Dr P.Chandana
@iare.ac.in
Coomputer Science and Engineering
Institute of Aeronautical Engineering
Dr. P.Chandana is currently working as an Associate Professor in CSE. She received Doctorate in Computer Science and Engineering from Andhra University for the research work on “Sketch Based Image Retrieval For Efficient Identification of Art Forensics”; MCA from IGNOU; M.Tech in Computer Science and Technology from Andhra University, Visakhapatnam; B.Sc in Maths, Electronics and Computers from Andhra University; She served in academics more than 12 years in various Engineering Colleges: VITS, Miracle Engineering College(Visakhapatnam), VIIT(Visakhapatnam), Raghu Engineering College (Visakhapatnam). Her research interests are Image Processing, Computer Vision, Internet of Things, Data Mining, Information Retrieval, Machine Learning, Artificial Intelligence, Natural Language Processing, Semantic Web, Wireless Networks. She has published 12 papers in international journals a book on “A Comprehensive Survey of Geographical Routing and its Impact on Mobility Models in Multi-hop Wireless
EDUCATION
Ph.D in Computer Science and Engineering, M.Tech in Computer Science and Technology, MCA from IGNOU
RESEARCH INTERESTS
Data Mining, Machine Learning, Data Analytics, Natural Language Processing, Image Proccessing, Computer Vision, Social Media Mining
FUTURE PROJECTS
Voice to Text Recognition for smart desktop applications
This project is intended to help out the blind persons and software employees to use resources of a computer efficiently with a desktop assistant gets activated with a voice.
Applications Invited
Collaborators and Students
Need and Resource management during Natural Calamities
This project is to provide the immediate help for the people who got stucked at the time of Natural calamities like Flood, EarthQuakes, Tornados etc. The aim of this project is to use Google Maps, Social Media to have a communication and tacking of nearby resource centers or ngos for extending the help.
Applications Invited
Collaborators and Students
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
S. L. Karthik, Madhavi Devi Botlagunta, Manjula Devarkonda Venkata, Mahendran Botlagunta, Anusha Kondam, and P. Chandana
IEEE
The Smart Bus Pass App transforms the bus pass experience in India through innovative mobile technology. With a user-friendly interface, it simplifies purchasing, recharging, and managing bus passes on smartphones. Secure transactions are ensured by integrating with popular payment systems and authentication methods. The app provides real-time tracking for bus locations and arrival times, reducing wait times. Personalized notifications keep users informed about schedule changes or delays. The app's paperless approach promotes sustainability, contributing to an eco-friendly transportation system. Rigorous testing and continuous maintenance ensure a reliable user experience, with regular updates introducing new features and improvements. In summary, the Smart Bus Pass App offers a comprehensive and efficient solution, providing convenience, real-time information, and sustainability for users in the public transportation system.
Allam Balaram, P. Chandana, Shaik Abdul Nabi, and M. SilpaRaj
Wiley
B. Padmaja, G. Chandra Sekhar, Ch. V. Rama Padmaja, P. Chandana, and E. Krishna Rao Patro
Springer Nature Singapore
P. Chandana, N.Pragnya Sree, V. Ramya, and G. Bhavana
IEEE
In today's e-commerce, online reviews play a critical part in decision-making. Customers, who make up a large portion of the population, read evaluations of items or businesses before deciding what to buy or where to get it from. Because there is a monetary reward in publishing false/fraudulent evaluations, there has been significant growth in misleading opinion spam on online review platforms. A fake review, often known as a fraudulent review or opinion spam, is a false review. Positive ratings of a specific object may attract more consumers and improve sales, while unfavourable evaluations may result in lower demand and sales. These fake/fraudulent reviews are produced with the intent of deceiving potential consumers to promote/hype or discredit their businesses. Our research aims to determine if a review is genuine or not.
P. Chandana, N. Pragnya Sree, V. Ramya, and G. Bhavana
IEEE
In today's e-commerce, online reviews play a critical part in decision-making. Customers, who make up a large portion of the population, read evaluations of items or businesses before deciding what to buy or where to get it from. Because there is a monetary reward in publishing false/fraudulent evaluations, there has been significant growth in misleading opinion spam on online review platforms. A fake review, often known as a fraudulent review or opinion spam, is a false review. Positive ratings of a specific object may attract more consumers and improve sales, while unfavourable evaluations may result in lower demand and sales. These fake/fraudulent reviews are produced with the intent of deceiving potential consumers to promote/hype or discredit their businesses. Our research aims to determine if a review is genuine or not.
