Arunmozhi Sinouvassane

RESEARCH INTERESTS

Wireless Communication
IOT and AI and ML

Scopus Publications

Healthy/Unhealthy Tomato Fruit Grading Using Deep-Learning with Features Fusion
M. Jayekumar, S. Arunmozhi, and S. Prabha
IEEE
Food products automatic grading is a necessary process for appropriate selection, packaging, and marketing. Food grading based on artificial-intelligence (AI) technique for automating the whole process. This work considered the tomato for the study and this procedure is employed to categorize vegetables according to their grade. Proposed work implements a Deep-Learning (DL) based technique to grade the tomato. The stages of this research includes; image collection and resizing, feature extraction with a chosen model, feature reduction with 50% dropout and serial features integration to generate Fused-features Vector (FV), and classification using 3-fold cross validation and confirmation. The efficacy of this tool is evaluated using different DL-model using the chosen image data and this work achieved a detection accuracy >98% when FV based classification is executed using SoftMax. This result confirms that the proposed scheme helps to achieve a better result on the chosen tomato image data.


Normal/Cataract Detection in Fundus Image Using Individual and Fused ResNet Features
S. Arunmozhi, P. Arunagiri, and S. Prabha
IEEE
The visual sensory information collected by the eyes is crucial for accurate perception and decision-making in the brain. Any ocular ailment will disrupt this process, perhaps resulting in mild to severe vision-related complications. Ocular ailments are primarily attributable to disease or senescence. Age-related eye illness is a prevalent concern that necessitates prompt detection and intervention. Cataract is a prevalent age-related ocular condition that results in mild to severe visual impairment and necessitates a small surgical intervention for correction. The image-guided identification of cataracts is a clinical practice, and this research intends to present a Deep Learning (DL) method to categorize Retinal Fundus Images (RFI) as normal or cataract. The proposed scheme comprises several phases: (i) image acquisition and resizing to 224×224 pixels, (ii) feature extraction utilizing a DL- model, (iii) optimal model selection, feature reduction with 50% dropout, and concatenation of serial features, and (iv) classification accompanied by 3-fold cross-validation to validate performance. This study evaluates the efficacy of the suggested DL-tool utilizing both traditional and fused-features. The experimental results of this work demonstrate that the fused-features technique achieves > 98% accuracy when applied to SM-based categorization.


A Delay Phase Precoder Design For Terahertz Massive MIMO Beyond 5G Communication System
R. Valli, Jayekumar M., Madhumitha. A, and S. Arunmozhi
IEEE
The future of wireless communication systems beyond 5G (B5G) is expected to leverage the terahertz (THz) band to achieve unprecedented data rates. Delay phase precoding in massive MIMO beyond 5G is like making sure each antenna talks at the right time and in the right way to improve communication. The proposed technique leverages the distinctive characteristics of the THz band, such as highly directional transmissions and sparse channel responses, to mitigate channel impairments and enhance signal quality. The design aims to exploit the spatial domain by incorporating delay and phase adjustments, ensuring efficient signal transmission and reception. By leveraging the unique properties of THz frequencies and massive MIMO configurations, the proposed precoder design enhances spectral efficiency and system reliability, thus facilitating the realization of high-data-rate communication systems beyond the capabilities of current 5G networks. This technique helps reduce interference and makes wireless signals stronger and more reliable, which is super important for faster and better connections in the next generation of wireless technology. The impact of hardware constraints, such as phase shifters and analog-to-digital converters (ADCs), on the practical implementation of the proposed precoding algorithm are explored. The proposed delay phase precoder is designed to selects the optimal delay and phase precoding Matrix at the transmitter end. Our simulation results also validates the improvement in performance in terms of spectral efficiency and energy efficiency.


