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Stock Market Price Prediction Using machine learning 5

Stock Market Price Prediction Using Machine Learning

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5th international conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Mohammed, Shariq Krishna, Somanchi Hari Mudalkar, Pralhad K. Verma, Narinder Karthikeyan, P. Yadav, Ajay Singh Dhofar University College of Commerce and Business Administration Department of Accounting Oman Vignana Bharathi Institute of Technology Department of Business Management India Institute of Management Kolhapur India University of Jammu Department of Computer Science and It Jammu and Kashmir India Kongu Engineering College Department of Management Studies Perundurai Tamilnadu Erode638060 India Srm Institute of Science and Technology Delhi-NCR Campus Department of Mathematics UP Ghaziabad India

ISBN: (纸本)9781665474672

the stock market is known for its high volatility, fast changes, and nonlinear behaviour;investors should be prepared for all three. It is highly challenging to precisely forecast the movements of stock prices due to the presence of multiple factors, both macro and micro, such as politics, the state of the global economy, unanticipated occurrences, and the financial success of a firm, among other factors. However, because there is such a wealth of information available, it can be challenging to draw conclusions. Because of this, researchers, analysts, and data scientists working in the financial sector are constantly looking for new analytical techniques that may be used to spot trends in the stock market. this development gave rise to the practice of algorithmic trading, which is characterized by the application of trading strategies that are pre-programmed and automated. machine learning models such as LSTM can accurately forecasts the prices of stocks as actual and predicted. © 2023 IEEE.

关键词： Financial markets

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4th international conference on computer and Communication Technologies, IC3T 2022

4th International Conference on Computer and Communication T...

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4th international conference on computer and Communication Technologies, IC3T 2022

ISBN: (纸本)9789811985621

the proceedings contain 63 papers. the special focus in this conference is on computer and Communication Technologies. the topics include: Study of Security for Edge Detection Based Image Steganography;fake Face Image Classification by Blending the Scalable Convolution Network and Hierarchical Vision Transformer;performance Analysis of Osteoarthritis from Knee Radiographs Using Convolutional Neural Networks;Efficient Motion Detection and Compensation Using FPGA;Discover Crypto-Jacker from Blockchain Using AFS Method;Automated Detection for Muscle Disease Using EMG Signal;drowsiness Detection for Automotive Drivers in Real-Time;prediction of Dementia Using Deep learning;performance Analysis of Universal Filtered Multicarrier Waveform with Various Design Parameters for 5G and Beyond Wireless Networks;Shuffled Frog Leap and Ant Lion Optimization for Intrusion Detection in IoT-Based WSN;Diabetic Retinopathy (DR) Detection Using Deep learning;comparative Analysis Using Data Mining Techniques to Predict the Air Quality and their Impact on Environment;Complexity Reduction by Signal Passing Technique in MIMO Decoders;A New Approach to Improve Reliability in UART Using Checksum Algorithm;Modified VHDL Implementation of 128-Bit Rijndael AES Algorithm by Asymmetric Keys;a Computationally Inexpensive Method Based on Transfer learning for Mobile Malware Detection;a Statistical Approach for Extractive Hindi Text Summarization Using machine Translation;Semantic Parser Using a Sequence-to-Sequence RNN Model to Generate Logical Forms;NFF: A Novel Nested Feature Fusion Method for Efficient and Early Detection of Colorectal Carcinoma;Arrhythmia Classification Using BiLSTM with DTCWT and MFCC Features;A Comprehensive Alert System Based on Social Distancing for Cautioning People Amidst the COVID-19 Pandemic Using Deep Neural Network Models;CNN-Based Breast Cancer Detection.

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An application of XGBoost algorithm for online transaction fraud detection based on improved sailfish optimizer 4

An application of XGBoost algorithm for online transaction f...

