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Performance Analysis of Breast Cancer: A machine learning Approach 3

Performance Analysis of Breast Cancer: A Machine Learning Ap...

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3rd international conference on Inventive Research in Computing Applications, ICIRCA 2021

作者： Khatun, Tania Utsho, Md. Mahfuzur Rahman Islam, Md. Ashiqul Zohura, Mst. Fatematuz Hossen, Md. Sagar Rimi, Robaiya Akter Anni, Sabiha Jannat Daffodil International University Dept. of Computer Science and Engineering Dhaka Bangladesh Daffodil International University Dept. of Software Engineering Dhaka Bangladesh Shahabuddin Medical College Gulshan-2 Dhaka Bangladesh

ISBN: (纸本)9780738146270

Nowadays, breast cancer is the most emerging diseaseamong women both in developed as well as developing countries. Due to increased life prospects, increased urbanization, and therelinquishment of western societies, the rareness of breast cancer is supersizing in the developing world. Even it became a second popular cause of cancer that has already been announced. It's very hard to identify the early symptom of this type of cancer for reducing numerous death. Different methods of machine learning and data mining techniques are using for medical diagnosis. In this study, four machine-learning algorithms are applying to analyze breast cancer in the inflammation stage and dig up the most cabbalistic and non-cabbalistic risk factors. To analyze breast cancer data from the Coimbra dataset from the UCI machine learning repository to create accurate prediction models for breast cancer. For getting better performance and to get higher accuracy Naïve Bayes (NB), Random Forest (RF), Multilayer Perceptron (MLP), Simple Logistic Regression (SLR) are using to find out some higher accuracy sequentially 70%, 68%, 85%, and 75%. Among all the above algorithms a better accuracy was achieved using Multi-layer Perceptron. Linear Regression (LiR) models are applying to dig up the most cabbalistic and non-cabbalistic risk factors of breast cancer. These results will help the doctor to detect breast cancer easily in the early stage and take the necessary steps. © 2021 IEEE.

关键词： Developing countries

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BDE 2021 - 2021 3rd international conference on Big data Engineering

BDE 2021 - 2021 3rd International Conference on Big Data Eng...

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3rd international conference on Big data Engineering, BDE 2021

ISBN: (纸本)9781450389426

The proceedings contain 24 papers. The topics discussed include: traffic jam analysis using multi-language twitter data;a retrospective analysis of social media bibliometrics in the era of big data;data-driven prediction of key attributes for tobacco products;massive small file storage scheme based on association rule mining;research on aerospace product quality data governance for resource integration;a super-resolution image reconstruction algorithm of molten droplet based on multi-feature fusion method;user portrait technology and its application scenario analysis;bone age assessment based on deep convolution neural network;collaborative filtering based on clustering and simulated annealing;deep learning network in relation inference from financial report;and depth factor decomposition machine based on multi-kernel convolutional neural network for click-through rate prediction.

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Towards Convergence of Blockchain and Self-sovereign Identity for Privacy-Preserving Secure Federated learning 3rd

Towards Convergence of Blockchain and Self-sovereign Identit...

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3rd international conference on Big data and Security, ICBDS 2021

作者： Haque, Rakib Ul Hasan, A. S. M. Touhidul Daria, Apubra Qu, Qiang Jiang, Qingshan School of Computer Science and Technology University of Chinese Academy of Sciences Shijingshan District Beijing100049 China Department of Computer Science and Engineering University of Asia Pacific Dhaka1205 Bangladesh Institute of Automation Research and Engineering Dhaka1205 Bangladesh Shenzhen Key Laboratory for High Performance Data Mining Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9789811908514

More and more researchers are eager to train their machine learning (ML) model by the distributed dataset in a federated manner. However, there are numerous privacy and security concerns in federated learning, i.e., adversarial attacks, authentication, and model inversion attacks. In this paper, we integrate self-sovereign identity for privacy-preserving secure federated learning. The proposed framework represents identity management and authentication scheme for edge devices, which are participated in FL. It guarantees participants’ privacy, where users’ private data are not shared with the centralized training module. The trust triangle and Blockchain ensure the authenticity of the distributed federated learning, where data privacy is handled with Differential Privacy. Result analysis is done based on measured score and time complexity. In terms of accuracy, BCWD, HDD, and DD achieve 94.69 %, 81.05 % and 78.00 %, respectively on standard ML, whereas 94.45 %, 80.59 % and 76.43 % on ϵ DP-based FL with privacy budget, ϵ = 3. For the time complexity measurement, the total number of transactions employed in the system is 1000. Transactions time and their verification took 3323.15 s. Moreover, the proposed system shows robust performance in both cases. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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machine learning in Wireless Communication

Machine Learning in Wireless Communication

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Electronic Information Engineering and Computer Science (EIECS), 2021 international conference on

作者： Dingxin Chen Xuancheng Wei Chenglan Ni Brunel London School North China University of Technology Beijing China School of Electronic and Optical Engineering Nanjing University of Posts and Telecommunications Zunyi China Wuhan Haidian Foreign Language Experimental School Wuhan China

