J Jesy Janet Kumari

EDUCATION

M.E(CSE)

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Networks and Communications, Computer Science, Hardware and Architecture, Artificial Intelligence

Scopus Publications

CSG_IACO: an efficient stigmergic-based improved ACO routing strategy to determine the effective path based on traffic density in V2V networks
J. Jesy Janet Kumari and S. Thangam
Springer Science and Business Media LLC



AvD ayur vriksha diagnostics for precise identification of medicinal leaves in ayurveda using deep learning techniques
J. Jesy Janet Kumari, S. Elilmaniyamma, P. Anushree, Mounika M. S., and Azalfa Maryam
IGI Global
Medicinal plants have been used for hundreds of years in conventional medical systems due to their therapeutic properties. However, accurately identifying these plants remains challenging, particularly for individuals lacking expertise in botany. This study introduces an innovative method for the automated recognition of medicinal plants employing DenseNet, a highly layered neural network architecture commonly used for image categorization tasks. The method centered on gathering a wide array of portraits of various medicinal plants, together with their respective labels. To improve image quality and uniformity, preprocessing methods shall be used. Overall, the work presents a promising solution in terms of the automatic identification of medicinal plants and harnessing capabilities of DL methodologies. This approach can significantly aid botanists, herbalists, and healthcare practitioners in identifying and utilizing medicinal plants for various therapeutic purposes.


Automatic autonomous heart monitoring device using machine learning for COVID patients
Leones Sherwin Vimalraj Stephen, Lydia Jeyakumar, Jesy Janet Kumari Johnson, and Revathi Patchamal
AIP Publishing



Gesture Recognition Technology in Smart Gloves Enhanced by Machine Learning
Thangam S, Nithin Kandi, Dorai Sai Charan, Vijay Gosu, J. Jesy Janet Kumari, and Virupaxappa G
IEEE
Gesture recognition systems are changing how people interact with devices, promising natural and intuitive hands-free control in various applications. Based on the ESP32 microcontroller and machine learning techniques, the proposed work is developing a compact, power-efficient system for recognizing hand gestures from data gathered by flex sensors and accelerometers. The entire system fits within memory constraints, as lightweight machine learning models are first trained on a computer using Python and then ideally deployed in optimized versions onto the ESP32. In this respect, the stream of pre-processed sensor data happens to be fed to the ML model for inference, sending back results to the ESP32, which hence triggers specific actions or commands. This fundamentally new approach shows the feasibility of machine learning on resource-constrained platforms, opening the road for wearable technologies that will be more clever and reactive.


Assistive Glasses Using Ultrasonic Sensors for Visually Impaired Individuals
J Jesy Janet Kumari, Saba Fatima, Surya Ramesh, and Zain Moosaraza
IEEE
To provide an efficient solution to the difficulty in navigation faced by the visually impaired, this paper presents a device built on the principle of object detection. This model utilizes ultrasonic sensors to detect an object that is present in front of the visually impaired individual. This device is built using Arduino Nano which serves as a processing unit, a battery as a power source, a buzzer for feedback audio cues, and an ultrasonic sensor that detects obstacles. The prototype is then implemented on a pair of glasses which makes it easy to use in everyday life. This is a portable, economical, and easy-to-use device that helps in safe indoor and outdoor navigation of the visually impaired thus, fulfilling the needs of visually impaired individuals.


SafeComm: An IOT-based System for enhancing rural child safety using LoRa Technology
Thangam S, Rohan Gamidi, Thotapalli Sri Surya, Sneha Saragadam, and J Jesy Janet Kumari
IEEE
The safety of kids who play outside while travelling in rural areas is a big concern. Safecomm’s new technical fix combines LoRa, NFC, as well as UVC cam systems. Thereby, the gadget has been engineered by merging LoRa modules, NFC tags, GSM module and ESP cams for real-time tracking of where their children are and instant alerting their parents or schools. Besides, when there is an emergency, the wearer can choose to press the emergency button for the SOS family contacts and the emergency services organisations to be alerted in case the battery level is still good. If required, images can also be captured and saved on the cloud storage. This IoT product leverages LoRa, NFC, and an UVC cam to cater for safety issues relating to modes of transport for rural schools. Every day when children mobile around, this machine secures them. The attendance is made easy and quick response in cases of emergency is also possible through this technology so the risks are averted. This device ensures that children are safe when they move about every day. These technologies make keeping track of attendance easier and being able to quickly respond during emergencies so that dangers are mitigated, therefore parents as well as teachers can be less worried. This undertaking is seen as one important stride toward using internet of things (IoT) technology for ensuring children from outside urban areas are safe and boosting safety inside schools or while commuting using linked transportation means.


