Dr G Manikandan

RESEARCH INTERESTS

Data Mining, Computer Network

Scopus Publications

A Security Control Strategy Based on Blockchain for Attack Detection in the Health Care Environment
M.Sandhya
Science Research Society
The usage of blockchain in healthcare spans a wide range of applications, including secure patient identification, clinical research, medication management, insurance, and the detection of medical fraud. In the hospital system, attack detection is also the most challenging responsibility. As a result, the data will be protected in this research by a revolutionary Blockchain-based African Buffalo Identity-Based Encryption (BABIBE) system. The system that is built in the Python program also collects and trains Electronic Health Records (EHR). Update the blockchain security to detect and ignore threats while also continuously monitoring them. The IBE approach, which encrypts data using the public key and decrypts it by private key, is used to secure data. As a result, blocks receive encrypted data that is then added to the blockchain using the private key. Additionally, depending on the threshold value, identify attacks, and compare the created technique's performance results with those of other traditional models, like energy consumption, throughput, latency, computation time, and detection rate.


Topological Information Embedded Convolution Neural Network–Dependent Energy Alert-Cluster Head Selection in WSN
Sivanantham Elumalai, Senthil Vadivu Mani, Bhuvaneswari Govinda Swamy, and Manikandan Gnanasundaram
Wiley
ABSTRACTEnergy efficiency is a major challenge in developing wireless sensor networks (WSNs). The cluster head (CH) can be selected at random or depending on one or more criteria that leads to increase the network lifespan directly. Nevertheless, the CH selection creates an optimization problem. For this purpose, a number of researches have been presented so far to select the optimum CH with the help of various optimization methods, but none of them effectively solves this problem. Therefore, a topological information embedded convolution neural network based energy alert‐cluster head selection in wireless sensor network (TIECNN‐EAC‐WSN) is proposed in this paper. In this method, cluster formation and CH selection is performed by topological information embedded convolution neural network (TIECNN). The CH selection is carried out by three features: energy stabilization, minimization of distance among nodes, and minimization of delay during data transmission. Then, the optimal route is selected by using improved manta ray foraging optimization (IMFO). The TIECNN‐EAC‐WSN approach is evaluated with some metrics, such as network lifetime, number of alive sensor node, and energy consumption with different scenrios. The stimulation results show that the proposed TIECNN‐EAC‐WSN method attains 23.20%, 27.22%, and 26.28% higher number of alive sensor node when compared with the existing models: energy‐aware optimization clustering for hierarchical routing in WSN (EAC‐HR‐WSN), energy‐aware clustering depending on fuzzy modeling in WSN utilizing modified invasive weed optimization (FMEAC‐WSN‐IWO), and quantum tunicate swarm approach–dependent energy‐aware clustering mode for WSN (QTSA‐EAC‐WSN), respectively.


CONVOLUTIONAL NEURAL NETWORKS AND DEEP LEARNING FOR THE DETECTION OF PNEUMONIA IN X-RAY IMAGES
Joel M Robinson, G Manikandan, R Gokulaselvam, R K Bharath, R Praveen Kumar, and V. Ebenezer
GN1 Sistemas e Publicacoes Ltd.



Hybrid methodology-based energy management of microgrid with grid-isolated electric vehicle charging system in smart distribution network
Kathirvel Kalaiselvan, Ragavan Saravanan, Balashanmugham Adhavan, and Gnana Sundaram Manikandan
Springer Science and Business Media LLC



Chronological bald eagle optimization based deep learning for image watermarking
G Suresh, G Bhuvaneswari, G Manikandan, and P Shanthakumar
Elsevier BV



Pelican Whale Optimization Enabled Deep Learning Framework for Video Steganography Using Arnold Transform-Based Embedding
G Suresh, G Manikandan, G Bhuvaneswari, and P Shanthakumar
World Scientific Pub Co Pte Ltd
Steganography refers to hiding a secret message from various sources, such as images, videos, audio and so on. The advantage of steganography is to avoid data hacking in transmission medium during the transmission of information sources. Video steganography is superior to image steganography since the videos can hide a substantial quantity of secret messages more than the image. Hence, this research introduced the video stereography technique, Arnold Transform with SqueezeNet-based Pelican Whale Optimization Algorithm (AT[Formula: see text]SqueezeNet_PWOA), for concealing the secret image on the video. To hide the secret image on the video, the proposed method follows three steps: key frame and feature extraction, pixel prediction and embedding. The extraction of the key frame process is carried out by the Structural Similarity Index Measure (SSIM), and then the neighborhood features and convolutional neural network (CNN) features are extracted from the frame to improve the robustness of the embedding process. Moreover, the pixel prediction is completed by the SqueezeNet model, wherein the learning factors are tuned by the PWOA. In addition, the embedding process is completed by applying the Arnold transform on the predicted pixel, and the transformed regions are combined with the secret image using the embedding function. Likewise, the extraction process extracts the secret image from the embedded video by substituting the predicted pixel and Arnold transform on the embedded video. The proposed method is used to hide chunks of secret data in the form of video sequences and it improves the performance. The Arnold transform used in this work provides security by encrypting the data. The use of SqueezeNet makes the proposed model a simple design and this reduces the computational time. Thus, the AT[Formula: see text]SqeezeNet_PWOA attained better correlation coefficient (CC), peak signal-to-noise ratio (PSNR) and mean square error (MSE) of 0.908, 48.66 and 0.001 dB with the Gaussian noise.


