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Reduce Emergency Response time using machine learning Technique 4

Reduce Emergency Response time using Machine learning Techni...

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4th international conference on Communication Technologies, ComTech 2023

作者： Lazuko, Alina Gui, Xiangquan Arshad, Ali Khan, Nauman Ali Quershi, Imran Lanzhou University of Technology College of Computer and Communication Lanzhou China Northwest Normal University College of Computer Science and Engineering Lanzhou China Military College of Signals Department of CSE Islamabad Pakistan

ISBN: (纸本)9798350337211

In reaction to any unusual circumstances, an ordinary man looks for the government's response system to play its pivotal role in providing a timely response by any respective field force. It is the responsibility of emergency response forces to ensure a rapid and appropriate reaction to any undesired situation. According to statistics obtained from emergency centers in Pakistan and different countries, it is brought into consideration that every day, around 20-25 thousand emergency calls are received, and on weekend's ratio increases by 20%. Extensively it is classified by different studies, facts, and feedback that 80% of the calls are hoax calls. Currently, the system is entirely dependent on a human resource where a human operator is required to listen to the person in the problem and decide which response force needs to be informed. this naturally brings a delay in response time. the system that is being developed first analyses voice-to-text conversions from a semantic perspective applies data parsing techniques to the text that has been extracted, and then creates tokens based on pre-learned keywords. the system is trained on a data set that was obtained from a well-known emergency command center. Keeping in mind the confidentiality of the data set, only the emergency-related descriptions were used to train our model. For each unique case, a keyword was listed in the database as a token, allowing the system to make decisions independently for each pattern it had learned. We have used SVM and logistic regression algorithms in a python framework for training and testing of research work. SVC will extract features from the data set then a pipelining algorithm will clean the data set and remove the unwanted data. It is a kind of pre-processing that is being done to achieve the best and the better accuracy measures. then these text tokens will be converted into vectors, and a vectorizer algorithm needs vectors/numbers for input and processing purposes. then a funct

关键词： Semantics

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Multimodal federated learning framework evaluation for lymph node metastasis in gynecologic malignanciese

Multimodal federated learning framework evaluation for lymph...

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pattern recognition and machine learning (PRML), IEEE international conference on

作者： Ling. Ma Zhijun. Hu Ding Yue Guangyu Wu Xiaohua Shi Shalayiding Sirejiding Kaijiang. Liu Library Shanghai Jiao Tong University Shanghai China Department of Gynecologic Oncology Renji Hospital School of Medicine Shanghai Jiao Tong University Shanghai China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Departments of Radiology Renji Hospital School of Medicine Shanghai Jiao Tong University Shanghai China Renji Hospital School of Medicine Shanghai Jiao Tong University Shanghai China

Gynecologic malignancies, including cervical cancer, endometrial cancer, and ovarian cancer, represent a significant threat to women’s health, and their diagnosis and treatment are crucial aspects of global public health. While imaging techniques play an important role in diagnosing gynecologic tumors, early detection and accurate diagnosis present challenges. Hence, we propose a framework for assessing lymph node metastasis based on multi-modal federated learning. By utilizing MRI images, we develop a hybrid neural network model that effectively predicts lymph node metastasis by combining a multi-layer perceptron (MLP) and a convolutional neural network (CNN). through federated learning, we ensure data privacy while facilitating data sharing and collaborative computation among multiple hospitals. Validation using a dataset from a single hospital illustrates the considerable benefits of the model in enhancing the sensitivity and specificity of lymph node metastasis diagnosis in gynecologic malignancies. this research offers a novel and efficient approach for assessing lymph node metastasis in gynecologic malignancies, showcasing the vast potential of federated learning in the analysis of medical images.

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A MobileNetV2-CBAM-based Model for Forest Fire Classification Using UAV Imagery

A MobileNetV2-CBAM-based Model for Forest Fire Classificatio...

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pattern recognition and machine learning (PRML), IEEE international conference on

作者： Xinjie Deng Burhan Khan Chee Peng Lim Ming-Yu Liao Chongqing Jianzhu College Institute for Intelligent Systems Research and Innovation Deakin University Chongqing China Institute for Intelligent Systems Research and Innovation Deakin University Waurn Ponds VIC Australia Department of International Business National Kaohsiung University of Science and Technology Taiwan

Forest fires are one of the worldwide severe natural disasters causing human and environmental losses. Early and accurate fire detection is critical to minimize damages caused by forest fires and reduce human risks in firefighting efforts. Recently, Unmanned Aerial Vehicles (UAVs) mounted with cameras and edge computing devices with Artificial Intelligence (AI)-based algorithms have been employed to tackle forest fire detection problems in real time. Considering that edge computing is limited in resources and power consumption, the developed AI algorithms need to be lightweight. this paper proposes lightweight deep learning (DL) algorithms based on MobileNetV2 and attention mechanism to detect forest fires using UAV imagery. Based on several experiments, the proposed model yields an accuracy rate of 86.14% and an F1-score of 0.8719, outperforming other related methods in forest fire detection and classification.

