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Intrusion Detection in Computer Networks using Optimized machine learning Algorithms 3

Intrusion Detection in Computer Networks using Optimized Mac...

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3rd international Informatics and Software Engineering conference, IISEC 2022

作者： Yilmaz, Abdullah Asim Republic of Türkiye Ministry of Agriculture and Forestry Department of Strategy Development Ankara Turkey

ISBN: (数字)9781665459952

ISBN: (纸本)9781665459952

Intrusion detection systems (IDSs) are employed to maintain computer networks from cyber attacks. Here, the aim is to detect intrusions once the data is transmitted across the internet. Intrusion detection methods (IDMs) developed in the literature are commonly focused on data mining, statistical and machine learning-based analysis of anomaly situations. In this paper, we suggested machine learning based methodology to detect intrusions in computer networks. The proposed method comprises four main phases, namely, preprocessing, feature selection, parameter optimization and classification. Most significant features are selected by utilizing the Correlation Based Feature Selection. For classification Random Tree, AdaBoost, K-Nearest Neighbor (KNN), Support-vector machine (SVM) are used while for parameter optimization particle swarm optimization is employed. The proposed method was tested on two extensive datasets, namely NSL-KDD and CIC-DDOS2019. The experimental results reveal that the proposed method can effectively classify intrusions with a high detection rate and that it outperforms the other machine learning techniques. © 2022 IEEE.

关键词： Network security

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Research on meteorological risk prediction method based on machine learning algorithm 3

Research on meteorological risk prediction method based on m...

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3rd international conference on Computer Vision and data mining, ICCVDM 2022

作者： Li, Ping Chen, Yu Liu, Shuo Tian, Li Lu, Jinglong Liaoning Provincial Meteorological Observatory Shenyang Liaoning110016 China Liaoning Meteorological Disaster Monitoring and Early Warning Centre Shenyang Liaoning110016 China Institute of Atmospheric Environment China Meteorological Administration Shenyang Liaoning110016 China

ISBN: (纸本)9781510661363

Among all kinds of natural disasters, meteorological disasters cause the greatest economic losses. Meteorological disasters have multiple causes. The characteristics of systematization, social amplification, unpredictability and urgency have caused various. The loss is incalculable. Therefore, the risk assessment and management of meteorological disasters is very important. So far, there is still no universally practical and systematic theory of meteorological disaster risk assessment and management. The research on meteorological disaster risk assessment and management in China started relatively late, Moreover, flood and drought are the main disasters, and there are few studies on other disasters, such as typhoons and hurricanes. Through the method of case analysis, this paper summarizes some internal, hoping to increase the research content in this field. © COPYRIGHT SPIE.

关键词： Disasters

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Brain Tumor recognition Leveraging machine learning and CNN 1

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3rd international conference on Artificial-Business Analytics, Quantum and machine learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

作者： Jaiswal, Updesh Kumar Chauhan, Ayushi Gupta, Raghav Department of Information Technology IMS Engineering College Ghaziabad India

ISBN: (数字)9789819748600

ISBN: (纸本)9789819748594

A mass or lump that can be formed by an abnormal growth of cells in the body is known as a tumor. Tumors are of two types: Non-cancerous or cancerous. Cells in the human brain essentially grow in a distorted manner, causing a brain tumor. A tumor in the brain is the harmful disease and can be analyzed by magnetic resonance imaging (MRI) images using automated techniques. Many machine learning (ML) methods for identifying and analyzing brain tumors have been proposed by several researchers so far. A comparative study of different methods was carried out. After an intensive analysis process, some issues were discovered in image segmentation, recovery, and enhancement. To sort out these problems, a new method is proposed by using convolutional neural network (CNN). CNN has the ability to identify complex patterns and relationships in images. CNN algorithms can accurately analyze large amounts of data such as MRI images to identify the presence of a tumor, its size, location, and type. To reduce the errors in images of the dataset, CNN classification is used. CNNs are superior to other traditional image analysis algorithms such as threshold or edge detection. To implement the proposed model, Python, TensorFlow, and Matplotlib environments are used. CNN can predict tumors in MRI images with high accuracy. The proposed method using CNN outperforms deep learning (DL), genetic algorithm (GA), support vector machine (SVM), and artificial neural network (ANN) and gives the validation accuracy up to 95.7%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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Requirements Prediction for Medical Products Based on Named Entity recognition 3

Requirements Prediction for Medical Products Based on Named ...

