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Fusion of Wavelet Decomposition and N-BEATS for Improved Stock Market Forecasting

SN computer science 2024年第7期5卷 869页

作者： Pramanick, Neha Singhal, Vatsal Neeraj Mathew, Jimson Agarwal, Mayank Department of Computer Science and Engineering Indian Institute of Technology Patna Patna 801103 India

Stock market forecasting is one of the most exciting areas of time series forecasting both for the industry and academia. Stock market is a complex, non-linear and non-stationary system with many governing factors and noise. Some of these factors can be quantified and modeled, whereas some factors possess a random walk behavior making the process of forecasting challenging. Various statistical methods, machine learning, and deep learning techniques are prevalent in stock market forecasting. Recently, there has been a paradigm shift towards hybrid models, showing some promising results. In this paper, we propose a technique that combines a recently proposed deep learning architecture N-BEATS with wavelet transformation for improved forecasting of future prices of stock market indices. This work uses daily time series data from five stock market indices, namely NIFTY 50, Dow Jones Industrial Average (DJIA), Nikkei 225, BSE SENSEX, and Hang Seng Index (HSI), for the experimental studies to compare the proposed technique with some traditional deep learning techniques. The empirical findings suggest that the proposed architecture has high accuracy as compared to some traditional time series forecasting methods and can improve the forecasting of non-linear and non-stationary stock market time series. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： 00-01 99-00 Deep learning Forecasting N-BEATS Stock market Time series

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Advanced Deep Learning Technique for Accurate Pest Detection in Medicinal Plants 4

Advanced Deep Learning Technique for Accurate Pest Detection...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Shreelakshmi, C.M. Channakrishnaraju Gsss Institute of Engineering and Technology for Women Department of Computer Science and Engineering Mysuru India Ssahe University Tumakuru India Sri Siddhartha Institute of Technology Department of Computer Science and Engineering Tumakuru India

ISBN: (纸本)9798350352931

Medicinal plants are widely used in traditional and modern Ayurveda for treating various diseases. However, pest infestation poses a significant challenge to maintaining their health and productivity. This paper proposes a method for detecting pest-infected medicinal plants, which may assist farmers, researchers, and agricultural professionals in identifying infestations early. The study utilizes three datasets of medicinal plants which are Neem, Aloe Vera, and Ashwagandha, and trains five deep learning models such as Basic CNN, VGG16, ResNet50, VGG32, and a newly proposed CFNET. These models process plant image data and predict pest presence using metrics such as accuracy, precision, recall, F1-score, and training time. The CFNET model, built on the EfficientNetB3 architecture, achieved the highest accuracy of 0.98 on the Neem dataset, 0.94 on Aloe Vera, and 0.96 on Ashwagandha, with a focus on reducing false negatives. The results highlight CFNET's superior performance and efficiency compared to other models. This system demonstrates the potential of deep learning for sustainable pest management in medicinal plant cultivation. © 2024 IEEE.

关键词： Medicinal chemistry

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A novel and proposed triad machine learning-based framework for the prognosis of Huntington’s disease

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International Journal of Information technology (Singapore) 2024年第4期16卷 2603-2610页

作者： Deepak, Shashikant Ojha, Ananta Acharjya, Kalyan Mishra, Reshmi Gantra, Amit Kalaiarasan, C. Upadhyay, Ramakant Walke, Suchita Department of UGDX ATLAS SkillTech University Mumbai India Department of Computer Science and Information Technology Jain (Deemed-to-be-University) Karnataka Bengaluru India Maharishi School of Engineering and Technology MUIT Lucknow India Department of Biotechnology Noida Institute of Engineering and Technology UP Greater Noida India Department of Computer Science and Engineering Parul Institute of Engineering and Technology Gujarat Vadodara India Department of Computer Science and Engineering Presidency University Karnataka Bengaluru India Department of Computer Science Sanskriti University UP Mathura India Department of Computer Engineering Pillai College of Engineering and Technology Mumbai University Mumbai India

A hereditary condition known as Huntington's Disease (HD) results in the gradual degeneration of brain nerve cells, impairing a person's capacity for speech, walking, and reasoning. Owing to its severity, novel strategies are crucial for the creation of techniques that support the accurate categorization of this illness. This research proposes a tripartite machine learning-based framework that uses gait dynamics and temporal parameter extraction to predict the prognosis of Huntington's illness. Phases including data pre-processing, feature extraction, classification, and projected output are included in the suggested triad method. The proposed framework uses a triad combination of classifiers for classification: Support vector machines (SVM), K-nearest neighbor (KNN), and Naïve Bayes (NB). The suggested framework's effectiveness is assessed by comparing it to current machine learning classifiers using parameters from the public repository on the dynamics of gait in neurodegenerative diseases. The experimental results show that the triad classifiers employed in the proposed approach are able to achieve higher accuracy (85.45%), higher sensitivity (78.37%) and higher specificity (76.55%) than the existing classifiers used in the recent studies. © The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Gait dynamics Huntington’s disease KNN Machine learning (ML) NB SVM

