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Two-Operation Buffering based on Dynamic Optical RAM 17

Two-Operation Buffering based on Dynamic Optical RAM

17th IEEE International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer engineering, TCSET 2024

作者： Semenov, Andriy Tsyrulnyk, Serhii Baraban, Serhii Semenova, Olena Rudyk, Andrii Khloba, Anton Vinnytsia National Technical University Faculty of Information Electronic Systems Vinnytsia Ukraine Vinnytsia National Agrarian University Department of Computer Sciences and Digital Economy Vinnytsia Ukraine Poznan University of Technology Faculty of Computing and Telecommunications Poznan Poland National University of Water and Environmental Engineering Department of Automation Electrical Engineering and Computer-Integrated Technologies Rivne Ukraine

ISBN: (纸本)9798331520564

The authors of this paper propose a block diagram for a dual-operating buffer on a dynamic optical RAM. The buffer consists of two dynamic optical RAMs, each controlled separately to allow for simultaneous writing and reading of data, thereby improving performance. This setup enables data to be read from the buffer while new data is being written to it. Implementing a dual-operating buffer on a dynamic optical RAM has the potential to enhance the performance of high-speed data transmission in telecommunications networks. The authors conducted numerical calculations to compare failure probabilities between single-operation and dual-operation buffers on dynamic optical RAM. The results indicated that using a dual-operating buffer significantly reduces the probability of failure with the same buffer memory capacity compared to a single-operating buffer. Additionally, the authors analyzed the effectiveness of the proposed dual-operating buffer. The analysis confirmed that exploiting dynamic optical memory for buffering enhances data transfer speed and efficiency due to the inherent properties of optical data transfer, such as high bandwidth and low latency. © 2024 IEEE.

关键词： Dynamic random access storage

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Fog based Edge Learning using IoT: Concepts, Challenges and Applications 4

Fog based Edge Learning using IoT: Concepts, Challenges and ...

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4th International Conference on Smart systems and Inventive Technology, ICSSIT 2022

作者： Sindhu, V. Sudhakaran, Pradeep Anitha, G. Mahendiran, P.D. Department of Information Technology Sri Krishna College of Engineering and Technology Coimbatore India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chengalpattu India Department of Information Technology Sri Shakthi Institute of Engineering and Technology Coimbatore India Department of Computer Science and Business Systems Sri Krishna College of Engineering and Technology Coimbatore India

ISBN: (纸本)9781665401180

In innovation world, fog and edge computing is extremely near the client which means making the processing capacities extremely nearer to the customer or user. The mushrooming of IoT devices and usage is increasing day by day. Huge amount of data will be generated by these connected devices. Thus, there is an indispensable need to process such heavy volume of data in a quick and efficient manner. In request to meet the above standards. In this paper the research challenges of fog and Edge computing, the role of fog computing in IoT, the working model and benefits of edge computing are given in a novel approach after a de tailed review, study and scrutiny. This enables and paves way to the researcher for a smoother and sustainable research scope in the fog and edge computing environment. © 2022 IEEE

关键词： Fog computing

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Statistical Modelling for Simulating and Interpreting an Egg Packaging Process for Giveaway Mitigation 33

Statistical Modelling for Simulating and Interpreting an Egg...

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33rd Australasian Conference on Information systems: The Changing Face of IS, ACIS 2022

作者： Tan, Lige Ko, Ben Tortorella, Guilherme Luz Cervantes, Abel Armas Palmer, Martin Kirley, Michael School of Computing and Information Systems University of Melbourne ParkvilleVIC Australia Department of Mechanical Engineering University of Melbourne ParkvilleVIC Australia Department of Chemical Engineering University of Melbourne ParkvilleVIC Australia

Giveaway, the excess product being packed into orders, contributes to revenue loss that pre-packaged food manufacturers care about the most. In collaboration with an egg packaging company, this study aims to discover operation rules to mitigate the giveaway in egg orders. For that, two variables have been raised as potential controllable factors of the giveaway. One statistical model has been developed to better interpret the experimental results by understanding the underlying rules of the egg grading machine. The experiments have been accurately reproduced by a simulation using the estimated model parameters, indicating the model's success. Based on the experiment results, we claim that the number of accepted egg grades significantly influences the final giveaway ratio. Limitations and further potentials of the statistical model have also been discussed. Copyright © 2022 Tan et al.

