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Short-term Portfolio Optimization using Doubly Regularized Exponential Growth Rate 27

Short-term Portfolio Optimization using Doubly Regularized E...

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27th international conference on computer Supported Cooperative Work in Design (CSCWD)

作者： Guan, Quanlong He, Jinneng Lai, Zhao-Rong Hou, Yuyu Jiang, Quming Chen, Ziliang Jinan Univ Coll Informat Sci & Technol Guangzhou Peoples R China Jinan Univ Guangdong Inst Smart Educ Guangzhou Peoples R China Guangdong Key Lab Data Secur & Privacy Preserving Guangzhou Peoples R China Chutian Dragon Co Ltd Dongguan Peoples R China Guangdong Macao Adv Intelligent Comp Joint Lab Zhuhai Peoples R China Jinan Univ Sch Intelligent Syst Sci & Engn Jinan Peoples R China

ISBN: (纸本)9798350349184;9798350349191

In the realm of short-term portfolio optimization, the integration of machine learning with exponential growth rate techniques is gaining prominence. this paper introduces a novel approach for short-term portfolio optimization, termed Short-term Portfolio Optimization using Doubly Regularized EGR (SPODR), to address the challenges posed by limited data availability. SPODR utilizes radial basis functions for the effective identification of market trends, enabling improved stock market forecasts. the approach uniquely combines l(1) and l(2)-regularization, adhering to empirical financial principles, to strike a balance between risk and return in short-term portfolios. A key aspect of SPODR is addressing the complexity of its ElasticNet-like objective, which poses a challenge for traditional methods due to its online learning nature. To overcome this, we have developed an algorithm based on the log barrier interior-point method. this algorithm is adept at efficiently optimizing portfolio allocation, taking into account the specific constraints inherent in our approach. Extensive comparative experiments across five benchmark datasets demonstrate that SPODR significantly outperforms existing short-term portfolio optimization models. It achieves a right balance between return and risk. Furthermore, SPODR showcases efficient computational speed, enhancing its applicability in real-world financial settings.

关键词： Short-term portfolio optimization machine learning in finance application FinTech exponential growth rate approach

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the role of artificial intelligence technology in diagnosis and treatment of obstructive sleep apnea 4

The role of artificial intelligence technology in diagnosis ...

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4th international Symposium on Artificial Intelligence for Medicine Science (ISAIMS)

作者： Zhang, Lanjing Yao, Dongyuan Jiangxi Univ Chinese Med Sch Humanities Nanchang Peoples R China Cent South Univ Jiangxi Neurol Inst Jiangxi Prov Nanchang Jiangxi Peoples R China Nanchang Med Coll Dept Neurol Jiangxi Prov Peoples Hosp Affiliated Hosp 1 Nanchang Jiangxi Peoples R China Cent South Univ Jiangxi Xiangya Hosp Nanchang Jiangxi Peoples R China

ISBN: (纸本)9798400708138

Obstructive sleep apnea (OSA) is the most common sleep-disordered breathing worldwide. the principal diagnosis method for OSA is polysomnographic (PSG) examination, but it requires a lot of workforces and is very expensive. Two of the most common treatment modalities are continuous positive airway pressure (CPAP) and surgery. the main problem of CPAP treatment is poor compliance, while surgery has large fluctuations in success rate. the rapid development of artificial intelligence technology leads to more convenient diagnosis and treatment of OSA. this paper reviews the application of artificial intelligence technology in the field of OSA, including pre-detection, monitoring and treatment.

关键词： Artificial intelligence Obstructive sleep apnea Clinical application machine learning

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Malware Detection and Analysis using machine learning 7

Malware Detection and Analysis using Machine Learning

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

作者： Sirigiri, Manoj Sirigiri, Divya Aishwarya, R. Yogitha, R. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Brillio Technologies Bangalore India

ISBN: (纸本)9781665464086

Online privacy for people is getting worse every day. computer malware is tainting the data records of some well-known companies. Hackers can gain access to a network and change data, once inside. this work discusses several types of malware and communication strategies, such as Trojans, keyloggers, port forwarding, source code obfuscation, application format converters, and social engineering techniques. three different machine learning algorithms are applied in this work to derive meaningful insights from the data. As a result of the work proposed, given malware can be categorized as a malicious or a non-malicious application. this is executed by analyzing the classification report, accuracy and f1-score metrics. the Random Forest is selected as a champion model based on the highest f1-score for the validation dataset. © 2023 IEEE.

关键词： Personal computing

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Optimization of the YOLOv5 deep learning model for peripheral blood cell detection and recognition 4

Optimization of the YOLOv5 deep learning model for periphera...

