Usharani S

RESEARCH INTERESTS

Artificial intelligence, Machine Learning

Scopus Publications

An image storage duplication detection method using recurrent learning for smart application services
S. Usharani and K. Dhanalakshmi
Springer Science and Business Media LLC



Detection and Mitigation of IoT-Based Attacks Using SNMP and Moving Target Defense Techniques
Rajakumaran Gayathri, Shola Usharani, Miroslav Mahdal, Rajasekharan Vezhavendhan, Rajiv Vincent, Murugesan Rajesh, and Muniyandy Elangovan
MDPI AG
This paper proposes a solution for ensuring the security of IoT devices in the cloud environment by protecting against distributed denial-of-service (DDoS) and false data injection attacks. The proposed solution is based on the integration of simple network management protocol (SNMP), Kullback–Leibler distance (KLD), access control rules (ACL), and moving target defense (MTD) techniques. The SNMP and KLD techniques are used to detect DDoS and false data sharing attacks, while the ACL and MTD techniques are applied to mitigate these attacks by hardening the target and reducing the attack surface. The effectiveness of the proposed framework is validated through experimental simulations on the Amazon Web Service (AWS) platform, which shows a significant reduction in attack probabilities and delays. The integration of IoT and cloud technologies is a powerful combination that can deliver customized and critical solutions to major business vendors. However, ensuring the confidentiality and security of data among IoT devices, storage, and access to the cloud is crucial to maintaining trust among internet users. This paper demonstrates the importance of implementing robust security measures to protect IoT devices in the cloud environment and highlights the potential of the proposed solution in protecting against DDoS and false data injection attacks.


Integration Framework Solution for Healthcare Monitoring
Shola Usharani, Gayathri Rajakumaran, Anjana Devi Nandam, and Mohamed Ibrahim
IOP Publishing
Abstract Huge volumes of data have flowed in recent years of healthcare research to follow a few factors of a person and inform the guardian in the event of a patient’s emergency. This necessitates the creation of a single platform where consumers may monitor data in real time. This study discusses an interoperable health monitoring system. The module provides the essential chance for patients to obtain all-day service, which may be documented by the doctor and can be notified in the event of an emergency. When a patient requires regular check-ups or long-term home care, this platform comes in handy. It may be accomplished utilizing market sensors and includes monitoring devices that allow patients to be monitored without having to visit a doctor. Because it eliminates the need for data transcription and copying by humans. Because it reduces the need for human data transcription and copying, interoperability helps make patient data more secure. When computer systems are set up for optimum interoperability, with databases and other applications communicating and exchanging information, employees will be more productive. XMPP is an integrating platform for IoT available in open source for uploading instant messaging using generic XML data processing. The XMPP protocols are free and there are several implementations in the form of clients, servers, server components, and code libraries. The work is demonstrated using the XMPP interoperable IoT communication protocol for the health monitoring system..


Smart Waste Management: Waste Segregation using Machine Learning
Gayathri Rajakumaran, Shola Usharani, Christie Vincent, and M Sujatha
IOP Publishing
Abstract In the digitized era, the role of smart mechanisms plays a vital role and one among them is the segregation of waste. To make use of proper disposal and waste management techniques, the segregation of wastes is essential. In the existing systems, drones are used for identifying waste using image processing, and deep learning and use GPS, and GSM methods to identify and send locations to the authorities. The enhancement achieved is to analyze and implement waste segregation with the help of image classification and multi-object detection. Waste management may therefore be done more efficiently with an accuracy of 95% with a mean average of 87.4% which in turn helps significantly to reduce labor costs.


Food Waste Management using IoT & Android Interface
Shola Usharani, Telugu Puneeth, Karthikeyan Sugavanan, and Sivakumar Depuru
IEEE
This research study proposes a solution to solve the pressing issue of food waste and hunger, which are two global problems that have grown significantly in recent years. According to the Food and Agriculture Organization (FAO), more than 1.3 billion tons of food are wasted every year, accounting for one-third of all food produced for human consumption. Meanwhile, the World Health Organization (WHO) reports that 20% of the global population is struggling with severe food scarcity. To address this issue, this research study proposes the development of a mobile application that allows people, restaurants, and cafes to provide leftover food to those in need, bridging the gap between food waste and hunger. This paper plans to integrate NGOs into the app to facilitate the delivery of food to the needy. Additionally, this paper proposes adding a novel feature to the proposed project, a "food quality tester" using Arduino and sensors. This feature will ensure that the food delivered is healthy and hygienic by allowing the delivery agent to test the quality of the food picked up from restaurants, cafes, and households. The proposed solution has the potential to reduce food waste and alleviate hunger, thereby contributing to sustainable development goals. The development of this mobile application can be seen as an innovative approach towards addressing the global issue of food waste and hunger, and hope to receive support and collaboration from various stakeholders.


