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CloudML: Privacy-Assured Healthcare Machine Learning Model for Cloud Network
Cloud computing is the need of the twenty-first century with an exponential increase in the volume of data. Compared to any other technologies, the cloud has seen fastest adoption in the industry. The popularity of cloud is closely linked to the benefits it offers which ranges from a group of stakeholders to huge number of entrepreneurs. This enables some prominent features such as elasticity, scalability, high availability, and accessibility. So, the increase in popularity of the cloud is linked to the influx of data that involves big data with some specialized techniques and tools. Many data analysis applications use clustering techniques incorporated with machine learning to derive useful information by grouping similar data, especially in healthcare and medical department for predicting symptoms of diseases. However, the security of healthcare data with a machine learning model for classifying patients information and genetic data is a major concern. So, to solve such problems, this paper proposes a Cloud-Machine Learning (CloudML) Model for encrypted heart disease datasets by employing a privacy preservation scheme in it. This model is designed in such a way that it does not vary in accuracy while clustering the datasets. The performance analysis of the model shows that the proposed approach yields significant results in terms of Communication Overhead, Storage Overhead, Runtime, Scalability, and Encryption Cost. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Cloud-enabled e-commerce negotiation framework using bayesian-based adaptive probabilistic trust management model
Enforcing a trust management model in the broker-based negotiation context is identified as a foremost challenge. Creating such trust model is not a pure technical issue, but the technology should enhance the cloud service negotiation framework for improving the utility value and success rate between the bargaining participants (consumer, broker, and service provider) during their negotiation progression. In the existing negotiation frameworks, trusts were established using reputation, self-assessment, identity, evidence, and policy-based evaluation techniques for maximizing the negotiators (cloud participants) utility value and success rate. To further maximization, a Bayesian-based adaptive probabilistic trust management model is enforced in the future broker-based trusted cloud service negotiation framework. This adaptive model dynamically ranks the service provider agents by estimating the success rate, cooperation rate and honesty rate factors to effectively measure the trustworthiness among the participants. The measured trustworthiness value will be used by the broker agents for prioritization of trusted provider agents over the non-trusted provider agents which minimizes the bargaining conflict between the participants and enhance future bargaining progression. In addition, the proposed adaptive probabilistic trust management model formulates the sequence of bilateral negotiation process among the participants as a Bayesian learning process. Finally, the performance of the projected cloud-enabled e-commerce negotiation framework with Bayesian-based adaptive probabilistic trust management model is compared with the existing frameworks by validating under different levels of negotiation rounds. The Author(s) 2025. -
Cloud-enabled Diabetic Retinopathy Prediction System using optimized deep Belief Network Classifier
Diabetic retinopathy disease is one of the notorious metabolic disorders happens due to increase of blood sugar level in human body. In computer vision, images are recognized as the indispensable tool for precise prediction and diagnosis of diabetic retinopathy. Therefore, the proposed research study considers the fundus images of various patients containing the diabetic disease. Basic idea behind this research is to introduce a stochastic neighbor embedding (SNE) feature extraction approach for the sake of dimensional reduction and unnecessary noise removal from the fundus images. After feature extraction, the proposed optimized deep belief network (O-DBN) classifier model is capable of measuring the image features into various classes that gives the severity levels of diabetic retinopathy disease. Moreover, the proposed cloud-enabled diabetic retinopathy prediction system using the SNE feature extraction and O-DBN classification model could outperform the existing online prediction systems in terms of sensitivity, specificity, F1-score, prediction time and accuracy. 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. -
Cloud-Based Diabetic Retinopathy Severity Recognition System Using Ensemble Deep Convolutional Neural Network Classifier Model