Smart IoT-Driven Monitoring and Control System for Enhancing Shrimp Aquaculture Health
Thangam S, V.S.S. Ashish Babu, K. Ashish Paul, Rishu Jaiswal, and J. Jesy Janet Kumari
IEEE
In the emerging field of aquaculture, the necessity for precise environmental monitoring is paramount for the sustainability and profitability of shrimp farming. This study explores the deployment of an IoT-based system to rigorously monitor water quality parameters such as temperature, pH, turbidity, and total dissolved solids in shrimp tanks. Employing a suite of sensors integrated with an ESP32 microcontroller and real-time data transmission to the Blynk platform, the methodology allows for continuous monitoring and immediate adjustment of tank conditions to minimize loss and optimize yield. The study highlights significant challenges such as high stocking densities, manual feeding practices that lead to growth irregularities, and the impact of erratic power supply and fixed market prices on operational costs. The results demonstrate that our advanced IoT solution not only enhances operational efficiency and shrimp health but also contributes to reducing resource wastage and increasing shrimp yield, thereby supporting the argument that technology-driven approaches can substantially improve the stability and control of farming conditions, fostering sustainable development in aquaculture.


Smart Steps to Sporting Success: IoT-Driven Footwear Innovations
S Thangam, Gadi Joshith, S Santhosh, V Harish, and J Jesy Janet Kumari
IEEE
Using the cloud platform Blynk IoT, smart sneakers with built-in step and calorie monitoring were created with athletes in mind. The primary characteristic of the smart shoe is its ability to detect early signals of cramping in the muscles during athletic exercises. This allows for prompt response to protect the athlete’s foot and performance. It also acknowledges the vulnerabilities that athletes may encounter during training; this feature guarantees that athletes can notify the user by sending them messages in the event of any potential hazard. Beyond features for health and safety, the smart shoe calculates steps and tracks activity, giving athletes useful information for their training and recuperation regimens. With a focus on safety first, this project tackles important issues like sensor accuracy, device longevity, user comfort, and data security to produce a solution that improves athletic performance. By providing athletes with a comprehensive tool for health monitoring and personal safety, the smart shoe seeks to redefine wearable technology in sports.


Technology Enhanced Cradle by using Sensors and Internet of Things
S Thangam, Mogili Greeshma, Mekala Varun, Nvs Sanjana, and J Jesy Janet Kumari
IEEE
Conventional cradles lack the ability to monitor and support infants continuously or adapt to varying needs. This study proposes a novel smart cradle equipped with advanced sensors and actuators, enabling parents to control environmental factors in real time, thereby ensuring the highest levels of safety and comfort for infants. Unlike previous models, the smart cradle automatically responds to stimuli, such as an infant’s cry, and allows for remote monitoring via the Blynk app. Comparative analysis demonstrates that the proposed smart cradle offers faster response times and greater accuracy in environmental control compared to recent models, establishing it as a new benchmark in infant care products. This innovation addresses the deficiencies of current cradle designs and represents a significant advancement in automated infant care systems, ultimately conserving caregivers’ resources and improving overall infant care.


Flex Sensor Data Analysis for Hand Rehabilitation using Wearable Glove
Thangam S, Anusha C Reddy, Ginnaram Varun, M Hemasri, Virupaxappa, and J Jesy Janet Kumari
IEEE
Every day we encounter many people with unexpected situations where they suffer from many neuropathological diseases that cause a severe impact on the daily lives of individuals, especially about hand movements, thereby directly affecting the quality of their lives. Traditional methods often lack insights into personalized progress and optimal medication outcomes, which take longer to recover. We have designed an approach based on a novel concept using wearable technology to monitor and assess finger movements. The proposed solution of wearable device integrated flex sensor technology to enhance the rehabilitation process by providing real-time insights by the input of the flex sensors captured by the finger movements of the patients. By sensor data analysis, people get a chance to know their progress and the professionals gain accurate tracking of the progress so that they enhance identifying the areas of improvement and customize the treatment. This innovative approach to designing a wearable device serves as a helping aid to improve patient outcomes by categorizing the stages of improvement into Normal, Mild, and Severe Impairment for a better understanding and helps improve the rehabilitation experience.


