A Deep Learning Modified Neural Network(DLMNN) based proficient sentiment analysis technique on Twitter data

Neelakandan, S.; Paulraj, D.; Ezhumalai, P.; Mohan, Prakash

doi:10.1080/0952813x.2022.2093405

Cited by 27 publications

(8 citation statements)

References 21 publications

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“…Because everyone on the network participates in Ethereum's consensus process, material recorded on the distributed ledger that requires a higher level of anonymity can be accessed by all users. Te privacy problems of patient medical data are addressed by Hyperledger's permissioned Blockchain, which only permits nodes approved by authorities to read such data [46].…”

Section: Resultsmentioning

confidence: 99%

An Efficient Secure Sharing of Electronic Health Records Using IoT-Based Hyperledger Blockchain

S.,

M.,

et al. 2024

International Journal of Intelligent Systems

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Electronic Health Record (EHR) systems are a valuable and effective tool for exchanging medical information about patients between hospitals and other significant healthcare sector stakeholders in order to improve patient diagnosis and treatment around the world. Nevertheless, the majority of the hospital infrastructures that are now in place lack the proper security, trusted access control, and management of privacy and confidentiality concerns that the current EHR systems are supposed to provide. Goal. For various EHR systems, this research proposes a Blockchain-enabled Hyperledger Fabric Architecture as a solution to this delicate issue. The three steps of the suggested system are the secure upload phase, the secure download phase, and authentication. Patient registration, login, and verification make up the authentication step. The administrator grants authorization to read, edit, delete, or revoke the files following user details verification. In the secure upload phase, feature extraction is carried out first, and then a hashed access policy is created from the extracted feature. Next, the hash value is stored in an IoT-based Hyperledger blockchain. The uploaded EHR files are additionally encrypted before being stored on the cloud server. In the secure download step, the physician uses a hashed access policy to send the request to the cloud and decrypts the corresponding files. The experimental findings demonstrate that the system outperformed cutting-edge techniques. The proposed Modified Key Policy Attribute-Based Encryption performs better for the remaining 10 to 25 mb file sizes. This IoT framework compares MKP-ABE with certain efficiency indicators, such as encryption, decryption period, protection level analysis and encrypted memory use, resource use on decryption, upload time, and transfer time, which are present in the KP-ABE, the ECC, RSA, and AES. Here, the IoT device suggested requires 4008 ms for data encryption and 4138 ms for the data decryption.

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Section: Resultsmentioning

confidence: 99%

An Efficient Secure Sharing of Electronic Health Records Using IoT-Based Hyperledger Blockchain

S.,

M.,

et al. 2024

International Journal of Intelligent Systems

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“…To create the standardized data set, the raw data of small companies that were taken out of the database were standardized. Standardized processing is not covered in detail because it is not the study's main emphasis [20].…”

Section: The Resultsmentioning

confidence: 99%

Developing Business Management Concepts in the Economic Field Based on Artificial Intelligence in Light of Increasing Data

Rashid,

Ali,

Mousa

et al. 2024

Preprint

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Asia has formally entered the big data era thanks to the ongoing advancement of information technology. the company business management has discovered bright operations management based on technological advances, and the arrival of the big data era has improved work efficiency and brought about a number of economic benefits while also posing a number of challenges, such as the ongoing expansion of business scope. The business's current management model is unable to keep up with the demands of development as its scope continues to grow. The primary development trend for the future is the integration of intelligence technologies with enterprise business management, big data technology application in corporate business management promotes fairness, transparency, and justice across corporate development and consumer activities; it also facilitates the production of goods and the creation of value for products in the direction of socialization. In order to further deepen the understanding of artificial intelligence among relevant personnel, we aim to further elaborate the future growth direction of the application of artificial intelligence in the field of enterprise business management in this paper, which begins with an in-depth awareness of the current situation of the combined use of artificial intelligence technology and big companies’ business management. We then focus on the utilization path of artificial cognitive ability technology in businesses business management.

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“…When making judgments, a multiplicity of characteristics and features are evaluated. The primary focus of Subramani, Neelakandan, and their colleagues' work [34] is task scheduling that takes numerous restrictions into consideration. Due to the current state of the art, the authors of this study are inspired to continue their investigation into the scheduling of tasks and the allocation of resources.…”

Section: Plos Onementioning

confidence: 99%

An Efficient Hybrid Job Scheduling Optimization (EHJSO) approach to enhance resource search using Cuckoo and Grey Wolf Job Optimization for cloud environment

Paulraj

Sethukarasi²,

Neelakandan³

et al. 2023

PLoS ONE

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Cloud computing has now evolved as an unavoidable technology in the fields of finance, education, internet business, and nearly all organisations. The cloud resources are practically accessible to cloud users over the internet to accomplish the desired task of the cloud users. The effectiveness and efficacy of cloud computing services depend on the tasks that the cloud users submit and the time taken to complete the task as well. By optimising resource allocation and utilisation, task scheduling is crucial to enhancing the effectiveness and performance of a cloud system. In this context, cloud computing offers a wide range of advantages, such as cost savings, security, flexibility, mobility, quality control, disaster recovery, automatic software upgrades, and sustainability. According to a recent research survey, more and more tech-savvy companies and industry executives are recognize and utilize the advantages of the Cloud computing. Hence, as the number of users of the Cloud increases, so did the need to regulate the resource allocation as well. However, the scheduling of jobs in the cloud necessitates a smart and fast algorithm that can discover the resources that are accessible and schedule the jobs that are requested by different users. Consequently, for better resource allocation and job scheduling, a fast, efficient, tolerable job scheduling algorithm is required. Efficient Hybrid Job Scheduling Optimization (EHJSO) utilises Cuckoo Search Optimization and Grey Wolf Job Optimization (GWO). Due to some cuckoo species’ obligate brood parasitism (laying eggs in other species’ nests), the Cuckoo search optimization approach was developed. Grey wolf optimization (GWO) is a population-oriented AI system inspired by grey wolf social structure and hunting strategies. Make span, computation time, fitness, iteration-based performance, and success rate were utilised to compare previous studies. Experiments show that the recommended method is superior.

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A Deep Learning Modified Neural Network(DLMNN) based proficient sentiment analysis technique on Twitter data

Cited by 27 publications

References 21 publications

An Efficient Secure Sharing of Electronic Health Records Using IoT-Based Hyperledger Blockchain

An Efficient Secure Sharing of Electronic Health Records Using IoT-Based Hyperledger Blockchain

Developing Business Management Concepts in the Economic Field Based on Artificial Intelligence in Light of Increasing Data

An Efficient Hybrid Job Scheduling Optimization (EHJSO) approach to enhance resource search using Cuckoo and Grey Wolf Job Optimization for cloud environment

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