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检索条件"任意字段=7th International Conference on Machine Learning and Soft Computing, ICMLSC 2023"

共 787 条记录，以下是201-210 订阅

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Utilization of CNN and Ensemble Methods to Support Medical Practitioners in Categorizing Lung Cancer: A Research Paper 7

Utilization of CNN and Ensemble Methods to Support Medical P...

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2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Chitale, Rohit Shah, Bindi Bhalgat, Sejal Pandey, Anuj Jakhete, Sumitra A. Pict Information Technology Pune India

ISBN: (纸本)9798350304268

Detecting lung cancer early is critical for successful treatment, and it is essential to evaluate C. T. scans effectively. To address this, we have developed a platform that aims to improve the accuracy of lung cancer detection by combining the strengths of these two machine-learning techniques. the proposed system employs an XGBoost classifier to preprocess the data and extract important features, followed by a CNN model to classify the images. the web application interface allows users to upload C. T. scans, and the output is generated instantly, indicating the presence or absence of lung cancer. the proposed system achieved a high accuracy rate of 96 % on the dataset used, demonstrating its effectiveness in the early detection of lung cancer. the system has potential as a screening tool for lung cancer, allowing for early detection and timely treatment. the proposed approach offers a promising solution for more effective and efficient lung cancer detection. © 2023 IEEE.

关键词： CNN CT scan deep learning healthcare lung cancer detection medical professionals XGBoost

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COVID - 19, Pneumonia and Bacterial Infection Detection Using machine learning 7

COVID - 19, Pneumonia and Bacterial Infection Detection Usin...

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7th international conference on Image Information Processing, ICIIP 2023

作者： Vaid, Dhruvin Pant, Sonu Saini, Rakesh Kumar Dit University School of Computing Dehradun India

ISBN: (纸本)9798350371406

the exponential growth in population has a large impact on availability of doctor to the public. A big part of India's population is below the poverty line which make it difficult for them to access medical health at cheaper price. But the advancement in technology and medicine have brought relief and easy access to health-care sector. Now disease like COVID19, Pneumonia and Bacterial Infection can be detected using computational algorithm like machine learning, Deep learning, CNN etc. this advancement brings faster medical diagnosis to the public. this abstract provides an overview of the key aspects of machine learning, highlighting its objectives, components, and impact. It outlines the role of machine learning in enhancing healthcare in case of detecting disease. © 2023 IEEE.

关键词： COVID-19

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A Novel Approach to Predict the Severity of Peripheral Neuropathy using machine learning 7

A Novel Approach to Predict the Severity of Peripheral Neuro...

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7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Muhaimeen, A. Aadithiyaprasana, K. Ranjith, A. Sasirekha, S.P. Reshma, R. Mekala, N. Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350397253

the occurrence of type 2 diabetes mellitus, a chronic metabolic condition, has been continuously increasing worldwide. the World Health Organization and Diabetes Association (DA) standards consider both clinical and laboratory factors and provide the framework for screening and diagnosis. the nerve disorder has no known cure yet. Pre-diabetes and morphological diabetes patients' impacts are yet apparent. the prolonged undiagnosed diabetes mellitus may cause major nerve disorders. It leads to severe nerve impairment that affects the brain and spinal cord which led peripheral neuropathy. this research model is designed to evaluate the clinical applications of percussion entropy index (PEI) and electrophysiological findings (EPF) for predicting the severity of nerve damage in patients. the dataset of five hundred and sixteen predefine diabetes patients were classified into mild, moderate, and severe based on clinical index values. Different machine learning prediction models like logistic regression (LR), support vector machine (SVM), light gradient boosting (LGB), and random forest (RF) are used to perform comparative analysis with the proposed hybrid model. the output of this research study shows that the Hybrid RF model was very effectively used to predict the severity level of diabetic neuropathy. © 2023 IEEE.

关键词： Forecasting

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Performance Analysis of machine learning-Based Scheduling Policies for Operating Systems 7

Performance Analysis of Machine Learning-Based Scheduling Po...

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7th international conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Nadh, Boddu Sasi Sai Sha, Akhbar Addanki, Veerababu Yerramreddy, Dhanvanth Reddy Nisha, K.S. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350340600

this study investigates the effectiveness of machine learning-based approaches for predicting a target variable in a given dataset. We compare the performance of several popular regression models, including linear regression, random forest regression, gradient boosting regression, support vector regression, and neural network regression, on a real-world dataset with a large number of features. For each model, several hyperparameters are tuned by using a grid search approach to find the configuration that achieves the best performance on a held-out test set. the obtained results show that the neural network regression model achieved the best test RMSE of 0.173, followed by the gradient boosting regression and random forest regression models with test RMSEs of around 0.18. the linear regression and support vector regression models had higher test RMSEs, indicating lower effectiveness in this particular dataset. the research findings suggest that more complex machine learning models with a higher number of hyperparameters to tune can perform better for this type of prediction task, but careful hyperparameter tuning is required to achieve the optimal performance. © 2023 IEEE.

