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Holt-Winters Algorithm to Predict the Stock Value Using Recurrent Neural Network

Intelligent Automation & Soft Computing 2023年第1期35卷 1151-1163页

作者： M.Mohan P.C.Kishore Raja P.Velmurugan A.Kulothungan Department of Computer Science&Engineering SRM UniversitySonepatHaryanaIndia Department of Electronics&Communication Engineering SRM UniversitySonepatHaryanaIndia Department of Computing Technologies SRM Institute of Science and TechnologyChennaiIndia Department of Computer Science&Engineering SRM Institute of Science and TechnologyModinagarGhaziabadIndia

Prediction of stock market value is highly risky because it is based on the concept of Time Series forecasting system that can be used for investments in a safe environment with minimized chances of *** proposed model uses a real time dataset offifteen Stocks as input into the system and based on the data,predicts or forecast future stock prices of different companies belonging to different *** dataset includes approximatelyfifteen companies from different sectors and forecasts their results based on which the user can decide whether to invest in the particular company or not;the forecasting is done for the next *** model uses 3 main concepts for forecasting *** one is for stocks that show periodic change throughout the season,the‘Holt-Winters Triple Exponential Smoothing’.3 basic things taken into conclusion by this algorithm are Base Level,Trend Level and Seasoning *** value of all these are calculated by us and then decomposition of all these factors is done by the Holt-Winters *** second concept is‘Recurrent Neural Network’.The specific model of recurrent neural network that is being used is Long-Short Term Memory and it’s the same as the Normal Neural Network,the only difference is that each intermediate cell is a memory cell and retails its value till the next feedback *** third concept is Recommendation System whichfilters and predict the rating based on the different factors.

关键词： Stock market stock market prediction time series forecasting efficient market hypothesis National stock exchange India smoothing observation trend level seasonal factor

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An Adaptation and Evaluation for Object Detection of Small Objects Using YOLO8

An Adaptation and Evaluation for Object Detection of Small O...

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2024 IEEE International Conference on Intelligent Computing and Emerging Communication Technologies, ICEC 2024

作者： Chitra, M. Balasubramanian, Haripriya Srinivaas, V Lakshan Jayanth, Korada SRM Institute of Science and Technology Computer Science Engineering Department Chennai600089 India

ISBN: (纸本)9798331508432

Detecting small objects in aerial imagery, particularly from UAVs, presents unique challenges due to the reduced size of targets, complex backgrounds, and scale variations. Despite advancements in deep learning and improvements in object detection models, capturing small objects in UAV-captured images remains difficult. Conditions such as object occlusion, varying resolutions, and densely populated environments continue to impact the precision and recall of small object detection, requiring further architectural enhancements to improve performance in these *** this research, we introduce an enhanced object detection model for small-scale objects in UAV-captured imagery, addressing the unique challenges posed by varying object scales and densely packed environments. Our model incorporates several state-of-the-art improvements to the YOLOv8 architecture. First, we introduce the Multi-Scale Dynamic Attention Mechanism (MSDAM) to optimize feature extraction across multiple scales, enhancing the detection of small objects. Next, we employ a dynamic non-monotonic loss function named Weighted-Intersection over Union (WIoU) to refine bounding box regression, improving localization accuracy, particularly for small, hard-to-detect targets. To further boost performance, Squeeze-and-Excitation (SE) Layers are integrated, adaptively recalibrating feature maps to emphasize the most informative channels, thus enhancing feature representation. Additionally, Soft Non-Maximum Suppression (Soft-NMS) is applied in post-processing, reducing missed detections by preserving overlapping objects in densely populated scenes. Our experiments show significant improvements in precision, recall, and mAP, demonstrating the efficiency of the proposed methodology in accomplishing accurate and computationally efficient object detection for UAV applications. © 2024 IEEE.

