A Hybrid mRMR-RSA Feature Selection Approach for Lung Cancer Diagnosis Using Gene Expression Data Punam Gulande, Raval Awale Biomedical and Pharmacology Journal, 2025 Worldwide Lung cancer is the leading causes of cancer-related death, thus emphasizing the need for early and accurate detection to improve patient outcomes. While imaging modalities such as Computerized Tomography (CT) are widely used for identifying abnormal tissues and tumor characteristics, integrating advanced computational methods offers transformative potential in diagnostics. This study focuses on leveraging a hybrid machine learning approach for lung cancer classification using microarray gene expression profiles. Gene expression profiling provides critical insights into genetic abnormalities associated with cancer, but the high dimensionality of the data relative to the sample size poses significant analytical challenges. To address this, a hybrid Minimum Redundancy Maximum Relevance (mRMR) and Recursive Feature Selection Algorithm (RSA) framework was developed to enhance feature selection and classification accuracy. The K-Nearest Neighbor (KNN) algorithm demonstrated superior performance, achieving high accuracy and notable improvements in precision and recall metrics. Among various models evaluated like SVM, ANN, the K-Nearest Neighbor (KNN) algorithm determined to give superior performance with achieved high accuracy of 92.37% with dataset1 and 92.01% with dataset2. These findings highlight the promise of hybrid machine learning techniques in early prediction for diagnosis, paving the way for more personalized and effective lung cancer detection and treatment strategies. The potential implications of the findings for personalized lung cancer detection and treatment are significant and transformative. The use of hybrid machine learning techniques enables earlier detection of lung cancer. This could lead to improving survival rates, Personalized Treatment Plans, Precision Medicine, Predictive Capabilities, Cost-Effectiveness.
Electroencephalogram (EEG) based prediction of attention deficit hyperactivity disorder (ADHD) using machine learning Nitin Ahire, R.N. Awale, Abhay Wagh Applied Neuropsychology Adult, 2025 "Attention-Deficit Hyperactivity Disorder (ADHD)" is a neuro-developmental disorder in children under 12 years old. Learning deficits, anxiety, depression, sensory processing disorder, and oppositional defiant disorder are the most frequent comorbidities of ADHD. This research focuses on ADHD in children, considering its common occurrence and frequent coexistence with other mental disorders. The study utilizes the resting-state open-eye "Electroencephalogram" (EEG) signals of 61 children with ADHD and 60 healthy children. Morphological and "Power Spectral Density" (PSD) features associated with ADHD are analysed and "Principal Component Analysis" (PCA) is employed to reduce data dimensionality. Classification algorithms including AdaBoost, "K-Nearest Neighbour" (KNN) classifier, Naive Bayes, and random forest are utilized, with the Bernoulli Naive Bayes classifier achieving the highest accuracy of 96%. This study found some relevant characteristics for classification at the frontal (F), central (C), and parietal (P) electrode placement sites. Finally, this reveals distinct EEG patterns in children with ADHD and the study provides a potential supplementary method for ADHD diagnosis.
Classification of attention deficit hyperactivity disorder using machine learning on an EEG dataset Nitin Ahire, R. N. Awale, Abhay Wagh Applied Neuropsychology Child, 2025 The neurodevelopmental disorder, Attention Deficit Hyperactivity Disorder (ADHD), frequently affecting youngsters, is characterized by persistent patterns of inattention, hyperactivity, and impulsivity, the etiology of which may involve a variety of genetic, environmental, and neurological factors. Electroencephalography (EEG) measures the electrical activity in the brain through neuronal activity, which is a function of cognitive processes. In this study, a previously recorded sample set of 121 children containing unbiased data from both ADHD and control group classes and EEG signals were analyzed to classify the ADHD patients. The samples were tested under different cognitive conditions, and multiple features were extracted using Euclidean distance. Many machine learning algorithms use Euclidean distance as their default distance metric to compare two recorded data points. The extracted features were trained using four supervised machine learning algorithms (linear regression, random forest, extreme gradient boosting, and K nearest neighbor (KNN)) based on the results of various frequency bands. The results suggest that the KNN algorithm produces the highest accuracy over other machine learning approaches, and results can be further improved with the application of hyperparameter tuning and used for classifying sub-groups of ADHD to identify the severity of the disorder.
