammad Fasha Ahmad Altarawneh Dept. of Bus. Intell. & Data Analytics University of Petra Amman Jordan Dept. of Software Engineering Princess Sumaya University for Technology Amman Jordan Dept. of Computer Science Brunel University of London London UK

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

The metaverse, a dynamic blend of virtual and augmented reality, holds extraordinary promise for reshaping the healthcare landscape. By integrating advanced technologies such as virtual reality (VR), artificial intelligence (AI), blockchain, and digital twins, the metaverse introduces transformative opportunities in patient care, medical education, and healthcare operations. These tools foster immersive, personalized, and data-driven environments, significantly improving diagnostic precision, therapeutic strategies, and training methodologies. This paper provides a framework to utilize this potential while navigating key concerns, including systems scale, user customization, and ethical adequacy. In that case, the framework's goal should be eliminating existing limitations and barriers within healthcare systems by utilizing state-of-the-art technologies with real-world applications. The framework involves virtual consultation sites that enable provider-patient interactions, intelligent virtual agents with specific care recommendations, blockchain integration for data integrity, and dynamic VR segments that are functional in delivering healthcare education and training. The study focuses on scenario-based modeling and the use of pilot deployment in various healthcare settings to test for practicability and feasibility. The research also emphasizes that ethical issues and inclusion during the design and implementation of the innovations should also consider society's values regarding the medical field and promote equal access to healthcare. This framework proves a comprehensive blueprint for continuous progress and places the metaverse as one cornerstone in the global healthcare system.

关键词： Training Ethics Technological innovation Solid modeling Metaverse Medical services Blockchains Digital twins Security Medical diagnostic imaging

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