Lightweight Deep-Learning Based Metal/Plastic Trash Detection with Fused Features
S. Arunmozhi and S. Prabha
IEEE
Recently, several approaches have been proposed for the proper management of waste to achieve environmental sustainability and resource conservation. Proper garbage disposal reduces pollution and prevents the contamination of water and soil. This study proposes a deep-learning tool (DLT) for the automatic detection of metal/plastic waste to enhance the recycling process. This work created the DLT by utilizing a Lightweight Deep Learning (LWDL) model to establish a more efficient and less intricate method. The stages in this tool encompass: the collection of trash images and their resizing to 224x224x3 pixels, feature extraction utilizing the selected LWDL model, performance evaluation through binary classification with three-fold cross-validation, and the identification of the top two LWDL models to produce fused deep features (FDF) to enhance classification accuracy. The experimental method employs the SoftMax utilizing individual LWDL features and FDF, and the findings affirm that the proposed tool yields superior metal/plastic waste classification results compared to the individual features. In the future, this technique may be utilized to categorize authentic waste photographs captured with a digital camera.


ReDiaSafe: A Novel Approach for Predicting 30-Day Diabetes Patient Readmission Risk
Auxilia Michael, Hema Arularasi Murugan, Arthi Manikandan, Jayapratha Natarajan, and S. Arunmozhi
IEEE
Diabetes is an ailment where life becomes dangerous because most readmissions occur within 30 days since patients do not have any means of awareness towards controlling this health issue. Hospital readmission prevention, especially regarding inpatient or outpatient care, has always been very important in improving patient care, satisfaction, and cost benefits in health care. This paper is on ReDiaSafe as a machine learning tool for predicting the probability of 30-day hospital readmissions in a diabetic patient. It intended to identify useful data attributes and build predictive models that are able to classify readmission risks with accuracy. Thorough research on user input data had been conducted and feature analysis done to elicit significant parameters that influence readmission likelihood. Several machine learning technologies, notably LightGBM and logistic regression; have been applied to classify patients into risk categories: low, medium, and high risk. Our results showed several features with strong relationships to readmission risk, including demographic variables, past admissions, lab results, and medication use patterns. Out of all models tested, the one found most promising with regard to predictions was LightGBM, as this learned well to differentiate risk categories. Furthermore, the combining with an interactive AI chatbot provides individual healthcare suggestions and recommendations based on personalized risk assessments to better engage and support the users. This is further enhanced by the ReDiaSafe tool, which goes a long way in harnessing state-of-the-art machine learning techniques for appalling and really meaningful hospital readmission risk estimates for diabetic patients. This way, better quality of life for the patients is achieved with this method towards life preservation. However, this will also potentially offer general healthcare practitioners segmented interventions towards a much more reduced readmission rate.


Monitoring Street light using Power Line Carrier Communication (PLCC) & SCADA
A. Baskaran, S. Arunmozhi, and S. Vishnu
IEEE
Streetlights provide illumination at night and provides safety on roads. Conventional monitoring of streetlights involves periodic human inspection which is time consuming, costly and sometimes unsafe. We proposed a remote monitoring and control of streetlights mounted on power transmission line posts using Power Line Carrie r Communication (PLCC) technology. PLC C allows transmission of data over existing power cables and does not require additional infrastructure. A PLCC modem is installed at each streetlight fixture which transmits status updates like lamp ON/OFF, voltage, current drawn etc. to a data concentrator unit via the low frequency power line network. The concentrator unit sends this information to a control center via a wireless or wired backhaul network. Such a solution ensures 24x7 monitoring of streetlights with minimum additional hardware investment. Mathematical models of different PLCC modulation schemes are developed and their performance is analyzed and compared through simulations. These Data were synchronized with SCADA to monitor the Streetlight. Practical implementation issues are also discussed.