关键词： Statistical tests

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A Novel Ransomware Virus Detection Technique using machine and Deep learning Methods 7

A Novel Ransomware Virus Detection Technique using Machine a...

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7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Charmilisri, A. Harshi, Ineni Madhushalini, V. Raja, Laxmi Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350397253

Malware is typically found only after the victim gets a ransom demand. Ransomware detection systems detect the threat more quickly, allowing victims to take action before irreversible damage occurs. the identification of ransomware will assist in avoiding the loss of crucial data. After an intrusion, many users never access their original data again. Data will be lost permanently if a recent backup is not created. Endpoint detection is one of the virus-defense techniques that can stop malware as soon as an attacker gains access. Hence, the sensitive data can be kept safe by protecting the data. this study proposes a system for detecting ransomware attacks that uses machine learning and deep learning approaches to analyse the malware that enters the network in order to stop these malware attacks. Each file was initially analysed and examined in a computer system to see whether it's vulnerable to attack. When there is network communication, each and every packet is detected. the assessed packets will be checked to see whether there was any unusual behaviour in the system's activities. the goal of this study is to determine when a harmful signal first arises and where it happens in a ransomware workflow, as well as to allow early identification and stop ransomware activities before they cause significant damage. © 2023 IEEE.

关键词： Intrusion detection

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Stock Price Prediction on Indian Share Market Using machine learning 7th

Stock Price Prediction on Indian Share Market Using Machine ...

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7th international conference on soft computing: theories and Applications, SoCTA 2023

作者： Kumar, Horesh Garg, Balendra Kumar Modi, Ronak Mayankar, Aaditya Kumar, Anshul Patel, Sunil Kumar G.L. Bajaj Institute of Technology and Management Greater Noida India Pandit S. N. Shukla University Madhya Pradesh Shahdol484001 India Manipal University Jaipur Jaipur India

ISBN: (纸本)9789819720309

Since the development of machine learning, predicting stock market trends has become a captivating task, impacted by economic data, politics, and investor mood. the complexity of these components makes reliable prediction challenging. In this work, we analyze multiple models, including statistics, linear regression, and deep learning, using a 10-year dataset of Asian Paints stock prices from the BSE also our results reveal that LSTM beat other models with an R2 score of 94.5, followed by RNN at 93, and this illustrates LSTM's capacity to capture long-term dependencies in stock price prediction. By utilizing rigorous measurements, we uncover model strengths and flaws, improving decision-making for academics and practitioners. Deep learning, particularly LSTM, gives useful insights for comprehending stock market difficulties, and further advances are conceivable through new features and political sentiment research. Our research intends to increase trust in decision-making for investors, financial analysts, and policymakers, highlighting the potential of deep learning. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Long short-term memory

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Key Analysis of Integer Factorizing based Public-Key Cryptosystems Using machine learning 7

Key Analysis of Integer Factorizing based Public-Key Cryptos...

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7th international conference on Advances in Artificial Intelligence, ICAAI 2023

作者： Alia, Mohammad Ahmad Jaradat, Yousef Alshehadeh, Abdul Razzak Faculty of Science and It Al-Zaytoonah University of Jordan Amman Jordan Faculty of Engineering and Technology Al-Zaytoonah University of Jordan Amman Jordan Faculty of Business Al-Zaytoonah University of Jordan Amman Jordan

ISBN: (纸本)9798400708985

this paper discusses the possibilities of using machine learning models to analyze the keys in public key encryption algorithms that are based on various mathematical hard problems. Given their distinct domains within computer science, machine learning and public key cryptosystems may appear unrelated at first glance. Nonetheless, there are intriguing correlations and potential points of convergence between these two domains. In general, machine learning models have the potential to help with differential cryptanalysis, a technique that uses statistical differences between plaintext-ciphertext pairs to deduce parts of the key. As a result, breaking encryption keys using machine learning is a complex and evolving research area that investigates cryptanalysis machine learning techniques. While public key encryption algorithms were designed to be secure against various attacks such as brute force, advances in computing power and machine learning have raised concerns about the potential emergence of new attack vectors. © 2023 ACM.