关键词： Grading

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Enhancing Traffic engineering Policy in SDN Addressing TCAM limitations using Bloom Filters 4

Enhancing Traffic Engineering Policy in SDN Addressing TCAM ...

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4th IEEE Global Conference for Advancement in Technology, GCAT 2023

作者： Ghosh, Akash Barik, Lalbihari Rout, Suchismita Rawat, Bhupender Singh Jena, Prakash Chandra Patra, Sudhansu Shekhar School of Computer Applications KIIT Deemed To be University Bhubaneswar India Department of Information Systems Faculty of Computing and Information Technology in Rabigh King Abdul Aziz University Saudi Arabia College of Smart Computing COER University Roorkee India Department of Computer Sc and Engg. Hi-Tech College of Engineering Bhubaneswar India

ISBN: (纸本)9798350305258

With the increasing demand for new technologies, the speed of delivering a packet is becoming the major focus of various industries. The generation of huge traffic is becoming the bottleneck of the current scenario. By segregating the control plane from the data plane, Software Defined Networking (SDN) is able to many such issues to certain extent. But the traffic engineering problem still remains as an important topic to focus on. Ternary Content Addressable Memory (TCAM) is capable of solving many such issues with the cost of its cost and power issues. For the effective use of TCAM, various algorithms and mechanisms have been proposed by many researchers. Bloom filter is one of the popular ways which is able to provide effective solutions after collaborating with TCAM. An algorithm has been proposed which is able to find the exact match of the entry in TCAM. The use of a CRC generator and multiple hashing tables makes it effective at finding the presence of a flow rule in TCAM, in very little time. The major focus is on the fast search procedure for finding the exact match of the flow rule. The number of false positives (FPs) is reduced to a lower percentage value as compared to the existing approaches. © 2023 IEEE.

关键词： Bloom Filter False positive IP addressing multi-hashing Prefix match SDN TCAM Traffic engineering

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Bidirectional Adversarial Autoencoders for the design of Plasmonic Metasurfaces

arXiv

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arXiv 2024年

作者： Liu, Yuansan Panisilvam, Jeygopi Dower, Peter Kim, Sejeong Bailey, James Department of Computing and Information Systems Faculty of Engineering and Information Technology University of Melbourne Australia Department of Electrical and Electronic Engineering Faculty of Engineering and Information Technology University of Melbourne Australia

Deep Learning has been a critical part of designing inverse design methods that are computationally efficient and accurate. An example of this is the design of photonic metasurfaces by using their photoluminescent spectrum as the input data to predict their topology. One fundamental challenge of these systems is their ability to represent nonlinear relationships between sets of data that have different dimensionalities. Existing design methods often implement a conditional Generative Adversarial Network in order to solve this problem, but in many cases the solution is unable to generate structures that provide multiple peaks when validated. It is demonstrated that in response to the target spectrum, the Bidirectional Adversarial Autoencoder is able to generate structures that provide multiple peaks on several occasions. As a result the proposed model represents an important advance towards the generation of nonlinear photonic metasurfaces that can be used in advanced metasurface design. © 2024, CC BY.

关键词： Design

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Prediction of Sprint Delivery Capability in Agile Software Development Using Machine Learning

Prediction of Sprint Delivery Capability in Agile Software D...

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International Research Conference on Smart computing and systems engineering (SCSE)

作者： Narayanasamy Lorshan P. M. A. K. Wijerathna B. T. G. S. Kumara Department of Computing and Information Systems Faculty of Computing Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka Department of Software Engineering Faculty of Computing Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka Department of Data Science Faculty of Computing Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka

ISBN: (数字)9798331523268

ISBN: (纸本)9798331523275

Scrum is one of the most prominent agile development frameworks as it delivers the project in many sprints in an incremental manner. Sprint delivery capability is a crucial concept in the scrum projects as it assesses whether sprint can capable of delivering its issues on its length. It is difficult to consider forecasting sprint delivery capability in large scrum projects as multiple sprints are processed simultaneously and agile dynamics. Machine Learning (ML) models were applied in the existing studies to forecast sprint delivery but, had limitations in finding non-linear and hidden patterns. To address these problems, this research exploited the untapped ensemble learning approach to bridge the gap to improve the prediction of sprint delivery capability in different project progression levels. The study gathered the data from one of the large open-source scrum projects, JIRA which included 1873 iterations and 10853 issues in table format spanning at 30%, 50%, and 80% project progression levels. Data preprocessing enabled to refinement of the data for analysis. The author enhanced the feature aggregation by using both statistical and automatic feature learning to derive the new features from the issue table and combine them with the iteration table. The key features affecting sprint delivery capability were identified using hybrid feature selection approach. The study developed the untapped ensemble models by choosing the different combinations of ML, Deep Learning (DL), and ensemble algorithms using appropriate ensemble methods and combination rules. The author examined the models and concluded that Categorical Boost (CatBoost) gained the highest accuracy of 96.8% at the 80% project progression level. This prediction assists in improving sprint planning, delivering the software on time, and getting more insights about sprint performance.