Improved Contextual Understanding and Emotion Detection in Large-Scale Text Data with Hybrid Deep Learning Models
Thilakavathy P, Manikandan G, Deepa R, Jayalakshmi V, and Surendran R
IEEE
With the growth of large-scale text datasets from reviews, social media, and other online sources, sentiment analysis is essential for understanding public opinion. Traditional models struggle to understand complicated linguistic sentiments and interdependencies. This research could improve sentiment classification in customer feedback analysis, market sentiment predictions, and social media monitoring. Addressed issues include ambiguous language, sentence context flipping, and the need for robust models that can manage massive datasets. Contextual Detection Text Analysis Using Deep Learning (CDTA-DL) improves contextual knowledge and emotion identification in this research. RNNs and CNNs are used in the CDTA-DL approach to extract spatial and sequential information from text. CDTA-DL helps large-scale sentiment analysis detect sarcasm, implicit emotions, and many expressions. In a comprehensive simulation investigation employing open-access sentiment analysis datasets, CDTA-DL surpassed standard deep learning models in accuracy, recall, and F1 scores. The recommended strategy may increase emotion recognition and contextual understanding, making it ideal for large-scale sentiment analysis in various domains.


Smart Control and Analysis of EV Battery Systems for Enhanced Performance and Longevity
A. Syed Musthafa, P. Sanmugavalli, Naveen R, Syed Musharaf S, Kiran Kumar S, and Manikandan S. G
IEEE
This research investigates the application of Random Forest regression for predicting the lifetime of electric vehicle (EV) batteries. By analyzing various factors influencing battery degradation, such as charging/discharging cycles, temperature, and usage patterns, the Random Forest model can effectively predict battery lifespan. This research contributes to the advancement of EV technology by providing valuable insights into battery degradation and enabling more accurate predictions of battery lifespan. These predictions will facilitate better battery management strategies, improve vehicle performance, and enhance the overall adoption of electric vehicles.


Enhanced Camouflage Detection Using Advanced Image Processing Techniques for Real-Time Object Recognition and Pattern Differentiation
Manikandan G, Deepa R, Jayalakshmi V, Thilakavathy P, and Surendran R
IEEE
The identification of objects that blend in with their surroundings has long been a concern in fields including defence, wildlife monitoring, and surveillance due to the difficulty of detecting such objects. This finding is significant because it could greatly enhance our capacity to detect camouflaged objects, transforming the field of real-time object detection. Existing image processing methods often fail miserably when adequately capturing this aspect. This dissertation's primary focus is identifying devices with complicated patterns, legacy interference, and inadequate contrast; nonetheless, in disguised scenarios, all of these features cause identification to be unsuccessful. This research often suggests a new approach called the Linear Edge Detecting Scheme for Camouflaged Object Detection (LEDS-COD) in these difficult cases. The LEDS-COD system can enhance item visibility in low-comparison settings by highlighting critical edges and descriptions. Pattern differentiation methodologies and modern facility-side recognition techniques are combined to accomplish this goal. The approach successfully differentiated items because it zeroed in on texture and comparative details that would otherwise go unnoticed by humans or advanced algorithms. LEDS-COD is useful in many areas, including the Navy, autonomous surveillance systems, and animal protection, where the real-time identification of hidden devices is essential. Results from a simulation study using various datasets, including concealed objects, show that LEDS-COD surpasses conventional detection methods in recognition accuracy, processing speed, and perseverance. The recommended technique exceeded the requirements for obtaining improved object detection estimates for challenging conditions like those with complex backdrops and changing illumination. According to this study, LEDS-COD can be devastating in improving real-time camouflage detection.


