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An Analytic Review on Stock Market Price Prediction using Machine Learning and Deep Learning Techniques

Recent Patents on engineering 2024年第2期18卷 88-104页

作者： Rath, Swarnalata Das, Nilima R. Pattanayak, Binod Kumar Department of Computer Science and Engineering Siksha ‘O’ Anusandhan Deemed to be University Odisha Bhubaneswar India Department of Computer Application Siksha ‘O’ Anusandhan Deemed to be University Odisha Bhubaneswar India

Anticipating stock market trends is a challenging endeavor that requires a lot of attention because correctly predicting stock prices can lead to significant rewards if the right judgments are made. Due to non-stationary, loud, and chaotic data, stock market prediction is challenging. Investors need help to forecast where they should spend their money to make a profit. Investment methods in the stock market are intricate and based on the analysis of large datasets. Expert analysts and investors have placed a high value on developments in stock price prediction. Due to intrinsically noisy settings and increased volatility concerning market trends, the stock market forecast for assessing trends is tricky. The intricacies of stock prices are influenced by several elements, including quarterly earnings releases, market news, and other altering habits. Traders use a number of technical indicators based on stocks that are collected on a daily basis to make decisions. Even though these indicators are used to analyze stock returns, predicting daily, and weekly market patterns are difficult. Machine learning techniques have been extensively studied in recent years to see if they might boost market predictions compared to legacy or conventional methods. The existing methodologies have devised several strategies for predicting stock market trends. Various machine learning and deep learning algorithms, such as SVM, DT, LR, NN, kNN, ANN, and CNN, can boost performance in predicting the stock market. Based on a survey of current literature, this work aims to identify future directions for machine learning stock market prediction research. This research aims to provide a systematic literature review process to discover relevant peer-reviewed journal papers from the last two decades and classify studies with similar methods and situations into the machine learning approach and deep learning. In the current article, the methods and the performance of those adopted methods will be id

关键词： Financial markets

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Real-time distance field acceleration based free-viewpoint video synthesis for large sports fields

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Computational Visual Media 2024年第2期10卷 331-353页

作者： Yanran Dai Jing Li Yuqi Jiang Haidong Qin Bang Liang Shikuan Hong Haozhe Pan Tao Yang School of Telecommunications Engineering Xidian UniversityXi’an 710071China National Engineering Laboratory for Integrated AeroSpace-Ground-Ocean Big Data Application Technology SAIIPthe School of Computer ScienceNorthwestern Polytechnical UniversityXi’an 710129China

Free-viewpoint video allows the user to view objects from any virtual perspective,creating an immersive visual *** technology enhances the interactivity and freedom of multimedia ***,many free-viewpoint video synthesis methods hardly satisfy the requirement to work in real time with high precision,particularly for sports fields having large areas and numerous moving *** address these issues,we propose a freeviewpoint video synthesis method based on distance field *** central idea is to fuse multiview distance field information and use it to adjust the search step size *** step size search is used in two ways:for fast estimation of multiobject three-dimensional surfaces,and synthetic view rendering based on global occlusion *** have implemented our ideas using parallel computing for interactive display,using CUDA and OpenGL frameworks,and have used real-world and simulated experimental datasets for *** results show that the proposed method can render free-viewpoint videos with multiple objects on large sports fields at 25 ***,the visual quality of our synthetic novel viewpoint images exceeds that of state-of-the-art neural-rendering-based methods.

关键词： free-viewpoint video view synthesis camera array distance field sports video

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Fall Detection and Alerts in Hearing Aids for Deaf using Generative AI 2

Fall Detection and Alerts in Hearing Aids for Deaf using Gen...

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Tripathi, Bharat Lal Saini, Madan Jain, Manish Saxena, Apoorva Pal, Narendra Department of Computer Application Allenhouse Institute of Technology Kanpur India Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab India Department of Computer Application Allenhouse Business School Kanpur India Department of Computer Science & Engineering Axis Institute of Technology & Management Kanpur India Department of Computer Science & Engineering Invertis University Bareilly India

ISBN: (纸本)9798331530389

Individuals with sensorineural hearing loss often experience difficulty comprehending speech when background noise is present. This paper investigates the extent of this problem in various listening scenarios and with different types of background sounds. Additionally, the section explores the contributing factors to this challenge such as reduced audibility, reduced frequency selectivity, loudness recruitment, and dead regions in the cochlea where there are no surviving inner hair cells and or neurons. The sections evaluate different techniques for compensating for the effects of these factors. While signal-processing methods using the output of a single microphone to compensate for reduced frequency selectivity have had limited success, techniques utilizing multiple microphones have proven effective. Amplitude compression has also been effective in compensating for the effects of loudness recruitment, enabling individuals to understand speech across a wide range of sound levels. Next, the paper delves into the technical aspects of implementing fall detection systems in hearing aids, learning algorithms are also investigated for their potential in enhancing fall detection accuracy and minimizing false alarms. Furthermore, the importance of user-friendly interfaces and customization features in ensuring the effectiveness and acceptance of fall detection alerts is also highlighted. The paper also addresses the ethical and privacy considerations associated with fall detection in hearing aids. It emphasizes the need for transparent data handling practices and user consent, while also addressing potential concerns related to user autonomy and intrusion. © 2025 IEEE.

