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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Garg, Bhavika Jain, Tarun Sharma, Vijay Shankar Manipal University Jaipur Department of Computer Science and Engineering India Manipal University Jaipur Department of Iot & Intelligent Systems India

ISBN: (纸本)9798350319125

Mesothelioma is an extremely severe cancer that can easily transform into lung cancer. Mesothelioma diagnosis takes several months and treatment, including surgery, is expensive. Given the risk, early detection of Mesothelioma is essential for patient health, as it is connected to asbestos exposure. Various machine learning algorithms has been used in this research paper to compare with accurate results for mesothelioma detection. Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), k Nearest Neighbourhood (k-NN), and Linear Regression (LR) are some of the machine learning methods being used. For this research paper, dataset is available on UCI, called the University of California Irvine [1]. The test dataset contains 264 instances, 35 characteristics and 8 performance measures, which is used to evaluate the classifiers accuracy. The average accuracy of XGB, RF, DT, SGD, LR and Voting Classifier are 100% each. Combing all the classifiers, helps us to break through the Mesothelioma data and the creation of data driven insights to improve patient care. © 2023 IEEE.

关键词： Asbestos Exposure DT Machine Learning Machine Learning Algorithms-SVM Mesothelioma

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Predicting the Hardness of Low Alloy Metal using Machine Learning Model 6

Predicting the Hardness of Low Alloy Metal using Machine Lea...

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6th International Conference on Computational Intelligence and Networks, CINE 2024

作者： Logeswaran, K. Savitha, S. Suresh, P. Sah, Swati Priyanka, B. Ragav, R. Vishal, R.S. Mohamed Arsath, H. Kongu Engineering College Department of Artificial Intelligence TamilNadu Erode638060 India Vellore Institute of Technology School of Computer Science and Engineering Department of Database Systems TamilNadu Vellore632014 India K.S.R College of Engineering Department of Computer Science and Engineering Tamilnadu Trichengode637215 India Sharda University School of Engineering and Technology Nodia India

ISBN: (纸本)9798331516796

In industries like materials engineering and manufacturing, predicting the hardness of low alloy metals is crucial for ensuring quality and performance. Traditional methods, which often rely on formulas or manual testing, can be slow and inaccurate due to variations in material properties. This paper presents a new approach for predicting metal hardness using machine learning techniques, including Multi-linear Regression, Decision Tree Regression, Random Forest Regression, and Gradient Boosting Regression. These algorithms are used to improve the accuracy and reliability of hardness predictions compared to older methods. The paper covers how the data was collected, which features were chosen, and how the models were trained. It also compares the performance of these algorithms, looking at how accurate and effective they are in different situations. The results show that the machine learning models offer better predictions than traditional methods, leading to more consistent and reliable outcomes. Finally, the research discusses how these models can be used in real-world applications to make processes more efficient and reduce errors. © 2024 IEEE.

关键词： Prediction models

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Product Recommendation System using Deep Learning based Recurrent Neural Network

Product Recommendation System using Deep Learning based Recu...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Shanthi, S. Nirmaladevi, K. Mathiarasi, N. Tharani, As. Krithika Monisha, A. Kuppuchamy, R. Krishnamoorthy, N. Kongu Engineering College Department of Computer Science & Engineering Perundurai India PSNA College of Engineering and Technology Department of Computer Applications Tamilnadu Dindigul India Vellore Institute of Technology Department of Software Systems and Engineering Vellore Campus India

ISBN: (纸本)9798350348217

Today's marketing strategies place a high priority on comprehending customer sentiments. It will not only give businesses a better understanding of how their clients view their goods and/or services, but it will also give them suggestions on how to enhance their offerings. We make an effort to comprehend the relationship between various factors in customer reviews on an online store for women's clothing. The proposed system also classifies each review, according to, whether it recommends the product in question or not, and whether it has a positive, negative, or neutral sentiment. With the exception of review titles and review texts, we used univariate and multivariate analyses on datasets features to accomplish these goals. Additionally, we have implemented a bidirectional recurrent neural network (RNN) with a long-short term memory unit (LSTM) for sentiment and recommendation. © 2023 IEEE.

