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Empirical Analysis of Digital Currency Herding and Contagion Effect Using Deep Learning Approaches 3

Empirical Analysis of Digital Currency Herding and Contagion...

3rd Odisha International Conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Tripathy, Nrusingha Hota, Sarbeswara Mishra, Debahuti Dash, Biswajit Nayak, Subrat Kumar Tripathy, Jogeswar Deemed to Be University Department of Computer Science and Engineering Siksha 'O' Anusandhan Odisha Bhubaneswar India Deemed to Be University Department of Computer Application Siksha 'O' Anusandhan Odisha Bhubaneswar India Raajdhani Engineering College Department of Computer Science and Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798350354379

In recent years, the use of virtual or cryptocurrency-based money has grown. Blockchain, a digital accounting system, is the foundation of cryptocurrency. The markets for cryptocurrencies are eminent for their high volatility and quick price swings. People tend to be more likely to follow the lead of others at times of notable price fluctuations out of a concern of losing money or missing out on possible gains. In this work, we taking the Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), the hybrid combinations of LSTM-Convolutional Neural Network (CNN), LSTM-Gated Recurrent Unit (GRU) and for Facebook Prophet (Fb-Prophet) model smoothing, triple exponential smoothing (Holt-Winters Method) is utilized, which adds seasonality into the forecasting model. In this work bitcoin exchanges providing real-time information casing the period from January 2012 to July 2020 is utilized. The price of cryptocurrencies fluctuates significantly throughout a range of time periods, from short-term intraday swings to long-term patterns that last for weeks or months. By altering the network layer design and the duration of the input sequences, LSTM-GRU models configured to adapt to various time horizons. In contrast to previous iterations, the hybrid LSTM-GRU performs well. When compared to other models, the model's Mean Absolute Error (MAE) score of 0.056 and Root Mean Square Error (RMSE) score of 0.073 are quite acceptable. Forecasting the value of cryptocurrencies will help traders and investors in making better-informed choices when purchasing, disposing of, and keeping assets. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep Learning with a Novel Concoction Loss Function for Identification of Ophthalmic Disease

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computers, Materials & Continua 2023年第9期76卷 3763-3781页

作者： Sayyid Kamran Hussain Ali Haider Khan Malek Alrashidi Sajid Iqbal Qazi Mudassar Ilyas Kamran Shah Department of Computer Science TIMES InstituteMultan60000Pakistan Department of Software Engineering Faculty of Computer ScienceLahore Garrison UniversityLahore54000Pakistan Department of Computer Science Applied CollegeUniversity of TabukTabukSaudi Arabia Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversityAl Hofuf31982Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl HofufSaudi Arabia

As ocular computer-aided diagnostic(CAD)tools become more widely accessible,many researchers are developing deep learning(DL)methods to aid in ocular disease(OHD)*** eye diseases like cataracts(CATR),glaucoma(GLU),and age-related macular degeneration(AMD)are the focus of this study,which uses DL to examine their *** imbalance and outliers are widespread in fundus images,which can make it difficult to apply manyDL algorithms to accomplish this analytical *** creation of efficient and reliable DL algorithms is seen to be the key to further enhancing detection *** the analysis of images of the color of the retinal fundus,this study offers a DL model that is combined with a one-of-a-kind concoction loss function(CLF)for the automated identification of *** study presents a combination of focal loss(FL)and correntropy-induced loss functions(CILF)in the proposed DL model to improve the recognition performance of classifiers for biomedical *** is done because of the good generalization and robustness of these two types of losses in addressing complex datasets with class imbalance and *** classification performance of the DL model with our proposed loss function is compared to that of the baseline models using accuracy(ACU),recall(REC),specificity(SPF),Kappa,and area under the receiver operating characteristic curve(AUC)as the evaluation *** testing shows that the method is reliable and efficient.

