Breast cancer (BC) disease is the most common and rapidly spreading disease across the globe. This disease can be prevented if identified early, and this eventually reduces the death rate. Machine learning (ML) is the most frequently utilized technology in research. Cancer patients can benefit from early detection and diagnosis. Using machine learning approaches, this research proposes an improved way of detecting breast cancer. To deal with the problem of imbalanced data in the class and noise, the Synthetic Minority Oversampling Technique (SMOTE) has been used. There are two steps in the suggested task. In the first phase, SMOTE is utilized to decrease the influence of imbalance data issues, and subsequently, in the next phase, data is classified using the Naive Bayes classifier, decision trees classifier, Random Forest, and their ensembles. According to the experimental analysis, the XGBoost-Random Forest ensemble classifier outperforms with 98.20% accuracy in the early detection of breast cancer.

Cervical Spondylosis (CS) is one of the most common chronic serious bone diseases that mainly affects patient's health and leads to human death also. It basically occurs in old age patients but spreading very much in young age patients too based on different age factors and side effects. Cervical Spondylosis (CS) is becoming most important research area in medical science field and medical image processing is very essential for this research study. Mainly, it causes mild pain in disk points of the neck, spinal cord and some other facts can build Cervical Spondylosis. There is high significance in image processing techniques, segmentation techniques and point-based techniques for early detection of error at particular point where the changes can be identified in few points of neck, spinal cord or other bones of human body. Identification of these changes, mild symptoms from images like X-ray, Computed Tomography (CT) scan or Magnetic Resonance Images (MRI) are very complex for further diagnosis and identification of spondylosis. So early detection of spondylosis using medical image processing techniques gives better time-consuming, costliness of hospital medical service reduces the risk level diagnosis also. In this research work, MRI cervical images are collected and analysis is carried out by using Principle Component Analysis (PCA) segmentation classifier image processing technique for early detection of spondylosis. By applying this method, performance metrics like Mean Square Error (MSE), Peak Signal Noise Ratio (PSNR) and Elapsed Time (ET) are calculated for input image −1 and achieved 0.000095 error value in particular point where the changes are identified easily with better Elapsed Time complexity 3.7 (in Seconds), but for input image – 2 achieved 0.000005 error value in particular point where the changes are identified easily with better Elapsed Time complexity 2.5 (in Seconds) less execution time.

The project was inspired by three factors: the lack of a dedicated device as a reminder for chronic patients' medicine timings, the lag of an easily accessible SOS button, and the third and most important, the lag of communication between the time of the accident and contact with guardians or hospitals. According to a study, approximately 85% of deaths caused by small and major heart attacks, strokes, and shortness of breath could be averted simply by treating them right away. As a result, a readily accessible SOS button would sufficient. Apart from that, statistics show that not taking medications on time and taking the wrong medicine at the wrong time are both harmful to one's health. In such a situation, a dedicated reminder device that can remind of medicine names at specific times is required. Furthermore, according to News18, 36,579 people die each year in vehicle accidents, 36,213 in two-wheeler accidents, 31,997 in truck and lorry accidents, and almost 45,000 individuals in other means of transportation. Nearly 76 percent of deaths in each vehicle are caused by a delay in getting the patient to the hospital. In half of the cases, guardians were unable to reach the injured or the victim was not assisted in arriving at the hospital on time. In such a situation, some gadget or technology that can detect any collision and alert the nearest hospital and local guardians as soon as possible is required.

Now-a-days all cloud service providers are providing all resources to end users at very cheap rate and at the same time by designing scheduling algorithms cloud service providers are ensuring that all users can get response data in quick time. Proper utilization of resources can be enhanced by better resource allocation which will decrease the total makespan and the response time of the task. In this proposed work, a novel scheduling algorithm called Prediction of Execution Time of Tasks algorithm (PETT) to estimate minimum task execution time for cloud computing environment is proposed. In PETT algorithm, an array with all tasks and processors is built as a Directed Acyclic Graph (DAG). The matrix contains estimated execution task time on each processor and all rows of the matrix are filled with all processors execution time for all tasks. On generated matrix, PCA algorithm has been applied to choose that processor, which consumes less execution time for selected task. This process continues till all tasks assigned to all processors. By applying PETT algorithm, further computation and communication cost can be reduced at the cloud end.

This research work provides the design and development of an online service booking platform with payment integration. The platform is designed to enable users to book and pay for services such as plumbing and electrical services online from the comfort of their own homes. The system uses HTML, CSS, and PHP to create an intuitive user interface, as well as a secure payment gateway for online transactions. The platform is designed to provide better user experience and the system is designed for both mobile and desktop devices. The study concludes by providing a set of recommendations for future development of the platform, and discusses the potential benefits and challenges of online service booking platforms.