关键词： Long short-term memory

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Ovarian Cancer Prediction using Machine Learning 3

Ovarian Cancer Prediction using Machine Learning

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Agnihotri, Yashika Jain, Tarun Sharma, Vijay Shankar Manipal University Jaipur Department of Computer Science and Engineering India Manipal University Jaipur Department of Iot & Intelligent Systems India

ISBN: (纸本)9798350319125

Ovarian cancer is the type of cancer that has the highest recurrence rate in women and poses a serious threat to women. Due to the lack of observable signs, this quiet invader often goes undetected in the beginning, leaving women susceptible to its sneaky development. Once it has advanced and its symptoms are obvious, it becomes arduous to treat. Machine learning is emerging as a ray of hope in the healthcare scene in the current era of rapid technological innovation. Machine learning provides a promising path for the early diagnosis and prediction of ovarian cancer by harnessing the power of algorithms and data analytics, perhaps turning the tide against this tough enemy. The dataset [17] comprises of 349 instances and 49 features. The goal here is to derive a model that helps in classification of instances into two classes: Benign Ovarian Tumors (BOT) and Ovarian Cancer (OC). Therefore, methodical machine learning algorithms are employed to detect ovarian cancer. Our research findings revealed that out of all the classifiers, the Random Forest Model has the highest accuracy (91.43 %). This approach is capable of accurately classifying BOT and OC and could have immense effect on diagnosis of patients, leading to better treatments and higher survival rate. © 2023 IEEE.

关键词： Classification Decision Tree Model KNN Logistic Regression Model Random Forest Model SGD Model and Voting Classifier Model SVM Model Used - Machine Learning XGB Model

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Portable Waste Bank for Plastic Bottles with Electronic-Money Payment 1

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International Conference on Innovative Technologies in Intelligent systems and Industrial Applications, CITISIA 2022

作者： Herawan, Safarudin Gazali Suryaningrum, Kristien Margi Kristin, Desi Maya Gavrila, Ardito Yunus, Afa Ahmad Tanawi, Welldelin Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia IInformation Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Plastic is the second largest amount of waste in Indonesia, reaching 17.3% of the total waste of 28.6 million tons for 2021. The type of plastic that is most often recycled is plastic bottle packaging. Plastic waste that is not managed properly can affect the health and safety of the ecosystem. The waste bank program is one of the government’s efforts to reduce the amount of waste heaps. The portable waste bank for plastic bottles has a function of automatically exchanging plastic bottle waste with points that can be exchanged into electronic-money. Therefore, This research are aims to design a mobile friendly website-based application to integrate in the use of portable waste banks by using the QR code scan feature to exchange points into electronic-money. The results of the design are found that the portable waste bank with the simulated application has functioned well and can be applied in the real condition. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Websites

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An Advanced Intrusion Detection Solution for Networks based on Honeypot Servers 6

An Advanced Intrusion Detection Solution for Networks based ...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Veena, K. Meena, K. Mercy Theresa, M. Hemalatha, C. Rajalakshmi, D. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai600119 India Department of Computer Science and Engineering GITAM School of Technology GITAM Bangalore561203 India Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai603203 India Computer Science and Engineering Sri Sairam Institute of Technology Chennai India

ISBN: (纸本)9798350398496

Network security is a critical issue in modern technology. Honey pot-based intrusion detection methods provide an additional layer of security and enhance network performance by analyzing hacker behaviour and detecting unauthorized clients. IP validation and vulnerability detection are used to ensure authorized customer access and prevent illegal client activity. A voice recognition system detects malicious attacks in stage two. If authorized, packets are passed to the server, otherwise, they are delivered to honeypot servers. Using Naive Bayes algorithm, the detection rate is 85.06% with an accuracy of 98.18% and a false alarm rate of 0.15 seconds. © 2023 IEEE.

关键词： Network security

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ChatGPT vs. Stack Overflow: An Exploratory Comparison of Programming Assistance Tools 23

ChatGPT vs. Stack Overflow: An Exploratory Comparison of Pro...

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23rd IEEE International Conference on Software Quality, Reliability, and Security Companion, QRS-C 2023

作者： Liu, Jinrun Tang, Xinyu Li, Linlin Chen, Panpan Liu, Yepang Southern University of Science and Technology Shenzhen Guangdong China Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering China

ISBN: (纸本)9798350359398

Programmers often seek help from Q&A websites to resolve issues they encounter during programming. Stack Overflow has been a widely used platform for this purpose for over a decade. Recently, revolutionary AI-powered platforms like ChatGPT have quickly gained popularity among programmers for their efficient and personalized programming assistance via natural language interactions. Both platforms can offer valuable assistance to programmers, but it's unclear which is more effective at enhancing programmer productivity. In our paper, we conducted an exploratory user study to compare the performance of Stack Overflow and ChatGPT in enhancing programmer productivity. Two groups of students with similar programming abilities were instructed to use the two platforms to solve three different types of programming tasks: Algorithmic challenges, library usage, and debugging. During the experiments, we measured and compared the quality of code produced and the time taken to complete tasks for the two groups. The results show that, concerning code quality, ChatGPT outperforms Stack Overflow significantly in helping complete algorithmic and library-related tasks, while Stack Overflow is better for debugging tasks. Regarding task completion speed, the ChatGPT group is obviously faster than the Stack Overflow group in the algorithmic challenge, but the two groups have a similar performance in the other two tasks. Additionally, we conducted a post-experiment survey with the participants to understand how the platforms have helped them complete the programming tasks. We analyzed the questionnaires to summarize ChatGPT and Stack Overflow's strengths and weaknesses pointed out by the participants. By comparing these, we identified the reasons behind the two platforms' divergent performances in programming assistance. © 2023 IEEE.

