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IJIRTM: Volume-6, Issue-4, 2022

Paper Title : AMC Using Statistical Signal Processing and Machine Learning Algorithms in the form of Cognitive Radios
Author Name : Ramdas, Prof.Anoop Kumar Khambra, Prof.Jitendra Mishra
Keywords : Automatic modulation classification, Statistical Signal Processing, Physical Layer, QAM, QPSK, Cognitive radio.
Abstract :

Programming characterized radio (SDR) frameworks definitely stand out enough to be noticed as of late for their reasonableness and effortlessness for involved trial and error. They can be utilized for execution of dynamic range allotment (DSA) calculations in mental radio (CR) stage. There has been a monstrous exploration in the DSA calculations both in AI and sign handling worldview, yet, these CRs are as yet unfit to choose which calculation suites for explicit situation. An examination between the range detecting calculations utilizing AI strategies and factual sign handling procedures is required to know which calculation suits best for asset compelled conditions for CRs and range observatories. Two difficulties; to be specific, multi-transmitter location and programmed adjustment arrangement (AMC) are picked. Novel AI based and measurable sign handling based multi-transmitter location calculation are proposed and utilized in the correlation. In the wake of contrasting precision, for multi-transmitter recognition, machine learning calculation has precision of 70% and 80% for 2 and 5 client framework, separately, while, the precision for factual sign handling calculation is half for 2 and 5 client framework. For AMC, both sign handling and AI calculation have an ideal precision past 10 dB for 100 test tests (64-QAM being an exemption) be that as it may, for 1000 test tests, the AI calculation outflanks the sign iii processing calculation. Time correlation showed that sign handling calculations, in the two cases, take part of the time expected by AI calculations. Thus, it is prescribed to utilize AI procedures where precision is significant and utilize signal handling approach where timing is significant. The interaction of choosing the calculations can be viewed as a trade off among precision and time.

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Paper Title : New Education Policy-2020: A Critical Analysis & Overview
Author Name : Ankit Singh Bisen, Dr.DD Bedia
Keywords : New Education Policy, Kendriya Vidyalaya, Navodaya Vidyalaya, 2020, Modi Ji 2.0.
Abstract :

It took the nation 34 years to implement the New Education Policy 2020. The drafting committee presented its final draft for approval to the union cabinet on July 29, 2020, and it was accepted and authorised. The new plan aims to prepare the way for revolutionary reforms in the country's primary and secondary education sectors. This was one of the most major steps taken to overhaul the education system of the nation. The objective of this study is to assess the deplorable condition of the places where the policy has advised action. It is inconceivable to have a policy that mandates the creation of a complete infrastructure. A fundamental reorganisation and a paradigm change must be considered during the execution of this policy. As it is well known that education is a concurrent issue, the execution of the New Education Policy 2020 idea is dependent on future legislation enacted by the centre and the state.

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Paper Title : Analyze the Performance of Breast Cancer Detection using Neural Network Techniques
Author Name : Kumari Deepshikha, Prof.Chetan Agrawal, Prof.Himanshu Yadav
Keywords : Diseases diagnosis, Classification, Machine learning, Image segmentation, CAD.
Abstract :

In the human body, there are several types of tissues formed by a plurality of cells. The inharmonious and vertiginous growth of these cells can cause a tumor, being able to be benign or malignant thus originating the cancer. Breast cancer is the type of cancer that affects women more; however, there is a small possibility of occurring in men, even in a very unusual way, since according to statistics, for every 1 man diagnosed with cancer 100 women present the disease. Breast cancer accounts for more than 1 in 10 new cancer diagnosis each year and is the leading cause of cancer death in women. For early and efficient diagnosis of breast cancer more and more techniques are being developed. The feed forward back propagation neural network is one of famous approaches in neural network. In this work we present the feed forward neural network classifier for the breast cancer detection and improved the accuracy rate over the previous approach.

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