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Aging population rate in Malaysia is getting higher every year. When people becoming older, they start losing their abilities to do some of the activities properly and easily that they were able to do when they were younger. Basic tasks like cooking and cleaning, which were once part of manageable daily routine, become too difficult to perform as they enter to the elderly age. Despite this, many elderlies choose to live independently in their own home. This paper presents a new approach to utilize advanced Internet of Things (IoT) based pulse sensors for elderly. This proposed solution is an embedded scheme that support elderly with controlled automation solution that control and operates various home electrical appliances such as lights, fans, and televisions based on the measurement of pulse rate and instructions. The solution consists of pulse sensors, microcontrollers which integrated with Blynk server as a communication protocol. The proposed solution aims to minimize elderly movement by providing substantial assistance to the physically challenged elderly without assistance from any third person. The scope of the article focuses on enhancing and improving quality life of elderly. The main advantage of the proposed solution is that it is sensible and required zero technology knowledge that provides users with an automation solution with minimum movement needed. The proposed solution reduces human labours, effort, and energy conservation in the meantime. This study is limited to the scale of elderly in Malaysia rather than the whole world.

Consumer/customer relationship management (CRM) can potentially influence business as it predicts changes in people’s perspectives, which could impact future sales. Accordingly, advancements in Information Technology are under investigation to see their capabilities to improve the work of CRM. Many prediction techniques, such as Data Mining, Machine Learning (ML), and Deep Learning (DL), were found to be utilized with CRM. ML methods were found to dominate other approaches in terms of the prediction of consumers’ intention to purchase. This review provides DL algorithms that are mostly used in the last five years, to support CRM to predict purchase intention for better product sales decisions. Prediction criteria related to online activities and behavior were found to be the most inputs of prediction models. DL approaches are slowly applied within purchase intention prediction due to their advanced capabilities in handling large and complicated datasets with minimum human supervision. DL models such as CNN and LSTM result in high accuracy in prediction intention with 98%. Future research uses the two algorithms (CNN, LSTM) compiled to make the best prediction consumption in CRM. Additionally, an effort is being made to create a framework for predicting purchases based on many DL algorithms and the most pertinent characteristics. [ABSTRACT FROM AUTHOR]