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检索条件"任意字段=2023 International Conference on Software Engineering and Machine Learning, CONF-SEML 2023"

共 3233 条记录，以下是501-510 订阅

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Data Mining of Ambulatory Blood Pressure Monitoring 13

Data Mining of Ambulatory Blood Pressure Monitoring

引用

13th international conference on Dependable Systems, Services and Technologies, DESSERT 2023

作者： Chuiko, Gennady Darnapuk, Yeugen Dvornik, Olga Gravenor, Michael Yaremchuk, Olga Petro Mohyla Black Sea National University Department of Computer Engineering Mykolaiv Ukraine Swansea University Medical School Singleton Park Swansea United Kingdom Petro Mohyla Black Sea National University Educational and Scientific Medical Institute Mykolaiv Ukraine

ISBN: (纸本)9798350396119

The paper's aim is data mining of the recently published (2017) Ambulatory Blood Pressure Monitoring database, which covers 270 patients. Preprocessing, denoising, unsupervised machine learning (clustering), and feature selection were performed within Java-based software (WEKA 3-9-6). The Blood Pressure Load Level showed the best congruence with the obtained clusters. It also has the lowest noise among other levels of the database. The methods are designed to simplify database mining for clinicians largely unfamiliar with machine learning algorithms and benefit from simple algorithm approaches and visual aids output. © 2023 IEEE.

关键词： Data mining

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Unraveling the Potential of machine learning in Lung Cancer Prediction

Unraveling the Potential of Machine Learning in Lung Cancer ...

引用

2023 international conference on Advanced Computing and Communication Technologies, ICACCTech 2023

作者： Gupta, Dakshesh Saini, Jyoti Rana, Meenakshi Chandigarh University Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350380880

Lung cancer is a disease in which some undesired cells grow inside the lungs and as they grow older they get transferred to nearby lymph nodes and then to different parts of the body. Lung cancer is a very common disease that affects people of all ages. Its causes can be many either due to smoking or peer pressure. Cancer is a type of disease that spreads very fast in different parts of the body but cannot be easily identified. There are various researches previously done but the model on which they are trained is not giving much accurate results. There are different machine learning models used to predict lung cancer such as Tree, Random Forest, and Neural Network. The Neural Network was one of the supervised learning models which provided us with the most accuracy and precision. The best fit is calculated by the Area under Curve, Classification Accuracy, Precision, Fl Score, and Recall. In this discussion, it is all about various machine learning techniques, Orange software used for data prediction, and Tableau software used for Data visualization are done. The model is trained on the dataset of 310 rows and 16 columns. The value of each columns decides how will are model behave for different unknown values. The results of the model are also shown, including its accuracy, and the results are visualized through graphs that help to predict the disease which is better than all the existing model's results. It will let the readers be able to understand lung cancer more accurately and what data to include to get the most accurate results for lung cancer prediction. © 2023 IEEE.

关键词： Diseases

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Experimental Design of machine learning based Enhanced Communication Efficiency and Noise Reduction over Mobile Cellular Networks

Experimental Design of Machine Learning based Enhanced Commu...

引用

2023 international conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Prasad, Lakkimsetty M B Swarnalatha, S. S.V University SVU College of Engineering Department of ECE Tirupati India

ISBN: (纸本)9798350319200

Modern communication relies heavily on mobile cellular networks, which can meet the increasing demands of users for higher data speeds, lower latency, and better communication quality. In order to improve communication efficiency and reduce noise in mobile cellular networks, this research presents a novel experimental design that uses machine learning. Essentially, this study makes use of the learning Assisted Communication Evaluator Protocol (LACEP), a clever method that blends machine learning algorithms into the network architecture with ease. Notable gains result from LACEP's intelligent optimization of network settings, adaptive response to changing environmental circumstances, and efficient mitigation of communication noise. Prominently, the suggested model attained a remarkable throughput of 97.24%, surpassing the throughput of 3.05% of current protocols such as Internet Protocol, Link Layer Protocol, and TDD Protocol. The experimental findings offer strong proof of the ability of machine learning-based techniques to deal with the dynamic and frequently noisy characteristics of mobile cellular networks. It's also critical to emphasize that Python software is used to achieve this creative approach, guaranteeing scalability and adaptability. This experimental design, which adopts machine learning via LACEP, represents a positive step towards more effective and resilient mobile communication systems. It also adds to the continuous endeavors to enhance the performance of mobile cellular networks and provide high-quality communication services even in difficult settings. © 2023 IEEE.