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3rd international conference on Electronic Information Engineering and Computer Science, EIECS 2023

作者： Ruan, Peiguang Liu, Ruanchong Guo, Bo Liu, Zhepeng Zhang, Chengying Alibaba Cloud Big Data Application College Zhuhai College of Science and Technology Zhuhai China Guangdong Pharmaceutical University The First Clinical Medicine School Guangzhou China College of Science and Technology School of Finance and Trade Zhuhai Zhuhai China Shantou University College of Engineering Shantou China Xihua University School of Management Chengdu China

ISBN: (纸本)9798350316674

Medical product demand forecasting is a necessary condition for building a medical intelligent system, and is an important means to alleviate the lack of medical resources, and the form of medical product demand is mainly unstructured text, including the number of medicines, medical record texts. These medical texts contain a large number of medical terms related to the patient health situation and medical terms are the most direct embodiment of medical knowledge. However, existing methods for requirements are concentrated on the neural network or machine learning algorithms, which ignores the tremendous information in the medical terms. In this work, we utilize the named entity recognition method to predict the demand of the medical products. Efficiently identifying these medical terms from medical texts is a key factor in ensuring the high performance of medical intelligence systems. Therefore, the goal of the medical named entity recognition task is to extract medically significant symptoms, medical history, diseases, etc. from medical texts and realize the downstream prediction task. From our extensive experiment simulation and comparison with existing prediction methods, we can conclude the proposed method can achieve the precise prediction accuracy with reasonable computation costs. © 2023 IEEE.

关键词： Computation Costs Medical Product Named Entity recognition Requirement Prediction

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A Review on Iris recognition System using machine and Deep learning 3

A Review on Iris Recognition System using Machine and Deep L...

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3rd IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2022

作者： Panwar, Vaibhav Pooja Sharda University Department of Computer Science and Engineering UP Greater Noida India

ISBN: (纸本)9781665462006

For security purposes, the use of an Iris recognition System has become standard practice. It is possible to achieve accurate results by employing Iris recognition. Since it never accepts fraudulent values, it is an excellent security tool. In the search for one of the most dependable and accurate biometric technologies available today, Iris recognition is a fantastic location to begin the research. Because of the complex texture of the Iris, which provides strict requirements for identifying individuals, Iris identification systems have caught the interest of many researchers. And its extensive use around the world, Iris recognition has made remarkable strides in recent years. The survey details the Iris recognition technology from the stage of data collection to recognition. Iris recognition involves several procedures, including image capture, pre-processing, feature analysis, recognition, and evaluation. machine learning and deep learning are taken in this article for Iris recognition. A closer look at the essential concepts of various techniques, as well as their inherent connections, will help the researcher to obtain a better understanding of the development of the Iris recognition system. Although many research and review articles have previously been published in this area, we utilized the knowledge from those articles to identify some unexplored areas of iris recognition for researchers. This study will be the beginning for researchers to start their work. © 2022 IEEE.

关键词： Deep learning

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Application of machine learning Algorithm: Forecasting Heart disease

Application of Machine Learning Algorithm: Forecasting Heart...

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Innovation in Technology (INOCON), IEEE international conference for

作者： Apurva Lohia Arti Pawar ICAS Manipal Academy of Higher Education Manipal India

The healthcare industry produces a huge amount of complex data about patient records. This data can be processed and mined to uncover hidden patterns, which will provide professionals in the healthcare field with additional information and help them in making better informed decisions. Using data in such a way is known as data mining. One such area in the healthcare industry where data mining can be used is early prediction of heart diseases so that patients can get the appropriate treatment. This paper compares the efficiencies of the following supervised machine learning models: Logistic Regression, Naïve Bayes, Support Vector machine, K-Nearest Neighbors, Decision Tree, Neural Networks, Random Forest. It was found that the Neural Networks algorithm provides the highest accuracy of 81.32%.

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Construction of Business English Needs Analysis Model Based on Computer Corpus 3rd

Construction of Business English Needs Analysis Model Based ...

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3rd EAI international conference on Application of Big data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2023

作者： Lu, Yao Zhang, Li Ma, Lingya Shandong Management University Shandong Jinan250357 China Hubei Three Gorges Polytechnic Hubei Yichang443000 China

ISBN: (纸本)9783031631290

In order to meet the needs of business English, this article constructs a computer corpus based business English requirement analysis model. By collecting and organizing relevant corpus in the field of business English, establish a large-scale and diverse corpus. Then, using natural language processing technology and machine learning algorithms, the corpus is analyzed and processed to extract common vocabulary, phrases, and grammatical structures in business English. Next, by clustering and mining association rules on the texts in the corpus, the actual needs and characteristics of business English are discovered. In the process of model construction, we will also consider different branches and specialties in the business field, such as marketing, international trade, business communication, etc., to adapt to the needs of different fields. Finally, based on the results of the needs analysis, we will provide targeted suggestions and guidance for business English teaching and training. The construction of this model helps educators and learners better understand the characteristics and needs of business English, in order to carry out business English teaching and training more effectively. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Big data

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From GPS to Maps: Path Reconstruction Techniques for Understanding People Mobility with Low-Sampling-Rate data 3

From GPS to Maps: Path Reconstruction Techniques for Underst...

