Bosom malignant growth is one of the most well-known infections among ladies around the world. It is viewed as one of the main sources of death among ladies. Accordingly, early recognition is important to save lives. Thermography imaging is a viable indicative method which is utilized for bosom disease recognition with the assistance of infrared innovation. In this paper, we propose a completely programmed bosom disease discovery framework. In the first place, U-Net organization is utilized to naturally remove and disconnect the bosom region from the remainder of the body which acts as commotion during the bosom malignant growth identification model. Second, we propose a two-class profound learning model, which is prepared without any preparation for the characterization of ordinary and unusual bosom tissues from warm pictures. Likewise, it is utilized to remove additional qualities from the dataset that is useful in preparing the organization and work on the effectiveness of the characterization cycle. The proposed framework is assessed utilizing genuine information (A benchmark, data set (DMR-IR)) and accomplished exactness = 99.33%, responsiveness = 100 percent and particularity = 98.67%. The proposed framework is supposed to be a useful device for doctors in clinical use.

Keywords : Dataset, Framework

Author : GOPIKA S, RITIKA .T, NAVYA C, PRAKASH BM, DR. GANESH BABU C

Title : BREAST CANCER DETECTION USING DEEP LEARNING ALGORITHM

Volume/Issue : 2023;05(03)

Page No : 1 - 6

Mobile Cardiac Telemetry, or Real-Time ECG Monitoring on an Outpatient Basis, is a term used today (MCT or MCOT). A mobile cardiac telemetry system monitors the patient continuously for up to 30 days while performing an outpatient ECG (electrocardiogram). MCT is a relatively new type of ambulatory cardiac monitoring that has been shown to be a reliable and effective way to monitor the patient's ECG data while the patient is awake or asleep. Because there is no patient interaction during MCT monitoring, the system automatically identifies and transmits ECG rhythm to a distant diagnostic monitoring laboratory. With the execution of this automated capability in real time, MCT monitoring technology has advanced beyond other types of cardiac monitoring. By using processor we connect the AD8232 heart rate sensor and ESP8266 wifi module which helps to transfer the collected data to the cloud and by using the exists iot remote app we retrieve the data and the final output signal will be visualized in the patient mobile phone and also it can be shared.

Keywords : Ecg Signal, Mobile Cardiac Telemetry, AD8232, ESP8266

Author : MEENA A, KAMALRAJ J, SARASWETHA G, KAVYA G S

Title : REAL TIME ECG MEASUREMENT AND VISUALIZATION ON MOBILE

Volume/Issue : 2023;05(03)

Page No : 7 - 15

The Ethereum blockchain digital currency transactions including smart contracts are first examined using a large data analysis, which reveals intriguing features in action. The public's interest in digital currencies such as Ether, Finney, Wei and Bitcoin, as determined by the number of web searches, is then compared with Similar-web. Analysis of cryptocurrency pricing and search trends indicates the presence of major actors (and not just everyday users) who influence the marketplace when prices are lower. The pairs that provide better accurate and current estimates of Ether prices are revealed through with a cross-correlation analysis of digital cryptocurrency values and searching trends.

Keywords : Ethereum, Blockchain, Similar-Web, Cryptocurrency, Smart Contract, Big Data.

Author : Dhivya K, Dr. T. Sivakumar

Title : Smart Contract Digital Cryptocurrency Analysis of Ethereum Blockchain Transactions for Similar-web

Volume/Issue : 2023;05(03)

Page No : 16 - 20

Wireless Sensor Network consists of a large number of Sensor nodes, which firstly sense the data and then transmit the sensed data to the base station which is known as the Sink. Network lifetime is one of the key challenges for Wireless Sensor networks because of the limited battery life of the nodes. As we know sensor nodes are energy constrained devices and to increase the lifetime of the network it is very necessary to minimize the consumption of energy of nodes while sensing and transmitting the data. Clustering in Wireless networks is one of the pre-eminent ways to improve the lifetime of the network. In EDEEC, a clustering-based hierarchical model is used where data is aggregated in the cluster and sent to a higher-level cluster head where the cluster head is selected randomly on the basis of residual energy of the network. EDEEC works on 3-level heterogeneous wireless sensor networks in which there are three type of sensor nodes named as normal nodes, advanced nodes and super node, which still have scope of improvement because if the levels of heterogeneous wireless sensor networks are to be increased then more complexity will be there in the network and then a more stable behaviour of network is required. In this work it is proposed that performance of the network can be enhanced if the level of heterogeneity in the network increases because in the real world there can exist more than three types of nodes in the network. In this work EDEEC protocol has been implemented with four types of nodes that includes normal nodes, advance nodes, super nodes and ultra-super nodes and the nodes count varies from 100 to 1000 nodes to check the change in energy consumption and stability of the network.

Keywords : Clustering, Energy, Stability period, Heterogeneous, Wireless Sensor Networks.

Author : Er. Harjit Kaur, Er. Harsimranpreet Kaur, Er. Pratibha Soram

Title : Implementation of EDEEC protocol for 4- Level Scalable Heterogeneous Wireless Sensor Networks

Volume/Issue : 2023;05(03)

Page No : 21 - 32