Intelligent Safety Helmet For Miners Using Arduino Leveraging Support Vector Machines
S. Thangam, Aryan Kothari, V. R. N. S. Nikhil, Namana Rohit, and J. Jesy Janet Kumari
IEEE
In the fast-evolving era of mining and development, ensuring the safety and well-being of miners remains paramount. Recent advancements in the Internet of Things (IoT) have paved the way for innovative solutions in mining techniques. This paper introduces an intelligent mining helmet equipped with Arduino UNO, ESP8266 wireless connectivity, and an array of sensors (gas, temperature, humidity, and ultrasonic). These sensors monitor environmental conditions in real-time, coupled with an alarm system to enhance miner safety and operational efficiency. This implementation of the safety helmet employs a multi-approach methodology, integrating support vector machines (SVMs) for predictive analysis of hazardous conditions. The SVM model is trained on historical data to predict potential risks based on current sensor readings and Our Model got a very good accuracy. The intelligent helmet continuously checks environmental conditions, and in the event of potential hazards, such as high gas levels or elevated temperatures, it alerts miners with a buzzer, prompting immediate evacuation to prevent accidents. The results demonstrate that the intelligent helmet significantly improves safety by reducing the response time to hazardous conditions. Additionally, it optimizes working conditions through real-time monitoring and predictive maintenance by analyzing environmental trends. Such an SVM-based predictive model is very important in minimizing false alarms and enhancing the reliability of the system.


A Smart Pill Container for Improved Medication
Thangam S, T. Sudeep Reddy, Dhanush, T. Krithin, and J. Jesy Janet Kumari
IEEE
Medication non-adherence is a significant problem in healthcare, leading to the gradual worsening of patient health, increased hospitalizations, and higher costs. Current patient-driven solutions rely heavily on the reliability of patient compliance and self-reporting, which are often inaccurate, leaving real-time monitoring and intervention gaps. The proposed system of a smart pill box, integrated with a Raspberry Pi 4, weight sensors, and a camera, addresses these weaknesses. The weight sensors will detect medication intake and verify dosages by sensing changes in weight, while the camera will capture images of the pills. Using a self-made dataset with over 400 images of each of 10 different tablets, the system will employ deep learning to identify the pills and verify dosages, alerting patients and caregivers to anomalies such as skipped doses. This automated adherence monitoring system reduces reliance on self-reporting and increases tracking accuracy. The expected outcomes of this research include improved medication adherence, reduced medication errors, and enhanced monitoring mechanisms, ultimately leading to better healthcare provision and outcomes for patients. Innovating health technology in this manner promises significant improvements in patient care.


Real-Time Stress Monitoring using IoT and Machine Learning for Cognitive Wellness
Thangam S, Garikipati Karthik, Tejaswi Muppala, Vinitha Chowdary A, and J Jesy Janet Kumari
IEEE
Stress monitoring and management became some of the most vital requirements for maintaining overall well-being in high-stress environments. This work deals with the challenging issue of real-time stress detection using a fusion of the Internet of Things and machine learning technology implemented on a Raspberry Pi. Using the proposed model, the following physiological signals are collected using a Pulse Sensor for heart rate, a DS18B20 sensor for temperature, a DHT22 sensor for humidity, and a Grove GSR sensor for galvanic skin response (GSR). Based on the collected data, a hybrid model of Support Vector Machines and Random forests was trained, which would ultimately classify stress into different levels. A Snake game dynamics were controlled through a joystick. Using the predicted stress levels, the game difficulty is automatically adjusted, to provide real-time biofeedback for the user's cognitive wellness and stress management. This ensures that the user is calm and focused. The system effectively adapted to the gaming environment based on the stress levels of the player and it will serve as a promising tool to enhance cognitive wellness through game interaction.