Automatic Concrete Surface Crack Recognition Using EfficientNetV2 Variants
A. Rama, Robertas Damaševičius, S. Arunmozhi, Mazin Abed Mohammed, Ragheed Hussam, and V. Rajinikanth
IEEE
Building health and structural integrity are important to ensure occupant safety and long-term durability. Monitoring and assessing building condition regularly ensures that potential problems are identified early. This research aims to develop and implement a deep-learning (DL) supported tool to examine the concrete surface images collected using digital camera. The data gathering forms the first stage and the chosen DL algorithm is used for feature extraction followed by the feature selection and then fusion is done and finally a fivefold cross verification schema is applied for a binary classification. This work considered the EfficientNetV2 (ENV2) model with variants like small, medium and large for the examination. The investigation is implemented using individual and fused deep-features and the performance of the developed tool is confirmed using the achieved accuracy. The outcome of this research confirms that the proposed tool produces a betterment of >96% accuracy with individual-features and 100% accuracy with fused-features. These results confirm that the proposed scheme provides significant result during the crack detection.


CNN Framework for Automatic Segmentation of Breast Section from Thermal Images
A. Rama, K.B. Sudeepa, S. Arunmozhi, Mazin Abed Mohammed, Aqeel Ali, and V. Rajinikanth
IEEE
Breast cancer is considered a severe illness in the female society, and if left untreated, it can be fatal. It is always desirable to detect the BC early utilizing a selected imaging strategy. Thermogram supported breast abnormality detection is one of the recent technique and this gives the necessary information in the form of the distributed thermal pattern. This research aims to implement the Convolutional-Neural-Network (CNN) based segmentation technique to extract breast region from the chosen thermogram. This scheme's multiple stages include: (i) data collecting and processing, (ii) implementation of CNN segmentation to extract the breast, (iii) comparing it to the binary-mask and computing performance metrics, and (iv) performance evaluation and verification of the chosen CNN techniques. Pre-trained CNN segmentations are used in this work to extract the necessary section from the thermogram, and the experimental results show that the VGG-UNet methodology helps to extract the essential region with an enhanced accuracy of 97.260.64% when compared to other CNN approaches.


Lightweight Deep-Learning Based Music Genre Classification: A Study
A. Rama, N. Mythili, M.P. Rajakumar, S. Arunmozhi, Mazin Abed Mohammed, and V. Rajinikanth
IEEE
Deep-learning (DL) applications that are used real-time across various industries have gained a lot of traction and have become increasingly popular, especially when it comes to data-driven recommendation systems. This work aims to develop a DL scheme to support the music-recommendation system (MS) based on the music data. The various phases of this scheme includes; (i) data collection and signal-image conversion to get the necessary RGB scale images from the data, (ii) pre-trained DL based feature extraction, and (iii) deep-features based detection to recommend the appropriate music. This research considered the classic- (CL) and pop-music (PO) for the examination and the achieved results are evaluated to substantiate the performance of this arrangement. In this work, the signal-image conversion procedure is implemented to convert 1D signal to 2D image and then it is examined using proposed technique. The experimental outcome is separately presented for (i) spectrogram and (ii) synchro-extracting-transform and obtained results are presented. The experimental investigation is presented with MobileNet variants and this study authorizes that the implemented scheme achieved a better detection MobileNetV2 (>99%) compared to other schemes in this study.


ResNet/ResNetV2 Supported Framework for Rice-Plant Disease Detection Using Leaf Data
R. Santhosh, S. Arunmozhi, and Nilanjan Tewari
IEEE
Computer algorithm supported data-analysis is one of the common practices to solve the chosen data-evaluation tasks. Recently, the computer algorithm assisted image-evaluation is emerged as one of the capable research field. The purpose of this research is to use leaf information to create a deep learning scheme to investigate rice plant disease (RD). This approach consists of three stages: (i) gathering and resizing leaf images; (ii) extracting deep features using selected DS; and (iii) using SoftMax based binary classification with 5-fold cross validation. In this work, 1000 photos from each class are examined, and the categorization result that is obtained is confirmed. This study considered the ResNet and ResNetV2 variants for the examination and the achieved result is separately verified for 50, 101 and 152 layered schemes. This investigation task confirms that the ResNet variants provided >91% accuracy and the ResNetV2 variants provided an accuracy of >94%. This demonstrates that the proposed method performs satisfactorily on the selected leaf data, and going forward, real-time data may be taken into consideration to validate the technique's effectiveness for RD detection.


