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"Akila, D"
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An Intelligent Task Scheduling Model for Hybrid Internet of Things and Cloud Environment for Big Data Applications
One of the most significant issues in Internet of Things (IoT) cloud computing is scheduling tasks. Recent developments in IoT-based technologies have led to a meteoric rise in the demand for cloud storage. In order to load the IoT services onto cloud resources efficiently even while satisfying the requirements of the applications, sophisticated planning methodologies are required. This is important because several processes must be well prepared on different virtual machines to maximize resource usage and minimize waiting times. Different IoT application tasks can be difficult to schedule in a cloud-based computing architecture due to the heterogeneous features of IoT. With the rise in IoT sensors and the need to access information quickly and reliably, fog cloud computing is proposed for the integration of fog and cloud networks to meet these demands. One of the most important necessities in a fog cloud setting is efficient task scheduling, as this can help to lessen the time it takes for data to be processed and improve QoS (quality of service). The overall processing time of IoT programs should be kept as short as possible by effectively planning and managing their workloads, taking into account limitations such as task scheduling. Finding the ideal approach is challenging, especially for big data systems, because task scheduling is a complex issue. This research provides a Deep Learning Algorithm for Big data Task Scheduling System (DLA-BDTSS) for the Internet of Things (IoT) and cloud computing applications. When it comes to reducing energy costs and end-to-end delay, an optimized scheduling model based on deep learning is used to analyze and process various tasks. The method employs a multi-objective strategy to shorten the makespan and maximize resource consumption. A regional exploration search technique improves the optimization algorithm’s capacity to exploit data and avoid becoming stuck in local optimization. DLA-BDTSS was compared to other well-known task allocation methods in accurate trace information and the CloudSim tools. The investigation showed that DLA-BDTSS performed better than other well-known algorithms. It converged faster than different approaches, making it beneficial for big data task scheduling scenarios, and it obtained an 8.43 percent improvement in the outcomes. DLA-BDTSS obtained an 8.43% improvement in the outcomes with an execution time of 34 s and fitness value evaluation of 76.8%.
Journal Article
Energy-Efficient Load Balancing Technique to optimize Average response time and Data Center Processing Time in Cloud Computing Environment
Cloud infrastructure is a modern computing system in which pooled services are made available to users at various times depending on their demands. The process of distributing workload among computing system nodes is known as load balancing. Loads include things like CPU power; ram capacity, and network traffic. A well-balanced load avoids the situation when some nodes are fully loaded while others are inactive or not working. Where there are many tasks in a virtual machine (VM), these tasks are delegated to underutilize VMs in the same or a separate datacenter. Modified Round Robin and Modified Honey Bee Algorithms are proposed in this article for effective load balancing based on honey bee and Round Robin foraging activity to control load through VMs. Tasks taken from VMs that are crowded are viewed as honey bees. In the suggested method which is a circular ribbon, filled VMs are considered. In order to ensure a fast responding time and a minimum number of task migrations, the planned protocol also explores the aims of tasks in VM queues. The results of the test indicate that the quality of service has improved considerably (QoS).
Journal Article
Deep Learning Enhancing Performance Using Support Vector Machine HTM Cortical Learning Algorithm
Deep Learning is a function of AI that duplicates the mechanisms of human thought in the processing of information and selection processes. The aim of this study is to apply a technology known as SVMHTMC to improve deep learning. The HTM Cortical Learning Approach and the Support Vector Machine have been combined in this suggested algorithm. The deep learning technique is based on the assumption that the mean absolute percentage error is reduced. Aside from the overlapping duty cycle, the high proportion of which shows the speed of the classifier’s processing function. The findings demonstrate that by halving the value, the suggested set of criteria minimizes the absolute proportion of mistakes. In addition, raise the percentage of overlapping duty cycles by 17%.
Journal Article
A review on game-changing approach for the oral administration of lipophilic drug: SEDDS
Conventional emulsifying agent forms a monolayer around the droplet to stabilize the emulsion, resulting from aqueous dilution hence reduce interfacial tension and preventing coalescence. [11] The optimized Surfactant is dissolved in the oil phase in different proportion in a glass test tube than each phase of surfactant and oil phase is titrated against aqueous phase, turbidity in test tube referred to as Endpoint. Some non-ionic surfactants have lesser degree of toxicitythan ionic surfactant but may cause moderate reversible change in intestinal wall permeability. Mechanical vibration is produced when the tip of sonicator touches liquid medium which results in cavitation. [...]ultrasound can directly produce nanoemulsion with droplet size as low as 0.2 mm. 3.Microfluidization:
Journal Article
Information-Centric IoT-Based Smart Farming with Dynamic Data Optimization
Smart farming has become a strategic approach of sustainable agriculture management and monitoring with the infrastructure to exploit modern technologies, including big data, the cloud, and the Internet of Things (IoT). Many researchers try to integrate IoT-based smart farming on cloud platforms effectively. They define various frameworks on smart farming and monitoring system and still lacks to define effective data management schemes. Since IoT-cloud systems involve massive structured and unstructured data, data optimization comes into the picture. Hence, this research designs an Information-Centric IoT-based Smart Farming with Dynamic Data Optimization (ICISF-DDO), which enhances the performance of the smart farming infrastructure with minimal energy consumption and improved lifetime. Here, a conceptual framework of the proposed scheme and statistical design model has been well defined. The information storage and management with DDO has been expanded individually to show the effective use of membership parameters in data optimization. The simulation outcomes state that the proposed ICISF-DDO can surpass existing smart farming systems with a data optimization ratio of 97.71%, reliability ratio of 98.63%, a coverage ratio of 99.67%, least sensor error rate of 8.96%, and efficient energy consumption ratio of 4.84%.
