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Proposed Model for Prediction of Stock Market Price of Netflix

Proposed Model for Prediction of Stock Market Price of Netfl...

2023 International Conference on Artificial Intelligence and Applications, ICAIA 2023 and Alliance Technology Conference, ATCON-1 2023

作者： Patwal, Pratap Singh Srivastava, Anoop Kumar Laxmi Devi Institute of Engineering & Technology Dept. of Computer Science & Engineering Rajasthan Alwar India Aced Alliance University Dept. of Computer Science & Engineering Karnataka Bengaluru India

ISBN: (纸本)9781665456272

Accurate prediction of stock market price is highly challenging. This paper presents a proposed model for prediction of stock market price of Netflix. We have considered a five-year data set (April, 2017 - April, 2022) of Netflix. An Exploratory Data Analysis (EDA) of Netflix's stock price data for predicting its stock market prices using time series is done. The implementation of the model is done using Python language. We imported five-years data and applied several techniques: importing libraries, calculating stock return, line plot, plot all, plot return year wise, plot histogram, plot kernel density, plot box plot, differencing method, resample daily to monthly data etc. EDA proved that using time series technique achieved better results in prediction of stock price and visualizing. © 2023 IEEE.

关键词： Forecasting

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EXTRACTING EXPLOITS AND ATTACK VECTORS FROM CYBERSECURITY NEWS USING NLP

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and computer science 2022年第2期84卷 63-78页

作者： Sandescu, Cristian Dinisor, Alexandra Vladescu, Cristina-Veronica Grigorescu, Octavian Corlatescu, Dragos Dascalu, Mihai Rughinis, Razvan Dept. of Software Development CODA Intelligence SRL Romania Dept. of Informatics Technical University of Munich Germany Dept. of Computer Science University POLITEHNICA of Bucharest Romania

Cybersecurity has an immense impact on society as it enables the digital protection of individuals and enterprises against an increasing number of online threats. Moreover, the rate at which attackers discover and exploit critical vulnerabilities outperforms the vendors’ capabilities to respond accordingly and provide security patches. As such, open-source intelligence data (OSINT) has become a valuable resource, from which details on zero-day vulnerabilities can be retrieved and timely actions can be taken before the patches become available. In this paper we propose a method to automatically label articles on vulnerabilities and cyberattacks from trusted sources. Using Named Entity Recognition, we extract essential information about new vulnerabilities, such as the exploit’s public release and the environment in which the attack’s exploitation is possible. Our balanced dataset contains 1095 samples out of which 250 entries are from cybersecurity articles;the rest of the articles were crawled and annotated from the U.S. Government’s Vulnerability Database, whereas automated text augmentation techniques were also considered. Our model built on top of spaCy obtained an overall performance of 75% recall on the Exploit Available task. When considering the Attack Vector metric, the model achieved the following recalls: Network 72%, Local 78%, and Physical 92%. © 2022, Politechnica University of Bucharest. All rights reserved.

关键词： Cybersecurity

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Predicting DDoS Attacks: A Machine Learning Approach using ALDDoS Dataset 15

Predicting DDoS Attacks: A Machine Learning Approach using A...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Shookdeb, Shantanu Muduli, Debendra Bhatta, Sandesh Adhikari, Aayush Sapkota, Aaron Chaturvedi, Prabhat C V Raman Global University Dept. of computer Science and Eng. Bhubaneswar India C V Raman Global University Dept. of computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9798350370249

The DDoS attack stands as Distributed Denial of Service attack refers a harmful effort to intercept on the regular traffic of a targeted web service or server. DDoS attack interferes with the normal network flow of the targeted server, potentially causing significant disruption to its *** send an incoming packet to every node except the arrived node,it used a non-adaptive routing algorithm. DDoS attacks are executed by using multiple compromised systems, which included combination of multiple computers system and other IoT device resources to make a large traffic on targeted network, rendering it inaccessible to legitimate users. In our study we experimented with various machine learning model like as RandomForest, DecisionTree, Xgbbost etc. with a high accuarcy of 99.99% to predict the DDoS attack with publicly available ALDDoS (Application-Layer DDoS) Dataset by Warda (Owner). © 2024 IEEE.

关键词： ALDDos Cyber defense DDoS RandomForest Web-proxy

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MSFFAR-Net: A Multi-Scale Feature Fusion and Attention-Guided Residual Network for Bangladeshi Medicinal Plants Classification 26

MSFFAR-Net: A Multi-Scale Feature Fusion and Attention-Guide...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Islam, Al Mohimanul Masud, Fatiha Binta Ahmed, Md. Rayhan Islam, Salekul United International University Dept. of Computer Science and Engineering Dhaka Bangladesh University of British Columbia Dept. of Computer Science Kelowna Canada

ISBN: (纸本)9798350359015

The classification of plant species, particularly medicinal ones, has gained much research attention due to challenges in manual identification and the urgency to protect endangered species. The growing need for automated identification of medicinal plants has led to a focus on using leaf images, which provide abundant information and are easily accessible. The importance of medicinal plants in healthcare underscores the need for efficient identification methods, given the time-consuming reliance on experts. Vision-based systems offer fast and accurate recognition, benefiting researchers and the public in identifying these essential plant species. This work proposed a Multi-Scale Feature Fusion and Attention-Guided Residual Network (MSFFAR-Net) that incorporates pre-trained MobileNetv2 as feature extractor and a series of newly introduced Attention Residual Convolution Module to classify Bangladeshi Medicinal plants. Our model, validated through a 4-fold cross-validation, achieved high classification mean accuracy of 98.12% for BDMediLeaves and 96.18% for the Bangladeshi Medicinal Plant Dataset. © 2023 IEEE.

