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                <text>An IoHT System Utilizing Smart Contracts for Machine Learning -Based Authentication</text>
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                <text>Artificial intelligence; blockchain technologies; E Healthcare; Internet of Healthcare Things (IoHT); Ma-chine learning; Random Forest; Support vector machines component</text>
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                <text>The Internet of Healthcare Things (IoHT) and blockchain technologies have made it feasible to share data in a secure and effective manner, but it is still challenging to ensure the data's veracity and privacy. This paper presents a blockchain authentication method that utilizes Machine Learning (ML) techniques that use smart contracts to ensure the security and privacy of IoHT data. The process utilizes smart contracts to manage access control and ensure data integrity, and deep learning algorithms to identify and validate the accuracy of user data. Furthermore, the approach improves the resilience and dependability of the authentication process and permits secure data ex-change between multiple IoHT systems. The proposed approach provides a potentially revolutionary solution to enhance the safety and confidentiality of IoHT data. It has the potential to fundamentally change how healthcare is provided in the future.  2023 IEEE.</text>
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                <text>2023 International Conference on Emerging Trends in Networks and Computer Communications, ETNCC 2023 - Proceedings, pp. 196-201.</text>
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                <text>Upreti K., CHRIST(Deemed to Be University), Dept. of Computer Science, Delhi NCR, Ghaziabad, India; Vats P., Scse Manipal University Jaipur, Dept. of Cse, Rajasthan, Jaipur, India; Borkhade G., Bharati Vidyapeeth College of Engineering, Dept. of Electronics &amp;amp; Telecommunication Engg, Maharashtra, Navi Mumbai, India; Raut R.D., Sant Gadge Baba Amravati University, Pgtd Applied Electronics, Maharashtra, Amravati, India; Hundekari S., Mit Adt University Loni Kalbhor, Pune, Pune, India; Parashar J., Adgitm, Dept. of Computer Science &amp;amp; Engg., New Delhi, India</text>
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                <text>Pressure sores are a common form of skin problem which occurs with patients who are bedridden or immobile. It is believed that the occurrence of ulcers due to pressure can be prevented. Making best use of resources available and providing comfort to the patient, it is very much important to identify people at risk and provide preventive measures. This work is associated with a method to analyze pressure from pressure points on bedridden patients. A system is presented in this work that continuously monitors the pressure from pressure points using force sensors and sends an alarm to the nurses or caretakers if there is a variation in the pressure exerted on a specific area.  2018 IEEE.</text>
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                <text>Vijayalakshmi A.; Jose D.V.</text>
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                <text>Proceedings 2018 3rd International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2018, pp. 282-284.</text>
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                <text>Vijayalakshmi A., Department of Computer Science, CHRIST (Deemed to Be University), Bangalore, India; Jose D.V., Department of Computer Science, CHRIST (Deemed to Be University), Bangalore, India</text>
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                <text>Patent Number: 202141010711, Applicant: Sathishkumar V E.&#13;
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Internet of Things (IOT) is the connection of things / objects through networks, in which things or objects can interact with each other without or minimal human intervention. Now-a-days, Security has grown to be the maximum tough task. Everyone wishes protection however in present scenario, not anything is secure now no longer even of their very own houses. In this work, we are proposing an IoT based system for Antitheft Security Detection in a Cloud Environment. In this system we used PIR sensor, ultrasonic sensor and Vibration sensor.</text>
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                <text>Patent Number: 202111041762, Applicant: Mr. Chandan Choubey.&#13;
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                <text>Classification; Crop harvesting; Detection of plant disease; RFO; Threshold segmentation</text>
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                <text>Purpose: In cultivation, early harvest offers farmers an opportunity to increase production while decreasing the chances of lower crop production rates, ensuring that the economy remains balanced. The significant reason is to predict the disease in plants and distinguish the type of syndrome with the help of segmentation and random forest optimization classification. In this investigation, the accurate prior phase of crop imagery has been collected from different datasets like cropscience, yesmodes and nelsonwisc. In the current study, the real-time earlier state of crop images has been gathered from numerous data sources similar to crop_science, yes_modes, nelson_wisc dataset. Design/methodology/approach: In this research work, random forest machine learning-based persuasive plants healthcare computing is provided. If proper ecological care is not applied to early harvesting, it can cause diseases in plants, decrease the cropping rate and less production. Until now different methods have been developed for crop analysis at an earlier stage, but it is necessary to implement methods to advanced techniques. So, the detection of plant diseases with the help of threshold segmentation and random forest classification has been involved in this investigation. This implemented design is verified on Python 3.7.8 software for simulation analysis. Findings: In this work, different methods are developed for crops at an earlier stage, but more methods are needed to implement methods with prior stage crop harvesting. Because of this, a disease-finding system has been implemented. The methodologies like Threshold segmentation and RFO classifier lends 97.8% identification precision with 99.3% real optimistic rate, and 59.823 peak signal-to-noise (PSNR), 0.99894 structure similarity index (SSIM), 0.00812 machine squared error (MSE) values are attained. Originality/value: The implemented machine learning design is outperformance methodology, and they are proving good application detection rate.  2021, Emerald Publishing Limited.</text>