关键词： Deep learning multi-classification focal loss CNN eye disease

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An ensemble deep learning model for cyber threat hunting in industrial internet of things

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Digital Communications and Networks 2023年第1期9卷 101-110页

作者： Abbas Yazdinejad Mostafa Kazemi Reza M.Parizi Ali Dehghantanha Hadis Karimipour Cyber Science Lab School of Computer ScienceUniversity of GuelphOntarioCanada Department of Electrical Engineering Faculty of EngineeringShahed UniversityTehranIran College of Computing and Software Engineering Kennesaw State UniversityGAUSA School of Engineering University of GuelphOntarioCanada

By the emergence of the fourth industrial revolution,interconnected devices and sensors generate large-scale,dynamic,and inharmonious data in Industrial Internet of Things(IIoT)*** vast heterogeneous data increase the challenges of security risks and data analysis *** IIoT grows,cyber-attacks become more diverse and complex,making existing anomaly detection models less effective to *** this paper,an ensemble deep learning model that uses the benefits of the Long Short-Term Memory(LSTM)and the AutoEncoder(AE)architecture to identify out-of-norm activities for cyber threat hunting in IIoT is *** this model,the LSTM is applied to create a model on normal time series of data(past and present data)to learn normal data patterns and the important features of data are identified by AE to reduce data *** addition,the imbalanced nature of IIoT datasets has not been considered in most of the previous literature,affecting low accuracy and *** solve this problem,the proposed model extracts new balanced data from the imbalanced datasets,and these new balanced data are fed into the deep LSTM AE anomaly detection *** this paper,the proposed model is evaluated on two real IIoT datasets-Gas Pipeline(GP)and Secure Water Treatment(SWaT)that are imbalanced and consist of long-term and short-term dependency on *** results are compared with conventional machine learning classifiers,Random Forest(RF),Multi-Layer Perceptron(MLP),Decision Tree(DT),and Super Vector Machines(SVM),in which higher performance in terms of accuracy is obtained,99.3%and 99.7%based on GP and SWaT datasets,***,the proposed ensemble model is compared with advanced related models,including Stacked Auto-Encoders(SAE),Naive Bayes(NB),Projective Adaptive Resonance Theory(PART),Convolutional Auto-Encoder(C-AE),and Package Signatures(PS)based LSTM(PS-LSTM)model.

关键词： Internet of things IIoT Anomaly detection Ensemble deep learning Neural networks LSTM

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Enhancing Password Security: Analyzing Password Management Practices Among IT Students 7

Enhancing Password Security: Analyzing Password Management P...

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7th IEEE International Conference and Workshop Obuda on Electrical and Power engineering, CANDO-EPE 2024

作者： Kollar, Anita Mihalovicsne Katona, Jozsef Institute of Computer Engineering University of Dunaujvaros Department of Software Development and Application Dunaujvaros Hungary John Von Neumann University GAMF Faculty of Engineering and Computer Science Kecskemet Hungary Budapest Business University Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Hungary

ISBN: (纸本)9798331531119

Today, with the digital world in its full swing, strong password management is key to protecting sensitive information from both internal and external threats. The current research explores the passwords managing behavior of the IT students at the University of Dunaujvaros. A survey made in May 2024 questioned 68 students, 59 males and 9 females, about behaviors such as using passwords with complexity, not repeating them for multiple accounts or machines, changing them regularly, and which applications they use for password management. The findings have depicted that, as a whole, students appear to be moderately sensitive about the password security instructions. Nonetheless, most continue to indulge in unsafe practices, including easily remembered or weak passwords and infrequent password changes. Furthermore, approximately half of the responders use password management applications, indicating room for significant improvement in the future. The results suggest that intervention and special education measures should be taken to enhance the practice of password security among future IT experts. This research has good potential to contribute toward strategies designed for people behaving safely in password management, and hence promote a secure digital environment. © 2024 IEEE.

关键词： Students

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Optimal Multi-Bitrate Video Caching and Processing in Edge Computing: A Stackelberg Game Approach

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IEEE Internet of Things Journal 2025年第13期12卷 25059-25076页

作者： Guo, Min Zhang, Di Xing, Weiwei Shao, Xun Liu, Zhi Zhang, Yaoxue Beijing Jiaotong University School of Software Engineering Beijing100044 China Toyohashi University of Technology Department of Electrical and Electronic Information Engineering Aichi441-8580 Japan The University of Electro-Communications Department of Computer and Network Engineering Tokyo182-8585 Japan Tsinghua University Department of Computer Science and Technology Beijing100084 China