Detection of TB from Chest X-ray: A Study with EfficientNet
A. Rama, M. P. Rajakumar, N. Mythili, S. Arunmozhi, Mazin Abed Mohammed, and V. Rajinikanth
IEEE
The lung is one of the prime organs, and any disease in the lung causes mild to severe breathing problems; untreated lung disease will lead to several complications. Tuberculosis (TB) is a lung ailment that needs premature recognition and handling. The primary objective is to employ the deep-learning (DL) based TB detection using chest $X$-rays. Various stages of the proposed scheme consist of (i) data collection and resizing, (ii) DL-supported feature extraction, (iii) binary classification and five-fold cross-validation, and (iv) comparison with earlier results and confirming the merit of the scheme. This research implements EfficientNet (EN) variants to classify the chosen $\\mathrm{X}$-rays into healthy/TB classes using the SoftMax classifier. The proposed scheme with EN_B2 (ENB2) has been successful in providing an accuracy of $96{\\% }$ as far as detection accuracy is considered when compared to other methods. The superiority of the suggested strategy is also confirmed by an analysis using the most recent technology, which confirms the worth of the proposed system on the chosen $\\mathrm{X}$-ray imagery.


A study on segmentation of leukocyte image with Shannon's entropy
N. Sri Madhava Raja, S. Arunmozhi, Hong Lin, Nilanjan Dey, and V. Rajinikanth
IGI Global
In recent years, a considerable number of approaches have been proposed by the researchers to evaluate infectious diseases by examining the digital images of peripheral blood cell (PBC) recorded using microscopes. In this chapter, a semi-automated approach is proposed by integrating the Shannon's entropy (SE) thresholding and DRLS-based segmentation procedure to extract the stained blood cell from digital PBC pictures. This work implements a two-step practice with cuckoo search (CS) and SE-based pre-processing and DRLS-based post-processing procedure to examine the PBC pictures. During the experimentation, the PBC pictures are adopted from the database leukocyte images for segmentation and classification (LISC). The proposed approach is implemented by considering the RGB scale and gray scale version of the PBC pictures, and the performance of the proposed approach is confirmed by computing the picture similarity and statistical measures computed with the extracted stained blood cell with the ground truth image.


Biometric Authentication for Intelligent and Privacy-Preserving Healthcare Systems
Dhananjay Nigam, Shilp Nirajbhai Patel, P. M. Durai Raj Vincent, Kathiravan Srinivasan, and Sinouvassane Arunmozhi
Hindawi Limited
Secure identification is a critical system requirement for patients seeking health-related services. In the event of critical, aged, or disabled patients who require frequent health treatments, quick and easy identification is vital. Researchers describe the notion of the unprotected environment in this study, in which patients can receive health services from the hospital’s smart and intelligent surroundings without the use of explicit equipment. Patients would interact directly with the environment and be identified through it. We suggest a biometric-based authentication technique for the unprotected hospital environment that also safeguards the patient’s identity privacy. Furthermore, we demonstrate that this authentication technique is resistant to many well-known assaults, including insider attacks, replay attacks, and identity privacy. Doctors and other staff members showed enthusiastic responses after installing 2-factor authentications, as it makes their workflow efficient and makes things easier for patients. It also lets them focus on other factors rather than worrying about data security; hence, we need biometric authentication in intelligent and privacy-preserving healthcare systems. The paper deals with two-factor biometric authentication, and despite the added security, two-factor authentication adoption is said to be poor. It is due to a lack of awareness and difficulty to use and configure two-factor authentication (2FA) into a particular application by some individuals who struggle with the concept of authentication and its technology. Also, many 2FA methods in widespread use today have not been subjected to adequate usability testing. Research focuses on the point that there is still a large section of people unaware of the use of biometric systems to protect their online data. Researchers collected quantitative and qualitative data from 96 individuals during a two-week between-subjects usability survey of some common and rarely used 2FA approaches. The survey allowed the researcher to investigate which authentication methods are given higher priority and why, along with the relationship between different usage patterns and perceived usability, and identify user misconceptions and insecure habits to determine ease of use. It was observed that the biometric-based method was given the utmost preferability.


