The Moment invariant is a feature extraction technique used to extract the global features for shape recognition and identification analysis. There are many types of Moment Invariants technique since it was introduced. To date, many applications still use the Moment Invariant technique as feature extraction technique to extract the features of any images. The reason why the Moment Invariants still valid till today because its capabilities to analyze the image due to its invariant features of an image based on rotation, translation and scaling factors. Therefore, this review paper focuses to elaborate the history of Moment invariants and its applications in related fields. The summary about the advantages and disadvantages of Moment Invariants techniques will be described at the end of this review paper.

Cervix cancer is one of Malaysia's most significant cancers for women (around 12.9%, with an age-standardised incidence rate of 19.7 per 100,000). It was higher than other Asian, West, and even worldwide nations. The National Strategic Plan for Cancer Control Program 2016-2020 (Health Ministry) was presented to minimize cancer and mortality. The high incidence of cervical cancer in Malaysia is mainly due to women's insufficient knowledge about its prevention and importance. Compared with traditional literature reviews, the systemic analysis provides many advantages. A clearer review process, a more prominent field of study, and essential priorities that can manage research bias can all help to enhance these reviews. However, better integration, cooperation, and coordination between government and private sector as well as NGOs and professional organisations are essential for optimal cancer control and treatment across the country.

Cervical cancer is a prevalent and fatal disease that affects women all over the world. This affects roughly 0.5 million women annually and kills over 0.3 million people. Recently, a significant amount of literature has emerged around the advancement of technologies for identifying cervical cancer cells in women. Previously, diagnosing cervical cancer was done manually, which could lead to false positives or negatives. The best way of interpreting Pap smear images and automatically diagnose cervical cancer are still up for debate among the researchers. Method used in this study is the contrast enhancement technique for pre-processing and edge detection-based for segmentation of the nucleus. In this study, the average performance results of the method showed an accuracy of 96.99% in the seven-class problem using Herlev dataset. The present finding also support this study which concluded the results of accuracy achieved for the algorithm used for nucleus detection is improved by 6.15% when comparing to previous work. The accuracy value is in the lines of earlier literature that achieved accuracy of the approach used above 90% for seven class of cells. The major feature of the suggested approach is an improvement in the ability to anticipate which cells are aberrant and which are normal. Adding more classifiers could improve the suggested system even further. Therefore, a cervical cancer screening system might utilize this framework to identify women who have precancerous lesions.

Electricity-saving can be achieved through the efficient use of energy such as turning off lights and electrical appliances when not in use. Therefore this work proposed the smart classroom for electricity-saving with an integrated IoT System to prevent wasting electricity in the classroom. Smart Classroom means that it will detect and count the number of students entering and exiting the classroom by using a sensor system automatically. The main objective of this work is to control the lighting systems and fans by using the IoT application and sensor system. This means that when the sensor is triggered the sensor will send data to the Blynk application software using IoT to display the status of the classroom. This proposed work is also able to detect whether a classroom is available to use or not based on the presence of people. If the classroom is being used the Blynk application software will show the lamp and fan are ON. Otherwise the lamps and fans are OFF if there are no people in the classroom. The result successfully shows that if the first student entering the classroom all the lamps and fans are ON. While if the last student exiting the classroom all the lamps and fans are OFF. This result also indicates that electricity can be saved if all appliances in the classroom are switch OFF at the right time.

Malaria is a very serious disease that caused by the transmitted of parasites through the bites of infected Anopheles mosquito. Malaria death cases can be reduced and prevented through early diagnosis and prompt treatment. A fast and easy-to-use method, with high performance is required to differentiate malaria from non-malarial fevers. Manual examination of blood smears is currently the gold standard, but it is time-consuming, labour-intensive, requires skilled microscopists and the sensitivity of the method depends heavily on the skills of the microscopist. Currently, microscopy-based diagnosis remains the most widely used approach for malaria diagnosis. The development of automated malaria detection techniques is still a field of interest. Automated detection is faster and high accuracy compared to the traditional technique using microscopy. This paper presents an exhaustive review of these studies and suggests a direction for future developments of the malaria detection techniques. This paper analysis of three popular computational approaches which is k-mean clustering, neural network, and morphological approach was presented. Based on overall performance, many research proposed based on the morphological approach in order to detect malaria.