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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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Comparative Analysis of Neurodegenerative disease detection using Artificial Intelligence

Comparative Analysis of Neurodegenerative disease detection ...

引用

2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Vidya, J. Yendapalli, Vamsidhar Gitam University Gitam School of Technology Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350359688

Neurodegeneration is one of the features of several debilitating diseases that are rising rapidly. As they are not curable and progressive, early detection may help the patients and the caretakers to maintain a good quality of life. Advancements in computational methods have made it possible to diagnose and classify NDD (neurodegenerative diseases) using the automated procedure, which will be more effective than manual detection. This paper aims to comprehensively describe the neurodegenerative disease classification using ML and DL techniques that may help the community working on early detection of the disease. © 2024 IEEE.

关键词： Contrastive Learning

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Comparative Analysis of Formalin Detection in Fish Using Deep Neural Networks 3

Comparative Analysis of Formalin Detection in Fish Using Dee...

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3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Islam, Rashedul Islam, Sabrin Jeba, Samiha Maisha Dihan, Rakibul Hasan International University of Business Agriculture and Technology Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350388282

For the people of Bangladesh, fish is a non-vegetarian and staple food. As Bangladesh is a riverine country, fish is available everywhere in our country. Fish rots easily. For this reason, the fish has to be preserved to fulfill people's nutritional needs. To meet the needs of the people of Bangladesh, fish is collected from the rivers and sometimes imported for this vast population. While importing fish or taking it from one place of the country to another, unscrupulous traders mixed formalin to prevent the fish from rotting. Formalin is very harmful to the human body. It creates so many health problems like cancer, miscarriage, tumors, etc. So, it is essential to detect formalin fish and non-formalin fish. There is a need for a more dependable and automated method of formalin detection because current approaches rely on human senses and are prone to inaccuracy. Digital image processing has become a promising technology for fish formalin detection in recent years. In this paper, an intelligent application is proposed. This model can identify formalin and fresh fish based on the images of fish eyes. The capability of digital image processing to offer a quick and non-intrusive examination technique is one of the main advantages of formalin detection. Without damaging sample preparation, it can swiftly and accurately identify the presence of formalin by examining photographs of the fish. This is especially helpful in large-scale operations when it is impractical to inspect each fish manually. Digital image processing algorithms are more accurate and consistent because they are not influenced by human biases or subjective judgments like human senses are. The architecture we proposed is VGG-16, and we compare it with EfficientNet-B3. VGG-16, renowned for its simplicity and effectiveness in various computer vision tasks, achieved a notable accuracy of 94.44%. © 2024 IEEE.

关键词： Deep neural networks

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Prediction of Drug-Target Interactions Using BERT for Protein Sequences and Drug Compound 16

Prediction of Drug-Target Interactions Using BERT for Protei...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Simon, Essmily Bankapur, Sanjay S. National Institute of Technology Puducherry Department of Computer Science and Engineering Puducherry Karaikal India

ISBN: (纸本)9798350383119

The current drug development crucially depends on identifying potential relationships between medicines and targets. However, anticipating such relationships is difficult due to the limits of current computational techniques. Hence, the use of deep learning is essential for identifying potential therapeutic drug compounds and providing support throughout the entire drug development process. This study discusses the deep learning technique of using bidirectional encoder representations from the Transformers (BERT) model which helped to build representations using protein and drug SMILES (Simplified Molecular Input Line Entry System) dataset to enhance DTI prediction. We used the pretrained Protein BERT model and ChemBERT for protein sequences and drug SMILES data respectively for feature extraction and resulting features are concatenated together and fed into a random forest (RF) for classification. BERT model helps to use protein and drug datasets for feature extraction without using the descriptor dataset for finding the interaction between drugs and proteins.. © 2024 IEEE.

关键词： BERT Deep learning Drug discovery Drug target interaction Machine learning Random forest

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Comparative Performance Analysis of Decision Tree Variants in Heart and Lung Disease Classification

Comparative Performance Analysis of Decision Tree Variants i...

