Computer Science, Computer Vision and Pattern Recognition, Human-Computer Interaction, Signal Processing
38
Scopus Publications
210
Scholar Citations
8
Scholar h-index
8
Scholar i10-index
Scopus Publications
Deep Convolution Learning of EEG Signals for Brain-Computer Interfaces: Applications in Cognitive State Recognition and Epilepsy Mahantesh K, Pooja R Rao, Shreya J 2025 9th International Conference on Computational System and Information Technology for Sustainable Solutions Csitss 2025, 2025 Brain-Computer Interfaces (BCIs) have shown significant potential in medical applications, such as epilepsy monitoring and cognitive state recognition. A key challenge in BCI systems is the accurate classification of electroencephalography (EEG) signals, which are often noisy and exhibit complex temporal patterns. The research conducted here describes a new approach for analyzing and classifying EEG signals using deep one-dimensional (1D) Convolutional Neural Networks (CNNs). The primary goal is to focus on two key Brain-Computer Interface (BCI) applications: epileptic seizures detection and cognitive states recognition(mental tasks). By directly processing the raw EEG time-series data, this method automates feature extraction, drastically reducing the dependency on manual feature engineering. The network architecture uses convolutional layers to capture temporal relationships, followed by pooling layers to lower dimensionality and prevent overfitting. Finally, a fully connected layer performs the classification, sorting the signals into categories like seizure/nonseizure or different cognitive states. The approach is demonstrated on publicly available epilepsy and cognitive state EEG datasets. Experimental results portrayed that the 1D CNN outperforms traditional machine learning and state-of-the-art methods like CNN encoders and REEGNet in accuracy.
Smart Assistive Solutions: Convolutional Neural Networks in BCI for Enhanced Accessibility K Mahantesh, N Hamsaveni, L Pranav, S Bhoomika, J K Gagana, S Aditi 2025 IEEE International Conference on Electronics Computing and Communication Technologies Conecct 2025, 2025 This paper investigates the design and implementation of a brain-computer interface (BCI) that empowers individuals who have significant motor impairments to manage a wheelchair using their brain signals. Traditional assistive technologies often rely on some degree of physical movement, which can be challenging or impossible for users with significant disabilities. The proposed BCI system utilizes electroencephalographic (EEG) data collected through a MITSAR-21 headset to interpret neural activity and translate it into actionable commands for wheelchair navigation. By employing sophisticated techniques for signal processing, which include noise reduction and feature extraction, the system enhances the precision of brain signal interpretation. Machine learning and CNN techniques are applied to classify user intentions in real-time, facilitating a seamless communication between the user and the wheelchair. The primary objective of this research is to restore independence and enhance the quality of life for individuals facing mobility challenges. Experimental results demonstrate the system’s effectiveness in allowing users to navigate the wheelchair intuitively, demonstrating its potential for wider use in assistive technology.
Optimising AI network resource allocation in healthcare with quantum-inspired techniques J. Ranjith, K. Mahantesh, S. B. G. Tilak Babu, N. Ashok Kumar, M. V. Rama Prasad, Venkatesan Hariram AI and Quantum Network Applications in Business and Medicine, 2024 This exploratory research investigates the optimization of artificial intelligence (AI) network resource allocation within healthcare contexts by employing methods that are motivated by amounts. In light of the ever-increasing complexity of healthcare data and the growing demand for efficient deployment of computer resources, it is possible that existing methods will abruptly fail to meet the requirements. This study intends to devise new techniques to effectively allocate resources within artificial intelligence networks that have been adapted for healthcare operations. These methodologies will be derived from the perceptivity of amount-inspired computing. One of the goals of this investigation is to improve the scalability, speed, and delicacy of AI-driven healthcare systems. This will be accomplished by incorporating principles inspired from amount computing, such as superposition and trap, into resource allocation algorithms. This paper is to provide insight into how quantum-inspired methods can be used to revise resource allocation processes in healthcare AI networks.
LW-PWECC: Cryptographic Framework of Attack Detection and Secure Data Transmission in IoT J Ranjith, K Mahantesh, C N Abhilash Journal of Robotics and Control Jrc, 2024 In the present era, the number of Internet of Health Things (IoHT) devices and applications has drastically expanded. Security and attack are major issues in the IoHT domain because of the nature of its architecture and sorts of devices. Over the recent few years, network attacks have dramatically increased. Many detection and encryption techniques are existing however they lack accuracy, training stability, insecurity, delay etc. By the above concerns, this manuscript introduces a novel deep learning technique called Agnostic Spiking Binarized neural network with Improved Billiards optimization for accurate detection of network attacks and Light Weight integrated Puzzle War Elliptic Curve Cryptographic framework for secure data transmission with high security and minimal delay. Optimal features from the datasets are selected by volcano eruption optimization algorithm with better convergence for reducing the overall processing time. Wilcoxon Rank Sum and Mc Neymar’s tests are performed for proving the statistical analyses. The outcomes show that the introduced approach performs with an overall accuracy of 99.93% which is better than the previous techniques demonstrating the effectiveness.