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4th international conference on Bioinformatics and Intelligent Computing (BIC)

作者： Gao, Yunfei Liu, Yanhong Zhao, Shilei Ma, Xingxing Zhou, Yun Inner Mongolia Univ Sci & Technol Baotou Med Coll Baotou City Inner Mongolia Peoples R China

ISBN: (纸本)9798400716645

In recent years, as neural networks continue to evolve, the use of YOLO deep learning algorithms in medical imaging and diagnosis has become increasingly prevalent. the detection and recognition of blood cells are crucial aspects of medical diagnosis. While deep learning-based object detection and recognition are garnering increasing interest, detecting and counting blood cells in medical imaging remains an essential and challenging task. In this paper, we optimize and improve the YOLOv5 algorithm to achieve efficient detection and recognition of peripheral blood cells. We begin by outlining the architecture and the training procedures of YOLOv5 and discuss its potential applications in medical imaging. Building on the characteristics of blood cell images, we introduce an optimization method using DenseNet based on YOLOv5. Enhancements in data preprocessing, network structuring, and training parameters have improved the accuracy and efficiency of blood cell recognition and counting. Experiments conducted on our proprietary dataset and subsequent comparisons with the base algorithm affirm our claims. Results suggest that our proposed method demonstrates strong performance in detecting and identifying blood cells, offering high practicality for applications.

关键词： peripheral blood cells detection and recognition YOLOv5 deep learning DenseNet

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A Method for Predicting Movie Box-Office using machine learning 4

A Method for Predicting Movie Box-Office using Machine Learn...

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4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Menaga, D. Lakshminarayanan, Akshaya St. Joseph's Institute of Technology Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798350300093

Movies are still an important source of entertainment in any country. But the industry loses a lot when the movies don't do well at the box office. A new area of data analysis is predicting how society will respond to new items in terms of popularity and adoption rates. this project helps analyze and predict the theatrical performance of films before they are released independently. When a movie becomes a blockbuster, it makes a huge profit. But when the movie fails, the loss is also huge. And both have direct positive and negative effects. this also helps investors associated with this business avoid investment risks. Researchers show that it accounts for almost 25% of the movie's revenue. Within 1-2 weeks after release as such, it is difficult to predict how a film will perform at the box office in advance of its release date. the proposed system attempts to predict the success rate of movies. Predictive analysis of various properties of the film. the film attributes such as crew, release date, Production Company, and plot are valuable sources of information and can be of great help in predicting the success of a film. there is a lot of data available on the internet from various sources such as TMDB about different movie attributes, and it is highly relevant and successful, making it an important use case for machine learning. © 2023 IEEE.

关键词： Forecasting

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Comparative Analysis of Deep learning Models for Early Skin Cancer Detection Using 3D Total Body Photography 4

Comparative Analysis of Deep Learning Models for Early Skin ...

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4th international conference on Sustainable Expert Systems, ICSES 2024

作者： Reddy, Keshavagari Smithin Polaki, Ramya Sulochana, V. Pallavi, Gundala Prasanna Kumar, R. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India Anna Administrative Staff College Chennai India

ISBN: (纸本)9798331540364

Skin cancer presents a significant public health challenge, with early detection being crucial in mitigating its life-threatening implications. this study explores the application of deep learning models for early skin cancer detection using 3D Total Body Photography (TBP) images from the ISIC dataset. the dataset includes single-lesion crops from a large and diverse population, enabling comprehensive algorithm training. the study evaluates four models: EfficientNet-B0, CNN, ResNet50, and Inception V3. Among these, EfficientNet-B0 achieved the highest accuracy of 98%, underscoring the importance of model selection in skin cancer diagnostics. the results emphasize the potential of deep learning models in aiding early detection and reducing mortality from skin cancer. © 2024 IEEE.

关键词： Lung cancer

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Edge Computing-based Intelligent Garbage Classification and Recognition application 4

Edge Computing-based Intelligent Garbage Classification and ...

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4th international conference on computers and Artificial Intelligence Technology, CAIT 2023

作者： Yu, Liang Mo, Fu Lai, Yangbing Tian, Liwei Guangdong University of Science and Technology College of Computer Science and AIoT Edge Computing Engineering Technology Research Center of Dongguan City DongGuan China Guangdong University of Science and Technology Coll. of Mech. and Elec. Eng. and Intelligent Mfg. and Environ. Monitoring Engineering Technology Research Center of Dongguan City DongGuan China

ISBN: (纸本)9798350326710

As the standard of living improves, there is a corresponding increase in the variety of materials, leading to a rise in household garbage. Dealing with the challenge of classifying numerous types of garbage can be a daunting task. the accuracy of waste separation cannot be effectively improved solely by relying on individuals' common sense perception, knowledge propaganda, and training. A more comprehensive and efficient approach is needed. this paper proposes an intelligent waste classification and recognition application based on edge computing to address the aforementioned issues. the edge mobile intelligent waste classification and recognition application utilizes a small-sample lightweight machine learning model for training compressed quantization on the Edge Impulse Platform (EI). the application is built using the MobileNetV2 model and migration learning theory. When a user scans a trash image by running the application through a mobile edge device, the tiny machine learning model is invoked to perform edge computation and identify the class of trash. System testing on the EI platform and field applications demonstrates that the average response time for intelligent waste classification and recognition is 0.66 seconds. Additionally, the average inference time is less than 5 milliseconds, and the application achieves an average recognition accuracy of 97% or higher. this intelligent waste classification and recognition system exhibits excellent overall performance and offers great convenience for individuals in their daily waste classification tasks. © 2023 IEEE.

