Efficient Middleware for the Portability of PaaS Services Consuming Applications among Heterogeneous Clouds

Bharany, Salil; Kaur, Kiranbir; Badotra, Sumit; Rani, Shalli; Kavita, Kavita; Woźniak, Marcin; Shafi, Jana; Ijaz, Muhammad Fazal

doi:10.3390/s22135013

Cited by 36 publications

(8 citation statements)

References 45 publications

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“…Numerous opinion mining approaches are used to extract the feelings hidden in the comments and reviews for a specific unlocked mobile. Additionally, a thorough analysis of sentiment classification methods is performed using the data set from mobile phone assessments [ 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ]. The study’s [ 24 ] findings provide a comparison of eight distinct classifiers based on the assessment parameters of accuracy, recall, precision, and F-measure.…”

Section: Results and Comparative Analysismentioning

confidence: 99%

Multinomial Naive Bayesian Classifier Framework for Systematic Analysis of Smart IoT Devices

Kaushik

Bhardwaj²,

Dahiya³

et al. 2022

Sensors

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Businesses need to use sentiment analysis, powered by artificial intelligence and machine learning to forecast accurately whether or not consumers are satisfied with their offerings. This paper uses a deep learning model to analyze thousands of reviews of Amazon Alexa to predict customer sentiment. The proposed model can be directly applied to any company with an online presence to detect customer sentiment from their reviews automatically. This research aims to present a suitable method for analyzing the users’ reviews of Amazon Echo and categorizing them into positive or negative thoughts. A dataset containing reviews of 3150 users has been used in this research work. Initially, a word cloud of positive and negative reviews was plotted, which gave a lot of insight from the text data. After that, a deep learning model using a multinomial naive Bayesian classifier was built and trained using 80% of the dataset. Then the remaining 20% of the dataset was used to test the model. The proposed model gives 93% accuracy. The proposed model has also been compared with four models used in the same domain, outperforming three.

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Section: Results and Comparative Analysismentioning

confidence: 99%

Multinomial Naive Bayesian Classifier Framework for Systematic Analysis of Smart IoT Devices

Kaushik

Bhardwaj²,

Dahiya³

et al. 2022

Sensors

Self Cite

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“…In this research, the designed system incorporates cloud computing technology as a platform as a service (PaaS) (Bharany et al, 2022;Zhong & Yuan, 2019). This design playing a crucial role in facilitating This integration of feedback into the control algorithm is instrumental in enhancing the system's adaptability.…”

Section: Cloud Computing Architecture Designmentioning

confidence: 99%

Enhancing low‐temperature long‐time milk pasteurization process with a C4.5 algorithm‐driven AIoT system for real‐time decision‐making

Hadi,

Sugiono,

Mizar

et al. 2024

J Food Process Engineering

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This research is driven by the imperative to improve the milk pasteurization process's efficiency and safety within the Industry 5.0 framework. It introduces the artificial intelligence of things (AIoT) into the pasteurization system and focuses on deploying the C4.5 algorithm, anticipated to render precise real‐time decisions by analyzing critical temperature and duration data essential for the low‐temperature long‐time (LTLT) pasteurization technique. Test results indicate that the C4.5 model is highly effective, demonstrating nearly perfect metrics with a precision of .78, a recall of .77, and an F1‐score of .73, while achieving an overall algorithm accuracy of .76. In terms of heating control, the double jacket method tested on a 300‐L system proved the control design's capability to quickly reach and maintain desired temperatures, hitting the set‐point within 70 min. However, it was observed that systems with larger capacities took longer to reach the set‐point, suggesting a potential for further optimization. The findings affirm that integrating AIoT into control systems can significantly enhance process efficiency and effectiveness, consistent with the goal of incorporating sophisticated technological solutions in the food sector.Practical ApplicationsThe article “Enhancing Low‐Temperature Long‐Time Milk Pasteurization Process with a C4.5 Algorithm‐Driven AIoT System for Real‐Time Decision‐Making” explores an AIoT system's potential to revolutionize the dairy industry's pasteurization methods. This system promises enhanced efficiency by optimizing energy and time, thereby reducing costs. It ensures superior quality control through continuous monitoring, maintaining nutritional integrity and safety. The high automation level minimizes human error and operational demands, while predictive maintenance forecasts prevent downtime. The system's scalability supports production expansion with minimal resource increase. Moreover, it guarantees product traceability and compliance with food safety regulations. The generated data offers insights for further supply chain optimizations. This integration of AI and IoT thus presents significant advancements in process management, quality assurance, and scalable operations for the dairy industry.

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“…It is a keyless hash function. The SHA-256 follows the same model as SHA-1 and begins by defining several constants [25][26][27][28][29]. Several operating systems frequently use hash methods to secure passwords.…”

Section: The Sha-256 Hash Functionmentioning

confidence: 99%

Secure Sensitive Data Sharing Using RSA and ElGamal Cryptographic Algorithms with Hash Functions

Adeniyi¹,

Falola²,

Maashi

et al. 2022

Information

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With the explosion of connected devices linked to one another, the amount of transmitted data grows day by day, posing new problems in terms of information security, such as unauthorized access to users’ credentials and sensitive information. Therefore, this study employed RSA and ElGamal cryptographic algorithms with the application of SHA-256 for digital signature formulation to enhance security and validate the sharing of sensitive information. Security is increasingly becoming a complex task to achieve. The goal of this study is to be able to authenticate shared data with the application of the SHA-256 function to the cryptographic algorithms. The methodology employed involved the use of C# programming language for the implementation of the RSA and ElGamal cryptographic algorithms using the SHA-256 hash function for digital signature. The experimental result shows that the RSA algorithm performs better than the ElGamal during the encryption and signature verification processes, while ElGamal performs better than RSA during the decryption and signature generation process.

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Efficient Middleware for the Portability of PaaS Services Consuming Applications among Heterogeneous Clouds

Cited by 36 publications

References 45 publications

Multinomial Naive Bayesian Classifier Framework for Systematic Analysis of Smart IoT Devices

Multinomial Naive Bayesian Classifier Framework for Systematic Analysis of Smart IoT Devices

Enhancing low‐temperature long‐time milk pasteurization process with a C4.5 algorithm‐driven AIoT system for real‐time decision‐making

Secure Sensitive Data Sharing Using RSA and ElGamal Cryptographic Algorithms with Hash Functions

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