引用

2024 IEEE Calcutta Conference, CALCON 2024

作者： Ghosh, Subhayu Kumari, Sonal Jana, Nanda Dulal National Institute of Technology Department of Computer Science and Engineering West Bengal Durgapur India

ISBN: (纸本)9798331516666

Machine learning (ML) algorithms are pivotal for disease detection, with decision tree (DT) models particularly prominent in classification tasks. This paper conducts a thorough comparative analysis of different variants of DT algorithm for heart and lung disease classification. DT variants, such as ID3, C4.5, CART, and CHAID were evaluated on medical image datasets, addressing missing values and class imbalances through preprocessing. Accuracy, precision, recall, and F1-score were employed for assessment, and CHAID exhibited superior accuracy and robustness across both diseases. CART and C4.5 were competitive but fell short in predictive power. The study emphasizes selecting the right DT variant based on diagnostic requirements and explores ensemble methods' potential to enhance classification outcomes. These findings offer valuable insights for developing more effective decision-support systems in clinical settings, thereby improving disease diagnosis accuracy and reliability. © 2024 IEEE.

关键词： Lung cancer

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Diagnosing Parkinson's Disease with KNN Classifier Utilizing Speech Feature Extraction

Diagnosing Parkinson's Disease with KNN Classifier Utilizing...

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Santhanam, Srikrithi Advika, A. Santhanam, Srinithi Faritha Banu, J. SRM Institute of Science and Technology Department of Computer Science and Engineering Ramapuram Chennai India

ISBN: (纸本)9798331522988

A significant portion of people have suffered from a form of Parkinson's disease (PD), widely attributed to be the second most frequently diagnosed form of neurological illness that significantly impairs motor and cognitive abilities. But as common a disease as it is, a lack of proper conclusive testing results in diagnoses that rely on historical analysis and records and can lead to errors, particularly in the early stages. Using a dataset from the University of Oxford that includes a variety of speech variables recognised for their non-invasive and distinctive qualities in the identification of this neurological ailment, this study investigates a machine-learning probability of diagnostic tests. This method leverages K-Nearest Neighbours (KNN) classification, using the features: fundamental frequency parameters, jitter, shimmer, and other vocal disturbances. Voice analysis shows great promise for improving early identification and ensuring proper protection of Parkinson's victims from this haunting disease. © 2024 IEEE.

关键词： Nearest neighbor search

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A Comprehensive Ensemble Approach with Real-Time Performance Metrics for Heart Failure Prediction 1

A Comprehensive Ensemble Approach with Real-Time Performance...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Reddy, Nomula Nagarjuna Lingadally, Nipun Shereesha, M. Shilpa, Thoutireddy B v Raju Institute of Technology Department of Computer Scienece and Engineering Narsapur India

ISBN: (纸本)9798331518943

Heart failure (HF) remains a significant global health concern, leading to approximately 17 million deaths annually from cardiovascular disease (CVD). Despite advances in predictive models, current approaches often lack the accuracy needed for effective early diagnosis and management. This study utilizes electronic medical records (EMRs) from 5000 patients, each characterized by 13 clinical features, to enhance HF prediction through advanced machine learning (ML) techniques. We propose a novel ensemble learning (EL) approach using a Stacking classifier (SC) with Logistic Regression (LR) as the meta-model, integrating RandomForest (RF), Multi-Layer Perceptron (MLP) and XGBoost (XGB). The ensemble model demonstrates exceptional performance, achieving an accuracy of 99.66% and an F1 score of 99.32%, significantly outperforming individual models. This study highlights the effectiveness of ensemble methods in improving predictive accuracy for HF, providing clinicians with more reliable tools for early diagnosis and intervention. Future research will explore additional model combinations and meta-models to further enhance prediction outcomes. © 2024 IEEE.