Energy-Efficient Technique to Improve the System Using MIMO Manjunath Managuli, K. Mahantesh, M. Lakshminarayana, Sangamesh C. Managuli Digital Convergence in Antenna Designs Applications for Real Time Solutions, 2024 Energy consumption can be reserved by the WSN method, which causes the nodes to be in sleep or awake states when nodes are transmitting information. Instead, the proposed system achieves energy efficiency using sleep/awake, which ensures a high PDR (packet delivery ratio). This concept does not use the WSN method. This methodology is asynchronous and self-adaptive, which it uses to switch between sleep and awake modes and doesn't utilize the duty cycle to assign these methods. In this methodology, the time slots for the nodes are divided and scheduling is done dependent on the self-governing, where the nodes choose to switch their modes.
Prosthetic AI Enabled Arm for Rehabilitation and Advanced Dynamics Mahantesh K, Shubha Rao A, Vyshnavi Shekhar B S, Preeti Karanji International Conference on Distributed Computing and Optimization Techniques Icdcot 2024, 2024 Brain Computer Interface (BCI) is wide range of system were signal generated by the human brain is transformed into commands/messages that are communicated via computer or robotic limb to the outside world. In the presented research here, Motor Imagery based Brain Computer Interface (MI-BCI) to control the prosthetic hand is proposed. The hand features an electric motor and an angle mechanism to deliver haptic feedback and enable local machine control. With the utilization of this system, participants demonstrated the capacity to regulate the grasp of the prosthesis with an accuracy close to that of the control scheme. The SVM classification algorithm is employed to interpret and transmit commands for operating the prostheses. Utilizing model predictions as commands for device control and other Brain-Computer Interface (BCI) applications, real-time brain signal input has been incorporated into the user interface. Based on the conducted pragmatic study, Random Forest delivers better efficiency in terms of accuracy in comparison to other machine learning classifiers.
Leveraging Ensemble Deep Learning for Enhanced Brain Tumor Analysis: Integrating YOLOv8, Mask R-CNN, and U-Net Suhas S, Ranjitha U N, Bhuvan C U, Mahantesh K B, Kalyan DS 8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions Csitss 2024, 2024 Brain tumors pose a significant health threat, and accurate detection and segmentation are crucial for treatment. This paper proposes a novel deep learning approach integrating U-Net for precise segmentation and YOLOv8 for efficient tumor detection in MRI scans. U-Net is optimized for accurate delineation, while YOLOv8, enhanced with attention and spatial pooling, facilitates efficient tumor localization. Image enhancement and data augmentation further boost performance. Furthermore, U-Net, an architecture known for its segmentation capabilities, will be explored in conjunction with fine-tuning for brain tumor segmentation This integrated approach is expected to achieve competitive accuracy and efficiency, potentially improving clinical decision-making in brain tumor diagnosis and treatment.
Optimizing Image Classification Using Bag of Features and Support Vector Machines K Mahantesh, K S Navyashree, Devika S Nairy, R Asha, B Anshitha 4th IEEE International Conference on Mobile Networks and Wireless Communications Icmnwc 2024, 2024 Image categorization is a fundamental task in computer vision, with applications in domains such as object recognition, medical imaging, and autonomous systems. Traditional approaches frequently fail to balance accuracy, computing efficiency, and scalability, particularly when dealing with big and complex datasets. This work presents a novel picture classification strategy that combines the Bag of Features (BoF) model with Support Vector Machines (SVM). The BoF model describes images by extracting local visual characteristics (such as SIFT, SURF, or ORB) from image patches and quantizing them into visual words to create a histogram representation. SVM, a powerful machine learning classifier, is used to classify these histograms, utilizing its capacity to handle high-dimensional, sparse data. Experiments using common image classification datasets show that the BoF-SVM system greatly outperforms previous methods, resulting in higher classification accuracy and lower processing costs. Furthermore, it has superior generalization to previously unseen data and is more resistant to noise and picture changes. The suggested BoF-SVM system produces promising results for boosting both accuracy and efficiency in image classification tasks, with room for further optimization in more complicated and diversified applications
