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Candlestick Chart Based Stock Analysis System using Ensemble Learning 1

Candlestick Chart Based Stock Analysis System using Ensemble...

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1st IEEE International Conference on Networking and Communications, ICNWC 2023

作者： Varghese, Angel Ann Krishnadas, J. Kumar, R. Satheesh Sahrdaya College of Engineering and Technology Department of Computer Science and Engineering Thrissur India

ISBN: (纸本)9798350336009

The 18^{\mathrm{t}\mathrm{h}}-century candlestick charts, which were first utilized in the Japanese rice market, are now frequently used in trading tactics across all financial markets. Candlestick charts make it possible to comprehend an asset's opening, high, low, and closing values all in one image. In addition to these benefits, the abundance of candlestick chart patterns makes practical application challenging. A software framework that employs candlestick charts to forecast trend direction was built for this study. There are four stages of the project. A system that can identify candle patterns is developed in the first stage. The second stage involves executing training and testing procedures on data sets with labeled candlestick chart types and trend directions in order to assess the model's performance. During the machine learning phase, open-source techniques like xgboost were applied. In the project's final stage, it was discovered that the strategy focused solely on identifying candlestick patterns and taking positions in line with the trend based on the suggested methodology produced larger profits in 11 global indices than the Buy and Hold strategy. In comparison to the current accuracy of 53.8%, the model's average forecast accuracy is 59.42% © 2023 IEEE.

关键词： Commerce

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Analysis and Prediction of COVID-19 Disease using Machine Learning 14

Analysis and Prediction of COVID-19 Disease using Machine Le...

引用

14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Parmar, Shabnam Rani, Rinkle Kalra, Nidhi Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala India

In this research, the symptoms and other factors of a patient are utilized to train machine learning algorithms to predict whether the patient would die from or recover from (COVID19). It is probable that the coronavirus (COVID19) will create the highly infectious Coronavirus illness COVID19 (SARSCoV2). By coughing, sneezing, speaking, or inhaling, this virus can be transmitted from an infected person's lips and nose to small liquid particles. The size of these tiny atoms' inhalation droplets and aerosols vary, with bigger droplets being larger than smaller atoms. COVID19 is transferred by inhalation or by touching your eyes, nose, or mouth with your fingers after meeting a contaminated surface. When a big number of people are present, the COVID-19 virus can spread rapidly. We will need to examine the COVID19 dataset to see which models are the most accurate in estimating fatality rates for the virus's most vulnerable victims. Machine learning is used to compute and evaluate the performance of a variety of prediction models. We have used K-nearest neighbor (KNN), Support Vector Machine (SVM) classifier, Gaussian naive Bayesian (GNB), Decision tree (DT), and Logistic regression (LR) for the prediction of death and recovery of symptomatic patients. In this research, a variety of feature selection and extraction strategies were used, and prediction accuracy for feature selection methods for the KNN model and feature extraction methods for the GNB model both reached up to 96 percent. The k-nearest neighbor has performed and predicted high accuracy of 96% in both feature selection and extraction techniques. © Grenze Scientific Society, 2023.

关键词： Support vector machines

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An overview of thalassemia: A review work

An overview of thalassemia: A review work

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Begum, Ruqqaiya Suryanarayana, G. Rama, B. V. Saketha Swapna, N. Department of Computer Science and Engineering Vardhaman College of Engineering Telangana Hyderabad India Department of Computer Science Engineering Indian Institute of Information Technology Design and Manufacturing Kancheepuram Tamil Nadu Chennai India Department of Computer Science & Engineering Vijay Rural Engineering College Telangana Nizamabad India

ISBN: (纸本)9781032669663

Thalassemia is a Disease that passes from parents to children through genes. It is a red blood cell disorder caused when the body doesn’t make enough of a protein in the blood called hemoglobin. Three classifications of Thalassemia are Alpha(a), Beta(b), DeltaBeta(db) And Beta thalassemia is the most effective disease compared to other types. Out of 5% of patients 3% comes in Beta Thalassemia. It is first identified in the early 19’s with the symptoms like poor growth, severe anemia, and huge abdominal organs. It is a disease of which most people are deceased due to unawareness, and not getting proper Medication, especially in rural areas as they cannot find symptoms because other disease symptoms differ from thalassemia. Out of 10000 newborns, approximately an average of 4.4 will be affected by thalassemia. Out of 32 districts In India, 10 to 12 thousand children were born with this disease. To date, no research work has shown the exact death rates of thalassemia worldwide. Blood transfusion is the major method used for the prevention of thalassemia-affected patients. Apart from that Bone marrow is another prevention method used for Beta Carriers. In this work, we gathered detailed Information about thalassemia and its types so that people will get aware of this disease. This work provides details of how this disease is getting inherited from parents to children, the depth of the characteristics of every subtype, and the statistics about causes and death rates from this disease. © 2024 The Author(s).

关键词： Blood

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Advances in Multi-Spectral Image Categorization Through Convolutional Neural Networks with an Optimized Activation Function 2

Advances in Multi-Spectral Image Categorization Through Conv...