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                <text>International Journal of Intelligent Computing and Cybernetics, Vol-15, No. 2, pp. 184-197.</text>
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                <text>Kailasam S., CSE, Koneru Lakshmaiah Education Foundation, Guntur, India; Achanta S.D.M., ECE, Vignan's Institute of Information Technology, Visakhapatnam, India; Rama Koteswara Rao P., ECE, NRI Institute of Technology, Krishna, India; Vatambeti R., CHRIST (Deemed to be University), Bengaluru, India; Kayam S., ECE, Koneru Lakshmaiah Education Foundation, Guntur, India</text>
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                <text>AN IOT-BASED COMPUTATIONAL INTELLIGENCE MODEL TO PERFORM GENE ANALYTICS IN PATERNITY TESTING AND COMPARISON FOR HEALTH 4.0</text>
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                <text>Correlation Clustering; Gene Analysis; Iot; Paternity Testing</text>
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                <text>Parental comparison and parenthood testing are essential in various legal and medical scenarios. The accuracy and reliability of these tests heavily rely on the gene analysis algorithms used. However, analyzing the quality of succession data are quite challenging due to the presence of detrimental characteristics. To address this issue, we propose using machine learning-based algorithms such as clustering (Correlation-based) and Classification (Modified Naive Bayesian) to separate these characteristics from the parent-child gene array. This progression helps to identify, validate, and select tools, techniques for scrutinizing indecent sequences, leading to accurate and reliable results. In this paper, we present an IoT-based intelligence tool for parental comparison that uses a secure gene analysis algorithm. The model employs multiple sensors and devices to collect genetic data, which is then securely processed and analyzed using contemporary algorithms. The suggested model uses advanced techniques such as encryption and decryption to ensure the privacy and confidentiality of the genetic information. Our experimental consequences reveal that the proposed model is reliable, secure, and provides accurate results. The model has the potential to be used in various legal and medical contexts where the security and reliability of genetic data are critical.  2023 Little Lion Scientific.</text>
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                <text>Arputharaj V.J.; Sankar K.; Kumar A.S.; Sridevi M.; Prasad D.D.</text>
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                <text>Journal of Theoretical and Applied Information Technology, Vol-101, No. 14, pp. 5781-5796.</text>
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                <text>Little Lion Scientific</text>
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                <text>ISSN: 19928645</text>
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                <text>Arputharaj V.J., Department of Computer Science, CHRIST (Deemed to Be University), India; Sankar K., Department of Computer Science and Engineering (Data Science), CVR College of Engineering, Telangana, Hyderabad, India; Kumar A.S., Department of Computer Science, Skyline University, Nigeria; Sridevi M., Department of Computer Science and Engineering, CVR College of Engineering, (V) Ibrahimpatnam (M), R R District, Telangana, Mangalpalli, 501 510, India; Prasad D.D., Department of ECE, Vishnu Institute of Technology, AP, Bhimavaram, India</text>
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                <text>An iot-based fog computing approach for retrieval of patient vitals</text>
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              <elementText elementTextId="158540">
                <text>Adafruit IO; Cloud computing; DHT11; Fog computing; Humidity; Internet of Things (IoT); Linux; MAX30100; Pulse oximeter sensor; Pulse rate; Python; Temperature; VNC viewer</text>
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                <text>Internet of Things (IoT) has been an interminable technology for providing real-time services to end users and has also been connected to various other technologies for an efficient use. Cloud computing has been a greater part in Internet of Things, since all the data from the sensors are stored in the cloud for later retrieval or comparison. To retrieve time-sensitive data to end users within a needed time, fog computing plays a vital role. Due to the necessity of fast retrieval of real-time data to end users, fog computing is coming into action. In this paper, a real-time data retrieval process has been done with minimal time delay using fog computing. The performance of data retrieval process using fog computing has been compared with that of cloud computing in terms of retrieval latency using parameters such as temperature, humidity, and heartbeat. With this experiment, it has been proved that fog computing performs better than cloud computing in terms of retrieval latency.  The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021.</text>
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                <text>Hilda Janice J.; Jingle I D.J.</text>