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data mining Approach in Predicting House Price for Automated Property Appraiser Systems 6th

Data Mining Approach in Predicting House Price for Automated...

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6th international conference on Innovative Computing and Communication, ICICC 2023

作者： Yousir, Naeem th. Abdulameer, Shaymaa Mohammed Mostafa, Salama A. College of Information Engineering Al-Nahrain University Baghdad10072 Iraq Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Batu Pahat Johor86400 Malaysia

ISBN: (纸本)9789819930098

the house price prediction model can be a logistic tool in assisting individuals and companies to determine the cost of a property or a house on sale and the best time to acquire a house. With the ever-increasing in house price rates, data mining approaches have been used as a property appraiser system to predict a house price based on house features before making a deal. Existing research in house prediction involves collecting a number of house price estimation attributes into a dataset of house price features. this study is aimed at building a model for predicting house price based on the house price features dataset. then dataset is run through machine learning (ML) algorithms to learn the pattern and predict new house price cases. the cross-industry standard process for data mining (CRISP-DM) method is used for this research. Decision tree regression (DTR), linear regression (LR), multiple linear regression (MLR), and random forest regression (RFR) are the ML algorithms applied in this research. the results have shown that RFR is the most suitable algorithm for this dataset, with an average coefficient of determination (R square) of 82.09%, followed by the DTR with an R square score of 73.32% when using different training and learning splitting ratios. An application of the RFR model can be used in real estate companies for estimating the prices of properties. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Multiple linear regression

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Security Scheduling Method of Cloud Network Big data Cluster Based on Association Rule Algorithm 4th

Security Scheduling Method of Cloud Network Big Data Cluster...

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4th international conference on machine learning for Cyber Security, ML4CS 2022

作者： Peng, Teng Wang, Xiaohong Yichun Vocational and Technical College Yichun336000 China

ISBN: (纸本)9783031200984

In order to avoid unreasonable deployment and realize the orderly deployment of big data cluster nodes, a cloud network big data cluster security scheduling method based on association rule algorithm is proposed. According to the execution steps of association rule mining algorithm, the connection form of hadoop/mapreduce framework is determined, and then the security mapping conditions are jointly solved to complete the security performance analysis of cloud network based on association rule algorithm. Combine the obtained big data information parameters, set the link bandwidth time list structure, and solve the specific value of packet routing index according to the connection form of SDN scheduling system. the experimental results show that the cloud network big data information migration amount of the method in this paper reaches 6.50 × 107 MB, and the continuous occupation time of cluster nodes is less than 0.7 ms, which increases the unit migration amount of big data information and reduces the continuous occupation time of cluster nodes. It can solve the problem of unreasonable allocation of information parameters and realize the orderly deployment of big data cluster nodes. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Big data

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machine learning Based Predicting Student's Grade 4

Machine Learning Based Predicting Student's Grade

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international conference on 4th Industrial Revolution Based Technology and Practices, ICFIRTP 2022

作者： Singh, Sanjay Kumar Goswami, Shreeyeshi Nagar, Shubhangi Kaur, Mandeep Rakesh, Nitin Goyal, Mayank Kumar Sharda University Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350345919

Many scholars have looked into student academic performance in monitoring and non-supervision supervised learning, and they have used a variety of data mining techniques. To gain significant guessing skill, neural networks often require a large number of observations. Due to the rising number of impoverished students with degrees, it is vital to develop a programme that aids in the reduction of this scourge as well as the incidence of recurrence due to poor performance or the necessity to drop out of school entirely in pursuit of their job. As a result, it is vital to understand each one's benefits and drawbacks in order to identify which one works best and which ones should be chosen many times. the study's goal is to create an Artificial Neutral Network-based student performance prediction system that uses student mathematical features to help the university select candidates (students) with a high prediction of admission success based on previous student academic records who will eventually earn a degree from the institution. the model was built using a set of input variables, including parental status. With parent's data, there is about 80.84 percent accuracy, whereas without information about parent's education, there is 81.37 percent, indicating that they are closely related. As a result, in the large scale of student's data, these differences will be so minor, according to this machine learning prediction process. there are many things, which need to do predictive process to check the possibility of happening that thing, as the advancement is rapidly increasing. © 2022 IEEE.

关键词： Linear Regression machine learning Parents' Education predictive Model Students' grade prediction

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Employing machine learning algorithms to discover risk factors of glaucoma 4

Employing machine learning algorithms to discover risk facto...