P. Chandana, Ch. Aishwarya, and Syeda Saniya Muskan
IEEE
One of the most concerning topics in smart cities is the internet of things, namely wireless sensor networks in indoor localization. Wi-Fi with received signal strengths (RSSs) is one of the most used indoor localization techniques. It generates signal intensity irregularities in Wi-Fi RSSs owing to reflection, refraction, incursion, and channel noise. RSS values cannot be defined (the location of each unknown node must be accurately stated). Here, Asymmetrical, abnormal circumstances are present in the Wi-Fi indoor localization area. Matplotlib, numpy, pandas, ploty, scipy, seaborn, scikitlearn, wordcloud, statsmodels, and streamlit were utilized in this study.
P. Chandana, G. S. Pradeep Ghantasala, J. Rethna Virgil Jeny, Kaushik Sekaran, Deepika N., Yunyoung Nam, and Seifedine Kadry
Institute of Advanced Engineering and Science
The majority of research Study is moving towards cognitive computing, ubiquitous computing, internet of things (IoT) which focus on some of the real time applications like smart cities, smart agriculture, wearable smart devices. The objective of the research in this paper is to integrate the image processing strategies to the smart agriculture techniques to help the farmers to use the latest innovations of technology in order to resolve the issues of crops like infections or diseases to their crops which may be due to bugs or due to climatic conditions or may be due to soil consistency. As IoT is playing a crucial role in smart agriculture, the concept of infection recognition using object recognition the image processing strategy can help out the farmers greatly without making them to learn much about the technology and also helps them to sort out the issues with respect to crop. In this paper, an attempt of integrating kissan application with expert systems and image processing is made in order to help the farmers to have an immediate solution for the problem identified in a crop.
B. Vijayalaxmi, Kaushik Sekaran, N. Neelima, P. Chandana, Maytham N. Meqdad, and Seifedine Kadry
Institute of Advanced Engineering and Science
Driver Assistance system is significant in drriver drowsiness to avoid on road accidents. The aim of this research work is to detect the position of driver’s eye for fatigue estimation. It is not unusual to see vehicles moving around even during the nights. In such circumstances there will be very high probability that a driver gets drowsy which may lead to fatal accidents. Providing a solution to this problem has become a motivating factor for this research, which aims at detecting driver fatigue. This research concentrates on locating the eye region failing which a warning signal is generated so as to alert the driver. In this paper, an efficient algorithm is proposed for detecting the location of an eye, which forms an invaluable insight for driver fatigue detection after the face detection stage. After detecting the eyes, eye tracking for input videos has to be achieved so that the blink rate of eyes can be determined.
Kaushik Sekaran, P. Chandana, N. Murali Krishna, and Seifedine Kadry
Springer Science and Business Media LLC
The tremendous research towards medical health systems are giving ample scope for the computing systems to emerge with the latest innovations. These innovations are leading to the efficient implementations of the medical systems which involve in automatic diagnosis of the health related problems. The most important health research is going on towards cancer prediction, which has different forms and can be affected on different portions of the body parts. One of the most affected cancer that predicted to be incurable are Pancreatic Cancer, which cannot be treated efficiently once identified, in most of the cases it found to be unpredictable as it lies in the abdomen region below the stomach. Therefore the advancements in the medical research is trending towards the implementations of an automated systems which identifies the stages of cancer if affected and provide the better diagnosis and treatment if identified. Deep learning is one such area which extended its research towards medical imaging, which automates the process of diagnosing the problems of the patients when appended with the set of machines like CT/PET Scan systems. In this paper, the deep learning strategy named Convolutional Neural network (CNN) model is used to predict the cancer images of the pancreas, which is embedded with the model of Gaussian Mixture model with EM algorithm to predict the essential features from the CT Scan and predicts the percentage of cancer spread in the pancreas with the threshold parameters taken as a markers. The experimentation is carried out on the CT Scan images dataset of pancreas collected from the Cancer Imaging Archive (TCIA) consists of approximately 19,000 images supported by the National Institutes of Health Clinical Center to analyze the performance of the model.