One of the key reasons for visual impairments is due to the ignorance of diabetic retinopathy disease. This research study focuses on the early recognition of diabetic retinopathy disease from the fundus images and identifies its severity stages to make successful treatments against blindness risk. Some traditional approaches explored the decision tree, kernel-based support vector machine, and Nae Bayes classifier models to extract the features from fundus images. Most of the researchers applied the modern approach of convolutional neural network model through transfer learning mechanism to extract relevant features from the fundus images. It helps in the diagnosis of diabetic retinopathy that may delay the prediction process and create inconsistency among the doctors. So, a deep learning-based approach is proposed in this research study to provide stage-wise prediction of diabetic retinopathy disease with a multi-task learning mechanism. As a result, the proposed deep convolutional neural network classifier with an ensemble model outperforms the existing classifier with EfficientNet-B4, EfficientNet-B5, SE-ResNeXt50 (380?380), and SE-ResNeXt50 (512?512) networking methods in the context of prediction correctness, sensitivity, specificity, macro F1, and quadratic weighted kappa (QWK) score metrics. Exploiting hyperparameter optimizations on the deep learning classifier model and multi-task regression learning approaches make significant improvements over the performance evaluation metrics. Finally, the proposed approaches make the effective recognition of diabetic retinopathy disease stages based on the human fundus image. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Cloud-Based Cataract Recognition System Using Hybrid Classifier Model
One of the key challenges of ophthalmologists is to diagnose the various ranges of ophthalmological illnesses such as diabetic retinopathy, cataract, and glaucoma. Here, cataract disease is identified as the one of the leading and most common ophthalmological problems that occurs due to aging. A computer-assisted cataract detection and diagnosis support system is required by the ophthalmologists to overcome the error that occurs during manual screening process. So, a cloud-based cataract recognition system is proposed using the convolutional neural network with support vector machine classifier model to improve the prediction accuracy, sensitivity, specificity, precision, recall, F1-score, and Mathews correlation coefficient. Moreover, the four-layer convolutional neural network is finetuned with a rich set of features and trained with various network models such as Inception V3, MobileNet, VGG-16, VGG-19, and ResNet-101. Therefore, the proposed hybrid combination of ResNet-101 with support vector machine classifier makes better cataract detection and outperforms the existing classifier models in terms of above-mentioned performance evaluation metrics. Moreover, the proposed hybrid approach provides the better telemedical solution to remote people by providing accurate disease prediction and severity grading such as normal, mild, premature, and severe cataract. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Cloud Virtualization with Data Security: Challenges and Opportunities
In recent years, Cloud Computing is emerging as a torrid research area for both academicians and industrialists. It provides effective ways to handle and store the data in advanced system processing applications. Furthermore, it also leverages a radical change in the way the users access and use the available resources. Despite the hype, it also has the challenge of slow data transition from present physical storage to the cloud based platform. This is mainly due to the security challenges associated with the Cloud Computing applications. Hence, data protection has become very critical and always requires an efficient and effective security protocol into the existence. So, the security and reliability of the cloud platform would definitely attract more researchers to this platform. This article discusses an overview of Cloud paradigm and the different virtualization techniques adopted to overcome the security issues associated with the cloud computing platform. Springer Nature Switzerland AG 2020. -
Cloud service negotiation framework for real-time E-commerce application using game theory decision system
A major demanding issue is developing a Service Level Agreement (SLA) based negotiation framework in the cloud. To provide personalized service access to consumers, a novel Automated Dynamic SLA Negotiation Framework (ADSLANF) is proposed using a dynamic SLA concept to negotiate on service terms and conditions. The existing frameworks exploit a direct negotiation mechanism where the provider and consumer can directly talk to each other, which may not be applicable in the future due to increasing demand on broker-based models. The proposed ADSLANF will take very less total negotiation time due to complicated negotiation mechanisms using a third-party broker agent. Also, a novel game theory decision system will suggest an optimal solution to the negotiating agent at the time of generating a proposal or counter proposal. This optimal suggestion will make the negotiating party aware of the optimal acceptance range of the proposal and avoid the negotiation break off by quickly reaching an agreement. 2021 - IOS Press. All rights reserved. -
Cloud security based attack detection using transductive learning integrated with Hidden Markov Model