An Adaptive on Demand Modified Ant Colony Optimization Routing for VANETs
J. Jesy Janet Kumari, S. Thangam, and A. Saleem Raja
Springer Nature Switzerland



QoS CBSC: An Enhanced Metaheuristic Strategy on QoS-Cloud-based Service in Cloud
J Jesy Janet Kumari, Thangam S, and A. Saleem Raja
IEEE
Due to the growing number of cloud users working on various cloud apps on specific infrastructure, resource allocation in cloud computing is implicitly difficult. Most resource allocation strategies currently in use focused on delivering efficiency determined by the workload of applications across many domains, such as business and scientific. Cloud service brokers compete strongly with one another to provide quality of service improvements based on the need, demand and the rapid growth of the services that are offered. Such conflict makes it difficult and complex to provide simple service selection and composition services in the cloud, this needs to be addressed to lighten the load on local resources. Because cloud-based services are too sophisticated and scalable, it is preferable to use an optimization strategy to choose the services that will meet the needs of the clients. To do this, a hybrid algorithm Modified Ant Colony System Cloud Services Composition (MACSCSC) is proposed to employ Ant Colony Optimization (ACO) incorporated with Genetic Algorithm (GA) to smoothly increase cloud-based services. When allocating cloud resources these meta-heuristic algorithms can achieve significantly better performance, lower costs and instances, better utilization of resources, and increased energy efficiency. The empirical findings on different real datasets have been demonstrated to show the improvements of the suggested method to overcome the drawbacks such as allocation of resources and high power consumption.


Adaptive Strategies for Scalable and Heterogeneous VANET Network
S Thangam, Priyanshu Jha, Jawed Hawari, Nabin Kumar Sah, Nikunj Jain, J Jesy Janet Kumari, and A.Saleem Raja
IEEE
Despite technological advancements, Vehicular Ad-hoc Networks (VANETs) continue to face challenges related to reliability and high-speed mobility. This study focuses on enhancing scalability, improving mobility, and addressing network heterogeneity in the context of data dissemination. Data dissemination involves the distribution or transmission of statistical data to users. Tools like NS3, SUMO utilizing OSM for realistic traffic simulations, MATLAB for Data Analysis and evaluation of various protocols like ADHOC, Dynamic Source Routing (DSR), Optimized Link State Routing Protocol (OLSR), Destination Sequenced Distance Vector Routing (DSDV). The research aimed to refine VANET simulations for effective decision making, as its primary objective data dissemination i.e. distribution or transmission of statistical data to users, forming the central focus of this investigation.


Improving Existing VANETs by Incorporating UAVS Using Ant Colony and PSO Algorithms
S Thangam, Aniket Dixit, Alok Kumar Karn, Arnav Thakur, Cg Arunbalaji, J Jesy Janet Kumari, and A. Saleem Raja
IEEE
Vehicular Ad Hoc Networks (VANETs) are a type of wireless communication network that enable vehicles to communicate with each other and with roadside infrastructure in a peer-to-peer fashion; despite their vast applications, they come with several shortcomings. The key challenges in their real-world implementation include scalability, connectivity, and coverage issues. Unmanned Aerial Vehicles (UAVs), also known as drones, can complement existing VANETs in several ways to enhance their functionality and address some of their limitations. This paper studies the incorporation of UAVs in VANETs to overcome the challenges faced by the present networks. The approach is based on the dynamic deployment of UAVs in the most optimal positions, found by utilizing Particle Swarm Optimization (PSO) and Ant Colony algorithms which analyze the vehicle density, and previous coverage information in the network. The deployment of UAVs is intended to provide a seamless network coverage for ground vehicles. The impact of dynamic UAV mobility in communicating VANETs is comparatively studied. The simulation is done using Network Simulator-3 (NS3) simulator to evaluate the performance of 4 VANET protocols, AODV, DSR, OLSR, and DSDV, in terms of packet delivery ratio (PDR), Average End-to-End Delay, Throughput, Average Throughput per Packet, Packet Drop Rate, and Normalized Routing Load after incorporating the proposed modification designs. The paper concludes that the incorporation of Ant Colony is better suited to enhance VANETs than PSO.


Performance Analysis of Routing Protocols in VANETs using OSM, SUMO, and NS2
Thangam. S, Aaditi, Ananya Avvaru, Aryan Tandon, J Jesy Janet Kumari, and A Saleem Raja
IEEE
With growing urbanization there is a need to manage vehicle-to-vehicle communication for intelligent communication in congested traffic. The performance of the widely used routing protocols AODV, DSDV, DSR, LAR, and ZRP is examined in this research in relation to traffic circumstances on a particular highway. Utilizing OSM data, integrated with NS2 and SUMO, realistic traffic scenarios are simulated. ZRP is a hybrid protocol that handles local communication within zones and LAR bridges the gap between the nodes and zones. This increases communication efficiency. Simulation of scenarios gives a clearer idea of how the dissemination is happening and helps in performance analysis. The protocols are assessed using important performance criteria in this study by means of a methodical approach that includes data collecting, simulation, and execution. Comparison of the metrics for the protocols helps in protocol selection for given scenarios, optimization, and standardized evaluation. The specific metrics considered for performance evaluation include Packet Delivery Ratio, Average End-to-End Delay, Average Throughput, Packet Drop Rate, and Normalized Routing Load.