By caching and transcoding video files on edge servers, video edge caching (VEC) can alleviate network congestion and improve user experience. To achieve this, VEC needs to address resource allocation and pricing problems and develop caching and transcoding strategies. However, existing works have not effectively coordinated and resolved these issues. Towards this end, we propose a game-theoretic multi-bitrate video caching and processing method for VEC. Specifically, to address these interdependent issues concurrently, we first incorporate multidimensional strategies into the system model and formalize the profit maximization problems of the network service provider and multiple video providers (VPs) as a constrained multi-objective optimization problem. Second, we transform the problem into a Stackelberg game and identify a Nash equilibrium as the solution. To solve the game problem, we decompose the game process into two stages focusing on resource competition and price determination, respectively. Then, we address the problems of transcoding and caching strategies development, and resource allocation in parallel across varying resource price conditions. The transcoding strategy is determined based on the constraints imposed by delay, value, and the source video. For the caching strategy development and resource allocation, we transform them into two grouped knapsack problems and apply the "dominant strategy» from game theory to identify the "feasible strategy» of VPs. Finally, we determine the resource prices in a traversal manner. Theoretical analysis shows that our proposed method achieves the optimal solution. Experimental results show that our algorithm achieve a 6.04% increase in overall profit compared to the greedy strategy algorithm. © 2025 IEEE.

关键词： Costs

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Classification of Online Sentiment Data tweets using Various Approaches in Machine Learning

Classification of Online Sentiment Data tweets using Various...

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2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Rawat, Vandana Bisht, Paritosh Suryakant Kothiyal, Shruti Singh, Neelam Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Department of Computer Application Dehradun India Department of Computer Science and Engineering France

ISBN: (纸本)9798350323917

Online data is the data that is present on an online platform such as social media. Social media contains various information through which it can analyze users' attitudes & behavior. Sentimental analysis enables the automatic analysis of expression whether it is positive or negative. It can be about places, people, events, organizations, brands, products, etc. By using tools provided by machine learning & natural language processing with different approaches sentiment analysis can be made possible. The ubiquitous use of the Online World has given people a new method for expressing their emotions. It also functions as a platform with a shedload of knowledge where viewers may examine other users' thoughts, which are categorized into so many sentiment classifications and have become more and more essential in judgment. The representation of the number of comments that contain complex multidimensional sentiments that are either good or bad or anything in between the two can really be analyzed by means of this paper's approach to sentiment classification for consumer review classification. In this research, sentiment contribution in the field of online data tweets are collected, and using various machine learning algorithms results are identified as collective actions using different accuracy. © 2023 IEEE.

关键词： Sentiment analysis

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Reliable and Stable Routing in VANET Using Multi-Objective Function Aided K-Means Clustering with Heuristic Algorithm 1

Reliable and Stable Routing in VANET Using Multi-Objective F...

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1st International Conference on Trends in engineering Systems and Technologies, ICTEST 2024

作者： Das, Nabanita Barooah, Maushumi Das, Anupam Assam Engineering College Jalukbari Department of Computer Application Assam Guwahati781013 India The Assam Royal Global University Betkuchi Department of Computer Science and Engineering Guwahati781035 India

ISBN: (纸本)9798350307795

The expansion of Vehicular Ad-hoc Networks (VANET) has brought a heightened focus on service delivery within the realm of the smart transit systems. Nevertheless, the mobility attributes inherent to vehicular networks often result in frequent route disruptions, particularly when transmitting data. This circumstance holds true in both advanced and emerging nations, where substantial time is wasted due to congested traffic conditions. These consequences are substantial, encompassing heightened stress levels for drivers, reduced efficiency for diverse personal and commercial vehicles, and escalated emissions of harmful gases, especially those that contribute to air pollution, affecting the well-being of residents in densely populated regions. Clustering stands as a formidable approach for establishing a stable topological arrangement. Therefore, an efficient reliable routing scheme is developed in VANET with the help of a clustering algorithm. The node clustering is initially performed with the help of the K-means Clustering algorithm (KMC) in VANET. An Advanced Pelican Optimization Algorithm (APOA) is utilized for choosing a shorter and more stable route between all available routes to make efficient routing in VANET. The multi-objective functions like delay, hop count, energy, and the ratio of packet delivery are considered for the efficient routing in VANET to make energy-efficient transmission. The effectiveness of the proposed KMC-based optimal routing in VANET is validated with the conventional routing schemes in accordance with some of the metrics to determine efficacy. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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Epidermis Bruise Analysis Using Deep Learning 6

Epidermis Bruise Analysis Using Deep Learning

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6th International Conference on Computational Intelligence and Communication Technologies, CCICT 2024

作者： Sharma, Vansheta Rani, Rupa Pathak, Priya Chakraborty, Sudeshna Department of Computer Science & Engineering- Data Science ABES Institute of Technology U.P. Ghaziabad India Department of Computer Science & Engineering Ajay Kumar Garg Engineering College U.P. Ghaziabad India Depatment of Computer Science & Engineering Banasthali Vidyapith Rajasthan India Department School of Computer Application and Technology Galgotias University UP India