Journal Article
Quantitative Sørensen-Dice Indexed Damgård-Jurik Cryptosystem For Secured Data Access Control In Cloud
Data Access Control has become a demanding issue in cloud storage systems. Access control is the protection method to control who can view or access the information in computing scenarios. Some techniques have been designed formost of the security strategiesprovidedtotheclients accessingtheuploadeddata. AQuantitative Sørensen-Dice Indexing Damgård-Jurik Cryptosystem based Data Access Control (QSDIDJC-DAC) method is introduced to avoid the illegitimate data access in the cloud server. Initially, the QSDIDJC-DAC method comprises five processes, namely registration, key generation, authentication, encryption and decryption for data access. At first, the clientsregister their information to the cloud server. After that, the cloud server generates the key pairs (i.e., public key, private key). Then the client encrypts the data with the general public key and sends it to the cloud server for storing the data. During the data access, the user transmits the request to the cloud server. Upon receiving the request, the authentication server verifies the cloud user is a legitimate user using the Quantitative Sørensen-Dice similarity coefficient with higher authentication accuracy. The Similarity Coefficient matches the requested user with user information stored in the cloud server on the time of registration. Based on the similarity value, the legitimate and illegitimate users are correctly identified with minimum time consumption. After performing the verification process, the cloud server allows legitimate users to access the data. Subsequently, the client decrypts the data with the help of their private key. This helps to enhance the data access control in the cloud server with a better security level. Experimental assessment is carried out on factors such as authentication accuracy, computation time and data confidentiality rate with recognize to some of the cloud users and thedata.
Journal Article
Stability Indicating Assay Method Development and Validation by RP-UPLC with PDA detector for Simultaneous Estimation of Glycopyrrolate and Neostigmine in Pharmaceutical dosage form
The presented research aimed to create a simple, precise, and accurate isocratic reversed-phase stability indicating Ultra Performance Liquid Chromatography (UPLC) method for simultaneous quantification of Glycopyrrolate and Neostigmine in bulk and injectable dosage form. On a Symmetry-C18(150mm X 4.6mm, 3.5m) column, isocratic separation was accomplished. The mobile phase is composed of acetonitrile and 1% orthophosphoric acid buffer (70:30 v/v) flowing at a rate of 1ml/min, with detection at 255nm utilising a photo-diode array detector. To apply stress conditions, the drug was treated to acid degradation, alkali degradation, peroxide degradation, photolysis, and heat. Specificity, precision, linearity, accuracy robustness, and solution stability were all validated. Glycopyrrolate and Neostigmine showed linearity in the range of 1-15mg mL-1 and 5-75mg mL-1, respectively. Glycopyrrolate accuracy was from 98.9 to 100.2 percent, whereas Neostigmine accuracy ranged from 98.4 to 100.2 percent. The detection of Glycopyrrolate and Neostigmine test does not interfere with degradation products produced as a result of stress studies, hence this method is regarded as stability-indicating.
Journal Article
A Hybrid Edge-Cloud System for Networking Service Components Optimization Using the Internet of Things
The need for data is growing steadily due to big data technologies and the Internet’s quick expansion, and the volume of data being generated is creating a significant need for data analysis. The Internet of Things (IoT) model has appeared as a crucial element for edge platforms. An IoT system has serious performance issues due to the enormous volume of data that many connected devices produce. Potential methods to increase resource consumption and responsive services’ adaptability in an IoT system include edge-cloud computation and networking function virtualization (NFV) techniques. In the edge environment, there is a service combination of many IoT applications. The significant transmission latency impacts the functionality of the entire network in the IoT communication procedure because of the data communication among various service components. As a result, this research proposes a new optimization technique for IoT service element installation in edge-cloud-hybrid systems, namely the IoT-based Service Components Optimization Model (IoT-SCOM), with the decrease of transmission latency as the optimization aim. Additionally, this research creates the IoT-SCOM model and optimizes it to choose the best deployment option with the least assured delay. The experimental findings demonstrate that the IoT-SCOM approach has greater accuracy and effectiveness for the difficulty of data-intensive service element installation in the edge-cloud environment compared to the existing methods and the stochastic optimization technique.
Journal Article
Development and validation of stability indicating UPLC method for the simultaneous estimation of triamterene and hydrochlorothiazide in combined dosage forms using quality by design approach
Background
According to the information gathered from the literature, no technique for UPLC of triamterene and hydrochlorothiazide employing QbD in the formulations has been published. The technique development by incorporating QbD and validating for accuracy, linearity, precision, LOQ, LOD, ruggedness and selectivity as per ICH is part of the work’s modernity.
Results
Screening investigations led to the selection of cmps. Peak tailing was evaluated as a metric of technique robustness based on these important analytical attributes, namely retention time. With a 0.1 percent OPN: methanol (40:60) mobile phase, a flow rate of 0.3 ml/min, a wave length of 224 nm, an injection volume of 41, and a run time of 6 min, the best chromatographic separation was attained.
Conclusions
The method was verified using ICH criteria, which ensure a high level of linearity, accuracy, precision, specificity and robustness. As a result, the suggested approach is regarded as a quick and accurate method for estimating triamterene and hydrochlorothiazide at the same time.
Journal Article