In recent years, the technology of wireless communication has developed rapidly. For instance, 5G and 6G are a next-generation communication technology. Because of the potential of 5G and 6 G, large quantities of data can be generated, which can be utilized to enhance the performance of neural networks. Recently, convolutional neural networks have achieved remarkable performance in various applications, including voice recognition, channel prediction, and so on. Recently, there have been a lot of studies in this field, including physical, radio, and wireless. Recently, great progress has been made in the area of deep learning in the physical layer. This research covers a broad spectrum of topics. A novel method for automatic modulation recognition is proposed in this paper. This paper gives a comparative analysis of traditional AMR and DL-based AMR, and also describes CNN arithmetic. In addition to CNN-based 2D AMRs, they are also included in Star Map, Time Rate Entry, Eye Graph, etc. In the following section, we will show you an AMR algorithm for RNNs. Finally, this paper sums up and forecasts its future.

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A collective data mining approach to predict customer behaviour 3

A collective data mining approach to predict customer behavi...

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3rd international conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2021

作者： Manohar, E. Jenifer, P. Nisha, M. Sharon Benita, B. Francis Xavier Engineering College Department of Computer Science and Engineering Tirunelveli Tamil Nadu India

ISBN: (纸本)9780738111834

The customer churn is a demanding area of research in e-commerce. This introduces a number of challenges in discovering a more accurate prediction of the information. The classification algorithms are the supervised machine learning techniques. The various classification algorithm has its own advantages. In the proposed approach, the various classification approaches such as support vector machine, Bayesian Classifier and the Random Forest Algorithm are used to analyze the customer churn and a collective approach is applied to develop an effective model. The collective value from various classification methods makes the proposed method more effective. The accuracy metric, precision metric, recall metric and F-Score metric values are calculated to show the effectiveness of this approach. The feature selection method is implemented on the data. The method will lead the education of institutions to predict the churn status and help them to increase the customer. © 2021 IEEE.

关键词： Forecasting

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Advanced Feature Extraction Techniques for machine learning-Based Fault Analysis in Power Grids

Advanced Feature Extraction Techniques for Machine Learning-...

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Electrical Power Engineering, Communication and Computing Technology(ODICON), Odisha international conference on

作者： Pratham Tatraiya Himanshu Priyadarshi Manipal University Jaipur Jaipur India

ISBN: (数字)9798350354379

ISBN: (纸本)9798350354386

As the infrastructure develops and more elements are added in the daily livelihood of humankind, dependable electrical power distribution is critical for keeping society and economies running smoothly. Modern power grids are complex, with wide networks and diverse components, requiring advanced problem detection and management systems for its upkeep. The growing demand of reliable power supply, as well as the necessity for long-term power infrastructure, emphasize the importance of reliable fault detection systems. This work aims to improve the resilience and reliability of electricity grids by using machine learning (ML) techniques to detect and analyze faults in power grids, using extensive feature extraction. The original base dataset, generated on an IEEE 6-bus grid system for consistent benchmarking, of three phase voltages and currents will be transformed into a dataset inclusive of time and other derivations, namely, voltage and current unbalance factor, volt-age and current harmonic distortion and the root-mean-square current and voltages. The results show a massive improvement in accuracy and related metrics between the base dataset, hovering between 34%-45% for various ML techniques to the improved dataset, yielding accuracy in the range of 87% -92% for respective techniques.

关键词： Analytical models Accuracy Computational modeling Sensor phenomena and characterization Feature extraction Power grids Sensor systems Reliability data mining Load modeling

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Research on Sentiment Analysis of hotel reviews based on Word2vec method 3

Research on Sentiment Analysis of hotel reviews based on Wor...

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3rd international conference on Artificial Intelligence and Electromechanical Automation, AIEA 2022

作者： Wan, Jiahua Yao, Lisha Tang, Qiang School of Big Data and Artificial Intelligence Anhui Xinhua University Anhui Hefei China College of Computing and Information Technologies National University Manila Philippines

ISBN: (纸本)9781510657281

This paper takes sentiment analysis as the research direction and collects the data of users' comments on hotel rooms. The research methods used include text corpus preprocessing, Word2vec model, support vector machine, AUC value calculation, feature classification based on supervised learning method, emotion analysis and ordered logistic regression model, etc. Word2vec is used to map words into k-dimensional vector space, and PCA algorithm is used to reduce the dimensionality of the results. According to Word2vec, the comment corpus can be divided into 7 categories of feature dimensions, and then feature recognition is carried out, and emotion scores are calculated by emotion analysis technology. Finally, classify and summarize the emotional scores of each comment in different feature categories. In this paper, through the establishment of Word2vec model, the analysis of consumers' emotional tendency to the hotel, to provide the direction of improving the service level for the hotel, is conducive to the management and development of the hotel. © 2022 SPIE.

关键词： Support vector machines

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Prediction of Efficient Driving Mode for Vehicle using machine learning

Prediction of Efficient Driving Mode for Vehicle using Machi...

