Garbage is a major problem, because it can harm human health, cause bad odors, and air pollution. With the existence of trash bins, it seems that it doesn't matter because most people prefer to litter, as well as cleaning workers to check the capacity of the trash can who often forget to cause garbage to accumulate so that it can pollute the environment.
To solve the waste problem, especially at universities, a smart campus concept was created to solve the problem of waste management. By utilizing GPS technology, Internet of Things, Wi-fi technology that is already available, and other hardware devices such as Arduino microcontrollers, ultrasonic sensors, and others.
With this concept, it is hoped that the cleaning staff will arrive on time to transport the garbage according to the information from the existing application, where the information has shown the coordinates of the full trash can so that cleanliness and comfort are maintained.

The cell cycle is a conserved process comprising of an organized series of interdependent and cross regulatory events that lead to controlled cell growth and proliferation. Genomic and volume regulatory processes are of special interest as they decide the fate of cell cycle. Signaling cascades including MAPK, PI3K, Sonic Hedgehog, Wnt and NOTCH signaling pathways are few well known conventional players contributing in controlling the cell cycle progression through different phases by expressing certain proteins. Moreover, the unconventional volume regulatory players exert influence by regulating membrane potential that is determined by ions influx or efflux across the plasma membrane via ion channels, controlling water movement and ultimately contributing to volume increase in growth phases of the cell cycle. Both of these players are interlinked, therefore, in order to establish a better understanding of the interdependence of these players, principles of machine learning were applied on data obtained on cell cycle. The data was processed by using neural networks and it shows that a significant understanding of conventional regulators is available in the literature and it has been under the limelight as well. However, when it comes to unconventional volume regulatory players, a limited understanding is available. Moreover, the precise role of each component and its interdependence with other is not yet fully understood. Due to which, they are not clearly evaluated for their potential role as cell cycle control elements for therapeutic purposes. Therefore, this study aims to summarize the data on cell cycle that is obtained through machine learning and to discuss the advances in cell cycle modelling mechanisms and designs that are based on different mathematical algorithms. Thus, this review will provide a basis to clearly understand and interlink the discoveries on cell cycle so that a comprehensive cell cycle model could be built which, if manipulated can be used for therapeutic purposes by identifying the least explored regulatory control elements.

Breast cancer is one of the most common cancer in women worldwide. Early detection of breast cancer can lead to better treatment and decrease in mortality. Mammogram image in medical technology, made it easier to analyze breast cancer. Mammography exam is a specialized imaging technique in medical to scan breast which results in mammogram image. Detecting breast cancer earlier, a patient can have several treatment options and also can save live. Early detection of breast cancer can leads to survive 93 percent or greater in the initial five years. This paper proposes a brseast cancer detection method from mammogram image sample by applying morphological operation on gray image rather than binary. Firstly, image is sent for gamma correction. Then it is converted to gray and applied morphological dilation. Again morphological opening operation is formed on the dilated image. Output of dilated and opening operation is then binarized. An AND operation is performed between both binary images. Some post processing like- small area filtering and hole filling task is took place. Then common unwanted object is removed. Finally rest of the region is the desired cancer infected region. Achieved performance is acceptable and satisfactory through the proposed method.

In today’s world having a smart reliable surveillance system is very much in need. In fact in many public places like banks, jewellery stores, malls, schools and colleges it is basic necessary to have a surveillance system (CCTV). Most of today’s implementations are not smart and they record videos during night even when there is no motion. This will lead to unnecessary storage usage and difficult to get the important part of the footage. And also, most of the today’s implementations are stationary, they can’t track the moving object. This report will outline a naive approach to implement a smart video surveillance system using object motion detection and tracking. Here we are using conventional Background subtraction model to detect motion and we estimate the direction of motion of object by comparing the centroid of the moving object in subsequent frames and track the moving object by rotating the camera using servo. Video recording takes place only when there is movement in the frame which helps in storage efficiency. We are also improving the speed of email alert delivery by using multithreading.

Diabetes has since become global pandemic – which must be diagnosed early enough if the patients are to survive a while longer. Traditional means of detection has its limitations and defects. The adoption of data mining tools and adaptation of machine intelligence is to yield an approach of predictive diagnosis that offers solution to task, which traditional means do not proffer low-cost-effective results. The significance thus, is to investigate data feats rippled with ambiguities and noise as well as simulate model tractability in order to yield a low-cost and robust solution. Thus, we explore a deep learning ensemble for detection of diabetes as a decision support. Model achieved a 95-percent accuracy, with a sensitivity of 0.98. It also agrees with other studies that age, obesity, environ-conditions and family relation to the first/second degrees are critical factors to be watched for type-I and type-II management. While, mothers with/without previous case of gestational diabetes is confirmed if there is: (a) history of babies with weight above 4.5kg at birth, (b) resistant to insulin showing polycystic ovary syndrome, and (c) have abnormal tolerance to insulin.