Cloud-Based Foot Pressure Analysis for Diabetic Care Using ANN
Pradeepa H, Raveendra N Amarnath, V G Sivakumar, Thamizhamuthu R, G Manikandan, and G Bhuvaneswari
IEEE
It introduces a new method for caring for diabetic feet combines smart pressure monitoring for the feet with wearable gear that is cloud-based and uses artificial neural networks (ANNs). It is essential to recognize diabetic foot ulcers early and monitor them continuously to avoid complications. Wearable pressure sensors record data on the user's foot pressure in real time make up the proposed system. ANNs trained to detect patterns recommend a possible risk of ulceration by analyzing this data once it is transferred to a cloud-based platform. By using cloud-based architecture, healthcare practitioners can remotely monitor patients and intervene as necessary. Clinical trials show the technology improves diabetic foot care by detecting irregularities in pressure distribution and lowering the risk of ulcers and their sequel. Improved patient outcomes and decreased healthcare expenses are achieved via the preventive treatment of diabetic feet made possible by this system, which makes use of breakthroughs in wearable technology and artificial intelligence (AI).


Cloud-Based AI Solutions for Early Wound Infection Detection and Treatment Recommendations
Mothiram Rajasekaran, Chitra Sabapathy Ranganathan, G Manikandan, G Bhuvaneswari, T. R. GaneshBabu, and M. Rajmohan
IEEE
Wound infections are a major problem for healthcare systems worldwide. Infections cause patients to remain in the hospital longer., which increases healthcare expenses and negatively impacts their health outcomes. For patient care to be successful, it is crucial to identify these illnesses quickly and treat them appropriately. Recent developments in cloud computing and artificial intelligence (AI) have encouraged optimism for the potential of these technologies to improve wound infection diagnosis and treatment. Within healthcare systems, this research outlines a new method for early wound infection diagnosis and treatment recommendations generation using cloud-based AI algorithms. The proposed method employs machine learning to appropriately detect early indications of infection by analyzing a variety of patient data inputs, such as wound features, vital signs, laboratory findings, and medical history. Also, healthcare practitioners may benefit from real-time monitoring and decision assistance due to cloud-based architecture's easy interaction with existing electronic health record (HER) systems. The system is designed with a dynamic feedback loop that can be updated with fresh data inputs to make it more accurate and flexible. The proposed cloud-based AI system has been thoroughly tested and proven reliable in improving the early diagnosis and treatment of wound infections. This has led to better patient outcomes and more efficient use of healthcare resources. Key challenges include data privacy issues, the integration of diverse technologies, the reliability of precise forecasts, and maintaining system resiliency in changing environments.


Automated Injury Detection and Alert Systems in Public Transportation Integrating IoT with Convolutional Neural Networks
S P Vimal, John Benito Jesudasan Peter, U. Kavitha, G. Bhuvaneswari, G. Manikandan, and R Thamizhamuthu
IEEE
Innovative methods to improve public transportation safety and response mechanisms have emerged due to Internet of Things (IoT) and artificial intelligence (AI) technologies. IoT devices and Convolutional Neural Networks (CNNs), a class of deep learning algorithms analyzing visual imagery, are used to create an innovative Automated Injury Detection and Alert System (AIDAS) for public transportation. The proposed AIDAS architecture employs IoT sensors and cameras in buses and trains to monitor and evaluate passengers’ physical health in real time. These sensors can detect unexpected impacts, odd motions, and distressing noises. A strong CNN model trained to detect passenger injury or bodily damage receives live images from the onboard cameras. An automatic alarm process is activated immediately upon injury detection. The closest emergency response teams and transportation system operators get complete event reports, including time, position (by GPS coordinates), and injury type. A quick reaction to accidents with this real-time alarm system might save lives and reduce public transportation injuries. AIDAS’ use of IoT and CNN technology is innovative in public transportation safety. The technology significantly improved emergency response times and reduced unattended injuries in public transport networks. The system’s excellent damage detection accuracy is due to CNNs’ advanced image processing and pattern recognition skills and IoT devices’ sensory inputs. It also addresses privacy issues and the necessity for strong data encryption and proposes solutions. Finally, it analyzes how AIDAS affects public transportation safety and future research and development in this critical field.