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4th international conference on machine learning, Big Data and Business Intelligence, MLBDBI 2022

作者： Chen, Hongwei Chen, Lun Hubei University of Technology School of Computer Science Wuhan China

ISBN: (纸本)9798350333947

Fraud detection is relevant in this context because of the large scale of online transactions, which conceal a small number of fraudulent *** learning (ML) fraud detection models have numerous hyperparameters of the eXtreme Gradient Boosting (XGBoost) algorithm, which are important for the performance impact of fraud detection models. In this paper, the propose an improved version based on Sailfish optimizer(SFO) applied to online transaction fraud detection to optimize the hyperparameters of the XGBoost algorithm. Firstly, the optimization-seeking and local-optimal-skipping capabilities of Sailfish optimizer are improved, and Improved Sailfish optimizer (ISFO) is compared with other classical optimization algorithms, and the results show that the algorithm has outstanding. the Improved Sailfish optimizer is then used to optimize the parameters of XGBoost (ISFO-XGBoost) and compared with several other machine learning classifiers such as: Decision Tree (DT), Random Forest (RF), XGBoost, and several optimization algorithms optimized for the XGBoost model. To validate the performance, a fraud dataset of online transactions is used for evaluation. the experimental results demonstrate that the proposed improved algorithm is highly promising in solving machine learning hyperparameter optimization challenges, and the ISFO-XGBoost algorithm achieves the best results. © 2022 IEEE.

关键词： Decision trees

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Banana Ripeness Identification and Classification Using Hybrid Models with RESNET-50, VGG-16 and machine learning Techniques 4th

Banana Ripeness Identification and Classification Using Hybr...

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4th international conference on machine Intelligence and Signal Processing, MISP 2022

作者： Mathew, Milan Bins Surya Manjunathan, G. Gokul, B. Mohana Ganesh, K. Sajidha, S.A. Nisha, V.M. School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India

ISBN: (纸本)9789819900848

For any fruit or vegetable consumers consider the ripeness of the item as one of the major indicators of its price and quality. Similarly, for bananas, the quality of the fruit is strongly associated with its ripeness and is a matter of concern for consumers, producers and traders of this fruit. this is because the ripeness of a fruit is associated with its taste, texture, and edibility which is important for any food. A manual method of classifying the bananas based on their ripeness is a tedious and impractical technique. this study aims to use modern day computer vision technologies to automatically identify and classify bananas based on their ripeness. We propose a hybrid model BanNet that aims to use a hybrid model that uses deep learning models like VGG-16 and ResNet-50 for feature extraction and a machine learning classifier to classify the banana based on its ripeness. the bananas will be classified into 4 categories—ripe, mid-ripe, overripe and unripe. the results are compared with other machine learning classifiers and another deep learning model used for feature extraction. the results reveal that the proposed system has a better overall recognition rate than the other techniques discussed. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural network models

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the Effect of a Weighted Mean Pooling on the Performance of Convolution Neural Network 4th

The Effect of a Weighted Mean Pooling on the Performance of ...

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4th international conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Shadoul, Inas Hossen, Abdulnasir Mesbah, Mostefa Al-Hmouz, Rami Sultan Qaboos Univ Dept Elect Engn Coll Engn Al Khoud Oman

ISBN: (纸本)9783031821493;9783031821509

Convolutional Neural Networks (CNNs), a deep learning application, are powerful tools particularly suited for image processing and classification applications. Pooling is a major component of CNNs and significantly influences learning. In this step, data is reduced in size through a specific algorithm or technique, resulting in the reduction of the computational load on the layers. the various pooling techniques in CNNs have specific uses, features, and effects, some desirable and others counterproductive to the training goal. Using one pooling technique can produce results that other techniques cannot. Some cases can benefit differently from different pooling techniques. this raises the question of whether combining these pooling techniques could achieve a collective positive impact, potentially leading to performance gains beyond those achievable by individual techniques used separately. A control parameter is added to optimize the selection of the pooling method or could be a weighted combination of more than one method. the results show that the presented method guarantees the same performance as a single pooling layer at least and could be improved when weighted pooling layers are involved in some datasets.

