19c96fd517d854497e8-2901 20220211071251715 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Recent Technology and Engineering (IJRTE) IJRTE 22773878 10.35940/ijrte.2277-3878 https://www.ijrte.org/ 01 30 2021 9 5 School of Computer Science and Engineering Vellore Institute of Technology-Chennai. R. Rohith S.P.Syed Ibrahim School of Computer Science and Engineering Vellore Institute of Technology-Chennai. Tuberculosis is a life-threatening disease that mainly affects underdeveloped as well as developing nations. While lethal it is often resistive to antibiotics and the safest way to treat a patient is to detect the disease’s presence as soon as possible. Various techniques have been developed to diagnose tuberculosis and radiography of the chest is one of such methods that works well for over a decade.. Though an effective method still the success depends on the medical officer who examines the chest X-rays. Thus ,this paper proposes an approach for detecting X-ray abnormalities using deep learning. The systems output is assessed on two open Montgomery and Shenz en chest X-ray datasets and accuracy of 84 percent is achieved. 01 30 2021 254 258 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.C4460.019521 https://www.ijrte.org/portfolio-item/C4460099320/ Assistant Professor, Electronics and Communication Engineering, Marathwada Institute of Technology(MIT), Bulandshahr(BSR), Uttar Pradesh, India. Mr. Nitin Kumar Agrawal Mr. Shaamshad Alam Assistant Professor, Electronics and Communication Engineering, Marathwada Institute of Technology(MIT), Bulandshahr(BSR), Uttar Pradesh, India. Harshit Raghav B. Tech Student, Electronics and Communication Engineering, Marathwada Institute of Technology(MIT), Bulandshahr (BSR), Uttar Pradesh, India. In the present time, if we see around the world we can realize that information transfer through one place to another is very easy. A person lives in America easy do business with the person live far away from it. All this can be achieved by the phenomenon known as Networking. And the device through which the information are transferred are called interconnected device. As we know, in present time our need is not only transfer or sharing of information but in a secure way. So with the help of this we are not just transferring the information but in a secure manner To understand the whole phenomenon of this networking, the basic requirement is OSI LAYER Model. This is not just a model but a complete frame which gives us whole information of its working as well as link between them. So through this paper we give some basics concept building of OSI LAYER which help in understanding the Networking. 01 30 2021 66 69 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.D4991.019521 https://www.ijrte.org/portfolio-item/D4991119420/ Emeritus Professor at Electronics Engineering and Electrical Communications, Ain Shams University-Faculty Of Engineering, Egypt. Abdel Halim A. Zikry Ashraf Y. Hassan head of the electrical engineering department of electrical engineering, Benha University, Egypty. Wageda I. Shaban Dr.lecture in Department of Basic Sciences, Benha University, Egypty. Sahar F. Abdel-Momen phd. student, department of electrical engineering, Benha University, Egypty. Low density parity checking codes (LDPC) are one of the most important issues in coding theory at present. LDPC-code are a type of linear-block LDPC-codes. Channel coding might be considered as the finest conversant and most potent components of cellular communications systems, that was employed for transmitting errors corrections imposed by noise, fading and interfering. LDPC-codes are advanced coding gain, i.e., new area in coding. the performances of LDPC-code are similar to the Shannon-limiting, this led to the usage of decoding in several applications in digital communications systems, like DVB-S2 and WLAN802.1..This paper aims to know what is LDPC,what its application and introduce encoding algorithms that gives rise to a linear encoding time and also show that the regular and irregular LDPC performance and also introduce different methods for decoding LDPC. I discuss in detail LDPC decoding algorithm: bit flipping algorithm, as a type from hard decision .belief propagation algorithm, sum product algorithm and minimum sum algorithm as examples from soft decision .I expect that at least some students or researchers involved in researching LDPC codes would find this paper helpful. 01 30 2021 17 26 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.E5067.019521 https://www.ijrte.org/portfolio-item/E5067019521/