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2nd IEEE International Conference on Data Science and Network Security, ICDSNS 2024

作者： Raman, Ramakrishnan Kumar, Vikram Pillai, Biju G. Verma, Apurv Rastogi, Shailesh Meenakshi, R. India Srv Media Sri Balaji University Pune India Parul Institute of Technology Parul University Department of Computer Science & Engineering Engineering and Technology India Pune India Chennai Institute of Technology Computer Science and Engineering India

ISBN: (纸本)9798350373110

In recent years, deep learning technologies have significantly advanced the field of multi-spectral image analysis. This paper presents a novel advancement through the development of a specialized Convolutional Neural Network (CNN) architecture that incorporates an optimized activation function designed specifically for categorizing multi-spectral images. Our research introduces a CNN architecture with depth-wise separable convolutions, batch normalization, dropout layers, and fully connected layers, engineered to efficiently analyze both spatial and spectral dimensions of multi-spectral data. The cornerstone of this innovation is the optimized activation function, empirically refined to enhance learning dynamics and computational efficiency, effectively addressing common issues like vanishing and exploding gradients. Through extensive testing on a diverse dataset of environmental and man-made objects, our model demonstrated superior performance metrics such as accuracy (94.2%), precision (93.8%), recall (92.9%), and F1 score (93.3%), significantly outperforming conventional models. These results highlight the effectiveness of the tailored activation function in learning complex patterns in multi-spectral images, thus improving categorization capabilities. This study underscores the potential of tailored deep learning structures in enhancing the accuracy and robustness of multi-spectral image categorization, with profound implications for environmental monitoring, agriculture, and remote sensing. © 2024 IEEE.

关键词： Convolutional neural networks

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MetaCampus: Advancing Online Education with Virtual Classroom 3

MetaCampus: Advancing Online Education with Virtual Classroo...

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3rd International Conference for Innovation in technology, INOCON 2024

作者： Malave, Sachin Takmare, Sachin Sarfare, Atharva Patil, Vihang Sawant, Siddhesh Patil, Pranay A. P. Shah Institute of Technology Department of Computer Engineering Mumbai India

ISBN: (纸本)9798350381931

In recent years, the world has experienced online style of schooling. In case of bad weather conditions or pandemic events like COVID-19, schools and universities changes to online form of instruction. Tools like Zoom call or Google Meet were used. These tools, although being a significant tool for enabling remote learning, pose problems such as reduced attention, interruptions, restricted involvement, screen fatigue, lack of interaction and teacher burnout. Virtual reality technologies are promising to effectively enrich our daily lives. Enhancing learning experiences with virtual reality technologies is one of important paths to make the technologies affect our daily lifestyle, because students in classes need not come together in the same spot. With the use of virtual reality (VR), students may attend lessons from anywhere at any time, feeling as though they are in the same classroom. Moreover, virtual reality technologies can bring extra effects that cannot be reproduced in current virtual class rooms. The study suggests VR Classroom that offers a virtual place where students and a teacher virtually remain in the same spot. VR Classroom also includes numerous options to inspire students through engaging features. © 2024 IEEE.

关键词： Virtual reality

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WBC Subtype Detection using Deep Learning with Optimizing Hyperparameters by Genetic Algorithm

WBC Subtype Detection using Deep Learning with Optimizing Hy...

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2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Shuvo, Mehedi Hasan Rahman, Md. Tahamidur Ahsan, Sk. Md. Masudul Khulna University of Engineering & Technology Department of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798350316001

The human body's defense mechanism, the immune system, heavily relies on white blood cells (WBCs). These cells come in five subtypes: Eosinophil, Basophil, Neutrophils, Monocyte, and Lymphocyte. Each subtype plays a vital role in the diagnosis of hematological diseases, such as leukemia, as their presence or absence can indicate the presence of a disease. In the past, hematologists have manually examined blood smears under a microscope to detect the subtype of WBCs. However, this method is both time-consuming and prone to errors. This study proposes a new approach, which uses a deep learning-based convolutional neural network (CNN) architecture in combination with an evolution-based algorithm (genetic algorithm) to detect WBC subtypes with greater accuracy. A large dataset of 12,000 images was used for training and analysis. The proposed model, which utilizes the You Only Look Once version 5 (YOLOv5) for image localization and segmentation, was able to accurately localize WBCs with an IoU score of 94% for more than 80% of images and detect the subtypes with an accuracy of 98%. This study aims to overcome the limitations of previous research by providing a more efficient and accurate method for detecting WBC subtypes. © 2023 IEEE.

