Quantitative analysis of cervical image to predict the complications of pregnancy N. Nagarani, Sivasankari Jothiraj, P. Venkatakrishnan, R. Senthil Kumar Predicting Pregnancy Complications Through Artificial Intelligence and Machine Learning, 2023 The period of life during pregnancy for young parents is pleasant, especially for the mother. Many factors are taken into account during pregnancy, including the fetal heart, head position, cervical dilation, thickness, position, and length. The cervical length should be routinely assessed by ultrasound if it is less than 25 mm. The authors hope to use this participatory framework to generate new ideas for defining normal and abnormal cervical function during pregnancy. Recently, deep learning techniques have revolutionized artificial intelligence (AI) research in pregnancy. Cervical image data obtained by ultrasound are often compared using computer vision pattern analysis, which promises to be a major revolution. In further research and development in AI-based ultrasonography, the clinical application of AI in medical ultrasonography faces unique obstacles. This chapter focuses on the utilization of machine learning approaches in prenatal medicine, with a particular emphasis on interpretable ML applications that produce objective results and assist doctors in identifying key parameters
Optimized channel prediction and auction-based channel allocation for personal cognitive networks Krishnan Jeyakanth, Perumalsamy Venkatakrishnan, Chinnasamy Chitra International Journal of Communication Systems, 2023 Summary Cognitive networks are stands out as intelligent technology which evolved to enhance spectrum utilization. Secondary users are allowed to utilize the primary user's frequency bands on idling times. Identifying the idle licensed spectrum is achieved through spectrum sensing. The spectrum holes should be explored such that a suitable spectrum can be selected and allocated to the secondary users. Existing spectrum sensing and selection schemes have limitations due to interferences. Thus, an optimization algorithm based on bio‐inspired improved weed optimization was presented in this research work for enhanced channel utilization. The optimization model explores the channel characteristics and reduces the primary network interferences through its optimal solution. Further, Markov greedy‐based auction scheme was presented for channel allocation. Considering the channel capacity, delay, and switching rates the allocation is performed to enhance the overall system performance. Simulation analysis demonstrates the superior performance of the proposed model over existing techniques like particle swarm optimization and genetic algorithm.
Spectrum and Power Allocation Scheme Using HoDEPSO-RP Approach for Cognitive Radio Network Jeyakanth Krishnan, Venkatakrishnan Perumalsamy, Chitra Chinnasamy, Dhivya Udhayasuriyan Proceedings of the 1st IEEE International Conference on Networking and Communications 2023 Icnwc 2023, 2023 In this research work, an evolutionary development approach is introduced for channel assignment in CRNs. These approaches are divine by some kind of adoptive development approach. In this proposed methodology, we utilized PSO with DE technique. It effectively maximize the throughput of CN while gratifying interference constraints of both primary and secondary users in network. It also solves the joint spectrum and power allocation problem in CRNs. In addition, a repair process is involved to ignore conflicts with secondary users to enhance spectrum in CRNs. The resulting algorithm is known as Hybridization of Differential Evolution based Particle Swarm optimization with repair process (HoDEPSO-RP). The performance analysis of this proposed HoDEPSO-RP is estimated by simulation techniques. This proposed work provides better performance result when compared with other existing algorithms.
RETRACTION:IoT based artificial intelligence indoor air quality monitoring system using enabled RNN algorithm techniques Senthilkumar Ramachandraarjunan, Venkatakrishnan Perumalsamy, Balaji Narayanan Journal of Intelligent and Fuzzy Systems, 2022 Monitoring indoor air quality stays needed for human health because people use more than 95% of air in their indoor rooms. An Intelligent Internal Air Quality Monitoring (IIAQM) system built on the Internet of Things (IoT) devices has been developed and tested in Quantanics Techserv Private Limited, Tamilnadu, India. To monitor the levels of CO2, PM2.5 (Particle Matters 2.5), and moisture measurement, the IIAQM model has been used to monitor the present level of air quality. The gateway collects IIAQM sensor data in a few seconds and transfers data to cloud server. Approved users can incorporate the cloud systems through mobile applications or web servers. Installation of sensor networks, instrument transformers, and IoT-powered microcontrollers will provide air quality monitoring for buildings. The proposed window controller configuration is designed with the help of a Recurrent Neural Network (RNN) to predict the air quality level in advance. If the air quality level is above the normal level, the window controller automatically will open with the help of sensor activity control system. After the AQI (Air Quality Index) becomes normal, hence the window controller is closed automatically. The air quality index, CO2, and humidity data are visualized on the Grafana dashboard.