Gain and Bandwidth Enhanced Single Cavity Backed Staggered Microstrip Antenna International Journal of Microwave and Optical Technology, 2024
Evaluation of Various Machine Learning Models in Forecasting Cricket Player Performance Rameshwari Lokhande, R. N. Awale, Rahul R. Ingle 2024 4th International Conference on Robotics Automation and Artificial Intelligence Raai 2024, 2024 Predicting player performance is a critical aspect of sports analytics, aiding in decision-making and strategy development. This study presents a comprehensive analysis of multiple machine learning algorithms to forecast player performance in the dynamic sport of cricket. The research evaluates a range of algorithms, including Decision Trees, Random Forests, Support Vector Regression (SVR), Adaboost Regressor, XGBoost, and LightGBM, to predict both batting and bowling outcomes. Performance metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared (R2) scores are used to rigorously compare these models. The findings reveal that certain algorithms outperform others in predicting player success, offering valuable insights for team selection, strategy planning, and player development. A key contribution of this study is its in-depth examination of feature importance and hyperparameter optimization, which enhances the predictive power of the models. The study also highlights practical applications by exploring how these algorithms can be integrated with existing sports analytics platforms, bridging the gap between theoretical analysis and real-world implementation. Furthermore, the results provide a data-driven foundation for cricket teams, coaches, and analysts to leverage machine learning for more informed decisions, setting the stage for future research in sports analytics.
UAV-Accelerometer Data Analysis for Cricket Player Performance Prediction Rameshwari Lokhande, Rahul Ingle, R. N. Awale Proceedings 2024 5th International Conference on Mobile Computing and Sustainable Informatics Icmcsi 2024, 2024 Cricket, a game where the playing surface has a significant impact on player performance, calls for a thorough study of this relationship. The pitch's dynamics, particularly its texture, bounce, spin, and swing, have a big effect on how games turn out. Accelerometer sensors have become increasingly common in recent years for analyzing pitch conditions and forecasting player performance. These sensors record information on ball vibration and movement, giving crucial information on the condition of the pitch. This study intends to investigate the various methods used to predict player performance using pitch circumstances and accelerometer sensor data. The evaluation includes a full examination of their fundamental ideas, benefits, and drawbacks. It also explores the methods used to handle and analyze data from accelerometer sensors. The report also discusses the future research paths in this area and investigates the possible benefits of adding accelerometer sensors to improve player performance prediction. This study adds to the body of knowledge in the field of player performance prediction by examining the improvements in accelerometer technology and its implications for cricket.
SVM-ABC based cancer microarray (gene expression) hybrid method for data classification Punam Gulande, R N Awale Computational Intelligence, 2023 Microarray technology presents a challenge due to the large dimensionality of the data, which can be difficult to interpret. To address this challenge, the article proposes a feature extraction‐based cancer classification technique coupled with artificial bee colony optimization (ABC) algorithm. The ABC‐support vector machine (SVM) method is used to classify the lung cancer datasets and compared them with existing techniques in terms of precision, recall, F‐measure, and accuracy. The proposed ABC‐SVM has the advantage of dealing with complex nonlinear data, providing good flexibility. Simulation analysis was conducted with 30% of the data reserved for testing the proposed method. The results indicate that the proposed attribute classification technique, which uses fewer genes, performs better than other modalities. The classifiers, such as naïve Bayes, multi‐class SVM, and linear discriminant analysis, were also compared and the proposed method outperformed these classifiers and state‐of‐the‐art techniques. Overall, this study demonstrates the potential of using intelligent algorithms and feature extraction techniques to improve the accuracy of cancer diagnosis using microarray gene expression data.