Diabetes Mellitus Diagnosis using Optical Ring Resonators
Manush Prajwal, J Jesy Janet Kumari, Maanas Mitrahass Uppu, Vanajaroselin Chirchi, S Vishalatchi, and D N Darshan
IEEE
The research shows the use of optical ring resonators (ORRs) as a promising non-invasive tool for diagnosing diabetes mellitus, potentially replacing invasive procedures. By accurately measuring glucose levels in biological samples through resonance frequency shifts closely linked to varying glucose concentrations, ORRs point to innovative and precise glucose measurement for early diabetes detection. This advancement could transform disease monitoring by reducing patient discomfort from current invasive techniques. The study develops a highly sensitive, non-invasive method for detecting diabetes by analyzing subtle optical signal changes related to glucose levels in urine is around 165-180 mg/dl (normal) and resonant frequency output is around 36.026459 but in case of blood resonant frequency output is around0.348478amusing advanced optical ring resonator technology. The methodology involves designing optical ring resonators capable of detecting minute refractive index variations connected to glucose concentrations. Major findings demonstrate the precision of diagnosing diabetes, showcasing the potential for a rapid and reliable diagnostic tool. Interpretations emphasize the innovative approach’s ability to significantly improve early detection, enabling timely intervention and enhanced diabetes management for better healthcare outcomes and quality of life for individuals with diabetes.


Efficacious Routing Approaches in Vehicular Ad hoc Network: An Empirical Study
J Jesy Janet Kumari, S. Thangam, and A. Saleem Raja
IEEE
VANET, or Vehicular Ad hoc Network is a transient wireless structure made up of different kinds of vehicles serving as network hubs and the links between them acting as links. VANET is an improving mechanism that has received an amount of growth in the fields of research, academia, and business. A VANET is a mobile network that uses vehicles as terminals. VANETs, similar to spontaneous systems, possess all the main features of independent connectivity, including processing and preservation constraints. To improve safety while driving and other services, a network of VANETs can be established. The effectiveness of VANETs has been demonstrated in numerous real-world applications, such as traffic management and driver-assist systems. Information exchanged over VANETs can undoubtedly help users experience a better driving experience and prevent accidents. academia, and government. VANET is an acronym used to describe the Vehicular ad hoc network that forms over-moving vehicles on the road. Vehicle networks are rapidly emerging as a platform for creating and implementing new and classic applications. VANETs are distinguished by their high mobility, frequently changing topology, and ephemeral, one-time connections. Vehicle interruptions are common due to how dynamic and moving vehicles are at present. Clustering techniques for vehicles that arrange to increase network scalability and connection dependability as VANET introduces vehicles in groups. Subsequently, other clustering strategies and techniques also exist, and it is necessary for a wide range of situations, including information transfer, routing, and detection of accidents. The methods used for vehicle clustering techniques are examined in this study in terms of networking techniques.


An Empirical Study on E-Commerce Site using Unique AI based Features and Data Science Tools
J Jesy Janet Kumari, Aniket Singh, R. Ch. A. Naidu, M Sathya, and M Ramya Sri
IEEE
With the advancement of modern-day techniques in the field of Information Technology, the way of shopping through E-Commerce site is becoming outdated. There are two ways through which an individual can do shopping first is the online method and second is the offline one in today’s world online shopping by having more variety of products available on individual platform with easy way of shopping because of this day by day the retailers with offline method are facing challenges to increase their sales and obtaining data of demanding products that are available in the market, now with the growth of artificial intelligence, they can use lot of beneficiary tools to boost their business. If a giant next generation E-Commerce site is made with which we can connect all the wholesalers, retailers and customers with their own point of profits, then it can bring a new revolution in the market where there will be different layers will be available with separate user friendly graphic user interface for all wholesalers, retailers and customers, where they will be allowed to access their own layers accordingly with several unique features and benefits to save time and making shopping more amazing for customers and selling their products and boosting daily sales for the retailers with the influence of top wholesalers available to help them with the unique kind of trading system and daily analytics and progress report using data science.