关键词： machine learning Convolution Neural Network (CNN) Pooling Mixed pooling Image classification

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MOODIFY: Web application to Detect Human Emotions 4

MOODIFY: Web Application to Detect Human Emotions

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Chawla, Kunal Kaul, Vanshika Singh, Baljap Soni, Shiv Sahil Kumar, Anubhav Gupta, Shikha Apex Institute of Technology Chandigarh University Punjab India

ISBN: (纸本)9798350300888

Human Emotions are very complex psychological responses to various events and stimuli that are experienced subjectively. these Human Emotions are often influenced by cultural, social, and individual factors. there are a lot of emotions that are recognized and some of them are Happiness, Sadness, Anger, Fear, Disgust, Surprise, Love, Excitement, Guilt, Shame, Jealousy, Empathy and many more. Recently, emotion detection is a popular topic due to a vast potential application such as marketing, psychology, human-computer interaction, political science and curing diseases such as depression, anxiety disorder, chronic pain, eating disorders, etc. According to World Health Organization (WHO) depression, anxiety, etc. are common metal disorder and it is estimated that almost 5% of the adult suffer from it. One out of every Eight people in the world is living with a mental disorder. there is a need of a comprehensive system, which can detect human emotion which are showcased by his/her text. In world of Internet, social media platforms have become very essential means of communication for the entire world. However, sometimes while communicating using text chats people get confused because they are unable to understand the emotions showcased by the messages. this paper aims at providing a sustainable, robust and efficient web application using which anybody can understand the human emotions behind the text. © 2023 IEEE.

关键词： Natural language processing systems

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EEG-Based Emotion Recognition Using Morlet Dual-Level Wavelet Contextual Neural Network with Snow Geese Algorithm 4

EEG-Based Emotion Recognition Using Morlet Dual-Level Wavele...

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4th international conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Sivaramkrishnan, M. Siva Ramkumar, M. Kannaiyan, Mathi Kanan, Mohammad Baig, Rahmath Ulla Giri, Jayant Karpagam College of Engnieering Dept of Eee India SNS College of Technology Dept of Ece India University College of Kancheepuram Anna University Department of Mechanical Engineer Kanchipuram631552 India University of Business and Technology College of Engineering Department of Industrial Engineering Jeddah21448 Saudi Arabia Zarqa University College of Engineering Department of Mechanical Engineering Zarqa Jordan Cambridge Institute of Technology Department of Artificial Intelligence and Machine Learning K R Puram Bangalore560086 India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India

ISBN: (纸本)9798331523923

EEG-based Emotion Recognition (ER) works by analyzing brainwave patterns from EEG signals to understand and identify a person's emotional state. the problem of emotion identification from EEG signals remains challenging because brain activity presents both non-linear dynamic properties and individual-specific EEG patterns along with biological noise. Traditional approaches to emotion detection become inefficient due to EEG signal complexity thereby complicating accuracy and reliability measures. To tackle the challenges in EEG-based emotion recognition, this study presents a new approach called the Morlet Dual-level Wavelet Contextual Neural Network with Snow Geese Algorithm (MorDWCNNet+SGA). the method uses the SEED dataset, starting with preprocessing through the Observability-Constrained Resampling-Free Cubature Kalman Filter (OCRCKF) to enhance important EEG patterns. For feature extraction, the Multi-Discrete Wavelet Transform (MDWT) is applied to capture key EEG characteristics. Emotion classification is then performed using the Morlet Dual-level Wavelet Contextual Neural Network (MorDWCNNet), which is further optimized by the Snow Geese Algorithm (SGA) to improve accuracy. Implemented in Python and tested on the SEED dataset, the MorDWCNNet+SGA framework achieves remarkable results, with 99.9% accuracy and 99.3% sensitivity, significantly outperforming existing methods. the outcomes of the proposed method achieved high accuracy in effectively distinguishing emotional states through brainwave patterns. this method demonstrates how advanced techniques can combine synergistically to enhance ER performance resulting in improved real-world application accuracy and speed. © 2025 IEEE.

关键词： Kalman filters

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ML-MT: A Study of e-Health application Framework by machine learning Techniques 4

ML-MT: A Study of e-Health Application Framework by Machine ...

