Dr B Kiran Bala
@krce.ac.in
Head of the Department/Artificial Intelligence and Data Science
K.Ramakrishnan College of Engineering
EDUCATION
2020
Ph.D
St. Peters Institute of Higher Education and Research
2013
M.B.A
Alagappa University
2012
M.E
MAM College of Engineering
2010
B.Tech
M.I.E.T. Engineering College
RESEARCH INTERESTS
Image Processing, Machine Learning
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
P Gayathri, N. Geetha, M. Sridhar, Ramu Kuchipudi, K. Suresh Babu, Lakshmana Phaneendra Maguluri, and B Kiran Bala
The Science and Information Organization
— Timely diagnosis of Alzheimer's Disease (AD) is pivotal for effective intervention and improved patient outcomes, utilizing Magnetic Resonance Imaging (MRI) to unveil structural brain changes associated with the disorder. This research presents an integrated methodology for early detection of Alzheimer's Disease from Magnetic Resonance Imaging, combining advanced techniques. The framework initiates with Convolutional Neural Networks (CNNs) for intricate feature extraction from structural MRI data indicative of Alzheimer's Disease. To address class imbalance in medical datasets, Synthetic Minority Over-sampling Technique (SMOTE) ensures a balanced representation of Alzheimer's Disease and non-Alzheimer's Disease instances. The classification phase employs Spider Monkey Optimization (SMO) to optimize model parameters, enhancing precision and sensitivity in Alzheimer's Disease diagnosis. This work aims to provide a comprehensive approach, improving accuracy and tackling imbalanced datasets challenges in early Alzheimer's detection. Experimental outcomes demonstrate the proposed approach outperforming conventional techniques in terms of classification accuracy, sensitivity, and specificity. With a notable 91% classification accuracy, particularly significant in medical diagnostics, this method holds promise for practical application in clinical settings, showcasing robustness and potential for enhancing patient outcomes in early-stage Alzheimer's diagnosis. The implementation is conducted in Python.
Myagmarsuren Orosoo, Santhakumar Govindasamy, Narmandakh Bayarsaikhan, Yaisna Rajkumari, Gulnaz Fatma, R. Manikandan, and B. Kiran Bala
Elsevier BV
Yousef Methkal Abd Algani, Orlando Juan Marquez Caro, Liz Maribel Robladillo Bravo, Chamandeep Kaur, Mohammed Saleh Al Ansari, and B. Kiran Bala
Elsevier BV
Sohail Imran Khan, Chamandeep Kaur, Mohammed Saleh Al Ansari, Iskandar Muda, Ricardo Fernando Cosio Borda, and B. Kiran Bala
Springer Science and Business Media LLC
Chamandeep Kaur, Tuhina Panda, Subhasis Panda, Abdul Rahman Mohammed Al Ansari, M. Nivetha, and B. Kiran Bala
IEEE
Machine learning is widely used in many aspects of healthcare. The development of medical technology has made it possible to gather better data for early disease symptom diagnosis. This study makes an effort to categorize Alzheimer’s disorder. Alzheimer’s disease is a fatal disorder that may result in memory loss and mental impairment. To prepare for medical attention, this needs early disease diagnosis. Magnetic resonance imaging (MRI) can be used to accurately and non-invasively diagnose Alzheimer’s disease. Effective feature extraction and segmentation techniques are necessary for the accurate diagnosis of MRI images. Utilizing MRI data of the brain’s white matter, grey matter, and cerebrospinal fluid, feature selection is carried out. Random forest trees are used in standard machine learning methods like regression and classification. The results of the utilized method were next contrasted with those of other machine learning techniques. As a result, RF model-based interpolation analysis surpasses the RF non-imputation method with greater accuracy, specificity, sensitivity, f-measure, and ROC.
Vishal M. Tidake, Nilanjan Mazumdar, A. Suresh Kumar, B. Nageswara Rao, Gulnaz Fatma, I. Infant Raj, and B. Kiran Bala
IEEE
There seems to be a growing amount of user-generated material online as more people become familiar with the Internet. Understanding hidden thoughts, emotions, and attitudes in tweets, emails, comments, and reviews is difficult yet essential for market analysis, brand tracking, social media tracking, and customer support. Sentiment Analysis (SA) identifies the emotional undertone of a string of words and also might basically be employed to comprehend a user’s attitude, thoughts, and emotions. The Harris Hawks Optimization - Sparrow S earch Algorithm with Convolutional Neural Network i.e., (HH-SSA-CNN) proposed in this study is an innovative SA algorithm. Pre-processing, sentiment categorization, and feature extraction make up the procedure. The preprocessing phase removes the unwanted info from input text evaluations using NLP algorithms. A hybrid technique that combines review-related features and aspect-related features has been presented for efficiently retrieving the features. This method creates unique composite features for every review. The created HH- SSA-CNN is used to accomplish sentiment categorization. This approach has been used in the IMDb dataset. To assess the model’s efficacy, the outcomes of the HH-SSA-CNN model are contrasted with those of alternative methodologies. The result indicates that the developed model accurately classifies the sentiments while compared to other existing methods.
