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Alaaudeen KM, Selvarajan S, Manoharan H, Jhaveri RH. Intelligent robotics harvesting system process for fruits grasping prediction. Sci Rep 2024; 14:2820. [PMID: 38307901 PMCID: PMC10837192 DOI: 10.1038/s41598-024-52743-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 01/23/2024] [Indexed: 02/04/2024] Open
Abstract
This paper proposes and executes an in-depth learning-based image processing approach for self-picking apples. The system includes a lightweight one-step detection network for fruit recognition. As well as computer vision to analyze the point class and anticipate a correct approach position for each fruit before grabbing. Using the raw inputs from a high-resolution camera, fruit recognition and instance segmentation are done on RGB photos. The computer vision classification and grasping systems are integrated and outcomes from tree-grown foods are provided as input information and output methodology poses for every apple and orange to robotic arm execution. Before RGB picture data is acquired from laboratory and plantation environments, the developed vision method will be evaluated. Robot harvest experiment is conducted in indoor as well as outdoor to evaluate the proposed harvesting system's performance. The research findings suggest that the proposed vision technique can control robotic harvesting effectively and precisely where the success rate of identification is increased above 95% in case of post prediction process with reattempts of less than 12%.
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Affiliation(s)
- K M Alaaudeen
- Department of Computer Science and Engineering, Grace College of Engineering, Mullakkadu, Thoothukoodi, India
| | - Shitharth Selvarajan
- School of Built Environment, Engineering and Computing, Leeds Beckett University, LS1 3HE, Leeds, UK.
- Department of Computer Science, Kebri Dehar University, Kebri Dehar, Ethiopia.
| | - Hariprasath Manoharan
- Department of Electronics and Communication Engineering, Panimalar Engineering College, Poonamallee, Chennai, Tamil Nadu, India
| | - Rutvij H Jhaveri
- Department of Computer Science and Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar, India
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2
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Manoharan H, Selvarajan S, Aluvalu R, Abdelhaq M, Alsaqour R, Uddin M. Diagnostic structure of visual robotic inundated systems with fuzzy clustering membership correlation. PeerJ Comput Sci 2023; 9:e1709. [PMID: 38192458 PMCID: PMC10773856 DOI: 10.7717/peerj-cs.1709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 10/29/2023] [Indexed: 01/10/2024]
Abstract
The process of using robotic technology to examine underwater systems is still a difficult undertaking because the majority of automated activities lack network connectivity. Therefore, the suggested approach finds the main hole in undersea systems and fills it using robotic automation. In the predicted model, an analytical framework is created to operate the robot within predetermined areas while maximizing communication ranges. Additionally, a clustering algorithm with a fuzzy membership function is implemented, allowing the robots to advance in accordance with predefined clusters and arrive at their starting place within a predetermined amount of time. A cluster node is connected in each clustered region and provides the central control center with the necessary data. The weights are evenly distributed, and the designed robotic system is installed to prevent an uncontrolled operational state. Five different scenarios are used to test and validate the created model, and in each case, the proposed method is found to be superior to the current methodology in terms of range, energy, density, time periods, and total metrics of operation.
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Affiliation(s)
- Hariprasath Manoharan
- Department of Electronics and Communication Engineering, Panimalar Engineering College, Poonamallee, Chennai, Tamil Nadu, India
| | | | - Rajanikanth Aluvalu
- Department of IT, Chaitanya Bharathi Institute of Technology, Hyderabad, India
| | - Maha Abdelhaq
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Raed Alsaqour
- Department of Information Technology, College of Computing and Informatics, Saudi Electronic University, Riyadh, Saudi Arabia
| | - Mueen Uddin
- College of Computing and IT, University of Doha for Science and Technology, Qatar
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3
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Selvarajan S, Manoharan H, Iwendi C, Al-Shehari T, Al-Razgan M, Alfakih T. SCBC: Smart city monitoring with blockchain using Internet of Things for and neuro fuzzy procedures. Math Biosci Eng 2023; 20:20828-20851. [PMID: 38124578 DOI: 10.3934/mbe.2023922] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
The security of the Internet of Things (IoT) is crucial in various application platforms, such as the smart city monitoring system, which encompasses comprehensive monitoring of various conditions. Therefore, this study conducts an analysis on the utilization of blockchain technology for the purpose of monitoring Internet of Things (IoT) systems. The analysis is carried out by employing parametric objective functions. In the context of the Internet of Things (IoT), it is imperative to establish well-defined intervals for job execution, ensuring that the completion status of each action is promptly monitored and assessed. The major significance of proposed method is to integrate a blockchain technique with neuro-fuzzy algorithm thereby improving the security of data processing units in all smart city applications. As the entire process is carried out with IoT the security of data in both processing and storage units are not secured therefore confidence level of monitoring units are maximized at each state. Due to the integration process the proposed system model is implemented with minimum energy conservation where 93% of tasks are completed with improved security for about 90%.