关键词： Genetic algorithms

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Detection of Phishing Websites Using Link Guard Algorithm 2nd

Detection of Phishing Websites Using Link Guard Algorithm

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2nd International Conference on Advances in Computational Intelligence and Informatics, ICACII 2023

作者： Laxmi Aishwarya, V. Srujan, T. Deepak, S. Bhavan, V. Pradeep Kumar, V. Department of Computer Science Engineering B V Raju Institute of Technology Narsapur India

ISBN: (纸本)9789819747269

Phishing is a brand-new form of network attack where the attacker creates a copy of an already-existing web page to deceive users into providing personal, financial, or password information to what they believe to be the website of their service provider (e.g., by using specially crafted emails or instant messaging). Using the common traits of hyperlinks used in phishing assaults, we suggested a novel end-host-based anti-phishing algorithm, which we term Link Guard, in this research article. Link Guard can recognize both known and unidentified phishing attacks because it is based on the general features of these attacks. In Windows 10, Link Guard has been implemented. Our tests showed that Link Guard can effectively identify and stop both known and unidentified phishing attacks with a low rate of false negatives. Eighty-five to ninety-five percent of phishing attempts are effectively detected by Link Guard. Our tests also demonstrated that Link Guard is lightweight and capable of quickly recognizing and preventing phishing assaults. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Phishing

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An Efficient Framework for Crime Prediction Using Feature engineering and Machine Learning 1

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5th International Conference on Data and Information Sciences, ICDIS 2023

作者： Vengadeswaran Binu, Dhanush Rai, Lokesh Department of Computer Science and Engineering Indian Institute of Information Technology Kottayam Kottayam India

ISBN: (数字)9789819969067

ISBN: (纸本)9789819969050

The growth of crime in society produces insecurity among people and severely impacts the country’s economic development. Understanding crime patterns is necessary to provide a proactive response to curb criminal activities. Most crime prediction models harnessing machine learning/deep learning techniques fail to exhibit significant results for the vague and inconsistent dataset. This is due to the non-consideration of suitable pre-processing and feature engineering techniques, resulting in an unreliable and inaccurate model. Hence in this work, an efficient framework for crime prediction using Feature engineering and Machine learning is proposed. Initially, the vague and inconsistent input dataset is pre-processed using Feature Scaling and Encoding input values. After pre-processing DBSCAN clustering is applied to extract geospatial features, which is used for improving model accuracy. Then SelectKbest library is used to select the 10 best features for the prediction model. Further, the SMOTE library is used to oversample crime types with the lowest sample size. Finally, for classifying the crime types, XGBoost and LGBM classifier models are evaluated. The proposed crime prediction model was validated by deploying an Amazon EC2 P3 instance in the cloud environment. The proposed model yields an improved accuracy of 62% for the XGBoost Classifier and 63% for the LGBM Classifier compared with KNN and Decision Tree. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Forecasting

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PPDHero: Requirements Elicitation and Development of a System to Empower New Mothers on Postpartum Depression 23th

PPDHero: Requirements Elicitation and Development of a Sy...

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23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Sara, Tasfia Akter Nazifa, Sadia Nur Tasneem, Shadmanee Shakib, Tasnim Ullah Islam, Muhammad Nazrul Department of Computer Science and Engineering Military Institute of Science and Technology Dhaka1216 Bangladesh

ISBN: (纸本)9783031648496

This research aims to develop a novel system for screening postpartum depression (PPD) and for providing education and support to new mothers to identify and manage symptoms and connect with health professionals. A requirement elicitation study was conducted following a semi-structured interview (n=6) and survey (n=102) and revealed a set of user requirements that need to be addressed in the proposed system. As such, extensive research and analysis of PPD and related information sources, including healthcare professionals, academic research papers, and online resources, served as a foundation for the development of the web-based system PPDHero. This system goes a step beyond by infusing gamification, setting us apart from the existing digital landscape. The usability of the proposed system was also evaluated through the System Usability Scale (SUS) approach, which was replicated with 18 participants including new mothers and doctors. The SUS scores received from the doctors (84.58) and new mothers (71.79) showed an acceptable quality of the proposed system from a usability perspective. The proposed PPDHero can improve the lives of new mothers by providing them with accessible and efficient tools to manage PPD symptoms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Usability engineering

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Face Tracking using Deep Reinforcement Learning on Zenbo Junior

Face Tracking using Deep Reinforcement Learning on Zenbo Jun...

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2024 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2024

作者： Tseng, Shih-Huan Lin, De-Yu Huang, Shih-Shinh National Kaohsiung University of Science and Technology Department of Computer and Communication Engineering Kaohsiung Taiwan

ISBN: (纸本)9798350389210

This paper aims to develop a deep reinforcement learning-based face tracking system for Zenbo Jr. As robots become increasingly integrated into daily life, human-robot interaction (HRI) has emerged as a critical research area, especially in adhering to social norms. This project combines deep learning with visual servo systems to enhance the accuracy and response speed of robotic face tracking. The primary objectives of the study include: adjusting robot movements based on face position, designing a virtual environment using OpenAI Gym that matches Zenbo Jr.'s perspective for model training, and testing and validating the model on Zenbo Jr. The research methods involve designing the virtual environment, establishing reward functions, and constructing and training the DQN model. Experimental results compare the training outcomes of Q-Learning and Deep Q-Learning, confirming that Q-Learning leads to an excessively large Q-table in scenarios with numerous state-action combinations, making effective training impossible, whereas Deep Q-Learning achieves stable convergence and good results. © 2024 IEEE.

关键词： Deep reinforcement learning

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