Jeyalakshmi Chelliah, KiranBala Benny, Revathi Arunachalam, and Viswanathan Balasubramanian
Zarqa University
Several research works in speaker recognition have grown recently due to its tremendous applications in security, criminal investigations and in other major fields. Identification of a speaker is represented by the way they speak, and not on the spoken words. Hence the identification of hearing-impaired speakers from their speech is a challenging task since their speech is highly distorted. In this paper, a new task has been introduced in recognizing Hearing Impaired (HI) speakers using speech as a biometric in native language Tamil. Though their speech is very hard to get recognized even by their parents and teachers, our proposed system accurately identifies them by adapting enhancement of their speeches. Due to the huge variety in their utterances, instead of applying the spectrogram of raw speech, Mel Frequency Cepstral Coefficient features are derived from speech and it is applied as spectrogram to Convolutional Neural Network (CNN), which is not necessary for ordinary speakers. In the proposed system of recognizing HI speakers, is used as a modelling technique to assess the performance of the system and this deep learning network provides 80% accuracy and the system is less complex. Auto Associative Neural Network (AANN) is used as a modelling technique and performance of AANN is only 9% accurate and it is found that CNN performs better than AANN for recognizing HI speakers. Hence this system is very much useful for the biometric system and other security related applications for hearing impaired speakers.
K Bapayya Naidu, B. Ravi Prasad, Samar Mansour Hassen, Chamandeep Kaur, Mohammed Saleh Al Ansari, R. Vinod, M. Nivetha, and B. Kiran Bala
Elsevier BV
Yousef Methkal Abd Algani, Mahyudin Ritonga, B. Kiran Bala, Mohammed Saleh Al Ansari, Malek Badr, and Ahmed I. Taloba
Elsevier BV
Yousef Methkal Abd Algani, G Arul Freeda Vinodhini, K. Ruth Isabels, Chamandeep Kaur, Mark Treve, B. Kiran Bala, S. Balaji, and G. Usha Devi
Elsevier BV
D. Venu, Babu J, R. Saravanakumar, Ricardo Fernando Cosio Borda, Yousef Methkal Abd Algani, and B. Kiran Bala
Elsevier BV
Sudha Rajesh, Yousef Methkal Abd Algani, Mohammed Saleh Al Ansari, Bhuvaneswari Balachander, Roop Raj, Iskandar Muda, B. Kiran Bala, and S. Balaji
Elsevier BV
Mark Treve, Indrajit Patra, P. Prabu, S. Rama Sree, N. Keerthi Kumar, Yousef Methkal Abd Algani, B. Kiran Bala, and S. Balaji
Elsevier BV
Yousef Methkal Abd Algani, K. Boopalan, G Elangovan, D. Teja Santosh, K. Chanthirasekaran, Indrajit Patra, N. Pughazendi, B. Kiranbala, R. Nikitha, and M. Saranya
Elsevier BV
Dr. M Anna Gustina Zainal, Ricardo Fernando Cosio Borda, Yousef Methkal Abd Algani, Mr. Bhaskarrao Yakkala, Dr. S Sanjith, Iskandar Muda, T. Kalaichelvi, M. Mahendran, and B. Kiran Bala
Elsevier BV
C. Kotteeswaran, Indrajit Patra, Regonda Nagaraju, D. Sungeetha, Bapayya Naidu Kommula, Yousef Methkal Abd Algani, S. Murugavalli, and B. Kiran Bala
Elsevier BV
M. Vijay Anand, B. KiranBala, S. R. Srividhya, Kavitha C., Mohammed Younus, and Md Habibur Rahman
Hindawi Limited
Cancer is a disease caused by uncontrollable cell growth. The disease is a constant subject of concern due to unavailability of treatment at a severe level. Patients who have suffered from the disease have the chance of getting saved if this fatal illness is identified in the beginning stage. The survival chance will be very low if it is detected in the final stage of cancer. As the patients could not survive in their last stage, to cure their disease, an early diagnosis is a key issue and is vital. For the classification of cancer, Gaussian Naïve Bayes is implemented in this work. By exerting it on two datasets, the algorithm is tested, in which the Wisconsin Breast Cancer Dataset (WBCD) is considered as earliest one and the next one is the Lung Cancer Dataset. The assessment result of the suggested algorithm attained 90% accuracy in the prediction of lung cancer, and in predicting breast cancer, the accuracy is 98%.
I. Infant Raj and B. Kiran Bala
Springer International Publishing
B. Kiran Bala and I. Infant Raj
Springer International Publishing
I. Infant Raj and B. Kiran Bala
Springer International Publishing
B. Kiran Bala and I. Infant Raj
Springer International Publishing
B. Kiran Bala and I. Infant Raj
IEEE
To identify the variation between tumor and breast cancer in earlier stage itself with the help of mammogram images by using MIAS database. To extract the best transforms in frequency domain with the help of comparative analysis of result, The proposed sectional metric for the identification for the input images like age wise, left and right image taken in this process and finally from the result system taken this transform for the entire process.
B Kiran Bala and S Audithan
Diva Enterprises Private Limited
B. Kiran Bala and S. Audithan
IEEE
Among the various carcinoma occurrences, breast cancer remains the most female malignancy in the world. The existence of MicroCalcifications (MCs) is a primary sign of breast cancer and their diagnosis process is still a complex problem. Nowadays, digital mammography technique is used as the most common and effective tool in screening mammography. In this study, an automated MCs classification system is proposed based on Spectral Graph Wavelet Theory (SGWT) and K-Nearest Neighbour (KNN) classifier. The decomposed mammogram at various resolution levels by SGWT provides more information than spatial domain. The energy of each coefficient in different sub-bands is computed and all sub-bands are summed together to form the feature vector and classification is achieved by KNN classifier. Results prove that the MCs classification system provides accurate results at 3rd level SGWT level with 100% accuracy.