In recent years, organizations and enterprises put huge attention on their network security. The attackers were able to influence vulnerabilities for the configuration of the network through the network. Zero-day (0-day) is defined as vulnerable software or application that is either defined by the vendor or not patched by any vendor of organization. When zero-day attack is identified within the network there is no proper mechanism when observed. To mitigate challenges related to the zero-day attack, this paper presented HMM_TDL, a deep learning model for detection and prevention of attack in the cloud platform. The presented model is carried out in three phases like at first, Hidden Markov Model (HMM) is incorporated for the detection of attacks. With the derived HMM model, hyper alerts are transmitted to the database for attack prevention. In the second stage, a transductive deep learning model with k-medoids clustering is adopted for attack identification. With k-medoids clustering, soft labels are assigned for attack and data and update to the database. In the last phase, with computed HMM_TDL database is updated with computed trust value for attack prevention within the cloud. 2022 -
Cloud Job Scheduling Using Deadline-Based Task Optimisation Algorithm in Internet of Things
The cloud-based Internet of Things (IoT) gadgets are becoming increasingly significant in todays current environment. Thoroughly examining the ever-changing relationship among these two domains, this literature review sheds light on how the field of research is developing and how important both domains are to defining our digital future. The analysis delves into the various uses of cloud-based computing in conjunction with IoT devices, highlighting how these two technologies have the combined power to transform companies, improve productivity, and improve user experiences. Blending cloud-based resources with IoT has become essential for advancement, from connected houses to industrial automation. The article provides a detailed overview of the complexities involved in this merger, highlighting the importance of computing in the cloud in tackling issues like data protection, immediate analysis, and resource optimisation. This study also points out significant gaps in current understanding, highlighting the need for more investigation to fully realise the promise of cloud computing when combined with IoT devices. Essentially, this analysis of the literature highlights the critical role in determining the integration of cloud technology and IoT devices by giving a more efficient and optimal scheduling Deadline-Based Task scheduling algorithm, which has proved to have the least average waiting time of five units when compared to all the scheduling algorithms taken into consideration. The beginning of a new era characterised by connectivity and data-driven decision-making, and the key to realising the full potential of IoT applications is to comprehend and leverage the power of cloud technology. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Cloud Intrusion Detection Using Hybrid Convolutional Neural Networks
Instead of storing data on a hard drive, cloud computing is seen as the best option. The Internet is used to deliver three different kinds of computing services to users all over the world. One advantage that cloud computing provides to its customers is greater access to resources and higher performance while at the same time increasing the risk of an attack. Intrusion detection systems that can handle a large volume of data packets, analyse them, and generate reports based on knowledge and behaviour analysis were developed as part of this research. As an added layer of protection, the Convolution Neural Network Algorithm is used to encrypt data during end-to-end transmission and to store it in the cloud. Intrusion detection increases the safety of data in the cloud. In this paper demonstrates the data is encrypted and decrypted using a model of an algorithm and explains how it is protected from attackers. It's important to take into account the amount of time and memory required to encrypt and decrypt large text files when evaluating the proposed system's performance. The security of the cloud has also been examined and compared to other existing encoding methods. 2024, Iquz Galaxy Publisher. All rights reserved. -
Cloud Enabled Smart Firefighting Drone Using Internet of Things
Internet of Things is fasted booming sector. This technology is evolved in various fields. The frequent updates in concerning the progress of Skyscraper fire or high-rise fire it is essential for us to ensure effective and safe firefighting. Since high-rise fire is typically inaccessible by ground vehicles due to some constraints or parameters. Due to less advancement in technology most skyscrapers are not furnished with proper fire monitoring and prevention system. To solve this issue this article is propose Unmanned Air Vehicles (UAVs) are making an appearance and making promises to prevent such kind of incidents. In this system, UAV can be launched from the Fire Control Unit (FCU). The proposed methodology is implemented with the help of Internet of Things (IoT). Sensors which are installed at the skyscraper detects the presence of fire and immediately send stress signals to the command and control unit from where further possible action can be taken. The pilot at the fire control unit continuously monitors the flight path and receives the video and fire scan information from the UAV. Upon detection of a stress signal or fire signal the Skyscraper position is determined with the help of Global Positioning System (GPS) and permission is requested from the applicable security agency to launch the extinguisher vehicle. The permission is granted, the coordinates of the location are filled in the system and the nearest station sends the UAV to the location. The fire suppressant are deployed it comes back to the nearest landing location and re-loaded with another fire suppressant to be carried to the fire location. The proposed methodology should improve the Quality of Service. 2019 IEEE. -
