Mobile Ad-hoc Network (MANET) is assumed an encouraging technology that constructs interim network connectivity without the aid of any prior architecture which is required during abnormal circumstances or in provisional events such as in emergencies, crisis conditions, and military conflicts. Source routing in MANET is challenged by arbitrary and random node mobility that triggers a lot of route discoveries due to frequent link breakages. This generates a massive number of Route Request (RREQ) packets resulting from the flooding procedure. The flooding procedure is used in the route discovery process and produces a storm of the broadcast, leading to an increase in packet loss and control overhead. In this work we study and evaluate the quality of services parameters for different routing protocol.

Early detection of the disease has become an important issue in the medical field due to the increasing population of the world. With the rapid growth of the population, the risk of dying from cancer is progressive each day. "Cancer" is a scary disease for most people. But in women, "breast cancer" can be even more frightening, because it is directly related to the woman's body. However, men can also get breast cancer, but men are much less likely to get the disease than women. In this paper we present the different techniques used for breast cancer diagnosis using machine learning.

Even though more than two-thirds of the planet is covered in water and the remaining third is made up of land, the number of people who have access to potable water is constantly shrinking. The majority of human illnesses are brought on by contaminated or unpurified water. Nowadays, due to pollution brought on by human activity, every country is struggling with a severe water shortage. A basic need for all humans on this planet is the ability to drink water that is reliable and of acceptable quality. The amount of fresh water that can be retrieved from rivers, lakes, and ponds is diminishing as a result of industrialization and population development. Because it is economical and environmentally benign, solar-powered water purification is gaining popularity. A common water purification device that uses no energy for water distillation is a solar still. A cheap way to make drinking water from brackish or poor-quality underground water is through solar distillation. This strategy might help with the world's water shortage problems. According to a peer-reviewed study, uniform dispersion of nanoparticles in the base fluid increases the solar still's productivity and efficiency while also improving solar absorption. A complete overview of the most recent advancements in the usage of nanofluid in various types of solar stills is provided by this in-depth analysis of the literature. Variable water depth and a proposed concentration of nanofluid are used to measure performance.

On a consistent basis, those working in the medical field must deal with enormous amounts of data. When dealing with enormous amounts of data, adopting the conventional approaches can have an effect on the findings. In the field of medical research, for instance, machine learning algorithms can be utilized to forecast the onset of disease. The review of a patient's medications and specialists cannot begin unless the sickness has been diagnosed at an early stage. The diagnosis of disease at an early stage Several different types of machine learning algorithms, including Decision Trees, Support Vector Machines, Multilayer Perceptrons, Bayes classifiers, and K-Nearest Neighbors Ensemble classifiers, are applied in order to efficiently diagnose a wide range of health problems and identify a variety of health problems. Using algorithms that are based on machine learning, it is feasible to swiftly and reliably forecast diseases. In this study, several types of diseases and their manifestations were predicted with the help of machine learning algorithms. This work covers a variety of problems, some of which include the prediction of chronic renal disease, the detection of diabetes, and the detection of breast cancer. Additionally, hybrid strategies for the enhancement of classifiers are investigated in this paper.