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Analyzing Sentiments: A Comprehensive Study of Roberta-Based Sentiment Analysis on Twitters 4

Analyzing Sentiments: A Comprehensive Study of Roberta-Based...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Krishnamoorthy, A. Sundhar, K.A. Naveen Kumar, V. Karthik, V. Vellore Institute of Technology School of Computer Science & Engineering Tamil Nadu India Vellore Institute of Technology Department of Computer Science Engineering Tamil Nadu India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364729

Social media platforms have become essential avenues for individuals to express opinions and emotions across diverse topics such as products, events, and policies, using both text and emojis. Understanding these sentiments is crucial, yet manually analyzing the vast data on platforms like Twitter is impractical. Automated sentiment analysis tools, exemplified by Roberta, offer a solution. Roberta, an evolving language model, employs advanced bidirectional learning to predict sentiment labels accurately. This study focuses on sentiment analysis on Twitter, leveraging Roberta to autonomously categorize tweets into positive, negative, or neutral sentiments. We utilizing natural language processing and machine learning algorithms for dataset preprocessing, incorporating sources like Kaggle and ensuring data integrity. Assessment metrics encompassing accuracy, precision, recall, and F1-score, in addition to confusion metrics, assess model performance. According to our findings, machine learning powered sentiment analysis models are dependable, even in the face of challenges and more so when processing Twitter data sets. The study gives direction on how this kind of model can be of help in analyzing sentiments expressed through social networking sites thus assisting scholars and professional Consultancy in the field. © 2024 IEEE.

关键词： Tweets

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Advanced Meta-Heuristic Algorithm Based on AlBiruni Earth Radius and Particle Swarm Optimization Methods for Oral Cancer Detection

Advanced Meta-Heuristic Algorithm Based on AlBiruni Earth Ra...

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

作者： Kumar, B.H. Manjunatha Ananya, G.R. Anusha, S.V. Paramanantti, Poornima Prakruthi, K. S J C Institute of Technology Computer Science & Engineering Chikkaballapur India S Jc Institute of Technology Department of Computer Science and Engineering Chikkaballapur India

ISBN: (纸本)9798350359688

Oral cancer is a widespread and potentially fatal disease, often diagnosed in later stages, leading to poor outcomes and increased mortality rates. Early detection is crucial for improving patient prognosis and survival rates. To address this challenge, a novel method for early oral cancer diagnosis is proposed in this study, utilizing advanced meta-heuristic algorithms such as Particle Swarm Optimization (PSO) and the Al Biruni Earth Radius Algorithm (ABERA). By incorporating PSO and ABERA, the method aims to enhance the accuracy of detection models. Initial testing on standard biomedical imaging datasets shows promising results. This suggests potential for more efficient screening procedures and personalized treatment strategies. However, further research is necessary to validate these findings and explore additional aspects of oral cancer diagnosis and management. © 2024 IEEE.

关键词： Lung cancer

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Hybrid Technique-based Optimal Energy Management in Smart Home Appliances

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IEIE Transactions on Smart Processing and Computing 2024年第5期13卷 425-434页

作者： Shirley, C.P. Depaa, R.A.B. Priya, A. Sarala, R. Solanki, Rajdeep Singh Malini, K.V. Venkatakrishna Reddy, Ch. Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Civil Engineering Dr. M.G.R Educational and Research Institute Maduravoyal Chennai95 India School of Architecture Koneru Lakshmaiah Educational Foundation Andhra Pradesh Guntur India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai Rameshwaram High Road Viraganoor Madurai625009 India Department of Computer Science Medi-Caps University Indore India Department of Electrical and Electronics Engineering Sri Sairam College of Engineering Anekal India Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of Technology Hyderabad75 India

This manuscript presents a hybrid method for optimal energy management in smart home appliances. The proposed approach combines the Ebola Optimization Search Algorithm (EOSA) with the performance of spiking neural networks (SNN). The key objective of the proposed strategy is cost reduction through day-ahead load scheduling while also considering the best demand response (DR), and self-consumption of photovoltaic (PV). The EOSA method manages air conditioning (AC) and ensures thermal comfort. In addition, the SNN method was used to predict the optimal system control signal. Comparative analysis with existing systems, such as PSO, RFA, and BCO, showed that the proposed strategy resulted in 13% cost savings. The proposed system considered parameters, such as solar radiation, occupant presence, and electricity prices. The proposed technique is implemented in the MATLAB software, and the performance is compared with existing approaches. The efficiency of the proposed approach is higher than the existing techniques. © 2024 The institute of Electronics and Information Engineers.