Novel AUD Likelihood detection based on EEG Classification Suprava Patnaik, Nitin Ahire, Sushilkumar Yadav, Vaibhav Patel, Jason Malliss, R.N. Awale 2019 6th IEEE International Conference on Advances in Computing Communication and Control Icac3 2019, 2019
Investigation of hexagonal ring microstrip antenna K. P. Ray, S. S. Kakatkar, S. M. Rathod, R. N. Awale, D. P. Rathod 2015 International Conference on Microwave Optical and Communication Engineering Icmoce 2015, 2016
Cross Layer Communication for wireless networks Satish Ket, Vijay Shinde, Ravindra Khandare, R. N. Awale Proceedings of the International Conference on Advances in Computing Communication and Control Icac3 09, 2009
RECENT SCHOLAR PUBLICATIONS
ACNN-DR: Adaptive convolutional neural network for diabetic retinopathy using bias correction and continual learning A Jadhav, RN Awale, RR Ingle Biomedical Signal Processing and Control 120, 110165 , 2026 2026
Electroencephalogram (EEG) based prediction of attention deficit hyperactivity disorder (ADHD) using machine learning N Ahire, RN Awale, A Wagh Applied Neuropsychology: Adult 32 (4), 966-977 , 2025 2025 Citations: 53
Classification of attention deficit hyperactivity disorder using machine learning on an EEG dataset N Ahire, RN Awale, A Wagh Applied Neuropsychology: Child 14 (3), 312-322 , 2025 2025 Citations: 10
Forecasting bowler performance in one-day international cricket using machine learning R Lokhande, RN Awale, RR Ingle Expert Systems with Applications 259, 125178 , 2025 2025 Citations: 11
Evaluation of Various Machine Learning Models in Forecasting Cricket Player Performance R Lokhande, RN Awale, RR Ingle 2024 4th International Conference on Robotics, Automation and Artificial … , 2024 2024
Gain and Bandwidth Enhanced Single Cavity Backed Staggered Microstrip Antenna. BS Jadhao, SM Rathod, RN Awale, KP Ray International Journal of Microwave & Optical Technology 19 (6) , 2024 2024 Citations: 1
Analysing the impact of field conditions, pitch features, and opponent strength on cricket performance: A machine learning approach R Lokhande, R Awale, R Ingle Vietnam Journal of Science, Technology and Engineering 66 (3), 3-14 , 2024 2024 Citations: 2
A Compressive Approach for Cancer Classification using Text Data and Machine Learning P Gulande, RN Awale Recent Advances in Science, Engineering & Technology, 37-44 , 2024 2024
Attention module-based fused deep cnn for learning disabilities identification using EEG signal NK Ahire, RN Awale, A Wagh Multimedia Tools and Applications 83 (16), 48331-48356 , 2024 2024 Citations: 5
Microarray Gene Expression Classification: An Efficient Feature Selection Using Hybrid Swarm Intelligence Algorithm. P Gulande, RN Awale Computer Systems Science & Engineering 48 (4) , 2024 2024 Citations: 2
UAV-Accelerometer Data Analysis for Cricket Player Performance Prediction R Lokhande, R Ingle, RN Awale 2024 5th International Conference on Mobile Computing and Sustainable … , 2024 2024
Development of EEG-based identification of learning disability using machine learning algorithms N Ahire, RN Awale, A Wagh Data Modelling and Analytics for the Internet of Medical Things, 141-152 , 2023 2023 Citations: 6
SVM‐ABC based cancer microarray (gene expression) hybrid method for data classification P Gulande, RN Awale Computational Intelligence 39 (6), 1054-1072 , 2023 2023 Citations: 5
Learning disability identification with EEG signal analysis using machine learning approach N Ahire, RN Awale, A Wagh AIP Conference Proceedings 2842 (1), 020002 , 2023 2023 Citations: 1
Designing a three-layer back propagation artificial neural network for breast thermogram classification A Hakim, RN Awale IETE Journal of Research 69 (7), 4053-4065 , 2023 2023 Citations: 6
An Efficient Classification Method Using GLCM and Decision Tree Classifier P Gulande, RN Awale International Conference on ICT for Sustainable Development, 431-443 , 2023 2023
Comprehensive review of EEG data classification techniques for ADHD detection using machine learning and deep learning. N Ahire, RN Awale, A Wagh Romanian Journal of Pediatrics/Revista Romana de Pediatrie 72 (2) , 2023 2023 Citations: 10
A comprehensive review of machine learning approaches for dyslexia diagnosis N Ahire, RN Awale, S Patnaik, A Wagh Multimedia Tools and Applications 82 (9), 13557-13577 , 2023 2023 Citations: 53