Performance Evaluation of FFNN, RNN ANFIS for Trauma Identification Using the Concept of ANOVA
R. Sofia, R. Valli, and S. Arunmozhi
IEEE
The state of trauma continues to be a contentious issue and inevitably causes a concern in the sense it disrupts the routine life of an individual. The increasing cases of trauma in recent times owing to a number of reasons become even more serious and can further hamper the life. It invites measures to quickly recognize the state and serve as a diagnostic platform where remedies can be initiated. So this paper has been done in motivation of identifying the trauma patients using the FFNN, RNN and ANFIS.


Deep-Learning based Automated Detection of Pneumonia in Chest Radiographs
S. Arunmozhi, V. Rajinikanth, and M.P. Rajakumar
IEEE
Pneumonia is one of the communicable illnesses in humans which generally affect lungs. The untreated pneumonia will cause severe problems in elderly people (age>65 years) and children (age<5 years) and hence the early detection and treatment is commonly preferred to recover the infected patients from the disease. The infection is lungs are commonly diagnosed with chest radiographs (X-ray) due to its clinical significance. This work implements the deep-learning (DL) scheme to detect the pneumonia. The disease detection performance of the DL scheme is confirmed using a binary classification achieved with SoftMax classifier unit. During this assessment 2000 (1000 healthy and 1000 pneumonia) images are considered for the appraisal and the necessary performance measures are computed to confirm the performance. The experimental outcome of AlexNet offered an accuracy of >98% on the considered image database.


Healthcare Framework for Risk Analysis of Hypertension
Anukirthika T. S., Dellecta Jessy Rashmi R, N. Sri Madhavaraja, S. Arunmozhi, and K. Suresh Manic
IEEE
Hypertension or high blood pressure is sometimes called a "silent killer", since it has no warning signs, yet it can lead to life-threatening conditions. The good news is prevention and treatment for hypertension can be done with early diagnosis. So, there is a need for real time monitoring of blood pressure that can be done using Internet of Things (IoT) assisted health monitoring system. The proposed system collects the user’s health parameters using IoT sensors and identifies the stage of hypertension. The vital point of this system is to continuously generate emergency alerts of blood pressure fluctuation and dangerous changes in any other health parameters to hypertensive users on their mobile phones.


Preface
IEEE



Detection of Tuberculosis in Chect X-Ray using Cancatinated Deep and Handcrafted Features
S. Arunmozhi, Aditya Prabhakara Kamath, and Venkatesan Rajinikanth
IEEE
The lung infection in human causes various respiratory problems, which affects the oxygen supply in blood stream. Tuberculosis (TB) is a severe lung disease in humans and the uncontrolled TB causes various respiratory problems, including death. TB is also a communicable disease and appropriate diagnosis and treatment will reduce the severity of the disease. This research aims to implement a novel disease diagnosis procedure to detect the TB infection in chest X-ray with better accuracy. This research employs the concatenation of deep-features (DF) with handcrafted-features (HF) to improve the diagnostic accuracy. In this work, the VGG16 is employed to extort the DF and the HF is obtained using the discrete-wavelet-transform (DWT) approach. The optimal values of DF and HF are arranged as per their rank and then a serial feature concatenation is employed to get a new 1D feature (DF+HF). This feature is then considered to train and validate the performance of considered classifiers using a 5-fold cross validation and it offered an accuracy of >97% with the Fine-Tree classifier.