关键词： Communication learning Assisted Communication Evaluator Protocol (LACEP) machine learning Mobile cellular networks Normalization

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Agricultural Crop Recommendation System 3

Agricultural Crop Recommendation System

引用

3rd IEEE international conference on Intelligent Technologies, CONIT 2023

作者： Balakrishnan, D. Kumar, Anumula Praneeth Sai Kiran Reddy, Kristipati Kumar, R. Ravindra Aadith, K. Madhan, Sudarsi Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil India

ISBN: (纸本)9798350338607

The Agricultural Crop Recommendation System using machine learning is a software application that utilizes artificial intelligence algorithms to provide recommendations to farmers on the best crops to plant based on their specific farming conditions. The system collects and analyzes various data sources, such as soil quality, climate, and historical crop yields, to make personalized crop recommendations. The system is easy to use and can be accessed through a web-based platform or mobile app. The implementation of this system can help farmers to optimize their crop production, reduce costs, and increase efficiency in agricultural *** implementation of a machine learning-based Agricultural Crop Recommendation System holds immense potential to transform the agricultural sector. By leveraging data-driven insights, this system can offer farmers recommendations that are both sustainable and profitable, enabling them to make informed decisions and enhance their overall productivity. © 2023 IEEE.

关键词： Crops

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Towards Classifying File Segments in Memory Using machine-learning 9

Towards Classifying File Segments in Memory Using Machine-Le...

引用

9th international conference on Information Technology Trends, ITT 2023

作者： Al-Saleh, Mohammed I. Alkouz, Akram Alarabeyyat, Abdulsalam Bouchahma, Majed Jordan University of Science and Technology Computer Science Department Jordan Higher Colleges of Technology Computer Information System United Arab Emirates Al-Balqa Applied University Software Engineering Department Jordan-Salt Jordan

ISBN: (纸本)9798350327502

Digital media triage is a main challenge that faces a digital investigator. Knowing what might be useful during crime investigation could greatly save the investigator's time and enhance outcomes. Memory investigation can get potential benefit from the triage process because of its scattered and diverse contents. The Operating System's paging scheme might map the contents of a file into non-consecutive page frames in the physical memory, making the file-type classification and identification process harder. This paper tackles the memory content triage problem at the page level using a machine-learning methodology. © 2023 IEEE.

关键词： Digital storage

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Enhanced Deep learning Approach for software Defect Forecasting

Enhanced Deep Learning Approach for Software Defect Forecast...

引用

2023 Annual international conference on Emerging Research Areas: international conference on Intelligent Systems, AICERA/ICIS 2023

作者： Anju, A.J. Judith, J.E. Eranad Knowledge City Technical Campus Department of Computer Science Engineering Kerala India Noorul Islam Centre for Higher Education Department of Computer Science Engineering Tamil Nadu India

ISBN: (纸本)9798350303452

In the realm of defect prediction in complex systems, the integration of advanced machine learning techniques holds promise for enhancing accuracy and efficiency. This research introduces a novel approach by combining a Deep Adaptive Neural Network (ARNN) with the optimization capabilities of Cuckoo Search (CSO) for defect and non-defect data classification. Th e proposed model leverages the temporal dependencies within the data using a deep ARNN architecture, allowing for the exploration of intricate patterns and correlations over time. To optimize the model's performance, the Cuckoo Search Optimization algorithm is employed. CSO, inspired by the reproductive behavior of cuckoos, offers an effective mechanism for fine-tuning the parameters of the deep ARNN, optimizing its ability to discern subtle variations between defect and non-defect instances. The efficacy of the proposed approach is evaluated on benchmark datasets, showcasing its capability to achieve superior predictive accuracy compared to conventional methods. © 2023 IEEE.