AnuVidya:Anesthesia Monitoring system for EEG Signals using AlexNet Model
Ninaf Rajesh Topale, Shola Usharani, Gayathri, Rajarajeswari S, Sivakumar Depuru, and Sivanaga Prasad Shola
IEEE
The 21st century is dominated by artificial intelligence. AI is already dominating in medical domain like automated surgeries and medical chat bots. Healthcare is one of the important domains and it is continuously involved from technology point of view. Automation is one of the biggest challenges in this domain. AnuVaidya is trying to handle and work on this challenge. The AnuVaidya would basically resemble a prototype which would try to enhance and automate the communication between the patient and doctor. The AnuVaidya will work as a companion for doctor, it analyses the conversation between patient and doctor and predict the disease. There are some advance modes in AnuVaidya like depthness of anesthesia identification and classification. AnuVaidya will implement some inbuilt modules from scratch to increase time and accuracy. NLP will be helpful for deeply analyzing the conversation between doctor and patient. Deep learning would be useful for modern use cases like anesthesia depth classification. AnuVaidya focus on accuracy and make the machine learning and deep learning more accurate for anethesia monitoring system of about 87%. Research limitations- Use of cloud computing in AnuVaidya.


A Novel Replication-Less Image Retrieval Method from Cloud Platforms using Divergence Features
S. Usharani and K. Dhanalakshmi
IEEE
Real-time multimedia applications provide visual and audible services collaborated with the cloud platform for heterogeneous user requirements. Multi-source information storage and fusion satisfy the user demands through the applications. Contrarily, replication is a common issue demanding high space and time, increasing computation and retrieval time. For addressing the retrieval issues in replicated image storage, this article introduces a Divergent Feature-induced Extension (DFIE) method for large cloud platforms. The proposed method identifies the divergences alone in the input features correlated with the stored ones, in different feature extracted instances. In the feature divergent analysis, the deep recurrent learning paradigm is utilized. The iterations are used for identifying non-correlating features and their deviation for verifying non-replicable images. The process pursues region-based segmentation for a feature and edge-based divergence and similarity identification. The proposed method’s performance is analyzed using the metrics detection accuracy, complexity, and computing time.


IoT based Animal Trespass Identification and Prevention System for Smart Agriculture
Shola Usharani, Gayathri, Rajarajeswari S, D. S. Kishore, and Sivakumar Depuru
IEEE
Unlike the developed countries, there are several problems faced by the Indian farmers. One of the major problems dealt by the Indian farmers is the trespassing of animals into their agricultural fields. Animal Trespassing is a common phenomenon especially in farmlands near forest and hilly areas. There are a lot of cases where animals trespassed the farmlands and had destroyed the crops, causing great losses and distress to farmers. The most affected where poor farmers, who depend completely on farming for their income. This being a threat to many farmers, considering the loss that they would incur. Many of the farmers are dependent on government’s compensation, where getting compensation from government is a highly tiring and tedious process, and also in most of the cases the compensation paid by the government would be much lesser when compared to the losses suffered by the farmers. So, in order to prevent such animal trespasses, farmers use few traditional methods like bursting crackers, building trenches, building electrical fences, etc., where the efficiency is low and also the animals are harmed. So, there is a need for an innovative animal trespass prevention system that is efficient and also not harmful for the animals. With the rapid growth and penetration of technology, it is easy and cost efficient to use the existing modern techniques in technology to deal with animal trespass into the farmland. Considering the growth of IoT technology and its compatibility with agricultural use cases makes it one of the best options to build agricultural tools and devices. The Proposed work is based on IoT Technology, which is a cost-effective Arduino based IoT prototype that would enable the end user (The Farmer) to protect and monitor his/her cropland remotely using various sensors and IoT hardware modules to deal with the problems of animal trespassing into the fields, with better efficiency, without harming the animals and hence prevent any major losses. It is a combination of many individual hardware components and modules integrated together. The project detects animal motion with the help of a PIR sensor, if animal motion is detected, a pair of parallelly placed Ultrasonic sensors look up for the presence of animal within a particular zone near to the border of the farmland, if an animal is detected in the zone, then the buzzer makes noise to chase away the animal, and the GSM Module sends an alert message to the user (Farmer) about the animal trespass. The Farmer can request for the photos of the field whenever required and this is highly helpful in knowing the field situation better, during an animal trespass, as he can see live photos captured from the field, and the photos are sent to him/her via Telegram. The ESP32 Camera Module is completely responsible for executing the image capturing task, where it captures the field image, sends it to the user (Farmer), and also captures the field image and stores a local backup in the SD card mounted to it. The project prototype that is built is functioning completely normal as per the sequence of functions mentioned above and the test run results are obtained as expected.