Artificial Vision Based Smart Urban Parking System
Ajanthwin Prabagar, N. Sri Madhavaraja, S. Arunmozhi, and K. Suresh Manic
IEEE
With the increasing vehicle population in urban areas, it is hard to find a suitable parking lot to park the vehicle. Also, traditional parking systems involve human labour to monitor parking of vehicles in the specified slot. This can be overcome by using computer vision to identify the available slots and notify the drivers about the availability of slots, as well as improve the security by monitoring the entry and exit of the vehicle with their number plate. This system uses image processing to identify unoccupied slots and provide users a hassle-free experience. The availability of slots is processed and suggestions are provided accordingly. This reduces enormous amount of time spent in search of a parking slot. The number plate of the vehicles parked are noted along with their entry and exit timings.


Arithmetical Analysis of WSN based Indoor Positioning Localization Systems with Kalman Filtering
R. SambathKumar, S. Gowshameed, and S. Arunmozhi
IEEE
(WSN) is used for determining the Indoor Positioning of objects and persons in recent years. WSN has been implemented in indoor positioning applications such as real-time tracking of humans/objects, patient monitoring in health care, navigation, warehouses for inventory monitoring, shopping malls, etc. But one of the problems while implementing WSN an Indoor positioning system is to ensure more coverage large number of sensors must be deployed which increases the installation cost. So, in this paper, MATLAB GUI named Sensor Network Localization Explorer to analyze the impact of node density on indoor aligning localization schemes. Later Kalman filter with the indoor positioning system to increase the reliability and reduce localization error of the system is introduced with a lesser number of nodes.


Automated classification of retinal images into AMD/non-AMD Class—a study using multi-threshold and Gassian-filter enhanced images
V. Rajinikanth, R. Sivakumar, D. Jude Hemanth, Seifedine Kadry, J. R. Mohanty, S. Arunmozhi, N. Sri Madhava Raja, and Nguyen Gia Nhu
Springer Science and Business Media LLC



Image fusion practice to improve the ischemic-stroke-lesion detection for efficient clinical decision making
D. Jude Hemanth, V. Rajinikanth, Vaddi Seshagiri Rao, Samaresh Mishra, Naeem M. S. Hannon, R. Vijayarajan, and S. Arunmozhi
Springer Science and Business Media LLC



Understanding Chinese cultural values and cultural psychology
Yongdong Wang, Liangliang Zhao, S. Arunmozhi, and N. Sri Madhava Raja
Elsevier BV



An automated person authentication system with photo to sketch matching technique
P. Resmi, R. Reshika, N. Sri Madhava Raja, S. Arunmozhi, and Vaddi Seshagiri Rao
Springer Singapore



Segmentation and assessment of leukocytes using entropy-based procedure
S. Manasi, M. Ramyaa, N. Sri Madhava Raja, S. Arunmozhi, and Suresh Chandra Satapathy
Springer Singapore

RECENT SCHOLAR PUBLICATIONS

Normal/Cataract Detection in Fundus Image Using Individual and Fused ResNet Features
S Arunmozhi, P Arunagiri, S Prabha
2024 International Conference on System, Computation, Automation and 2024



Lightweight Deep-Learning Based Metal/Plastic Trash Detection with Fused Features
S Arunmozhi, S Prabha
2024 International Conference on System, Computation, Automation and 2024



ReDiaSafe: A Novel Approach for Predicting 30-Day Diabetes Patient Readmission Risk
A Michael, HA Murugan, A Manikandan, J Natarajan, S Arunmozhi
2024 International Conference on System, Computation, Automation and 2024



A Delay Phase Precoder Design For Terahertz Massive MIMO Beyond 5G Communication System
R Valli, M Jayekumar, S Arunmozhi
2024 International Conference on System, Computation, Automation and 2024



Healthy/Unhealthy Tomato Fruit Grading Using Deep-Learning with Features Fusion
M Jayekumar, S Arunmozhi, S Prabha
2024 International Conference on System, Computation, Automation and 2024