关键词： Representation learning Software algorithms Predictive models Feature extraction Prediction algorithms Software Planning Ensemble learning Modeling Forecasting

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Comparative Analysis of Cloud computing and the Gig Economy Pricing Strategies

Comparative Analysis of Cloud Computing and the Gig Economy ...

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Innovative computing, Intelligent Communication and Smart Electrical systems (ICSES), International Conference on

作者： M Jothish Kumar Gajulapalli Venkata Karthik Kumar Reddy Iyapparaja S Nowritha Department of Smart Computing School of Computer Science Engineering and Information Systems VIT Vellore Vellore Tamil Nadu India School of Computer Science Engineering and Information Systems VIT Vellore Vellore Tamil Nadu India Department of Smart Computing School of Computer Science Engineering and Information Systems VIT University Vellore Tamil Nadu India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Cloud computing services and gig economy platforms vary in flexibility, efficiency, and scalability due to their pricing schemes. Cloud computing services provide flexibility and cost control via pay-as-you-go, subscription, and freemium models. Like gig economy platforms, workers and customers pay for task completion, time-based contracts, and premium services. Assessing how successfully different pricing techniques fulfil varied demands, from short-term initiatives to long-term commitments, requires identifying their strengths and weaknesses. The goal is to evaluate these models' capacity to meet shifting demand, save money, and scale. The methodology also assesses cost predictability and company and consumer advantages of each pricing structure. The comparison seeks to improve resource allocation and financial planning by revealing how various pricing systems function in different circumstances. The research helps decision-makers use cloud services or gig economy platforms for different operational purposes by emphasizing their economic consequences. From datasets, simulated pricing model data was examined. The first example includes settings such as Hours Used (5–25), Pay-as-You-Go (10–50 USD), and Subscription-Based (30 USD). The second instance has Job Size (Resources) from 5 to 25, Freemium Model (USD) from 10 to 30, and Usage-Based Pricing (USD) from 10 to 50.

关键词： Cloud computing Adaptation models Costs Biological system modeling Computational modeling Scalability Pricing Predictive models Data models Resource management

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Unveiling Public Perception of ChatGPT Through Twitter Data and Machine Learning 4

Unveiling Public Perception of ChatGPT Through Twitter Data ...

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4th International Conference on Data Analytics for Business and Industry, ICDABI 2023

作者： Abishethvarman, Vadivel Banujan, Kuhaneswaran Wijeratne, Ashansa Nirubikaa, Ravikumar Kumara, Samantha Prasanth, Senthan Sabaragamuwa University of Sri Lanka Department of Computing and Information Systems Belihuloya Sri Lanka Sabaragamuwa University of Sri Lanka Department of Software Engineering Belihuloya Sri Lanka Sabaragamuwa University of Sri Lanka Department of Physical Sciences and Technology Belihuloya Sri Lanka

ISBN: (纸本)9798350369786

The study comprehensively analysed public sentiment towards ChatGPT, a language processing AI tool, using approximately 135,000 tweets from an initial dataset of 236,275 entries. After cleaning and pre-processing the data using basic NLP techniques, Machine Learning (ML) models - Long Short-Term Memory (LSTM), Decision Tree (DT), and Support Vector Machine (SVM) - were applied to classify the tweets into positive, negative, and neutral categories. The LSTM model demonstrated superior performance across all evaluation metrics - accuracy, precision, recall, and F1 score - for all tweet categories. Word clouds generated for each category revealed that the most frequent words in positive tweets included 'student,' 'education,' 'research,' and 'teacher,' indicating a strong association of ChatGPT with educational and research contexts. Similarly, 'AI,' 'student,' 'research,' 'course,' and 'education' dominated neutral and negative tweets, highlighting prevalent discussion topics. However, the study had limitations, including dependency on a single dataset, potential loss of contextual information during pre-processing, and a focus on English tweets only. Despite these limitations, the study provides valuable insights into the public's perception of ChatGPT, underscoring its significant association with education and research and the effectiveness of the LSTM model in classifying tweets. The findings suggest that future research should incorporate diverse data sources and languages better to understand global sentiment towards ChatGPT and similar AI tools. © 2023 IEEE.