关键词： Demand response

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A fully flexible key delegation mechanism with efficient fine-grained access control in CP-ABE

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Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 12837-12856页

作者： Porwal, Shardha Mittal, Sangeeta Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Uttar Pradesh Noida India

Ciphertext Policy-Attribute Based Encryption (CP-ABE) is a secure one-to-many asymmetric encryption schemes where access control of a shared resource is defined in terms of a set of attributes possessed by a user. Key delegation service, where a user is able to temporally transfer its full or partial access rights, is an important requirement to make CP-ABE based systems practically useful. In existing CP-ABE delegation schemes, a delegator can delegate its entire or partial set of attributes such that all content accessible using those attributes becomes available to the delegatee also. Flexibility to delegate some specific content, which is a practical requirement, is not feasible with these schemes. Further, for a delegated resource, flexibility of fine grained delegation like delegating only a specific subset of read, write and update permissions is not yet available. In this work, CP-ABE key delegation has been enhanced to support specific content and specific permission delegation. The proposed scheme is fully flexible as it additionally supports limits on the number of times delegation can be done, delegatee revocation and traceability of forbidden delegation. Theoretical and practical analysis show that the proposed scheme achieves its objectives and is computationally efficient as well as secure against chosen plaintext and key abuse attacks. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Access control

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An Extensive Analysis of Neuromorphic Computing

An Extensive Analysis of Neuromorphic Computing

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2024 International Conference on Advances in Computing Research on science engineering and technology, ACROSET 2024

作者： Vajpayee, Amit Kaur, Palak Preet Sharma, Ankit Varshney, Santosh Parul Institute of Engineering and Technology Department of Computer Science and Engineering Vadodara India Apex Institute of Technology Chandigarh University Department of Bigdata Analytics Mohali India Acropolis Institute of Technology and Research Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798350388800

Neuromorphic computing is a new paradigm that emerges from the structure and function of the human brain and aims to revolutionize computing. The technology is designed to simulate the high speed, low power consumption and large contraction of the brain. Neuromorphic systems provide great potential for intelligence and big data by using special algorithms and special designs to simulate neural networks. Despite encouraging progress from technology companies such as Intel and IBM, the development of neural computing chips still faces major challenges. This approach brings significant benefits to artificial intelligence, machine learning and big data. This technology is still in its infancy but has great potential to revolutionize intelligence. By mimicking the brain's performance and learning potential, neuromorphic computing could pave the way for smarter, faster, and more flexible systems that work well on water, performing tasks such as complex data analysis and on-the-fly decision making. © 2024 IEEE.

关键词： Adversarial machine learning

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Two Stage Video Classification Approach Using Convolution Neural Network 2

Two Stage Video Classification Approach Using Convolution Ne...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Veena, H.N. Rajani Gowda, Naveen Chandra Ramani, D Roja SJB Institute of Technology Department of Computer Science and Engineering Bengaluru India School of Computer Science and Engineering REVA University Bengaluru India Newhorizon College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350327533

This research study focuses on video categorization, which is a crucial area of computer vision with uses in entertainment, education, and surveillance. Convolutional Neural Networks (CNNs) are used in a two-stage approach in the suggested methodology. The video is first broken down into frames, which are subsequently supplied into a CNN model. Another CNN model that predicts the video class uses the output from this model as input. A publicly accessible dataset is used to assess the efficacy of this approach, demonstrating its competitive accuracy performance in comparison to the most advanced techniques. The outcomes indicate that the technique can effectively classify videos and has the potential to be used in a variety of real-world scenarios. © 2024 IEEE.

关键词： 3D CNN Autoencoder Convolution Neural Networks (CNN) Long-Term Recurrent Convolutional Network (LRCN) Recurrent Neural Network(RNN)

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FQBDDA: fuzzy Q-learning based DDoS attack detection algorithm for cloud computing environment

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International Journal of Information technology (Singapore) 2024年第2期16卷 891-900页

作者： Kumar, Animesh Dutta, Sandip Pranav, Prashant Department of Computer Science and Engineering Birla Institute of Technology Mesra Ranchi 835215 India

With on-demand resources, flexibility, scalability, dynamic nature, and cheaper maintenance costs, cloud computing technology has revolutionized the Information technology sector, and almost everyone using the internet relies in some manner on the use of cloud services. Distributed denial of service (DDoS) attack blocks the services by flooding high or low volumes of malicious traffic to exhaust the servers, resources, etc. of the Cloud environment. In today’s era, they are challenging to detect because of low-rate traffic and its hidden approach in the cloud. Studying all DDoS attacks with their possible solution is essential to protect the cloud computing environment. In this paper, we have proposed a fuzzy Q learning algorithm and Chebyshev’s Inequality principle to counter the problem of DDoS attacks. The proposed framework follows the inclusion of Chebyshev’s inequality for workload prediction in the cloud in the analysis phase and fuzzy Q-learning in the planning phase. Experimental results prove that our proposed fuzzy Q-learning based DDoS attack detection algorithm for cloud computing environment (FQBDDA) model prevent DDoS attack. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management.

关键词： Cloud computing DDoS attack Fuzzy logic Fuzzy Q-learning Optimization Q-learning Security

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