关键词： Data privacy

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Efficient net-based deep learning: A deep-fake image detection 3

Efficient net-based deep learning: A deep-fake image detecti...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Singh, Rohan Sharma, Dilip Kumar Kumar Jain, Praphula Gla University Department of Computer Science and Engineering Mathura India Gla University Department of Computer Engineering and Application Mathura India Mits Gwalior Department of Computer Science and Engineering MP India

ISBN: (纸本)9798331528201

With the substantial volume of social media data generated daily and the concurrent emergence of powerful multimedia modification techniques, detecting manipulations in photographs and videos has become an important task. This study examines the strength of a deep learning-based efficient net model for detecting manipulation in multimedia images. This study examines the publicly accessible benchmark dataset DFDC to prove its efficiency. We also highlighted the real-world challenges and emerging trends to combat deep-fakes. The research experiment resulted in and confirmed the efficiency of Efficient-Net as it performed well in both the training and testing stages, exhibiting more accuracy and reduced loss. The test accuracy varies between 50 and 95, with epochs spanning from 0 to 10. © 2024 IEEE.

关键词： Deep Learning Deepfakes Efficient Net Fake Image Video

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Medication Recommendation System for Skin Diseases using NLP 2

Medication Recommendation System for Skin Diseases using NLP

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Mishra, Anjana Bisoy, Sukant Kishoro Naik, Bighnaraj Computer Science and Engineering C.V. Raman Global University Bhubaneswar India Vssut Computer Science and Application Burla India

ISBN: (纸本)9798350370188

Skin diseases are common health problems that affect a large number of people globally nowadays;due to busy schedules they are not able to take proper care. There is an imbalance among the healthcare experts and the patients, lack of time and trustworthy resources. Due to which individuals initiate self-administration of medicinal drugs without proper consultation, which makes the circumstance worse than normal. The recommended treatments for these diseases may vary depending on severity, type and cause of the condition. A proposed tool to tackle this problem is natural language processing (NLP). The aim of this paper is to examine the potential of NLP for prescribing medications for skin conditions. The proposed medication recommendation system analyses the user-reviews using Vader Sentiment Analysis to determine their sentiments and assigns a score to each review. The SVM algorithm with Gradient Descent and RNN with LSTM is used to classify the reviews into different categories based on their score and verifies the Vader score for recommendation of medications. Therefore, the aim is to provide accurate and personalised medications to patients suffering from skin diseases. © 2024 IEEE.

关键词： Recommender systems

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Face Classification & Recognition Using Stacking Ensemble Learning with Principal Component Analysis Approach 3

Face Classification & Recognition Using Stacking Ensemble Le...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Devi, Shivali Saini, Jyoti Kaur, Harjinder Kaur, Harmanpreet Chandigarh University Computer Science & Engineering Punjab India Ssiet Dinanagar Computer Science & Engineering Punjab India Rayat Bahra University Computer Application Department Punjab India

ISBN: (纸本)9798350369083

Developing a high-level automated security system for identification/authentication has long been an active research subject in practically all fields. Traditional security systems utilize a key or a password to secure a process/product, whereas biometric security systems use a person's physical or behavioral attributes. Face patterns play a significant part in several potential recognition/authentication applications because of their uniqueness, universality, dependability, and stability. This study provides a face identification system that starts with eye detection and ends with face image recognition. AN ensemble stacking approach for face classification is presented in this study that combines various Machine learning (ML) algorithms with and without Principal Component Analysis (PCA). The proposed system is trained and tested on a well-known publically available Olivetti Face Dataset. The dataset is split into 75% train and 25% test dataset which is also transformed using PCA. The results show that the accuracy of each base model having PCA data is on par or better than the one without PCA. The paper also proves that the performance of the new stacking ensemble system results in an average or better performance than the other base models. For Olivetti face dataset, the proposed Stacked Ensemble Learning model's accuracy with PCA is 94%, which is on average than the accuracy of the other base models, including Neural Network (NN) (100%), Random Forest (92%), Support Vector Machine (SVM)(96%), Gaussian Naives Bayes(81%), K-Nearest Neighbour (KNN) (89%), and Logistic Regression (LR) (97%) respectively. © 2024 IEEE.

