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Clinical Intelligence: Deep Reinforcement Learning for Healthcare and Biomedical Advancements
Deep reinforcement learning (DRL) is showing a remarkable impact in the healthcare and biomedical domains, leveraging its ability to learn complex decision-making policies from raw data through trial-and-error interactions. DRL can effectively extract the characteristic information in the environment, propose effective behavior strategies, and correct errors that occurred during the training process. Targeted toward healthcare professionals, researchers, and technology enthusiasts, this chapter begins with notable applications of DRL in healthcare, including personalized treatment recommendations, clinical trial optimization, disease diagnosis, robotic surgery and assistance, mental health support systems, chronic disease management and scheduling, and a few more. It also delves on challenges such as data privacy, interpretability, regulatory compliance, validation, and the need for domain expertise to ensure safe and effective deployment. Next, the chapter seamlessly transitions into DRL algorithms contributing to the biomedical field which are gaining traction due to their potential to provide timely and personalized interventions. Over time, the research community has proposed several methods and algorithms within the field of deep reinforcement learning that help agents learn optimal policies from rich data. Healthcare data is often complex, high-dimensional, and unstructured, such as medical images, genomics data, and patient records. The healthcare-suitable DRL algorithms such as Q-learning, SARSA, Bayesian, actor-critic, reinforcement learning (RL), Deep-Q-Networks (DQN), and Monte Carlo Tree Search (MCTS) are highlighted. In addition, the section offers guidelines for the application of DRL to healthcare and biomedical problems, aiming at providing indications to the designer of new applications in order to choose among different RL methods. Furthermore, a case study is included to fully realize the revolutionary benefits of DRL in healthcare environments, aiming to bridge the gap between theory and practice. The case study presents a remarkable impact on categories such as precision medicine, dynamic treatment regime, medical imaging, diagnostic systems, control systems, chat-bots and advanced interfaces, and healthcare management systems. 2024 Scrivener Publishing LLC. -
Clinical Study Macular Oedema
Prior to the development of the ophthalmoscope, macular oedema remained mostly unknown. Macular oedema is caused by fluid buildup in the retinal layers around the fovea. It causes vision loss by changing the functional cell connection in the retina and stimulating an inflammatory reparative response. The clinical profile, aetiology, and varied types of Macular Oedema are hence the focus of research, and also to investigate the aetiology of macular oedema as well as the various forms of macular oedema in patients attending Krishna Hospital in Karad. The male to female ratio among the 60 participants was 2.53:1. Macular oedema is the major cause for loss in vision which is common vitreo retinal diseases, with diabetes being the most prevalent cause (35% of cases) in our study. Its early detection and treatment are critical for preventing blindness. It is consequently critical to understand the aetiology, pattern, and chronicity of macular oedema in order to customize treatment and monitor response to it. RJPT All right reserved. -
Clinical Text Classification of Medical Transcriptions Based on Different Diseases
Clinical text classification is the process of extracting the information from clinical narratives. Clinical narratives are the voice files, notes taken during a lecture, or other spoken material given by physicians. Because of the rapid rise in data in the healthcare sector, text mining and information extraction (IE) have acquired a few applications in the previous few years. This research attempts to use machine learning algorithms to diagnose diseases from the given medical transcriptions. Proposed clinical text classification models could decrease human efforts of labeled training data creation and feature engineering and for designing for applying machine learning models to clinical text classification by leveraging weak supervision. The main aim of this paper is to compare the multiclass logistic regression model and support vector classifier model which is implemented for performing clinical text classification on medical transcriptions. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
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Clonning and Characterization of An Exported Protein Present in the RD7 Region of Clinical Isolates of Mycobacterium Tuberculosis
The bacterium Mycobacterium tuberculosis is responsible for causing the disease newlinetuberculosis in mammals, which is regarded as one of the oldest diseases haunting the human race. The only available tuberculosis vaccine Bacillus Calmette-Guerine (BCG), is effective against childhood tuberculosis but is regarded as having low efficacy in conferring protection in the case of tuberculosis in adults. A comparison of the M. tuberculosis H37Rv strain and clinical isolates from Kerala had earlier revealed that the clinical strains have a distinctive 4.5 kb genomic sequence that is lacking from the H37Rv strain in the RD7 region. The RD7 is a distinctive genomic region that is absent in M. tuberculosis H37Rv and Mycobacterium bovis BCG strain. The 4.5 kb genomic sequence is projected to include 6 potential ORFs by newlineNCBI ORF prediction tool, one of which Novel Hypothetical Protein (NHP2) is anticipated to encode an exported protein with a length of 268 amino acids. Studies demonstrate that Mycobacterium tuberculosis secretory proteins such as the Ag85 complex, the ESAT-6 family protein, and the PE-PPE family proteins were newlineeffective vaccine candidates because they trigger T cells. Here, we present an indepth analysis of the exported protein, which is 268 amino acids long. The putative exported protein with a gene 807 bp long was PCR amplified and cloned in the expression vector pET-32a for expression. The protein was over expressed using Isopropyl D-1-thiogalactopyranoside (IPTG) and was isolated and purified using column chromatography. Bioinformatics studies were conducted to study the characteristics of the expressed protein. A novel putative mycobacterial protein discovered by subtractive hybridization was studied for its potential as a vaccine candidate using cutting-edge computer technologies. -
Cloud and IOT based alcohol and health monitoring system /
Patent Number: 202141022529, Applicant: Dr.AR.Sivakumaran.
Wireless alcohol and health monitoring system that can monitor a human 24x7. Vehicle driving to manufacturing plants, Offices, Hospitals, Military, and other such ventures need to screen their staff/faculty follow all hard-working attitudes that incorporate, not coming to premises affected by liquor. This guarantees legitimate hard-working attitudes are followed. Our proposed framework takes into consideration liquor and wellbeing checking in addition to a detailing framework that screens this and reports it to concerned staff distantly over the web. -
Cloud and IOT based smart forest fire detection and warming system /
Patent Number: 202141048693, Applicant: Arumugam Ranjith.
The development of modern industrial civilizations has caused in the establishment of manufacturing plants, office buildings, and housing blocks throughout urban parts. Because of the combustible substances contained in these facilities, there are gas and oil tanks all over these areas. Because of the densely packed buildings, extreme heat and smoke, and the possibility of explosives, putting out a fire in one of these places is nearly impossible. -
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. -
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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. -
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 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, 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 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 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 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 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 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 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.