STUDY ON THE USE OF POLYMERIC TREATMENT WITH RICE HUSK SILICA ON DIRECT TENSION BEHAVIOUR AND ADHERENCE OF SISAL FIBRE IN CEMENTICIOUS COMPOSITES




SVM-RFE enabled feature selection with DMN based centroid update model for incremental data clustering using COVID-19
Robinson Joel M, Manikandan G, Bhuvaneswari G, and Shanthakumar P
Informa UK Limited
This research introduces an efficacious model for incremental data clustering using Entropy weighted-Gradient Namib Beetle Mayfly Algorithm (NBMA). Here, feature selection is done based upon support vector machine recursive feature elimination (SVM-RFE), where the weight parameter is optimally fine-tuned using NBMA. After that, clustering is carried out utilizing entropy weighted power k-means clustering algorithm and weight is updated employing designed Gradient NBMA. Finally, incremental data clustering takes place in which centroid matching is carried out based on RV coefficient, whereas centroid is updated based on deep maxout network (DMN). Also, the result shows the better performance of the proposed method..


Enhanced Ai-Based Machine Learning Model for an Accurate Segmentation and Classification Methods
G Manikandan, Bui Thanh Hung, Siva Shankar S, and Prasun Chakrabarti
Auricle Technologies, Pvt., Ltd.
Phone Laser Scanner becomes the versatile sensor module that is premised on Lamp Identification and Spanning methodology and is used in a spectrum of uses. There are several prior editorials in the literary works that concentrate on the implementations or attributes of these processes; even so, evaluations of all those inventive computational techniques reported in the literature have not even been performed in the required thickness. At ToAT that finish, we examine and summarize the latest advances in Artificial Intelligence based machine learning data processing approaches such as extracting features, fragmentation, machine vision, and categorization. In this survey, we have reviewed total 48 papers based on an enhanced AI based machine learning model for accurate classification and segmentation methods. Here, we have reviewed the sections on segmentation and classification of images based on machine learning models.


Blockchain Technology's Role in an Electronic Voting System for Developing Countries to Produce Better Results
I Milcah Blessy, G Manikandan, and M Robinson Joel
IEEE
The centralized approach is used by traditional electronic voting systems. These systems have a central administration that controls the database and the entire system as a whole as well as the complete voting process. Accidental or deliberate, this might lead to issues including possible database tampering and duplicate voting. Many of these problems have been addressed by the application of permissionless blockchain technology in modern voting platforms; nonetheless, each voting action requires a certain amount of processing effort due to the conventional consensus process of such blockchains. The removal of the standard consensus procedures employed by public blockchains delay has a major influence on power consumption, impairs system productivity, and increases system utilization of energy. The main goal of this review was to evaluate the current status of voting systems that are electronic and powered by blockchain election research, along with any related difficulties, in order to anticipate potential future developments. This investigation led to the discovery that some of the problems now plaguing election systems may be resolved by blockchain technologies. In order to create an all-encompassing facial recognition system, the project suggests a unique Connected Neural Network noted as CNN architecture for recognising facial features. This pipeline involves the collection of real-time data, such as visages of people, pre-processing, training of models, and ultra-parameter optimisation. Additionally, an online application to post participation utilising Face Recognition and the newly built innovative CNN model is being developed. Age detection AGES expanded as Ageing Pattern Subspace, which is frequently done with the aid of technology or algorithms, is a method of estimating or identifying a person's age according to a variety of criteria.


Artificial Intelligence to the Assessment, Monitoring, and Forecasting of Drought in Developing Countries
G. Manikandan, G Bhuvaneswari, and M Robinson Joel
IEEE
In order for plants to respond to specific degrees of moisture stress that affect both vegetative development and crop production, circumstances called “drought” must exist. It happens when the amount of moisture that can be held in the soil to suit a specific crop's needs is insufficient. India's drought has two main causes: climate change and a lack of surface water supplies. In some cases, it may be able to pinpoint the direct cause of a drought in a specific area, but this is not always the case. Consequently, it is imperative to establish an effective method for communicating the Standardized Precipitation Index SPI data revealing drought indices to farmers and strengthen drought and climate resilience in order to improve all these services in favour of improving agricultural productivity and decreasing food insecurity in India. Understanding past drought experiences with precise indicators is essential to developing future plans and policies in India's agriculture industry. Since this study would aid in India's agricultural development, it is obvious that a standardised drought index must be used to comprehend how frequently droughts are occurring across the country. The major goal of this study is to establish a suitable baseline for drought index forecasting using Standardized Precipitation Index SPI data. As a result, the project's ultimate result would be a knowledge base from which appropriate forecasting tools and distribution networks for farmers might be updated or established. Also, experiment with the logistic regression algorithm to get the best prediction.