Modified spider monkey optimization—An enhanced optimization of spectrum sharing in cognitive radio networks G. Dinesh, P. Venkatakrishnan, K. Meena Alias Jeyanthi International Journal of Communication Systems, 2021 Summary At present, the demand for wireless communications is growing tremendously. Cognitive radio network plays an important role in making the spectrum to be used effectively. Uncertainty in channels and interference were generally occurs that reduced the system efficiency. To overcome the load usage problem, the proposed system of spectrum sensing and scheduling algorithms is introduced, where the available spectrums are sensed or detected and scheduled to load the free spectrum. Spectrum sensing is a cognitive radio basic function to thwart the harmful interference with licensed users and detect the available spectrum for enhancing the spectrum's utilization. In the proposed technique, initially, the spectrum sharing and sensing method is put forward to raise the throughput and quality of service necessity. At this time, spectrum sharing in common with scheduling process is presented, where the available spectrum, the load is scheduled and sensed to free the spectrum. Here, the modified spider monkey optimization (MSMO) technique is used for spectrum sensing and detecting free spectrums, thereby enhancing the energy efficiency of the available spectrum. This technique will found the optimal solution and increases the expectation of some decisions. Modified round robin algorithm is used for scheduling load. In this algorithm, every packet flow has its packet queue presented in the network interface controller. The performance analysis is finally measured using metrics such as throughput, handoff, success probability, and false alarm probability.
Detection of Singularity in the Cell Nucleus of Synovial Sarcoma Using Wavelet Leaders P. Arunachalam, P. Venkatakrishnan, N. Janakiraman Proceedings of the 6th International Conference on Inventive Computation Technologies Icict 2021, 2021 Pathological examination is important for an accurate diagnosis of Synovial Sarcoma (SS). It is the most common cancer of the soft tissues of the limb in adolescents and adults. In this work, SS was used to determine the discriminant singularity characteristics using Wavelet Leaders (WL). The most popular technique for measuring the discriminant singularity characteristics of an image signal is the Lipschitz Exponent (LE). The singularity measurement was based on LE function by taking a slope of logarithmic scale versus logarithmic Wavelet Transform Modulus Maxima (WTMM). Here, the presence of the singularity was measured using WTMM and WL by summing each color component of an image signal. The performance characteristics of the statistical discrimination are evaluated and compared with the non-parametric hypothesis using the Wilcoxon rank-sum test. The most important difference between WTMM and WL was analyzed using the Receiver Operating Characteristics (ROC) curve. From the experimental analysis that the WL method provides excellent discriminant singularities or discontinuities performance characteristics such as area, standard error, z-statistics, and p-values. Finally, the results of experiments have proven that a WL can express practical, precise, robust, and satisfactory performance in practice.
