Underwater wireless sensor network (UWSN) has limited bandwidth, long propagation delays, and unrechargeable batteries, which forces the development of techniques minimizing the energy consumption of UWSNs. One solution is clustering that outperforms in terms of energy optimization and, in addition, can also provide scalability and data reliability. The development of clustering algorithms depends on UWSN architectures. In our case, we have proposed a hybrid UWSN network architecture named ACOP-UWSN (ACoustic OPtical Underwater Wireless Sensor Network), which aims for reliable data transmission at the fastest speed with higher data rates and minimized propagation delays. In ACOP-UWSN, optical sensors sense data, whereas acoustic sensors only relay data towards the surface buoy. Clustering of all these nodes may result in unnecessary wastage of the node’s energy and memory. Therefore, we undergo clustering of only optical sensors, as comparatively, sensing and processing of data consume maximum energy and memory than relaying data. In the proposed clustering algorithm, the criteria for cluster head selection are OSNR (optical signal-to-noise ratio) and residual energy. The mathematical derivation of OSNR for underwater wireless optical communication is a key part of the algorithm. The results achieved using the simulation tool show that our proposed OSNR-based energy efficient clustering algorithm (OECA) is efficient in terms of energy and as well data reliability as compared to state of art. The quantitative analysis of the results showed that the total energy consumed for the existing scheme is 450 J, which is more as compared to 400 J of OECA. Similarly, the residual energy of nodes using OECA is 1% greater than the existing scheme. To our knowledge, research on the clustering of optical sensors in UWSN is least available, and we are among the few to work on an OSNR-based clustering algorithm for underwater optical nodes.

All over the world, there are a significant number of patients suffering every year from blood cancer. Most of the people are unaware of the risk involved in such a disease. A majority of these diseases are dangerous and may cause death. The patient who have been diagnosed with such a disease, feels very afraid. The patient may feel that the disease is very uncontrolled. Such diseases are very uncommon, and the patient may get very less assistance and information available about this disease. This symptom is called as acute lymphocytic leukemia (ALL) in medical science. In such a kind of cancer, white blood cells are mostly affected. In case of children, this disease is mainly detected i.e. children are more prone to this disease. If the disease is diagnosed in the early stage, the chances of recovery are maximum. Hence, there should be an accurate and guaranteed mechanism available to detect such type of blood cancers in the patients. This work proposes a system to distinguish the three different types of ALL using a convolutional neural network (CNN) by means of microscopic pictures of peripheral blood smears (PBS) and obtain accuracy levels that surpass those of practicing physicians.

Graph theory plays significant role in every field of science as well as technology. Every situation can be articulated in terms of suitable graphs by using various approaches of graph theory. Considering the recent pandemic in the world and the precautions taken for prevention of the COVID 19, it is the most appropriate way to exercise the graph models with theoretical as well as practical aspects to control this epidemic. This paper defines the variable set, variable graphs and their types with respect to variable vertex sets and variable edge sets. Depending upon the nature of the pandemic, there are four types of Virus graphs. Virus graph I and III are not so perilous for all living beings. Although, Virus graphs II and IV are extremely hazardous for the harmony of the world. In view of different aspects for expand of pandemic, growth types of virus graphs are divided in 1-1, 1-P and 1-all growth types. The COVID19 initially was in Virus graph-I type, but presently it is in Virus graph-II types. We present the table involving the number of infected people after n days with respect to different values of P and growth rates with I0 = 100. Moreover, the country wise starting dates of stages of the virus graph-I and II are specified. The concept of cut sets is applicable for prevention of COVID19 and the whole world is using the same analogy.

We defined the concept of \(\pi\)-coloring of graphs and incident vertex \(\pi\) coloring of graphs. The incident vertex \(\pi\) coloring number \((IV \pi CN)\) of graphs is different from all existing coloring techniques. The \(IV \pi CN\) of complete graph \((K_n)\) is \(n\). \(IV \pi CN\) of wheel, star, double star graph are \((n+1)\). Also, \(IV \pi CN\) of friendship, diamond and fan graphs are \(\Delta+1\). The \(IV \pi CN\) of double fan graph is \(\Delta+2\). The \(IV \pi CN\) of complete bipartite graphs \(K_{m,n}\) is \((m+n)\). The \(IV \pi CN\) of bipartite graph is bounded. Moreover, some results associated to enumeration of the number of graphs having equal incident vertex \(\pi\) chromatic number of few families are proved.