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Affiliation(s)
- Shitharth Selvarajan
- School of Built Environment, Engineering and Computing, Leeds Beckett University, LS1 3HE Leeds, United Kingdom
| | - Hariprasath Manoharan
- Department of Electronics and Communication Engineering, Panimalar Engineering College, Poonamallee, Chennai-600123, Tamil Nadu, India
| | | | - Taher Al-Shehari
- Department of Self-Development Skills-Computer Skills, Common First Year Deanship, King Saud University, 11362, Riyadh, Saudi Arabia
| | - Muna Al-Razgan
- Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11345, Saudi Arabia
| | - Taha Alfakih
- Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
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4
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Manoharan H, Yuvaraja T, Kuppusamy R, Radhakrishnan A. Implementation of explainable artificial intelligence in commercial communication systems using micro systems. Sci Prog 2023; 106:368504231191657. [PMID: 37533330 PMCID: PMC10399265 DOI: 10.1177/00368504231191657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/04/2023]
Abstract
The developments in the field of artificial intelligence (AI) and decision making systems are identified as virtuous models for banking and finance sector (BFS) applications. Even though AI provides great advantage in application changes it is essential to remodel the system using explainable artificial intelligence (XAI) design system. Also the standard sensing models provides appropriate monitoring values but huge size of sensors is considered as a major drawback. Thus two diverse problems are addressed in this research work where XAI has been integrated with micro electro-mechanical systems (MEMS) for solving the problems related to BFS applications. Further the data security has been enhanced as XAI is implemented with conviction managements and real time experiments are carried out by developing a unique application by integrating new mathematical designs. To validate the effectiveness of BFS application the developed model is tested with five scenarios which includes multiple parametric arrangements with interpretability process. The tested and compared outcomes with existing models indicates that XAI and MEMS provides inordinate improvements in terms of data impairments thus increasing the transparency of the projected system to an average 96%.
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Affiliation(s)
- Hariprasath Manoharan
- Department of Electronics and Communication Engineering, Panimalar Engineering College, Poonamallee, Chennai, Tamil Nadu, India
| | - Teekaraman Yuvaraja
- School of Engineering and Computing, American International University, Saad Al Abdullah, Al Jahra, Kuwait
| | - Ramya Kuppusamy
- Department of Electrical and Electronics Engineering, Sri Sairam College of Engineering, Bengaluru, Karnataka, India
| | - Arun Radhakrishnan
- Faculty of Electrical and Computer Engineering, Jimma Institute of Technology, Jimma University, Jimma, Oromia, Ethiopia
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5
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Selvarajan S, Manoharan H, Iwendi C, Alsowail RA, Pandiaraj S. A comparative recognition research on excretory organism in medical applications using artificial neural networks. Front Bioeng Biotechnol 2023; 11:1211143. [PMID: 37397968 PMCID: PMC10312079 DOI: 10.3389/fbioe.2023.1211143] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 06/08/2023] [Indexed: 07/04/2023] Open
Abstract
Purpose: In the contemporary era, a significant number of individuals encounter various health issues, including digestive system ailments, even during their advanced years. The major purpose of this study is based on certain observations that are made in internal digestive systems in order to prevent severe cause that usually occurs in elderly people. Approach: To solve the purpose of the proposed method the proposed system is introduced with advanced features and parametric monitoring system that are based on wireless sensor setups. The parametric monitoring system is integrated with neural network where certain control actions are taken to prevent gastrointestinal activities at reduced data loss. Results: The outcome of the combined process is examined based on four different cases that is designed based on analytical model where control parameters and weight establishments are also determined. As the internal digestive system is monitored the data loss that is present with wireless sensor network must be reduced and proposed approach prevents such data loss with an optimized value of 1.39%. Conclusion: Parametric cases were conducted to evaluate the efficacy of neural networks. The findings indicate a significantly higher effectiveness rate of approximately 68% when compared to the control cases.
