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8th International Conference on Business and Industrial Research, ICBIR 2023

作者： Phon-Amnuaisuk, Somnuk Tan, Soon-Jiann Khor, Kok-Chin Tham, Mau-Luen Chong, Yung-Wey Omar, Saiful Ibrahim, Noor Farizah Binti Yusoff, Mohd Najwadi Mashud, Idham School of Computing and Informatics Universiti Teknologi Brunei Brunei Universiti Teknologi Brunei Faculty of Engineering Brunei Universiti Tunku Abdul Rahman Lkc Faculty of Engineering and Science Malaysia Universiti Sains Malaysia School of Computer Sciences Malaysia

ISBN: (纸本)9798350399646

Without GPS data, ETA can be estimated with good precision from historical GPS data and the context of the traffic environment. In this paper, the estimated travel duration between bus stops was computed based on historical GPS data with time stamps. Three approaches were employed to compute the duration between bus stops: (i) statistical central tendencies, (ii) nonlinear regression techniques i.e., decision tree regressor, random forest regressor and k-nearest neighbors regressor, and (iii) an ANN predictive model. These three approaches capture different characteristics of the traffic environment. Statistical central tendencies give the overall view of the ETA information while nonlinear regressors can capture context from the time stamps (e.g., weekdays vs holidays). Finally, predicting the duration required to reach the next bus stop based on previous timespans on the same route implicitly captures temporal dynamics of the traffic environment from previous time steps and the ANN predictive model exploits this temporal information. The feasibility of ETA estimation using these three approaches was explained, evaluated and discussed. © 2023 IEEE.

关键词： Decision trees

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Optimization of Traffic and Time Control with Sensor-Driven Transmission Control System using MANET and Machine Learning 7

Optimization of Traffic and Time Control with Sensor-Driven ...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Kathirvel, N. Vidyalakshmi, R. Raihana, A. Mohanraj, A. Uma, S. Saranya, N. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science And Technology School of Computing Department of Information Technology India Karpagam College of Engineerng Department of Eee Coimbatore India Sri Krishna College of Engineering And Technology Department of Information Technology Coimbatore India Sri Eshwar College of Engineering Department of Computer Science And Engineering India Hindustan College of Engineering And Technology Department of Computer Science And Engineering India

ISBN: (纸本)9798350359299

In the dynamic landscape of modern urban environments, the optimization of transportation and traffic management systems is paramount for ensuring efficiency, sustainability, and the overall well-being of inhabitants. This study introduces an innovative solution through the deployment of a sophisticated network of strategically positioned sensors at crucial locations, encompassing roadway sections, entry and exit points, and parking spaces within a smart city framework. These sensors continuously capture real-time data on traffic flow, vehicle counts, and parking spot occupancy, forming the foundational elements of an intelligent system. Robust machine learning models, notably Convolutional Neural Networks (CNN), VGG19, k-Nearest Neighbors (KNN), and Support Vector Machines (SVM), are employed to process and analyze the acquired sensor data. CNN, renowned for its prowess in image recognition, emerges as the leading performer, achieving an impressive accuracy of 97.6%. This model excels in deciphering complex spatial patterns, significantly enhancing the precision of traffic forecasts. Additionally, performance metrics, encompassing precision, recall, F1 score, and accuracy, underscore the efficacy of each machine learning model. The exceptional precision and recall values across all models showcase their robustness in identifying and predicting traffic situations, while accuracy scores affirm their overall efficiency in optimizing transportation and traffic management within smart city environments. © 2024 IEEE.

关键词： Sensor networks

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Empowering the Blind: AI-Assisted Solutions for Visually Impaired People 9

Empowering the Blind: AI-Assisted Solutions for Visually Imp...

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9th IEEE International Smart Cities Conference, ISC2 2023

作者： Oureshi, Muhammad Shuaib Khan, Inam Ullah Qureshi, SMuhammad Bilal Khan, Fida Muhammad Aleshaiker, Sama Chungnam National University Department of Computer Engineering Daejeon Korea Republic of University of Lakki Marwat Department of Computer Science & It KPK Pakistan University of Science & Technology Department of Computer Science Bannu Pakistan School of Computing and Engineering University of West London United Kingdom

ISBN: (纸本)9798350397758

Physical disability has an influence on individuals everywhere. One of these disabilities that significantly affect a large number of individuals is visual loss. Basic mobility problems, including as crossing the street, reading, driving, and socializing, are commonly a problem for blind people. To go around or complete other daily duties, they typically need on particular assistance devices, including walking sticks. There is still more work to be done in the field of blindness treatment, despite ongoing scientific research. In addition, research has suggested solutions to the problems faced by blind individuals, but these solutions need the necessary technology support. The goal of this research project is to employ smart devices to make daily routines simpler for blind persons of all categories. This smart gadget can recognize faces, different objects, and colors by employing artificial intelligence and picture processing. The notification of the visually impaired individual results from the detecting procedure and might take the form of a vibration or sound alarm. This study also includes a tangible survey that includes local residents who are blind or visually handicapped. In order to program and implement the project, Open CV and Python are both used. This project prototype's effort looks into the methods that are employed for object detection. It also shows how this smart gadget can recognize certain physical objects and how it may alert the user when faced with hurdles. By helping blind individuals with the use of smart technology, this research will, overall, be a helpful contribution to the field of healthcare. © 2023 IEEE.