关键词： Aerial photography

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Effective Malware Classification using Fine-tuned CNN Architecture: An Image-Based Approach 2

Effective Malware Classification using Fine-tuned CNN Archit...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Sasikala, L. Shanmuganathan, C. SRM Institute of Science and Technology Department of Computer Science and Engineering Ramapuram Chennai India

ISBN: (纸本)9798350365269

Malware detection and classification are crucial in cybersecurity to protect systems and networks against malicious attacks. This study introduces a proficient image-based method employing convolutional neural network (CNN) architecture. It aims to enhance malware classification by addressing the challenge of detecting polymorphic or obfuscated malware variants, which can be difficult with conventional techniques reliant on static or dynamic analysis of executable files. Our proposed model tackles this constraint by representing malware samples as grayscale and RGB images, capturing both structural and visual aspects. To improve classification accuracy, we fine-tuned a pre-trained CNN model on a Malign dataset of 25 families and 9342 malware samples, leveraging transfer learning. Our experimental results show that our method accurately classifies distinct types of malware with excellent precision and recall rates. Additionally, image-based representation is resistant to evasion strategies used by advanced malware. Our proposed approach demonstrates potential for enhancing malware detection and classification in practical cybersecurity settings © 2024 IEEE.

关键词： Cybersecurity

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Improving the Prediction and Fine-Grained Approach for Predicting and Forecasting Cryptocurrency Price Using ARIMA 4

Improving the Prediction and Fine-Grained Approach for Predi...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Sega, J.Juslin Shivam Gautam, Yash Yadav, Kshitij Devendra Srm Institute of Science and Technology Department of Computer Science and Engineering Ramapuram Chennai India

ISBN: (纸本)9798350349900

A capacity of foreseeing price fluctuations in bitcoin with exceptionally precise is very worthwhile to investigators and funding sources. However, as the cryptocurrency market is nonlinear, it can be challenging to determine the distinctive features of time-series data, which renders it challenging to forecast accurate price estimations. Massive oscillations in non-stationary cryptocurrency values underscore the pressing necessity to precise forecasting models. The most effective methodology for cryptocurrency price forecasting is machine learning, foremost ensemble and deep learning. Traditional statistical methods are difficult to execute accurately because to the lack of seasonal variations and the need to meet a number of naive requirements. The suggested methodology builds upon the random walk theory, commonly utilized in financial markets to model stock values. To simulate market volatility, this methodology utilizes randomization into the observed feature activations of neural networks at a layer-wise level. Moreover, a mechanism to assess the market's reaction pattern is incorporated into the prediction model. Training was conducted on ARIMA and LSTM, short for Long Short-Term Memory models using Ripple, Ethereum, and Tron as illustrative examples. © 2024 IEEE.

关键词： Prediction models

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A Review on Decoding Palm Leaf Manuscripts to Digital Information using Deep Learning Techniques 8

A Review on Decoding Palm Leaf Manuscripts to Digital Inform...

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8th International Conference on Inventive Systems and Control, ICISC 202

作者： Sega, J.Juslin Duela, J.Shiny Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai Ramapuram India

ISBN: (纸本)9798350386578

Ancient Tamil letters hold significant historical, archaeological, and linguistic value. This study explores various deep learning techniques employed to recognize handwritten Tamil characters in ancient palm leaf manuscripts. This study discusses about the five distinct layers of deep learning models: convolution, pooling, activation, fully connected, and softmax classifier. Further, this comprehensive study analyzes the morphological operations involved, feature processing, connected component analysis and compared the performance of various deep learning models such as MobileNet, ResNet and InceptionV3. © 2024 IEEE.