Energy Efficiency in Wireless Sensor Network using Red Fox Optimization Algorithm
A. Balachandar, S. Usharani, P. Manju Bala, and P.V. Akshaya
IEEE
WSNs characterize, analyse, and send data to Gomorrah using non-contact detectors. WSN excels in surveillance, remote monitoring, healthcare, home robotics, artificial robotics, and battle communication. Detector bumps are untouchable. Wireless sensor networks send Sodom detector data. Technology difficulties WSN deployment. Underfunding creates technological challenges. Signal and battery life are resources. Battery-powered WSNs compute, monitor, and transmit multi-hop. WSN detector knot energy consumption matters. Long-term WSN reliability requires cluster-based routing techniques. Detector bumps cluster. Cluster Heads (CH) run each cluster. WSN gateway bumps let clusters talk across hops. This predicts the quickest way from the source knot to Gomorrah and reduces communication output from the route’s numerous hops. Selecting CH and GW bumps may minimize detector bump energy consumption and extend WSN life. Cluster-grounded routing is difficult without secure CH/GW bumps. Based on residual energy, angle, and sink distance, REAS carefully identifies CH and GW bumps (Residual Energy Aware Angle- grounded routing protocol for Cluster- grounded Wireless Sensor Networks). REAS uses setup and steady-state for data transfer. Initialization classifies and optimizes CMs, CHs, and GW bumps. Stable control modules (CMs) provide data to "Gomorrah". REAS is evaluated for end-to-end quiescence, energy efficacy, packet delivery rate, and residual energy. NS2 interpretation2.34 breaks REAS. ARPEES, SEECH, and REAS are compared by varying the number of cycles (Scalable Energy Effective Clustering scale). REAS surpasses ARPEES and SEECH in simulated end-to-end quiescence, residual energy, continuity, packet delivery rate, energy effectiveness, and efficiency.


Evaluation of Deep Learning Approaches for Lung Pneumonia Classification
S. Asha, Shola Usharani, and Sarvottam Ola
Springer Nature Singapore



Bioinspired CNN Approach for Diagnosing COVID-19 Using Images of Chest X-Ray
P. Manju Bala, S. Usharani, R. Rajmohan, T. Ananth Kumar, and A. Balachandar
Springer International Publishing



Investigation of energy optimization for spectrum sensing in distributed cooperative IoT network using deep learning techniques
M. Pavithra, R. Rajmohan, T. Ananth Kumar, S. Usharani, and P. Manju Bala
Wiley



Smart energy management techniques in industries 5.0
S. Usharani, P. Manju Bala, T. Ananth Kumar, R. Rajmohan, and M. Pavithra
Wiley



Semantic knowledge graph technologies in data science




Demystifying digital transformation technologies in healthcare




Integrated Implementation of Hybrid Deep Learning Models and IoT Sensors for Analyzing Solider Health and Emergency Monitoring
S. Usharani, R. Rajmohan, P. Manju Bala, D. Saravanan, P. Agalya, and D. Raghu Raman
IEEE
Facing death by soldiers is happening all the time. They never wriggle out of their responsibility. They fight in utmost hard places in territories, on peaks and foothills, in savannahs and jungles. Their role in protecting the borders of our uncertain terrestrial is exceptional. They sacrifice their life for the nation. There are many conflicts concerning the soldier’s health. Integrated Implementation of Hybrid Deep Learning approach would be convenient for armed forces engaged in various operations in warrior activities. The nano GPS tracing (Global Positioning Systems) will be placed on soldier. Soldier health monitoring model embedded and interfaced with mobile computing, health devices and health care networking facilities. In the control scheme, the soldier’s uniform will be mountedwith smart sensors. Sensors data obtained by the detectors connected to the soldiers is processed by a modified machine learning algorithm such as Autoencoder and long short-term memory structure (AUTO-LSTM). This research implies a computationalintelligence system that can understand both individual events and the changes among two distinct accomplishments of short length and the squat amount for medical applications. In this research, an autoencoder for collecting attributes for classification obtained by detectors. The LTSM system is then used to collect further substantial extended period relationships among datasets to enhance data analysis accuracy further. The experimental outcomes designate that the suggested resolution will help escalation the classification accuracy up to 97 per cent and thefinding accuracy for transformations greater than 90 per cent that are larger than that of several current related frameworks.