Cloud Dynamic Scheduling for Multimedia Data Encryption Using Tabu Search Algorithm
The cloud computing is interlinked with recent and out-dated technology. The cloud data storage industry is earning billion and millions of money through this technology. The cloud remote server storage is on-demand technology. The cloud users are expecting higher quality in minimal cost. The quality of service is playing a vital role in any latest technology. The cloud user always depends on thirty party service providers. This service provider is facing higher competition. The customer is choosing a service based on two parameters one is security and another one is cost. The reason behind this is all our personal data is stored on some third party server. The customer is expecting higher security level. The service provider is choosing many techniques for data security, best one is encryption mechanism. This encryption method is having many algorithms. Then again one problem is raised, that is which algorithm is best for encryption. The prediction of algorithm is one of major task. Each and every algorithm is having unique advantage. The algorithm performance is varying depends on file type. The proposed method of this article is to solve this encryption algorithm selection problem by using tabu search concept. The proposed method is to ensure best encryption method to reducing the average encode and decode time in multimedia data. The local search scheduling concept is to schedule the encryption algorithm and store that data in local memory table. The quality of service is improved by using proposed scheduling technique. 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Cloud databases: A resilient and robust framework to dissolve vendor lock-in
Vendor lock-in has become a major concern in cloud computing. The term vendor lock-in describes situations where the subscriber cannot move data or services to another cloud vendor. This is due to heavy data volumes, high network bandwidth costs, dependencies, or unacceptable downtime. The proposed vendor lock-in dissolution practice migrates the database effectively in noticeably less time, regardless of database size and with a nominal network bandwidth requirement. Through this new practice, databases can be migrated to very remote regions, even across continents. A real-time implementation of the proposed method presented in this paper. 2024 The Author(s) -
Cloud Computing, Machine Learning, and Secure Data Sharing enabled through Blockchain
Blockchain technologies are sweeping the globe. Cloud computing & secure data sharing have emerged as new technologies, owing to current advances in machine learning. Conventional machine learning algorithms need the collection & processing of training information on centralized systems. With the introduction of new decentralized machine learning algorithms & cloud computing, ML on-device information learning is now a reality. IoT gadgets may outsource training duties to cloud computing services to enable AI at the network's perimeter. Furthermore, these dispersed edges intelligence architectures bring additional issues, also including consumer confidentiality & information safety. Blockchain has been proposed as a viable alternative to these issues. Blockchain, as a dispersed intelligent database, has evolved as a revolutionary innovation for the future phase of multiple industries' uses due to its decentralized, accessible, & safe structure. This system also includes trustworthy automatic scripting running & unchangeable information recordings. As quantum technologies have proven more viable in the latest days, blockchain has faced prospective challenges from quantum computations. In this paper, we summarize the existing material in the study fields of blockchain-based cloud computing, machine learning, and secure data sharing, as well as a basic orientation to post-quantum blockchain to offer a summary of the existing state-of-the-art in these cutting-edge innovations. 2022 IEEE. -
Cloud Computing with Machine Learning Could Help Us in the Early Diagnosis of Breast Cancer
The purpose of this study is to develop tools which could help the clinicians in the primary care hospitals with the early diagnosis of breast cancer diagnosis. Breast cancer is one of the leading forms of cancer in developing countries and often gets detected at the lateral stages. The detection of cancer at later stages results not only in pain and in agony to the patients but also puts lot of financial burden on the caregivers. In this work, we are presenting the preliminary results of the project code named BCDM (Breast Cancer Diagnosis using Machine Learning) developed using Mat lab. The algorithm developed in this research cancer work based on adaptive resonance theory. In this research work, we concluded how Art 1 network will help in classification of breast. The aim of the project is to eventually run the algorithm on a cloud computer and a clinician at a primary healthcare can use the system for the early diagnosis of the patients using web based interface from anywhere in the world. 2015 IEEE. -
Cloud computing security for public cloud using ciphers and queueing petri nets