关键词： Image segmentation

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Metrics Evaluation of Bell Pepper Disease Classification Using Deep Convolutional Neural Network (DCNN) 1

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2nd International Conference on Women Researchers in Electronics and Computing, WREC 2023

作者： Sowmiya, K. Thenmozhi, M. Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Chennai India Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9789819970773

ISBN: (纸本)9789819970766

Crop disease is one of the major issues in agricultural fields. It reduces the food production process and produces a huge economic loss for farmers in agricultural lands. Deep learning models are built for detecting and identifying diseases at an early stage to help the farmers and provide food security. In this paper, transfer learning approach is used to identify and classify the diseased portion of leaves in farming lands. The benchmark dataset was taken from the Internet (Kaggle) for bell pepper leaf for identifying the diseases such as pepper leaf spot, mosaic virus, blight, damping off disease, verticillium wilt, blossom end rot, yellowing leaves, curling leaves, and healthy leaves. Ten pre-trained models are built for recognizing diseases and compared to obtain better accuracy. Adam optimizer and binary cross-entropy is used in the model for calculating loss function. In this paper, experimental results are presented on bell pepper diseases for MobileNetV2 with better accuracy of 99.42%. The various performance metrics such as accuracy, precision, recall and F1-score, ROC curve are used to determine the accuracy of the model. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Crops

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Multifaceted Understanding of Student Success: A Data Mining Approach 2

Multifaceted Understanding of Student Success: A Data Mining...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Udit Krishna, S. Barath Lakshman, A.N. Mukund, R. Srm Institute Of Science And Technology Department Of Computer Science And Engineering Chennai Ramapuram India

ISBN: (纸本)9798350381665

This research explores the potential of Data Mining in Education, particularly in the capability to predict student performance and identify the factors that influence academic outcomes. Employing the UC Irvine Student Performance dataset, the study investigates the efficiency of data mining strategies for predicting student outcomes and the co-relating elements. Amongst the nine ML models evaluated, including an ANN model, the linear SVM emerged as the optimal choice, achieving a significantly higher accuracy of 94.9% and an ROC AUC score of 0.959 with a Logarithmic loss of 1.74 on the test set compared to the other candidates. Beyond prediction, the study examines the influence of various factors on academic performance using the coefficients of the Logistic Regression model. Apart from the two mid-term grades, history of failures, internet access, external school support and the area of residence influence the student's performance in the final examination. By interpreting these findings, the research lays the groundwork for developing data-driven and personalized learning strategies, potentially encouraging a more fair and effective learning environment for all students. © 2024 IEEE.

关键词： Data mining

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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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Infectious Disease Detection and Drugs Recommendation Using Machine Learning 2

Infectious Disease Detection and Drugs Recommendation Using ...

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2nd International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2024

作者： Archana, T. Gupta, Sandeep Kr Hembrom, Kankan Mandal, Sayantan Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (纸本)9798331541217

This paper introduces a machine learning system that combines Logistic Regression, Decision Tree, and Random Forest algorithms to analyze patient data, focusing on symptoms and case histories to predict likely diseases and recommend effective treatments. The objective is to improve early disease detection and personalized medication recommendations, reducing side effects and enhancing clinical judgment. Extensive medical data trains these models, enabling integration with electronic health records to support clinical decisions, which strengthens healthcare delivery. This work contributes to the field by providing a model that increases diagnostic accuracy and offers a tailored treatment plan for individual patients, marking a step towards enhanced personalized healthcare. © 2024 IEEE.

关键词： Electronic health record

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Sentiment Classification of Movie Reviews with Logistic Regression 5

Sentiment Classification of Movie Reviews with Logistic Regr...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Swathi, R. Sri, Aiswaryaa Roshini, Padma SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai600089 India

ISBN: (纸本)9798331530013

This research focuses on developing an automated sentiment analysis system for Tamil text. The system utilizes Natural Language Processing (NLP) techniques, specifically the Bag of Words (BoW) model with CountVectorizer, to extract features from the text data. These extracted features are then fed into a Logistic Regression classifier to predict sentiment as positive, negative, or neutral. The system demonstrates an F1-score of 0.82, indicating high accuracy in sentiment classification. This research has significant implications for businesses and organizations, enabling them to effectively analyze customer feedback, monitor brand reputation, and make data-driven decisions based on public opinion. © 2024 IEEE.

关键词： Logistic regression

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