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4th international conference on pattern recognition and Artificial Intelligence, PRAI 2021

作者： Al-Shamiri, Abdulkawi Yahya Radman School of Computer Science and Engineering Hodeidah University Department of Computer Science Al-Hodaidah Yemen

ISBN: (纸本)9781665413220

the statistics of the World Health Organization indicated that blindness or loss of vision in one of the eyes is one of the many threats and challenges in the world due to the increasing number of people infected annually. One of the many serious eye diseases is glaucoma, which leads to the loss of vision in the affected eye. Most studies on glaucoma are based on clinical trials and thus lack insights into the risk factors of glaucoma;these factors could be obtained from larger number of doctors and patients and analyzed sufficiently. With the recent development of technology, the need of utilizing machine learning algorithms in the medical field has increased to achieve a better and accurate health care system. thus, this study aims to effectively discover the hidden risk factors of glaucoma by machine learning algorithms using real data collected from doctors and patients. the author used one of decision tree algorithms so-called J48 and one of association rule algorithm socalled apriori to analyze the collected data. the study shows that some of the risk factors of glaucoma which have been proven by medical trials are also obtained by means of the two algorithms. therefore, machine learning algorithms can be used to effectively determine causes of any disease. the results not only provide a novel method to discover the risk factors of glaucoma but also reveal new risk factors of glaucoma which have not been discovered previously by studies based on medical trials such as discovery the relation chronic constipation and stomach upset with glaucoma as reasons leading to glaucoma. this study will contribute to avoiding blindness and preventing it by determining the recognition factors of glaucoma. © 2021 IEEE.

关键词： pattern recognition

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Mushroom Classification Based on Deep Residual Network

Mushroom Classification Based on Deep Residual Network

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pattern recognition and machine learning (PRML), IEEE international conference on

作者： Ju Feng Xufeng Ling Yubo Wang Jie Yang School of Artificial Intelligence Shanghai Normal University Tianhua College Shanghai China Shanghai Acoustics Laboratory Chinese Academy of Sciences Shanghai China Institute of Image Processing and Pattern Recognition and Institute of Medical Robotics Shanghai Jiaotong University Shanghai China

Due to the similarity in mushroom features and the difficulty in distinguishing between poisonous and nonpoisonous varieties, mushrooms pose a threat to human health. To address the challenge of mushroom classification and identification, this paper proposes a mushroom classification method based on residual networks. Firstly, a network architecture with multiple residual blocks is designed, and it is trained using an image dataset. then, a transfer learning strategy is employed to initialize the network parameters from a pre-trained model, followed by fine-tuning to adapt to the mushroom classification task. Finally, multiple testing experiments are conducted to evaluate the effectiveness of the proposed method. the experimental results demonstrate excellent performance of the proposed method in mushroom classification tasks. Compared to traditional feature extraction methods, it can better capture the details and texture features of mushrooms, thereby improving classification accuracy. In conclusion, the mushroom classification method based on residual networks exhibits high accuracy and generalization capability. this method has potential applications in the field of mushroom classification, aiding in the better identification and differentiation of poisonous mushrooms, thereby protecting human health.

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4th international Symposium on Intelligent Computing Systems, ISICS 2022

4th International Symposium on Intelligent Computing Systems...

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4th international Symposium on Intelligent Computing Systems, ISICS 2022

ISBN: (纸本)9783030984564

the proceedings contain 11 papers. the special focus in this conference is on Intelligent Computing Systems. the topics include: On the Performance of Preconditioned Methods to Solve Lp -Norm Phase Unwrapping;Fault Diagnosis of Combustion Engines in MTU 16VS4000-G81 Generator Sets Using Fuzzy Logic: An Approach to Normalize Specific Fuel Consumption;DDoS Attack Preventing and Detection with the Artificial Intelligence Approach;iris recognition Using Supervised learning Based on Matching Features;convolutional Neural Network for Segmentation of Single Cell Gel Electrophoresis Assay;Evaluation of Human SCD Test by Digital Image Analysis;machine Vision-Based Expert System for Automated Skin Cancer Detection;AI Used to Identify Car Accident Injury and Mortality Risk Factors in Dubai UAE;toward Automatic Water Pollution Analysis: A machine learning Approach for Water-Quality Monitoring through pattern Classification of Water Crystallization.

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Proceedings of the 2022 IEEE Nigeria 4th international conference on Disruptive Technologies for Sustainable Development, NIGERCON 2022

Proceedings of the 2022 IEEE Nigeria 4th International Confe...

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4th IEEE Nigeria international conference on Disruptive Technologies for Sustainable Development, NIGERCON 2022

ISBN: (纸本)9781665479783

the proceedings contain 140 papers. the topics discussed include: machine learning models for predicting bank loan eligibility;semantic relation evaluation of data science articles using network of mention;effect of training algorithms and network architecture on the performance of multi-band ANN-based path loss prediction model;digital transformation in Nigeria: the prospects and challenges of the gig economy;assessing the vulnerabilities of internet users to cyber-attacks using their password login patterns;benefits of smart devices and grid-tied minigrids on the Nigerian electricity supply industry;development of Bluetooth enabled switching device for handoff selection in multiple operators enabled sim card system;a spatio-temporal non-concurrent data gathering and energy replenishment in wireless rechargeable sensor networks (ST-NCDR);tech mining analysis: renewable energy forecasting using artificial intelligence technologies;and proactive fuzzy-based backup channel selection scheme for spectrum handoff in cognitive radio networks.

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