EEG Signal Classification Automation using Novel Modified Random Forest Approach Journal of Scientific and Industrial Research, 2023 Digitalization and automation are the two aspects in the medical industry that define compliance with industry 4.0. Automation is essential for speeding up the diagnosis process, while digitalization leads to smart medicine and efficient diagnosis. Epilepsy is one such disease that can use these automation techniques. The automatic monitoring of epilepsy EEG is of great significance in clinical medicine. Aiming at the non-stationary characteristics of EEG signals, the classification of EEG signals is based on the combination of overall empirical mode. It is proposed using the random forest method. The EEG signal data set has an epileptic interval over 200 single-channel signals with a seizure period. A total of 819,400 data are used as samples. First, the overall epileptic EEG signal modal is decomposed into multiple intrinsic modal functions. The effective features are extracted from the first-order intrinsic modal function. Finally, random forest and Least Square SVM (LS-SVM) are considered to classify the EEG signals characteristics. The correct recognition rate of random forest and LS-SVM is compared. The results show that random forest classification method has an ideal classification effect on epilepsy EEG signals during and between seizures. The recognition accuracy is 99% and 60%, which is higher than the accuracy of the LS-SVM. The proposed method improves clinical epilepsy. The efficiency of EEG signals analysis.
A Real Time 5G Ultra-Reliable and Low Latency Communications Using Collaborative Hypothesis Coding M Jasperlite, M. Mahesh, K. Prathyusha, V Ramesh Babu Proceedings IEEE International Conference on Advances in Computing Communication and Applied Informatics Accai 2022, 2022 Ultra-reliable and low latency communications (URLLC) are the most prominent technology for 5G technology, in this configuration, optimization and accurate communication is very important. The earlier technologies like 2G, 3G & 4G radio access mechanism is providing accurate operations, but these are facing complex operations. Therefore, advanced URLLC networks are compulsory to cross over complex functions. In this work, a collaborative hypothesis coding based URLLC method is proposed. This proposed method is configured, optimized and self-healing the phenomenon. At final calculating the network coverage, capacity, probability of error, false alarm, signal to noise ratio, load balancing, throughput and energy saving. These all parameters are more improved compared to earlier stage self-organization network.
Deep learning algorithms in eeg signal decoding application: A review Ramesh Babu Vallabhaneni, Pankaj Sharma, Vinit Kumar, Vyom Kulshreshtha, Koya Jeevan Reddy, S. Selva Kumar, V. Sandeep Kumar, Surendra Kumar Bitra IEEE Access, 2021 In recent years, deep learning algorithms have been developed rapidly, and they are becoming a powerful tool in biomedical engineering. Especially, there has been an increasing focus on the use of deep learning algorithms for decoding physiological or pathological status of the brain from electroencephalographic (EEG). This paper overviews current application of deep learning algorithms in various EEG decoding tasks, and introduces commonly used algorithms, typical application scenarios, important progresses and existing problems. Firstly, the basic principles of deep learning algorithms used in EEG decoding is briefly described, including convolutional neural network, deep belief network, auto-encoder and recurrent neural network. In this paper, existing applications of deep learning on EEG is discussed, including brain-computer interfaces, cognitive neuroscience and diagnosis of brain disorders. Finally, this paper outlines some key problems that will be addressed in future applications of deep learning for EEG decoding, such as parameter selection, computational complexity, and the capability of generalization.
Brain tumour detection using mean shift clustering and GLCM features with edge adaptive total variation denoising technique Ramesh Babu Vallabhaneni, V. Rajesh Alexandria Engineering Journal, 2018 The paper presents an automatic brain tumour detection technique in noise corrupted images. The Denoising of the image is implemented using Edge Adaptive Total Variation Denoising Technique (EATVD). The technique is used to preserve the edges in the process of Denoising image. Once the noise is removed from the image, the image is segmented using mean shift clustering. The segmented parts are sent to gray level co-occurrence matrix for feature extraction. The features are used by multi class SVM to detect the tumour in the images. The step followed extracts the tumour with increased precision in noisy images.