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Affiliation(s)
| | - Hariprasath Manoharan
- Department of Electronics and Communication Engineering, Panimalar Engineering College, Chennai, India
| | - Celestine Iwendi
- School of Creative Technologies, University of Bolton, Bolton, United Kingdom
| | - Rakan A. Alsowail
- Computer Skills, Self-Development Skills Department, Deanship of Common First Year, King Saud University, Riyadh, Saudi Arabia
| | - Saravanan Pandiaraj
- Computer Skills, Self-Development Skills Department, Deanship of Common First Year, King Saud University, Riyadh, Saudi Arabia
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Hasanin T, Manoharan H, Alterazi HA, Srivastava G, Selvarajan S, Lin JCW. Mathematical approach of fiber optics for renewable energy sources using general adversarial networks. Front Ecol Evol 2023. [DOI: 10.3389/fevo.2023.1132678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023] Open
Abstract
It is significantly more challenging to extend the visibility factor to a higher depth during the development phase of a communication system for subterranean places. Even if there are numerous optical fiber systems that provide the right energy sources for intended panels, the visibility parameter is not optimized past a certain point. Therefore, the suggested method looks at the properties of a fiber optic communication system that is integrated with a certain energy source while having external panels. A regulating state is established in addition to characteristic analysis by minimizing the reflection index, and the integration of the general adversarial network (GAN) optimizes both central and layer formations in exterior panels. Thus, the suggested technique uses the external noise factor to provide relevant data to the control center via fiber optic shackles. As a result, the normalized error is smaller, boosting the suggested method's effectiveness in all subsurface areas. The created mathematical model is divided into five different situations, and the results are simulated using MATLAB to test the effectiveness of the anticipated strategy. Additionally, comparisons are done for each of the five scenarios, and it is found that the proposed fiber-optic method for energy sources is far more effective than current methodologies.
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Shitharth S, Khadidos AO, Alshareef AM, Manoharan H, Khadidos AO. Application Of Improved Support Vector Machine For Pulmonary Syndrome Exposure With Computer Vision Measures. Curr Bioinform 2023. [DOI: 10.2174/1574893618666230206121127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Background:
In many medically developed applications, the process of early diagnosis in cases of pulmonary disease does not exist. Many people experience immediate suffering due to the lack of early diagnosis, even after becoming aware of breathing difficulties in daily life. Because of this, identifying such hazardous diseases is crucial, and the suggested solution combines computer vision and communication processing techniques. As computing technology advances, a more sophisticated mechanism is required for decision-making.
Objective:
The major objective of the proposed method is to use image processing to demonstrate computer vision-based experimentation for identifying lung illness. In order to characterize all the uncertainties that are present in nodule segments, an improved support vector machine is also integrated into the decision-making process.
Method:
As a result, the suggested method incorporates an Improved Support Vector Machine (ISVM) with a clear correlation between various margins. Additionally, an image processing technique is introduced where all impacted sites are marked at high intensity to detect the presence of pulmonary syndrome. Contrary to other methods, the suggested method divides the image processing methodology into groups, making the loop generation process much simpler.
Results:
Five situations are taken into account to demonstrate the effectiveness of the suggested technique, and test results are compared with those from existing models.
Conclusion:
The proposed technique with ISVM produces 83 percent of successful results.
other:
This is real-time work done with the designed operative device for pulmonary disease identification.