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                <text>Lecture Notes in Networks and Systems, Vol-132, pp. 177-183.</text>
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                <text>ISSN: 23673370</text>
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                <text>Balakrishnan D., Kalasalingam Academy of Research and Education, Krishnankoil, Tamilnadu, Anand Nagar, 626126, India; Raja J., Sri Manakula Vinyagar Engineering College, Pondicherry, Autonomous Institution, Pondicherry University, India; Rajagopal M., CHRIST (Deemed to Be University), Bangalore, India; Sudhakar K., Madanapalle Institute of Technology &amp;amp; Science, Madanapalle, India; Janani K., Dr.N.G.P. Institute of Technology, Coimbatore, India</text>
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                <text>Designing a secured transportation system to handover food items to various fair price shops is one of the objectives of smart city development in India. In this paper, an IoT-based tracking solution for moving goods carrying vehicle is proposed. A hardware prototype model is developed using different sensors with GPS/GPRS tracking module and is attached to the vehicle. An alarm is raised to make decision in case of trouble or malfunction. The data generated by the model during the movement of vehicle is encrypted using RSA algorithm and sent to cloud for monitoring by an application developed using PHP and analysis using MapReduce programming model. Experiments are conducted to study the feasibility of the developed model during deployment. From the experiment it is observed that, the developed hardware model and the application meet the objective of monitoring vehicle, safer recovery in case of malfunction and secured delivery of items. Copyright  2021 Inderscience Enterprises Ltd.</text>
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                <text>Ravichandran S.K., Department of CSE, Christ University, Karnataka, Bangalore, India; Chellasamy R., Government College of Engineering Salem, Tamil Nadu, Salem, India; Pugalendhi G.K., Anna University Regional Campus, Coimbatore, Tamil Nadu, Coimbatore, India</text>
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                <text>Materials Advances;Volume;6;Issue;14;pp.4905-4912</text>
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                <text>&lt;a href="https://doi.org/10.1039/d5ma00329f" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1039/d5ma00329f&lt;/a&gt; &lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/pages/publications/105008902082?origin=resultslist" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/pages/publications/105008902082?origin=resultslist&lt;/a&gt;</text>
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                <text>Salian R.D., Department of Physics and Electronics, Christ University, Bangalore, 560029, India; Kumar Das A., Department of Chemistry, Christ University, Bangalore, 560029, India; Kumbhakar P., Department of Physics and Electronics, Christ University, Bangalore, 560029, India</text>
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                <text>The development of sophisticated smart devices heavily relies on flexible soft actuators combined with near infrared (NIR) light responsive two-dimensional (2D) materials. Soft robots provide a number of benefits, such as flexibility, high sensitivity, compliance and security. Amidst many manufacturing and driving approaches, light has surfaced as a facilitator, aiding in the fabrication of soft actuators. Using few-layered molybdenum disulphide (MoS2) and poly(3,4-ethylenedioxythiophene)/poly(styrene sulfonate) (PEDOT:PSS), the current work aims to introduce a polymer nanocomposite film for soft actuator applications under NIR light exposure. The actuation behavior was impacted by PEDOT:PSS under NIR light exposure. In order to incorporate controllable deformation of the actuator, the photothermal properties of the composite film were investigated. In situ Raman spectroscopy and the density functional theory (DFT) calculation explain the structural change and energy optimization of PEDOT:PSS. A soft insect was further designed based on this photothermal property, which can deform under light exposure. Therefore, such flexible design has huge potential for soft robotics applications in modern technologies.  2025 RSC.</text>
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                <text>An Novel Cutting Edge ANN Machine Learning Algorithm for Sepsis Early Prediction and Diagnosis</text>
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                <text>Early detection and diagnosis of sepsis can significantly improve patient outcomes, but current diagnostic methods are limited. The problem addressed in this paper is the early detection and diagnosis of sepsis using machine learning algorithms. Sepsis is a life-threatening condition that can rapidly progress and cause organ failure, leading to increased mortality rates. Early detection and treatment of sepsis are critical for improving patient outcomes and reducing healthcare costs. However, sepsis can be challenging to diagnose, and existing methods have limitations in terms of accuracy and timeliness This research proposes a new cutting-edge Optimized Artificial Neural Network machine learning algorithm for sepsis early prediction and diagnosis. The proposed algorithm combines different data sources, including patient vital signs, laboratory results, and clinical notes, to predict the likelihood of sepsis development. The algorithm was evaluated on a large dataset of patient records and achieved promising results in terms of accuracy, Precision and Recall. The proposed algorithm can potentially serve as a valuable tool for clinicians in the early detection and diagnosis of sepsis, leading to better patient outcomes.  2023 American Institute of Physics Inc.. All rights reserved.</text>