Harnessing the power of machine learning for breast anomaly prediction using thermograms A Hakim, RN Awale International Journal of Medical Engineering and Informatics 15 (1), 1-22 , 2023 2023 Citations: 8
Performance Analysis of Various Cancers Using Genetic Data with Variance Threshold AA Dhumkekar, S Ghorpade, RN Awale 2022 OITS International Conference on Information Technology (OCIT), 67-72 , 2022 2022 Citations: 4
MOST CITED SCHOLAR PUBLICATIONS
Thermal imaging-an emerging modality for breast cancer detection: a comprehensive review A Hakim, RN Awale Journal of Medical systems 44 (8), 136 , 2020 2020 Citations: 101
Electroencephalogram (EEG) based prediction of attention deficit hyperactivity disorder (ADHD) using machine learning N Ahire, RN Awale, A Wagh Applied Neuropsychology: Adult 32 (4), 966-977 , 2025 2025 Citations: 53
A comprehensive review of machine learning approaches for dyslexia diagnosis N Ahire, RN Awale, S Patnaik, A Wagh Multimedia Tools and Applications 82 (9), 13557-13577 , 2023 2023 Citations: 53
Analysis of facial EMG signal for emotion recognition using wavelet packet transform and SVM V Kehri, R Ingle, S Patil, RN Awale Machine intelligence and signal analysis, 247-257 , 2018 2018 Citations: 49
Techniques of EMG signal analysis and classification of neuromuscular diseases V Kehri, R Ingle, R Awale, S Oimbe International Conference on Communication and Signal Processing 2016 (ICCASP … , 2016 2016 Citations: 44
EMG signal analysis for diagnosis of muscular dystrophy using wavelet transform, SVM and ANN V Kehri, RN Awale Biomedical and Pharmacology Journal 11 (3), 1583-1591 , 2018 2018 Citations: 30
Real time feature extraction of ECG signal on android platform PK Gakare, AM Patel, JR Vaghela, RN Awale 2012 international conference on communication, information & computing … , 2012 2012 Citations: 28
Statistical steganalysis of high capacity image steganography with cryptography SK Sabnis, RN Awale Procedia Computer Science 79, 321-327 , 2016 2016 Citations: 22
SmartCAC: Call admission control scheme to guarantee QoS for voice over IEEE 802.11 WLANs P Shete, R Awale International Journal of Computer Applications 42 (6), 1-5 , 2012 2012 Citations: 20
Security challenges in software defined network and their solutions V Patil, C Patil, RN Awale 2017 8th International Conference on Computing, Communication and Networking … , 2017 2017 Citations: 18
Extraction of hottest blood vessels from breast thermograms using state-of-the-art image segmentation methods AS Hakim, RN Awale Quantitative InfraRed Thermography Journal 19 (5), 347-365 , 2022 2022 Citations: 17
A facial EMG data analysis for emotion classification based on spectral kurtogram and CNN V Kehri, RN Awale International Journal of digital signals and smart systems 4 (1-3), 50-63 , 2020 2020 Citations: 17
Shorted circular microstrip antennas for 50 Ω microstrip line feed with very low cross polarization SM Rathod, RN Awale, KP Ray Progress In Electromagnetics Research Letters 74, 91-98 , 2018 2018 Citations: 14
Wavelet packet sub-band based classification of alcoholic and controlled state EEG signals D Puri, R Ingle, P Kachare, M Patil, R Awale International Conference on Communication and Signal Processing 2016 (ICCASP … , 2016 2016 Citations: 13
Drain current models for single-gate MOSFETs & undoped symmetric & asymmetric double-gate SOI MOSFETs and quantum mechanical effects: a review S Subramaniam, RN Awale, SM Joshi International Journal of Engineering Science and Technology 5 (1), 96-105 , 2013 2013 Citations: 12
Energy conservation through energy efficient technologies at thermal power plant ND Adate, RN Awale International Journal of Power System Operation and Energy Management, ISSN … , 2013 2013 Citations: 12
Forecasting bowler performance in one-day international cricket using machine learning R Lokhande, RN Awale, RR Ingle Expert Systems with Applications 259, 125178 , 2025 2025 Citations: 11
Directivity enhancement of a circular microstrip antenna with shorting post SM Rathod, RN Awale, KP Ray, SS Kakatkar IETE Journal of Research 68 (1), 504-513 , 2022 2022 Citations: 11
Detection of breast pathology using thermography as a screening tool A Hakim, RN Awale 15th Quantitative InfraRed Thermography Conference , 2020 2020 Citations: 11
Broadband gap‐coupled half‐hexagonal microstrip antenna fed by microstrip‐line resonator SM Rathod, RN Awale, KP Ray, AD Chaudhari International Journal of RF and Microwave Computer‐Aided Engineering 28 (6 … , 2018 2018 Citations: 11