关键词： Face recognition

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Investigation of Security Attacks in IoMT Devices and Federated Learning as a Mitigation Strategy

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Procedia Computer science 2025年 258卷 3426-3435页

作者： Kiruthika M T Poongodi Assistant Professor Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Tamilnadu 603203 India Professor Department of Computer Science and Engineering School of Engineering Dayananda Sagar University Bangalore Karnataka India

Internet of Medical Things (IoMT) is transforming the healthcare business with an increasing number of wearables, gadgets, and sensing devices. As a result, several patients are under daily monitoring for their cardio health, blood glucose level, blood pressure, etc. The data generated by these devices are very voluminous, and there is an increasing need for safeguarding these sensitive data in order to protect them from malicious attacks. This paper suggests to incorporate Federated learning model to IoMT as a mitigative measure. It is apt to induct FL into IoMT as it operates on a collaborative environment which do not allow transfer of the raw data. Patients’ data are stored at the client end itself and only model updates are shared to the server. In this way, the data and network attacks can be reduced to a great extent. This work proposes an integrated architecture where the layers of FL and IoMT can be knotted up for ensuring privacy and security. This proposed architecture model, further elaborates a distributed working environment. This paper illustrates how FL helps in securing the data produced by the different IoMT devices through the proposed architecture. The challenges involved in the deployment of FL in the framework of IoMT along with potential research directions are discussed further in this research.

关键词： : Federated Learning IoMT Security Internet of Medical Things IoMT Data Attacks Optimization

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Emerging theory and phenomena in thermal conduction: A selective review

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science China(Physics,Mechanics & Astronomy) 2022年第11期65卷 2-17页

作者： Jie Chen Jia He Dongkai Pan Xiaotian Wang Nuo Yang Jiaojiao Zhu Shengyuan A.Yang Gang Zhang Center for Phononics and Thermal Energy Science China-EU Joint Lab for NanophononicsMOE Key Laboratory of Advanced Microstructured MaterialsSchool of Physics Science and EngineeringTongji UniversityShanghai 200092China School of Energy and Power Engineering Huazhong University of Science and Technology(HUST)Wuhan 430074China School of Physical Science and Technology Southwest UniversityChongqing 400715China Research Laboratory for Quantum Materials Singapore University of Technology and DesignSingapore 487372Singapore Institute of High Performance Computing Agency for ScienceTechnology and Research(A*STAR)Singapore 138632Singapore

Recently, emerging phonon phenomena have been discovered and rapidly developed, which have become an active hot research topic. In this review article, we present state-of-the-art advances in several fascinating phonon transport phenomena. First, we summarize the recent progress on the wave nature of phonons, including phonon coherence and its effects on thermal conductivity and the topological properties of phonons. Then, we discuss the particle nature of phonons, including the weak coupling of phonons and the high-order phonon anharmonicity. Finally, we present the summary and a brief outlook. This review presents the advanced understanding of some emerging phonon phenomena in solid materials, which provides new opportunities for further advancement in a wide variety of applications.

关键词： phonon transport coherence topological phonon high-order phonon interaction

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Research on Geometry Problem Text Understanding Based on Bidirectional LSTM-CRF 9

Research on Geometry Problem Text Understanding Based on Bid...

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9th International Conference on Digital Home, ICDH 2022

作者： Zhou, Wei Xu, Ruixi Guan, Hao Zhao, Jietong Rao, Yongsheng Guangzhou University Institute of Computing Science and Technology Guangzhou China Guangzhou University School of Computer Science and Cyber Engineering Guangzhou China

ISBN: (数字)9781665454780

ISBN: (纸本)9781665454780

The challenge of enabling a computer to understand a geometry problem in natural language has been attracting increasingly attention. As society becomes more data oriented, knowledge graphs (KG) that represents structured relations between entities are also gaining ground. However, it is still brand new that to combine knowledge graph with geometry problem understanding. Focusing on understanding geometry problem text in Chinese natural language, we propose two methods: (1) three-level representation normalization;(2) acquiring structured geometric relation by integrating entity extraction using Bilstm-CRF and constructing relation triad using template mapping. Based on these, we construct a system that implements obtaining geometric relation triads and finally put them in knowledge graph. With our three-level representation normalization, the accuracy in its subsequent processing, entity recognition and extraction, has significantly and constantly improved about 3 percent compared with directly processing on the raw problem text with simple data cleaning. The results of the final test confirm the possibility that the system successfully recognizes and obtains all geometry relation triads of each problem sentence in the entire testing set, achieving complete transformation from an unstructured geometry problem text to a structured relation triad set. © 2022 IEEE.

关键词： Knowledge graph

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Maximizing Crop Yield: Crop Yield Prediction using Advanced ML Algorithms

Maximizing Crop Yield: Crop Yield Prediction using Advanced ...