Medical Assistance for Alzheimer's Disease Using Smart Specs
Seema Patil, T Shobha, J Jesy Janet Kumari, Ruksar Khanum, Ruzaina Anjum, Sneha Manjunath, and Suriya Jan
IEEE
The Internet of Things (IoT) has firmly established itself as a popular technology. It strives to make it easier for people across domains to live an “Easy & Smart” lifestyle. Machine vision and artificial intelligence (AI) are the sources of the idea of image recognition. Chronic illnesses, a catch-all phrase for conditions including Alzheimer's, Parkinson's, other types of dementia, cardiovascular disorders, and more, are on the rise. They come with challenges relating to ongoing supervision, care, and aid. Not everyone is prepared to handle the financial and ethical issues involving patients and their caretakers. Wearable IoT devices and AI offer a potential quick aid for dependable remote monitoring and help through ambient medical aid without jeopardizing the patient's confidentiality or privacy. The proposed work seeks to design a prototype that is inexpensive and provides pleasant patient healthcare. Concerning Alzheimer's, applications and IoT and AI devices are used to create a long-term fix. The patient will be able to recognize familiar people around them and capture knowledge for future reminders with the use of the proposed wearable camera-aided device in conjunction with a Bluetooth ear-complementary device. The AI component triggers constant reminders, which helps the patient stay more aware of their surroundings & situation.

RECENT SCHOLAR PUBLICATIONS

Enhancing Connectivity of AODV in Urban Scenarios for VANETs: A QoS-Aware Approach
JJJ Kumari, S Thangam, A Ghosh, DV Kolhe, S Ghosh
2025 4th International Conference on Sentiment Analysis and Deep Learning 2025



CSG_IACO: an efficient stigmergic-based improved ACO routing strategy to determine the effective path based on traffic density in V2V networks
JJJ Kumari, S Thangam
International Journal of Information Technology, 1-26 2025



AvD Ayur Vriksha Diagnostics for Precise Identification of Medicinal Leaves in Ayurveda Using Deep Learning Techniques
JJJ Kumari, S Elilmaniyamma, P Anushree, M MS, A Maryam
Future Innovations in the Convergence of AI and Internet of Things in 2025



ERSA Enhanced RSA: Advanced Security to Overcome Cyber-Vulnerability
JJJ Kumari, S Thangam
Advancing Cyber Security Through Quantum Cryptography, 413-440 2025



Assistive Glasses Using Ultrasonic Sensors for Visually Impaired Individuals
JJJ Kumari, S Fatima, S Ramesh, Z Moosaraza
2024 4th International Conference on Ubiquitous Computing and Intelligent 2024



Gesture Recognition Technology in Smart Gloves Enhanced by Machine Learning
S Thangam, N Kandi, DS Charan, V Gosu, JJJ Kumari, G Virupaxappa
2024 4th International Conference on Ubiquitous Computing and Intelligent 2024



Smart IoT-Driven Monitoring and Control System for Enhancing Shrimp Aquaculture Health
S Thangam, VSSA Babu, KA Paul, R Jaiswal, JJJ Kumari
2024 8th International Conference on Computational System and Information 2024



SafeComm: An IOT-based System for enhancing rural child safety using LoRa Technology
S Thangam, R Gamidi, TS Surya, S Saragadam, JJJ Kumari
2024 Global Conference on Communications and Information Technologies (GCCIT 2024



Computer Networks: Data Communicationhttps://www.amazon.in/Computer-Networks-Communication-Dr-Thangam-ebook/dp/B0DHZ66QPL/ref=sr_1_2?crid=308D4V82H1VJS&dib=eyJ2IjoiMSJ9
JJJA Dr S Thangam (Author)
2024



Technology Enhanced Cradle by using Sensors and Internet of Things
S Thangam, M Greeshma, M Varun, N Sanjana, JJJ Kumari
2024 5th International Conference on Smart Electronics and Communication 2024



Intelligent Safety Helmet For Miners Using Arduino Leveraging Support Vector Machines
S Thangam, A Kothari, V Nikhil, N Rohit, JJJ Kumari
2024 Second International Conference on Networks, Multimedia and Information 2024



Real-Time Stress Monitoring using IoT and Machine Learning for Cognitive Wellness
S Thangam, G Karthik, T Muppala, V Chowdary, JJJ Kumari
2024 5th International Conference on Electronics and Sustainable 2024



A smart pill container for improved medication
S Thangam, TS Reddy, T Krithin, JJJ Kumari
2024 5th International Conference on Electronics and Sustainable 2024