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Affiliation(s)
- S Shitharth
- Department of Computer Science & Engineering, Kebri Dehar University, Kebri Dehar, Ethiopia
| | - Adil O. Khadidos
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Abdulrhman M. Alshareef
- Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Hariprasath Manoharan
- Department of Electronics and Communication Engineering, Panimalar Engineering College, Poonamallee, Chennai, India
| | - Alaa O. Khadidos
- Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
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8
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Shitharth S, Alotaibi FS, Manoharan H, Khadidos AO, Alyoubi KH, Alshareef AM. Reconnoitering the significance of securty using multiple cloud environments for conveyance applications with blowfish algorithm. J Cloud Comp 2022. [DOI: 10.1186/s13677-022-00351-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
AbstractIn recent years the process of transportation needs a highly effective traffic system in order to monitor all consumer goods as many goods are left out at different locations. To handle such moving cases cloud platform is highly helpful as with respect to geographical location the goods are mapped in correct form. However incorporation of single cloud platform does not provide sufficient amount of storage about all goods thus a multiple cloud platform is introduced in proposed system. As multiple cloud platform is provided the security features of each data base system is also checked and enhanced using encryption keys. Moreover for proper operating conditions of multiple cloud platforms an analytical model is designed that synchronizes necessary data at end system. The defined analytical model focuses on solving multiple objectives that are related to critical energy problems where demand problems are reduced. Further the encryption process is carried out using Improved BlowFish Algorithm (IBFA) by allocating proper resources with decryption keys. To validate the effectiveness of proposed method five scenarios are considered where all scenario outcomes proves to be much higher than existing models by an average of 43%.
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9
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Alterazi HA, Kshirsagar PR, Manoharan H, Selvarajan S, Alhebaishi N, Srivastava G, Lin JCW. Prevention of Cyber Security with the Internet of Things Using Particle Swarm Optimization. Sensors (Basel) 2022; 22:6117. [PMID: 36015878 PMCID: PMC9413110 DOI: 10.3390/s22166117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 08/11/2022] [Accepted: 08/12/2022] [Indexed: 06/15/2023]
Abstract
High security for physical items such as intelligent machinery and residential appliances is provided via the Internet of Things (IoT). The physical objects are given a distinct online address known as the Internet Protocol to communicate with the network's external foreign entities through the Internet (IP). IoT devices are in danger of security issues due to the surge in hacker attacks during Internet data exchange. If such strong attacks are to create a reliable security system, attack detection is essential. Attacks and abnormalities such as user-to-root (U2R), denial-of-service, and data-type probing could have an impact on an IoT system. This article examines various performance-based AI models to predict attacks and problems with IoT devices with accuracy. Particle Swarm Optimization (PSO), genetic algorithms, and ant colony optimization were used to demonstrate the effectiveness of the suggested technique concerning four different parameters. The results of the proposed method employing PSO outperformed those of the existing systems by roughly 73 percent.
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Affiliation(s)
- Hassan A. Alterazi
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 22254, Saudi Arabia
| | - Pravin R. Kshirsagar
- Department of Artificial Intelligence, G. H Raisoni College of Engineering, Nagpur 440016, India
| | - Hariprasath Manoharan
- Department of Electronics and Communication Engineering, Panimalar Engineering College, Poonamallee, Chennai 600123, India
| | - Shitharth Selvarajan
- Department of Computer Science, Kebri Dehar University, Kebri Dehar 001, Ethiopia
| | - Nawaf Alhebaishi
- Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 22254, Saudi Arabia
| | - Gautam Srivastava
- Department of Mathematics and Computer Science, Brandon University, Brandon, MB R7A 6A9, Canada
- Research Center for Interneural Computing, China Medical University, Taichung 406040, Taiwan
| | - Jerry Chun-Wei Lin
- Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, 5063 Bergen, Norway
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10
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Kshirsagar PR, Manoharan H, Selvarajan S, Alterazi HA, Singh D, Lee HN. Perception Exploration on Robustness Syndromes With Pre-processing Entities Using Machine Learning Algorithm. Front Public Health 2022; 10:893989. [PMID: 35784247 PMCID: PMC9243559 DOI: 10.3389/fpubh.2022.893989] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 04/27/2022] [Indexed: 11/13/2022] Open
Abstract
The majority of the current-generation individuals all around the world are dealing with a variety of health-related issues. The most common cause of health problems has been found as depression, which is caused by intellectual difficulties. However, most people are unable to recognize such occurrences in them, and no procedures for discriminating them from normal people have been created so far. Even some advanced technologies do not support distinct classes of individuals as language writing skills vary greatly across numerous places, making the central operations cumbersome. As a result, the primary goal of the proposed research is to create a unique model that can detect a variety of diseases in humans, thereby averting a high level of depression. A machine learning method known as the Convolutional Neural Network (CNN) model has been included into this evolutionary process for extracting numerous features in three distinct units. The CNN also detects early-stage problems since it accepts input in the form of writing and sketching, both of which are turned to images. Furthermore, with this sort of image emotion analysis, ordinary reactions may be easily differentiated, resulting in more accurate prediction results. The characteristics such as reference line, tilt, length, edge, constraint, alignment, separation, and sectors are analyzed to test the usefulness of CNN for recognizing abnormalities, and the extracted features provide an enhanced value of around 74%higher than the conventional models.