关键词： Electronic health record

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Anomaly Detection from CCTV Camera Feed 10

Anomaly Detection from CCTV Camera Feed

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10th IEEE International Symposium on Smart Electronic Systems, iSES 2024

作者： Aakash Chauhan, Lakshay Sharma, Shubham Deb, Sujay Indraprastha Institute of Information Technology Delhi Computer Science & Engineering Department Delhi India

ISBN: (纸本)9798331533229

The paper presents a novel approach for detecting anomalies like arson, explosion, vandalism, etc in public spaces using CCTV camera feeds. We propose a deep learning-based system that analyzes video streams to identify unusual events or behaviours that deviate from normal patterns. Our method combines Convolutional Neural Networks (CNNs) and Spiking Neural Networks (SNNs) for feature extraction with Long Short-Term Memory (LSTM) networks to model temporal dependencies in the video sequences. The system is trained on the UCF Crime Dataset to learn normal activity patterns and can flag potential anomalies in emulated real-time situations. To enhance accuracy, we incorporate additional computer vision technique, like optical flow, with CNN/SNN and LSTM to get various combinations of models (OF-CNN, OF-CNNLSTM, OF-SNN, OF-SNNLSTM, CNNLSTM) for improved anomaly detection. The proposed system has potential applications in enhancing public safety and security monitoring in urban environments. © 2024 IEEE.

关键词： Convolutional neural networks

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RRPoS: A Fair and Efficient Approach to Blockchain Consensus using Round Robin 3

RRPoS: A Fair and Efficient Approach to Blockchain Consensus...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Sanober, Adla Anwar, Shamama Birla Institute of Technology Department of Computer Science and Engineering Mesra Ranchi835215 India

ISBN: (纸本)9798350354348

Due to advanced technological development, the application of blockchain is not just for financial services. It has inherent potential uses in various industries, including healthcare, supply chain, industrial goods, E-commerce, and others. Any group or individuals agree in a blockchain network to make transactions on the basis of a consensus mechanism. The critical role of the consensus mechanism is that it improves the efficiency and security of the blockchain system. Proof of Work (PoW), Proof of Stake (PoS), Delegated Proof of Stake (DPoS), Proof of Authority (PoA), Practical Byzantine Fault Tolerance (PBFT) and some other recent proof of concepts such as Proof of Burn (PoB), Proof of Learning, Proof of Luck (PoLu), etc. are the various existing consensus algorithms. However, there are problems with these algorithms including centralization risk, security, efficiency, and waste of resources. Proof of Work (PoW) consumes high computational power to solve the cryptographic puzzle and makes the system more costly. Proof of Stake (PoS) encourages high-stakes node selected as a validator to propose and validate the new block in the blockchain, which promotes inequality and monopolies. The paper introduces Round Robin Proof of Stake (RRPoS) based on the existing Proof of Stake (PoS) that involves block creation in a round-robin fashion to increase the chances of high- to low-stakes validators for the validation of the proposed block. The proposed consensus algorithm is able to ascertain the existence of malicious validator nodes and eliminate them to enhance system security. The performance analysis demonstrates that RRPoS is better and more efficient than PoW and PoS. © 2024 IEEE.

关键词： Supply chains

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Detection of Brain Tumor using Convalutional Neural Network 15

Detection of Brain Tumor using Convalutional Neural Network

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Kavyashree, N. Amrutha, J. Bhoomika, N. Dimple, A.R. Sinchana, Y.D. Department of Computer Science and Engineering BGS Institute of Technology Karanataka BG Nagar India

ISBN: (纸本)9798331300579

It is challenging to identify disease regions from images since mind developments differ in how they present and propose different features from ordinary ***, it actually takes a monotonous effort to manage a significant amount of *** the use of the information increase approach, CNN model, and pre-prepared model, this study developed a model for identifying mental cancers from two-layered X-ray *** this experiment, tumors with different sizes, shapes, and intensities were taken from the Kaggle *** quite some time, deep learning has been used to remove unusual growth regions from the *** is one of the best specialized methods for organizing and classifying *** detection of brain tumors may benefit from sophisticated computer reasoning and computations of the brain's organization order. © Grenze Scientific Society, 2024.

关键词： Neural networks

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