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Advances in computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Narra Naga Nitin R Sai Srikar P Dileep D Deva Hema Department of Computer Science and Engineering School of Computing SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

For food security and economic stability, effective crop damage management is vital, as agriculture is vital to human survival. This research endeavor presents a data-centric methodology for forecasting crop damage and yield by employing machine learning methods on an extensive collection of agricultural parameters, including historical damage records, DESIS Hyperspectral images, crop attributes, pesticide usage, and environmental factors. Robust feature engineering and exploratory data analysis (EDA) are implemented to identify intricate relationships within the dataset. This research paper integrates conventional algorithmic approaches such as Random Forest, K-Nearest Neighbors, Gaussian Naïve Bayes, and Support Vector Classifier (SVC) with a Long Short-Term Memory (LSTM) neural network model of advanced complexity. The fundamental objective is to develop precise and resilient models that can accurately categorize levels of crop injury. This research paper employs LSTM models to enhance predictive capabilities and offer significant insights into the intricate correlations that exist between agricultural parameters and crop damage. In addition to advancing precision agriculture, the findings of this study equip producers with useful instruments for risk management and proactive decision-making.

关键词： Static VAr compensators Predictive models Vectors Stability analysis Classification algorithms Bayes methods Risk management

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Identifying Influential Communicators based on Correlation and Minimum Distance in Social Media Data Streams 9

Identifying Influential Communicators based on Correlation a...

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9th IEEE Smart World Congress, SWC 2023

作者： Zhu, Jie Shi, Lei-Lei Liu, Lu Han, Zi-Xuan Jiang, Liang Panneerselvam, John Jiangsu University School of Computer Science and Telecommunication Engineering China Jiangsu University Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace China University of Leicester School of Computing and Mathematical Sciences United Kingdom Jiangsu University of Science and Technology School of Ocean China

ISBN: (纸本)9798350319804

Twitter has become a crucial social networking platform for users to receive and exchange information about current events. Identifying the most influential users in social networks is pivotal for the efficient propagation of information. However, traditional influencer identification models often fall short of assessing users' influence comprehensively and neglect overlapping influence, which greatly restrains their efficiency and accuracy in identifying key users. To address such issues, we propose a method for identifying key users based on correlation and minimum distance (CM-UDM). This method can evaluate users' influence based on their individual, global, local, and propagation path characteristics in social networks. Additionally, we propose a key user decision graph in order to address the problem of overlapping users' influence. We have conducted experiments using one classical dataset and three real Twitter datasets, where the performance of our proposed method in identifying highly influential users is demonstrated. © 2023 IEEE.

关键词： Influence maximization Influence overlap Twitter

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Plant Disease Detection Using Deep Learning Model

Plant Disease Detection Using Deep Learning Model

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2022 International Conference on Machine Learning, Big Data, Cloud and Parallel computing, COM-IT-CON 2022

作者： Johri, Prashant Parashar, Nidhi Saxena, Utkarsh Pushpanjali, Kumari Galgotias University School of Computing Science Greater Noida India Meerut Institute of Technology Department of Computer Science and Engineering Meerut India

ISBN: (数字)9781665496025

ISBN: (纸本)9781665496025

The need of plant disease detection in early days is getting very crucial with time. The plant diseases affect the growth of the plant and decrease the productivity of the plant. Plant diseases results in significant loss of the cultivated crop. With advancement of deep learning the power to detect the plant disease has significantly increased as compared to machine learning. Many performance matrix are being created to evaluate techniques. Deep learning is a great approach for classification. It quicken the pace, automate the system and increase the accuracy of the image classification. Fast and accurate models in agricultural systema are the need of the hour. The use of deep learning in sector of agriculture faten the pace of agricultural growth with high yielding of crops and good quality of crop. The use of CNN methodology which gives best accuracy with economical equipment played a lead and vital role in enhancement of agricultural sector. © 2022 IEEE.

关键词： Crops

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Comparative Study of Supervised Machine Learning Methods for Prediction of Heart Disease 3

Comparative Study of Supervised Machine Learning Methods for...

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3rd IEEE Conference on VLSI Device, Circuit and System, VLSI DCS 2022

作者： Rana, Meghavi Ur Rehman, Mohammad Zia Jain, Srishti Dit University School of Computing Dehradun India Indian Institute of Technology Computer Science and Engineering Indore India

ISBN: (纸本)9781665438018

With the ever-growing medical data, it became possible to use the data for prediction of diseases using Machine Learning (ML) methods. ML methods have been widely employed in healthcare. In the study, some of the mostly used ML methods such as Support Vector Machine, Naïve Bayes classifier, Random Forest, Decision tree, and K-Nearest Neighbor were used for prediction of heart disease. Further, we aim to provide a comparative analysis of the ML algorithms applied for heart disease prediction using their accuracy metrics. Dataset for the study was taken from Kaggle *** format, where data mining steps such as data collection, data cleaning, data preprocessing, and exploratory data analysis have been done. The study highlights the ML methods used for classification, providing the comparative analysis between them. As a result, it was concluded that the random forest gave the highest accuracy rate with the dataset used in the study. © 2022 IEEE.

关键词： Forecasting

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