Histopathology Image Classification for Soft Tissue Sarcoma in Limbs using Artificial Neural Networks P. Arunachalam, P. Venkatakrishnan, N. Janakiraman Proceedings of the 6th International Conference on Inventive Computation Technologies Icict 2021, 2021 Clinical imaging techniques have been widely used in the classification of cancer biopsy specimen histopathology images of limb soft tissue sarcoma (STS). Here, by automatically differentiating cell patterns in malignant and non-malignant tumors, an efficient classifier based on both accuracy and time requirements is significantly improved, which further reduces intra-inter-observer variations. Color normalization is carried out using a linear transformation into a grayscale image and the region of interest (ROI) of the image is selected by the pathologist. The wavelet transform has been used to extract statistical texture features (SFT) from the grayscale image of this ROI, and neural correlates with extracted features networks were trained For the purpose test, the features of a new limb STS tissue sample image are extracted and these extracted values are presented to the already trained networks for classification. In this case, the proposed research uses an artificial neural network (ANN), which leads to prominence by improving the classification methods based on accuracy, sensitivity and specificity. Here, two different types of ANN classifiers are discussed with back propagation neural network (BPNN) and radial basis function network (RBFN) classifiers. Furthermore, here the most significant difference between BPNN and RBFN is analyzed using the receiver operating characteristics (ROC) area under the curve. The performance accuracy of these two classification methods reaches 96.36% and 90.91 % for RBFN and BPNN, respectively. Based on these accuracy values, RBFN is found to be more efficient than BPNN classifiers. Finally the cancer cell classification accuracy is increased, decision- making time is reduced, and the initial treatment plan for chronic disease of the limbs tumor has been achieved
Dispersed spectrum sensing and scheduling in cognitive radio network based on SSOA-RR G. Dinesh, P. Venkatakrishnan, K. Meena Alias Jeyanthi International Journal of Communication Systems, 2020 Summary Wireless communication is an emerging technology in recent days which involves the transmission of data or information over a wide range of distance. The wireless network is capable of using the unlicensed spectrum for the transmission of data for various applications like medical, science, and industries. There are cases where the licensed spectrum is not utilized up to the level. In such cases, the cognitive radio network (CRN) technology allows cognitive devices to sense it and further enables the dynamic access of the scarce resource for proper utilization. However, the excessive number of bandwidth access may lead to the occurrence of interferences among the system. This is the major issue faced in all CRNs. To resolve this, effective band scheduling mechanism in CRN has been proposed. In this research, efficient spectrum sensing is performed using stochastic optimization algorithm called Salp Swarm Optimization Algorithm (SSOA). This SSOA algorithm utilizes the best fitness function through three phases such as leaving, contention and joining of bands and provides a list of nonacquired list of bands. From the list of bands, the best band with majority bandwidth has been selected using Round Robin (RR) Algorithm. Here, the load system is allotted dynamically. Based on the load factor, the spectrum is scheduled. As a matter of fact, the whole spectrum scheduling methodology depends primarily on the base contributions made by the operation of spectrum sensing. Finally, the performance analysis is estimated for the throughput, settling time, number of bands occupied by base station (BS), and the bands occupied by each BS. From the performance analysis, the proposed system attains better results than other conventional approaches.
Bispectral analysis of human electroencephalogram (EEG) signals during various sleep stages New Trends in Audio and Video Signal Processing Algorithms Architectures Arrangements and Applications Ntav SPA 2008 Conference Proceedings, 2008
Spectrum and power allocation scheme using HoDEPSO-RP approach for cognitive radio network J Krishnan, V Perumalsamy, C Chinnasamy, D Udhayasuriyan 2023 International Conference on Networking and Communications (ICNWC), 1-8 , 2023 2023 Citations: 6