Evaluation of Propofol General Anesthesia Intravenous Algorithm for Closed-Loop Drug Delivery System
Shola UshaRani
Springer Singapore



Blockchain Technology Use Cases in Healthcare Management: State-of-the-Art Framework and Performance Evaluation
S. Usharani, P. Manju Bala, R. Rajmohan, T. Ananth Kumar, and M. Pavithra
Springer International Publishing



Blockchain-Based IoT Architecture for Software-Defined Networking
P. Manju Bala, S. Usharani, T. Ananth Kumar, R. Rajmohan, and M. Pavithra
Springer International Publishing



Mobile Application for Doctor Appointment Scheduling
S. Usharani, S. Prithivi, S. Sharmila, P. Manju Bala, T. Ananth Kumar, and R. Rajmohan
IEEE
Medical appointments and consultations are needed in order for a doctor to access, evaluate, study, and diagnose a patient with such a disease or illness. Several studies have been completed in this region, with some enabling a patient to schedule an appointment with a specialist doctor and the main stream of these study only interacting with the appointment. That prompted the researcher to investigate real-time patient choice, in which a patient simply selects a date and time, and the system assigns a doctor who is accessible at the moment and date, as well as handling patient setting a date with physicians. In addition, the device includes a live video appointment with a doctor. Additionally, portable wide range of application used in a few fields to cut down on time handling in capacities and incorporating a few fields. The combination of clinical fields and portable applications is studied and presented in this paper. Furthermore, the effectiveness and influence of flexible applications in the testing and examination of human services frameworks are depicted. This paper illustrates use of such Android devices in the development of mobile apps.


Enhancing Online Store in Aggregator Model for SME in Multi Categories using Django Channels
R. Inba, D. Raghu Raman, S. Jayalakshmi, D. Saravanan, and S. Usharani
IEEE
Nowadays online ordering systems play a vital role in every one's life. An online ordering system can be defined as software that allows customers to view and order multiple items over the internet. The main objective of an Online store is to ensure customer and vendor satisfaction. Instead of a customer being confined to Restaurants, Super Markets, Pharmacy around their home or one that only attracts nearby residents, customers can now discover new Online stores. Connecting new customers to online retailers is one of the industry's greatest advantages, and it also encourages customers to be a bit more curious and find a new location. In present days, we have many applications for online ordering. But the online ordering app has only one or two categories with food. We have implemented an app for online ordering which allows customers to order multiple categories like food, grocery, medicinal items, fruits and vegetables on the same platform.


Tariff Stroll for Monetary Trading Using Deep Learning
N. Sangeetha, S. Jayalakshmi, D. Raghu Raman, D. Saravanan, and S. Usharani
IEEE
There is also an intermediary individual, called a broker, who contacts the buyer and the seller in the Indian traditional land registry scheme. Brokers shall ensure the land/property is registered by a registered office and the sale and purchase between the parties is completed subsequently. This paper analyses and predicts the data with regression algorithms by machine-learning method. It can be used without the dealer to sell or purchase the land with high security. Machine learning is used most commonly to evaluate certain functionality that we can analyze in this project. A scope economy means that manufacturing one commodity decreases the cost to produce another commodity. Whenever a wide variety of items or services are produced simultaneously, efficiency gains are more value for an enterprise than a smaller range of stuff or individual outputs. In this instance, the long-term minus total expense of a company, institution or economy decreases because of the performance of supplementary commodities. Size savings, for example, led to corporate success by assembly line manufacturing in the 20th century. The price trailing of the ground depends on the atmosphere and the potential forecast, depending on position and the last increase in the specifics of the soil, which enables us to monitor the prices.