An Analysis of Security Challenges in Internet of Things (IoT) based Smart Homes
M. Robinson Joel, G. Manikandan, and G Bhuvaneswari
IEEE
The term "Internet of things (IoT) security" refers to the software industry concerned with protecting the IoT and connected devices. Internet of Things (IoT) is a network of devices connected with computers, sensors, actuators, or users. In IoT, each device has a distinct identity and is required to automatically transmit data over the network. Allowing computers to connect to the Internet exposes them to a number of major vulnerabilities if they are not properly secured. IoT security concerns must be monitored and analyzed to ensure the proper working of IoT models. Protecting personal safety while ensuring accessibility is the main objective of IoT security. This article has surveyed some of the methods and techniques used to secure data. Accuracy, precision, recall, f1 score, and area under the Receiver Operating Characteristic Curve are the assessment metrics utilized to compare the performance of the existing techniques. Further the utilization of machine learning algorithms like Decision Tree, Random Forest, and ANN tests have resulted in an accuracy of 99.4%. Despite the results, Random Forest (RF) performs significantly better. This study will help to gain more knowledge on the smart home automation and its security challenges.


Design of an IoT approach for security surveillance system for industrial process monitoring using Raspberry-Pi
G. Manikandan, D. Karunkuzhali, D. Geetha, and V. Kavitha
AIP Publishing



KNOWLEDGE DISCOVERY IN DATA OF PROSTATE CANCER BY APPLYING ENSEMBLE LEARNING
Dr.Manikandan G. and Dr.Bhuvaneswari G.
ENGG Journals Publications
AI to help researchers in analyzing larger data sets and providing faster and more accurate diagnoses of prostate cancer lesions. This research work finds AdaBoost M1 model gives an optimal results. This research work finds Ada Boost M1 of ensemble model gives an optimal results. The highest accuracy value is 89% of accuracy which is produced by Filtered Classifier. The least accuracy value is 83% of accuracy which is produced by Iterative Classifier Optimizer algorithm. The highest positive predictive value is 0.90 of positive predictive value which is produced by Filtered Classifier. The least positive predictive value is 0.83 of positive predictive value which is produced by Iterative Classifier Optimizer algorithm. The highest true positive rate value is 0.89 of true positive rate which is produced by Filtered Classifier. The least true positive rate is 0.83 of true positive rate which is produced by Iterative Classifier Optimizer algorithm. The highest F1-Score value is 0.89 of F1-Score value which is produced by Filtered Classifier. The least F1-Score value is 0.83 of F1-Score value which is produced by Iterative Classifier Optimizer algorithm. . The highest phi coefficient value is 0.77 of phi coefficient value which is produced by Filtered Classifier. The least phi coefficient is 0.65 of phi coefficient value which is produced by Iterative Classifier Optimizer algorithm. The highest AUC value is 0.91 of ACU-ROC value which is produced by Iterative Classifier Optimizer algorithm. The least AUC is 0.65 of ACU-ROC value which is produced by Attribute Selected Classifier and Filtered Classifier. The highest AUC-PR value is 0.89 of ACU-ROC value which is produced by Iterative Classifier Optimizer algorithm, Bagging and Classification via Regression models. The least AUC-PR is 0.80 of AUC-PR value which is produced by Attribute Selected Classifier and Filtered Classifier. This work concludes that the Ada Boost M1 Classifier gives best outcomes compare with other models.


Artificial Intelligence and Advanced Technology based Bridge Safety Monitoring System
D Karunkuzhali, D Geetha, G Manikandan, J. Manikandan, and V Kavitha
IEEE
In this study, wireless technology is used to provide a bridge security checking framework based on IoT. The robotized continuous scaffold wellness checking framework was developed with the assistance of breakthroughs in sensor technology. This method will help CEOs plan for and recover from disasters. The Wireless Technology is employed in the development of an IOT-based bridge security checking framework. Remote sensor hubs can collect several forms of data, such as vibration, water level, and bridge weight. These particulars would also be relevant for verification and observation. The primary purpose of this research is to develop a system that can detect and avoid flyover and extension mistakes, as well as underlying disasters. This study provides an overview of the various techniques used to screen the states of the scaffolds and proposes a framework for assessing constant designs as well as a water level sensor for monitoring the water level in the stream in order to keep traffic away from flood situations using AI calculations. If a crisis occurs, the Bridge’s doors will close as a result. The collected data is delivered to the server and data set, allowing managers to monitor the extension situation using portable telecom devices.