On the performance characteristics of embedded techniques for medical image compression Journal of Scientific and Industrial Research, 2017
Brain tumor detection using mean shift clustering and glcm features with edge adaptive total variation denoising technique Arpn Journal of Engineering and Applied Sciences, 2017
BTSWASH: Brain tumour segmentation by water shed algorithm Ramesh Babu Vallabhaneni, V. Rajesh International Conference on Signal Processing and Communication Engineering Systems Proceedings of Spaces 2015 in Association with IEEE, 2015
An effective technique for brain tumour segmentation and detection using cuckoo-based neuro-fuzzy classifier Journal of Theoretical and Applied Information Technology, 2014
Retraction Note: Parkinson’s disease detection using modified ResNeXt deep learning model from brain MRI images B Balnarsaiah, BA Nayak, GS Sujeetha, BS Babu, RB Vallabhaneni Soft Computing 28 (Suppl 1), 311-311 , 2024 2024
RETRACTED ARTICLE: Parkinson’s disease detection using modified ResNeXt deep learning model from brain MRI images B Balnarsaiah, BA Nayak, GS Sujeetha, BS Babu, RB Vallabhaneni Soft Computing 27 (16), 11905-11914 , 2023 2023 Citations: 21
EEG signal classification automation using novel modified random forest approach G Mary, A Anuja, MP Kishore, S Chitti, RB Vallabhaneni, N Renuka Journal of Scientific & Industrial Research 82 (1), 101-108 , 2023 2023 Citations: 9
Implementation of Anti-Collision Robot Using FPGA and IR Sensor BA Dr.Vallabaneni.Ramesh Babu,K. Jahnavi, M. Naga Siva Kumar, B. Sujitha Journal of Information and Computational Analysis 12 (4), 8 , 2022 2022
Automatic Water Quality Monitoring And Management TBP Dr. V. Ramesh Babu,T. Rajeswari, T. Hema Bharathi, G. Harika Journal of Scientific Computing 11 (4), 18-31 , 2022 2022
A Real-Time 5G Ultra-Reliable And Low Latency Communications Using Collaborative Hypothesis Coding RB Vallabhaneni ICECA 2021: 5th International Conference on Electronics, Communication and … , 2021 2021
Deep learning algorithms in eeg signal decoding application: a review RB Vallabhaneni, P Sharma, V Kumar, V Kulshreshtha, KJ Reddy, ... IEEE Access 9, 125778-125786 , 2021 2021 Citations: 55
Automatic Detection of B- Lines In Vivo Lung UltraSound By Using Bottom Hat Transform DRBV PAMARTHY SRAVANTHI The International journal of analytical and experimental modal analysis 12 … , 2020 2020
On-Processing of Brain Tumor Detection Segmentation and Compression BPK Ramesh Babu Vallabhaneni Young Scientist Conference as a Part of the INDIA International Science … , 2019 2019
Particle Swarm Optimization for Image Enhancement AA Ramesh Babu Vallabhaneni International Journal of Research 11 (7), 1217-1224 , 2018 2018
SEMI SUPERVISED BASED SEGEMENTATION FOR BRAIN TUMOR DETECTION DVR Ramesh Babu Vallabhaneni, Abdul Azeez International Journal of Pure and Applied Mathematics 117 (18), 279-283 , 2017 2017
Performance analysis of total variant techniques for efficient segmentation of medical images RB Vallabhaneni, V Rajesh Journal of Engineering and Applied Sciences 12 (20), 5343-5346 , 2017 2017 Citations: 6
Design and Implementation of Higher Order Multi-Bit Pasta Adders RBV Nainatara International Journal of Applied Sciences, Engineering and Management 5 (5 … , 2016 2016
Comparative Study of DSP Architectures for Wireless Sensor Nodes RBV S.Kalyan Chakravarthi International Journal of Applied Sciences, Engineering & Management 4 (4), 11-18 , 2015 2015
BTSWASH: Brain tumour segmentation by water shed algorithm RB Vallabhaneni, V Rajesh 2015 International Conference on Signal Processing and Communication … , 2015 2015
AN EFFECTIVE TECHNIQUE FOR BRAIN TUMOUR SEGMENTATION AND DETECTION USING CUCKOO-BASED NEURO-FUZZY CLASSIFIER BV RAMESH, V RAJESH, M ABAZEED, N FAISAL, ALI ADEL, S ZUBAIR, ... Journal of Theoretical and Applied Information Technology 70 (2) , 2014 2014
Brain Tumor Segmentation With Wavelet Watershed and Detection Using Multi-SVM Classifier VR Ramesh Babu Vallabhaneni International Review on Computers and Software (I.RE.CO.S.) 9 (11), 1807-1815 , 2014 2014
Reducing Area In SoC Using Viterbi Row Multiplication DVR M.Sumalatha, Ramesh Babu Vallabhaneni International Journal of Engineering & Science Research (IJESR) 1 (9), 321-326 , 2014 2014