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Mysore Sub Section international conference (MysuruCon), IEEE

作者： Ramakrishna Hegde Sujan D Kripa Das Sriraksha M S Siri R Siri Gowri H Soumyasri S M Computer Science Engineering Vidyavardhaka College of Engg. Mysuru India Computer Science Engineering PES University Bengaluru India Master of Computer Applications The National Institute of Engineering Mysuru India

The field of autonomous driving cars is a rapidly growing area of research, machine learning and image recognition technology plays a pivotal role in ensuring their safe navigation on the roads. Different driving modes are offered by modern cars to accommodate various road conditions and driver preferences. However, manually selecting these modes can be cumbersome. Addressing the dynamic nature of mode switching in autonomous driving systems presents a multifaceted challenge. The system must continuously assess a multitude of variables such as traffic patterns, road conditions, weather conditions, and the wear and tear of vehicle components. Traffic conditions can change rapidly, requiring real-time analysis of traffic flow and congestion. Road conditions, including the presence of obstacles, construction zones, or slippery surfaces, demand instant adaptation for safe navigation. Weather conditions further complicate the scenario, as rain, snow, fog, or even intense sunlight can impact visibility and road grip. Addressing these challenges requires sophisticated algorithms capable of processing vast amounts of data from various sensors in real time. machine learning models must be trained to recognize patterns in diverse conditions, enabling the system to make informed decisions swiftly and accurately. Moreover, the system must possess the ability to prioritize these factors based on their immediate impact on driving safety and efficiency. This can be a future enhancement of the paper. By leveraging machine learning algorithms, the mode selection process can be automated. The system constantly monitors driving conditions, learns from real-time camera sensor data and selects the most optimal mode to adapt to current situation. Different models such as Convolutional Neural Network (CNN), CNN with MobileNetV2 as base and decision trees are analyzed.

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machine learning-based Prediction Model for Academic Performance 3

Machine Learning-based Prediction Model for Academic Perform...

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3rd international conference on Advancements in Computing, ICAC 2021

作者： Tharsha, S. Dilogera, J. Mohanashiyaam, B. Kirushan, S. Chathurika, K.B.A.B. Ravi Supunya Swarnakantha, N.H.P.R.S. Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9781665408622

This paper represents the work of a new integrated and collaborative Smart application for managing students online through data mining techniques. Nowadays especially in this pandemic situation, there is a necessity for academic management to incorporate and change all study methods online. By considering all these conditions this research is focused to discuss the solution to manage and engage students smartly and easily. Thou technology advancements have a serious impact on the day-to-day life people face troubles when using complex applications, this implemented Smart application is simple to use and a great tool for Student Management systems. The survey feedback from students, academic staff, and the public illustrate that this project helps to improve the effectiveness and efficiency of learning capability among the targeted group. The main objective of this project is to build up a smart model using machine learning, Deep learning, and Artificial Intelligence to overcome generic learning problems. Therefore, this paper aims to present the concept behind the development and implementation of the Smart Study Application for Student Management System. © 2021 IEEE.

关键词： Students

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3rd international conference on Information and Communication Technology and Applications, ICTA 2020

3rd International Conference on Information and Communicatio...

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3rd international conference on Information and Communication Technology and Applications, ICTA 2020

ISBN: (纸本)9783030691424

The proceedings contain 55 papers. The special focus in this conference is on Information and Communication Technology and Applications. The topics include: Application of Supervised machine learning Based on Gaussian Process Regression for Extrapolative Cell Availability Evaluation in Cellular Communication Systems;anomaly Android Malware Detection: A Comparative Analysis of Six Classifiers;Credit Risk Prediction in Commercial Bank Using Chi-Square with SVM-RBF;A Conceptual Hybrid Model of Deep Convolutional Neural Network (DCNN) and Long Short-Term Memory (LSTM) for Masquerade Attack Detection;An Automated Framework for Swift Lecture Evaluation Using Speech recognition and NLP;DeepFacematch: A Convolutional Neural Network Model for Contactless Attendance on e-SIWES Portal;hausa Intelligence Chatbot System;an Empirical Study to Investigate data Sampling Techniques for Improving Code-Smell Prediction Using Imbalanced data;a Statistical Linguistic Terms Interrelationship Approach to Query Expansion Based on Terms Selection Value;application of Big data Analytics for Improving learning Process in Technical Vocational Education and Training;validation of Student Psychological Player Types for Game-Based learning in University Math Lectures;outlier Detection in Multivariate Time Series data Using a Fusion of K-Medoid, Standardized Euclidean Distance and Z-Score;an Improved Hybridization in the Diagnosis of Diabetes Mellitus Using Selected Computational Intelligence;optimizing the Classification of Network Intrusion Detection Using Ensembles of Decision Trees Algorithm;identification of Bacterial Leaf Blight and Powdery Mildew Diseases Based on a Combination of Histogram of Oriented Gradient and Local Binary pattern Features;feature Weighting and Classification Modeling for Network Intrusion Detection Using machine learning Algorithms;comparative Performance Analysis of Anti-virus Software.

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