Optimized channel prediction and auction‐based channel allocation for personal cognitive networks K Jeyakanth, P Venkatakrishnan, C Chitra International Journal of Communication Systems 36 (3), e5391 , 2023 2023 Citations: 11
Quantitative analysis of cervical image to predict the complications of pregnancy N Nagarani, S Jothiraj, P Venkatakrishnan, RS Kumar Predicting Pregnancy Complications Through Artificial Intelligence and … , 2023 2023 Citations: 9
RETRACTED: IoT based artificial intelligence indoor air quality monitoring system using enabled RNN algorithm techniques S Ramachandraarjunan, V Perumalsamy, B Narayanan Journal of Intelligent & Fuzzy Systems 43 (3), 2853-2868 , 2022 2022 Citations: 52
Detection of structure characteristics and its discontinuity based field programmable gate array processor in cancer cell by wavelet transform P Arunachalam, P Venkatakrishnan, N Janakiraman, S Sangeetha Journal of Medical Imaging and Health Informatics 11 (12), 3066-3081 , 2021 2021 Citations: 1
Modified spider monkey optimization—An enhanced optimization of spectrum sharing in cognitive radio networks G Dinesh, P Venkatakrishnan, KMA Jeyanthi International Journal of Communication Systems 34 (3), e4658 , 2021 2021 Citations: 29
Detection of singularity in the cell nucleus of synovial sarcoma using wavelet leaders P Arunachalam, P Venkatakrishnan, N Janakiraman 2021 6th International Conference on Inventive Computation Technologies … , 2021 2021 Citations: 9
Histopathology image classification for soft tissue sarcoma in limbs using artificial neural networks P Arunachalam, P Venkatakrishnan, N Janakiraman 2021 6th International Conference on Inventive Computation Technologies … , 2021 2021 Citations: 19
Dispersed spectrum sensing and scheduling in cognitive radio network based on SSOA‐RR G Dinesh, P Venkatakrishnan, KMA Jeyanthi International Journal of Communication Systems 33 (16), e4588 , 2020 2020 Citations: 6
Prediction of Air Pollution by the Contribution of Road Traffic—Signal Processing and Higher-Order Statistics (HOS) Spectra Approach S Sangeetha, P Venkatakrishnan, G Srikanth Urban Air Quality Monitoring, Modelling and Human Exposure Assessment, 155-168 , 2020 2020
Intelligent based novel embedded system based IoT enabled air pollution monitoring system R Senthilkumar, P Venkatakrishnan, N Balaji Microprocessors and Microsystems 77, 103172 , 2020 2020 Citations: 144
Unmanned Aerial vehicle’s runway landing system with efficient target detection by using morphological fusion for military surveillance system N Nagarani, P Venkatakrishnan, N Balaji Computer Communications 151, 463-472 , 2020 2020 Citations: 43
Singularity detection in human EEG signal using wavelet leaders P Venkatakrishnan, S Sangeetha Biomedical Signal Processing and Control 13, 282-294 , 2014 2014 Citations: 17
Analysis of Vibration in gearbox sensor data using Lipschitz Exponent (LE) function: A Wavelet approach P Venkatakrishnan, S Sangeetha, JS Gnanasekaran, MG Vishnukumar, ... IFAC Proceedings Volumes 47 (1), 1067-1071 , 2014 2014 Citations: 11
Measurement of Lipschitz exponent (LE) using wavelet transform modulus maxima (WTMM) P Venkatakrishnan, S Sangeetha, M Sundar International Journal of Scientific & Engineering Research 3 (6), 1-4 , 2012 2012 Citations: 13
Performance analysis of life time efficiency of Machines using Wavelet Transform Modulus Maxima P Venkatakrishnan, S Sangeetha, M Muthukumaran International Journal of Scientific & Engineering Research 3 (6) , 2012 2012 Citations: 3
Detection of quadratic phase coupling from human EEG signals using higher order statistics and spectra P Venkatakrishnan, R Sukanesh, S Sangeetha Signal, Image and Video Processing 5 (2), 217-229 , 2011 2011 Citations: 15
NONLINEAR DETECTION IN HUMAN EEG SIGNALS USING HIGHER ORDER STATISTICS AND SPECTRA P VENKATAKRISHNAN 2010
Sleep spindles detection from human sleep EEG signals using autoregressive (AR) model: a surrogate data approach V Perumalsamy, S Sankaranarayanan, S Rajamony Journal of Biomedical Science and Engineering 2 (5), 294 , 2009 2009 Citations: 9
Bispectral analysis of human electroencephalogram (EEG) signals during various sleep stages P Venkatakrishnan, S Sangeetha, R Sukanesh New Trends in Audio and Video/Signal Processing Algorithms, Architectures … , 2008 2008 Citations: 4
MOST CITED SCHOLAR PUBLICATIONS
Intelligent based novel embedded system based IoT enabled air pollution monitoring system R Senthilkumar, P Venkatakrishnan, N Balaji Microprocessors and Microsystems 77, 103172 , 2020 2020 Citations: 144