关键词： Tweets

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Factors Influencing the Acceptance of Blockchain Technology: Real Estate Industry 2021

Factors Influencing the Acceptance of Blockchain Technology:...

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4th International Conference on Software engineering and Information Management, ICSIM 2021

作者： Ng, Claudia Siew Hoong Lee, Angela Department of Computing and Information Systems School of Engineering and Technology Sunway University Malaysia

ISBN: (纸本)9781450388955

Blockchain is an innovative distributed ledger technology enabling the exchange of value online without the need of a central authority. Blockchain provides a decentralized and trustless network, secured by cryptography and consensus mechanisms. The acceptance of blockchain technology greatly impact the real estate owners, financiers, builders and more. Hence, this study attempts to find out how buyers and sellers of real estates are accepting blockchain technology by integrating and analysing a proposed hybrid model of UTAUT (Unified Theory of Acceptance and Use of Technology) combined with TRI (Technology Readiness Index). This study utilizes a quantitative research method and aims to identify the key influencing factors in the acceptance of blockchain technology. It is crucial to fully comprehend the potential of blockchain technology as it is a new revolutionary technology. This paper proposed a conceptual model that tests the performance expectancy, effort, social influence, facilitating condition, innovativeness, optimism, discomfort and insecurity in influencing the user intention behaviour in accepting blockchain technology. A pilot-test was conducted to test the research model. The results showed that all composite reliability values except for ĝ€Social Influence' were found to be above the recommended value of 0.70, suggesting strong indicator reliability of the constructs. © 2021 ACM.

关键词： Blockchain

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Image Enhancement Using Adaptive Fractional Order Filter

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Computer systems Science & engineering 2023年第5期45卷 1409-1422页

作者： Ayesha Heena Nagashettappa Biradar Najmuddin M.Maroof Surbhi Bhatia Arwa Mashat Shakila Basheer Department of Electronics and Communication Engineering Bheemanna Khandre Institute of TechnologyBhalkiVisveswaraya Technological UniversityBelagaviIndia Department of Electronics and Communication Engineering Khaja Banda Nawaz College of EngineeringVisveswaraya Technological UniversityBelagaviIndia Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversityAl-HofufAl-AhsaSaudi Arabia Faculty of Computing&Information Technology King Abdulaziz UniversityP.O.Box 344Rabigh21911Saudi Arabia Department of Information Systems College of Computer and Information Science Princess Nourah bint Abdulrahman UniversityP.O.BOX 84428Riyadh11671Saudi Arabia

Image enhancement is an important preprocessing task as the contrast is low in most of the medical images,Therefore,enhancement becomes the mandatory process before actual image processing should *** research article proposes an enhancement of the model-based differential operator for the images in general and Echocardiographic images,the proposed operators are based on Grunwald-Letnikov(G-L),Riemann-Liouville(R-L)and Caputo(Li&Xie),which are the definitions of fractional order *** this fractional-order,differentiation is well focused on the enhancement of echocardiographic *** provoked for developing a non-linear filter mask for image *** designed filter is simple and effective in terms of improving the contrast of the input low contrast images and preserving the textural features,particularly in smooth *** novelty of the proposed method involves a procedure of partitioning the image into homogenous regions,details,and ***,a fractional differential mask is appropriately chosen adaptively for enhancing the partitioned pixels present in the *** is also incorporated into the Hessian matrix with is a second-order derivative for every pixel and the parameters such as average gradient and entropy are used for qualitative *** wide range of existing state-of-the-art techniques such as fixed order fractional differential filter for enhancement,histogram equalization,integer-order differential methods have been *** proposed algorithm resulted in the enhancement of the input images with an increased value of average gradient as well as entropy in comparison to the previous *** values obtained are very close(almost equal to 99.9%)to the original values of the average gradient and entropy of the *** results of the simulation validate the effectiveness of the proposed algorithm.

关键词： Adaptive filter differential filter enhancement mask fractional differential mask fractional-order calculus hessian matrix

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