Review of Cuckoo-Based Nero-Fuzzy Classifier for Detection and Segmentation of Brain Tumour RB Vallabhaneni National Level Conference on Advanced Signal Processing (NCASPA-2013) at K.L … , 2013 2013
An efficient de noising based clustering algorithm for detecting dead centers and removal of noise in digital images LP Maguluri, RB Vallabhaneni, V Rajesh 2013 Tenth International Conference on Wireless and Optical Communications … , 2013 2013 Citations: 1
MOST CITED SCHOLAR PUBLICATIONS
Brain tumour detection using mean shift clustering and GLCM features with edge adaptive total variation denoising technique 2018 Citations: 83
Deep learning algorithms in eeg signal decoding application: a review RB Vallabhaneni, P Sharma, V Kumar, V Kulshreshtha, KJ Reddy, ... IEEE Access 9, 125778-125786 , 2021 2021 Citations: 55
RETRACTED ARTICLE: Parkinson’s disease detection using modified ResNeXt deep learning model from brain MRI images B Balnarsaiah, BA Nayak, GS Sujeetha, BS Babu, RB Vallabhaneni Soft Computing 27 (16), 11905-11914 , 2023 2023 Citations: 21
On the performance characteristics of embedded techniques for medical image compression 2017 Citations: 14
EEG signal classification automation using novel modified random forest approach G Mary, A Anuja, MP Kishore, S Chitti, RB Vallabhaneni, N Renuka Journal of Scientific & Industrial Research 82 (1), 101-108 , 2023 2023 Citations: 9
Performance analysis of total variant techniques for efficient segmentation of medical images RB Vallabhaneni, V Rajesh Journal of Engineering and Applied Sciences 12 (20), 5343-5346 , 2017 2017 Citations: 6
An efficient de noising based clustering algorithm for detecting dead centers and removal of noise in digital images LP Maguluri, RB Vallabhaneni, V Rajesh 2013 Tenth International Conference on Wireless and Optical Communications … , 2013 2013 Citations: 1
Retraction Note: Parkinson’s disease detection using modified ResNeXt deep learning model from brain MRI images B Balnarsaiah, BA Nayak, GS Sujeetha, BS Babu, RB Vallabhaneni Soft Computing 28 (Suppl 1), 311-311 , 2024 2024
Implementation of Anti-Collision Robot Using FPGA and IR Sensor BA Dr.Vallabaneni.Ramesh Babu,K. Jahnavi, M. Naga Siva Kumar, B. Sujitha Journal of Information and Computational Analysis 12 (4), 8 , 2022 2022
Automatic Water Quality Monitoring And Management TBP Dr. V. Ramesh Babu,T. Rajeswari, T. Hema Bharathi, G. Harika Journal of Scientific Computing 11 (4), 18-31 , 2022 2022
A Real-Time 5G Ultra-Reliable And Low Latency Communications Using Collaborative Hypothesis Coding RB Vallabhaneni ICECA 2021: 5th International Conference on Electronics, Communication and … , 2021 2021
Automatic Detection of B- Lines In Vivo Lung UltraSound By Using Bottom Hat Transform DRBV PAMARTHY SRAVANTHI The International journal of analytical and experimental modal analysis 12 … , 2020 2020
On-Processing of Brain Tumor Detection Segmentation and Compression BPK Ramesh Babu Vallabhaneni Young Scientist Conference as a Part of the INDIA International Science … , 2019 2019
Particle Swarm Optimization for Image Enhancement AA Ramesh Babu Vallabhaneni International Journal of Research 11 (7), 1217-1224 , 2018 2018
SEMI SUPERVISED BASED SEGEMENTATION FOR BRAIN TUMOR DETECTION DVR Ramesh Babu Vallabhaneni, Abdul Azeez International Journal of Pure and Applied Mathematics 117 (18), 279-283 , 2017 2017
Design and Implementation of Higher Order Multi-Bit Pasta Adders RBV Nainatara International Journal of Applied Sciences, Engineering and Management 5 (5 … , 2016 2016
Comparative Study of DSP Architectures for Wireless Sensor Nodes RBV S.Kalyan Chakravarthi International Journal of Applied Sciences, Engineering & Management 4 (4), 11-18 , 2015 2015
BTSWASH: Brain tumour segmentation by water shed algorithm RB Vallabhaneni, V Rajesh 2015 International Conference on Signal Processing and Communication … , 2015 2015
AN EFFECTIVE TECHNIQUE FOR BRAIN TUMOUR SEGMENTATION AND DETECTION USING CUCKOO-BASED NEURO-FUZZY CLASSIFIER BV RAMESH, V RAJESH, M ABAZEED, N FAISAL, ALI ADEL, S ZUBAIR, ... Journal of Theoretical and Applied Information Technology 70 (2) , 2014 2014
Brain Tumor Segmentation With Wavelet Watershed and Detection Using Multi-SVM Classifier VR Ramesh Babu Vallabhaneni International Review on Computers and Software (I.RE.CO.S.) 9 (11), 1807-1815 , 2014 2014