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                <text>Kanna R.R.; Priya T.M.; Immanuel V.A.; Kirubanand V.B.; Senthilnathan T.; Rohini V.</text>
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                <text>AIP Conference Proceedings, Vol-2909, No. 1</text>
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                <text>American Institute of Physics Inc.</text>
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                <text>&lt;a href="https://doi.org/10.1063/5.0181885" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1063/5.0181885&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179878686&amp;amp;doi=10.1063%2F5.0181885&amp;amp;partnerID=40&amp;amp;md5=18cfb15733598513772dfe4db5e8471b" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179878686&amp;amp;doi=10.1063%2f5.0181885&amp;amp;partnerID=40&amp;amp;md5=18cfb15733598513772dfe4db5e8471b&lt;/a&gt;</text>
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                <text>ISSN: 0094243X; ISBN: 978-073544750-9</text>
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                <text>Kanna R.R., Department of Computer Science, CHRIST(Deemed to be University), Bangalore, India; Priya T.M., Department of Computer Science, CHRIST(Deemed to be University), Bangalore, India; Immanuel V.A., Department of Computer Science, CHRIST(Deemed to be University), Bangalore, India; Kirubanand V.B., Department of Computer Science, CHRIST(Deemed to be University), Bangalore, India; Senthilnathan T., Department of Computer Science, CHRIST(Deemed to be University), Bangalore, India; Rohini V., Department of Computer Science, CHRIST(Deemed to be University), Bangalore, India</text>
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                <text>An Objective Evaluation of Harris Corner and FAST Feature Extraction Techniques for 3D Reconstruction of Face in Forensic Investigation</text>
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              <elementText elementTextId="93202">
                <text>3D reconstruction; Features from accelerated segment test; Harris corner detection</text>
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                <text>3d reconstructed face images are the volumetric data from two dimensions, it can provide geometric information, which is very helpful for different application like facial recognition, forensic analysis, animation. Reconstructed face images can provide better visualization, than a two dimensional image can provide. For a proper 3d reconstruction one of primary step is feature extraction. The objective of this study is to conduct a comprehensive evaluation of two prominent traditional feature extraction techniques, namely Harris Corner and FAST (Features from Accelerated Segment Test), for the purpose of 3D reconstruction of face images in forensic analysis. In this research paper feature extraction was carried out using the Harris corner detection and FAST Feature technique. 3D reconstruction is completed using the retrieved features. In this study a comparative analysis was conducted assessing the aspect ratio, depth resolution. The results of the assessment provide valuable insights into the strengths and limitations of both techniques, aiding researchers and practitioners in selecting the most suitable method for 3D face image reconstruction applications.  2023, Ismail Saritas. All rights reserved.</text>
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              <elementText elementTextId="93204">
                <text>John S.; Danti A.</text>
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              <elementText elementTextId="93205">
                <text>International Journal of Intelligent Systems and Applications in Engineering, Vol-11, No. 4, pp. 535-545.</text>
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              <elementText elementTextId="93206">
                <text>Ismail Saritas</text>
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&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174803209&amp;amp;partnerID=40&amp;amp;md5=d2a15f0da2cdeccb915655ea9eb20e9c" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174803209&amp;amp;partnerID=40&amp;amp;md5=d2a15f0da2cdeccb915655ea9eb20e9c&lt;/a&gt;</text>
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                <text>ISSN: 21476799</text>
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                <text>John S., Department of Computer Science and Engineering, Christ University, Bengaluru, India; Danti A., Department of Computer Science and Engineering, Christ University, Bengaluru, India</text>
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      <name>Article</name>
      <description>Faculty Publications -Articles</description>
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          <element elementId="50">
            <name>Title</name>
            <description>A name given to the resource</description>
            <elementTextContainer>
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                <text>An objective function based technique for devignetting fundus imagery using MST</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="132180">
                <text>Average gradient of illumination component; Devignetting; Error of enhancement; Fundus imagery; Illumination correction; Modified sigmoid transform (MST); Objective function</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="41">
            <name>Description</name>
            <description>An account of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="132181">