RETRACTED: IoT based artificial intelligence indoor air quality monitoring system using enabled RNN algorithm techniques S Ramachandraarjunan, V Perumalsamy, B Narayanan Journal of Intelligent & Fuzzy Systems 43 (3), 2853-2868 , 2022 2022 Citations: 52
Unmanned Aerial vehicle’s runway landing system with efficient target detection by using morphological fusion for military surveillance system N Nagarani, P Venkatakrishnan, N Balaji Computer Communications 151, 463-472 , 2020 2020 Citations: 43
Modified spider monkey optimization—An enhanced optimization of spectrum sharing in cognitive radio networks G Dinesh, P Venkatakrishnan, KMA Jeyanthi International Journal of Communication Systems 34 (3), e4658 , 2021 2021 Citations: 29
Histopathology image classification for soft tissue sarcoma in limbs using artificial neural networks P Arunachalam, P Venkatakrishnan, N Janakiraman 2021 6th International Conference on Inventive Computation Technologies … , 2021 2021 Citations: 19
Singularity detection in human EEG signal using wavelet leaders P Venkatakrishnan, S Sangeetha Biomedical Signal Processing and Control 13, 282-294 , 2014 2014 Citations: 17
Detection of quadratic phase coupling from human EEG signals using higher order statistics and spectra P Venkatakrishnan, R Sukanesh, S Sangeetha Signal, Image and Video Processing 5 (2), 217-229 , 2011 2011 Citations: 15
Measurement of Lipschitz exponent (LE) using wavelet transform modulus maxima (WTMM) P Venkatakrishnan, S Sangeetha, M Sundar International Journal of Scientific & Engineering Research 3 (6), 1-4 , 2012 2012 Citations: 13
Optimized channel prediction and auction‐based channel allocation for personal cognitive networks K Jeyakanth, P Venkatakrishnan, C Chitra International Journal of Communication Systems 36 (3), e5391 , 2023 2023 Citations: 11
Analysis of Vibration in gearbox sensor data using Lipschitz Exponent (LE) function: A Wavelet approach P Venkatakrishnan, S Sangeetha, JS Gnanasekaran, MG Vishnukumar, ... IFAC Proceedings Volumes 47 (1), 1067-1071 , 2014 2014 Citations: 11
Quantitative analysis of cervical image to predict the complications of pregnancy N Nagarani, S Jothiraj, P Venkatakrishnan, RS Kumar Predicting Pregnancy Complications Through Artificial Intelligence and … , 2023 2023 Citations: 9
Detection of singularity in the cell nucleus of synovial sarcoma using wavelet leaders P Arunachalam, P Venkatakrishnan, N Janakiraman 2021 6th International Conference on Inventive Computation Technologies … , 2021 2021 Citations: 9
Sleep spindles detection from human sleep EEG signals using autoregressive (AR) model: a surrogate data approach V Perumalsamy, S Sankaranarayanan, S Rajamony Journal of Biomedical Science and Engineering 2 (5), 294 , 2009 2009 Citations: 9
Spectrum and power allocation scheme using HoDEPSO-RP approach for cognitive radio network J Krishnan, V Perumalsamy, C Chinnasamy, D Udhayasuriyan 2023 International Conference on Networking and Communications (ICNWC), 1-8 , 2023 2023 Citations: 6
Dispersed spectrum sensing and scheduling in cognitive radio network based on SSOA‐RR G Dinesh, P Venkatakrishnan, KMA Jeyanthi International Journal of Communication Systems 33 (16), e4588 , 2020 2020 Citations: 6
Bispectral analysis of human electroencephalogram (EEG) signals during various sleep stages P Venkatakrishnan, S Sangeetha, R Sukanesh New Trends in Audio and Video/Signal Processing Algorithms, Architectures … , 2008 2008 Citations: 4
Performance analysis of life time efficiency of Machines using Wavelet Transform Modulus Maxima P Venkatakrishnan, S Sangeetha, M Muthukumaran International Journal of Scientific & Engineering Research 3 (6) , 2012 2012 Citations: 3
Detection of sleep spindles from electroencephalogram (EEG) signals using auto recursive (AR) model P Venkatakrishnan, S Sangeetha, R Sukanesh 2008 First International Conference on Emerging Trends in Engineering and … , 2008 2008 Citations: 3
Detection of structure characteristics and its discontinuity based field programmable gate array processor in cancer cell by wavelet transform P Arunachalam, P Venkatakrishnan, N Janakiraman, S Sangeetha Journal of Medical Imaging and Health Informatics 11 (12), 3066-3081 , 2021 2021 Citations: 1
Prediction of Air Pollution by the Contribution of Road Traffic—Signal Processing and Higher-Order Statistics (HOS) Spectra Approach S Sangeetha, P Venkatakrishnan, G Srikanth Urban Air Quality Monitoring, Modelling and Human Exposure Assessment, 155-168 , 2020 2020