Cloud computing is the most used word in the domain of Information Technology, which is making colossal differentiations in the IT business. Nowadays, a massive proportion of data is being made, and the masters are discovering better approaches for managing this data. In a general sense, the word cloud implies a virtual database that stores immense data from various clients. There are three sorts of cloud public, private and hybrid. A public cloud is fundamental for general customers where customers can use cloud benefits free or by paying. Private cloud is for explicit associations, and hybrid one is in a broad sense a mix of both. Cloud offers diverse kind of administrations, for instance, IAAS, PAAS, SAAS where administrations like a stage for running any application, getting to the enormous information extra room, can use any application running under the cloud are given. The cloud similarly has a shortcoming concerning the security for the data warehouse. In a general sense, public cloud is inclined to data modification, data hacking and therefore, the integrity and privacy of the data are being undermined. Here in our work our motive is to verify the information that will be taken care of in the public cloud by using the multi-stage encryption. The estimation that we have proposed is a mix of Rail Fence cipher and Play Fair cipher. 2020, IJSTR. -
Cloud Computing Application: Research Challenges and Opportunity
In a world with intensive computational services and require optimal solutions, cloud security is a critical concern. As a known fact, the cloud is a diverse field in which data is crucial, and as a result, it invites the dark world to enter and create a virtual menace to businesses, governments, and technology that is facilitated by the cloud. This article addresses the fundamentals of cloud computing, as well as security and threats in various applications. This research study will explore how security is remaining as a potential risk for cloud users across the globe by listing some of the cloud applications. Some viable solutions and security measures that could help us in analyzing cloud security threats are reviewed. The analyzed solutions include profound analytical thinking on how to render the solutions more impactful in each scenario. Several cloud security solutions are available to assist businesses in reducing costs and enhancing security. This study discover that if the risks are taken into consideration without any delay then the matter of solutions gets divided into four pillars, which will assist us in obtaining a more comprehensive knowledge. Visibility, compute-based security, network protection, and lastly identity security are referred as four pillars. 2022 IEEE. -
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Cloud based ERP Model using Optimized Load Balancer
Enterprise Resource Planning (ERP) and Cloud computing are turning out to be increasingly more significant in the field of Information Technology (IT) furthermore, Communication. These are two distinct segments of current data frameworks, and there are a few inside and out examinations about Enterprise Resource Planning on cloud computing framework. ERP frameworks are related with a few issues, for example, shared synchronization of multi-composed assets, constrained customization, massive overhauling cost, arrangement mix, industry usefulness, reinforcement support and innovation refreshes. These issues render ERP frameworks execution excruciating, complex and time-devouring and create the need for a huge change in ERP structure to upgrade ERP frameworks foundation and usefulness. Cloud Computing (CC) stages can defeat ERP frameworks inconsistencies with financially savvy, redid and profoundly accessible figuring assets. The objective of this examination is to blend ERP and CC benefits to lessen the factor of consumption cost and execution delays through a proposed system. For this reason, investigate the unmistakable issues in current ERP frameworks through a complete correlation between ERP when moving to CC condition. Also, a conventional structure is proposed for 'Cloud-based ERP frameworks'. 2020 IEEE. -
Cloud and IoT-Driven Smart Irrigation: A Modern Approach to Water Management in Agriculture
Agriculture faces the dual challenge of meeting global food demand while conserving scarce water resources under climate change. Conventional irrigation systems often result in water wastage and high manual intervention. This study proposes a Cloud- and IoT-driven smart irrigation framework that integrates Wireless Sensor Networks (WSNs), an NI CompactRIO controller, renewable energy, and real-time weather forecasting services. The system collects and analyzes heterogeneous data streams (soil moisture, humidity, temperature, and water levels) to dynamically control irrigation schedules. Experimental validation on a prototype farm in Morocco demonstrates that the proposed system reduces weekly water consumption by 26, lowers irrigation events from 10 to 6, eliminates manual interventions, and achieves 13.6 energy savings compared with traditional methods. The integration of predictive weather data prevents over-irrigation during rainfall, while cloud-based analytics enhance scalability and monitoring. These results highlight the system's potential for resource-efficient, autonomous, and sustainable agriculture, particularly in water-scarce regions. Future work will focus on extending the system with LoRa-based sensor networks and machine learning-based solar energy forecasting to further improve adaptability and scalability. 2026, International Association of Engineers. All right reserved.