关键词： Bangladeshi medicinal plant Classification Convolutional Neural Network Deep learning Transfer learning

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Hybrid Nature-Inspired Based Oversampling and Feature Selection Approach for Imbalance Data Streams Classification

Hybrid Nature-Inspired Based Oversampling and Feature Select...

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2022 OPJU International Technology Conference on Emerging Technologies for Sustainable Development, OTCON 2022

作者： Arya, Monika Dewangan, Bhupesh Kumar Verma, Monika Rohini, M. Motwani, Anand Sar, Sumit Kumar Bhilai Institute of Technology Dept. of Computer Science & Engineering CG Durg India Op Jindal University Dept. of Computer Science Engineering Raigarh India Sri Krishna College of Engineering and Technology Dept. of Computer Science & Engineering Coimbatore India Vit Bhopal University School of Computing Science & Engineering MP Sehore India

ISBN: (纸本)9781665492942

A big data stream is described using 5 mathrm{V}primes (Volume, Variety, Velocity, Variability, and Veracity). These characteristics impose various challenges. In many cases, data streams are unbalanced, making traditional data mining approaches impossible to employ. The standard data mining approaches are not suitable for imbalanced data streams for achieving analytical efficiency because they require periodic analyses, but big data requires real-time analytics. Additionally, the induction model must be re-run and rebuilt each time to add up the most recent data. Mining these unique streams, on the other hand, is one of the most intriguing research areas. Deep learning (DL) algorithms were developed to increase classification performance for issues requiring large data sets with varying types and characteristics. Feature selection (F.S.) is a critical stage in any classification application. F.S. entails eliminating superfluous and redundant characteristics, resulting in a prediction model that is more efficient, interpretable, and fast. While complete solutions are available for F.S., managing massive data streams that need instantaneous processing is challenging by its own nature. This work provides a hybrid metaheuristic strategy with FFSMOTE for oversampling imbalanced data stream and Honey Bee algorithm for feature selection. Hybridization aims to improve feature selection processes by combing the advantage of both the algorithms. Furthermore, the Ensemble Deep classifiers are used for data stream classification. © 2023 IEEE.

关键词： Classification (of information)

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On-chain Off-chain Blockchain Model for IoT using IPFS 27

On-chain Off-chain Blockchain Model for IoT using IPFS

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27th International Conference on Advanced Computing and Communications, ADCOM 2022

作者： Khobragade, Pooja Kumar, Ashok Department of Computer Science and Engineering NIT Rourkela India Turuk Dept. Computer Science and Engineering NIT Rourkela India

ISBN: (纸本)9781839539107

Blockchain has become the most revolutionary and trending technology since 2008 with the development of Bitcoin. Besides bitcoin, blockchain can be applied in different sectors, such as finance, healthcare, IoT, security, etc. Blockchain and IoT are two new emerging technologies. IoT has become an integral part of human digital life with the exponential growth of digitally connected devices to the internet. With the development of connected devices, data communication has also increased. Till now, IoT data management has been based on a centralized server. Blockchain technology can protect IoT devices against malicious attacks and single-point failure. This study analyzes the integration of blockchain technology with IoT and also discusses the advantages, challenges in integration, and associated cyber security threats. Also, it provides an architecture for blockchain-enabled secure IoT devices with the Interplanetary File System(IPFS). © ADCOM 2022. All rights *** right reserved.

关键词： Smart contract

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Using IOT and WSNs Technology for Monitoring an Aquaponic Greenhouse System 4

Using IOT and WSNs Technology for Monitoring an Aquaponic Gr...

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4th International Conference on Embedded and Distributed Systems, EDiS 2024

作者： Touhami, Achouak Bourouis, Amina Benahmed, Tariq Touhami, Sana Touhami, Nawal Benahmed, Khelifa Ali Kafi university center Dept. Mathematics and Computer Science Tindouf Algeria Tahri Mohammed university Dept. Mathematics and Computer Science Bechar Algeria Ahmed Draria university Dept. Electrical Engineering Adrar Algeria

ISBN: (纸本)9798331519872

The increasing population, climate fluctuations, soil erosion, limited water resources, and concerns about food security highlight the need for effective and sustainable approaches to food production. Aquaponics represents a sustainable method that conserves land and water while minimizing its environmental impact. This technique operates as a closed-loop, soil-less farming system, requiring meticulous monitoring, control, and management. Monitoring an aquaponic greenhouse system involves tracking various parameters to maintain the health and productivity of both plants and fish. Our research introduces a design and algorithms for monitoring an aquaponic greenhouse, utilizing wireless sensor networks (WSN), aquaponic systems, and Internet of Things (IoT) technology. © 2024 IEEE.