U-In-Effnet: Semantic Segmentation with the Effect of Magnifying Glasss International Journal of Intelligent Systems and Applications in Engineering, 2023
Privacy and Security issues in Smart Health Care J. Ranjith, K. Mahantesh 4th International Conference on Electrical Electronics Communication Computer Technologies and Optimization Techniques Iceeccot 2019, 2019
Deep Convolution Learning of EEG Signals for Brain-Computer Interfaces: Applications in Cognitive State Recognition and Epilepsy K Mahantesh, PR Rao, J Shreya 2025 9th International Conference on Computational System and Information … , 2025 2025
Smart Assistive Solutions: Convolutional Neural Networks in BCI for Enhanced Accessibility K Mahantesh, N Hamsaveni, L Pranav, S Bhoomika, JK Gagana, S Aditi 2025 IEEE International Conference on Electronics, Computing and … , 2025 2025
Optimising AI Network Resource Allocation in Healthcare With Quantum-Inspired Techniques J Ranjith, K Mahantesh, SBGT Babu, NA Kumar, MVR Prasad, V Hariram AI and Quantum Network Applications in Business and Medicine, 101-118 , 2025 2025 Citations: 2
Optimizing Image Classification Using Bag of Features and Support Vector Machines K Mahantesh, KS Navyashree, DS Nairy, R Asha, B Anshitha 2024 4th International Conference on Mobile Networks and Wireless … , 2024 2024
Leveraging Ensemble Deep Learning for Enhanced Brain Tumor Analysis: Integrating YOLOv8, Mask R-CNN, and U-Net S Suhas, UN Ranjitha, CU Bhuvan, KB Mahantesh, DS Kalyan 2024 8th International Conference on Computational System and Information … , 2024 2024 Citations: 2
Prosthetic AI Enabled Arm for Rehabilitation and Advanced Dynamics K Mahantesh, S Rao, V Shekhar, P Karanji 2024 International Conference on Distributed Computing and Optimization … , 2024 2024 Citations: 2
Energy‐efficient technique to improve the system using MIMO M Managuli, K Mahantesh, M Lakshminarayana, SC Managuli Digital Convergence in Antenna Designs: Applications for Real‐Time Solutions … , 2024 2024 Citations: 8
LW-PWECC: cryptographic framework of attack detection and secure data transmission in IoT J Ranjith, K Mahantesh, CN Abhilash Journal of Robotics and Control (JRC) 5 (1), 228-238 , 2024 2024 Citations: 12
Positioning and Quantification of Cracks by Sensors Using Algorithms SLA Gowda, H Ananya, BP Kumar, DL Chethan, PA Gowda, K Mahantesh, ... International Conference on Sustainable Infrastructure: Innovation … , 2023 2023
Learning Cognitive Features to Classify EEG Signals for Mind-Controlled Locomotive K Mahantesh, B Pranesh, T Nitin, S Charan, M Rathna International Conference on Emerging Research in Computing, Information … , 2023 2023 Citations: 1
Ensemble Architecture for Improved Image A ShubhaRao, K Mahantesh Cognition and Recognition: 8th International Conference, ICCR 2021, Mandya … , 2023 2023
Application of conv-1D and Bi-LSTM to classify and detect epilepsy in EEG Data R Chetana, AS Rao, K Mahantesh International Journal of Advanced Computer Science and Applications 14 (6) , 2023 2023 Citations: 13
Impact of computer vision based secure image enrichment techniques on image classification model AS Rao, K Mahantesh Journal of Discrete Mathematical Sciences & Cryptography 26 (3), 899-911 , 2023 2023 Citations: 3
Hybrid ensemble learning framework for epileptic seizure detection using electroencephalograph signals C Rachappa, M Kapanaiah, V Nagaraju Indonesian Journal of Electrical Engineering and Computer Science 28 (3 … , 2022 2022 Citations: 4
Image Classification in Large Datasets B Pranesh, T Nitin, S Charan, DP Tejash, K Mahantesh Distributed Computing and Optimization Techniques: Select Proceedings of … , 2022 2022
Image classification based on inception-v3 and a mixture of handcrafted features A Shubha Rao, K Mahantesh Distributed Computing and Optimization Techniques: Select Proceedings of … , 2022 2022 Citations: 5
An ensemble model to extract discriminative features for semantic image classification in large datasets B Pranesh, T Nitin, S Charan, DP Tejash, K Mahantesh Distributed Computing and Optimization Techniques: Select Proceedings of … , 2022 2022 Citations: 1
Dominating set based arbitrary oriented bilingual scene text localization. RM Jayanth, M Kapanaiah International Journal of Electrical & Computer Engineering (2088-8708) 12 (4) , 2022 2022 Citations: 3