关键词： Edge computing

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Deep learning Methods of Dynamic Sign Language Translation: A Rapid Umbrella Review 4

Deep Learning Methods of Dynamic Sign Language Translation: ...

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4th IEEE international conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Bopape, Lebogang Puree Tsoeu, Mohohlo Samuel Durban University of Technology Department of Electronic and Computer Engineering South Africa Durban University of Technology Department of Electronic and Computer Engineering Durban South Africa

ISBN: (纸本)9798350395914

Countries globally are recognizing sign languages, which are used by more than 5% of national and global populations as official languages. In Africa, South Africa was the fourth country to officialize sign language in 2023. this trend combined with advances in computer vision and machine learning has resulted in an increase of scholarly literature on the translation between spoken and signed languages to bridge the language and communication divide. this has been accompanied by a high volume of published literature reviews on the subject, most of them distinguished by time and geographical contexts. Despite numerous published review papers on the subject, to date, no formal or umbrella reviews of these reviews have been performed to consolidate the similarities, contrasts, gaps, and opportunities reported in these reviews. In this paper, we conduct a first-of-its-kind, rapid systematic umbrella review of 11 literature reviews about machine translation and recognition of dynamic sign languages using machine learning. three lenses namely, the evolution of machine learning and computer vision technologies and resulting applications to sign language translation;geographical contexts and associated applications;and the unique linguistic context of sign languages are used. We found that most reported literature has solved the sign language translation and recognition problem from a purely data-driven perspective, without due attention to specific linguistic complexities of the languages. Consequently, we found that most literature has not paid attention to proper corpora design, model design, and meaningful performance evaluation besides the generic and inherited measures of performance from spoken language processing and computer vision literature. © 2024 IEEE.

关键词： Adversarial machine learning

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machine learning application for Diabetes Prediction Using Ensemble Classification 5

Machine Learning Application for Diabetes Prediction Using E...

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5th international conference on Inventive Research in Computing applications, ICIRCA 2023

作者： Dhiman, Tanu Goyal, Shefali Singh, Nongmeikapam thoiba Singhal, Rohit Kumar Chandigarh University Department of Computer Science Engineering Punjab India

ISBN: (纸本)9798350321425

Data mining is a useful method for extracting meaningful information from massive volumes of records. Statistics and machine learning may be utilised to evaluate large-scale patterns in massive databases. Numerous researchers use image processing methods to predict disease, utilising data mining and machine learning approaches. Disease data can be collected from various sources. A bioinformatics technology will be used for keeping, retrieving, and interpreting information with biological molecules. A forecast can be explained as a description of upcoming events based on current conditions. the aim of this study is to create machine learning and data mining algorithms for diabetes prediction. there are various steps in predicting diabetes. In this study, a vote-based classifier is developed to predict diabetes. the proposed algorithm, is used to optimize the diabetes prediction performance up to 2%. © 2023 IEEE.

关键词： Data mining

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machine learning-Based Network Intrusion Detection System 4

Machine Learning-Based Network Intrusion Detection System

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4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Mirlashari, Mahsa Murtaza Rizvi, Syed Afzal Jamia Millia Islamia Department of Computer Science Delhi India

ISBN: (纸本)9798350306118

the Internet is an extensive and interconnected network of computers and gadgets worldwide. the demand for security measures is more critical as usage and traffic increase. there are both benign and malignant users on the Internet, and they all have equal access to information. Unfortunately, malicious users, also known as hackers, use this access to infiltrate organizations' systems and inflict significant harm. these attacks can take many forms, including stealing sensitive data, disrupting operations, and spreading viruses or malware. Organizations must implement robust security mechanisms to counteract these threats to safeguard their networks, devices, and data. Robust security mechanisms include measures such as firewalls, encryption, antivirus software, and employee training on best practices for online security. By adopting a preventive security strategy, organizations can mitigate the risk of cyber-Attacks and protect their critical data. the firewall defends the enterprise from malicious attacks, but no network can be completely secure. So, it is proposed to install IDS on top of the firewall;it monitors any attempt to breach it, gets access to systems on the trusted side, and issues an alert. Using machine learning (ML), an IDS is suggested in this paper. the NSL-KDD dataset is used to test the Logistic Regression (LR), Stacking CV Classifier, K-Nearest Neighbor (KNN), MLP classifier, Catboost Classifier, Linear Support Vector machine (LSVM), Random Forest (RF), Decision Tree (DT), and XGBoost Classifier technique. © 2023 IEEE.

关键词： Decision trees

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