关键词： Aquaponic greenhouse Closed-loop Fish Internet of things Plant Wireless sensor networks

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Automatic Image Registration using SIFT and AKAZE: An application for Preservation of Cultural Heritage 21

Automatic Image Registration using SIFT and AKAZE: An applic...

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21st OITS International Conference on Information Technology, OCIT 2023

作者： Sawkmie, Lumlang Samal, Sonali Balabantaray, Bunil Kumar NIT Meghalaya Dept. of Computer Science and Engineering Shillong India

ISBN: (纸本)9798350358230

Automatic image registration, which aligns several images acquired from different viewpoints or timeframes, plays an important role in the analysis and preservation of cultural heritage. It serves as a crucial preprocessing step for performing comparative analysis, restoration, and reconstruction of cultural heritage images. In this paper, an automatic image registration approach is proposed that combines the strengths of both the Scale Invariant Feature Transform (SIFT) and the Accelerated KAZE (AKAZE) for accurate and robust registration of heritage images. While SIFT excels in matching images with varying scales and rotations, AKAZE is renowned for its robustness to non-linear transformations and challenging imaging conditions. This fusion of SIFT and AKAZE feature detection leads to a substantial increase in accurate correspondences, enhancing registration results. The effectiveness of this proposed approach has been assessed using the Affine Covariant Regions Dataset and the Indian Heritage Image Retrieval Dataset (IHIRD). The outcomes of the experiments show that the proposed method increases the number of correct correspondences when compared to those of SIFT or AKAZE alone. © 2023 IEEE.

关键词： Image registration

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Automating Dermatological Diagnosis - A Comparative Analysis 5

Automating Dermatological Diagnosis - A Comparative Analysis

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5th IEEE International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Johnson, Aleen Arora, Vishnupriya Aggarwal, Lakshita Goswami, Puneet Srm University Dept. Of Computer Science & Engineering Haryana Sonepat India Srm University Dept. Of Computer Science Haryana Sonepat India

ISBN: (纸本)9798350330861

Evolving and adapting healthcare along with growing technology is an urgent need of an hour using machine learning is an addition to classify health data for the large aspect of identifying dermatological illness. Convolution Neural Network (CNN) can accurately classify a variety of skin illnesses by extracting complex information from images. CNN do away with the requirement for manual feature engineering because they are able to learn from large amounts of data. They are resistant to noise and changes in lighting, ensuring accurate analysis every time. CNN help in early detection, increasing the efficacy of treatment, and lowering the possibility of a false positive. Their combination has the potential to greatly improve patient care and change dermatological procedures. Objective: The goal of this study is to determine the best effective algorithm, whether it comes from deep learning or machine learning, based on a thorough comparative analysis that considers elements like accuracy, interpret ability, and scalability. Methodology: This study uses a comparative analysis methodology to decide which machine learning and deep learning algorithms are the most effective. It entails a review of the literature, the gathering of data, the design and execution of experiments, statistical analysis, and result interpretation. Findings: This study's findings shows that deep learning approaches are more effective than machine learning ones. In contrast to machine learning, which depends on manual, subjective techniques, deep learning allows for automatic and objective feature extraction. Deep learning achieves greater prediction accuracy and generalization ability despite requiring more training time and computational resources, demonstrating its superior learning powers. © 2023 IEEE.

关键词： Learning algorithms

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Driving Behavior Classification Using Smartphone Sensor Data 8

Driving Behavior Classification Using Smartphone Sensor Data

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8th International Conference on computer science and engineering, UBMK 2023

作者： Dikbiyik, Deniz Alagoz, Fatih Boǧaziçi University Dept. of Software Engineering İstanbul Turkey Boǧaziçi University Dept. of Computer Engineering İstanbul Turkey

ISBN: (纸本)9798350340815

Given the substantial number of accidents because of human error, the development of automatic driver behavior monitoring systems has become a pressing need. By providing real-time monitoring and analysis of driving behavior, these systems have the potential to decrease the incidence of accidents and improve overall road safety. This study presents a novel analysis framework for classifying driving behavior based on data gathered from passengers' smartphones. The data were collected using our mobile application installed on the smartphones and processed using machine learning algorithms. The study utilized several machine learning classification t echniques, w ith a focus on developing a Long Short Term Memory (LSTM) algorithm for improved accuracy. A Federated Learning algorithm was also developed to collect the data not into one area and train a global model to apply for labeling the driving behavior. The results show the efficacy of the proposed approach in accurately classifying driving behavior based on data obtained from smartphones. © 2023 IEEE.

关键词： Smartphones

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