A Completely Distributed Blockchain Period Authentication Framework
V Kavitha, D Geetha, D Karunkuzhali, and G Manikandan
IOP Publishing
Abstract The time capsule that would be opened in the future without third-party intervention was always a difficult issue. Although many researchers work on various systems, there are potential limitations, such as unreliable decryption period not entirely decentralised, which are difficult to estimate the needed data resources. In this post, we introduced a protocol and a safe cryptographic way to open a timely message in an advanced, decentralised environment to match in with several computing power conditions. The methodology also allows participants to gain extensive benefits of adding their computing resources, making our system more suited for applications in real life.


Enhancement and Development of Next Generation Data Mining Photolithographic Mechanism
D Geetha, V Kavitha, G Manikandan, and D Karunkuzhali
IOP Publishing
Abstract The analytical data of project management was established. In a stereolithography method, the APC system was already implemented in essential dimensions and overlays. Productivity and system efficiency have been enhanced. The new APC, however, is created on the inspection information where the method anomalies are blended with the fluctuation of the system and which have to evaluate very small quantities, and it has the impact cap. The inspection data for the CD, overlay and log information of the acquaintance tool in ainteractive data base have been compiled and processed. We have also investigated how the earlier in this thread problem can be paid and resolved. First of all, in the enormous tool log data we have extracted ties between inspection informationbesides several parameters, particularly factor loadings. We then discussed problems with big relationships and have, thus, gathered valuable knowledge which did not come out of the traditional system. In order to show the stabilising machine fluctuation effect, we developed, along with APC, a second-generationinformation mining system.


Traffic Control Loss and to Handle Seamless Mobility in a Heterogeneous Network with Lesser Transmission Delay
G. Manikandan, G. Bhuvaneswari, Suhasini, K.G. Saravanan, M. Parameswari, and D.Sterlin Rani
IEEE
Consistent versatility the board is a capacity to offer the different types of assistance during the correspondence in remote heterogeneous organizations. Because of the irregular versatility of the portable terminals, the availability between various cell phones gets lost. To give the lossless network between the cell phones, the handover from the purpose of current connection to another point is fundamental. To improve the Seamless portability the board and traffic signal, an effective model called Generalized Light Gradient Boost Decision Tree-based Traffic-Aware Seamless Mobility (GLGBDT-TASM) model is presented in the heterogeneous organization. At the point when a portable hub in the organization moves out of its correspondence range, the sign strength of the hubs is determined. In view of the sign strength assessment, the Generalized Light Gradient Boost Decision Tree classifier orders the versatile hubs into the feeble and solid sign strength with the limit esteem. The boosting calculation at first develops' frail students for example double choice tree to distinguish the frail sign strength of the portable hub. At that point the group classifier joins the consequences of frail students and limits the speculation mistake. This assists with playing out the handover just with the powerless sign strength of the hub coming about in limits the repetitive handover. Furthermore, the powerless sign strength of the portable hub from the current connection point handover towards the closest accessible connection highlight improve the consistent information conveyance. Followed by, transmission capacity accessibility is estimated for diminishing the bundle misfortune because of the organization traffic coming about in improves the consistent information conveyance between the hubs. The reenactment is completed to assess the exhibition of the GLGBDT-TASM model with two related methodologies. The outcomes show that the GLGBDT-TASM model viably improved traffic-mindful consistent versatility in a heterogeneous organization with least deferral and bundle misfortune just as a higher information conveyance rate when contrasted with best in class techniques.


Input Based Resource Allocation in Motion Estimation using Re-configurable Architecture
S. Suhasini, J. M. SheelaLavanya, M. Parameswari, G. Manikandan, and S. Gracia Nissi
IEEE
Reconfigurable engineering can dynamically assign the assets during runtime. It tends to be adequately utilized in computationally escalated applications like media processing. In media processing, video compression is one of the most computationally intensive applications. ME is the basic undertaking in video pressure as it devours enormous measure of computational time for finding the best block match by calculating Sum of Absolute Difference (S AD) of different blocks in successive video frames. To overcome this problem, its inherent parallel execution nature is analysed and mapped into customized parallel reconfigurable engineering to adequately deal with the force and asset usage by unique reconfiguration. Application of reconfigurations in the hardware for block matching and comparator modules based on the level of motion in the input video can produce substantial optimization in terms of power and resource utilization.