关键词： Defects

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Viability of machine learning in Android Scareware Detection 38th

Viability of Machine Learning in Android Scareware Detection

引用

38th international conference on Computers and Their Applications, CATA 2023

作者： Gautam, Aayush Rahimi, Nick School of Computing Sciences & Computer Engineering The University of Southern Mississippi USM United States

With the rapid increase in the use of mobile phones and other technologies, there has been a proportional growth in malware that tries to collect sensitive user data. Android is the most popular operating system for smartphones and has a great potential of becoming a target for malware threats. Scareware is a type of malware that tries to get users to provide valuable information or download malicious software through social engineering. This research aims to find out if machine learning is a viable option to prevent the consequences of scareware by accurately detecting them. In this investigation, four supervised machine learning (ML) algorithms were used in a dataset called CICAndMal2017 with 85 attributes for each of 11 Android scareware families and benign samples. We were able to achieve an accuracy of 96% which helped us to conclude that machine learning can and should be used to detect scareware. The machine learning models were then tested to calculate the accuracy for classifying each scareware family. © 2023, EasyChair. All rights reserved.

关键词： Android (operating system)

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Interpreting machine and Deep learning Models for PDF Malware Detection using XAI and SHAP Framework 2

Interpreting Machine and Deep Learning Models for PDF Malwar...

引用

2nd international conference for Innovation in Technology, INOCON 2023

作者： Rahman, Tahsinur Ahmed, Nusaiba Monjur, Shama Haque, Fasbeer Mohammad Hossain, Muhammad Iqbal BRAC University Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350320923

As the world progresses towards a digital era, the transfer of data in Portable Document Format (PDF) has become ubiquitous. Regrettably, this format is susceptible to malware attacks and the conventional anti-malware and anti-virus software may not be able to detect PDF malware effectively. In response to this problem, the implementation of machine learning algorithms and neural networks has been proposed in the past. However, the lack of transparency in these models raises concerns regarding their ethical and responsible decision-making. To address this concern, the utilization of Explainable AI (XAI) with the SHAP framework is proposed to classify PDF files as either malicious or clean, providing both a global and local understanding of the models' decisions. The algorithms employed in this endeavor include Stochastic Gradient Descent (SGD), XGBoost Classifier, Single Layer Perceptron, and Artificial Neural Network (ANN). © 2023 IEEE.

关键词： Decision making

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Research on IPFS resource retrieval system based on inverted index 4

Research on IPFS resource retrieval system based on inverted...

引用

4th international conference on machine learning and Computer Application, ICMLCA 2023

作者： Shi, Lin Lu, Jun College of Software Engineering Chengdu University of Information Technology Chengdu China

ISBN: (纸本)9798400709449

In recent years, the spread of epidemics has prompted many industries to explore online services. The Inter Planetary File System (IPFS), as a peer-to-peer network-based protocol, this paper proposes a system architecture that adopts the idea of functional separation. Specifically, the retrieval nodes and data nodes are designed in separate clusters, and an inverted index is used in the retrieval cluster to improve the query speed of the index table, thereby optimizing the query process. © 2023 ACM.

关键词： Peer to peer networks

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A Performance Comparison on machine learning for Forecasting Heart Disease 3

A Performance Comparison on Machine Learning for Forecasting...

引用

3rd international conference on Smart Data Intelligence, ICSMDI 2023

作者： Bharath, Kakarla Sai Sanakkayala, Anudeep Kadiyam, Abhishek Chandra, Gudapati Pradeep Joseph S, Iwin Thanakumar Brahma Rao, K.B.V. Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vijayawada India

ISBN: (纸本)9781665464871

Heart is a vital organ in the human body. It is one of the superior organs, which receives more attention from the internal organs. According to various research studies, heart diseases were considered as the leading cause of death worldwide. The heart disease prediction techniques require more accuracy and precision to identify and forecast various disorders. Any improper disease diagnosis can cause death. Many researchers have been experimenting to develop a software system to predict heart disease using machine learning. The primary goal of this research work is to predict cardiac disease in humans using a machine learning algorithm. This study has reviewed some data mining and machine learning methodologies to perform heart disease prediction. © 2023 IEEE.

关键词： Support vector machines

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