KNN-Based ML Model for the Symbol Prediction in TCM Trellis Coded Modulation TCM Decoder

Machine Learning is a booming technology today. In a machine learning set of training, data is to be provided to the model for training and that model predicts the output. Machine Learning models are trained using a computer program known as ML algorithms.The new machine learning-based Transition Metric Unit (TMU) of 4D- 8PSK Trellis coded Modulation TCM Decoder is presented in this work. The classic Viterbi decoder's branch metric unit, or TMU, takes on a complex structure. Trellis coded Modulation (TCM) is a combination of 8 PSK modulations and Error Correcting Code (ECC). TMU is one of the complex units of the TCM decoder, which is essentially a Viterbi decoder. Similar to how the first Branch metric is determined in the straightforward Viterbi decoder, the TCM decoder performs this BM computation via the TMU unit. The TMU becomes challenging and uses more dynamic power as a result of the enormous constraint length and the vast number of encoder states.In the proposed algorithm innovative KNN (K nearest neighbours) based ML model is developed. It is a supervised learning model in which input and output both are provided to the model, training data also called the labels, when a new set of data will come the model will give output based on its previous set experience and data.Here we are using this ML model for the symbol prediction at the receiver end of the TCM decoder based on the previous learning. Using the proposed innovation, the paper perceives the optimization of the TCM Decoder which will further reduce the H/W requirements and low latency which results in less power consumption

Novel hybrid approach based on combination of textural features and clinical parameters for reliable prediction of thyroid malignancy

The dramatic increase in thyroid cancer, particularly among the younger population demands development of an automated decision support system for timely and reliable prognosis of the disease so as to facilitate improved chances of recovery in the subjects.  While numerous methods are already reported by the researchers for the detection of Thyroid malignancy, the most crucial parameter of Thyroid Malignancy Index (TMI) has received very less attention. TMI is of paramount importance in diagnosis and treatment of the patients having malignant thyroid and its consideration is therefore inevitable. This research aims to develop an automated and a reliable decision support system for detection of thyroid malignancy.  The proposed hybrid approach incorporates a novel combination of texture features and clinically observable parameters to initially identify a malignant thyroid tumor using support vector machines and further predicts its TMI value, thus exhibiting a performance like a trained radiologist. Publically available database comprising of 99 cases and 134 ultrasound images are used to validate the superiority of the proposed approach. Apt consideration and reliable prediction of the TMI in this research makes the designed approach novel and marks a mega leap towards its practical deployment in the clinical environment