Kaushik Sekaran, P. Chandana, J. Rethna Virgil Jeny, Maytham N. Meqdad, and Seifedine Kadry
Universitas Ahmad Dahlan
Natural language processing is the trending topic in the latest research areas, which allows the developers to create the human-computer interactions to come into existence. The natural language processing is an integration of artificial intelligence, computer science and computer linguistics. The research towards natural Language Processing is focused on creating innovations towards creating the devices or machines which operates basing on the single command of a human. It allows various Bot creations to innovate the instructions from the mobile devices to control the physical devices by allowing the speech-tagging. In our paper, we design a search engine which not only displays the data according to user query but also performs the detailed display of the content or topic user is interested for using the summarization concept. We find the designed search engine is having optimal response time for the user queries by analyzing with number of transactions as inputs. Also, the result findings in the performance analysis show that the text summarization method has been an efficient way for improving the response time in the search engine optimizations.
N. Murali Krishna, Kaushik Sekaran, Annepu Venkata Naga Vamsi, G. S. Pradeep Ghantasala, P. Chandana, Seifedine Kadry, Tomas Blazauskas, and Robertas Damasevicius
Institute of Electrical and Electronics Engineers (IEEE)
We propose an efficient mixture classification technique, which uses electroencephalography (EEG) signals for establishing a communication channel for the physically challenged or immobilized people, by the usage of the brain signals. In order to identify the emotion expressions by an immobilized person, we introduce a novel approach for emotion recognition based on the generalized mixture distribution model. The main benefit of utilizing this model is that it is an asymmetric distribution, which helps to extract the EEG signals, which are either in symmetric or asymmetric form. The skew Gaussian distribution helps to identify the small duration EEG signal sample and helps toward better recognition of emotions in both clean and noisy EEG signals. The proposed method is particularly well suited for the high variability of the EEG signal allowing the emotions to be identified appropriately. The features of the brain signals are extracted by using cepstral coefficients. The extracted features are classified into different emotions using mixture classification techniques. In order to validate the model, six mentally impaired subjects are considered in the age group of 60–68, and an 8-channel EEG signal is utilized to collect the EEG signals under audio-visual stimuli. The basic emotions considered in this study include happy, sad, neutral, and boredom and an average emotion recognition accuracy of 89% is achieved.
Harshita R. Jhurani, Pindi Chandana, Yarram Vijay Kumar Reddy, and Mangipudi Sharada
IEEE
Security has been a major issue since the evolution of mankind. Access control refers to providing access to data only to specified users. In the present time, various mechanisms are used and many policies have been implemented to secure the confidential data. These mechanisms are not always easy to be implemented. Many challenges are met during the execution and various devices have been designed in an ad-hoc fashion to meet the present day requirements. The deployment of these devices and methods in various fields has solved the protection issue to a large extent. This paper represents the SQLIA and XSS attacks, the techniques that are compared to few mechanisms used to restrict unethical access to database, cloud and femtocells that followed in day-to-day life, and the challenges met to obtain them.
P. Chandana, P. Srinivas Rao, C. H. Satyanarayana, Y. Srinivas, and A. Gauthami Latha
Springer Singapore
Today, modern technology led to a faster growth of digital media collection, and it contains both still images and videos. Storage devices contain large amount of digital images, increasing the response time of a system to retrieve images required from such collections, which degrades the performance. Various search skills are needed to find what we are searching for in such large collections. The annotations are given manually for images by describing with the set of keywords. By doing so, the contents of an image retrieve images of interest, but it is time-consuming. Also, different individuals may annotate the same image using different keywords, which make it difficult to create a suitable classification and annotate images with the exact keywords. To overcome all these reasons, content-based image retrieval (CBIR) is the area which is used for extracting images. The technique gray-level co-occurrence matrix (GLCM) is discussed and analyzed for retrieval of image. It considers the various features such as color histogram, texture, and edge density. In this paper, we mainly concentrate on texture feature for accurate and effective content-based image retrieval system.