One of the deadly diseases among humans is Cancer, which occurs almost anywhere in the human body. Cancer is caused by the cells that spread into the surrounding tissues by dividing itself uncontrollably. Breast Cancer is the most common cancer among women. Early detection and diagnosis of breast cancer are treatable and curable. Many women have no symptoms for this cancer at an early stage. The abnormal cells in the breast will risk for the development of breast cancer. So, it is important for women to regularly examine their breast. Technologies can be utilized in a smarter way with Artificial Intelligence techniques to assist the women during their examination of the breast at their living place to avoid the risk of breast cancer. The main aim is to develop a lowcost self-examining device for the detection of breast cancer and abnormality in the breast using an efficient optical method, Deep-learning algorithm and Internet of Things.

The thyroid is one of the most important parts of our body. As part of the endocrine system, this tiny gland in our neck releases thyroid hormone, which is responsible for directing all your metabolic functions which means controlling everything from digestion to conversion to energy. When thyroid dysfunction, it can affect all aspects of our health. Both researchers and doctors face challenges in fighting thyroid disease. In that thyroid disease is a major cause of the emergence of medical diagnostics and prognosis, the beginning of which is a difficult confirmation in medical research. Thyroid hormones are suspected to regulate metabolism. Hyperthyroidism and hypothyroidism are one of the two most common thyroid diseases that release thyroid hormones to regulate the rate of digestion. Early detection of thyroid disease is a major factor in saving many lives. Frequently, visual tests and hand techniques are used for these types of diagnostic thyroid diseases. This manual interpretation of medical images requires the use of time and is highly affected by errors. This work is developed to successfully diagnose and detect the presence of five different thyroid diseases such as Hyperthyroidism, Hypothyroidism, Thyroid cancer, thyroid gland, Thyroiditis and general thyroid screening without the need for several consultations. This leads to predictable disease progression and allows us to take immediate steps to avoid further consequences in an effective and cost-effective way to avoid the human error rate. A web application will also be developed where a scanned image of the inclusion will provide the removal of the most time-consuming thyroid type and patient investment.

In recent years, food wastage becomes the major problem of the world and researchers indicate that 20-60% of the total production is lost in the food supply chain.[1] Due to perishable nature and the cost of the products fresh food companies face more challenges throughout the supply chains. An order proposal is generated for all the products for a time period of a week by the integration of Machine Learning and loud and also taking into supply chain with some barriers such as supplier delivery times and also the maximum and minimum number of orders. The whole process of prediction is done using Random Forest Regression algorithm. This paper focuses particularly on perishable goods and analyzed based on the accuracy of the training and testing data.

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