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Affiliation(s)
- Pravin R. Kshirsagar
- Department of Artificial Intelligence, G.H. Raisoni College of Engineering, Nagpur, India
| | - Hariprasath Manoharan
- Department of Electronics and Communication Engineering, Panimalar Institute of Technology, Chennai, India
- Hariprasath Manoharan
| | - Shitharth Selvarajan
- Department of Computer Science and Engineering, Kebri Dehar University, Kebri Dehar, Ethiopia
| | - Hassan A. Alterazi
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Dilbag Singh
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, South Korea
| | - Heung-No Lee
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, South Korea
- *Correspondence: Heung-No Lee
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Manoharan H, Selvarajan S, Yafoz A, Alterazi HA, Uddin M, Chen CL, Wu CM. Deep Conviction Systems for Biomedical Applications Using Intuiting Procedures With Cross Point Approach. Front Public Health 2022; 10:909628. [PMID: 35677767 PMCID: PMC9168426 DOI: 10.3389/fpubh.2022.909628] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 04/14/2022] [Indexed: 11/26/2022] Open
Abstract
The production, testing, and processing of signals without any interpretation is a crucial task with time scale periods in today's biological applications. As a result, the proposed work attempts to use a deep learning model to handle difficulties that arise during the processing stage of biomedical information. Deep Conviction Systems (DCS) are employed at the integration step for this procedure, which uses classification processes with a large number of characteristics. In addition, a novel system model for analyzing the behavior of biomedical signals has been developed, complete with an output tracking mechanism that delivers transceiver results in a low-power implementation approach. Because low-power transceivers are integrated, the cost of implementation for designated output units will be decreased. To prove the effectiveness of DCS feasibility, convergence and robustness characteristics are observed by incorporating an interface system that is processed with a deep learning toolbox. They compared test results using DCS to prove that all experimental scenarios prove to be much more effective for about 79 percent for variations with time periods.
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Affiliation(s)
- Hariprasath Manoharan
- Department of Electronics and Communication Engineering, Panimalar Institute of Technology, Chennai, India
| | - Shitharth Selvarajan
- Department of Computer Science & Engineering, Kebri Dehar University, Kebri Dehar, Ethiopia
| | - Ayman Yafoz
- Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Hassan A. Alterazi
- Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Mueen Uddin
- School of Digital Science, University Brunei Darussalam, Bandar Seri Begawan, Brunei
| | - Chin-Ling Chen
- School of Information Engineering, Changchun Sci-Tech University, Changchun, China
- Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung, Taiwan
- School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China
| | - Chih-Ming Wu
- School of Civil Engineering and Architecture, Xiamen University of Technology, Xiamen, China
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Teekaraman Y, Manoharan H, Kuppusamy R. Examining the effect of intellectual devices for healthiness using flower bee algorithm. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07172-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
AbstractIn modern days, for the perseverance of monitoring the vigor of a specific individual, the Wireless Body Sensor Networks have been emerging as an auspicious platform. The foremost challenge such as energy, path loss, and transmission distance that is prevalent from early days has been addressed in this article. The focal notion specified in this exploration is to discover the sensor locations on a human body that is much suitable for interconnecting the information about the intact body thus satisfying all the objectives. The focal difference between the proposed technique and the existing methods is that the projected methodology enacts multiple objectives as an alternative of distinct impartial. Further, the problem of finding the locations of sensors has been executed on an online monitoring system using MATLAB platform where the consequences are found to be improved practically for about 65% when compared with existing literature.