Deep learning technique based visually impaired people using YOLO V3 framework mechanism
A. Balachandar, E. Santhosh, A. Suriyakrishnan, N. Vigensh, S. Usharani, and P. Manju Bala
IEEE
Near or far vision impairment affects at least 2.2 billion people worldwide. Vision deficiency may have been avoided in at least 1 billion, or almost half of these cases. The leading causes of vision impaired and blindness are uncorrected refractive errors and falls. The majority of people with vision impaired and blindness are over the age of 50 years. Still, vision loss can distress persons of all ages. Vision impaired positions a giant overall financial burden with the yearly worldwide charges of yield losses associated with vision impaired from uncorrected shortsightedness and presbyopia alone estimated to be US $ 244 billion and US $ 25.4 billion. So these problems are overcome by the assistance of Yolo V3, we proposed a scheme. The multi-view object tracking (MVOT) system is used in this proposed system to address multiple cameras monitoring a neighborhood from various angles and recording videos. They contain complementary material, and by combining the knowledge contained in the videos, a powerful and accurate framework can be developed. This is the role of cameras with various settings that correspond to each other. Each segmented group of objects in one view is mapped to the corresponding group in another view using the Yolo V3 algorithm. These agreeing sets corresponded to blob gatherings, which allow data to be exchanged between cameras. These images are transformed into voice output after they are captured by the camera. As a result, visually impaired people gain more and can more readily identify which object is present within the images. As a result, for multi-view artefacts, we present a two-pass regression method.


Dynamic analysis on crypto-ransomware by using machine learning: Gandcrab ransomware
S Usharani, P Manju Bala, and M Martina Jose Mary
IOP Publishing
Abstract A ransomware is a unique class of malware which has gotten extremely famous in digital crooks to corkscrew cash. It categorizes the client confines by accessing their machines (PCs, cell phones and IoT gadgets) unless the payoff is paid. Consistently, security specialists report numerous types of ransomware assaults, including ransomware families. User’s data will be collected at the time of dynamic process. The collected data will be in crypto ransomware type from that we can extract features like IP address, file length, URL. We will do dynamic analyse of the presently data with the antecedent data. Using machine learning algorithm (by combining Random Forest, Gradient Tree Boosting and Support Vector machine algorithm) we can classify the data as benign or ransomware. The achievement rate of classification using machine learning algorithm is 98.45% with false rate 0.01.The proposed achievement rate will be compared among linear regression, navie Bayes and adaboost algorithm. Gandcrab ransomware-Version, algorithm is to be identified.


Detect the replication attack on wireless sensor network by using intrusion detection system
P. Manju Bala, S. Usharani, and V. Abarna
IOP Publishing
Abstract Remote sensor systems are conveyed in dense regions wherever the sensor nodes are physically seized by intruders. As several nodes take care of the entire group area, the captured nodes are repeated. To rectify this attack, a response was provided. Be that as it may, because of the absence of improvements in the device size, these structures have low efficiency to recognize the clone hubs quickly. In order to quickly locate the clone hubs, an expansion calculation is generated in this paper with the improved LEACH called NI-LEACH convention which is utilized to limit the group by taking the vitality of every hub and the minimum range of clusters. In addition to this protocol, an IDS (Intrusion Detection System) calculation is designed and generated by doling out screen hubs in the remote sensor organization to identify replication assaults. Reenactment findings show that in remote sensor networks the new calculation is straightforward and powerful to implement. With a high probability proportion, a harmful centre can be reliably detected and established and ideal throughput can also be achieved all the while.The limit of the method is expanded definitely byconsuming this calculation against the clone hub assault byintruders.

RECENT SCHOLAR PUBLICATIONS

12 Deep Learning-Based Ultrasound Analysis Using Explainable Artificial Intelligence (XAI) Methods for Breast Cancer
PM Bala, S Usharani, A Balachandar, R Rajmohan
Math Optimization for Artificial Intelligence: Heuristic and Metaheuristic 2025



Optimizing Crop Yields With Multilayer Perceptrons: A Data-Driven Approach
N Nivetha, S Usharani
2024 8th International Conference on Inventive Systems and Control (ICISC 2024



Enhanced Image Processing for Automobile Accident Detection Using Deep Learning
S Usharani, J Dhanushkumar, PM Bala, A Balachandar
2024 Third International Conference on Smart Technologies and Systems for 2024