Classification and Recognition of Bilingual Text Using Graph Edit Distance Based Degree of Similarity MJ Roopa, K Mahantesh Indian Journal of Science and Technology 15 (27), 1336-1343 , 2022 2022 Citations: 1
VIRNet for Image Retrieval: One for All Top Based on Feature Fusion Technique AS Rao, K Mahantesh, V Nagaraju International Conference on Human-Computer Interaction, 378-386 , 2022 2022
MOST CITED SCHOLAR PUBLICATIONS
Privacy and security issues in smart health care J Ranjith, K Mahantesh 2019 4th International conference on electrical, electronics, communication … , 2019 2019 Citations: 18
Assessment and application of EEG: A literature review J Reaves, T Flavin, B Mitra, K Mahantesh, V Nagaraju Journal of Applied Bioinformatics & Computational Biology 10 (7) , 2021 2021 Citations: 16
Blockchain-based knapsack system for security and privacy preserving to medical data J Ranjith, K Mahantesh SN Computer Science 2 (4), 245 , 2021 2021 Citations: 14
An impact of complex hybrid color space in image segmentation K Mahantesh, VNM Aradhya, SK Niranjan Recent Advances in Intelligent Informatics: Proceedings of the Second … , 2014 2014 Citations: 14
Application of conv-1D and Bi-LSTM to classify and detect epilepsy in EEG Data R Chetana, AS Rao, K Mahantesh International Journal of Advanced Computer Science and Applications 14 (6) , 2023 2023 Citations: 13
Coslets: a novel approach to explore object taxonomy in compressed DCT domain for large image datasets K Mahantesh, VNM Aradhya, SK Niranjan Advances in Intelligent Informatics, 39-48 , 2015 2015 Citations: 13
LW-PWECC: cryptographic framework of attack detection and secure data transmission in IoT J Ranjith, K Mahantesh, CN Abhilash Journal of Robotics and Control (JRC) 5 (1), 228-238 , 2024 2024 Citations: 12
An impact of PCA-mixture models and different similarity distance measure techniques to identify latent image features for object categorization K Mahantesh, VN Manjunath Aradhya, C Naveena Advances in Signal Processing and Intelligent Recognition Systems, 371-378 , 2014 2014 Citations: 11
Energy‐efficient technique to improve the system using MIMO M Managuli, K Mahantesh, M Lakshminarayana, SC Managuli Digital Convergence in Antenna Designs: Applications for Real‐Time Solutions … , 2024 2024 Citations: 8
Content based image retrieval-inspired by computer vision & deep learning techniques K Mahantesh, S Rao 2019 4th international conference on electrical, electronics, communication … , 2019 2019 Citations: 8
Learning semantic features for classifying very large image datasets using convolution neural network AS Rao, K Mahantesh SN Computer Science 2 (3), 187 , 2021 2021 Citations: 7
Blockchain-based knapsack system for security and privacy preserving to medical data (2021) in SN COMPUT R Jagadeesh, K Mahantesh Scientifur 2, 245 , 2021 2021 Citations: 6
Image classification based on inception-v3 and a mixture of handcrafted features A Shubha Rao, K Mahantesh Distributed Computing and Optimization Techniques: Select Proceedings of … , 2022 2022 Citations: 5
An investigation of fSVD and ridgelet transform for illumination and expression invariant face recognition B Bhaskar, K Mahantesh, GP Geetha Advances in Intelligent Informatics, 31-38 , 2015 2015 Citations: 5
An investigation of combining gradient descriptor and diverse classifiers to improve object taxonomy in very large image dataset TR Anusha, N Hemavathi, K Mahantesh, R Chetana 2014 International Conference on Contemporary Computing and Informatics … , 2014 2014 Citations: 5
A study of subspace mixture models with different classifiers for very large object classification K Mahantesh, VNM Aradhya, SK Niranjan 2014 International Conference on Advances in Computing, Communications and … , 2014 2014 Citations: 5
Hybrid ensemble learning framework for epileptic seizure detection using electroencephalograph signals C Rachappa, M Kapanaiah, V Nagaraju Indonesian Journal of Electrical Engineering and Computer Science 28 (3 … , 2022 2022 Citations: 4
A Novel Approach for Image Retrieval System Combining Color, Shape & Texture Features K Mahantesh, M Anusha, KR Manasa International Journal Technology and Advanced Engineering (IJETAE) 3 (3) , 2013 2013 Citations: 4
Impact of computer vision based secure image enrichment techniques on image classification model AS Rao, K Mahantesh Journal of Discrete Mathematical Sciences & Cryptography 26 (3), 899-911 , 2023 2023 Citations: 3
Dominating set based arbitrary oriented bilingual scene text localization. RM Jayanth, M Kapanaiah International Journal of Electrical & Computer Engineering (2088-8708) 12 (4) , 2022 2022 Citations: 3