ResNet/ResNetV2 Supported Framework for Rice-Plant Disease Detection Using Leaf Data
R Santhosh, S Arunmozhi, N Tewari
2023 International Conference on System, Computation, Automation and 2023



Automatic Concrete Surface Crack Recognition Using EfficientNetV2 Variants
A Rama, R Damaševičius, S Arunmozhi, MA Mohammed, R Hussam, ...
2023 International Conference on System, Computation, Automation and 2023



Monitoring Street light using Power Line Carrier Communication (PLCC) & SCADA
A Baskaran, S Arunmozhi, S Vishnu
2023 International Conference on System, Computation, Automation and 2023



Lightweight Deep-Learning Based Music Genre Classification: A Study
A Rama, N Mythili, MP Rajakumar, S Arunmozhi, MA Mohammed, ...
2023 International Conference on System, Computation, Automation and 2023



CNN Framework for Automatic Segmentation of Breast Section from Thermal Images
A Rama, KB Sudeepa, S Arunmozhi, MA Mohammed, A Ali, V Rajinikanth
2023 International Conference on System, Computation, Automation and 2023



Detection of TB from chest x-ray: A study with EfficientNet
A Rama, MP Rajakumar, N Mythili, S Arunmozhi, MA Mohammed, ...
2023 International Conference on System, Computation, Automation and 2023



Design and Implementation of a Sweep Generator for Precise Frequency Control
AS Vishnu.S, Baskaran.A
METSZET 8 (6), 373-377 2023



Multi-Purpose Potential of RFID Technology for Access Control, Asset Tracking, and SOS Messaging Integration.
Baskaran.A, Arunmozhi.S, Vishnu.S
METSZET 8 (6), 245-251 2023



A study on segmentation of leukocyte image with Shannon's entropy
NSM Raja, S Arunmozhi, H Lin, N Dey, V Rajinikanth
Research Anthology on Improving Medical Imaging Techniques for Analysis and 2023



[Retracted] Biometric Authentication for Intelligent and Privacy‐Preserving Healthcare Systems
D Nigam, SN Patel, PMD Raj Vincent, K Srinivasan, S Arunmozhi
Journal of Healthcare Engineering 2022 (1), 1789996 2022



Digital Future of Healthcare
VR S. Arunmozhi, Vaddi Satya Sai Sarojini, T. Pavithra, Varsha Varghese, V ...
2021



Automated detection of COVID-19 lesion in lung CT slices with VGG-UNet and handcrafted features
S Arunmozhi, VSS Sarojini, T Pavithra, V Varghese, V Deepti, ...
Digital Future of Healthcare, 185-200 2021



Foldable Wheel Chair with Location Identifier
KM S. Arunmozhi, R. Valli, S. Malarkkan
IN Patent 202,141,035,038 2021



Saline Solution Rundown Monitoring and Alerting Device
VR R.Valli, S. Arunmozhi, K. Manojkumar
IN Patent 202,141,035,035 2021



Arithmetical analysis of WSN based indoor positioning localization systems with Kalman filtering
R SambathKumar, S Gowshameed, S Arunmozhi
2021 International Conference on System, Computation, Automation and 2021

MOST CITED SCHOLAR PUBLICATIONS

Automated classification of retinal images into AMD/non-AMD Class—a study using multi-threshold and Gassian-filter enhanced images
V Rajinikanth, R Sivakumar, DJ Hemanth, S Kadry, JR Mohanty, ...
Evolutionary Intelligence 14, 1163-1171 2021
Citations: 28



[Retracted] Biometric Authentication for Intelligent and Privacy‐Preserving Healthcare Systems
D Nigam, SN Patel, PMD Raj Vincent, K Srinivasan, S Arunmozhi
Journal of Healthcare Engineering 2022 (1), 1789996 2022
Citations: 22



Deep-learning based automated detection of pneumonia in chest radiographs
S Arunmozhi, V Rajinikanth, MP Rajakumar
2021 International conference on system, computation, automation and 2021
Citations: 15