                <text>Fundus photography is a powerful imaging modality that is utilized for detecting macular degeneration, retinal neoplasms, choroid disturbances, glaucoma and diabetic retinopathy. As the illumination source in fundus imaging is situated at the center of the fundus camera, the illumination at the peripheral regions of the images would be relatively less than the center, which is termed vignetting. Vignetting adversely affects the performance of computerized methods for analyzing fundus imagery. A devignetting method for fundus imagery based on the Modified Sigmoid Transform (MST) is proposed in this paper. Gain (A) and centering parameter (?) of MST have a crucial influence on its performance. For low values of the gain, local contrast is penalized, and the overall dynamic range is compressed. When the value of gain is very high, the images after the illumination correction will have a washed out appearance. The optimum value of gain is determined in this paper from an objective method based on two statistical indices, Average Gradient of Illumination Component (AGIC) and Error of Enhancement (EME). MST with gain value defined via objective methods is able to correct the uneven illumination in fundus images without penalizing the local contrast. The proposed method is compared with illumination equalization model, homomorphic filtering and Adaptive Gamma Correction (AGC) and was found to be superior in terms of naturality uniformity of background illumination, and computational speed.  2018</text>
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            <elementTextContainer>
              <elementText elementTextId="132182">
                <text>Shamsudeen F.M.; Raju G.</text>
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            <description>A related resource from which the described resource is derived</description>
            <elementTextContainer>
              <elementText elementTextId="132183">
                <text>Informatics in Medicine Unlocked, Vol-14, pp. 82-91.</text>
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                <text>Elsevier Ltd</text>
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            <description>A point or period of time associated with an event in the lifecycle of the resource</description>
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                <text>&lt;a href="https://doi.org/10.1016/j.imu.2018.10.001" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1016/j.imu.2018.10.001&lt;/a&gt;
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            <elementTextContainer>
              <elementText elementTextId="132187">
                <text>All Open Access; Gold Open Access</text>
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                <text>ISSN: 23529148</text>
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                <text>Online</text>
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                <text>Shamsudeen F.M., School of Computer Science, MG University, Kottayam, India, Department of Computer Applications, TKM College of Engineering, India; Raju G., Department of Computer Science and Engineering, Faculty of Engineering, Christ (deemed to be University), Kanminike, Kumbalagod Post, Bengaluru, 560074, India</text>
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                <text>An online signature method using DNA based bio-hash for positive identification and non-repudiation</text>
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                <text>This work focuses on using biological data as a unique feature to generate e-Signature. DNA, the blue print of life is of unique nature. The signature created using biological data will be difficult to repudiate in the scenario of a legal dispute. Applications of human DNA are not limited to molecular biology, with the advents of fast growing technologies it is possible to inject DNA into e-Signature for positive identification. The proposed methodology uses Signature DNA as a unique biological feature for the registrant. This work has various phases, the first phase includes creating the Signature DNA using hybridized unique DNA segments of the individual (Registrant) which is the unique identification of the user and difficult to duplicate and repudiate. It generates a Bio-Hash of the Signature DNA. The DNA-Hash generated serves for positive identification of the user which computed with the hash of the e-Document and a random value serve as a Bio-Sign (e-Signature) for the e-Document in the second phase. Bio-Sign converted into QR code with a link to the e-Sign service providers website will ensure usability for verification. In the verification phase the verifier scans the QR code which connects to the e-Service provider's web link. The service provider computes and verifies the document and ensures the e-Signature is valid or not to the verifier. If the signer repudiates the signature, positive identification using DNA helps to achieve Non-Repudiation, the last phase. In the scenario of a legal dispute, the registrant cannot repudiate as the authorities can provide positive identification using the DNA Signature for greater assurance. The proposed technique ensures authentication, integrity and Non-Repudiation with Zero knowledge scenario to the verifier.  2017 IEEE.</text>
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                <text>Sreeja C.S.; Misbahuddin M.</text>
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                <text>2017 International Conference on Public Key Infrastructure and its Applications, PKIA 2017 - Proceedings, Vol-2018-January, pp. 28-35.</text>
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                <text>Institute of Electrical and Electronics Engineers Inc.</text>
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                <text>&lt;a href="https://doi.org/10.1109/PKIA.2017.8278957" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/PKIA.2017.8278957&lt;/a&gt;
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                <text>ISBN: 978-153862356-5</text>
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                <text>Sreeja C.S., Dept. of Computer Science, Christ University, Bengaluru, India; Misbahuddin M., Computer Networks and Internet Engineering Division, C-DAC, Electronic City, Bengaluru, India</text>
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