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3rd international conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2023

作者： Rakhman, Arkham Zahri Nugroho, Lukito Edi Widyawan, Widyawan Utami, Nurul Khairiza Universitas Gadjah Mada Dept. of Electrical Engineering and Information Technology Yogyakarta Indonesia

ISBN: (纸本)9798350394559

Urban mobility is a critical aspect of city planning and transportation management, with the Global Positioning System (GPS) playing an essential role in analyzing and understanding patterns of human movement. Particularly using smartphones, GPS provides highly detailed digital trace data, enabling in-depth analysis of human movement behavior. However, there are significant challenges when working with GPS datasets that have a low frequency, which often occurs due to storage capacity limitations or data transmission costs. This study explores methodologies for reconstructing movement paths from low-frequency GPS datasets. The process includes several key steps: data pre-processing involving cleaning and normalizing data;aligning GPS data with existing road networks using the nearest-neighbor KD-Tree algorithm;and accurate path reconstruction. In addition, the study develops a method for time interpolation estimation to determine movement times between the aligned coordinate points. The findings of this research indicate that with the proposed approach, movement paths can be reconstructed effectively and accurately, even from data with low sampling frequencies. This approach promises a wide potential application, ranging from transportation infrastructure planning to the development of more sophisticated navigation systems. With ongoing research, the integration of additional sensors and machine learning techniques is anticipated to further enhance the accuracy and utility of this methodology. © 2023 IEEE.

关键词： Global positioning system

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3rdWorkshop on Patent Text mining and Semantic Technologies (PatentSemTech2022) 22

3rdWorkshop on Patent Text Mining and Semantic Technologies ...

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45th international ACM SIGIR conference on Research and Development in Information Retrieval (SIGIR)

作者： Krestel, Ralf Aras, Hidir Andersson, Linda Piroi, Florina Hanbury, Allan Alderucci, Dean ZBW Leibniz Informat Ctr Econ Kiel Germany Univ Kiel Kiel Germany FIZ Karlsruhe Karlsruhe Germany Artificial Researcher IT GmbH Vienna Austria TU Wien Vienna Austria Res Studios Austria Vienna Austria Carnegie Mellon Univ Ctr AI & Patent Anal Pittsburgh PA 15213 USA

ISBN: (纸本)9781450387323

Steadily increasing numbers of patent applications per year and large amounts of available patent data necessitate highly efficient and interactive next-generation information retrieval systems in the patent domain. AI and machine learning (ML) methods such as Deep learning (DL) are successfully adopted in many domains, so patent researchers and practitioners start to employ AI-based approaches as well, to support experts in the patenting process or to automate patent analysis and retrieval processes. AI-enhanced Information Retrieval systems can improve patent search and analysis but also require millions of annotated sample data for training the ML models. When working with patent data, particular challenges arise that call for adaption of existing IR and AI methods as well as development of novel approaches suited for the patent domain. The focus of the 3rd edition of this workshop will be on two-way communication between industry and academia from all areas of Information Retrieval, such as Natural Language Processing (NLP), Text and data mining (TDM), and Semantic Technologies (ST). We want to bring together novel research results and the latest systems and methods employed by the Intellectual Property (IP) industry.

关键词： patent analysis text mining semantic technology deep learning

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machine learning Based Citrus Fruit Disease Prediction Using Support Vector machine Algorithm 3rd

Machine Learning Based Citrus Fruit Disease Prediction Using...

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3rd international conference on Emerging Electronics and Automation, E2A 2023

作者： Mohanapriya, D. Kabilesh, S.K. Swaranambigai, R. Saritha, B. Deepikapriya, S.V.K. Jai Shriram Engineering College Tamil Nadu Tiruppur638660 India KGISL Institute of Technology Tamil Nadu Coimbatore641035 India Jansons Institute of Technology Tamilnadu Coimbatore641659 India Erode Sengunthar Engineering College Tamilnadu Erode638057 India

ISBN: (纸本)9789819730896

The recognition of fruit diseases is rapidly becoming a significant area in computer vision. Besides reducing fruit production, plant diseases also have a profound impact on the country's economy. Therefore, predicting plant illnesses and implementing remedial actions are of utmost importance. Plant illness directly affects the crop’s quality and overall productivity. To achieve swift and accurate disease prediction, an automated method is used to identify and diagnose affected leaves within a short span of time. Through the appropriate use of diagnosis and classification techniques, potential losses can be prevented. The features used for identifying plant diseases include color, strength, area of attack, and dimension. The current research primarily focuses on guava leaf disease prediction using K-means clustering techniques. In this proposed study, we aim to identify orange fruit and leaf diseases. To achieve this, K-means clustering is applied to segment the orange fruit images into distinct clusters. We leverage the gray-level co-occurrence matrix (GLCM) approach with the clustered data to extract the global features as feature vectors, and the orientation gradient approach is utilized to uncover real features. Implementing machine learning algorithms has significantly enhanced the accuracy of the system, achieving a remarkable 96% accuracy rate. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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