关键词： Authentication

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Multispectral point cloud superpoint segmentation

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science China(Technological sciences) 2024年第4期67卷 1270-1281页

作者： WANG QingWang WANG MingYe ZHANG ZiFeng SONG Jian ZENG Kai SHEN Tao GU YanFeng Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming 650500China Yunnan Key Laboratory of Computer Technologies Application Kunming 650500China School of Electronics and Information Engineering Harbin Institute of TechnologyHarbin 150001China

The multitude of airborne point clouds limits the point cloud processing *** are grouped based on similar points,which can effectively alleviate the demand for computing resources and improve processing ***,existing superpoint segmentation methods focus only on local geometric structures,resulting in inconsistent spectral features of points within a *** feature inconsistencies degrade the performance of subsequent ***,this study proposes a novel Superpoint Segmentation method that jointly utilizes spatial Geometric and Spectral Information for multispectral point cloud superpoint segmentation(GSI-SS).Specifically,a similarity metric that combines spatial geometry and spectral information is proposed to facilitate the consistency of geometric structures and object attributes within segmented *** the formation of the primary superpoints,an intersuperpoint pointexchange mechanism that maximizes feature consistency within the final superpoints is *** are conducted on two real multispectral point cloud datasets,and the proposed method achieved higher recall,precision,F score,and lower global consistency and feature classification *** experimental results demonstrate the superiority of the proposed GSI-SS over several state-of-the-art methods.

关键词： multispectral point cloud superpoint segmentation over-segmentation spatial-spectral joint metric

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Graph Based Keywords Extraction from Crime Data 16

Graph Based Keywords Extraction from Crime Data

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Das, Priyanka Munshi, Anuvab Naha, Annwesha Dey, Adrija Chakraborty, Sriparno Dept. of Computer Application and Science Institute of Engineering & Management Kolkata University of Engineering and Management Kolkata India

ISBN: (纸本)9798331505264

It takes a lot of work to gather a large amount of data to help in criminal investigation and prevention of crimes. The most effective methods for reporting crimes are insufficient for both civilians and law enforcement. The police and witness narrative summaries are frequently deemed to be too lengthy to read. Hence, there exists many research works that represents summary of those descriptive reports. But to make it easier, there should be certain phrases or words so that Police investigators can quickly understand criminal scenarios saving them from having to read the entire report. This can be accomplished with the use of criminal information extraction. Here, the proposed study uses a graph-based methodology and natural language processing techniques to extract crimerelated keywords from in-depth police and witness narrative descriptions. Initially, the suggested work selected nouns, verbs, and adjectives from the crime reports and evaluated their importance scores. Based on the words' importance scores, a graph partitioning technique has been applied;following repeated runs of the algorithm, the words are classified into clusters. Each cluster in this instance, together with the words that reside within it, represent a different aspect of crime. These clustered words are considered as the keywords for representing the crime. © 2024 IEEE.

关键词： Crime

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Machine Learning Techniques for Identifying Distressed Individuals on Social Media - A Survey

Machine Learning Techniques for Identifying Distressed Indiv...

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2024 International Conference on Big Data Analytics in Bioinformatics, DABCon 2024

作者： Sadhukhan, Ankan Samanta, Sreejani Pasari, Abhijit Samui, Sayantika Mandal, Dipayan Ghosh, Sandipta Guria, Mrinmoy Pal, Debrupa Computer Science and Engineering Narula Institute of Technology Kolkata India Computer Application Narula Institute of Technology Kolkata India

ISBN: (纸本)9798350378047

The pervasive presence of social media platforms has turned them into reservoirs of human emotions, providing a distinctive perspective into the intricate tapestry of digital sentiments. This research delves into the realm of emotional upheaval prevalent in online spaces, aiming to uncover signs of distress through the utilization of machine learning algorithms. By scrutinizing extensive volumes of social media data, this survey seeks to establish a pioneering framework for predicting the identification of individuals undergoing emotional distress. Many papers are published on machine learning approaches over the past few decades. In this paper, we provide a comprehensive survey of all best machine learning approaches in the current year to study or identify distress calls on social video through sentiment and media analysis. Furthermore, the challenges and benefits of using Machine Learning, along with its shortcomings, are summarized. Finally, open issues and their consequences for further research in the field of identifying individuals who are mentally depressed on social media are presented. © 2024 IEEE.

关键词： Adversarial machine learning

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Unsupervised Bearing Fault Diagnosis Method for Complex Electromechanical Hydraulic System 4

Unsupervised Bearing Fault Diagnosis Method for Complex Elec...

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4th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2023

作者： He, Zizhou Shen, Wenwen Li, Suicheng Yuan, Tianyou Zhou, Xiaowei Ni, Zhigao Institute of Computer Application CAEP Sichuan Province Department of Computer Engineering and Science China

ISBN: (纸本)9798350324303

Finding the optimal combination of feature extraction and classifier has always been a hot issue in intelligent health monitoring. Traditional artificial features are difficult to achieve the ability of deep learning to mine deep abstract features of signals. However, compared with ensemble learning models, deep learning classifiers have the problems of easy overfitting, poor generalization performance, and the need for a large number of labeled samples. Autoencoder has the advantage of unsupervised feature extraction, LightGBM has the advantage of feature selection, so this paper proposes a fault diagnosis method combining deep convolutional sparse autoencoder with LightGBM, which has the advantages of unsupervised feature extraction and distributed gradient boosting. Experiments are carried out on the CWRU bearing dataset to verify the superiority of the proposed method. © 2023 IEEE.

关键词： Feature extraction

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