关键词： ChatGPT programming Stack Overflow user study

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Facial Expression Recognition Model Depending on Optimized Support Vector Machine

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computers, Materials & Continua 2023年第7期76卷 499-515页

作者： Amel Ali Alhussan Fatma M.Talaat El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Doaa Sami Khafaga Mona Alnaggar Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Machine Learning&Information Retrieval Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Robotics and Intelligent Machines Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt

In computer vision,emotion recognition using facial expression images is considered an important research *** learning advances in recent years have aided in attaining improved results in this *** to recent studies,multiple facial expressions may be included in facial photographs representing a particular type of *** is feasible and useful to convert face photos into collections of visual words and carry out global expression *** main contribution of this paper is to propose a facial expression recognitionmodel(FERM)depending on an optimized Support Vector Machine(SVM).To test the performance of the proposed model(FERM),AffectNet is *** uses 1250 emotion-related keywords in six different languages to search three major search engines and get over 1,000,000 facial photos *** FERM is composed of three main phases:(i)the Data preparation phase,(ii)Applying grid search for optimization,and(iii)the categorization *** discriminant analysis(LDA)is used to categorize the data into eight labels(neutral,happy,sad,surprised,fear,disgust,angry,and contempt).Due to using LDA,the performance of categorization via SVM has been obviously *** search is used to find the optimal values for hyperparameters of SVM(C and gamma).The proposed optimized SVM algorithm has achieved an accuracy of 99%and a 98%F1 score.

关键词： Facial expression recognition machine learning linear dis-criminant analysis(LDA) support vector machine(SVM) grid search

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Network-Agnostic Voltage-to-Power Sensitivities of a Real-Time Reduced Active Distribution Network

Network-Agnostic Voltage-to-Power Sensitivities of a Real-Ti...

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2023 IEEE PES Innovative Smart Grid Technologies - Asia, ISGT Asia 2023

作者： Prasad, Manorath Rather, Zakir Hussain Razzaghi, Reza Doolla, Suryanarayana Indian Institute of Technology Bombay Department of Energy Science and Engineering Mumbai India Monash University Department of Electrical and Computer Systems Engineering Melbourne Australia

ISBN: (纸本)9798350327748

The advent of distribution-level phasor measurement units (D-PMUs) has enhanced the observability of active distribution networks (ADNs) and improved their energy management and control. Recently, various ADN control strategies that require detailed network parameter information have been proposed based on the voltage-to-power sensitivities of the ADN nodes. This paper proposes a new realistic approach to determining nodal voltage-to-power sensitivities of a synchrophasor measurement-based reduced ADN model. The real-time synchrophasor data dynamically updates the parameters of the reduced equivalent model to follow the time-varying operating conditions of the ADN accurately. The voltage-to-power sensitivities of the retained observable nodes are derived from the power flow Jacobian matrix using reduced equivalent model parameters. Thus, the proposed approach is agnostic about detailed line impedance data of the complex ADN. Further, the computational complexity is significantly decreased with the reduced number of nodes in the equivalent ADN model. The proposed approach has been tested on modified IEEE-33 bus benchmark system with high penetration of photovoltaic inverters using a DIgSILENT PowerFactory MATLAB co-simulation platform. © 2023 IEEE.

关键词： Electric power distribution

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A Novel Method for Pneumothorax Diagnosis and Segmentation Using Deep Convolution Neural Network

A Novel Method for Pneumothorax Diagnosis and Segmentation U...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

ISBN: (纸本)9798350348217

A pneumothorax is the accumulation of air in the pleural space between the chest and lungs. Air that is in the lungs during breathing can leak out of the lungs and become trapped between the chest and lungs in the thoracic cavity, causing chest pain, difficulty breathing, and ultimately the complete collapse of the taut lungs. pneumothorax. Therefore, early diagnosis and treatment should be started. Neural networks have proven effective in image classification tasks, and many researchers believe that training neural network models can detect radiologist-diagnosed chest abnormalities such as pneumonia and pneumothorax. is proving. Moreover, we utilize various techniques, including Stochastic Weight Averaging (SWA), aggressive data augmentation, and learning rate schedule. We use the chest x-ray dataset provided by the 2019 SIIM-ACR In this study, a ResNet-34 neural network is trained on a data set of chest x-rays containing over 10,675 images to create a model that predicts the presence of pneumothorax from chest *** suggested system receives a Dice Similarity Coefficient (DSC) of 0.8410 © 2023 IEEE.

关键词： Image classification

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