RECENT SCHOLAR PUBLICATIONS

AI-Enhanced Public Safety Lighting for Smart Sustainable Cities Using Cloud Computing
B Tidke, S Pragadeeswaran, RP Lakshmi, G Bhuvaneswari, ...
2025 International Conference on Multi-Agent Systems for Collaborative 2025



Integrating Blockchain IoT and 6G Technologies for Secure Efficient and Sustainable Smart City Applications Enhancing Urban Living Through Innovation
S Cloudin, MR Joel, G Manikandan, IM Blessy
Building Tomorrow's Smart Cities With 6G Infrastructure Technology, 483-508 2025



Making Clinical Decisions to Treat Patients by Using Health Information Technology
S Ruban, S Prabagar, C Moorthy, JP Manimozhi, MR Joel, G Manikandan
Responsible AI for Digital Health and Medical Analytics, 87-112 2025



Important Concerns With Comorbidities and Type 2 Diabetes in Clinical Decision Support Systems Based on Mobile Solutions
S Ruban, S Anitha, VP Arulkumar, G Bhuvaneswari, G Manikandan
Impact of Digital Solutions for Improved Healthcare Delivery, 231-256 2025



Cloud-Based Foot Pressure Analysis for Diabetic Care Using ANN
H Pradeepa, RN Amarnath, VG Sivakumar, R Thamizhamuthu, ...
2024 5th International Conference on Data Intelligence and Cognitive 2024



Automated Injury Detection and Alert Systems in Public Transportation Integrating IoT with Convolutional Neural Networks
SP Vimal, JBJ Peter, U Kavitha, G Bhuvaneswari, G Manikandan, ...
2024 2nd International Conference on Self Sustainable Artificial 2024



Cloud-Based AI Solutions for Early Wound Infection Detection and Treatment Recommendations
M Rajasekaran, CS Ranganathan, G Manikandan, G Bhuvaneswari, ...
2024 4th International Conference on Sustainable Expert Systems (ICSES), 591-596 2024



Convolutional Neural Networks and Deep Learning for the detection of pneumonia in X-RAY images
R Joel, G Manikandan, R Gokulaselvam, RK Bharath, P Kumar, ...
ITEGAM-JETIA 10 (49), 1-11 2024



Hybrid methodology-based energy management of microgrid with grid-isolated electric vehicle charging system in smart distribution network
K Kalaiselvan, R Saravanan, B Adhavan, GS Manikandan
Electrical Engineering 106 (3), 2705-2720 2024



Leaf Disease Detection Using Machine Learning in Conjunction with Image Processing
G Manikandan, G Bhuvaneswari, M Robinson Joel, J Prince Immanuel
International Conference on Innovations and Advances in Cognitive Systems 2024



Chronological bald eagle optimization based deep learning for image watermarking
G Suresh, G Bhuvaneswari, G Manikandan, P Shanthakumar
Expert Systems with Applications 238, 121545 2024



Pelican Whale Optimization Enabled Deep Learning Framework for Video Steganography Using Arnold Transform-Based Embedding
G Suresh, G Manikandan, G Bhuvaneswari, P Shanthakumar
International Journal of Pattern Recognition and Artificial Intelligence 38 2024



Measuring the Influence of Artificial Intelligence (AI) on Online Purchase Decisions-In Case of Indian Consumers
BG Manikandan G
International Journal of Scientific Research in Science, Engineering and 2024



Alzheimer Disease Using Machine Learning
MSH S Dennis Emmanuel, Dr G Manikandan, Ms. Vilma Veronica
International Journal of Scientific Research in Science and Technology 11 (2 2024



Malicious Social Bot Using Twitter Network Analysis in Django
MVV Ms. N. Ezhil Arasi, Dr G Manikandan, Ms. S. Hemalatha
International Journal of Scientific Research in Science and Technology 11 (2 2024



Sign Language Detection and Recognition Using Media Pipe and Deep Learning Algorithm
MSH Ms. E J Honesty Praiselin, Dr G Manikandan, Ms. Vilma Veronica
International Journal of Scientific Research in Science and Technology 11 (2 2024



Detection Of Cardiovascular Disease Using ECG Images in Machine Learning and Deep Learning
MVV Ms. K Jebima Jessy, Dr G Manikandan, Ms. S. Hemalatha
International Journal of Scientific Research in Science and Technology 11 (2 2024



Abnormal Event Detection in Human Activity Using Deep Learning
MVV Ms. G Roshini, Dr. G Manikandan, Ms. S. Hemalatha
International Journal of Scientific Research in Science and Technology 11 (2 2024



Smart Agriculture: Enhancing Security Through Animal Detection Via Deep Learning and Computer Vision
MSH A Samuvel, Dr G Manikandan, Ms. Vilma Veronica
International Journal of Scientific Research in Science and Technology 11 (2 2024