关键词： Public key cryptography

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Applying Deep learning Methods on Spam Review Detection 7

Applying Deep Learning Methods on Spam Review Detection

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7th international conference on computing Methodologies and Communication, ICCMC 2023

作者： Ramu, M. Raj, Chinnakotla Jayanth Nithish, Apthiri Boggula, Chandhu Ganesh Babu, G. Srikanth Goud, K.I. Dept. of Computer Science and Systems Engineering SVEC Andhrapradesh Tirupati India

ISBN: (纸本)9781665464086

In today's environment, a reliable and effective technique for identifying spam reviews is essential if you want to purchase things online without being taken advantage of. there are possibilities for publishing reviews in many internet locations, which opens the door for sponsored or deceptive fake reviews. these fabricated evaluations may mislead the general audience and leave them unsure of whether or not to believe them. the issue of spam review finding has been solved by the introduction of prominent deep literacy methods. the focus of recent research has been on supervised literacy practices that contain labelled data, which is inadequate for online review. this initiative aims to expose any dishonest textbook reviews. To do this, we've used both labelled and unlabeled data and suggested deep learning techniques for spam review detection, including Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNN), and a Long Short-Term Memory (LSTM) variation of Recurrent Neural Networks (RNN). We also used standard machine learning classifiers to identify spam reviews, including Naive Bayes (NB), K Nearest Neighbor (KNN), and Support Vector machine (SVM). Finally, we compared the effectiveness of traditional and deep literacy classifiers. We'll use deep literacy classifiers to boost the finesse and efficiency. © 2023 IEEE.

关键词： Long short-term memory

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Enhancing Security for Online Transactions through Supervised machine learning and Block Chain Technology in Credit Card Fraud Detection 7

Enhancing Security for Online Transactions through Supervise...

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7th international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Raj, A. thilakesh Shobana, J. Nassa, Vinay Kumar Painuly, Sakshi Savaram, Mythreya Sridevi, M. Sri Venkateswara College of Engineering Department of Information Technology Tamilnadu India Srm Institute of Science and Technology School of Computing Tamilnadu India Rajarambapu Institute of Technology Department of Computer Science and Engineering Maharashtra Uran India Graphic Era Hill University Dehradun R/S Graphic Era Deemed to Be University School of Computing Dehradun India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh India Cvr College of Engineering Department of Cse Telangana India

ISBN: (纸本)9798350341485

this research probed the capabilities of supervised machine learning algorithms in credit card fraud detection, an essential facet of contemporary digital finance, and integrated the transformative idea of blockchain for added security and transparency. Findings highlighted the predominant efficiency of the XGBoost algorithm, which recorded an accuracy of 97%, a precision of 94%, and an AUC of 0.97. Other notable performers included the Gradient Boosting machine and Random Forest. the study underscores the potential of integrating advanced machine learning techniques and blockchain technology to significantly enhance fraud detection systems, ultimately enhancing user trust and the security of online transactions. Recommendations for the adoption of algorithms like XGBoost and the utilization of blockchain ensures immutable transaction records and continuous monitoring for adaptive defense against evolving cyber threats. Future research directions might encompass the exploration of hybrid models, blockchain integration, and deep learning techniques to further enhance fraud detection mechanisms. this research provides research reference for financial institutions that strive to optimize the digital transaction standards. © 2023 IEEE.

关键词： Blockchain

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machine learning for Accurate and Efficient Pomegranate Fruit Disease Detection: A Novel Approach to Improve Crop Yield and Quality 7

Machine Learning for Accurate and Efficient Pomegranate Frui...

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2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Wakhare, Prashant Neduncheliyan, S. Mane, Pradeep B. Bharath Institute of Higher Education and Research Tamil Nadu India Aissms Institute of Information Technology Maharashtra Pune India

ISBN: (纸本)9798350304268

this paper presents a web-based tool for identifying diseases in fruit crops to address the negative impact of climate change on agriculture. With the increase in population, there is a growing demand for more food production, which puts pressure on farmers to increase crop yields. However, climate change is causing uneven weather conditions, leading to decreased crop yields. this paper proposes a solution that allows farmers to upload an image of a fruit to a system that has a trained dataset of fruit images. the system uses several image processing steps, including feature extraction based on parameters such as color, morphology, and CCV, followed by clustering using CNN Algorithm and classification using SVM to determine if the fruit is infected or not. An intent search technique is used to find the user intention from the features extracted. the experimental evaluation of the approach shows that it is effective, with a 98.38% accuracy rate in identifying diseases in fruit crops. the proposed tool can be used to identify the diseases that affect fruit crops and provide farmers with timely and accurate information, enabling them to take preventive measures to protect their crops, improve crop yields, and contribute to the overall agricultural economy. the recommendations for the application of the developed machine learning model for pomegranate fruit disease detection include using high-quality data, regularly updating the model, integrating with other detection methods, providing an easy-to-use interface, and providing technical support to growers. © 2023 IEEE.

关键词： Agricultural Productivity Crop Yield Deep learning fruit Disease Detection machine learning Algorithms Pomegranate

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