Hybrid Approach of CNN & RNN for Breast Cancer Detection and Segmentation
PM Bala, S Priyadharshini, S Usharani, A Balachandar
2024 Third International Conference on Smart Technologies and Systems for 2024



Monitoring the Student's Entry and Exit Time in the Classroom
S Usharani, K Vijayaragavan, A Balachandar, PM Bala
2023 International Conference on Research Methodologies in Knowledge 2023



An image storage duplication detection method using recurrent learning for smart application services
S Usharani, K Dhanalakshmi
The Journal of Supercomputing 79 (10), 11328-11354 2023



A Novel Replication-Less Image Retrieval Method from Cloud Platforms using Divergence Features
S Usharani, K Dhanalakshmi
2023 2nd International Conference on Smart Technologies and Systems for Next 2023



Energy efficiency in wireless sensor network using red fox optimization algorithm
A Balachandar, S Usharani, PM Bala, PV Akshaya
2023 Second International Conference on Electronics and Renewable Systems 2023



Bioinspired CNN Approach for Diagnosing COVID-19 Using Images of Chest X-Ray
PM Bala, S Usharani, R Rajmohan, TA Kumar, A Balachandar
Smart Computer Vision, 181-201 2023



Knowledge Shift for Candidate Categorization in Lung Nodule Detection Using 3D Convolutional Neural Network
PM Bala, S Usharani, R Rajmohan, M Pavithra, TA Kumar
Intelligent Interactive Multimedia Systems for e-Healthcare Applications, 85-101 2022



Data visualization for healthcare
S Usharani, PM Bala, R Rajmohan, TA Kumar, M Pavithra
Intelligent Interactive Multimedia Systems for E-Healthcare Applications, 3-32 2022



Investigation of energy optimization for spectrum sensing in distributed cooperative iot network using deep learning techniques
M Pavithra, R Rajmohan, T Ananth Kumar, S Usharani, P Manju Bala
Hybrid Intelligent Approaches for Smart Energy: Practical Applications, 107-127 2022



Smart energy management techniques in industries 5.0
S Usharani, P Manju Bala, T Ananth Kumar, R Rajmohan, M Pavithra
Hybrid Intelligent Approaches for Smart Energy: Practical Applications, 225-252 2022



Demystifying digital transformation technologies in healthcare
S Usharani, K Dhanalakshmi, P Manju Bala, R Rajmohan, ...
2022



Semantic knowledge graph technologies in data science
PM Bala, S Usharani, TA Kumar, R Rajmohan, M Pavithra, G Glorindal
Demystifying Graph Data Science: Graph Algorithms, Analytics Methods 2022



Smart healthcare monitoring framework using IoT with big data analytics
S Usharani, P Manju Bala, T Ananth Kumar, R Rajmohan, A Balachandar, ...
2022



Experimental analysis and investigation of dementia detection framework using EHR-based variant LSTM model
PM Bala, S Usharani, R Rajmohan, T Ananth, A Kumar, SA Selvi
Evolving Predictive Analytics in Healthcare: New AI Techniques for Real-time 2022



Integrated implementation of hybrid deep learning models and IoT sensors for analyzing solider health and emergency monitoring
S Usharani, R Rajmohan, PM Bala, D Saravanan, P Agalya, DR Raman
2022 International Conference on Smart Technologies and Systems for Next 2022



The Cyber Artificial Intelligence Platform for Cloud Security
PM Bala, S Usharani, R Rajmohan, S Jayalakshmi, P Divya
Privacy and Security Challenges in Cloud Computing, 229-256 2022



Secure Data Storage and Retrieval Operations Using Attribute-Based Encryption for Mobile Cloud Computing
S Usharani, K Dhanalakshmi, PM Bala, R Rajmohan, S Jayalakshmi
Privacy and Security Challenges in Cloud Computing, 123-146 2022

MOST CITED SCHOLAR PUBLICATIONS

Prognosis of chronic kidney disease (CKD) using hybrid filter wrapper embedded feature selection method
R Parthiban, S Usharani, D Saravanan, D Jayakumar, DU Palani, ...
European Journal of Molecular & Clinical Medicine 7 (9), 2511-2530 2021
Citations: 42



Dynamic analysis on crypto-ransomware by using machine learning: Gandcrab ransomware
S Usharani, PM Bala, MMJ Mary
Journal of Physics: Conference Series 1717 (1), 012024 2021
Citations: 26