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13
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Kshirsagar PR, Manoharan H, Shitharth S, Alshareef AM, Albishry N, Balachandran PK. Deep Learning Approaches for Prognosis of Automated Skin Disease. Life (Basel) 2022; 12:life12030426. [PMID: 35330177 PMCID: PMC8951408 DOI: 10.3390/life12030426] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 03/12/2022] [Accepted: 03/13/2022] [Indexed: 01/19/2023] Open
Abstract
Skin problems are among the most common ailments on Earth. Despite its popularity, assessing it is not easy because of the complexities in skin tones, hair colors, and hairstyles. Skin disorders provide a significant public health risk across the globe. They become dangerous when they enter the invasive phase. Dermatological illnesses are a significant concern for the medical community. Because of increased pollution and poor diet, the number of individuals with skin disorders is on the rise at an alarming rate. People often overlook the early signs of skin illness. The current approach for diagnosing and treating skin conditions relies on a biopsy process examined and administered by physicians. Human assessment can be avoided with a hybrid technique, thus providing hopeful findings on time. Approaches to a thorough investigation indicate that deep learning methods might be used to construct frameworks capable of identifying diverse skin conditions. Skin and non-skin tissue must be distinguished to detect skin diseases. This research developed a skin disease classification system using MobileNetV2 and LSTM. For this system, accuracy in skin disease forecasting is the primary aim while ensuring excellent efficiency in storing complete state information for exact forecasts.
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Affiliation(s)
- Pravin R. Kshirsagar
- Department of Artificial Intelligence, G.H. Raisoni College of Engineering, Nagpur 412207, India;
| | - Hariprasath Manoharan
- Department of Electronics and Communication Engineering, Panimalar Institute of Technology, Poonamallee, Chennai 600123, India;
| | - S. Shitharth
- Department of Computer Science & Engineering, Kebri Dehar University, Kebri Dahar P.O. Box 250, Ethiopia;
| | - Abdulrhman M. Alshareef
- Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
| | - Nabeel Albishry
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
| | - Praveen Kumar Balachandran
- Department of Electrical and Electronics Engineering, Vardhaman College of Engineering, Hyderabad 501218, India
- Correspondence:
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14
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Nicol MJ, Nichol MJ, Manoharan H, Marfell-Jones MJ, Meha-Hoerara K, Milne R, O'Connell M, Olliver J, Teekman B. Issues in adolescent health: a challenge for nursing. Contemp Nurse 2002; 12:155-63. [PMID: 12188150 DOI: 10.5172/conu.12.2.155] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The young people of today are the greatest investment we as adults have in our future. The care and nurturing we afford the adolescent is just as important as that which we afford to children or the elderly. Although most adolescents have a preoccupation with their bodies, they do not always engage in activities that will protect and develop them. Adolescents are often exposed to peer pressure, the effects of which may impact negatively on their behaviour and their health. Many adolescent health and behavioural issues evolve from developmental changes and can manifest in a confrontational attitude toward society, parents and others. They are hormonally 'fully charged', and their adolescent sexuality can have enormous effects on their future physical, psychosocial, moral and sexual development. Nurses have a pivotal role to play in ensuring children and adolescents learn the facts relating to the consequences of engaging in unhealthy behaviour and lifestyle. Nurses must also encourage parents to model and reinforce good health practices, such as serving balanced and nutritious meals at regular times and planning positive family activities. In this paper we review some of the salient issues in adolescent health today.
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Affiliation(s)
- M J Nicol
- Division of Nursing, Universal College of Learning, Palmerston North, New Zealand
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Sturm JC, Manoharan H, Lenchyshyn LC, Thewalt ML, Rowell NL, Noël J, Houghton DC. Well-resolved band-edge photoluminescence of excitons confined in strained Si1-xGex quantum wells. Phys Rev Lett 1991; 66:1362-1365. [PMID: 10043186 DOI: 10.1103/physrevlett.66.1362] [Citation(s) in RCA: 18] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
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