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4th international conference on Cybernetics, Cognition and machine learning applications, ICCCMLA 2022

作者： Mridha, Krishna Ritu, Ananya Chowdhury, Md Minhazul Alam Arefin, Nazmul Marwadi University Computer Engineering Gujarat Rajkot India Brac University Computer Science and Engineering Bangladesh Shahjalal University of Science and Technology Mathematics Bangladesh Southwest University of Science and Technology Computer Science and Technology China

ISBN: (数字)9781665462464

ISBN: (纸本)9781665462464

One of the crucial challenges of the healthcare sector is to provide services efficiently so that everyone can get the care they need without having to get caught up in the administrative processes or a long waiting list. machine learning has been used to solve various problems in healthcare and this research paper aims to increase efficiency by providing an easy-to-use platform that not only works as a bridge between the doctors and the patients but also expedites the diagnosis process with the assistance of machine learning. the proposed framework is composed of mainly two core components which are the patient management system and the doctor management system. the patient management system allows patients to request an appointment, collects and monitors patient data, and predicts the chances of the patient having heart disease, kidney disease, liver disease, cancer, and diabetes. the doctor views their appointments, patients list, and status, and provides diagnosis and treatment where the prediction from the patient management system comes in handy to make decisions faster. the doctor's assistant and hospital administrative staff work behind the scene so that appointment scheduling is running smoothly and data is properly available for the models to make predictions. Among the machine learning algorithms explored, Logistic regression had the highest accuracy values in predicting cancer (97%), diabetes (92%), and liver disease (91%). Naive Bayes was 97% accurate in predicting kidney disease and Random Forest was 94% accurate in predicting heart disease which was the best performance in the case of these two diseases. © 2022 IEEE.

关键词： Forecasting

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application of machine learning Techniques to Classify Intention to Pay for Forest Ecosystem Services 12th

Application of Machine Learning Techniques to Classify Inten...

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12th EAI international conference on Context-Aware Systems and applications, ICCASA 2023

作者： thuy, Pham thu Tung, Nguyen thanh Dat, Luu Quoc VNU School of Interdisciplinary Studies Vietnam National University 144 Xuan Thuy Road Hanoi100000 Viet Nam Science and Technology Department Vietnam National University 144 Xuan Thuy Road Hanoi100000 Viet Nam International School Vietnam National University 144 Xuan Thuy Road Hanoi100000 Viet Nam VNU University of Economics and Business Vietnam National University 144 Xuan Thuy Road Hanoi100000 Viet Nam

ISBN: (纸本)9783031588778

Capturing the ability to take part in the payment of forest ecosystem services by beneficiaries is the result that policy-making agencies are always concerned. this research selects several machine learning techniques, including single classifiers (Multilayer Perceptron, Naive Bayes, SMO) and ensemble classifiers (LogitBoost, Random Forest, Bagging) to evaluate and classify willingness-to-pay intention for mangrove ecosystem services of people in PhuLong commune, Vietnam. Research data is inherited from a previous contingent valuation survey, with a sample size of 235. the results show that the machine learning algorithms are workt with small sample-size data sets with feasibility prediction results in behavioral intent classification. the LogitBoost model achieves the best classification performance compared to the remaining models. Besides, socio-psychological factors are ranked as important factors in classifying behavioral intentions related to payment for forest ecosystem services. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Adversarial machine learning

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Research on material transportation robot based on PSO-PID controller 4

Research on material transportation robot based on PSO-PID c...

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4th international conference on Mechanical Engineering, Intelligent Manufacturing, and Automation Technology, MEMAT 2023

作者： Zhang, Yongjie GuangXi Vocational Normal University China Guangxi Xixiangtang District Luowen Avenue 17 Nanning China Key Laboratory of Application Technology of intelligent connected vehicle [Guangxi Vocational Normal University Education Department of Guangxi Zhuang Autonomous Region China Guangxi Nanning China

ISBN: (纸本)9781510674820

the paper introduces a small material handling robot system, the system uses Arduino MEGA2560 + NVIDIA Jetson Nano master control to build the whole machine architecture, making full use of its underlying control advantages and arithmetic advantages, which can solve the problem of insufficient arithmetic performance of MCU system and insufficient convenience of the underlying hardware control of ROS system in the traditional robot control scheme. the system uses serial proportional integral derivative(PID) control algorithm to achieve closed-loop control of the robot, and realises self-tuning of PID parameters by particle swarm optimization(PSO) to solve the problems of difficult to guarantee the accuracy of manual parameter adjustment and long parameter adjustment cycle. the paper uses the test vehicle to test 2 kinds of motion control scenarios, and uses MATLAB to track the process of particle swarm parameter self-tuning, and obtains the conclusion that the particle swarm self-tuning is highly efficient and the accuracy is reliable. © 2024 SPIE · 0277-786X.

关键词： three term control systems

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