Flex Sensor Data Analysis for Hand Rehabilitation using Wearable Glove
S Thangam, AC Reddy, G Varun, M Hemasri, JJJ Kumari
2024 Third International Conference on Electrical, Electronics, Information 2024



Smart Steps to Sporting Success: IoT-Driven Footwear Innovations
S Thangam, G Joshith, S Santhosh, V Harish, JJJ Kumari
2024 15th International Conference on Computing Communication and Networking 2024



AEGSDQ: An Enhancement of Eye Gaze System for Quadriplegia People using a Mouse Pointer
JJJ Kumari, A Aaron, A Salman, N Das
Journal of Information Systems Research and Practice 6 (1), 1-11 2024



Improving Existing VANETs by Incorporating UAVS Using Ant Colony and PSO Algorithms
S Thangam, A Dixit, AK Karn, A Thakur, C Arunbalaji, JJJ Kumari, ...
2024 4th International Conference on Data Engineering and Communication 2024



Smart Traffic Management in Green VANETs: Ant-Inspired Routing Strategies with Local Search Optimization
S Thangam, M Jahnavi, K Chandana, K Dheeraj
2024 International Conference on Recent Innovation in Smart and Sustainable 2024



OLBR: Optimization of Location-Based Routing Protocols in Vehicular Ad Hoc Networks
S Thangam, MVS Reddy, K Ullas, NAK Jilani, K Venkatesh, JJJ Kumari, ...
2024 International Conference on Recent Innovation in Smart and Sustainable 2024



Performance Analysis of Routing Protocols in VANETs using OSM, SUMO, and NS2
A Avvaru, A Tandon, JJJ Kumari, AS Raja
2024 IEEE International Conference on Interdisciplinary Approaches in 2024

MOST CITED SCHOLAR PUBLICATIONS

Efficacious Routing Approaches in Vehicular Ad hoc Network: An Empirical Study
JJJ Kumari, S Thangam, AS Raja
2023 First International Conference on Advances in Electrical, Electronics 2023
Citations: 8



Medical Assistance for Alzheimer's Disease Using Smart Specs
S Patil, T Shobha, JJJ Kumari, R Khanum, R Anjum, S Manjunath, S Jan
2022 4th International Conference on Circuits, Control, Communication and 2022
Citations: 5



An optimal navigation model for realistic traffic network scenarios in VANET
JJJ Kumari, S Thangam, AS Raja
2023
Citations: 4



An Empirical Study on E-Commerce Site using Unique AI based Features and Data Sceinece Tools
JJJ Kumari
4th International Conference on Electronics and Sustainable Communication 2023
Citations: 4



Flex Sensor Data Analysis for Hand Rehabilitation using Wearable Glove
S Thangam, AC Reddy, G Varun, M Hemasri, JJJ Kumari
2024 Third International Conference on Electrical, Electronics, Information 2024
Citations: 3



Adaptive Strategies for Scalable and Heterogeneous VANET Network
S Thangam, P Jha, J Hawari, NK Sah, N Jain, JJJ Kumari, AS Raja
2024 5th International Conference on Intelligent Communication Technologies 2024
Citations: 3



A smart pill container for improved medication
S Thangam, TS Reddy, T Krithin, JJJ Kumari
2024 5th International Conference on Electronics and Sustainable 2024
Citations: 2



Improving Existing VANETs by Incorporating UAVS Using Ant Colony and PSO Algorithms
S Thangam, A Dixit, AK Karn, A Thakur, C Arunbalaji, JJJ Kumari, ...
2024 4th International Conference on Data Engineering and Communication 2024
Citations: 2



Performance Analysis of Routing Protocols in VANETs using OSM, SUMO, and NS2
A Avvaru, A Tandon, JJJ Kumari, AS Raja
2024 IEEE International Conference on Interdisciplinary Approaches in 2024
Citations: 2



CSG_IACO: an efficient stigmergic-based improved ACO routing strategy to determine the effective path based on traffic density in V2V networks
JJJ Kumari, S Thangam
International Journal of Information Technology, 1-26 2025
Citations: 1



Diabetes Mellitus Diagnosis using Optical Ring Resonators
M Prajwal, JJJ Kumari, MM Uppu, V Chirchi, S Vishalatchi, DN Darshan
2024 11th International Conference on Computing for Sustainable Global 2024
Citations: 1