QR Code Recognition Based on Image Processing
MVV Ms. J Seetha, Dr. G Manikandan, Ms. S. Hemalatha
International Journal of Scientific Research in Science and Technology 11 (2 2024

MOST CITED SCHOLAR PUBLICATIONS

A novel machine learning framework for diagnosing the type 2 diabetics using temporal fuzzy ant miner decision tree classifier with temporal weighted genetic algorithm
G Bhuvaneswari, G Manikandan
Computing 100, 759-772 2018
Citations: 58



An intelligent intrusion detection system for secure wireless communication using IPSO and negative selection classifier
G Bhuvaneswari, G Manikandan
Cluster Computing 22 (Suppl 5), 12429-12441 2019
Citations: 29



Mining spatially co-located objects from vehicle moving data
G Manikandan, S Srinivasan
Eur. J. of Sci. Res 68 (3) 2012
Citations: 27



A smart speed governor device for vehicle using IoT
G Bhuvaneswari, G Manikandan
Webology 19 (2) 2022
Citations: 24



Mining of spatial co-location pattern implementation by FP growth
G Manikandan, S Srinivasan
Ind. J. Comput. Sci. Eng 3, 344-348 2012
Citations: 23



An efficient algorithm for mining spatially co-located moving objects
G Manikandan, S Srinivasan
American Journal of Applied Sciences 10 (3), 195-208 2013
Citations: 22



KNOWLEDGE DISCOVERY IN DATA OF PROSTATE CANCER BY APPLYING ENSEMBLE LEARNING
DGB Dr G Manikandan
INDIAN JOURNAL OF COMPUTER SCIENCE AND ENGINEERING 13 (3), 907-916 2022
Citations: 14



Hybrid methodology-based energy management of microgrid with grid-isolated electric vehicle charging system in smart distribution network
K Kalaiselvan, R Saravanan, B Adhavan, GS Manikandan
Electrical Engineering 106 (3), 2705-2720 2024
Citations: 12



An analysis of security challenges in internet of things (iot) based smart homes
MR Joel, G Manikandan, G Bhuvaneswari
2023 Second International Conference on Electronics and Renewable Systems 2023
Citations: 11



Recognition of Ancient stone Inscription Characters Using Histogram of Oriented Gradients
G Bhuvaneswari, G Manikandan
Proceedings of International Conference on Recent Trends in Computing 2019
Citations: 8



Chronological bald eagle optimization based deep learning for image watermarking
G Suresh, G Bhuvaneswari, G Manikandan, P Shanthakumar
Expert Systems with Applications 238, 121545 2024
Citations: 7



Traffic control by bluetooth enabled mobile phone
G Manikandan, S Srinivasan
International Journal of Computer and Communication Engineering 1 (1), 66 2012
Citations: 6



Input Based Resource Allocation in Motion Estimation using Re-configurable Architecture
S Suhasini, JM SheelaLavanya, M Parameswari, G Manikandan, SG Nissi
2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile 2021
Citations: 5



Enhancement and Development of Next Generation Data Mining Photolithographic Mechanism
D Geetha, V Kavitha, G Manikandan, D Karunkuzhali
Journal of Physics: Conference Series 1964 (4), 042092 2021
Citations: 5



2021 international conference on Emerging Smart Computing and Informatics (ESCI)
G Manikandan, PS Kumar, NS Sivakumar, S Srinivasan, VS Perumal, ...
IEEE 2021
Citations: 5



Artificial Intelligence to the Assessment, Monitoring, and Forecasting of Drought in Developing Countries
G Manikandan, G Bhuvaneswari, MR Joel
2023 International Conference on Circuit Power and Computing Technologies 2023
Citations: 4



Design of an IoT approach for security surveillance system for industrial process monitoring using Raspberry-Pi
G Manikandan, D Karunkuzhali, D Geetha, V Kavitha
AIP Conference Proceedings 2519 (1) 2022
Citations: 4



A completely distributed blockchain period authentication framework
V Kavitha, D Geetha, D Karunkuzhali, G Manikandan
Journal of Physics: Conference Series 1964 (4), 042047 2021
Citations: 4



SVM-RFE enabled feature selection with DMN based centroid update model for incremental data clustering using COVID-19
M Robinson Joel, G Manikandan, G Bhuvaneswari, P Shanthakumar
2023
Citations: 3



Measuring the Influence of Artificial Intelligence (AI) on Online Purchase Decisions-In Case of Indian Consumers
BG Manikandan G
International Journal of Scientific Research in Science, Engineering and 2024
Citations: 2