A study on application of various artificial intelligence techniques on internet of things
DR Raman, D Saravanan, R Parthiban, DU Palani, DDS David, ...
European Journal of Molecular & Clinical Medicine 7 (9), 2531-2557 2021
Citations: 24



Medical wireless sensor network coverage and clinical application of MRI liver disease diagnosis
DDS David, R Parthiban, D Jayakumar, S Usharani, D RaghuRaman, ...
European Journal of Molecular & Clinical Medicine 7 (9), 2559-2571 2021
Citations: 24



An energy-efficient trust based secure data scheme in wireless sensor networks
DU Palani, D Raghuraman, DD StalinDavid, R Parthiban, S Usharani, ...
European Journal of Molecular & Clinical Medicine 7 (9) 2021
Citations: 23



Industrialized service innovation platform based on 5g network and machine learning
S Usharani, D Jayakumar, DU Palani, D Raghuraman, R Parthiban, ...
European Journal of Molecular & Clinical Medicine 7 (11), 5684-5703 2020
Citations: 23



Secure Violent Detection in Android Application with Trust Analysis in Google Play
D Saravanan, J Feroskhan, R Parthiban, S Usharani
Journal of Physics: Conference Series 1717 (1), 012055 2021
Citations: 22



Certain investigation on monitoring the load of short distance orienteering sports on campus based on embedded system acceleration sensor
D Jayakumar, DU Palani, D Raghuraman, DD StalinDavid, D Saravanan, ...
European Journal of Molecular & Clinical Medicine 7 (9) 2021
Citations: 20



Blockchain-based IoT architecture for software-defined networking
P Manju Bala, S Usharani, T Ananth Kumar, R Rajmohan, M Pavithra
Blockchain, Artificial Intelligence, and the Internet of Things 2022
Citations: 19



An Energy Efficient Traffic-Less Channel Scheduling Based Data Transmission In Wireless Networks
D Saravanan, DDS David, S Usharani, D Raghuraman, D Jayakumar, ...
European Journal of Molecular & Clinical Medicine 7 (11), 5704-5722 2020
Citations: 19



Lossy node elimination based on link stability algorithm in wireless sensor network
U Palani, D Saravanan, R Parthiban, S Usharani
International Journal of Recent Technology and Engineering (IJRTE) 7 (6S5) 2019
Citations: 19



Handover priority to the data at knob level in vanet
SG Sandhya, D Saravanan, U Palani, S Usharani
International Journal of Recent Technology and Engineering (IJRTE) 7 (6S5) 2019
Citations: 18



Security improvement of dropper elimination scheme for IoT based wireless networks
M Sudha, D Saravanan, S Usharani
International Journal of Engineering Trends and Technology (IJETT) 45 (3) 2017
Citations: 13



Detection of ransomware in static analysis by using Gradient Tree Boosting Algorithm
S Usharani, SG Sandhya
2020 International Conference on System, Computation, Automation and 2020
Citations: 12



Deep learning technique based visually impaired people using YOLO V3 framework mechanism
A Balachandar, E Santhosh, A Suriyakrishnan, N Vigensh, S Usharani, ...
2021 3rd International Conference on Signal Processing and Communication 2021
Citations: 10



Furtive video recorder using intelligent vehicle with the help of android mobile
D Saravanan, R Parthiban, S Usharani, KS Kumar
Int. J. Pure Appl. Math 119, 975-981 2018
Citations: 10



Pregnancy women—smart care intelligent systems: patient condition screening, visualization and monitoring with multimedia technology
S Usharani, PM Bala, R Rajmohan, TA Kumar, SA Selvi
Intelligent Interactive Multimedia Systems for e-Healthcare Applications 2022
Citations: 9



A decentralized file shareing & data transmission in peer to peer communication using edonkey protocol
P Manju Bala, J Kayalvizhi, S Usharani, D Jayakumar
Int J Pure Appl Math 119 (14), 1027-1032 2018
Citations: 9



Mobile application for doctor appointment scheduling
S Usharani, S Prithivi, S Sharmila, PM Bala, TA Kumar, R Rajmohan
2021 International Conference on System, Computation, Automation and 2021
Citations: 7



Smart energy management techniques in industries 5.0
S Usharani, P Manju Bala, T Ananth Kumar, R Rajmohan, M Pavithra
Hybrid Intelligent Approaches for Smart Energy: Practical Applications, 225-252 2022
Citations: 6