ABCD rule implementation for the skin melanoma assesment–a study
V Rajinikanth, NSM Raja, S Arunmozhi
2019 IEEE International Conference on System, Computation, Automation and 2019
Citations: 15



Image fusion practice to improve the ischemic-stroke-lesion detection for efficient clinical decision making
DJ Hemanth, V Rajinikanth, VS Rao, S Mishra, NMS Hannon, ...
Evolutionary Intelligence 14, 1089-1099 2021
Citations: 13



A study on segmentation of leukocyte image with Shannon's entropy
NSM Raja, S Arunmozhi, H Lin, N Dey, V Rajinikanth
Research Anthology on Improving Medical Imaging Techniques for Analysis and 2023
Citations: 12



Automated detection of COVID-19 lesion in lung CT slices with VGG-UNet and handcrafted features
S Arunmozhi, VSS Sarojini, T Pavithra, V Varghese, V Deepti, ...
Digital Future of Healthcare, 185-200 2021
Citations: 9



Machine learning based intrusion detection framework using recursive feature elimination method
JDS WS, B Parvathavarthini
2020 International Conference on System, Computation, Automation and 2020
Citations: 9



Enhancement of energy storage capacity in lithium polymer batteries incorporated with zirconium oxide nano powders
D Murugandhan, R Valli, N Senthilkumar, S Arunmozhi
Materials Today: Proceedings 37, 1313-1319 2021
Citations: 8



Assesment of Tumor in Breast MRI using Kapur's Thresholding and Active Contour Segmentation
A Kirthika, NSM Raja, R Sivakumar, S Arunmozhi
2020 international conference on system, computation, automation and 2020
Citations: 7



A study on segmentation of leukocyte image with Shannon’s entropy. Histopathol Image Anal Med Decis Mak 1–27
NSM Raja, S Arunmozhi, H Lin, N Dey, V Rajinikanth
2019
Citations: 6



A novel complexity PAPR reduction scheme for MIMO-OFDM systems
L Arunjeeva, S Arunmozhi
IEEE-International Conference On Advances In Engineering, Science And 2012
Citations: 6



A study on brain tumor extraction using various segmentation techniques
S Arunmozhi, G Sivagurunathan, PK Meenakshi, S Karishma, ...
2020 international conference on system, computation, automation and 2020
Citations: 5



CNN Framework for Automatic Segmentation of Breast Section from Thermal Images
A Rama, KB Sudeepa, S Arunmozhi, MA Mohammed, A Ali, V Rajinikanth
2023 International Conference on System, Computation, Automation and 2023
Citations: 4



Detection of TB from chest x-ray: A study with EfficientNet
A Rama, MP Rajakumar, N Mythili, S Arunmozhi, MA Mohammed, ...
2023 International Conference on System, Computation, Automation and 2023
Citations: 4



Arithmetical analysis of WSN based indoor positioning localization systems with Kalman filtering
R SambathKumar, S Gowshameed, S Arunmozhi
2021 International Conference on System, Computation, Automation and 2021
Citations: 4



Schizophrenia detection using brain MRI—A study with watershed algorithm
S Arunmozhi, NSM Raja, V Rajinikanth, K Aparna, V Vallinayagam
2020 International Conference on System, Computation, Automation and 2020
Citations: 4



Performance analysis of quadrature spatial modulation based cooperative relaying MIMO networks
S Arunmozhi, SL Prasannadurga, G Nagarajan
2017 International Conference on Inventive Systems and Control (ICISC), 1-4 2017
Citations: 4



Detection of tuberculosis in chect x-ray using cancatinated deep and handcrafted features
S Arunmozhi, AP Kamath, V Rajinikanth
2021 International Conference on System, Computation, Automation and 2021
Citations: 3



Performance of Full-Duplex One-Way and Two-Way Cooperative Relaying Networks
S Arunmozhi, G Nagarajan
Indonesian Journal of Electrical Engineering and Computer Science 9 (2), 526-538 2018
Citations: 3