ISBN: (纸本)9798350374629

Among the most vital organs that protect the body of a human from the external environment is the skin. Early skin illness identification is essential for reducing mortality because it prevents skin cancer, and any other skin disease from developing. But because it resembles skin patches, insect bites etc in appearance, early detection that might lengthen life expectancy is a fantastic experiment. We would be working with different skin diseases namely ‘Acne’, ‘Eczema or Atopic Dermatitis’, ‘melanoma’. A working model that uses image analysis and machine learning to classify and identify skin conditions. A brand-new, cutting edge automated system that can swiftly and accurately identify skin lesions is needed to deal with biomedical pictures. There is growing curiosity concerning the application of deep learning to the diagnosis and management of different diseases. For the early detection of skin disease, we therefore suggested a deep learning model based on CNN and the merger of the U-Net and ResNet models is presented as a fusion model that has been used to perform encoding decoding of the disorders using skin scans. This model integrates image processing, image enhancement, machine learning and segmentation techniques. The model yielded an overall accuracy of 95.6%. Incorporate additional skin conditions in the future to strengthen our model's suitability for medical applications. ©2024 IEEE.

关键词： Image enhancement

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Parallel Cloth Simulation Using OpenGL Shading Language

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computer Systems science & engineering 2022年第5期41卷 427-443页

作者： Hongly Va Min-Hyung Choi Min Hong Department of Software Convergence Soonchunhyang UniversityAsan31538Korea Department of Computer Science and Engineering University of Colorado DenverDenverCO 80217USA Department of Computer Software Engineering Soonchunhyang UniversityAsan31538Korea

The primary goal of cloth simulation is to express object behavior in a realistic manner and achieve real-time performance by following the fundamental concept of *** general,the mass–spring system is applied to real-time cloth simulation with three types of ***,hard spring cloth simulation using the mass–spring system requires a small integration time-step in order to use a large stiffness ***,to obtain stable behavior,constraint enforcement is used instead of maintenance of the force of each *** force computation involves a large sparse linear solving *** to the large computation,we implement a cloth simulation using adaptive constraint activation and deactivation techniques that involve the mass-spring system and constraint enforcement method to prevent excessive elongation of *** the same time,when the length of the spring is stretched or compressed over a defined threshold,adaptive constraint activation and deactivation method deactivates the spring and generate the implicit *** method that uses a serial process of the Central Processing Unit(CPU)to solve the system in every frame cannot handle the complex structure of cloth model in *** simulation utilizes the Graphic Processing Unit(GPU)parallel processing with compute shader in OpenGL Shading Language(GLSL)to solve the system *** this paper,we design and implement parallel method for cloth simulation,and experiment on the performance and behavior comparison of the mass-spring system,constraint enforcement,and adaptive constraint activation and deactivation techniques the using GPU-based parallel method.

关键词： Adaptive constraint cloth simulation constraint enforcement GLSL compute shader mass–spring system parallel GPU

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Cardinality estimation for property graph queries with gated learning approach on the graph database

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Multimedia Tools and applications 2025年第11期84卷 9159-9183页

作者： He, Zhenzhen Yu, Jiong Du, Xusheng Guo, Binglei Li, Ziyang Li, Zhe School of Information Science and Engineering Xinjiang University Urumqi China School of Computer Engineering Hubei University of Arts and Science Xiangyang China School of Software Xinjiang University Urumqi China Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong

With the increasing complexity of graph query processing tasks, it is difficult for users to obtain the accurate cardinality before or during the execution of query tasks. Accurate estimate query cardinality is crucial for property graph data model, which usually involves entities, multiple joins, and various types of properties (key-value pairs). While learning-based approaches have been used in query optimization, estimating the cardinality of property graph queries is still particularly challenging. In this paper, we first formally represent each property graph query and then divide the estimation process into three phases: query execution, training data generation, and model training. Secondly, we construct a data pool to deal the updating query workloads in the training data generation phase. Thirdly, we utilize the gate mechanism to develop a cardinality estimation framework based on deep learning neural networks for property graph queries in the Neo4j database, and we propose a hybrid loss function to optimize the training process. Finally, we adopt a method of pre-aggregating the underlying data to speed up query execution in the first phase. The result of the experiment on two real-world datasets shows that our optimized methods can effectively improve the estimation accuracy of the deep learning model, and our query estimator outperforms other deep learning models compared in terms of estimation accuracy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Query processing

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